diff --git a/.gitattributes b/.gitattributes
new file mode 100644
index 0000000000000000000000000000000000000000..c7d9f3332a950355d5a77d85000f05e6f45435ea
--- /dev/null
+++ b/.gitattributes
@@ -0,0 +1,34 @@
+*.7z filter=lfs diff=lfs merge=lfs -text
+*.arrow filter=lfs diff=lfs merge=lfs -text
+*.bin filter=lfs diff=lfs merge=lfs -text
+*.bz2 filter=lfs diff=lfs merge=lfs -text
+*.ckpt filter=lfs diff=lfs merge=lfs -text
+*.ftz filter=lfs diff=lfs merge=lfs -text
+*.gz filter=lfs diff=lfs merge=lfs -text
+*.h5 filter=lfs diff=lfs merge=lfs -text
+*.joblib filter=lfs diff=lfs merge=lfs -text
+*.lfs.* filter=lfs diff=lfs merge=lfs -text
+*.mlmodel filter=lfs diff=lfs merge=lfs -text
+*.model filter=lfs diff=lfs merge=lfs -text
+*.msgpack filter=lfs diff=lfs merge=lfs -text
+*.npy filter=lfs diff=lfs merge=lfs -text
+*.npz filter=lfs diff=lfs merge=lfs -text
+*.onnx filter=lfs diff=lfs merge=lfs -text
+*.ot filter=lfs diff=lfs merge=lfs -text
+*.parquet filter=lfs diff=lfs merge=lfs -text
+*.pb filter=lfs diff=lfs merge=lfs -text
+*.pickle filter=lfs diff=lfs merge=lfs -text
+*.pkl filter=lfs diff=lfs merge=lfs -text
+*.pt filter=lfs diff=lfs merge=lfs -text
+*.pth filter=lfs diff=lfs merge=lfs -text
+*.rar filter=lfs diff=lfs merge=lfs -text
+*.safetensors filter=lfs diff=lfs merge=lfs -text
+saved_model/**/* filter=lfs diff=lfs merge=lfs -text
+*.tar.* filter=lfs diff=lfs merge=lfs -text
+*.tflite filter=lfs diff=lfs merge=lfs -text
+*.tgz filter=lfs diff=lfs merge=lfs -text
+*.wasm filter=lfs diff=lfs merge=lfs -text
+*.xz filter=lfs diff=lfs merge=lfs -text
+*.zip filter=lfs diff=lfs merge=lfs -text
+*.zst filter=lfs diff=lfs merge=lfs -text
+*tfevents* filter=lfs diff=lfs merge=lfs -text
diff --git a/.gitignore b/.gitignore
new file mode 100644
index 0000000000000000000000000000000000000000..672d3b80c30dbcd809d5a6a093502500c9f1fcea
--- /dev/null
+++ b/.gitignore
@@ -0,0 +1,45 @@
+*_video
+*_video.py
+extras/
+
+# output dir
+.DS_Store
+output
+
+*.json
+*.diff
+*.jpg
+*.ckpt
+
+# compilation and distribution
+__pycache__
+_ext
+*.pyc
+*.pyd
+*.so
+*.dll
+*.egg-info/
+build/
+dist/
+wheels/
+
+# pytorch/python/numpy formats
+*.pth
+*.pkl
+*.npy
+*.ts
+model_ts*.txt
+
+# ipython/jupyter notebooks
+**/.ipynb_checkpoints/
+
+# Editor temporaries
+*.swn
+*.swo
+*.swp
+*~
+
+# editor settings
+.idea
+.vscode
+_darcs
\ No newline at end of file
diff --git a/Dockerfile b/Dockerfile
new file mode 100644
index 0000000000000000000000000000000000000000..64a6eca483c57db8d8321199018d83eaad58dac3
--- /dev/null
+++ b/Dockerfile
@@ -0,0 +1,68 @@
+FROM nvidia/cuda:11.3.1-cudnn8-devel-ubuntu18.04
+CMD nvidia-smi
+
+ENV DEBIAN_FRONTEND noninteractive
+RUN apt-get update && apt-get install -y \
+ git \
+ make build-essential libssl-dev zlib1g-dev \
+ libbz2-dev libreadline-dev libsqlite3-dev wget curl llvm \
+ libncursesw5-dev xz-utils tk-dev libxml2-dev libxmlsec1-dev libffi-dev liblzma-dev \
+ ffmpeg libsm6 libxext6 cmake libgl1-mesa-glx \
+ && rm -rf /var/lib/apt/lists/*
+
+RUN useradd -ms /bin/bash user
+USER user
+
+ENV HOME=/home/user \
+ PATH=/home/user/.local/bin:$PATH
+
+RUN curl https://pyenv.run | bash
+ENV PATH=$HOME/.pyenv/shims:$HOME/.pyenv/bin:$PATH
+RUN pyenv install 3.8.15 && \
+ pyenv global 3.8.15 && \
+ pyenv rehash && \
+ pip install --no-cache-dir --upgrade pip setuptools wheel
+
+ENV WORKDIR=/code
+WORKDIR $WORKDIR
+RUN chown -R user:user $WORKDIR
+RUN chmod -R 777 $WORKDIR
+
+COPY requirements.txt $WORKDIR/requirements.txt
+RUN pip install --no-cache-dir --upgrade -r $WORKDIR/requirements.txt
+RUN pip install ninja
+
+COPY . .
+
+ARG TORCH_CUDA_ARCH_LIST=7.5+PTX
+
+USER root
+RUN chown -R user:user $HOME
+RUN chmod -R 777 $HOME
+RUN chown -R user:user $WORKDIR
+RUN chmod -R 777 $WORKDIR
+
+USER user
+RUN ln -s $WORKDIR/annotator/OneFormer/oneformer/modeling/pixel_decoder/ops $WORKDIR/ && ls
+RUN cd ops/ && FORCE_CUDA=1 python setup.py build --build-base=$WORKDIR/ install --user && cd ..
+RUN sh deform_setup.sh
+
+USER user
+RUN sh deform_setup.sh
+
+USER user
+
+ENV PYTHONPATH=${HOME}/app \
+ PYTHONUNBUFFERED=1 \
+ GRADIO_ALLOW_FLAGGING=never \
+ GRADIO_NUM_PORTS=1 \
+ GRADIO_SERVER_NAME=0.0.0.0 \
+ GRADIO_THEME=huggingface \
+ SYSTEM=spaces
+
+RUN --mount=type=secret,id=ACCESS_TOKEN,mode=0444,required=true
+
+
+EXPOSE 7860
+
+ENTRYPOINT ["python", "app.py"]
\ No newline at end of file
diff --git a/README.md b/README.md
new file mode 100644
index 0000000000000000000000000000000000000000..4368be22b5a39ef23354993d53a668aa1e607415
--- /dev/null
+++ b/README.md
@@ -0,0 +1,10 @@
+---
+title: PAIR Diffusion
+emoji: 📚
+colorFrom: purple
+colorTo: gray
+sdk: docker
+pinned: false
+---
+
+Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/annotator/OneFormer/__init__.py b/annotator/OneFormer/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..56a56c9a3fdfc6c20f2e97b635174ebabe213665
--- /dev/null
+++ b/annotator/OneFormer/__init__.py
@@ -0,0 +1,61 @@
+# ------------------------------------------------------------------------------
+# Reference: https://github.com/SHI-Labs/OneFormer
+# Modified by Vidit Goel (https://github.com/vidit98)
+# ------------------------------------------------------------------------------
+
+import os
+import random
+# fmt: off
+import sys
+sys.path.insert(1, './annotator/OneFormer')
+# fmt: on
+
+import imutils
+import cv2
+import numpy as np
+
+from detectron2.config import get_cfg
+from detectron2.projects.deeplab import add_deeplab_config
+from detectron2.data import MetadataCatalog
+
+from oneformer import (
+ add_oneformer_config,
+ add_common_config,
+ add_swin_config,
+ add_dinat_config,
+ add_convnext_config,
+)
+from demo.defaults import DefaultPredictor
+
+
+def setup_cfg(config_file, wts):
+ # load config from file and command-line arguments
+ cfg = get_cfg()
+ add_deeplab_config(cfg)
+ add_common_config(cfg)
+ add_swin_config(cfg)
+ add_dinat_config(cfg)
+ add_convnext_config(cfg)
+ add_oneformer_config(cfg)
+ cfg.merge_from_file(config_file)
+ cfg.MODEL.WEIGHTS = wts
+ cfg.freeze()
+ return cfg
+
+
+class OneformerSegmenter:
+ def __init__(self, wts, config='./annotator/OneFormer/configs/coco/swin/oneformer_swin_large_bs16_100ep.yaml',confidence_thresh=0.5):
+ cfg = setup_cfg(config, wts)
+ metadata = MetadataCatalog.get(cfg.DATASETS.TEST_PANOPTIC[0] if len(cfg.DATASETS.TEST_PANOPTIC) else "__unused")
+ self.predictor = DefaultPredictor(cfg)
+ self.metadata = metadata
+
+ def __call__(self, img, task):
+ if task == 'panoptic':
+ predictions = self.predictor(img, "panoptic")
+ panoptic_seg, segments_info = predictions["panoptic_seg"]
+ return panoptic_seg, segments_info
+ elif task == 'semantic':
+ predictions = self.predictor(img, "semantic")
+ semask = predictions["sem_seg"].argmax(dim=0)
+ return semask
\ No newline at end of file
diff --git a/annotator/OneFormer/configs/ade20k/Base-ADE20K-UnifiedSegmentation.yaml b/annotator/OneFormer/configs/ade20k/Base-ADE20K-UnifiedSegmentation.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..31eab45b878433fc844a13dbdd54f97c936d9b89
--- /dev/null
+++ b/annotator/OneFormer/configs/ade20k/Base-ADE20K-UnifiedSegmentation.yaml
@@ -0,0 +1,68 @@
+MODEL:
+ BACKBONE:
+ FREEZE_AT: 0
+ NAME: "build_resnet_backbone"
+ WEIGHTS: "detectron2://ImageNetPretrained/torchvision/R-50.pkl"
+ PIXEL_MEAN: [123.675, 116.280, 103.530]
+ PIXEL_STD: [58.395, 57.120, 57.375]
+ RESNETS:
+ DEPTH: 50
+ STEM_TYPE: "basic" # not used
+ STEM_OUT_CHANNELS: 64
+ STRIDE_IN_1X1: False
+ OUT_FEATURES: ["res2", "res3", "res4", "res5"]
+ # NORM: "SyncBN"
+ RES5_MULTI_GRID: [1, 1, 1] # not used
+DATASETS:
+ TRAIN: ("ade20k_panoptic_train",)
+ TEST_PANOPTIC: ("ade20k_panoptic_val",)
+ TEST_INSTANCE: ("ade20k_instance_val",)
+ TEST_SEMANTIC: ("ade20k_sem_seg_val",)
+SOLVER:
+ IMS_PER_BATCH: 16
+ BASE_LR: 0.0001
+ MAX_ITER: 160000
+ WARMUP_FACTOR: 1.0
+ WARMUP_ITERS: 0
+ WEIGHT_DECAY: 0.05
+ OPTIMIZER: "ADAMW"
+ LR_SCHEDULER_NAME: "WarmupPolyLR"
+ BACKBONE_MULTIPLIER: 0.1
+ CLIP_GRADIENTS:
+ ENABLED: True
+ CLIP_TYPE: "full_model"
+ CLIP_VALUE: 0.01
+ NORM_TYPE: 2.0
+ AMP:
+ ENABLED: True
+INPUT:
+ MIN_SIZE_TRAIN: !!python/object/apply:eval ["[int(x * 0.1 * 512) for x in range(5, 21)]"]
+ MIN_SIZE_TRAIN_SAMPLING: "choice"
+ MIN_SIZE_TEST: 512
+ MAX_SIZE_TRAIN: 2048
+ MAX_SIZE_TEST: 2048
+ CROP:
+ ENABLED: True
+ TYPE: "absolute"
+ SIZE: (512, 512)
+ SINGLE_CATEGORY_MAX_AREA: 1.0
+ COLOR_AUG_SSD: True
+ SIZE_DIVISIBILITY: 512 # used in dataset mapper
+ FORMAT: "RGB"
+ DATASET_MAPPER_NAME: "oneformer_unified"
+ MAX_SEQ_LEN: 77
+ TASK_SEQ_LEN: 77
+ TASK_PROB:
+ SEMANTIC: 0.33
+ INSTANCE: 0.66
+TEST:
+ EVAL_PERIOD: 5000
+ AUG:
+ ENABLED: False
+ MIN_SIZES: [256, 384, 512, 640, 768, 896]
+ MAX_SIZE: 3584
+ FLIP: True
+DATALOADER:
+ FILTER_EMPTY_ANNOTATIONS: True
+ NUM_WORKERS: 4
+VERSION: 2
\ No newline at end of file
diff --git a/annotator/OneFormer/configs/ade20k/convnext/oneformer_convnext_large_bs16_160k.yaml b/annotator/OneFormer/configs/ade20k/convnext/oneformer_convnext_large_bs16_160k.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..4072dc52877317162858ab7e723b8b764005c931
--- /dev/null
+++ b/annotator/OneFormer/configs/ade20k/convnext/oneformer_convnext_large_bs16_160k.yaml
@@ -0,0 +1,38 @@
+_BASE_: ../oneformer_R50_bs16_160k.yaml
+MODEL:
+ BACKBONE:
+ NAME: "D2ConvNeXt"
+ CONVNEXT:
+ IN_CHANNELS: 3
+ DEPTHS: [3, 3, 27, 3]
+ DIMS: [192, 384, 768, 1536]
+ DROP_PATH_RATE: 0.4
+ LSIT: 1.0
+ OUT_INDICES: [0, 1, 2, 3]
+ WEIGHTS: "convnext_large_22k_1k_384.pkl"
+ PIXEL_MEAN: [123.675, 116.280, 103.530]
+ PIXEL_STD: [58.395, 57.120, 57.375]
+ ONE_FORMER:
+ NUM_OBJECT_QUERIES: 250
+INPUT:
+ MIN_SIZE_TRAIN: !!python/object/apply:eval ["[int(x * 0.1 * 640) for x in range(5, 21)]"]
+ MIN_SIZE_TRAIN_SAMPLING: "choice"
+ MIN_SIZE_TEST: 640
+ MAX_SIZE_TRAIN: 2560
+ MAX_SIZE_TEST: 2560
+ CROP:
+ ENABLED: True
+ TYPE: "absolute"
+ SIZE: (640, 640)
+ SINGLE_CATEGORY_MAX_AREA: 1.0
+ COLOR_AUG_SSD: True
+ SIZE_DIVISIBILITY: 640 # used in dataset mapper
+ FORMAT: "RGB"
+TEST:
+ DETECTIONS_PER_IMAGE: 250
+ EVAL_PERIOD: 5000
+ AUG:
+ ENABLED: False
+ MIN_SIZES: [320, 480, 640, 800, 960, 1120]
+ MAX_SIZE: 4480
+ FLIP: True
diff --git a/annotator/OneFormer/configs/ade20k/convnext/oneformer_convnext_xlarge_bs16_160k.yaml b/annotator/OneFormer/configs/ade20k/convnext/oneformer_convnext_xlarge_bs16_160k.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..7cf2d206dbe17b6a42567ad9c8810cea6b0e1467
--- /dev/null
+++ b/annotator/OneFormer/configs/ade20k/convnext/oneformer_convnext_xlarge_bs16_160k.yaml
@@ -0,0 +1,38 @@
+_BASE_: ../oneformer_R50_bs16_160k.yaml
+MODEL:
+ BACKBONE:
+ NAME: "D2ConvNeXt"
+ CONVNEXT:
+ IN_CHANNELS: 3
+ DEPTHS: [3, 3, 27, 3]
+ DIMS: [256, 512, 1024, 2048]
+ DROP_PATH_RATE: 0.4
+ LSIT: 1.0
+ OUT_INDICES: [0, 1, 2, 3]
+ WEIGHTS: "convnext_xlarge_22k_1k_384_ema.pkl"
+ PIXEL_MEAN: [123.675, 116.280, 103.530]
+ PIXEL_STD: [58.395, 57.120, 57.375]
+ ONE_FORMER:
+ NUM_OBJECT_QUERIES: 250
+INPUT:
+ MIN_SIZE_TRAIN: !!python/object/apply:eval ["[int(x * 0.1 * 640) for x in range(5, 21)]"]
+ MIN_SIZE_TRAIN_SAMPLING: "choice"
+ MIN_SIZE_TEST: 640
+ MAX_SIZE_TRAIN: 2560
+ MAX_SIZE_TEST: 2560
+ CROP:
+ ENABLED: True
+ TYPE: "absolute"
+ SIZE: (640, 640)
+ SINGLE_CATEGORY_MAX_AREA: 1.0
+ COLOR_AUG_SSD: True
+ SIZE_DIVISIBILITY: 640 # used in dataset mapper
+ FORMAT: "RGB"
+TEST:
+ DETECTIONS_PER_IMAGE: 250
+ EVAL_PERIOD: 5000
+ AUG:
+ ENABLED: False
+ MIN_SIZES: [320, 480, 640, 800, 960, 1120]
+ MAX_SIZE: 4480
+ FLIP: True
diff --git a/annotator/OneFormer/configs/ade20k/dinat/coco_pretrain_oneformer_dinat_large_bs16_160k_1280x1280.yaml b/annotator/OneFormer/configs/ade20k/dinat/coco_pretrain_oneformer_dinat_large_bs16_160k_1280x1280.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..dcada2bc4c94b594339ea73bd2c68f11e2ca81f2
--- /dev/null
+++ b/annotator/OneFormer/configs/ade20k/dinat/coco_pretrain_oneformer_dinat_large_bs16_160k_1280x1280.yaml
@@ -0,0 +1,42 @@
+_BASE_: ../oneformer_R50_bs16_160k.yaml
+MODEL:
+ BACKBONE:
+ NAME: "D2DiNAT"
+ DiNAT:
+ EMBED_DIM: 192
+ MLP_RATIO: 2.0
+ DEPTHS: [3, 4, 18, 5]
+ NUM_HEADS: [6, 12, 24, 48]
+ KERNEL_SIZE: 11
+ DROP_PATH_RATE: 0.3
+ DILATIONS: [[1, 28, 1], [1, 7, 1, 14], [1, 3, 1, 5, 1, 5, 1, 7, 1, 3, 1, 5, 1, 5, 1, 7, 1, 7], [1, 3, 1, 3, 1]]
+ WEIGHTS: "150_16_dinat_l_oneformer_coco_100ep.pth"
+ PIXEL_MEAN: [123.675, 116.280, 103.530]
+ PIXEL_STD: [58.395, 57.120, 57.375]
+ ONE_FORMER:
+ NUM_OBJECT_QUERIES: 150
+SOLVER:
+ AMP:
+ ENABLED: False
+INPUT:
+ MIN_SIZE_TRAIN: !!python/object/apply:eval ["[int(x * 0.1 * 1280) for x in range(5, 21)]"]
+ MIN_SIZE_TRAIN_SAMPLING: "choice"
+ MIN_SIZE_TEST: 1280
+ MAX_SIZE_TRAIN: 5120
+ MAX_SIZE_TEST: 5120
+ CROP:
+ ENABLED: True
+ TYPE: "absolute"
+ SIZE: (1280, 1280)
+ SINGLE_CATEGORY_MAX_AREA: 1.0
+ COLOR_AUG_SSD: True
+ SIZE_DIVISIBILITY: 1280 # used in dataset mapper
+ FORMAT: "RGB"
+TEST:
+ DETECTIONS_PER_IMAGE: 150
+ EVAL_PERIOD: 5000
+ AUG:
+ ENABLED: False
+ MIN_SIZES: [640, 960, 1280, 1600, 1920, 2240]
+ MAX_SIZE: 8960
+ FLIP: True
\ No newline at end of file
diff --git a/annotator/OneFormer/configs/ade20k/dinat/oneformer_dinat_large_bs16_160k.yaml b/annotator/OneFormer/configs/ade20k/dinat/oneformer_dinat_large_bs16_160k.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..ac861808ca03a9f69addd518a589933886d8d0d8
--- /dev/null
+++ b/annotator/OneFormer/configs/ade20k/dinat/oneformer_dinat_large_bs16_160k.yaml
@@ -0,0 +1,42 @@
+_BASE_: ../oneformer_R50_bs16_160k.yaml
+MODEL:
+ BACKBONE:
+ NAME: "D2DiNAT"
+ DiNAT:
+ EMBED_DIM: 192
+ MLP_RATIO: 2.0
+ DEPTHS: [3, 4, 18, 5]
+ NUM_HEADS: [6, 12, 24, 48]
+ KERNEL_SIZE: 11
+ DROP_PATH_RATE: 0.3
+ DILATIONS: [[1, 20, 1], [1, 5, 1, 10], [1, 2, 1, 3, 1, 4, 1, 5, 1, 2, 1, 3, 1, 4, 1, 5, 1, 5], [1, 2, 1, 2, 1]]
+ WEIGHTS: "dinat_large_in22k_in1k_384_11x11.pkl"
+ PIXEL_MEAN: [123.675, 116.280, 103.530]
+ PIXEL_STD: [58.395, 57.120, 57.375]
+ ONE_FORMER:
+ NUM_OBJECT_QUERIES: 250
+SOLVER:
+ AMP:
+ ENABLED: False
+INPUT:
+ MIN_SIZE_TRAIN: !!python/object/apply:eval ["[int(x * 0.1 * 640) for x in range(5, 21)]"]
+ MIN_SIZE_TRAIN_SAMPLING: "choice"
+ MIN_SIZE_TEST: 640
+ MAX_SIZE_TRAIN: 2560
+ MAX_SIZE_TEST: 2560
+ CROP:
+ ENABLED: True
+ TYPE: "absolute"
+ SIZE: (640, 640)
+ SINGLE_CATEGORY_MAX_AREA: 1.0
+ COLOR_AUG_SSD: True
+ SIZE_DIVISIBILITY: 640 # used in dataset mapper
+ FORMAT: "RGB"
+TEST:
+ DETECTIONS_PER_IMAGE: 250
+ EVAL_PERIOD: 5000
+ AUG:
+ ENABLED: False
+ MIN_SIZES: [320, 480, 640, 800, 960, 1120]
+ MAX_SIZE: 4480
+ FLIP: True
diff --git a/annotator/OneFormer/configs/ade20k/dinat/oneformer_dinat_large_bs16_160k_1280x1280.yaml b/annotator/OneFormer/configs/ade20k/dinat/oneformer_dinat_large_bs16_160k_1280x1280.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..95506844b629d2f9a608495adafa5739950b1dbe
--- /dev/null
+++ b/annotator/OneFormer/configs/ade20k/dinat/oneformer_dinat_large_bs16_160k_1280x1280.yaml
@@ -0,0 +1,42 @@
+_BASE_: ../oneformer_R50_bs16_160k.yaml
+MODEL:
+ BACKBONE:
+ NAME: "D2DiNAT"
+ DiNAT:
+ EMBED_DIM: 192
+ MLP_RATIO: 2.0
+ DEPTHS: [3, 4, 18, 5]
+ NUM_HEADS: [6, 12, 24, 48]
+ KERNEL_SIZE: 11
+ DROP_PATH_RATE: 0.3
+ DILATIONS: [[1, 28, 1], [1, 7, 1, 14], [1, 3, 1, 5, 1, 5, 1, 7, 1, 3, 1, 5, 1, 5, 1, 7, 1, 7], [1, 3, 1, 3, 1]]
+ WEIGHTS: "dinat_large_in22k_in1k_384_11x11.pkl"
+ PIXEL_MEAN: [123.675, 116.280, 103.530]
+ PIXEL_STD: [58.395, 57.120, 57.375]
+ ONE_FORMER:
+ NUM_OBJECT_QUERIES: 250
+SOLVER:
+ AMP:
+ ENABLED: False
+INPUT:
+ MIN_SIZE_TRAIN: !!python/object/apply:eval ["[int(x * 0.1 * 1280) for x in range(5, 21)]"]
+ MIN_SIZE_TRAIN_SAMPLING: "choice"
+ MIN_SIZE_TEST: 1280
+ MAX_SIZE_TRAIN: 5120
+ MAX_SIZE_TEST: 5120
+ CROP:
+ ENABLED: True
+ TYPE: "absolute"
+ SIZE: (1280, 1280)
+ SINGLE_CATEGORY_MAX_AREA: 1.0
+ COLOR_AUG_SSD: True
+ SIZE_DIVISIBILITY: 1280 # used in dataset mapper
+ FORMAT: "RGB"
+TEST:
+ DETECTIONS_PER_IMAGE: 250
+ EVAL_PERIOD: 5000
+ AUG:
+ ENABLED: False
+ MIN_SIZES: [640, 960, 1280, 1600, 1920, 2240]
+ MAX_SIZE: 8960
+ FLIP: True
\ No newline at end of file
diff --git a/annotator/OneFormer/configs/ade20k/dinat/oneformer_dinat_large_bs16_160k_896x896.yaml b/annotator/OneFormer/configs/ade20k/dinat/oneformer_dinat_large_bs16_160k_896x896.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..264ebf6ce7fcd3510f9a2954003752955eb8c1e6
--- /dev/null
+++ b/annotator/OneFormer/configs/ade20k/dinat/oneformer_dinat_large_bs16_160k_896x896.yaml
@@ -0,0 +1,42 @@
+_BASE_: ../oneformer_R50_bs16_160k.yaml
+MODEL:
+ BACKBONE:
+ NAME: "D2DiNAT"
+ DiNAT:
+ EMBED_DIM: 192
+ MLP_RATIO: 2.0
+ DEPTHS: [3, 4, 18, 5]
+ NUM_HEADS: [6, 12, 24, 48]
+ KERNEL_SIZE: 11
+ DROP_PATH_RATE: 0.3
+ DILATIONS: [[1, 20, 1], [1, 5, 1, 10], [1, 2, 1, 3, 1, 4, 1, 5, 1, 2, 1, 3, 1, 4, 1, 5, 1, 5], [1, 2, 1, 2, 1]]
+ WEIGHTS: "dinat_large_in22k_in1k_384_11x11.pkl"
+ PIXEL_MEAN: [123.675, 116.280, 103.530]
+ PIXEL_STD: [58.395, 57.120, 57.375]
+ ONE_FORMER:
+ NUM_OBJECT_QUERIES: 250
+SOLVER:
+ AMP:
+ ENABLED: False
+INPUT:
+ MIN_SIZE_TRAIN: !!python/object/apply:eval ["[int(x * 0.1 * 896) for x in range(5, 21)]"]
+ MIN_SIZE_TRAIN_SAMPLING: "choice"
+ MIN_SIZE_TEST: 896
+ MAX_SIZE_TRAIN: 3584
+ MAX_SIZE_TEST: 3584
+ CROP:
+ ENABLED: True
+ TYPE: "absolute"
+ SIZE: (896, 896)
+ SINGLE_CATEGORY_MAX_AREA: 1.0
+ COLOR_AUG_SSD: True
+ SIZE_DIVISIBILITY: 896 # used in dataset mapper
+ FORMAT: "RGB"
+TEST:
+ DETECTIONS_PER_IMAGE: 250
+ EVAL_PERIOD: 5000
+ AUG:
+ ENABLED: False
+ MIN_SIZES: [448, 678, 896, 1120, 1344, 1568]
+ MAX_SIZE: 6272
+ FLIP: True
diff --git a/annotator/OneFormer/configs/ade20k/oneformer_R50_bs16_160k.yaml b/annotator/OneFormer/configs/ade20k/oneformer_R50_bs16_160k.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..7db0e410e3130807a1b2ab9dc0772ceef7af75db
--- /dev/null
+++ b/annotator/OneFormer/configs/ade20k/oneformer_R50_bs16_160k.yaml
@@ -0,0 +1,58 @@
+_BASE_: Base-ADE20K-UnifiedSegmentation.yaml
+MODEL:
+ META_ARCHITECTURE: "OneFormer"
+ SEM_SEG_HEAD:
+ NAME: "OneFormerHead"
+ IGNORE_VALUE: 255
+ NUM_CLASSES: 150
+ LOSS_WEIGHT: 1.0
+ CONVS_DIM: 256
+ MASK_DIM: 256
+ NORM: "GN"
+ # pixel decoder
+ PIXEL_DECODER_NAME: "MSDeformAttnPixelDecoder"
+ IN_FEATURES: ["res2", "res3", "res4", "res5"]
+ DEFORMABLE_TRANSFORMER_ENCODER_IN_FEATURES: ["res3", "res4", "res5"]
+ COMMON_STRIDE: 4
+ TRANSFORMER_ENC_LAYERS: 6
+ ONE_FORMER:
+ TRANSFORMER_DECODER_NAME: "ContrastiveMultiScaleMaskedTransformerDecoder"
+ TRANSFORMER_IN_FEATURE: "multi_scale_pixel_decoder"
+ DEEP_SUPERVISION: True
+ NO_OBJECT_WEIGHT: 0.1
+ CLASS_WEIGHT: 2.0
+ MASK_WEIGHT: 5.0
+ DICE_WEIGHT: 5.0
+ CONTRASTIVE_WEIGHT: 0.5
+ CONTRASTIVE_TEMPERATURE: 0.07
+ HIDDEN_DIM: 256
+ NUM_OBJECT_QUERIES: 150
+ USE_TASK_NORM: True
+ NHEADS: 8
+ DROPOUT: 0.1
+ DIM_FEEDFORWARD: 2048
+ ENC_LAYERS: 0
+ PRE_NORM: False
+ ENFORCE_INPUT_PROJ: False
+ SIZE_DIVISIBILITY: 32
+ CLASS_DEC_LAYERS: 2
+ DEC_LAYERS: 10 # 9 decoder layers, add one for the loss on learnable query
+ TRAIN_NUM_POINTS: 12544
+ OVERSAMPLE_RATIO: 3.0
+ IMPORTANCE_SAMPLE_RATIO: 0.75
+ TEXT_ENCODER:
+ WIDTH: 256
+ CONTEXT_LENGTH: 77
+ NUM_LAYERS: 6
+ VOCAB_SIZE: 49408
+ PROJ_NUM_LAYERS: 2
+ N_CTX: 16
+ TEST:
+ SEMANTIC_ON: True
+ INSTANCE_ON: True
+ PANOPTIC_ON: True
+ OVERLAP_THRESHOLD: 0.8
+ OBJECT_MASK_THRESHOLD: 0.5
+ TASK: "panoptic"
+TEST:
+ DETECTIONS_PER_IMAGE: 150
diff --git a/annotator/OneFormer/configs/ade20k/swin/oneformer_swin_large_bs16_160k.yaml b/annotator/OneFormer/configs/ade20k/swin/oneformer_swin_large_bs16_160k.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..c065d5f2fbbc018b019c9e0431bcdfe94be61f67
--- /dev/null
+++ b/annotator/OneFormer/configs/ade20k/swin/oneformer_swin_large_bs16_160k.yaml
@@ -0,0 +1,40 @@
+_BASE_: ../oneformer_R50_bs16_160k.yaml
+MODEL:
+ BACKBONE:
+ NAME: "D2SwinTransformer"
+ SWIN:
+ EMBED_DIM: 192
+ DEPTHS: [2, 2, 18, 2]
+ NUM_HEADS: [6, 12, 24, 48]
+ WINDOW_SIZE: 12
+ APE: False
+ DROP_PATH_RATE: 0.3
+ PATCH_NORM: True
+ PRETRAIN_IMG_SIZE: 384
+ WEIGHTS: "swin_large_patch4_window12_384_22kto1k.pkl"
+ PIXEL_MEAN: [123.675, 116.280, 103.530]
+ PIXEL_STD: [58.395, 57.120, 57.375]
+ ONE_FORMER:
+ NUM_OBJECT_QUERIES: 250
+INPUT:
+ MIN_SIZE_TRAIN: !!python/object/apply:eval ["[int(x * 0.1 * 640) for x in range(5, 21)]"]
+ MIN_SIZE_TRAIN_SAMPLING: "choice"
+ MIN_SIZE_TEST: 640
+ MAX_SIZE_TRAIN: 2560
+ MAX_SIZE_TEST: 2560
+ CROP:
+ ENABLED: True
+ TYPE: "absolute"
+ SIZE: (640, 640)
+ SINGLE_CATEGORY_MAX_AREA: 1.0
+ COLOR_AUG_SSD: True
+ SIZE_DIVISIBILITY: 640 # used in dataset mapper
+ FORMAT: "RGB"
+TEST:
+ DETECTIONS_PER_IMAGE: 250
+ EVAL_PERIOD: 5000
+ AUG:
+ ENABLED: False
+ MIN_SIZES: [320, 480, 640, 800, 960, 1120]
+ MAX_SIZE: 4480
+ FLIP: True
diff --git a/annotator/OneFormer/configs/ade20k/swin/oneformer_swin_large_bs16_160k_1280x1280.yaml b/annotator/OneFormer/configs/ade20k/swin/oneformer_swin_large_bs16_160k_1280x1280.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..37ed5cc138cc32369b4ffa997d05f661b27af50e
--- /dev/null
+++ b/annotator/OneFormer/configs/ade20k/swin/oneformer_swin_large_bs16_160k_1280x1280.yaml
@@ -0,0 +1,40 @@
+_BASE_: ../oneformer_R50_bs16_160k.yaml
+MODEL:
+ BACKBONE:
+ NAME: "D2SwinTransformer"
+ SWIN:
+ EMBED_DIM: 192
+ DEPTHS: [2, 2, 18, 2]
+ NUM_HEADS: [6, 12, 24, 48]
+ WINDOW_SIZE: 12
+ APE: False
+ DROP_PATH_RATE: 0.3
+ PATCH_NORM: True
+ PRETRAIN_IMG_SIZE: 384
+ WEIGHTS: "swin_large_patch4_window12_384_22kto1k.pkl"
+ PIXEL_MEAN: [123.675, 116.280, 103.530]
+ PIXEL_STD: [58.395, 57.120, 57.375]
+ ONE_FORMER:
+ NUM_OBJECT_QUERIES: 250
+INPUT:
+ MIN_SIZE_TRAIN: !!python/object/apply:eval ["[int(x * 0.1 * 1280) for x in range(5, 21)]"]
+ MIN_SIZE_TRAIN_SAMPLING: "choice"
+ MIN_SIZE_TEST: 1280
+ MAX_SIZE_TRAIN: 5120
+ MAX_SIZE_TEST: 5120
+ CROP:
+ ENABLED: True
+ TYPE: "absolute"
+ SIZE: (1280, 1280)
+ SINGLE_CATEGORY_MAX_AREA: 1.0
+ COLOR_AUG_SSD: True
+ SIZE_DIVISIBILITY: 1280 # used in dataset mapper
+ FORMAT: "RGB"
+TEST:
+ DETECTIONS_PER_IMAGE: 250
+ EVAL_PERIOD: 5000
+ AUG:
+ ENABLED: False
+ MIN_SIZES: [640, 960, 1280, 1600, 1920, 2240]
+ MAX_SIZE: 8960
+ FLIP: True
diff --git a/annotator/OneFormer/configs/ade20k/swin/oneformer_swin_large_bs16_160k_896x896.yaml b/annotator/OneFormer/configs/ade20k/swin/oneformer_swin_large_bs16_160k_896x896.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..4ccc081194b9ef5b3b28a74b294f6297042d3968
--- /dev/null
+++ b/annotator/OneFormer/configs/ade20k/swin/oneformer_swin_large_bs16_160k_896x896.yaml
@@ -0,0 +1,40 @@
+_BASE_: ../oneformer_R50_bs16_160k.yaml
+MODEL:
+ BACKBONE:
+ NAME: "D2SwinTransformer"
+ SWIN:
+ EMBED_DIM: 192
+ DEPTHS: [2, 2, 18, 2]
+ NUM_HEADS: [6, 12, 24, 48]
+ WINDOW_SIZE: 12
+ APE: False
+ DROP_PATH_RATE: 0.3
+ PATCH_NORM: True
+ PRETRAIN_IMG_SIZE: 384
+ WEIGHTS: "swin_large_patch4_window12_384_22kto1k.pkl"
+ PIXEL_MEAN: [123.675, 116.280, 103.530]
+ PIXEL_STD: [58.395, 57.120, 57.375]
+ ONE_FORMER:
+ NUM_OBJECT_QUERIES: 250
+INPUT:
+ MIN_SIZE_TRAIN: !!python/object/apply:eval ["[int(x * 0.1 * 896) for x in range(5, 21)]"]
+ MIN_SIZE_TRAIN_SAMPLING: "choice"
+ MIN_SIZE_TEST: 896
+ MAX_SIZE_TRAIN: 3584
+ MAX_SIZE_TEST: 3584
+ CROP:
+ ENABLED: True
+ TYPE: "absolute"
+ SIZE: (896, 896)
+ SINGLE_CATEGORY_MAX_AREA: 1.0
+ COLOR_AUG_SSD: True
+ SIZE_DIVISIBILITY: 896 # used in dataset mapper
+ FORMAT: "RGB"
+TEST:
+ DETECTIONS_PER_IMAGE: 250
+ EVAL_PERIOD: 5000
+ AUG:
+ ENABLED: False
+ MIN_SIZES: [448, 678, 896, 1120, 1344, 1568]
+ MAX_SIZE: 6272
+ FLIP: True
diff --git a/annotator/OneFormer/configs/ade20k/swin/oneformer_swin_tiny_bs16_160k.yaml b/annotator/OneFormer/configs/ade20k/swin/oneformer_swin_tiny_bs16_160k.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..1f7b72b3c067c471fb577058356d2631925c6a0a
--- /dev/null
+++ b/annotator/OneFormer/configs/ade20k/swin/oneformer_swin_tiny_bs16_160k.yaml
@@ -0,0 +1,15 @@
+_BASE_: ../oneformer_R50_bs16_160k.yaml
+MODEL:
+ BACKBONE:
+ NAME: "D2SwinTransformer"
+ SWIN:
+ EMBED_DIM: 96
+ DEPTHS: [2, 2, 6, 2]
+ NUM_HEADS: [3, 6, 12, 24]
+ WINDOW_SIZE: 7
+ APE: False
+ DROP_PATH_RATE: 0.3
+ PATCH_NORM: True
+ WEIGHTS: "swin_tiny_patch4_window7_224.pkl"
+ PIXEL_MEAN: [123.675, 116.280, 103.530]
+ PIXEL_STD: [58.395, 57.120, 57.375]
diff --git a/annotator/OneFormer/configs/cityscapes/Base-Cityscapes-UnifiedSegmentation.yaml b/annotator/OneFormer/configs/cityscapes/Base-Cityscapes-UnifiedSegmentation.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..88ea243cd09b5e695bc236babc39c6889e628a1c
--- /dev/null
+++ b/annotator/OneFormer/configs/cityscapes/Base-Cityscapes-UnifiedSegmentation.yaml
@@ -0,0 +1,68 @@
+MODEL:
+ BACKBONE:
+ FREEZE_AT: 0
+ NAME: "build_resnet_backbone"
+ WEIGHTS: "detectron2://ImageNetPretrained/torchvision/R-50.pkl"
+ PIXEL_MEAN: [123.675, 116.280, 103.530]
+ PIXEL_STD: [58.395, 57.120, 57.375]
+ RESNETS:
+ DEPTH: 50
+ STEM_TYPE: "basic" # not used
+ STEM_OUT_CHANNELS: 64
+ STRIDE_IN_1X1: False
+ OUT_FEATURES: ["res2", "res3", "res4", "res5"]
+ NORM: "SyncBN" # use syncbn for cityscapes dataset
+ RES5_MULTI_GRID: [1, 1, 1] # not used
+DATASETS:
+ TRAIN: ("cityscapes_fine_panoptic_train",)
+ TEST_PANOPTIC: ("cityscapes_fine_panoptic_val",)
+ TEST_INSTANCE: ("cityscapes_fine_instance_seg_val",)
+ TEST_SEMANTIC: ("cityscapes_fine_sem_seg_val",)
+SOLVER:
+ IMS_PER_BATCH: 16
+ BASE_LR: 0.0001
+ MAX_ITER: 90000
+ WARMUP_FACTOR: 1.0
+ WARMUP_ITERS: 0
+ WEIGHT_DECAY: 0.05
+ OPTIMIZER: "ADAMW"
+ LR_SCHEDULER_NAME: "WarmupPolyLR"
+ BACKBONE_MULTIPLIER: 0.1
+ CLIP_GRADIENTS:
+ ENABLED: True
+ CLIP_TYPE: "full_model"
+ CLIP_VALUE: 0.01
+ NORM_TYPE: 2.0
+ AMP:
+ ENABLED: True
+INPUT:
+ MIN_SIZE_TRAIN: !!python/object/apply:eval ["[int(x * 0.1 * 1024) for x in range(5, 21)]"]
+ MIN_SIZE_TRAIN_SAMPLING: "choice"
+ MIN_SIZE_TEST: 1024
+ MAX_SIZE_TRAIN: 4096
+ MAX_SIZE_TEST: 2048
+ CROP:
+ ENABLED: True
+ TYPE: "absolute"
+ SIZE: (512, 1024)
+ SINGLE_CATEGORY_MAX_AREA: 1.0
+ COLOR_AUG_SSD: True
+ SIZE_DIVISIBILITY: -1
+ FORMAT: "RGB"
+ DATASET_MAPPER_NAME: "oneformer_unified"
+ MAX_SEQ_LEN: 77
+ TASK_SEQ_LEN: 77
+ TASK_PROB:
+ SEMANTIC: 0.33
+ INSTANCE: 0.66
+TEST:
+ EVAL_PERIOD: 5000
+ AUG:
+ ENABLED: False
+ MIN_SIZES: [512, 768, 1024, 1280, 1536, 1792]
+ MAX_SIZE: 4096
+ FLIP: True
+DATALOADER:
+ FILTER_EMPTY_ANNOTATIONS: True
+ NUM_WORKERS: 4
+VERSION: 2
\ No newline at end of file
diff --git a/annotator/OneFormer/configs/cityscapes/convnext/mapillary_pretrain_oneformer_convnext_large_bs16_90k.yaml b/annotator/OneFormer/configs/cityscapes/convnext/mapillary_pretrain_oneformer_convnext_large_bs16_90k.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..81d8ff1013834c1422cc35ce26c04d792f888ef6
--- /dev/null
+++ b/annotator/OneFormer/configs/cityscapes/convnext/mapillary_pretrain_oneformer_convnext_large_bs16_90k.yaml
@@ -0,0 +1,18 @@
+_BASE_: ../oneformer_R50_bs16_90k.yaml
+MODEL:
+ BACKBONE:
+ NAME: "D2ConvNeXt"
+ CONVNEXT:
+ IN_CHANNELS: 3
+ DEPTHS: [3, 3, 27, 3]
+ DIMS: [192, 384, 768, 1536]
+ DROP_PATH_RATE: 0.4
+ LSIT: 1.0
+ OUT_INDICES: [0, 1, 2, 3]
+ WEIGHTS: "250_16_convnext_l_oneformer_mapillary_300k.pth"
+ PIXEL_MEAN: [123.675, 116.280, 103.530]
+ PIXEL_STD: [58.395, 57.120, 57.375]
+ ONE_FORMER:
+ NUM_OBJECT_QUERIES: 250
+TEST:
+ DETECTIONS_PER_IMAGE: 250
diff --git a/annotator/OneFormer/configs/cityscapes/convnext/oneformer_convnext_large_bs16_90k.yaml b/annotator/OneFormer/configs/cityscapes/convnext/oneformer_convnext_large_bs16_90k.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..0bec3c0ad3964c17d1f1a00fd4ebc80c51ca7213
--- /dev/null
+++ b/annotator/OneFormer/configs/cityscapes/convnext/oneformer_convnext_large_bs16_90k.yaml
@@ -0,0 +1,18 @@
+_BASE_: ../oneformer_R50_bs16_90k.yaml
+MODEL:
+ BACKBONE:
+ NAME: "D2ConvNeXt"
+ CONVNEXT:
+ IN_CHANNELS: 3
+ DEPTHS: [3, 3, 27, 3]
+ DIMS: [192, 384, 768, 1536]
+ DROP_PATH_RATE: 0.4
+ LSIT: 1.0
+ OUT_INDICES: [0, 1, 2, 3]
+ WEIGHTS: "convnext_large_22k_1k_384.pkl"
+ PIXEL_MEAN: [123.675, 116.280, 103.530]
+ PIXEL_STD: [58.395, 57.120, 57.375]
+ ONE_FORMER:
+ NUM_OBJECT_QUERIES: 250
+TEST:
+ DETECTIONS_PER_IMAGE: 250
diff --git a/annotator/OneFormer/configs/cityscapes/convnext/oneformer_convnext_xlarge_bs16_90k.yaml b/annotator/OneFormer/configs/cityscapes/convnext/oneformer_convnext_xlarge_bs16_90k.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..d4c6047efb5a765d1a43a6790023dcf18acad85e
--- /dev/null
+++ b/annotator/OneFormer/configs/cityscapes/convnext/oneformer_convnext_xlarge_bs16_90k.yaml
@@ -0,0 +1,18 @@
+_BASE_: ../oneformer_R50_bs16_90k.yaml
+MODEL:
+ BACKBONE:
+ NAME: "D2ConvNeXt"
+ CONVNEXT:
+ IN_CHANNELS: 3
+ DEPTHS: [3, 3, 27, 3]
+ DIMS: [256, 512, 1024, 2048]
+ DROP_PATH_RATE: 0.4
+ LSIT: 1.0
+ OUT_INDICES: [0, 1, 2, 3]
+ WEIGHTS: "convnext_xlarge_22k_1k_384_ema.pkl"
+ PIXEL_MEAN: [123.675, 116.280, 103.530]
+ PIXEL_STD: [58.395, 57.120, 57.375]
+ ONE_FORMER:
+ NUM_OBJECT_QUERIES: 250
+TEST:
+ DETECTIONS_PER_IMAGE: 250
diff --git a/annotator/OneFormer/configs/cityscapes/dinat/oneformer_dinat_large_bs16_90k.yaml b/annotator/OneFormer/configs/cityscapes/dinat/oneformer_dinat_large_bs16_90k.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..09484df0c9b196601a246c1a6a8b1631efc9c8ad
--- /dev/null
+++ b/annotator/OneFormer/configs/cityscapes/dinat/oneformer_dinat_large_bs16_90k.yaml
@@ -0,0 +1,22 @@
+_BASE_: ../oneformer_R50_bs16_90k.yaml
+MODEL:
+ BACKBONE:
+ NAME: "D2DiNAT"
+ DiNAT:
+ EMBED_DIM: 192
+ MLP_RATIO: 2.0
+ DEPTHS: [3, 4, 18, 5]
+ NUM_HEADS: [6, 12, 24, 48]
+ KERNEL_SIZE: 7
+ DROP_PATH_RATE: 0.3
+ DILATIONS: [[1, 18, 1], [1, 5, 1, 9], [1, 2, 1, 3, 1, 4, 1, 2, 1, 3, 1, 4, 1, 2, 1, 3, 1, 4], [1, 2, 1, 2, 1]]
+ WEIGHTS: "dinat_large_in22k_224.pkl"
+ PIXEL_MEAN: [123.675, 116.280, 103.530]
+ PIXEL_STD: [58.395, 57.120, 57.375]
+ ONE_FORMER:
+ NUM_OBJECT_QUERIES: 250
+SOLVER:
+ AMP:
+ ENABLED: False
+TEST:
+ DETECTIONS_PER_IMAGE: 250
\ No newline at end of file
diff --git a/annotator/OneFormer/configs/cityscapes/oneformer_R50_bs16_90k.yaml b/annotator/OneFormer/configs/cityscapes/oneformer_R50_bs16_90k.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..a4746e47729978a623af3319315f7201ebe84841
--- /dev/null
+++ b/annotator/OneFormer/configs/cityscapes/oneformer_R50_bs16_90k.yaml
@@ -0,0 +1,59 @@
+_BASE_: Base-Cityscapes-UnifiedSegmentation.yaml
+MODEL:
+ META_ARCHITECTURE: "OneFormer"
+ SEM_SEG_HEAD:
+ NAME: "OneFormerHead"
+ IGNORE_VALUE: 255
+ NUM_CLASSES: 19
+ LOSS_WEIGHT: 1.0
+ CONVS_DIM: 256
+ MASK_DIM: 256
+ NORM: "GN"
+ # pixel decoder
+ PIXEL_DECODER_NAME: "MSDeformAttnPixelDecoder"
+ IN_FEATURES: ["res2", "res3", "res4", "res5"]
+ DEFORMABLE_TRANSFORMER_ENCODER_IN_FEATURES: ["res3", "res4", "res5"]
+ COMMON_STRIDE: 4
+ TRANSFORMER_ENC_LAYERS: 6
+ ONE_FORMER:
+ TRANSFORMER_DECODER_NAME: "ContrastiveMultiScaleMaskedTransformerDecoder"
+ TRANSFORMER_IN_FEATURE: "multi_scale_pixel_decoder"
+ DEEP_SUPERVISION: True
+ NO_OBJECT_WEIGHT: 0.1
+ CLASS_WEIGHT: 2.0
+ MASK_WEIGHT: 5.0
+ DICE_WEIGHT: 5.0
+ CONTRASTIVE_WEIGHT: 0.5
+ CONTRASTIVE_TEMPERATURE: 0.07
+ HIDDEN_DIM: 256
+ NUM_OBJECT_QUERIES: 150
+ USE_TASK_NORM: True
+ NHEADS: 8
+ DROPOUT: 0.1
+ DIM_FEEDFORWARD: 2048
+ ENC_LAYERS: 0
+ PRE_NORM: False
+ ENFORCE_INPUT_PROJ: False
+ SIZE_DIVISIBILITY: 32
+ ENC_LAYERS: 0
+ CLASS_DEC_LAYERS: 2
+ DEC_LAYERS: 10 # 9 decoder layers, add one for the loss on learnable query
+ TRAIN_NUM_POINTS: 12544
+ OVERSAMPLE_RATIO: 3.0
+ IMPORTANCE_SAMPLE_RATIO: 0.75
+ TEXT_ENCODER:
+ WIDTH: 256
+ CONTEXT_LENGTH: 77
+ NUM_LAYERS: 6
+ VOCAB_SIZE: 49408
+ PROJ_NUM_LAYERS: 2
+ N_CTX: 16
+ TEST:
+ SEMANTIC_ON: True
+ INSTANCE_ON: True
+ PANOPTIC_ON: True
+ OVERLAP_THRESHOLD: 0.8
+ OBJECT_MASK_THRESHOLD: 0.8
+ TASK: "panoptic"
+TEST:
+ DETECTIONS_PER_IMAGE: 150
diff --git a/annotator/OneFormer/configs/cityscapes/swin/oneformer_swin_large_bs16_90k.yaml b/annotator/OneFormer/configs/cityscapes/swin/oneformer_swin_large_bs16_90k.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..eeb202aa4d874bde88e3b355531f54b8ae6055f2
--- /dev/null
+++ b/annotator/OneFormer/configs/cityscapes/swin/oneformer_swin_large_bs16_90k.yaml
@@ -0,0 +1,20 @@
+_BASE_: ../oneformer_R50_bs16_90k.yaml
+MODEL:
+ BACKBONE:
+ NAME: "D2SwinTransformer"
+ SWIN:
+ EMBED_DIM: 192
+ DEPTHS: [2, 2, 18, 2]
+ NUM_HEADS: [6, 12, 24, 48]
+ WINDOW_SIZE: 12
+ APE: False
+ DROP_PATH_RATE: 0.3
+ PATCH_NORM: True
+ PRETRAIN_IMG_SIZE: 384
+ WEIGHTS: "swin_large_patch4_window12_384_22kto1k.pkl"
+ PIXEL_MEAN: [123.675, 116.280, 103.530]
+ PIXEL_STD: [58.395, 57.120, 57.375]
+ ONE_FORMER:
+ NUM_OBJECT_QUERIES: 250
+TEST:
+ DETECTIONS_PER_IMAGE: 250
diff --git a/annotator/OneFormer/configs/coco/Base-COCO-UnifiedSegmentation.yaml b/annotator/OneFormer/configs/coco/Base-COCO-UnifiedSegmentation.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..ccd24f348f9bc7d60dcdc4b74d887708e57cb8a8
--- /dev/null
+++ b/annotator/OneFormer/configs/coco/Base-COCO-UnifiedSegmentation.yaml
@@ -0,0 +1,54 @@
+MODEL:
+ BACKBONE:
+ FREEZE_AT: 0
+ NAME: "build_resnet_backbone"
+ WEIGHTS: "detectron2://ImageNetPretrained/torchvision/R-50.pkl"
+ PIXEL_MEAN: [123.675, 116.280, 103.530]
+ PIXEL_STD: [58.395, 57.120, 57.375]
+ RESNETS:
+ DEPTH: 50
+ STEM_TYPE: "basic" # not used
+ STEM_OUT_CHANNELS: 64
+ STRIDE_IN_1X1: False
+ OUT_FEATURES: ["res2", "res3", "res4", "res5"]
+ # NORM: "SyncBN"
+ RES5_MULTI_GRID: [1, 1, 1] # not used
+DATASETS:
+ TRAIN: ("coco_2017_train_panoptic_with_sem_seg",)
+ TEST_PANOPTIC: ("coco_2017_val_panoptic_with_sem_seg",) # to evaluate instance and semantic performance as well
+ TEST_INSTANCE: ("coco_2017_val",)
+ TEST_SEMANTIC: ("coco_2017_val_panoptic_with_sem_seg",)
+SOLVER:
+ IMS_PER_BATCH: 16
+ BASE_LR: 0.0001
+ STEPS: (327778, 355092)
+ MAX_ITER: 368750
+ WARMUP_FACTOR: 1.0
+ WARMUP_ITERS: 10
+ WEIGHT_DECAY: 0.05
+ OPTIMIZER: "ADAMW"
+ BACKBONE_MULTIPLIER: 0.1
+ CLIP_GRADIENTS:
+ ENABLED: True
+ CLIP_TYPE: "full_model"
+ CLIP_VALUE: 0.01
+ NORM_TYPE: 2.0
+ AMP:
+ ENABLED: True
+INPUT:
+ IMAGE_SIZE: 1024
+ MIN_SCALE: 0.1
+ MAX_SCALE: 2.0
+ FORMAT: "RGB"
+ DATASET_MAPPER_NAME: "coco_unified_lsj"
+ MAX_SEQ_LEN: 77
+ TASK_SEQ_LEN: 77
+ TASK_PROB:
+ SEMANTIC: 0.33
+ INSTANCE: 0.66
+TEST:
+ EVAL_PERIOD: 5000
+DATALOADER:
+ FILTER_EMPTY_ANNOTATIONS: True
+ NUM_WORKERS: 4
+VERSION: 2
diff --git a/annotator/OneFormer/configs/coco/dinat/oneformer_dinat_large_bs16_100ep.yaml b/annotator/OneFormer/configs/coco/dinat/oneformer_dinat_large_bs16_100ep.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..f3f96a7826d96039515056567562f393534493cc
--- /dev/null
+++ b/annotator/OneFormer/configs/coco/dinat/oneformer_dinat_large_bs16_100ep.yaml
@@ -0,0 +1,24 @@
+_BASE_: ../oneformer_R50_bs16_50ep.yaml
+MODEL:
+ BACKBONE:
+ NAME: "D2DiNAT"
+ DiNAT:
+ EMBED_DIM: 192
+ MLP_RATIO: 2.0
+ DEPTHS: [3, 4, 18, 5]
+ NUM_HEADS: [6, 12, 24, 48]
+ KERNEL_SIZE: 11
+ DROP_PATH_RATE: 0.3
+ DILATIONS: [[1, 20, 1], [1, 5, 1, 10], [1, 2, 1, 3, 1, 4, 1, 5, 1, 2, 1, 3, 1, 4, 1, 5, 1, 5], [1, 2, 1, 2, 1]]
+ WEIGHTS: "dinat_large_in22k_in1k_384_11x11.pkl"
+ PIXEL_MEAN: [123.675, 116.280, 103.530]
+ PIXEL_STD: [58.395, 57.120, 57.375]
+ ONE_FORMER:
+ NUM_OBJECT_QUERIES: 150
+SOLVER:
+ STEPS: (655556, 710184)
+ MAX_ITER: 737500
+ AMP:
+ ENABLED: False
+TEST:
+ DETECTIONS_PER_IMAGE: 150
\ No newline at end of file
diff --git a/annotator/OneFormer/configs/coco/oneformer_R50_bs16_50ep.yaml b/annotator/OneFormer/configs/coco/oneformer_R50_bs16_50ep.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..f768c8fa8b5e4fc1121e65e050053e0d8870cd73
--- /dev/null
+++ b/annotator/OneFormer/configs/coco/oneformer_R50_bs16_50ep.yaml
@@ -0,0 +1,59 @@
+_BASE_: Base-COCO-UnifiedSegmentation.yaml
+MODEL:
+ META_ARCHITECTURE: "OneFormer"
+ SEM_SEG_HEAD:
+ NAME: "OneFormerHead"
+ IGNORE_VALUE: 255
+ NUM_CLASSES: 133
+ LOSS_WEIGHT: 1.0
+ CONVS_DIM: 256
+ MASK_DIM: 256
+ NORM: "GN"
+ # pixel decoder
+ PIXEL_DECODER_NAME: "MSDeformAttnPixelDecoder"
+ IN_FEATURES: ["res2", "res3", "res4", "res5"]
+ DEFORMABLE_TRANSFORMER_ENCODER_IN_FEATURES: ["res3", "res4", "res5"]
+ COMMON_STRIDE: 4
+ TRANSFORMER_ENC_LAYERS: 6
+ ONE_FORMER:
+ TRANSFORMER_DECODER_NAME: "ContrastiveMultiScaleMaskedTransformerDecoder"
+ TRANSFORMER_IN_FEATURE: "multi_scale_pixel_decoder"
+ DEEP_SUPERVISION: True
+ NO_OBJECT_WEIGHT: 0.1
+ CLASS_WEIGHT: 2.0
+ MASK_WEIGHT: 5.0
+ DICE_WEIGHT: 5.0
+ CONTRASTIVE_WEIGHT: 0.5
+ CONTRASTIVE_TEMPERATURE: 0.07
+ HIDDEN_DIM: 256
+ NUM_OBJECT_QUERIES: 150
+ USE_TASK_NORM: True
+ NHEADS: 8
+ DROPOUT: 0.1
+ DIM_FEEDFORWARD: 2048
+ ENC_LAYERS: 0
+ PRE_NORM: False
+ ENFORCE_INPUT_PROJ: False
+ SIZE_DIVISIBILITY: 32
+ CLASS_DEC_LAYERS: 2
+ DEC_LAYERS: 10 # 9 decoder layers, add one for the loss on learnable query
+ TRAIN_NUM_POINTS: 12544
+ OVERSAMPLE_RATIO: 3.0
+ IMPORTANCE_SAMPLE_RATIO: 0.75
+ TEXT_ENCODER:
+ WIDTH: 256
+ CONTEXT_LENGTH: 77
+ NUM_LAYERS: 6
+ VOCAB_SIZE: 49408
+ PROJ_NUM_LAYERS: 2
+ N_CTX: 16
+ TEST:
+ SEMANTIC_ON: True
+ INSTANCE_ON: True
+ PANOPTIC_ON: True
+ DETECTION_ON: False
+ OVERLAP_THRESHOLD: 0.8
+ OBJECT_MASK_THRESHOLD: 0.8
+ TASK: "panoptic"
+TEST:
+ DETECTIONS_PER_IMAGE: 150
diff --git a/annotator/OneFormer/configs/coco/swin/oneformer_swin_large_bs16_100ep.yaml b/annotator/OneFormer/configs/coco/swin/oneformer_swin_large_bs16_100ep.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..39b45289a3f40895bd10fcd554d7852427d35a66
--- /dev/null
+++ b/annotator/OneFormer/configs/coco/swin/oneformer_swin_large_bs16_100ep.yaml
@@ -0,0 +1,28 @@
+_BASE_: ../oneformer_R50_bs16_50ep.yaml
+MODEL:
+ BACKBONE:
+ NAME: "D2SwinTransformer"
+
+ IS_DEMO: True
+ IS_TRAIN: False
+ SWIN:
+ EMBED_DIM: 192
+ DEPTHS: [2, 2, 18, 2]
+ NUM_HEADS: [6, 12, 24, 48]
+ WINDOW_SIZE: 12
+ APE: False
+ DROP_PATH_RATE: 0.3
+ PATCH_NORM: True
+ PRETRAIN_IMG_SIZE: 384
+ WEIGHTS: "./checkpoints/150_16_swin_l_oneformer_coco_100ep.pth"
+ PIXEL_MEAN: [123.675, 116.280, 103.530]
+ PIXEL_STD: [58.395, 57.120, 57.375]
+ ONE_FORMER:
+ NUM_OBJECT_QUERIES: 150
+SOLVER:
+ STEPS: (655556, 735184)
+ MAX_ITER: 737500
+ AMP:
+ ENABLED: False
+TEST:
+ DETECTIONS_PER_IMAGE: 150
diff --git a/annotator/OneFormer/configs/coco/swin/oneformer_swin_tiny_bs16_50ep.yaml b/annotator/OneFormer/configs/coco/swin/oneformer_swin_tiny_bs16_50ep.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..e2b5ff1a06aabf42bf5da922473f690a4681e160
--- /dev/null
+++ b/annotator/OneFormer/configs/coco/swin/oneformer_swin_tiny_bs16_50ep.yaml
@@ -0,0 +1,15 @@
+_BASE_: ../oneformer_R50_bs16_50ep.yaml
+MODEL:
+ BACKBONE:
+ NAME: "D2SwinTransformer"
+ SWIN:
+ EMBED_DIM: 96
+ DEPTHS: [2, 2, 6, 2]
+ NUM_HEADS: [3, 6, 12, 24]
+ WINDOW_SIZE: 7
+ APE: False
+ DROP_PATH_RATE: 0.3
+ PATCH_NORM: True
+ WEIGHTS: "swin_tiny_patch4_window7_224.pkl"
+ PIXEL_MEAN: [123.675, 116.280, 103.530]
+ PIXEL_STD: [58.395, 57.120, 57.375]
diff --git a/annotator/OneFormer/configs/mapillary_vistas/Base-Mapillary-UnifiedSegmentation.yaml b/annotator/OneFormer/configs/mapillary_vistas/Base-Mapillary-UnifiedSegmentation.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..b83daf8f5d3dc47fa241c6d5cf3c7b6b8273470c
--- /dev/null
+++ b/annotator/OneFormer/configs/mapillary_vistas/Base-Mapillary-UnifiedSegmentation.yaml
@@ -0,0 +1,68 @@
+MODEL:
+ BACKBONE:
+ FREEZE_AT: 0
+ NAME: "build_resnet_backbone"
+ WEIGHTS: "detectron2://ImageNetPretrained/torchvision/R-50.pkl"
+ PIXEL_MEAN: [123.675, 116.280, 103.530]
+ PIXEL_STD: [58.395, 57.120, 57.375]
+ RESNETS:
+ DEPTH: 50
+ STEM_TYPE: "basic" # not used
+ STEM_OUT_CHANNELS: 64
+ STRIDE_IN_1X1: False
+ OUT_FEATURES: ["res2", "res3", "res4", "res5"]
+ NORM: "SyncBN" # use syncbn for cityscapes dataset
+ RES5_MULTI_GRID: [1, 1, 1] # not used
+DATASETS:
+ TRAIN: ("mapillary_vistas_panoptic_train",)
+ TEST_PANOPTIC: ("mapillary_vistas_panoptic_val",)
+ TEST_INSTANCE: ("mapillary_vistas_panoptic_val",)
+ TEST_SEMANTIC: ("mapillary_vistas_sem_seg_val",)
+SOLVER:
+ IMS_PER_BATCH: 16
+ BASE_LR: 0.0001
+ MAX_ITER: 300000
+ WARMUP_FACTOR: 1.0
+ WARMUP_ITERS: 0
+ WEIGHT_DECAY: 0.05
+ OPTIMIZER: "ADAMW"
+ LR_SCHEDULER_NAME: "WarmupPolyLR"
+ BACKBONE_MULTIPLIER: 0.1
+ CLIP_GRADIENTS:
+ ENABLED: True
+ CLIP_TYPE: "full_model"
+ CLIP_VALUE: 0.01
+ NORM_TYPE: 2.0
+ AMP:
+ ENABLED: True
+INPUT:
+ MIN_SIZE_TRAIN: !!python/object/apply:eval ["[int(x * 0.1 * 2048) for x in range(5, 21)]"]
+ MIN_SIZE_TRAIN_SAMPLING: "choice"
+ MIN_SIZE_TEST: 2048
+ MAX_SIZE_TRAIN: 8192
+ MAX_SIZE_TEST: 2048
+ CROP:
+ ENABLED: True
+ TYPE: "absolute"
+ SIZE: (1024, 1024)
+ SINGLE_CATEGORY_MAX_AREA: 1.0
+ COLOR_AUG_SSD: True
+ SIZE_DIVISIBILITY: 1024 # used in dataset mapper
+ FORMAT: "RGB"
+ DATASET_MAPPER_NAME: "oneformer_unified"
+ MAX_SEQ_LEN: 77
+ TASK_SEQ_LEN: 77
+ TASK_PROB:
+ SEMANTIC: 0.50
+ INSTANCE: 0.50
+TEST:
+ EVAL_PERIOD: 30000
+ AUG:
+ ENABLED: False
+ MIN_SIZES: [512, 768, 1024, 1280, 1536, 1792]
+ MAX_SIZE: 4096
+ FLIP: True
+DATALOADER:
+ FILTER_EMPTY_ANNOTATIONS: True
+ NUM_WORKERS: 10
+VERSION: 2
\ No newline at end of file
diff --git a/annotator/OneFormer/configs/mapillary_vistas/convnext/cityscapes_pretrain_oneformer_convnext_large_bs16_300k.yaml b/annotator/OneFormer/configs/mapillary_vistas/convnext/cityscapes_pretrain_oneformer_convnext_large_bs16_300k.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..1baade11239c67200cc6bb8f041d20200ba214d3
--- /dev/null
+++ b/annotator/OneFormer/configs/mapillary_vistas/convnext/cityscapes_pretrain_oneformer_convnext_large_bs16_300k.yaml
@@ -0,0 +1,22 @@
+_BASE_: ../oneformer_R50_bs16_300k.yaml
+MODEL:
+ BACKBONE:
+ NAME: "D2ConvNeXt"
+ CONVNEXT:
+ IN_CHANNELS: 3
+ DEPTHS: [3, 3, 27, 3]
+ DIMS: [192, 384, 768, 1536]
+ DROP_PATH_RATE: 0.4
+ LSIT: 1.0
+ OUT_INDICES: [0, 1, 2, 3]
+ WEIGHTS: "convnext_large_22k_1k_384.pkl"
+ PIXEL_MEAN: [123.675, 116.280, 103.530]
+ PIXEL_STD: [58.395, 57.120, 57.375]
+ ONE_FORMER:
+ NUM_OBJECT_QUERIES: 250
+INPUT:
+ TASK_PROB:
+ SEMANTIC: 0.33
+ INSTANCE: 0.66
+TEST:
+ DETECTIONS_PER_IMAGE: 250
diff --git a/annotator/OneFormer/configs/mapillary_vistas/convnext/oneformer_convnext_large_bs16_300k.yaml b/annotator/OneFormer/configs/mapillary_vistas/convnext/oneformer_convnext_large_bs16_300k.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..615c140a5843b157e135cae4c0331ab52e4a456b
--- /dev/null
+++ b/annotator/OneFormer/configs/mapillary_vistas/convnext/oneformer_convnext_large_bs16_300k.yaml
@@ -0,0 +1,18 @@
+_BASE_: ../oneformer_R50_bs16_300k.yaml
+MODEL:
+ BACKBONE:
+ NAME: "D2ConvNeXt"
+ CONVNEXT:
+ IN_CHANNELS: 3
+ DEPTHS: [3, 3, 27, 3]
+ DIMS: [192, 384, 768, 1536]
+ DROP_PATH_RATE: 0.4
+ LSIT: 1.0
+ OUT_INDICES: [0, 1, 2, 3]
+ WEIGHTS: "convnext_large_22k_1k_384.pkl"
+ PIXEL_MEAN: [123.675, 116.280, 103.530]
+ PIXEL_STD: [58.395, 57.120, 57.375]
+ ONE_FORMER:
+ NUM_OBJECT_QUERIES: 250
+TEST:
+ DETECTIONS_PER_IMAGE: 250
diff --git a/annotator/OneFormer/configs/mapillary_vistas/dinat/oneformer_dinat_large_bs16_300k.yaml b/annotator/OneFormer/configs/mapillary_vistas/dinat/oneformer_dinat_large_bs16_300k.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..b2819fe67d4bbff1c9d19540ece96576fdafb302
--- /dev/null
+++ b/annotator/OneFormer/configs/mapillary_vistas/dinat/oneformer_dinat_large_bs16_300k.yaml
@@ -0,0 +1,22 @@
+_BASE_: ../oneformer_R50_bs16_300k.yaml
+MODEL:
+ BACKBONE:
+ NAME: "D2DiNAT"
+ DiNAT:
+ EMBED_DIM: 192
+ MLP_RATIO: 2.0
+ DEPTHS: [3, 4, 18, 5]
+ NUM_HEADS: [6, 12, 24, 48]
+ KERNEL_SIZE: 11
+ DROP_PATH_RATE: 0.3
+ DILATIONS: [[1, 20, 1], [1, 5, 1, 10], [1, 2, 1, 3, 1, 4, 1, 5, 1, 2, 1, 3, 1, 4, 1, 5, 1, 5], [1, 2, 1, 2, 1]]
+ WEIGHTS: "dinat_large_in22k_in1k_384_11x11.pkl"
+ PIXEL_MEAN: [123.675, 116.280, 103.530]
+ PIXEL_STD: [58.395, 57.120, 57.375]
+ ONE_FORMER:
+ NUM_OBJECT_QUERIES: 250
+SOLVER:
+ AMP:
+ ENABLED: False
+TEST:
+ DETECTIONS_PER_IMAGE: 250
\ No newline at end of file
diff --git a/annotator/OneFormer/configs/mapillary_vistas/oneformer_R50_bs16_300k.yaml b/annotator/OneFormer/configs/mapillary_vistas/oneformer_R50_bs16_300k.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..2833784911a0caf9adf26640a07d8002bf9b976b
--- /dev/null
+++ b/annotator/OneFormer/configs/mapillary_vistas/oneformer_R50_bs16_300k.yaml
@@ -0,0 +1,59 @@
+_BASE_: Base-Mapillary-UnifiedSegmentation.yaml
+MODEL:
+ META_ARCHITECTURE: "OneFormer"
+ SEM_SEG_HEAD:
+ NAME: "OneFormerHead"
+ IGNORE_VALUE: 65
+ NUM_CLASSES: 65
+ LOSS_WEIGHT: 1.0
+ CONVS_DIM: 256
+ MASK_DIM: 256
+ NORM: "GN"
+ # pixel decoder
+ PIXEL_DECODER_NAME: "MSDeformAttnPixelDecoder"
+ IN_FEATURES: ["res2", "res3", "res4", "res5"]
+ DEFORMABLE_TRANSFORMER_ENCODER_IN_FEATURES: ["res3", "res4", "res5"]
+ COMMON_STRIDE: 4
+ TRANSFORMER_ENC_LAYERS: 6
+ ONE_FORMER:
+ TRANSFORMER_DECODER_NAME: "ContrastiveMultiScaleMaskedTransformerDecoder"
+ TRANSFORMER_IN_FEATURE: "multi_scale_pixel_decoder"
+ DEEP_SUPERVISION: True
+ NO_OBJECT_WEIGHT: 0.1
+ CLASS_WEIGHT: 2.0
+ MASK_WEIGHT: 5.0
+ DICE_WEIGHT: 5.0
+ CONTRASTIVE_WEIGHT: 0.5
+ CONTRASTIVE_TEMPERATURE: 0.07
+ HIDDEN_DIM: 256
+ NUM_OBJECT_QUERIES: 150
+ USE_TASK_NORM: True
+ NHEADS: 8
+ DROPOUT: 0.1
+ DIM_FEEDFORWARD: 2048
+ ENC_LAYERS: 0
+ PRE_NORM: False
+ ENFORCE_INPUT_PROJ: False
+ SIZE_DIVISIBILITY: 32
+ ENC_LAYERS: 0
+ CLASS_DEC_LAYERS: 2
+ DEC_LAYERS: 10 # 9 decoder layers, add one for the loss on learnable query
+ TRAIN_NUM_POINTS: 12544
+ OVERSAMPLE_RATIO: 3.0
+ IMPORTANCE_SAMPLE_RATIO: 0.75
+ TEXT_ENCODER:
+ WIDTH: 256
+ CONTEXT_LENGTH: 77
+ NUM_LAYERS: 6
+ VOCAB_SIZE: 49408
+ PROJ_NUM_LAYERS: 2
+ N_CTX: 16
+ TEST:
+ SEMANTIC_ON: True
+ INSTANCE_ON: True
+ PANOPTIC_ON: True
+ OVERLAP_THRESHOLD: 0.8
+ OBJECT_MASK_THRESHOLD: 0.8
+ TASK: "panoptic"
+TEST:
+ DETECTIONS_PER_IMAGE: 150
diff --git a/annotator/OneFormer/configs/mapillary_vistas/swin/oneformer_swin_large_bs16_300k.yaml b/annotator/OneFormer/configs/mapillary_vistas/swin/oneformer_swin_large_bs16_300k.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..86da20f346e015f73c4d736537e130846ca921d7
--- /dev/null
+++ b/annotator/OneFormer/configs/mapillary_vistas/swin/oneformer_swin_large_bs16_300k.yaml
@@ -0,0 +1,20 @@
+_BASE_: ../oneformer_R50_bs16_300k.yaml
+MODEL:
+ BACKBONE:
+ NAME: "D2SwinTransformer"
+ SWIN:
+ EMBED_DIM: 192
+ DEPTHS: [2, 2, 18, 2]
+ NUM_HEADS: [6, 12, 24, 48]
+ WINDOW_SIZE: 12
+ APE: False
+ DROP_PATH_RATE: 0.3
+ PATCH_NORM: True
+ PRETRAIN_IMG_SIZE: 384
+ WEIGHTS: "swin_large_patch4_window12_384_22kto1k.pkl"
+ PIXEL_MEAN: [123.675, 116.280, 103.530]
+ PIXEL_STD: [58.395, 57.120, 57.375]
+ ONE_FORMER:
+ NUM_OBJECT_QUERIES: 250
+TEST:
+ DETECTIONS_PER_IMAGE: 250
diff --git a/annotator/OneFormer/datasets/README.md b/annotator/OneFormer/datasets/README.md
new file mode 100644
index 0000000000000000000000000000000000000000..b7b441d8678ba7d01d10890bdea0394e503ed6f3
--- /dev/null
+++ b/annotator/OneFormer/datasets/README.md
@@ -0,0 +1,168 @@
+# Prepare Datasets for OneFormer
+
+- A dataset can be used by accessing [DatasetCatalog](https://detectron2.readthedocs.io/modules/data.html#detectron2.data.DatasetCatalog) for its data, or [MetadataCatalog](https://detectron2.readthedocs.io/modules/data.html#detectron2.data.MetadataCatalog) for its metadata (class names, etc).
+- This document explains how to setup the builtin datasets so they can be used by the above APIs. [Training OneFormer with Custom Datasets](https://github.com/SHI-Labs/OneFormer/tree/main/datasets/custom_datasets) gives a deeper dive on how to train OneFormer with custom datasets.
+- Detectron2 has builtin support for a few datasets. The datasets are assumed to exist in a directory specified by the environment variable `DETECTRON2_DATASETS`. Under this directory, detectron2 will look for datasets in the structure described below, if needed.
+
+ ```text
+ $DETECTRON2_DATASETS/
+ ADEChallengeData2016/
+ cityscapes/
+ coco/
+ mapillary_vistas/
+ ```
+
+- You can set the location for builtin datasets by `export DETECTRON2_DATASETS=/path/to/datasets`. If left unset, the default is `./datasets` relative to your current working directory.
+
+
+## Expected dataset structure for [ADE20K](http://sceneparsing.csail.mit.edu/)
+
+```text
+ADEChallengeData2016/
+ images/
+ annotations/
+ objectInfo150.txt
+ # download instance annotation
+ annotations_instance/
+ # generated by prepare_ade20k_sem_seg.py
+ annotations_detectron2/
+ # below are generated by prepare_ade20k_pan_seg.py
+ ade20k_panoptic_{train,val}.json
+ ade20k_panoptic_{train,val}/
+ # below are generated by prepare_ade20k_ins_seg.py
+ ade20k_instance_{train,val}.json
+```
+
+- Generate `annotations_detectron2`:
+
+ ```bash
+ python datasets/prepare_ade20k_sem_seg.py
+ ```
+
+- Install panopticapi by:
+
+ ```bash
+ pip install git+https://github.com/cocodataset/panopticapi.git
+ ```
+
+- Download the instance annotation from :
+
+ ```bash
+ wget http://sceneparsing.csail.mit.edu/data/ChallengeData2017/annotations_instance.tar
+ ```
+
+- Then, run `python datasets/prepare_ade20k_pan_seg.py`, to combine semantic and instance annotations for panoptic annotations.
+
+- Run `python datasets/prepare_ade20k_ins_seg.py`, to extract instance annotations in COCO format.
+
+## Expected dataset structure for [Cityscapes](https://www.cityscapes-dataset.com/downloads/)
+
+```text
+cityscapes/
+ gtFine/
+ train/
+ aachen/
+ color.png, instanceIds.png, labelIds.png, polygons.json,
+ labelTrainIds.png
+ ...
+ val/
+ test/
+ # below are generated Cityscapes panoptic annotation
+ cityscapes_panoptic_train.json
+ cityscapes_panoptic_train/
+ cityscapes_panoptic_val.json
+ cityscapes_panoptic_val/
+ cityscapes_panoptic_test.json
+ cityscapes_panoptic_test/
+ leftImg8bit/
+ train/
+ val/
+ test/
+```
+
+- Login and download the dataset
+
+ ```bash
+ wget --keep-session-cookies --save-cookies=cookies.txt --post-data 'username=myusername&password=mypassword&submit=Login' https://www.cityscapes-dataset.com/login/
+ ######## gtFine
+ wget --load-cookies cookies.txt --content-disposition https://www.cityscapes-dataset.com/file-handling/?packageID=1
+ ######## leftImg8bit
+ wget --load-cookies cookies.txt --content-disposition https://www.cityscapes-dataset.com/file-handling/?packageID=3
+ ```
+
+- Install cityscapes scripts by:
+
+ ```bash
+ pip install git+https://github.com/mcordts/cityscapesScripts.git
+ ```
+
+- To create labelTrainIds.png, first prepare the above structure, then run cityscapesescript with:
+
+ ```bash
+ git clone https://github.com/mcordts/cityscapesScripts.git
+ ```
+
+ ```bash
+ CITYSCAPES_DATASET=/path/to/abovementioned/cityscapes python cityscapesScripts/cityscapesscripts/preparation/createTrainIdLabelImgs.py
+ ```
+
+ These files are not needed for instance segmentation.
+
+- To generate Cityscapes panoptic dataset, run cityscapesescript with:
+
+ ```bash
+ CITYSCAPES_DATASET=/path/to/abovementioned/cityscapes python cityscapesScripts/cityscapesscripts/preparation/createPanopticImgs.py
+ ```
+
+ These files are not needed for semantic and instance segmentation.
+
+## Expected dataset structure for [COCO](https://cocodataset.org/#download)
+
+```text
+coco/
+ annotations/
+ instances_{train,val}2017.json
+ panoptic_{train,val}2017.json
+ caption_{train,val}2017.json
+ # evaluate on instance labels derived from panoptic annotations
+ panoptic2instances_val2017.json
+ {train,val}2017/
+ # image files that are mentioned in the corresponding json
+ panoptic_{train,val}2017/ # png annotations
+ panoptic_semseg_{train,val}2017/ # generated by the script mentioned below
+```
+
+- Install panopticapi by:
+
+ ```bash
+ pip install git+https://github.com/cocodataset/panopticapi.git
+ ```
+
+- Then, run `python datasets/prepare_coco_semantic_annos_from_panoptic_annos.py`, to extract semantic annotations from panoptic annotations (only used for evaluation).
+
+- Then run the following command to convert the panoptic json into instance json format (used for evaluation on instance segmentation task):
+
+ ```bash
+ python datasets/panoptic2detection_coco_format.py --things_only
+ ```
+
+## Expected dataset structure for [Mapillary Vistas](https://www.mapillary.com/dataset/vistas)
+
+```text
+mapillary_vistas/
+ training/
+ images/
+ instances/
+ labels/
+ panoptic/
+ validation/
+ images/
+ instances/
+ labels/
+ panoptic/
+ mapillary_vistas_instance_{train,val}.json # generated by the script mentioned below
+```
+
+No preprocessing is needed for Mapillary Vistas on semantic and panoptic segmentation.
+
+We do not evaluate for the instance segmentation task on the Mapillary Vistas dataset.
diff --git a/annotator/OneFormer/datasets/ade20k_instance_catid_mapping.txt b/annotator/OneFormer/datasets/ade20k_instance_catid_mapping.txt
new file mode 100644
index 0000000000000000000000000000000000000000..ba388d09cc00fa92a733d2a605f7068dc2271f82
--- /dev/null
+++ b/annotator/OneFormer/datasets/ade20k_instance_catid_mapping.txt
@@ -0,0 +1,104 @@
+Instacne100 SceneParse150 FullADE20K
+1 8 165
+2 9 3055
+3 11 350
+4 13 1831
+5 15 774
+5 15 783
+6 16 2684
+7 19 687
+8 20 471
+9 21 401
+10 23 1735
+11 24 2473
+12 25 2329
+13 28 1564
+14 31 57
+15 32 2272
+16 33 907
+17 34 724
+18 36 2985
+18 36 533
+19 37 1395
+20 38 155
+21 39 2053
+22 40 689
+23 42 266
+24 43 581
+25 44 2380
+26 45 491
+27 46 627
+28 48 2388
+29 50 943
+30 51 2096
+31 54 2530
+32 56 420
+33 57 1948
+34 58 1869
+35 59 2251
+36 63 239
+37 65 571
+38 66 2793
+39 67 978
+40 68 236
+41 70 181
+42 71 629
+43 72 2598
+44 73 1744
+45 74 1374
+46 75 591
+47 76 2679
+48 77 223
+49 79 47
+50 81 327
+51 82 2821
+52 83 1451
+53 84 2880
+54 86 480
+55 87 77
+56 88 2616
+57 89 246
+57 89 247
+58 90 2733
+59 91 14
+60 93 38
+61 94 1936
+62 96 120
+63 98 1702
+64 99 249
+65 103 2928
+66 104 2337
+67 105 1023
+68 108 2989
+69 109 1930
+70 111 2586
+71 112 131
+72 113 146
+73 116 95
+74 117 1563
+75 119 1708
+76 120 103
+77 121 1002
+78 122 2569
+79 124 2833
+80 125 1551
+81 126 1981
+82 127 29
+83 128 187
+84 130 747
+85 131 2254
+86 133 2262
+87 134 1260
+88 135 2243
+89 136 2932
+90 137 2836
+91 138 2850
+92 139 64
+93 140 894
+94 143 1919
+95 144 1583
+96 145 318
+97 147 2046
+98 148 1098
+99 149 530
+100 150 954
diff --git a/annotator/OneFormer/datasets/custom_datasets/README.md b/annotator/OneFormer/datasets/custom_datasets/README.md
new file mode 100644
index 0000000000000000000000000000000000000000..47563b8bff6971ad615f0316aa6feb3f4bd97ce8
--- /dev/null
+++ b/annotator/OneFormer/datasets/custom_datasets/README.md
@@ -0,0 +1,35 @@
+# Training OneFormer with Custom Datasets
+
+OneFormer advocates the usage of panoptic annotations along with its task-conditioned joint training strategy. However, if panoptic annotations are not available, then also OneFormer can be trained using only the instance or semantic annotations on custom datasets. We provide some guidelines for training with custom datasets.
+
+## Register your New Dataset
+
+- OneFormer uses the information (class names, thing classes, etc.) stored in a dataset's metadata while preparing a dataset dictionary using a [`dataset_mapper`](https://github.com/SHI-Labs/OneFormer/tree/main/oneformer/data/dataset_mappers).
+
+- [Use Custom Datasets](https://detectron2.readthedocs.io/en/latest/tutorials/datasets.html) gives a deeper dive into registering a new custom dataset.
+
+## Training with Available Panoptic Annotations
+
+- To prepare the dataset dictionary for each iteration during training, OneFormer uses a [`dataset_mapper`](https://github.com/SHI-Labs/OneFormer/tree/main/oneformer/data/dataset_mappers) class.
+
+- Originally, we provide two `dataset_mapper` classes which support task-conditioned joint training using the panoptic annotations:
+ - [`COCOUnifiedNewBaselineDatasetMapper`](https://github.com/SHI-Labs/OneFormer/blob/5e04c9aaffd9bc73020d2238757f62346fe778c0/oneformer/data/dataset_mappers/coco_unified_new_baseline_dataset_mapper.py#L56): Specifically designed for COCO annotation format.
+ - [`OneFormerUnifiedDatasetMapper`](https://github.com/SHI-Labs/OneFormer/blob/5e04c9aaffd9bc73020d2238757f62346fe778c0/oneformer/data/dataset_mappers/oneformer_unified_dataset_mapper.py#L26): General annotation format.
+
+- If you have panoptic annotations for your custom dataset, you may use these dataset_mapper classes directly after registering your dataset. You may also tune the [task sampling probabilities in the corresponding config file](https://github.com/SHI-Labs/OneFormer/blob/5e04c9aaffd9bc73020d2238757f62346fe778c0/configs/ade20k/Base-ADE20K-UnifiedSegmentation.yaml#L55).
+
+- If you want to train using only the instance or semantic annotation, please follow the next section on preparing a custom dataset mapper class.
+
+## Write a Custom Dataset Mapper Class
+
+- If you want to train using only instance or semantic annotations, write your custom dataset mapper class and add it to the [`build_train_loader`](https://github.com/SHI-Labs/OneFormer/blob/5e04c9aaffd9bc73020d2238757f62346fe778c0/train_net.py#L156) method.
+
+- We provide a few templates for custom dataset mappers:
+ - [`InstanceCOCOCustomNewBaselineDatasetMapper`](https://github.com/SHI-Labs/OneFormer/blob/a7fae86ce5791a93132c059c1bdfc79c9f842820/datasets/custom_datasets/instance_coco_custom_dataset_mapper.py#L72): Specifically designed for COCO instance annotation format.
+ - [`InstanceOneFormerCustomDatasetMapper`](https://github.com/SHI-Labs/OneFormer/blob/a7fae86ce5791a93132c059c1bdfc79c9f842820/datasets/custom_datasets/instance_oneformer_custom_dataset_mapper.py#L26): General instance annotation format.
+ - [`SemanticOneFormerCustomDatasetMapper`](https://github.com/SHI-Labs/OneFormer/blob/a7fae86ce5791a93132c059c1bdfc79c9f842820/datasets/custom_datasets/semantic_oneformer_custom_dataset_mapper.py#L26): General semantic annotation format.
+
+- Remember to register your custom dataset before training.
+
+
+Now you are all set to train OneFormer using your custom dataset!
\ No newline at end of file
diff --git a/annotator/OneFormer/datasets/custom_datasets/instance_coco_custom_dataset_mapper.py b/annotator/OneFormer/datasets/custom_datasets/instance_coco_custom_dataset_mapper.py
new file mode 100644
index 0000000000000000000000000000000000000000..794310884c3ba4762ec7ad17e3f54199900c44d5
--- /dev/null
+++ b/annotator/OneFormer/datasets/custom_datasets/instance_coco_custom_dataset_mapper.py
@@ -0,0 +1,235 @@
+# ------------------------------------------------------------------------------
+# Reference: https://github.com/facebookresearch/Mask2Former/blob/main/mask2former/data/dataset_mappers/coco_instance_new_baseline_dataset_mapper.py
+# Modified by Jitesh Jain (https://github.com/praeclarumjj3)
+# ------------------------------------------------------------------------------
+
+import copy
+import logging
+
+import numpy as np
+import torch
+
+from detectron2.data import MetadataCatalog
+from detectron2.config import configurable
+from detectron2.data import detection_utils as utils
+from detectron2.data import transforms as T
+from oneformer.data.tokenizer import SimpleTokenizer, Tokenize
+from pycocotools import mask as coco_mask
+
+__all__ = ["InstanceCOCOCustomNewBaselineDatasetMapper"]
+
+
+def convert_coco_poly_to_mask(segmentations, height, width):
+ masks = []
+ for polygons in segmentations:
+ rles = coco_mask.frPyObjects(polygons, height, width)
+ mask = coco_mask.decode(rles)
+ if len(mask.shape) < 3:
+ mask = mask[..., None]
+ mask = torch.as_tensor(mask, dtype=torch.uint8)
+ mask = mask.any(dim=2)
+ masks.append(mask)
+ if masks:
+ masks = torch.stack(masks, dim=0)
+ else:
+ masks = torch.zeros((0, height, width), dtype=torch.uint8)
+ return masks
+
+
+def build_transform_gen(cfg, is_train):
+ """
+ Create a list of default :class:`Augmentation` from config.
+ Now it includes resizing and flipping.
+ Returns:
+ list[Augmentation]
+ """
+ assert is_train, "Only support training augmentation"
+ image_size = cfg.INPUT.IMAGE_SIZE
+ min_scale = cfg.INPUT.MIN_SCALE
+ max_scale = cfg.INPUT.MAX_SCALE
+
+ augmentation = []
+
+ if cfg.INPUT.RANDOM_FLIP != "none":
+ augmentation.append(
+ T.RandomFlip(
+ horizontal=cfg.INPUT.RANDOM_FLIP == "horizontal",
+ vertical=cfg.INPUT.RANDOM_FLIP == "vertical",
+ )
+ )
+
+ augmentation.extend([
+ T.ResizeScale(
+ min_scale=min_scale, max_scale=max_scale, target_height=image_size, target_width=image_size
+ ),
+ T.FixedSizeCrop(crop_size=(image_size, image_size)),
+ ])
+
+ return augmentation
+
+
+# This is specifically designed for the COCO Instance Segmentation dataset.
+class InstanceCOCOCustomNewBaselineDatasetMapper:
+ """
+ A callable which takes a dataset dict in Detectron2 Dataset format,
+ and map it into a format used by OneFormer for custom instance segmentation using COCO format.
+
+ The callable currently does the following:
+
+ 1. Read the image from "file_name"
+ 2. Applies geometric transforms to the image and annotation
+ 3. Find and applies suitable cropping to the image and annotation
+ 4. Prepare image and annotation to Tensors
+ """
+
+ @configurable
+ def __init__(
+ self,
+ is_train=True,
+ *,
+ num_queries,
+ tfm_gens,
+ meta,
+ image_format,
+ max_seq_len,
+ task_seq_len,
+ ):
+ """
+ NOTE: this interface is experimental.
+ Args:
+ is_train: for training or inference
+ augmentations: a list of augmentations or deterministic transforms to apply
+ crop_gen: crop augmentation
+ tfm_gens: data augmentation
+ image_format: an image format supported by :func:`detection_utils.read_image`.
+ """
+ self.tfm_gens = tfm_gens
+ logging.getLogger(__name__).info(
+ "[InstanceCOCOCustomNewBaselineDatasetMapper] Full TransformGens used in training: {}".format(
+ str(self.tfm_gens)
+ )
+ )
+
+ self.img_format = image_format
+ self.is_train = is_train
+ self.meta = meta
+ self.num_queries = num_queries
+
+ self.things = []
+ for k,v in self.meta.thing_dataset_id_to_contiguous_id.items():
+ self.things.append(v)
+ self.class_names = self.meta.thing_classes
+ self.text_tokenizer = Tokenize(SimpleTokenizer(), max_seq_len=max_seq_len)
+ self.task_tokenizer = Tokenize(SimpleTokenizer(), max_seq_len=task_seq_len)
+
+ @classmethod
+ def from_config(cls, cfg, is_train=True):
+ # Build augmentation
+ tfm_gens = build_transform_gen(cfg, is_train)
+ dataset_names = cfg.DATASETS.TRAIN
+ meta = MetadataCatalog.get(dataset_names[0])
+
+ ret = {
+ "is_train": is_train,
+ "meta": meta,
+ "tfm_gens": tfm_gens,
+ "image_format": cfg.INPUT.FORMAT,
+ "num_queries": cfg.MODEL.ONE_FORMER.NUM_OBJECT_QUERIES - cfg.MODEL.TEXT_ENCODER.N_CTX,
+ "task_seq_len": cfg.INPUT.TASK_SEQ_LEN,
+ "max_seq_len": cfg.INPUT.MAX_SEQ_LEN,
+ }
+ return ret
+
+ def _get_texts(self, classes, num_class_obj):
+
+ classes = list(np.array(classes))
+ texts = ["an instance photo"] * self.num_queries
+
+ for class_id in classes:
+ cls_name = self.class_names[class_id]
+ num_class_obj[cls_name] += 1
+
+ num = 0
+ for i, cls_name in enumerate(self.class_names):
+ if num_class_obj[cls_name] > 0:
+ for _ in range(num_class_obj[cls_name]):
+ if num >= len(texts):
+ break
+ texts[num] = f"a photo with a {cls_name}"
+ num += 1
+
+ return texts
+
+ def __call__(self, dataset_dict):
+ """
+ Args:
+ dataset_dict (dict): Metadata of one image, in Detectron2 Dataset format.
+
+ Returns:
+ dict: a format that builtin models in detectron2 accept
+ """
+ dataset_dict = copy.deepcopy(dataset_dict) # it will be modified by code below
+ image = utils.read_image(dataset_dict["file_name"], format=self.img_format)
+ utils.check_image_size(dataset_dict, image)
+
+ # TODO: get padding mask
+ # by feeding a "segmentation mask" to the same transforms
+ padding_mask = np.ones(image.shape[:2])
+
+ image, transforms = T.apply_transform_gens(self.tfm_gens, image)
+ # the crop transformation has default padding value 0 for segmentation
+ padding_mask = transforms.apply_segmentation(padding_mask)
+ padding_mask = ~ padding_mask.astype(bool)
+
+ image_shape = image.shape[:2] # h, w
+
+ # Pytorch's dataloader is efficient on torch.Tensor due to shared-memory,
+ # but not efficient on large generic data structures due to the use of pickle & mp.Queue.
+ # Therefore it's important to use torch.Tensor.
+ dataset_dict["image"] = torch.as_tensor(np.ascontiguousarray(image.transpose(2, 0, 1)))
+ dataset_dict["padding_mask"] = torch.as_tensor(np.ascontiguousarray(padding_mask))
+
+ if not self.is_train:
+ # USER: Modify this if you want to keep them for some reason.
+ dataset_dict.pop("annotations", None)
+ return dataset_dict
+
+ if "annotations" in dataset_dict:
+ # USER: Modify this if you want to keep them for some reason.
+ for anno in dataset_dict["annotations"]:
+ anno.pop("keypoints", None)
+
+ # USER: Implement additional transformations if you have other types of data
+ annos = [
+ utils.transform_instance_annotations(obj, transforms, image_shape)
+ for obj in dataset_dict.pop("annotations")
+ if obj.get("iscrowd", 0) == 0
+ ]
+
+ instances = utils.annotations_to_instances(annos, image_shape)
+
+ instances.gt_boxes = instances.gt_masks.get_bounding_boxes()
+ # Need to filter empty instances first (due to augmentation)
+ instances = utils.filter_empty_instances(instances)
+ # Generate masks from polygon
+ h, w = instances.image_size
+ # image_size_xyxy = torch.as_tensor([w, h, w, h], dtype=torch.float)
+ if hasattr(instances, 'gt_masks'):
+ gt_masks = instances.gt_masks
+ gt_masks = convert_coco_poly_to_mask(gt_masks.polygons, h, w)
+ instances.gt_masks = gt_masks
+
+ num_class_obj = {}
+ for name in self.class_names:
+ num_class_obj[name] = 0
+
+ task = "The task is instance"
+ text = self._get_texts(instances.gt_classes, num_class_obj)
+
+ dataset_dict["instances"] = instances
+ dataset_dict["orig_shape"] = image_shape
+ dataset_dict["task"] = task
+ dataset_dict["text"] = text
+ dataset_dict["thing_ids"] = self.things
+
+ return dataset_dict
diff --git a/annotator/OneFormer/datasets/custom_datasets/instance_oneformer_custom_dataset_mapper.py b/annotator/OneFormer/datasets/custom_datasets/instance_oneformer_custom_dataset_mapper.py
new file mode 100644
index 0000000000000000000000000000000000000000..5743e637f940c1d963879ca5d3afc57159539e51
--- /dev/null
+++ b/annotator/OneFormer/datasets/custom_datasets/instance_oneformer_custom_dataset_mapper.py
@@ -0,0 +1,245 @@
+# ------------------------------------------------------------------------------
+# Reference: https://github.com/facebookresearch/Mask2Former/blob/main/mask2former/data/dataset_mappers/mask_former_instance_dataset_mapper.py
+# Modified by Jitesh Jain (https://github.com/praeclarumjj3)
+# ------------------------------------------------------------------------------
+
+import copy
+import logging
+import os
+
+import numpy as np
+import torch
+from torch.nn import functional as F
+
+from detectron2.config import configurable
+from detectron2.data import detection_utils as utils
+from detectron2.data import transforms as T
+from detectron2.structures import BitMasks, Instances, polygons_to_bitmask
+from detectron2.data import MetadataCatalog
+from detectron2.projects.point_rend import ColorAugSSDTransform
+from oneformer.data.tokenizer import SimpleTokenizer, Tokenize
+import pycocotools.mask as mask_util
+
+__all__ = ["InstanceOneFormerCustomDatasetMapper"]
+
+
+class InstanceOneFormerCustomDatasetMapper:
+ """
+ A callable which takes a dataset dict in Detectron2 Dataset format,
+ and map it into a format used by OneFormer custom instance segmentation.
+
+ The callable currently does the following:
+
+ 1. Read the image from "file_name"
+ 2. Applies geometric transforms to the image and annotation
+ 3. Find and applies suitable cropping to the image and annotation
+ 4. Prepare image and annotation to Tensors
+ """
+
+ @configurable
+ def __init__(
+ self,
+ is_train=True,
+ *,
+ name,
+ num_queries,
+ meta,
+ augmentations,
+ image_format,
+ size_divisibility,
+ task_seq_len,
+ max_seq_len,
+ ):
+ """
+ NOTE: this interface is experimental.
+ Args:
+ is_train: for training or inference
+ augmentations: a list of augmentations or deterministic transforms to apply
+ image_format: an image format supported by :func:`detection_utils.read_image`.
+ ignore_label: the label that is ignored to evaluation
+ size_divisibility: pad image size to be divisible by this value
+ """
+ self.is_train = is_train
+ self.meta = meta
+ self.name = name
+ self.tfm_gens = augmentations
+ self.img_format = image_format
+ self.size_divisibility = size_divisibility
+ self.num_queries = num_queries
+
+ logger = logging.getLogger(__name__)
+ mode = "training" if is_train else "inference"
+ logger.info(f"[{self.__class__.__name__}] Augmentations used in {mode}: {augmentations}")
+
+ self.things = []
+ for k,v in self.meta.thing_dataset_id_to_contiguous_id.items():
+ self.things.append(v)
+ self.class_names = self.meta.thing_classes
+ self.text_tokenizer = Tokenize(SimpleTokenizer(), max_seq_len=max_seq_len)
+ self.task_tokenizer = Tokenize(SimpleTokenizer(), max_seq_len=task_seq_len)
+
+ @classmethod
+ def from_config(cls, cfg, is_train=True):
+ # Build augmentation
+ augs = [
+ T.ResizeShortestEdge(
+ cfg.INPUT.MIN_SIZE_TRAIN,
+ cfg.INPUT.MAX_SIZE_TRAIN,
+ cfg.INPUT.MIN_SIZE_TRAIN_SAMPLING,
+ )
+ ]
+ if cfg.INPUT.CROP.ENABLED:
+ augs.append(
+ T.RandomCrop(
+ cfg.INPUT.CROP.TYPE,
+ cfg.INPUT.CROP.SIZE,
+ )
+ )
+ if cfg.INPUT.COLOR_AUG_SSD:
+ augs.append(ColorAugSSDTransform(img_format=cfg.INPUT.FORMAT))
+ augs.append(T.RandomFlip())
+
+ # Assume always applies to the training set.
+ dataset_names = cfg.DATASETS.TRAIN
+ meta = MetadataCatalog.get(dataset_names[0])
+
+ ret = {
+ "is_train": is_train,
+ "meta": meta,
+ "name": dataset_names[0],
+ "num_queries": cfg.MODEL.ONE_FORMER.NUM_OBJECT_QUERIES - cfg.MODEL.TEXT_ENCODER.N_CTX,
+ "task_seq_len": cfg.INPUT.TASK_SEQ_LEN,
+ "max_seq_len": cfg.INPUT.MAX_SEQ_LEN,
+ "augmentations": augs,
+ "image_format": cfg.INPUT.FORMAT,
+ "size_divisibility": cfg.INPUT.SIZE_DIVISIBILITY,
+ "semantic_prob": cfg.INPUT.TASK_PROB.SEMANTIC,
+ "instance_prob": cfg.INPUT.TASK_PROB.INSTANCE,
+ }
+ return ret
+
+ def _get_texts(self, classes, num_class_obj):
+
+ classes = list(np.array(classes))
+ texts = ["an instance photo"] * self.num_queries
+
+ for class_id in classes:
+ cls_name = self.class_names[class_id]
+ num_class_obj[cls_name] += 1
+
+ num = 0
+ for i, cls_name in enumerate(self.class_names):
+ if num_class_obj[cls_name] > 0:
+ for _ in range(num_class_obj[cls_name]):
+ if num >= len(texts):
+ break
+ texts[num] = f"a photo with a {cls_name}"
+ num += 1
+
+ return texts
+
+ def __call__(self, dataset_dict):
+ """
+ Args:
+ dataset_dict (dict): Metadata of one image, in Detectron2 Dataset format.
+
+ Returns:
+ dict: a format that builtin models in detectron2 accept
+ """
+ assert self.is_train, "OneFormerDatasetMapper should only be used for training!"
+
+ dataset_dict = copy.deepcopy(dataset_dict) # it will be modified by code below
+ image = utils.read_image(dataset_dict["file_name"], format=self.img_format)
+ utils.check_image_size(dataset_dict, image)
+
+ aug_input = T.AugInput(image)
+ aug_input, transforms = T.apply_transform_gens(self.tfm_gens, aug_input)
+ image = aug_input.image
+
+ # transform instnace masks
+ assert "annotations" in dataset_dict
+ for anno in dataset_dict["annotations"]:
+ anno.pop("keypoints", None)
+
+ annos = [
+ utils.transform_instance_annotations(obj, transforms, image.shape[:2])
+ for obj in dataset_dict.pop("annotations")
+ if obj.get("iscrowd", 0) == 0
+ ]
+
+ if len(annos):
+ assert "segmentation" in annos[0]
+ segms = [obj["segmentation"] for obj in annos]
+ masks = []
+ for segm in segms:
+ if isinstance(segm, list):
+ # polygon
+ masks.append(polygons_to_bitmask(segm, *image.shape[:2]))
+ elif isinstance(segm, dict):
+ # COCO RLE
+ masks.append(mask_util.decode(segm))
+ elif isinstance(segm, np.ndarray):
+ assert segm.ndim == 2, "Expect segmentation of 2 dimensions, got {}.".format(
+ segm.ndim
+ )
+ # mask array
+ masks.append(segm)
+ else:
+ raise ValueError(
+ "Cannot convert segmentation of type '{}' to BitMasks!"
+ "Supported types are: polygons as list[list[float] or ndarray],"
+ " COCO-style RLE as a dict, or a binary segmentation mask "
+ " in a 2D numpy array of shape HxW.".format(type(segm))
+ )
+
+ # Pad image and segmentation label here!
+ image = torch.as_tensor(np.ascontiguousarray(image.transpose(2, 0, 1)))
+ masks = [torch.from_numpy(np.ascontiguousarray(x)) for x in masks]
+
+ classes = [int(obj["category_id"]) for obj in annos]
+ classes = torch.tensor(classes, dtype=torch.int64)
+
+ if self.size_divisibility > 0:
+ image_size = (image.shape[-2], image.shape[-1])
+ padding_size = [
+ 0,
+ self.size_divisibility - image_size[1],
+ 0,
+ self.size_divisibility - image_size[0],
+ ]
+ # pad image
+ image = F.pad(image, padding_size, value=128).contiguous()
+ # pad mask
+ masks = [F.pad(x, padding_size, value=0).contiguous() for x in masks]
+
+ image_shape = (image.shape[-2], image.shape[-1]) # h, w
+
+ # Pytorch's dataloader is efficient on torch.Tensor due to shared-memory,
+ # but not efficient on large generic data structures due to the use of pickle & mp.Queue.
+ # Therefore it's important to use torch.Tensor.
+ dataset_dict["image"] = image
+
+ # Prepare per-category binary masks
+ instances = Instances(image_shape)
+ instances.gt_classes = classes
+ if len(masks) == 0:
+ # Some image does not have annotation (all ignored)
+ instances.gt_masks = torch.zeros((0, image.shape[-2], image.shape[-1]))
+ else:
+ masks = BitMasks(torch.stack(masks))
+ instances.gt_masks = masks.tensor
+
+ num_class_obj = {}
+ for name in self.class_names:
+ num_class_obj[name] = 0
+
+ task = "The task is instance"
+ text = self._get_texts(instances.gt_classes, num_class_obj)
+
+ dataset_dict["instances"] = instances
+ dataset_dict["orig_shape"] = image_shape
+ dataset_dict["task"] = task
+ dataset_dict["text"] = text
+ dataset_dict["thing_ids"] = self.things
+
+ return dataset_dict
diff --git a/annotator/OneFormer/datasets/custom_datasets/semantic_oneformer_custom_dataset_mapper.py b/annotator/OneFormer/datasets/custom_datasets/semantic_oneformer_custom_dataset_mapper.py
new file mode 100644
index 0000000000000000000000000000000000000000..5636bf66e551ac9ccd6f8453add17ad1739eb9b0
--- /dev/null
+++ b/annotator/OneFormer/datasets/custom_datasets/semantic_oneformer_custom_dataset_mapper.py
@@ -0,0 +1,238 @@
+# ------------------------------------------------------------------------------
+# Reference: https://github.com/facebookresearch/Mask2Former/blob/main/mask2former/data/dataset_mappers/mask_former_semantic_dataset_mapper.py
+# Modified by Jitesh Jain (https://github.com/praeclarumjj3)
+# ------------------------------------------------------------------------------
+
+import copy
+import logging
+import os
+
+import numpy as np
+import torch
+from torch.nn import functional as F
+
+from detectron2.config import configurable
+from detectron2.data import detection_utils as utils
+from detectron2.data import transforms as T
+from detectron2.structures import BitMasks, Instances, polygons_to_bitmask
+from detectron2.data import MetadataCatalog
+from detectron2.projects.point_rend import ColorAugSSDTransform
+from oneformer.data.tokenizer import SimpleTokenizer, Tokenize
+import pycocotools.mask as mask_util
+
+__all__ = ["SemanticOneFormerCustomDatasetMapper"]
+
+
+class SemanticOneFormerCustomDatasetMapper:
+ """
+ A callable which takes a dataset dict in Detectron2 Dataset format,
+ and map it into a format used by OneFormer custom semantic segmentation.
+
+ The callable currently does the following:
+
+ 1. Read the image from "file_name"
+ 2. Applies geometric transforms to the image and annotation
+ 3. Find and applies suitable cropping to the image and annotation
+ 4. Prepare image and annotation to Tensors
+ """
+
+ @configurable
+ def __init__(
+ self,
+ is_train=True,
+ *,
+ name,
+ num_queries,
+ meta,
+ augmentations,
+ image_format,
+ ignore_label,
+ size_divisibility,
+ task_seq_len,
+ max_seq_len,
+ ):
+ """
+ NOTE: this interface is experimental.
+ Args:
+ is_train: for training or inference
+ augmentations: a list of augmentations or deterministic transforms to apply
+ image_format: an image format supported by :func:`detection_utils.read_image`.
+ ignore_label: the label that is ignored to evaluation
+ size_divisibility: pad image size to be divisible by this value
+ """
+ self.is_train = is_train
+ self.meta = meta
+ self.name = name
+ self.tfm_gens = augmentations
+ self.img_format = image_format
+ self.ignore_label = ignore_label
+ self.size_divisibility = size_divisibility
+ self.num_queries = num_queries
+
+ logger = logging.getLogger(__name__)
+ mode = "training" if is_train else "inference"
+ logger.info(f"[{self.__class__.__name__}] Augmentations used in {mode}: {augmentations}")
+
+ self.class_names = self.meta.stuff_classes
+ self.text_tokenizer = Tokenize(SimpleTokenizer(), max_seq_len=max_seq_len)
+ self.task_tokenizer = Tokenize(SimpleTokenizer(), max_seq_len=task_seq_len)
+
+ @classmethod
+ def from_config(cls, cfg, is_train=True):
+ # Build augmentation
+ augs = [
+ T.ResizeShortestEdge(
+ cfg.INPUT.MIN_SIZE_TRAIN,
+ cfg.INPUT.MAX_SIZE_TRAIN,
+ cfg.INPUT.MIN_SIZE_TRAIN_SAMPLING,
+ )
+ ]
+ if cfg.INPUT.CROP.ENABLED:
+ augs.append(
+ T.RandomCrop_CategoryAreaConstraint(
+ cfg.INPUT.CROP.TYPE,
+ cfg.INPUT.CROP.SIZE,
+ cfg.INPUT.CROP.SINGLE_CATEGORY_MAX_AREA,
+ cfg.MODEL.SEM_SEG_HEAD.IGNORE_VALUE,
+ )
+ )
+ if cfg.INPUT.COLOR_AUG_SSD:
+ augs.append(ColorAugSSDTransform(img_format=cfg.INPUT.FORMAT))
+ augs.append(T.RandomFlip())
+
+ # Assume always applies to the training set.
+ dataset_names = cfg.DATASETS.TRAIN
+ meta = MetadataCatalog.get(dataset_names[0])
+ ignore_label = meta.ignore_label
+
+ ret = {
+ "is_train": is_train,
+ "meta": meta,
+ "name": dataset_names[0],
+ "num_queries": cfg.MODEL.ONE_FORMER.NUM_OBJECT_QUERIES - cfg.MODEL.TEXT_ENCODER.N_CTX,
+ "task_seq_len": cfg.INPUT.TASK_SEQ_LEN,
+ "max_seq_len": cfg.INPUT.MAX_SEQ_LEN,
+ "augmentations": augs,
+ "image_format": cfg.INPUT.FORMAT,
+ "ignore_label": ignore_label,
+ "size_divisibility": cfg.INPUT.SIZE_DIVISIBILITY,
+ }
+ return ret
+
+ def _get_texts(self, classes, num_class_obj):
+
+ classes = list(np.array(classes))
+ texts = ["an semantic photo"] * self.num_queries
+
+ for class_id in classes:
+ cls_name = self.class_names[class_id]
+ num_class_obj[cls_name] += 1
+
+ num = 0
+ for i, cls_name in enumerate(self.class_names):
+ if num_class_obj[cls_name] > 0:
+ for _ in range(num_class_obj[cls_name]):
+ if num >= len(texts):
+ break
+ texts[num] = f"a photo with a {cls_name}"
+ num += 1
+
+ return texts
+
+ def __call__(self, dataset_dict):
+ """
+ Args:
+ dataset_dict (dict): Metadata of one image, in Detectron2 Dataset format.
+
+ Returns:
+ dict: a format that builtin models in detectron2 accept
+ """
+ assert self.is_train, "SemanticOneFormerCustomDatasetMapper should only be used for training!"
+
+ dataset_dict = copy.deepcopy(dataset_dict) # it will be modified by code below
+ image = utils.read_image(dataset_dict["file_name"], format=self.img_format)
+ utils.check_image_size(dataset_dict, image)
+
+ if "sem_seg_file_name" in dataset_dict:
+ # PyTorch transformation not implemented for uint16, so converting it to double first
+ sem_seg_gt = utils.read_image(dataset_dict.pop("sem_seg_file_name")).astype("double")
+ else:
+ sem_seg_gt = None
+
+ if sem_seg_gt is None:
+ raise ValueError(
+ "Cannot find 'sem_seg_file_name' for semantic segmentation dataset {}.".format(
+ dataset_dict["file_name"]
+ )
+ )
+
+ aug_input = T.AugInput(image, sem_seg=sem_seg_gt)
+ aug_input, transforms = T.apply_transform_gens(self.tfm_gens, aug_input)
+ image = aug_input.image
+ sem_seg_gt = aug_input.sem_seg
+
+ # Pad image and segmentation label here!
+ image = torch.as_tensor(np.ascontiguousarray(image.transpose(2, 0, 1)))
+ if sem_seg_gt is not None:
+ sem_seg_gt = torch.as_tensor(sem_seg_gt.astype("long"))
+
+ if self.size_divisibility > 0:
+ image_size = (image.shape[-2], image.shape[-1])
+ padding_size = [
+ 0,
+ self.size_divisibility - image_size[1],
+ 0,
+ self.size_divisibility - image_size[0],
+ ]
+ image = F.pad(image, padding_size, value=128).contiguous()
+ if sem_seg_gt is not None:
+ sem_seg_gt = F.pad(sem_seg_gt, padding_size, value=self.ignore_label).contiguous()
+
+ image_shape = (image.shape[-2], image.shape[-1]) # h, w
+
+ # Pytorch's dataloader is efficient on torch.Tensor due to shared-memory,
+ # but not efficient on large generic data structures due to the use of pickle & mp.Queue.
+ # Therefore it's important to use torch.Tensor.
+ dataset_dict["image"] = image
+
+ if sem_seg_gt is not None:
+ dataset_dict["sem_seg"] = sem_seg_gt.long()
+
+ if "annotations" in dataset_dict:
+ raise ValueError("Semantic segmentation dataset should not have 'annotations'.")
+
+ # Prepare per-category binary masks
+ if sem_seg_gt is not None:
+ sem_seg_gt = sem_seg_gt.numpy()
+ instances = Instances(image_shape)
+ classes = np.unique(sem_seg_gt)
+ # remove ignored region
+ classes = classes[classes != self.ignore_label]
+ instances.gt_classes = torch.tensor(classes, dtype=torch.int64)
+
+ masks = []
+ for class_id in classes:
+ masks.append(sem_seg_gt == class_id)
+
+ if len(masks) == 0:
+ # Some image does not have annotation (all ignored)
+ instances.gt_masks = torch.zeros((0, sem_seg_gt.shape[-2], sem_seg_gt.shape[-1]))
+ else:
+ masks = BitMasks(
+ torch.stack([torch.from_numpy(np.ascontiguousarray(x.copy())) for x in masks])
+ )
+ instances.gt_masks = masks.tensor
+
+ num_class_obj = {}
+ for name in self.class_names:
+ num_class_obj[name] = 0
+
+ task = "The task is semantic"
+ text = self._get_texts(instances.gt_classes, num_class_obj)
+
+ dataset_dict["instances"] = instances
+ dataset_dict["orig_shape"] = image_shape
+ dataset_dict["task"] = task
+ dataset_dict["text"] = text
+
+ return dataset_dict
diff --git a/annotator/OneFormer/datasets/fg_ids.py b/annotator/OneFormer/datasets/fg_ids.py
new file mode 100644
index 0000000000000000000000000000000000000000..886b6255f2d0e4de01f062f45173c265602aaff2
--- /dev/null
+++ b/annotator/OneFormer/datasets/fg_ids.py
@@ -0,0 +1,108 @@
+ADE20K_FG_IDS = {
+ 1: 8,
+ 2: 9,
+ 3: 11,
+ 4: 13,
+ 5: 15,
+ 5: 15,
+ 6: 16,
+ 7: 19,
+ 8: 20,
+ 9: 21,
+ 10: 23,
+ 11: 24,
+ 12: 25,
+ 13: 28,
+ 14: 31,
+ 15: 32,
+ 16: 33,
+ 17: 34,
+ 18: 36,
+ 18: 36,
+ 19: 37,
+ 20: 38,
+ 21: 39,
+ 22: 40,
+ 23: 42,
+ 24: 43,
+ 25: 44,
+ 26: 45,
+ 27: 46,
+ 28: 48,
+ 29: 50,
+ 30: 51,
+ 31: 54,
+ 32: 56,
+ 33: 57,
+ 34: 58,
+ 35: 59,
+ 36: 63,
+ 37: 65,
+ 38: 66,
+ 39: 67,
+ 40: 68,
+ 41: 70,
+ 42: 71,
+ 43: 72,
+ 44: 73,
+ 45: 74,
+ 46: 75,
+ 47: 76,
+ 48: 77,
+ 49: 79,
+ 50: 81,
+ 51: 82,
+ 52: 83,
+ 53: 84,
+ 54: 86,
+ 55: 87,
+ 56: 88,
+ 57: 89,
+ 57: 89,
+ 58: 90,
+ 59: 91,
+ 60: 93,
+ 61: 94,
+ 62: 96,
+ 63: 98,
+ 64: 99,
+ 65: 103,
+ 66: 104,
+ 67: 105,
+ 68: 108,
+ 69: 109,
+ 70: 111,
+ 71: 112,
+ 72: 113,
+ 73: 116,
+ 74: 117,
+ 75: 119,
+ 76: 120,
+ 77: 121,
+ 78: 122,
+ 79: 124,
+ 80: 125,
+ 81: 126,
+ 82: 127,
+ 83: 128,
+ 84: 130,
+ 85: 131,
+ 86: 133,
+ 87: 134,
+ 88: 135,
+ 89: 136,
+ 90: 137,
+ 91: 138,
+ 92: 139,
+ 93: 140,
+ 94: 143,
+ 95: 144,
+ 96: 145,
+ 97: 147,
+ 98: 148,
+ 99: 149,
+ 100: 150
+ }
+
+
+CITYSCAPES_FG_NAMES = ['person', 'rider', 'car', 'truck', 'bus', 'train', 'motorcycle', 'bicycle']
\ No newline at end of file
diff --git a/annotator/OneFormer/datasets/panoptic2detection_coco_format.py b/annotator/OneFormer/datasets/panoptic2detection_coco_format.py
new file mode 100644
index 0000000000000000000000000000000000000000..14baa07a94d3048dcb8493c262f3b692b71a9370
--- /dev/null
+++ b/annotator/OneFormer/datasets/panoptic2detection_coco_format.py
@@ -0,0 +1,152 @@
+#!/usr/bin/env python
+# ------------------------------------------------------------------------------
+# Reference: https://github.com/cocodataset/panopticapi/blob/master/converters/panoptic2detection_coco_format.py
+# Modified by Jitesh Jain (https://github.com/praeclarumjj3)
+# ------------------------------------------------------------------------------
+'''
+This script converts panoptic COCO format to detection COCO format. More
+information about the formats can be found here:
+http://cocodataset.org/#format-data. All segments will be stored in RLE format.
+
+Additional option:
+- using option '--things_only' the script can discard all stuff
+segments, saving segments of things classes only.
+'''
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+from __future__ import unicode_literals
+import os, sys
+import argparse
+import numpy as np
+import json
+import time
+import multiprocessing
+
+import PIL.Image as Image
+
+from panopticapi.utils import get_traceback, rgb2id, save_json
+
+try:
+ # set up path for pycocotools
+ # sys.path.append('./cocoapi-master/PythonAPI/')
+ from pycocotools import mask as COCOmask
+except Exception:
+ raise Exception("Please install pycocotools module from https://github.com/cocodataset/cocoapi")
+
+@get_traceback
+def convert_panoptic_to_detection_coco_format_single_core(
+ proc_id, annotations_set, categories, segmentations_folder, things_only
+):
+ annotations_detection = []
+ for working_idx, annotation in enumerate(annotations_set):
+ if working_idx % 100 == 0:
+ print('Core: {}, {} from {} images processed'.format(proc_id,
+ working_idx,
+ len(annotations_set)))
+
+ file_name = '{}.png'.format(annotation['file_name'].rsplit('.')[0])
+ try:
+ pan_format = np.array(
+ Image.open(os.path.join(segmentations_folder, file_name)), dtype=np.uint32
+ )
+ except IOError:
+ raise KeyError('no prediction png file for id: {}'.format(annotation['image_id']))
+ pan = rgb2id(pan_format)
+
+ for segm_info in annotation['segments_info']:
+ if things_only and categories[segm_info['category_id']]['isthing'] != 1:
+ continue
+ mask = (pan == segm_info['id']).astype(np.uint8)
+ mask = np.expand_dims(mask, axis=2)
+ segm_info.pop('id')
+ segm_info['image_id'] = annotation['image_id']
+ rle = COCOmask.encode(np.asfortranarray(mask))[0]
+ rle['counts'] = rle['counts'].decode('utf8')
+ segm_info['segmentation'] = rle
+ annotations_detection.append(segm_info)
+
+ print('Core: {}, all {} images processed'.format(proc_id, len(annotations_set)))
+ return annotations_detection
+
+
+def convert_panoptic_to_detection_coco_format(input_json_file,
+ segmentations_folder,
+ output_json_file,
+ categories_json_file,
+ things_only):
+ start_time = time.time()
+
+ if segmentations_folder is None:
+ segmentations_folder = input_json_file.rsplit('.', 1)[0]
+
+ print("CONVERTING...")
+ print("COCO panoptic format:")
+ print("\tSegmentation folder: {}".format(segmentations_folder))
+ print("\tJSON file: {}".format(input_json_file))
+ print("TO")
+ print("COCO detection format")
+ print("\tJSON file: {}".format(output_json_file))
+ if things_only:
+ print("Saving only segments of things classes.")
+ print('\n')
+
+ print("Reading annotation information from {}".format(input_json_file))
+ with open(input_json_file, 'r') as f:
+ d_coco = json.load(f)
+ annotations_panoptic = d_coco['annotations']
+
+ with open(categories_json_file, 'r') as f:
+ categories_list = json.load(f)
+ categories = {category['id']: category for category in categories_list}
+
+ cpu_num = multiprocessing.cpu_count()
+ annotations_split = np.array_split(annotations_panoptic, cpu_num)
+ print("Number of cores: {}, images per core: {}".format(cpu_num, len(annotations_split[0])))
+ workers = multiprocessing.Pool(processes=cpu_num)
+ processes = []
+ for proc_id, annotations_set in enumerate(annotations_split):
+ p = workers.apply_async(convert_panoptic_to_detection_coco_format_single_core,
+ (proc_id, annotations_set, categories, segmentations_folder, things_only))
+ processes.append(p)
+ annotations_coco_detection = []
+ for p in processes:
+ annotations_coco_detection.extend(p.get())
+ for idx, ann in enumerate(annotations_coco_detection):
+ ann['id'] = idx
+
+ d_coco['annotations'] = annotations_coco_detection
+ categories_coco_detection = []
+ for category in d_coco['categories']:
+ if things_only and category['isthing'] != 1:
+ continue
+ category.pop('isthing')
+ categories_coco_detection.append(category)
+ d_coco['categories'] = categories_coco_detection
+ save_json(d_coco, output_json_file)
+
+ t_delta = time.time() - start_time
+ print("Time elapsed: {:0.2f} seconds".format(t_delta))
+
+
+if __name__ == "__main__":
+ parser = argparse.ArgumentParser(
+ description="The script converts panoptic COCO format to detection \
+ COCO format. See this file's head for more information."
+ )
+ parser.add_argument('--things_only', action='store_true',
+ help="discard stuff classes")
+ args = parser.parse_args()
+
+ _root = os.getenv("DETECTRON2_DATASETS", "datasets")
+ root = os.path.join(_root, "coco")
+ input_json_file = os.path.join(root, "annotations", "panoptic_val2017.json")
+ output_json_file = os.path.join(root, "annotations", "panoptic2instances_val2017.json")
+ categories_json_file = "datasets/panoptic_coco_categories.json"
+ segmentations_folder = os.path.join(root, "panoptic_val2017")
+
+ convert_panoptic_to_detection_coco_format(input_json_file,
+ segmentations_folder,
+ output_json_file,
+ categories_json_file,
+ args.things_only)
diff --git a/annotator/OneFormer/datasets/prepare_ade20k_ins_seg.py b/annotator/OneFormer/datasets/prepare_ade20k_ins_seg.py
new file mode 100644
index 0000000000000000000000000000000000000000..e4e951adcd84dbd08b3d6570aee56887bf1c69a6
--- /dev/null
+++ b/annotator/OneFormer/datasets/prepare_ade20k_ins_seg.py
@@ -0,0 +1,112 @@
+#!/usr/bin/env python3
+# -*- coding: utf-8 -*-
+# Copyright (c) Facebook, Inc. and its affiliates.
+import glob
+import json
+import os
+from collections import Counter
+
+import numpy as np
+import tqdm
+from panopticapi.utils import IdGenerator, save_json
+from PIL import Image
+import pycocotools.mask as mask_util
+
+
+if __name__ == "__main__":
+ dataset_dir = os.getenv("DETECTRON2_DATASETS", "datasets")
+
+ for name, dirname in [("train", "training"), ("val", "validation")]:
+ image_dir = os.path.join(dataset_dir, f"ADEChallengeData2016/images/{dirname}/")
+ instance_dir = os.path.join(
+ dataset_dir, f"ADEChallengeData2016/annotations_instance/{dirname}/"
+ )
+
+ # img_id = 0
+ ann_id = 1
+
+ # json
+ out_file = os.path.join(dataset_dir, f"ADEChallengeData2016/ade20k_instance_{name}.json")
+
+ # json config
+ instance_config_file = "datasets/ade20k_instance_imgCatIds.json"
+ with open(instance_config_file) as f:
+ category_dict = json.load(f)["categories"]
+
+ # load catid mapping
+ # it is important to share category id for both instance and panoptic annotations
+ mapping_file = "datasets/ade20k_instance_catid_mapping.txt"
+ with open(mapping_file) as f:
+ map_id = {}
+ for i, line in enumerate(f.readlines()):
+ if i == 0:
+ continue
+ ins_id, sem_id, _ = line.strip().split()
+ # shift id by 1 because we want it to start from 0!
+ # ignore_label becomes 255
+ map_id[int(ins_id)] = int(sem_id) - 1
+
+ for cat in category_dict:
+ cat["id"] = map_id[cat["id"]]
+
+ filenames = sorted(glob.glob(os.path.join(image_dir, "*.jpg")))
+
+ ann_dict = {}
+ images = []
+ annotations = []
+
+ for idx, filename in enumerate(tqdm.tqdm(filenames)):
+ image = {}
+ image_id = os.path.basename(filename).split(".")[0]
+
+ image["id"] = image_id
+ image["file_name"] = os.path.basename(filename)
+
+ original_format = np.array(Image.open(filename))
+ image["width"] = original_format.shape[1]
+ image["height"] = original_format.shape[0]
+
+ images.append(image)
+
+ filename_instance = os.path.join(instance_dir, image_id + ".png")
+ ins_seg = np.asarray(Image.open(filename_instance))
+ assert ins_seg.dtype == np.uint8
+
+ instance_cat_ids = ins_seg[..., 0]
+ # instance id starts from 1!
+ # because 0 is reserved as VOID label
+ instance_ins_ids = ins_seg[..., 1]
+
+ # process things
+ for thing_id in np.unique(instance_ins_ids):
+ if thing_id == 0:
+ continue
+ mask = instance_ins_ids == thing_id
+ instance_cat_id = np.unique(instance_cat_ids[mask])
+ assert len(instance_cat_id) == 1
+
+ anno = {}
+ anno['id'] = ann_id
+ ann_id += 1
+ anno['image_id'] = image['id']
+ anno["iscrowd"] = int(0)
+ anno["category_id"] = int(map_id[instance_cat_id[0]])
+
+ inds = np.nonzero(mask)
+ ymin, ymax = inds[0].min(), inds[0].max()
+ xmin, xmax = inds[1].min(), inds[1].max()
+ anno["bbox"] = [int(xmin), int(ymin), int(xmax - xmin + 1), int(ymax - ymin + 1)]
+ # if xmax <= xmin or ymax <= ymin:
+ # continue
+ rle = mask_util.encode(np.array(mask[:, :, None], order="F", dtype="uint8"))[0]
+ rle["counts"] = rle["counts"].decode("utf-8")
+ anno["segmentation"] = rle
+ anno["area"] = int(mask_util.area(rle))
+ annotations.append(anno)
+
+ # save this
+ ann_dict['images'] = images
+ ann_dict['categories'] = category_dict
+ ann_dict['annotations'] = annotations
+
+ save_json(ann_dict, out_file)
diff --git a/annotator/OneFormer/datasets/prepare_ade20k_pan_seg.py b/annotator/OneFormer/datasets/prepare_ade20k_pan_seg.py
new file mode 100644
index 0000000000000000000000000000000000000000..8b1b03281dd4307c6dad2dc7e2505aacad3e72a3
--- /dev/null
+++ b/annotator/OneFormer/datasets/prepare_ade20k_pan_seg.py
@@ -0,0 +1,500 @@
+#!/usr/bin/env python3
+# -*- coding: utf-8 -*-
+# Copyright (c) Facebook, Inc. and its affiliates.
+import glob
+import json
+import os
+from collections import Counter
+
+import numpy as np
+import tqdm
+from panopticapi.utils import IdGenerator, save_json
+from PIL import Image
+
+ADE20K_SEM_SEG_CATEGORIES = [
+ "wall",
+ "building",
+ "sky",
+ "floor",
+ "tree",
+ "ceiling",
+ "road, route",
+ "bed",
+ "window ",
+ "grass",
+ "cabinet",
+ "sidewalk, pavement",
+ "person",
+ "earth, ground",
+ "door",
+ "table",
+ "mountain, mount",
+ "plant",
+ "curtain",
+ "chair",
+ "car",
+ "water",
+ "painting, picture",
+ "sofa",
+ "shelf",
+ "house",
+ "sea",
+ "mirror",
+ "rug",
+ "field",
+ "armchair",
+ "seat",
+ "fence",
+ "desk",
+ "rock, stone",
+ "wardrobe, closet, press",
+ "lamp",
+ "tub",
+ "rail",
+ "cushion",
+ "base, pedestal, stand",
+ "box",
+ "column, pillar",
+ "signboard, sign",
+ "chest of drawers, chest, bureau, dresser",
+ "counter",
+ "sand",
+ "sink",
+ "skyscraper",
+ "fireplace",
+ "refrigerator, icebox",
+ "grandstand, covered stand",
+ "path",
+ "stairs",
+ "runway",
+ "case, display case, showcase, vitrine",
+ "pool table, billiard table, snooker table",
+ "pillow",
+ "screen door, screen",
+ "stairway, staircase",
+ "river",
+ "bridge, span",
+ "bookcase",
+ "blind, screen",
+ "coffee table",
+ "toilet, can, commode, crapper, pot, potty, stool, throne",
+ "flower",
+ "book",
+ "hill",
+ "bench",
+ "countertop",
+ "stove",
+ "palm, palm tree",
+ "kitchen island",
+ "computer",
+ "swivel chair",
+ "boat",
+ "bar",
+ "arcade machine",
+ "hovel, hut, hutch, shack, shanty",
+ "bus",
+ "towel",
+ "light",
+ "truck",
+ "tower",
+ "chandelier",
+ "awning, sunshade, sunblind",
+ "street lamp",
+ "booth",
+ "tv",
+ "plane",
+ "dirt track",
+ "clothes",
+ "pole",
+ "land, ground, soil",
+ "bannister, banister, balustrade, balusters, handrail",
+ "escalator, moving staircase, moving stairway",
+ "ottoman, pouf, pouffe, puff, hassock",
+ "bottle",
+ "buffet, counter, sideboard",
+ "poster, posting, placard, notice, bill, card",
+ "stage",
+ "van",
+ "ship",
+ "fountain",
+ "conveyer belt, conveyor belt, conveyer, conveyor, transporter",
+ "canopy",
+ "washer, automatic washer, washing machine",
+ "plaything, toy",
+ "pool",
+ "stool",
+ "barrel, cask",
+ "basket, handbasket",
+ "falls",
+ "tent",
+ "bag",
+ "minibike, motorbike",
+ "cradle",
+ "oven",
+ "ball",
+ "food, solid food",
+ "step, stair",
+ "tank, storage tank",
+ "trade name",
+ "microwave",
+ "pot",
+ "animal",
+ "bicycle",
+ "lake",
+ "dishwasher",
+ "screen",
+ "blanket, cover",
+ "sculpture",
+ "hood, exhaust hood",
+ "sconce",
+ "vase",
+ "traffic light",
+ "tray",
+ "trash can",
+ "fan",
+ "pier",
+ "crt screen",
+ "plate",
+ "monitor",
+ "bulletin board",
+ "shower",
+ "radiator",
+ "glass, drinking glass",
+ "clock",
+ "flag", # noqa
+]
+
+PALETTE = [
+ [120, 120, 120],
+ [180, 120, 120],
+ [6, 230, 230],
+ [80, 50, 50],
+ [4, 200, 3],
+ [120, 120, 80],
+ [140, 140, 140],
+ [204, 5, 255],
+ [230, 230, 230],
+ [4, 250, 7],
+ [224, 5, 255],
+ [235, 255, 7],
+ [150, 5, 61],
+ [120, 120, 70],
+ [8, 255, 51],
+ [255, 6, 82],
+ [143, 255, 140],
+ [204, 255, 4],
+ [255, 51, 7],
+ [204, 70, 3],
+ [0, 102, 200],
+ [61, 230, 250],
+ [255, 6, 51],
+ [11, 102, 255],
+ [255, 7, 71],
+ [255, 9, 224],
+ [9, 7, 230],
+ [220, 220, 220],
+ [255, 9, 92],
+ [112, 9, 255],
+ [8, 255, 214],
+ [7, 255, 224],
+ [255, 184, 6],
+ [10, 255, 71],
+ [255, 41, 10],
+ [7, 255, 255],
+ [224, 255, 8],
+ [102, 8, 255],
+ [255, 61, 6],
+ [255, 194, 7],
+ [255, 122, 8],
+ [0, 255, 20],
+ [255, 8, 41],
+ [255, 5, 153],
+ [6, 51, 255],
+ [235, 12, 255],
+ [160, 150, 20],
+ [0, 163, 255],
+ [140, 140, 200],
+ [250, 10, 15],
+ [20, 255, 0],
+ [31, 255, 0],
+ [255, 31, 0],
+ [255, 224, 0],
+ [153, 255, 0],
+ [0, 0, 255],
+ [255, 71, 0],
+ [0, 235, 255],
+ [0, 173, 255],
+ [31, 0, 255],
+ [11, 200, 200],
+ [255, 82, 0],
+ [0, 255, 245],
+ [0, 61, 255],
+ [0, 255, 112],
+ [0, 255, 133],
+ [255, 0, 0],
+ [255, 163, 0],
+ [255, 102, 0],
+ [194, 255, 0],
+ [0, 143, 255],
+ [51, 255, 0],
+ [0, 82, 255],
+ [0, 255, 41],
+ [0, 255, 173],
+ [10, 0, 255],
+ [173, 255, 0],
+ [0, 255, 153],
+ [255, 92, 0],
+ [255, 0, 255],
+ [255, 0, 245],
+ [255, 0, 102],
+ [255, 173, 0],
+ [255, 0, 20],
+ [255, 184, 184],
+ [0, 31, 255],
+ [0, 255, 61],
+ [0, 71, 255],
+ [255, 0, 204],
+ [0, 255, 194],
+ [0, 255, 82],
+ [0, 10, 255],
+ [0, 112, 255],
+ [51, 0, 255],
+ [0, 194, 255],
+ [0, 122, 255],
+ [0, 255, 163],
+ [255, 153, 0],
+ [0, 255, 10],
+ [255, 112, 0],
+ [143, 255, 0],
+ [82, 0, 255],
+ [163, 255, 0],
+ [255, 235, 0],
+ [8, 184, 170],
+ [133, 0, 255],
+ [0, 255, 92],
+ [184, 0, 255],
+ [255, 0, 31],
+ [0, 184, 255],
+ [0, 214, 255],
+ [255, 0, 112],
+ [92, 255, 0],
+ [0, 224, 255],
+ [112, 224, 255],
+ [70, 184, 160],
+ [163, 0, 255],
+ [153, 0, 255],
+ [71, 255, 0],
+ [255, 0, 163],
+ [255, 204, 0],
+ [255, 0, 143],
+ [0, 255, 235],
+ [133, 255, 0],
+ [255, 0, 235],
+ [245, 0, 255],
+ [255, 0, 122],
+ [255, 245, 0],
+ [10, 190, 212],
+ [214, 255, 0],
+ [0, 204, 255],
+ [20, 0, 255],
+ [255, 255, 0],
+ [0, 153, 255],
+ [0, 41, 255],
+ [0, 255, 204],
+ [41, 0, 255],
+ [41, 255, 0],
+ [173, 0, 255],
+ [0, 245, 255],
+ [71, 0, 255],
+ [122, 0, 255],
+ [0, 255, 184],
+ [0, 92, 255],
+ [184, 255, 0],
+ [0, 133, 255],
+ [255, 214, 0],
+ [25, 194, 194],
+ [102, 255, 0],
+ [92, 0, 255],
+]
+
+
+if __name__ == "__main__":
+ dataset_dir = os.getenv("DETECTRON2_DATASETS", "datasets")
+
+ for name, dirname in [("train", "training"), ("val", "validation")]:
+ image_dir = os.path.join(dataset_dir, f"ADEChallengeData2016/images/{dirname}/")
+ semantic_dir = os.path.join(dataset_dir, f"ADEChallengeData2016/annotations/{dirname}/")
+ instance_dir = os.path.join(
+ dataset_dir, f"ADEChallengeData2016/annotations_instance/{dirname}/"
+ )
+
+ # folder to store panoptic PNGs
+ out_folder = os.path.join(dataset_dir, f"ADEChallengeData2016/ade20k_panoptic_{name}/")
+ # json with segmentations information
+ out_file = os.path.join(dataset_dir, f"ADEChallengeData2016/ade20k_panoptic_{name}.json")
+
+ if not os.path.isdir(out_folder):
+ print("Creating folder {} for panoptic segmentation PNGs".format(out_folder))
+ os.mkdir(out_folder)
+
+ # json config
+ config_file = "datasets/ade20k_instance_imgCatIds.json"
+ with open(config_file) as f:
+ config = json.load(f)
+
+ # load catid mapping
+ mapping_file = "datasets/ade20k_instance_catid_mapping.txt"
+ with open(mapping_file) as f:
+ map_id = {}
+ for i, line in enumerate(f.readlines()):
+ if i == 0:
+ continue
+ ins_id, sem_id, _ = line.strip().split()
+ # shift id by 1 because we want it to start from 0!
+ # ignore_label becomes 255
+ map_id[int(ins_id) - 1] = int(sem_id) - 1
+
+ ADE20K_150_CATEGORIES = []
+ for cat_id, cat_name in enumerate(ADE20K_SEM_SEG_CATEGORIES):
+ ADE20K_150_CATEGORIES.append(
+ {
+ "name": cat_name,
+ "id": cat_id,
+ "isthing": int(cat_id in map_id.values()),
+ "color": PALETTE[cat_id],
+ }
+ )
+ categories_dict = {cat["id"]: cat for cat in ADE20K_150_CATEGORIES}
+
+ panoptic_json_categories = ADE20K_150_CATEGORIES[:]
+ panoptic_json_images = []
+ panoptic_json_annotations = []
+
+ filenames = sorted(glob.glob(os.path.join(image_dir, "*.jpg")))
+ for idx, filename in enumerate(tqdm.tqdm(filenames)):
+ panoptic_json_image = {}
+ panoptic_json_annotation = {}
+
+ image_id = os.path.basename(filename).split(".")[0]
+
+ panoptic_json_image["id"] = image_id
+ panoptic_json_image["file_name"] = os.path.basename(filename)
+
+ original_format = np.array(Image.open(filename))
+ panoptic_json_image["width"] = original_format.shape[1]
+ panoptic_json_image["height"] = original_format.shape[0]
+
+ pan_seg = np.zeros(
+ (original_format.shape[0], original_format.shape[1], 3), dtype=np.uint8
+ )
+ id_generator = IdGenerator(categories_dict)
+
+ filename_semantic = os.path.join(semantic_dir, image_id + ".png")
+ filename_instance = os.path.join(instance_dir, image_id + ".png")
+
+ sem_seg = np.asarray(Image.open(filename_semantic))
+ ins_seg = np.asarray(Image.open(filename_instance))
+
+ assert sem_seg.dtype == np.uint8
+ assert ins_seg.dtype == np.uint8
+
+ semantic_cat_ids = sem_seg - 1
+ instance_cat_ids = ins_seg[..., 0] - 1
+ # instance id starts from 1!
+ # because 0 is reserved as VOID label
+ instance_ins_ids = ins_seg[..., 1]
+
+ segm_info = []
+
+ # NOTE: there is some overlap between semantic and instance annotation
+ # thus we paste stuffs first
+
+ # process stuffs
+ for semantic_cat_id in np.unique(semantic_cat_ids):
+ if semantic_cat_id == 255:
+ continue
+ if categories_dict[semantic_cat_id]["isthing"]:
+ continue
+ mask = semantic_cat_ids == semantic_cat_id
+ # should not have any overlap
+ assert pan_seg[mask].sum() == 0
+
+ segment_id, color = id_generator.get_id_and_color(semantic_cat_id)
+ pan_seg[mask] = color
+
+ area = np.sum(mask) # segment area computation
+ # bbox computation for a segment
+ hor = np.sum(mask, axis=0)
+ hor_idx = np.nonzero(hor)[0]
+ x = hor_idx[0]
+ width = hor_idx[-1] - x + 1
+ vert = np.sum(mask, axis=1)
+ vert_idx = np.nonzero(vert)[0]
+ y = vert_idx[0]
+ height = vert_idx[-1] - y + 1
+ bbox = [int(x), int(y), int(width), int(height)]
+
+ segm_info.append(
+ {
+ "id": int(segment_id),
+ "category_id": int(semantic_cat_id),
+ "area": int(area),
+ "bbox": bbox,
+ "iscrowd": 0,
+ }
+ )
+
+ # process things
+ for thing_id in np.unique(instance_ins_ids):
+ if thing_id == 0:
+ continue
+ mask = instance_ins_ids == thing_id
+ instance_cat_id = np.unique(instance_cat_ids[mask])
+ assert len(instance_cat_id) == 1
+
+ semantic_cat_id = map_id[instance_cat_id[0]]
+
+ segment_id, color = id_generator.get_id_and_color(semantic_cat_id)
+ pan_seg[mask] = color
+
+ area = np.sum(mask) # segment area computation
+ # bbox computation for a segment
+ hor = np.sum(mask, axis=0)
+ hor_idx = np.nonzero(hor)[0]
+ x = hor_idx[0]
+ width = hor_idx[-1] - x + 1
+ vert = np.sum(mask, axis=1)
+ vert_idx = np.nonzero(vert)[0]
+ y = vert_idx[0]
+ height = vert_idx[-1] - y + 1
+ bbox = [int(x), int(y), int(width), int(height)]
+
+ segm_info.append(
+ {
+ "id": int(segment_id),
+ "category_id": int(semantic_cat_id),
+ "area": int(area),
+ "bbox": bbox,
+ "iscrowd": 0,
+ }
+ )
+
+ panoptic_json_annotation = {
+ "image_id": image_id,
+ "file_name": image_id + ".png",
+ "segments_info": segm_info,
+ }
+
+ Image.fromarray(pan_seg).save(os.path.join(out_folder, image_id + ".png"))
+
+ panoptic_json_images.append(panoptic_json_image)
+ panoptic_json_annotations.append(panoptic_json_annotation)
+
+ # save this
+ d = {
+ "images": panoptic_json_images,
+ "annotations": panoptic_json_annotations,
+ "categories": panoptic_json_categories,
+ }
+
+ save_json(d, out_file)
diff --git a/annotator/OneFormer/datasets/prepare_ade20k_sem_seg.py b/annotator/OneFormer/datasets/prepare_ade20k_sem_seg.py
new file mode 100644
index 0000000000000000000000000000000000000000..b0edfeb340edaff45beb14b3f9438aef2c65e78f
--- /dev/null
+++ b/annotator/OneFormer/datasets/prepare_ade20k_sem_seg.py
@@ -0,0 +1,27 @@
+#!/usr/bin/env python3
+# -*- coding: utf-8 -*-
+# Copyright (c) Facebook, Inc. and its affiliates.
+import os
+from pathlib import Path
+
+import numpy as np
+import tqdm
+from PIL import Image
+
+
+def convert(input, output):
+ img = np.asarray(Image.open(input))
+ assert img.dtype == np.uint8
+ img = img - 1 # 0 (ignore) becomes 255. others are shifted by 1
+ Image.fromarray(img).save(output)
+
+
+if __name__ == "__main__":
+ dataset_dir = Path(os.getenv("DETECTRON2_DATASETS", "datasets")) / "ADEChallengeData2016"
+ for name in ["training", "validation"]:
+ annotation_dir = dataset_dir / "annotations" / name
+ output_dir = dataset_dir / "annotations_detectron2" / name
+ output_dir.mkdir(parents=True, exist_ok=True)
+ for file in tqdm.tqdm(list(annotation_dir.iterdir())):
+ output_file = output_dir / file.name
+ convert(file, output_file)
diff --git a/annotator/OneFormer/datasets/prepare_coco_semantic_annos_from_panoptic_annos.py b/annotator/OneFormer/datasets/prepare_coco_semantic_annos_from_panoptic_annos.py
new file mode 100644
index 0000000000000000000000000000000000000000..3090c9bc2f9a63156a4132e89c635613691eb350
--- /dev/null
+++ b/annotator/OneFormer/datasets/prepare_coco_semantic_annos_from_panoptic_annos.py
@@ -0,0 +1,84 @@
+#!/usr/bin/env python3
+# -*- coding: utf-8 -*-
+# Copyright (c) Facebook, Inc. and its affiliates.
+
+import functools
+import json
+import multiprocessing as mp
+import numpy as np
+import os
+import time
+from fvcore.common.download import download
+from panopticapi.utils import rgb2id
+from PIL import Image
+
+from detectron2.data.datasets.builtin_meta import COCO_CATEGORIES
+
+
+def _process_panoptic_to_semantic(input_panoptic, output_semantic, segments, id_map):
+ panoptic = np.asarray(Image.open(input_panoptic), dtype=np.uint32)
+ panoptic = rgb2id(panoptic)
+ output = np.zeros_like(panoptic, dtype=np.uint8) + 255
+ for seg in segments:
+ cat_id = seg["category_id"]
+ new_cat_id = id_map[cat_id]
+ output[panoptic == seg["id"]] = new_cat_id
+ Image.fromarray(output).save(output_semantic)
+
+
+def separate_coco_semantic_from_panoptic(panoptic_json, panoptic_root, sem_seg_root, categories):
+ """
+ Create semantic segmentation annotations from panoptic segmentation
+ annotations, to be used by PanopticFPN.
+ It maps all thing categories to class 0, and maps all unlabeled pixels to class 255.
+ It maps all stuff categories to contiguous ids starting from 1.
+ Args:
+ panoptic_json (str): path to the panoptic json file, in COCO's format.
+ panoptic_root (str): a directory with panoptic annotation files, in COCO's format.
+ sem_seg_root (str): a directory to output semantic annotation files
+ categories (list[dict]): category metadata. Each dict needs to have:
+ "id": corresponds to the "category_id" in the json annotations
+ "isthing": 0 or 1
+ """
+ os.makedirs(sem_seg_root, exist_ok=True)
+
+ id_map = {} # map from category id to id in the output semantic annotation
+ assert len(categories) <= 254
+ for i, k in enumerate(categories):
+ id_map[k["id"]] = i
+ # what is id = 0?
+ # id_map[0] = 255
+ print(id_map)
+
+ with open(panoptic_json) as f:
+ obj = json.load(f)
+
+ pool = mp.Pool(processes=max(mp.cpu_count() // 2, 4))
+
+ def iter_annotations():
+ for anno in obj["annotations"]:
+ file_name = anno["file_name"]
+ segments = anno["segments_info"]
+ input = os.path.join(panoptic_root, file_name)
+ output = os.path.join(sem_seg_root, file_name)
+ yield input, output, segments
+
+ print("Start writing to {} ...".format(sem_seg_root))
+ start = time.time()
+ pool.starmap(
+ functools.partial(_process_panoptic_to_semantic, id_map=id_map),
+ iter_annotations(),
+ chunksize=100,
+ )
+ print("Finished. time: {:.2f}s".format(time.time() - start))
+
+
+if __name__ == "__main__":
+ dataset_dir = os.path.join(os.getenv("DETECTRON2_DATASETS", "datasets"), "coco")
+ for s in ["val2017", "train2017"]:
+ separate_coco_semantic_from_panoptic(
+ os.path.join(dataset_dir, "annotations/panoptic_{}.json".format(s)),
+ os.path.join(dataset_dir, "panoptic_{}".format(s)),
+ os.path.join(dataset_dir, "panoptic_semseg_{}".format(s)),
+ COCO_CATEGORIES,
+ )
diff --git a/annotator/OneFormer/demo/defaults.py b/annotator/OneFormer/demo/defaults.py
new file mode 100644
index 0000000000000000000000000000000000000000..331e5baadfd297e8cbd86df7cae9afc2eeb18680
--- /dev/null
+++ b/annotator/OneFormer/demo/defaults.py
@@ -0,0 +1,82 @@
+# ------------------------------------------------------------------------------
+# Reference: https://github.com/facebookresearch/detectron2/blob/main/detectron2/engine/defaults.py
+# Modified by Jitesh Jain (https://github.com/praeclarumjj3)
+# ------------------------------------------------------------------------------
+
+import torch
+import detectron2.data.transforms as T
+from detectron2.checkpoint import DetectionCheckpointer
+from detectron2.data import (
+ MetadataCatalog,
+)
+from detectron2.modeling import build_model
+
+
+__all__ = [
+ "DefaultPredictor",
+]
+
+
+class DefaultPredictor:
+ """
+ Create a simple end-to-end predictor with the given config that runs on
+ single device for a single input image.
+ Compared to using the model directly, this class does the following additions:
+ 1. Load checkpoint from `cfg.MODEL.WEIGHTS`.
+ 2. Always take BGR image as the input and apply conversion defined by `cfg.INPUT.FORMAT`.
+ 3. Apply resizing defined by `cfg.INPUT.{MIN,MAX}_SIZE_TEST`.
+ 4. Take one input image and produce a single output, instead of a batch.
+ This is meant for simple demo purposes, so it does the above steps automatically.
+ This is not meant for benchmarks or running complicated inference logic.
+ If you'd like to do anything more complicated, please refer to its source code as
+ examples to build and use the model manually.
+ Attributes:
+ metadata (Metadata): the metadata of the underlying dataset, obtained from
+ cfg.DATASETS.TEST.
+ Examples:
+ ::
+ pred = DefaultPredictor(cfg)
+ inputs = cv2.imread("input.jpg")
+ outputs = pred(inputs)
+ """
+
+ def __init__(self, cfg):
+ self.cfg = cfg.clone() # cfg can be modified by model
+ self.model = build_model(self.cfg)
+ self.model.eval()
+ if len(cfg.DATASETS.TEST):
+ self.metadata = MetadataCatalog.get(cfg.DATASETS.TEST[0])
+
+ checkpointer = DetectionCheckpointer(self.model)
+ checkpointer.load(cfg.MODEL.WEIGHTS)
+
+ self.aug = T.ResizeShortestEdge(
+ [cfg.INPUT.MIN_SIZE_TEST, cfg.INPUT.MIN_SIZE_TEST], cfg.INPUT.MAX_SIZE_TEST
+ )
+
+ self.input_format = cfg.INPUT.FORMAT
+ assert self.input_format in ["RGB", "BGR"], self.input_format
+
+ def __call__(self, original_image, task):
+ """
+ Args:
+ original_image (np.ndarray): an image of shape (H, W, C) (in BGR order).
+ Returns:
+ predictions (dict):
+ the output of the model for one image only.
+ See :doc:`/tutorials/models` for details about the format.
+ """
+ with torch.no_grad(): # https://github.com/sphinx-doc/sphinx/issues/4258
+ # Apply pre-processing to image.
+ if self.input_format == "RGB":
+ # whether the model expects BGR inputs or RGB
+ original_image = original_image[:, :, ::-1]
+ height, width = original_image.shape[:2]
+ image = self.aug.get_transform(original_image).apply_image(original_image)
+ image = torch.as_tensor(image.astype("float32").transpose(2, 0, 1))
+
+ task = f"The task is {task}"
+
+ inputs = {"image": image, "height": height, "width": width, "task": task}
+ predictions = self.model([inputs])[0]
+ return predictions
\ No newline at end of file
diff --git a/annotator/OneFormer/oneformer/__init__.py b/annotator/OneFormer/oneformer/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..f9e0460603f7d98ac9e44f793729e28ac15a01a5
--- /dev/null
+++ b/annotator/OneFormer/oneformer/__init__.py
@@ -0,0 +1,18 @@
+from . import data # register all new datasets
+from . import modeling
+
+# config
+from .config import *
+
+# dataset loading
+from .data.dataset_mappers.coco_unified_new_baseline_dataset_mapper import COCOUnifiedNewBaselineDatasetMapper
+from .data.dataset_mappers.oneformer_unified_dataset_mapper import (
+ OneFormerUnifiedDatasetMapper,
+)
+
+# models
+from .oneformer_model import OneFormer
+from .test_time_augmentation import SemanticSegmentorWithTTA
+
+# evaluation
+from .evaluation.instance_evaluation import InstanceSegEvaluator
diff --git a/annotator/OneFormer/oneformer/config.py b/annotator/OneFormer/oneformer/config.py
new file mode 100644
index 0000000000000000000000000000000000000000..0dc8320dfb8b7e718cf59b31c5a3f4f018c94d9e
--- /dev/null
+++ b/annotator/OneFormer/oneformer/config.py
@@ -0,0 +1,210 @@
+# -*- coding: utf-8 -*-
+# Copyright (c) Facebook, Inc. and its affiliates.
+from detectron2.config import CfgNode as CN
+
+__all__ = ["add_common_config", "add_oneformer_config", "add_swin_config",
+ "add_dinat_config", "add_convnext_config"]
+
+def add_common_config(cfg):
+ """
+ Add config for common configuration
+ """
+
+ # data config
+ # select the dataset mapper
+ cfg.INPUT.DATASET_MAPPER_NAME = "oneformer_unified"
+ # Color augmentation
+ cfg.INPUT.COLOR_AUG_SSD = False
+ # We retry random cropping until no single category in semantic segmentation GT occupies more
+ # than `SINGLE_CATEGORY_MAX_AREA` part of the crop.
+ cfg.INPUT.CROP.SINGLE_CATEGORY_MAX_AREA = 1.0
+ # Pad image and segmentation GT in dataset mapper.
+ cfg.INPUT.SIZE_DIVISIBILITY = -1
+
+ cfg.INPUT.TASK_SEQ_LEN = 77
+ cfg.INPUT.MAX_SEQ_LEN = 77
+
+ cfg.INPUT.TASK_PROB = CN()
+ cfg.INPUT.TASK_PROB.SEMANTIC = 0.33
+ cfg.INPUT.TASK_PROB.INSTANCE = 0.66
+
+ # test dataset
+ cfg.DATASETS.TEST_PANOPTIC = ("",)
+ cfg.DATASETS.TEST_INSTANCE = ("",)
+ cfg.DATASETS.TEST_SEMANTIC = ("",)
+
+ # solver config
+ # weight decay on embedding
+ cfg.SOLVER.WEIGHT_DECAY_EMBED = 0.0
+ # optimizer
+ cfg.SOLVER.OPTIMIZER = "ADAMW"
+ cfg.SOLVER.BACKBONE_MULTIPLIER = 0.1
+
+ # wandb
+ cfg.WANDB = CN()
+ cfg.WANDB.PROJECT = "OneFormer"
+ cfg.WANDB.NAME = None
+
+ cfg.MODEL.IS_TRAIN = True
+ cfg.MODEL.IS_DEMO = False
+
+ # text encoder config
+ cfg.MODEL.TEXT_ENCODER = CN()
+
+ cfg.MODEL.TEXT_ENCODER.WIDTH = 256
+ cfg.MODEL.TEXT_ENCODER.CONTEXT_LENGTH = 77
+ cfg.MODEL.TEXT_ENCODER.NUM_LAYERS = 12
+ cfg.MODEL.TEXT_ENCODER.VOCAB_SIZE = 49408
+ cfg.MODEL.TEXT_ENCODER.PROJ_NUM_LAYERS = 2
+ cfg.MODEL.TEXT_ENCODER.N_CTX = 16
+
+ # oneformer inference config
+ cfg.MODEL.TEST = CN()
+ cfg.MODEL.TEST.SEMANTIC_ON = True
+ cfg.MODEL.TEST.INSTANCE_ON = False
+ cfg.MODEL.TEST.PANOPTIC_ON = False
+ cfg.MODEL.TEST.DETECTION_ON = False
+ cfg.MODEL.TEST.OBJECT_MASK_THRESHOLD = 0.0
+ cfg.MODEL.TEST.OVERLAP_THRESHOLD = 0.0
+ cfg.MODEL.TEST.SEM_SEG_POSTPROCESSING_BEFORE_INFERENCE = False
+ cfg.MODEL.TEST.TASK = "panoptic"
+
+ # TEST AUG Slide
+ cfg.TEST.AUG.IS_SLIDE = False
+ cfg.TEST.AUG.CROP_SIZE = (640, 640)
+ cfg.TEST.AUG.STRIDE = (426, 426)
+ cfg.TEST.AUG.SCALE = (2048, 640)
+ cfg.TEST.AUG.SETR_MULTI_SCALE = True
+ cfg.TEST.AUG.KEEP_RATIO = True
+ cfg.TEST.AUG.SIZE_DIVISOR = 32
+
+ # pixel decoder config
+ cfg.MODEL.SEM_SEG_HEAD.MASK_DIM = 256
+ # adding transformer in pixel decoder
+ cfg.MODEL.SEM_SEG_HEAD.TRANSFORMER_ENC_LAYERS = 0
+ # pixel decoder
+ cfg.MODEL.SEM_SEG_HEAD.PIXEL_DECODER_NAME = "BasePixelDecoder"
+ cfg.MODEL.SEM_SEG_HEAD.SEM_EMBED_DIM = 256
+ cfg.MODEL.SEM_SEG_HEAD.INST_EMBED_DIM = 256
+
+ # LSJ aug
+ cfg.INPUT.IMAGE_SIZE = 1024
+ cfg.INPUT.MIN_SCALE = 0.1
+ cfg.INPUT.MAX_SCALE = 2.0
+
+ # MSDeformAttn encoder configs
+ cfg.MODEL.SEM_SEG_HEAD.DEFORMABLE_TRANSFORMER_ENCODER_IN_FEATURES = ["res3", "res4", "res5"]
+ cfg.MODEL.SEM_SEG_HEAD.DEFORMABLE_TRANSFORMER_ENCODER_N_POINTS = 4
+ cfg.MODEL.SEM_SEG_HEAD.DEFORMABLE_TRANSFORMER_ENCODER_N_HEADS = 8
+
+def add_oneformer_config(cfg):
+ """
+ Add config for ONE_FORMER.
+ """
+
+ # oneformer model config
+ cfg.MODEL.ONE_FORMER = CN()
+
+ # loss
+ cfg.MODEL.ONE_FORMER.DEEP_SUPERVISION = True
+ cfg.MODEL.ONE_FORMER.NO_OBJECT_WEIGHT = 0.1
+ cfg.MODEL.ONE_FORMER.CLASS_WEIGHT = 1.0
+ cfg.MODEL.ONE_FORMER.DICE_WEIGHT = 1.0
+ cfg.MODEL.ONE_FORMER.MASK_WEIGHT = 20.0
+ cfg.MODEL.ONE_FORMER.CONTRASTIVE_WEIGHT = 0.5
+ cfg.MODEL.ONE_FORMER.CONTRASTIVE_TEMPERATURE = 0.07
+
+ # transformer config
+ cfg.MODEL.ONE_FORMER.NHEADS = 8
+ cfg.MODEL.ONE_FORMER.DROPOUT = 0.1
+ cfg.MODEL.ONE_FORMER.DIM_FEEDFORWARD = 2048
+ cfg.MODEL.ONE_FORMER.ENC_LAYERS = 0
+ cfg.MODEL.ONE_FORMER.CLASS_DEC_LAYERS = 2
+ cfg.MODEL.ONE_FORMER.DEC_LAYERS = 6
+ cfg.MODEL.ONE_FORMER.PRE_NORM = False
+
+ cfg.MODEL.ONE_FORMER.HIDDEN_DIM = 256
+ cfg.MODEL.ONE_FORMER.NUM_OBJECT_QUERIES = 120
+ cfg.MODEL.ONE_FORMER.NUM_OBJECT_CTX = 16
+ cfg.MODEL.ONE_FORMER.USE_TASK_NORM = True
+
+ cfg.MODEL.ONE_FORMER.TRANSFORMER_IN_FEATURE = "res5"
+ cfg.MODEL.ONE_FORMER.ENFORCE_INPUT_PROJ = False
+
+ # Sometimes `backbone.size_divisibility` is set to 0 for some backbone (e.g. ResNet)
+ # you can use this config to override
+ cfg.MODEL.ONE_FORMER.SIZE_DIVISIBILITY = 32
+
+ # transformer module
+ cfg.MODEL.ONE_FORMER.TRANSFORMER_DECODER_NAME = "ContrastiveMultiScaleMaskedTransformerDecoder"
+
+ # point loss configs
+ # Number of points sampled during training for a mask point head.
+ cfg.MODEL.ONE_FORMER.TRAIN_NUM_POINTS = 112 * 112
+ # Oversampling parameter for PointRend point sampling during training. Parameter `k` in the
+ # original paper.
+ cfg.MODEL.ONE_FORMER.OVERSAMPLE_RATIO = 3.0
+ # Importance sampling parameter for PointRend point sampling during training. Parametr `beta` in
+ # the original paper.
+ cfg.MODEL.ONE_FORMER.IMPORTANCE_SAMPLE_RATIO = 0.75
+
+def add_swin_config(cfg):
+ """
+ Add config forSWIN Backbone.
+ """
+
+ # swin transformer backbone
+ cfg.MODEL.SWIN = CN()
+ cfg.MODEL.SWIN.PRETRAIN_IMG_SIZE = 224
+ cfg.MODEL.SWIN.PATCH_SIZE = 4
+ cfg.MODEL.SWIN.EMBED_DIM = 96
+ cfg.MODEL.SWIN.DEPTHS = [2, 2, 6, 2]
+ cfg.MODEL.SWIN.NUM_HEADS = [3, 6, 12, 24]
+ cfg.MODEL.SWIN.WINDOW_SIZE = 7
+ cfg.MODEL.SWIN.MLP_RATIO = 4.0
+ cfg.MODEL.SWIN.QKV_BIAS = True
+ cfg.MODEL.SWIN.QK_SCALE = None
+ cfg.MODEL.SWIN.DROP_RATE = 0.0
+ cfg.MODEL.SWIN.ATTN_DROP_RATE = 0.0
+ cfg.MODEL.SWIN.DROP_PATH_RATE = 0.3
+ cfg.MODEL.SWIN.APE = False
+ cfg.MODEL.SWIN.PATCH_NORM = True
+ cfg.MODEL.SWIN.OUT_FEATURES = ["res2", "res3", "res4", "res5"]
+ cfg.MODEL.SWIN.USE_CHECKPOINT = False
+
+def add_dinat_config(cfg):
+ """
+ Add config for NAT Backbone.
+ """
+
+ # DINAT transformer backbone
+ cfg.MODEL.DiNAT = CN()
+ cfg.MODEL.DiNAT.DEPTHS = [3, 4, 18, 5]
+ cfg.MODEL.DiNAT.OUT_FEATURES = ["res2", "res3", "res4", "res5"]
+ cfg.MODEL.DiNAT.EMBED_DIM = 64
+ cfg.MODEL.DiNAT.MLP_RATIO = 3.0
+ cfg.MODEL.DiNAT.NUM_HEADS = [2, 4, 8, 16]
+ cfg.MODEL.DiNAT.DROP_PATH_RATE = 0.2
+ cfg.MODEL.DiNAT.KERNEL_SIZE = 7
+ cfg.MODEL.DiNAT.DILATIONS = [[1, 16, 1], [1, 4, 1, 8], [1, 2, 1, 3, 1, 4], [1, 2, 1, 2, 1]]
+ cfg.MODEL.DiNAT.OUT_INDICES = (0, 1, 2, 3)
+ cfg.MODEL.DiNAT.QKV_BIAS = True
+ cfg.MODEL.DiNAT.QK_SCALE = None
+ cfg.MODEL.DiNAT.DROP_RATE = 0
+ cfg.MODEL.DiNAT.ATTN_DROP_RATE = 0.
+ cfg.MODEL.DiNAT.IN_PATCH_SIZE = 4
+
+def add_convnext_config(cfg):
+ """
+ Add config for ConvNeXt Backbone.
+ """
+
+ # swin transformer backbone
+ cfg.MODEL.CONVNEXT = CN()
+ cfg.MODEL.CONVNEXT.IN_CHANNELS = 3
+ cfg.MODEL.CONVNEXT.DEPTHS = [3, 3, 27, 3]
+ cfg.MODEL.CONVNEXT.DIMS = [192, 384, 768, 1536]
+ cfg.MODEL.CONVNEXT.DROP_PATH_RATE = 0.4
+ cfg.MODEL.CONVNEXT.LSIT = 1.0
+ cfg.MODEL.CONVNEXT.OUT_INDICES = [0, 1, 2, 3]
+ cfg.MODEL.CONVNEXT.OUT_FEATURES = ["res2", "res3", "res4", "res5"]
\ No newline at end of file
diff --git a/annotator/OneFormer/oneformer/data/__init__.py b/annotator/OneFormer/oneformer/data/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..62a23dfafae1ce22264771517fd7eb44bbe4c600
--- /dev/null
+++ b/annotator/OneFormer/oneformer/data/__init__.py
@@ -0,0 +1 @@
+from . import datasets
diff --git a/annotator/OneFormer/oneformer/data/bpe_simple_vocab_16e6.txt b/annotator/OneFormer/oneformer/data/bpe_simple_vocab_16e6.txt
new file mode 100644
index 0000000000000000000000000000000000000000..aff3ec8f3d5f3d6c859b5751eda5e870dffd7eaf
--- /dev/null
+++ b/annotator/OneFormer/oneformer/data/bpe_simple_vocab_16e6.txt
@@ -0,0 +1,262145 @@
+"bpe_simple_vocab_16e6.txt#version: 0.2
+i n
+t h
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+r e
+a r
+e r
+th e
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+o u
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+) |
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+--- >>>
+*: ãĥ»ãĤļ
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+ðŁĶ ĵ
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+ðŁĺ¹ðŁĺ¹ ðŁĺ¹ðŁĺ¹
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+____ ___
+! ðŁĻĪ
+! ðŁĺħ
+ðŁĻĮðŁı½ ðŁĻĮðŁı½
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+======== ====
+ðŁļ İ
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+ðŁĴļ .
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+-------------------------------- ----------------
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+si menon
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+linux foundation
+l ct
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+er ca
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+bag anac
+atten bury
+att aboy
+as kea
+ari ano
+ann nnnn
+alimi ballard
+ad mn
+acher on
+> ...
+" "@
+ðŁĺĺ ðŁĺľ
+ðŁĴķðŁĴķðŁĴķðŁĴķ ðŁĴķðŁĴķðŁĴķðŁĴķ
+íĺķ ìĽIJ
+ë· Ķ
+âļª âļ«
+ઠľ
+zin nias
+zale wski
+yogag irl
+wyr ley
+woyz eck
+worth morealive
+world sight
+won u
+wil drose
+wei hen
+vu ko
+vendredi lecture
+tz on
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+tur non
+total football
+top golf
+thur sley
+thr on
+the pi
+thatsmy girl
+terrific tuesday
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+soule ater
+sop rou
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+smo or
+sketch noting
+sidec inema
+shubhan gi
+sh indler
+sed its
+se dat
+sd chat
+scotti e
+sap inker
+sab ba
+river park
+ri mowa
+reason ableness
+readju sted
+ran jana
+quer as
+py ong
+pu ggy
+promo products
+prayforthe world
+phosphat ase
+ph v
+pau lone
+open studio
+nr sv
+nose k
+nis ch
+ng wa
+new rules
+ne scafe
+my wolfsong
+morphe tt
+mon n
+mon kee
+mini atura
+micdro premix
+mesop orous
+mc pher
+mc coo
+mbl season
+marin ka
+mal asada
+m do
+luni z
+lse plc
+love youu
+lo ew
+libret tist
+letter sto
+lair g
+lac on
+kou ga
+kid shealth
+kend riya
+kay han
+kau shiki
+kan de
+june jo
+ju styn
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+johninne scentre
+jo bad
+jharsu guda
+jer ri
+jame spear
+jal na
+infl iction
+ine gypt
+incarcer ating
+im hof
+i asb
+hel pre
+head casey
+headcasey mike
+hallucino gen
+hal lettsville
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+goo din
+go sparks
+global summit
+ger an
+freethe arctic
+fre aker
+for trose
+for summer
+fe her
+fcu trecht
+face time
+ext gen
+er awan
+enew man
+ellef sondavid
+el il
+ed ball
+duc ted
+dr marty
+doh ring
+dhar ti
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+climate breakdown
+ci wf
+chri sperkinsdnd
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+chevening fco
+can ids
+bro mides
+bid dies
+bham donkeys
+bed in
+be idou
+be honest
+bat en
+bare la
+bac ademy
+b ment
+b dl
+anastaciam usic
+an erjee
+am pex
+al fas
+al ben
+ak hir
+aed as
+adam aofficial
+ack in
+achievement unlocked
+abutil on
+abdel rahman
+aar sbl
+?? "@
+! ðŁĶ¥ðŁĶ¥ðŁĶ¥
+! ðŁijī
+! :(
+ðŁĺį ðŁĺĻ
+ðŁİĬ ðŁİī
+ë§ ¤
+ë¦ ´
+åĵ ģ
+ze ma
+youngen terprise
+yaqu b
+xavier daniel
+wit tic
+win st
+wag ar
+vovin am
+vor ov
+vijay filmaker
+ver ilog
+vapor ware
+uri m
+up ends
+tre sham
+tre mpe
+total y
+tor ie
+today at
+thimble weed
+the chase
+swit chers
+swati jaihind
+swap meet
+stru ms
+stock photos
+stin ch
+speed prayers
+southernmiss fb
+so ca
+simon webbe
+shep newsteam
+ser via
+sean cardo
+sam ish
+saakshisra wat
+sa otome
+rough rider
+rond ina
+roadand track
+righ tist
+ric c
+renew ui
+ren lagann
+re treat
+rc psychic
+rae bareli
+py x
+pron o
+present ando
+poo jai
+pau gh
+pat roberts
+ovi de
+over development
+ometal lic
+nfl pabowl
+nep tunes
+mt dna
+mother and
+moore senate
+mjin nocent
+mistre s
+mike tirico
+med itech
+marky ramone
+maquilla je
+low theband
+love tour
+lift back
+li arin
+latt in
+labor atorium
+ky bourbon
+kr ach
+ken sit
+kazakh stan
+kati ed
+kaan ur
+jones jr
+jac kel
+it ain
+isser lis
+interrup tus
+international youthday
+insan ity
+indo chinese
+im mbm
+ig awa
+hydro graphy
+hi mitsu
+herak les
+her se
+hend all
+he ssel
+havil ah
+happy jhopeday
+ham ina
+h wu
+h wp
+h kt
+gy res
+guy ra
+gun sense
+gul fair
+greglou ganis
+grease paint
+ghaz ala
+gang neung
+gambrin us
+foodis medicine
+fon ua
+fi ii
+felice herrig
+ero icab
+ero ars
+er lend
+engie group
+ell p
+eli sir
+einstein maga
+efra im
+earl ham
+e peeps
+dr marth
+do towsky
+dis love
+dim witted
+de juan
+daniel s
+dak os
+d scc
+cri an
+contr alto
+commun is
+clergy men
+christmas music
+chi ded
+chest nu
+chazz palminteri
+cd na
+castle martyr
+cab by
+brighton pride
+bm v
+big and
+bel mon
+be oplay
+bat sky
+bag amoyo
+australian shepherd
+audio boom
+ath boy
+as chi
+arcadia unified
+ap ure
+ap ate
+anc ities
+amand ac
+alife dotowsky
+al san
+ake elah
+af sar
+adam rank
+. ;)
+" ¡
+!! ðŁİīðŁİī
+ðŁĴļðŁĴļ ðŁĴļðŁĴļðŁĴļ
+ðŁİīðŁİĬ ðŁİĪ
+ðŁĮ¼ ðŁĮ¼
+ðŁ§Ł âĢįâĻĢï¸ı
+íĭ° ìķĦëĿ¼
+éī Ħ
+âĿ¤ï¸ı ðŁĴª
+ᥠ±
+о ÑĤ
+your car
+york theatre
+ye sil
+yam ma
+ya hog
+wt fff
+writers festival
+worldfood prize
+wor dle
+we yer
+vo xx
+vendee globe
+var ico
+vancity buzz
+under development
+tran scultural
+tor neo
+tor mentors
+tiltro tor
+thisisse th
+thisisseth sblog
+the bonni
+th year
+tayl an
+tahaw y
+sw fclive
+super soft
+suburban ites
+str ator
+speaker series
+sp sm
+sou ad
+slo per
+sla visa
+skipp ered
+sketch cards
+show pieces
+sho wi
+shin han
+sf hs
+seg no
+se chs
+school meals
+schel de
+sau rian
+sag ami
+ry p
+rump f
+rou lston
+ro ffey
+riz al
+renega des
+ren alovelis
+regionalli ga
+region ale
+rb k
+ravin dersingh
+ra ux
+pu plovers
+pro scribed
+present an
+pre heated
+pr illy
+poul son
+po ids
+pm dc
+pik app
+photo video
+phi bbs
+pc j
+par ash
+pa tha
+p rum
+p drm
+our community
+oph ils
+ny rd
+nonprofit day
+non resident
+nin ny
+ne ira
+nat oli
+nalim bachia
+my app
+muke sham
+msccruise susa
+morning rush
+monagh angaa
+mol let
+mile posts
+mi thu
+meaning lessness
+mcken ny
+mathi sen
+masterchef sheep
+mar one
+mamat abanerjee
+ly dden
+lon ie
+lo bat
+lin foot
+lgb thistory
+lec ats
+kilmac ud
+kerry katona
+ke ens
+karol sevilla
+kam akshi
+jrod fromoz
+jose cuervo
+jai my
+interior designideas
+ino ise
+inder jit
+hon gi
+hill erman
+hemam alini
+hayley mcqueen
+hand maids
+hal kett
+gui z
+gross ers
+gre el
+gib let
+gay les
+fou sey
+fli k
+female entrepreneur
+feels badman
+fanni bal
+f staste
+erc p
+ent el
+en in
+eli kes
+ei ichiro
+ed postcards
+ec kankar
+duc s
+dor mammu
+domin ik
+dogand puplovers
+disp leasing
+dge l
+del fonics
+del at
+de ase
+d pm
+cu cks
+cronkit enews
+congress muktbharat
+color smarathi
+col avita
+clen buter
+cipri an
+cbc news
+carleton college
+car nal
+black nose
+bior xiv
+bhav nalimbachia
+best t
+be chard
+bar ma
+babs draws
+b fest
+az atho
+avon rep
+att ac
+asph alt
+ap sf
+anton etti
+annane trebko
+ang om
+ameth asone
+am oner
+ah hh
+ag proud
+affor dance
+ad joint
+: ~
+//// //
+$ ]
+! :))
+ðŁĴį ðŁĴİ
+ðŁijįðŁı½ ðŁijįðŁı½
+zar da
+yuk ito
+work athome
+women scienceday
+wit s
+where withal
+vic ta
+vas elines
+v any
+utaware ru
+us fda
+un competitive
+un believe
+twoo odley
+tw ofer
+truck driver
+tri stin
+trans acted
+threep wood
+the yyam
+the shoe
+the il
+tari kh
+tali m
+sussex lifemag
+sus open
+sugi mori
+su long
+stati stik
+sta this
+spring side
+smo ss
+set su
+sch aer
+sc ana
+sat oh
+sar la
+santa anita
+sambu cus
+saig on
+ru perts
+ru ia
+roman i
+robb ins
+real muto
+reading festival
+read justment
+provision als
+pro kabaddi
+ppl summit
+poe mas
+pig tail
+pdp nigeria
+party yyy
+ow lets
+news sport
+neptun o
+naray ang
+nan tong
+nai ad
+mur rin
+mo scon
+ml h
+merq ury
+mc t
+marcus butler
+makha chkala
+lucky man
+loe hr
+ll cc
+liveyour dream
+lin acre
+lime stone
+lifeof an
+li ffs
+lesmiz bway
+leh ri
+learn coding
+le user
+lan l
+lake eri
+lado ga
+ko chunni
+k dd
+justicele ague
+jen nar
+iyanla vanzant
+im pi
+i stre
+i ib
+hondac anada
+hey ns
+har diest
+hannah bronfman
+h pr
+h fc
+gw m
+guar in
+greatbritish menu
+ghu ggi
+ghost story
+gec dsb
+ge ats
+gaslight anthem
+garci aparra
+friend sday
+fl ye
+fis chl
+fent anil
+fair vote
+ery ri
+episco po
+eng cro
+electrocardio gram
+ek mainaure
+dun ces
+duc ators
+dub nyk
+distru stful
+deton ators
+deceler ate
+debbie reynolds
+de mais
+day z
+d th
+cuar ón
+cu pich
+cros well
+courts matter
+cool kid
+chol ar
+cha que
+catholic faith
+cap olitics
+c pride
+by land
+bran sholme
+bla zek
+bicy clec
+be calmed
+bas combe
+bae jin
+bad on
+auri fil
+audi op
+attenu ated
+ar ki
+ar dara
+anti theism
+ames waran
+am paio
+al isms
+ag api
+abo g
+!!! ??
+! âĿ¤ï¸ıâĿ¤ï¸ıâĿ¤ï¸ı
+ðŁĴĻ ðŁĴļðŁĴĽ
+ðŁĩ´ ðŁĩ²
+ìĨ Ķë
+미ë Ĥĺ
+é İ
+åį ģ
+âĢĶâĢĶâĢĶâĢĶâĢĶâĢĶâĢĶâĢĶâĢĶâĢĶâĢĶâĢĶ âĢĶâĢĶâĢĶâĢĶ
+z abu
+ye young
+ye ji
+y ma
+wild boyz
+weald stonefc
+vote ariana
+ven ne
+v bn
+ut zon
+uise du
+ufo table
+ufo sighting
+uff ington
+tu le
+tt itz
+troph ication
+travel awesome
+transcendent alism
+tra pper
+tik va
+ti sed
+thisis england
+teenwolf season
+tech support
+sundar c
+suare zg
+su sy
+stron gheart
+street food
+story bots
+sto c
+st gile
+srsg vac
+southeast ward
+soccer saturday
+so zone
+smid t
+sm city
+sli mey
+sin claire
+sd reader
+scare d
diff --git a/annotator/OneFormer/oneformer/data/bpe_simple_vocab_16e6.txt.gz b/annotator/OneFormer/oneformer/data/bpe_simple_vocab_16e6.txt.gz
new file mode 100644
index 0000000000000000000000000000000000000000..36a15856e00a06a9fbed8cdd34d2393fea4a3113
--- /dev/null
+++ b/annotator/OneFormer/oneformer/data/bpe_simple_vocab_16e6.txt.gz
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:924691ac288e54409236115652ad4aa250f48203de50a9e4722a6ecd48d6804a
+size 1356917
diff --git a/annotator/OneFormer/oneformer/data/build.py b/annotator/OneFormer/oneformer/data/build.py
new file mode 100644
index 0000000000000000000000000000000000000000..ea371c1dad91cc5a97394f4b81fe7a7ea50017bb
--- /dev/null
+++ b/annotator/OneFormer/oneformer/data/build.py
@@ -0,0 +1,121 @@
+# ------------------------------------------------------------------------------
+# Reference: https://github.com/facebookresearch/detectron2/blob/main/detectron2/data/build.py
+# Modified by Jitesh Jain (https://github.com/praeclarumjj3)
+# ------------------------------------------------------------------------------
+
+from typing import Any, Callable, Dict, List, Optional, Union
+import torch.utils.data as torchdata
+
+from detectron2.config import configurable
+
+
+from detectron2.data.common import DatasetFromList, MapDataset
+from detectron2.data.dataset_mapper import DatasetMapper
+from detectron2.data.samplers import (
+ InferenceSampler,
+)
+from detectron2.data.build import (
+ get_detection_dataset_dicts,
+ trivial_batch_collator
+)
+"""
+This file contains the default logic to build a dataloader for training or testing.
+"""
+
+__all__ = [
+ "build_detection_test_loader",
+]
+
+
+def _test_loader_from_config(cfg, dataset_name, mapper=None):
+ """
+ Uses the given `dataset_name` argument (instead of the names in cfg), because the
+ standard practice is to evaluate each test set individually (not combining them).
+ """
+ if isinstance(dataset_name, str):
+ dataset_name = [dataset_name]
+
+ dataset = get_detection_dataset_dicts(
+ dataset_name,
+ filter_empty=False,
+ proposal_files=[
+ cfg.DATASETS.PROPOSAL_FILES_TEST[list(cfg.DATASETS.TEST).index(x)] for x in dataset_name
+ ]
+ if cfg.MODEL.LOAD_PROPOSALS
+ else None,
+ )
+ if mapper is None:
+ mapper = DatasetMapper(cfg, False)
+ return {
+ "dataset": dataset,
+ "mapper": mapper,
+ "num_workers": cfg.DATALOADER.NUM_WORKERS,
+ "sampler": InferenceSampler(len(dataset))
+ if not isinstance(dataset, torchdata.IterableDataset)
+ else None,
+ }
+
+
+@configurable(from_config=_test_loader_from_config)
+def build_detection_test_loader(
+ dataset: Union[List[Any], torchdata.Dataset],
+ *,
+ mapper: Callable[[Dict[str, Any]], Any],
+ sampler: Optional[torchdata.Sampler] = None,
+ batch_size: int = 1,
+ num_workers: int = 0,
+ collate_fn: Optional[Callable[[List[Any]], Any]] = None,
+) -> torchdata.DataLoader:
+ """
+ Similar to `build_detection_train_loader`, with default batch size = 1,
+ and sampler = :class:`InferenceSampler`. This sampler coordinates all workers
+ to produce the exact set of all samples.
+
+ Args:
+ dataset: a list of dataset dicts,
+ or a pytorch dataset (either map-style or iterable). They can be obtained
+ by using :func:`DatasetCatalog.get` or :func:`get_detection_dataset_dicts`.
+ mapper: a callable which takes a sample (dict) from dataset
+ and returns the format to be consumed by the model.
+ When using cfg, the default choice is ``DatasetMapper(cfg, is_train=False)``.
+ sampler: a sampler that produces
+ indices to be applied on ``dataset``. Default to :class:`InferenceSampler`,
+ which splits the dataset across all workers. Sampler must be None
+ if `dataset` is iterable.
+ batch_size: the batch size of the data loader to be created.
+ Default to 1 image per worker since this is the standard when reporting
+ inference time in papers.
+ num_workers: number of parallel data loading workers
+ collate_fn: same as the argument of `torch.utils.data.DataLoader`.
+ Defaults to do no collation and return a list of data.
+
+ Returns:
+ DataLoader: a torch DataLoader, that loads the given detection
+ dataset, with test-time transformation and batching.
+
+ Examples:
+ ::
+ data_loader = build_detection_test_loader(
+ DatasetRegistry.get("my_test"),
+ mapper=DatasetMapper(...))
+
+ # or, instantiate with a CfgNode:
+ data_loader = build_detection_test_loader(cfg, "my_test")
+ """
+ if isinstance(dataset, list):
+ dataset = DatasetFromList(dataset, copy=False)
+ if mapper is not None:
+ dataset = MapDataset(dataset, mapper)
+ if isinstance(dataset, torchdata.IterableDataset):
+ assert sampler is None, "sampler must be None if dataset is IterableDataset"
+ else:
+ if sampler is None:
+ sampler = InferenceSampler(len(dataset))
+ return torchdata.DataLoader(
+ dataset,
+ batch_size=batch_size,
+ sampler=sampler,
+ drop_last=False,
+ num_workers=num_workers,
+ collate_fn=trivial_batch_collator if collate_fn is None else collate_fn,
+ )
\ No newline at end of file
diff --git a/annotator/OneFormer/oneformer/data/dataset_mappers/__init__.py b/annotator/OneFormer/oneformer/data/dataset_mappers/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..8b137891791fe96927ad78e64b0aad7bded08bdc
--- /dev/null
+++ b/annotator/OneFormer/oneformer/data/dataset_mappers/__init__.py
@@ -0,0 +1 @@
+
diff --git a/annotator/OneFormer/oneformer/data/dataset_mappers/coco_unified_new_baseline_dataset_mapper.py b/annotator/OneFormer/oneformer/data/dataset_mappers/coco_unified_new_baseline_dataset_mapper.py
new file mode 100644
index 0000000000000000000000000000000000000000..677b9eaf25e25e98c6e7d39a6c77a29f2f313d3c
--- /dev/null
+++ b/annotator/OneFormer/oneformer/data/dataset_mappers/coco_unified_new_baseline_dataset_mapper.py
@@ -0,0 +1,341 @@
+# ------------------------------------------------------------------------------
+# Reference: https://github.com/facebookresearch/Mask2Former/blob/main/mask2former/data/dataset_mappers/coco_panoptic_new_baseline_dataset_mapper.py
+# Modified by Jitesh Jain (https://github.com/praeclarumjj3)
+# ------------------------------------------------------------------------------
+
+import copy
+import logging
+
+import numpy as np
+import torch
+
+from detectron2.data import MetadataCatalog
+from detectron2.config import configurable
+from detectron2.data import detection_utils as utils
+from detectron2.data import transforms as T
+from detectron2.structures import BitMasks, Instances
+from oneformer.utils.box_ops import masks_to_boxes
+from oneformer.data.tokenizer import SimpleTokenizer, Tokenize
+
+__all__ = ["COCOUnifiedNewBaselineDatasetMapper"]
+
+
+def build_transform_gen(cfg, is_train):
+ """
+ Create a list of default :class:`Augmentation` from config.
+ Now it includes resizing and flipping.
+ Returns:
+ list[Augmentation]
+ """
+ assert is_train, "Only support training augmentation"
+ image_size = cfg.INPUT.IMAGE_SIZE
+ min_scale = cfg.INPUT.MIN_SCALE
+ max_scale = cfg.INPUT.MAX_SCALE
+
+ augmentation = []
+
+ if cfg.INPUT.RANDOM_FLIP != "none":
+ augmentation.append(
+ T.RandomFlip(
+ horizontal=cfg.INPUT.RANDOM_FLIP == "horizontal",
+ vertical=cfg.INPUT.RANDOM_FLIP == "vertical",
+ )
+ )
+
+ augmentation.extend([
+ T.ResizeScale(
+ min_scale=min_scale, max_scale=max_scale, target_height=image_size, target_width=image_size
+ ),
+ T.FixedSizeCrop(crop_size=(image_size, image_size)),
+ ])
+
+ return augmentation
+
+
+# This is specifically designed for the COCO dataset.
+class COCOUnifiedNewBaselineDatasetMapper:
+ """
+ A callable which takes a dataset dict in Detectron2 Dataset format,
+ and map it into a format used by OneFormer.
+
+ This dataset mapper applies the same transformation as DETR for COCO panoptic segmentation.
+
+ The callable currently does the following:
+
+ 1. Read the image from "file_name"
+ 2. Applies geometric transforms to the image and annotation
+ 3. Find and applies suitable cropping to the image and annotation
+ 4. Prepare image and annotation to Tensors
+ """
+
+ @configurable
+ def __init__(
+ self,
+ is_train=True,
+ *,
+ num_queries,
+ tfm_gens,
+ meta,
+ image_format,
+ max_seq_len,
+ task_seq_len,
+ semantic_prob,
+ instance_prob,
+ ):
+ """
+ NOTE: this interface is experimental.
+ Args:
+ is_train: for training or inference
+ augmentations: a list of augmentations or deterministic transforms to apply
+ crop_gen: crop augmentation
+ tfm_gens: data augmentation
+ image_format: an image format supported by :func:`detection_utils.read_image`.
+ """
+ self.tfm_gens = tfm_gens
+ logging.getLogger(__name__).info(
+ "[COCOUnifiedNewBaselineDatasetMapper] Full TransformGens used in training: {}".format(
+ str(self.tfm_gens)
+ )
+ )
+
+ self.img_format = image_format
+ self.is_train = is_train
+ self.meta = meta
+ self.ignore_label = self.meta.ignore_label
+ self.num_queries = num_queries
+
+ self.things = []
+ for k,v in self.meta.thing_dataset_id_to_contiguous_id.items():
+ self.things.append(v)
+ self.class_names = self.meta.stuff_classes
+ self.text_tokenizer = Tokenize(SimpleTokenizer(), max_seq_len=max_seq_len)
+ self.task_tokenizer = Tokenize(SimpleTokenizer(), max_seq_len=task_seq_len)
+ self.semantic_prob = semantic_prob
+ self.instance_prob = instance_prob
+
+ @classmethod
+ def from_config(cls, cfg, is_train=True):
+ # Build augmentation
+ tfm_gens = build_transform_gen(cfg, is_train)
+ dataset_names = cfg.DATASETS.TRAIN
+ meta = MetadataCatalog.get(dataset_names[0])
+
+ ret = {
+ "is_train": is_train,
+ "meta": meta,
+ "tfm_gens": tfm_gens,
+ "image_format": cfg.INPUT.FORMAT,
+ "num_queries": cfg.MODEL.ONE_FORMER.NUM_OBJECT_QUERIES - cfg.MODEL.TEXT_ENCODER.N_CTX,
+ "task_seq_len": cfg.INPUT.TASK_SEQ_LEN,
+ "max_seq_len": cfg.INPUT.MAX_SEQ_LEN,
+ "semantic_prob": cfg.INPUT.TASK_PROB.SEMANTIC,
+ "instance_prob": cfg.INPUT.TASK_PROB.INSTANCE,
+ }
+ return ret
+
+ def _get_semantic_dict(self, pan_seg_gt, image_shape, segments_info, num_class_obj):
+ instances = Instances(image_shape)
+
+ classes = []
+ texts = ["a semantic photo"] * self.num_queries
+ masks = []
+ label = np.ones_like(pan_seg_gt) * self.ignore_label
+
+ for segment_info in segments_info:
+ class_id = segment_info["category_id"]
+ if not segment_info["iscrowd"]:
+ mask = pan_seg_gt == segment_info["id"]
+ if not np.all(mask == False):
+ if class_id not in classes:
+ cls_name = self.class_names[class_id]
+ classes.append(class_id)
+ masks.append(mask)
+ num_class_obj[cls_name] += 1
+ else:
+ idx = classes.index(class_id)
+ masks[idx] += mask
+ masks[idx] = np.clip(masks[idx], 0, 1).astype(np.bool)
+ label[mask] = class_id
+
+ num = 0
+ for i, cls_name in enumerate(self.class_names):
+ if num_class_obj[cls_name] > 0:
+ for _ in range(num_class_obj[cls_name]):
+ if num >= len(texts):
+ break
+ texts[num] = f"a photo with a {cls_name}"
+ num += 1
+
+ classes = np.array(classes)
+ instances.gt_classes = torch.tensor(classes, dtype=torch.int64)
+ if len(masks) == 0:
+ # Some image does not have annotation (all ignored)
+ instances.gt_masks = torch.zeros((0, pan_seg_gt.shape[-2], pan_seg_gt.shape[-1]))
+ instances.gt_bboxes = torch.zeros((0, 4))
+ else:
+ masks = BitMasks(
+ torch.stack([torch.from_numpy(np.ascontiguousarray(x.copy())) for x in masks])
+ )
+ instances.gt_masks = masks.tensor
+ # Placeholder bounding boxes for stuff regions. Note that these are not used during training.
+ instances.gt_bboxes = torch.stack([torch.tensor([0., 0., 1., 1.])] * instances.gt_masks.shape[0])
+ return instances, texts, label
+
+ def _get_instance_dict(self, pan_seg_gt, image_shape, segments_info, num_class_obj):
+ instances = Instances(image_shape)
+
+ classes = []
+ texts = ["an instance photo"] * self.num_queries
+ masks = []
+ label = np.ones_like(pan_seg_gt) * self.ignore_label
+
+ for segment_info in segments_info:
+ class_id = segment_info["category_id"]
+ if class_id in self.things:
+ if not segment_info["iscrowd"]:
+ mask = pan_seg_gt == segment_info["id"]
+ if not np.all(mask == False):
+ cls_name = self.class_names[class_id]
+ classes.append(class_id)
+ masks.append(mask)
+ num_class_obj[cls_name] += 1
+ label[mask] = class_id
+
+ num = 0
+ for i, cls_name in enumerate(self.class_names):
+ if num_class_obj[cls_name] > 0:
+ for _ in range(num_class_obj[cls_name]):
+ if num >= len(texts):
+ break
+ texts[num] = f"a photo with a {cls_name}"
+ num += 1
+
+ classes = np.array(classes)
+ instances.gt_classes = torch.tensor(classes, dtype=torch.int64)
+ if len(masks) == 0:
+ # Some image does not have annotation (all ignored)
+ instances.gt_masks = torch.zeros((0, pan_seg_gt.shape[-2], pan_seg_gt.shape[-1]))
+ instances.gt_bboxes = torch.zeros((0, 4))
+ else:
+ masks = BitMasks(
+ torch.stack([torch.from_numpy(np.ascontiguousarray(x.copy())) for x in masks])
+ )
+ instances.gt_masks = masks.tensor
+ instances.gt_bboxes = masks_to_boxes(instances.gt_masks)
+ return instances, texts, label
+
+ def _get_panoptic_dict(self, pan_seg_gt, image_shape, segments_info, num_class_obj):
+ instances = Instances(image_shape)
+
+ classes = []
+ texts = ["a panoptic photo"] * self.num_queries
+ masks = []
+ label = np.ones_like(pan_seg_gt) * self.ignore_label
+
+ for segment_info in segments_info:
+ class_id = segment_info["category_id"]
+ if not segment_info["iscrowd"]:
+ mask = pan_seg_gt == segment_info["id"]
+ if not np.all(mask == False):
+ cls_name = self.class_names[class_id]
+ classes.append(class_id)
+ masks.append(mask)
+ num_class_obj[cls_name] += 1
+ label[mask] = class_id
+
+ num = 0
+ for i, cls_name in enumerate(self.class_names):
+ if num_class_obj[cls_name] > 0:
+ for _ in range(num_class_obj[cls_name]):
+ if num >= len(texts):
+ break
+ texts[num] = f"a photo with a {cls_name}"
+ num += 1
+
+ classes = np.array(classes)
+ instances.gt_classes = torch.tensor(classes, dtype=torch.int64)
+ if len(masks) == 0:
+ # Some image does not have annotation (all ignored)
+ instances.gt_masks = torch.zeros((0, pan_seg_gt.shape[-2], pan_seg_gt.shape[-1]))
+ instances.gt_bboxes = torch.zeros((0, 4))
+ else:
+ masks = BitMasks(
+ torch.stack([torch.from_numpy(np.ascontiguousarray(x.copy())) for x in masks])
+ )
+ instances.gt_masks = masks.tensor
+ instances.gt_bboxes = masks_to_boxes(instances.gt_masks)
+ for i in range(instances.gt_classes.shape[0]):
+ # Placeholder bounding boxes for stuff regions. Note that these are not used during training.
+ if instances.gt_classes[i].item() not in self.things:
+ instances.gt_bboxes[i] = torch.tensor([0., 0., 1., 1.])
+ return instances, texts, label
+
+ def __call__(self, dataset_dict):
+ """
+ Args:
+ dataset_dict (dict): Metadata of one image, in Detectron2 Dataset format.
+
+ Returns:
+ dict: a format that builtin models in detectron2 accept
+ """
+ dataset_dict = copy.deepcopy(dataset_dict) # it will be modified by code below
+ image = utils.read_image(dataset_dict["file_name"], format=self.img_format)
+ utils.check_image_size(dataset_dict, image)
+
+ image, transforms = T.apply_transform_gens(self.tfm_gens, image)
+ image_shape = image.shape[:2] # h, w
+
+ # Pytorch's dataloader is efficient on torch.Tensor due to shared-memory,
+ # but not efficient on large generic data structures due to the use of pickle & mp.Queue.
+ # Therefore it's important to use torch.Tensor.
+ dataset_dict["image"] = torch.as_tensor(np.ascontiguousarray(image.transpose(2, 0, 1)))
+
+ if not self.is_train:
+ # USER: Modify this if you want to keep them for some reason.
+ dataset_dict.pop("annotations", None)
+ return dataset_dict
+
+ # semantic segmentation
+ if "sem_seg_file_name" in dataset_dict:
+ # PyTorch transformation not implemented for uint16, so converting it to double first
+ sem_seg_gt = utils.read_image(dataset_dict.pop("sem_seg_file_name")).astype("double")
+ sem_seg_gt = transforms.apply_segmentation(sem_seg_gt)
+ else:
+ sem_seg_gt = None
+
+ if "pan_seg_file_name" in dataset_dict:
+ pan_seg_gt = utils.read_image(dataset_dict.pop("pan_seg_file_name"), "RGB")
+ segments_info = dataset_dict["segments_info"]
+
+ # apply the same transformation to panoptic segmentation
+ pan_seg_gt = transforms.apply_segmentation(pan_seg_gt)
+
+ from panopticapi.utils import rgb2id
+ pan_seg_gt = rgb2id(pan_seg_gt)
+
+ prob_task = np.random.uniform(0,1.)
+
+ num_class_obj = {}
+
+ for name in self.class_names:
+ num_class_obj[name] = 0
+
+ if prob_task < self.semantic_prob:
+ task = "The task is semantic"
+ instances, text, sem_seg = self._get_semantic_dict(pan_seg_gt, image_shape, segments_info, num_class_obj)
+ elif prob_task < self.instance_prob:
+ task = "The task is instance"
+ instances, text, sem_seg = self._get_instance_dict(pan_seg_gt, image_shape, segments_info, num_class_obj)
+ else:
+ task = "The task is panoptic"
+ instances, text, sem_seg = self._get_panoptic_dict(pan_seg_gt, image_shape, segments_info, num_class_obj)
+
+
+ dataset_dict["sem_seg"] = torch.from_numpy(sem_seg).long()
+ dataset_dict["instances"] = instances
+ dataset_dict["orig_shape"] = image_shape
+ dataset_dict["task"] = task
+ dataset_dict["text"] = text
+ dataset_dict["thing_ids"] = self.things
+
+ return dataset_dict
diff --git a/annotator/OneFormer/oneformer/data/dataset_mappers/dataset_mapper.py b/annotator/OneFormer/oneformer/data/dataset_mappers/dataset_mapper.py
new file mode 100644
index 0000000000000000000000000000000000000000..b8617e8da4ddd851bf8c8bb97432a87503aa4afc
--- /dev/null
+++ b/annotator/OneFormer/oneformer/data/dataset_mappers/dataset_mapper.py
@@ -0,0 +1,203 @@
+# ------------------------------------------------------------------------------
+# Reference: https://github.com/facebookresearch/detectron2/blob/main/detectron2/data/dataset_mapper.py
+# Modified by Jitesh Jain (https://github.com/praeclarumjj3)
+# ------------------------------------------------------------------------------
+
+import copy
+import logging
+import numpy as np
+from typing import List, Optional, Union
+import torch
+
+from detectron2.config import configurable
+
+from detectron2.data import detection_utils as utils
+from detectron2.data import transforms as T
+from oneformer.data.tokenizer import SimpleTokenizer, Tokenize
+
+__all__ = ["DatasetMapper"]
+
+
+class DatasetMapper:
+ """
+ A callable which takes a dataset dict in Detectron2 Dataset format,
+ and map it into a format used by the model.
+
+ This is the default callable to be used to map your dataset dict into training data.
+ You may need to follow it to implement your own one for customized logic,
+ such as a different way to read or transform images.
+ See :doc:`/tutorials/data_loading` for details.
+
+ The callable currently does the following:
+
+ 1. Read the image from "file_name"
+ 2. Applies cropping/geometric transforms to the image and annotations
+ 3. Prepare data and annotations to Tensor and :class:`Instances`
+ """
+
+ @configurable
+ def __init__(
+ self,
+ is_train: bool,
+ *,
+ augmentations: List[Union[T.Augmentation, T.Transform]],
+ image_format: str,
+ task_seq_len: int,
+ task: str = "panoptic",
+ use_instance_mask: bool = False,
+ use_keypoint: bool = False,
+ instance_mask_format: str = "polygon",
+ keypoint_hflip_indices: Optional[np.ndarray] = None,
+ precomputed_proposal_topk: Optional[int] = None,
+ recompute_boxes: bool = False,
+ ):
+ """
+ NOTE: this interface is experimental.
+
+ Args:
+ is_train: whether it's used in training or inference
+ augmentations: a list of augmentations or deterministic transforms to apply
+ image_format: an image format supported by :func:`detection_utils.read_image`.
+ use_instance_mask: whether to process instance segmentation annotations, if available
+ use_keypoint: whether to process keypoint annotations if available
+ instance_mask_format: one of "polygon" or "bitmask". Process instance segmentation
+ masks into this format.
+ keypoint_hflip_indices: see :func:`detection_utils.create_keypoint_hflip_indices`
+ precomputed_proposal_topk: if given, will load pre-computed
+ proposals from dataset_dict and keep the top k proposals for each image.
+ recompute_boxes: whether to overwrite bounding box annotations
+ by computing tight bounding boxes from instance mask annotations.
+ """
+ if recompute_boxes:
+ assert use_instance_mask, "recompute_boxes requires instance masks"
+ # fmt: off
+ self.is_train = is_train
+ self.augmentations = T.AugmentationList(augmentations)
+ self.image_format = image_format
+ self.use_instance_mask = use_instance_mask
+ self.instance_mask_format = instance_mask_format
+ self.use_keypoint = use_keypoint
+ self.keypoint_hflip_indices = keypoint_hflip_indices
+ self.proposal_topk = precomputed_proposal_topk
+ self.recompute_boxes = recompute_boxes
+ self.task_tokenizer = Tokenize(SimpleTokenizer(), max_seq_len=task_seq_len)
+ self.task = task
+ assert self.task in ["panoptic", "semantic", "instance"]
+
+ # fmt: on
+ logger = logging.getLogger(__name__)
+ mode = "training" if is_train else "inference"
+ logger.info(f"[DatasetMapper] Augmentations used in {mode}: {augmentations}")
+
+ @classmethod
+ def from_config(cls, cfg, is_train: bool = True):
+ augs = utils.build_augmentation(cfg, is_train)
+ if cfg.INPUT.CROP.ENABLED and is_train:
+ augs.insert(0, T.RandomCrop(cfg.INPUT.CROP.TYPE, cfg.INPUT.CROP.SIZE))
+ recompute_boxes = cfg.MODEL.MASK_ON
+ else:
+ recompute_boxes = False
+
+ ret = {
+ "is_train": is_train,
+ "augmentations": augs,
+ "image_format": cfg.INPUT.FORMAT,
+ "use_instance_mask": cfg.MODEL.MASK_ON,
+ "instance_mask_format": cfg.INPUT.MASK_FORMAT,
+ "use_keypoint": cfg.MODEL.KEYPOINT_ON,
+ "task_seq_len": cfg.INPUT.TASK_SEQ_LEN,
+ "recompute_boxes": recompute_boxes,
+ "task": cfg.MODEL.TEST.TASK,
+ }
+
+ if cfg.MODEL.KEYPOINT_ON:
+ ret["keypoint_hflip_indices"] = utils.create_keypoint_hflip_indices(cfg.DATASETS.TRAIN)
+
+ if cfg.MODEL.LOAD_PROPOSALS:
+ ret["precomputed_proposal_topk"] = (
+ cfg.DATASETS.PRECOMPUTED_PROPOSAL_TOPK_TRAIN
+ if is_train
+ else cfg.DATASETS.PRECOMPUTED_PROPOSAL_TOPK_TEST
+ )
+ return ret
+
+ def _transform_annotations(self, dataset_dict, transforms, image_shape):
+ # USER: Modify this if you want to keep them for some reason.
+ for anno in dataset_dict["annotations"]:
+ if not self.use_instance_mask:
+ anno.pop("segmentation", None)
+ if not self.use_keypoint:
+ anno.pop("keypoints", None)
+
+ # USER: Implement additional transformations if you have other types of data
+ annos = [
+ utils.transform_instance_annotations(
+ obj, transforms, image_shape, keypoint_hflip_indices=self.keypoint_hflip_indices
+ )
+ for obj in dataset_dict.pop("annotations")
+ if obj.get("iscrowd", 0) == 0
+ ]
+ instances = utils.annotations_to_instances(
+ annos, image_shape, mask_format=self.instance_mask_format
+ )
+
+ # After transforms such as cropping are applied, the bounding box may no longer
+ # tightly bound the object. As an example, imagine a triangle object
+ # [(0,0), (2,0), (0,2)] cropped by a box [(1,0),(2,2)] (XYXY format). The tight
+ # bounding box of the cropped triangle should be [(1,0),(2,1)], which is not equal to
+ # the intersection of original bounding box and the cropping box.
+ if self.recompute_boxes:
+ instances.gt_boxes = instances.gt_masks.get_bounding_boxes()
+ dataset_dict["instances"] = utils.filter_empty_instances(instances)
+
+ def __call__(self, dataset_dict):
+ """
+ Args:
+ dataset_dict (dict): Metadata of one image, in Detectron2 Dataset format.
+
+ Returns:
+ dict: a format that builtin models in detectron2 accept
+ """
+ dataset_dict = copy.deepcopy(dataset_dict) # it will be modified by code below
+ # USER: Write your own image loading if it's not from a file
+ image = utils.read_image(dataset_dict["file_name"], format=self.image_format)
+ utils.check_image_size(dataset_dict, image)
+
+ task = f"The task is {self.task}"
+ dataset_dict["task"] = task
+
+ # USER: Remove if you don't do semantic/panoptic segmentation.
+ if "sem_seg_file_name" in dataset_dict:
+ sem_seg_gt = utils.read_image(dataset_dict.pop("sem_seg_file_name"), "L").squeeze(2)
+ else:
+ sem_seg_gt = None
+
+ aug_input = T.AugInput(image, sem_seg=sem_seg_gt)
+ transforms = self.augmentations(aug_input)
+ image, sem_seg_gt = aug_input.image, aug_input.sem_seg
+
+ image_shape = image.shape[:2] # h, w
+ # Pytorch's dataloader is efficient on torch.Tensor due to shared-memory,
+ # but not efficient on large generic data structures due to the use of pickle & mp.Queue.
+ # Therefore it's important to use torch.Tensor.
+ dataset_dict["image"] = torch.as_tensor(np.ascontiguousarray(image.transpose(2, 0, 1)))
+ if sem_seg_gt is not None:
+ dataset_dict["sem_seg"] = torch.as_tensor(sem_seg_gt.astype("long"))
+
+ # USER: Remove if you don't use pre-computed proposals.
+ # Most users would not need this feature.
+ if self.proposal_topk is not None:
+ utils.transform_proposals(
+ dataset_dict, image_shape, transforms, proposal_topk=self.proposal_topk
+ )
+
+ if not self.is_train:
+ # USER: Modify this if you want to keep them for some reason.
+ dataset_dict.pop("annotations", None)
+ dataset_dict.pop("sem_seg_file_name", None)
+ return dataset_dict
+
+ if "annotations" in dataset_dict:
+ self._transform_annotations(dataset_dict, transforms, image_shape)
+
+ return dataset_dict
\ No newline at end of file
diff --git a/annotator/OneFormer/oneformer/data/dataset_mappers/oneformer_unified_dataset_mapper.py b/annotator/OneFormer/oneformer/data/dataset_mappers/oneformer_unified_dataset_mapper.py
new file mode 100644
index 0000000000000000000000000000000000000000..f70ef4ede37744ef4f252e1d776ba1088d87f421
--- /dev/null
+++ b/annotator/OneFormer/oneformer/data/dataset_mappers/oneformer_unified_dataset_mapper.py
@@ -0,0 +1,375 @@
+# ------------------------------------------------------------------------------
+# Reference: https://github.com/facebookresearch/Mask2Former/blob/main/mask2former/data/dataset_mappers/mask_former_panoptic_dataset_mapper.py
+# Modified by Jitesh Jain (https://github.com/praeclarumjj3)
+# ------------------------------------------------------------------------------
+
+import copy
+import logging
+import os
+
+import numpy as np
+import torch
+from torch.nn import functional as F
+
+from detectron2.config import configurable
+from detectron2.data import detection_utils as utils
+from detectron2.data import transforms as T
+from detectron2.structures import BitMasks, Instances
+from detectron2.data import MetadataCatalog
+from detectron2.projects.point_rend import ColorAugSSDTransform
+from oneformer.utils.box_ops import masks_to_boxes
+from oneformer.data.tokenizer import SimpleTokenizer, Tokenize
+
+__all__ = ["OneFormerUnifiedDatasetMapper"]
+
+
+class OneFormerUnifiedDatasetMapper:
+ """
+ A callable which takes a dataset dict in Detectron2 Dataset format,
+ and map it into a format used by OneFormer for universal segmentation.
+
+ The callable currently does the following:
+
+ 1. Read the image from "file_name"
+ 2. Applies geometric transforms to the image and annotation
+ 3. Find and applies suitable cropping to the image and annotation
+ 4. Prepare image and annotation to Tensors
+ """
+
+ @configurable
+ def __init__(
+ self,
+ is_train=True,
+ *,
+ name,
+ num_queries,
+ meta,
+ augmentations,
+ image_format,
+ ignore_label,
+ size_divisibility,
+ task_seq_len,
+ max_seq_len,
+ semantic_prob,
+ instance_prob,
+ ):
+ """
+ NOTE: this interface is experimental.
+ Args:
+ is_train: for training or inference
+ augmentations: a list of augmentations or deterministic transforms to apply
+ image_format: an image format supported by :func:`detection_utils.read_image`.
+ ignore_label: the label that is ignored to evaluation
+ size_divisibility: pad image size to be divisible by this value
+ """
+ self.is_train = is_train
+ self.meta = meta
+ self.name = name
+ self.tfm_gens = augmentations
+ self.img_format = image_format
+ self.ignore_label = ignore_label
+ self.size_divisibility = size_divisibility
+ self.num_queries = num_queries
+
+ logger = logging.getLogger(__name__)
+ mode = "training" if is_train else "inference"
+ logger.info(f"[{self.__class__.__name__}] Augmentations used in {mode}: {augmentations}")
+
+ self.things = []
+ for k,v in self.meta.thing_dataset_id_to_contiguous_id.items():
+ self.things.append(v)
+ self.class_names = self.meta.stuff_classes
+ self.text_tokenizer = Tokenize(SimpleTokenizer(), max_seq_len=max_seq_len)
+ self.task_tokenizer = Tokenize(SimpleTokenizer(), max_seq_len=task_seq_len)
+ self.semantic_prob = semantic_prob
+ self.instance_prob = instance_prob
+
+ @classmethod
+ def from_config(cls, cfg, is_train=True):
+ # Build augmentation
+ augs = [
+ T.ResizeShortestEdge(
+ cfg.INPUT.MIN_SIZE_TRAIN,
+ cfg.INPUT.MAX_SIZE_TRAIN,
+ cfg.INPUT.MIN_SIZE_TRAIN_SAMPLING,
+ )
+ ]
+ if cfg.INPUT.CROP.ENABLED:
+ augs.append(
+ T.RandomCrop_CategoryAreaConstraint(
+ cfg.INPUT.CROP.TYPE,
+ cfg.INPUT.CROP.SIZE,
+ cfg.INPUT.CROP.SINGLE_CATEGORY_MAX_AREA,
+ cfg.MODEL.SEM_SEG_HEAD.IGNORE_VALUE,
+ )
+ )
+ if cfg.INPUT.COLOR_AUG_SSD:
+ augs.append(ColorAugSSDTransform(img_format=cfg.INPUT.FORMAT))
+ augs.append(T.RandomFlip())
+
+ # Assume always applies to the training set.
+ dataset_names = cfg.DATASETS.TRAIN
+ meta = MetadataCatalog.get(dataset_names[0])
+ ignore_label = meta.ignore_label
+
+ ret = {
+ "is_train": is_train,
+ "meta": meta,
+ "name": dataset_names[0],
+ "num_queries": cfg.MODEL.ONE_FORMER.NUM_OBJECT_QUERIES - cfg.MODEL.TEXT_ENCODER.N_CTX,
+ "task_seq_len": cfg.INPUT.TASK_SEQ_LEN,
+ "max_seq_len": cfg.INPUT.MAX_SEQ_LEN,
+ "augmentations": augs,
+ "image_format": cfg.INPUT.FORMAT,
+ "ignore_label": ignore_label,
+ "size_divisibility": cfg.INPUT.SIZE_DIVISIBILITY,
+ "semantic_prob": cfg.INPUT.TASK_PROB.SEMANTIC,
+ "instance_prob": cfg.INPUT.TASK_PROB.INSTANCE,
+ }
+ return ret
+
+ def _get_semantic_dict(self, pan_seg_gt, image_shape, segments_info, num_class_obj):
+ pan_seg_gt = pan_seg_gt.numpy()
+ instances = Instances(image_shape)
+
+ classes = []
+ texts = ["a semantic photo"] * self.num_queries
+ masks = []
+ label = np.ones_like(pan_seg_gt) * self.ignore_label
+
+ for segment_info in segments_info:
+ class_id = segment_info["category_id"]
+ if not segment_info["iscrowd"]:
+ mask = pan_seg_gt == segment_info["id"]
+ if not np.all(mask == False):
+ if class_id not in classes:
+ cls_name = self.class_names[class_id]
+ classes.append(class_id)
+ masks.append(mask)
+ num_class_obj[cls_name] += 1
+ else:
+ idx = classes.index(class_id)
+ masks[idx] += mask
+ masks[idx] = np.clip(masks[idx], 0, 1).astype(np.bool)
+ label[mask] = class_id
+
+ num = 0
+ for i, cls_name in enumerate(self.class_names):
+ if num_class_obj[cls_name] > 0:
+ for _ in range(num_class_obj[cls_name]):
+ if num >= len(texts):
+ break
+ texts[num] = f"a photo with a {cls_name}"
+ num += 1
+
+ classes = np.array(classes)
+ instances.gt_classes = torch.tensor(classes, dtype=torch.int64)
+ if len(masks) == 0:
+ # Some image does not have annotation (all ignored)
+ instances.gt_masks = torch.zeros((0, pan_seg_gt.shape[-2], pan_seg_gt.shape[-1]))
+ instances.gt_bboxes = torch.zeros((0, 4))
+ else:
+ masks = BitMasks(
+ torch.stack([torch.from_numpy(np.ascontiguousarray(x.copy())) for x in masks])
+ )
+ instances.gt_masks = masks.tensor
+ # Placeholder bounding boxes for stuff regions. Note that these are not used during training.
+ instances.gt_bboxes = torch.stack([torch.tensor([0., 0., 1., 1.])] * instances.gt_masks.shape[0])
+ return instances, texts, label
+
+ def _get_instance_dict(self, pan_seg_gt, image_shape, segments_info, num_class_obj):
+ pan_seg_gt = pan_seg_gt.numpy()
+ instances = Instances(image_shape)
+
+ classes = []
+ texts = ["an instance photo"] * self.num_queries
+ masks = []
+ label = np.ones_like(pan_seg_gt) * self.ignore_label
+
+ for segment_info in segments_info:
+ class_id = segment_info["category_id"]
+ if class_id in self.things:
+ if not segment_info["iscrowd"]:
+ mask = pan_seg_gt == segment_info["id"]
+ if not np.all(mask == False):
+ cls_name = self.class_names[class_id]
+ classes.append(class_id)
+ masks.append(mask)
+ num_class_obj[cls_name] += 1
+ label[mask] = class_id
+
+ num = 0
+ for i, cls_name in enumerate(self.class_names):
+ if num_class_obj[cls_name] > 0:
+ for _ in range(num_class_obj[cls_name]):
+ if num >= len(texts):
+ break
+ texts[num] = f"a photo with a {cls_name}"
+ num += 1
+
+ classes = np.array(classes)
+ instances.gt_classes = torch.tensor(classes, dtype=torch.int64)
+ if len(masks) == 0:
+ # Some image does not have annotation (all ignored)
+ instances.gt_masks = torch.zeros((0, pan_seg_gt.shape[-2], pan_seg_gt.shape[-1]))
+ instances.gt_bboxes = torch.zeros((0, 4))
+ else:
+ masks = BitMasks(
+ torch.stack([torch.from_numpy(np.ascontiguousarray(x.copy())) for x in masks])
+ )
+ instances.gt_masks = masks.tensor
+ instances.gt_bboxes = masks_to_boxes(instances.gt_masks)
+ return instances, texts, label
+
+ def _get_panoptic_dict(self, pan_seg_gt, image_shape, segments_info, num_class_obj):
+ pan_seg_gt = pan_seg_gt.numpy()
+ instances = Instances(image_shape)
+
+ classes = []
+ texts = ["a panoptic photo"] * self.num_queries
+ masks = []
+ label = np.ones_like(pan_seg_gt) * self.ignore_label
+
+ for segment_info in segments_info:
+ class_id = segment_info["category_id"]
+ if not segment_info["iscrowd"]:
+ mask = pan_seg_gt == segment_info["id"]
+ if not np.all(mask == False):
+ cls_name = self.class_names[class_id]
+ classes.append(class_id)
+ masks.append(mask)
+ num_class_obj[cls_name] += 1
+ label[mask] = class_id
+
+ num = 0
+ for i, cls_name in enumerate(self.class_names):
+ if num_class_obj[cls_name] > 0:
+ for _ in range(num_class_obj[cls_name]):
+ if num >= len(texts):
+ break
+ texts[num] = f"a photo with a {cls_name}"
+ num += 1
+
+ classes = np.array(classes)
+ instances.gt_classes = torch.tensor(classes, dtype=torch.int64)
+ if len(masks) == 0:
+ # Some image does not have annotation (all ignored)
+ instances.gt_masks = torch.zeros((0, pan_seg_gt.shape[-2], pan_seg_gt.shape[-1]))
+ instances.gt_bboxes = torch.zeros((0, 4))
+ else:
+ masks = BitMasks(
+ torch.stack([torch.from_numpy(np.ascontiguousarray(x.copy())) for x in masks])
+ )
+ instances.gt_masks = masks.tensor
+ instances.gt_bboxes = masks_to_boxes(instances.gt_masks)
+ for i in range(instances.gt_classes.shape[0]):
+ # Placeholder bounding boxes for stuff regions. Note that these are not used during training.
+ if instances.gt_classes[i].item() not in self.things:
+ instances.gt_bboxes[i] = torch.tensor([0., 0., 1., 1.])
+ return instances, texts, label
+
+ def __call__(self, dataset_dict):
+ """
+ Args:
+ dataset_dict (dict): Metadata of one image, in Detectron2 Dataset format.
+
+ Returns:
+ dict: a format that builtin models in detectron2 accept
+ """
+ assert self.is_train, "OneFormerDatasetMapper should only be used for training!"
+
+ dataset_dict = copy.deepcopy(dataset_dict) # it will be modified by code below
+ image = utils.read_image(dataset_dict["file_name"], format=self.img_format)
+ utils.check_image_size(dataset_dict, image)
+
+ # semantic segmentation
+ if "sem_seg_file_name" in dataset_dict:
+ # PyTorch transformation not implemented for uint16, so converting it to double first
+ sem_seg_gt = utils.read_image(dataset_dict.pop("sem_seg_file_name")).astype("double")
+ else:
+ sem_seg_gt = None
+
+ # panoptic segmentation
+ if "pan_seg_file_name" in dataset_dict:
+ pan_seg_gt = utils.read_image(dataset_dict.pop("pan_seg_file_name"), "RGB")
+ segments_info = dataset_dict["segments_info"]
+ else:
+ pan_seg_gt = None
+ segments_info = None
+
+ if pan_seg_gt is None:
+ raise ValueError(
+ "Cannot find 'pan_seg_file_name' for panoptic segmentation dataset {}.".format(
+ dataset_dict["file_name"]
+ )
+ )
+
+ aug_input = T.AugInput(image, sem_seg=sem_seg_gt)
+ aug_input, transforms = T.apply_transform_gens(self.tfm_gens, aug_input)
+ image = aug_input.image
+ if sem_seg_gt is not None:
+ sem_seg_gt = aug_input.sem_seg
+
+ # apply the same transformation to panoptic segmentation
+ pan_seg_gt = transforms.apply_segmentation(pan_seg_gt)
+
+ from panopticapi.utils import rgb2id
+
+ pan_seg_gt = rgb2id(pan_seg_gt)
+
+ # Pad image and segmentation label here!
+ image = torch.as_tensor(np.ascontiguousarray(image.transpose(2, 0, 1)))
+ if sem_seg_gt is not None:
+ sem_seg_gt = torch.as_tensor(sem_seg_gt.astype("long"))
+ pan_seg_gt = torch.as_tensor(pan_seg_gt.astype("long"))
+
+ if self.size_divisibility > 0:
+ image_size = (image.shape[-2], image.shape[-1])
+ padding_size = [
+ 0,
+ self.size_divisibility - image_size[1],
+ 0,
+ self.size_divisibility - image_size[0],
+ ]
+ image = F.pad(image, padding_size, value=128).contiguous()
+ if sem_seg_gt is not None:
+ sem_seg_gt = F.pad(sem_seg_gt, padding_size, value=self.ignore_label).contiguous()
+ pan_seg_gt = F.pad(
+ pan_seg_gt, padding_size, value=0
+ ).contiguous() # 0 is the VOID panoptic label
+
+ image_shape = (image.shape[-2], image.shape[-1]) # h, w
+
+ # Pytorch's dataloader is efficient on torch.Tensor due to shared-memory,
+ # but not efficient on large generic data structures due to the use of pickle & mp.Queue.
+ # Therefore it's important to use torch.Tensor.
+ dataset_dict["image"] = image
+
+ if "annotations" in dataset_dict:
+ raise ValueError("Pemantic segmentation dataset should not have 'annotations'.")
+
+ prob_task = np.random.uniform(0,1.)
+
+ num_class_obj = {}
+
+ for name in self.class_names:
+ num_class_obj[name] = 0
+
+ if prob_task < self.semantic_prob:
+ task = "The task is semantic"
+ instances, text, sem_seg = self._get_semantic_dict(pan_seg_gt, image_shape, segments_info, num_class_obj)
+ elif prob_task < self.instance_prob:
+ task = "The task is instance"
+ instances, text, sem_seg = self._get_instance_dict(pan_seg_gt, image_shape, segments_info, num_class_obj)
+ else:
+ task = "The task is panoptic"
+ instances, text, sem_seg = self._get_panoptic_dict(pan_seg_gt, image_shape, segments_info, num_class_obj)
+
+ dataset_dict["sem_seg"] = torch.from_numpy(sem_seg).long()
+ dataset_dict["instances"] = instances
+ dataset_dict["orig_shape"] = image_shape
+ dataset_dict["task"] = task
+ dataset_dict["text"] = text
+ dataset_dict["thing_ids"] = self.things
+
+ return dataset_dict
diff --git a/annotator/OneFormer/oneformer/data/datasets/__init__.py b/annotator/OneFormer/oneformer/data/datasets/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..bfb31c1afbb74c61680a58654d45a3f930658a1f
--- /dev/null
+++ b/annotator/OneFormer/oneformer/data/datasets/__init__.py
@@ -0,0 +1,9 @@
+from . import (
+ register_ade20k_panoptic,
+ register_cityscapes_panoptic,
+ register_coco_panoptic_annos_semseg,
+ register_ade20k_instance,
+ register_coco_panoptic2instance,
+ register_mapillary_vistas,
+ register_mapillary_vistas_panoptic,
+)
diff --git a/annotator/OneFormer/oneformer/data/datasets/register_ade20k_instance.py b/annotator/OneFormer/oneformer/data/datasets/register_ade20k_instance.py
new file mode 100644
index 0000000000000000000000000000000000000000..0cf5466eb7e203de8c78679f622d30c902bd61d9
--- /dev/null
+++ b/annotator/OneFormer/oneformer/data/datasets/register_ade20k_instance.py
@@ -0,0 +1,56 @@
+# ------------------------------------------------------------------------------
+# Reference: https://github.com/facebookresearch/Mask2Former/blob/main/mask2former/data/datasets/register_ade20k_instance.py
+# ------------------------------------------------------------------------------
+
+import json
+import logging
+import numpy as np
+import os
+from PIL import Image
+
+from detectron2.data import DatasetCatalog, MetadataCatalog
+from detectron2.data.datasets.coco import load_coco_json, register_coco_instances
+from detectron2.utils.file_io import PathManager
+
+ADE_CATEGORIES = [{'id': 7, 'name': 'bed'}, {'id': 8, 'name': 'windowpane'}, {'id': 10, 'name': 'cabinet'}, {'id': 12, 'name': 'person'}, {'id': 14, 'name': 'door'}, {'id': 15, 'name': 'table'}, {'id': 18, 'name': 'curtain'}, {'id': 19, 'name': 'chair'}, {'id': 20, 'name': 'car'}, {'id': 22, 'name': 'painting'}, {'id': 23, 'name': 'sofa'}, {'id': 24, 'name': 'shelf'}, {'id': 27, 'name': 'mirror'}, {'id': 30, 'name': 'armchair'}, {'id': 31, 'name': 'seat'}, {'id': 32, 'name': 'fence'}, {'id': 33, 'name': 'desk'}, {'id': 35, 'name': 'wardrobe'}, {'id': 36, 'name': 'lamp'}, {'id': 37, 'name': 'bathtub'}, {'id': 38, 'name': 'railing'}, {'id': 39, 'name': 'cushion'}, {'id': 41, 'name': 'box'}, {'id': 42, 'name': 'column'}, {'id': 43, 'name': 'signboard'}, {'id': 44, 'name': 'chest of drawers'}, {'id': 45, 'name': 'counter'}, {'id': 47, 'name': 'sink'}, {'id': 49, 'name': 'fireplace'}, {'id': 50, 'name': 'refrigerator'}, {'id': 53, 'name': 'stairs'}, {'id': 55, 'name': 'case'}, {'id': 56, 'name': 'pool table'}, {'id': 57, 'name': 'pillow'}, {'id': 58, 'name': 'screen door'}, {'id': 62, 'name': 'bookcase'}, {'id': 64, 'name': 'coffee table'}, {'id': 65, 'name': 'toilet'}, {'id': 66, 'name': 'flower'}, {'id': 67, 'name': 'book'}, {'id': 69, 'name': 'bench'}, {'id': 70, 'name': 'countertop'}, {'id': 71, 'name': 'stove'}, {'id': 72, 'name': 'palm'}, {'id': 73, 'name': 'kitchen island'}, {'id': 74, 'name': 'computer'}, {'id': 75, 'name': 'swivel chair'}, {'id': 76, 'name': 'boat'}, {'id': 78, 'name': 'arcade machine'}, {'id': 80, 'name': 'bus'}, {'id': 81, 'name': 'towel'}, {'id': 82, 'name': 'light'}, {'id': 83, 'name': 'truck'}, {'id': 85, 'name': 'chandelier'}, {'id': 86, 'name': 'awning'}, {'id': 87, 'name': 'streetlight'}, {'id': 88, 'name': 'booth'}, {'id': 89, 'name': 'television receiver'}, {'id': 90, 'name': 'airplane'}, {'id': 92, 'name': 'apparel'}, {'id': 93, 'name': 'pole'}, {'id': 95, 'name': 'bannister'}, {'id': 97, 'name': 'ottoman'}, {'id': 98, 'name': 'bottle'}, {'id': 102, 'name': 'van'}, {'id': 103, 'name': 'ship'}, {'id': 104, 'name': 'fountain'}, {'id': 107, 'name': 'washer'}, {'id': 108, 'name': 'plaything'}, {'id': 110, 'name': 'stool'}, {'id': 111, 'name': 'barrel'}, {'id': 112, 'name': 'basket'}, {'id': 115, 'name': 'bag'}, {'id': 116, 'name': 'minibike'}, {'id': 118, 'name': 'oven'}, {'id': 119, 'name': 'ball'}, {'id': 120, 'name': 'food'}, {'id': 121, 'name': 'step'}, {'id': 123, 'name': 'trade name'}, {'id': 124, 'name': 'microwave'}, {'id': 125, 'name': 'pot'}, {'id': 126, 'name': 'animal'}, {'id': 127, 'name': 'bicycle'}, {'id': 129, 'name': 'dishwasher'}, {'id': 130, 'name': 'screen'}, {'id': 132, 'name': 'sculpture'}, {'id': 133, 'name': 'hood'}, {'id': 134, 'name': 'sconce'}, {'id': 135, 'name': 'vase'}, {'id': 136, 'name': 'traffic light'}, {'id': 137, 'name': 'tray'}, {'id': 138, 'name': 'ashcan'}, {'id': 139, 'name': 'fan'}, {'id': 142, 'name': 'plate'}, {'id': 143, 'name': 'monitor'}, {'id': 144, 'name': 'bulletin board'}, {'id': 146, 'name': 'radiator'}, {'id': 147, 'name': 'glass'}, {'id': 148, 'name': 'clock'}, {'id': 149, 'name': 'flag'}]
+
+
+_PREDEFINED_SPLITS = {
+ # point annotations without masks
+ "ade20k_instance_train": (
+ "ADEChallengeData2016/images/training",
+ "ADEChallengeData2016/ade20k_instance_train.json",
+ ),
+ "ade20k_instance_val": (
+ "ADEChallengeData2016/images/validation",
+ "ADEChallengeData2016/ade20k_instance_val.json",
+ ),
+}
+
+
+def _get_ade_instances_meta():
+ thing_ids = [k["id"] for k in ADE_CATEGORIES]
+ assert len(thing_ids) == 100, len(thing_ids)
+ # Mapping from the incontiguous ADE category id to an id in [0, 99]
+ thing_dataset_id_to_contiguous_id = {k: i for i, k in enumerate(thing_ids)}
+ thing_classes = [k["name"] for k in ADE_CATEGORIES]
+ ret = {
+ "thing_dataset_id_to_contiguous_id": thing_dataset_id_to_contiguous_id,
+ "thing_classes": thing_classes,
+ }
+ return ret
+
+
+def register_all_ade20k_instance(root):
+ for key, (image_root, json_file) in _PREDEFINED_SPLITS.items():
+ # Assume pre-defined datasets live in `./datasets`.
+ register_coco_instances(
+ key,
+ _get_ade_instances_meta(),
+ os.path.join(root, json_file) if "://" not in json_file else json_file,
+ os.path.join(root, image_root),
+ )
+
+
+_root = os.getenv("DETECTRON2_DATASETS", "datasets")
+register_all_ade20k_instance(_root)
diff --git a/annotator/OneFormer/oneformer/data/datasets/register_ade20k_panoptic.py b/annotator/OneFormer/oneformer/data/datasets/register_ade20k_panoptic.py
new file mode 100644
index 0000000000000000000000000000000000000000..527910f19f70e9e632ce91e350964b50300046c7
--- /dev/null
+++ b/annotator/OneFormer/oneformer/data/datasets/register_ade20k_panoptic.py
@@ -0,0 +1,394 @@
+# ------------------------------------------------------------------------------
+# Reference: https://github.com/facebookresearch/Mask2Former/blob/main/mask2former/data/datasets/register_ade20k_panoptic.py
+# Modified by Jitesh Jain (https://github.com/praeclarumjj3)
+# ------------------------------------------------------------------------------
+
+import json
+import os
+
+from detectron2.data import DatasetCatalog, MetadataCatalog
+from detectron2.utils.file_io import PathManager
+
+ADE20K_150_CATEGORIES = [
+ {"color": [120, 120, 120], "id": 0, "isthing": 0, "name": "wall"},
+ {"color": [180, 120, 120], "id": 1, "isthing": 0, "name": "building"},
+ {"color": [6, 230, 230], "id": 2, "isthing": 0, "name": "sky"},
+ {"color": [80, 50, 50], "id": 3, "isthing": 0, "name": "floor"},
+ {"color": [4, 200, 3], "id": 4, "isthing": 0, "name": "tree"},
+ {"color": [120, 120, 80], "id": 5, "isthing": 0, "name": "ceiling"},
+ {"color": [140, 140, 140], "id": 6, "isthing": 0, "name": "road, route"},
+ {"color": [204, 5, 255], "id": 7, "isthing": 1, "name": "bed"},
+ {"color": [230, 230, 230], "id": 8, "isthing": 1, "name": "window "},
+ {"color": [4, 250, 7], "id": 9, "isthing": 0, "name": "grass"},
+ {"color": [224, 5, 255], "id": 10, "isthing": 1, "name": "cabinet"},
+ {"color": [235, 255, 7], "id": 11, "isthing": 0, "name": "sidewalk, pavement"},
+ {"color": [150, 5, 61], "id": 12, "isthing": 1, "name": "person"},
+ {"color": [120, 120, 70], "id": 13, "isthing": 0, "name": "earth, ground"},
+ {"color": [8, 255, 51], "id": 14, "isthing": 1, "name": "door"},
+ {"color": [255, 6, 82], "id": 15, "isthing": 1, "name": "table"},
+ {"color": [143, 255, 140], "id": 16, "isthing": 0, "name": "mountain, mount"},
+ {"color": [204, 255, 4], "id": 17, "isthing": 0, "name": "plant"},
+ {"color": [255, 51, 7], "id": 18, "isthing": 1, "name": "curtain"},
+ {"color": [204, 70, 3], "id": 19, "isthing": 1, "name": "chair"},
+ {"color": [0, 102, 200], "id": 20, "isthing": 1, "name": "car"},
+ {"color": [61, 230, 250], "id": 21, "isthing": 0, "name": "water"},
+ {"color": [255, 6, 51], "id": 22, "isthing": 1, "name": "painting, picture"},
+ {"color": [11, 102, 255], "id": 23, "isthing": 1, "name": "sofa"},
+ {"color": [255, 7, 71], "id": 24, "isthing": 1, "name": "shelf"},
+ {"color": [255, 9, 224], "id": 25, "isthing": 0, "name": "house"},
+ {"color": [9, 7, 230], "id": 26, "isthing": 0, "name": "sea"},
+ {"color": [220, 220, 220], "id": 27, "isthing": 1, "name": "mirror"},
+ {"color": [255, 9, 92], "id": 28, "isthing": 0, "name": "rug"},
+ {"color": [112, 9, 255], "id": 29, "isthing": 0, "name": "field"},
+ {"color": [8, 255, 214], "id": 30, "isthing": 1, "name": "armchair"},
+ {"color": [7, 255, 224], "id": 31, "isthing": 1, "name": "seat"},
+ {"color": [255, 184, 6], "id": 32, "isthing": 1, "name": "fence"},
+ {"color": [10, 255, 71], "id": 33, "isthing": 1, "name": "desk"},
+ {"color": [255, 41, 10], "id": 34, "isthing": 0, "name": "rock, stone"},
+ {"color": [7, 255, 255], "id": 35, "isthing": 1, "name": "wardrobe, closet, press"},
+ {"color": [224, 255, 8], "id": 36, "isthing": 1, "name": "lamp"},
+ {"color": [102, 8, 255], "id": 37, "isthing": 1, "name": "tub"},
+ {"color": [255, 61, 6], "id": 38, "isthing": 1, "name": "rail"},
+ {"color": [255, 194, 7], "id": 39, "isthing": 1, "name": "cushion"},
+ {"color": [255, 122, 8], "id": 40, "isthing": 0, "name": "base, pedestal, stand"},
+ {"color": [0, 255, 20], "id": 41, "isthing": 1, "name": "box"},
+ {"color": [255, 8, 41], "id": 42, "isthing": 1, "name": "column, pillar"},
+ {"color": [255, 5, 153], "id": 43, "isthing": 1, "name": "signboard, sign"},
+ {
+ "color": [6, 51, 255],
+ "id": 44,
+ "isthing": 1,
+ "name": "chest of drawers, chest, bureau, dresser",
+ },
+ {"color": [235, 12, 255], "id": 45, "isthing": 1, "name": "counter"},
+ {"color": [160, 150, 20], "id": 46, "isthing": 0, "name": "sand"},
+ {"color": [0, 163, 255], "id": 47, "isthing": 1, "name": "sink"},
+ {"color": [140, 140, 140], "id": 48, "isthing": 0, "name": "skyscraper"},
+ {"color": [250, 10, 15], "id": 49, "isthing": 1, "name": "fireplace"},
+ {"color": [20, 255, 0], "id": 50, "isthing": 1, "name": "refrigerator, icebox"},
+ {"color": [31, 255, 0], "id": 51, "isthing": 0, "name": "grandstand, covered stand"},
+ {"color": [255, 31, 0], "id": 52, "isthing": 0, "name": "path"},
+ {"color": [255, 224, 0], "id": 53, "isthing": 1, "name": "stairs"},
+ {"color": [153, 255, 0], "id": 54, "isthing": 0, "name": "runway"},
+ {"color": [0, 0, 255], "id": 55, "isthing": 1, "name": "case, display case, showcase, vitrine"},
+ {
+ "color": [255, 71, 0],
+ "id": 56,
+ "isthing": 1,
+ "name": "pool table, billiard table, snooker table",
+ },
+ {"color": [0, 235, 255], "id": 57, "isthing": 1, "name": "pillow"},
+ {"color": [0, 173, 255], "id": 58, "isthing": 1, "name": "screen door, screen"},
+ {"color": [31, 0, 255], "id": 59, "isthing": 0, "name": "stairway, staircase"},
+ {"color": [11, 200, 200], "id": 60, "isthing": 0, "name": "river"},
+ {"color": [255, 82, 0], "id": 61, "isthing": 0, "name": "bridge, span"},
+ {"color": [0, 255, 245], "id": 62, "isthing": 1, "name": "bookcase"},
+ {"color": [0, 61, 255], "id": 63, "isthing": 0, "name": "blind, screen"},
+ {"color": [0, 255, 112], "id": 64, "isthing": 1, "name": "coffee table"},
+ {
+ "color": [0, 255, 133],
+ "id": 65,
+ "isthing": 1,
+ "name": "toilet, can, commode, crapper, pot, potty, stool, throne",
+ },
+ {"color": [255, 0, 0], "id": 66, "isthing": 1, "name": "flower"},
+ {"color": [255, 163, 0], "id": 67, "isthing": 1, "name": "book"},
+ {"color": [255, 102, 0], "id": 68, "isthing": 0, "name": "hill"},
+ {"color": [194, 255, 0], "id": 69, "isthing": 1, "name": "bench"},
+ {"color": [0, 143, 255], "id": 70, "isthing": 1, "name": "countertop"},
+ {"color": [51, 255, 0], "id": 71, "isthing": 1, "name": "stove"},
+ {"color": [0, 82, 255], "id": 72, "isthing": 1, "name": "palm, palm tree"},
+ {"color": [0, 255, 41], "id": 73, "isthing": 1, "name": "kitchen island"},
+ {"color": [0, 255, 173], "id": 74, "isthing": 1, "name": "computer"},
+ {"color": [10, 0, 255], "id": 75, "isthing": 1, "name": "swivel chair"},
+ {"color": [173, 255, 0], "id": 76, "isthing": 1, "name": "boat"},
+ {"color": [0, 255, 153], "id": 77, "isthing": 0, "name": "bar"},
+ {"color": [255, 92, 0], "id": 78, "isthing": 1, "name": "arcade machine"},
+ {"color": [255, 0, 255], "id": 79, "isthing": 0, "name": "hovel, hut, hutch, shack, shanty"},
+ {"color": [255, 0, 245], "id": 80, "isthing": 1, "name": "bus"},
+ {"color": [255, 0, 102], "id": 81, "isthing": 1, "name": "towel"},
+ {"color": [255, 173, 0], "id": 82, "isthing": 1, "name": "light"},
+ {"color": [255, 0, 20], "id": 83, "isthing": 1, "name": "truck"},
+ {"color": [255, 184, 184], "id": 84, "isthing": 0, "name": "tower"},
+ {"color": [0, 31, 255], "id": 85, "isthing": 1, "name": "chandelier"},
+ {"color": [0, 255, 61], "id": 86, "isthing": 1, "name": "awning, sunshade, sunblind"},
+ {"color": [0, 71, 255], "id": 87, "isthing": 1, "name": "street lamp"},
+ {"color": [255, 0, 204], "id": 88, "isthing": 1, "name": "booth"},
+ {"color": [0, 255, 194], "id": 89, "isthing": 1, "name": "tv"},
+ {"color": [0, 255, 82], "id": 90, "isthing": 1, "name": "plane"},
+ {"color": [0, 10, 255], "id": 91, "isthing": 0, "name": "dirt track"},
+ {"color": [0, 112, 255], "id": 92, "isthing": 1, "name": "clothes"},
+ {"color": [51, 0, 255], "id": 93, "isthing": 1, "name": "pole"},
+ {"color": [0, 194, 255], "id": 94, "isthing": 0, "name": "land, ground, soil"},
+ {
+ "color": [0, 122, 255],
+ "id": 95,
+ "isthing": 1,
+ "name": "bannister, banister, balustrade, balusters, handrail",
+ },
+ {
+ "color": [0, 255, 163],
+ "id": 96,
+ "isthing": 0,
+ "name": "escalator, moving staircase, moving stairway",
+ },
+ {
+ "color": [255, 153, 0],
+ "id": 97,
+ "isthing": 1,
+ "name": "ottoman, pouf, pouffe, puff, hassock",
+ },
+ {"color": [0, 255, 10], "id": 98, "isthing": 1, "name": "bottle"},
+ {"color": [255, 112, 0], "id": 99, "isthing": 0, "name": "buffet, counter, sideboard"},
+ {
+ "color": [143, 255, 0],
+ "id": 100,
+ "isthing": 0,
+ "name": "poster, posting, placard, notice, bill, card",
+ },
+ {"color": [82, 0, 255], "id": 101, "isthing": 0, "name": "stage"},
+ {"color": [163, 255, 0], "id": 102, "isthing": 1, "name": "van"},
+ {"color": [255, 235, 0], "id": 103, "isthing": 1, "name": "ship"},
+ {"color": [8, 184, 170], "id": 104, "isthing": 1, "name": "fountain"},
+ {
+ "color": [133, 0, 255],
+ "id": 105,
+ "isthing": 0,
+ "name": "conveyer belt, conveyor belt, conveyer, conveyor, transporter",
+ },
+ {"color": [0, 255, 92], "id": 106, "isthing": 0, "name": "canopy"},
+ {
+ "color": [184, 0, 255],
+ "id": 107,
+ "isthing": 1,
+ "name": "washer, automatic washer, washing machine",
+ },
+ {"color": [255, 0, 31], "id": 108, "isthing": 1, "name": "plaything, toy"},
+ {"color": [0, 184, 255], "id": 109, "isthing": 0, "name": "pool"},
+ {"color": [0, 214, 255], "id": 110, "isthing": 1, "name": "stool"},
+ {"color": [255, 0, 112], "id": 111, "isthing": 1, "name": "barrel, cask"},
+ {"color": [92, 255, 0], "id": 112, "isthing": 1, "name": "basket, handbasket"},
+ {"color": [0, 224, 255], "id": 113, "isthing": 0, "name": "falls"},
+ {"color": [112, 224, 255], "id": 114, "isthing": 0, "name": "tent"},
+ {"color": [70, 184, 160], "id": 115, "isthing": 1, "name": "bag"},
+ {"color": [163, 0, 255], "id": 116, "isthing": 1, "name": "minibike, motorbike"},
+ {"color": [153, 0, 255], "id": 117, "isthing": 0, "name": "cradle"},
+ {"color": [71, 255, 0], "id": 118, "isthing": 1, "name": "oven"},
+ {"color": [255, 0, 163], "id": 119, "isthing": 1, "name": "ball"},
+ {"color": [255, 204, 0], "id": 120, "isthing": 1, "name": "food, solid food"},
+ {"color": [255, 0, 143], "id": 121, "isthing": 1, "name": "step, stair"},
+ {"color": [0, 255, 235], "id": 122, "isthing": 0, "name": "tank, storage tank"},
+ {"color": [133, 255, 0], "id": 123, "isthing": 1, "name": "trade name"},
+ {"color": [255, 0, 235], "id": 124, "isthing": 1, "name": "microwave"},
+ {"color": [245, 0, 255], "id": 125, "isthing": 1, "name": "pot"},
+ {"color": [255, 0, 122], "id": 126, "isthing": 1, "name": "animal"},
+ {"color": [255, 245, 0], "id": 127, "isthing": 1, "name": "bicycle"},
+ {"color": [10, 190, 212], "id": 128, "isthing": 0, "name": "lake"},
+ {"color": [214, 255, 0], "id": 129, "isthing": 1, "name": "dishwasher"},
+ {"color": [0, 204, 255], "id": 130, "isthing": 1, "name": "screen"},
+ {"color": [20, 0, 255], "id": 131, "isthing": 0, "name": "blanket, cover"},
+ {"color": [255, 255, 0], "id": 132, "isthing": 1, "name": "sculpture"},
+ {"color": [0, 153, 255], "id": 133, "isthing": 1, "name": "hood, exhaust hood"},
+ {"color": [0, 41, 255], "id": 134, "isthing": 1, "name": "sconce"},
+ {"color": [0, 255, 204], "id": 135, "isthing": 1, "name": "vase"},
+ {"color": [41, 0, 255], "id": 136, "isthing": 1, "name": "traffic light"},
+ {"color": [41, 255, 0], "id": 137, "isthing": 1, "name": "tray"},
+ {"color": [173, 0, 255], "id": 138, "isthing": 1, "name": "trash can"},
+ {"color": [0, 245, 255], "id": 139, "isthing": 1, "name": "fan"},
+ {"color": [71, 0, 255], "id": 140, "isthing": 0, "name": "pier"},
+ {"color": [122, 0, 255], "id": 141, "isthing": 0, "name": "crt screen"},
+ {"color": [0, 255, 184], "id": 142, "isthing": 1, "name": "plate"},
+ {"color": [0, 92, 255], "id": 143, "isthing": 1, "name": "monitor"},
+ {"color": [184, 255, 0], "id": 144, "isthing": 1, "name": "bulletin board"},
+ {"color": [0, 133, 255], "id": 145, "isthing": 0, "name": "shower"},
+ {"color": [255, 214, 0], "id": 146, "isthing": 1, "name": "radiator"},
+ {"color": [25, 194, 194], "id": 147, "isthing": 1, "name": "glass, drinking glass"},
+ {"color": [102, 255, 0], "id": 148, "isthing": 1, "name": "clock"},
+ {"color": [92, 0, 255], "id": 149, "isthing": 1, "name": "flag"},
+]
+
+ADE20k_COLORS = [k["color"] for k in ADE20K_150_CATEGORIES]
+
+MetadataCatalog.get("ade20k_sem_seg_train").set(
+ stuff_colors=ADE20k_COLORS[:],
+)
+
+MetadataCatalog.get("ade20k_sem_seg_val").set(
+ stuff_colors=ADE20k_COLORS[:],
+)
+
+
+def load_ade20k_panoptic_json(json_file, image_dir, gt_dir, semseg_dir, meta):
+ """
+ Args:
+ image_dir (str): path to the raw dataset. e.g., "~/coco/train2017".
+ gt_dir (str): path to the raw annotations. e.g., "~/coco/panoptic_train2017".
+ json_file (str): path to the json file. e.g., "~/coco/annotations/panoptic_train2017.json".
+ Returns:
+ list[dict]: a list of dicts in Detectron2 standard format. (See
+ `Using Custom Datasets `_ )
+ """
+
+ def _convert_category_id(segment_info, meta):
+ if segment_info["category_id"] in meta["thing_dataset_id_to_contiguous_id"]:
+ segment_info["category_id"] = meta["thing_dataset_id_to_contiguous_id"][
+ segment_info["category_id"]
+ ]
+ segment_info["isthing"] = True
+ else:
+ segment_info["category_id"] = meta["stuff_dataset_id_to_contiguous_id"][
+ segment_info["category_id"]
+ ]
+ segment_info["isthing"] = False
+ return segment_info
+
+ with PathManager.open(json_file) as f:
+ json_info = json.load(f)
+
+ ret = []
+ for ann in json_info["annotations"]:
+ image_id = ann["image_id"]
+ # TODO: currently we assume image and label has the same filename but
+ # different extension, and images have extension ".jpg" for COCO. Need
+ # to make image extension a user-provided argument if we extend this
+ # function to support other COCO-like datasets.
+ image_file = os.path.join(image_dir, os.path.splitext(ann["file_name"])[0] + ".jpg")
+ label_file = os.path.join(gt_dir, ann["file_name"])
+ sem_label_file = os.path.join(semseg_dir, ann["file_name"])
+ segments_info = [_convert_category_id(x, meta) for x in ann["segments_info"]]
+ ret.append(
+ {
+ "file_name": image_file,
+ "image_id": image_id,
+ "pan_seg_file_name": label_file,
+ "sem_seg_file_name": sem_label_file,
+ "segments_info": segments_info,
+ }
+ )
+ assert len(ret), f"No images found in {image_dir}!"
+ assert PathManager.isfile(ret[0]["file_name"]), ret[0]["file_name"]
+ assert PathManager.isfile(ret[0]["pan_seg_file_name"]), ret[0]["pan_seg_file_name"]
+ assert PathManager.isfile(ret[0]["sem_seg_file_name"]), ret[0]["sem_seg_file_name"]
+ return ret
+
+
+def register_ade20k_panoptic(
+ name, metadata, image_root, panoptic_root, semantic_root, panoptic_json, instances_json=None,
+):
+ """
+ Register a "standard" version of ADE20k panoptic segmentation dataset named `name`.
+ The dictionaries in this registered dataset follows detectron2's standard format.
+ Hence it's called "standard".
+ Args:
+ name (str): the name that identifies a dataset,
+ e.g. "ade20k_panoptic_train"
+ metadata (dict): extra metadata associated with this dataset.
+ image_root (str): directory which contains all the images
+ panoptic_root (str): directory which contains panoptic annotation images in COCO format
+ panoptic_json (str): path to the json panoptic annotation file in COCO format
+ sem_seg_root (none): not used, to be consistent with
+ `register_coco_panoptic_separated`.
+ instances_json (str): path to the json instance annotation file
+ """
+ panoptic_name = name
+ DatasetCatalog.register(
+ panoptic_name,
+ lambda: load_ade20k_panoptic_json(
+ panoptic_json, image_root, panoptic_root, semantic_root, metadata
+ ),
+ )
+ MetadataCatalog.get(panoptic_name).set(
+ panoptic_root=panoptic_root,
+ image_root=image_root,
+ panoptic_json=panoptic_json,
+ json_file=instances_json,
+ evaluator_type="ade20k_panoptic_seg",
+ ignore_label=255,
+ label_divisor=1000,
+ **metadata,
+ )
+
+
+_PREDEFINED_SPLITS_ADE20K_PANOPTIC = {
+ "ade20k_panoptic_train": (
+ "ADEChallengeData2016/images/training",
+ "ADEChallengeData2016/ade20k_panoptic_train",
+ "ADEChallengeData2016/ade20k_panoptic_train.json",
+ "ADEChallengeData2016/annotations_detectron2/training",
+ "ADEChallengeData2016/ade20k_instance_train.json",
+ ),
+ "ade20k_panoptic_val": (
+ "ADEChallengeData2016/images/validation",
+ "ADEChallengeData2016/ade20k_panoptic_val",
+ "ADEChallengeData2016/ade20k_panoptic_val.json",
+ "ADEChallengeData2016/annotations_detectron2/validation",
+ "ADEChallengeData2016/ade20k_instance_val.json",
+ ),
+}
+
+
+def get_metadata():
+ meta = {}
+ # The following metadata maps contiguous id from [0, #thing categories +
+ # #stuff categories) to their names and colors. We have to replica of the
+ # same name and color under "thing_*" and "stuff_*" because the current
+ # visualization function in D2 handles thing and class classes differently
+ # due to some heuristic used in Panoptic FPN. We keep the same naming to
+ # enable reusing existing visualization functions.
+ thing_classes = [k["name"] for k in ADE20K_150_CATEGORIES if k["isthing"] == 1]
+ thing_colors = [k["color"] for k in ADE20K_150_CATEGORIES if k["isthing"] == 1]
+ stuff_classes = [k["name"] for k in ADE20K_150_CATEGORIES]
+ stuff_colors = [k["color"] for k in ADE20K_150_CATEGORIES]
+
+ meta["thing_classes"] = thing_classes
+ meta["thing_colors"] = thing_colors
+ meta["stuff_classes"] = stuff_classes
+ meta["stuff_colors"] = stuff_colors
+
+ # Convert category id for training:
+ # category id: like semantic segmentation, it is the class id for each
+ # pixel. Since there are some classes not used in evaluation, the category
+ # id is not always contiguous and thus we have two set of category ids:
+ # - original category id: category id in the original dataset, mainly
+ # used for evaluation.
+ # - contiguous category id: [0, #classes), in order to train the linear
+ # softmax classifier.
+ thing_dataset_id_to_contiguous_id = {}
+ stuff_dataset_id_to_contiguous_id = {}
+
+ for i, cat in enumerate(ADE20K_150_CATEGORIES):
+ if cat["isthing"]:
+ thing_dataset_id_to_contiguous_id[cat["id"]] = i
+ # else:
+ # stuff_dataset_id_to_contiguous_id[cat["id"]] = i
+
+ # in order to use sem_seg evaluator
+ stuff_dataset_id_to_contiguous_id[cat["id"]] = i
+
+ meta["thing_dataset_id_to_contiguous_id"] = thing_dataset_id_to_contiguous_id
+ meta["stuff_dataset_id_to_contiguous_id"] = stuff_dataset_id_to_contiguous_id
+
+ return meta
+
+
+def register_all_ade20k_panoptic(root):
+ metadata = get_metadata()
+ for (
+ prefix,
+ (image_root, panoptic_root, panoptic_json, semantic_root, instance_json),
+ ) in _PREDEFINED_SPLITS_ADE20K_PANOPTIC.items():
+ # The "standard" version of COCO panoptic segmentation dataset,
+ # e.g. used by Panoptic-DeepLab
+ register_ade20k_panoptic(
+ prefix,
+ metadata,
+ os.path.join(root, image_root),
+ os.path.join(root, panoptic_root),
+ os.path.join(root, semantic_root),
+ os.path.join(root, panoptic_json),
+ os.path.join(root, instance_json),
+ )
+
+
+_root = os.getenv("DETECTRON2_DATASETS", "datasets")
+register_all_ade20k_panoptic(_root)
diff --git a/annotator/OneFormer/oneformer/data/datasets/register_cityscapes_panoptic.py b/annotator/OneFormer/oneformer/data/datasets/register_cityscapes_panoptic.py
new file mode 100644
index 0000000000000000000000000000000000000000..07ecb23ba6422ac24e4a21aa6bb3125b07f71f33
--- /dev/null
+++ b/annotator/OneFormer/oneformer/data/datasets/register_cityscapes_panoptic.py
@@ -0,0 +1,199 @@
+# ------------------------------------------------------------------------------
+# Reference: https://github.com/facebookresearch/detectron2/blob/main/detectron2/data/datasets/cityscapes_panoptic.py
+# Modified by Jitesh Jain (https://github.com/praeclarumjj3)
+# ------------------------------------------------------------------------------
+
+import json
+import logging
+import os
+
+from detectron2.data import DatasetCatalog, MetadataCatalog
+from detectron2.data.datasets.builtin_meta import CITYSCAPES_CATEGORIES
+from detectron2.utils.file_io import PathManager
+
+"""
+This file contains functions to register the Cityscapes panoptic dataset to the DatasetCatalog.
+"""
+
+
+logger = logging.getLogger(__name__)
+
+
+def get_cityscapes_panoptic_files(image_dir, gt_dir, json_info):
+ files = []
+ # scan through the directory
+ cities = PathManager.ls(image_dir)
+ logger.info(f"{len(cities)} cities found in '{image_dir}'.")
+ image_dict = {}
+ for city in cities:
+ city_img_dir = os.path.join(image_dir, city)
+ for basename in PathManager.ls(city_img_dir):
+ image_file = os.path.join(city_img_dir, basename)
+
+ suffix = "_leftImg8bit.png"
+ assert basename.endswith(suffix), basename
+ basename = os.path.basename(basename)[: -len(suffix)]
+
+ image_dict[basename] = image_file
+
+ for ann in json_info["annotations"]:
+ image_file = image_dict.get(ann["image_id"], None)
+ assert image_file is not None, "No image {} found for annotation {}".format(
+ ann["image_id"], ann["file_name"]
+ )
+ label_file = os.path.join(gt_dir, ann["file_name"])
+ segments_info = ann["segments_info"]
+ files.append((image_file, label_file, segments_info))
+
+ assert len(files), "No images found in {}".format(image_dir)
+ assert PathManager.isfile(files[0][0]), files[0][0]
+ assert PathManager.isfile(files[0][1]), files[0][1]
+ return files
+
+
+def load_cityscapes_panoptic(image_dir, gt_dir, gt_json, meta):
+ """
+ Args:
+ image_dir (str): path to the raw dataset. e.g., "~/cityscapes/leftImg8bit/train".
+ gt_dir (str): path to the raw annotations. e.g.,
+ "~/cityscapes/gtFine/cityscapes_panoptic_train".
+ gt_json (str): path to the json file. e.g.,
+ "~/cityscapes/gtFine/cityscapes_panoptic_train.json".
+ meta (dict): dictionary containing "thing_dataset_id_to_contiguous_id"
+ and "stuff_dataset_id_to_contiguous_id" to map category ids to
+ contiguous ids for training.
+
+ Returns:
+ list[dict]: a list of dicts in Detectron2 standard format. (See
+ `Using Custom Datasets `_ )
+ """
+
+ def _convert_category_id(segment_info, meta):
+ if segment_info["category_id"] in meta["thing_dataset_id_to_contiguous_id"]:
+ segment_info["category_id"] = meta["thing_dataset_id_to_contiguous_id"][
+ segment_info["category_id"]
+ ]
+ else:
+ segment_info["category_id"] = meta["stuff_dataset_id_to_contiguous_id"][
+ segment_info["category_id"]
+ ]
+ return segment_info
+
+ assert os.path.exists(
+ gt_json
+ ), "Please run `python cityscapesscripts/preparation/createPanopticImgs.py` to generate label files." # noqa
+
+
+ with open(gt_json) as f:
+ json_info = json.load(f)
+
+ files = get_cityscapes_panoptic_files(image_dir, gt_dir, json_info)
+ ret = []
+ for image_file, label_file, segments_info in files:
+ sem_label_file = (
+ image_file.replace("leftImg8bit", "gtFine").split(".")[0] + "_labelTrainIds.png"
+ )
+ segments_info = [_convert_category_id(x, meta) for x in segments_info]
+ ret.append(
+ {
+ "file_name": image_file,
+ "image_id": "_".join(
+ os.path.splitext(os.path.basename(image_file))[0].split("_")[:3]
+ ),
+ "sem_seg_file_name": sem_label_file,
+ "pan_seg_file_name": label_file,
+ "segments_info": segments_info,
+ }
+ )
+ assert len(ret), f"No images found in {image_dir}!"
+ assert PathManager.isfile(
+ ret[0]["sem_seg_file_name"]
+ ), "Please generate labelTrainIds.png with cityscapesscripts/preparation/createTrainIdLabelImgs.py" # noqa
+ assert PathManager.isfile(
+ ret[0]["pan_seg_file_name"]
+ ), "Please generate panoptic annotation with python cityscapesscripts/preparation/createPanopticImgs.py" # noqa
+ return ret
+
+
+_RAW_CITYSCAPES_PANOPTIC_SPLITS = {
+ "cityscapes_fine_panoptic_train": (
+ "cityscapes/leftImg8bit/train",
+ "cityscapes/gtFine/cityscapes_panoptic_train",
+ "cityscapes/gtFine/cityscapes_panoptic_train.json",
+ ),
+ "cityscapes_fine_panoptic_val": (
+ "cityscapes/leftImg8bit/val",
+ "cityscapes/gtFine/cityscapes_panoptic_val",
+ "cityscapes/gtFine/cityscapes_panoptic_val.json",
+ ),
+ # "cityscapes_fine_panoptic_test": not supported yet
+}
+
+
+def register_all_cityscapes_panoptic(root):
+ meta = {}
+ # The following metadata maps contiguous id from [0, #thing categories +
+ # #stuff categories) to their names and colors. We have to replica of the
+ # same name and color under "thing_*" and "stuff_*" because the current
+ # visualization function in D2 handles thing and class classes differently
+ # due to some heuristic used in Panoptic FPN. We keep the same naming to
+ # enable reusing existing visualization functions.
+ thing_classes = [k["name"] for k in CITYSCAPES_CATEGORIES]
+ thing_colors = [k["color"] for k in CITYSCAPES_CATEGORIES]
+ stuff_classes = [k["name"] for k in CITYSCAPES_CATEGORIES]
+ stuff_colors = [k["color"] for k in CITYSCAPES_CATEGORIES]
+
+ meta["thing_classes"] = thing_classes
+ meta["thing_colors"] = thing_colors
+ meta["stuff_classes"] = stuff_classes
+ meta["stuff_colors"] = stuff_colors
+
+ # There are three types of ids in cityscapes panoptic segmentation:
+ # (1) category id: like semantic segmentation, it is the class id for each
+ # pixel. Since there are some classes not used in evaluation, the category
+ # id is not always contiguous and thus we have two set of category ids:
+ # - original category id: category id in the original dataset, mainly
+ # used for evaluation.
+ # - contiguous category id: [0, #classes), in order to train the classifier
+ # (2) instance id: this id is used to differentiate different instances from
+ # the same category. For "stuff" classes, the instance id is always 0; for
+ # "thing" classes, the instance id starts from 1 and 0 is reserved for
+ # ignored instances (e.g. crowd annotation).
+ # (3) panoptic id: this is the compact id that encode both category and
+ # instance id by: category_id * 1000 + instance_id.
+ thing_dataset_id_to_contiguous_id = {}
+ stuff_dataset_id_to_contiguous_id = {}
+
+ for k in CITYSCAPES_CATEGORIES:
+ if k["isthing"] == 1:
+ thing_dataset_id_to_contiguous_id[k["id"]] = k["trainId"]
+ else:
+ stuff_dataset_id_to_contiguous_id[k["id"]] = k["trainId"]
+
+ meta["thing_dataset_id_to_contiguous_id"] = thing_dataset_id_to_contiguous_id
+ meta["stuff_dataset_id_to_contiguous_id"] = stuff_dataset_id_to_contiguous_id
+
+ for key, (image_dir, gt_dir, gt_json) in _RAW_CITYSCAPES_PANOPTIC_SPLITS.items():
+ image_dir = os.path.join(root, image_dir)
+ gt_dir = os.path.join(root, gt_dir)
+ gt_json = os.path.join(root, gt_json)
+
+ if key in DatasetCatalog.list():
+ DatasetCatalog.remove(key)
+
+ DatasetCatalog.register(
+ key, lambda x=image_dir, y=gt_dir, z=gt_json: load_cityscapes_panoptic(x, y, z, meta)
+ )
+ MetadataCatalog.get(key).set(
+ panoptic_root=gt_dir,
+ image_root=image_dir,
+ panoptic_json=gt_json,
+ gt_dir=gt_dir.replace("cityscapes_panoptic_", ""),
+ evaluator_type="cityscapes_panoptic_seg",
+ ignore_label=255,
+ label_divisor=1000,
+ **meta,
+ )
+
+_root = os.getenv("DETECTRON2_DATASETS", "datasets")
+register_all_cityscapes_panoptic(_root)
\ No newline at end of file
diff --git a/annotator/OneFormer/oneformer/data/datasets/register_coco_panoptic2instance.py b/annotator/OneFormer/oneformer/data/datasets/register_coco_panoptic2instance.py
new file mode 100644
index 0000000000000000000000000000000000000000..79e1a95c95b2ec9e39e0ca0750dc44bf19bba3a0
--- /dev/null
+++ b/annotator/OneFormer/oneformer/data/datasets/register_coco_panoptic2instance.py
@@ -0,0 +1,44 @@
+# ------------------------------------------------------------------------------
+# Reference: https://github.com/facebookresearch/detectron2/blob/main/detectron2/data/datasets/builtin.py
+# Modified by Jitesh Jain (https://github.com/praeclarumjj3)
+# ------------------------------------------------------------------------------
+
+
+"""
+This file registers pre-defined datasets at hard-coded paths, and their metadata.
+
+We hard-code metadata for common datasets. This will enable:
+1. Consistency check when loading the datasets
+2. Use models on these standard datasets directly and run demos,
+ without having to download the dataset annotations
+
+We hard-code some paths to the dataset that's assumed to
+exist in "./datasets/".
+
+Users SHOULD NOT use this file to create new dataset / metadata for new dataset.
+To add new dataset, refer to the tutorial "docs/DATASETS.md".
+"""
+
+import os
+from detectron2.data.datasets.builtin_meta import _get_builtin_metadata
+from detectron2.data.datasets.coco import register_coco_instances
+
+
+_PREDEFINED_SPLITS_COCO = {
+ "coco_2017_val_panoptic2instance": ("coco/val2017", "coco/annotations/panoptic2instances_val2017.json"),
+}
+
+
+def register_panoptic2instances_coco(root):
+ for key, (image_root, json_file) in _PREDEFINED_SPLITS_COCO.items():
+ # Assume pre-defined datasets live in `./datasets`.
+ register_coco_instances(
+ key,
+ _get_builtin_metadata("coco"),
+ os.path.join(root, json_file) if "://" not in json_file else json_file,
+ os.path.join(root, image_root),
+ )
+
+
+_root = os.path.expanduser(os.getenv("DETECTRON2_DATASETS", "datasets"))
+register_panoptic2instances_coco(_root)
\ No newline at end of file
diff --git a/annotator/OneFormer/oneformer/data/datasets/register_coco_panoptic_annos_semseg.py b/annotator/OneFormer/oneformer/data/datasets/register_coco_panoptic_annos_semseg.py
new file mode 100644
index 0000000000000000000000000000000000000000..ac1118bcb1a8e7cc991a820ff17c4ae889d2d7e9
--- /dev/null
+++ b/annotator/OneFormer/oneformer/data/datasets/register_coco_panoptic_annos_semseg.py
@@ -0,0 +1,367 @@
+# ------------------------------------------------------------------------------
+# Reference: https://github.com/facebookresearch/Mask2Former/blob/main/mask2former/data/datasets/register_coco_panoptic_annos_semseg.py
+# Modified by Jitesh Jain (https://github.com/praeclarumjj3)
+# ------------------------------------------------------------------------------
+
+import json
+import os
+
+from detectron2.data import DatasetCatalog, MetadataCatalog
+from detectron2.data.datasets import load_sem_seg
+from detectron2.data.datasets.builtin_meta import COCO_CATEGORIES
+from detectron2.utils.file_io import PathManager
+import contextlib
+import logging
+import io
+from fvcore.common.timer import Timer
+import pycocotools.mask as mask_util
+from detectron2.structures import BoxMode
+
+
+logger = logging.getLogger(__name__)
+
+
+_PREDEFINED_SPLITS_COCO_PANOPTIC = {
+ "coco_2017_train_panoptic": (
+ # This is the original panoptic annotation directory
+ "coco/panoptic_train2017",
+ "coco/annotations/panoptic_train2017.json",
+ # This directory contains semantic annotations that are
+ # converted from panoptic annotations.
+ # It is used by PanopticFPN.
+ # You can use the script at detectron2/datasets/prepare_panoptic_fpn.py
+ # to create these directories.
+ "coco/panoptic_semseg_train2017",
+ ),
+ "coco_2017_val_panoptic": (
+ "coco/panoptic_val2017",
+ "coco/annotations/panoptic_val2017.json",
+ "coco/panoptic_semseg_val2017",
+ ),
+}
+
+def load_coco_instance_json(json_file, image_root, dataset_name=None):
+ from pycocotools.coco import COCO
+
+ timer = Timer()
+ json_file = PathManager.get_local_path(json_file)
+ with contextlib.redirect_stdout(io.StringIO()):
+ coco_api = COCO(json_file)
+ if timer.seconds() > 1:
+ logger.info("Loading {} takes {:.2f} seconds.".format(json_file, timer.seconds()))
+
+ id_map = None
+ if dataset_name is not None:
+ meta = MetadataCatalog.get(dataset_name)
+ cat_ids = sorted(coco_api.getCatIds())
+ cats = coco_api.loadCats(cat_ids)
+ # The categories in a custom json file may not be sorted.
+ thing_classes = [c["name"] for c in sorted(cats, key=lambda x: x["id"])]
+ meta.thing_classes = thing_classes
+
+ # In COCO, certain category ids are artificially removed,
+ # and by convention they are always ignored.
+ # We deal with COCO's id issue and translate
+ # the category ids to contiguous ids in [0, 80).
+
+ # It works by looking at the "categories" field in the json, therefore
+ # if users' own json also have incontiguous ids, we'll
+ # apply this mapping as well but print a warning.
+ if not (min(cat_ids) == 1 and max(cat_ids) == len(cat_ids)):
+ if "coco" not in dataset_name:
+ logger.warning(
+ """
+Category ids in annotations are not in [1, #categories]! We'll apply a mapping for you.
+"""
+ )
+ id_map = {v: i for i, v in enumerate(cat_ids)}
+ meta.thing_dataset_id_to_contiguous_id = id_map
+
+ # sort indices for reproducible results
+ img_ids = sorted(coco_api.imgs.keys())
+ # imgs is a list of dicts, each looks something like:
+ # {'license': 4,
+ # 'url': 'http://farm6.staticflickr.com/5454/9413846304_881d5e5c3b_z.jpg',
+ # 'file_name': 'COCO_val2014_000000001268.jpg',
+ # 'height': 427,
+ # 'width': 640,
+ # 'date_captured': '2013-11-17 05:57:24',
+ # 'id': 1268}
+ imgs = coco_api.loadImgs(img_ids)
+ # anns is a list[list[dict]], where each dict is an annotation
+ # record for an object. The inner list enumerates the objects in an image
+ # and the outer list enumerates over images. Example of anns[0]:
+ # [{'segmentation': [[192.81,
+ # 247.09,
+ # ...
+ # 219.03,
+ # 249.06]],
+ # 'area': 1035.749,
+ # 'iscrowd': 0,
+ # 'image_id': 1268,
+ # 'bbox': [192.81, 224.8, 74.73, 33.43],
+ # 'category_id': 16,
+ # 'id': 42986},
+ # ...]
+ anns = [coco_api.imgToAnns[img_id] for img_id in img_ids]
+ total_num_valid_anns = sum([len(x) for x in anns])
+ total_num_anns = len(coco_api.anns)
+ if total_num_valid_anns < total_num_anns:
+ logger.warning(
+ f"{json_file} contains {total_num_anns} annotations, but only "
+ f"{total_num_valid_anns} of them match to images in the file."
+ )
+
+ if "minival" not in json_file:
+ # The popular valminusminival & minival annotations for COCO2014 contain this bug.
+ # However the ratio of buggy annotations there is tiny and does not affect accuracy.
+ # Therefore we explicitly white-list them.
+ ann_ids = [ann["id"] for anns_per_image in anns for ann in anns_per_image]
+ assert len(set(ann_ids)) == len(ann_ids), "Annotation ids in '{}' are not unique!".format(
+ json_file
+ )
+
+ imgs_anns = list(zip(imgs, anns))
+ logger.info("Loaded {} images in COCO format from {}".format(len(imgs_anns), json_file))
+
+ dataset_dicts = {}
+
+ ann_keys = ["iscrowd", "bbox", "keypoints", "category_id"]
+
+ num_instances_without_valid_segmentation = 0
+
+ for (img_dict, anno_dict_list) in imgs_anns:
+ record = {}
+ record["file_name"] = os.path.join(image_root, img_dict["file_name"])
+ record["height"] = img_dict["height"]
+ record["width"] = img_dict["width"]
+ image_id = record["image_id"] = img_dict["id"]
+
+ objs = []
+ for anno in anno_dict_list:
+ # Check that the image_id in this annotation is the same as
+ # the image_id we're looking at.
+ # This fails only when the data parsing logic or the annotation file is buggy.
+
+ # The original COCO valminusminival2014 & minival2014 annotation files
+ # actually contains bugs that, together with certain ways of using COCO API,
+ # can trigger this assertion.
+ assert anno["image_id"] == image_id
+
+ assert anno.get("ignore", 0) == 0, '"ignore" in COCO json file is not supported.'
+
+ obj = {key: anno[key] for key in ann_keys if key in anno}
+ if "bbox" in obj and len(obj["bbox"]) == 0:
+ raise ValueError(
+ f"One annotation of image {image_id} contains empty 'bbox' value! "
+ "This json does not have valid COCO format."
+ )
+
+ segm = anno.get("segmentation", None)
+ if segm: # either list[list[float]] or dict(RLE)
+ if isinstance(segm, dict):
+ if isinstance(segm["counts"], list):
+ # convert to compressed RLE
+ segm = mask_util.frPyObjects(segm, *segm["size"])
+ else:
+ # filter out invalid polygons (< 3 points)
+ segm = [poly for poly in segm if len(poly) % 2 == 0 and len(poly) >= 6]
+ if len(segm) == 0:
+ num_instances_without_valid_segmentation += 1
+ continue # ignore this instance
+ obj["segmentation"] = segm
+
+ keypts = anno.get("keypoints", None)
+ if keypts: # list[int]
+ for idx, v in enumerate(keypts):
+ if idx % 3 != 2:
+ # COCO's segmentation coordinates are floating points in [0, H or W],
+ # but keypoint coordinates are integers in [0, H-1 or W-1]
+ # Therefore we assume the coordinates are "pixel indices" and
+ # add 0.5 to convert to floating point coordinates.
+ keypts[idx] = v + 0.5
+ obj["keypoints"] = keypts
+
+ obj["bbox_mode"] = BoxMode.XYWH_ABS
+ if id_map:
+ annotation_category_id = obj["category_id"]
+ try:
+ obj["category_id"] = id_map[annotation_category_id]
+ except KeyError as e:
+ raise KeyError(
+ f"Encountered category_id={annotation_category_id} "
+ "but this id does not exist in 'categories' of the json file."
+ ) from e
+ objs.append(obj)
+ record["annotations"] = objs
+ dataset_dicts[image_id] = record
+
+ if num_instances_without_valid_segmentation > 0:
+ logger.warning(
+ "Filtered out {} instances without valid segmentation. ".format(
+ num_instances_without_valid_segmentation
+ )
+ + "There might be issues in your dataset generation process. Please "
+ "check https://detectron2.readthedocs.io/en/latest/tutorials/datasets.html carefully"
+ )
+ return dataset_dicts
+
+def get_metadata():
+ meta = {}
+ # The following metadata maps contiguous id from [0, #thing categories +
+ # #stuff categories) to their names and colors. We have to replica of the
+ # same name and color under "thing_*" and "stuff_*" because the current
+ # visualization function in D2 handles thing and class classes differently
+ # due to some heuristic used in Panoptic FPN. We keep the same naming to
+ # enable reusing existing visualization functions.
+ thing_classes = [k["name"] for k in COCO_CATEGORIES if k["isthing"] == 1]
+ thing_colors = [k["color"] for k in COCO_CATEGORIES if k["isthing"] == 1]
+ stuff_classes = [k["name"] for k in COCO_CATEGORIES]
+ stuff_colors = [k["color"] for k in COCO_CATEGORIES]
+
+ meta["thing_classes"] = thing_classes
+ meta["thing_colors"] = thing_colors
+ meta["stuff_classes"] = stuff_classes
+ meta["stuff_colors"] = stuff_colors
+
+ # Convert category id for training:
+ # category id: like semantic segmentation, it is the class id for each
+ # pixel. Since there are some classes not used in evaluation, the category
+ # id is not always contiguous and thus we have two set of category ids:
+ # - original category id: category id in the original dataset, mainly
+ # used for evaluation.
+ # - contiguous category id: [0, #classes), in order to train the linear
+ # softmax classifier.
+ thing_dataset_id_to_contiguous_id = {}
+ stuff_dataset_id_to_contiguous_id = {}
+
+ for i, cat in enumerate(COCO_CATEGORIES):
+ if cat["isthing"]:
+ thing_dataset_id_to_contiguous_id[cat["id"]] = i
+ # else:
+ # stuff_dataset_id_to_contiguous_id[cat["id"]] = i
+
+ # in order to use sem_seg evaluator
+ stuff_dataset_id_to_contiguous_id[cat["id"]] = i
+
+ meta["thing_dataset_id_to_contiguous_id"] = thing_dataset_id_to_contiguous_id
+ meta["stuff_dataset_id_to_contiguous_id"] = stuff_dataset_id_to_contiguous_id
+
+ return meta
+
+
+def load_coco_panoptic_json(json_file, instances_json, instances_name, image_dir, gt_dir, semseg_dir, meta):
+ """
+ Args:
+ image_dir (str): path to the raw dataset. e.g., "~/coco/train2017".
+ gt_dir (str): path to the raw annotations. e.g., "~/coco/panoptic_train2017".
+ json_file (str): path to the json file. e.g., "~/coco/annotations/panoptic_train2017.json".
+ Returns:
+ list[dict]: a list of dicts in Detectron2 standard format. (See
+ `Using Custom Datasets `_ )
+ """
+
+ def _convert_category_id(segment_info, meta):
+ if segment_info["category_id"] in meta["thing_dataset_id_to_contiguous_id"]:
+ segment_info["category_id"] = meta["thing_dataset_id_to_contiguous_id"][
+ segment_info["category_id"]
+ ]
+ segment_info["isthing"] = True
+ else:
+ segment_info["category_id"] = meta["stuff_dataset_id_to_contiguous_id"][
+ segment_info["category_id"]
+ ]
+ segment_info["isthing"] = False
+ return segment_info
+
+ with PathManager.open(json_file) as f:
+ json_info = json.load(f)
+
+ instance_data_dicts = load_coco_instance_json(instances_json, image_dir.replace("panoptic_", ""), instances_name)
+
+ ret = []
+ for ann in json_info["annotations"]:
+ image_id = int(ann["image_id"])
+ # TODO: currently we assume image and label has the same filename but
+ # different extension, and images have extension ".jpg" for COCO. Need
+ # to make image extension a user-provided argument if we extend this
+ # function to support other COCO-like datasets.
+ image_file = os.path.join(image_dir, os.path.splitext(ann["file_name"])[0] + ".jpg")
+ label_file = os.path.join(gt_dir, ann["file_name"])
+ sem_label_file = os.path.join(semseg_dir, ann["file_name"])
+ segments_info = [_convert_category_id(x, meta) for x in ann["segments_info"]]
+ ret.append(
+ {
+ "file_name": image_file,
+ "image_id": image_id,
+ "pan_seg_file_name": label_file,
+ "sem_seg_file_name": sem_label_file,
+ "segments_info": segments_info,
+ "annotations": instance_data_dicts[image_id]["annotations"],
+ }
+ )
+ assert len(ret), f"No images found in {image_dir}!"
+ assert PathManager.isfile(ret[0]["file_name"]), ret[0]["file_name"]
+ assert PathManager.isfile(ret[0]["pan_seg_file_name"]), ret[0]["pan_seg_file_name"]
+ assert PathManager.isfile(ret[0]["sem_seg_file_name"]), ret[0]["sem_seg_file_name"]
+ return ret
+
+
+def register_coco_panoptic_annos_sem_seg(
+ name, metadata, image_root, panoptic_root, panoptic_json, sem_seg_root, instances_json, instances_name,
+):
+ panoptic_name = name
+ delattr(MetadataCatalog.get(panoptic_name), "thing_classes")
+ delattr(MetadataCatalog.get(panoptic_name), "thing_colors")
+ MetadataCatalog.get(panoptic_name).set(
+ thing_classes=metadata["thing_classes"],
+ thing_colors=metadata["thing_colors"],
+ # thing_dataset_id_to_contiguous_id=metadata["thing_dataset_id_to_contiguous_id"],
+ )
+
+ # the name is "coco_2017_train_panoptic_with_sem_seg" and "coco_2017_val_panoptic_with_sem_seg"
+ semantic_name = name + "_with_sem_seg"
+ DatasetCatalog.register(
+ semantic_name,
+ lambda: load_coco_panoptic_json(panoptic_json, instances_json, instances_name, image_root, panoptic_root, sem_seg_root, metadata),
+ )
+ MetadataCatalog.get(semantic_name).set(
+ sem_seg_root=sem_seg_root,
+ panoptic_root=panoptic_root,
+ image_root=image_root,
+ panoptic_json=panoptic_json,
+ json_file=instances_json,
+ evaluator_type="coco_panoptic_seg",
+ ignore_label=255,
+ label_divisor=1000,
+ **metadata,
+ )
+
+
+def register_all_coco_panoptic_annos_sem_seg(root):
+ for (
+ prefix,
+ (panoptic_root, panoptic_json, semantic_root),
+ ) in _PREDEFINED_SPLITS_COCO_PANOPTIC.items():
+
+ prefix_instances = prefix[: -len("_panoptic")]
+ instances_meta = MetadataCatalog.get(prefix_instances)
+ image_root, instances_json = instances_meta.image_root, instances_meta.json_file
+
+ if 'val' in instances_json:
+ instances_json = instances_json.replace('instances_', 'panoptic2instances_')
+
+ register_coco_panoptic_annos_sem_seg(
+ prefix,
+ get_metadata(),
+ image_root,
+ os.path.join(root, panoptic_root),
+ os.path.join(root, panoptic_json),
+ os.path.join(root, semantic_root),
+ instances_json,
+ prefix_instances,
+ )
+
+
+_root = os.getenv("DETECTRON2_DATASETS", "datasets")
+register_all_coco_panoptic_annos_sem_seg(_root)
diff --git a/annotator/OneFormer/oneformer/data/datasets/register_mapillary_vistas.py b/annotator/OneFormer/oneformer/data/datasets/register_mapillary_vistas.py
new file mode 100644
index 0000000000000000000000000000000000000000..12fa1e2b5db6bc7898a8dc6488d9045ec29f0a27
--- /dev/null
+++ b/annotator/OneFormer/oneformer/data/datasets/register_mapillary_vistas.py
@@ -0,0 +1,507 @@
+# Copyright (c) Facebook, Inc. and its affiliates.
+import os
+
+from detectron2.data import DatasetCatalog, MetadataCatalog
+from detectron2.data.datasets import load_sem_seg
+
+MAPILLARY_VISTAS_SEM_SEG_CATEGORIES = [
+ {
+ "color": [165, 42, 42],
+ "instances": True,
+ "readable": "Bird",
+ "name": "animal--bird",
+ "evaluate": True,
+ },
+ {
+ "color": [0, 192, 0],
+ "instances": True,
+ "readable": "Ground Animal",
+ "name": "animal--ground-animal",
+ "evaluate": True,
+ },
+ {
+ "color": [196, 196, 196],
+ "instances": False,
+ "readable": "Curb",
+ "name": "construction--barrier--curb",
+ "evaluate": True,
+ },
+ {
+ "color": [190, 153, 153],
+ "instances": False,
+ "readable": "Fence",
+ "name": "construction--barrier--fence",
+ "evaluate": True,
+ },
+ {
+ "color": [180, 165, 180],
+ "instances": False,
+ "readable": "Guard Rail",
+ "name": "construction--barrier--guard-rail",
+ "evaluate": True,
+ },
+ {
+ "color": [90, 120, 150],
+ "instances": False,
+ "readable": "Barrier",
+ "name": "construction--barrier--other-barrier",
+ "evaluate": True,
+ },
+ {
+ "color": [102, 102, 156],
+ "instances": False,
+ "readable": "Wall",
+ "name": "construction--barrier--wall",
+ "evaluate": True,
+ },
+ {
+ "color": [128, 64, 255],
+ "instances": False,
+ "readable": "Bike Lane",
+ "name": "construction--flat--bike-lane",
+ "evaluate": True,
+ },
+ {
+ "color": [140, 140, 200],
+ "instances": True,
+ "readable": "Crosswalk - Plain",
+ "name": "construction--flat--crosswalk-plain",
+ "evaluate": True,
+ },
+ {
+ "color": [170, 170, 170],
+ "instances": False,
+ "readable": "Curb Cut",
+ "name": "construction--flat--curb-cut",
+ "evaluate": True,
+ },
+ {
+ "color": [250, 170, 160],
+ "instances": False,
+ "readable": "Parking",
+ "name": "construction--flat--parking",
+ "evaluate": True,
+ },
+ {
+ "color": [96, 96, 96],
+ "instances": False,
+ "readable": "Pedestrian Area",
+ "name": "construction--flat--pedestrian-area",
+ "evaluate": True,
+ },
+ {
+ "color": [230, 150, 140],
+ "instances": False,
+ "readable": "Rail Track",
+ "name": "construction--flat--rail-track",
+ "evaluate": True,
+ },
+ {
+ "color": [128, 64, 128],
+ "instances": False,
+ "readable": "Road",
+ "name": "construction--flat--road",
+ "evaluate": True,
+ },
+ {
+ "color": [110, 110, 110],
+ "instances": False,
+ "readable": "Service Lane",
+ "name": "construction--flat--service-lane",
+ "evaluate": True,
+ },
+ {
+ "color": [244, 35, 232],
+ "instances": False,
+ "readable": "Sidewalk",
+ "name": "construction--flat--sidewalk",
+ "evaluate": True,
+ },
+ {
+ "color": [150, 100, 100],
+ "instances": False,
+ "readable": "Bridge",
+ "name": "construction--structure--bridge",
+ "evaluate": True,
+ },
+ {
+ "color": [70, 70, 70],
+ "instances": False,
+ "readable": "Building",
+ "name": "construction--structure--building",
+ "evaluate": True,
+ },
+ {
+ "color": [150, 120, 90],
+ "instances": False,
+ "readable": "Tunnel",
+ "name": "construction--structure--tunnel",
+ "evaluate": True,
+ },
+ {
+ "color": [220, 20, 60],
+ "instances": True,
+ "readable": "Person",
+ "name": "human--person",
+ "evaluate": True,
+ },
+ {
+ "color": [255, 0, 0],
+ "instances": True,
+ "readable": "Bicyclist",
+ "name": "human--rider--bicyclist",
+ "evaluate": True,
+ },
+ {
+ "color": [255, 0, 100],
+ "instances": True,
+ "readable": "Motorcyclist",
+ "name": "human--rider--motorcyclist",
+ "evaluate": True,
+ },
+ {
+ "color": [255, 0, 200],
+ "instances": True,
+ "readable": "Other Rider",
+ "name": "human--rider--other-rider",
+ "evaluate": True,
+ },
+ {
+ "color": [200, 128, 128],
+ "instances": True,
+ "readable": "Lane Marking - Crosswalk",
+ "name": "marking--crosswalk-zebra",
+ "evaluate": True,
+ },
+ {
+ "color": [255, 255, 255],
+ "instances": False,
+ "readable": "Lane Marking - General",
+ "name": "marking--general",
+ "evaluate": True,
+ },
+ {
+ "color": [64, 170, 64],
+ "instances": False,
+ "readable": "Mountain",
+ "name": "nature--mountain",
+ "evaluate": True,
+ },
+ {
+ "color": [230, 160, 50],
+ "instances": False,
+ "readable": "Sand",
+ "name": "nature--sand",
+ "evaluate": True,
+ },
+ {
+ "color": [70, 130, 180],
+ "instances": False,
+ "readable": "Sky",
+ "name": "nature--sky",
+ "evaluate": True,
+ },
+ {
+ "color": [190, 255, 255],
+ "instances": False,
+ "readable": "Snow",
+ "name": "nature--snow",
+ "evaluate": True,
+ },
+ {
+ "color": [152, 251, 152],
+ "instances": False,
+ "readable": "Terrain",
+ "name": "nature--terrain",
+ "evaluate": True,
+ },
+ {
+ "color": [107, 142, 35],
+ "instances": False,
+ "readable": "Vegetation",
+ "name": "nature--vegetation",
+ "evaluate": True,
+ },
+ {
+ "color": [0, 170, 30],
+ "instances": False,
+ "readable": "Water",
+ "name": "nature--water",
+ "evaluate": True,
+ },
+ {
+ "color": [255, 255, 128],
+ "instances": True,
+ "readable": "Banner",
+ "name": "object--banner",
+ "evaluate": True,
+ },
+ {
+ "color": [250, 0, 30],
+ "instances": True,
+ "readable": "Bench",
+ "name": "object--bench",
+ "evaluate": True,
+ },
+ {
+ "color": [100, 140, 180],
+ "instances": True,
+ "readable": "Bike Rack",
+ "name": "object--bike-rack",
+ "evaluate": True,
+ },
+ {
+ "color": [220, 220, 220],
+ "instances": True,
+ "readable": "Billboard",
+ "name": "object--billboard",
+ "evaluate": True,
+ },
+ {
+ "color": [220, 128, 128],
+ "instances": True,
+ "readable": "Catch Basin",
+ "name": "object--catch-basin",
+ "evaluate": True,
+ },
+ {
+ "color": [222, 40, 40],
+ "instances": True,
+ "readable": "CCTV Camera",
+ "name": "object--cctv-camera",
+ "evaluate": True,
+ },
+ {
+ "color": [100, 170, 30],
+ "instances": True,
+ "readable": "Fire Hydrant",
+ "name": "object--fire-hydrant",
+ "evaluate": True,
+ },
+ {
+ "color": [40, 40, 40],
+ "instances": True,
+ "readable": "Junction Box",
+ "name": "object--junction-box",
+ "evaluate": True,
+ },
+ {
+ "color": [33, 33, 33],
+ "instances": True,
+ "readable": "Mailbox",
+ "name": "object--mailbox",
+ "evaluate": True,
+ },
+ {
+ "color": [100, 128, 160],
+ "instances": True,
+ "readable": "Manhole",
+ "name": "object--manhole",
+ "evaluate": True,
+ },
+ {
+ "color": [142, 0, 0],
+ "instances": True,
+ "readable": "Phone Booth",
+ "name": "object--phone-booth",
+ "evaluate": True,
+ },
+ {
+ "color": [70, 100, 150],
+ "instances": False,
+ "readable": "Pothole",
+ "name": "object--pothole",
+ "evaluate": True,
+ },
+ {
+ "color": [210, 170, 100],
+ "instances": True,
+ "readable": "Street Light",
+ "name": "object--street-light",
+ "evaluate": True,
+ },
+ {
+ "color": [153, 153, 153],
+ "instances": True,
+ "readable": "Pole",
+ "name": "object--support--pole",
+ "evaluate": True,
+ },
+ {
+ "color": [128, 128, 128],
+ "instances": True,
+ "readable": "Traffic Sign Frame",
+ "name": "object--support--traffic-sign-frame",
+ "evaluate": True,
+ },
+ {
+ "color": [0, 0, 80],
+ "instances": True,
+ "readable": "Utility Pole",
+ "name": "object--support--utility-pole",
+ "evaluate": True,
+ },
+ {
+ "color": [250, 170, 30],
+ "instances": True,
+ "readable": "Traffic Light",
+ "name": "object--traffic-light",
+ "evaluate": True,
+ },
+ {
+ "color": [192, 192, 192],
+ "instances": True,
+ "readable": "Traffic Sign (Back)",
+ "name": "object--traffic-sign--back",
+ "evaluate": True,
+ },
+ {
+ "color": [220, 220, 0],
+ "instances": True,
+ "readable": "Traffic Sign (Front)",
+ "name": "object--traffic-sign--front",
+ "evaluate": True,
+ },
+ {
+ "color": [140, 140, 20],
+ "instances": True,
+ "readable": "Trash Can",
+ "name": "object--trash-can",
+ "evaluate": True,
+ },
+ {
+ "color": [119, 11, 32],
+ "instances": True,
+ "readable": "Bicycle",
+ "name": "object--vehicle--bicycle",
+ "evaluate": True,
+ },
+ {
+ "color": [150, 0, 255],
+ "instances": True,
+ "readable": "Boat",
+ "name": "object--vehicle--boat",
+ "evaluate": True,
+ },
+ {
+ "color": [0, 60, 100],
+ "instances": True,
+ "readable": "Bus",
+ "name": "object--vehicle--bus",
+ "evaluate": True,
+ },
+ {
+ "color": [0, 0, 142],
+ "instances": True,
+ "readable": "Car",
+ "name": "object--vehicle--car",
+ "evaluate": True,
+ },
+ {
+ "color": [0, 0, 90],
+ "instances": True,
+ "readable": "Caravan",
+ "name": "object--vehicle--caravan",
+ "evaluate": True,
+ },
+ {
+ "color": [0, 0, 230],
+ "instances": True,
+ "readable": "Motorcycle",
+ "name": "object--vehicle--motorcycle",
+ "evaluate": True,
+ },
+ {
+ "color": [0, 80, 100],
+ "instances": False,
+ "readable": "On Rails",
+ "name": "object--vehicle--on-rails",
+ "evaluate": True,
+ },
+ {
+ "color": [128, 64, 64],
+ "instances": True,
+ "readable": "Other Vehicle",
+ "name": "object--vehicle--other-vehicle",
+ "evaluate": True,
+ },
+ {
+ "color": [0, 0, 110],
+ "instances": True,
+ "readable": "Trailer",
+ "name": "object--vehicle--trailer",
+ "evaluate": True,
+ },
+ {
+ "color": [0, 0, 70],
+ "instances": True,
+ "readable": "Truck",
+ "name": "object--vehicle--truck",
+ "evaluate": True,
+ },
+ {
+ "color": [0, 0, 192],
+ "instances": True,
+ "readable": "Wheeled Slow",
+ "name": "object--vehicle--wheeled-slow",
+ "evaluate": True,
+ },
+ {
+ "color": [32, 32, 32],
+ "instances": False,
+ "readable": "Car Mount",
+ "name": "void--car-mount",
+ "evaluate": True,
+ },
+ {
+ "color": [120, 10, 10],
+ "instances": False,
+ "readable": "Ego Vehicle",
+ "name": "void--ego-vehicle",
+ "evaluate": True,
+ },
+ {
+ "color": [0, 0, 0],
+ "instances": False,
+ "readable": "Unlabeled",
+ "name": "void--unlabeled",
+ "evaluate": False,
+ },
+]
+
+
+def _get_mapillary_vistas_meta():
+ stuff_classes = [k["readable"] for k in MAPILLARY_VISTAS_SEM_SEG_CATEGORIES if k["evaluate"]]
+ assert len(stuff_classes) == 65
+
+ stuff_colors = [k["color"] for k in MAPILLARY_VISTAS_SEM_SEG_CATEGORIES if k["evaluate"]]
+ assert len(stuff_colors) == 65
+
+ ret = {
+ "stuff_classes": stuff_classes,
+ "stuff_colors": stuff_colors,
+ }
+ return ret
+
+
+def register_all_mapillary_vistas(root):
+ root = os.path.join(root, "mapillary_vistas")
+ meta = _get_mapillary_vistas_meta()
+ for name, dirname in [("train", "training"), ("val", "validation")]:
+ image_dir = os.path.join(root, dirname, "images")
+ gt_dir = os.path.join(root, dirname, "labels")
+ name = f"mapillary_vistas_sem_seg_{name}"
+ DatasetCatalog.register(
+ name, lambda x=image_dir, y=gt_dir: load_sem_seg(y, x, gt_ext="png", image_ext="jpg")
+ )
+ MetadataCatalog.get(name).set(
+ image_root=image_dir,
+ sem_seg_root=gt_dir,
+ evaluator_type="sem_seg",
+ ignore_label=65, # different from other datasets, Mapillary Vistas sets ignore_label to 65
+ **meta,
+ )
+
+
+_root = os.getenv("DETECTRON2_DATASETS", "datasets")
+register_all_mapillary_vistas(_root)
\ No newline at end of file
diff --git a/annotator/OneFormer/oneformer/data/datasets/register_mapillary_vistas_panoptic.py b/annotator/OneFormer/oneformer/data/datasets/register_mapillary_vistas_panoptic.py
new file mode 100644
index 0000000000000000000000000000000000000000..aa7033950bf36f8d1e8eb5ebb6c61ac13a3d8acb
--- /dev/null
+++ b/annotator/OneFormer/oneformer/data/datasets/register_mapillary_vistas_panoptic.py
@@ -0,0 +1,508 @@
+# Copyright (c) Facebook, Inc. and its affiliates.
+import json
+import os
+
+from detectron2.data import DatasetCatalog, MetadataCatalog
+from detectron2.utils.file_io import PathManager
+
+
+MAPILLARY_VISTAS_SEM_SEG_CATEGORIES = [
+ {'color': [165, 42, 42],
+ 'id': 1,
+ 'isthing': 1,
+ 'name': 'Bird',
+ 'supercategory': 'animal--bird'},
+ {'color': [0, 192, 0],
+ 'id': 2,
+ 'isthing': 1,
+ 'name': 'Ground Animal',
+ 'supercategory': 'animal--ground-animal'},
+ {'color': [196, 196, 196],
+ 'id': 3,
+ 'isthing': 0,
+ 'name': 'Curb',
+ 'supercategory': 'construction--barrier--curb'},
+ {'color': [190, 153, 153],
+ 'id': 4,
+ 'isthing': 0,
+ 'name': 'Fence',
+ 'supercategory': 'construction--barrier--fence'},
+ {'color': [180, 165, 180],
+ 'id': 5,
+ 'isthing': 0,
+ 'name': 'Guard Rail',
+ 'supercategory': 'construction--barrier--guard-rail'},
+ {'color': [90, 120, 150],
+ 'id': 6,
+ 'isthing': 0,
+ 'name': 'Barrier',
+ 'supercategory': 'construction--barrier--other-barrier'},
+ {'color': [102, 102, 156],
+ 'id': 7,
+ 'isthing': 0,
+ 'name': 'Wall',
+ 'supercategory': 'construction--barrier--wall'},
+ {'color': [128, 64, 255],
+ 'id': 8,
+ 'isthing': 0,
+ 'name': 'Bike Lane',
+ 'supercategory': 'construction--flat--bike-lane'},
+ {'color': [140, 140, 200],
+ 'id': 9,
+ 'isthing': 1,
+ 'name': 'Crosswalk - Plain',
+ 'supercategory': 'construction--flat--crosswalk-plain'},
+ {'color': [170, 170, 170],
+ 'id': 10,
+ 'isthing': 0,
+ 'name': 'Curb Cut',
+ 'supercategory': 'construction--flat--curb-cut'},
+ {'color': [250, 170, 160],
+ 'id': 11,
+ 'isthing': 0,
+ 'name': 'Parking',
+ 'supercategory': 'construction--flat--parking'},
+ {'color': [96, 96, 96],
+ 'id': 12,
+ 'isthing': 0,
+ 'name': 'Pedestrian Area',
+ 'supercategory': 'construction--flat--pedestrian-area'},
+ {'color': [230, 150, 140],
+ 'id': 13,
+ 'isthing': 0,
+ 'name': 'Rail Track',
+ 'supercategory': 'construction--flat--rail-track'},
+ {'color': [128, 64, 128],
+ 'id': 14,
+ 'isthing': 0,
+ 'name': 'Road',
+ 'supercategory': 'construction--flat--road'},
+ {'color': [110, 110, 110],
+ 'id': 15,
+ 'isthing': 0,
+ 'name': 'Service Lane',
+ 'supercategory': 'construction--flat--service-lane'},
+ {'color': [244, 35, 232],
+ 'id': 16,
+ 'isthing': 0,
+ 'name': 'Sidewalk',
+ 'supercategory': 'construction--flat--sidewalk'},
+ {'color': [150, 100, 100],
+ 'id': 17,
+ 'isthing': 0,
+ 'name': 'Bridge',
+ 'supercategory': 'construction--structure--bridge'},
+ {'color': [70, 70, 70],
+ 'id': 18,
+ 'isthing': 0,
+ 'name': 'Building',
+ 'supercategory': 'construction--structure--building'},
+ {'color': [150, 120, 90],
+ 'id': 19,
+ 'isthing': 0,
+ 'name': 'Tunnel',
+ 'supercategory': 'construction--structure--tunnel'},
+ {'color': [220, 20, 60],
+ 'id': 20,
+ 'isthing': 1,
+ 'name': 'Person',
+ 'supercategory': 'human--person'},
+ {'color': [255, 0, 0],
+ 'id': 21,
+ 'isthing': 1,
+ 'name': 'Bicyclist',
+ 'supercategory': 'human--rider--bicyclist'},
+ {'color': [255, 0, 100],
+ 'id': 22,
+ 'isthing': 1,
+ 'name': 'Motorcyclist',
+ 'supercategory': 'human--rider--motorcyclist'},
+ {'color': [255, 0, 200],
+ 'id': 23,
+ 'isthing': 1,
+ 'name': 'Other Rider',
+ 'supercategory': 'human--rider--other-rider'},
+ {'color': [200, 128, 128],
+ 'id': 24,
+ 'isthing': 1,
+ 'name': 'Lane Marking - Crosswalk',
+ 'supercategory': 'marking--crosswalk-zebra'},
+ {'color': [255, 255, 255],
+ 'id': 25,
+ 'isthing': 0,
+ 'name': 'Lane Marking - General',
+ 'supercategory': 'marking--general'},
+ {'color': [64, 170, 64],
+ 'id': 26,
+ 'isthing': 0,
+ 'name': 'Mountain',
+ 'supercategory': 'nature--mountain'},
+ {'color': [230, 160, 50],
+ 'id': 27,
+ 'isthing': 0,
+ 'name': 'Sand',
+ 'supercategory': 'nature--sand'},
+ {'color': [70, 130, 180],
+ 'id': 28,
+ 'isthing': 0,
+ 'name': 'Sky',
+ 'supercategory': 'nature--sky'},
+ {'color': [190, 255, 255],
+ 'id': 29,
+ 'isthing': 0,
+ 'name': 'Snow',
+ 'supercategory': 'nature--snow'},
+ {'color': [152, 251, 152],
+ 'id': 30,
+ 'isthing': 0,
+ 'name': 'Terrain',
+ 'supercategory': 'nature--terrain'},
+ {'color': [107, 142, 35],
+ 'id': 31,
+ 'isthing': 0,
+ 'name': 'Vegetation',
+ 'supercategory': 'nature--vegetation'},
+ {'color': [0, 170, 30],
+ 'id': 32,
+ 'isthing': 0,
+ 'name': 'Water',
+ 'supercategory': 'nature--water'},
+ {'color': [255, 255, 128],
+ 'id': 33,
+ 'isthing': 1,
+ 'name': 'Banner',
+ 'supercategory': 'object--banner'},
+ {'color': [250, 0, 30],
+ 'id': 34,
+ 'isthing': 1,
+ 'name': 'Bench',
+ 'supercategory': 'object--bench'},
+ {'color': [100, 140, 180],
+ 'id': 35,
+ 'isthing': 1,
+ 'name': 'Bike Rack',
+ 'supercategory': 'object--bike-rack'},
+ {'color': [220, 220, 220],
+ 'id': 36,
+ 'isthing': 1,
+ 'name': 'Billboard',
+ 'supercategory': 'object--billboard'},
+ {'color': [220, 128, 128],
+ 'id': 37,
+ 'isthing': 1,
+ 'name': 'Catch Basin',
+ 'supercategory': 'object--catch-basin'},
+ {'color': [222, 40, 40],
+ 'id': 38,
+ 'isthing': 1,
+ 'name': 'CCTV Camera',
+ 'supercategory': 'object--cctv-camera'},
+ {'color': [100, 170, 30],
+ 'id': 39,
+ 'isthing': 1,
+ 'name': 'Fire Hydrant',
+ 'supercategory': 'object--fire-hydrant'},
+ {'color': [40, 40, 40],
+ 'id': 40,
+ 'isthing': 1,
+ 'name': 'Junction Box',
+ 'supercategory': 'object--junction-box'},
+ {'color': [33, 33, 33],
+ 'id': 41,
+ 'isthing': 1,
+ 'name': 'Mailbox',
+ 'supercategory': 'object--mailbox'},
+ {'color': [100, 128, 160],
+ 'id': 42,
+ 'isthing': 1,
+ 'name': 'Manhole',
+ 'supercategory': 'object--manhole'},
+ {'color': [142, 0, 0],
+ 'id': 43,
+ 'isthing': 1,
+ 'name': 'Phone Booth',
+ 'supercategory': 'object--phone-booth'},
+ {'color': [70, 100, 150],
+ 'id': 44,
+ 'isthing': 0,
+ 'name': 'Pothole',
+ 'supercategory': 'object--pothole'},
+ {'color': [210, 170, 100],
+ 'id': 45,
+ 'isthing': 1,
+ 'name': 'Street Light',
+ 'supercategory': 'object--street-light'},
+ {'color': [153, 153, 153],
+ 'id': 46,
+ 'isthing': 1,
+ 'name': 'Pole',
+ 'supercategory': 'object--support--pole'},
+ {'color': [128, 128, 128],
+ 'id': 47,
+ 'isthing': 1,
+ 'name': 'Traffic Sign Frame',
+ 'supercategory': 'object--support--traffic-sign-frame'},
+ {'color': [0, 0, 80],
+ 'id': 48,
+ 'isthing': 1,
+ 'name': 'Utility Pole',
+ 'supercategory': 'object--support--utility-pole'},
+ {'color': [250, 170, 30],
+ 'id': 49,
+ 'isthing': 1,
+ 'name': 'Traffic Light',
+ 'supercategory': 'object--traffic-light'},
+ {'color': [192, 192, 192],
+ 'id': 50,
+ 'isthing': 1,
+ 'name': 'Traffic Sign (Back)',
+ 'supercategory': 'object--traffic-sign--back'},
+ {'color': [220, 220, 0],
+ 'id': 51,
+ 'isthing': 1,
+ 'name': 'Traffic Sign (Front)',
+ 'supercategory': 'object--traffic-sign--front'},
+ {'color': [140, 140, 20],
+ 'id': 52,
+ 'isthing': 1,
+ 'name': 'Trash Can',
+ 'supercategory': 'object--trash-can'},
+ {'color': [119, 11, 32],
+ 'id': 53,
+ 'isthing': 1,
+ 'name': 'Bicycle',
+ 'supercategory': 'object--vehicle--bicycle'},
+ {'color': [150, 0, 255],
+ 'id': 54,
+ 'isthing': 1,
+ 'name': 'Boat',
+ 'supercategory': 'object--vehicle--boat'},
+ {'color': [0, 60, 100],
+ 'id': 55,
+ 'isthing': 1,
+ 'name': 'Bus',
+ 'supercategory': 'object--vehicle--bus'},
+ {'color': [0, 0, 142],
+ 'id': 56,
+ 'isthing': 1,
+ 'name': 'Car',
+ 'supercategory': 'object--vehicle--car'},
+ {'color': [0, 0, 90],
+ 'id': 57,
+ 'isthing': 1,
+ 'name': 'Caravan',
+ 'supercategory': 'object--vehicle--caravan'},
+ {'color': [0, 0, 230],
+ 'id': 58,
+ 'isthing': 1,
+ 'name': 'Motorcycle',
+ 'supercategory': 'object--vehicle--motorcycle'},
+ {'color': [0, 80, 100],
+ 'id': 59,
+ 'isthing': 0,
+ 'name': 'On Rails',
+ 'supercategory': 'object--vehicle--on-rails'},
+ {'color': [128, 64, 64],
+ 'id': 60,
+ 'isthing': 1,
+ 'name': 'Other Vehicle',
+ 'supercategory': 'object--vehicle--other-vehicle'},
+ {'color': [0, 0, 110],
+ 'id': 61,
+ 'isthing': 1,
+ 'name': 'Trailer',
+ 'supercategory': 'object--vehicle--trailer'},
+ {'color': [0, 0, 70],
+ 'id': 62,
+ 'isthing': 1,
+ 'name': 'Truck',
+ 'supercategory': 'object--vehicle--truck'},
+ {'color': [0, 0, 192],
+ 'id': 63,
+ 'isthing': 1,
+ 'name': 'Wheeled Slow',
+ 'supercategory': 'object--vehicle--wheeled-slow'},
+ {'color': [32, 32, 32],
+ 'id': 64,
+ 'isthing': 0,
+ 'name': 'Car Mount',
+ 'supercategory': 'void--car-mount'},
+ {'color': [120, 10, 10],
+ 'id': 65,
+ 'isthing': 0,
+ 'name': 'Ego Vehicle',
+ 'supercategory': 'void--ego-vehicle'}
+]
+
+
+def load_mapillary_vistas_panoptic_json(json_file, image_dir, gt_dir, semseg_dir, meta):
+ """
+ Args:
+ image_dir (str): path to the raw dataset. e.g., "~/coco/train2017".
+ gt_dir (str): path to the raw annotations. e.g., "~/coco/panoptic_train2017".
+ json_file (str): path to the json file. e.g., "~/coco/annotations/panoptic_train2017.json".
+ Returns:
+ list[dict]: a list of dicts in Detectron2 standard format. (See
+ `Using Custom Datasets `_ )
+ """
+
+ def _convert_category_id(segment_info, meta):
+ if segment_info["category_id"] in meta["thing_dataset_id_to_contiguous_id"]:
+ segment_info["category_id"] = meta["thing_dataset_id_to_contiguous_id"][
+ segment_info["category_id"]
+ ]
+ segment_info["isthing"] = True
+ else:
+ segment_info["category_id"] = meta["stuff_dataset_id_to_contiguous_id"][
+ segment_info["category_id"]
+ ]
+ segment_info["isthing"] = False
+ return segment_info
+
+ with PathManager.open(json_file) as f:
+ json_info = json.load(f)
+
+ ret = []
+ for ann in json_info["annotations"]:
+ image_id = ann["image_id"]
+ # TODO: currently we assume image and label has the same filename but
+ # different extension, and images have extension ".jpg" for COCO. Need
+ # to make image extension a user-provided argument if we extend this
+ # function to support other COCO-like datasets.
+ image_file = os.path.join(image_dir, os.path.splitext(ann["file_name"])[0] + ".jpg")
+ label_file = os.path.join(gt_dir, ann["file_name"])
+ sem_label_file = os.path.join(semseg_dir, ann["file_name"])
+ segments_info = [_convert_category_id(x, meta) for x in ann["segments_info"]]
+ ret.append(
+ {
+ "file_name": image_file,
+ "image_id": image_id,
+ "pan_seg_file_name": label_file,
+ "sem_seg_file_name": sem_label_file,
+ "segments_info": segments_info,
+ }
+ )
+ assert len(ret), f"No images found in {image_dir}!"
+ assert PathManager.isfile(ret[0]["file_name"]), ret[0]["file_name"]
+ assert PathManager.isfile(ret[0]["pan_seg_file_name"]), ret[0]["pan_seg_file_name"]
+ assert PathManager.isfile(ret[0]["sem_seg_file_name"]), ret[0]["sem_seg_file_name"]
+ return ret
+
+
+def register_mapillary_vistas_panoptic(
+ name, metadata, image_root, panoptic_root, semantic_root, panoptic_json, instances_json=None
+):
+ """
+ Register a "standard" version of ADE20k panoptic segmentation dataset named `name`.
+ The dictionaries in this registered dataset follows detectron2's standard format.
+ Hence it's called "standard".
+ Args:
+ name (str): the name that identifies a dataset,
+ e.g. "ade20k_panoptic_train"
+ metadata (dict): extra metadata associated with this dataset.
+ image_root (str): directory which contains all the images
+ panoptic_root (str): directory which contains panoptic annotation images in COCO format
+ panoptic_json (str): path to the json panoptic annotation file in COCO format
+ sem_seg_root (none): not used, to be consistent with
+ `register_coco_panoptic_separated`.
+ instances_json (str): path to the json instance annotation file
+ """
+ panoptic_name = name
+ DatasetCatalog.register(
+ panoptic_name,
+ lambda: load_mapillary_vistas_panoptic_json(
+ panoptic_json, image_root, panoptic_root, semantic_root, metadata
+ ),
+ )
+ MetadataCatalog.get(panoptic_name).set(
+ panoptic_root=panoptic_root,
+ image_root=image_root,
+ panoptic_json=panoptic_json,
+ json_file=instances_json,
+ evaluator_type="mapillary_vistas_panoptic_seg",
+ ignore_label=65, # different from other datasets, Mapillary Vistas sets ignore_label to 65
+ label_divisor=1000,
+ **metadata,
+ )
+
+
+_PREDEFINED_SPLITS_ADE20K_PANOPTIC = {
+ "mapillary_vistas_panoptic_train": (
+ "mapillary_vistas/training/images",
+ "mapillary_vistas/training/panoptic",
+ "mapillary_vistas/training/panoptic/panoptic_2018.json",
+ "mapillary_vistas/training/labels",
+ ),
+ "mapillary_vistas_panoptic_val": (
+ "mapillary_vistas/validation/images",
+ "mapillary_vistas/validation/panoptic",
+ "mapillary_vistas/validation/panoptic/panoptic_2018.json",
+ "mapillary_vistas/validation/labels",
+ ),
+}
+
+
+def get_metadata():
+ meta = {}
+ # The following metadata maps contiguous id from [0, #thing categories +
+ # #stuff categories) to their names and colors. We have to replica of the
+ # same name and color under "thing_*" and "stuff_*" because the current
+ # visualization function in D2 handles thing and class classes differently
+ # due to some heuristic used in Panoptic FPN. We keep the same naming to
+ # enable reusing existing visualization functions.
+ thing_classes = [k["name"] for k in MAPILLARY_VISTAS_SEM_SEG_CATEGORIES]
+ thing_colors = [k["color"] for k in MAPILLARY_VISTAS_SEM_SEG_CATEGORIES]
+ stuff_classes = [k["name"] for k in MAPILLARY_VISTAS_SEM_SEG_CATEGORIES]
+ stuff_colors = [k["color"] for k in MAPILLARY_VISTAS_SEM_SEG_CATEGORIES]
+
+ meta["thing_classes"] = thing_classes
+ meta["thing_colors"] = thing_colors
+ meta["stuff_classes"] = stuff_classes
+ meta["stuff_colors"] = stuff_colors
+
+ # Convert category id for training:
+ # category id: like semantic segmentation, it is the class id for each
+ # pixel. Since there are some classes not used in evaluation, the category
+ # id is not always contiguous and thus we have two set of category ids:
+ # - original category id: category id in the original dataset, mainly
+ # used for evaluation.
+ # - contiguous category id: [0, #classes), in order to train the linear
+ # softmax classifier.
+ thing_dataset_id_to_contiguous_id = {}
+ stuff_dataset_id_to_contiguous_id = {}
+
+ for i, cat in enumerate(MAPILLARY_VISTAS_SEM_SEG_CATEGORIES):
+ if cat["isthing"]:
+ thing_dataset_id_to_contiguous_id[cat["id"]] = i
+ # else:
+ # stuff_dataset_id_to_contiguous_id[cat["id"]] = i
+
+ # in order to use sem_seg evaluator
+ stuff_dataset_id_to_contiguous_id[cat["id"]] = i
+
+ meta["thing_dataset_id_to_contiguous_id"] = thing_dataset_id_to_contiguous_id
+ meta["stuff_dataset_id_to_contiguous_id"] = stuff_dataset_id_to_contiguous_id
+
+ return meta
+
+
+def register_all_mapillary_vistas_panoptic(root):
+ metadata = get_metadata()
+ for (
+ prefix,
+ (image_root, panoptic_root, panoptic_json, semantic_root),
+ ) in _PREDEFINED_SPLITS_ADE20K_PANOPTIC.items():
+ # The "standard" version of COCO panoptic segmentation dataset,
+ # e.g. used by Panoptic-DeepLab
+ register_mapillary_vistas_panoptic(
+ prefix,
+ metadata,
+ os.path.join(root, image_root),
+ os.path.join(root, panoptic_root),
+ os.path.join(root, semantic_root),
+ os.path.join(root, panoptic_json),
+ )
+
+
+_root = os.getenv("DETECTRON2_DATASETS", "datasets")
+register_all_mapillary_vistas_panoptic(_root)
\ No newline at end of file
diff --git a/annotator/OneFormer/oneformer/data/tokenizer.py b/annotator/OneFormer/oneformer/data/tokenizer.py
new file mode 100644
index 0000000000000000000000000000000000000000..df2c99ffe927a105d0f1dbb9033d2973536fa4dc
--- /dev/null
+++ b/annotator/OneFormer/oneformer/data/tokenizer.py
@@ -0,0 +1,193 @@
+# -------------------------------------------------------------------------
+# MIT License
+#
+# Copyright (c) 2021 OpenAI
+#
+# Permission is hereby granted, free of charge, to any person obtaining a copy
+# of this software and associated documentation files (the "Software"), to deal
+# in the Software without restriction, including without limitation the rights
+# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+# copies of the Software, and to permit persons to whom the Software is
+# furnished to do so, subject to the following conditions:
+#
+# The above copyright notice and this permission notice shall be included in all
+# copies or substantial portions of the Software.
+#
+# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+# SOFTWARE.
+#
+# Modified by Jiarui Xu
+# -------------------------------------------------------------------------
+
+import gzip
+import html
+import os
+from functools import lru_cache
+
+import ftfy
+import regex as re
+import torch
+
+
+@lru_cache()
+def default_bpe():
+ return os.path.join(os.path.dirname(os.path.abspath(__file__)), 'bpe_simple_vocab_16e6.txt.gz')
+
+
+@lru_cache()
+def bytes_to_unicode():
+ """Returns list of utf-8 byte and a corresponding list of unicode strings.
+
+ The reversible bpe codes work on unicode strings. This means you need a large # of unicode characters in your vocab
+ if you want to avoid UNKs. When you're at something like a 10B token dataset you end up needing around 5K for decent
+ coverage. This is a significant percentage of your normal, say, 32K bpe vocab. To avoid that, we want lookup tables
+ between utf-8 bytes and unicode strings. And avoids mapping to whitespace/control characters the bpe code barfs on.
+ """
+ bs = list(range(ord('!'), ord('~') + 1)) + list(range(ord('¡'), ord('¬') + 1)) + list(range(ord('®'), ord('ÿ') + 1))
+ cs = bs[:]
+ n = 0
+ for b in range(2**8):
+ if b not in bs:
+ bs.append(b)
+ cs.append(2**8 + n)
+ n += 1
+ cs = [chr(n) for n in cs]
+ return dict(zip(bs, cs))
+
+
+def get_pairs(word):
+ """Return set of symbol pairs in a word.
+
+ Word is represented as tuple of symbols (symbols being variable-length strings).
+ """
+ pairs = set()
+ prev_char = word[0]
+ for char in word[1:]:
+ pairs.add((prev_char, char))
+ prev_char = char
+ return pairs
+
+
+def basic_clean(text):
+ text = ftfy.fix_text(text)
+ text = html.unescape(html.unescape(text))
+ return text.strip()
+
+
+def whitespace_clean(text):
+ text = re.sub(r'\s+', ' ', text)
+ text = text.strip()
+ return text
+
+class Tokenize:
+
+ def __init__(self, tokenizer, max_seq_len=77, truncate=True):
+ self.tokenizer = tokenizer
+ self.max_seq_len = max_seq_len
+ self.truncate = truncate
+
+ def __call__(self, texts):
+ expanded_dim = False
+ if isinstance(texts, str):
+ texts = [texts]
+ expanded_dim = True
+
+ sot_token = self.tokenizer.encoder['<|startoftext|>']
+ eot_token = self.tokenizer.encoder['<|endoftext|>']
+ all_tokens = [[sot_token] + self.tokenizer.encode(text) + [eot_token] for text in texts]
+ result = torch.zeros(len(all_tokens), self.max_seq_len, dtype=torch.long)
+
+ for i, tokens in enumerate(all_tokens):
+ if len(tokens) > self.max_seq_len:
+ if self.truncate:
+ tokens = tokens[:self.max_seq_len]
+ tokens[-1] = eot_token
+ else:
+ raise RuntimeError(f'Input {texts[i]} is too long for context length {self.max_seq_len}')
+ result[i, :len(tokens)] = torch.tensor(tokens)
+
+ if expanded_dim:
+ return result[0]
+
+ return result
+
+
+class SimpleTokenizer(object):
+
+ def __init__(self, bpe_path: str = default_bpe()):
+ self.byte_encoder = bytes_to_unicode()
+ self.byte_decoder = {v: k for k, v in self.byte_encoder.items()}
+ merges = gzip.open(bpe_path).read().decode('utf-8').split('\n')
+ merges = merges[1:49152 - 256 - 2 + 1]
+ merges = [tuple(merge.split()) for merge in merges]
+ vocab = list(bytes_to_unicode().values())
+ vocab = vocab + [v + '' for v in vocab]
+ for merge in merges:
+ vocab.append(''.join(merge))
+ vocab.extend(['<|startoftext|>', '<|endoftext|>'])
+ self.encoder = dict(zip(vocab, range(len(vocab))))
+ self.decoder = {v: k for k, v in self.encoder.items()}
+ self.bpe_ranks = dict(zip(merges, range(len(merges))))
+ self.cache = {'<|startoftext|>': '<|startoftext|>', '<|endoftext|>': '<|endoftext|>'}
+ self.pat = re.compile(
+ r"""<\|startoftext\|>|<\|endoftext\|>|'s|'t|'re|'ve|'m|'ll|'d|[\p{L}]+|[\p{N}]|[^\s\p{L}\p{N}]+""",
+ re.IGNORECASE)
+
+ def bpe(self, token):
+ if token in self.cache:
+ return self.cache[token]
+ word = tuple(token[:-1]) + (token[-1] + '', )
+ pairs = get_pairs(word)
+
+ if not pairs:
+ return token + ''
+
+ while True:
+ bigram = min(pairs, key=lambda pair: self.bpe_ranks.get(pair, float('inf')))
+ if bigram not in self.bpe_ranks:
+ break
+ first, second = bigram
+ new_word = []
+ i = 0
+ while i < len(word):
+ try:
+ j = word.index(first, i)
+ new_word.extend(word[i:j])
+ i = j
+ except: # noqa: E722
+ new_word.extend(word[i:])
+ break
+
+ if word[i] == first and i < len(word) - 1 and word[i + 1] == second:
+ new_word.append(first + second)
+ i += 2
+ else:
+ new_word.append(word[i])
+ i += 1
+ new_word = tuple(new_word)
+ word = new_word
+ if len(word) == 1:
+ break
+ else:
+ pairs = get_pairs(word)
+ word = ' '.join(word)
+ self.cache[token] = word
+ return word
+
+ def encode(self, text):
+ bpe_tokens = []
+ text = whitespace_clean(basic_clean(text)).lower()
+ for token in re.findall(self.pat, text):
+ token = ''.join(self.byte_encoder[b] for b in token.encode('utf-8'))
+ bpe_tokens.extend(self.encoder[bpe_token] for bpe_token in self.bpe(token).split(' '))
+ return bpe_tokens
+
+ def decode(self, tokens):
+ text = ''.join([self.decoder[token] for token in tokens])
+ text = bytearray([self.byte_decoder[c] for c in text]).decode('utf-8', errors='replace').replace('', ' ')
+ return text
\ No newline at end of file
diff --git a/annotator/OneFormer/oneformer/datasetmapper_tta.py b/annotator/OneFormer/oneformer/datasetmapper_tta.py
new file mode 100644
index 0000000000000000000000000000000000000000..4d554690c3a8e891eef0380acc1579befed3932d
--- /dev/null
+++ b/annotator/OneFormer/oneformer/datasetmapper_tta.py
@@ -0,0 +1,88 @@
+import copy
+import numpy as np
+from typing import List
+import torch
+from fvcore.transforms import NoOpTransform
+from torch import nn
+
+from detectron2.config import configurable
+from detectron2.data.transforms import (
+ RandomFlip,
+ ResizeShortestEdge,
+ ResizeTransform,
+ apply_augmentations,
+)
+
+__all__ = ["DatasetMapperTTA"]
+
+
+class DatasetMapperTTA:
+ """
+ Implement test-time augmentation for detection data.
+ It is a callable which takes a dataset dict from a detection dataset,
+ and returns a list of dataset dicts where the images
+ are augmented from the input image by the transformations defined in the config.
+ This is used for test-time augmentation.
+ """
+
+ @configurable
+ def __init__(self, min_sizes: List[int], max_size: int, flip: bool):
+ """
+ Args:
+ min_sizes: list of short-edge size to resize the image to
+ max_size: maximum height or width of resized images
+ flip: whether to apply flipping augmentation
+ """
+ self.min_sizes = min_sizes
+ self.max_size = max_size
+ self.flip = flip
+
+ @classmethod
+ def from_config(cls, cfg):
+ return {
+ "min_sizes": cfg.TEST.AUG.MIN_SIZES,
+ "max_size": cfg.TEST.AUG.MAX_SIZE,
+ "flip": cfg.TEST.AUG.FLIP,
+ }
+
+ def __call__(self, dataset_dict):
+ """
+ Args:
+ dict: a dict in standard model input format. See tutorials for details.
+ Returns:
+ list[dict]:
+ a list of dicts, which contain augmented version of the input image.
+ The total number of dicts is ``len(min_sizes) * (2 if flip else 1)``.
+ Each dict has field "transforms" which is a TransformList,
+ containing the transforms that are used to generate this image.
+ """
+ numpy_image = dataset_dict["image"].permute(1, 2, 0).numpy()
+ shape = numpy_image.shape
+ orig_shape = (dataset_dict["height"], dataset_dict["width"])
+
+ if shape[:2] != orig_shape:
+ # It transforms the "original" image in the dataset to the input image
+ pre_tfm = ResizeTransform(orig_shape[0], orig_shape[1], shape[0], shape[1])
+ else:
+ pre_tfm = NoOpTransform()
+
+ # Create all combinations of augmentations to use
+ aug_candidates = [] # each element is a list[Augmentation]
+ for min_size in self.min_sizes:
+ resize = ResizeShortestEdge(min_size, self.max_size)
+ aug_candidates.append([resize]) # resize only
+ if self.flip:
+ flip = RandomFlip(prob=1.0)
+ aug_candidates.append([resize, flip]) # resize + flip
+
+ # Apply all the augmentations
+ ret = []
+ for aug in aug_candidates:
+ new_image, tfms = apply_augmentations(aug, np.copy(numpy_image))
+ torch_image = torch.from_numpy(np.ascontiguousarray(new_image.transpose(2, 0, 1)))
+
+ dic = copy.deepcopy(dataset_dict)
+ dic["transforms"] = pre_tfm + tfms
+ dic["image"] = torch_image
+ ret.append(dic)
+ return ret
\ No newline at end of file
diff --git a/annotator/OneFormer/oneformer/evaluation/__init__.py b/annotator/OneFormer/oneformer/evaluation/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..49f62369cca38a3c85884f8dea6baea674cb9060
--- /dev/null
+++ b/annotator/OneFormer/oneformer/evaluation/__init__.py
@@ -0,0 +1,3 @@
+from .detection_coco_evaluator import *
+from .coco_evaluator import *
+from .cityscapes_evaluation import CityscapesInstanceEvaluator
\ No newline at end of file
diff --git a/annotator/OneFormer/oneformer/evaluation/cityscapes_evaluation.py b/annotator/OneFormer/oneformer/evaluation/cityscapes_evaluation.py
new file mode 100644
index 0000000000000000000000000000000000000000..4e06ab8cbe6b43a355c0a9cfb3f2d688438d2c64
--- /dev/null
+++ b/annotator/OneFormer/oneformer/evaluation/cityscapes_evaluation.py
@@ -0,0 +1,201 @@
+# ------------------------------------------------------------------------------
+# Reference: https://github.com/facebookresearch/detectron2/blob/main/detectron2/evaluation/cityscapes_evaluation.py
+# Modified by Jitesh Jain (https://github.com/praeclarumjj3)
+# ------------------------------------------------------------------------------
+
+import glob
+import logging
+import numpy as np
+import os
+import tempfile
+from collections import OrderedDict
+import torch
+from PIL import Image
+
+from detectron2.data import MetadataCatalog
+from detectron2.utils import comm
+from detectron2.utils.file_io import PathManager
+
+from .evaluator import DatasetEvaluator
+
+
+class CityscapesEvaluator(DatasetEvaluator):
+ """
+ Base class for evaluation using cityscapes API.
+ """
+
+ def __init__(self, dataset_name):
+ """
+ Args:
+ dataset_name (str): the name of the dataset.
+ It must have the following metadata associated with it:
+ "thing_classes", "gt_dir".
+ """
+ self._metadata = MetadataCatalog.get(dataset_name)
+ self._cpu_device = torch.device("cpu")
+ self._logger = logging.getLogger(__name__)
+
+ def reset(self):
+ self._working_dir = tempfile.TemporaryDirectory(prefix="cityscapes_eval_")
+ self._temp_dir = self._working_dir.name
+ # All workers will write to the same results directory
+ # TODO this does not work in distributed training
+ assert (
+ comm.get_local_size() == comm.get_world_size()
+ ), "CityscapesEvaluator currently do not work with multiple machines."
+ self._temp_dir = comm.all_gather(self._temp_dir)[0]
+ if self._temp_dir != self._working_dir.name:
+ self._working_dir.cleanup()
+ self._logger.info(
+ "Writing cityscapes results to temporary directory {} ...".format(self._temp_dir)
+ )
+
+
+class CityscapesInstanceEvaluator(CityscapesEvaluator):
+ """
+ Evaluate instance segmentation results on cityscapes dataset using cityscapes API.
+
+ Note:
+ * It does not work in multi-machine distributed training.
+ * It contains a synchronization, therefore has to be used on all ranks.
+ * Only the main process runs evaluation.
+ """
+
+ def process(self, inputs, outputs):
+ from cityscapesscripts.helpers.labels import name2label
+
+ for input, output in zip(inputs, outputs):
+ file_name = input["file_name"]
+ basename = os.path.splitext(os.path.basename(file_name))[0]
+ pred_txt = os.path.join(self._temp_dir, basename + "_pred.txt")
+
+ if "instances" in output:
+ output = output["instances"].to(self._cpu_device)
+ num_instances = len(output)
+ with open(pred_txt, "w") as fout:
+ for i in range(num_instances):
+ pred_class = output.pred_classes[i]
+ classes = self._metadata.stuff_classes[pred_class]
+ class_id = name2label[classes].id
+ score = output.scores[i]
+ mask = output.pred_masks[i].numpy().astype("uint8")
+ png_filename = os.path.join(
+ self._temp_dir, basename + "_{}_{}.png".format(i, classes)
+ )
+
+ Image.fromarray(mask * 255).save(png_filename)
+ fout.write(
+ "{} {} {}\n".format(os.path.basename(png_filename), class_id, score)
+ )
+ else:
+ # Cityscapes requires a prediction file for every ground truth image.
+ with open(pred_txt, "w") as fout:
+ pass
+
+ def evaluate(self):
+ """
+ Returns:
+ dict: has a key "segm", whose value is a dict of "AP" and "AP50".
+ """
+ comm.synchronize()
+ if comm.get_rank() > 0:
+ return
+ import cityscapesscripts.evaluation.evalInstanceLevelSemanticLabeling as cityscapes_eval
+
+ self._logger.info("Evaluating results under {} ...".format(self._temp_dir))
+
+ # set some global states in cityscapes evaluation API, before evaluating
+ cityscapes_eval.args.predictionPath = os.path.abspath(self._temp_dir)
+ cityscapes_eval.args.predictionWalk = None
+ cityscapes_eval.args.JSONOutput = False
+ cityscapes_eval.args.colorized = False
+ cityscapes_eval.args.gtInstancesFile = os.path.join(self._temp_dir, "gtInstances.json")
+
+ # These lines are adopted from
+ # https://github.com/mcordts/cityscapesScripts/blob/master/cityscapesscripts/evaluation/evalInstanceLevelSemanticLabeling.py # noqa
+ gt_dir = PathManager.get_local_path(self._metadata.gt_dir)
+ groundTruthImgList = glob.glob(os.path.join(gt_dir, "*", "*_gtFine_instanceIds.png"))
+ assert len(
+ groundTruthImgList
+ ), "Cannot find any ground truth images to use for evaluation. Searched for: {}".format(
+ cityscapes_eval.args.groundTruthSearch
+ )
+ predictionImgList = []
+ for gt in groundTruthImgList:
+ predictionImgList.append(cityscapes_eval.getPrediction(gt, cityscapes_eval.args))
+ results = cityscapes_eval.evaluateImgLists(
+ predictionImgList, groundTruthImgList, cityscapes_eval.args
+ )["averages"]
+
+ ret = OrderedDict()
+ ret["segm"] = {"AP": results["allAp"] * 100, "AP50": results["allAp50%"] * 100}
+ self._working_dir.cleanup()
+ return ret
+
+
+class CityscapesSemSegEvaluator(CityscapesEvaluator):
+ """
+ Evaluate semantic segmentation results on cityscapes dataset using cityscapes API.
+
+ Note:
+ * It does not work in multi-machine distributed training.
+ * It contains a synchronization, therefore has to be used on all ranks.
+ * Only the main process runs evaluation.
+ """
+
+ def process(self, inputs, outputs):
+ from cityscapesscripts.helpers.labels import trainId2label
+
+ for input, output in zip(inputs, outputs):
+ file_name = input["file_name"]
+ basename = os.path.splitext(os.path.basename(file_name))[0]
+ pred_filename = os.path.join(self._temp_dir, basename + "_pred.png")
+
+ output = output["sem_seg"].argmax(dim=0).to(self._cpu_device).numpy()
+ pred = 255 * np.ones(output.shape, dtype=np.uint8)
+ for train_id, label in trainId2label.items():
+ if label.ignoreInEval:
+ continue
+ pred[output == train_id] = label.id
+ Image.fromarray(pred).save(pred_filename)
+
+ def evaluate(self):
+ comm.synchronize()
+ if comm.get_rank() > 0:
+ return
+ # Load the Cityscapes eval script *after* setting the required env var,
+ # since the script reads CITYSCAPES_DATASET into global variables at load time.
+ import cityscapesscripts.evaluation.evalPixelLevelSemanticLabeling as cityscapes_eval
+
+ self._logger.info("Evaluating results under {} ...".format(self._temp_dir))
+
+ # set some global states in cityscapes evaluation API, before evaluating
+ cityscapes_eval.args.predictionPath = os.path.abspath(self._temp_dir)
+ cityscapes_eval.args.predictionWalk = None
+ cityscapes_eval.args.JSONOutput = False
+ cityscapes_eval.args.colorized = False
+
+ # These lines are adopted from
+ # https://github.com/mcordts/cityscapesScripts/blob/master/cityscapesscripts/evaluation/evalPixelLevelSemanticLabeling.py # noqa
+ gt_dir = PathManager.get_local_path(self._metadata.gt_dir)
+ groundTruthImgList = glob.glob(os.path.join(gt_dir, "*", "*_gtFine_labelIds.png"))
+ assert len(
+ groundTruthImgList
+ ), "Cannot find any ground truth images to use for evaluation. Searched for: {}".format(
+ cityscapes_eval.args.groundTruthSearch
+ )
+ predictionImgList = []
+ for gt in groundTruthImgList:
+ predictionImgList.append(cityscapes_eval.getPrediction(cityscapes_eval.args, gt))
+ results = cityscapes_eval.evaluateImgLists(
+ predictionImgList, groundTruthImgList, cityscapes_eval.args
+ )
+ ret = OrderedDict()
+ ret["sem_seg"] = {
+ "IoU": 100.0 * results["averageScoreClasses"],
+ "iIoU": 100.0 * results["averageScoreInstClasses"],
+ "IoU_sup": 100.0 * results["averageScoreCategories"],
+ "iIoU_sup": 100.0 * results["averageScoreInstCategories"],
+ }
+ self._working_dir.cleanup()
+ return ret
diff --git a/annotator/OneFormer/oneformer/evaluation/coco_evaluator.py b/annotator/OneFormer/oneformer/evaluation/coco_evaluator.py
new file mode 100644
index 0000000000000000000000000000000000000000..dd85578589d1e5f4b39594486c771c5ff940eaa4
--- /dev/null
+++ b/annotator/OneFormer/oneformer/evaluation/coco_evaluator.py
@@ -0,0 +1,563 @@
+# ------------------------------------------------------------------------------
+# Reference: https://github.com/facebookresearch/detectron2/blob/main/detectron2/evaluation/coco_evaluation.py
+# Modified by Jitesh Jain (https://github.com/praeclarumjj3)
+# ------------------------------------------------------------------------------
+
+import contextlib
+import copy
+import io
+import itertools
+import json
+import logging
+import numpy as np
+import os
+import pickle
+from collections import OrderedDict
+import pycocotools.mask as mask_util
+import torch
+from pycocotools.coco import COCO
+from pycocotools.cocoeval import COCOeval
+from tabulate import tabulate
+
+import detectron2.utils.comm as comm
+from detectron2.config import CfgNode
+from detectron2.data import MetadataCatalog
+from detectron2.data.datasets.coco import convert_to_coco_json
+from detectron2.structures import Boxes, BoxMode, pairwise_iou
+from detectron2.utils.file_io import PathManager
+from detectron2.utils.logger import create_small_table
+
+from .evaluator import DatasetEvaluator
+
+try:
+ from detectron2.evaluation.fast_eval_api import COCOeval_opt
+except ImportError:
+ COCOeval_opt = COCOeval
+
+
+class COCOEvaluator(DatasetEvaluator):
+ """
+ Evaluate AP for instance detection/segmentation, AP
+ for keypoint detection outputs using COCO's metrics.
+ See http://cocodataset.org/#detection-eval and
+ http://cocodataset.org/#keypoints-eval to understand its metrics.
+ The metrics range from 0 to 100 (instead of 0 to 1), where a -1 or NaN means
+ the metric cannot be computed (e.g. due to no predictions made).
+
+ In addition to COCO, this evaluator is able to support any bounding box detection,
+ instance segmentation, or keypoint detection dataset.
+ """
+
+ def __init__(
+ self,
+ dataset_name,
+ tasks=None,
+ distributed=True,
+ output_dir=None,
+ *,
+ max_dets_per_image=None,
+ use_fast_impl=True,
+ kpt_oks_sigmas=(),
+ allow_cached_coco=True,
+ ):
+ """
+ Args:
+ dataset_name (str): name of the dataset to be evaluated.
+ It must have either the following corresponding metadata:
+
+ "json_file": the path to the COCO format annotation
+
+ Or it must be in detectron2's standard dataset format
+ so it can be converted to COCO format automatically.
+ tasks (tuple[str]): tasks that can be evaluated under the given
+ configuration. A task is one of "bbox", "segm", "keypoints".
+ By default, will infer this automatically from predictions.
+ distributed (True): if True, will collect results from all ranks and run evaluation
+ in the main process.
+ Otherwise, will only evaluate the results in the current process.
+ output_dir (str): optional, an output directory to dump all
+ results predicted on the dataset. The dump contains two files:
+
+ 1. "instances_predictions.pth" a file that can be loaded with `torch.load` and
+ contains all the results in the format they are produced by the model.
+ 2. "coco_instances_results.json" a json file in COCO's result format.
+ max_dets_per_image (int): limit on the maximum number of detections per image.
+ By default in COCO, this limit is to 100, but this can be customized
+ to be greater, as is needed in evaluation metrics AP fixed and AP pool
+ (see https://arxiv.org/pdf/2102.01066.pdf)
+ This doesn't affect keypoint evaluation.
+ use_fast_impl (bool): use a fast but **unofficial** implementation to compute AP.
+ Although the results should be very close to the official implementation in COCO
+ API, it is still recommended to compute results with the official API for use in
+ papers. The faster implementation also uses more RAM.
+ kpt_oks_sigmas (list[float]): The sigmas used to calculate keypoint OKS.
+ See http://cocodataset.org/#keypoints-eval
+ When empty, it will use the defaults in COCO.
+ Otherwise it should be the same length as ROI_KEYPOINT_HEAD.NUM_KEYPOINTS.
+ allow_cached_coco (bool): Whether to use cached coco json from previous validation
+ runs. You should set this to False if you need to use different validation data.
+ Defaults to True.
+ """
+ self._logger = logging.getLogger(__name__)
+ self._distributed = distributed
+ self._output_dir = output_dir
+
+ if use_fast_impl and (COCOeval_opt is COCOeval):
+ self._logger.info("Fast COCO eval is not built. Falling back to official COCO eval.")
+ use_fast_impl = False
+ self._use_fast_impl = use_fast_impl
+
+ # COCOeval requires the limit on the number of detections per image (maxDets) to be a list
+ # with at least 3 elements. The default maxDets in COCOeval is [1, 10, 100], in which the
+ # 3rd element (100) is used as the limit on the number of detections per image when
+ # evaluating AP. COCOEvaluator expects an integer for max_dets_per_image, so for COCOeval,
+ # we reformat max_dets_per_image into [1, 10, max_dets_per_image], based on the defaults.
+ if max_dets_per_image is None:
+ max_dets_per_image = [1, 10, 100]
+ else:
+ max_dets_per_image = [1, 10, max_dets_per_image]
+ self._max_dets_per_image = max_dets_per_image
+
+ if tasks is not None and isinstance(tasks, CfgNode):
+ kpt_oks_sigmas = (
+ tasks.TEST.KEYPOINT_OKS_SIGMAS if not kpt_oks_sigmas else kpt_oks_sigmas
+ )
+ self._logger.warn(
+ "COCO Evaluator instantiated using config, this is deprecated behavior."
+ " Please pass in explicit arguments instead."
+ )
+ self._tasks = None # Infering it from predictions should be better
+ else:
+ self._tasks = tasks
+
+ self._cpu_device = torch.device("cpu")
+
+ self._metadata = MetadataCatalog.get(dataset_name)
+ if not hasattr(self._metadata, "json_file"):
+ if output_dir is None:
+ raise ValueError(
+ "output_dir must be provided to COCOEvaluator "
+ "for datasets not in COCO format."
+ )
+ self._logger.info(f"Trying to convert '{dataset_name}' to COCO format ...")
+
+ cache_path = os.path.join(output_dir, f"{dataset_name}_coco_format.json")
+ self._metadata.json_file = cache_path
+ convert_to_coco_json(dataset_name, cache_path, allow_cached=allow_cached_coco)
+
+ json_file = PathManager.get_local_path(self._metadata.json_file)
+ with contextlib.redirect_stdout(io.StringIO()):
+ self._coco_api = COCO(json_file)
+
+ # Test set json files do not contain annotations (evaluation must be
+ # performed using the COCO evaluation server).
+ self._do_evaluation = "annotations" in self._coco_api.dataset
+ if self._do_evaluation:
+ self._kpt_oks_sigmas = kpt_oks_sigmas
+
+ def reset(self):
+ self._predictions = []
+
+ def process(self, inputs, outputs):
+ """
+ Args:
+ inputs: the inputs to a COCO model (e.g., GeneralizedRCNN).
+ It is a list of dict. Each dict corresponds to an image and
+ contains keys like "height", "width", "file_name", "image_id".
+ outputs: the outputs of a COCO model. It is a list of dicts with key
+ "instances" that contains :class:`Instances`.
+ """
+ for input, output in zip(inputs, outputs):
+ prediction = {"image_id": input["image_id"]}
+
+ if "instances" in output:
+ instances = output["instances"].to(self._cpu_device)
+ prediction["instances"] = instances_to_coco_json(instances, input["image_id"])
+ if len(prediction) > 1:
+ self._predictions.append(prediction)
+
+ def evaluate(self, img_ids=None):
+ """
+ Args:
+ img_ids: a list of image IDs to evaluate on. Default to None for the whole dataset
+ """
+ if self._distributed:
+ comm.synchronize()
+ predictions = comm.gather(self._predictions, dst=0)
+ predictions = list(itertools.chain(*predictions))
+
+ if not comm.is_main_process():
+ return {}
+ else:
+ predictions = self._predictions
+
+ if len(predictions) == 0:
+ self._logger.warning("[COCOEvaluator] Did not receive valid predictions.")
+ return {}
+
+ if self._output_dir:
+ PathManager.mkdirs(self._output_dir)
+ file_path = os.path.join(self._output_dir, "instances_predictions.pth")
+ with PathManager.open(file_path, "wb") as f:
+ torch.save(predictions, f)
+
+ self._results = OrderedDict()
+ if "instances" in predictions[0]:
+ self._eval_predictions(predictions, img_ids=img_ids)
+ # Copy so the caller can do whatever with results
+ return copy.deepcopy(self._results)
+
+ def _tasks_from_predictions(self, predictions):
+ """
+ Get COCO API "tasks" (i.e. iou_type) from COCO-format predictions.
+ """
+ for pred in predictions:
+ if "segmentation" in pred:
+ tasks = {"segm"}
+ if "keypoints" in pred:
+ tasks.add("keypoints")
+ return sorted(tasks)
+
+ def _eval_predictions(self, predictions, img_ids=None):
+ """
+ Evaluate predictions. Fill self._results with the metrics of the tasks.
+ """
+ self._logger.info("Preparing results for COCO format ...")
+ coco_results = list(itertools.chain(*[x["instances"] for x in predictions]))
+ tasks = self._tasks or self._tasks_from_predictions(coco_results)
+
+ # unmap the category ids for COCO
+ if hasattr(self._metadata, "thing_dataset_id_to_contiguous_id"):
+ dataset_id_to_contiguous_id = self._metadata.thing_dataset_id_to_contiguous_id
+ all_contiguous_ids = list(dataset_id_to_contiguous_id.values())
+ num_classes = len(all_contiguous_ids)
+ assert min(all_contiguous_ids) == 0 and max(all_contiguous_ids) == num_classes - 1
+
+ reverse_id_mapping = {v: k for k, v in dataset_id_to_contiguous_id.items()}
+ for result in coco_results:
+ category_id = result["category_id"]
+ assert category_id < num_classes, (
+ f"A prediction has class={category_id}, "
+ f"but the dataset only has {num_classes} classes and "
+ f"predicted class id should be in [0, {num_classes - 1}]."
+ )
+ result["category_id"] = reverse_id_mapping[category_id]
+
+ if self._output_dir:
+ file_path = os.path.join(self._output_dir, "coco_instances_results.json")
+ self._logger.info("Saving results to {}".format(file_path))
+ with PathManager.open(file_path, "w") as f:
+ f.write(json.dumps(coco_results))
+ f.flush()
+
+ if not self._do_evaluation:
+ self._logger.info("Annotations are not available for evaluation.")
+ return
+
+ self._logger.info(
+ "Evaluating predictions with {} COCO API...".format(
+ "unofficial" if self._use_fast_impl else "official"
+ )
+ )
+ for task in sorted(tasks):
+ assert task in {"segm", "keypoints"}, f"Got unknown task: {task}!"
+ coco_eval = (
+ _evaluate_predictions_on_coco(
+ self._coco_api,
+ coco_results,
+ task,
+ kpt_oks_sigmas=self._kpt_oks_sigmas,
+ use_fast_impl=self._use_fast_impl,
+ img_ids=img_ids,
+ max_dets_per_image=self._max_dets_per_image,
+ )
+ if len(coco_results) > 0
+ else None # cocoapi does not handle empty results very well
+ )
+
+ res = self._derive_coco_results(
+ coco_eval, task, class_names=self._metadata.get("thing_classes")
+ )
+ self._results[task] = res
+
+ def _derive_coco_results(self, coco_eval, iou_type, class_names=None):
+ """
+ Derive the desired score numbers from summarized COCOeval.
+
+ Args:
+ coco_eval (None or COCOEval): None represents no predictions from model.
+ iou_type (str):
+ class_names (None or list[str]): if provided, will use it to predict
+ per-category AP.
+
+ Returns:
+ a dict of {metric name: score}
+ """
+
+ metrics = {
+ "segm": ["AP", "AP50", "AP75", "APs", "APm", "APl"],
+ "keypoints": ["AP", "AP50", "AP75", "APm", "APl"],
+ }[iou_type]
+
+ if coco_eval is None:
+ self._logger.warn("No predictions from the model!")
+ return {metric: float("nan") for metric in metrics}
+
+ # the standard metrics
+ results = {
+ metric: float(coco_eval.stats[idx] * 100 if coco_eval.stats[idx] >= 0 else "nan")
+ for idx, metric in enumerate(metrics)
+ }
+ self._logger.info(
+ "Evaluation results for {}: \n".format(iou_type) + create_small_table(results)
+ )
+ if not np.isfinite(sum(results.values())):
+ self._logger.info("Some metrics cannot be computed and is shown as NaN.")
+
+ if class_names is None or len(class_names) <= 1:
+ return results
+ # Compute per-category AP
+ # from https://github.com/facebookresearch/Detectron/blob/a6a835f5b8208c45d0dce217ce9bbda915f44df7/detectron/datasets/json_dataset_evaluator.py#L222-L252 # noqa
+ precisions = coco_eval.eval["precision"]
+ # precision has dims (iou, recall, cls, area range, max dets)
+ assert len(class_names) == precisions.shape[2]
+
+ results_per_category = []
+ for idx, name in enumerate(class_names):
+ # area range index 0: all area ranges
+ # max dets index -1: typically 100 per image
+ precision = precisions[:, :, idx, 0, -1]
+ precision = precision[precision > -1]
+ ap = np.mean(precision) if precision.size else float("nan")
+ results_per_category.append(("{}".format(name), float(ap * 100)))
+
+ # tabulate it
+ N_COLS = min(6, len(results_per_category) * 2)
+ results_flatten = list(itertools.chain(*results_per_category))
+ results_2d = itertools.zip_longest(*[results_flatten[i::N_COLS] for i in range(N_COLS)])
+ table = tabulate(
+ results_2d,
+ tablefmt="pipe",
+ floatfmt=".3f",
+ headers=["category", "AP"] * (N_COLS // 2),
+ numalign="left",
+ )
+ self._logger.info("Per-category {} AP: \n".format(iou_type) + table)
+
+ results.update({"AP-" + name: ap for name, ap in results_per_category})
+ return results
+
+
+def instances_to_coco_json(instances, img_id):
+ """
+ Dump an "Instances" object to a COCO-format json that's used for evaluation.
+
+ Args:
+ instances (Instances):
+ img_id (int): the image id
+
+ Returns:
+ list[dict]: list of json annotations in COCO format.
+ """
+ num_instance = len(instances)
+ if num_instance == 0:
+ return []
+
+ scores = instances.scores.tolist()
+ classes = instances.pred_classes.tolist()
+
+ has_mask = instances.has("pred_masks")
+ if has_mask:
+ # use RLE to encode the masks, because they are too large and takes memory
+ # since this evaluator stores outputs of the entire dataset
+ rles = [
+ mask_util.encode(np.array(mask[:, :, None], order="F", dtype="uint8"))[0]
+ for mask in instances.pred_masks
+ ]
+ for rle in rles:
+ # "counts" is an array encoded by mask_util as a byte-stream. Python3's
+ # json writer which always produces strings cannot serialize a bytestream
+ # unless you decode it. Thankfully, utf-8 works out (which is also what
+ # the pycocotools/_mask.pyx does).
+ rle["counts"] = rle["counts"].decode("utf-8")
+
+ has_keypoints = instances.has("pred_keypoints")
+ if has_keypoints:
+ keypoints = instances.pred_keypoints
+
+ results = []
+ for k in range(num_instance):
+ result = {
+ "image_id": img_id,
+ "category_id": classes[k],
+ "score": scores[k],
+ }
+ if has_mask:
+ result["segmentation"] = rles[k]
+ if has_keypoints:
+ # In COCO annotations,
+ # keypoints coordinates are pixel indices.
+ # However our predictions are floating point coordinates.
+ # Therefore we subtract 0.5 to be consistent with the annotation format.
+ # This is the inverse of data loading logic in `datasets/coco.py`.
+ keypoints[k][:, :2] -= 0.5
+ result["keypoints"] = keypoints[k].flatten().tolist()
+ results.append(result)
+ return results
+
+def _evaluate_predictions_on_coco(
+ coco_gt,
+ coco_results,
+ iou_type,
+ kpt_oks_sigmas=None,
+ use_fast_impl=True,
+ img_ids=None,
+ max_dets_per_image=None,
+):
+ """
+ Evaluate the coco results using COCOEval API.
+ """
+ assert len(coco_results) > 0
+
+ if iou_type == "segm":
+ coco_results = copy.deepcopy(coco_results)
+ # When evaluating mask AP, if the results contain bbox, cocoapi will
+ # use the box area as the area of the instance, instead of the mask area.
+ # This leads to a different definition of small/medium/large.
+ # We remove the bbox field to let mask AP use mask area.
+ for c in coco_results:
+ c.pop("bbox", None)
+
+ coco_dt = coco_gt.loadRes(coco_results)
+ coco_eval = (COCOeval_opt if use_fast_impl else COCOeval)(coco_gt, coco_dt, iou_type)
+ # For COCO, the default max_dets_per_image is [1, 10, 100].
+ if max_dets_per_image is None:
+ max_dets_per_image = [1, 10, 100] # Default from COCOEval
+ else:
+ assert (
+ len(max_dets_per_image) >= 3
+ ), "COCOeval requires maxDets (and max_dets_per_image) to have length at least 3"
+ # In the case that user supplies a custom input for max_dets_per_image,
+ # apply COCOevalMaxDets to evaluate AP with the custom input.
+ if max_dets_per_image[2] != 100:
+ coco_eval = COCOevalMaxDets(coco_gt, coco_dt, iou_type)
+ if iou_type != "keypoints":
+ coco_eval.params.maxDets = max_dets_per_image
+
+ if img_ids is not None:
+ coco_eval.params.imgIds = img_ids
+
+ if iou_type == "keypoints":
+ # Use the COCO default keypoint OKS sigmas unless overrides are specified
+ if kpt_oks_sigmas:
+ assert hasattr(coco_eval.params, "kpt_oks_sigmas"), "pycocotools is too old!"
+ coco_eval.params.kpt_oks_sigmas = np.array(kpt_oks_sigmas)
+ # COCOAPI requires every detection and every gt to have keypoints, so
+ # we just take the first entry from both
+ num_keypoints_dt = len(coco_results[0]["keypoints"]) // 3
+ num_keypoints_gt = len(next(iter(coco_gt.anns.values()))["keypoints"]) // 3
+ num_keypoints_oks = len(coco_eval.params.kpt_oks_sigmas)
+ assert num_keypoints_oks == num_keypoints_dt == num_keypoints_gt, (
+ f"[COCOEvaluator] Prediction contain {num_keypoints_dt} keypoints. "
+ f"Ground truth contains {num_keypoints_gt} keypoints. "
+ f"The length of cfg.TEST.KEYPOINT_OKS_SIGMAS is {num_keypoints_oks}. "
+ "They have to agree with each other. For meaning of OKS, please refer to "
+ "http://cocodataset.org/#keypoints-eval."
+ )
+
+ coco_eval.evaluate()
+ coco_eval.accumulate()
+ coco_eval.summarize()
+
+ return coco_eval
+
+
+class COCOevalMaxDets(COCOeval):
+ """
+ Modified version of COCOeval for evaluating AP with a custom
+ maxDets (by default for COCO, maxDets is 100)
+ """
+
+ def summarize(self):
+ """
+ Compute and display summary metrics for evaluation results given
+ a custom value for max_dets_per_image
+ """
+
+ def _summarize(ap=1, iouThr=None, areaRng="all", maxDets=100):
+ p = self.params
+ iStr = " {:<18} {} @[ IoU={:<9} | area={:>6s} | maxDets={:>3d} ] = {:0.3f}"
+ titleStr = "Average Precision" if ap == 1 else "Average Recall"
+ typeStr = "(AP)" if ap == 1 else "(AR)"
+ iouStr = (
+ "{:0.2f}:{:0.2f}".format(p.iouThrs[0], p.iouThrs[-1])
+ if iouThr is None
+ else "{:0.2f}".format(iouThr)
+ )
+
+ aind = [i for i, aRng in enumerate(p.areaRngLbl) if aRng == areaRng]
+ mind = [i for i, mDet in enumerate(p.maxDets) if mDet == maxDets]
+ if ap == 1:
+ # dimension of precision: [TxRxKxAxM]
+ s = self.eval["precision"]
+ # IoU
+ if iouThr is not None:
+ t = np.where(iouThr == p.iouThrs)[0]
+ s = s[t]
+ s = s[:, :, :, aind, mind]
+ else:
+ # dimension of recall: [TxKxAxM]
+ s = self.eval["recall"]
+ if iouThr is not None:
+ t = np.where(iouThr == p.iouThrs)[0]
+ s = s[t]
+ s = s[:, :, aind, mind]
+ if len(s[s > -1]) == 0:
+ mean_s = -1
+ else:
+ mean_s = np.mean(s[s > -1])
+ print(iStr.format(titleStr, typeStr, iouStr, areaRng, maxDets, mean_s))
+ return mean_s
+
+ def _summarizeDets():
+ stats = np.zeros((12,))
+ # Evaluate AP using the custom limit on maximum detections per image
+ stats[0] = _summarize(1, maxDets=self.params.maxDets[2])
+ stats[1] = _summarize(1, iouThr=0.5, maxDets=self.params.maxDets[2])
+ stats[2] = _summarize(1, iouThr=0.75, maxDets=self.params.maxDets[2])
+ stats[3] = _summarize(1, areaRng="small", maxDets=self.params.maxDets[2])
+ stats[4] = _summarize(1, areaRng="medium", maxDets=self.params.maxDets[2])
+ stats[5] = _summarize(1, areaRng="large", maxDets=self.params.maxDets[2])
+ stats[6] = _summarize(0, maxDets=self.params.maxDets[0])
+ stats[7] = _summarize(0, maxDets=self.params.maxDets[1])
+ stats[8] = _summarize(0, maxDets=self.params.maxDets[2])
+ stats[9] = _summarize(0, areaRng="small", maxDets=self.params.maxDets[2])
+ stats[10] = _summarize(0, areaRng="medium", maxDets=self.params.maxDets[2])
+ stats[11] = _summarize(0, areaRng="large", maxDets=self.params.maxDets[2])
+ return stats
+
+ def _summarizeKps():
+ stats = np.zeros((10,))
+ stats[0] = _summarize(1, maxDets=20)
+ stats[1] = _summarize(1, maxDets=20, iouThr=0.5)
+ stats[2] = _summarize(1, maxDets=20, iouThr=0.75)
+ stats[3] = _summarize(1, maxDets=20, areaRng="medium")
+ stats[4] = _summarize(1, maxDets=20, areaRng="large")
+ stats[5] = _summarize(0, maxDets=20)
+ stats[6] = _summarize(0, maxDets=20, iouThr=0.5)
+ stats[7] = _summarize(0, maxDets=20, iouThr=0.75)
+ stats[8] = _summarize(0, maxDets=20, areaRng="medium")
+ stats[9] = _summarize(0, maxDets=20, areaRng="large")
+ return stats
+
+ if not self.eval:
+ raise Exception("Please run accumulate() first")
+ iouType = self.params.iouType
+ if iouType == "segm":
+ summarize = _summarizeDets
+ elif iouType == "keypoints":
+ summarize = _summarizeKps
+ self.stats = summarize()
+
+ def __str__(self):
+ self.summarize()
\ No newline at end of file
diff --git a/annotator/OneFormer/oneformer/evaluation/detection_coco_evaluator.py b/annotator/OneFormer/oneformer/evaluation/detection_coco_evaluator.py
new file mode 100644
index 0000000000000000000000000000000000000000..b3834ecbca97d01262edd13c9939874c07613428
--- /dev/null
+++ b/annotator/OneFormer/oneformer/evaluation/detection_coco_evaluator.py
@@ -0,0 +1,723 @@
+# ------------------------------------------------------------------------------
+# Reference: https://github.com/facebookresearch/detectron2/blob/main/detectron2/evaluation/coco_evaluation.py
+# Modified by Jitesh Jain (https://github.com/praeclarumjj3)
+# ------------------------------------------------------------------------------
+
+import contextlib
+import copy
+import io
+import itertools
+import json
+import logging
+import numpy as np
+import os
+import pickle
+from collections import OrderedDict
+import pycocotools.mask as mask_util
+import torch
+from pycocotools.coco import COCO
+from pycocotools.cocoeval import COCOeval
+from tabulate import tabulate
+
+import detectron2.utils.comm as comm
+from detectron2.config import CfgNode
+from detectron2.data import MetadataCatalog
+from detectron2.data.datasets.coco import convert_to_coco_json
+from detectron2.structures import Boxes, BoxMode, pairwise_iou
+from detectron2.utils.file_io import PathManager
+from detectron2.utils.logger import create_small_table
+
+from .evaluator import DatasetEvaluator
+
+try:
+ from detectron2.evaluation.fast_eval_api import COCOeval_opt
+except ImportError:
+ COCOeval_opt = COCOeval
+
+
+class DetectionCOCOEvaluator(DatasetEvaluator):
+ """
+ Evaluate AR for object proposals, AP for instance detection/segmentation, AP
+ for keypoint detection outputs using COCO's metrics.
+ See http://cocodataset.org/#detection-eval and
+ http://cocodataset.org/#keypoints-eval to understand its metrics.
+ The metrics range from 0 to 100 (instead of 0 to 1), where a -1 or NaN means
+ the metric cannot be computed (e.g. due to no predictions made).
+
+ In addition to COCO, this evaluator is able to support any bounding box detection,
+ instance segmentation, or keypoint detection dataset.
+ """
+
+ def __init__(
+ self,
+ dataset_name,
+ tasks=None,
+ distributed=True,
+ output_dir=None,
+ *,
+ max_dets_per_image=None,
+ use_fast_impl=True,
+ kpt_oks_sigmas=(),
+ allow_cached_coco=True,
+ ):
+ """
+ Args:
+ dataset_name (str): name of the dataset to be evaluated.
+ It must have either the following corresponding metadata:
+
+ "json_file": the path to the COCO format annotation
+
+ Or it must be in detectron2's standard dataset format
+ so it can be converted to COCO format automatically.
+ tasks (tuple[str]): tasks that can be evaluated under the given
+ configuration. A task is one of "bbox", "segm", "keypoints".
+ By default, will infer this automatically from predictions.
+ distributed (True): if True, will collect results from all ranks and run evaluation
+ in the main process.
+ Otherwise, will only evaluate the results in the current process.
+ output_dir (str): optional, an output directory to dump all
+ results predicted on the dataset. The dump contains two files:
+
+ 1. "instances_predictions.pth" a file that can be loaded with `torch.load` and
+ contains all the results in the format they are produced by the model.
+ 2. "coco_instances_results.json" a json file in COCO's result format.
+ max_dets_per_image (int): limit on the maximum number of detections per image.
+ By default in COCO, this limit is to 100, but this can be customized
+ to be greater, as is needed in evaluation metrics AP fixed and AP pool
+ (see https://arxiv.org/pdf/2102.01066.pdf)
+ This doesn't affect keypoint evaluation.
+ use_fast_impl (bool): use a fast but **unofficial** implementation to compute AP.
+ Although the results should be very close to the official implementation in COCO
+ API, it is still recommended to compute results with the official API for use in
+ papers. The faster implementation also uses more RAM.
+ kpt_oks_sigmas (list[float]): The sigmas used to calculate keypoint OKS.
+ See http://cocodataset.org/#keypoints-eval
+ When empty, it will use the defaults in COCO.
+ Otherwise it should be the same length as ROI_KEYPOINT_HEAD.NUM_KEYPOINTS.
+ allow_cached_coco (bool): Whether to use cached coco json from previous validation
+ runs. You should set this to False if you need to use different validation data.
+ Defaults to True.
+ """
+ self._logger = logging.getLogger(__name__)
+ self._distributed = distributed
+ self._output_dir = output_dir
+
+ if use_fast_impl and (COCOeval_opt is COCOeval):
+ self._logger.info("Fast COCO eval is not built. Falling back to official COCO eval.")
+ use_fast_impl = False
+ self._use_fast_impl = use_fast_impl
+
+ # COCOeval requires the limit on the number of detections per image (maxDets) to be a list
+ # with at least 3 elements. The default maxDets in COCOeval is [1, 10, 100], in which the
+ # 3rd element (100) is used as the limit on the number of detections per image when
+ # evaluating AP. COCOEvaluator expects an integer for max_dets_per_image, so for COCOeval,
+ # we reformat max_dets_per_image into [1, 10, max_dets_per_image], based on the defaults.
+ if max_dets_per_image is None:
+ max_dets_per_image = [1, 10, 100]
+ else:
+ max_dets_per_image = [1, 10, max_dets_per_image]
+ self._max_dets_per_image = max_dets_per_image
+
+ if tasks is not None and isinstance(tasks, CfgNode):
+ kpt_oks_sigmas = (
+ tasks.TEST.KEYPOINT_OKS_SIGMAS if not kpt_oks_sigmas else kpt_oks_sigmas
+ )
+ self._logger.warn(
+ "COCO Evaluator instantiated using config, this is deprecated behavior."
+ " Please pass in explicit arguments instead."
+ )
+ self._tasks = None # Infering it from predictions should be better
+ else:
+ self._tasks = tasks
+
+ self._cpu_device = torch.device("cpu")
+
+ self._metadata = MetadataCatalog.get(dataset_name)
+ if not hasattr(self._metadata, "json_file"):
+ if output_dir is None:
+ raise ValueError(
+ "output_dir must be provided to COCOEvaluator "
+ "for datasets not in COCO format."
+ )
+ self._logger.info(f"Trying to convert '{dataset_name}' to COCO format ...")
+
+ cache_path = os.path.join(output_dir, f"{dataset_name}_coco_format.json")
+ self._metadata.json_file = cache_path
+ convert_to_coco_json(dataset_name, cache_path, allow_cached=allow_cached_coco)
+
+ json_file = PathManager.get_local_path(self._metadata.json_file)
+ with contextlib.redirect_stdout(io.StringIO()):
+ self._coco_api = COCO(json_file)
+
+ # Test set json files do not contain annotations (evaluation must be
+ # performed using the COCO evaluation server).
+ self._do_evaluation = "annotations" in self._coco_api.dataset
+ if self._do_evaluation:
+ self._kpt_oks_sigmas = kpt_oks_sigmas
+
+ def reset(self):
+ self._predictions = []
+
+ def process(self, inputs, outputs):
+ """
+ Args:
+ inputs: the inputs to a COCO model (e.g., GeneralizedRCNN).
+ It is a list of dict. Each dict corresponds to an image and
+ contains keys like "height", "width", "file_name", "image_id".
+ outputs: the outputs of a COCO model. It is a list of dicts with key
+ "box_instances" that contains :class:`Instances`.
+ """
+ for input, output in zip(inputs, outputs):
+ prediction = {"image_id": input["image_id"]}
+
+ if "box_instances" in output:
+ instances = output["box_instances"].to(self._cpu_device)
+ prediction["box_instances"] = instances_to_coco_json(instances, input["image_id"])
+ if "proposals" in output:
+ prediction["proposals"] = output["proposals"].to(self._cpu_device)
+ if len(prediction) > 1:
+ self._predictions.append(prediction)
+
+ def evaluate(self, img_ids=None):
+ """
+ Args:
+ img_ids: a list of image IDs to evaluate on. Default to None for the whole dataset
+ """
+ if self._distributed:
+ comm.synchronize()
+ predictions = comm.gather(self._predictions, dst=0)
+ predictions = list(itertools.chain(*predictions))
+
+ if not comm.is_main_process():
+ return {}
+ else:
+ predictions = self._predictions
+
+ if len(predictions) == 0:
+ self._logger.warning("[COCOEvaluator] Did not receive valid predictions.")
+ return {}
+
+ if self._output_dir:
+ PathManager.mkdirs(self._output_dir)
+ file_path = os.path.join(self._output_dir, "instances_predictions.pth")
+ with PathManager.open(file_path, "wb") as f:
+ torch.save(predictions, f)
+
+ self._results = OrderedDict()
+ if "proposals" in predictions[0]:
+ self._eval_box_proposals(predictions)
+ if "box_instances" in predictions[0]:
+ self._eval_predictions(predictions, img_ids=img_ids)
+ # Copy so the caller can do whatever with results
+ return copy.deepcopy(self._results)
+
+ def _tasks_from_predictions(self, predictions):
+ """
+ Get COCO API "tasks" (i.e. iou_type) from COCO-format predictions.
+ """
+ tasks = {"bbox"}
+ for pred in predictions:
+ if "keypoints" in pred:
+ tasks.add("keypoints")
+ return sorted(tasks)
+
+ def _eval_predictions(self, predictions, img_ids=None):
+ """
+ Evaluate predictions. Fill self._results with the metrics of the tasks.
+ """
+ self._logger.info("Preparing results for COCO format ...")
+ coco_results = list(itertools.chain(*[x["box_instances"] for x in predictions]))
+ tasks = self._tasks or self._tasks_from_predictions(coco_results)
+
+ # unmap the category ids for COCO
+ if hasattr(self._metadata, "thing_dataset_id_to_contiguous_id"):
+ dataset_id_to_contiguous_id = self._metadata.thing_dataset_id_to_contiguous_id
+ all_contiguous_ids = list(dataset_id_to_contiguous_id.values())
+ num_classes = len(all_contiguous_ids)
+ assert min(all_contiguous_ids) == 0 and max(all_contiguous_ids) == num_classes - 1
+
+ reverse_id_mapping = {v: k for k, v in dataset_id_to_contiguous_id.items()}
+ for result in coco_results:
+ category_id = result["category_id"]
+ assert category_id < num_classes, (
+ f"A prediction has class={category_id}, "
+ f"but the dataset only has {num_classes} classes and "
+ f"predicted class id should be in [0, {num_classes - 1}]."
+ )
+ result["category_id"] = reverse_id_mapping[category_id]
+
+ if self._output_dir:
+ file_path = os.path.join(self._output_dir, "coco_instances_results.json")
+ self._logger.info("Saving results to {}".format(file_path))
+ with PathManager.open(file_path, "w") as f:
+ f.write(json.dumps(coco_results))
+ f.flush()
+
+ if not self._do_evaluation:
+ self._logger.info("Annotations are not available for evaluation.")
+ return
+
+ self._logger.info(
+ "Evaluating predictions with {} COCO API...".format(
+ "unofficial" if self._use_fast_impl else "official"
+ )
+ )
+ for task in sorted(tasks):
+ assert task in {"bbox", "keypoints"}, f"Got unknown task: {task}!"
+ coco_eval = (
+ _evaluate_predictions_on_coco(
+ self._coco_api,
+ coco_results,
+ task,
+ kpt_oks_sigmas=self._kpt_oks_sigmas,
+ use_fast_impl=self._use_fast_impl,
+ img_ids=img_ids,
+ max_dets_per_image=self._max_dets_per_image,
+ )
+ if len(coco_results) > 0
+ else None # cocoapi does not handle empty results very well
+ )
+
+ res = self._derive_coco_results(
+ coco_eval, task, class_names=self._metadata.get("thing_classes")
+ )
+ self._results[task] = res
+
+ def _eval_box_proposals(self, predictions):
+ """
+ Evaluate the box proposals in predictions.
+ Fill self._results with the metrics for "box_proposals" task.
+ """
+ if self._output_dir:
+ # Saving generated box proposals to file.
+ # Predicted box_proposals are in XYXY_ABS mode.
+ bbox_mode = BoxMode.XYXY_ABS.value
+ ids, boxes, objectness_logits = [], [], []
+ for prediction in predictions:
+ ids.append(prediction["image_id"])
+ boxes.append(prediction["proposals"].proposal_boxes.tensor.numpy())
+ objectness_logits.append(prediction["proposals"].objectness_logits.numpy())
+
+ proposal_data = {
+ "boxes": boxes,
+ "objectness_logits": objectness_logits,
+ "ids": ids,
+ "bbox_mode": bbox_mode,
+ }
+ with PathManager.open(os.path.join(self._output_dir, "box_proposals.pkl"), "wb") as f:
+ pickle.dump(proposal_data, f)
+
+ if not self._do_evaluation:
+ self._logger.info("Annotations are not available for evaluation.")
+ return
+
+ self._logger.info("Evaluating bbox proposals ...")
+ res = {}
+ areas = {"all": "", "small": "s", "medium": "m", "large": "l"}
+ for limit in [100, 1000]:
+ for area, suffix in areas.items():
+ stats = _evaluate_box_proposals(predictions, self._coco_api, area=area, limit=limit)
+ key = "AR{}@{:d}".format(suffix, limit)
+ res[key] = float(stats["ar"].item() * 100)
+ self._logger.info("Proposal metrics: \n" + create_small_table(res))
+ self._results["box_proposals"] = res
+
+ def _derive_coco_results(self, coco_eval, iou_type, class_names=None):
+ """
+ Derive the desired score numbers from summarized COCOeval.
+
+ Args:
+ coco_eval (None or COCOEval): None represents no predictions from model.
+ iou_type (str):
+ class_names (None or list[str]): if provided, will use it to predict
+ per-category AP.
+
+ Returns:
+ a dict of {metric name: score}
+ """
+
+ metrics = {
+ "bbox": ["AP", "AP50", "AP75", "APs", "APm", "APl"],
+ "keypoints": ["AP", "AP50", "AP75", "APm", "APl"],
+ }[iou_type]
+
+ if coco_eval is None:
+ self._logger.warn("No predictions from the model!")
+ return {metric: float("nan") for metric in metrics}
+
+ # the standard metrics
+ results = {
+ metric: float(coco_eval.stats[idx] * 100 if coco_eval.stats[idx] >= 0 else "nan")
+ for idx, metric in enumerate(metrics)
+ }
+ self._logger.info(
+ "Evaluation results for {}: \n".format(iou_type) + create_small_table(results)
+ )
+ if not np.isfinite(sum(results.values())):
+ self._logger.info("Some metrics cannot be computed and is shown as NaN.")
+
+ if class_names is None or len(class_names) <= 1:
+ return results
+ # Compute per-category AP
+ # from https://github.com/facebookresearch/Detectron/blob/a6a835f5b8208c45d0dce217ce9bbda915f44df7/detectron/datasets/json_dataset_evaluator.py#L222-L252 # noqa
+ precisions = coco_eval.eval["precision"]
+ # precision has dims (iou, recall, cls, area range, max dets)
+ assert len(class_names) == precisions.shape[2]
+
+ results_per_category = []
+ for idx, name in enumerate(class_names):
+ # area range index 0: all area ranges
+ # max dets index -1: typically 100 per image
+ precision = precisions[:, :, idx, 0, -1]
+ precision = precision[precision > -1]
+ ap = np.mean(precision) if precision.size else float("nan")
+ results_per_category.append(("{}".format(name), float(ap * 100)))
+
+ # tabulate it
+ N_COLS = min(6, len(results_per_category) * 2)
+ results_flatten = list(itertools.chain(*results_per_category))
+ results_2d = itertools.zip_longest(*[results_flatten[i::N_COLS] for i in range(N_COLS)])
+ table = tabulate(
+ results_2d,
+ tablefmt="pipe",
+ floatfmt=".3f",
+ headers=["category", "AP"] * (N_COLS // 2),
+ numalign="left",
+ )
+ self._logger.info("Per-category {} AP: \n".format(iou_type) + table)
+
+ results.update({"AP-" + name: ap for name, ap in results_per_category})
+ return results
+
+
+def instances_to_coco_json(instances, img_id):
+ """
+ Dump an "Instances" object to a COCO-format json that's used for evaluation.
+
+ Args:
+ instances (Instances):
+ img_id (int): the image id
+
+ Returns:
+ list[dict]: list of json annotations in COCO format.
+ """
+ num_instance = len(instances)
+ if num_instance == 0:
+ return []
+
+ boxes = instances.pred_boxes.tensor.numpy()
+ boxes = BoxMode.convert(boxes, BoxMode.XYXY_ABS, BoxMode.XYWH_ABS)
+ boxes = boxes.tolist()
+ scores = instances.scores.tolist()
+ classes = instances.pred_classes.tolist()
+
+ has_mask = instances.has("pred_masks")
+ if has_mask:
+ # use RLE to encode the masks, because they are too large and takes memory
+ # since this evaluator stores outputs of the entire dataset
+ rles = [
+ mask_util.encode(np.array(mask[:, :, None], order="F", dtype="uint8"))[0]
+ for mask in instances.pred_masks
+ ]
+ for rle in rles:
+ # "counts" is an array encoded by mask_util as a byte-stream. Python3's
+ # json writer which always produces strings cannot serialize a bytestream
+ # unless you decode it. Thankfully, utf-8 works out (which is also what
+ # the pycocotools/_mask.pyx does).
+ rle["counts"] = rle["counts"].decode("utf-8")
+
+ has_keypoints = instances.has("pred_keypoints")
+ if has_keypoints:
+ keypoints = instances.pred_keypoints
+
+ results = []
+ for k in range(num_instance):
+ result = {
+ "image_id": img_id,
+ "category_id": classes[k],
+ "bbox": boxes[k],
+ "score": scores[k],
+ }
+ if has_mask:
+ result["segmentation"] = rles[k]
+ if has_keypoints:
+ # In COCO annotations,
+ # keypoints coordinates are pixel indices.
+ # However our predictions are floating point coordinates.
+ # Therefore we subtract 0.5 to be consistent with the annotation format.
+ # This is the inverse of data loading logic in `datasets/coco.py`.
+ keypoints[k][:, :2] -= 0.5
+ result["keypoints"] = keypoints[k].flatten().tolist()
+ results.append(result)
+ return results
+
+
+# inspired from Detectron:
+# https://github.com/facebookresearch/Detectron/blob/a6a835f5b8208c45d0dce217ce9bbda915f44df7/detectron/datasets/json_dataset_evaluator.py#L255 # noqa
+def _evaluate_box_proposals(dataset_predictions, coco_api, thresholds=None, area="all", limit=None):
+ """
+ Evaluate detection proposal recall metrics. This function is a much
+ faster alternative to the official COCO API recall evaluation code. However,
+ it produces slightly different results.
+ """
+ # Record max overlap value for each gt box
+ # Return vector of overlap values
+ areas = {
+ "all": 0,
+ "small": 1,
+ "medium": 2,
+ "large": 3,
+ "96-128": 4,
+ "128-256": 5,
+ "256-512": 6,
+ "512-inf": 7,
+ }
+ area_ranges = [
+ [0**2, 1e5**2], # all
+ [0**2, 32**2], # small
+ [32**2, 96**2], # medium
+ [96**2, 1e5**2], # large
+ [96**2, 128**2], # 96-128
+ [128**2, 256**2], # 128-256
+ [256**2, 512**2], # 256-512
+ [512**2, 1e5**2],
+ ] # 512-inf
+ assert area in areas, "Unknown area range: {}".format(area)
+ area_range = area_ranges[areas[area]]
+ gt_overlaps = []
+ num_pos = 0
+
+ for prediction_dict in dataset_predictions:
+ predictions = prediction_dict["proposals"]
+
+ # sort predictions in descending order
+ # TODO maybe remove this and make it explicit in the documentation
+ inds = predictions.objectness_logits.sort(descending=True)[1]
+ predictions = predictions[inds]
+
+ ann_ids = coco_api.getAnnIds(imgIds=prediction_dict["image_id"])
+ anno = coco_api.loadAnns(ann_ids)
+ gt_boxes = [
+ BoxMode.convert(obj["bbox"], BoxMode.XYWH_ABS, BoxMode.XYXY_ABS)
+ for obj in anno
+ if obj["iscrowd"] == 0
+ ]
+ gt_boxes = torch.as_tensor(gt_boxes).reshape(-1, 4) # guard against no boxes
+ gt_boxes = Boxes(gt_boxes)
+ gt_areas = torch.as_tensor([obj["area"] for obj in anno if obj["iscrowd"] == 0])
+
+ if len(gt_boxes) == 0 or len(predictions) == 0:
+ continue
+
+ valid_gt_inds = (gt_areas >= area_range[0]) & (gt_areas <= area_range[1])
+ gt_boxes = gt_boxes[valid_gt_inds]
+
+ num_pos += len(gt_boxes)
+
+ if len(gt_boxes) == 0:
+ continue
+
+ if limit is not None and len(predictions) > limit:
+ predictions = predictions[:limit]
+
+ overlaps = pairwise_iou(predictions.proposal_boxes, gt_boxes)
+
+ _gt_overlaps = torch.zeros(len(gt_boxes))
+ for j in range(min(len(predictions), len(gt_boxes))):
+ # find which proposal box maximally covers each gt box
+ # and get the iou amount of coverage for each gt box
+ max_overlaps, argmax_overlaps = overlaps.max(dim=0)
+
+ # find which gt box is 'best' covered (i.e. 'best' = most iou)
+ gt_ovr, gt_ind = max_overlaps.max(dim=0)
+ assert gt_ovr >= 0
+ # find the proposal box that covers the best covered gt box
+ box_ind = argmax_overlaps[gt_ind]
+ # record the iou coverage of this gt box
+ _gt_overlaps[j] = overlaps[box_ind, gt_ind]
+ assert _gt_overlaps[j] == gt_ovr
+ # mark the proposal box and the gt box as used
+ overlaps[box_ind, :] = -1
+ overlaps[:, gt_ind] = -1
+
+ # append recorded iou coverage level
+ gt_overlaps.append(_gt_overlaps)
+ gt_overlaps = (
+ torch.cat(gt_overlaps, dim=0) if len(gt_overlaps) else torch.zeros(0, dtype=torch.float32)
+ )
+ gt_overlaps, _ = torch.sort(gt_overlaps)
+
+ if thresholds is None:
+ step = 0.05
+ thresholds = torch.arange(0.5, 0.95 + 1e-5, step, dtype=torch.float32)
+ recalls = torch.zeros_like(thresholds)
+ # compute recall for each iou threshold
+ for i, t in enumerate(thresholds):
+ recalls[i] = (gt_overlaps >= t).float().sum() / float(num_pos)
+ # ar = 2 * np.trapz(recalls, thresholds)
+ ar = recalls.mean()
+ return {
+ "ar": ar,
+ "recalls": recalls,
+ "thresholds": thresholds,
+ "gt_overlaps": gt_overlaps,
+ "num_pos": num_pos,
+ }
+
+
+def _evaluate_predictions_on_coco(
+ coco_gt,
+ coco_results,
+ iou_type,
+ kpt_oks_sigmas=None,
+ use_fast_impl=True,
+ img_ids=None,
+ max_dets_per_image=None,
+):
+ """
+ Evaluate the coco results using COCOEval API.
+ """
+ assert len(coco_results) > 0
+
+ if iou_type == "segm":
+ coco_results = copy.deepcopy(coco_results)
+ # When evaluating mask AP, if the results contain bbox, cocoapi will
+ # use the box area as the area of the instance, instead of the mask area.
+ # This leads to a different definition of small/medium/large.
+ # We remove the bbox field to let mask AP use mask area.
+ for c in coco_results:
+ c.pop("bbox", None)
+
+ coco_dt = coco_gt.loadRes(coco_results)
+ coco_eval = (COCOeval_opt if use_fast_impl else COCOeval)(coco_gt, coco_dt, iou_type)
+ # For COCO, the default max_dets_per_image is [1, 10, 100].
+ if max_dets_per_image is None:
+ max_dets_per_image = [1, 10, 100] # Default from COCOEval
+ else:
+ assert (
+ len(max_dets_per_image) >= 3
+ ), "COCOeval requires maxDets (and max_dets_per_image) to have length at least 3"
+ # In the case that user supplies a custom input for max_dets_per_image,
+ # apply COCOevalMaxDets to evaluate AP with the custom input.
+ if max_dets_per_image[2] != 100:
+ coco_eval = COCOevalMaxDets(coco_gt, coco_dt, iou_type)
+ if iou_type != "keypoints":
+ coco_eval.params.maxDets = max_dets_per_image
+
+ if img_ids is not None:
+ coco_eval.params.imgIds = img_ids
+
+ if iou_type == "keypoints":
+ # Use the COCO default keypoint OKS sigmas unless overrides are specified
+ if kpt_oks_sigmas:
+ assert hasattr(coco_eval.params, "kpt_oks_sigmas"), "pycocotools is too old!"
+ coco_eval.params.kpt_oks_sigmas = np.array(kpt_oks_sigmas)
+ # COCOAPI requires every detection and every gt to have keypoints, so
+ # we just take the first entry from both
+ num_keypoints_dt = len(coco_results[0]["keypoints"]) // 3
+ num_keypoints_gt = len(next(iter(coco_gt.anns.values()))["keypoints"]) // 3
+ num_keypoints_oks = len(coco_eval.params.kpt_oks_sigmas)
+ assert num_keypoints_oks == num_keypoints_dt == num_keypoints_gt, (
+ f"[COCOEvaluator] Prediction contain {num_keypoints_dt} keypoints. "
+ f"Ground truth contains {num_keypoints_gt} keypoints. "
+ f"The length of cfg.TEST.KEYPOINT_OKS_SIGMAS is {num_keypoints_oks}. "
+ "They have to agree with each other. For meaning of OKS, please refer to "
+ "http://cocodataset.org/#keypoints-eval."
+ )
+
+ coco_eval.evaluate()
+ coco_eval.accumulate()
+ coco_eval.summarize()
+
+ return coco_eval
+
+
+class COCOevalMaxDets(COCOeval):
+ """
+ Modified version of COCOeval for evaluating AP with a custom
+ maxDets (by default for COCO, maxDets is 100)
+ """
+
+ def summarize(self):
+ """
+ Compute and display summary metrics for evaluation results given
+ a custom value for max_dets_per_image
+ """
+
+ def _summarize(ap=1, iouThr=None, areaRng="all", maxDets=100):
+ p = self.params
+ iStr = " {:<18} {} @[ IoU={:<9} | area={:>6s} | maxDets={:>3d} ] = {:0.3f}"
+ titleStr = "Average Precision" if ap == 1 else "Average Recall"
+ typeStr = "(AP)" if ap == 1 else "(AR)"
+ iouStr = (
+ "{:0.2f}:{:0.2f}".format(p.iouThrs[0], p.iouThrs[-1])
+ if iouThr is None
+ else "{:0.2f}".format(iouThr)
+ )
+
+ aind = [i for i, aRng in enumerate(p.areaRngLbl) if aRng == areaRng]
+ mind = [i for i, mDet in enumerate(p.maxDets) if mDet == maxDets]
+ if ap == 1:
+ # dimension of precision: [TxRxKxAxM]
+ s = self.eval["precision"]
+ # IoU
+ if iouThr is not None:
+ t = np.where(iouThr == p.iouThrs)[0]
+ s = s[t]
+ s = s[:, :, :, aind, mind]
+ else:
+ # dimension of recall: [TxKxAxM]
+ s = self.eval["recall"]
+ if iouThr is not None:
+ t = np.where(iouThr == p.iouThrs)[0]
+ s = s[t]
+ s = s[:, :, aind, mind]
+ if len(s[s > -1]) == 0:
+ mean_s = -1
+ else:
+ mean_s = np.mean(s[s > -1])
+ print(iStr.format(titleStr, typeStr, iouStr, areaRng, maxDets, mean_s))
+ return mean_s
+
+ def _summarizeDets():
+ stats = np.zeros((12,))
+ # Evaluate AP using the custom limit on maximum detections per image
+ stats[0] = _summarize(1, maxDets=self.params.maxDets[2])
+ stats[1] = _summarize(1, iouThr=0.5, maxDets=self.params.maxDets[2])
+ stats[2] = _summarize(1, iouThr=0.75, maxDets=self.params.maxDets[2])
+ stats[3] = _summarize(1, areaRng="small", maxDets=self.params.maxDets[2])
+ stats[4] = _summarize(1, areaRng="medium", maxDets=self.params.maxDets[2])
+ stats[5] = _summarize(1, areaRng="large", maxDets=self.params.maxDets[2])
+ stats[6] = _summarize(0, maxDets=self.params.maxDets[0])
+ stats[7] = _summarize(0, maxDets=self.params.maxDets[1])
+ stats[8] = _summarize(0, maxDets=self.params.maxDets[2])
+ stats[9] = _summarize(0, areaRng="small", maxDets=self.params.maxDets[2])
+ stats[10] = _summarize(0, areaRng="medium", maxDets=self.params.maxDets[2])
+ stats[11] = _summarize(0, areaRng="large", maxDets=self.params.maxDets[2])
+ return stats
+
+ def _summarizeKps():
+ stats = np.zeros((10,))
+ stats[0] = _summarize(1, maxDets=20)
+ stats[1] = _summarize(1, maxDets=20, iouThr=0.5)
+ stats[2] = _summarize(1, maxDets=20, iouThr=0.75)
+ stats[3] = _summarize(1, maxDets=20, areaRng="medium")
+ stats[4] = _summarize(1, maxDets=20, areaRng="large")
+ stats[5] = _summarize(0, maxDets=20)
+ stats[6] = _summarize(0, maxDets=20, iouThr=0.5)
+ stats[7] = _summarize(0, maxDets=20, iouThr=0.75)
+ stats[8] = _summarize(0, maxDets=20, areaRng="medium")
+ stats[9] = _summarize(0, maxDets=20, areaRng="large")
+ return stats
+
+ if not self.eval:
+ raise Exception("Please run accumulate() first")
+ iouType = self.params.iouType
+ if iouType == "segm" or iouType == "bbox":
+ summarize = _summarizeDets
+ elif iouType == "keypoints":
+ summarize = _summarizeKps
+ self.stats = summarize()
+
+ def __str__(self):
+ self.summarize()
\ No newline at end of file
diff --git a/annotator/OneFormer/oneformer/evaluation/evaluator.py b/annotator/OneFormer/oneformer/evaluation/evaluator.py
new file mode 100644
index 0000000000000000000000000000000000000000..7d0848c7ec511f7000f4230c914a8b32f690dee0
--- /dev/null
+++ b/annotator/OneFormer/oneformer/evaluation/evaluator.py
@@ -0,0 +1,228 @@
+# ------------------------------------------------------------------------------
+# Reference: https://github.com/facebookresearch/detectron2/blob/main/detectron2/evaluation/evaluator.py
+# Modified by Jitesh Jain (https://github.com/praeclarumjj3)
+# ------------------------------------------------------------------------------
+
+import datetime
+import logging
+import time
+from collections import OrderedDict, abc
+from contextlib import ExitStack, contextmanager
+from typing import List, Union
+import torch
+from torch import nn
+
+from detectron2.utils.comm import get_world_size, is_main_process
+from detectron2.utils.logger import log_every_n_seconds
+
+
+class DatasetEvaluator:
+ """
+ Base class for a dataset evaluator.
+
+ The function :func:`inference_on_dataset` runs the model over
+ all samples in the dataset, and have a DatasetEvaluator to process the inputs/outputs.
+
+ This class will accumulate information of the inputs/outputs (by :meth:`process`),
+ and produce evaluation results in the end (by :meth:`evaluate`).
+ """
+
+ def reset(self):
+ """
+ Preparation for a new round of evaluation.
+ Should be called before starting a round of evaluation.
+ """
+ pass
+
+ def process(self, inputs, outputs):
+ """
+ Process the pair of inputs and outputs.
+ If they contain batches, the pairs can be consumed one-by-one using `zip`:
+
+ .. code-block:: python
+
+ for input_, output in zip(inputs, outputs):
+ # do evaluation on single input/output pair
+ ...
+
+ Args:
+ inputs (list): the inputs that's used to call the model.
+ outputs (list): the return value of `model(inputs)`
+ """
+ pass
+
+ def evaluate(self):
+ """
+ Evaluate/summarize the performance, after processing all input/output pairs.
+
+ Returns:
+ dict:
+ A new evaluator class can return a dict of arbitrary format
+ as long as the user can process the results.
+ In our train_net.py, we expect the following format:
+
+ * key: the name of the task (e.g., bbox)
+ * value: a dict of {metric name: score}, e.g.: {"AP50": 80}
+ """
+ pass
+
+
+class DatasetEvaluators(DatasetEvaluator):
+ """
+ Wrapper class to combine multiple :class:`DatasetEvaluator` instances.
+
+ This class dispatches every evaluation call to
+ all of its :class:`DatasetEvaluator`.
+ """
+
+ def __init__(self, evaluators):
+ """
+ Args:
+ evaluators (list): the evaluators to combine.
+ """
+ super().__init__()
+ self._evaluators = evaluators
+
+ def reset(self):
+ for evaluator in self._evaluators:
+ evaluator.reset()
+
+ def process(self, inputs, outputs):
+ for evaluator in self._evaluators:
+ evaluator.process(inputs, outputs)
+
+ def evaluate(self):
+ results = OrderedDict()
+ for evaluator in self._evaluators:
+ result = evaluator.evaluate()
+ if is_main_process() and result is not None:
+ for k, v in result.items():
+ assert (
+ k not in results
+ ), "Different evaluators produce results with the same key {}".format(k)
+ results[k] = v
+ return results
+
+
+def inference_on_dataset(
+ model, data_loader, evaluator: Union[DatasetEvaluator, List[DatasetEvaluator], None]
+):
+ """
+ Run model on the data_loader and evaluate the metrics with evaluator.
+ Also benchmark the inference speed of `model.__call__` accurately.
+ The model will be used in eval mode.
+
+ Args:
+ model (callable): a callable which takes an object from
+ `data_loader` and returns some outputs.
+
+ If it's an nn.Module, it will be temporarily set to `eval` mode.
+ If you wish to evaluate a model in `training` mode instead, you can
+ wrap the given model and override its behavior of `.eval()` and `.train()`.
+ data_loader: an iterable object with a length.
+ The elements it generates will be the inputs to the model.
+ evaluator: the evaluator(s) to run. Use `None` if you only want to benchmark,
+ but don't want to do any evaluation.
+
+ Returns:
+ The return value of `evaluator.evaluate()`
+ """
+ num_devices = get_world_size()
+ logger = logging.getLogger(__name__)
+ logger.info("Start inference on {} batches".format(len(data_loader)))
+
+ total = len(data_loader) # inference data loader must have a fixed length
+ if evaluator is None:
+ # create a no-op evaluator
+ evaluator = DatasetEvaluators([])
+ if isinstance(evaluator, abc.MutableSequence):
+ evaluator = DatasetEvaluators(evaluator)
+ evaluator.reset()
+
+ num_warmup = min(5, total - 1)
+ start_time = time.perf_counter()
+ total_data_time = 0
+ total_compute_time = 0
+ total_eval_time = 0
+ with ExitStack() as stack:
+ if isinstance(model, nn.Module):
+ stack.enter_context(inference_context(model))
+ stack.enter_context(torch.no_grad())
+
+ start_data_time = time.perf_counter()
+ for idx, inputs in enumerate(data_loader):
+ total_data_time += time.perf_counter() - start_data_time
+ if idx == num_warmup:
+ start_time = time.perf_counter()
+ total_data_time = 0
+ total_compute_time = 0
+ total_eval_time = 0
+
+ start_compute_time = time.perf_counter()
+ outputs = model(inputs)
+ if torch.cuda.is_available():
+ torch.cuda.synchronize()
+ total_compute_time += time.perf_counter() - start_compute_time
+
+ start_eval_time = time.perf_counter()
+ evaluator.process(inputs, outputs)
+ total_eval_time += time.perf_counter() - start_eval_time
+
+ iters_after_start = idx + 1 - num_warmup * int(idx >= num_warmup)
+ data_seconds_per_iter = total_data_time / iters_after_start
+ compute_seconds_per_iter = total_compute_time / iters_after_start
+ eval_seconds_per_iter = total_eval_time / iters_after_start
+ total_seconds_per_iter = (time.perf_counter() - start_time) / iters_after_start
+ if idx >= num_warmup * 2 or compute_seconds_per_iter > 5:
+ eta = datetime.timedelta(seconds=int(total_seconds_per_iter * (total - idx - 1)))
+ log_every_n_seconds(
+ logging.INFO,
+ (
+ f"Inference done {idx + 1}/{total}. "
+ f"Dataloading: {data_seconds_per_iter:.4f} s/iter. "
+ f"Inference: {compute_seconds_per_iter:.4f} s/iter. "
+ f"Eval: {eval_seconds_per_iter:.4f} s/iter. "
+ f"Total: {total_seconds_per_iter:.4f} s/iter. "
+ f"ETA={eta}"
+ ),
+ n=5,
+ )
+ start_data_time = time.perf_counter()
+
+ # Measure the time only for this worker (before the synchronization barrier)
+ total_time = time.perf_counter() - start_time
+ total_time_str = str(datetime.timedelta(seconds=total_time))
+ # NOTE this format is parsed by grep
+ logger.info(
+ "Total inference time: {} ({:.6f} s / iter per device, on {} devices)".format(
+ total_time_str, total_time / (total - num_warmup), num_devices
+ )
+ )
+ total_compute_time_str = str(datetime.timedelta(seconds=int(total_compute_time)))
+ logger.info(
+ "Total inference pure compute time: {} ({:.6f} s / iter per device, on {} devices)".format(
+ total_compute_time_str, total_compute_time / (total - num_warmup), num_devices
+ )
+ )
+
+ results = evaluator.evaluate()
+ # An evaluator may return None when not in main process.
+ # Replace it by an empty dict instead to make it easier for downstream code to handle
+ if results is None:
+ results = {}
+ return results
+
+
+@contextmanager
+def inference_context(model):
+ """
+ A context where the model is temporarily changed to eval mode,
+ and restored to previous mode afterwards.
+
+ Args:
+ model: a torch Module
+ """
+ training_mode = model.training
+ model.eval()
+ yield
+ model.train(training_mode)
diff --git a/annotator/OneFormer/oneformer/evaluation/instance_evaluation.py b/annotator/OneFormer/oneformer/evaluation/instance_evaluation.py
new file mode 100644
index 0000000000000000000000000000000000000000..7c5e429f97fb74c957fa5be76b4b0349d30e0459
--- /dev/null
+++ b/annotator/OneFormer/oneformer/evaluation/instance_evaluation.py
@@ -0,0 +1,110 @@
+# ------------------------------------------------------------------------------
+# Reference: https://github.com/facebookresearch/Mask2Former/blob/main/mask2former/evaluation/instance_evaluation.py
+# ------------------------------------------------------------------------------
+
+import contextlib
+import copy
+import io
+import itertools
+import json
+import logging
+import numpy as np
+import os
+import pickle
+from collections import OrderedDict
+import pycocotools.mask as mask_util
+import torch
+from pycocotools.coco import COCO
+from pycocotools.cocoeval import COCOeval
+from tabulate import tabulate
+
+import detectron2.utils.comm as comm
+from detectron2.config import CfgNode
+from detectron2.data import MetadataCatalog
+from detectron2.data.datasets.coco import convert_to_coco_json
+from detectron2.evaluation.coco_evaluation import COCOEvaluator, _evaluate_predictions_on_coco
+from detectron2.evaluation.fast_eval_api import COCOeval_opt
+from detectron2.structures import Boxes, BoxMode, pairwise_iou
+from detectron2.utils.file_io import PathManager
+from detectron2.utils.logger import create_small_table
+
+
+# modified from COCOEvaluator for instance segmetnat
+class InstanceSegEvaluator(COCOEvaluator):
+ """
+ Evaluate AR for object proposals, AP for instance detection/segmentation, AP
+ for keypoint detection outputs using COCO's metrics.
+ See http://cocodataset.org/#detection-eval and
+ http://cocodataset.org/#keypoints-eval to understand its metrics.
+ The metrics range from 0 to 100 (instead of 0 to 1), where a -1 or NaN means
+ the metric cannot be computed (e.g. due to no predictions made).
+
+ In addition to COCO, this evaluator is able to support any bounding box detection,
+ instance segmentation, or keypoint detection dataset.
+ """
+
+ def _eval_predictions(self, predictions, img_ids=None):
+ """
+ Evaluate predictions. Fill self._results with the metrics of the tasks.
+ """
+ self._logger.info("Preparing results for COCO format ...")
+ coco_results = list(itertools.chain(*[x["instances"] for x in predictions]))
+ tasks = self._tasks or self._tasks_from_predictions(coco_results)
+
+ # unmap the category ids for COCO
+ if hasattr(self._metadata, "thing_dataset_id_to_contiguous_id"):
+ dataset_id_to_contiguous_id = self._metadata.thing_dataset_id_to_contiguous_id
+ # all_contiguous_ids = list(dataset_id_to_contiguous_id.values())
+ # num_classes = len(all_contiguous_ids)
+ # assert min(all_contiguous_ids) == 0 and max(all_contiguous_ids) == num_classes - 1
+
+ reverse_id_mapping = {v: k for k, v in dataset_id_to_contiguous_id.items()}
+ for result in coco_results:
+ category_id = result["category_id"]
+ # assert category_id < num_classes, (
+ # f"A prediction has class={category_id}, "
+ # f"but the dataset only has {num_classes} classes and "
+ # f"predicted class id should be in [0, {num_classes - 1}]."
+ # )
+ assert category_id in reverse_id_mapping, (
+ f"A prediction has class={category_id}, "
+ f"but the dataset only has class ids in {dataset_id_to_contiguous_id}."
+ )
+ result["category_id"] = reverse_id_mapping[category_id]
+
+ if self._output_dir:
+ file_path = os.path.join(self._output_dir, "coco_instances_results.json")
+ self._logger.info("Saving results to {}".format(file_path))
+ with PathManager.open(file_path, "w") as f:
+ f.write(json.dumps(coco_results))
+ f.flush()
+
+ if not self._do_evaluation:
+ self._logger.info("Annotations are not available for evaluation.")
+ return
+
+ self._logger.info(
+ "Evaluating predictions with {} COCO API...".format(
+ "unofficial" if self._use_fast_impl else "official"
+ )
+ )
+ for task in sorted(tasks):
+ assert task in {"bbox", "segm", "keypoints"}, f"Got unknown task: {task}!"
+ coco_eval = (
+ _evaluate_predictions_on_coco(
+ self._coco_api,
+ coco_results,
+ task,
+ kpt_oks_sigmas=self._kpt_oks_sigmas,
+ use_fast_impl=self._use_fast_impl,
+ img_ids=img_ids,
+ max_dets_per_image=self._max_dets_per_image,
+ )
+ if len(coco_results) > 0
+ else None # cocoapi does not handle empty results very well
+ )
+
+ res = self._derive_coco_results(
+ coco_eval, task, class_names=self._metadata.get("thing_classes")
+ )
+ self._results[task] = res
diff --git a/annotator/OneFormer/oneformer/modeling/__init__.py b/annotator/OneFormer/oneformer/modeling/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..29e09f4e888e0c73c49d1db7d10ef6a82ba79488
--- /dev/null
+++ b/annotator/OneFormer/oneformer/modeling/__init__.py
@@ -0,0 +1,6 @@
+from .backbone.swin import D2SwinTransformer
+# from .backbone.dinat import D2DiNAT
+from .backbone.convnext import D2ConvNeXt
+from .pixel_decoder.fpn import BasePixelDecoder
+from .pixel_decoder.msdeformattn import MSDeformAttnPixelDecoder
+from .meta_arch.oneformer_head import OneFormerHead
diff --git a/annotator/OneFormer/oneformer/modeling/backbone/__init__.py b/annotator/OneFormer/oneformer/modeling/backbone/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..9020c2df23e2af280b7bb168b996ae9eaf312eb8
--- /dev/null
+++ b/annotator/OneFormer/oneformer/modeling/backbone/__init__.py
@@ -0,0 +1 @@
+# Copyright (c) Facebook, Inc. and its affiliates.
diff --git a/annotator/OneFormer/oneformer/modeling/backbone/convnext.py b/annotator/OneFormer/oneformer/modeling/backbone/convnext.py
new file mode 100644
index 0000000000000000000000000000000000000000..ebe148dbfdd76c9d03715520ae48887249ed7a4b
--- /dev/null
+++ b/annotator/OneFormer/oneformer/modeling/backbone/convnext.py
@@ -0,0 +1,214 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+
+# All rights reserved.
+
+# This source code is licensed under the license found in the
+# LICENSE file in the root directory of this source tree.
+
+
+from functools import partial
+
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+from timm.models.layers import DropPath
+
+from detectron2.modeling import BACKBONE_REGISTRY, Backbone, ShapeSpec
+
+
+class Block(nn.Module):
+ r""" ConvNeXt Block. There are two equivalent implementations:
+ (1) DwConv -> LayerNorm (channels_first) -> 1x1 Conv -> GELU -> 1x1 Conv; all in (N, C, H, W)
+ (2) DwConv -> Permute to (N, H, W, C); LayerNorm (channels_last) -> Linear -> GELU -> Linear; Permute back
+ We use (2) as we find it slightly faster in PyTorch
+
+ Args:
+ dim (int): Number of input channels.
+ drop_path (float): Stochastic depth rate. Default: 0.0
+ layer_scale_init_value (float): Init value for Layer Scale. Default: 1e-6.
+ """
+ def __init__(self, dim, drop_path=0., layer_scale_init_value=1e-6):
+ super().__init__()
+ self.dwconv = nn.Conv2d(dim, dim, kernel_size=7, padding=3, groups=dim) # depthwise conv
+ self.norm = LayerNorm(dim, eps=1e-6)
+ self.pwconv1 = nn.Linear(dim, 4 * dim) # pointwise/1x1 convs, implemented with linear layers
+ self.act = nn.GELU()
+ self.pwconv2 = nn.Linear(4 * dim, dim)
+ self.gamma = nn.Parameter(layer_scale_init_value * torch.ones((dim)),
+ requires_grad=True) if layer_scale_init_value > 0 else None
+ self.drop_path = DropPath(drop_path) if drop_path > 0. else nn.Identity()
+
+ def forward(self, x):
+ input = x
+ x = self.dwconv(x)
+ x = x.permute(0, 2, 3, 1) # (N, C, H, W) -> (N, H, W, C)
+ x = self.norm(x)
+ x = self.pwconv1(x)
+ x = self.act(x)
+ x = self.pwconv2(x)
+ if self.gamma is not None:
+ x = self.gamma * x
+ x = x.permute(0, 3, 1, 2) # (N, H, W, C) -> (N, C, H, W)
+
+ x = input + self.drop_path(x)
+ return x
+
+class LayerNorm(nn.Module):
+ r""" LayerNorm that supports two data formats: channels_last (default) or channels_first.
+ The ordering of the dimensions in the inputs. channels_last corresponds to inputs with
+ shape (batch_size, height, width, channels) while channels_first corresponds to inputs
+ with shape (batch_size, channels, height, width).
+ """
+ def __init__(self, normalized_shape, eps=1e-6, data_format="channels_last"):
+ super().__init__()
+ self.weight = nn.Parameter(torch.ones(normalized_shape))
+ self.bias = nn.Parameter(torch.zeros(normalized_shape))
+ self.eps = eps
+ self.data_format = data_format
+ if self.data_format not in ["channels_last", "channels_first"]:
+ raise NotImplementedError
+ self.normalized_shape = (normalized_shape, )
+
+ def forward(self, x):
+ if self.data_format == "channels_last":
+ return F.layer_norm(x, self.normalized_shape, self.weight, self.bias, self.eps)
+ elif self.data_format == "channels_first":
+ u = x.mean(1, keepdim=True)
+ s = (x - u).pow(2).mean(1, keepdim=True)
+ x = (x - u) / torch.sqrt(s + self.eps)
+ x = self.weight[:, None, None] * x + self.bias[:, None, None]
+ return x
+
+
+class ConvNeXt(nn.Module):
+ r""" ConvNeXt
+ A PyTorch impl of : `A ConvNet for the 2020s` -
+ https://arxiv.org/pdf/2201.03545.pdf
+
+ Args:
+ in_chans (int): Number of input image channels. Default: 3
+ num_classes (int): Number of classes for classification head. Default: 1000
+ depths (tuple(int)): Number of blocks at each stage. Default: [3, 3, 9, 3]
+ dims (int): Feature dimension at each stage. Default: [96, 192, 384, 768]
+ drop_path_rate (float): Stochastic depth rate. Default: 0.
+ layer_scale_init_value (float): Init value for Layer Scale. Default: 1e-6.
+ head_init_scale (float): Init scaling value for classifier weights and biases. Default: 1.
+ """
+ def __init__(self, in_chans=3, depths=[3, 3, 9, 3], dims=[96, 192, 384, 768],
+ drop_path_rate=0., layer_scale_init_value=1e-6, out_indices=[0, 1, 2, 3],
+ ):
+ super().__init__()
+
+ self.num_features = dims
+
+ self.downsample_layers = nn.ModuleList() # stem and 3 intermediate downsampling conv layers
+ stem = nn.Sequential(
+ nn.Conv2d(in_chans, dims[0], kernel_size=4, stride=4),
+ LayerNorm(dims[0], eps=1e-6, data_format="channels_first")
+ )
+ self.downsample_layers.append(stem)
+ for i in range(3):
+ downsample_layer = nn.Sequential(
+ LayerNorm(dims[i], eps=1e-6, data_format="channels_first"),
+ nn.Conv2d(dims[i], dims[i+1], kernel_size=2, stride=2),
+ )
+ self.downsample_layers.append(downsample_layer)
+
+ self.stages = nn.ModuleList() # 4 feature resolution stages, each consisting of multiple residual blocks
+ dp_rates=[x.item() for x in torch.linspace(0, drop_path_rate, sum(depths))]
+ cur = 0
+ for i in range(4):
+ stage = nn.Sequential(
+ *[Block(dim=dims[i], drop_path=dp_rates[cur + j],
+ layer_scale_init_value=layer_scale_init_value) for j in range(depths[i])]
+ )
+ self.stages.append(stage)
+ cur += depths[i]
+
+ self.out_indices = out_indices
+
+ norm_layer = partial(LayerNorm, eps=1e-6, data_format="channels_first")
+ for i_layer in range(4):
+ layer = norm_layer(dims[i_layer])
+ layer_name = f'norm{i_layer}'
+ self.add_module(layer_name, layer)
+
+ def forward_features(self, x):
+ outs = {}
+ for i in range(4):
+ x = self.downsample_layers[i](x)
+ x = self.stages[i](x)
+ if i in self.out_indices:
+ norm_layer = getattr(self, f'norm{i}')
+ x_out = norm_layer(x)
+ outs["res{}".format(i + 2)] = x_out
+
+ return outs
+
+ def forward(self, x):
+ x = self.forward_features(x)
+ return x
+
+@BACKBONE_REGISTRY.register()
+class D2ConvNeXt(ConvNeXt, Backbone):
+ def __init__(self, cfg, input_shape):
+
+ in_chans = cfg.MODEL.CONVNEXT.IN_CHANNELS
+ depths = cfg.MODEL.CONVNEXT.DEPTHS
+ dims = cfg.MODEL.CONVNEXT.DIMS
+ drop_path_rate = cfg.MODEL.CONVNEXT.DROP_PATH_RATE
+ layer_scale_init_value = cfg.MODEL.CONVNEXT.LSIT
+ out_indices = cfg.MODEL.CONVNEXT.OUT_INDICES
+
+ super().__init__(
+ in_chans=in_chans,
+ depths=depths,
+ dims=dims,
+ drop_path_rate=drop_path_rate,
+ layer_scale_init_value=layer_scale_init_value,
+ out_indices=out_indices,
+ )
+
+ self._out_features = cfg.MODEL.CONVNEXT.OUT_FEATURES
+
+ self._out_feature_strides = {
+ "res2": 4,
+ "res3": 8,
+ "res4": 16,
+ "res5": 32,
+ }
+ self._out_feature_channels = {
+ "res2": self.num_features[0],
+ "res3": self.num_features[1],
+ "res4": self.num_features[2],
+ "res5": self.num_features[3],
+ }
+
+ def forward(self, x):
+ """
+ Args:
+ x: Tensor of shape (N,C,H,W). H, W must be a multiple of ``self.size_divisibility``.
+ Returns:
+ dict[str->Tensor]: names and the corresponding features
+ """
+ assert (
+ x.dim() == 4
+ ), f"ConvNeXt takes an input of shape (N, C, H, W). Got {x.shape} instead!"
+ outputs = {}
+ y = super().forward(x)
+ for k in y.keys():
+ if k in self._out_features:
+ outputs[k] = y[k]
+ return outputs
+
+ def output_shape(self):
+ return {
+ name: ShapeSpec(
+ channels=self._out_feature_channels[name], stride=self._out_feature_strides[name]
+ )
+ for name in self._out_features
+ }
+
+ @property
+ def size_divisibility(self):
+ return 32
\ No newline at end of file
diff --git a/annotator/OneFormer/oneformer/modeling/backbone/dinat.py b/annotator/OneFormer/oneformer/modeling/backbone/dinat.py
new file mode 100644
index 0000000000000000000000000000000000000000..ba0af1073b8390c79ff7cc9e0be5d70677abcd57
--- /dev/null
+++ b/annotator/OneFormer/oneformer/modeling/backbone/dinat.py
@@ -0,0 +1,296 @@
+# --------------------------------------------------------
+# Neighborhood Attention Transformer
+# Licensed under The MIT License
+# Written by Ali Hassani
+# --------------------------------------------------------
+
+# Modified by Jitesh Jain
+
+import torch
+import torch.nn as nn
+from timm.models.layers import DropPath
+from detectron2.modeling import BACKBONE_REGISTRY, Backbone, ShapeSpec
+
+from natten import NeighborhoodAttention2D as NeighborhoodAttention
+
+
+class ConvTokenizer(nn.Module):
+ def __init__(self, in_chans=3, embed_dim=96, norm_layer=None):
+ super().__init__()
+ self.proj = nn.Sequential(
+ nn.Conv2d(in_chans, embed_dim // 2, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1)),
+ nn.Conv2d(embed_dim // 2, embed_dim, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1)),
+ )
+ if norm_layer is not None:
+ self.norm = norm_layer(embed_dim)
+ else:
+ self.norm = None
+
+ def forward(self, x):
+ x = self.proj(x).permute(0, 2, 3, 1)
+ if self.norm is not None:
+ x = self.norm(x)
+ return x
+
+
+class ConvDownsampler(nn.Module):
+ def __init__(self, dim, norm_layer=nn.LayerNorm):
+ super().__init__()
+ self.reduction = nn.Conv2d(dim, 2 * dim, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)
+ self.norm = norm_layer(2 * dim)
+
+ def forward(self, x):
+ x = self.reduction(x.permute(0, 3, 1, 2)).permute(0, 2, 3, 1)
+ x = self.norm(x)
+ return x
+
+
+class Mlp(nn.Module):
+ def __init__(self, in_features, hidden_features=None, out_features=None, act_layer=nn.GELU, drop=0.):
+ super().__init__()
+ out_features = out_features or in_features
+ hidden_features = hidden_features or in_features
+ self.fc1 = nn.Linear(in_features, hidden_features)
+ self.act = act_layer()
+ self.fc2 = nn.Linear(hidden_features, out_features)
+ self.drop = nn.Dropout(drop)
+
+ def forward(self, x):
+ x = self.fc1(x)
+ x = self.act(x)
+ x = self.drop(x)
+ x = self.fc2(x)
+ x = self.drop(x)
+ return x
+
+
+class NATLayer(nn.Module):
+ def __init__(self, dim, num_heads, kernel_size=7, dilation=None,
+ mlp_ratio=4., qkv_bias=True, qk_scale=None, drop=0., attn_drop=0., drop_path=0.,
+ act_layer=nn.GELU, norm_layer=nn.LayerNorm, layer_scale=None):
+ super().__init__()
+ self.dim = dim
+ self.num_heads = num_heads
+ self.mlp_ratio = mlp_ratio
+
+ self.norm1 = norm_layer(dim)
+ self.attn = NeighborhoodAttention(
+ dim, kernel_size=kernel_size, dilation=dilation, num_heads=num_heads,
+ qkv_bias=qkv_bias, qk_scale=qk_scale, attn_drop=attn_drop, proj_drop=drop)
+
+ self.drop_path = DropPath(drop_path) if drop_path > 0. else nn.Identity()
+ self.norm2 = norm_layer(dim)
+ self.mlp = Mlp(in_features=dim, hidden_features=int(dim * mlp_ratio), act_layer=act_layer, drop=drop)
+ self.layer_scale = False
+ if layer_scale is not None and type(layer_scale) in [int, float]:
+ self.layer_scale = True
+ self.gamma1 = nn.Parameter(layer_scale * torch.ones(dim), requires_grad=True)
+ self.gamma2 = nn.Parameter(layer_scale * torch.ones(dim), requires_grad=True)
+
+ def forward(self, x):
+ if not self.layer_scale:
+ shortcut = x
+ x = self.norm1(x)
+ x = self.attn(x)
+ x = shortcut + self.drop_path(x)
+ x = x + self.drop_path(self.mlp(self.norm2(x)))
+ return x
+ shortcut = x
+ x = self.norm1(x)
+ x = self.attn(x)
+ x = shortcut + self.drop_path(self.gamma1 * x)
+ x = x + self.drop_path(self.gamma2 * self.mlp(self.norm2(x)))
+ return x
+
+
+
+class NATBlock(nn.Module):
+ def __init__(self, dim, depth, num_heads, kernel_size, dilations=None,
+ downsample=True,
+ mlp_ratio=4., qkv_bias=True, qk_scale=None, drop=0., attn_drop=0.,
+ drop_path=0., norm_layer=nn.LayerNorm, layer_scale=None):
+ super().__init__()
+ self.dim = dim
+ self.depth = depth
+
+ self.blocks = nn.ModuleList([
+ NATLayer(dim=dim,
+ num_heads=num_heads,
+ kernel_size=kernel_size,
+ dilation=None if dilations is None else dilations[i],
+ mlp_ratio=mlp_ratio,
+ qkv_bias=qkv_bias, qk_scale=qk_scale,
+ drop=drop, attn_drop=attn_drop,
+ drop_path=drop_path[i] if isinstance(drop_path, list) else drop_path,
+ norm_layer=norm_layer,
+ layer_scale=layer_scale)
+ for i in range(depth)])
+
+ self.downsample = None if not downsample else ConvDownsampler(dim=dim, norm_layer=norm_layer)
+
+ def forward(self, x):
+ for blk in self.blocks:
+ x = blk(x)
+ if self.downsample is None:
+ return x, x
+ return self.downsample(x), x
+
+
+class DiNAT(nn.Module):
+ def __init__(self,
+ embed_dim,
+ mlp_ratio,
+ depths,
+ num_heads,
+ drop_path_rate=0.2,
+ in_chans=3,
+ kernel_size=7,
+ dilations=None,
+ out_indices=(0, 1, 2, 3),
+ qkv_bias=True,
+ qk_scale=None,
+ drop_rate=0.,
+ attn_drop_rate=0.,
+ norm_layer=nn.LayerNorm,
+ frozen_stages=-1,
+ layer_scale=None,
+ **kwargs):
+ super().__init__()
+ self.num_levels = len(depths)
+ self.embed_dim = embed_dim
+ self.num_features = [int(embed_dim * 2 ** i) for i in range(self.num_levels)]
+ self.mlp_ratio = mlp_ratio
+
+ self.patch_embed = ConvTokenizer(in_chans=in_chans, embed_dim=embed_dim, norm_layer=norm_layer)
+
+ self.pos_drop = nn.Dropout(p=drop_rate)
+
+ dpr = [x.item() for x in torch.linspace(0, drop_path_rate, sum(depths))]
+ self.levels = nn.ModuleList()
+ for i in range(self.num_levels):
+ level = NATBlock(dim=int(embed_dim * 2 ** i),
+ depth=depths[i],
+ num_heads=num_heads[i],
+ kernel_size=kernel_size,
+ dilations=None if dilations is None else dilations[i],
+ mlp_ratio=self.mlp_ratio,
+ qkv_bias=qkv_bias, qk_scale=qk_scale,
+ drop=drop_rate, attn_drop=attn_drop_rate,
+ drop_path=dpr[sum(depths[:i]):sum(depths[:i + 1])],
+ norm_layer=norm_layer,
+ downsample=(i < self.num_levels - 1),
+ layer_scale=layer_scale)
+ self.levels.append(level)
+
+ # add a norm layer for each output
+ self.out_indices = out_indices
+ for i_layer in self.out_indices:
+ layer = norm_layer(self.num_features[i_layer])
+ layer_name = f'norm{i_layer}'
+ self.add_module(layer_name, layer)
+
+ self.frozen_stages = frozen_stages
+
+ def _freeze_stages(self):
+ if self.frozen_stages >= 0:
+ self.patch_embed.eval()
+ for param in self.patch_embed.parameters():
+ param.requires_grad = False
+
+ if self.frozen_stages >= 2:
+ for i in range(0, self.frozen_stages - 1):
+ m = self.network[i]
+ m.eval()
+ for param in m.parameters():
+ param.requires_grad = False
+
+ def train(self, mode=True):
+ super(DiNAT, self).train(mode)
+ self._freeze_stages()
+
+ def forward_embeddings(self, x):
+ x = self.patch_embed(x)
+ return x
+
+ def forward_tokens(self, x):
+ outs = {}
+ for idx, level in enumerate(self.levels):
+ x, xo = level(x)
+ if idx in self.out_indices:
+ norm_layer = getattr(self, f'norm{idx}')
+ x_out = norm_layer(xo)
+ outs["res{}".format(idx + 2)] = x_out.permute(0, 3, 1, 2).contiguous()
+ return outs
+
+ def forward(self, x):
+ x = self.forward_embeddings(x)
+ return self.forward_tokens(x)
+
+
+@BACKBONE_REGISTRY.register()
+class D2DiNAT(DiNAT, Backbone):
+ def __init__(self, cfg, input_shape):
+
+ embed_dim = cfg.MODEL.DiNAT.EMBED_DIM
+ mlp_ratio = cfg.MODEL.DiNAT.MLP_RATIO
+ depths = cfg.MODEL.DiNAT.DEPTHS
+ num_heads = cfg.MODEL.DiNAT.NUM_HEADS
+ drop_path_rate = cfg.MODEL.DiNAT.DROP_PATH_RATE
+ kernel_size = cfg.MODEL.DiNAT.KERNEL_SIZE
+ out_indices = cfg.MODEL.DiNAT.OUT_INDICES
+ dilations = cfg.MODEL.DiNAT.DILATIONS
+
+ super().__init__(
+ embed_dim=embed_dim,
+ mlp_ratio=mlp_ratio,
+ depths=depths,
+ num_heads=num_heads,
+ drop_path_rate=drop_path_rate,
+ kernel_size=kernel_size,
+ out_indices=out_indices,
+ dilations=dilations,
+ )
+
+ self._out_features = cfg.MODEL.DiNAT.OUT_FEATURES
+
+ self._out_feature_strides = {
+ "res2": 4,
+ "res3": 8,
+ "res4": 16,
+ "res5": 32,
+ }
+ self._out_feature_channels = {
+ "res2": self.num_features[0],
+ "res3": self.num_features[1],
+ "res4": self.num_features[2],
+ "res5": self.num_features[3],
+ }
+
+ def forward(self, x):
+ """
+ Args:
+ x: Tensor of shape (N,C,H,W). H, W must be a multiple of ``self.size_divisibility``.
+ Returns:
+ dict[str->Tensor]: names and the corresponding features
+ """
+ assert (
+ x.dim() == 4
+ ), f"DiNAT takes an input of shape (N, C, H, W). Got {x.shape} instead!"
+ outputs = {}
+ y = super().forward(x)
+ for k in y.keys():
+ if k in self._out_features:
+ outputs[k] = y[k]
+ return outputs
+
+ def output_shape(self):
+ return {
+ name: ShapeSpec(
+ channels=self._out_feature_channels[name], stride=self._out_feature_strides[name]
+ )
+ for name in self._out_features
+ }
+
+ @property
+ def size_divisibility(self):
+ return 32
diff --git a/annotator/OneFormer/oneformer/modeling/backbone/swin.py b/annotator/OneFormer/oneformer/modeling/backbone/swin.py
new file mode 100644
index 0000000000000000000000000000000000000000..3b099d84396ac31d22881e5b6c9e53d2d0abaef3
--- /dev/null
+++ b/annotator/OneFormer/oneformer/modeling/backbone/swin.py
@@ -0,0 +1,770 @@
+# --------------------------------------------------------
+# Swin Transformer
+# Copyright (c) 2021 Microsoft
+# Licensed under The MIT License [see LICENSE for details]
+# Written by Ze Liu, Yutong Lin, Yixuan Wei
+# --------------------------------------------------------
+
+# Copyright (c) Facebook, Inc. and its affiliates.
+# Modified by Bowen Cheng from https://github.com/SwinTransformer/Swin-Transformer-Semantic-Segmentation/blob/main/mmseg/models/backbones/swin_transformer.py
+
+import numpy as np
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+import torch.utils.checkpoint as checkpoint
+from timm.models.layers import DropPath, to_2tuple, trunc_normal_
+
+from detectron2.modeling import BACKBONE_REGISTRY, Backbone, ShapeSpec
+
+
+class Mlp(nn.Module):
+ """Multilayer perceptron."""
+
+ def __init__(
+ self, in_features, hidden_features=None, out_features=None, act_layer=nn.GELU, drop=0.0
+ ):
+ super().__init__()
+ out_features = out_features or in_features
+ hidden_features = hidden_features or in_features
+ self.fc1 = nn.Linear(in_features, hidden_features)
+ self.act = act_layer()
+ self.fc2 = nn.Linear(hidden_features, out_features)
+ self.drop = nn.Dropout(drop)
+
+ def forward(self, x):
+ x = self.fc1(x)
+ x = self.act(x)
+ x = self.drop(x)
+ x = self.fc2(x)
+ x = self.drop(x)
+ return x
+
+
+def window_partition(x, window_size):
+ """
+ Args:
+ x: (B, H, W, C)
+ window_size (int): window size
+ Returns:
+ windows: (num_windows*B, window_size, window_size, C)
+ """
+ B, H, W, C = x.shape
+ x = x.view(B, H // window_size, window_size, W // window_size, window_size, C)
+ windows = x.permute(0, 1, 3, 2, 4, 5).contiguous().view(-1, window_size, window_size, C)
+ return windows
+
+
+def window_reverse(windows, window_size, H, W):
+ """
+ Args:
+ windows: (num_windows*B, window_size, window_size, C)
+ window_size (int): Window size
+ H (int): Height of image
+ W (int): Width of image
+ Returns:
+ x: (B, H, W, C)
+ """
+ B = int(windows.shape[0] / (H * W / window_size / window_size))
+ x = windows.view(B, H // window_size, W // window_size, window_size, window_size, -1)
+ x = x.permute(0, 1, 3, 2, 4, 5).contiguous().view(B, H, W, -1)
+ return x
+
+
+class WindowAttention(nn.Module):
+ """Window based multi-head self attention (W-MSA) module with relative position bias.
+ It supports both of shifted and non-shifted window.
+ Args:
+ dim (int): Number of input channels.
+ window_size (tuple[int]): The height and width of the window.
+ num_heads (int): Number of attention heads.
+ qkv_bias (bool, optional): If True, add a learnable bias to query, key, value. Default: True
+ qk_scale (float | None, optional): Override default qk scale of head_dim ** -0.5 if set
+ attn_drop (float, optional): Dropout ratio of attention weight. Default: 0.0
+ proj_drop (float, optional): Dropout ratio of output. Default: 0.0
+ """
+
+ def __init__(
+ self,
+ dim,
+ window_size,
+ num_heads,
+ qkv_bias=True,
+ qk_scale=None,
+ attn_drop=0.0,
+ proj_drop=0.0,
+ ):
+
+ super().__init__()
+ self.dim = dim
+ self.window_size = window_size # Wh, Ww
+ self.num_heads = num_heads
+ head_dim = dim // num_heads
+ self.scale = qk_scale or head_dim ** -0.5
+
+ # define a parameter table of relative position bias
+ self.relative_position_bias_table = nn.Parameter(
+ torch.zeros((2 * window_size[0] - 1) * (2 * window_size[1] - 1), num_heads)
+ ) # 2*Wh-1 * 2*Ww-1, nH
+
+ # get pair-wise relative position index for each token inside the window
+ coords_h = torch.arange(self.window_size[0])
+ coords_w = torch.arange(self.window_size[1])
+ coords = torch.stack(torch.meshgrid([coords_h, coords_w])) # 2, Wh, Ww
+ coords_flatten = torch.flatten(coords, 1) # 2, Wh*Ww
+ relative_coords = coords_flatten[:, :, None] - coords_flatten[:, None, :] # 2, Wh*Ww, Wh*Ww
+ relative_coords = relative_coords.permute(1, 2, 0).contiguous() # Wh*Ww, Wh*Ww, 2
+ relative_coords[:, :, 0] += self.window_size[0] - 1 # shift to start from 0
+ relative_coords[:, :, 1] += self.window_size[1] - 1
+ relative_coords[:, :, 0] *= 2 * self.window_size[1] - 1
+ relative_position_index = relative_coords.sum(-1) # Wh*Ww, Wh*Ww
+ self.register_buffer("relative_position_index", relative_position_index)
+
+ self.qkv = nn.Linear(dim, dim * 3, bias=qkv_bias)
+ self.attn_drop = nn.Dropout(attn_drop)
+ self.proj = nn.Linear(dim, dim)
+ self.proj_drop = nn.Dropout(proj_drop)
+
+ trunc_normal_(self.relative_position_bias_table, std=0.02)
+ self.softmax = nn.Softmax(dim=-1)
+
+ def forward(self, x, mask=None):
+ """Forward function.
+ Args:
+ x: input features with shape of (num_windows*B, N, C)
+ mask: (0/-inf) mask with shape of (num_windows, Wh*Ww, Wh*Ww) or None
+ """
+ B_, N, C = x.shape
+ qkv = (
+ self.qkv(x)
+ .reshape(B_, N, 3, self.num_heads, C // self.num_heads)
+ .permute(2, 0, 3, 1, 4)
+ )
+ q, k, v = qkv[0], qkv[1], qkv[2] # make torchscript happy (cannot use tensor as tuple)
+
+ q = q * self.scale
+ attn = q @ k.transpose(-2, -1)
+
+ relative_position_bias = self.relative_position_bias_table[
+ self.relative_position_index.view(-1)
+ ].view(
+ self.window_size[0] * self.window_size[1], self.window_size[0] * self.window_size[1], -1
+ ) # Wh*Ww,Wh*Ww,nH
+ relative_position_bias = relative_position_bias.permute(
+ 2, 0, 1
+ ).contiguous() # nH, Wh*Ww, Wh*Ww
+ attn = attn + relative_position_bias.unsqueeze(0)
+
+ if mask is not None:
+ nW = mask.shape[0]
+ attn = attn.view(B_ // nW, nW, self.num_heads, N, N) + mask.unsqueeze(1).unsqueeze(0)
+ attn = attn.view(-1, self.num_heads, N, N)
+ attn = self.softmax(attn)
+ else:
+ attn = self.softmax(attn)
+
+ attn = self.attn_drop(attn)
+
+ x = (attn @ v).transpose(1, 2).reshape(B_, N, C)
+ x = self.proj(x)
+ x = self.proj_drop(x)
+ return x
+
+
+class SwinTransformerBlock(nn.Module):
+ """Swin Transformer Block.
+ Args:
+ dim (int): Number of input channels.
+ num_heads (int): Number of attention heads.
+ window_size (int): Window size.
+ shift_size (int): Shift size for SW-MSA.
+ mlp_ratio (float): Ratio of mlp hidden dim to embedding dim.
+ qkv_bias (bool, optional): If True, add a learnable bias to query, key, value. Default: True
+ qk_scale (float | None, optional): Override default qk scale of head_dim ** -0.5 if set.
+ drop (float, optional): Dropout rate. Default: 0.0
+ attn_drop (float, optional): Attention dropout rate. Default: 0.0
+ drop_path (float, optional): Stochastic depth rate. Default: 0.0
+ act_layer (nn.Module, optional): Activation layer. Default: nn.GELU
+ norm_layer (nn.Module, optional): Normalization layer. Default: nn.LayerNorm
+ """
+
+ def __init__(
+ self,
+ dim,
+ num_heads,
+ window_size=7,
+ shift_size=0,
+ mlp_ratio=4.0,
+ qkv_bias=True,
+ qk_scale=None,
+ drop=0.0,
+ attn_drop=0.0,
+ drop_path=0.0,
+ act_layer=nn.GELU,
+ norm_layer=nn.LayerNorm,
+ ):
+ super().__init__()
+ self.dim = dim
+ self.num_heads = num_heads
+ self.window_size = window_size
+ self.shift_size = shift_size
+ self.mlp_ratio = mlp_ratio
+ assert 0 <= self.shift_size < self.window_size, "shift_size must in 0-window_size"
+
+ self.norm1 = norm_layer(dim)
+ self.attn = WindowAttention(
+ dim,
+ window_size=to_2tuple(self.window_size),
+ num_heads=num_heads,
+ qkv_bias=qkv_bias,
+ qk_scale=qk_scale,
+ attn_drop=attn_drop,
+ proj_drop=drop,
+ )
+
+ self.drop_path = DropPath(drop_path) if drop_path > 0.0 else nn.Identity()
+ self.norm2 = norm_layer(dim)
+ mlp_hidden_dim = int(dim * mlp_ratio)
+ self.mlp = Mlp(
+ in_features=dim, hidden_features=mlp_hidden_dim, act_layer=act_layer, drop=drop
+ )
+
+ self.H = None
+ self.W = None
+
+ def forward(self, x, mask_matrix):
+ """Forward function.
+ Args:
+ x: Input feature, tensor size (B, H*W, C).
+ H, W: Spatial resolution of the input feature.
+ mask_matrix: Attention mask for cyclic shift.
+ """
+ B, L, C = x.shape
+ H, W = self.H, self.W
+ assert L == H * W, "input feature has wrong size"
+
+ shortcut = x
+ x = self.norm1(x)
+ x = x.view(B, H, W, C)
+
+ # pad feature maps to multiples of window size
+ pad_l = pad_t = 0
+ pad_r = (self.window_size - W % self.window_size) % self.window_size
+ pad_b = (self.window_size - H % self.window_size) % self.window_size
+ x = F.pad(x, (0, 0, pad_l, pad_r, pad_t, pad_b))
+ _, Hp, Wp, _ = x.shape
+
+ # cyclic shift
+ if self.shift_size > 0:
+ shifted_x = torch.roll(x, shifts=(-self.shift_size, -self.shift_size), dims=(1, 2))
+ attn_mask = mask_matrix
+ else:
+ shifted_x = x
+ attn_mask = None
+
+ # partition windows
+ x_windows = window_partition(
+ shifted_x, self.window_size
+ ) # nW*B, window_size, window_size, C
+ x_windows = x_windows.view(
+ -1, self.window_size * self.window_size, C
+ ) # nW*B, window_size*window_size, C
+
+ # W-MSA/SW-MSA
+ attn_windows = self.attn(x_windows, mask=attn_mask) # nW*B, window_size*window_size, C
+
+ # merge windows
+ attn_windows = attn_windows.view(-1, self.window_size, self.window_size, C)
+ shifted_x = window_reverse(attn_windows, self.window_size, Hp, Wp) # B H' W' C
+
+ # reverse cyclic shift
+ if self.shift_size > 0:
+ x = torch.roll(shifted_x, shifts=(self.shift_size, self.shift_size), dims=(1, 2))
+ else:
+ x = shifted_x
+
+ if pad_r > 0 or pad_b > 0:
+ x = x[:, :H, :W, :].contiguous()
+
+ x = x.view(B, H * W, C)
+
+ # FFN
+ x = shortcut + self.drop_path(x)
+ x = x + self.drop_path(self.mlp(self.norm2(x)))
+
+ return x
+
+
+class PatchMerging(nn.Module):
+ """Patch Merging Layer
+ Args:
+ dim (int): Number of input channels.
+ norm_layer (nn.Module, optional): Normalization layer. Default: nn.LayerNorm
+ """
+
+ def __init__(self, dim, norm_layer=nn.LayerNorm):
+ super().__init__()
+ self.dim = dim
+ self.reduction = nn.Linear(4 * dim, 2 * dim, bias=False)
+ self.norm = norm_layer(4 * dim)
+
+ def forward(self, x, H, W):
+ """Forward function.
+ Args:
+ x: Input feature, tensor size (B, H*W, C).
+ H, W: Spatial resolution of the input feature.
+ """
+ B, L, C = x.shape
+ assert L == H * W, "input feature has wrong size"
+
+ x = x.view(B, H, W, C)
+
+ # padding
+ pad_input = (H % 2 == 1) or (W % 2 == 1)
+ if pad_input:
+ x = F.pad(x, (0, 0, 0, W % 2, 0, H % 2))
+
+ x0 = x[:, 0::2, 0::2, :] # B H/2 W/2 C
+ x1 = x[:, 1::2, 0::2, :] # B H/2 W/2 C
+ x2 = x[:, 0::2, 1::2, :] # B H/2 W/2 C
+ x3 = x[:, 1::2, 1::2, :] # B H/2 W/2 C
+ x = torch.cat([x0, x1, x2, x3], -1) # B H/2 W/2 4*C
+ x = x.view(B, -1, 4 * C) # B H/2*W/2 4*C
+
+ x = self.norm(x)
+ x = self.reduction(x)
+
+ return x
+
+
+class BasicLayer(nn.Module):
+ """A basic Swin Transformer layer for one stage.
+ Args:
+ dim (int): Number of feature channels
+ depth (int): Depths of this stage.
+ num_heads (int): Number of attention head.
+ window_size (int): Local window size. Default: 7.
+ mlp_ratio (float): Ratio of mlp hidden dim to embedding dim. Default: 4.
+ qkv_bias (bool, optional): If True, add a learnable bias to query, key, value. Default: True
+ qk_scale (float | None, optional): Override default qk scale of head_dim ** -0.5 if set.
+ drop (float, optional): Dropout rate. Default: 0.0
+ attn_drop (float, optional): Attention dropout rate. Default: 0.0
+ drop_path (float | tuple[float], optional): Stochastic depth rate. Default: 0.0
+ norm_layer (nn.Module, optional): Normalization layer. Default: nn.LayerNorm
+ downsample (nn.Module | None, optional): Downsample layer at the end of the layer. Default: None
+ use_checkpoint (bool): Whether to use checkpointing to save memory. Default: False.
+ """
+
+ def __init__(
+ self,
+ dim,
+ depth,
+ num_heads,
+ window_size=7,
+ mlp_ratio=4.0,
+ qkv_bias=True,
+ qk_scale=None,
+ drop=0.0,
+ attn_drop=0.0,
+ drop_path=0.0,
+ norm_layer=nn.LayerNorm,
+ downsample=None,
+ use_checkpoint=False,
+ ):
+ super().__init__()
+ self.window_size = window_size
+ self.shift_size = window_size // 2
+ self.depth = depth
+ self.use_checkpoint = use_checkpoint
+
+ # build blocks
+ self.blocks = nn.ModuleList(
+ [
+ SwinTransformerBlock(
+ dim=dim,
+ num_heads=num_heads,
+ window_size=window_size,
+ shift_size=0 if (i % 2 == 0) else window_size // 2,
+ mlp_ratio=mlp_ratio,
+ qkv_bias=qkv_bias,
+ qk_scale=qk_scale,
+ drop=drop,
+ attn_drop=attn_drop,
+ drop_path=drop_path[i] if isinstance(drop_path, list) else drop_path,
+ norm_layer=norm_layer,
+ )
+ for i in range(depth)
+ ]
+ )
+
+ # patch merging layer
+ if downsample is not None:
+ self.downsample = downsample(dim=dim, norm_layer=norm_layer)
+ else:
+ self.downsample = None
+
+ def forward(self, x, H, W):
+ """Forward function.
+ Args:
+ x: Input feature, tensor size (B, H*W, C).
+ H, W: Spatial resolution of the input feature.
+ """
+
+ # calculate attention mask for SW-MSA
+ Hp = int(np.ceil(H / self.window_size)) * self.window_size
+ Wp = int(np.ceil(W / self.window_size)) * self.window_size
+ img_mask = torch.zeros((1, Hp, Wp, 1), device=x.device) # 1 Hp Wp 1
+ h_slices = (
+ slice(0, -self.window_size),
+ slice(-self.window_size, -self.shift_size),
+ slice(-self.shift_size, None),
+ )
+ w_slices = (
+ slice(0, -self.window_size),
+ slice(-self.window_size, -self.shift_size),
+ slice(-self.shift_size, None),
+ )
+ cnt = 0
+ for h in h_slices:
+ for w in w_slices:
+ img_mask[:, h, w, :] = cnt
+ cnt += 1
+
+ mask_windows = window_partition(
+ img_mask, self.window_size
+ ) # nW, window_size, window_size, 1
+ mask_windows = mask_windows.view(-1, self.window_size * self.window_size)
+ attn_mask = mask_windows.unsqueeze(1) - mask_windows.unsqueeze(2)
+ attn_mask = attn_mask.masked_fill(attn_mask != 0, float(-100.0)).masked_fill(
+ attn_mask == 0, float(0.0)
+ )
+
+ for blk in self.blocks:
+ blk.H, blk.W = H, W
+ if self.use_checkpoint:
+ x = checkpoint.checkpoint(blk, x, attn_mask)
+ else:
+ x = blk(x, attn_mask)
+ if self.downsample is not None:
+ x_down = self.downsample(x, H, W)
+ Wh, Ww = (H + 1) // 2, (W + 1) // 2
+ return x, H, W, x_down, Wh, Ww
+ else:
+ return x, H, W, x, H, W
+
+
+class PatchEmbed(nn.Module):
+ """Image to Patch Embedding
+ Args:
+ patch_size (int): Patch token size. Default: 4.
+ in_chans (int): Number of input image channels. Default: 3.
+ embed_dim (int): Number of linear projection output channels. Default: 96.
+ norm_layer (nn.Module, optional): Normalization layer. Default: None
+ """
+
+ def __init__(self, patch_size=4, in_chans=3, embed_dim=96, norm_layer=None):
+ super().__init__()
+ patch_size = to_2tuple(patch_size)
+ self.patch_size = patch_size
+
+ self.in_chans = in_chans
+ self.embed_dim = embed_dim
+
+ self.proj = nn.Conv2d(in_chans, embed_dim, kernel_size=patch_size, stride=patch_size)
+ if norm_layer is not None:
+ self.norm = norm_layer(embed_dim)
+ else:
+ self.norm = None
+
+ def forward(self, x):
+ """Forward function."""
+ # padding
+ _, _, H, W = x.size()
+ if W % self.patch_size[1] != 0:
+ x = F.pad(x, (0, self.patch_size[1] - W % self.patch_size[1]))
+ if H % self.patch_size[0] != 0:
+ x = F.pad(x, (0, 0, 0, self.patch_size[0] - H % self.patch_size[0]))
+
+ x = self.proj(x) # B C Wh Ww
+ if self.norm is not None:
+ Wh, Ww = x.size(2), x.size(3)
+ x = x.flatten(2).transpose(1, 2)
+ x = self.norm(x)
+ x = x.transpose(1, 2).view(-1, self.embed_dim, Wh, Ww)
+
+ return x
+
+
+class SwinTransformer(nn.Module):
+ """Swin Transformer backbone.
+ A PyTorch impl of : `Swin Transformer: Hierarchical Vision Transformer using Shifted Windows` -
+ https://arxiv.org/pdf/2103.14030
+ Args:
+ pretrain_img_size (int): Input image size for training the pretrained model,
+ used in absolute postion embedding. Default 224.
+ patch_size (int | tuple(int)): Patch size. Default: 4.
+ in_chans (int): Number of input image channels. Default: 3.
+ embed_dim (int): Number of linear projection output channels. Default: 96.
+ depths (tuple[int]): Depths of each Swin Transformer stage.
+ num_heads (tuple[int]): Number of attention head of each stage.
+ window_size (int): Window size. Default: 7.
+ mlp_ratio (float): Ratio of mlp hidden dim to embedding dim. Default: 4.
+ qkv_bias (bool): If True, add a learnable bias to query, key, value. Default: True
+ qk_scale (float): Override default qk scale of head_dim ** -0.5 if set.
+ drop_rate (float): Dropout rate.
+ attn_drop_rate (float): Attention dropout rate. Default: 0.
+ drop_path_rate (float): Stochastic depth rate. Default: 0.2.
+ norm_layer (nn.Module): Normalization layer. Default: nn.LayerNorm.
+ ape (bool): If True, add absolute position embedding to the patch embedding. Default: False.
+ patch_norm (bool): If True, add normalization after patch embedding. Default: True.
+ out_indices (Sequence[int]): Output from which stages.
+ frozen_stages (int): Stages to be frozen (stop grad and set eval mode).
+ -1 means not freezing any parameters.
+ use_checkpoint (bool): Whether to use checkpointing to save memory. Default: False.
+ """
+
+ def __init__(
+ self,
+ pretrain_img_size=224,
+ patch_size=4,
+ in_chans=3,
+ embed_dim=96,
+ depths=[2, 2, 6, 2],
+ num_heads=[3, 6, 12, 24],
+ window_size=7,
+ mlp_ratio=4.0,
+ qkv_bias=True,
+ qk_scale=None,
+ drop_rate=0.0,
+ attn_drop_rate=0.0,
+ drop_path_rate=0.2,
+ norm_layer=nn.LayerNorm,
+ ape=False,
+ patch_norm=True,
+ out_indices=(0, 1, 2, 3),
+ frozen_stages=-1,
+ use_checkpoint=False,
+ ):
+ super().__init__()
+
+ self.pretrain_img_size = pretrain_img_size
+ self.num_layers = len(depths)
+ self.embed_dim = embed_dim
+ self.ape = ape
+ self.patch_norm = patch_norm
+ self.out_indices = out_indices
+ self.frozen_stages = frozen_stages
+
+ # split image into non-overlapping patches
+ self.patch_embed = PatchEmbed(
+ patch_size=patch_size,
+ in_chans=in_chans,
+ embed_dim=embed_dim,
+ norm_layer=norm_layer if self.patch_norm else None,
+ )
+
+ # absolute position embedding
+ if self.ape:
+ pretrain_img_size = to_2tuple(pretrain_img_size)
+ patch_size = to_2tuple(patch_size)
+ patches_resolution = [
+ pretrain_img_size[0] // patch_size[0],
+ pretrain_img_size[1] // patch_size[1],
+ ]
+
+ self.absolute_pos_embed = nn.Parameter(
+ torch.zeros(1, embed_dim, patches_resolution[0], patches_resolution[1])
+ )
+ trunc_normal_(self.absolute_pos_embed, std=0.02)
+
+ self.pos_drop = nn.Dropout(p=drop_rate)
+
+ # stochastic depth
+ dpr = [
+ x.item() for x in torch.linspace(0, drop_path_rate, sum(depths))
+ ] # stochastic depth decay rule
+
+ # build layers
+ self.layers = nn.ModuleList()
+ for i_layer in range(self.num_layers):
+ layer = BasicLayer(
+ dim=int(embed_dim * 2 ** i_layer),
+ depth=depths[i_layer],
+ num_heads=num_heads[i_layer],
+ window_size=window_size,
+ mlp_ratio=mlp_ratio,
+ qkv_bias=qkv_bias,
+ qk_scale=qk_scale,
+ drop=drop_rate,
+ attn_drop=attn_drop_rate,
+ drop_path=dpr[sum(depths[:i_layer]) : sum(depths[: i_layer + 1])],
+ norm_layer=norm_layer,
+ downsample=PatchMerging if (i_layer < self.num_layers - 1) else None,
+ use_checkpoint=use_checkpoint,
+ )
+ self.layers.append(layer)
+
+ num_features = [int(embed_dim * 2 ** i) for i in range(self.num_layers)]
+ self.num_features = num_features
+
+ # add a norm layer for each output
+ for i_layer in out_indices:
+ layer = norm_layer(num_features[i_layer])
+ layer_name = f"norm{i_layer}"
+ self.add_module(layer_name, layer)
+
+ self._freeze_stages()
+
+ def _freeze_stages(self):
+ if self.frozen_stages >= 0:
+ self.patch_embed.eval()
+ for param in self.patch_embed.parameters():
+ param.requires_grad = False
+
+ if self.frozen_stages >= 1 and self.ape:
+ self.absolute_pos_embed.requires_grad = False
+
+ if self.frozen_stages >= 2:
+ self.pos_drop.eval()
+ for i in range(0, self.frozen_stages - 1):
+ m = self.layers[i]
+ m.eval()
+ for param in m.parameters():
+ param.requires_grad = False
+
+ def init_weights(self, pretrained=None):
+ """Initialize the weights in backbone.
+ Args:
+ pretrained (str, optional): Path to pre-trained weights.
+ Defaults to None.
+ """
+
+ def _init_weights(m):
+ if isinstance(m, nn.Linear):
+ trunc_normal_(m.weight, std=0.02)
+ if isinstance(m, nn.Linear) and m.bias is not None:
+ nn.init.constant_(m.bias, 0)
+ elif isinstance(m, nn.LayerNorm):
+ nn.init.constant_(m.bias, 0)
+ nn.init.constant_(m.weight, 1.0)
+
+ def forward(self, x):
+ """Forward function."""
+ x = self.patch_embed(x)
+
+ Wh, Ww = x.size(2), x.size(3)
+ if self.ape:
+ # interpolate the position embedding to the corresponding size
+ absolute_pos_embed = F.interpolate(
+ self.absolute_pos_embed, size=(Wh, Ww), mode="bicubic"
+ )
+ x = (x + absolute_pos_embed).flatten(2).transpose(1, 2) # B Wh*Ww C
+ else:
+ x = x.flatten(2).transpose(1, 2)
+ x = self.pos_drop(x)
+
+ outs = {}
+ for i in range(self.num_layers):
+ layer = self.layers[i]
+ x_out, H, W, x, Wh, Ww = layer(x, Wh, Ww)
+
+ if i in self.out_indices:
+ norm_layer = getattr(self, f"norm{i}")
+ x_out = norm_layer(x_out)
+
+ out = x_out.view(-1, H, W, self.num_features[i]).permute(0, 3, 1, 2).contiguous()
+ outs["res{}".format(i + 2)] = out
+
+ return outs
+
+ def train(self, mode=True):
+ """Convert the model into training mode while keep layers freezed."""
+ super(SwinTransformer, self).train(mode)
+ self._freeze_stages()
+
+
+@BACKBONE_REGISTRY.register()
+class D2SwinTransformer(SwinTransformer, Backbone):
+ def __init__(self, cfg, input_shape):
+
+ pretrain_img_size = cfg.MODEL.SWIN.PRETRAIN_IMG_SIZE
+ patch_size = cfg.MODEL.SWIN.PATCH_SIZE
+ in_chans = 3
+ embed_dim = cfg.MODEL.SWIN.EMBED_DIM
+ depths = cfg.MODEL.SWIN.DEPTHS
+ num_heads = cfg.MODEL.SWIN.NUM_HEADS
+ window_size = cfg.MODEL.SWIN.WINDOW_SIZE
+ mlp_ratio = cfg.MODEL.SWIN.MLP_RATIO
+ qkv_bias = cfg.MODEL.SWIN.QKV_BIAS
+ qk_scale = cfg.MODEL.SWIN.QK_SCALE
+ drop_rate = cfg.MODEL.SWIN.DROP_RATE
+ attn_drop_rate = cfg.MODEL.SWIN.ATTN_DROP_RATE
+ drop_path_rate = cfg.MODEL.SWIN.DROP_PATH_RATE
+ norm_layer = nn.LayerNorm
+ ape = cfg.MODEL.SWIN.APE
+ patch_norm = cfg.MODEL.SWIN.PATCH_NORM
+ use_checkpoint = cfg.MODEL.SWIN.USE_CHECKPOINT
+
+ super().__init__(
+ pretrain_img_size,
+ patch_size,
+ in_chans,
+ embed_dim,
+ depths,
+ num_heads,
+ window_size,
+ mlp_ratio,
+ qkv_bias,
+ qk_scale,
+ drop_rate,
+ attn_drop_rate,
+ drop_path_rate,
+ norm_layer,
+ ape,
+ patch_norm,
+ use_checkpoint=use_checkpoint,
+ )
+
+ self._out_features = cfg.MODEL.SWIN.OUT_FEATURES
+
+ self._out_feature_strides = {
+ "res2": 4,
+ "res3": 8,
+ "res4": 16,
+ "res5": 32,
+ }
+ self._out_feature_channels = {
+ "res2": self.num_features[0],
+ "res3": self.num_features[1],
+ "res4": self.num_features[2],
+ "res5": self.num_features[3],
+ }
+
+ def forward(self, x):
+ """
+ Args:
+ x: Tensor of shape (N,C,H,W). H, W must be a multiple of ``self.size_divisibility``.
+ Returns:
+ dict[str->Tensor]: names and the corresponding features
+ """
+ assert (
+ x.dim() == 4
+ ), f"SwinTransformer takes an input of shape (N, C, H, W). Got {x.shape} instead!"
+ outputs = {}
+ y = super().forward(x)
+ for k in y.keys():
+ if k in self._out_features:
+ outputs[k] = y[k]
+ return outputs
+
+ def output_shape(self):
+ return {
+ name: ShapeSpec(
+ channels=self._out_feature_channels[name], stride=self._out_feature_strides[name]
+ )
+ for name in self._out_features
+ }
+
+ @property
+ def size_divisibility(self):
+ return 32
diff --git a/annotator/OneFormer/oneformer/modeling/criterion.py b/annotator/OneFormer/oneformer/modeling/criterion.py
new file mode 100644
index 0000000000000000000000000000000000000000..dbc5c5e78b11be8996a22f99518add5a01341adf
--- /dev/null
+++ b/annotator/OneFormer/oneformer/modeling/criterion.py
@@ -0,0 +1,330 @@
+# ------------------------------------------------------------------------------
+# Reference: https://github.com/facebookresearch/Mask2Former/blob/main/mask2former/modeling/criterion.py
+# Modified by Jitesh Jain (https://github.com/praeclarumjj3)
+# ------------------------------------------------------------------------------
+
+"""
+OneFormer criterion.
+"""
+import logging
+
+import torch
+import torch.nn.functional as F
+from torch import nn
+
+from detectron2.utils.comm import get_world_size
+from detectron2.projects.point_rend.point_features import (
+ get_uncertain_point_coords_with_randomness,
+ point_sample,
+)
+
+from ..utils.misc import is_dist_avail_and_initialized, nested_tensor_from_tensor_list
+from ..utils import box_ops
+import torch.distributed as dist
+import diffdist.functional as diff_dist
+import numpy as np
+
+def dist_collect(x):
+ """ collect all tensor from all GPUs
+ args:
+ x: shape (mini_batch, ...)
+ returns:
+ shape (mini_batch * num_gpu, ...)
+ """
+ x = x.contiguous()
+ out_list = [torch.zeros_like(x, device=x.device, dtype=x.dtype).contiguous() for _ in range(dist.get_world_size())]
+ out_list = diff_dist.all_gather(out_list, x)
+ return torch.cat(out_list, dim=0).contiguous()
+
+def dice_loss(
+ inputs: torch.Tensor,
+ targets: torch.Tensor,
+ num_masks: float,
+ ):
+ """
+ Compute the DICE loss, similar to generalized IOU for masks
+ Args:
+ inputs: A float tensor of arbitrary shape.
+ The predictions for each example.
+ targets: A float tensor with the same shape as inputs. Stores the binary
+ classification label for each element in inputs
+ (0 for the negative class and 1 for the positive class).
+ """
+ inputs = inputs.sigmoid()
+ inputs = inputs.flatten(1)
+ numerator = 2 * (inputs * targets).sum(-1)
+ denominator = inputs.sum(-1) + targets.sum(-1)
+ loss = 1 - (numerator + 1) / (denominator + 1)
+ return loss.sum() / num_masks
+
+
+dice_loss_jit = torch.jit.script(
+ dice_loss
+) # type: torch.jit.ScriptModule
+
+
+def sigmoid_ce_loss(
+ inputs: torch.Tensor,
+ targets: torch.Tensor,
+ num_masks: float,
+ ):
+ """
+ Args:
+ inputs: A float tensor of arbitrary shape.
+ The predictions for each example.
+ targets: A float tensor with the same shape as inputs. Stores the binary
+ classification label for each element in inputs
+ (0 for the negative class and 1 for the positive class).
+ Returns:
+ Loss tensor
+ """
+ loss = F.binary_cross_entropy_with_logits(inputs, targets, reduction="none")
+ loss = loss.mean(1)
+ return loss.sum() / num_masks
+
+
+sigmoid_ce_loss_jit = torch.jit.script(
+ sigmoid_ce_loss
+) # type: torch.jit.ScriptModule
+
+
+def calculate_uncertainty(logits):
+ """
+ We estimate uncerainty as L1 distance between 0.0 and the logit prediction in 'logits' for the
+ foreground class in `classes`.
+ Args:
+ logits (Tensor): A tensor of shape (R, 1, ...) for class-specific or
+ class-agnostic, where R is the total number of predicted masks in all images and C is
+ the number of foreground classes. The values are logits.
+ Returns:
+ scores (Tensor): A tensor of shape (R, 1, ...) that contains uncertainty scores with
+ the most uncertain locations having the highest uncertainty score.
+ """
+ assert logits.shape[1] == 1
+ gt_class_logits = logits.clone()
+ return -(torch.abs(gt_class_logits))
+
+
+class SetCriterion(nn.Module):
+ """This class computes the loss for DETR.
+ The process happens in two steps:
+ 1) we compute hungarian assignment between ground truth boxes and the outputs of the model
+ 2) we supervise each pair of matched ground-truth / prediction (supervise class and box)
+ """
+
+ def __init__(self, num_classes, matcher, weight_dict, eos_coef, losses,
+ num_points, oversample_ratio, importance_sample_ratio, contrast_temperature=None):
+ """Create the criterion.
+ Parameters:
+ num_classes: number of object categories, omitting the special no-object category
+ matcher: module able to compute a matching between targets and proposals
+ weight_dict: dict containing as key the names of the losses and as values their relative weight.
+ eos_coef: relative classification weight applied to the no-object category
+ losses: list of all the losses to be applied. See get_loss for list of available losses.
+ """
+ super().__init__()
+ self.num_classes = num_classes
+ self.matcher = matcher
+ self.weight_dict = weight_dict
+ self.eos_coef = eos_coef
+ self.losses = losses
+ empty_weight = torch.ones(self.num_classes + 1)
+ empty_weight[-1] = self.eos_coef
+ self.register_buffer("empty_weight", empty_weight)
+ self.cross_entropy = nn.CrossEntropyLoss()
+
+ # pointwise mask loss parameters
+ self.num_points = num_points
+ self.oversample_ratio = oversample_ratio
+ self.importance_sample_ratio = importance_sample_ratio
+ self.contrast_temperature = contrast_temperature
+ if self.contrast_temperature is not None:
+ self.logit_scale = nn.Parameter(torch.ones([]) * np.log(1 / contrast_temperature))
+
+
+ def loss_contrastive(self, outputs, targets, indices, num_masks):
+ assert "contrastive_logits" in outputs
+ assert "texts" in outputs
+ image_x = outputs["contrastive_logits"].float()
+
+ batch_size = image_x.shape[0]
+ # get label globally
+ if is_dist_avail_and_initialized():
+ labels = torch.arange(batch_size, dtype=torch.long, device=image_x.device) + batch_size * dist.get_rank()
+ else:
+ labels = torch.arange(batch_size, dtype=torch.long, device=image_x.device)
+
+ text_x = outputs["texts"]
+
+ # [B, C]
+ image_x = F.normalize(image_x.flatten(1), dim=-1)
+ text_x = F.normalize(text_x.flatten(1), dim=-1)
+
+ if is_dist_avail_and_initialized():
+ logits_per_img = image_x @ dist_collect(text_x).t()
+ logits_per_text = text_x @ dist_collect(image_x).t()
+ else:
+ logits_per_img = image_x @ text_x.t()
+ logits_per_text = text_x @ image_x.t()
+
+ logit_scale = torch.clamp(self.logit_scale.exp(), max=100)
+ loss_img = self.cross_entropy(logits_per_img * logit_scale, labels)
+ loss_text = self.cross_entropy(logits_per_text * logit_scale, labels)
+
+ loss_contrastive = loss_img + loss_text
+
+ losses = {"loss_contrastive": loss_contrastive}
+ return losses
+
+ def loss_labels(self, outputs, targets, indices, num_masks):
+ """Classification loss (NLL)
+ targets dicts must contain the key "labels" containing a tensor of dim [nb_target_boxes]
+ """
+ assert "pred_logits" in outputs
+ src_logits = outputs["pred_logits"].float()
+
+ idx = self._get_src_permutation_idx(indices)
+ target_classes_o = torch.cat([t["labels"][J] for t, (_, J) in zip(targets, indices)])
+ target_classes = torch.full(
+ src_logits.shape[:2], self.num_classes, dtype=torch.int64, device=src_logits.device
+ )
+ target_classes[idx] = target_classes_o
+
+ ce_weight = torch.full(
+ src_logits.shape[:2], self.eos_coef, dtype=torch.float32, device=src_logits.device
+ )
+ ce_weight[idx] = torch.tensor(1.).to(target_classes.device)
+
+ loss_ce = F.cross_entropy(src_logits.transpose(1, 2), target_classes, self.empty_weight, reduce=False, reduction="none")
+ loss_ce = loss_ce.sum(1) / ce_weight.sum()
+ loss_ce = loss_ce.sum()
+ losses = {"loss_ce": loss_ce}
+ return losses
+
+ def loss_masks(self, outputs, targets, indices, num_masks):
+ """Compute the losses related to the masks: the focal loss and the dice loss.
+ targets dicts must contain the key "masks" containing a tensor of dim [nb_target_boxes, h, w]
+ """
+ assert "pred_masks" in outputs
+
+ src_idx = self._get_src_permutation_idx(indices)
+ tgt_idx = self._get_tgt_permutation_idx(indices)
+ src_masks = outputs["pred_masks"]
+ src_masks = src_masks[src_idx]
+ masks = [t["masks"] for t in targets]
+ # TODO use valid to mask invalid areas due to padding in loss
+ target_masks, valid = nested_tensor_from_tensor_list(masks).decompose()
+ target_masks = target_masks.to(src_masks)
+ target_masks = target_masks[tgt_idx]
+
+ # No need to upsample predictions as we are using normalized coordinates :)
+ # N x 1 x H x W
+ src_masks = src_masks[:, None]
+ target_masks = target_masks[:, None]
+
+ with torch.no_grad():
+ # sample point_coords
+ point_coords = get_uncertain_point_coords_with_randomness(
+ src_masks,
+ lambda logits: calculate_uncertainty(logits),
+ self.num_points,
+ self.oversample_ratio,
+ self.importance_sample_ratio,
+ )
+ # get gt labels
+ point_labels = point_sample(
+ target_masks,
+ point_coords,
+ align_corners=False,
+ ).squeeze(1)
+
+ point_logits = point_sample(
+ src_masks,
+ point_coords,
+ align_corners=False,
+ ).squeeze(1)
+
+ losses = {
+ "loss_mask": sigmoid_ce_loss_jit(point_logits, point_labels, num_masks),
+ "loss_dice": dice_loss_jit(point_logits, point_labels, num_masks),
+ }
+
+ del src_masks
+ del target_masks
+ return losses
+
+ def _get_src_permutation_idx(self, indices):
+ # permute predictions following indices
+ batch_idx = torch.cat([torch.full_like(src, i) for i, (src, _) in enumerate(indices)])
+ src_idx = torch.cat([src for (src, _) in indices])
+ return batch_idx, src_idx
+
+ def _get_tgt_permutation_idx(self, indices):
+ # permute targets following indices
+ batch_idx = torch.cat([torch.full_like(tgt, i) for i, (_, tgt) in enumerate(indices)])
+ tgt_idx = torch.cat([tgt for (_, tgt) in indices])
+ return batch_idx, tgt_idx
+
+ def get_loss(self, loss, outputs, targets, indices, num_masks):
+ loss_map = {
+ 'labels': self.loss_labels,
+ 'masks': self.loss_masks,
+ 'contrastive': self.loss_contrastive,
+ }
+ assert loss in loss_map, f"do you really want to compute {loss} loss?"
+ return loss_map[loss](outputs, targets, indices, num_masks)
+
+ def forward(self, outputs, targets):
+ """This performs the loss computation.
+ Parameters:
+ outputs: dict of tensors, see the output specification of the model for the format
+ targets: list of dicts, such that len(targets) == batch_size.
+ The expected keys in each dict depends on the losses applied, see each loss' doc
+ """
+ outputs_without_aux = {k: v for k, v in outputs.items() if k != "aux_outputs"}
+
+ # Retrieve the matching between the outputs of the last layer and the targets
+ indices = self.matcher(outputs_without_aux, targets)
+
+ # Compute the average number of target boxes accross all nodes, for normalization purposes
+ num_masks = sum(len(t["labels"]) for t in targets)
+ num_masks = torch.as_tensor(
+ [num_masks], dtype=torch.float, device=next(iter(outputs.values())).device
+ )
+ if is_dist_avail_and_initialized():
+ torch.distributed.all_reduce(num_masks)
+ num_masks = torch.clamp(num_masks / get_world_size(), min=1).item()
+
+ # Compute all the requested losses
+ losses = {}
+ for loss in self.losses:
+ losses.update(self.get_loss(loss, outputs, targets, indices, num_masks))
+
+ # In case of auxiliary losses, we repeat this process with the output of each intermediate layer.
+ if "aux_outputs" in outputs:
+ for i, aux_outputs in enumerate(outputs["aux_outputs"]):
+ indices = self.matcher(aux_outputs, targets)
+ for loss in self.losses:
+ if loss == "contrastive":
+ continue
+ l_dict = self.get_loss(loss, aux_outputs, targets, indices, num_masks)
+ l_dict = {k + f"_{i}": v for k, v in l_dict.items()}
+ losses.update(l_dict)
+
+ return losses
+
+ def __repr__(self):
+ head = "Criterion " + self.__class__.__name__
+ body = [
+ "matcher: {}".format(self.matcher.__repr__(_repr_indent=8)),
+ "losses: {}".format(self.losses),
+ "weight_dict: {}".format(self.weight_dict),
+ "num_classes: {}".format(self.num_classes),
+ "eos_coef: {}".format(self.eos_coef),
+ "num_points: {}".format(self.num_points),
+ "oversample_ratio: {}".format(self.oversample_ratio),
+ "importance_sample_ratio: {}".format(self.importance_sample_ratio),
+ ]
+ _repr_indent = 4
+ lines = [head] + [" " * _repr_indent + line for line in body]
+ return "\n".join(lines)
\ No newline at end of file
diff --git a/annotator/OneFormer/oneformer/modeling/matcher.py b/annotator/OneFormer/oneformer/modeling/matcher.py
new file mode 100644
index 0000000000000000000000000000000000000000..27f99c197b6865c053edbf2282e6347b83cf861f
--- /dev/null
+++ b/annotator/OneFormer/oneformer/modeling/matcher.py
@@ -0,0 +1,212 @@
+# ------------------------------------------------------------------------------
+# Reference: https://github.com/facebookresearch/Mask2Former/blob/main/mask2former/modeling/matcher.py
+# Modified by Jitesh Jain (https://github.com/praeclarumjj3)
+# ------------------------------------------------------------------------------
+
+"""
+Modules to compute the matching cost and solve the corresponding LSAP.
+"""
+import torch
+import torch.nn.functional as F
+from scipy.optimize import linear_sum_assignment
+from torch import nn
+from torch.cuda.amp import autocast
+import numpy as np
+
+from detectron2.projects.point_rend.point_features import point_sample
+
+
+def linear_sum_assignment_with_nan(cost_matrix):
+ cost_matrix = np.asarray(cost_matrix)
+ nan = np.isnan(cost_matrix).any()
+ nan_all = np.isnan(cost_matrix).all()
+ empty = cost_matrix.size == 0
+
+ if not empty:
+ if nan_all:
+ print('Matrix contains all NaN values!')
+ elif nan:
+ print('Matrix contains NaN values!')
+
+ if nan_all:
+ cost_matrix = np.empty(shape=(0, 0))
+ elif nan:
+ cost_matrix[np.isnan(cost_matrix)] = 100
+
+ return linear_sum_assignment(cost_matrix)
+
+def batch_dice_loss(inputs: torch.Tensor, targets: torch.Tensor):
+ """
+ Compute the DICE loss, similar to generalized IOU for masks
+ Args:
+ inputs: A float tensor of arbitrary shape.
+ The predictions for each example.
+ targets: A float tensor with the same shape as inputs. Stores the binary
+ classification label for each element in inputs
+ (0 for the negative class and 1 for the positive class).
+ """
+ inputs = inputs.sigmoid()
+ inputs = inputs.flatten(1)
+ numerator = 2 * torch.einsum("nc,mc->nm", inputs, targets)
+ denominator = inputs.sum(-1)[:, None] + targets.sum(-1)[None, :]
+ loss = 1 - (numerator + 1) / (denominator + 1)
+ return loss
+
+
+batch_dice_loss_jit = torch.jit.script(
+ batch_dice_loss
+) # type: torch.jit.ScriptModule
+
+
+def batch_sigmoid_ce_loss(inputs: torch.Tensor, targets: torch.Tensor):
+ """
+ Args:
+ inputs: A float tensor of arbitrary shape.
+ The predictions for each example.
+ targets: A float tensor with the same shape as inputs. Stores the binary
+ classification label for each element in inputs
+ (0 for the negative class and 1 for the positive class).
+ Returns:
+ Loss tensor
+ """
+ hw = inputs.shape[1]
+
+ pos = F.binary_cross_entropy_with_logits(
+ inputs, torch.ones_like(inputs), reduction="none"
+ )
+ neg = F.binary_cross_entropy_with_logits(
+ inputs, torch.zeros_like(inputs), reduction="none"
+ )
+
+ loss = torch.einsum("nc,mc->nm", pos, targets) + torch.einsum(
+ "nc,mc->nm", neg, (1 - targets)
+ )
+
+ return loss / hw
+
+
+batch_sigmoid_ce_loss_jit = torch.jit.script(
+ batch_sigmoid_ce_loss
+) # type: torch.jit.ScriptModule
+
+
+class HungarianMatcher(nn.Module):
+ """This class computes an assignment between the targets and the predictions of the network
+
+ For efficiency reasons, the targets don't include the no_object. Because of this, in general,
+ there are more predictions than targets. In this case, we do a 1-to-1 matching of the best predictions,
+ while the others are un-matched (and thus treated as non-objects).
+ """
+
+ def __init__(self, cost_class: float = 1, cost_mask: float = 1,
+ cost_dice: float = 1, num_points: int = 0):
+ """Creates the matcher
+
+ Params:
+ cost_class: This is the relative weight of the classification error in the matching cost
+ cost_mask: This is the relative weight of the focal loss of the binary mask in the matching cost
+ cost_dice: This is the relative weight of the dice loss of the binary mask in the matching cost
+ """
+ super().__init__()
+ self.cost_class = cost_class
+ self.cost_mask = cost_mask
+ self.cost_dice = cost_dice
+
+ assert cost_class != 0 or cost_mask != 0 or cost_dice != 0, "all costs cant be 0"
+
+ self.num_points = num_points
+
+ @torch.no_grad()
+ def memory_efficient_forward(self, outputs, targets):
+ """More memory-friendly matching"""
+ bs, num_queries = outputs["pred_logits"].shape[:2]
+
+ indices = []
+
+ # Iterate through batch size
+ for b in range(bs):
+ out_prob = outputs["pred_logits"][b].softmax(-1) # [num_queries, num_classes]
+ tgt_ids = targets[b]["labels"]
+
+ # Compute the classification cost. Contrary to the loss, we don't use the NLL,
+ # but approximate it in 1 - proba[target class].
+ # The 1 is a constant that doesn't change the matching, it can be ommitted.
+ cost_class = -out_prob[:, tgt_ids]
+
+ out_mask = outputs["pred_masks"][b] # [num_queries, H_pred, W_pred]
+ # gt masks are already padded when preparing target
+ tgt_mask = targets[b]["masks"].to(out_mask)
+
+ out_mask = out_mask[:, None]
+ tgt_mask = tgt_mask[:, None]
+ # all masks share the same set of points for efficient matching!
+ point_coords = torch.rand(1, self.num_points, 2, device=out_mask.device)
+ # get gt labels
+ tgt_mask = point_sample(
+ tgt_mask,
+ point_coords.repeat(tgt_mask.shape[0], 1, 1),
+ align_corners=False,
+ ).squeeze(1)
+
+ out_mask = point_sample(
+ out_mask,
+ point_coords.repeat(out_mask.shape[0], 1, 1),
+ align_corners=False,
+ ).squeeze(1)
+
+ with autocast(enabled=False):
+ out_mask = out_mask.float()
+ tgt_mask = tgt_mask.float()
+ # Compute the focal loss between masks
+ cost_mask = batch_sigmoid_ce_loss_jit(out_mask, tgt_mask)
+ # Compute the dice loss betwen masks
+ cost_dice = batch_dice_loss(out_mask, tgt_mask)
+
+ # Final cost matrix
+ C = (
+ self.cost_mask * cost_mask
+ + self.cost_class * cost_class
+ + self.cost_dice * cost_dice
+ )
+ C = C.reshape(num_queries, -1).cpu()
+
+ indices.append(linear_sum_assignment_with_nan(C))
+
+ return [
+ (torch.as_tensor(i, dtype=torch.int64), torch.as_tensor(j, dtype=torch.int64))
+ for i, j in indices
+ ]
+
+ @torch.no_grad()
+ def forward(self, outputs, targets):
+ """Performs the matching
+
+ Params:
+ outputs: This is a dict that contains at least these entries:
+ "pred_logits": Tensor of dim [batch_size, num_queries, num_classes] with the classification logits
+ "pred_masks": Tensor of dim [batch_size, num_queries, H_pred, W_pred] with the predicted masks
+
+ targets: This is a list of targets (len(targets) = batch_size), where each target is a dict containing:
+ "labels": Tensor of dim [num_target_boxes] (where num_target_boxes is the number of ground-truth
+ objects in the target) containing the class labels
+ "masks": Tensor of dim [num_target_boxes, H_gt, W_gt] containing the target masks
+
+ Returns:
+ A list of size batch_size, containing tuples of (index_i, index_j) where:
+ - index_i is the indices of the selected predictions (in order)
+ - index_j is the indices of the corresponding selected targets (in order)
+ For each batch element, it holds:
+ len(index_i) = len(index_j) = min(num_queries, num_target_boxes)
+ """
+
+ return self.memory_efficient_forward(outputs, targets)
+
+ def __repr__(self, _repr_indent=4):
+ head = "Matcher " + self.__class__.__name__
+ body = [
+ "cost_class: {}".format(self.cost_class),
+ "cost_mask: {}".format(self.cost_mask),
+ "cost_dice: {}".format(self.cost_dice),
+ ]
+ lines = [head] + [" " * _repr_indent + line for line in body]
+ return "\n".join(lines)
diff --git a/annotator/OneFormer/oneformer/modeling/meta_arch/__init__.py b/annotator/OneFormer/oneformer/modeling/meta_arch/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..8b137891791fe96927ad78e64b0aad7bded08bdc
--- /dev/null
+++ b/annotator/OneFormer/oneformer/modeling/meta_arch/__init__.py
@@ -0,0 +1 @@
+
diff --git a/annotator/OneFormer/oneformer/modeling/meta_arch/oneformer_head.py b/annotator/OneFormer/oneformer/modeling/meta_arch/oneformer_head.py
new file mode 100644
index 0000000000000000000000000000000000000000..cf6dbd9f5d734acd3a895a15fa67544f872e80ea
--- /dev/null
+++ b/annotator/OneFormer/oneformer/modeling/meta_arch/oneformer_head.py
@@ -0,0 +1,135 @@
+# ------------------------------------------------------------------------------
+# Reference: https://github.com/facebookresearch/Mask2Former/blob/main/mask2former/modeling/meta_arch/mask_former_head.py
+# Modified by Jitesh Jain (https://github.com/praeclarumjj3)
+# ------------------------------------------------------------------------------
+
+import logging
+from copy import deepcopy
+from typing import Callable, Dict, List, Optional, Tuple, Union
+
+import fvcore.nn.weight_init as weight_init
+from torch import nn
+from torch.nn import functional as F
+
+from detectron2.config import configurable
+from detectron2.layers import Conv2d, ShapeSpec, get_norm
+from detectron2.modeling import SEM_SEG_HEADS_REGISTRY
+from ..pixel_decoder.fpn import build_pixel_decoder
+from ..transformer_decoder.oneformer_transformer_decoder import build_transformer_decoder
+
+@SEM_SEG_HEADS_REGISTRY.register()
+class OneFormerHead(nn.Module):
+
+ _version = 2
+
+ def _load_from_state_dict(
+ self, state_dict, prefix, local_metadata, strict, missing_keys, unexpected_keys, error_msgs
+ ):
+ version = local_metadata.get("version", None)
+ if version is None or version < 2:
+ # Do not warn if train from scratch
+ scratch = True
+ logger = logging.getLogger(__name__)
+ for k in list(state_dict.keys()):
+ newk = k
+ if "sem_seg_head" in k and not k.startswith(prefix + "predictor"):
+ newk = k.replace(prefix, prefix + "pixel_decoder.")
+ # logger.debug(f"{k} ==> {newk}")
+ if newk != k:
+ state_dict[newk] = state_dict[k]
+ del state_dict[k]
+ scratch = False
+
+ if not scratch:
+ logger.warning(
+ f"Weight format of {self.__class__.__name__} have changed! "
+ "Please upgrade your models. Applying automatic conversion now ..."
+ )
+
+ @configurable
+ def __init__(
+ self,
+ input_shape: Dict[str, ShapeSpec],
+ *,
+ num_classes: int,
+ pixel_decoder: nn.Module,
+ loss_weight: float = 1.0,
+ ignore_value: int = -1,
+ # extra parameters
+ transformer_predictor: nn.Module,
+ transformer_in_feature: str,
+ ):
+ """
+ NOTE: this interface is experimental.
+ Args:
+ input_shape: shapes (channels and stride) of the input features
+ num_classes: number of classes to predict
+ pixel_decoder: the pixel decoder module
+ loss_weight: loss weight
+ ignore_value: category id to be ignored during training.
+ transformer_predictor: the transformer decoder that makes prediction
+ transformer_in_feature: input feature name to the transformer_predictor
+ """
+ super().__init__()
+ input_shape = sorted(input_shape.items(), key=lambda x: x[1].stride)
+ self.in_features = [k for k, v in input_shape]
+ feature_strides = [v.stride for k, v in input_shape]
+ feature_channels = [v.channels for k, v in input_shape]
+
+ self.ignore_value = ignore_value
+ self.common_stride = 4
+ self.loss_weight = loss_weight
+
+ self.pixel_decoder = pixel_decoder
+ self.predictor = transformer_predictor
+ self.transformer_in_feature = transformer_in_feature
+
+ self.num_classes = num_classes
+
+ @classmethod
+ def from_config(cls, cfg, input_shape: Dict[str, ShapeSpec]):
+ # figure out in_channels to transformer predictor
+ if cfg.MODEL.ONE_FORMER.TRANSFORMER_IN_FEATURE == "transformer_encoder":
+ transformer_predictor_in_channels = cfg.MODEL.SEM_SEG_HEAD.CONVS_DIM
+ elif cfg.MODEL.ONE_FORMER.TRANSFORMER_IN_FEATURE == "pixel_embedding":
+ transformer_predictor_in_channels = cfg.MODEL.SEM_SEG_HEAD.MASK_DIM
+ elif cfg.MODEL.ONE_FORMER.TRANSFORMER_IN_FEATURE == "multi_scale_pixel_decoder":
+ transformer_predictor_in_channels = cfg.MODEL.SEM_SEG_HEAD.CONVS_DIM
+ else:
+ transformer_predictor_in_channels = input_shape[cfg.MODEL.ONE_FORMER.TRANSFORMER_IN_FEATURE].channels
+
+ return {
+ "input_shape": {
+ k: v for k, v in input_shape.items() if k in cfg.MODEL.SEM_SEG_HEAD.IN_FEATURES
+ },
+ "ignore_value": cfg.MODEL.SEM_SEG_HEAD.IGNORE_VALUE,
+ "num_classes": cfg.MODEL.SEM_SEG_HEAD.NUM_CLASSES,
+ "pixel_decoder": build_pixel_decoder(cfg, input_shape),
+ "loss_weight": cfg.MODEL.SEM_SEG_HEAD.LOSS_WEIGHT,
+ "transformer_in_feature": cfg.MODEL.ONE_FORMER.TRANSFORMER_IN_FEATURE,
+ "transformer_predictor": build_transformer_decoder(
+ cfg,
+ transformer_predictor_in_channels,
+ mask_classification=True,
+ ),
+ }
+
+ def forward(self, features, tasks, mask=None):
+ return self.layers(features, tasks, mask)
+
+ def layers(self, features, tasks, mask=None):
+ mask_features, transformer_encoder_features, multi_scale_features, _, _ = self.pixel_decoder.forward_features(features)
+
+ if self.transformer_in_feature == "multi_scale_pixel_decoder":
+ predictions = self.predictor(multi_scale_features, mask_features, tasks, mask)
+ else:
+ if self.transformer_in_feature == "transformer_encoder":
+ assert (
+ transformer_encoder_features is not None
+ ), "Please use the TransformerEncoderPixelDecoder."
+ predictions = self.predictor(transformer_encoder_features, mask_features, mask)
+ elif self.transformer_in_feature == "pixel_embedding":
+ predictions = self.predictor(mask_features, mask_features, mask)
+ else:
+ predictions = self.predictor(features[self.transformer_in_feature], mask_features, mask)
+ return predictions
diff --git a/annotator/OneFormer/oneformer/modeling/pixel_decoder/__init__.py b/annotator/OneFormer/oneformer/modeling/pixel_decoder/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..8b137891791fe96927ad78e64b0aad7bded08bdc
--- /dev/null
+++ b/annotator/OneFormer/oneformer/modeling/pixel_decoder/__init__.py
@@ -0,0 +1 @@
+
diff --git a/annotator/OneFormer/oneformer/modeling/pixel_decoder/fpn.py b/annotator/OneFormer/oneformer/modeling/pixel_decoder/fpn.py
new file mode 100644
index 0000000000000000000000000000000000000000..1828c74c27a1bd726af71e478d6e6cbb3a4e84ae
--- /dev/null
+++ b/annotator/OneFormer/oneformer/modeling/pixel_decoder/fpn.py
@@ -0,0 +1,314 @@
+# ------------------------------------------------------------------------------
+# Reference: https://github.com/facebookresearch/Mask2Former/blob/main/mask2former/modeling/pixel_decoder/fpn.py
+# ------------------------------------------------------------------------------
+import logging
+import numpy as np
+from typing import Callable, Dict, List, Optional, Tuple, Union
+
+import fvcore.nn.weight_init as weight_init
+import torch
+from torch import nn
+from torch.nn import functional as F
+from torch.nn.init import xavier_uniform_, constant_, uniform_, normal_
+from torch.cuda.amp import autocast
+
+from detectron2.config import configurable
+from detectron2.layers import Conv2d, DeformConv, ShapeSpec, get_norm
+from detectron2.modeling import SEM_SEG_HEADS_REGISTRY
+
+from ..transformer_decoder.position_encoding import PositionEmbeddingSine
+from ..transformer_decoder.transformer import TransformerEncoder, TransformerEncoderLayer, _get_clones, _get_activation_fn
+
+
+def build_pixel_decoder(cfg, input_shape):
+ """
+ Build a pixel decoder from `cfg.MODEL.ONE_FORMER.PIXEL_DECODER_NAME`.
+ """
+ name = cfg.MODEL.SEM_SEG_HEAD.PIXEL_DECODER_NAME
+ model = SEM_SEG_HEADS_REGISTRY.get(name)(cfg, input_shape)
+ forward_features = getattr(model, "forward_features", None)
+ if not callable(forward_features):
+ raise ValueError(
+ "Only SEM_SEG_HEADS with forward_features method can be used as pixel decoder. "
+ f"Please implement forward_features for {name} to only return mask features."
+ )
+ return model
+
+
+# This is a modified FPN decoder.
+@SEM_SEG_HEADS_REGISTRY.register()
+class BasePixelDecoder(nn.Module):
+ @configurable
+ def __init__(
+ self,
+ input_shape: Dict[str, ShapeSpec],
+ *,
+ conv_dim: int,
+ mask_dim: int,
+ norm: Optional[Union[str, Callable]] = None,
+ ):
+ """
+ NOTE: this interface is experimental.
+ Args:
+ input_shape: shapes (channels and stride) of the input features
+ conv_dims: number of output channels for the intermediate conv layers.
+ mask_dim: number of output channels for the final conv layer.
+ norm (str or callable): normalization for all conv layers
+ """
+ super().__init__()
+
+ input_shape = sorted(input_shape.items(), key=lambda x: x[1].stride)
+ self.in_features = [k for k, v in input_shape] # starting from "res2" to "res5"
+ feature_channels = [v.channels for k, v in input_shape]
+
+ lateral_convs = []
+ output_convs = []
+
+ use_bias = norm == ""
+ for idx, in_channels in enumerate(feature_channels):
+ if idx == len(self.in_features) - 1:
+ output_norm = get_norm(norm, conv_dim)
+ output_conv = Conv2d(
+ in_channels,
+ conv_dim,
+ kernel_size=3,
+ stride=1,
+ padding=1,
+ bias=use_bias,
+ norm=output_norm,
+ activation=F.relu,
+ )
+ weight_init.c2_xavier_fill(output_conv)
+ self.add_module("layer_{}".format(idx + 1), output_conv)
+
+ lateral_convs.append(None)
+ output_convs.append(output_conv)
+ else:
+ lateral_norm = get_norm(norm, conv_dim)
+ output_norm = get_norm(norm, conv_dim)
+
+ lateral_conv = Conv2d(
+ in_channels, conv_dim, kernel_size=1, bias=use_bias, norm=lateral_norm
+ )
+ output_conv = Conv2d(
+ conv_dim,
+ conv_dim,
+ kernel_size=3,
+ stride=1,
+ padding=1,
+ bias=use_bias,
+ norm=output_norm,
+ activation=F.relu,
+ )
+ weight_init.c2_xavier_fill(lateral_conv)
+ weight_init.c2_xavier_fill(output_conv)
+ self.add_module("adapter_{}".format(idx + 1), lateral_conv)
+ self.add_module("layer_{}".format(idx + 1), output_conv)
+
+ lateral_convs.append(lateral_conv)
+ output_convs.append(output_conv)
+ # Place convs into top-down order (from low to high resolution)
+ # to make the top-down computation in forward clearer.
+ self.lateral_convs = lateral_convs[::-1]
+ self.output_convs = output_convs[::-1]
+
+ self.mask_dim = mask_dim
+ self.mask_features = Conv2d(
+ conv_dim,
+ mask_dim,
+ kernel_size=3,
+ stride=1,
+ padding=1,
+ )
+ weight_init.c2_xavier_fill(self.mask_features)
+
+ self.oneformer_num_feature_levels = 3 # always use 3 scales
+
+ @classmethod
+ def from_config(cls, cfg, input_shape: Dict[str, ShapeSpec]):
+ ret = {}
+ ret["input_shape"] = {
+ k: v for k, v in input_shape.items() if k in cfg.MODEL.SEM_SEG_HEAD.IN_FEATURES
+ }
+ ret["conv_dim"] = cfg.MODEL.SEM_SEG_HEAD.CONVS_DIM
+ ret["mask_dim"] = cfg.MODEL.SEM_SEG_HEAD.MASK_DIM
+ ret["norm"] = cfg.MODEL.SEM_SEG_HEAD.NORM
+ return ret
+
+ def forward_features(self, features):
+ multi_scale_features = []
+ num_cur_levels = 0
+ # Reverse feature maps into top-down order (from low to high resolution)
+ for idx, f in enumerate(self.in_features[::-1]):
+ x = features[f]
+ lateral_conv = self.lateral_convs[idx]
+ output_conv = self.output_convs[idx]
+ if lateral_conv is None:
+ y = output_conv(x)
+ else:
+ cur_fpn = lateral_conv(x)
+ # Following FPN implementation, we use nearest upsampling here
+ y = cur_fpn + F.interpolate(y, size=cur_fpn.shape[-2:], mode="nearest")
+ y = output_conv(y)
+ if num_cur_levels < self.oneformer_num_feature_levels:
+ multi_scale_features.append(y)
+ num_cur_levels += 1
+ return self.mask_features(y), None, multi_scale_features
+
+ def forward(self, features, targets=None):
+ logger = logging.getLogger(__name__)
+ logger.warning("Calling forward() may cause unpredicted behavior of PixelDecoder module.")
+ return self.forward_features(features)
+
+
+class TransformerEncoderOnly(nn.Module):
+ def __init__(
+ self,
+ d_model=512,
+ nhead=8,
+ num_encoder_layers=6,
+ dim_feedforward=2048,
+ dropout=0.1,
+ activation="relu",
+ normalize_before=False,
+ ):
+ super().__init__()
+
+ encoder_layer = TransformerEncoderLayer(
+ d_model, nhead, dim_feedforward, dropout, activation, normalize_before
+ )
+ encoder_norm = nn.LayerNorm(d_model) if normalize_before else None
+ self.encoder = TransformerEncoder(encoder_layer, num_encoder_layers, encoder_norm)
+
+ self._reset_parameters()
+
+ self.d_model = d_model
+ self.nhead = nhead
+
+ def _reset_parameters(self):
+ for p in self.parameters():
+ if p.dim() > 1:
+ nn.init.xavier_uniform_(p)
+
+ def forward(self, src, mask, pos_embed):
+ # flatten NxCxHxW to HWxNxC
+ bs, c, h, w = src.shape
+ src = src.flatten(2).permute(2, 0, 1)
+ pos_embed = pos_embed.flatten(2).permute(2, 0, 1)
+ if mask is not None:
+ mask = mask.flatten(1)
+
+ memory = self.encoder(src, src_key_padding_mask=mask, pos=pos_embed)
+ return memory.permute(1, 2, 0).view(bs, c, h, w)
+
+
+# This is a modified FPN decoder with extra Transformer encoder that processes the lowest-resolution feature map.
+@SEM_SEG_HEADS_REGISTRY.register()
+class TransformerEncoderPixelDecoder(BasePixelDecoder):
+ @configurable
+ def __init__(
+ self,
+ input_shape: Dict[str, ShapeSpec],
+ *,
+ transformer_dropout: float,
+ transformer_nheads: int,
+ transformer_dim_feedforward: int,
+ transformer_enc_layers: int,
+ transformer_pre_norm: bool,
+ conv_dim: int,
+ mask_dim: int,
+ norm: Optional[Union[str, Callable]] = None,
+ ):
+ """
+ NOTE: this interface is experimental.
+ Args:
+ input_shape: shapes (channels and stride) of the input features
+ transformer_dropout: dropout probability in transformer
+ transformer_nheads: number of heads in transformer
+ transformer_dim_feedforward: dimension of feedforward network
+ transformer_enc_layers: number of transformer encoder layers
+ transformer_pre_norm: whether to use pre-layernorm or not
+ conv_dims: number of output channels for the intermediate conv layers.
+ mask_dim: number of output channels for the final conv layer.
+ norm (str or callable): normalization for all conv layers
+ """
+ super().__init__(input_shape, conv_dim=conv_dim, mask_dim=mask_dim, norm=norm)
+
+ input_shape = sorted(input_shape.items(), key=lambda x: x[1].stride)
+ self.in_features = [k for k, v in input_shape] # starting from "res2" to "res5"
+ feature_strides = [v.stride for k, v in input_shape]
+ feature_channels = [v.channels for k, v in input_shape]
+
+ in_channels = feature_channels[len(self.in_features) - 1]
+ self.input_proj = Conv2d(in_channels, conv_dim, kernel_size=1)
+ weight_init.c2_xavier_fill(self.input_proj)
+ self.transformer = TransformerEncoderOnly(
+ d_model=conv_dim,
+ dropout=transformer_dropout,
+ nhead=transformer_nheads,
+ dim_feedforward=transformer_dim_feedforward,
+ num_encoder_layers=transformer_enc_layers,
+ normalize_before=transformer_pre_norm,
+ )
+ N_steps = conv_dim // 2
+ self.pe_layer = PositionEmbeddingSine(N_steps, normalize=True)
+
+ # update layer
+ use_bias = norm == ""
+ output_norm = get_norm(norm, conv_dim)
+ output_conv = Conv2d(
+ conv_dim,
+ conv_dim,
+ kernel_size=3,
+ stride=1,
+ padding=1,
+ bias=use_bias,
+ norm=output_norm,
+ activation=F.relu,
+ )
+ weight_init.c2_xavier_fill(output_conv)
+ delattr(self, "layer_{}".format(len(self.in_features)))
+ self.add_module("layer_{}".format(len(self.in_features)), output_conv)
+ self.output_convs[0] = output_conv
+
+ @classmethod
+ def from_config(cls, cfg, input_shape: Dict[str, ShapeSpec]):
+ ret = super().from_config(cfg, input_shape)
+ ret["transformer_dropout"] = cfg.MODEL.ONE_FORMER.DROPOUT
+ ret["transformer_nheads"] = cfg.MODEL.ONE_FORMER.NHEADS
+ ret["transformer_dim_feedforward"] = cfg.MODEL.ONE_FORMER.DIM_FEEDFORWARD
+ ret[
+ "transformer_enc_layers"
+ ] = cfg.MODEL.SEM_SEG_HEAD.TRANSFORMER_ENC_LAYERS # a separate config
+ ret["transformer_pre_norm"] = cfg.MODEL.ONE_FORMER.PRE_NORM
+ return ret
+
+ def forward_features(self, features):
+ multi_scale_features = []
+ num_cur_levels = 0
+ # Reverse feature maps into top-down order (from low to high resolution)
+ for idx, f in enumerate(self.in_features[::-1]):
+ x = features[f]
+ lateral_conv = self.lateral_convs[idx]
+ output_conv = self.output_convs[idx]
+ if lateral_conv is None:
+ transformer = self.input_proj(x)
+ pos = self.pe_layer(x)
+ transformer = self.transformer(transformer, None, pos)
+ y = output_conv(transformer)
+ # save intermediate feature as input to Transformer decoder
+ transformer_encoder_features = transformer
+ else:
+ cur_fpn = lateral_conv(x)
+ # Following FPN implementation, we use nearest upsampling here
+ y = cur_fpn + F.interpolate(y, size=cur_fpn.shape[-2:], mode="nearest")
+ y = output_conv(y)
+ if num_cur_levels < self.oneformer_num_feature_levels:
+ multi_scale_features.append(y)
+ num_cur_levels += 1
+ return self.mask_features(y), transformer_encoder_features, multi_scale_features
+
+ def forward(self, features, targets=None):
+ logger = logging.getLogger(__name__)
+ logger.warning("Calling forward() may cause unpredicted behavior of PixelDecoder module.")
+ return self.forward_features(features)
diff --git a/annotator/OneFormer/oneformer/modeling/pixel_decoder/msdeformattn.py b/annotator/OneFormer/oneformer/modeling/pixel_decoder/msdeformattn.py
new file mode 100644
index 0000000000000000000000000000000000000000..9b6d0aa078fba389ce2eafe1e43e5834df475ccb
--- /dev/null
+++ b/annotator/OneFormer/oneformer/modeling/pixel_decoder/msdeformattn.py
@@ -0,0 +1,362 @@
+# ------------------------------------------------------------------------------
+# Reference: https://github.com/facebookresearch/Mask2Former/blob/main/mask2former/modeling/pixel_decoder/msdeformattn.py
+# Modified by Jitesh Jain (https://github.com/praeclarumjj3)
+# ------------------------------------------------------------------------------
+
+import logging
+import numpy as np
+from typing import Callable, Dict, List, Optional, Tuple, Union
+
+import fvcore.nn.weight_init as weight_init
+import torch
+from torch import nn
+from torch.nn import functional as F
+from torch.nn.init import xavier_uniform_, constant_, uniform_, normal_
+from torch.cuda.amp import autocast
+
+from detectron2.config import configurable
+from detectron2.layers import Conv2d, ShapeSpec, get_norm
+from detectron2.modeling import SEM_SEG_HEADS_REGISTRY
+
+from ..transformer_decoder.position_encoding import PositionEmbeddingSine
+from ..transformer_decoder.transformer import _get_clones, _get_activation_fn
+from .ops.modules import MSDeformAttn
+
+
+# MSDeformAttn Transformer encoder in deformable detr
+class MSDeformAttnTransformerEncoderOnly(nn.Module):
+ def __init__(self, d_model=256, nhead=8,
+ num_encoder_layers=6, dim_feedforward=1024, dropout=0.1,
+ activation="relu",
+ num_feature_levels=4, enc_n_points=4,
+ ):
+ super().__init__()
+
+ self.d_model = d_model
+ self.nhead = nhead
+
+ encoder_layer = MSDeformAttnTransformerEncoderLayer(d_model, dim_feedforward,
+ dropout, activation,
+ num_feature_levels, nhead, enc_n_points)
+ self.encoder = MSDeformAttnTransformerEncoder(encoder_layer, num_encoder_layers)
+
+ self.level_embed = nn.Parameter(torch.Tensor(num_feature_levels, d_model))
+
+ self._reset_parameters()
+
+ def _reset_parameters(self):
+ for p in self.parameters():
+ if p.dim() > 1:
+ nn.init.xavier_uniform_(p)
+ for m in self.modules():
+ if isinstance(m, MSDeformAttn):
+ m._reset_parameters()
+ normal_(self.level_embed)
+
+ def get_valid_ratio(self, mask):
+ _, H, W = mask.shape
+ valid_H = torch.sum(~mask[:, :, 0], 1)
+ valid_W = torch.sum(~mask[:, 0, :], 1)
+ valid_ratio_h = valid_H.float() / H
+ valid_ratio_w = valid_W.float() / W
+ valid_ratio = torch.stack([valid_ratio_w, valid_ratio_h], -1)
+ return valid_ratio
+
+ def forward(self, srcs, pos_embeds):
+ masks = [torch.zeros((x.size(0), x.size(2), x.size(3)), device=x.device, dtype=torch.bool) for x in srcs]
+ # prepare input for encoder
+ src_flatten = []
+ mask_flatten = []
+ lvl_pos_embed_flatten = []
+ spatial_shapes = []
+ for lvl, (src, mask, pos_embed) in enumerate(zip(srcs, masks, pos_embeds)):
+ bs, c, h, w = src.shape
+ spatial_shape = (h, w)
+ spatial_shapes.append(spatial_shape)
+ src = src.flatten(2).transpose(1, 2)
+ mask = mask.flatten(1)
+ pos_embed = pos_embed.flatten(2).transpose(1, 2)
+ lvl_pos_embed = pos_embed + self.level_embed[lvl].view(1, 1, -1)
+ lvl_pos_embed_flatten.append(lvl_pos_embed)
+ src_flatten.append(src)
+ mask_flatten.append(mask)
+ src_flatten = torch.cat(src_flatten, 1)
+ mask_flatten = torch.cat(mask_flatten, 1)
+ lvl_pos_embed_flatten = torch.cat(lvl_pos_embed_flatten, 1)
+ spatial_shapes = torch.as_tensor(spatial_shapes, dtype=torch.long, device=src_flatten.device)
+ level_start_index = torch.cat((spatial_shapes.new_zeros((1, )), spatial_shapes.prod(1).cumsum(0)[:-1]))
+ valid_ratios = torch.stack([self.get_valid_ratio(m) for m in masks], 1)
+
+ # encoder
+ memory = self.encoder(src_flatten, spatial_shapes, level_start_index, valid_ratios, lvl_pos_embed_flatten, mask_flatten)
+
+ return memory, spatial_shapes, level_start_index, valid_ratios
+
+
+class MSDeformAttnTransformerEncoderLayer(nn.Module):
+ def __init__(self,
+ d_model=256, d_ffn=1024,
+ dropout=0.1, activation="relu",
+ n_levels=4, n_heads=8, n_points=4):
+ super().__init__()
+
+ # self attention
+ self.self_attn = MSDeformAttn(d_model, n_levels, n_heads, n_points)
+ self.dropout1 = nn.Dropout(dropout)
+ self.norm1 = nn.LayerNorm(d_model)
+
+ # ffn
+ self.linear1 = nn.Linear(d_model, d_ffn)
+ self.activation = _get_activation_fn(activation)
+ self.dropout2 = nn.Dropout(dropout)
+ self.linear2 = nn.Linear(d_ffn, d_model)
+ self.dropout3 = nn.Dropout(dropout)
+ self.norm2 = nn.LayerNorm(d_model)
+
+ @staticmethod
+ def with_pos_embed(tensor, pos):
+ return tensor if pos is None else tensor + pos
+
+ def forward_ffn(self, src):
+ src2 = self.linear2(self.dropout2(self.activation(self.linear1(src))))
+ src = src + self.dropout3(src2)
+ src = self.norm2(src)
+ return src
+
+ def forward(self, src, pos, reference_points, spatial_shapes, level_start_index, padding_mask=None):
+ # self attention
+ src2 = self.self_attn(self.with_pos_embed(src, pos), reference_points, src, spatial_shapes, level_start_index, padding_mask)
+ src = src + self.dropout1(src2)
+ src = self.norm1(src)
+
+ # ffn
+ src = self.forward_ffn(src)
+
+ return src
+
+
+class MSDeformAttnTransformerEncoder(nn.Module):
+ def __init__(self, encoder_layer, num_layers):
+ super().__init__()
+ self.layers = _get_clones(encoder_layer, num_layers)
+ self.num_layers = num_layers
+
+ @staticmethod
+ def get_reference_points(spatial_shapes, valid_ratios, device):
+ reference_points_list = []
+ for lvl, (H_, W_) in enumerate(spatial_shapes):
+
+ ref_y, ref_x = torch.meshgrid(torch.linspace(0.5, H_ - 0.5, H_, dtype=torch.float32, device=device),
+ torch.linspace(0.5, W_ - 0.5, W_, dtype=torch.float32, device=device))
+ ref_y = ref_y.reshape(-1)[None] / (valid_ratios[:, None, lvl, 1] * H_)
+ ref_x = ref_x.reshape(-1)[None] / (valid_ratios[:, None, lvl, 0] * W_)
+ ref = torch.stack((ref_x, ref_y), -1)
+ reference_points_list.append(ref)
+ reference_points = torch.cat(reference_points_list, 1)
+ reference_points = reference_points[:, :, None] * valid_ratios[:, None]
+ return reference_points
+
+ def forward(self, src, spatial_shapes, level_start_index, valid_ratios, pos=None, padding_mask=None):
+ output = src
+ reference_points = self.get_reference_points(spatial_shapes, valid_ratios, device=src.device)
+ for _, layer in enumerate(self.layers):
+ output = layer(output, pos, reference_points, spatial_shapes, level_start_index, padding_mask)
+
+ return output
+
+
+@SEM_SEG_HEADS_REGISTRY.register()
+class MSDeformAttnPixelDecoder(nn.Module):
+ @configurable
+ def __init__(
+ self,
+ input_shape: Dict[str, ShapeSpec],
+ *,
+ transformer_dropout: float,
+ transformer_nheads: int,
+ transformer_dim_feedforward: int,
+ transformer_enc_layers: int,
+ conv_dim: int,
+ mask_dim: int,
+ norm: Optional[Union[str, Callable]] = None,
+ # deformable transformer encoder args
+ transformer_in_features: List[str],
+ common_stride: int,
+ ):
+ """
+ NOTE: this interface is experimental.
+ Args:
+ input_shape: shapes (channels and stride) of the input features
+ transformer_dropout: dropout probability in transformer
+ transformer_nheads: number of heads in transformer
+ transformer_dim_feedforward: dimension of feedforward network
+ transformer_enc_layers: number of transformer encoder layers
+ conv_dims: number of output channels for the intermediate conv layers.
+ mask_dim: number of output channels for the final conv layer.
+ norm (str or callable): normalization for all conv layers
+ """
+ super().__init__()
+ transformer_input_shape = {
+ k: v for k, v in input_shape.items() if k in transformer_in_features
+ }
+
+ # this is the input shape of pixel decoder
+ input_shape = sorted(input_shape.items(), key=lambda x: x[1].stride)
+ self.in_features = [k for k, v in input_shape] # starting from "res2" to "res5"
+ self.feature_strides = [v.stride for k, v in input_shape]
+ self.feature_channels = [v.channels for k, v in input_shape]
+
+ # this is the input shape of transformer encoder (could use less features than pixel decoder
+ transformer_input_shape = sorted(transformer_input_shape.items(), key=lambda x: x[1].stride)
+ self.transformer_in_features = [k for k, v in transformer_input_shape] # starting from "res2" to "res5"
+ transformer_in_channels = [v.channels for k, v in transformer_input_shape]
+ self.transformer_feature_strides = [v.stride for k, v in transformer_input_shape] # to decide extra FPN layers
+
+ self.transformer_num_feature_levels = len(self.transformer_in_features)
+ if self.transformer_num_feature_levels > 1:
+ input_proj_list = []
+ # from low resolution to high resolution (res5 -> res2)
+ for in_channels in transformer_in_channels[::-1]:
+ input_proj_list.append(nn.Sequential(
+ nn.Conv2d(in_channels, conv_dim, kernel_size=1),
+ nn.GroupNorm(32, conv_dim),
+ ))
+ self.input_proj = nn.ModuleList(input_proj_list)
+ else:
+ self.input_proj = nn.ModuleList([
+ nn.Sequential(
+ nn.Conv2d(transformer_in_channels[-1], conv_dim, kernel_size=1),
+ nn.GroupNorm(32, conv_dim),
+ )])
+
+ for proj in self.input_proj:
+ nn.init.xavier_uniform_(proj[0].weight, gain=1)
+ nn.init.constant_(proj[0].bias, 0)
+
+ self.transformer = MSDeformAttnTransformerEncoderOnly(
+ d_model=conv_dim,
+ dropout=transformer_dropout,
+ nhead=transformer_nheads,
+ dim_feedforward=transformer_dim_feedforward,
+ num_encoder_layers=transformer_enc_layers,
+ num_feature_levels=self.transformer_num_feature_levels,
+ )
+ N_steps = conv_dim // 2
+ self.pe_layer = PositionEmbeddingSine(N_steps, normalize=True)
+
+ self.mask_dim = mask_dim
+ # use 1x1 conv instead
+ self.mask_features = Conv2d(
+ conv_dim,
+ mask_dim,
+ kernel_size=1,
+ stride=1,
+ padding=0,
+ )
+ weight_init.c2_xavier_fill(self.mask_features)
+
+ self.oneformer_num_feature_levels = 3 # always use 3 scales
+ self.common_stride = common_stride
+
+ # extra fpn levels
+ stride = min(self.transformer_feature_strides)
+ self.num_fpn_levels = int(np.log2(stride) - np.log2(self.common_stride))
+
+ lateral_convs = []
+ output_convs = []
+
+ use_bias = norm == ""
+ for idx, in_channels in enumerate(self.feature_channels[:self.num_fpn_levels]):
+ lateral_norm = get_norm(norm, conv_dim)
+ output_norm = get_norm(norm, conv_dim)
+
+ lateral_conv = Conv2d(
+ in_channels, conv_dim, kernel_size=1, bias=use_bias, norm=lateral_norm
+ )
+ output_conv = Conv2d(
+ conv_dim,
+ conv_dim,
+ kernel_size=3,
+ stride=1,
+ padding=1,
+ bias=use_bias,
+ norm=output_norm,
+ activation=F.relu,
+ )
+ weight_init.c2_xavier_fill(lateral_conv)
+ weight_init.c2_xavier_fill(output_conv)
+ self.add_module("adapter_{}".format(idx + 1), lateral_conv)
+ self.add_module("layer_{}".format(idx + 1), output_conv)
+
+ lateral_convs.append(lateral_conv)
+ output_convs.append(output_conv)
+ # Place convs into top-down order (from low to high resolution)
+ # to make the top-down computation in forward clearer.
+ self.lateral_convs = lateral_convs[::-1]
+ self.output_convs = output_convs[::-1]
+
+ @classmethod
+ def from_config(cls, cfg, input_shape: Dict[str, ShapeSpec]):
+ ret = {}
+ ret["input_shape"] = {
+ k: v for k, v in input_shape.items() if k in cfg.MODEL.SEM_SEG_HEAD.IN_FEATURES
+ }
+ ret["conv_dim"] = cfg.MODEL.SEM_SEG_HEAD.CONVS_DIM
+ ret["mask_dim"] = cfg.MODEL.SEM_SEG_HEAD.MASK_DIM
+ ret["norm"] = cfg.MODEL.SEM_SEG_HEAD.NORM
+ ret["transformer_dropout"] = cfg.MODEL.ONE_FORMER.DROPOUT
+ ret["transformer_nheads"] = cfg.MODEL.ONE_FORMER.NHEADS
+ # ret["transformer_dim_feedforward"] = cfg.MODEL.ONE_FORMER.DIM_FEEDFORWARD
+ ret["transformer_dim_feedforward"] = 1024 # use 1024 for deformable transformer encoder
+ ret[
+ "transformer_enc_layers"
+ ] = cfg.MODEL.SEM_SEG_HEAD.TRANSFORMER_ENC_LAYERS # a separate config
+ ret["transformer_in_features"] = cfg.MODEL.SEM_SEG_HEAD.DEFORMABLE_TRANSFORMER_ENCODER_IN_FEATURES
+ ret["common_stride"] = cfg.MODEL.SEM_SEG_HEAD.COMMON_STRIDE
+ return ret
+
+ @autocast(enabled=False)
+ def forward_features(self, features):
+ srcs = []
+ pos = []
+ # Reverse feature maps into top-down order (from low to high resolution)
+ for idx, f in enumerate(self.transformer_in_features[::-1]):
+ x = features[f].float() # deformable detr does not support half precision
+ srcs.append(self.input_proj[idx](x))
+ pos.append(self.pe_layer(x))
+
+ y, spatial_shapes, level_start_index, valid_ratios = self.transformer(srcs, pos)
+ bs = y.shape[0]
+
+ split_size_or_sections = [None] * self.transformer_num_feature_levels
+ for i in range(self.transformer_num_feature_levels):
+ if i < self.transformer_num_feature_levels - 1:
+ split_size_or_sections[i] = level_start_index[i + 1] - level_start_index[i]
+ else:
+ split_size_or_sections[i] = y.shape[1] - level_start_index[i]
+ y = torch.split(y, split_size_or_sections, dim=1)
+
+ out = []
+ multi_scale_features = []
+ num_cur_levels = 0
+ for i, z in enumerate(y):
+ out.append(z.transpose(1, 2).view(bs, -1, spatial_shapes[i][0], spatial_shapes[i][1]))
+
+ # append `out` with extra FPN levels
+ # Reverse feature maps into top-down order (from low to high resolution)
+ for idx, f in enumerate(self.in_features[:self.num_fpn_levels][::-1]):
+ x = features[f].float()
+ lateral_conv = self.lateral_convs[idx]
+ output_conv = self.output_convs[idx]
+ cur_fpn = lateral_conv(x)
+ # Following FPN implementation, we use nearest upsampling here
+ y = cur_fpn + F.interpolate(out[-1], size=cur_fpn.shape[-2:], mode="bilinear", align_corners=False)
+ y = output_conv(y)
+ out.append(y)
+
+ for o in out:
+ if num_cur_levels < self.oneformer_num_feature_levels:
+ multi_scale_features.append(o)
+ num_cur_levels += 1
+
+ return self.mask_features(out[-1]), out[0], multi_scale_features, spatial_shapes, level_start_index
diff --git a/annotator/OneFormer/oneformer/modeling/pixel_decoder/ops/functions/__init__.py b/annotator/OneFormer/oneformer/modeling/pixel_decoder/ops/functions/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..8a2197bda3199aa32cafc5b9d396479609853dd2
--- /dev/null
+++ b/annotator/OneFormer/oneformer/modeling/pixel_decoder/ops/functions/__init__.py
@@ -0,0 +1,10 @@
+# ------------------------------------------------------------------------------------------------
+# Deformable DETR
+# Copyright (c) 2020 SenseTime. All Rights Reserved.
+# Licensed under the Apache License, Version 2.0 [see LICENSE for details]
+# ------------------------------------------------------------------------------------------------
+# Modified from https://github.com/chengdazhi/Deformable-Convolution-V2-PyTorch/tree/pytorch_1.0.0
+# ------------------------------------------------------------------------------------------------
+
+from .ms_deform_attn_func import MSDeformAttnFunction
+
diff --git a/annotator/OneFormer/oneformer/modeling/pixel_decoder/ops/functions/ms_deform_attn_func.py b/annotator/OneFormer/oneformer/modeling/pixel_decoder/ops/functions/ms_deform_attn_func.py
new file mode 100644
index 0000000000000000000000000000000000000000..42f2597235ed11cec681e21112dcdebd0c9a1149
--- /dev/null
+++ b/annotator/OneFormer/oneformer/modeling/pixel_decoder/ops/functions/ms_deform_attn_func.py
@@ -0,0 +1,75 @@
+# ------------------------------------------------------------------------------------------------
+# Deformable DETR
+# Copyright (c) 2020 SenseTime. All Rights Reserved.
+# Licensed under the Apache License, Version 2.0 [see LICENSE for details]
+# ------------------------------------------------------------------------------------------------
+# Modified from https://github.com/chengdazhi/Deformable-Convolution-V2-PyTorch/tree/pytorch_1.0.0
+# ------------------------------------------------------------------------------------------------
+
+# Copyright (c) Facebook, Inc. and its affiliates.
+# Modified by Bowen Cheng from https://github.com/fundamentalvision/Deformable-DETR
+
+from __future__ import absolute_import
+from __future__ import print_function
+from __future__ import division
+
+import torch
+import torch.nn.functional as F
+from torch.autograd import Function
+from torch.autograd.function import once_differentiable
+
+if torch.cuda.is_available():
+ try:
+ import MultiScaleDeformableAttention as MSDA
+ except ModuleNotFoundError as e:
+ info_string = (
+ "\n\nPlease compile MultiScaleDeformableAttention CUDA op with the following commands:\n"
+ "\t`cd mask2former/modeling/pixel_decoder/ops`\n"
+ "\t`sh make.sh`\n"
+ )
+ raise ModuleNotFoundError(info_string)
+else:
+ MultiScaleDeformableAttention = None
+
+
+class MSDeformAttnFunction(Function):
+ @staticmethod
+ def forward(ctx, value, value_spatial_shapes, value_level_start_index, sampling_locations, attention_weights, im2col_step):
+ ctx.im2col_step = im2col_step
+ output = MSDA.ms_deform_attn_forward(
+ value, value_spatial_shapes, value_level_start_index, sampling_locations, attention_weights, ctx.im2col_step)
+ ctx.save_for_backward(value, value_spatial_shapes, value_level_start_index, sampling_locations, attention_weights)
+ return output
+
+ @staticmethod
+ @once_differentiable
+ def backward(ctx, grad_output):
+ value, value_spatial_shapes, value_level_start_index, sampling_locations, attention_weights = ctx.saved_tensors
+ grad_value, grad_sampling_loc, grad_attn_weight = \
+ MSDA.ms_deform_attn_backward(
+ value, value_spatial_shapes, value_level_start_index, sampling_locations, attention_weights, grad_output, ctx.im2col_step)
+
+ return grad_value, None, None, grad_sampling_loc, grad_attn_weight, None
+
+
+def ms_deform_attn_core_pytorch(value, value_spatial_shapes, sampling_locations, attention_weights):
+ # for debug and test only,
+ # need to use cuda version instead
+ N_, S_, M_, D_ = value.shape
+ _, Lq_, M_, L_, P_, _ = sampling_locations.shape
+ value_list = value.split([H_ * W_ for H_, W_ in value_spatial_shapes], dim=1)
+ sampling_grids = 2 * sampling_locations - 1
+ sampling_value_list = []
+ for lid_, (H_, W_) in enumerate(value_spatial_shapes):
+ # N_, H_*W_, M_, D_ -> N_, H_*W_, M_*D_ -> N_, M_*D_, H_*W_ -> N_*M_, D_, H_, W_
+ value_l_ = value_list[lid_].flatten(2).transpose(1, 2).reshape(N_*M_, D_, H_, W_)
+ # N_, Lq_, M_, P_, 2 -> N_, M_, Lq_, P_, 2 -> N_*M_, Lq_, P_, 2
+ sampling_grid_l_ = sampling_grids[:, :, :, lid_].transpose(1, 2).flatten(0, 1)
+ # N_*M_, D_, Lq_, P_
+ sampling_value_l_ = F.grid_sample(value_l_, sampling_grid_l_,
+ mode='bilinear', padding_mode='zeros', align_corners=False)
+ sampling_value_list.append(sampling_value_l_)
+ # (N_, Lq_, M_, L_, P_) -> (N_, M_, Lq_, L_, P_) -> (N_, M_, 1, Lq_, L_*P_)
+ attention_weights = attention_weights.transpose(1, 2).reshape(N_*M_, 1, Lq_, L_*P_)
+ output = (torch.stack(sampling_value_list, dim=-2).flatten(-2) * attention_weights).sum(-1).view(N_, M_*D_, Lq_)
+ return output.transpose(1, 2).contiguous()
diff --git a/annotator/OneFormer/oneformer/modeling/pixel_decoder/ops/make.sh b/annotator/OneFormer/oneformer/modeling/pixel_decoder/ops/make.sh
new file mode 100755
index 0000000000000000000000000000000000000000..7b38cdbf48f3571d986a33e7563b517952b51bb2
--- /dev/null
+++ b/annotator/OneFormer/oneformer/modeling/pixel_decoder/ops/make.sh
@@ -0,0 +1,13 @@
+#!/usr/bin/env bash
+# ------------------------------------------------------------------------------------------------
+# Deformable DETR
+# Copyright (c) 2020 SenseTime. All Rights Reserved.
+# Licensed under the Apache License, Version 2.0 [see LICENSE for details]
+# ------------------------------------------------------------------------------------------------
+# Modified from https://github.com/chengdazhi/Deformable-Convolution-V2-PyTorch/tree/pytorch_1.0.0
+# ------------------------------------------------------------------------------------------------
+
+# Copyright (c) Facebook, Inc. and its affiliates.
+# Modified by Bowen Cheng from https://github.com/fundamentalvision/Deformable-DETR
+
+python setup.py build install
diff --git a/annotator/OneFormer/oneformer/modeling/pixel_decoder/ops/modules/__init__.py b/annotator/OneFormer/oneformer/modeling/pixel_decoder/ops/modules/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..855ca1095aacc2a0418722497a8fdcaa56427b45
--- /dev/null
+++ b/annotator/OneFormer/oneformer/modeling/pixel_decoder/ops/modules/__init__.py
@@ -0,0 +1,10 @@
+# ------------------------------------------------------------------------------------------------
+# Deformable DETR
+# Copyright (c) 2020 SenseTime. All Rights Reserved.
+# Licensed under the Apache License, Version 2.0 [see LICENSE for details]
+# ------------------------------------------------------------------------------------------------
+# Modified from https://github.com/chengdazhi/Deformable-Convolution-V2-PyTorch/tree/pytorch_1.0.0
+# ------------------------------------------------------------------------------------------------
+
+
+from .ms_deform_attn import MSDeformAttn
diff --git a/annotator/OneFormer/oneformer/modeling/pixel_decoder/ops/modules/ms_deform_attn.py b/annotator/OneFormer/oneformer/modeling/pixel_decoder/ops/modules/ms_deform_attn.py
new file mode 100644
index 0000000000000000000000000000000000000000..6ba6422f134b5ad547019b536f37ec345b0cc59c
--- /dev/null
+++ b/annotator/OneFormer/oneformer/modeling/pixel_decoder/ops/modules/ms_deform_attn.py
@@ -0,0 +1,126 @@
+# ------------------------------------------------------------------------------------------------
+# Deformable DETR
+# Copyright (c) 2020 SenseTime. All Rights Reserved.
+# Licensed under the Apache License, Version 2.0 [see LICENSE for details]
+# ------------------------------------------------------------------------------------------------
+# Modified from https://github.com/chengdazhi/Deformable-Convolution-V2-PyTorch/tree/pytorch_1.0.0
+# ------------------------------------------------------------------------------------------------
+
+# Copyright (c) Facebook, Inc. and its affiliates.
+# Modified by Bowen Cheng from https://github.com/fundamentalvision/Deformable-DETR
+
+from __future__ import absolute_import
+from __future__ import print_function
+from __future__ import division
+
+import warnings
+import math
+
+import torch
+from torch import nn
+import torch.nn.functional as F
+from torch.nn.init import xavier_uniform_, constant_
+
+if torch.cuda.is_available():
+ from ..functions import MSDeformAttnFunction
+else:
+ MSDeformAttnFunction = None
+from ..functions.ms_deform_attn_func import ms_deform_attn_core_pytorch
+
+
+def _is_power_of_2(n):
+ if (not isinstance(n, int)) or (n < 0):
+ raise ValueError("invalid input for _is_power_of_2: {} (type: {})".format(n, type(n)))
+ return (n & (n-1) == 0) and n != 0
+
+
+class MSDeformAttn(nn.Module):
+ def __init__(self, d_model=256, n_levels=4, n_heads=8, n_points=4):
+ """
+ Multi-Scale Deformable Attention Module
+ :param d_model hidden dimension
+ :param n_levels number of feature levels
+ :param n_heads number of attention heads
+ :param n_points number of sampling points per attention head per feature level
+ """
+ super().__init__()
+ if d_model % n_heads != 0:
+ raise ValueError('d_model must be divisible by n_heads, but got {} and {}'.format(d_model, n_heads))
+ _d_per_head = d_model // n_heads
+ # you'd better set _d_per_head to a power of 2 which is more efficient in our CUDA implementation
+ if not _is_power_of_2(_d_per_head):
+ warnings.warn("You'd better set d_model in MSDeformAttn to make the dimension of each attention head a power of 2 "
+ "which is more efficient in our CUDA implementation.")
+
+ self.im2col_step = 128
+
+ self.d_model = d_model
+ self.n_levels = n_levels
+ self.n_heads = n_heads
+ self.n_points = n_points
+
+ self.sampling_offsets = nn.Linear(d_model, n_heads * n_levels * n_points * 2)
+ self.attention_weights = nn.Linear(d_model, n_heads * n_levels * n_points)
+ self.value_proj = nn.Linear(d_model, d_model)
+ self.output_proj = nn.Linear(d_model, d_model)
+
+ self._reset_parameters()
+
+ def _reset_parameters(self):
+ constant_(self.sampling_offsets.weight.data, 0.)
+ thetas = torch.arange(self.n_heads, dtype=torch.float32) * (2.0 * math.pi / self.n_heads)
+ grid_init = torch.stack([thetas.cos(), thetas.sin()], -1)
+ grid_init = (grid_init / grid_init.abs().max(-1, keepdim=True)[0]).view(self.n_heads, 1, 1, 2).repeat(1, self.n_levels, self.n_points, 1)
+ for i in range(self.n_points):
+ grid_init[:, :, i, :] *= i + 1
+ with torch.no_grad():
+ self.sampling_offsets.bias = nn.Parameter(grid_init.view(-1))
+ constant_(self.attention_weights.weight.data, 0.)
+ constant_(self.attention_weights.bias.data, 0.)
+ xavier_uniform_(self.value_proj.weight.data)
+ constant_(self.value_proj.bias.data, 0.)
+ xavier_uniform_(self.output_proj.weight.data)
+ constant_(self.output_proj.bias.data, 0.)
+
+ def forward(self, query, reference_points, input_flatten, input_spatial_shapes, input_level_start_index, input_padding_mask=None):
+ """
+ :param query (N, Length_{query}, C)
+ :param reference_points (N, Length_{query}, n_levels, 2), range in [0, 1], top-left (0,0), bottom-right (1, 1), including padding area
+ or (N, Length_{query}, n_levels, 4), add additional (w, h) to form reference boxes
+ :param input_flatten (N, \sum_{l=0}^{L-1} H_l \cdot W_l, C)
+ :param input_spatial_shapes (n_levels, 2), [(H_0, W_0), (H_1, W_1), ..., (H_{L-1}, W_{L-1})]
+ :param input_level_start_index (n_levels, ), [0, H_0*W_0, H_0*W_0+H_1*W_1, H_0*W_0+H_1*W_1+H_2*W_2, ..., H_0*W_0+H_1*W_1+...+H_{L-1}*W_{L-1}]
+ :param input_padding_mask (N, \sum_{l=0}^{L-1} H_l \cdot W_l), True for padding elements, False for non-padding elements
+
+ :return output (N, Length_{query}, C)
+ """
+ N, Len_q, _ = query.shape
+ N, Len_in, _ = input_flatten.shape
+ assert (input_spatial_shapes[:, 0] * input_spatial_shapes[:, 1]).sum() == Len_in
+
+ value = self.value_proj(input_flatten)
+ if input_padding_mask is not None:
+ value = value.masked_fill(input_padding_mask[..., None], float(0))
+ value = value.view(N, Len_in, self.n_heads, self.d_model // self.n_heads)
+ sampling_offsets = self.sampling_offsets(query).view(N, Len_q, self.n_heads, self.n_levels, self.n_points, 2)
+ attention_weights = self.attention_weights(query).view(N, Len_q, self.n_heads, self.n_levels * self.n_points)
+ attention_weights = F.softmax(attention_weights, -1).view(N, Len_q, self.n_heads, self.n_levels, self.n_points)
+ # N, Len_q, n_heads, n_levels, n_points, 2
+ if reference_points.shape[-1] == 2:
+ offset_normalizer = torch.stack([input_spatial_shapes[..., 1], input_spatial_shapes[..., 0]], -1)
+ sampling_locations = reference_points[:, :, None, :, None, :] \
+ + sampling_offsets / offset_normalizer[None, None, None, :, None, :]
+ elif reference_points.shape[-1] == 4:
+ sampling_locations = reference_points[:, :, None, :, None, :2] \
+ + sampling_offsets / self.n_points * reference_points[:, :, None, :, None, 2:] * 0.5
+ else:
+ raise ValueError(
+ 'Last dim of reference_points must be 2 or 4, but get {} instead.'.format(reference_points.shape[-1]))
+ if torch.cuda.is_available():
+ output = MSDeformAttnFunction.apply(
+ value, input_spatial_shapes, input_level_start_index, sampling_locations, attention_weights, self.im2col_step)
+ else:
+ ## CPU
+ output = ms_deform_attn_core_pytorch(value, input_spatial_shapes, sampling_locations, attention_weights)
+ output = self.output_proj(output)
+ return output
diff --git a/annotator/OneFormer/oneformer/modeling/pixel_decoder/ops/setup.py b/annotator/OneFormer/oneformer/modeling/pixel_decoder/ops/setup.py
new file mode 100644
index 0000000000000000000000000000000000000000..3b57ad313ac8f9b6586892142da8ba943e516cec
--- /dev/null
+++ b/annotator/OneFormer/oneformer/modeling/pixel_decoder/ops/setup.py
@@ -0,0 +1,78 @@
+# ------------------------------------------------------------------------------------------------
+# Deformable DETR
+# Copyright (c) 2020 SenseTime. All Rights Reserved.
+# Licensed under the Apache License, Version 2.0 [see LICENSE for details]
+# ------------------------------------------------------------------------------------------------
+# Modified from https://github.com/chengdazhi/Deformable-Convolution-V2-PyTorch/tree/pytorch_1.0.0
+# ------------------------------------------------------------------------------------------------
+
+# Copyright (c) Facebook, Inc. and its affiliates.
+# Modified by Bowen Cheng from https://github.com/fundamentalvision/Deformable-DETR
+
+import os
+import glob
+
+import torch
+
+from torch.utils.cpp_extension import CUDA_HOME
+from torch.utils.cpp_extension import CppExtension
+from torch.utils.cpp_extension import CUDAExtension
+
+from setuptools import find_packages
+from setuptools import setup
+
+requirements = ["torch", "torchvision"]
+
+def get_extensions():
+ this_dir = os.path.dirname(os.path.abspath(__file__))
+ extensions_dir = os.path.join(this_dir, "src")
+
+ main_file = glob.glob(os.path.join(extensions_dir, "*.cpp"))
+ source_cpu = glob.glob(os.path.join(extensions_dir, "cpu", "*.cpp"))
+ source_cuda = glob.glob(os.path.join(extensions_dir, "cuda", "*.cu"))
+
+ sources = main_file + source_cpu
+ extension = CppExtension
+ extra_compile_args = {"cxx": []}
+ define_macros = []
+
+ # Force cuda since torch ask for a device, not if cuda is in fact available.
+ if (os.environ.get('FORCE_CUDA') or torch.cuda.is_available()) and CUDA_HOME is not None:
+ extension = CUDAExtension
+ sources += source_cuda
+ define_macros += [("WITH_CUDA", None)]
+ extra_compile_args["nvcc"] = [
+ "-DCUDA_HAS_FP16=1",
+ "-D__CUDA_NO_HALF_OPERATORS__",
+ "-D__CUDA_NO_HALF_CONVERSIONS__",
+ "-D__CUDA_NO_HALF2_OPERATORS__",
+ ]
+ else:
+ if CUDA_HOME is None:
+ raise NotImplementedError('CUDA_HOME is None. Please set environment variable CUDA_HOME.')
+ else:
+ raise NotImplementedError('No CUDA runtime is found. Please set FORCE_CUDA=1 or test it by running torch.cuda.is_available().')
+
+ sources = [os.path.join(extensions_dir, s) for s in sources]
+ include_dirs = [extensions_dir]
+ ext_modules = [
+ extension(
+ "MultiScaleDeformableAttention",
+ sources,
+ include_dirs=include_dirs,
+ define_macros=define_macros,
+ extra_compile_args=extra_compile_args,
+ )
+ ]
+ return ext_modules
+
+setup(
+ name="MultiScaleDeformableAttention",
+ version="1.0",
+ author="Weijie Su",
+ url="https://github.com/fundamentalvision/Deformable-DETR",
+ description="PyTorch Wrapper for CUDA Functions of Multi-Scale Deformable Attention",
+ packages=find_packages(exclude=("configs", "tests",)),
+ ext_modules=get_extensions(),
+ cmdclass={"build_ext": torch.utils.cpp_extension.BuildExtension},
+)
diff --git a/annotator/OneFormer/oneformer/modeling/pixel_decoder/ops/src/cpu/ms_deform_attn_cpu.cpp b/annotator/OneFormer/oneformer/modeling/pixel_decoder/ops/src/cpu/ms_deform_attn_cpu.cpp
new file mode 100644
index 0000000000000000000000000000000000000000..48757e2b0156b2c1513b615d2a17e5aee5172ae7
--- /dev/null
+++ b/annotator/OneFormer/oneformer/modeling/pixel_decoder/ops/src/cpu/ms_deform_attn_cpu.cpp
@@ -0,0 +1,46 @@
+/*!
+**************************************************************************************************
+* Deformable DETR
+* Copyright (c) 2020 SenseTime. All Rights Reserved.
+* Licensed under the Apache License, Version 2.0 [see LICENSE for details]
+**************************************************************************************************
+* Modified from https://github.com/chengdazhi/Deformable-Convolution-V2-PyTorch/tree/pytorch_1.0.0
+**************************************************************************************************
+*/
+
+/*!
+* Copyright (c) Facebook, Inc. and its affiliates.
+* Modified by Bowen Cheng from https://github.com/fundamentalvision/Deformable-DETR
+*/
+
+#include
+
+#include
+#include
+
+
+at::Tensor
+ms_deform_attn_cpu_forward(
+ const at::Tensor &value,
+ const at::Tensor &spatial_shapes,
+ const at::Tensor &level_start_index,
+ const at::Tensor &sampling_loc,
+ const at::Tensor &attn_weight,
+ const int im2col_step)
+{
+ AT_ERROR("Not implement on cpu");
+}
+
+std::vector
+ms_deform_attn_cpu_backward(
+ const at::Tensor &value,
+ const at::Tensor &spatial_shapes,
+ const at::Tensor &level_start_index,
+ const at::Tensor &sampling_loc,
+ const at::Tensor &attn_weight,
+ const at::Tensor &grad_output,
+ const int im2col_step)
+{
+ AT_ERROR("Not implement on cpu");
+}
+
diff --git a/annotator/OneFormer/oneformer/modeling/pixel_decoder/ops/src/cpu/ms_deform_attn_cpu.h b/annotator/OneFormer/oneformer/modeling/pixel_decoder/ops/src/cpu/ms_deform_attn_cpu.h
new file mode 100644
index 0000000000000000000000000000000000000000..51bb27e9ee828f967e8aa854c2d55574040c6d7e
--- /dev/null
+++ b/annotator/OneFormer/oneformer/modeling/pixel_decoder/ops/src/cpu/ms_deform_attn_cpu.h
@@ -0,0 +1,38 @@
+/*!
+**************************************************************************************************
+* Deformable DETR
+* Copyright (c) 2020 SenseTime. All Rights Reserved.
+* Licensed under the Apache License, Version 2.0 [see LICENSE for details]
+**************************************************************************************************
+* Modified from https://github.com/chengdazhi/Deformable-Convolution-V2-PyTorch/tree/pytorch_1.0.0
+**************************************************************************************************
+*/
+
+/*!
+* Copyright (c) Facebook, Inc. and its affiliates.
+* Modified by Bowen Cheng from https://github.com/fundamentalvision/Deformable-DETR
+*/
+
+#pragma once
+#include
+
+at::Tensor
+ms_deform_attn_cpu_forward(
+ const at::Tensor &value,
+ const at::Tensor &spatial_shapes,
+ const at::Tensor &level_start_index,
+ const at::Tensor &sampling_loc,
+ const at::Tensor &attn_weight,
+ const int im2col_step);
+
+std::vector
+ms_deform_attn_cpu_backward(
+ const at::Tensor &value,
+ const at::Tensor &spatial_shapes,
+ const at::Tensor &level_start_index,
+ const at::Tensor &sampling_loc,
+ const at::Tensor &attn_weight,
+ const at::Tensor &grad_output,
+ const int im2col_step);
+
+
diff --git a/annotator/OneFormer/oneformer/modeling/pixel_decoder/ops/src/cuda/ms_deform_attn_cuda.cu b/annotator/OneFormer/oneformer/modeling/pixel_decoder/ops/src/cuda/ms_deform_attn_cuda.cu
new file mode 100644
index 0000000000000000000000000000000000000000..0c465dab3d636dfd6a44523c63f148b6e15084d9
--- /dev/null
+++ b/annotator/OneFormer/oneformer/modeling/pixel_decoder/ops/src/cuda/ms_deform_attn_cuda.cu
@@ -0,0 +1,158 @@
+/*!
+**************************************************************************************************
+* Deformable DETR
+* Copyright (c) 2020 SenseTime. All Rights Reserved.
+* Licensed under the Apache License, Version 2.0 [see LICENSE for details]
+**************************************************************************************************
+* Modified from https://github.com/chengdazhi/Deformable-Convolution-V2-PyTorch/tree/pytorch_1.0.0
+**************************************************************************************************
+*/
+
+/*!
+* Copyright (c) Facebook, Inc. and its affiliates.
+* Modified by Bowen Cheng from https://github.com/fundamentalvision/Deformable-DETR
+*/
+
+#include
+#include "cuda/ms_deform_im2col_cuda.cuh"
+
+#include
+#include
+#include
+#include
+
+
+at::Tensor ms_deform_attn_cuda_forward(
+ const at::Tensor &value,
+ const at::Tensor &spatial_shapes,
+ const at::Tensor &level_start_index,
+ const at::Tensor &sampling_loc,
+ const at::Tensor &attn_weight,
+ const int im2col_step)
+{
+ AT_ASSERTM(value.is_contiguous(), "value tensor has to be contiguous");
+ AT_ASSERTM(spatial_shapes.is_contiguous(), "spatial_shapes tensor has to be contiguous");
+ AT_ASSERTM(level_start_index.is_contiguous(), "level_start_index tensor has to be contiguous");
+ AT_ASSERTM(sampling_loc.is_contiguous(), "sampling_loc tensor has to be contiguous");
+ AT_ASSERTM(attn_weight.is_contiguous(), "attn_weight tensor has to be contiguous");
+
+ AT_ASSERTM(value.type().is_cuda(), "value must be a CUDA tensor");
+ AT_ASSERTM(spatial_shapes.type().is_cuda(), "spatial_shapes must be a CUDA tensor");
+ AT_ASSERTM(level_start_index.type().is_cuda(), "level_start_index must be a CUDA tensor");
+ AT_ASSERTM(sampling_loc.type().is_cuda(), "sampling_loc must be a CUDA tensor");
+ AT_ASSERTM(attn_weight.type().is_cuda(), "attn_weight must be a CUDA tensor");
+
+ const int batch = value.size(0);
+ const int spatial_size = value.size(1);
+ const int num_heads = value.size(2);
+ const int channels = value.size(3);
+
+ const int num_levels = spatial_shapes.size(0);
+
+ const int num_query = sampling_loc.size(1);
+ const int num_point = sampling_loc.size(4);
+
+ const int im2col_step_ = std::min(batch, im2col_step);
+
+ AT_ASSERTM(batch % im2col_step_ == 0, "batch(%d) must divide im2col_step(%d)", batch, im2col_step_);
+
+ auto output = at::zeros({batch, num_query, num_heads, channels}, value.options());
+
+ const int batch_n = im2col_step_;
+ auto output_n = output.view({batch/im2col_step_, batch_n, num_query, num_heads, channels});
+ auto per_value_size = spatial_size * num_heads * channels;
+ auto per_sample_loc_size = num_query * num_heads * num_levels * num_point * 2;
+ auto per_attn_weight_size = num_query * num_heads * num_levels * num_point;
+ for (int n = 0; n < batch/im2col_step_; ++n)
+ {
+ auto columns = output_n.select(0, n);
+ AT_DISPATCH_FLOATING_TYPES(value.type(), "ms_deform_attn_forward_cuda", ([&] {
+ ms_deformable_im2col_cuda(at::cuda::getCurrentCUDAStream(),
+ value.data() + n * im2col_step_ * per_value_size,
+ spatial_shapes.data(),
+ level_start_index.data(),
+ sampling_loc.data() + n * im2col_step_ * per_sample_loc_size,
+ attn_weight.data() + n * im2col_step_ * per_attn_weight_size,
+ batch_n, spatial_size, num_heads, channels, num_levels, num_query, num_point,
+ columns.data());
+
+ }));
+ }
+
+ output = output.view({batch, num_query, num_heads*channels});
+
+ return output;
+}
+
+
+std::vector ms_deform_attn_cuda_backward(
+ const at::Tensor &value,
+ const at::Tensor &spatial_shapes,
+ const at::Tensor &level_start_index,
+ const at::Tensor &sampling_loc,
+ const at::Tensor &attn_weight,
+ const at::Tensor &grad_output,
+ const int im2col_step)
+{
+
+ AT_ASSERTM(value.is_contiguous(), "value tensor has to be contiguous");
+ AT_ASSERTM(spatial_shapes.is_contiguous(), "spatial_shapes tensor has to be contiguous");
+ AT_ASSERTM(level_start_index.is_contiguous(), "level_start_index tensor has to be contiguous");
+ AT_ASSERTM(sampling_loc.is_contiguous(), "sampling_loc tensor has to be contiguous");
+ AT_ASSERTM(attn_weight.is_contiguous(), "attn_weight tensor has to be contiguous");
+ AT_ASSERTM(grad_output.is_contiguous(), "grad_output tensor has to be contiguous");
+
+ AT_ASSERTM(value.type().is_cuda(), "value must be a CUDA tensor");
+ AT_ASSERTM(spatial_shapes.type().is_cuda(), "spatial_shapes must be a CUDA tensor");
+ AT_ASSERTM(level_start_index.type().is_cuda(), "level_start_index must be a CUDA tensor");
+ AT_ASSERTM(sampling_loc.type().is_cuda(), "sampling_loc must be a CUDA tensor");
+ AT_ASSERTM(attn_weight.type().is_cuda(), "attn_weight must be a CUDA tensor");
+ AT_ASSERTM(grad_output.type().is_cuda(), "grad_output must be a CUDA tensor");
+
+ const int batch = value.size(0);
+ const int spatial_size = value.size(1);
+ const int num_heads = value.size(2);
+ const int channels = value.size(3);
+
+ const int num_levels = spatial_shapes.size(0);
+
+ const int num_query = sampling_loc.size(1);
+ const int num_point = sampling_loc.size(4);
+
+ const int im2col_step_ = std::min(batch, im2col_step);
+
+ AT_ASSERTM(batch % im2col_step_ == 0, "batch(%d) must divide im2col_step(%d)", batch, im2col_step_);
+
+ auto grad_value = at::zeros_like(value);
+ auto grad_sampling_loc = at::zeros_like(sampling_loc);
+ auto grad_attn_weight = at::zeros_like(attn_weight);
+
+ const int batch_n = im2col_step_;
+ auto per_value_size = spatial_size * num_heads * channels;
+ auto per_sample_loc_size = num_query * num_heads * num_levels * num_point * 2;
+ auto per_attn_weight_size = num_query * num_heads * num_levels * num_point;
+ auto grad_output_n = grad_output.view({batch/im2col_step_, batch_n, num_query, num_heads, channels});
+
+ for (int n = 0; n < batch/im2col_step_; ++n)
+ {
+ auto grad_output_g = grad_output_n.select(0, n);
+ AT_DISPATCH_FLOATING_TYPES(value.type(), "ms_deform_attn_backward_cuda", ([&] {
+ ms_deformable_col2im_cuda(at::cuda::getCurrentCUDAStream(),
+ grad_output_g.data(),
+ value.data() + n * im2col_step_ * per_value_size,
+ spatial_shapes.data(),
+ level_start_index.data(),
+ sampling_loc.data() + n * im2col_step_ * per_sample_loc_size,
+ attn_weight.data() + n * im2col_step_ * per_attn_weight_size,
+ batch_n, spatial_size, num_heads, channels, num_levels, num_query, num_point,
+ grad_value.data() + n * im2col_step_ * per_value_size,
+ grad_sampling_loc.data() + n * im2col_step_ * per_sample_loc_size,
+ grad_attn_weight.data() + n * im2col_step_ * per_attn_weight_size);
+
+ }));
+ }
+
+ return {
+ grad_value, grad_sampling_loc, grad_attn_weight
+ };
+}
\ No newline at end of file
diff --git a/annotator/OneFormer/oneformer/modeling/pixel_decoder/ops/src/cuda/ms_deform_attn_cuda.h b/annotator/OneFormer/oneformer/modeling/pixel_decoder/ops/src/cuda/ms_deform_attn_cuda.h
new file mode 100644
index 0000000000000000000000000000000000000000..4f0658e8668a11f0e7d71deff9adac71884f2e87
--- /dev/null
+++ b/annotator/OneFormer/oneformer/modeling/pixel_decoder/ops/src/cuda/ms_deform_attn_cuda.h
@@ -0,0 +1,35 @@
+/*!
+**************************************************************************************************
+* Deformable DETR
+* Copyright (c) 2020 SenseTime. All Rights Reserved.
+* Licensed under the Apache License, Version 2.0 [see LICENSE for details]
+**************************************************************************************************
+* Modified from https://github.com/chengdazhi/Deformable-Convolution-V2-PyTorch/tree/pytorch_1.0.0
+**************************************************************************************************
+*/
+
+/*!
+* Copyright (c) Facebook, Inc. and its affiliates.
+* Modified by Bowen Cheng from https://github.com/fundamentalvision/Deformable-DETR
+*/
+
+#pragma once
+#include
+
+at::Tensor ms_deform_attn_cuda_forward(
+ const at::Tensor &value,
+ const at::Tensor &spatial_shapes,
+ const at::Tensor &level_start_index,
+ const at::Tensor &sampling_loc,
+ const at::Tensor &attn_weight,
+ const int im2col_step);
+
+std::vector ms_deform_attn_cuda_backward(
+ const at::Tensor &value,
+ const at::Tensor &spatial_shapes,
+ const at::Tensor &level_start_index,
+ const at::Tensor &sampling_loc,
+ const at::Tensor &attn_weight,
+ const at::Tensor &grad_output,
+ const int im2col_step);
+
diff --git a/annotator/OneFormer/oneformer/modeling/pixel_decoder/ops/src/cuda/ms_deform_im2col_cuda.cuh b/annotator/OneFormer/oneformer/modeling/pixel_decoder/ops/src/cuda/ms_deform_im2col_cuda.cuh
new file mode 100644
index 0000000000000000000000000000000000000000..c04e0d4ab97d25c1756fcd8d08dd1e5a6d280b7c
--- /dev/null
+++ b/annotator/OneFormer/oneformer/modeling/pixel_decoder/ops/src/cuda/ms_deform_im2col_cuda.cuh
@@ -0,0 +1,1332 @@
+/*!
+**************************************************************************
+* Deformable DETR
+* Copyright (c) 2020 SenseTime. All Rights Reserved.
+* Licensed under the Apache License, Version 2.0 [see LICENSE for details]
+**************************************************************************
+* Modified from DCN (https://github.com/msracver/Deformable-ConvNets)
+* Copyright (c) 2018 Microsoft
+**************************************************************************
+*/
+
+/*!
+* Copyright (c) Facebook, Inc. and its affiliates.
+* Modified by Bowen Cheng from https://github.com/fundamentalvision/Deformable-DETR
+*/
+
+#include
+#include
+#include
+
+#include
+#include
+
+#include
+
+#define CUDA_KERNEL_LOOP(i, n) \
+ for (int i = blockIdx.x * blockDim.x + threadIdx.x; \
+ i < (n); \
+ i += blockDim.x * gridDim.x)
+
+const int CUDA_NUM_THREADS = 1024;
+inline int GET_BLOCKS(const int N, const int num_threads)
+{
+ return (N + num_threads - 1) / num_threads;
+}
+
+
+template
+__device__ scalar_t ms_deform_attn_im2col_bilinear(const scalar_t* &bottom_data,
+ const int &height, const int &width, const int &nheads, const int &channels,
+ const scalar_t &h, const scalar_t &w, const int &m, const int &c)
+{
+ const int h_low = floor(h);
+ const int w_low = floor(w);
+ const int h_high = h_low + 1;
+ const int w_high = w_low + 1;
+
+ const scalar_t lh = h - h_low;
+ const scalar_t lw = w - w_low;
+ const scalar_t hh = 1 - lh, hw = 1 - lw;
+
+ const int w_stride = nheads * channels;
+ const int h_stride = width * w_stride;
+ const int h_low_ptr_offset = h_low * h_stride;
+ const int h_high_ptr_offset = h_low_ptr_offset + h_stride;
+ const int w_low_ptr_offset = w_low * w_stride;
+ const int w_high_ptr_offset = w_low_ptr_offset + w_stride;
+ const int base_ptr = m * channels + c;
+
+ scalar_t v1 = 0;
+ if (h_low >= 0 && w_low >= 0)
+ {
+ const int ptr1 = h_low_ptr_offset + w_low_ptr_offset + base_ptr;
+ v1 = bottom_data[ptr1];
+ }
+ scalar_t v2 = 0;
+ if (h_low >= 0 && w_high <= width - 1)
+ {
+ const int ptr2 = h_low_ptr_offset + w_high_ptr_offset + base_ptr;
+ v2 = bottom_data[ptr2];
+ }
+ scalar_t v3 = 0;
+ if (h_high <= height - 1 && w_low >= 0)
+ {
+ const int ptr3 = h_high_ptr_offset + w_low_ptr_offset + base_ptr;
+ v3 = bottom_data[ptr3];
+ }
+ scalar_t v4 = 0;
+ if (h_high <= height - 1 && w_high <= width - 1)
+ {
+ const int ptr4 = h_high_ptr_offset + w_high_ptr_offset + base_ptr;
+ v4 = bottom_data[ptr4];
+ }
+
+ const scalar_t w1 = hh * hw, w2 = hh * lw, w3 = lh * hw, w4 = lh * lw;
+
+ const scalar_t val = (w1 * v1 + w2 * v2 + w3 * v3 + w4 * v4);
+ return val;
+}
+
+
+template
+__device__ void ms_deform_attn_col2im_bilinear(const scalar_t* &bottom_data,
+ const int &height, const int &width, const int &nheads, const int &channels,
+ const scalar_t &h, const scalar_t &w, const int &m, const int &c,
+ const scalar_t &top_grad,
+ const scalar_t &attn_weight,
+ scalar_t* &grad_value,
+ scalar_t* grad_sampling_loc,
+ scalar_t* grad_attn_weight)
+{
+ const int h_low = floor(h);
+ const int w_low = floor(w);
+ const int h_high = h_low + 1;
+ const int w_high = w_low + 1;
+
+ const scalar_t lh = h - h_low;
+ const scalar_t lw = w - w_low;
+ const scalar_t hh = 1 - lh, hw = 1 - lw;
+
+ const int w_stride = nheads * channels;
+ const int h_stride = width * w_stride;
+ const int h_low_ptr_offset = h_low * h_stride;
+ const int h_high_ptr_offset = h_low_ptr_offset + h_stride;
+ const int w_low_ptr_offset = w_low * w_stride;
+ const int w_high_ptr_offset = w_low_ptr_offset + w_stride;
+ const int base_ptr = m * channels + c;
+
+ const scalar_t w1 = hh * hw, w2 = hh * lw, w3 = lh * hw, w4 = lh * lw;
+ const scalar_t top_grad_value = top_grad * attn_weight;
+ scalar_t grad_h_weight = 0, grad_w_weight = 0;
+
+ scalar_t v1 = 0;
+ if (h_low >= 0 && w_low >= 0)
+ {
+ const int ptr1 = h_low_ptr_offset + w_low_ptr_offset + base_ptr;
+ v1 = bottom_data[ptr1];
+ grad_h_weight -= hw * v1;
+ grad_w_weight -= hh * v1;
+ atomicAdd(grad_value+ptr1, w1*top_grad_value);
+ }
+ scalar_t v2 = 0;
+ if (h_low >= 0 && w_high <= width - 1)
+ {
+ const int ptr2 = h_low_ptr_offset + w_high_ptr_offset + base_ptr;
+ v2 = bottom_data[ptr2];
+ grad_h_weight -= lw * v2;
+ grad_w_weight += hh * v2;
+ atomicAdd(grad_value+ptr2, w2*top_grad_value);
+ }
+ scalar_t v3 = 0;
+ if (h_high <= height - 1 && w_low >= 0)
+ {
+ const int ptr3 = h_high_ptr_offset + w_low_ptr_offset + base_ptr;
+ v3 = bottom_data[ptr3];
+ grad_h_weight += hw * v3;
+ grad_w_weight -= lh * v3;
+ atomicAdd(grad_value+ptr3, w3*top_grad_value);
+ }
+ scalar_t v4 = 0;
+ if (h_high <= height - 1 && w_high <= width - 1)
+ {
+ const int ptr4 = h_high_ptr_offset + w_high_ptr_offset + base_ptr;
+ v4 = bottom_data[ptr4];
+ grad_h_weight += lw * v4;
+ grad_w_weight += lh * v4;
+ atomicAdd(grad_value+ptr4, w4*top_grad_value);
+ }
+
+ const scalar_t val = (w1 * v1 + w2 * v2 + w3 * v3 + w4 * v4);
+ *grad_attn_weight = top_grad * val;
+ *grad_sampling_loc = width * grad_w_weight * top_grad_value;
+ *(grad_sampling_loc + 1) = height * grad_h_weight * top_grad_value;
+}
+
+
+template
+__device__ void ms_deform_attn_col2im_bilinear_gm(const scalar_t* &bottom_data,
+ const int &height, const int &width, const int &nheads, const int &channels,
+ const scalar_t &h, const scalar_t &w, const int &m, const int &c,
+ const scalar_t &top_grad,
+ const scalar_t &attn_weight,
+ scalar_t* &grad_value,
+ scalar_t* grad_sampling_loc,
+ scalar_t* grad_attn_weight)
+{
+ const int h_low = floor(h);
+ const int w_low = floor(w);
+ const int h_high = h_low + 1;
+ const int w_high = w_low + 1;
+
+ const scalar_t lh = h - h_low;
+ const scalar_t lw = w - w_low;
+ const scalar_t hh = 1 - lh, hw = 1 - lw;
+
+ const int w_stride = nheads * channels;
+ const int h_stride = width * w_stride;
+ const int h_low_ptr_offset = h_low * h_stride;
+ const int h_high_ptr_offset = h_low_ptr_offset + h_stride;
+ const int w_low_ptr_offset = w_low * w_stride;
+ const int w_high_ptr_offset = w_low_ptr_offset + w_stride;
+ const int base_ptr = m * channels + c;
+
+ const scalar_t w1 = hh * hw, w2 = hh * lw, w3 = lh * hw, w4 = lh * lw;
+ const scalar_t top_grad_value = top_grad * attn_weight;
+ scalar_t grad_h_weight = 0, grad_w_weight = 0;
+
+ scalar_t v1 = 0;
+ if (h_low >= 0 && w_low >= 0)
+ {
+ const int ptr1 = h_low_ptr_offset + w_low_ptr_offset + base_ptr;
+ v1 = bottom_data[ptr1];
+ grad_h_weight -= hw * v1;
+ grad_w_weight -= hh * v1;
+ atomicAdd(grad_value+ptr1, w1*top_grad_value);
+ }
+ scalar_t v2 = 0;
+ if (h_low >= 0 && w_high <= width - 1)
+ {
+ const int ptr2 = h_low_ptr_offset + w_high_ptr_offset + base_ptr;
+ v2 = bottom_data[ptr2];
+ grad_h_weight -= lw * v2;
+ grad_w_weight += hh * v2;
+ atomicAdd(grad_value+ptr2, w2*top_grad_value);
+ }
+ scalar_t v3 = 0;
+ if (h_high <= height - 1 && w_low >= 0)
+ {
+ const int ptr3 = h_high_ptr_offset + w_low_ptr_offset + base_ptr;
+ v3 = bottom_data[ptr3];
+ grad_h_weight += hw * v3;
+ grad_w_weight -= lh * v3;
+ atomicAdd(grad_value+ptr3, w3*top_grad_value);
+ }
+ scalar_t v4 = 0;
+ if (h_high <= height - 1 && w_high <= width - 1)
+ {
+ const int ptr4 = h_high_ptr_offset + w_high_ptr_offset + base_ptr;
+ v4 = bottom_data[ptr4];
+ grad_h_weight += lw * v4;
+ grad_w_weight += lh * v4;
+ atomicAdd(grad_value+ptr4, w4*top_grad_value);
+ }
+
+ const scalar_t val = (w1 * v1 + w2 * v2 + w3 * v3 + w4 * v4);
+ atomicAdd(grad_attn_weight, top_grad * val);
+ atomicAdd(grad_sampling_loc, width * grad_w_weight * top_grad_value);
+ atomicAdd(grad_sampling_loc + 1, height * grad_h_weight * top_grad_value);
+}
+
+
+template
+__global__ void ms_deformable_im2col_gpu_kernel(const int n,
+ const scalar_t *data_value,
+ const int64_t *data_spatial_shapes,
+ const int64_t *data_level_start_index,
+ const scalar_t *data_sampling_loc,
+ const scalar_t *data_attn_weight,
+ const int batch_size,
+ const int spatial_size,
+ const int num_heads,
+ const int channels,
+ const int num_levels,
+ const int num_query,
+ const int num_point,
+ scalar_t *data_col)
+{
+ CUDA_KERNEL_LOOP(index, n)
+ {
+ int _temp = index;
+ const int c_col = _temp % channels;
+ _temp /= channels;
+ const int sampling_index = _temp;
+ const int m_col = _temp % num_heads;
+ _temp /= num_heads;
+ const int q_col = _temp % num_query;
+ _temp /= num_query;
+ const int b_col = _temp;
+
+ scalar_t *data_col_ptr = data_col + index;
+ int data_weight_ptr = sampling_index * num_levels * num_point;
+ int data_loc_w_ptr = data_weight_ptr << 1;
+ const int qid_stride = num_heads * channels;
+ const int data_value_ptr_init_offset = b_col * spatial_size * qid_stride;
+ scalar_t col = 0;
+
+ for (int l_col=0; l_col < num_levels; ++l_col)
+ {
+ const int level_start_id = data_level_start_index[l_col];
+ const int spatial_h_ptr = l_col << 1;
+ const int spatial_h = data_spatial_shapes[spatial_h_ptr];
+ const int spatial_w = data_spatial_shapes[spatial_h_ptr + 1];
+ const scalar_t *data_value_ptr = data_value + (data_value_ptr_init_offset + level_start_id * qid_stride);
+ for (int p_col=0; p_col < num_point; ++p_col)
+ {
+ const scalar_t loc_w = data_sampling_loc[data_loc_w_ptr];
+ const scalar_t loc_h = data_sampling_loc[data_loc_w_ptr + 1];
+ const scalar_t weight = data_attn_weight[data_weight_ptr];
+
+ const scalar_t h_im = loc_h * spatial_h - 0.5;
+ const scalar_t w_im = loc_w * spatial_w - 0.5;
+
+ if (h_im > -1 && w_im > -1 && h_im < spatial_h && w_im < spatial_w)
+ {
+ col += ms_deform_attn_im2col_bilinear(data_value_ptr, spatial_h, spatial_w, num_heads, channels, h_im, w_im, m_col, c_col) * weight;
+ }
+
+ data_weight_ptr += 1;
+ data_loc_w_ptr += 2;
+ }
+ }
+ *data_col_ptr = col;
+ }
+}
+
+template
+__global__ void ms_deformable_col2im_gpu_kernel_shm_blocksize_aware_reduce_v1(const int n,
+ const scalar_t *grad_col,
+ const scalar_t *data_value,
+ const int64_t *data_spatial_shapes,
+ const int64_t *data_level_start_index,
+ const scalar_t *data_sampling_loc,
+ const scalar_t *data_attn_weight,
+ const int batch_size,
+ const int spatial_size,
+ const int num_heads,
+ const int channels,
+ const int num_levels,
+ const int num_query,
+ const int num_point,
+ scalar_t *grad_value,
+ scalar_t *grad_sampling_loc,
+ scalar_t *grad_attn_weight)
+{
+ CUDA_KERNEL_LOOP(index, n)
+ {
+ __shared__ scalar_t cache_grad_sampling_loc[blockSize * 2];
+ __shared__ scalar_t cache_grad_attn_weight[blockSize];
+ unsigned int tid = threadIdx.x;
+ int _temp = index;
+ const int c_col = _temp % channels;
+ _temp /= channels;
+ const int sampling_index = _temp;
+ const int m_col = _temp % num_heads;
+ _temp /= num_heads;
+ const int q_col = _temp % num_query;
+ _temp /= num_query;
+ const int b_col = _temp;
+
+ const scalar_t top_grad = grad_col[index];
+
+ int data_weight_ptr = sampling_index * num_levels * num_point;
+ int data_loc_w_ptr = data_weight_ptr << 1;
+ const int grad_sampling_ptr = data_weight_ptr;
+ grad_sampling_loc += grad_sampling_ptr << 1;
+ grad_attn_weight += grad_sampling_ptr;
+ const int grad_weight_stride = 1;
+ const int grad_loc_stride = 2;
+ const int qid_stride = num_heads * channels;
+ const int data_value_ptr_init_offset = b_col * spatial_size * qid_stride;
+
+ for (int l_col=0; l_col < num_levels; ++l_col)
+ {
+ const int level_start_id = data_level_start_index[l_col];
+ const int spatial_h_ptr = l_col << 1;
+ const int spatial_h = data_spatial_shapes[spatial_h_ptr];
+ const int spatial_w = data_spatial_shapes[spatial_h_ptr + 1];
+ const int value_ptr_offset = data_value_ptr_init_offset + level_start_id * qid_stride;
+ const scalar_t *data_value_ptr = data_value + value_ptr_offset;
+ scalar_t *grad_value_ptr = grad_value + value_ptr_offset;
+
+ for (int p_col=0; p_col < num_point; ++p_col)
+ {
+ const scalar_t loc_w = data_sampling_loc[data_loc_w_ptr];
+ const scalar_t loc_h = data_sampling_loc[data_loc_w_ptr + 1];
+ const scalar_t weight = data_attn_weight[data_weight_ptr];
+
+ const scalar_t h_im = loc_h * spatial_h - 0.5;
+ const scalar_t w_im = loc_w * spatial_w - 0.5;
+ *(cache_grad_sampling_loc+(threadIdx.x << 1)) = 0;
+ *(cache_grad_sampling_loc+((threadIdx.x << 1) + 1)) = 0;
+ *(cache_grad_attn_weight+threadIdx.x)=0;
+ if (h_im > -1 && w_im > -1 && h_im < spatial_h && w_im < spatial_w)
+ {
+ ms_deform_attn_col2im_bilinear(
+ data_value_ptr, spatial_h, spatial_w, num_heads, channels, h_im, w_im, m_col, c_col,
+ top_grad, weight, grad_value_ptr,
+ cache_grad_sampling_loc+(threadIdx.x << 1), cache_grad_attn_weight+threadIdx.x);
+ }
+
+ __syncthreads();
+ if (tid == 0)
+ {
+ scalar_t _grad_w=cache_grad_sampling_loc[0], _grad_h=cache_grad_sampling_loc[1], _grad_a=cache_grad_attn_weight[0];
+ int sid=2;
+ for (unsigned int tid = 1; tid < blockSize; ++tid)
+ {
+ _grad_w += cache_grad_sampling_loc[sid];
+ _grad_h += cache_grad_sampling_loc[sid + 1];
+ _grad_a += cache_grad_attn_weight[tid];
+ sid += 2;
+ }
+
+
+ *grad_sampling_loc = _grad_w;
+ *(grad_sampling_loc + 1) = _grad_h;
+ *grad_attn_weight = _grad_a;
+ }
+ __syncthreads();
+
+ data_weight_ptr += 1;
+ data_loc_w_ptr += 2;
+ grad_attn_weight += grad_weight_stride;
+ grad_sampling_loc += grad_loc_stride;
+ }
+ }
+ }
+}
+
+
+template
+__global__ void ms_deformable_col2im_gpu_kernel_shm_blocksize_aware_reduce_v2(const int n,
+ const scalar_t *grad_col,
+ const scalar_t *data_value,
+ const int64_t *data_spatial_shapes,
+ const int64_t *data_level_start_index,
+ const scalar_t *data_sampling_loc,
+ const scalar_t *data_attn_weight,
+ const int batch_size,
+ const int spatial_size,
+ const int num_heads,
+ const int channels,
+ const int num_levels,
+ const int num_query,
+ const int num_point,
+ scalar_t *grad_value,
+ scalar_t *grad_sampling_loc,
+ scalar_t *grad_attn_weight)
+{
+ CUDA_KERNEL_LOOP(index, n)
+ {
+ __shared__ scalar_t cache_grad_sampling_loc[blockSize * 2];
+ __shared__ scalar_t cache_grad_attn_weight[blockSize];
+ unsigned int tid = threadIdx.x;
+ int _temp = index;
+ const int c_col = _temp % channels;
+ _temp /= channels;
+ const int sampling_index = _temp;
+ const int m_col = _temp % num_heads;
+ _temp /= num_heads;
+ const int q_col = _temp % num_query;
+ _temp /= num_query;
+ const int b_col = _temp;
+
+ const scalar_t top_grad = grad_col[index];
+
+ int data_weight_ptr = sampling_index * num_levels * num_point;
+ int data_loc_w_ptr = data_weight_ptr << 1;
+ const int grad_sampling_ptr = data_weight_ptr;
+ grad_sampling_loc += grad_sampling_ptr << 1;
+ grad_attn_weight += grad_sampling_ptr;
+ const int grad_weight_stride = 1;
+ const int grad_loc_stride = 2;
+ const int qid_stride = num_heads * channels;
+ const int data_value_ptr_init_offset = b_col * spatial_size * qid_stride;
+
+ for (int l_col=0; l_col < num_levels; ++l_col)
+ {
+ const int level_start_id = data_level_start_index[l_col];
+ const int spatial_h_ptr = l_col << 1;
+ const int spatial_h = data_spatial_shapes[spatial_h_ptr];
+ const int spatial_w = data_spatial_shapes[spatial_h_ptr + 1];
+ const int value_ptr_offset = data_value_ptr_init_offset + level_start_id * qid_stride;
+ const scalar_t *data_value_ptr = data_value + value_ptr_offset;
+ scalar_t *grad_value_ptr = grad_value + value_ptr_offset;
+
+ for (int p_col=0; p_col < num_point; ++p_col)
+ {
+ const scalar_t loc_w = data_sampling_loc[data_loc_w_ptr];
+ const scalar_t loc_h = data_sampling_loc[data_loc_w_ptr + 1];
+ const scalar_t weight = data_attn_weight[data_weight_ptr];
+
+ const scalar_t h_im = loc_h * spatial_h - 0.5;
+ const scalar_t w_im = loc_w * spatial_w - 0.5;
+ *(cache_grad_sampling_loc+(threadIdx.x << 1)) = 0;
+ *(cache_grad_sampling_loc+((threadIdx.x << 1) + 1)) = 0;
+ *(cache_grad_attn_weight+threadIdx.x)=0;
+ if (h_im > -1 && w_im > -1 && h_im < spatial_h && w_im < spatial_w)
+ {
+ ms_deform_attn_col2im_bilinear(
+ data_value_ptr, spatial_h, spatial_w, num_heads, channels, h_im, w_im, m_col, c_col,
+ top_grad, weight, grad_value_ptr,
+ cache_grad_sampling_loc+(threadIdx.x << 1), cache_grad_attn_weight+threadIdx.x);
+ }
+
+ __syncthreads();
+
+ for (unsigned int s=blockSize/2; s>0; s>>=1)
+ {
+ if (tid < s) {
+ const unsigned int xid1 = tid << 1;
+ const unsigned int xid2 = (tid + s) << 1;
+ cache_grad_attn_weight[tid] += cache_grad_attn_weight[tid + s];
+ cache_grad_sampling_loc[xid1] += cache_grad_sampling_loc[xid2];
+ cache_grad_sampling_loc[xid1 + 1] += cache_grad_sampling_loc[xid2 + 1];
+ }
+ __syncthreads();
+ }
+
+ if (tid == 0)
+ {
+ *grad_sampling_loc = cache_grad_sampling_loc[0];
+ *(grad_sampling_loc + 1) = cache_grad_sampling_loc[1];
+ *grad_attn_weight = cache_grad_attn_weight[0];
+ }
+ __syncthreads();
+
+ data_weight_ptr += 1;
+ data_loc_w_ptr += 2;
+ grad_attn_weight += grad_weight_stride;
+ grad_sampling_loc += grad_loc_stride;
+ }
+ }
+ }
+}
+
+
+template
+__global__ void ms_deformable_col2im_gpu_kernel_shm_reduce_v1(const int n,
+ const scalar_t *grad_col,
+ const scalar_t *data_value,
+ const int64_t *data_spatial_shapes,
+ const int64_t *data_level_start_index,
+ const scalar_t *data_sampling_loc,
+ const scalar_t *data_attn_weight,
+ const int batch_size,
+ const int spatial_size,
+ const int num_heads,
+ const int channels,
+ const int num_levels,
+ const int num_query,
+ const int num_point,
+ scalar_t *grad_value,
+ scalar_t *grad_sampling_loc,
+ scalar_t *grad_attn_weight)
+{
+ CUDA_KERNEL_LOOP(index, n)
+ {
+ extern __shared__ int _s[];
+ scalar_t* cache_grad_sampling_loc = (scalar_t*)_s;
+ scalar_t* cache_grad_attn_weight = cache_grad_sampling_loc + 2 * blockDim.x;
+ unsigned int tid = threadIdx.x;
+ int _temp = index;
+ const int c_col = _temp % channels;
+ _temp /= channels;
+ const int sampling_index = _temp;
+ const int m_col = _temp % num_heads;
+ _temp /= num_heads;
+ const int q_col = _temp % num_query;
+ _temp /= num_query;
+ const int b_col = _temp;
+
+ const scalar_t top_grad = grad_col[index];
+
+ int data_weight_ptr = sampling_index * num_levels * num_point;
+ int data_loc_w_ptr = data_weight_ptr << 1;
+ const int grad_sampling_ptr = data_weight_ptr;
+ grad_sampling_loc += grad_sampling_ptr << 1;
+ grad_attn_weight += grad_sampling_ptr;
+ const int grad_weight_stride = 1;
+ const int grad_loc_stride = 2;
+ const int qid_stride = num_heads * channels;
+ const int data_value_ptr_init_offset = b_col * spatial_size * qid_stride;
+
+ for (int l_col=0; l_col < num_levels; ++l_col)
+ {
+ const int level_start_id = data_level_start_index[l_col];
+ const int spatial_h_ptr = l_col << 1;
+ const int spatial_h = data_spatial_shapes[spatial_h_ptr];
+ const int spatial_w = data_spatial_shapes[spatial_h_ptr + 1];
+ const int value_ptr_offset = data_value_ptr_init_offset + level_start_id * qid_stride;
+ const scalar_t *data_value_ptr = data_value + value_ptr_offset;
+ scalar_t *grad_value_ptr = grad_value + value_ptr_offset;
+
+ for (int p_col=0; p_col < num_point; ++p_col)
+ {
+ const scalar_t loc_w = data_sampling_loc[data_loc_w_ptr];
+ const scalar_t loc_h = data_sampling_loc[data_loc_w_ptr + 1];
+ const scalar_t weight = data_attn_weight[data_weight_ptr];
+
+ const scalar_t h_im = loc_h * spatial_h - 0.5;
+ const scalar_t w_im = loc_w * spatial_w - 0.5;
+ *(cache_grad_sampling_loc+(threadIdx.x << 1)) = 0;
+ *(cache_grad_sampling_loc+((threadIdx.x << 1) + 1)) = 0;
+ *(cache_grad_attn_weight+threadIdx.x)=0;
+ if (h_im > -1 && w_im > -1 && h_im < spatial_h && w_im < spatial_w)
+ {
+ ms_deform_attn_col2im_bilinear(
+ data_value_ptr, spatial_h, spatial_w, num_heads, channels, h_im, w_im, m_col, c_col,
+ top_grad, weight, grad_value_ptr,
+ cache_grad_sampling_loc+(threadIdx.x << 1), cache_grad_attn_weight+threadIdx.x);
+ }
+
+ __syncthreads();
+ if (tid == 0)
+ {
+ scalar_t _grad_w=cache_grad_sampling_loc[0], _grad_h=cache_grad_sampling_loc[1], _grad_a=cache_grad_attn_weight[0];
+ int sid=2;
+ for (unsigned int tid = 1; tid < blockDim.x; ++tid)
+ {
+ _grad_w += cache_grad_sampling_loc[sid];
+ _grad_h += cache_grad_sampling_loc[sid + 1];
+ _grad_a += cache_grad_attn_weight[tid];
+ sid += 2;
+ }
+
+
+ *grad_sampling_loc = _grad_w;
+ *(grad_sampling_loc + 1) = _grad_h;
+ *grad_attn_weight = _grad_a;
+ }
+ __syncthreads();
+
+ data_weight_ptr += 1;
+ data_loc_w_ptr += 2;
+ grad_attn_weight += grad_weight_stride;
+ grad_sampling_loc += grad_loc_stride;
+ }
+ }
+ }
+}
+
+template
+__global__ void ms_deformable_col2im_gpu_kernel_shm_reduce_v2(const int n,
+ const scalar_t *grad_col,
+ const scalar_t *data_value,
+ const int64_t *data_spatial_shapes,
+ const int64_t *data_level_start_index,
+ const scalar_t *data_sampling_loc,
+ const scalar_t *data_attn_weight,
+ const int batch_size,
+ const int spatial_size,
+ const int num_heads,
+ const int channels,
+ const int num_levels,
+ const int num_query,
+ const int num_point,
+ scalar_t *grad_value,
+ scalar_t *grad_sampling_loc,
+ scalar_t *grad_attn_weight)
+{
+ CUDA_KERNEL_LOOP(index, n)
+ {
+ extern __shared__ int _s[];
+ scalar_t* cache_grad_sampling_loc = (scalar_t*)_s;
+ scalar_t* cache_grad_attn_weight = cache_grad_sampling_loc + 2 * blockDim.x;
+ unsigned int tid = threadIdx.x;
+ int _temp = index;
+ const int c_col = _temp % channels;
+ _temp /= channels;
+ const int sampling_index = _temp;
+ const int m_col = _temp % num_heads;
+ _temp /= num_heads;
+ const int q_col = _temp % num_query;
+ _temp /= num_query;
+ const int b_col = _temp;
+
+ const scalar_t top_grad = grad_col[index];
+
+ int data_weight_ptr = sampling_index * num_levels * num_point;
+ int data_loc_w_ptr = data_weight_ptr << 1;
+ const int grad_sampling_ptr = data_weight_ptr;
+ grad_sampling_loc += grad_sampling_ptr << 1;
+ grad_attn_weight += grad_sampling_ptr;
+ const int grad_weight_stride = 1;
+ const int grad_loc_stride = 2;
+ const int qid_stride = num_heads * channels;
+ const int data_value_ptr_init_offset = b_col * spatial_size * qid_stride;
+
+ for (int l_col=0; l_col < num_levels; ++l_col)
+ {
+ const int level_start_id = data_level_start_index[l_col];
+ const int spatial_h_ptr = l_col << 1;
+ const int spatial_h = data_spatial_shapes[spatial_h_ptr];
+ const int spatial_w = data_spatial_shapes[spatial_h_ptr + 1];
+ const int value_ptr_offset = data_value_ptr_init_offset + level_start_id * qid_stride;
+ const scalar_t *data_value_ptr = data_value + value_ptr_offset;
+ scalar_t *grad_value_ptr = grad_value + value_ptr_offset;
+
+ for (int p_col=0; p_col < num_point; ++p_col)
+ {
+ const scalar_t loc_w = data_sampling_loc[data_loc_w_ptr];
+ const scalar_t loc_h = data_sampling_loc[data_loc_w_ptr + 1];
+ const scalar_t weight = data_attn_weight[data_weight_ptr];
+
+ const scalar_t h_im = loc_h * spatial_h - 0.5;
+ const scalar_t w_im = loc_w * spatial_w - 0.5;
+ *(cache_grad_sampling_loc+(threadIdx.x << 1)) = 0;
+ *(cache_grad_sampling_loc+((threadIdx.x << 1) + 1)) = 0;
+ *(cache_grad_attn_weight+threadIdx.x)=0;
+ if (h_im > -1 && w_im > -1 && h_im < spatial_h && w_im < spatial_w)
+ {
+ ms_deform_attn_col2im_bilinear(
+ data_value_ptr, spatial_h, spatial_w, num_heads, channels, h_im, w_im, m_col, c_col,
+ top_grad, weight, grad_value_ptr,
+ cache_grad_sampling_loc+(threadIdx.x << 1), cache_grad_attn_weight+threadIdx.x);
+ }
+
+ __syncthreads();
+
+ for (unsigned int s=blockDim.x/2, spre=blockDim.x; s>0; s>>=1, spre>>=1)
+ {
+ if (tid < s) {
+ const unsigned int xid1 = tid << 1;
+ const unsigned int xid2 = (tid + s) << 1;
+ cache_grad_attn_weight[tid] += cache_grad_attn_weight[tid + s];
+ cache_grad_sampling_loc[xid1] += cache_grad_sampling_loc[xid2];
+ cache_grad_sampling_loc[xid1 + 1] += cache_grad_sampling_loc[xid2 + 1];
+ if (tid + (s << 1) < spre)
+ {
+ cache_grad_attn_weight[tid] += cache_grad_attn_weight[tid + (s << 1)];
+ cache_grad_sampling_loc[xid1] += cache_grad_sampling_loc[xid2 + (s << 1)];
+ cache_grad_sampling_loc[xid1 + 1] += cache_grad_sampling_loc[xid2 + 1 + (s << 1)];
+ }
+ }
+ __syncthreads();
+ }
+
+ if (tid == 0)
+ {
+ *grad_sampling_loc = cache_grad_sampling_loc[0];
+ *(grad_sampling_loc + 1) = cache_grad_sampling_loc[1];
+ *grad_attn_weight = cache_grad_attn_weight[0];
+ }
+ __syncthreads();
+
+ data_weight_ptr += 1;
+ data_loc_w_ptr += 2;
+ grad_attn_weight += grad_weight_stride;
+ grad_sampling_loc += grad_loc_stride;
+ }
+ }
+ }
+}
+
+template
+__global__ void ms_deformable_col2im_gpu_kernel_shm_reduce_v2_multi_blocks(const int n,
+ const scalar_t *grad_col,
+ const scalar_t *data_value,
+ const int64_t *data_spatial_shapes,
+ const int64_t *data_level_start_index,
+ const scalar_t *data_sampling_loc,
+ const scalar_t *data_attn_weight,
+ const int batch_size,
+ const int spatial_size,
+ const int num_heads,
+ const int channels,
+ const int num_levels,
+ const int num_query,
+ const int num_point,
+ scalar_t *grad_value,
+ scalar_t *grad_sampling_loc,
+ scalar_t *grad_attn_weight)
+{
+ CUDA_KERNEL_LOOP(index, n)
+ {
+ extern __shared__ int _s[];
+ scalar_t* cache_grad_sampling_loc = (scalar_t*)_s;
+ scalar_t* cache_grad_attn_weight = cache_grad_sampling_loc + 2 * blockDim.x;
+ unsigned int tid = threadIdx.x;
+ int _temp = index;
+ const int c_col = _temp % channels;
+ _temp /= channels;
+ const int sampling_index = _temp;
+ const int m_col = _temp % num_heads;
+ _temp /= num_heads;
+ const int q_col = _temp % num_query;
+ _temp /= num_query;
+ const int b_col = _temp;
+
+ const scalar_t top_grad = grad_col[index];
+
+ int data_weight_ptr = sampling_index * num_levels * num_point;
+ int data_loc_w_ptr = data_weight_ptr << 1;
+ const int grad_sampling_ptr = data_weight_ptr;
+ grad_sampling_loc += grad_sampling_ptr << 1;
+ grad_attn_weight += grad_sampling_ptr;
+ const int grad_weight_stride = 1;
+ const int grad_loc_stride = 2;
+ const int qid_stride = num_heads * channels;
+ const int data_value_ptr_init_offset = b_col * spatial_size * qid_stride;
+
+ for (int l_col=0; l_col < num_levels; ++l_col)
+ {
+ const int level_start_id = data_level_start_index[l_col];
+ const int spatial_h_ptr = l_col << 1;
+ const int spatial_h = data_spatial_shapes[spatial_h_ptr];
+ const int spatial_w = data_spatial_shapes[spatial_h_ptr + 1];
+ const int value_ptr_offset = data_value_ptr_init_offset + level_start_id * qid_stride;
+ const scalar_t *data_value_ptr = data_value + value_ptr_offset;
+ scalar_t *grad_value_ptr = grad_value + value_ptr_offset;
+
+ for (int p_col=0; p_col < num_point; ++p_col)
+ {
+ const scalar_t loc_w = data_sampling_loc[data_loc_w_ptr];
+ const scalar_t loc_h = data_sampling_loc[data_loc_w_ptr + 1];
+ const scalar_t weight = data_attn_weight[data_weight_ptr];
+
+ const scalar_t h_im = loc_h * spatial_h - 0.5;
+ const scalar_t w_im = loc_w * spatial_w - 0.5;
+ *(cache_grad_sampling_loc+(threadIdx.x << 1)) = 0;
+ *(cache_grad_sampling_loc+((threadIdx.x << 1) + 1)) = 0;
+ *(cache_grad_attn_weight+threadIdx.x)=0;
+ if (h_im > -1 && w_im > -1 && h_im < spatial_h && w_im < spatial_w)
+ {
+ ms_deform_attn_col2im_bilinear(
+ data_value_ptr, spatial_h, spatial_w, num_heads, channels, h_im, w_im, m_col, c_col,
+ top_grad, weight, grad_value_ptr,
+ cache_grad_sampling_loc+(threadIdx.x << 1), cache_grad_attn_weight+threadIdx.x);
+ }
+
+ __syncthreads();
+
+ for (unsigned int s=blockDim.x/2, spre=blockDim.x; s>0; s>>=1, spre>>=1)
+ {
+ if (tid < s) {
+ const unsigned int xid1 = tid << 1;
+ const unsigned int xid2 = (tid + s) << 1;
+ cache_grad_attn_weight[tid] += cache_grad_attn_weight[tid + s];
+ cache_grad_sampling_loc[xid1] += cache_grad_sampling_loc[xid2];
+ cache_grad_sampling_loc[xid1 + 1] += cache_grad_sampling_loc[xid2 + 1];
+ if (tid + (s << 1) < spre)
+ {
+ cache_grad_attn_weight[tid] += cache_grad_attn_weight[tid + (s << 1)];
+ cache_grad_sampling_loc[xid1] += cache_grad_sampling_loc[xid2 + (s << 1)];
+ cache_grad_sampling_loc[xid1 + 1] += cache_grad_sampling_loc[xid2 + 1 + (s << 1)];
+ }
+ }
+ __syncthreads();
+ }
+
+ if (tid == 0)
+ {
+ atomicAdd(grad_sampling_loc, cache_grad_sampling_loc[0]);
+ atomicAdd(grad_sampling_loc + 1, cache_grad_sampling_loc[1]);
+ atomicAdd(grad_attn_weight, cache_grad_attn_weight[0]);
+ }
+ __syncthreads();
+
+ data_weight_ptr += 1;
+ data_loc_w_ptr += 2;
+ grad_attn_weight += grad_weight_stride;
+ grad_sampling_loc += grad_loc_stride;
+ }
+ }
+ }
+}
+
+
+template
+__global__ void ms_deformable_col2im_gpu_kernel_gm(const int n,
+ const scalar_t *grad_col,
+ const scalar_t *data_value,
+ const int64_t *data_spatial_shapes,
+ const int64_t *data_level_start_index,
+ const scalar_t *data_sampling_loc,
+ const scalar_t *data_attn_weight,
+ const int batch_size,
+ const int spatial_size,
+ const int num_heads,
+ const int channels,
+ const int num_levels,
+ const int num_query,
+ const int num_point,
+ scalar_t *grad_value,
+ scalar_t *grad_sampling_loc,
+ scalar_t *grad_attn_weight)
+{
+ CUDA_KERNEL_LOOP(index, n)
+ {
+ int _temp = index;
+ const int c_col = _temp % channels;
+ _temp /= channels;
+ const int sampling_index = _temp;
+ const int m_col = _temp % num_heads;
+ _temp /= num_heads;
+ const int q_col = _temp % num_query;
+ _temp /= num_query;
+ const int b_col = _temp;
+
+ const scalar_t top_grad = grad_col[index];
+
+ int data_weight_ptr = sampling_index * num_levels * num_point;
+ int data_loc_w_ptr = data_weight_ptr << 1;
+ const int grad_sampling_ptr = data_weight_ptr;
+ grad_sampling_loc += grad_sampling_ptr << 1;
+ grad_attn_weight += grad_sampling_ptr;
+ const int grad_weight_stride = 1;
+ const int grad_loc_stride = 2;
+ const int qid_stride = num_heads * channels;
+ const int data_value_ptr_init_offset = b_col * spatial_size * qid_stride;
+
+ for (int l_col=0; l_col < num_levels; ++l_col)
+ {
+ const int level_start_id = data_level_start_index[l_col];
+ const int spatial_h_ptr = l_col << 1;
+ const int spatial_h = data_spatial_shapes[spatial_h_ptr];
+ const int spatial_w = data_spatial_shapes[spatial_h_ptr + 1];
+ const int value_ptr_offset = data_value_ptr_init_offset + level_start_id * qid_stride;
+ const scalar_t *data_value_ptr = data_value + value_ptr_offset;
+ scalar_t *grad_value_ptr = grad_value + value_ptr_offset;
+
+ for (int p_col=0; p_col < num_point; ++p_col)
+ {
+ const scalar_t loc_w = data_sampling_loc[data_loc_w_ptr];
+ const scalar_t loc_h = data_sampling_loc[data_loc_w_ptr + 1];
+ const scalar_t weight = data_attn_weight[data_weight_ptr];
+
+ const scalar_t h_im = loc_h * spatial_h - 0.5;
+ const scalar_t w_im = loc_w * spatial_w - 0.5;
+ if (h_im > -1 && w_im > -1 && h_im < spatial_h && w_im < spatial_w)
+ {
+ ms_deform_attn_col2im_bilinear_gm(
+ data_value_ptr, spatial_h, spatial_w, num_heads, channels, h_im, w_im, m_col, c_col,
+ top_grad, weight, grad_value_ptr,
+ grad_sampling_loc, grad_attn_weight);
+ }
+ data_weight_ptr += 1;
+ data_loc_w_ptr += 2;
+ grad_attn_weight += grad_weight_stride;
+ grad_sampling_loc += grad_loc_stride;
+ }
+ }
+ }
+}
+
+
+template
+void ms_deformable_im2col_cuda(cudaStream_t stream,
+ const scalar_t* data_value,
+ const int64_t* data_spatial_shapes,
+ const int64_t* data_level_start_index,
+ const scalar_t* data_sampling_loc,
+ const scalar_t* data_attn_weight,
+ const int batch_size,
+ const int spatial_size,
+ const int num_heads,
+ const int channels,
+ const int num_levels,
+ const int num_query,
+ const int num_point,
+ scalar_t* data_col)
+{
+ const int num_kernels = batch_size * num_query * num_heads * channels;
+ const int num_actual_kernels = batch_size * num_query * num_heads * channels;
+ const int num_threads = CUDA_NUM_THREADS;
+ ms_deformable_im2col_gpu_kernel
+ <<>>(
+ num_kernels, data_value, data_spatial_shapes, data_level_start_index, data_sampling_loc, data_attn_weight,
+ batch_size, spatial_size, num_heads, channels, num_levels, num_query, num_point, data_col);
+
+ cudaError_t err = cudaGetLastError();
+ if (err != cudaSuccess)
+ {
+ printf("error in ms_deformable_im2col_cuda: %s\n", cudaGetErrorString(err));
+ }
+
+}
+
+template
+void ms_deformable_col2im_cuda(cudaStream_t stream,
+ const scalar_t* grad_col,
+ const scalar_t* data_value,
+ const int64_t * data_spatial_shapes,
+ const int64_t * data_level_start_index,
+ const scalar_t * data_sampling_loc,
+ const scalar_t * data_attn_weight,
+ const int batch_size,
+ const int spatial_size,
+ const int num_heads,
+ const int channels,
+ const int num_levels,
+ const int num_query,
+ const int num_point,
+ scalar_t* grad_value,
+ scalar_t* grad_sampling_loc,
+ scalar_t* grad_attn_weight)
+{
+ const int num_threads = (channels > CUDA_NUM_THREADS)?CUDA_NUM_THREADS:channels;
+ const int num_kernels = batch_size * num_query * num_heads * channels;
+ const int num_actual_kernels = batch_size * num_query * num_heads * channels;
+ if (channels > 1024)
+ {
+ if ((channels & 1023) == 0)
+ {
+ ms_deformable_col2im_gpu_kernel_shm_reduce_v2_multi_blocks
+ <<>>(
+ num_kernels,
+ grad_col,
+ data_value,
+ data_spatial_shapes,
+ data_level_start_index,
+ data_sampling_loc,
+ data_attn_weight,
+ batch_size,
+ spatial_size,
+ num_heads,
+ channels,
+ num_levels,
+ num_query,
+ num_point,
+ grad_value,
+ grad_sampling_loc,
+ grad_attn_weight);
+ }
+ else
+ {
+ ms_deformable_col2im_gpu_kernel_gm
+ <<>>(
+ num_kernels,
+ grad_col,
+ data_value,
+ data_spatial_shapes,
+ data_level_start_index,
+ data_sampling_loc,
+ data_attn_weight,
+ batch_size,
+ spatial_size,
+ num_heads,
+ channels,
+ num_levels,
+ num_query,
+ num_point,
+ grad_value,
+ grad_sampling_loc,
+ grad_attn_weight);
+ }
+ }
+ else{
+ switch(channels)
+ {
+ case 1:
+ ms_deformable_col2im_gpu_kernel_shm_blocksize_aware_reduce_v1
+ <<>>(
+ num_kernels,
+ grad_col,
+ data_value,
+ data_spatial_shapes,
+ data_level_start_index,
+ data_sampling_loc,
+ data_attn_weight,
+ batch_size,
+ spatial_size,
+ num_heads,
+ channels,
+ num_levels,
+ num_query,
+ num_point,
+ grad_value,
+ grad_sampling_loc,
+ grad_attn_weight);
+ break;
+ case 2:
+ ms_deformable_col2im_gpu_kernel_shm_blocksize_aware_reduce_v1
+ <<>>(
+ num_kernels,
+ grad_col,
+ data_value,
+ data_spatial_shapes,
+ data_level_start_index,
+ data_sampling_loc,
+ data_attn_weight,
+ batch_size,
+ spatial_size,
+ num_heads,
+ channels,
+ num_levels,
+ num_query,
+ num_point,
+ grad_value,
+ grad_sampling_loc,
+ grad_attn_weight);
+ break;
+ case 4:
+ ms_deformable_col2im_gpu_kernel_shm_blocksize_aware_reduce_v1
+ <<>>(
+ num_kernels,
+ grad_col,
+ data_value,
+ data_spatial_shapes,
+ data_level_start_index,
+ data_sampling_loc,
+ data_attn_weight,
+ batch_size,
+ spatial_size,
+ num_heads,
+ channels,
+ num_levels,
+ num_query,
+ num_point,
+ grad_value,
+ grad_sampling_loc,
+ grad_attn_weight);
+ break;
+ case 8:
+ ms_deformable_col2im_gpu_kernel_shm_blocksize_aware_reduce_v1
+ <<>>(
+ num_kernels,
+ grad_col,
+ data_value,
+ data_spatial_shapes,
+ data_level_start_index,
+ data_sampling_loc,
+ data_attn_weight,
+ batch_size,
+ spatial_size,
+ num_heads,
+ channels,
+ num_levels,
+ num_query,
+ num_point,
+ grad_value,
+ grad_sampling_loc,
+ grad_attn_weight);
+ break;
+ case 16:
+ ms_deformable_col2im_gpu_kernel_shm_blocksize_aware_reduce_v1
+ <<>>(
+ num_kernels,
+ grad_col,
+ data_value,
+ data_spatial_shapes,
+ data_level_start_index,
+ data_sampling_loc,
+ data_attn_weight,
+ batch_size,
+ spatial_size,
+ num_heads,
+ channels,
+ num_levels,
+ num_query,
+ num_point,
+ grad_value,
+ grad_sampling_loc,
+ grad_attn_weight);
+ break;
+ case 32:
+ ms_deformable_col2im_gpu_kernel_shm_blocksize_aware_reduce_v1
+ <<>>(
+ num_kernels,
+ grad_col,
+ data_value,
+ data_spatial_shapes,
+ data_level_start_index,
+ data_sampling_loc,
+ data_attn_weight,
+ batch_size,
+ spatial_size,
+ num_heads,
+ channels,
+ num_levels,
+ num_query,
+ num_point,
+ grad_value,
+ grad_sampling_loc,
+ grad_attn_weight);
+ break;
+ case 64:
+ ms_deformable_col2im_gpu_kernel_shm_blocksize_aware_reduce_v2
+ <<>>(
+ num_kernels,
+ grad_col,
+ data_value,
+ data_spatial_shapes,
+ data_level_start_index,
+ data_sampling_loc,
+ data_attn_weight,
+ batch_size,
+ spatial_size,
+ num_heads,
+ channels,
+ num_levels,
+ num_query,
+ num_point,
+ grad_value,
+ grad_sampling_loc,
+ grad_attn_weight);
+ break;
+ case 128:
+ ms_deformable_col2im_gpu_kernel_shm_blocksize_aware_reduce_v2
+ <<>>(
+ num_kernels,
+ grad_col,
+ data_value,
+ data_spatial_shapes,
+ data_level_start_index,
+ data_sampling_loc,
+ data_attn_weight,
+ batch_size,
+ spatial_size,
+ num_heads,
+ channels,
+ num_levels,
+ num_query,
+ num_point,
+ grad_value,
+ grad_sampling_loc,
+ grad_attn_weight);
+ break;
+ case 256:
+ ms_deformable_col2im_gpu_kernel_shm_blocksize_aware_reduce_v2
+ <<>>(
+ num_kernels,
+ grad_col,
+ data_value,
+ data_spatial_shapes,
+ data_level_start_index,
+ data_sampling_loc,
+ data_attn_weight,
+ batch_size,
+ spatial_size,
+ num_heads,
+ channels,
+ num_levels,
+ num_query,
+ num_point,
+ grad_value,
+ grad_sampling_loc,
+ grad_attn_weight);
+ break;
+ case 512:
+ ms_deformable_col2im_gpu_kernel_shm_blocksize_aware_reduce_v2
+ <<>>(
+ num_kernels,
+ grad_col,
+ data_value,
+ data_spatial_shapes,
+ data_level_start_index,
+ data_sampling_loc,
+ data_attn_weight,
+ batch_size,
+ spatial_size,
+ num_heads,
+ channels,
+ num_levels,
+ num_query,
+ num_point,
+ grad_value,
+ grad_sampling_loc,
+ grad_attn_weight);
+ break;
+ case 1024:
+ ms_deformable_col2im_gpu_kernel_shm_blocksize_aware_reduce_v2
+ <<>>(
+ num_kernels,
+ grad_col,
+ data_value,
+ data_spatial_shapes,
+ data_level_start_index,
+ data_sampling_loc,
+ data_attn_weight,
+ batch_size,
+ spatial_size,
+ num_heads,
+ channels,
+ num_levels,
+ num_query,
+ num_point,
+ grad_value,
+ grad_sampling_loc,
+ grad_attn_weight);
+ break;
+ default:
+ if (channels < 64)
+ {
+ ms_deformable_col2im_gpu_kernel_shm_reduce_v1
+ <<>>(
+ num_kernels,
+ grad_col,
+ data_value,
+ data_spatial_shapes,
+ data_level_start_index,
+ data_sampling_loc,
+ data_attn_weight,
+ batch_size,
+ spatial_size,
+ num_heads,
+ channels,
+ num_levels,
+ num_query,
+ num_point,
+ grad_value,
+ grad_sampling_loc,
+ grad_attn_weight);
+ }
+ else
+ {
+ ms_deformable_col2im_gpu_kernel_shm_reduce_v2
+ <<>>(
+ num_kernels,
+ grad_col,
+ data_value,
+ data_spatial_shapes,
+ data_level_start_index,
+ data_sampling_loc,
+ data_attn_weight,
+ batch_size,
+ spatial_size,
+ num_heads,
+ channels,
+ num_levels,
+ num_query,
+ num_point,
+ grad_value,
+ grad_sampling_loc,
+ grad_attn_weight);
+ }
+ }
+ }
+ cudaError_t err = cudaGetLastError();
+ if (err != cudaSuccess)
+ {
+ printf("error in ms_deformable_col2im_cuda: %s\n", cudaGetErrorString(err));
+ }
+
+}
\ No newline at end of file
diff --git a/annotator/OneFormer/oneformer/modeling/pixel_decoder/ops/src/ms_deform_attn.h b/annotator/OneFormer/oneformer/modeling/pixel_decoder/ops/src/ms_deform_attn.h
new file mode 100644
index 0000000000000000000000000000000000000000..2f80a1b294c55b37d13bb3558ff7aeadba3b37de
--- /dev/null
+++ b/annotator/OneFormer/oneformer/modeling/pixel_decoder/ops/src/ms_deform_attn.h
@@ -0,0 +1,67 @@
+/*!
+**************************************************************************************************
+* Deformable DETR
+* Copyright (c) 2020 SenseTime. All Rights Reserved.
+* Licensed under the Apache License, Version 2.0 [see LICENSE for details]
+**************************************************************************************************
+* Modified from https://github.com/chengdazhi/Deformable-Convolution-V2-PyTorch/tree/pytorch_1.0.0
+**************************************************************************************************
+*/
+
+/*!
+* Copyright (c) Facebook, Inc. and its affiliates.
+* Modified by Bowen Cheng from https://github.com/fundamentalvision/Deformable-DETR
+*/
+
+#pragma once
+
+#include "cpu/ms_deform_attn_cpu.h"
+
+#ifdef WITH_CUDA
+#include "cuda/ms_deform_attn_cuda.h"
+#endif
+
+
+at::Tensor
+ms_deform_attn_forward(
+ const at::Tensor &value,
+ const at::Tensor &spatial_shapes,
+ const at::Tensor &level_start_index,
+ const at::Tensor &sampling_loc,
+ const at::Tensor &attn_weight,
+ const int im2col_step)
+{
+ if (value.type().is_cuda())
+ {
+#ifdef WITH_CUDA
+ return ms_deform_attn_cuda_forward(
+ value, spatial_shapes, level_start_index, sampling_loc, attn_weight, im2col_step);
+#else
+ AT_ERROR("Not compiled with GPU support");
+#endif
+ }
+ AT_ERROR("Not implemented on the CPU");
+}
+
+std::vector
+ms_deform_attn_backward(
+ const at::Tensor &value,
+ const at::Tensor &spatial_shapes,
+ const at::Tensor &level_start_index,
+ const at::Tensor &sampling_loc,
+ const at::Tensor &attn_weight,
+ const at::Tensor &grad_output,
+ const int im2col_step)
+{
+ if (value.type().is_cuda())
+ {
+#ifdef WITH_CUDA
+ return ms_deform_attn_cuda_backward(
+ value, spatial_shapes, level_start_index, sampling_loc, attn_weight, grad_output, im2col_step);
+#else
+ AT_ERROR("Not compiled with GPU support");
+#endif
+ }
+ AT_ERROR("Not implemented on the CPU");
+}
+
diff --git a/annotator/OneFormer/oneformer/modeling/pixel_decoder/ops/src/vision.cpp b/annotator/OneFormer/oneformer/modeling/pixel_decoder/ops/src/vision.cpp
new file mode 100644
index 0000000000000000000000000000000000000000..4a08821e0121a77556aa7a263ec8ebfa928b13b6
--- /dev/null
+++ b/annotator/OneFormer/oneformer/modeling/pixel_decoder/ops/src/vision.cpp
@@ -0,0 +1,21 @@
+/*!
+**************************************************************************************************
+* Deformable DETR
+* Copyright (c) 2020 SenseTime. All Rights Reserved.
+* Licensed under the Apache License, Version 2.0 [see LICENSE for details]
+**************************************************************************************************
+* Modified from https://github.com/chengdazhi/Deformable-Convolution-V2-PyTorch/tree/pytorch_1.0.0
+**************************************************************************************************
+*/
+
+/*!
+* Copyright (c) Facebook, Inc. and its affiliates.
+* Modified by Bowen Cheng from https://github.com/fundamentalvision/Deformable-DETR
+*/
+
+#include "ms_deform_attn.h"
+
+PYBIND11_MODULE(TORCH_EXTENSION_NAME, m) {
+ m.def("ms_deform_attn_forward", &ms_deform_attn_forward, "ms_deform_attn_forward");
+ m.def("ms_deform_attn_backward", &ms_deform_attn_backward, "ms_deform_attn_backward");
+}
diff --git a/annotator/OneFormer/oneformer/modeling/pixel_decoder/ops/test.py b/annotator/OneFormer/oneformer/modeling/pixel_decoder/ops/test.py
new file mode 100644
index 0000000000000000000000000000000000000000..6e1b545459f6fd3235767e721eb5a1090ae14bef
--- /dev/null
+++ b/annotator/OneFormer/oneformer/modeling/pixel_decoder/ops/test.py
@@ -0,0 +1,92 @@
+# ------------------------------------------------------------------------------------------------
+# Deformable DETR
+# Copyright (c) 2020 SenseTime. All Rights Reserved.
+# Licensed under the Apache License, Version 2.0 [see LICENSE for details]
+# ------------------------------------------------------------------------------------------------
+# Modified from https://github.com/chengdazhi/Deformable-Convolution-V2-PyTorch/tree/pytorch_1.0.0
+# ------------------------------------------------------------------------------------------------
+
+# Copyright (c) Facebook, Inc. and its affiliates.
+# Modified by Bowen Cheng from https://github.com/fundamentalvision/Deformable-DETR
+
+from __future__ import absolute_import
+from __future__ import print_function
+from __future__ import division
+
+import time
+import torch
+import torch.nn as nn
+from torch.autograd import gradcheck
+
+from functions.ms_deform_attn_func import MSDeformAttnFunction, ms_deform_attn_core_pytorch
+
+
+N, M, D = 1, 2, 2
+Lq, L, P = 2, 2, 2
+shapes = torch.as_tensor([(6, 4), (3, 2)], dtype=torch.long).cuda()
+level_start_index = torch.cat((shapes.new_zeros((1, )), shapes.prod(1).cumsum(0)[:-1]))
+S = sum([(H*W).item() for H, W in shapes])
+
+
+torch.manual_seed(3)
+
+
+@torch.no_grad()
+def check_forward_equal_with_pytorch_double():
+ value = torch.rand(N, S, M, D).cuda() * 0.01
+ sampling_locations = torch.rand(N, Lq, M, L, P, 2).cuda()
+ attention_weights = torch.rand(N, Lq, M, L, P).cuda() + 1e-5
+ attention_weights /= attention_weights.sum(-1, keepdim=True).sum(-2, keepdim=True)
+ im2col_step = 2
+ output_pytorch = ms_deform_attn_core_pytorch(value.double(), shapes, sampling_locations.double(), attention_weights.double()).detach().cpu()
+ output_cuda = MSDeformAttnFunction.apply(value.double(), shapes, level_start_index, sampling_locations.double(), attention_weights.double(), im2col_step).detach().cpu()
+ fwdok = torch.allclose(output_cuda, output_pytorch)
+ max_abs_err = (output_cuda - output_pytorch).abs().max()
+ max_rel_err = ((output_cuda - output_pytorch).abs() / output_pytorch.abs()).max()
+
+ print(f'* {fwdok} check_forward_equal_with_pytorch_double: max_abs_err {max_abs_err:.2e} max_rel_err {max_rel_err:.2e}')
+
+
+@torch.no_grad()
+def check_forward_equal_with_pytorch_float():
+ value = torch.rand(N, S, M, D).cuda() * 0.01
+ sampling_locations = torch.rand(N, Lq, M, L, P, 2).cuda()
+ attention_weights = torch.rand(N, Lq, M, L, P).cuda() + 1e-5
+ attention_weights /= attention_weights.sum(-1, keepdim=True).sum(-2, keepdim=True)
+ im2col_step = 2
+ output_pytorch = ms_deform_attn_core_pytorch(value, shapes, sampling_locations, attention_weights).detach().cpu()
+ output_cuda = MSDeformAttnFunction.apply(value, shapes, level_start_index, sampling_locations, attention_weights, im2col_step).detach().cpu()
+ fwdok = torch.allclose(output_cuda, output_pytorch, rtol=1e-2, atol=1e-3)
+ max_abs_err = (output_cuda - output_pytorch).abs().max()
+ max_rel_err = ((output_cuda - output_pytorch).abs() / output_pytorch.abs()).max()
+
+ print(f'* {fwdok} check_forward_equal_with_pytorch_float: max_abs_err {max_abs_err:.2e} max_rel_err {max_rel_err:.2e}')
+
+
+def check_gradient_numerical(channels=4, grad_value=True, grad_sampling_loc=True, grad_attn_weight=True):
+
+ value = torch.rand(N, S, M, channels).cuda() * 0.01
+ sampling_locations = torch.rand(N, Lq, M, L, P, 2).cuda()
+ attention_weights = torch.rand(N, Lq, M, L, P).cuda() + 1e-5
+ attention_weights /= attention_weights.sum(-1, keepdim=True).sum(-2, keepdim=True)
+ im2col_step = 2
+ func = MSDeformAttnFunction.apply
+
+ value.requires_grad = grad_value
+ sampling_locations.requires_grad = grad_sampling_loc
+ attention_weights.requires_grad = grad_attn_weight
+
+ gradok = gradcheck(func, (value.double(), shapes, level_start_index, sampling_locations.double(), attention_weights.double(), im2col_step))
+
+ print(f'* {gradok} check_gradient_numerical(D={channels})')
+
+
+if __name__ == '__main__':
+ check_forward_equal_with_pytorch_double()
+ check_forward_equal_with_pytorch_float()
+
+ for channels in [30, 32, 64, 71, 1025, 2048, 3096]:
+ check_gradient_numerical(channels, True, True, True)
+
+
+
diff --git a/annotator/OneFormer/oneformer/modeling/transformer_decoder/__init__.py b/annotator/OneFormer/oneformer/modeling/transformer_decoder/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..b84bd4ecb48f134ccc218c4d5f02c50f7033bcd9
--- /dev/null
+++ b/annotator/OneFormer/oneformer/modeling/transformer_decoder/__init__.py
@@ -0,0 +1,2 @@
+# Copyright (c) Facebook, Inc. and its affiliates.
+from .oneformer_transformer_decoder import ContrastiveMultiScaleMaskedTransformerDecoder
\ No newline at end of file
diff --git a/annotator/OneFormer/oneformer/modeling/transformer_decoder/oneformer_transformer_decoder.py b/annotator/OneFormer/oneformer/modeling/transformer_decoder/oneformer_transformer_decoder.py
new file mode 100644
index 0000000000000000000000000000000000000000..a06d586f70131c86604ee0113993b99effaba340
--- /dev/null
+++ b/annotator/OneFormer/oneformer/modeling/transformer_decoder/oneformer_transformer_decoder.py
@@ -0,0 +1,528 @@
+# ------------------------------------------------------------------------------
+# Reference: https://github.com/facebookresearch/Mask2Former/blob/main/mask2former/modeling/transformer_decoder/mask2former_transformer_decoder.py
+# Modified by Jitesh Jain (https://github.com/praeclarumjj3)
+# ------------------------------------------------------------------------------
+
+import logging
+import fvcore.nn.weight_init as weight_init
+from typing import Optional
+import torch
+from torch import nn, Tensor
+from torch.nn import functional as F
+
+from detectron2.config import configurable
+from detectron2.layers import Conv2d
+
+from .position_encoding import PositionEmbeddingSine
+from .transformer import Transformer
+
+from detectron2.utils.registry import Registry
+
+
+TRANSFORMER_DECODER_REGISTRY = Registry("TRANSFORMER_MODULE")
+TRANSFORMER_DECODER_REGISTRY.__doc__ = """
+Registry for transformer module in OneFormer.
+"""
+
+
+def build_transformer_decoder(cfg, in_channels, mask_classification=True):
+ """
+ Build a instance embedding branch from `cfg.MODEL.INS_EMBED_HEAD.NAME`.
+ """
+ name = cfg.MODEL.ONE_FORMER.TRANSFORMER_DECODER_NAME
+ return TRANSFORMER_DECODER_REGISTRY.get(name)(cfg, in_channels, mask_classification)
+
+
+class SelfAttentionLayer(nn.Module):
+
+ def __init__(self, d_model, nhead, dropout=0.0,
+ activation="relu", normalize_before=False):
+ super().__init__()
+ self.self_attn = nn.MultiheadAttention(d_model, nhead, dropout=dropout)
+
+ self.norm = nn.LayerNorm(d_model)
+ self.dropout = nn.Dropout(dropout)
+
+ self.activation = _get_activation_fn(activation)
+ self.normalize_before = normalize_before
+
+ self._reset_parameters()
+
+ def _reset_parameters(self):
+ for p in self.parameters():
+ if p.dim() > 1:
+ nn.init.xavier_uniform_(p)
+
+ def with_pos_embed(self, tensor, pos: Optional[Tensor]):
+ return tensor if pos is None else tensor + pos
+
+ def forward_post(self, tgt,
+ tgt_mask: Optional[Tensor] = None,
+ tgt_key_padding_mask: Optional[Tensor] = None,
+ query_pos: Optional[Tensor] = None):
+ q = k = self.with_pos_embed(tgt, query_pos)
+ tgt2 = self.self_attn(q, k, value=tgt, attn_mask=tgt_mask,
+ key_padding_mask=tgt_key_padding_mask)[0]
+ tgt = tgt + self.dropout(tgt2)
+ tgt = self.norm(tgt)
+
+ return tgt
+
+ def forward_pre(self, tgt,
+ tgt_mask: Optional[Tensor] = None,
+ tgt_key_padding_mask: Optional[Tensor] = None,
+ query_pos: Optional[Tensor] = None):
+ tgt2 = self.norm(tgt)
+ q = k = self.with_pos_embed(tgt2, query_pos)
+ tgt2 = self.self_attn(q, k, value=tgt2, attn_mask=tgt_mask,
+ key_padding_mask=tgt_key_padding_mask)[0]
+ tgt = tgt + self.dropout(tgt2)
+
+ return tgt
+
+ def forward(self, tgt,
+ tgt_mask: Optional[Tensor] = None,
+ tgt_key_padding_mask: Optional[Tensor] = None,
+ query_pos: Optional[Tensor] = None):
+ if self.normalize_before:
+ return self.forward_pre(tgt, tgt_mask,
+ tgt_key_padding_mask, query_pos)
+ return self.forward_post(tgt, tgt_mask,
+ tgt_key_padding_mask, query_pos)
+
+
+class CrossAttentionLayer(nn.Module):
+
+ def __init__(self, d_model, nhead, dropout=0.0,
+ activation="relu", normalize_before=False):
+ super().__init__()
+ self.multihead_attn = nn.MultiheadAttention(d_model, nhead, dropout=dropout)
+
+ self.norm = nn.LayerNorm(d_model)
+ self.dropout = nn.Dropout(dropout)
+
+ self.activation = _get_activation_fn(activation)
+ self.normalize_before = normalize_before
+
+ self._reset_parameters()
+
+ def _reset_parameters(self):
+ for p in self.parameters():
+ if p.dim() > 1:
+ nn.init.xavier_uniform_(p)
+
+ def with_pos_embed(self, tensor, pos: Optional[Tensor]):
+ return tensor if pos is None else tensor + pos
+
+ def forward_post(self, tgt, memory,
+ memory_mask: Optional[Tensor] = None,
+ memory_key_padding_mask: Optional[Tensor] = None,
+ pos: Optional[Tensor] = None,
+ query_pos: Optional[Tensor] = None):
+ tgt2 = self.multihead_attn(query=self.with_pos_embed(tgt, query_pos),
+ key=self.with_pos_embed(memory, pos),
+ value=memory, attn_mask=memory_mask,
+ key_padding_mask=memory_key_padding_mask)[0]
+ tgt = tgt + self.dropout(tgt2)
+ tgt = self.norm(tgt)
+
+ return tgt
+
+ def forward_pre(self, tgt, memory,
+ memory_mask: Optional[Tensor] = None,
+ memory_key_padding_mask: Optional[Tensor] = None,
+ pos: Optional[Tensor] = None,
+ query_pos: Optional[Tensor] = None):
+ tgt2 = self.norm(tgt)
+ tgt2 = self.multihead_attn(query=self.with_pos_embed(tgt2, query_pos),
+ key=self.with_pos_embed(memory, pos),
+ value=memory, attn_mask=memory_mask,
+ key_padding_mask=memory_key_padding_mask)[0]
+ tgt = tgt + self.dropout(tgt2)
+
+ return tgt
+
+ def forward(self, tgt, memory,
+ memory_mask: Optional[Tensor] = None,
+ memory_key_padding_mask: Optional[Tensor] = None,
+ pos: Optional[Tensor] = None,
+ query_pos: Optional[Tensor] = None):
+ if self.normalize_before:
+ return self.forward_pre(tgt, memory, memory_mask,
+ memory_key_padding_mask, pos, query_pos)
+ return self.forward_post(tgt, memory, memory_mask,
+ memory_key_padding_mask, pos, query_pos)
+
+
+class FFNLayer(nn.Module):
+
+ def __init__(self, d_model, dim_feedforward=2048, dropout=0.0,
+ activation="relu", normalize_before=False):
+ super().__init__()
+ # Implementation of Feedforward model
+ self.linear1 = nn.Linear(d_model, dim_feedforward)
+ self.dropout = nn.Dropout(dropout)
+ self.linear2 = nn.Linear(dim_feedforward, d_model)
+
+ self.norm = nn.LayerNorm(d_model)
+
+ self.activation = _get_activation_fn(activation)
+ self.normalize_before = normalize_before
+
+ self._reset_parameters()
+
+ def _reset_parameters(self):
+ for p in self.parameters():
+ if p.dim() > 1:
+ nn.init.xavier_uniform_(p)
+
+ def with_pos_embed(self, tensor, pos: Optional[Tensor]):
+ return tensor if pos is None else tensor + pos
+
+ def forward_post(self, tgt):
+ tgt2 = self.linear2(self.dropout(self.activation(self.linear1(tgt))))
+ tgt = tgt + self.dropout(tgt2)
+ tgt = self.norm(tgt)
+ return tgt
+
+ def forward_pre(self, tgt):
+ tgt2 = self.norm(tgt)
+ tgt2 = self.linear2(self.dropout(self.activation(self.linear1(tgt2))))
+ tgt = tgt + self.dropout(tgt2)
+ return tgt
+
+ def forward(self, tgt):
+ if self.normalize_before:
+ return self.forward_pre(tgt)
+ return self.forward_post(tgt)
+
+
+def _get_activation_fn(activation):
+ """Return an activation function given a string"""
+ if activation == "relu":
+ return F.relu
+ if activation == "gelu":
+ return F.gelu
+ if activation == "glu":
+ return F.glu
+ raise RuntimeError(F"activation should be relu/gelu, not {activation}.")
+
+
+class MLP(nn.Module):
+ """ Very simple multi-layer perceptron (also called FFN)"""
+
+ def __init__(self, input_dim, hidden_dim, output_dim, num_layers):
+ super().__init__()
+ self.num_layers = num_layers
+ h = [hidden_dim] * (num_layers - 1)
+ self.layers = nn.ModuleList(nn.Linear(n, k) for n, k in zip([input_dim] + h, h + [output_dim]))
+
+ def forward(self, x):
+ for i, layer in enumerate(self.layers):
+ x = F.relu(layer(x)) if i < self.num_layers - 1 else layer(x)
+ return x
+
+
+@TRANSFORMER_DECODER_REGISTRY.register()
+class ContrastiveMultiScaleMaskedTransformerDecoder(nn.Module):
+
+ _version = 2
+
+ def _load_from_state_dict(
+ self, state_dict, prefix, local_metadata, strict, missing_keys, unexpected_keys, error_msgs
+ ):
+ version = local_metadata.get("version", None)
+ if version is None or version < 2:
+ # Do not warn if train from scratch
+ scratch = True
+ logger = logging.getLogger(__name__)
+ for k in list(state_dict.keys()):
+ newk = k
+ if "static_query" in k:
+ newk = k.replace("static_query", "query_feat")
+ if newk != k:
+ state_dict[newk] = state_dict[k]
+ del state_dict[k]
+ scratch = False
+
+ if not scratch:
+ logger.warning(
+ f"Weight format of {self.__class__.__name__} have changed! "
+ "Please upgrade your models. Applying automatic conversion now ..."
+ )
+
+ @configurable
+ def __init__(
+ self,
+ in_channels,
+ mask_classification=True,
+ *,
+ num_classes: int,
+ hidden_dim: int,
+ num_queries: int,
+ nheads: int,
+ dropout: float,
+ dim_feedforward: int,
+ enc_layers: int,
+ is_train: bool,
+ dec_layers: int,
+ class_dec_layers: int,
+ pre_norm: bool,
+ mask_dim: int,
+ enforce_input_project: bool,
+ use_task_norm: bool,
+ ):
+ """
+ NOTE: this interface is experimental.
+ Args:
+ in_channels: channels of the input features
+ mask_classification: whether to add mask classifier or not
+ num_classes: number of classes
+ hidden_dim: Transformer feature dimension
+ num_queries: number of queries
+ nheads: number of heads
+ dim_feedforward: feature dimension in feedforward network
+ enc_layers: number of Transformer encoder layers
+ dec_layers: number of Transformer decoder layers
+ pre_norm: whether to use pre-LayerNorm or not
+ mask_dim: mask feature dimension
+ enforce_input_project: add input project 1x1 conv even if input
+ channels and hidden dim is identical
+ """
+ super().__init__()
+
+ assert mask_classification, "Only support mask classification model"
+ self.mask_classification = mask_classification
+ self.is_train = is_train
+ self.use_task_norm = use_task_norm
+
+ # positional encoding
+ N_steps = hidden_dim // 2
+ self.pe_layer = PositionEmbeddingSine(N_steps, normalize=True)
+
+ self.class_transformer = Transformer(
+ d_model=hidden_dim,
+ dropout=dropout,
+ nhead=nheads,
+ dim_feedforward=dim_feedforward,
+ num_encoder_layers=enc_layers,
+ num_decoder_layers=class_dec_layers,
+ normalize_before=pre_norm,
+ return_intermediate_dec=False,
+ )
+
+ # define Transformer decoder here
+ self.num_heads = nheads
+ self.num_layers = dec_layers
+ self.transformer_self_attention_layers = nn.ModuleList()
+ self.transformer_cross_attention_layers = nn.ModuleList()
+ self.transformer_ffn_layers = nn.ModuleList()
+
+ for _ in range(self.num_layers):
+ self.transformer_self_attention_layers.append(
+ SelfAttentionLayer(
+ d_model=hidden_dim,
+ nhead=nheads,
+ dropout=0.0,
+ normalize_before=pre_norm,
+ )
+ )
+
+ self.transformer_cross_attention_layers.append(
+ CrossAttentionLayer(
+ d_model=hidden_dim,
+ nhead=nheads,
+ dropout=0.0,
+ normalize_before=pre_norm,
+ )
+ )
+
+ self.transformer_ffn_layers.append(
+ FFNLayer(
+ d_model=hidden_dim,
+ dim_feedforward=dim_feedforward,
+ dropout=0.0,
+ normalize_before=pre_norm,
+ )
+ )
+
+ self.decoder_norm = nn.LayerNorm(hidden_dim)
+
+ self.num_queries = num_queries
+ # learnable query p.e.
+ self.query_embed = nn.Embedding(num_queries, hidden_dim)
+
+ # level embedding (we always use 3 scales)
+ self.num_feature_levels = 3
+ self.level_embed = nn.Embedding(self.num_feature_levels, hidden_dim)
+ self.input_proj = nn.ModuleList()
+ for _ in range(self.num_feature_levels):
+ if in_channels != hidden_dim or enforce_input_project:
+ self.input_proj.append(Conv2d(in_channels, hidden_dim, kernel_size=1))
+ weight_init.c2_xavier_fill(self.input_proj[-1])
+ else:
+ self.input_proj.append(nn.Sequential())
+
+ self.class_input_proj = Conv2d(in_channels, hidden_dim, kernel_size=1)
+ weight_init.c2_xavier_fill(self.class_input_proj)
+
+ # output FFNs
+ if self.mask_classification:
+ self.class_embed = nn.Linear(hidden_dim, num_classes + 1)
+ self.mask_embed = MLP(hidden_dim, hidden_dim, mask_dim, 3)
+
+ @classmethod
+ def from_config(cls, cfg, in_channels, mask_classification):
+ ret = {}
+ ret["in_channels"] = in_channels
+ ret["mask_classification"] = mask_classification
+
+ ret["num_classes"] = cfg.MODEL.SEM_SEG_HEAD.NUM_CLASSES
+ ret["hidden_dim"] = cfg.MODEL.ONE_FORMER.HIDDEN_DIM
+ ret["num_queries"] = cfg.MODEL.ONE_FORMER.NUM_OBJECT_QUERIES
+ # Transformer parameters:
+ ret["nheads"] = cfg.MODEL.ONE_FORMER.NHEADS
+ ret["dim_feedforward"] = cfg.MODEL.ONE_FORMER.DIM_FEEDFORWARD
+
+ # NOTE: because we add learnable query features which requires supervision,
+ # we add minus 1 to decoder layers to be consistent with our loss
+ # implementation: that is, number of auxiliary losses is always
+ # equal to number of decoder layers. With learnable query features, the number of
+ # auxiliary losses equals number of decoders plus 1.
+ assert cfg.MODEL.ONE_FORMER.DEC_LAYERS >= 1
+ ret["dec_layers"] = cfg.MODEL.ONE_FORMER.DEC_LAYERS - 1
+ ret["class_dec_layers"] = cfg.MODEL.ONE_FORMER.CLASS_DEC_LAYERS
+ ret["enc_layers"] = cfg.MODEL.ONE_FORMER.ENC_LAYERS
+ ret["dropout"] = cfg.MODEL.ONE_FORMER.DROPOUT
+ ret["pre_norm"] = cfg.MODEL.ONE_FORMER.PRE_NORM
+ ret["enforce_input_project"] = cfg.MODEL.ONE_FORMER.ENFORCE_INPUT_PROJ
+ ret["is_train"] = cfg.MODEL.IS_TRAIN
+ ret["mask_dim"] = cfg.MODEL.SEM_SEG_HEAD.MASK_DIM
+ ret["use_task_norm"] = cfg.MODEL.ONE_FORMER.USE_TASK_NORM
+
+ return ret
+
+ def forward(self, x, mask_features, tasks, mask = None):
+ # x is a list of multi-scale feature
+ assert len(x) == self.num_feature_levels
+ src = []
+ pos = []
+ size_list = []
+
+ # disable mask, it does not affect performance
+ del mask
+
+ for i in range(self.num_feature_levels):
+ size_list.append(x[i].shape[-2:])
+ pos.append(self.pe_layer(x[i], None).flatten(2))
+ src.append(self.input_proj[i](x[i]).flatten(2) + self.level_embed.weight[i][None, :, None])
+
+ # flatten NxCxHxW to HWxNxC
+ pos[-1] = pos[-1].permute(2, 0, 1)
+ src[-1] = src[-1].permute(2, 0, 1)
+
+ _, bs, _ = src[0].shape
+
+ # QxNxC
+ query_embed = self.query_embed.weight.unsqueeze(1).repeat(1, bs, 1)
+ tasks = tasks.unsqueeze(0)
+ if self.use_task_norm:
+ tasks = self.decoder_norm(tasks)
+
+ feats = self.pe_layer(mask_features, None)
+
+ out_t, _ = self.class_transformer(feats, None,
+ self.query_embed.weight[:-1],
+ self.class_input_proj(mask_features),
+ tasks if self.use_task_norm else None)
+ out_t = out_t[0].permute(1, 0, 2)
+
+ out = torch.cat([out_t, tasks], dim=0)
+
+ output = out.clone()
+
+ predictions_class = []
+ predictions_mask = []
+
+ # prediction heads on learnable query features
+ outputs_class, outputs_mask, attn_mask = self.forward_prediction_heads(output, mask_features, attn_mask_target_size=size_list[0], i=0)
+ predictions_class.append(outputs_class)
+ predictions_mask.append(outputs_mask)
+
+ for i in range(self.num_layers):
+ level_index = i % self.num_feature_levels
+ attn_mask[torch.where(attn_mask.sum(-1) == attn_mask.shape[-1])] = False
+ # attention: cross-attention first
+ output = self.transformer_cross_attention_layers[i](
+ output, src[level_index],
+ memory_mask=attn_mask,
+ memory_key_padding_mask=None, # here we do not apply masking on padded region
+ pos=pos[level_index], query_pos=query_embed
+ )
+
+ output = self.transformer_self_attention_layers[i](
+ output, tgt_mask=None,
+ tgt_key_padding_mask=None,
+ query_pos=query_embed
+ )
+
+ # FFN
+ output = self.transformer_ffn_layers[i](
+ output
+ )
+
+ outputs_class, outputs_mask, attn_mask = self.forward_prediction_heads(output, mask_features, attn_mask_target_size=size_list[(i + 1) % self.num_feature_levels], i=i+1)
+ predictions_class.append(outputs_class)
+ predictions_mask.append(outputs_mask)
+
+ assert len(predictions_class) == self.num_layers + 1
+ if self.is_train:
+ query_class = out.permute(1, 0, 2)
+ else:
+ query_class = None
+ out = {
+ 'contrastive_logits': query_class,
+ 'pred_logits': predictions_class[-1],
+ 'pred_masks': predictions_mask[-1],
+ 'aux_outputs': self._set_aux_loss(
+ predictions_class if self.mask_classification else None,
+ predictions_mask,
+ )
+ }
+
+ return out
+
+ def forward_prediction_heads(self, output, mask_features, attn_mask_target_size, i):
+ decoder_output = self.decoder_norm(output)
+ decoder_output = decoder_output.transpose(0, 1)
+ outputs_class = self.class_embed(decoder_output)
+ mask_embed = self.mask_embed(decoder_output)
+ outputs_mask = torch.einsum("bqc,bchw->bqhw", mask_embed, mask_features)
+
+ # NOTE: prediction is of higher-resolution
+ # [B, Q, H, W] -> [B, Q, H*W] -> [B, h, Q, H*W] -> [B*h, Q, HW]
+ attn_mask = F.interpolate(outputs_mask, size=attn_mask_target_size, mode="bilinear", align_corners=False)
+
+ # save_attn_masks(attn_mask.sigmoid() < 0.5, fname=f'demo/maps/{i}_pre_bool')
+
+ # must use bool type
+ # If a BoolTensor is provided, positions with ``True`` are not allowed to attend while ``False`` values will be unchanged.
+ attn_mask = (attn_mask.sigmoid().flatten(2).unsqueeze(1).repeat(1, self.num_heads, 1, 1).flatten(0, 1) < 0.5).bool()
+ attn_mask = attn_mask.detach()
+
+ return outputs_class, outputs_mask, attn_mask
+
+ @torch.jit.unused
+ def _set_aux_loss(self, outputs_class, outputs_seg_masks):
+ # this is a workaround to make torchscript happy, as torchscript
+ # doesn't support dictionary with non-homogeneous values, such
+ # as a dict having both a Tensor and a list.
+ if self.mask_classification:
+ aux_list = [
+ {"pred_logits": a, "pred_masks": b}
+ for a, b in zip(outputs_class[:-1], outputs_seg_masks[:-1])
+ ]
+ else:
+ aux_list = [{"pred_masks": b} for b, in outputs_seg_masks[:-1]]
+
+ return aux_list
\ No newline at end of file
diff --git a/annotator/OneFormer/oneformer/modeling/transformer_decoder/position_encoding.py b/annotator/OneFormer/oneformer/modeling/transformer_decoder/position_encoding.py
new file mode 100644
index 0000000000000000000000000000000000000000..051984d9ea6e04e834f6fae3daf7d8317c2f0819
--- /dev/null
+++ b/annotator/OneFormer/oneformer/modeling/transformer_decoder/position_encoding.py
@@ -0,0 +1,67 @@
+# ------------------------------------------------------------------------------
+# Reference: https://github.com/facebookresearch/Mask2Former/blob/main/mask2former/modeling/transformer_decoder/position_encoding.py
+# Modified by Jitesh Jain (https://github.com/praeclarumjj3)
+# ------------------------------------------------------------------------------
+
+"""
+Various positional encodings for the transformer.
+"""
+import math
+
+import torch
+from torch import nn
+
+
+class PositionEmbeddingSine(nn.Module):
+ """
+ This is a more standard version of the position embedding, very similar to the one
+ used by the Attention is all you need paper, generalized to work on images.
+ """
+
+ def __init__(self, num_pos_feats=64, temperature=10000, normalize=False, scale=None):
+ super().__init__()
+ self.num_pos_feats = num_pos_feats
+ self.temperature = temperature
+ self.normalize = normalize
+ if scale is not None and normalize is False:
+ raise ValueError("normalize should be True if scale is passed")
+ if scale is None:
+ scale = 2 * math.pi
+ self.scale = scale
+
+ def forward(self, x, mask=None):
+ if mask is None:
+ mask = torch.zeros((x.size(0), x.size(2), x.size(3)), device=x.device, dtype=torch.bool)
+ not_mask = ~mask
+ y_embed = not_mask.cumsum(1, dtype=torch.float32)
+ x_embed = not_mask.cumsum(2, dtype=torch.float32)
+ if self.normalize:
+ eps = 1e-6
+ y_embed = y_embed / (y_embed[:, -1:, :] + eps) * self.scale
+ x_embed = x_embed / (x_embed[:, :, -1:] + eps) * self.scale
+
+ dim_t = torch.arange(self.num_pos_feats, dtype=torch.float32, device=x.device)
+ dim_t = self.temperature ** (2 * (dim_t // 2) / self.num_pos_feats)
+
+ pos_x = x_embed[:, :, :, None] / dim_t
+ pos_y = y_embed[:, :, :, None] / dim_t
+ pos_x = torch.stack(
+ (pos_x[:, :, :, 0::2].sin(), pos_x[:, :, :, 1::2].cos()), dim=4
+ ).flatten(3)
+ pos_y = torch.stack(
+ (pos_y[:, :, :, 0::2].sin(), pos_y[:, :, :, 1::2].cos()), dim=4
+ ).flatten(3)
+ pos = torch.cat((pos_y, pos_x), dim=3).permute(0, 3, 1, 2)
+ return pos
+
+ def __repr__(self, _repr_indent=4):
+ head = "Positional encoding " + self.__class__.__name__
+ body = [
+ "num_pos_feats: {}".format(self.num_pos_feats),
+ "temperature: {}".format(self.temperature),
+ "normalize: {}".format(self.normalize),
+ "scale: {}".format(self.scale),
+ ]
+ # _repr_indent = 4
+ lines = [head] + [" " * _repr_indent + line for line in body]
+ return "\n".join(lines)
diff --git a/annotator/OneFormer/oneformer/modeling/transformer_decoder/text_transformer.py b/annotator/OneFormer/oneformer/modeling/transformer_decoder/text_transformer.py
new file mode 100644
index 0000000000000000000000000000000000000000..d0b7292018ecfbf4111c0da9c90444d0e1e41cb6
--- /dev/null
+++ b/annotator/OneFormer/oneformer/modeling/transformer_decoder/text_transformer.py
@@ -0,0 +1,257 @@
+# -------------------------------------------------------------------------
+# MIT License
+#
+# Copyright (c) 2021 OpenAI
+#
+# Permission is hereby granted, free of charge, to any person obtaining a copy
+# of this software and associated documentation files (the "Software"), to deal
+# in the Software without restriction, including without limitation the rights
+# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+# copies of the Software, and to permit persons to whom the Software is
+# furnished to do so, subject to the following conditions:
+#
+# The above copyright notice and this permission notice shall be included in all
+# copies or substantial portions of the Software.
+#
+# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+# SOFTWARE.
+#
+# -------------------------------------------------------------------------
+
+import torch
+import torch.utils.checkpoint as checkpoint
+from torch import nn
+from collections import OrderedDict
+from timm.models.layers import trunc_normal_
+
+class Attention(nn.Module):
+ def __init__(self, dim, num_heads=8, qkv_bias=False, qk_scale=None, attn_drop=0., proj_drop=0.):
+ super().__init__()
+ self.num_heads = num_heads
+ head_dim = dim // num_heads
+ # NOTE scale factor was wrong in my original version, can set manually to be compat with prev weights
+ self.scale = qk_scale or head_dim ** -0.5
+
+ self.q_proj = nn.Linear(dim, dim, bias=qkv_bias)
+ self.k_proj = nn.Linear(dim, dim, bias=qkv_bias)
+ self.v_proj = nn.Linear(dim, dim, bias=qkv_bias)
+
+
+ self.attn_drop = nn.Dropout(attn_drop)
+ self.proj = nn.Linear(dim, dim)
+ self.proj_drop = nn.Dropout(proj_drop)
+
+ def forward(self, q, k, v):
+ B, N, C = q.shape
+ assert k.shape == v.shape
+ B, M, C = k.shape
+ q = self.q_proj(q).reshape(B, N, self.num_heads, C // self.num_heads)
+ k = self.k_proj(k).reshape(B, M, self.num_heads, C // self.num_heads)
+ v = self.v_proj(v).reshape(B, M, self.num_heads, C // self.num_heads)
+
+ attn = torch.einsum('bnkc,bmkc->bknm', q, k) * self.scale
+
+ attn = attn.softmax(dim=-1)
+
+ x = torch.einsum('bknm,bmkc->bnkc', attn, v).reshape(B, N, C)
+
+ x = self.proj(x)
+ x = self.proj_drop(x)
+ return x
+
+class TransformerDecoderLayer(nn.Module):
+ def __init__(
+ self,
+ d_model,
+ nhead,
+ dropout=0.1,
+ ):
+ super().__init__()
+ self.self_attn = Attention(d_model, nhead, proj_drop=dropout)
+ self.cross_attn = Attention(d_model, nhead, proj_drop=dropout)
+
+ self.norm1 = nn.LayerNorm(d_model)
+ self.norm2 = nn.LayerNorm(d_model)
+ self.norm3 = nn.LayerNorm(d_model)
+ self.dropout = nn.Dropout(dropout)
+
+ self.mlp = nn.Sequential(
+ nn.Linear(d_model, d_model * 4),
+ nn.GELU(),
+ nn.Dropout(dropout),
+ nn.Linear(d_model * 4, d_model)
+ )
+
+ def forward(self, x, mem):
+ q = k = v = self.norm1(x)
+ x = x + self.self_attn(q, k, v)
+ q = self.norm2(x)
+ x = x + self.cross_attn(q, mem, mem)
+ x = x + self.dropout(self.mlp(self.norm3(x)))
+ return x
+
+
+class ContextDecoder(nn.Module):
+ def __init__(self,
+ transformer_width=256,
+ transformer_heads=4,
+ transformer_layers=6,
+ visual_dim=1024,
+ dropout=0.1,
+ **kwargs):
+ super().__init__()
+
+ self.memory_proj = nn.Sequential(
+ nn.LayerNorm(visual_dim),
+ nn.Linear(visual_dim, transformer_width),
+ nn.LayerNorm(transformer_width),
+ )
+
+ self.text_proj = nn.Sequential(
+ nn.LayerNorm(visual_dim),
+ nn.Linear(visual_dim, transformer_width),
+ )
+
+ self.decoder = nn.ModuleList([
+ TransformerDecoderLayer(transformer_width, transformer_heads, dropout) for _ in range(transformer_layers)
+ ])
+
+ self.out_proj = nn.Sequential(
+ nn.LayerNorm(transformer_width),
+ nn.Linear(transformer_width, visual_dim)
+ )
+
+ self.apply(self._init_weights)
+
+ def _init_weights(self, m):
+ if isinstance(m, nn.Linear):
+ trunc_normal_(m.weight, std=.02)
+ if isinstance(m, nn.Linear) and m.bias is not None:
+ nn.init.constant_(m.bias, 0)
+ elif isinstance(m, nn.LayerNorm):
+ nn.init.constant_(m.bias, 0)
+ nn.init.constant_(m.weight, 1.0)
+
+
+ def forward(self, text, visual):
+ B, N, C = visual.shape
+ visual = self.memory_proj(visual)
+ x = self.text_proj(text)
+
+ for layer in self.decoder:
+ x = layer(x, visual)
+
+ return self.out_proj(x)
+
+
+class QuickGELU(nn.Module):
+
+ def forward(self, x: torch.Tensor):
+ return x * torch.sigmoid(1.702 * x)
+
+
+class ResidualAttentionBlock(nn.Module):
+
+ def __init__(self, d_model: int, n_head: int, attn_mask: torch.Tensor = None):
+ super().__init__()
+
+ self.attn = nn.MultiheadAttention(d_model, n_head)
+ self.ln_1 = nn.LayerNorm(d_model)
+ self.mlp = nn.Sequential(
+ OrderedDict([('c_fc', nn.Linear(d_model, d_model * 4)), ('gelu', QuickGELU()),
+ ('c_proj', nn.Linear(d_model * 4, d_model))]))
+ self.ln_2 = nn.LayerNorm(d_model)
+ self.attn_mask = attn_mask
+
+ def attention(self, x: torch.Tensor, key_padding_mask: torch.Tensor):
+ self.attn_mask = self.attn_mask.to(dtype=x.dtype, device=x.device) if self.attn_mask is not None else None
+ return self.attn(x, x, x, need_weights=False, attn_mask=self.attn_mask, key_padding_mask=key_padding_mask)[0]
+
+ def forward(self, x: torch.Tensor, key_padding_mask=None):
+ x = x + self.attention(self.ln_1(x), key_padding_mask=key_padding_mask)
+ x = x + self.mlp(self.ln_2(x))
+ return x
+
+class Transformer(nn.Module):
+
+ def __init__(self, width: int, layers: int, heads: int, attn_mask: torch.Tensor = None, use_checkpoint=False):
+ super().__init__()
+ self.width = width
+ self.layers = layers
+ self.resblocks = nn.Sequential(*[ResidualAttentionBlock(width, heads, attn_mask) for _ in range(layers)])
+ proj_std = (self.width**-0.5) * ((2 * self.layers)**-0.5)
+ attn_std = self.width**-0.5
+ fc_std = (2 * self.width)**-0.5
+ for block in self.resblocks:
+ nn.init.normal_(block.attn.in_proj_weight, std=attn_std)
+ nn.init.normal_(block.attn.out_proj.weight, std=proj_std)
+ nn.init.normal_(block.mlp.c_fc.weight, std=fc_std)
+ nn.init.normal_(block.mlp.c_proj.weight, std=proj_std)
+
+ self.use_checkpoint = use_checkpoint
+
+ def forward(self, x: torch.Tensor):
+ for resblock in self.resblocks:
+ if self.use_checkpoint:
+ x = checkpoint.checkpoint(resblock, x)
+ else:
+ x = resblock(x)
+ return x
+
+
+class TextTransformer(nn.Module):
+
+ def __init__(
+ self,
+ context_length: int,
+ width: int,
+ layers: int,
+ vocab_size,
+ use_checkpoint=False,
+ ):
+
+ super().__init__()
+ heads = width // 64
+ self.context_length = context_length
+ self.width = width
+ self.transformer = Transformer(
+ width=width,
+ layers=layers,
+ heads=heads,
+ attn_mask=self.build_attention_mask(),
+ use_checkpoint=use_checkpoint)
+
+ self.positional_embedding = nn.Parameter(torch.empty(self.context_length, width))
+ self.ln_final = nn.LayerNorm(width)
+ self.token_embedding = nn.Embedding(vocab_size, width)
+ nn.init.normal_(self.token_embedding.weight, std=0.02)
+
+ # initialization
+ nn.init.normal_(self.positional_embedding, std=0.01)
+
+ def build_attention_mask(self):
+ # lazily create causal attention mask, with full attention between the vision tokens
+ # pytorch uses additive attention mask; fill with -inf
+ mask = torch.empty(self.context_length, self.context_length)
+ mask.fill_(float('-inf'))
+ mask.triu_(1) # zero out the lower diagonal
+ return mask
+
+ def forward(self, text):
+ x = self.token_embedding(text)
+ x = x + self.positional_embedding
+ x = x.permute(1, 0, 2) # NLD -> LND
+ x = self.transformer(x)
+ x = x.permute(1, 0, 2) # LND -> NLD
+ x = self.ln_final(x)
+
+ # x.shape = [batch_size, n_ctx, transformer.width]
+ # take features from the eot embedding (eot_token is the highest number in each sequence)
+ x = x[torch.arange(x.shape[0]), text.argmax(dim=-1)]
+
+ return x
\ No newline at end of file
diff --git a/annotator/OneFormer/oneformer/modeling/transformer_decoder/transformer.py b/annotator/OneFormer/oneformer/modeling/transformer_decoder/transformer.py
new file mode 100644
index 0000000000000000000000000000000000000000..cd07525673b9b1165e1fdd0c9990a8f29c84f199
--- /dev/null
+++ b/annotator/OneFormer/oneformer/modeling/transformer_decoder/transformer.py
@@ -0,0 +1,376 @@
+# ------------------------------------------------------------------------------
+# Reference: https://github.com/facebookresearch/Mask2Former/blob/main/mask2former/modeling/transformer_decoder/transformer.py
+# Modified by Jitesh Jain (https://github.com/praeclarumjj3)
+# ------------------------------------------------------------------------------
+
+"""
+Transformer class.
+
+Copy-paste from torch.nn.Transformer with modifications:
+ * positional encodings are passed in MHattention
+ * extra LN at the end of encoder is removed
+ * decoder returns a stack of activations from all decoding layers
+"""
+import copy
+from typing import List, Optional
+
+import torch
+import torch.nn.functional as F
+from torch import Tensor, nn
+
+
+class Transformer(nn.Module):
+ def __init__(
+ self,
+ d_model=512,
+ nhead=8,
+ num_encoder_layers=6,
+ num_decoder_layers=6,
+ dim_feedforward=2048,
+ dropout=0.1,
+ activation="relu",
+ normalize_before=False,
+ return_intermediate_dec=False,
+ ):
+ super().__init__()
+
+ encoder_layer = TransformerEncoderLayer(
+ d_model, nhead, dim_feedforward, dropout, activation, normalize_before
+ )
+ encoder_norm = nn.LayerNorm(d_model) if normalize_before else None
+ self.encoder = TransformerEncoder(encoder_layer, num_encoder_layers, encoder_norm)
+
+ decoder_layer = TransformerDecoderLayer(
+ d_model, nhead, dim_feedforward, dropout, activation, normalize_before
+ )
+ decoder_norm = nn.LayerNorm(d_model)
+ self.decoder = TransformerDecoder(
+ decoder_layer,
+ num_decoder_layers,
+ decoder_norm,
+ return_intermediate=return_intermediate_dec,
+ )
+
+ self._reset_parameters()
+
+ self.d_model = d_model
+ self.nhead = nhead
+
+ def _reset_parameters(self):
+ for p in self.parameters():
+ if p.dim() > 1:
+ nn.init.xavier_uniform_(p)
+
+ def forward(self, src, mask, query_embed, pos_embed, task_token=None):
+ # flatten NxCxHxW to HWxNxC
+ bs, c, h, w = src.shape
+ src = src.flatten(2).permute(2, 0, 1)
+ pos_embed = pos_embed.flatten(2).permute(2, 0, 1)
+ query_embed = query_embed.unsqueeze(1).repeat(1, bs, 1)
+ if mask is not None:
+ mask = mask.flatten(1)
+
+ if task_token is None:
+ tgt = torch.zeros_like(query_embed)
+ else:
+ tgt = task_token.repeat(query_embed.shape[0], 1, 1)
+
+ memory = self.encoder(src, src_key_padding_mask=mask, pos=pos_embed)
+ hs = self.decoder(
+ tgt, memory, memory_key_padding_mask=mask, pos=pos_embed, query_pos=query_embed
+ )
+ return hs.transpose(1, 2), memory.permute(1, 2, 0).view(bs, c, h, w)
+
+
+class TransformerEncoder(nn.Module):
+ def __init__(self, encoder_layer, num_layers, norm=None):
+ super().__init__()
+ self.layers = _get_clones(encoder_layer, num_layers)
+ self.num_layers = num_layers
+ self.norm = norm
+
+ def forward(
+ self,
+ src,
+ mask: Optional[Tensor] = None,
+ src_key_padding_mask: Optional[Tensor] = None,
+ pos: Optional[Tensor] = None,
+ ):
+ output = src
+
+ for layer in self.layers:
+ output = layer(
+ output, src_mask=mask, src_key_padding_mask=src_key_padding_mask, pos=pos
+ )
+
+ if self.norm is not None:
+ output = self.norm(output)
+
+ return output
+
+
+class TransformerDecoder(nn.Module):
+ def __init__(self, decoder_layer, num_layers, norm=None, return_intermediate=False):
+ super().__init__()
+ self.layers = _get_clones(decoder_layer, num_layers)
+ self.num_layers = num_layers
+ self.norm = norm
+ self.return_intermediate = return_intermediate
+
+ def forward(
+ self,
+ tgt,
+ memory,
+ tgt_mask: Optional[Tensor] = None,
+ memory_mask: Optional[Tensor] = None,
+ tgt_key_padding_mask: Optional[Tensor] = None,
+ memory_key_padding_mask: Optional[Tensor] = None,
+ pos: Optional[Tensor] = None,
+ query_pos: Optional[Tensor] = None,
+ ):
+ output = tgt
+
+ intermediate = []
+
+ for layer in self.layers:
+ output = layer(
+ output,
+ memory,
+ tgt_mask=tgt_mask,
+ memory_mask=memory_mask,
+ tgt_key_padding_mask=tgt_key_padding_mask,
+ memory_key_padding_mask=memory_key_padding_mask,
+ pos=pos,
+ query_pos=query_pos,
+ )
+ if self.return_intermediate:
+ intermediate.append(self.norm(output))
+
+ if self.norm is not None:
+ output = self.norm(output)
+ if self.return_intermediate:
+ intermediate.pop()
+ intermediate.append(output)
+
+ if self.return_intermediate:
+ return torch.stack(intermediate)
+
+ return output.unsqueeze(0)
+
+
+class TransformerEncoderLayer(nn.Module):
+ def __init__(
+ self,
+ d_model,
+ nhead,
+ dim_feedforward=2048,
+ dropout=0.1,
+ activation="relu",
+ normalize_before=False,
+ ):
+ super().__init__()
+ self.self_attn = nn.MultiheadAttention(d_model, nhead, dropout=dropout)
+ # Implementation of Feedforward model
+ self.linear1 = nn.Linear(d_model, dim_feedforward)
+ self.dropout = nn.Dropout(dropout)
+ self.linear2 = nn.Linear(dim_feedforward, d_model)
+
+ self.norm1 = nn.LayerNorm(d_model)
+ self.norm2 = nn.LayerNorm(d_model)
+ self.dropout1 = nn.Dropout(dropout)
+ self.dropout2 = nn.Dropout(dropout)
+
+ self.activation = _get_activation_fn(activation)
+ self.normalize_before = normalize_before
+
+ def with_pos_embed(self, tensor, pos: Optional[Tensor]):
+ return tensor if pos is None else tensor + pos
+
+ def forward_post(
+ self,
+ src,
+ src_mask: Optional[Tensor] = None,
+ src_key_padding_mask: Optional[Tensor] = None,
+ pos: Optional[Tensor] = None,
+ ):
+ q = k = self.with_pos_embed(src, pos)
+ src2 = self.self_attn(
+ q, k, value=src, attn_mask=src_mask, key_padding_mask=src_key_padding_mask
+ )[0]
+ src = src + self.dropout1(src2)
+ src = self.norm1(src)
+ src2 = self.linear2(self.dropout(self.activation(self.linear1(src))))
+ src = src + self.dropout2(src2)
+ src = self.norm2(src)
+ return src
+
+ def forward_pre(
+ self,
+ src,
+ src_mask: Optional[Tensor] = None,
+ src_key_padding_mask: Optional[Tensor] = None,
+ pos: Optional[Tensor] = None,
+ ):
+ src2 = self.norm1(src)
+ q = k = self.with_pos_embed(src2, pos)
+ src2 = self.self_attn(
+ q, k, value=src2, attn_mask=src_mask, key_padding_mask=src_key_padding_mask
+ )[0]
+ src = src + self.dropout1(src2)
+ src2 = self.norm2(src)
+ src2 = self.linear2(self.dropout(self.activation(self.linear1(src2))))
+ src = src + self.dropout2(src2)
+ return src
+
+ def forward(
+ self,
+ src,
+ src_mask: Optional[Tensor] = None,
+ src_key_padding_mask: Optional[Tensor] = None,
+ pos: Optional[Tensor] = None,
+ ):
+ if self.normalize_before:
+ return self.forward_pre(src, src_mask, src_key_padding_mask, pos)
+ return self.forward_post(src, src_mask, src_key_padding_mask, pos)
+
+
+class TransformerDecoderLayer(nn.Module):
+ def __init__(
+ self,
+ d_model,
+ nhead,
+ dim_feedforward=2048,
+ dropout=0.1,
+ activation="relu",
+ normalize_before=False,
+ ):
+ super().__init__()
+ self.self_attn = nn.MultiheadAttention(d_model, nhead, dropout=dropout)
+ self.multihead_attn = nn.MultiheadAttention(d_model, nhead, dropout=dropout)
+ # Implementation of Feedforward model
+ self.linear1 = nn.Linear(d_model, dim_feedforward)
+ self.dropout = nn.Dropout(dropout)
+ self.linear2 = nn.Linear(dim_feedforward, d_model)
+
+ self.norm1 = nn.LayerNorm(d_model)
+ self.norm2 = nn.LayerNorm(d_model)
+ self.norm3 = nn.LayerNorm(d_model)
+ self.dropout1 = nn.Dropout(dropout)
+ self.dropout2 = nn.Dropout(dropout)
+ self.dropout3 = nn.Dropout(dropout)
+
+ self.activation = _get_activation_fn(activation)
+ self.normalize_before = normalize_before
+
+ def with_pos_embed(self, tensor, pos: Optional[Tensor]):
+ return tensor if pos is None else tensor + pos
+
+ def forward_post(
+ self,
+ tgt,
+ memory,
+ tgt_mask: Optional[Tensor] = None,
+ memory_mask: Optional[Tensor] = None,
+ tgt_key_padding_mask: Optional[Tensor] = None,
+ memory_key_padding_mask: Optional[Tensor] = None,
+ pos: Optional[Tensor] = None,
+ query_pos: Optional[Tensor] = None,
+ ):
+ q = k = self.with_pos_embed(tgt, query_pos)
+ tgt2 = self.self_attn(
+ q, k, value=tgt, attn_mask=tgt_mask, key_padding_mask=tgt_key_padding_mask
+ )[0]
+ tgt = tgt + self.dropout1(tgt2)
+ tgt = self.norm1(tgt)
+ tgt2 = self.multihead_attn(
+ query=self.with_pos_embed(tgt, query_pos),
+ key=self.with_pos_embed(memory, pos),
+ value=memory,
+ attn_mask=memory_mask,
+ key_padding_mask=memory_key_padding_mask,
+ )[0]
+ tgt = tgt + self.dropout2(tgt2)
+ tgt = self.norm2(tgt)
+ tgt2 = self.linear2(self.dropout(self.activation(self.linear1(tgt))))
+ tgt = tgt + self.dropout3(tgt2)
+ tgt = self.norm3(tgt)
+ return tgt
+
+ def forward_pre(
+ self,
+ tgt,
+ memory,
+ tgt_mask: Optional[Tensor] = None,
+ memory_mask: Optional[Tensor] = None,
+ tgt_key_padding_mask: Optional[Tensor] = None,
+ memory_key_padding_mask: Optional[Tensor] = None,
+ pos: Optional[Tensor] = None,
+ query_pos: Optional[Tensor] = None,
+ ):
+ tgt2 = self.norm1(tgt)
+ q = k = self.with_pos_embed(tgt2, query_pos)
+ tgt2 = self.self_attn(
+ q, k, value=tgt2, attn_mask=tgt_mask, key_padding_mask=tgt_key_padding_mask
+ )[0]
+ tgt = tgt + self.dropout1(tgt2)
+ tgt2 = self.norm2(tgt)
+ tgt2 = self.multihead_attn(
+ query=self.with_pos_embed(tgt2, query_pos),
+ key=self.with_pos_embed(memory, pos),
+ value=memory,
+ attn_mask=memory_mask,
+ key_padding_mask=memory_key_padding_mask,
+ )[0]
+ tgt = tgt + self.dropout2(tgt2)
+ tgt2 = self.norm3(tgt)
+ tgt2 = self.linear2(self.dropout(self.activation(self.linear1(tgt2))))
+ tgt = tgt + self.dropout3(tgt2)
+ return tgt
+
+ def forward(
+ self,
+ tgt,
+ memory,
+ tgt_mask: Optional[Tensor] = None,
+ memory_mask: Optional[Tensor] = None,
+ tgt_key_padding_mask: Optional[Tensor] = None,
+ memory_key_padding_mask: Optional[Tensor] = None,
+ pos: Optional[Tensor] = None,
+ query_pos: Optional[Tensor] = None,
+ ):
+ if self.normalize_before:
+ return self.forward_pre(
+ tgt,
+ memory,
+ tgt_mask,
+ memory_mask,
+ tgt_key_padding_mask,
+ memory_key_padding_mask,
+ pos,
+ query_pos,
+ )
+ return self.forward_post(
+ tgt,
+ memory,
+ tgt_mask,
+ memory_mask,
+ tgt_key_padding_mask,
+ memory_key_padding_mask,
+ pos,
+ query_pos,
+ )
+
+
+def _get_clones(module, N):
+ return nn.ModuleList([copy.deepcopy(module) for i in range(N)])
+
+
+def _get_activation_fn(activation):
+ """Return an activation function given a string"""
+ if activation == "relu":
+ return F.relu
+ if activation == "gelu":
+ return F.gelu
+ if activation == "glu":
+ return F.glu
+ raise RuntimeError(f"activation should be relu/gelu, not {activation}.")
diff --git a/annotator/OneFormer/oneformer/oneformer_model.py b/annotator/OneFormer/oneformer/oneformer_model.py
new file mode 100644
index 0000000000000000000000000000000000000000..01508df74cfe8a722dd937b7f54b12296258c5a1
--- /dev/null
+++ b/annotator/OneFormer/oneformer/oneformer_model.py
@@ -0,0 +1,486 @@
+# ------------------------------------------------------------------------------
+# Reference: https://github.com/facebookresearch/Mask2Former/blob/main/mask2former/maskformer_model.py
+# Modified by Jitesh Jain (https://github.com/praeclarumjj3)
+# ------------------------------------------------------------------------------
+
+from typing import Tuple
+
+import torch
+from torch import nn
+from torch.nn import functional as F
+
+from detectron2.config import configurable
+from detectron2.data import MetadataCatalog
+from detectron2.modeling import META_ARCH_REGISTRY, build_backbone, build_sem_seg_head
+from detectron2.modeling.backbone import Backbone
+from detectron2.modeling.postprocessing import sem_seg_postprocess
+from detectron2.structures import Boxes, ImageList, Instances, BitMasks
+from detectron2.utils.memory import retry_if_cuda_oom
+
+from .modeling.criterion import SetCriterion
+from .modeling.matcher import HungarianMatcher
+from einops import rearrange
+from .modeling.transformer_decoder.text_transformer import TextTransformer
+from .modeling.transformer_decoder.oneformer_transformer_decoder import MLP
+from oneformer.data.tokenizer import SimpleTokenizer, Tokenize
+
+@META_ARCH_REGISTRY.register()
+class OneFormer(nn.Module):
+ """
+ Main class for mask classification semantic segmentation architectures.
+ """
+
+ @configurable
+ def __init__(
+ self,
+ *,
+ backbone: Backbone,
+ sem_seg_head: nn.Module,
+ task_mlp: nn.Module,
+ text_encoder: nn.Module,
+ text_projector: nn.Module,
+ criterion: nn.Module,
+ prompt_ctx: nn.Embedding,
+ num_queries: int,
+ object_mask_threshold: float,
+ overlap_threshold: float,
+ metadata,
+ size_divisibility: int,
+ sem_seg_postprocess_before_inference: bool,
+ pixel_mean: Tuple[float],
+ pixel_std: Tuple[float],
+ # inference
+ semantic_on: bool,
+ panoptic_on: bool,
+ instance_on: bool,
+ detection_on: bool,
+ test_topk_per_image: int,
+ task_seq_len: int,
+ max_seq_len: int,
+ is_demo: bool,
+ ):
+ """
+ Args:
+ backbone: a backbone module, must follow detectron2's backbone interface
+ sem_seg_head: a module that predicts semantic segmentation from backbone features
+ criterion: a module that defines the loss
+ num_queries: int, number of queries
+ object_mask_threshold: float, threshold to filter query based on classification score
+ for panoptic segmentation inference
+ overlap_threshold: overlap threshold used in general inference for panoptic segmentation
+ metadata: dataset meta, get `thing` and `stuff` category names for panoptic
+ segmentation inference
+ size_divisibility: Some backbones require the input height and width to be divisible by a
+ specific integer. We can use this to override such requirement.
+ sem_seg_postprocess_before_inference: whether to resize the prediction back
+ to original input size before semantic segmentation inference or after.
+ For high-resolution dataset like Mapillary, resizing predictions before
+ inference will cause OOM error.
+ pixel_mean, pixel_std: list or tuple with #channels element, representing
+ the per-channel mean and std to be used to normalize the input image
+ semantic_on: bool, whether to output semantic segmentation prediction
+ instance_on: bool, whether to output instance segmentation prediction
+ panoptic_on: bool, whether to output panoptic segmentation prediction
+ test_topk_per_image: int, instance segmentation parameter, keep topk instances per image
+ """
+ super().__init__()
+ self.backbone = backbone
+ self.sem_seg_head = sem_seg_head
+ self.task_mlp = task_mlp
+ self.text_encoder = text_encoder
+ self.text_projector = text_projector
+ self.prompt_ctx = prompt_ctx
+ self.criterion = criterion
+ self.num_queries = num_queries
+ self.overlap_threshold = overlap_threshold
+ self.object_mask_threshold = object_mask_threshold
+ self.metadata = metadata
+ if size_divisibility < 0:
+ # use backbone size_divisibility if not set
+ size_divisibility = self.backbone.size_divisibility
+ self.size_divisibility = size_divisibility
+ self.sem_seg_postprocess_before_inference = sem_seg_postprocess_before_inference
+ self.register_buffer("pixel_mean", torch.Tensor(pixel_mean).view(-1, 1, 1), False)
+ self.register_buffer("pixel_std", torch.Tensor(pixel_std).view(-1, 1, 1), False)
+
+ # additional args
+ self.semantic_on = semantic_on
+ self.instance_on = instance_on
+ self.panoptic_on = panoptic_on
+ self.detection_on = detection_on
+ self.test_topk_per_image = test_topk_per_image
+
+ self.text_tokenizer = Tokenize(SimpleTokenizer(), max_seq_len=max_seq_len)
+ self.task_tokenizer = Tokenize(SimpleTokenizer(), max_seq_len=task_seq_len)
+ self.is_demo = is_demo
+
+ self.thing_indices = [k for k in self.metadata.thing_dataset_id_to_contiguous_id.keys()]
+
+ if not self.semantic_on:
+ assert self.sem_seg_postprocess_before_inference
+
+ @classmethod
+ def from_config(cls, cfg):
+ backbone = build_backbone(cfg)
+ sem_seg_head = build_sem_seg_head(cfg, backbone.output_shape())
+
+ if cfg.MODEL.IS_TRAIN:
+ text_encoder = TextTransformer(context_length=cfg.MODEL.TEXT_ENCODER.CONTEXT_LENGTH,
+ width=cfg.MODEL.TEXT_ENCODER.WIDTH,
+ layers=cfg.MODEL.TEXT_ENCODER.NUM_LAYERS,
+ vocab_size=cfg.MODEL.TEXT_ENCODER.VOCAB_SIZE)
+ text_projector = MLP(text_encoder.width, cfg.MODEL.ONE_FORMER.HIDDEN_DIM,
+ cfg.MODEL.ONE_FORMER.HIDDEN_DIM, cfg.MODEL.TEXT_ENCODER.PROJ_NUM_LAYERS)
+ if cfg.MODEL.TEXT_ENCODER.N_CTX > 0:
+ prompt_ctx = nn.Embedding(cfg.MODEL.TEXT_ENCODER.N_CTX, cfg.MODEL.TEXT_ENCODER.WIDTH)
+ else:
+ prompt_ctx = None
+ else:
+ text_encoder = None
+ text_projector = None
+ prompt_ctx = None
+
+ task_mlp = MLP(cfg.INPUT.TASK_SEQ_LEN, cfg.MODEL.ONE_FORMER.HIDDEN_DIM,
+ cfg.MODEL.ONE_FORMER.HIDDEN_DIM, 2)
+
+ # Loss parameters:
+ deep_supervision = cfg.MODEL.ONE_FORMER.DEEP_SUPERVISION
+ no_object_weight = cfg.MODEL.ONE_FORMER.NO_OBJECT_WEIGHT
+
+ # loss weights
+ class_weight = cfg.MODEL.ONE_FORMER.CLASS_WEIGHT
+ dice_weight = cfg.MODEL.ONE_FORMER.DICE_WEIGHT
+ mask_weight = cfg.MODEL.ONE_FORMER.MASK_WEIGHT
+ contrastive_weight = cfg.MODEL.ONE_FORMER.CONTRASTIVE_WEIGHT
+
+ # building criterion
+ matcher = HungarianMatcher(
+ cost_class=class_weight,
+ cost_mask=mask_weight,
+ cost_dice=dice_weight,
+ num_points=cfg.MODEL.ONE_FORMER.TRAIN_NUM_POINTS,
+ )
+
+ weight_dict = {"loss_ce": class_weight, "loss_mask": mask_weight,
+ "loss_dice": dice_weight, "loss_contrastive": contrastive_weight}
+
+
+ if deep_supervision:
+ dec_layers = cfg.MODEL.ONE_FORMER.DEC_LAYERS
+ aux_weight_dict = {}
+ for i in range(dec_layers - 1):
+ aux_weight_dict.update({k + f"_{i}": v for k, v in weight_dict.items()})
+ weight_dict.update(aux_weight_dict)
+
+ losses = ["labels", "masks", "contrastive"]
+
+ criterion = SetCriterion(
+ sem_seg_head.num_classes,
+ matcher=matcher,
+ weight_dict=weight_dict,
+ eos_coef=no_object_weight,
+ contrast_temperature=cfg.MODEL.ONE_FORMER.CONTRASTIVE_TEMPERATURE,
+ losses=losses,
+ num_points=cfg.MODEL.ONE_FORMER.TRAIN_NUM_POINTS,
+ oversample_ratio=cfg.MODEL.ONE_FORMER.OVERSAMPLE_RATIO,
+ importance_sample_ratio=cfg.MODEL.ONE_FORMER.IMPORTANCE_SAMPLE_RATIO,
+ )
+
+ return {
+ "backbone": backbone,
+ "sem_seg_head": sem_seg_head,
+ "task_mlp": task_mlp,
+ "prompt_ctx": prompt_ctx,
+ "text_encoder": text_encoder,
+ "text_projector": text_projector,
+ "criterion": criterion,
+ "num_queries": cfg.MODEL.ONE_FORMER.NUM_OBJECT_QUERIES,
+ "object_mask_threshold": cfg.MODEL.TEST.OBJECT_MASK_THRESHOLD,
+ "overlap_threshold": cfg.MODEL.TEST.OVERLAP_THRESHOLD,
+ "metadata": MetadataCatalog.get(cfg.DATASETS.TRAIN[0]),
+ "size_divisibility": cfg.MODEL.ONE_FORMER.SIZE_DIVISIBILITY,
+ "sem_seg_postprocess_before_inference": (
+ cfg.MODEL.TEST.SEM_SEG_POSTPROCESSING_BEFORE_INFERENCE
+ or cfg.MODEL.TEST.PANOPTIC_ON
+ or cfg.MODEL.TEST.INSTANCE_ON
+ ),
+ "pixel_mean": cfg.MODEL.PIXEL_MEAN,
+ "pixel_std": cfg.MODEL.PIXEL_STD,
+ # inference
+ "semantic_on": cfg.MODEL.TEST.SEMANTIC_ON,
+ "instance_on": cfg.MODEL.TEST.INSTANCE_ON,
+ "panoptic_on": cfg.MODEL.TEST.PANOPTIC_ON,
+ "detection_on": cfg.MODEL.TEST.DETECTION_ON,
+ "test_topk_per_image": cfg.TEST.DETECTIONS_PER_IMAGE,
+ "task_seq_len": cfg.INPUT.TASK_SEQ_LEN,
+ "max_seq_len": cfg.INPUT.MAX_SEQ_LEN,
+ "is_demo": cfg.MODEL.IS_DEMO,
+ }
+
+ @property
+ def device(self):
+ return self.pixel_mean.device
+
+ def encode_text(self, text):
+ assert text.ndim in [2, 3], text.ndim
+ b = text.shape[0]
+ squeeze_dim = False
+ num_text = 1
+ if text.ndim == 3:
+ num_text = text.shape[1]
+ text = rearrange(text, 'b n l -> (b n) l', n=num_text)
+ squeeze_dim = True
+
+ # [B, C]
+ x = self.text_encoder(text)
+
+ text_x = self.text_projector(x)
+
+ if squeeze_dim:
+ text_x = rearrange(text_x, '(b n) c -> b n c', n=num_text)
+ if self.prompt_ctx is not None:
+ text_ctx = self.prompt_ctx.weight.unsqueeze(0).repeat(text_x.shape[0], 1, 1)
+ text_x = torch.cat([text_x, text_ctx], dim=1)
+
+ return {"texts": text_x}
+
+ def forward(self, batched_inputs):
+ """
+ Args:
+ batched_inputs: a list, batched outputs of :class:`DatasetMapper`.
+ Each item in the list contains the inputs for one image.
+ For now, each item in the list is a dict that contains:
+ * "image": Tensor, image in (C, H, W) format.
+ * "instances": per-region ground truth
+ * Other information that's included in the original dicts, such as:
+ "height", "width" (int): the output resolution of the model (may be different
+ from input resolution), used in inference.
+ Returns:
+ list[dict]:
+ each dict has the results for one image. The dict contains the following keys:
+ * "sem_seg":
+ A Tensor that represents the
+ per-pixel segmentation prediced by the head.
+ The prediction has shape KxHxW that represents the logits of
+ each class for each pixel.
+ * "panoptic_seg":
+ A tuple that represent panoptic output
+ panoptic_seg (Tensor): of shape (height, width) where the values are ids for each segment.
+ segments_info (list[dict]): Describe each segment in `panoptic_seg`.
+ Each dict contains keys "id", "category_id", "isthing".
+ """
+ images = [x["image"].to(self.device) for x in batched_inputs]
+ images = [(x - self.pixel_mean) / self.pixel_std for x in images]
+ images = ImageList.from_tensors(images, self.size_divisibility)
+
+ tasks = torch.cat([self.task_tokenizer(x["task"]).to(self.device).unsqueeze(0) for x in batched_inputs], dim=0)
+ tasks = self.task_mlp(tasks.float())
+
+ features = self.backbone(images.tensor)
+ outputs = self.sem_seg_head(features, tasks)
+
+ if self.training:
+ texts = torch.cat([self.text_tokenizer(x["text"]).to(self.device).unsqueeze(0) for x in batched_inputs], dim=0)
+ texts_x = self.encode_text(texts)
+
+ outputs = {**outputs, **texts_x}
+
+ # mask classification target
+ if "instances" in batched_inputs[0]:
+ gt_instances = [x["instances"].to(self.device) for x in batched_inputs]
+ targets = self.prepare_targets(gt_instances, images)
+ else:
+ targets = None
+
+ # bipartite matching-based loss
+ losses = self.criterion(outputs, targets)
+
+ for k in list(losses.keys()):
+ if k in self.criterion.weight_dict:
+ losses[k] *= self.criterion.weight_dict[k]
+ else:
+ # remove this loss if not specified in `weight_dict`
+ losses.pop(k)
+ return losses
+ else:
+ mask_cls_results = outputs["pred_logits"]
+ mask_pred_results = outputs["pred_masks"]
+ # upsample masks
+ mask_pred_results = F.interpolate(
+ mask_pred_results,
+ size=(images.tensor.shape[-2], images.tensor.shape[-1]),
+ mode="bilinear",
+ align_corners=False,
+ )
+
+ del outputs
+
+ processed_results = []
+ for i, data in enumerate(zip(
+ mask_cls_results, mask_pred_results, batched_inputs, images.image_sizes
+ )):
+ mask_cls_result, mask_pred_result, input_per_image, image_size = data
+ height = input_per_image.get("height", image_size[0])
+ width = input_per_image.get("width", image_size[1])
+ processed_results.append({})
+
+ if self.sem_seg_postprocess_before_inference:
+ mask_pred_result = retry_if_cuda_oom(sem_seg_postprocess)(
+ mask_pred_result, image_size, height, width
+ )
+ mask_cls_result = mask_cls_result.to(mask_pred_result)
+
+ # semantic segmentation inference
+ if self.semantic_on:
+ r = retry_if_cuda_oom(self.semantic_inference)(mask_cls_result, mask_pred_result)
+ if not self.sem_seg_postprocess_before_inference:
+ r = retry_if_cuda_oom(sem_seg_postprocess)(r, image_size, height, width)
+ processed_results[-1]["sem_seg"] = r
+
+ # panoptic segmentation inference
+ if self.panoptic_on:
+ panoptic_r = retry_if_cuda_oom(self.panoptic_inference)(mask_cls_result, mask_pred_result)
+ processed_results[-1]["panoptic_seg"] = panoptic_r
+
+ # instance segmentation inference
+ if self.instance_on:
+ instance_r = retry_if_cuda_oom(self.instance_inference)(mask_cls_result, mask_pred_result, input_per_image["task"])
+ processed_results[-1]["instances"] = instance_r
+
+ if self.detection_on:
+ bbox_r = retry_if_cuda_oom(self.instance_inference)(mask_cls_result, mask_pred_result, input_per_image["task"])
+ processed_results[-1]["box_instances"] = bbox_r
+
+ return processed_results
+
+ def prepare_targets(self, targets, images):
+ h_pad, w_pad = images.tensor.shape[-2:]
+ new_targets = []
+ for targets_per_image in targets:
+ # pad gt
+ gt_masks = targets_per_image.gt_masks
+ padded_masks = torch.zeros((gt_masks.shape[0], h_pad, w_pad), dtype=gt_masks.dtype, device=gt_masks.device)
+ padded_masks[:, : gt_masks.shape[1], : gt_masks.shape[2]] = gt_masks
+ new_targets.append(
+ {
+ "labels": targets_per_image.gt_classes,
+ "masks": padded_masks,
+ }
+ )
+ return new_targets
+
+ def semantic_inference(self, mask_cls, mask_pred):
+ mask_cls = F.softmax(mask_cls, dim=-1)[..., :-1]
+ mask_pred = mask_pred.sigmoid()
+ semseg = torch.einsum("qc,qhw->chw", mask_cls, mask_pred)
+ return semseg
+
+ def panoptic_inference(self, mask_cls, mask_pred):
+ scores, labels = F.softmax(mask_cls, dim=-1).max(-1)
+ mask_pred = mask_pred.sigmoid()
+
+ keep = labels.ne(self.sem_seg_head.num_classes) & (scores > self.object_mask_threshold)
+ cur_scores = scores[keep]
+ cur_classes = labels[keep]
+ cur_masks = mask_pred[keep]
+ cur_mask_cls = mask_cls[keep]
+ cur_mask_cls = cur_mask_cls[:, :-1]
+
+ cur_prob_masks = cur_scores.view(-1, 1, 1) * cur_masks
+
+ h, w = cur_masks.shape[-2:]
+ panoptic_seg = torch.zeros((h, w), dtype=torch.int32, device=cur_masks.device)
+ segments_info = []
+
+ current_segment_id = 0
+
+ if cur_masks.shape[0] == 0:
+ # We didn't detect any mask :(
+ return panoptic_seg, segments_info
+ else:
+ # take argmax
+ cur_mask_ids = cur_prob_masks.argmax(0)
+ stuff_memory_list = {}
+ for k in range(cur_classes.shape[0]):
+ pred_class = cur_classes[k].item()
+ isthing = pred_class in self.metadata.thing_dataset_id_to_contiguous_id.values()
+ mask_area = (cur_mask_ids == k).sum().item()
+ original_area = (cur_masks[k] >= 0.5).sum().item()
+ mask = (cur_mask_ids == k) & (cur_masks[k] >= 0.5)
+
+ if mask_area > 0 and original_area > 0 and mask.sum().item() > 0:
+ if mask_area / original_area < self.overlap_threshold:
+ continue
+
+ # merge stuff regions
+ if not isthing:
+ if int(pred_class) in stuff_memory_list.keys():
+ panoptic_seg[mask] = stuff_memory_list[int(pred_class)]
+ continue
+ else:
+ stuff_memory_list[int(pred_class)] = current_segment_id + 1
+
+ current_segment_id += 1
+ panoptic_seg[mask] = current_segment_id
+
+ segments_info.append(
+ {
+ "id": current_segment_id,
+ "isthing": bool(isthing),
+ "category_id": int(pred_class),
+ }
+ )
+
+ return panoptic_seg, segments_info
+
+ def instance_inference(self, mask_cls, mask_pred, task_type):
+ # mask_pred is already processed to have the same shape as original input
+ image_size = mask_pred.shape[-2:]
+
+ # [Q, K]
+ scores = F.softmax(mask_cls, dim=-1)[:, :-1]
+ labels = torch.arange(self.sem_seg_head.num_classes, device=self.device).unsqueeze(0).repeat(self.num_queries, 1).flatten(0, 1)
+
+ # scores_per_image, topk_indices = scores.flatten(0, 1).topk(self.num_queries, sorted=False)
+ scores_per_image, topk_indices = scores.flatten(0, 1).topk(self.test_topk_per_image, sorted=False)
+ labels_per_image = labels[topk_indices]
+
+ topk_indices = topk_indices // self.sem_seg_head.num_classes
+ # mask_pred = mask_pred.unsqueeze(1).repeat(1, self.sem_seg_head.num_classes, 1).flatten(0, 1)
+ mask_pred = mask_pred[topk_indices]
+
+ # Only consider scores with confidence over [self.object_mask_threshold] for demo
+ if self.is_demo:
+ keep = scores_per_image > self.object_mask_threshold
+ scores_per_image = scores_per_image[keep]
+ labels_per_image = labels_per_image[keep]
+ mask_pred = mask_pred[keep]
+
+ # if this is panoptic segmentation, we only keep the "thing" classes
+ if self.panoptic_on:
+ keep = torch.zeros_like(scores_per_image).bool()
+ for i, lab in enumerate(labels_per_image):
+ keep[i] = lab in self.metadata.thing_dataset_id_to_contiguous_id.values()
+
+ scores_per_image = scores_per_image[keep]
+ labels_per_image = labels_per_image[keep]
+ mask_pred = mask_pred[keep]
+
+ if 'ade20k' in self.metadata.name and not self.is_demo and "instance" in task_type:
+ for i in range(labels_per_image.shape[0]):
+ labels_per_image[i] = self.thing_indices.index(labels_per_image[i].item())
+
+ result = Instances(image_size)
+ # mask (before sigmoid)
+ result.pred_masks = (mask_pred > 0).float()
+ if self.detection_on:
+ # Uncomment the following to get boxes from masks (this is slow)
+ result.pred_boxes = BitMasks(mask_pred > 0).get_bounding_boxes()
+ else:
+ result.pred_boxes = Boxes(torch.zeros(mask_pred.size(0), 4))
+
+ # calculate average mask prob
+ mask_scores_per_image = (mask_pred.sigmoid().flatten(1) * result.pred_masks.flatten(1)).sum(1) / (result.pred_masks.flatten(1).sum(1) + 1e-6)
+ result.scores = scores_per_image * mask_scores_per_image
+ result.pred_classes = labels_per_image
+ return result
\ No newline at end of file
diff --git a/annotator/OneFormer/oneformer/test_time_augmentation.py b/annotator/OneFormer/oneformer/test_time_augmentation.py
new file mode 100644
index 0000000000000000000000000000000000000000..a913cd4ae0ed8e22121380929ffcd51b9f3500a6
--- /dev/null
+++ b/annotator/OneFormer/oneformer/test_time_augmentation.py
@@ -0,0 +1,107 @@
+# ------------------------------------------------------------------------------
+# Reference: https://github.com/facebookresearch/Mask2Former/blob/main/mask2former/test_time_augmentation.py
+# ------------------------------------------------------------------------------
+
+import copy
+import logging
+from itertools import count
+
+import numpy as np
+import torch
+from fvcore.transforms import HFlipTransform
+from torch import nn
+from torch.nn.parallel import DistributedDataParallel
+
+from detectron2.data.detection_utils import read_image
+from .datasetmapper_tta import DatasetMapperTTA
+import torch.nn.functional as F
+
+__all__ = [
+ "SemanticSegmentorWithTTA",
+]
+
+
+class SemanticSegmentorWithTTA(nn.Module):
+ """
+ A SemanticSegmentor with test-time augmentation enabled.
+ Its :meth:`__call__` method has the same interface as :meth:`SemanticSegmentor.forward`.
+ """
+
+ def __init__(self, cfg, model, tta_mapper=None, batch_size=1):
+ """
+ Args:
+ cfg (CfgNode):
+ model (SemanticSegmentor): a SemanticSegmentor to apply TTA on.
+ tta_mapper (callable): takes a dataset dict and returns a list of
+ augmented versions of the dataset dict. Defaults to
+ `DatasetMapperTTA(cfg)`.
+ batch_size (int): batch the augmented images into this batch size for inference.
+ """
+ super().__init__()
+ if isinstance(model, DistributedDataParallel):
+ model = model.module
+ self.cfg = cfg.clone()
+ self.num_classes = self.cfg.MODEL.SEM_SEG_HEAD.NUM_CLASSES
+
+ self.model = model
+
+ if tta_mapper is None:
+ tta_mapper = DatasetMapperTTA(cfg)
+ self.tta_mapper = tta_mapper
+ self.batch_size = batch_size
+
+ def __call__(self, batched_inputs):
+ """
+ Same input/output format as :meth:`SemanticSegmentor.forward`
+ """
+
+ def _maybe_read_image(dataset_dict):
+ ret = copy.copy(dataset_dict)
+ if "image" not in ret:
+ image = read_image(ret.pop("file_name"), self.model.input_format)
+ image = torch.from_numpy(np.ascontiguousarray(image.transpose(2, 0, 1))) # CHW
+ ret["image"] = image
+ if "height" not in ret and "width" not in ret:
+ ret["height"] = image.shape[1]
+ ret["width"] = image.shape[2]
+ return ret
+
+ processed_results = []
+ for x in batched_inputs:
+ result = self._inference_one_image(_maybe_read_image(x))
+ processed_results.append(result)
+ return processed_results
+
+ def _inference_one_image(self, input):
+ """
+ Args:
+ input (dict): one dataset dict with "image" field being a CHW tensor
+ Returns:
+ dict: one output dict
+ """
+ orig_shape = (input["height"], input["width"])
+ augmented_inputs, tfms = self._get_augmented_inputs(input)
+
+ final_predictions = None
+ count_predictions = 0
+ for input, tfm in zip(augmented_inputs, tfms):
+ count_predictions += 1
+ with torch.no_grad():
+ if final_predictions is None:
+ if any(isinstance(t, HFlipTransform) for t in tfm.transforms):
+ final_predictions = self.model([input])[0].pop("sem_seg").flip(dims=[2])
+ else:
+ final_predictions = self.model([input])[0].pop("sem_seg")
+ else:
+ if any(isinstance(t, HFlipTransform) for t in tfm.transforms):
+ final_predictions += self.model([input])[0].pop("sem_seg").flip(dims=[2])
+ else:
+ final_predictions += self.model([input])[0].pop("sem_seg")
+
+ final_predictions = final_predictions / count_predictions
+ return {"sem_seg": final_predictions}
+
+ def _get_augmented_inputs(self, input):
+ augmented_inputs = self.tta_mapper(input)
+ tfms = [x.pop("transforms") for x in augmented_inputs]
+ return augmented_inputs, tfms
\ No newline at end of file
diff --git a/annotator/OneFormer/oneformer/utils/__init__.py b/annotator/OneFormer/oneformer/utils/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..130d3011b032f91df1a9cf965625e54922f6c81b
--- /dev/null
+++ b/annotator/OneFormer/oneformer/utils/__init__.py
@@ -0,0 +1,2 @@
+# Copyright (c) Facebook, Inc. and its affiliates.
+from .events import setup_wandb, WandbWriter
\ No newline at end of file
diff --git a/annotator/OneFormer/oneformer/utils/box_ops.py b/annotator/OneFormer/oneformer/utils/box_ops.py
new file mode 100644
index 0000000000000000000000000000000000000000..a2b62ad99ed1fc35cdb10a9e11acdeb0ff1abcc4
--- /dev/null
+++ b/annotator/OneFormer/oneformer/utils/box_ops.py
@@ -0,0 +1,133 @@
+# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
+"""
+Utilities for bounding box manipulation and GIoU.
+"""
+import torch, os
+from torchvision.ops.boxes import box_area
+
+
+def box_cxcywh_to_xyxy(x):
+ x_c, y_c, w, h = x.unbind(-1)
+ b = [(x_c - 0.5 * w), (y_c - 0.5 * h),
+ (x_c + 0.5 * w), (y_c + 0.5 * h)]
+ return torch.stack(b, dim=-1)
+
+
+def box_xyxy_to_cxcywh(x):
+ x0, y0, x1, y1 = x.unbind(-1)
+ b = [(x0 + x1) / 2, (y0 + y1) / 2,
+ (x1 - x0), (y1 - y0)]
+ return torch.stack(b, dim=-1)
+
+
+# modified from torchvision to also return the union
+def box_iou(boxes1, boxes2):
+ area1 = box_area(boxes1)
+ area2 = box_area(boxes2)
+
+ # import ipdb; ipdb.set_trace()
+ lt = torch.max(boxes1[:, None, :2], boxes2[:, :2]) # [N,M,2]
+ rb = torch.min(boxes1[:, None, 2:], boxes2[:, 2:]) # [N,M,2]
+
+ wh = (rb - lt).clamp(min=0) # [N,M,2]
+ inter = wh[:, :, 0] * wh[:, :, 1] # [N,M]
+
+ union = area1[:, None] + area2 - inter
+
+ iou = inter / (union + 1e-6)
+ return iou, union
+
+
+def generalized_box_iou(boxes1, boxes2):
+ """
+ Generalized IoU from https://giou.stanford.edu/
+ The boxes should be in [x0, y0, x1, y1] format
+ Returns a [N, M] pairwise matrix, where N = len(boxes1)
+ and M = len(boxes2)
+ """
+ # degenerate boxes gives inf / nan results
+ # so do an early check
+ assert (boxes1[:, 2:] >= boxes1[:, :2]).all()
+ assert (boxes2[:, 2:] >= boxes2[:, :2]).all()
+ # except:
+ # import ipdb; ipdb.set_trace()
+ iou, union = box_iou(boxes1, boxes2)
+
+ lt = torch.min(boxes1[:, None, :2], boxes2[:, :2])
+ rb = torch.max(boxes1[:, None, 2:], boxes2[:, 2:])
+
+ wh = (rb - lt).clamp(min=0) # [N,M,2]
+ area = wh[:, :, 0] * wh[:, :, 1]
+
+ return iou - (area - union) / (area + 1e-6)
+
+
+
+# modified from torchvision to also return the union
+def box_iou_pairwise(boxes1, boxes2):
+ area1 = box_area(boxes1)
+ area2 = box_area(boxes2)
+
+ lt = torch.max(boxes1[:, :2], boxes2[:, :2]) # [N,2]
+ rb = torch.min(boxes1[:, 2:], boxes2[:, 2:]) # [N,2]
+
+ wh = (rb - lt).clamp(min=0) # [N,2]
+ inter = wh[:, 0] * wh[:, 1] # [N]
+
+ union = area1 + area2 - inter
+
+ iou = inter / union
+ return iou, union
+
+
+def generalized_box_iou_pairwise(boxes1, boxes2):
+ """
+ Generalized IoU from https://giou.stanford.edu/
+ Input:
+ - boxes1, boxes2: N,4
+ Output:
+ - giou: N, 4
+ """
+ # degenerate boxes gives inf / nan results
+ # so do an early check
+ assert (boxes1[:, 2:] >= boxes1[:, :2]).all()
+ assert (boxes2[:, 2:] >= boxes2[:, :2]).all()
+ assert boxes1.shape == boxes2.shape
+ iou, union = box_iou_pairwise(boxes1, boxes2) # N, 4
+
+ lt = torch.min(boxes1[:, :2], boxes2[:, :2])
+ rb = torch.max(boxes1[:, 2:], boxes2[:, 2:])
+
+ wh = (rb - lt).clamp(min=0) # [N,2]
+ area = wh[:, 0] * wh[:, 1]
+
+ return iou - (area - union) / area
+
+def masks_to_boxes(masks):
+ """Compute the bounding boxes around the provided masks
+ The masks should be in format [N, H, W] where N is the number of masks, (H, W) are the spatial dimensions.
+ Returns a [N, 4] tensors, with the boxes in xyxy format
+ """
+ if masks.numel() == 0:
+ return torch.zeros((0, 4), device=masks.device)
+
+ h, w = masks.shape[-2:]
+
+ y = torch.arange(0, h, dtype=torch.float)
+ x = torch.arange(0, w, dtype=torch.float)
+ y, x = torch.meshgrid(y, x)
+
+ x_mask = (masks * x.unsqueeze(0))
+ x_max = x_mask.flatten(1).max(-1)[0]
+ x_min = x_mask.masked_fill(~(masks.bool()), 1e8).flatten(1).min(-1)[0]
+
+ y_mask = (masks * y.unsqueeze(0))
+ y_max = y_mask.flatten(1).max(-1)[0]
+ y_min = y_mask.masked_fill(~(masks.bool()), 1e8).flatten(1).min(-1)[0]
+
+ return torch.stack([x_min, y_min, x_max, y_max], 1)
+
+if __name__ == '__main__':
+ x = torch.rand(5, 4)
+ y = torch.rand(3, 4)
+ iou, union = box_iou(x, y)
\ No newline at end of file
diff --git a/annotator/OneFormer/oneformer/utils/events.py b/annotator/OneFormer/oneformer/utils/events.py
new file mode 100644
index 0000000000000000000000000000000000000000..d1d27ac6ecef656f1aa86649ceacb54470765821
--- /dev/null
+++ b/annotator/OneFormer/oneformer/utils/events.py
@@ -0,0 +1,120 @@
+import os
+import wandb
+from detectron2.utils import comm
+from detectron2.utils.events import EventWriter, get_event_storage
+
+
+def setup_wandb(cfg, args):
+ if comm.is_main_process():
+ init_args = {
+ k.lower(): v
+ for k, v in cfg.WANDB.items()
+ if isinstance(k, str) and k not in ["config"]
+ }
+ # only include most related part to avoid too big table
+ # TODO: add configurable params to select which part of `cfg` should be saved in config
+ if "config_exclude_keys" in init_args:
+ init_args["config"] = cfg
+ init_args["config"]["cfg_file"] = args.config_file
+ else:
+ init_args["config"] = {
+ "model": cfg.MODEL,
+ "solver": cfg.SOLVER,
+ "cfg_file": args.config_file,
+ }
+ if ("name" not in init_args) or (init_args["name"] is None):
+ init_args["name"] = os.path.basename(args.config_file)
+ else:
+ init_args["name"] = init_args["name"] + '_' + os.path.basename(args.config_file)
+ wandb.init(**init_args)
+
+
+class BaseRule(object):
+ def __call__(self, target):
+ return target
+
+
+class IsIn(BaseRule):
+ def __init__(self, keyword: str):
+ self.keyword = keyword
+
+ def __call__(self, target):
+ return self.keyword in target
+
+
+class Prefix(BaseRule):
+ def __init__(self, keyword: str):
+ self.keyword = keyword
+
+ def __call__(self, target):
+ return "/".join([self.keyword, target])
+
+
+class WandbWriter(EventWriter):
+ """
+ Write all scalars to a tensorboard file.
+ """
+
+ def __init__(self):
+ """
+ Args:
+ log_dir (str): the directory to save the output events
+ kwargs: other arguments passed to `torch.utils.tensorboard.SummaryWriter(...)`
+ """
+ self._last_write = -1
+ self._group_rules = [
+ (IsIn("/"), BaseRule()),
+ (IsIn("loss"), Prefix("train")),
+ ]
+
+ def write(self):
+
+ storage = get_event_storage()
+
+ def _group_name(scalar_name):
+ for (rule, op) in self._group_rules:
+ if rule(scalar_name):
+ return op(scalar_name)
+ return scalar_name
+
+ stats = {
+ _group_name(name): scalars[0]
+ for name, scalars in storage.latest().items()
+ if scalars[1] > self._last_write
+ }
+ if len(stats) > 0:
+ self._last_write = max([v[1] for k, v in storage.latest().items()])
+
+ # storage.put_{image,histogram} is only meant to be used by
+ # tensorboard writer. So we access its internal fields directly from here.
+ if len(storage._vis_data) >= 1:
+ stats["image"] = [
+ wandb.Image(img, caption=img_name)
+ for img_name, img, step_num in storage._vis_data
+ ]
+ # Storage stores all image data and rely on this writer to clear them.
+ # As a result it assumes only one writer will use its image data.
+ # An alternative design is to let storage store limited recent
+ # data (e.g. only the most recent image) that all writers can access.
+ # In that case a writer may not see all image data if its period is long.
+ storage.clear_images()
+
+ if len(storage._histograms) >= 1:
+
+ def create_bar(tag, bucket_limits, bucket_counts, **kwargs):
+ data = [
+ [label, val] for (label, val) in zip(bucket_limits, bucket_counts)
+ ]
+ table = wandb.Table(data=data, columns=["label", "value"])
+ return wandb.plot.bar(table, "label", "value", title=tag)
+
+ stats["hist"] = [create_bar(**params) for params in storage._histograms]
+
+ storage.clear_histograms()
+
+ if len(stats) == 0:
+ return
+ wandb.log(stats, step=storage.iter)
+
+ def close(self):
+ wandb.finish()
\ No newline at end of file
diff --git a/annotator/OneFormer/oneformer/utils/misc.py b/annotator/OneFormer/oneformer/utils/misc.py
new file mode 100644
index 0000000000000000000000000000000000000000..f2bca7733278c3a4b1f145bd7e5da23683b74961
--- /dev/null
+++ b/annotator/OneFormer/oneformer/utils/misc.py
@@ -0,0 +1,197 @@
+# Copyright (c) Facebook, Inc. and its affiliates.
+# Modified by Bowen Cheng from https://github.com/facebookresearch/detr/blob/master/util/misc.py
+"""
+Misc functions, including distributed helpers.
+
+Mostly copy-paste from torchvision references.
+"""
+from typing import List, Optional
+
+import torch
+import torch.distributed as dist
+import torchvision
+from torch import Tensor
+import warnings
+import torch.nn.functional as F
+import math
+
+def inverse_sigmoid(x, eps=1e-3):
+ x = x.clamp(min=0, max=1)
+ x1 = x.clamp(min=eps)
+ x2 = (1 - x).clamp(min=eps)
+ return torch.log(x1/x2)
+
+def _no_grad_trunc_normal_(tensor, mean, std, a, b):
+ # Cut & paste from PyTorch official master until it's in a few official releases - RW
+ # Method based on https://people.sc.fsu.edu/~jburkardt/presentations/truncated_normal.pdf
+ def norm_cdf(x):
+ # Computes standard normal cumulative distribution function
+ return (1. + math.erf(x / math.sqrt(2.))) / 2.
+
+ if (mean < a - 2 * std) or (mean > b + 2 * std):
+ warnings.warn("mean is more than 2 std from [a, b] in nn.init.trunc_normal_. "
+ "The distribution of values may be incorrect.",
+ stacklevel=2)
+
+ with torch.no_grad():
+ # Values are generated by using a truncated uniform distribution and
+ # then using the inverse CDF for the normal distribution.
+ # Get upper and lower cdf values
+ l = norm_cdf((a - mean) / std)
+ u = norm_cdf((b - mean) / std)
+
+ # Uniformly fill tensor with values from [l, u], then translate to
+ # [2l-1, 2u-1].
+ tensor.uniform_(2 * l - 1, 2 * u - 1)
+
+ # Use inverse cdf transform for normal distribution to get truncated
+ # standard normal
+ tensor.erfinv_()
+
+ # Transform to proper mean, std
+ tensor.mul_(std * math.sqrt(2.))
+ tensor.add_(mean)
+
+ # Clamp to ensure it's in the proper range
+ tensor.clamp_(min=a, max=b)
+ return tensor
+
+def trunc_normal_(tensor, mean=0., std=1., a=-2., b=2.):
+ # type: (Tensor, float, float, float, float) -> Tensor
+ r"""Fills the input Tensor with values drawn from a truncated
+ normal distribution. The values are effectively drawn from the
+ normal distribution :math:`\mathcal{N}(\text{mean}, \text{std}^2)`
+ with values outside :math:`[a, b]` redrawn until they are within
+ the bounds. The method used for generating the random values works
+ best when :math:`a \leq \text{mean} \leq b`.
+ Args:
+ tensor: an n-dimensional `torch.Tensor`
+ mean: the mean of the normal distribution
+ std: the standard deviation of the normal distribution
+ a: the minimum cutoff value
+ b: the maximum cutoff value
+ Examples:
+ >>> w = torch.empty(3, 5)
+ >>> nn.init.trunc_normal_(w)
+ """
+ return _no_grad_trunc_normal_(tensor, mean, std, a, b)
+
+def resize(input,
+ size=None,
+ scale_factor=None,
+ mode='nearest',
+ align_corners=None,
+ warning=True):
+ if warning:
+ if size is not None and align_corners:
+ input_h, input_w = tuple(int(x) for x in input.shape[2:])
+ output_h, output_w = tuple(int(x) for x in size)
+ if output_h > input_h or output_w > output_h:
+ if ((output_h > 1 and output_w > 1 and input_h > 1
+ and input_w > 1) and (output_h - 1) % (input_h - 1)
+ and (output_w - 1) % (input_w - 1)):
+ warnings.warn(
+ f'When align_corners={align_corners}, '
+ 'the output would more aligned if '
+ f'input size {(input_h, input_w)} is `x+1` and '
+ f'out size {(output_h, output_w)} is `nx+1`')
+ if isinstance(size, torch.Size):
+ size = tuple(int(x) for x in size)
+ return F.interpolate(input, size, scale_factor, mode, align_corners)
+
+def _max_by_axis(the_list):
+ # type: (List[List[int]]) -> List[int]
+ maxes = the_list[0]
+ for sublist in the_list[1:]:
+ for index, item in enumerate(sublist):
+ maxes[index] = max(maxes[index], item)
+ return maxes
+
+
+class NestedTensor(object):
+ def __init__(self, tensors, mask: Optional[Tensor]):
+ self.tensors = tensors
+ self.mask = mask
+
+ def to(self, device):
+ # type: (Device) -> NestedTensor # noqa
+ cast_tensor = self.tensors.to(device)
+ mask = self.mask
+ if mask is not None:
+ assert mask is not None
+ cast_mask = mask.to(device)
+ else:
+ cast_mask = None
+ return NestedTensor(cast_tensor, cast_mask)
+
+ def decompose(self):
+ return self.tensors, self.mask
+
+ def __repr__(self):
+ return str(self.tensors)
+
+
+def nested_tensor_from_tensor_list(tensor_list: List[Tensor]):
+ # TODO make this more general
+ if tensor_list[0].ndim == 3:
+ if torchvision._is_tracing():
+ # nested_tensor_from_tensor_list() does not export well to ONNX
+ # call _onnx_nested_tensor_from_tensor_list() instead
+ return _onnx_nested_tensor_from_tensor_list(tensor_list)
+
+ # TODO make it support different-sized images
+ max_size = _max_by_axis([list(img.shape) for img in tensor_list])
+ # min_size = tuple(min(s) for s in zip(*[img.shape for img in tensor_list]))
+ batch_shape = [len(tensor_list)] + max_size
+ b, c, h, w = batch_shape
+ dtype = tensor_list[0].dtype
+ device = tensor_list[0].device
+ tensor = torch.zeros(batch_shape, dtype=dtype, device=device)
+ mask = torch.ones((b, h, w), dtype=torch.bool, device=device)
+ for img, pad_img, m in zip(tensor_list, tensor, mask):
+ pad_img[: img.shape[0], : img.shape[1], : img.shape[2]].copy_(img)
+ m[: img.shape[1], : img.shape[2]] = False
+ else:
+ raise ValueError("not supported")
+ return NestedTensor(tensor, mask)
+
+
+# _onnx_nested_tensor_from_tensor_list() is an implementation of
+# nested_tensor_from_tensor_list() that is supported by ONNX tracing.
+@torch.jit.unused
+def _onnx_nested_tensor_from_tensor_list(tensor_list: List[Tensor]) -> NestedTensor:
+ max_size = []
+ for i in range(tensor_list[0].dim()):
+ max_size_i = torch.max(
+ torch.stack([img.shape[i] for img in tensor_list]).to(torch.float32)
+ ).to(torch.int64)
+ max_size.append(max_size_i)
+ max_size = tuple(max_size)
+
+ # work around for
+ # pad_img[: img.shape[0], : img.shape[1], : img.shape[2]].copy_(img)
+ # m[: img.shape[1], :img.shape[2]] = False
+ # which is not yet supported in onnx
+ padded_imgs = []
+ padded_masks = []
+ for img in tensor_list:
+ padding = [(s1 - s2) for s1, s2 in zip(max_size, tuple(img.shape))]
+ padded_img = torch.nn.functional.pad(img, (0, padding[2], 0, padding[1], 0, padding[0]))
+ padded_imgs.append(padded_img)
+
+ m = torch.zeros_like(img[0], dtype=torch.int, device=img.device)
+ padded_mask = torch.nn.functional.pad(m, (0, padding[2], 0, padding[1]), "constant", 1)
+ padded_masks.append(padded_mask.to(torch.bool))
+
+ tensor = torch.stack(padded_imgs)
+ mask = torch.stack(padded_masks)
+
+ return NestedTensor(tensor, mask=mask)
+
+
+def is_dist_avail_and_initialized():
+ if not dist.is_available():
+ return False
+ if not dist.is_initialized():
+ return False
+ return True
diff --git a/annotator/OneFormer/oneformer/utils/pos_embed.py b/annotator/OneFormer/oneformer/utils/pos_embed.py
new file mode 100644
index 0000000000000000000000000000000000000000..aa11d60db65fa98c140e7d75bdf985ff7ece8f18
--- /dev/null
+++ b/annotator/OneFormer/oneformer/utils/pos_embed.py
@@ -0,0 +1,122 @@
+# --------------------------------------------------------
+# Position embedding utils
+# --------------------------------------------------------
+
+from typing import Tuple
+
+import numpy as np
+import torch
+
+
+# --------------------------------------------------------
+# 2D sine-cosine position embedding
+# References:
+# Transformer: https://github.com/tensorflow/models/blob/master/official/nlp/transformer/model_utils.py
+# MoCo v3: https://github.com/facebookresearch/moco-v3
+# --------------------------------------------------------
+def get_2d_sincos_pos_embed(embed_dim, grid_size, cls_token=False):
+ """
+ grid_size: int of the grid height and width
+ return:
+ pos_embed: [grid_size*grid_size, embed_dim] or [1+grid_size*grid_size, embed_dim] (w/ or w/o cls_token)
+ """
+ grid_h = np.arange(grid_size, dtype=np.float32)
+ grid_w = np.arange(grid_size, dtype=np.float32)
+ grid = np.meshgrid(grid_w, grid_h) # here w goes first
+ grid = np.stack(grid, axis=0)
+
+ grid = grid.reshape([2, 1, grid_size, grid_size])
+ pos_embed = get_2d_sincos_pos_embed_from_grid(embed_dim, grid)
+ if cls_token:
+ pos_embed = np.concatenate([np.zeros([1, embed_dim]), pos_embed], axis=0)
+ return pos_embed
+
+
+def get_2d_sincos_pos_embed_from_grid(embed_dim, grid):
+ assert embed_dim % 2 == 0
+
+ # use half of dimensions to encode grid_h
+ emb_h = get_1d_sincos_pos_embed_from_grid(embed_dim // 2, grid[0]) # (H*W, D/2)
+ emb_w = get_1d_sincos_pos_embed_from_grid(embed_dim // 2, grid[1]) # (H*W, D/2)
+
+ emb = np.concatenate([emb_h, emb_w], axis=1) # (H*W, D)
+ return emb
+
+
+def get_1d_sincos_pos_embed_from_grid(embed_dim, pos):
+ """
+ embed_dim: output dimension for each position
+ pos: a list of positions to be encoded: size (M,)
+ out: (M, D)
+ """
+ assert embed_dim % 2 == 0
+ omega = np.arange(embed_dim // 2, dtype=np.float)
+ omega /= embed_dim / 2.0
+ omega = 1.0 / 10000 ** omega # (D/2,)
+
+ pos = pos.reshape(-1) # (M,)
+ out = np.einsum("m,d->md", pos, omega) # (M, D/2), outer product
+
+ emb_sin = np.sin(out) # (M, D/2)
+ emb_cos = np.cos(out) # (M, D/2)
+
+ emb = np.concatenate([emb_sin, emb_cos], axis=1) # (M, D)
+ return emb
+
+
+# --------------------------------------------------------
+# Interpolate position embeddings for high-resolution
+# References:
+# DeiT: https://github.com/facebookresearch/deit
+# --------------------------------------------------------
+def interpolate_pos_embed(model, checkpoint_model, pos_embed_key):
+ if pos_embed_key in checkpoint_model:
+ pos_embed_checkpoint = checkpoint_model[pos_embed_key]
+ embedding_size = pos_embed_checkpoint.shape[-1]
+ num_patches = model.num_patches
+ if pos_embed_key.startswith("decoder"):
+ num_extra_tokens = model.decoder_pos_embed.shape[-2] - num_patches
+ else:
+ num_extra_tokens = model.pos_embed.shape[-2] - num_patches
+ # height (== width) for the checkpoint position embedding
+ orig_size = int((pos_embed_checkpoint.shape[-2] - num_extra_tokens) ** 0.5)
+ # height (== width) for the new position embedding
+ new_size = int(num_patches ** 0.5)
+ # class_token and dist_token are kept unchanged
+ if orig_size != new_size:
+ print(
+ "Position interpolate from %dx%d to %dx%d"
+ % (orig_size, orig_size, new_size, new_size)
+ )
+ extra_tokens = pos_embed_checkpoint[:, :num_extra_tokens]
+ # only the position tokens are interpolated
+ pos_tokens = pos_embed_checkpoint[:, num_extra_tokens:]
+ pos_tokens = pos_tokens.reshape(
+ -1, orig_size, orig_size, embedding_size
+ ).permute(0, 3, 1, 2)
+ pos_tokens = torch.nn.functional.interpolate(
+ pos_tokens,
+ size=(new_size, new_size),
+ mode="bicubic",
+ align_corners=False,
+ )
+ pos_tokens = pos_tokens.permute(0, 2, 3, 1).flatten(1, 2)
+ new_pos_embed = torch.cat((extra_tokens, pos_tokens), dim=1)
+ checkpoint_model[pos_embed_key] = new_pos_embed
+
+
+def interpolate_pos_embed_online(
+ pos_embed, orig_size: Tuple[int], new_size: Tuple[int], num_extra_tokens: int
+):
+ extra_tokens = pos_embed[:, :num_extra_tokens]
+ pos_tokens = pos_embed[:, num_extra_tokens:]
+ embedding_size = pos_tokens.shape[-1]
+ pos_tokens = pos_tokens.reshape(
+ -1, orig_size[0], orig_size[1], embedding_size
+ ).permute(0, 3, 1, 2)
+ pos_tokens = torch.nn.functional.interpolate(
+ pos_tokens, size=new_size, mode="bicubic", align_corners=False,
+ )
+ pos_tokens = pos_tokens.permute(0, 2, 3, 1).flatten(1, 2)
+ new_pos_embed = torch.cat((extra_tokens, pos_tokens), dim=1)
+ return new_pos_embed
diff --git a/annotator/util.py b/annotator/util.py
new file mode 100644
index 0000000000000000000000000000000000000000..8fd735a5d3d5dd900d0b84b37a4ca123f9408ec0
--- /dev/null
+++ b/annotator/util.py
@@ -0,0 +1,41 @@
+import numpy as np
+import cv2
+import os
+
+
+annotator_ckpts_path = os.path.join(os.path.dirname(__file__), 'ckpts')
+
+
+def HWC3(x):
+ assert x.dtype == np.uint8
+ if x.ndim == 2:
+ x = x[:, :, None]
+ assert x.ndim == 3
+ H, W, C = x.shape
+ assert C == 1 or C == 3 or C == 4
+ if C == 3:
+ return x
+ if C == 1:
+ return np.concatenate([x, x, x], axis=2)
+ if C == 4:
+ color = x[:, :, 0:3].astype(np.float32)
+ alpha = x[:, :, 3:4].astype(np.float32) / 255.0
+ y = color * alpha + 255.0 * (1.0 - alpha)
+ y = y.clip(0, 255).astype(np.uint8)
+ return y
+
+
+def resize_image(input_image, resolution):
+ if len(input_image.shape) == 3:
+ H, W, C = input_image.shape
+ else:
+ H, W = input_image.shape
+ H = float(H)
+ W = float(W)
+ k = float(resolution) / min(H, W)
+ H *= k
+ W *= k
+ H = int(np.round(H / 64.0)) * 64
+ W = int(np.round(W / 64.0)) * 64
+ img = cv2.resize(input_image, (W, H), interpolation=cv2.INTER_LANCZOS4 if k > 1 else cv2.INTER_AREA)
+ return img
diff --git a/app.py b/app.py
new file mode 100644
index 0000000000000000000000000000000000000000..9bfcb2baaeb955dab7f80352e33075b8a55e72ba
--- /dev/null
+++ b/app.py
@@ -0,0 +1,413 @@
+
+
+import einops
+import gradio as gr
+import numpy as np
+import torch
+import random
+import os
+import subprocess
+import shlex
+
+from huggingface_hub import hf_hub_url, hf_hub_download
+from share import *
+
+from pytorch_lightning import seed_everything
+from annotator.util import resize_image, HWC3
+from annotator.OneFormer import OneformerSegmenter
+from cldm.model import create_model, load_state_dict
+from cldm.ddim_hacked import DDIMSamplerSpaCFG
+from ldm.models.autoencoder import DiagonalGaussianDistribution
+
+urls = {
+ 'shi-labs/oneformer_coco_swin_large': ['150_16_swin_l_oneformer_coco_100ep.pth'],
+ 'PAIR/PAIR-diffusion-sdv15-coco-finetune': ['pair_diffusion_epoch62.ckpt']
+}
+
+WTS_DICT = {
+
+}
+
+if os.path.exists('checkpoints') == False:
+ os.mkdir('checkpoints')
+for repo in urls:
+ files = urls[repo]
+ for file in files:
+ url = hf_hub_url(repo, file)
+ name_ckp = url.split('/')[-1]
+ save_path = os.path.join('checkpoints', name_ckp)
+ if os.path.exists(save_path) == False:
+ WTS_DICT[repo] = hf_hub_download(repo_id=repo, filename=file, token=os.environ.get("ACCESS_TOKEN"))
+
+print(WTS_DICT)
+apply_segmentor = OneformerSegmenter(WTS_DICT['shi-labs/oneformer_coco_swin_large'])
+
+model = create_model('./configs/sap_fixed_hintnet_v15.yaml').cpu()
+model.load_state_dict(load_state_dict(WTS_DICT['PAIR/PAIR-diffusion-sdv15-coco-finetune'], location='cuda'))
+model = model.cuda()
+ddim_sampler = DDIMSamplerSpaCFG(model)
+_COLORS = []
+save_memory = False
+
+def gen_color():
+ color = tuple(np.round(np.random.choice(range(256), size=3), 3))
+ if color not in _COLORS and np.mean(color) != 0.0:
+ _COLORS.append(color)
+ else:
+ gen_color()
+
+
+for _ in range(300):
+ gen_color()
+
+
+class ImageComp:
+ def __init__(self, edit_operation):
+ self.input_img = None
+ self.input_pmask = None
+ self.input_segmask = None
+
+ self.ref_img = None
+ self.ref_pmask = None
+ self.ref_segmask = None
+
+ self.H = None
+ self.W = None
+ self.baseoutput = None
+ self.kernel = np.ones((5, 5), np.uint8)
+ self.edit_operation = edit_operation
+
+ def init_input_canvas(self, img):
+ img = HWC3(img)
+ img = resize_image(img, 512)
+ detected_mask = apply_segmentor(img, 'panoptic')[0]
+ detected_seg = apply_segmentor(img, 'semantic')
+
+ self.input_img = img
+ self.input_pmask = detected_mask
+ self.input_segmask = detected_seg
+ self.H = img.shape[0]
+ self.W = img.shape[1]
+
+ detected_mask = detected_mask.cpu().numpy()
+
+ uni = np.unique(detected_mask)
+ color_mask = np.zeros((detected_mask.shape[0], detected_mask.shape[1], 3))
+ for i in uni:
+ color_mask[detected_mask == i] = _COLORS[i]
+
+ output = color_mask*0.8 + img * 0.2
+ self.baseoutput = output.astype(np.uint8)
+ return self.baseoutput
+
+ def init_ref_canvas(self, img):
+ img = HWC3(img)
+ img = resize_image(img, 512)
+ detected_mask = apply_segmentor(img, 'panoptic')[0]
+ detected_seg = apply_segmentor(img, 'semantic')
+
+ self.ref_img = img
+ self.ref_pmask = detected_mask
+ self.ref_segmask = detected_seg
+
+ detected_mask = detected_mask.cpu().numpy()
+
+ uni = np.unique(detected_mask)
+ color_mask = np.zeros((detected_mask.shape[0], detected_mask.shape[1], 3))
+ for i in uni:
+ color_mask[detected_mask == i] = _COLORS[i]
+
+ output = color_mask*0.8 + img * 0.2
+ self.baseoutput = output.astype(np.uint8)
+ return self.baseoutput
+
+ def process_mask(self, mask, panoptic_mask, segmask):
+ panoptic_mask_ = panoptic_mask + 1
+ mask_ = resize_image(mask['mask'][:, :, 0], min(panoptic_mask.shape))
+ mask_ = torch.tensor(mask_)
+ maski = torch.zeros_like(mask_).cuda()
+ maski[mask_ > 127] = 1
+ mask = maski * panoptic_mask_
+ unique_ids, counts = torch.unique(mask, return_counts=True)
+ # print(unique_ids, counts)
+ mask_id = unique_ids[torch.argmax(counts[1:]) + 1]
+ final_mask = torch.zeros(mask.shape).cuda()
+ final_mask[panoptic_mask_ == mask_id] = 1
+
+ obj_class = maski * (segmask + 1)
+ unique_ids, counts = torch.unique(obj_class, return_counts=True)
+ obj_class = unique_ids[torch.argmax(counts[1:]) + 1] - 1
+ return final_mask, obj_class
+
+
+ def edit_app(self, input_mask, ref_mask, whole_ref):
+ input_pmask = self.input_pmask
+ input_segmask = self.input_segmask
+
+ if whole_ref:
+ reference_mask = torch.ones(self.ref_pmask.shape).cuda()
+ else:
+ reference_mask, _ = self.process_mask(ref_mask, self.ref_pmask, self.ref_segmask)
+
+ edit_mask, _ = self.process_mask(input_mask, self.input_pmask, self.input_segmask)
+ ma = torch.max(input_pmask)
+ input_pmask[edit_mask == 1] = ma + 1
+ return reference_mask, input_pmask, input_segmask, edit_mask, ma
+
+
+ def edit(self, input_mask, ref_mask, whole_ref=False, inter=1):
+ input_img = (self.input_img/127.5 - 1)
+ input_img = torch.from_numpy(input_img.astype(np.float32)).cuda().unsqueeze(0).permute(0,3,1,2)
+
+ reference_img = (self.ref_img/127.5 - 1)
+ reference_img = torch.from_numpy(reference_img.astype(np.float32)).cuda().unsqueeze(0).permute(0,3,1,2)
+
+ reference_mask, input_pmask, input_segmask, region_mask, ma = self.edit_app(input_mask, ref_mask, whole_ref)
+
+ input_pmask = input_pmask.float().cuda().unsqueeze(0).unsqueeze(1)
+ _, mean_feat_inpt, one_hot_inpt, empty_mask_flag_inpt = model.get_appearance(input_img, input_pmask, return_all=True)
+
+ reference_mask = reference_mask.float().cuda().unsqueeze(0).unsqueeze(1)
+ _, mean_feat_ref, _, _ = model.get_appearance(reference_img, reference_mask, return_all=True)
+
+ if mean_feat_ref.shape[1] > 1:
+ mean_feat_inpt[:, ma + 1] = (1 - inter) * mean_feat_inpt[:, ma + 1] + inter*mean_feat_ref[:, 1]
+
+ splatted_feat = torch.einsum('nmc, nmhw->nchw', mean_feat_inpt, one_hot_inpt)
+ appearance = torch.nn.functional.normalize(splatted_feat) #l2 normaliz
+ input_segmask = ((input_segmask+1)/ 127.5 - 1.0).cuda().unsqueeze(0).unsqueeze(1)
+ structure = torch.nn.functional.interpolate(input_segmask, (self.H, self.W))
+ appearance = torch.nn.functional.interpolate(appearance, (self.H, self.W))
+
+
+ return structure, appearance, region_mask, input_img
+
+ def process(self, input_mask, ref_mask, prompt, a_prompt, n_prompt,
+ num_samples, ddim_steps, guess_mode, strength,
+ scale_s, scale_f, scale_t, seed, eta, masking=True,whole_ref=False,inter=1):
+ structure, appearance, mask, img = self.edit(input_mask, ref_mask,
+ whole_ref=whole_ref, inter=inter)
+
+ null_structure = torch.zeros(structure.shape).cuda() - 1
+ null_appearance = torch.zeros(appearance.shape).cuda()
+
+ null_control = torch.cat([null_structure, null_appearance], dim=1)
+ structure_control = torch.cat([structure, null_appearance], dim=1)
+ full_control = torch.cat([structure, appearance], dim=1)
+
+ null_control = torch.cat([null_control for _ in range(num_samples)], dim=0)
+ structure_control = torch.cat([structure_control for _ in range(num_samples)], dim=0)
+ full_control = torch.cat([full_control for _ in range(num_samples)], dim=0)
+
+ #Masking for local edit
+ if not masking:
+ mask, x0 = None, None
+ else:
+ x0 = model.encode_first_stage(img)
+ x0 = x0.sample() if isinstance(x0, DiagonalGaussianDistribution) else x0 # todo: check if we can set random number
+ x0 = x0 * model.scale_factor
+ mask = 1 - torch.tensor(mask).unsqueeze(0).unsqueeze(1).cuda()
+ mask = torch.nn.functional.interpolate(mask, x0.shape[2:]).float()
+
+ if seed == -1:
+ seed = random.randint(0, 65535)
+ seed_everything(seed)
+
+ scale = [scale_s, scale_f, scale_t]
+ print(scale)
+ if save_memory:
+ model.low_vram_shift(is_diffusing=False)
+ # uc_cross = model.get_unconditional_conditioning(num_samples)
+ uc_cross = model.get_learned_conditioning([n_prompt] * num_samples)
+ cond = {"c_concat": [full_control], "c_crossattn": [model.get_learned_conditioning([prompt + ', ' + a_prompt] * num_samples)]}
+ un_cond = {"c_concat": None if guess_mode else [null_control], "c_crossattn": [uc_cross]}
+ un_cond_struct = {"c_concat": None if guess_mode else [structure_control], "c_crossattn": [uc_cross]}
+ un_cond_struct_app = {"c_concat": None if guess_mode else [full_control], "c_crossattn": [uc_cross]}
+
+ shape = (4, self.H // 8, self.W // 8)
+
+ if save_memory:
+ model.low_vram_shift(is_diffusing=True)
+
+ model.control_scales = [strength * (0.825 ** float(12 - i)) for i in range(13)] if guess_mode else ([strength] * 13) # Magic number. IDK why. Perhaps because 0.825**12<0.01 but 0.826**12>0.01
+ samples, _ = ddim_sampler.sample(ddim_steps, num_samples,
+ shape, cond, verbose=False, eta=eta,
+ unconditional_guidance_scale=scale, mask=mask, x0=x0,
+ unconditional_conditioning=[un_cond, un_cond_struct, un_cond_struct_app ])
+
+ if save_memory:
+ model.low_vram_shift(is_diffusing=False)
+
+ x_samples = (model.decode_first_stage(samples) + 1) * 127.5
+ x_samples = (einops.rearrange(x_samples, 'b c h w -> b h w c')).cpu().numpy().clip(0, 255).astype(np.uint8)
+
+ results = [x_samples[i] for i in range(num_samples)]
+ return [] + results
+
+
+css = """
+ h1 {
+ text-align: center;
+}
+
+"""
+
+def create_app_demo():
+
+ with gr.Row():
+ gr.Markdown("## Object Level Appearance Editing")
+ print('first row')
+ with gr.Row():
+ gr.HTML(
+ """
+
+
+ Instructions
+
+ - 1. Upload an Input Image.
+ - 2. Mark one of segmented objects in the Select Object to Edit tab.
+ - 3. Upload an Reference Image.
+ - 4. Mark one of segmented objects in the Select Reference Object tab, whose appearance needs to used in the selected input object.
+ - 5. Enter a prompt and press Run button. (A very simple would also work)
+
+
+
""")
+ print('second row')
+ with gr.Column():
+ with gr.Row():
+ img_edit = ImageComp('edit_app')
+ with gr.Column():
+ btn1 = gr.Button("Input Image")
+ input_image = gr.Image(source='upload', label='Input Image', type="numpy",)
+ with gr.Column():
+ btn2 = gr.Button("Select Object to Edit")
+ input_mask = gr.Image(source="upload", label='Select Object in Input Image', type="numpy", tool="sketch")
+ input_image.change(fn=img_edit.init_input_canvas, inputs=[input_image], outputs=[input_mask], queue=False)
+
+ # with gr.Row():
+ with gr.Column():
+ btn3 = gr.Button("Reference Image")
+ ref_img = gr.Image(source='upload', label='Reference Image', type="numpy")
+ with gr.Column():
+ btn4 = gr.Button("Select Reference Object")
+ reference_mask = gr.Image(source="upload", label='Select Object in Refernce Image', type="numpy", tool="sketch")
+
+ ref_img.change(fn=img_edit.init_ref_canvas, inputs=[ref_img], outputs=[reference_mask], queue=False)
+
+ with gr.Row():
+ prompt = gr.Textbox(label="Prompt", value='A picture of truck')
+ with gr.Column():
+ interpolation = gr.Slider(label="Mixing ratio of appearance from reference object", minimum=0.1, maximum=1, value=1.0, step=0.1)
+ whole_ref = gr.Checkbox(label='Use whole reference Image for appearance (Only useful for style transfers)', value=False)
+ with gr.Row():
+ run_button = gr.Button(label="Run")
+
+ with gr.Row():
+ result_gallery = gr.Gallery(label='Output', show_label=False, elem_id="gallery").style(grid=4, height='auto')
+
+ with gr.Accordion("Advanced options", open=False):
+ num_samples = gr.Slider(label="Images", minimum=1, maximum=12, value=1, step=1)
+ image_resolution = gr.Slider(label="Image Resolution", minimum=512, maximum=512, value=512, step=64)
+ strength = gr.Slider(label="Control Strength", minimum=0.0, maximum=2.0, value=1.0, step=0.01)
+ guess_mode = gr.Checkbox(label='Guess Mode', value=False)
+ ddim_steps = gr.Slider(label="Steps", minimum=1, maximum=100, value=20, step=1)
+ scale_t = gr.Slider(label="Guidance Scale Text", minimum=0.1, maximum=30.0, value=9.0, step=0.1)
+ scale_f = gr.Slider(label="Guidance Scale Appearance", minimum=0.1, maximum=30.0, value=8.0, step=0.1)
+ scale_s = gr.Slider(label="Guidance Scale Structure", minimum=0.1, maximum=30.0, value=5.0, step=0.1)
+ seed = gr.Slider(label="Seed", minimum=-1, maximum=2147483647, step=1, randomize=True)
+ eta = gr.Number(label="eta (DDIM)", value=0.0)
+ masking = gr.Checkbox(label='Only edit the local region', value=True)
+ a_prompt = gr.Textbox(label="Added Prompt", value='best quality, extremely detailed')
+ n_prompt = gr.Textbox(label="Negative Prompt",
+ value='longbody, lowres, bad anatomy, bad hands, missing fingers, extra digit, fewer digits, cropped, worst quality, low quality')
+
+ with gr.Column():
+ gr.Examples(
+ examples=[['A picture of a truck', 'assets/truck.png','assets/truck2.jpeg', 892905419, 9, 7.6, 4.3],
+ ['A picture of a ironman', 'assets/ironman.webp','assets/hulk.jpeg', 709736989, 9, 7.7, 8.1],
+ ['A person skiing', 'assets/ski.jpg','assets/lava.jpg', 917723061, 9, 7.5, 4.4]],
+ inputs=[prompt, input_image, ref_img, seed, scale_t, scale_f, scale_s],
+ outputs=None,
+ fn=None,
+ cache_examples=False,
+ )
+ ips = [input_mask, reference_mask, prompt, a_prompt, n_prompt, num_samples, ddim_steps, guess_mode, strength,
+ scale_s, scale_f, scale_t, seed, eta, masking, whole_ref, interpolation]
+ run_button.click(fn=img_edit.process, inputs=ips, outputs=[result_gallery])
+
+
+
+def create_struct_demo():
+ with gr.Row():
+ gr.Markdown("## Edit Structure (Comming soon!)")
+
+def create_both_demo():
+ with gr.Row():
+ gr.Markdown("## Edit Structure and Appearance Together (Comming soon!)")
+
+
+
+block = gr.Blocks(css=css).queue()
+with block:
+ gr.HTML(
+ """
+
+
+ PAIR Diffusion
+
+
+
+ 1Picsart AI Resarch (PAIR), 2UTrenton, 3UT Austin, 4UC Berkeley, 5UOregon, 6UIUC
+
+
+ We built Structure and Appearance Paired (PAIR) Diffusion that allows reference image-guided appearance manipulation and
+ structure editing of an image at an object level. PAIR diffusion models an image as composition of multiple objects and enables control
+ over structure and appearance properties of the object. Describing object appearances using text can be challenging and ambiguous, PAIR Diffusion
+ enables a user to control the appearance of an object using images. User can further use text as another degree of control for appearance.
+ Having fine-grained control over appearance and structure at object level can be beneficial for future works in video and 3D beside image editing,
+ where we need to have consistent appearance across time in case of video or across various viewing positions in case of 3D.
+
+
+
+ """)
+
+ with gr.Tab('Edit Appearance'):
+ create_app_demo()
+ with gr.Tab('Edit Structure'):
+ create_struct_demo()
+ with gr.Tab('Edit Both'):
+ create_both_demo()
+
+
+
+
+
+print('Launching')
+block.launch(debug=True)
+
+
+# import gradio as gr
+# from transformers import pipeline
+
+# # pipeline = pipeline(task="image-classification", model="julien-c/hotdog-not-hotdog")
+
+# def predict(image):
+# return {"hot dog": 0.1 for p in range(2)}
+
+# gr.Interface(
+# predict,
+# inputs=gr.inputs.Image(label="Upload hot dog candidate", type="filepath"),
+# outputs=gr.outputs.Label(num_top_classes=2),
+# title="Hot Dog? Or Not?",
+# ).launch()
\ No newline at end of file
diff --git a/assets/hulk.jpeg b/assets/hulk.jpeg
new file mode 100644
index 0000000000000000000000000000000000000000..5790e3baf7277cfe5a55a9341cddc50667f81ba3
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diff --git a/assets/ironman.webp b/assets/ironman.webp
new file mode 100644
index 0000000000000000000000000000000000000000..7d8a44de9141b89a834d40f3d8378fce2c83e6d2
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diff --git a/assets/lava.jpg b/assets/lava.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..8378d998e3e1b24c3541e054b41206f28e3a21cc
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diff --git a/assets/ski.jpg b/assets/ski.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..34b963fd138539ab5e6748809eaf6fe14ca27bba
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diff --git a/assets/truck.png b/assets/truck.png
new file mode 100644
index 0000000000000000000000000000000000000000..d6ef3ecc1483c7158f8f25e840a706d991e1eadb
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diff --git a/assets/truck2.jpeg b/assets/truck2.jpeg
new file mode 100644
index 0000000000000000000000000000000000000000..0e54795dad6c894f6f85976338d15019304b4297
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diff --git a/cldm/__init__.py b/cldm/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/cldm/cldm.py b/cldm/cldm.py
new file mode 100644
index 0000000000000000000000000000000000000000..0a9e6ab954cdec7994e9bd8adffce15d99eb34e8
--- /dev/null
+++ b/cldm/cldm.py
@@ -0,0 +1,648 @@
+import einops
+import torch
+import torch as th
+import torch.nn as nn
+import math
+
+from ldm.modules.diffusionmodules.util import (
+ conv_nd,
+ linear,
+ zero_module,
+ timestep_embedding,
+)
+import torchvision
+from einops import rearrange, repeat
+from torchvision.utils import make_grid
+from ldm.modules.attention import SpatialTransformer
+from ldm.modules.diffusionmodules.openaimodel import UNetModel, TimestepEmbedSequential, ResBlock, Downsample, AttentionBlock
+from ldm.models.diffusion.ddpm import LatentDiffusion
+from ldm.util import log_txt_as_img, exists, instantiate_from_config
+from ldm.models.diffusion.ddim import DDIMSampler
+
+
+class VGGPerceptualLoss(torch.nn.Module):
+ def __init__(self, resize=True):
+ super(VGGPerceptualLoss, self).__init__()
+ blocks = []
+ vgg_model = torchvision.models.vgg16(pretrained=True)
+ print('Loaded VGG weights')
+ blocks.append(vgg_model.features[:4].eval())
+ blocks.append(vgg_model.features[4:9].eval())
+ blocks.append(vgg_model.features[9:16].eval())
+ blocks.append(vgg_model.features[16:23].eval())
+
+ for bl in blocks:
+ for p in bl.parameters():
+ p.requires_grad = False
+ self.blocks = torch.nn.ModuleList(blocks)
+ self.transform = torch.nn.functional.interpolate
+ self.resize = resize
+ self.register_buffer("mean", torch.tensor([0.485, 0.456, 0.406]).view(1, 3, 1, 1))
+ self.register_buffer("std", torch.tensor([0.229, 0.224, 0.225]).view(1, 3, 1, 1))
+ print('Initialized VGG model')
+
+ def forward(self, input, feature_layers=[0, 1, 2, 3], style_layers=[1,]):
+ if input.shape[1] != 3:
+ input = input.repeat(1, 3, 1, 1)
+ target = target.repeat(1, 3, 1, 1)
+ input = (input-self.mean) / self.std
+ if self.resize:
+ input = self.transform(input, mode='bilinear', size=(224, 224), align_corners=False)
+ x = input
+ gram_matrices_all = []
+ feats = []
+ for i, block in enumerate(self.blocks):
+ x = block(x)
+ if i in style_layers:
+ feats.append(x)
+
+ return feats
+
+
+
+class ControlledUnetModel(UNetModel):
+ def forward(self, x, timesteps=None, context=None, control=None, only_mid_control=False, **kwargs):
+ hs = []
+ with torch.no_grad():
+ t_emb = timestep_embedding(timesteps, self.model_channels, repeat_only=False)
+ emb = self.time_embed(t_emb)
+ h = x.type(self.dtype)
+ for module in self.input_blocks:
+ h = module(h, emb, context)
+ hs.append(h)
+ h = self.middle_block(h, emb, context)
+
+ if control is not None:
+ h += control.pop()
+
+ for i, module in enumerate(self.output_blocks):
+ if only_mid_control or control is None:
+ h = torch.cat([h, hs.pop()], dim=1)
+ else:
+ h = torch.cat([h, hs.pop() + control.pop()], dim=1)
+ h = module(h, emb, context)
+
+ h = h.type(x.dtype)
+ return self.out(h)
+
+
+class ControlNet(nn.Module):
+ def __init__(
+ self,
+ image_size,
+ in_channels,
+ model_channels,
+ hint_channels,
+ num_res_blocks,
+ attention_resolutions,
+ dropout=0,
+ channel_mult=(1, 2, 4, 8),
+ conv_resample=True,
+ dims=2,
+ use_checkpoint=False,
+ use_fp16=False,
+ num_heads=-1,
+ num_head_channels=-1,
+ num_heads_upsample=-1,
+ use_scale_shift_norm=False,
+ resblock_updown=False,
+ use_new_attention_order=False,
+ use_spatial_transformer=False, # custom transformer support
+ transformer_depth=1, # custom transformer support
+ context_dim=None, # custom transformer support
+ n_embed=None, # custom support for prediction of discrete ids into codebook of first stage vq model
+ legacy=True,
+ disable_self_attentions=None,
+ num_attention_blocks=None,
+ disable_middle_self_attn=False,
+ use_linear_in_transformer=False,
+ ):
+ super().__init__()
+ if use_spatial_transformer:
+ assert context_dim is not None, 'Fool!! You forgot to include the dimension of your cross-attention conditioning...'
+
+ if context_dim is not None:
+ assert use_spatial_transformer, 'Fool!! You forgot to use the spatial transformer for your cross-attention conditioning...'
+ from omegaconf.listconfig import ListConfig
+ if type(context_dim) == ListConfig:
+ context_dim = list(context_dim)
+
+ if num_heads_upsample == -1:
+ num_heads_upsample = num_heads
+
+ if num_heads == -1:
+ assert num_head_channels != -1, 'Either num_heads or num_head_channels has to be set'
+
+ if num_head_channels == -1:
+ assert num_heads != -1, 'Either num_heads or num_head_channels has to be set'
+
+ self.dims = dims
+ self.image_size = image_size
+ self.in_channels = in_channels
+ self.model_channels = model_channels
+ if isinstance(num_res_blocks, int):
+ self.num_res_blocks = len(channel_mult) * [num_res_blocks]
+ else:
+ if len(num_res_blocks) != len(channel_mult):
+ raise ValueError("provide num_res_blocks either as an int (globally constant) or "
+ "as a list/tuple (per-level) with the same length as channel_mult")
+ self.num_res_blocks = num_res_blocks
+ if disable_self_attentions is not None:
+ # should be a list of booleans, indicating whether to disable self-attention in TransformerBlocks or not
+ assert len(disable_self_attentions) == len(channel_mult)
+ if num_attention_blocks is not None:
+ assert len(num_attention_blocks) == len(self.num_res_blocks)
+ assert all(map(lambda i: self.num_res_blocks[i] >= num_attention_blocks[i], range(len(num_attention_blocks))))
+ print(f"Constructor of UNetModel received num_attention_blocks={num_attention_blocks}. "
+ f"This option has LESS priority than attention_resolutions {attention_resolutions}, "
+ f"i.e., in cases where num_attention_blocks[i] > 0 but 2**i not in attention_resolutions, "
+ f"attention will still not be set.")
+
+ self.attention_resolutions = attention_resolutions
+ self.dropout = dropout
+ self.channel_mult = channel_mult
+ self.conv_resample = conv_resample
+ self.use_checkpoint = use_checkpoint
+ self.dtype = th.float16 if use_fp16 else th.float32
+ self.num_heads = num_heads
+ self.num_head_channels = num_head_channels
+ self.num_heads_upsample = num_heads_upsample
+ self.predict_codebook_ids = n_embed is not None
+
+ time_embed_dim = model_channels * 4
+ self.time_embed = nn.Sequential(
+ linear(model_channels, time_embed_dim),
+ nn.SiLU(),
+ linear(time_embed_dim, time_embed_dim),
+ )
+
+ self.input_blocks = nn.ModuleList(
+ [
+ TimestepEmbedSequential(
+ conv_nd(dims, in_channels, model_channels, 3, padding=1)
+ )
+ ]
+ )
+ self.zero_convs = nn.ModuleList([self.make_zero_conv(model_channels)])
+
+ self.input_hint_block = TimestepEmbedSequential(
+ conv_nd(dims, hint_channels, 16, 3, padding=1),
+ nn.SiLU(),
+ conv_nd(dims, 16, 16, 3, padding=1),
+ nn.SiLU(),
+ conv_nd(dims, 16, 32, 3, padding=1, stride=2),
+ nn.SiLU(),
+ conv_nd(dims, 32, 32, 3, padding=1),
+ nn.SiLU(),
+ conv_nd(dims, 32, 96, 3, padding=1, stride=2),
+ nn.SiLU(),
+ conv_nd(dims, 96, 96, 3, padding=1),
+ nn.SiLU(),
+ conv_nd(dims, 96, 256, 3, padding=1, stride=2),
+ nn.SiLU(),
+ zero_module(conv_nd(dims, 256, model_channels, 3, padding=1))
+ )
+
+ self._feature_size = model_channels
+ input_block_chans = [model_channels]
+ ch = model_channels
+ ds = 1
+ for level, mult in enumerate(channel_mult):
+ for nr in range(self.num_res_blocks[level]):
+ layers = [
+ ResBlock(
+ ch,
+ time_embed_dim,
+ dropout,
+ out_channels=mult * model_channels,
+ dims=dims,
+ use_checkpoint=use_checkpoint,
+ use_scale_shift_norm=use_scale_shift_norm,
+ )
+ ]
+ ch = mult * model_channels
+ if ds in attention_resolutions:
+ if num_head_channels == -1:
+ dim_head = ch // num_heads
+ else:
+ num_heads = ch // num_head_channels
+ dim_head = num_head_channels
+ if legacy:
+ # num_heads = 1
+ dim_head = ch // num_heads if use_spatial_transformer else num_head_channels
+ if exists(disable_self_attentions):
+ disabled_sa = disable_self_attentions[level]
+ else:
+ disabled_sa = False
+
+ if not exists(num_attention_blocks) or nr < num_attention_blocks[level]:
+ layers.append(
+ AttentionBlock(
+ ch,
+ use_checkpoint=use_checkpoint,
+ num_heads=num_heads,
+ num_head_channels=dim_head,
+ use_new_attention_order=use_new_attention_order,
+ ) if not use_spatial_transformer else SpatialTransformer(
+ ch, num_heads, dim_head, depth=transformer_depth, context_dim=context_dim,
+ disable_self_attn=disabled_sa, use_linear=use_linear_in_transformer,
+ use_checkpoint=use_checkpoint
+ )
+ )
+ self.input_blocks.append(TimestepEmbedSequential(*layers))
+ self.zero_convs.append(self.make_zero_conv(ch))
+ self._feature_size += ch
+ input_block_chans.append(ch)
+ if level != len(channel_mult) - 1:
+ out_ch = ch
+ self.input_blocks.append(
+ TimestepEmbedSequential(
+ ResBlock(
+ ch,
+ time_embed_dim,
+ dropout,
+ out_channels=out_ch,
+ dims=dims,
+ use_checkpoint=use_checkpoint,
+ use_scale_shift_norm=use_scale_shift_norm,
+ down=True,
+ )
+ if resblock_updown
+ else Downsample(
+ ch, conv_resample, dims=dims, out_channels=out_ch
+ )
+ )
+ )
+ ch = out_ch
+ input_block_chans.append(ch)
+ self.zero_convs.append(self.make_zero_conv(ch))
+ ds *= 2
+ self._feature_size += ch
+
+ if num_head_channels == -1:
+ dim_head = ch // num_heads
+ else:
+ num_heads = ch // num_head_channels
+ dim_head = num_head_channels
+ if legacy:
+ # num_heads = 1
+ dim_head = ch // num_heads if use_spatial_transformer else num_head_channels
+ self.middle_block = TimestepEmbedSequential(
+ ResBlock(
+ ch,
+ time_embed_dim,
+ dropout,
+ dims=dims,
+ use_checkpoint=use_checkpoint,
+ use_scale_shift_norm=use_scale_shift_norm,
+ ),
+ AttentionBlock(
+ ch,
+ use_checkpoint=use_checkpoint,
+ num_heads=num_heads,
+ num_head_channels=dim_head,
+ use_new_attention_order=use_new_attention_order,
+ ) if not use_spatial_transformer else SpatialTransformer( # always uses a self-attn
+ ch, num_heads, dim_head, depth=transformer_depth, context_dim=context_dim,
+ disable_self_attn=disable_middle_self_attn, use_linear=use_linear_in_transformer,
+ use_checkpoint=use_checkpoint
+ ),
+ ResBlock(
+ ch,
+ time_embed_dim,
+ dropout,
+ dims=dims,
+ use_checkpoint=use_checkpoint,
+ use_scale_shift_norm=use_scale_shift_norm,
+ ),
+ )
+ self.middle_block_out = self.make_zero_conv(ch)
+ self._feature_size += ch
+
+ def make_zero_conv(self, channels):
+ return TimestepEmbedSequential(zero_module(conv_nd(self.dims, channels, channels, 1, padding=0)))
+
+ def forward(self, x, hint, timesteps, context, **kwargs):
+ t_emb = timestep_embedding(timesteps, self.model_channels, repeat_only=False)
+ emb = self.time_embed(t_emb)
+ guided_hint = self.input_hint_block(hint, emb, context, x.shape)
+
+ outs = []
+
+ h = x.type(self.dtype)
+ for module, zero_conv in zip(self.input_blocks, self.zero_convs):
+ if guided_hint is not None:
+ h = module(h, emb, context)
+ h += guided_hint
+ guided_hint = None
+ else:
+ h = module(h, emb, context)
+ outs.append(zero_conv(h, emb, context))
+
+ h = self.middle_block(h, emb, context)
+ outs.append(self.middle_block_out(h, emb, context))
+
+ return outs
+
+class Interpolate(nn.Module):
+ def __init__(self, size, mode):
+ super(Interpolate, self).__init__()
+ self.interp = torch.nn.functional.interpolate
+ self.size = size
+ self.mode = mode
+ self.factor = 8
+
+ def forward(self, x):
+ h,w = x.shape[2]//self.factor, x.shape[3]//self.factor
+ x = self.interp(x, size=(h,w), mode=self.mode)
+ return x
+
+class ControlNetSAP(ControlNet):
+ def __init__(
+ self,
+ hint_channels,
+ model_channels,
+ input_hint_block='fixed',
+ size = 64,
+ mode='nearest',
+ *args,
+ **kwargs
+ ):
+ super().__init__( hint_channels=hint_channels, model_channels=model_channels, *args, **kwargs)
+ #hint channels are atleast 128 dims
+
+ if input_hint_block == 'learnable':
+ ch = 2 ** (int(math.log2(hint_channels)))
+ self.input_hint_block = TimestepEmbedSequential(
+ conv_nd(self.dims, hint_channels, hint_channels, 3, padding=1),
+ nn.SiLU(),
+ conv_nd(self.dims, hint_channels, 2*ch, 3, padding=1, stride=2),
+ nn.SiLU(),
+ conv_nd(self.dims, 2*ch, 2*ch, 3, padding=1),
+ nn.SiLU(),
+ conv_nd(self.dims, 2*ch, 2*ch, 3, padding=1, stride=2),
+ nn.SiLU(),
+ conv_nd(self.dims, 2*ch, 2*ch, 3, padding=1),
+ nn.SiLU(),
+ conv_nd(self.dims, 2*ch, model_channels, 3, padding=1, stride=2),
+ nn.SiLU(),
+ zero_module(conv_nd(self.dims, model_channels, model_channels, 3, padding=1))
+ )
+ else:
+ print("Only interpolation")
+ self.input_hint_block = TimestepEmbedSequential(
+ Interpolate(size, mode),
+ zero_module(conv_nd(self.dims, hint_channels, model_channels, 3, padding=1)))
+
+
+class ControlLDM(LatentDiffusion):
+
+ def __init__(self, control_stage_config, control_key, only_mid_control, *args, **kwargs):
+ super().__init__(*args, **kwargs)
+ self.control_model = instantiate_from_config(control_stage_config)
+ self.control_key = control_key
+ self.only_mid_control = only_mid_control
+ self.control_scales = [1.0] * 13
+
+ @torch.no_grad()
+ def get_input(self, batch, k, bs=None, *args, **kwargs):
+ x, c = super().get_input(batch, self.first_stage_key, *args, **kwargs)
+ control = batch[self.control_key]
+ if bs is not None:
+ control = control[:bs]
+ control = control.to(self.device)
+ control = einops.rearrange(control, 'b h w c -> b c h w')
+ control = control.to(memory_format=torch.contiguous_format).float()
+ return x, dict(c_crossattn=[c], c_concat=[control])
+
+ def apply_model(self, x_noisy, t, cond, *args, **kwargs):
+ assert isinstance(cond, dict)
+ diffusion_model = self.model.diffusion_model
+
+ cond_txt = torch.cat(cond['c_crossattn'], 1)
+
+ if cond['c_concat'] is None:
+ eps = diffusion_model(x=x_noisy, timesteps=t, context=cond_txt, control=None, only_mid_control=self.only_mid_control)
+ else:
+ control = self.control_model(x=x_noisy, hint=torch.cat(cond['c_concat'], 1), timesteps=t, context=cond_txt)
+ control = [c * scale for c, scale in zip(control, self.control_scales)]
+ eps = diffusion_model(x=x_noisy, timesteps=t, context=cond_txt, control=control, only_mid_control=self.only_mid_control)
+
+ return eps
+
+ @torch.no_grad()
+ def get_unconditional_conditioning(self, N):
+ return self.get_learned_conditioning([""] * N)
+
+ @torch.no_grad()
+ def log_images(self, batch, N=4, n_row=2, sample=False, ddim_steps=50, ddim_eta=0.0, return_keys=None,
+ quantize_denoised=True, inpaint=True, plot_denoise_rows=False, plot_progressive_rows=True,
+ plot_diffusion_rows=False, unconditional_guidance_scale=9.0, unconditional_guidance_label=None,
+ use_ema_scope=True,
+ **kwargs):
+ use_ddim = ddim_steps is not None
+
+ log = dict()
+ z, c = self.get_input(batch, self.first_stage_key, bs=N)
+ c_cat, c = c["c_concat"][0][:N], c["c_crossattn"][0][:N]
+ N = min(z.shape[0], N)
+ n_row = min(z.shape[0], n_row)
+ log["reconstruction"] = self.decode_first_stage(z)
+ log["control"] = c_cat * 2.0 - 1.0
+ log["conditioning"] = log_txt_as_img((512, 512), batch[self.cond_stage_key], size=16)
+
+ if plot_diffusion_rows:
+ # get diffusion row
+ diffusion_row = list()
+ z_start = z[:n_row]
+ for t in range(self.num_timesteps):
+ if t % self.log_every_t == 0 or t == self.num_timesteps - 1:
+ t = repeat(torch.tensor([t]), '1 -> b', b=n_row)
+ t = t.to(self.device).long()
+ noise = torch.randn_like(z_start)
+ z_noisy = self.q_sample(x_start=z_start, t=t, noise=noise)
+ diffusion_row.append(self.decode_first_stage(z_noisy))
+
+ diffusion_row = torch.stack(diffusion_row) # n_log_step, n_row, C, H, W
+ diffusion_grid = rearrange(diffusion_row, 'n b c h w -> b n c h w')
+ diffusion_grid = rearrange(diffusion_grid, 'b n c h w -> (b n) c h w')
+ diffusion_grid = make_grid(diffusion_grid, nrow=diffusion_row.shape[0])
+ log["diffusion_row"] = diffusion_grid
+
+ if sample:
+ # get denoise row
+ samples, z_denoise_row = self.sample_log(cond={"c_concat": [c_cat], "c_crossattn": [c]},
+ batch_size=N, ddim=use_ddim,
+ ddim_steps=ddim_steps, eta=ddim_eta)
+ x_samples = self.decode_first_stage(samples)
+ log["samples"] = x_samples
+ if plot_denoise_rows:
+ denoise_grid = self._get_denoise_row_from_list(z_denoise_row)
+ log["denoise_row"] = denoise_grid
+
+ if unconditional_guidance_scale > 1.0:
+ uc_cross = self.get_unconditional_conditioning(N)
+ uc_cat = c_cat # torch.zeros_like(c_cat)
+ uc_full = {"c_concat": [uc_cat], "c_crossattn": [uc_cross]}
+ samples_cfg, _ = self.sample_log(cond={"c_concat": [c_cat], "c_crossattn": [c]},
+ batch_size=N, ddim=use_ddim,
+ ddim_steps=ddim_steps, eta=ddim_eta,
+ unconditional_guidance_scale=unconditional_guidance_scale,
+ unconditional_conditioning=uc_full,
+ )
+ x_samples_cfg = self.decode_first_stage(samples_cfg)
+ log[f"samples_cfg_scale_{unconditional_guidance_scale:.2f}"] = x_samples_cfg
+
+ return log
+
+ @torch.no_grad()
+ def sample_log(self, cond, batch_size, ddim, ddim_steps, **kwargs):
+ ddim_sampler = DDIMSampler(self)
+ b, c, h, w = cond["c_concat"][0].shape
+ shape = (self.channels, h // 8, w // 8)
+ samples, intermediates = ddim_sampler.sample(ddim_steps, batch_size, shape, cond, verbose=False, **kwargs)
+ return samples, intermediates
+
+ def configure_optimizers(self):
+ lr = self.learning_rate
+ params = list(self.control_model.parameters())
+ if not self.sd_locked:
+ params += list(self.model.diffusion_model.output_blocks.parameters())
+ params += list(self.model.diffusion_model.out.parameters())
+ opt = torch.optim.AdamW(params, lr=lr)
+ return opt
+
+ def low_vram_shift(self, is_diffusing):
+ if is_diffusing:
+ self.model = self.model.cuda()
+ self.control_model = self.control_model.cuda()
+ self.first_stage_model = self.first_stage_model.cpu()
+ self.cond_stage_model = self.cond_stage_model.cpu()
+ else:
+ self.model = self.model.cpu()
+ self.control_model = self.control_model.cpu()
+ self.first_stage_model = self.first_stage_model.cuda()
+ self.cond_stage_model = self.cond_stage_model.cuda()
+
+
+class SAP(ControlLDM):
+ @torch.no_grad()
+ def __init__(self,control_stage_config, control_key, only_mid_control, *args, **kwargs):
+ super().__init__(control_stage_config=control_stage_config,
+ control_key=control_key,
+ only_mid_control=only_mid_control,
+ *args, **kwargs)
+ self.appearance_net = VGGPerceptualLoss().to(self.device)
+ print("Loaded VGG model")
+
+ def get_appearance(self, img, mask, return_all=False):
+ img = (img + 1) * 0.5
+ feat = self.appearance_net(img)[0]
+ empty_mask_flag = torch.sum(mask, dim=(1,2,3)) == 0
+
+
+ empty_appearance = torch.zeros(feat.shape).to(self.device)
+ mask = torch.nn.functional.interpolate(mask.float(), (feat.shape[2:])).long()
+ one_hot = torch.nn.functional.one_hot(mask[:,0]).permute(0,3,1,2).float()
+
+ feat = torch.einsum('nchw, nmhw->nmchw', feat, one_hot)
+ feat = torch.sum(feat, dim=(3,4))
+ norm = torch.sum(one_hot, dim=(2,3)) + 1e-6 #nm
+ mean_feat = feat/norm[:,:,None] #nmc
+ mean_feat[:, 0] = torch.zeros(mean_feat[:,0].shape).to(self.device) #set edges in panopitc mask to empty appearance feature
+
+ splatted_feat = torch.einsum('nmc, nmhw->nchw', mean_feat, one_hot)
+ splatted_feat[empty_mask_flag] = empty_appearance[empty_mask_flag]
+ splatted_feat = torch.nn.functional.normalize(splatted_feat) #l2 normalize on c dim
+
+ if return_all:
+ return splatted_feat, mean_feat, one_hot, empty_mask_flag
+
+ return splatted_feat
+
+ def get_input(self, batch, k, bs=None, *args, **kwargs):
+ z, c, x_orig, x_recon = super(ControlLDM, self).get_input(batch, self.first_stage_key, return_first_stage_outputs=True , *args, **kwargs)
+ structure = batch['seg'].unsqueeze(1)
+ mask = batch['mask'].unsqueeze(1).to(self.device)
+ appearance = self.get_appearance(x_orig, mask)
+ if bs is not None:
+ structure = structure[:bs]
+ appearance = appearance[:bs]
+
+ structure = structure.to(self.device)
+ appearance = appearance.to(self.device)
+ structure = structure.to(memory_format=torch.contiguous_format).float()
+ appearance = appearance.to(memory_format=torch.contiguous_format).float()
+ structure = torch.nn.functional.interpolate(structure, x_orig.shape[2:])
+ appearance = torch.nn.functional.interpolate(appearance, x_orig.shape[2:])
+ control = torch.cat([structure, appearance], dim=1)
+ return z, dict(c_crossattn=[c], c_concat=[control])
+
+ @torch.no_grad()
+ def log_images(self, batch, N=4, n_row=2, sample=False, ddim_steps=50, ddim_eta=0.0, return_keys=None,
+ quantize_denoised=True, inpaint=True, plot_denoise_rows=False, plot_progressive_rows=False,
+ plot_diffusion_rows=False, unconditional_guidance_scale=9.0, unconditional_guidance_label=None,
+ use_ema_scope=True,
+ **kwargs):
+ use_ddim = ddim_steps is not None
+
+ log = dict()
+ z, c = self.get_input(batch, self.first_stage_key, bs=N)
+ c_cat, c = c["c_concat"][0][:N,], c["c_crossattn"][0][:N]
+ N = min(z.shape[0], N)
+ n_row = min(z.shape[0], n_row)
+ log["reconstruction"] = self.decode_first_stage(z)
+ log["control"] = c_cat[:, :1]
+ log["conditioning"] = log_txt_as_img((512, 512), batch[self.cond_stage_key], size=16)
+
+ if plot_diffusion_rows:
+ # get diffusion row
+ diffusion_row = list()
+ z_start = z[:n_row]
+ for t in range(self.num_timesteps):
+ if t % self.log_every_t == 0 or t == self.num_timesteps - 1:
+ t = repeat(torch.tensor([t]), '1 -> b', b=n_row)
+ t = t.to(self.device).long()
+ noise = torch.randn_like(z_start)
+ z_noisy = self.q_sample(x_start=z_start, t=t, noise=noise)
+ diffusion_row.append(self.decode_first_stage(z_noisy))
+
+ diffusion_row = torch.stack(diffusion_row) # n_log_step, n_row, C, H, W
+ diffusion_grid = rearrange(diffusion_row, 'n b c h w -> b n c h w')
+ diffusion_grid = rearrange(diffusion_grid, 'b n c h w -> (b n) c h w')
+ diffusion_grid = make_grid(diffusion_grid, nrow=diffusion_row.shape[0])
+ log["diffusion_row"] = diffusion_grid
+
+ if plot_progressive_rows:
+ with self.ema_scope("Plotting Progressives"):
+ img, progressives = self.progressive_denoising({"c_concat": [c_cat], "c_crossattn": [c]},
+ shape=(self.channels, self.image_size, self.image_size),
+ batch_size=N)
+ prog_row = self._get_denoise_row_from_list(progressives, desc="Progressive Generation")
+ log["progressive_row"] = prog_row
+
+ if sample:
+ # get denoise row
+ samples, z_denoise_row = self.sample_log(cond={"c_concat": [c_cat], "c_crossattn": [c]},
+ batch_size=N, ddim=use_ddim,
+ ddim_steps=ddim_steps, eta=ddim_eta)
+ x_samples = self.decode_first_stage(samples)
+ log["samples"] = x_samples
+ if plot_denoise_rows:
+ denoise_grid = self._get_denoise_row_from_list(z_denoise_row)
+ log["denoise_row"] = denoise_grid
+
+ if unconditional_guidance_scale > 1.0:
+ uc_cross = self.get_unconditional_conditioning(N)
+ uc_cat = c_cat # torch.zeros_like(c_cat)
+ uc_full = {"c_concat": [uc_cat], "c_crossattn": [uc_cross]}
+ samples_cfg, _ = self.sample_log(cond={"c_concat": [c_cat], "c_crossattn": [c]},
+ batch_size=N, ddim=use_ddim,
+ ddim_steps=ddim_steps, eta=ddim_eta,
+ unconditional_guidance_scale=unconditional_guidance_scale,
+ unconditional_conditioning=uc_full,
+ )
+ x_samples_cfg = self.decode_first_stage(samples_cfg)
+ log[f"samples_cfg_scale_{unconditional_guidance_scale:.2f}"] = x_samples_cfg
+
+ return log
diff --git a/cldm/data.py b/cldm/data.py
new file mode 100644
index 0000000000000000000000000000000000000000..38ae14a1a3d0ec0be874211e4931959c67afee28
--- /dev/null
+++ b/cldm/data.py
@@ -0,0 +1,99 @@
+import os
+import torch
+import pytorch_lightning as pl
+from omegaconf import OmegaConf
+from functools import partial
+from ldm.util import instantiate_from_config
+from torch.utils.data import random_split, DataLoader, Dataset, Subset
+
+class WrappedDataset(Dataset):
+ """Wraps an arbitrary object with __len__ and __getitem__ into a pytorch dataset"""
+
+ def __init__(self, dataset):
+ self.data = dataset
+
+ def __len__(self):
+ return len(self.data)
+
+ def __getitem__(self, idx):
+ return self.data[idx]
+
+class DataModuleFromConfig(pl.LightningDataModule):
+ def __init__(self, batch_size, train=None, validation=None, test=None, predict=None,
+ wrap=False, num_workers=None, shuffle_test_loader=False, use_worker_init_fn=False,
+ shuffle_val_dataloader=False):
+ super().__init__()
+ self.batch_size = batch_size
+ self.dataset_configs = dict()
+ self.num_workers = num_workers if num_workers is not None else batch_size * 2
+ self.use_worker_init_fn = use_worker_init_fn
+ if train is not None:
+ self.dataset_configs["train"] = train
+ self.train_dataloader = self._train_dataloader
+ if validation is not None:
+ self.dataset_configs["validation"] = validation
+ self.val_dataloader = partial(self._val_dataloader, shuffle=shuffle_val_dataloader)
+ if test is not None:
+ self.dataset_configs["test"] = test
+ self.test_dataloader = partial(self._test_dataloader, shuffle=shuffle_test_loader)
+ if predict is not None:
+ self.dataset_configs["predict"] = predict
+ self.predict_dataloader = self._predict_dataloader
+ self.wrap = wrap
+
+ def prepare_data(self):
+ for data_cfg in self.dataset_configs.values():
+ instantiate_from_config(data_cfg)
+
+ def setup(self, stage=None):
+ self.datasets = dict(
+ (k, instantiate_from_config(self.dataset_configs[k]))
+ for k in self.dataset_configs)
+ if self.wrap:
+ for k in self.datasets:
+ self.datasets[k] = WrappedDataset(self.datasets[k])
+
+ def _train_dataloader(self):
+ init_fn = None
+ return DataLoader(self.datasets["train"], batch_size=self.batch_size,
+ num_workers=self.num_workers, shuffle= True,
+ worker_init_fn=init_fn)
+
+ def _val_dataloader(self, shuffle=False):
+ init_fn = None
+ return DataLoader(self.datasets["validation"],
+ batch_size=self.batch_size,
+ num_workers=self.num_workers,
+ worker_init_fn=init_fn,
+ shuffle=shuffle)
+
+ def _test_dataloader(self, shuffle=False):
+ is_iterable_dataset = isinstance(self.datasets['train'], Txt2ImgIterableBaseDataset)
+ if is_iterable_dataset or self.use_worker_init_fn:
+ init_fn = worker_init_fn
+ else:
+ init_fn = None
+
+ # do not shuffle dataloader for iterable dataset
+ shuffle = shuffle and (not is_iterable_dataset)
+
+ return DataLoader(self.datasets["test"], batch_size=self.batch_size,
+ num_workers=self.num_workers, worker_init_fn=init_fn, shuffle=shuffle)
+
+ def _predict_dataloader(self, shuffle=False):
+ if isinstance(self.datasets['predict'], Txt2ImgIterableBaseDataset) or self.use_worker_init_fn:
+ init_fn = worker_init_fn
+ else:
+ init_fn = None
+ return DataLoader(self.datasets["predict"], batch_size=self.batch_size,
+ num_workers=self.num_workers, worker_init_fn=init_fn)
+
+
+def create_data(config):
+ data = instantiate_from_config(config.data)
+ # NOTE according to https://pytorch-lightning.readthedocs.io/en/latest/datamodules.html
+ # calling these ourselves should not be necessary but it is.
+ # lightning still takes care of proper multiprocessing though
+ data.prepare_data()
+ data.setup()
+ return data
\ No newline at end of file
diff --git a/cldm/ddim_hacked.py b/cldm/ddim_hacked.py
new file mode 100644
index 0000000000000000000000000000000000000000..478cb6e5c71a15ad6e7d16a04d20183f137c113b
--- /dev/null
+++ b/cldm/ddim_hacked.py
@@ -0,0 +1,375 @@
+"""SAMPLING ONLY."""
+
+import torch
+import numpy as np
+from tqdm import tqdm
+
+from ldm.modules.diffusionmodules.util import make_ddim_sampling_parameters, make_ddim_timesteps, noise_like, extract_into_tensor
+
+
+class DDIMSampler(object):
+ def __init__(self, model, schedule="linear", **kwargs):
+ super().__init__()
+ self.model = model
+ self.ddpm_num_timesteps = model.num_timesteps
+ self.schedule = schedule
+
+ def register_buffer(self, name, attr):
+ if type(attr) == torch.Tensor:
+ if attr.device != torch.device("cuda"):
+ attr = attr.to(torch.device("cuda"))
+ setattr(self, name, attr)
+
+ def make_schedule(self, ddim_num_steps, ddim_discretize="uniform", ddim_eta=0., verbose=True):
+ self.ddim_timesteps = make_ddim_timesteps(ddim_discr_method=ddim_discretize, num_ddim_timesteps=ddim_num_steps,
+ num_ddpm_timesteps=self.ddpm_num_timesteps,verbose=verbose)
+ alphas_cumprod = self.model.alphas_cumprod
+ assert alphas_cumprod.shape[0] == self.ddpm_num_timesteps, 'alphas have to be defined for each timestep'
+ to_torch = lambda x: x.clone().detach().to(torch.float32).to(self.model.device)
+
+ self.register_buffer('betas', to_torch(self.model.betas))
+ self.register_buffer('alphas_cumprod', to_torch(alphas_cumprod))
+ self.register_buffer('alphas_cumprod_prev', to_torch(self.model.alphas_cumprod_prev))
+
+ # calculations for diffusion q(x_t | x_{t-1}) and others
+ self.register_buffer('sqrt_alphas_cumprod', to_torch(np.sqrt(alphas_cumprod.cpu())))
+ self.register_buffer('sqrt_one_minus_alphas_cumprod', to_torch(np.sqrt(1. - alphas_cumprod.cpu())))
+ self.register_buffer('log_one_minus_alphas_cumprod', to_torch(np.log(1. - alphas_cumprod.cpu())))
+ self.register_buffer('sqrt_recip_alphas_cumprod', to_torch(np.sqrt(1. / alphas_cumprod.cpu())))
+ self.register_buffer('sqrt_recipm1_alphas_cumprod', to_torch(np.sqrt(1. / alphas_cumprod.cpu() - 1)))
+
+ # ddim sampling parameters
+ ddim_sigmas, ddim_alphas, ddim_alphas_prev = make_ddim_sampling_parameters(alphacums=alphas_cumprod.cpu(),
+ ddim_timesteps=self.ddim_timesteps,
+ eta=ddim_eta,verbose=verbose)
+ self.register_buffer('ddim_sigmas', ddim_sigmas)
+ self.register_buffer('ddim_alphas', ddim_alphas)
+ self.register_buffer('ddim_alphas_prev', ddim_alphas_prev)
+ self.register_buffer('ddim_sqrt_one_minus_alphas', np.sqrt(1. - ddim_alphas))
+ sigmas_for_original_sampling_steps = ddim_eta * torch.sqrt(
+ (1 - self.alphas_cumprod_prev) / (1 - self.alphas_cumprod) * (
+ 1 - self.alphas_cumprod / self.alphas_cumprod_prev))
+ self.register_buffer('ddim_sigmas_for_original_num_steps', sigmas_for_original_sampling_steps)
+
+ @torch.no_grad()
+ def sample(self,
+ S,
+ batch_size,
+ shape,
+ conditioning=None,
+ callback=None,
+ normals_sequence=None,
+ img_callback=None,
+ quantize_x0=False,
+ eta=0.,
+ mask=None,
+ x0=None,
+ temperature=1.,
+ noise_dropout=0.,
+ score_corrector=None,
+ corrector_kwargs=None,
+ verbose=True,
+ x_T=None,
+ log_every_t=100,
+ unconditional_guidance_scale=1.,
+ unconditional_conditioning=None, # this has to come in the same format as the conditioning, # e.g. as encoded tokens, ...
+ dynamic_threshold=None,
+ ucg_schedule=None,
+ **kwargs
+ ):
+ if conditioning is not None:
+ if isinstance(conditioning, dict):
+ ctmp = conditioning[list(conditioning.keys())[0]]
+ while isinstance(ctmp, list): ctmp = ctmp[0]
+ cbs = ctmp.shape[0]
+ if cbs != batch_size:
+ print(f"Warning: Got {cbs} conditionings but batch-size is {batch_size}")
+
+ elif isinstance(conditioning, list):
+ for ctmp in conditioning:
+ if ctmp.shape[0] != batch_size:
+ print(f"Warning: Got {cbs} conditionings but batch-size is {batch_size}")
+
+ else:
+ if conditioning.shape[0] != batch_size:
+ print(f"Warning: Got {conditioning.shape[0]} conditionings but batch-size is {batch_size}")
+
+ self.make_schedule(ddim_num_steps=S, ddim_eta=eta, verbose=verbose)
+ # sampling
+ C, H, W = shape
+ size = (batch_size, C, H, W)
+ print(f'Data shape for DDIM sampling is {size}, eta {eta}')
+
+ samples, intermediates = self.ddim_sampling(conditioning, size,
+ callback=callback,
+ img_callback=img_callback,
+ quantize_denoised=quantize_x0,
+ mask=mask, x0=x0,
+ ddim_use_original_steps=False,
+ noise_dropout=noise_dropout,
+ temperature=temperature,
+ score_corrector=score_corrector,
+ corrector_kwargs=corrector_kwargs,
+ x_T=x_T,
+ log_every_t=log_every_t,
+ unconditional_guidance_scale=unconditional_guidance_scale,
+ unconditional_conditioning=unconditional_conditioning,
+ dynamic_threshold=dynamic_threshold,
+ ucg_schedule=ucg_schedule
+ )
+ return samples, intermediates
+
+ @torch.no_grad()
+ def ddim_sampling(self, cond, shape,
+ x_T=None, ddim_use_original_steps=False,
+ callback=None, timesteps=None, quantize_denoised=False,
+ mask=None, x0=None, img_callback=None, log_every_t=100,
+ temperature=1., noise_dropout=0., score_corrector=None, corrector_kwargs=None,
+ unconditional_guidance_scale=1., unconditional_conditioning=None, dynamic_threshold=None,
+ ucg_schedule=None):
+ device = self.model.betas.device
+ b = shape[0]
+ if x_T is None:
+ img = torch.randn(shape, device=device)
+ else:
+ img = x_T
+
+ if timesteps is None:
+ timesteps = self.ddpm_num_timesteps if ddim_use_original_steps else self.ddim_timesteps
+ elif timesteps is not None and not ddim_use_original_steps:
+ subset_end = int(min(timesteps / self.ddim_timesteps.shape[0], 1) * self.ddim_timesteps.shape[0]) - 1
+ timesteps = self.ddim_timesteps[:subset_end]
+
+ intermediates = {'x_inter': [img], 'pred_x0': [img]}
+ time_range = reversed(range(0,timesteps)) if ddim_use_original_steps else np.flip(timesteps)
+ total_steps = timesteps if ddim_use_original_steps else timesteps.shape[0]
+ print(f"Running DDIM Sampling with {total_steps} timesteps")
+
+ iterator = tqdm(time_range, desc='DDIM Sampler', total=total_steps)
+
+ for i, step in enumerate(iterator):
+ index = total_steps - i - 1
+ ts = torch.full((b,), step, device=device, dtype=torch.long)
+
+ if mask is not None:
+ assert x0 is not None
+ img_orig = self.model.q_sample(x0, ts) # TODO: deterministic forward pass?
+ img = img_orig * mask + (1. - mask) * img
+
+ if ucg_schedule is not None:
+ assert len(ucg_schedule) == len(time_range)
+ unconditional_guidance_scale = ucg_schedule[i]
+
+ outs = self.p_sample_ddim(img, cond, ts, index=index, use_original_steps=ddim_use_original_steps,
+ quantize_denoised=quantize_denoised, temperature=temperature,
+ noise_dropout=noise_dropout, score_corrector=score_corrector,
+ corrector_kwargs=corrector_kwargs,
+ unconditional_guidance_scale=unconditional_guidance_scale,
+ unconditional_conditioning=unconditional_conditioning,
+ dynamic_threshold=dynamic_threshold)
+ img, pred_x0 = outs
+ if callback: callback(i)
+ if img_callback: img_callback(pred_x0, i)
+
+ if index % log_every_t == 0 or index == total_steps - 1:
+ intermediates['x_inter'].append(img)
+ intermediates['pred_x0'].append(pred_x0)
+
+ return img, intermediates
+
+ @torch.no_grad()
+ def p_sample_ddim(self, x, c, t, index, repeat_noise=False, use_original_steps=False, quantize_denoised=False,
+ temperature=1., noise_dropout=0., score_corrector=None, corrector_kwargs=None,
+ unconditional_guidance_scale=1., unconditional_conditioning=None,
+ dynamic_threshold=None):
+ b, *_, device = *x.shape, x.device
+
+ if unconditional_conditioning is None or unconditional_guidance_scale == 1.:
+ model_output = self.model.apply_model(x, t, c)
+ else:
+ model_t = self.model.apply_model(x, t, c)
+ model_uncond = self.model.apply_model(x, t, unconditional_conditioning)
+ model_output = model_uncond + unconditional_guidance_scale * (model_t - model_uncond)
+
+ if self.model.parameterization == "v":
+ e_t = self.model.predict_eps_from_z_and_v(x, t, model_output)
+ else:
+ e_t = model_output
+
+ if score_corrector is not None:
+ assert self.model.parameterization == "eps", 'not implemented'
+ e_t = score_corrector.modify_score(self.model, e_t, x, t, c, **corrector_kwargs)
+
+ alphas = self.model.alphas_cumprod if use_original_steps else self.ddim_alphas
+ alphas_prev = self.model.alphas_cumprod_prev if use_original_steps else self.ddim_alphas_prev
+ sqrt_one_minus_alphas = self.model.sqrt_one_minus_alphas_cumprod if use_original_steps else self.ddim_sqrt_one_minus_alphas
+ sigmas = self.model.ddim_sigmas_for_original_num_steps if use_original_steps else self.ddim_sigmas
+ # select parameters corresponding to the currently considered timestep
+ a_t = torch.full((b, 1, 1, 1), alphas[index], device=device)
+ a_prev = torch.full((b, 1, 1, 1), alphas_prev[index], device=device)
+ sigma_t = torch.full((b, 1, 1, 1), sigmas[index], device=device)
+ sqrt_one_minus_at = torch.full((b, 1, 1, 1), sqrt_one_minus_alphas[index],device=device)
+
+ # current prediction for x_0
+ if self.model.parameterization != "v":
+ pred_x0 = (x - sqrt_one_minus_at * e_t) / a_t.sqrt()
+ else:
+ pred_x0 = self.model.predict_start_from_z_and_v(x, t, model_output)
+
+ if quantize_denoised:
+ pred_x0, _, *_ = self.model.first_stage_model.quantize(pred_x0)
+
+ if dynamic_threshold is not None:
+ raise NotImplementedError()
+
+ # direction pointing to x_t
+ dir_xt = (1. - a_prev - sigma_t**2).sqrt() * e_t
+ noise = sigma_t * noise_like(x.shape, device, repeat_noise) * temperature
+ if noise_dropout > 0.:
+ noise = torch.nn.functional.dropout(noise, p=noise_dropout)
+ x_prev = a_prev.sqrt() * pred_x0 + dir_xt + noise
+ return x_prev, pred_x0
+
+ @torch.no_grad()
+ def encode(self, x0, c, t_enc, use_original_steps=False, return_intermediates=None,
+ unconditional_guidance_scale=1.0, unconditional_conditioning=None, callback=None):
+ num_reference_steps = self.ddpm_num_timesteps if use_original_steps else self.ddim_timesteps.shape[0]
+
+ assert t_enc <= num_reference_steps
+ num_steps = t_enc
+
+ if use_original_steps:
+ alphas_next = self.alphas_cumprod[:num_steps]
+ alphas = self.alphas_cumprod_prev[:num_steps]
+ else:
+ alphas_next = self.ddim_alphas[:num_steps]
+ alphas = torch.tensor(self.ddim_alphas_prev[:num_steps])
+
+ x_next = x0
+ intermediates = []
+ inter_steps = []
+ for i in tqdm(range(num_steps), desc='Encoding Image'):
+ t = torch.full((x0.shape[0],), i, device=self.model.device, dtype=torch.long)
+ if unconditional_guidance_scale == 1.:
+ noise_pred = self.model.apply_model(x_next, t, c)
+ else:
+ assert unconditional_conditioning is not None
+ e_t_uncond, noise_pred = torch.chunk(
+ self.model.apply_model(torch.cat((x_next, x_next)), torch.cat((t, t)),
+ torch.cat((unconditional_conditioning, c))), 2)
+ noise_pred = e_t_uncond + unconditional_guidance_scale * (noise_pred - e_t_uncond)
+
+ xt_weighted = (alphas_next[i] / alphas[i]).sqrt() * x_next
+ weighted_noise_pred = alphas_next[i].sqrt() * (
+ (1 / alphas_next[i] - 1).sqrt() - (1 / alphas[i] - 1).sqrt()) * noise_pred
+ x_next = xt_weighted + weighted_noise_pred
+ if return_intermediates and i % (
+ num_steps // return_intermediates) == 0 and i < num_steps - 1:
+ intermediates.append(x_next)
+ inter_steps.append(i)
+ elif return_intermediates and i >= num_steps - 2:
+ intermediates.append(x_next)
+ inter_steps.append(i)
+ if callback: callback(i)
+
+ out = {'x_encoded': x_next, 'intermediate_steps': inter_steps}
+ if return_intermediates:
+ out.update({'intermediates': intermediates})
+ return x_next, out
+
+ @torch.no_grad()
+ def stochastic_encode(self, x0, t, use_original_steps=False, noise=None):
+ # fast, but does not allow for exact reconstruction
+ # t serves as an index to gather the correct alphas
+ if use_original_steps:
+ sqrt_alphas_cumprod = self.sqrt_alphas_cumprod
+ sqrt_one_minus_alphas_cumprod = self.sqrt_one_minus_alphas_cumprod
+ else:
+ sqrt_alphas_cumprod = torch.sqrt(self.ddim_alphas)
+ sqrt_one_minus_alphas_cumprod = self.ddim_sqrt_one_minus_alphas
+
+ if noise is None:
+ noise = torch.randn_like(x0)
+ return (extract_into_tensor(sqrt_alphas_cumprod, t, x0.shape) * x0 +
+ extract_into_tensor(sqrt_one_minus_alphas_cumprod, t, x0.shape) * noise)
+
+ @torch.no_grad()
+ def decode(self, x_latent, cond, t_start, unconditional_guidance_scale=1.0, unconditional_conditioning=None,
+ use_original_steps=False, callback=None):
+
+ timesteps = np.arange(self.ddpm_num_timesteps) if use_original_steps else self.ddim_timesteps
+ timesteps = timesteps[:t_start]
+
+ time_range = np.flip(timesteps)
+ total_steps = timesteps.shape[0]
+ print(f"Running DDIM Sampling with {total_steps} timesteps")
+
+ iterator = tqdm(time_range, desc='Decoding image', total=total_steps)
+ x_dec = x_latent
+ for i, step in enumerate(iterator):
+ index = total_steps - i - 1
+ ts = torch.full((x_latent.shape[0],), step, device=x_latent.device, dtype=torch.long)
+ x_dec, _ = self.p_sample_ddim(x_dec, cond, ts, index=index, use_original_steps=use_original_steps,
+ unconditional_guidance_scale=unconditional_guidance_scale,
+ unconditional_conditioning=unconditional_conditioning)
+ if callback: callback(i)
+ return x_dec
+
+
+
+class DDIMSamplerSpaCFG(DDIMSampler):
+ @torch.no_grad()
+ def p_sample_ddim(self, x, c, t, index, repeat_noise=False, use_original_steps=False, quantize_denoised=False,
+ temperature=1., noise_dropout=0., score_corrector=None, corrector_kwargs=None,
+ unconditional_guidance_scale=[1.,1.,1.], unconditional_conditioning=None,
+ dynamic_threshold=None):
+ b, *_, device = *x.shape, x.device
+
+ if unconditional_conditioning is None or np.all(np.array(unconditional_guidance_scale == 1.)):
+ model_output = self.model.apply_model(x, t, c)
+ else:
+ model_t = self.model.apply_model(x, t, c)
+ model_uncond = self.model.apply_model(x, t, unconditional_conditioning[0])
+ model_struct = self.model.apply_model(x, t, unconditional_conditioning[1])
+ model_struct_app = self.model.apply_model(x, t, unconditional_conditioning[2])
+ sT, sS, sF = unconditional_guidance_scale
+ model_output = model_uncond + sS * (model_struct - model_uncond) + sF * (model_struct_app - model_struct) + sT * (model_t - model_struct_app)
+
+ if self.model.parameterization == "v":
+ e_t = self.model.predict_eps_from_z_and_v(x, t, model_output)
+ else:
+ e_t = model_output
+
+ if score_corrector is not None:
+ assert self.model.parameterization == "eps", 'not implemented'
+ e_t = score_corrector.modify_score(self.model, e_t, x, t, c, **corrector_kwargs)
+
+ alphas = self.model.alphas_cumprod if use_original_steps else self.ddim_alphas
+ alphas_prev = self.model.alphas_cumprod_prev if use_original_steps else self.ddim_alphas_prev
+ sqrt_one_minus_alphas = self.model.sqrt_one_minus_alphas_cumprod if use_original_steps else self.ddim_sqrt_one_minus_alphas
+ sigmas = self.model.ddim_sigmas_for_original_num_steps if use_original_steps else self.ddim_sigmas
+ # select parameters corresponding to the currently considered timestep
+ a_t = torch.full((b, 1, 1, 1), alphas[index], device=device)
+ a_prev = torch.full((b, 1, 1, 1), alphas_prev[index], device=device)
+ sigma_t = torch.full((b, 1, 1, 1), sigmas[index], device=device)
+ sqrt_one_minus_at = torch.full((b, 1, 1, 1), sqrt_one_minus_alphas[index],device=device)
+
+ # current prediction for x_0
+ if self.model.parameterization != "v":
+ pred_x0 = (x - sqrt_one_minus_at * e_t) / a_t.sqrt()
+ else:
+ pred_x0 = self.model.predict_start_from_z_and_v(x, t, model_output)
+
+ if quantize_denoised:
+ pred_x0, _, *_ = self.model.first_stage_model.quantize(pred_x0)
+
+ if dynamic_threshold is not None:
+ raise NotImplementedError()
+
+ # direction pointing to x_t
+ dir_xt = (1. - a_prev - sigma_t**2).sqrt() * e_t
+ noise = sigma_t * noise_like(x.shape, device, repeat_noise) * temperature
+ if noise_dropout > 0.:
+ noise = torch.nn.functional.dropout(noise, p=noise_dropout)
+ x_prev = a_prev.sqrt() * pred_x0 + dir_xt + noise
+ return x_prev, pred_x0
diff --git a/cldm/hack.py b/cldm/hack.py
new file mode 100644
index 0000000000000000000000000000000000000000..454361e9d036cd1a6a79122c2fd16b489e4767b1
--- /dev/null
+++ b/cldm/hack.py
@@ -0,0 +1,111 @@
+import torch
+import einops
+
+import ldm.modules.encoders.modules
+import ldm.modules.attention
+
+from transformers import logging
+from ldm.modules.attention import default
+
+
+def disable_verbosity():
+ logging.set_verbosity_error()
+ print('logging improved.')
+ return
+
+
+def enable_sliced_attention():
+ ldm.modules.attention.CrossAttention.forward = _hacked_sliced_attentin_forward
+ print('Enabled sliced_attention.')
+ return
+
+
+def hack_everything(clip_skip=0):
+ disable_verbosity()
+ ldm.modules.encoders.modules.FrozenCLIPEmbedder.forward = _hacked_clip_forward
+ ldm.modules.encoders.modules.FrozenCLIPEmbedder.clip_skip = clip_skip
+ print('Enabled clip hacks.')
+ return
+
+
+# Written by Lvmin
+def _hacked_clip_forward(self, text):
+ PAD = self.tokenizer.pad_token_id
+ EOS = self.tokenizer.eos_token_id
+ BOS = self.tokenizer.bos_token_id
+
+ def tokenize(t):
+ return self.tokenizer(t, truncation=False, add_special_tokens=False)["input_ids"]
+
+ def transformer_encode(t):
+ if self.clip_skip > 1:
+ rt = self.transformer(input_ids=t, output_hidden_states=True)
+ return self.transformer.text_model.final_layer_norm(rt.hidden_states[-self.clip_skip])
+ else:
+ return self.transformer(input_ids=t, output_hidden_states=False).last_hidden_state
+
+ def split(x):
+ return x[75 * 0: 75 * 1], x[75 * 1: 75 * 2], x[75 * 2: 75 * 3]
+
+ def pad(x, p, i):
+ return x[:i] if len(x) >= i else x + [p] * (i - len(x))
+
+ raw_tokens_list = tokenize(text)
+ tokens_list = []
+
+ for raw_tokens in raw_tokens_list:
+ raw_tokens_123 = split(raw_tokens)
+ raw_tokens_123 = [[BOS] + raw_tokens_i + [EOS] for raw_tokens_i in raw_tokens_123]
+ raw_tokens_123 = [pad(raw_tokens_i, PAD, 77) for raw_tokens_i in raw_tokens_123]
+ tokens_list.append(raw_tokens_123)
+
+ tokens_list = torch.IntTensor(tokens_list).to(self.device)
+
+ feed = einops.rearrange(tokens_list, 'b f i -> (b f) i')
+ y = transformer_encode(feed)
+ z = einops.rearrange(y, '(b f) i c -> b (f i) c', f=3)
+
+ return z
+
+
+# Stolen from https://github.com/basujindal/stable-diffusion/blob/main/optimizedSD/splitAttention.py
+def _hacked_sliced_attentin_forward(self, x, context=None, mask=None):
+ h = self.heads
+
+ q = self.to_q(x)
+ context = default(context, x)
+ k = self.to_k(context)
+ v = self.to_v(context)
+ del context, x
+
+ q, k, v = map(lambda t: einops.rearrange(t, 'b n (h d) -> (b h) n d', h=h), (q, k, v))
+
+ limit = k.shape[0]
+ att_step = 1
+ q_chunks = list(torch.tensor_split(q, limit // att_step, dim=0))
+ k_chunks = list(torch.tensor_split(k, limit // att_step, dim=0))
+ v_chunks = list(torch.tensor_split(v, limit // att_step, dim=0))
+
+ q_chunks.reverse()
+ k_chunks.reverse()
+ v_chunks.reverse()
+ sim = torch.zeros(q.shape[0], q.shape[1], v.shape[2], device=q.device)
+ del k, q, v
+ for i in range(0, limit, att_step):
+ q_buffer = q_chunks.pop()
+ k_buffer = k_chunks.pop()
+ v_buffer = v_chunks.pop()
+ sim_buffer = torch.einsum('b i d, b j d -> b i j', q_buffer, k_buffer) * self.scale
+
+ del k_buffer, q_buffer
+ # attention, what we cannot get enough of, by chunks
+
+ sim_buffer = sim_buffer.softmax(dim=-1)
+
+ sim_buffer = torch.einsum('b i j, b j d -> b i d', sim_buffer, v_buffer)
+ del v_buffer
+ sim[i:i + att_step, :, :] = sim_buffer
+
+ del sim_buffer
+ sim = einops.rearrange(sim, '(b h) n d -> b n (h d)', h=h)
+ return self.to_out(sim)
diff --git a/cldm/logger.py b/cldm/logger.py
new file mode 100644
index 0000000000000000000000000000000000000000..fd2798af63e43c8d048c043aa83dc140925e2dea
--- /dev/null
+++ b/cldm/logger.py
@@ -0,0 +1,233 @@
+import os
+
+import numpy as np
+import torch
+import torchvision
+from PIL import Image
+from pytorch_lightning.callbacks import Callback
+import pytorch_lightning as pl
+from pytorch_lightning.utilities.distributed import rank_zero_only
+from omegaconf import OmegaConf
+
+# class ImageLogger(Callback):
+# def __init__(self, batch_frequency=2000, max_images=4, clamp=True, increase_log_steps=True,
+# rescale=True, disabled=False, log_on_batch_idx=False, log_first_step=False,
+# log_images_kwargs=None):
+# super().__init__()
+# self.rescale = rescale
+# self.batch_freq = batch_frequency
+# self.max_images = max_images
+# if not increase_log_steps:
+# self.log_steps = [self.batch_freq]
+# self.clamp = clamp
+# self.disabled = disabled
+# self.log_on_batch_idx = log_on_batch_idx
+# self.log_images_kwargs = log_images_kwargs if log_images_kwargs else {}
+# self.log_first_step = log_first_step
+
+# @rank_zero_only
+# def log_local(self, save_dir, split, images, global_step, current_epoch, batch_idx):
+# root = os.path.join(save_dir, "image_log", split)
+# for k in images:
+# grid = torchvision.utils.make_grid(images[k], nrow=4)
+# if self.rescale:
+# grid = (grid + 1.0) / 2.0 # -1,1 -> 0,1; c,h,w
+# grid = grid.transpose(0, 1).transpose(1, 2).squeeze(-1)
+# grid = grid.numpy()
+# grid = (grid * 255).astype(np.uint8)
+# filename = "{}_gs-{:06}_e-{:06}_b-{:06}.png".format(k, global_step, current_epoch, batch_idx)
+# path = os.path.join(root, filename)
+# os.makedirs(os.path.split(path)[0], exist_ok=True)
+# Image.fromarray(grid).save(path)
+
+# def log_img(self, pl_module, batch, batch_idx, split="train"):
+# check_idx = batch_idx # if self.log_on_batch_idx else pl_module.global_step
+# if (self.check_frequency(check_idx) and # batch_idx % self.batch_freq == 0
+# hasattr(pl_module, "log_images") and
+# callable(pl_module.log_images) and
+# self.max_images > 0):
+# logger = type(pl_module.logger)
+
+# is_train = pl_module.training
+# if is_train:
+# pl_module.eval()
+
+# with torch.no_grad():
+# images = pl_module.log_images(batch, split=split, **self.log_images_kwargs)
+
+# for k in images:
+# N = min(images[k].shape[0], self.max_images)
+# images[k] = images[k][:N]
+# if isinstance(images[k], torch.Tensor):
+# images[k] = images[k].detach().cpu()
+# if self.clamp:
+# images[k] = torch.clamp(images[k], -1., 1.)
+
+# self.log_local(pl_module.logger.save_dir, split, images,
+# pl_module.global_step, pl_module.current_epoch, batch_idx)
+
+# if is_train:
+# pl_module.train()
+
+# def check_frequency(self, check_idx):
+# return check_idx % self.batch_freq == 0
+
+# def on_train_batch_end(self, trainer, pl_module, outputs, batch, batch_idx, dataloader_idx):
+# if not self.disabled:
+# self.log_img(pl_module, batch, batch_idx, split="train")
+
+
+class SetupCallback(Callback):
+ def __init__(self, resume, now, logdir, ckptdir, cfgdir, config, lightning_config):
+ super().__init__()
+ self.resume = resume
+ self.now = now
+ self.logdir = logdir
+ self.ckptdir = ckptdir
+ self.cfgdir = cfgdir
+ self.config = config
+ self.lightning_config = lightning_config
+
+ def on_keyboard_interrupt(self, trainer, pl_module):
+ if trainer.global_rank == 0:
+ print("Summoning checkpoint.")
+ ckpt_path = os.path.join(self.ckptdir, "last.ckpt")
+ trainer.save_checkpoint(ckpt_path)
+
+ def on_pretrain_routine_start(self, trainer, pl_module):
+ if trainer.global_rank == 0:
+ # Create logdirs and save configs
+ os.makedirs(self.logdir, exist_ok=True)
+ os.makedirs(self.ckptdir, exist_ok=True)
+ os.makedirs(self.cfgdir, exist_ok=True)
+
+ if "callbacks" in self.lightning_config:
+ if 'metrics_over_trainsteps_checkpoint' in self.lightning_config['callbacks']:
+ os.makedirs(os.path.join(self.ckptdir, 'trainstep_checkpoints'), exist_ok=True)
+ print("Project config")
+ print(OmegaConf.to_yaml(self.config))
+ OmegaConf.save(self.config,
+ os.path.join(self.cfgdir, "{}-project.yaml".format(self.now)))
+
+ print("Lightning config")
+ print(OmegaConf.to_yaml(self.lightning_config))
+ OmegaConf.save(OmegaConf.create({"lightning": self.lightning_config}),
+ os.path.join(self.cfgdir, "{}-lightning.yaml".format(self.now)))
+
+ else:
+ # ModelCheckpoint callback created log directory --- remove it
+ if not self.resume and os.path.exists(self.logdir):
+ dst, name = os.path.split(self.logdir)
+ dst = os.path.join(dst, "child_runs", name)
+ os.makedirs(os.path.split(dst)[0], exist_ok=True)
+ try:
+ os.rename(self.logdir, dst)
+ except FileNotFoundError:
+ pass
+
+
+class ImageLogger(Callback):
+ def __init__(self, batch_frequency, max_images, clamp=True, increase_log_steps=True,
+ rescale=True, disabled=False, log_on_batch_idx=False, log_first_step=False,
+ log_images_kwargs=None):
+ super().__init__()
+ self.rescale = rescale
+ self.batch_freq = batch_frequency
+ self.max_images = max_images
+ self.logger_log_images = {
+ pl.loggers.TestTubeLogger: self._testtube,
+ }
+ self.log_steps = [2 ** n for n in range(int(np.log2(self.batch_freq)) + 1)]
+ if not increase_log_steps:
+ self.log_steps = [self.batch_freq]
+ self.clamp = clamp
+ self.disabled = disabled
+ self.log_on_batch_idx = log_on_batch_idx
+ self.log_images_kwargs = log_images_kwargs if log_images_kwargs else {}
+ self.log_first_step = log_first_step
+
+ @rank_zero_only
+ def _testtube(self, pl_module, images, batch_idx, split):
+ for k in images:
+ grid = torchvision.utils.make_grid(images[k])
+ grid = (grid + 1.0) / 2.0 # -1,1 -> 0,1; c,h,w
+
+ tag = f"{split}/{k}"
+ pl_module.logger.experiment.add_image(
+ tag, grid,
+ global_step=pl_module.global_step)
+
+ @rank_zero_only
+ def log_local(self, save_dir, split, images,
+ global_step, current_epoch, batch_idx):
+ root = os.path.join(save_dir, "images", split)
+ for k in images:
+ grid = torchvision.utils.make_grid(images[k], nrow=4)
+ if self.rescale:
+ grid = (grid + 1.0) / 2.0 # -1,1 -> 0,1; c,h,w
+ grid = grid.transpose(0, 1).transpose(1, 2).squeeze(-1)
+ grid = grid.numpy()
+ grid = (grid * 255).astype(np.uint8)
+ filename = "{}_gs-{:06}_e-{:06}_b-{:06}.png".format(
+ k,
+ global_step,
+ current_epoch,
+ batch_idx)
+ path = os.path.join(root, filename)
+ os.makedirs(os.path.split(path)[0], exist_ok=True)
+ Image.fromarray(grid).save(path)
+
+ def log_img(self, pl_module, batch, batch_idx, split="train"):
+ check_idx = batch_idx if self.log_on_batch_idx else pl_module.global_step
+ if (self.check_frequency(check_idx) and # batch_idx % self.batch_freq == 0
+ hasattr(pl_module, "log_images") and
+ callable(pl_module.log_images) and
+ self.max_images > 0):
+ logger = type(pl_module.logger)
+
+ is_train = pl_module.training
+ if is_train:
+ pl_module.eval()
+
+ with torch.no_grad():
+ images = pl_module.log_images(batch, split=split, **self.log_images_kwargs)
+
+ for k in images:
+ N = min(images[k].shape[0], self.max_images)
+ images[k] = images[k][:N]
+ if isinstance(images[k], torch.Tensor):
+ images[k] = images[k].detach().cpu()
+ if self.clamp:
+ images[k] = torch.clamp(images[k], -1., 1.)
+
+ self.log_local(pl_module.logger.save_dir, split, images,
+ pl_module.global_step, pl_module.current_epoch, batch_idx)
+
+ logger_log_images = self.logger_log_images.get(logger, lambda *args, **kwargs: None)
+ logger_log_images(pl_module, images, pl_module.global_step, split)
+
+ if is_train:
+ pl_module.train()
+
+ def check_frequency(self, check_idx):
+ if ((check_idx % self.batch_freq) == 0 or (check_idx in self.log_steps)) and (
+ check_idx > 0 or self.log_first_step):
+ try:
+ self.log_steps.pop(0)
+ except IndexError as e:
+ print(e)
+ pass
+ return True
+ return False
+
+ def on_train_batch_end(self, trainer, pl_module, outputs, batch, batch_idx, dataloader_idx):
+ if not self.disabled and (pl_module.global_step > 0 or self.log_first_step):
+ self.log_img(pl_module, batch, batch_idx, split="train")
+
+ def on_validation_batch_end(self, trainer, pl_module, outputs, batch, batch_idx, dataloader_idx):
+ # if not self.disabled and pl_module.global_step > 0:
+ # self.log_img(pl_module, batch, batch_idx, split="val")
+ # if hasattr(pl_module, 'calibrate_grad_norm'):
+ # if (pl_module.calibrate_grad_norm and batch_idx % 25 == 0) and batch_idx > 0:
+ # self.log_gradients(trainer, pl_module, batch_idx=batch_idx)
+ pass
\ No newline at end of file
diff --git a/cldm/model.py b/cldm/model.py
new file mode 100644
index 0000000000000000000000000000000000000000..63a3c5e79b5e88d7590e369e7303fd9804673e5b
--- /dev/null
+++ b/cldm/model.py
@@ -0,0 +1,33 @@
+import os
+import torch
+
+from omegaconf import OmegaConf
+from ldm.util import instantiate_from_config
+
+
+def get_state_dict(d):
+ return d.get('state_dict', d)
+
+
+def load_state_dict(ckpt_path, location='cpu'):
+ _, extension = os.path.splitext(ckpt_path)
+ if extension.lower() == ".safetensors":
+ import safetensors.torch
+ state_dict = safetensors.torch.load_file(ckpt_path, device=location)
+ else:
+ state_dict = get_state_dict(torch.load(ckpt_path, map_location=torch.device(location)))
+ state_dict = get_state_dict(state_dict)
+ print(f'Loaded state_dict from [{ckpt_path}]')
+ return state_dict
+
+
+def create_model(config):
+ if isinstance(config, str):
+ config = OmegaConf.load(config)
+ model = instantiate_from_config(config.model).cpu()
+ print(f'Loaded model config from [{config}]')
+ return model
+ else:
+ model = instantiate_from_config(config.model).cpu()
+ return model
+
diff --git a/configs/sap_fixed_hintnet_v15.yaml b/configs/sap_fixed_hintnet_v15.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..d308ceddb0db134950332014ca8f0181b2250b24
--- /dev/null
+++ b/configs/sap_fixed_hintnet_v15.yaml
@@ -0,0 +1,114 @@
+model:
+ target: cldm.cldm.SAP
+ learning_rate: 1.5e-05
+ sd_locked: True
+ only_mid_control: False
+ init_ckpt: './models/sap_sd15_ini_fixed.ckpt'
+ params:
+ linear_start: 0.00085
+ linear_end: 0.0120
+ num_timesteps_cond: 1
+ log_every_t: 200
+ timesteps: 1000
+ first_stage_key: "image"
+ cond_stage_key: "caption"
+ control_key: "hint"
+ image_size: 64
+ channels: 4
+ cond_stage_trainable: false
+ conditioning_key: crossattn
+ monitor: val/loss_simple_ema
+ scale_factor: 0.18215
+ use_ema: False
+ only_mid_control: False
+
+ control_stage_config:
+ target: cldm.cldm.ControlNetSAP
+ params:
+ input_hint_block: 'fixed'
+ image_size: 32 # unused
+ in_channels: 4
+ hint_channels: 129 #(128 + 1)
+ model_channels: 320
+ attention_resolutions: [ 4, 2, 1 ]
+ num_res_blocks: 2
+ channel_mult: [ 1, 2, 4, 4 ]
+ num_heads: 8
+ use_spatial_transformer: True
+ transformer_depth: 1
+ context_dim: 768
+ use_checkpoint: True
+ legacy: False
+
+ unet_config:
+ target: cldm.cldm.ControlledUnetModel
+ params:
+ image_size: 32 # unused
+ in_channels: 4
+ out_channels: 4
+ model_channels: 320
+ attention_resolutions: [ 4, 2, 1 ]
+ num_res_blocks: 2
+ channel_mult: [ 1, 2, 4, 4 ]
+ num_heads: 8
+ use_spatial_transformer: True
+ transformer_depth: 1
+ context_dim: 768
+ use_checkpoint: True
+ legacy: False
+
+ first_stage_config:
+ target: ldm.models.autoencoder.AutoencoderKL
+ params:
+ embed_dim: 4
+ monitor: val/rec_loss
+ ddconfig:
+ double_z: true
+ z_channels: 4
+ resolution: 256
+ in_channels: 3
+ out_ch: 3
+ ch: 128
+ ch_mult:
+ - 1
+ - 2
+ - 4
+ - 4
+ num_res_blocks: 2
+ attn_resolutions: []
+ dropout: 0.0
+ lossconfig:
+ target: torch.nn.Identity
+
+ cond_stage_config:
+ target: ldm.modules.encoders.modules.FrozenCLIPEmbedder
+
+
+data:
+ target: cldm.data.DataModuleFromConfig
+ params:
+ batch_size: 4
+ wrap: True
+ train:
+ target: dataset.txtseg.COCOTrain
+ params:
+ size: 512
+ validation:
+ target: dataset.txtseg.COCOValidation
+ params:
+ size: 512
+
+
+lightning:
+ callbacks:
+ image_logger:
+ target: main.ImageLogger
+ params:
+ batch_frequency: 2000
+ max_images: 4
+ increase_log_steps: False
+ log_first_step: True
+
+ trainer:
+ benchmark: True
+ accumulate_grad_batches: 4
\ No newline at end of file
diff --git a/deform_setup.sh b/deform_setup.sh
new file mode 100644
index 0000000000000000000000000000000000000000..e6afa379fb41812699f2efce980c8d86d09ed7f6
--- /dev/null
+++ b/deform_setup.sh
@@ -0,0 +1,20 @@
+#!/usr/bin/env bash
+
+# ln -s ./oneformer/modeling/pixel_decoder/ops/ ./
+# ls
+# cd ops/ && bash make.sh && cd ..
+echo '----------------------------------------------------------------'
+echo '----------------------------------------------------------------'
+pip3 freeze | grep MultiScaleDeformableAttention
+pip3 freeze | grep torch
+pip3 freeze | grep detectron2
+echo '----------------------------------------------------------------'
+echo '----------------------------------------------------------------'
+
+# echo '----------------------------------------------------------------'
+# echo '----------------------------------------------------------------'
+# cd /home/user/.pyenv/versions/3.8.15/lib/python3.8/site-packages
+# ls
+# ls | grep MultiScale
+# echo '----------------------------------------------------------------'
+# echo '-----------
\ No newline at end of file
diff --git a/ldm/data/__init__.py b/ldm/data/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/ldm/data/util.py b/ldm/data/util.py
new file mode 100644
index 0000000000000000000000000000000000000000..5b60ceb2349e3bd7900ff325740e2022d2903b1c
--- /dev/null
+++ b/ldm/data/util.py
@@ -0,0 +1,24 @@
+import torch
+
+from ldm.modules.midas.api import load_midas_transform
+
+
+class AddMiDaS(object):
+ def __init__(self, model_type):
+ super().__init__()
+ self.transform = load_midas_transform(model_type)
+
+ def pt2np(self, x):
+ x = ((x + 1.0) * .5).detach().cpu().numpy()
+ return x
+
+ def np2pt(self, x):
+ x = torch.from_numpy(x) * 2 - 1.
+ return x
+
+ def __call__(self, sample):
+ # sample['jpg'] is tensor hwc in [-1, 1] at this point
+ x = self.pt2np(sample['jpg'])
+ x = self.transform({"image": x})["image"]
+ sample['midas_in'] = x
+ return sample
\ No newline at end of file
diff --git a/ldm/models/autoencoder.py b/ldm/models/autoencoder.py
new file mode 100644
index 0000000000000000000000000000000000000000..d122549995ce2cd64092c81a58419ed4a15a02fd
--- /dev/null
+++ b/ldm/models/autoencoder.py
@@ -0,0 +1,219 @@
+import torch
+import pytorch_lightning as pl
+import torch.nn.functional as F
+from contextlib import contextmanager
+
+from ldm.modules.diffusionmodules.model import Encoder, Decoder
+from ldm.modules.distributions.distributions import DiagonalGaussianDistribution
+
+from ldm.util import instantiate_from_config
+from ldm.modules.ema import LitEma
+
+
+class AutoencoderKL(pl.LightningModule):
+ def __init__(self,
+ ddconfig,
+ lossconfig,
+ embed_dim,
+ ckpt_path=None,
+ ignore_keys=[],
+ image_key="image",
+ colorize_nlabels=None,
+ monitor=None,
+ ema_decay=None,
+ learn_logvar=False
+ ):
+ super().__init__()
+ self.learn_logvar = learn_logvar
+ self.image_key = image_key
+ self.encoder = Encoder(**ddconfig)
+ self.decoder = Decoder(**ddconfig)
+ self.loss = instantiate_from_config(lossconfig)
+ assert ddconfig["double_z"]
+ self.quant_conv = torch.nn.Conv2d(2*ddconfig["z_channels"], 2*embed_dim, 1)
+ self.post_quant_conv = torch.nn.Conv2d(embed_dim, ddconfig["z_channels"], 1)
+ self.embed_dim = embed_dim
+ if colorize_nlabels is not None:
+ assert type(colorize_nlabels)==int
+ self.register_buffer("colorize", torch.randn(3, colorize_nlabels, 1, 1))
+ if monitor is not None:
+ self.monitor = monitor
+
+ self.use_ema = ema_decay is not None
+ if self.use_ema:
+ self.ema_decay = ema_decay
+ assert 0. < ema_decay < 1.
+ self.model_ema = LitEma(self, decay=ema_decay)
+ print(f"Keeping EMAs of {len(list(self.model_ema.buffers()))}.")
+
+ if ckpt_path is not None:
+ self.init_from_ckpt(ckpt_path, ignore_keys=ignore_keys)
+
+ def init_from_ckpt(self, path, ignore_keys=list()):
+ sd = torch.load(path, map_location="cpu")["state_dict"]
+ keys = list(sd.keys())
+ for k in keys:
+ for ik in ignore_keys:
+ if k.startswith(ik):
+ print("Deleting key {} from state_dict.".format(k))
+ del sd[k]
+ self.load_state_dict(sd, strict=False)
+ print(f"Restored from {path}")
+
+ @contextmanager
+ def ema_scope(self, context=None):
+ if self.use_ema:
+ self.model_ema.store(self.parameters())
+ self.model_ema.copy_to(self)
+ if context is not None:
+ print(f"{context}: Switched to EMA weights")
+ try:
+ yield None
+ finally:
+ if self.use_ema:
+ self.model_ema.restore(self.parameters())
+ if context is not None:
+ print(f"{context}: Restored training weights")
+
+ def on_train_batch_end(self, *args, **kwargs):
+ if self.use_ema:
+ self.model_ema(self)
+
+ def encode(self, x):
+ h = self.encoder(x)
+ moments = self.quant_conv(h)
+ posterior = DiagonalGaussianDistribution(moments)
+ return posterior
+
+ def decode(self, z):
+ z = self.post_quant_conv(z)
+ dec = self.decoder(z)
+ return dec
+
+ def forward(self, input, sample_posterior=True):
+ posterior = self.encode(input)
+ if sample_posterior:
+ z = posterior.sample()
+ else:
+ z = posterior.mode()
+ dec = self.decode(z)
+ return dec, posterior
+
+ def get_input(self, batch, k):
+ x = batch[k]
+ if len(x.shape) == 3:
+ x = x[..., None]
+ x = x.permute(0, 3, 1, 2).to(memory_format=torch.contiguous_format).float()
+ return x
+
+ def training_step(self, batch, batch_idx, optimizer_idx):
+ inputs = self.get_input(batch, self.image_key)
+ reconstructions, posterior = self(inputs)
+
+ if optimizer_idx == 0:
+ # train encoder+decoder+logvar
+ aeloss, log_dict_ae = self.loss(inputs, reconstructions, posterior, optimizer_idx, self.global_step,
+ last_layer=self.get_last_layer(), split="train")
+ self.log("aeloss", aeloss, prog_bar=True, logger=True, on_step=True, on_epoch=True)
+ self.log_dict(log_dict_ae, prog_bar=False, logger=True, on_step=True, on_epoch=False)
+ return aeloss
+
+ if optimizer_idx == 1:
+ # train the discriminator
+ discloss, log_dict_disc = self.loss(inputs, reconstructions, posterior, optimizer_idx, self.global_step,
+ last_layer=self.get_last_layer(), split="train")
+
+ self.log("discloss", discloss, prog_bar=True, logger=True, on_step=True, on_epoch=True)
+ self.log_dict(log_dict_disc, prog_bar=False, logger=True, on_step=True, on_epoch=False)
+ return discloss
+
+ def validation_step(self, batch, batch_idx):
+ log_dict = self._validation_step(batch, batch_idx)
+ with self.ema_scope():
+ log_dict_ema = self._validation_step(batch, batch_idx, postfix="_ema")
+ return log_dict
+
+ def _validation_step(self, batch, batch_idx, postfix=""):
+ inputs = self.get_input(batch, self.image_key)
+ reconstructions, posterior = self(inputs)
+ aeloss, log_dict_ae = self.loss(inputs, reconstructions, posterior, 0, self.global_step,
+ last_layer=self.get_last_layer(), split="val"+postfix)
+
+ discloss, log_dict_disc = self.loss(inputs, reconstructions, posterior, 1, self.global_step,
+ last_layer=self.get_last_layer(), split="val"+postfix)
+
+ self.log(f"val{postfix}/rec_loss", log_dict_ae[f"val{postfix}/rec_loss"])
+ self.log_dict(log_dict_ae)
+ self.log_dict(log_dict_disc)
+ return self.log_dict
+
+ def configure_optimizers(self):
+ lr = self.learning_rate
+ ae_params_list = list(self.encoder.parameters()) + list(self.decoder.parameters()) + list(
+ self.quant_conv.parameters()) + list(self.post_quant_conv.parameters())
+ if self.learn_logvar:
+ print(f"{self.__class__.__name__}: Learning logvar")
+ ae_params_list.append(self.loss.logvar)
+ opt_ae = torch.optim.Adam(ae_params_list,
+ lr=lr, betas=(0.5, 0.9))
+ opt_disc = torch.optim.Adam(self.loss.discriminator.parameters(),
+ lr=lr, betas=(0.5, 0.9))
+ return [opt_ae, opt_disc], []
+
+ def get_last_layer(self):
+ return self.decoder.conv_out.weight
+
+ @torch.no_grad()
+ def log_images(self, batch, only_inputs=False, log_ema=False, **kwargs):
+ log = dict()
+ x = self.get_input(batch, self.image_key)
+ x = x.to(self.device)
+ if not only_inputs:
+ xrec, posterior = self(x)
+ if x.shape[1] > 3:
+ # colorize with random projection
+ assert xrec.shape[1] > 3
+ x = self.to_rgb(x)
+ xrec = self.to_rgb(xrec)
+ log["samples"] = self.decode(torch.randn_like(posterior.sample()))
+ log["reconstructions"] = xrec
+ if log_ema or self.use_ema:
+ with self.ema_scope():
+ xrec_ema, posterior_ema = self(x)
+ if x.shape[1] > 3:
+ # colorize with random projection
+ assert xrec_ema.shape[1] > 3
+ xrec_ema = self.to_rgb(xrec_ema)
+ log["samples_ema"] = self.decode(torch.randn_like(posterior_ema.sample()))
+ log["reconstructions_ema"] = xrec_ema
+ log["inputs"] = x
+ return log
+
+ def to_rgb(self, x):
+ assert self.image_key == "segmentation"
+ if not hasattr(self, "colorize"):
+ self.register_buffer("colorize", torch.randn(3, x.shape[1], 1, 1).to(x))
+ x = F.conv2d(x, weight=self.colorize)
+ x = 2.*(x-x.min())/(x.max()-x.min()) - 1.
+ return x
+
+
+class IdentityFirstStage(torch.nn.Module):
+ def __init__(self, *args, vq_interface=False, **kwargs):
+ self.vq_interface = vq_interface
+ super().__init__()
+
+ def encode(self, x, *args, **kwargs):
+ return x
+
+ def decode(self, x, *args, **kwargs):
+ return x
+
+ def quantize(self, x, *args, **kwargs):
+ if self.vq_interface:
+ return x, None, [None, None, None]
+ return x
+
+ def forward(self, x, *args, **kwargs):
+ return x
+
diff --git a/ldm/models/diffusion/__init__.py b/ldm/models/diffusion/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/ldm/models/diffusion/ddim.py b/ldm/models/diffusion/ddim.py
new file mode 100644
index 0000000000000000000000000000000000000000..27ead0ea914c64c747b64e690662899fb3801144
--- /dev/null
+++ b/ldm/models/diffusion/ddim.py
@@ -0,0 +1,336 @@
+"""SAMPLING ONLY."""
+
+import torch
+import numpy as np
+from tqdm import tqdm
+
+from ldm.modules.diffusionmodules.util import make_ddim_sampling_parameters, make_ddim_timesteps, noise_like, extract_into_tensor
+
+
+class DDIMSampler(object):
+ def __init__(self, model, schedule="linear", **kwargs):
+ super().__init__()
+ self.model = model
+ self.ddpm_num_timesteps = model.num_timesteps
+ self.schedule = schedule
+
+ def register_buffer(self, name, attr):
+ if type(attr) == torch.Tensor:
+ if attr.device != torch.device("cuda"):
+ attr = attr.to(torch.device("cuda"))
+ setattr(self, name, attr)
+
+ def make_schedule(self, ddim_num_steps, ddim_discretize="uniform", ddim_eta=0., verbose=True):
+ self.ddim_timesteps = make_ddim_timesteps(ddim_discr_method=ddim_discretize, num_ddim_timesteps=ddim_num_steps,
+ num_ddpm_timesteps=self.ddpm_num_timesteps,verbose=verbose)
+ alphas_cumprod = self.model.alphas_cumprod
+ assert alphas_cumprod.shape[0] == self.ddpm_num_timesteps, 'alphas have to be defined for each timestep'
+ to_torch = lambda x: x.clone().detach().to(torch.float32).to(self.model.device)
+
+ self.register_buffer('betas', to_torch(self.model.betas))
+ self.register_buffer('alphas_cumprod', to_torch(alphas_cumprod))
+ self.register_buffer('alphas_cumprod_prev', to_torch(self.model.alphas_cumprod_prev))
+
+ # calculations for diffusion q(x_t | x_{t-1}) and others
+ self.register_buffer('sqrt_alphas_cumprod', to_torch(np.sqrt(alphas_cumprod.cpu())))
+ self.register_buffer('sqrt_one_minus_alphas_cumprod', to_torch(np.sqrt(1. - alphas_cumprod.cpu())))
+ self.register_buffer('log_one_minus_alphas_cumprod', to_torch(np.log(1. - alphas_cumprod.cpu())))
+ self.register_buffer('sqrt_recip_alphas_cumprod', to_torch(np.sqrt(1. / alphas_cumprod.cpu())))
+ self.register_buffer('sqrt_recipm1_alphas_cumprod', to_torch(np.sqrt(1. / alphas_cumprod.cpu() - 1)))
+
+ # ddim sampling parameters
+ ddim_sigmas, ddim_alphas, ddim_alphas_prev = make_ddim_sampling_parameters(alphacums=alphas_cumprod.cpu(),
+ ddim_timesteps=self.ddim_timesteps,
+ eta=ddim_eta,verbose=verbose)
+ self.register_buffer('ddim_sigmas', ddim_sigmas)
+ self.register_buffer('ddim_alphas', ddim_alphas)
+ self.register_buffer('ddim_alphas_prev', ddim_alphas_prev)
+ self.register_buffer('ddim_sqrt_one_minus_alphas', np.sqrt(1. - ddim_alphas))
+ sigmas_for_original_sampling_steps = ddim_eta * torch.sqrt(
+ (1 - self.alphas_cumprod_prev) / (1 - self.alphas_cumprod) * (
+ 1 - self.alphas_cumprod / self.alphas_cumprod_prev))
+ self.register_buffer('ddim_sigmas_for_original_num_steps', sigmas_for_original_sampling_steps)
+
+ @torch.no_grad()
+ def sample(self,
+ S,
+ batch_size,
+ shape,
+ conditioning=None,
+ callback=None,
+ normals_sequence=None,
+ img_callback=None,
+ quantize_x0=False,
+ eta=0.,
+ mask=None,
+ x0=None,
+ temperature=1.,
+ noise_dropout=0.,
+ score_corrector=None,
+ corrector_kwargs=None,
+ verbose=True,
+ x_T=None,
+ log_every_t=100,
+ unconditional_guidance_scale=1.,
+ unconditional_conditioning=None, # this has to come in the same format as the conditioning, # e.g. as encoded tokens, ...
+ dynamic_threshold=None,
+ ucg_schedule=None,
+ **kwargs
+ ):
+ if conditioning is not None:
+ if isinstance(conditioning, dict):
+ ctmp = conditioning[list(conditioning.keys())[0]]
+ while isinstance(ctmp, list): ctmp = ctmp[0]
+ cbs = ctmp.shape[0]
+ if cbs != batch_size:
+ print(f"Warning: Got {cbs} conditionings but batch-size is {batch_size}")
+
+ elif isinstance(conditioning, list):
+ for ctmp in conditioning:
+ if ctmp.shape[0] != batch_size:
+ print(f"Warning: Got {cbs} conditionings but batch-size is {batch_size}")
+
+ else:
+ if conditioning.shape[0] != batch_size:
+ print(f"Warning: Got {conditioning.shape[0]} conditionings but batch-size is {batch_size}")
+
+ self.make_schedule(ddim_num_steps=S, ddim_eta=eta, verbose=verbose)
+ # sampling
+ C, H, W = shape
+ size = (batch_size, C, H, W)
+ print(f'Data shape for DDIM sampling is {size}, eta {eta}')
+
+ samples, intermediates = self.ddim_sampling(conditioning, size,
+ callback=callback,
+ img_callback=img_callback,
+ quantize_denoised=quantize_x0,
+ mask=mask, x0=x0,
+ ddim_use_original_steps=False,
+ noise_dropout=noise_dropout,
+ temperature=temperature,
+ score_corrector=score_corrector,
+ corrector_kwargs=corrector_kwargs,
+ x_T=x_T,
+ log_every_t=log_every_t,
+ unconditional_guidance_scale=unconditional_guidance_scale,
+ unconditional_conditioning=unconditional_conditioning,
+ dynamic_threshold=dynamic_threshold,
+ ucg_schedule=ucg_schedule
+ )
+ return samples, intermediates
+
+ @torch.no_grad()
+ def ddim_sampling(self, cond, shape,
+ x_T=None, ddim_use_original_steps=False,
+ callback=None, timesteps=None, quantize_denoised=False,
+ mask=None, x0=None, img_callback=None, log_every_t=100,
+ temperature=1., noise_dropout=0., score_corrector=None, corrector_kwargs=None,
+ unconditional_guidance_scale=1., unconditional_conditioning=None, dynamic_threshold=None,
+ ucg_schedule=None):
+ device = self.model.betas.device
+ b = shape[0]
+ if x_T is None:
+ img = torch.randn(shape, device=device)
+ else:
+ img = x_T
+
+ if timesteps is None:
+ timesteps = self.ddpm_num_timesteps if ddim_use_original_steps else self.ddim_timesteps
+ elif timesteps is not None and not ddim_use_original_steps:
+ subset_end = int(min(timesteps / self.ddim_timesteps.shape[0], 1) * self.ddim_timesteps.shape[0]) - 1
+ timesteps = self.ddim_timesteps[:subset_end]
+
+ intermediates = {'x_inter': [img], 'pred_x0': [img]}
+ time_range = reversed(range(0,timesteps)) if ddim_use_original_steps else np.flip(timesteps)
+ total_steps = timesteps if ddim_use_original_steps else timesteps.shape[0]
+ print(f"Running DDIM Sampling with {total_steps} timesteps")
+
+ iterator = tqdm(time_range, desc='DDIM Sampler', total=total_steps)
+
+ for i, step in enumerate(iterator):
+ index = total_steps - i - 1
+ ts = torch.full((b,), step, device=device, dtype=torch.long)
+
+ if mask is not None:
+ assert x0 is not None
+ img_orig = self.model.q_sample(x0, ts) # TODO: deterministic forward pass?
+ img = img_orig * mask + (1. - mask) * img
+
+ if ucg_schedule is not None:
+ assert len(ucg_schedule) == len(time_range)
+ unconditional_guidance_scale = ucg_schedule[i]
+
+ outs = self.p_sample_ddim(img, cond, ts, index=index, use_original_steps=ddim_use_original_steps,
+ quantize_denoised=quantize_denoised, temperature=temperature,
+ noise_dropout=noise_dropout, score_corrector=score_corrector,
+ corrector_kwargs=corrector_kwargs,
+ unconditional_guidance_scale=unconditional_guidance_scale,
+ unconditional_conditioning=unconditional_conditioning,
+ dynamic_threshold=dynamic_threshold)
+ img, pred_x0 = outs
+ if callback: callback(i)
+ if img_callback: img_callback(pred_x0, i)
+
+ if index % log_every_t == 0 or index == total_steps - 1:
+ intermediates['x_inter'].append(img)
+ intermediates['pred_x0'].append(pred_x0)
+
+ return img, intermediates
+
+ @torch.no_grad()
+ def p_sample_ddim(self, x, c, t, index, repeat_noise=False, use_original_steps=False, quantize_denoised=False,
+ temperature=1., noise_dropout=0., score_corrector=None, corrector_kwargs=None,
+ unconditional_guidance_scale=1., unconditional_conditioning=None,
+ dynamic_threshold=None):
+ b, *_, device = *x.shape, x.device
+
+ if unconditional_conditioning is None or unconditional_guidance_scale == 1.:
+ model_output = self.model.apply_model(x, t, c)
+ else:
+ x_in = torch.cat([x] * 2)
+ t_in = torch.cat([t] * 2)
+ if isinstance(c, dict):
+ assert isinstance(unconditional_conditioning, dict)
+ c_in = dict()
+ for k in c:
+ if isinstance(c[k], list):
+ c_in[k] = [torch.cat([
+ unconditional_conditioning[k][i],
+ c[k][i]]) for i in range(len(c[k]))]
+ else:
+ c_in[k] = torch.cat([
+ unconditional_conditioning[k],
+ c[k]])
+ elif isinstance(c, list):
+ c_in = list()
+ assert isinstance(unconditional_conditioning, list)
+ for i in range(len(c)):
+ c_in.append(torch.cat([unconditional_conditioning[i], c[i]]))
+ else:
+ c_in = torch.cat([unconditional_conditioning, c])
+ model_uncond, model_t = self.model.apply_model(x_in, t_in, c_in).chunk(2)
+ model_output = model_uncond + unconditional_guidance_scale * (model_t - model_uncond)
+
+ if self.model.parameterization == "v":
+ e_t = self.model.predict_eps_from_z_and_v(x, t, model_output)
+ else:
+ e_t = model_output
+
+ if score_corrector is not None:
+ assert self.model.parameterization == "eps", 'not implemented'
+ e_t = score_corrector.modify_score(self.model, e_t, x, t, c, **corrector_kwargs)
+
+ alphas = self.model.alphas_cumprod if use_original_steps else self.ddim_alphas
+ alphas_prev = self.model.alphas_cumprod_prev if use_original_steps else self.ddim_alphas_prev
+ sqrt_one_minus_alphas = self.model.sqrt_one_minus_alphas_cumprod if use_original_steps else self.ddim_sqrt_one_minus_alphas
+ sigmas = self.model.ddim_sigmas_for_original_num_steps if use_original_steps else self.ddim_sigmas
+ # select parameters corresponding to the currently considered timestep
+ a_t = torch.full((b, 1, 1, 1), alphas[index], device=device)
+ a_prev = torch.full((b, 1, 1, 1), alphas_prev[index], device=device)
+ sigma_t = torch.full((b, 1, 1, 1), sigmas[index], device=device)
+ sqrt_one_minus_at = torch.full((b, 1, 1, 1), sqrt_one_minus_alphas[index],device=device)
+
+ # current prediction for x_0
+ if self.model.parameterization != "v":
+ pred_x0 = (x - sqrt_one_minus_at * e_t) / a_t.sqrt()
+ else:
+ pred_x0 = self.model.predict_start_from_z_and_v(x, t, model_output)
+
+ if quantize_denoised:
+ pred_x0, _, *_ = self.model.first_stage_model.quantize(pred_x0)
+
+ if dynamic_threshold is not None:
+ raise NotImplementedError()
+
+ # direction pointing to x_t
+ dir_xt = (1. - a_prev - sigma_t**2).sqrt() * e_t
+ noise = sigma_t * noise_like(x.shape, device, repeat_noise) * temperature
+ if noise_dropout > 0.:
+ noise = torch.nn.functional.dropout(noise, p=noise_dropout)
+ x_prev = a_prev.sqrt() * pred_x0 + dir_xt + noise
+ return x_prev, pred_x0
+
+ @torch.no_grad()
+ def encode(self, x0, c, t_enc, use_original_steps=False, return_intermediates=None,
+ unconditional_guidance_scale=1.0, unconditional_conditioning=None, callback=None):
+ num_reference_steps = self.ddpm_num_timesteps if use_original_steps else self.ddim_timesteps.shape[0]
+
+ assert t_enc <= num_reference_steps
+ num_steps = t_enc
+
+ if use_original_steps:
+ alphas_next = self.alphas_cumprod[:num_steps]
+ alphas = self.alphas_cumprod_prev[:num_steps]
+ else:
+ alphas_next = self.ddim_alphas[:num_steps]
+ alphas = torch.tensor(self.ddim_alphas_prev[:num_steps])
+
+ x_next = x0
+ intermediates = []
+ inter_steps = []
+ for i in tqdm(range(num_steps), desc='Encoding Image'):
+ t = torch.full((x0.shape[0],), i, device=self.model.device, dtype=torch.long)
+ if unconditional_guidance_scale == 1.:
+ noise_pred = self.model.apply_model(x_next, t, c)
+ else:
+ assert unconditional_conditioning is not None
+ e_t_uncond, noise_pred = torch.chunk(
+ self.model.apply_model(torch.cat((x_next, x_next)), torch.cat((t, t)),
+ torch.cat((unconditional_conditioning, c))), 2)
+ noise_pred = e_t_uncond + unconditional_guidance_scale * (noise_pred - e_t_uncond)
+
+ xt_weighted = (alphas_next[i] / alphas[i]).sqrt() * x_next
+ weighted_noise_pred = alphas_next[i].sqrt() * (
+ (1 / alphas_next[i] - 1).sqrt() - (1 / alphas[i] - 1).sqrt()) * noise_pred
+ x_next = xt_weighted + weighted_noise_pred
+ if return_intermediates and i % (
+ num_steps // return_intermediates) == 0 and i < num_steps - 1:
+ intermediates.append(x_next)
+ inter_steps.append(i)
+ elif return_intermediates and i >= num_steps - 2:
+ intermediates.append(x_next)
+ inter_steps.append(i)
+ if callback: callback(i)
+
+ out = {'x_encoded': x_next, 'intermediate_steps': inter_steps}
+ if return_intermediates:
+ out.update({'intermediates': intermediates})
+ return x_next, out
+
+ @torch.no_grad()
+ def stochastic_encode(self, x0, t, use_original_steps=False, noise=None):
+ # fast, but does not allow for exact reconstruction
+ # t serves as an index to gather the correct alphas
+ if use_original_steps:
+ sqrt_alphas_cumprod = self.sqrt_alphas_cumprod
+ sqrt_one_minus_alphas_cumprod = self.sqrt_one_minus_alphas_cumprod
+ else:
+ sqrt_alphas_cumprod = torch.sqrt(self.ddim_alphas)
+ sqrt_one_minus_alphas_cumprod = self.ddim_sqrt_one_minus_alphas
+
+ if noise is None:
+ noise = torch.randn_like(x0)
+ return (extract_into_tensor(sqrt_alphas_cumprod, t, x0.shape) * x0 +
+ extract_into_tensor(sqrt_one_minus_alphas_cumprod, t, x0.shape) * noise)
+
+ @torch.no_grad()
+ def decode(self, x_latent, cond, t_start, unconditional_guidance_scale=1.0, unconditional_conditioning=None,
+ use_original_steps=False, callback=None):
+
+ timesteps = np.arange(self.ddpm_num_timesteps) if use_original_steps else self.ddim_timesteps
+ timesteps = timesteps[:t_start]
+
+ time_range = np.flip(timesteps)
+ total_steps = timesteps.shape[0]
+ print(f"Running DDIM Sampling with {total_steps} timesteps")
+
+ iterator = tqdm(time_range, desc='Decoding image', total=total_steps)
+ x_dec = x_latent
+ for i, step in enumerate(iterator):
+ index = total_steps - i - 1
+ ts = torch.full((x_latent.shape[0],), step, device=x_latent.device, dtype=torch.long)
+ x_dec, _ = self.p_sample_ddim(x_dec, cond, ts, index=index, use_original_steps=use_original_steps,
+ unconditional_guidance_scale=unconditional_guidance_scale,
+ unconditional_conditioning=unconditional_conditioning)
+ if callback: callback(i)
+ return x_dec
\ No newline at end of file
diff --git a/ldm/models/diffusion/ddpm.py b/ldm/models/diffusion/ddpm.py
new file mode 100644
index 0000000000000000000000000000000000000000..f71a44af48c8cba8e97849b7e6813b3e6f9fe83c
--- /dev/null
+++ b/ldm/models/diffusion/ddpm.py
@@ -0,0 +1,1797 @@
+"""
+wild mixture of
+https://github.com/lucidrains/denoising-diffusion-pytorch/blob/7706bdfc6f527f58d33f84b7b522e61e6e3164b3/denoising_diffusion_pytorch/denoising_diffusion_pytorch.py
+https://github.com/openai/improved-diffusion/blob/e94489283bb876ac1477d5dd7709bbbd2d9902ce/improved_diffusion/gaussian_diffusion.py
+https://github.com/CompVis/taming-transformers
+-- merci
+"""
+
+import torch
+import torch.nn as nn
+import numpy as np
+import pytorch_lightning as pl
+from torch.optim.lr_scheduler import LambdaLR
+from einops import rearrange, repeat
+from contextlib import contextmanager, nullcontext
+from functools import partial
+import itertools
+from tqdm import tqdm
+from torchvision.utils import make_grid
+from pytorch_lightning.utilities.distributed import rank_zero_only
+from omegaconf import ListConfig
+
+from ldm.util import log_txt_as_img, exists, default, ismap, isimage, mean_flat, count_params, instantiate_from_config
+from ldm.modules.ema import LitEma
+from ldm.modules.distributions.distributions import normal_kl, DiagonalGaussianDistribution
+from ldm.models.autoencoder import IdentityFirstStage, AutoencoderKL
+from ldm.modules.diffusionmodules.util import make_beta_schedule, extract_into_tensor, noise_like
+from ldm.models.diffusion.ddim import DDIMSampler
+
+
+__conditioning_keys__ = {'concat': 'c_concat',
+ 'crossattn': 'c_crossattn',
+ 'adm': 'y'}
+
+
+def disabled_train(self, mode=True):
+ """Overwrite model.train with this function to make sure train/eval mode
+ does not change anymore."""
+ return self
+
+
+def uniform_on_device(r1, r2, shape, device):
+ return (r1 - r2) * torch.rand(*shape, device=device) + r2
+
+
+class DDPM(pl.LightningModule):
+ # classic DDPM with Gaussian diffusion, in image space
+ def __init__(self,
+ unet_config,
+ timesteps=1000,
+ beta_schedule="linear",
+ loss_type="l2",
+ ckpt_path=None,
+ ignore_keys=[],
+ load_only_unet=False,
+ monitor="val/loss",
+ use_ema=True,
+ first_stage_key="image",
+ image_size=256,
+ channels=3,
+ log_every_t=100,
+ clip_denoised=True,
+ linear_start=1e-4,
+ linear_end=2e-2,
+ cosine_s=8e-3,
+ given_betas=None,
+ original_elbo_weight=0.,
+ v_posterior=0., # weight for choosing posterior variance as sigma = (1-v) * beta_tilde + v * beta
+ l_simple_weight=1.,
+ conditioning_key=None,
+ parameterization="eps", # all assuming fixed variance schedules
+ scheduler_config=None,
+ use_positional_encodings=False,
+ learn_logvar=False,
+ logvar_init=0.,
+ make_it_fit=False,
+ ucg_training=None,
+ reset_ema=False,
+ reset_num_ema_updates=False,
+ ):
+ super().__init__()
+ assert parameterization in ["eps", "x0", "v"], 'currently only supporting "eps" and "x0" and "v"'
+ self.parameterization = parameterization
+ print(f"{self.__class__.__name__}: Running in {self.parameterization}-prediction mode")
+ self.cond_stage_model = None
+ self.clip_denoised = clip_denoised
+ self.log_every_t = log_every_t
+ self.first_stage_key = first_stage_key
+ self.image_size = image_size # try conv?
+ self.channels = channels
+ self.use_positional_encodings = use_positional_encodings
+ self.model = DiffusionWrapper(unet_config, conditioning_key)
+ count_params(self.model, verbose=True)
+ self.use_ema = use_ema
+ if self.use_ema:
+ self.model_ema = LitEma(self.model)
+ print(f"Keeping EMAs of {len(list(self.model_ema.buffers()))}.")
+
+ self.use_scheduler = scheduler_config is not None
+ if self.use_scheduler:
+ self.scheduler_config = scheduler_config
+
+ self.v_posterior = v_posterior
+ self.original_elbo_weight = original_elbo_weight
+ self.l_simple_weight = l_simple_weight
+
+ if monitor is not None:
+ self.monitor = monitor
+ self.make_it_fit = make_it_fit
+ if reset_ema: assert exists(ckpt_path)
+ if ckpt_path is not None:
+ self.init_from_ckpt(ckpt_path, ignore_keys=ignore_keys, only_model=load_only_unet)
+ if reset_ema:
+ assert self.use_ema
+ print(f"Resetting ema to pure model weights. This is useful when restoring from an ema-only checkpoint.")
+ self.model_ema = LitEma(self.model)
+ if reset_num_ema_updates:
+ print(" +++++++++++ WARNING: RESETTING NUM_EMA UPDATES TO ZERO +++++++++++ ")
+ assert self.use_ema
+ self.model_ema.reset_num_updates()
+
+ self.register_schedule(given_betas=given_betas, beta_schedule=beta_schedule, timesteps=timesteps,
+ linear_start=linear_start, linear_end=linear_end, cosine_s=cosine_s)
+
+ self.loss_type = loss_type
+
+ self.learn_logvar = learn_logvar
+ logvar = torch.full(fill_value=logvar_init, size=(self.num_timesteps,))
+ if self.learn_logvar:
+ self.logvar = nn.Parameter(self.logvar, requires_grad=True)
+ else:
+ self.register_buffer('logvar', logvar)
+
+ self.ucg_training = ucg_training or dict()
+ if self.ucg_training:
+ self.ucg_prng = np.random.RandomState()
+
+ def register_schedule(self, given_betas=None, beta_schedule="linear", timesteps=1000,
+ linear_start=1e-4, linear_end=2e-2, cosine_s=8e-3):
+ if exists(given_betas):
+ betas = given_betas
+ else:
+ betas = make_beta_schedule(beta_schedule, timesteps, linear_start=linear_start, linear_end=linear_end,
+ cosine_s=cosine_s)
+ alphas = 1. - betas
+ alphas_cumprod = np.cumprod(alphas, axis=0)
+ alphas_cumprod_prev = np.append(1., alphas_cumprod[:-1])
+
+ timesteps, = betas.shape
+ self.num_timesteps = int(timesteps)
+ self.linear_start = linear_start
+ self.linear_end = linear_end
+ assert alphas_cumprod.shape[0] == self.num_timesteps, 'alphas have to be defined for each timestep'
+
+ to_torch = partial(torch.tensor, dtype=torch.float32)
+
+ self.register_buffer('betas', to_torch(betas))
+ self.register_buffer('alphas_cumprod', to_torch(alphas_cumprod))
+ self.register_buffer('alphas_cumprod_prev', to_torch(alphas_cumprod_prev))
+
+ # calculations for diffusion q(x_t | x_{t-1}) and others
+ self.register_buffer('sqrt_alphas_cumprod', to_torch(np.sqrt(alphas_cumprod)))
+ self.register_buffer('sqrt_one_minus_alphas_cumprod', to_torch(np.sqrt(1. - alphas_cumprod)))
+ self.register_buffer('log_one_minus_alphas_cumprod', to_torch(np.log(1. - alphas_cumprod)))
+ self.register_buffer('sqrt_recip_alphas_cumprod', to_torch(np.sqrt(1. / alphas_cumprod)))
+ self.register_buffer('sqrt_recipm1_alphas_cumprod', to_torch(np.sqrt(1. / alphas_cumprod - 1)))
+
+ # calculations for posterior q(x_{t-1} | x_t, x_0)
+ posterior_variance = (1 - self.v_posterior) * betas * (1. - alphas_cumprod_prev) / (
+ 1. - alphas_cumprod) + self.v_posterior * betas
+ # above: equal to 1. / (1. / (1. - alpha_cumprod_tm1) + alpha_t / beta_t)
+ self.register_buffer('posterior_variance', to_torch(posterior_variance))
+ # below: log calculation clipped because the posterior variance is 0 at the beginning of the diffusion chain
+ self.register_buffer('posterior_log_variance_clipped', to_torch(np.log(np.maximum(posterior_variance, 1e-20))))
+ self.register_buffer('posterior_mean_coef1', to_torch(
+ betas * np.sqrt(alphas_cumprod_prev) / (1. - alphas_cumprod)))
+ self.register_buffer('posterior_mean_coef2', to_torch(
+ (1. - alphas_cumprod_prev) * np.sqrt(alphas) / (1. - alphas_cumprod)))
+
+ if self.parameterization == "eps":
+ lvlb_weights = self.betas ** 2 / (
+ 2 * self.posterior_variance * to_torch(alphas) * (1 - self.alphas_cumprod))
+ elif self.parameterization == "x0":
+ lvlb_weights = 0.5 * np.sqrt(torch.Tensor(alphas_cumprod)) / (2. * 1 - torch.Tensor(alphas_cumprod))
+ elif self.parameterization == "v":
+ lvlb_weights = torch.ones_like(self.betas ** 2 / (
+ 2 * self.posterior_variance * to_torch(alphas) * (1 - self.alphas_cumprod)))
+ else:
+ raise NotImplementedError("mu not supported")
+ lvlb_weights[0] = lvlb_weights[1]
+ self.register_buffer('lvlb_weights', lvlb_weights, persistent=False)
+ assert not torch.isnan(self.lvlb_weights).all()
+
+ @contextmanager
+ def ema_scope(self, context=None):
+ if self.use_ema:
+ self.model_ema.store(self.model.parameters())
+ self.model_ema.copy_to(self.model)
+ if context is not None:
+ print(f"{context}: Switched to EMA weights")
+ try:
+ yield None
+ finally:
+ if self.use_ema:
+ self.model_ema.restore(self.model.parameters())
+ if context is not None:
+ print(f"{context}: Restored training weights")
+
+ @torch.no_grad()
+ def init_from_ckpt(self, path, ignore_keys=list(), only_model=False):
+ sd = torch.load(path, map_location="cpu")
+ if "state_dict" in list(sd.keys()):
+ sd = sd["state_dict"]
+ keys = list(sd.keys())
+ for k in keys:
+ for ik in ignore_keys:
+ if k.startswith(ik):
+ print("Deleting key {} from state_dict.".format(k))
+ del sd[k]
+ if self.make_it_fit:
+ n_params = len([name for name, _ in
+ itertools.chain(self.named_parameters(),
+ self.named_buffers())])
+ for name, param in tqdm(
+ itertools.chain(self.named_parameters(),
+ self.named_buffers()),
+ desc="Fitting old weights to new weights",
+ total=n_params
+ ):
+ if not name in sd:
+ continue
+ old_shape = sd[name].shape
+ new_shape = param.shape
+ assert len(old_shape) == len(new_shape)
+ if len(new_shape) > 2:
+ # we only modify first two axes
+ assert new_shape[2:] == old_shape[2:]
+ # assumes first axis corresponds to output dim
+ if not new_shape == old_shape:
+ new_param = param.clone()
+ old_param = sd[name]
+ if len(new_shape) == 1:
+ for i in range(new_param.shape[0]):
+ new_param[i] = old_param[i % old_shape[0]]
+ elif len(new_shape) >= 2:
+ for i in range(new_param.shape[0]):
+ for j in range(new_param.shape[1]):
+ new_param[i, j] = old_param[i % old_shape[0], j % old_shape[1]]
+
+ n_used_old = torch.ones(old_shape[1])
+ for j in range(new_param.shape[1]):
+ n_used_old[j % old_shape[1]] += 1
+ n_used_new = torch.zeros(new_shape[1])
+ for j in range(new_param.shape[1]):
+ n_used_new[j] = n_used_old[j % old_shape[1]]
+
+ n_used_new = n_used_new[None, :]
+ while len(n_used_new.shape) < len(new_shape):
+ n_used_new = n_used_new.unsqueeze(-1)
+ new_param /= n_used_new
+
+ sd[name] = new_param
+
+ missing, unexpected = self.load_state_dict(sd, strict=False) if not only_model else self.model.load_state_dict(
+ sd, strict=False)
+ print(f"Restored from {path} with {len(missing)} missing and {len(unexpected)} unexpected keys")
+ if len(missing) > 0:
+ print(f"Missing Keys:\n {missing}")
+ if len(unexpected) > 0:
+ print(f"\nUnexpected Keys:\n {unexpected}")
+
+ def q_mean_variance(self, x_start, t):
+ """
+ Get the distribution q(x_t | x_0).
+ :param x_start: the [N x C x ...] tensor of noiseless inputs.
+ :param t: the number of diffusion steps (minus 1). Here, 0 means one step.
+ :return: A tuple (mean, variance, log_variance), all of x_start's shape.
+ """
+ mean = (extract_into_tensor(self.sqrt_alphas_cumprod, t, x_start.shape) * x_start)
+ variance = extract_into_tensor(1.0 - self.alphas_cumprod, t, x_start.shape)
+ log_variance = extract_into_tensor(self.log_one_minus_alphas_cumprod, t, x_start.shape)
+ return mean, variance, log_variance
+
+ def predict_start_from_noise(self, x_t, t, noise):
+ return (
+ extract_into_tensor(self.sqrt_recip_alphas_cumprod, t, x_t.shape) * x_t -
+ extract_into_tensor(self.sqrt_recipm1_alphas_cumprod, t, x_t.shape) * noise
+ )
+
+ def predict_start_from_z_and_v(self, x_t, t, v):
+ # self.register_buffer('sqrt_alphas_cumprod', to_torch(np.sqrt(alphas_cumprod)))
+ # self.register_buffer('sqrt_one_minus_alphas_cumprod', to_torch(np.sqrt(1. - alphas_cumprod)))
+ return (
+ extract_into_tensor(self.sqrt_alphas_cumprod, t, x_t.shape) * x_t -
+ extract_into_tensor(self.sqrt_one_minus_alphas_cumprod, t, x_t.shape) * v
+ )
+
+ def predict_eps_from_z_and_v(self, x_t, t, v):
+ return (
+ extract_into_tensor(self.sqrt_alphas_cumprod, t, x_t.shape) * v +
+ extract_into_tensor(self.sqrt_one_minus_alphas_cumprod, t, x_t.shape) * x_t
+ )
+
+ def q_posterior(self, x_start, x_t, t):
+ posterior_mean = (
+ extract_into_tensor(self.posterior_mean_coef1, t, x_t.shape) * x_start +
+ extract_into_tensor(self.posterior_mean_coef2, t, x_t.shape) * x_t
+ )
+ posterior_variance = extract_into_tensor(self.posterior_variance, t, x_t.shape)
+ posterior_log_variance_clipped = extract_into_tensor(self.posterior_log_variance_clipped, t, x_t.shape)
+ return posterior_mean, posterior_variance, posterior_log_variance_clipped
+
+ def p_mean_variance(self, x, t, clip_denoised: bool):
+ model_out = self.model(x, t)
+ if self.parameterization == "eps":
+ x_recon = self.predict_start_from_noise(x, t=t, noise=model_out)
+ elif self.parameterization == "x0":
+ x_recon = model_out
+ if clip_denoised:
+ x_recon.clamp_(-1., 1.)
+
+ model_mean, posterior_variance, posterior_log_variance = self.q_posterior(x_start=x_recon, x_t=x, t=t)
+ return model_mean, posterior_variance, posterior_log_variance
+
+ @torch.no_grad()
+ def p_sample(self, x, t, clip_denoised=True, repeat_noise=False):
+ b, *_, device = *x.shape, x.device
+ model_mean, _, model_log_variance = self.p_mean_variance(x=x, t=t, clip_denoised=clip_denoised)
+ noise = noise_like(x.shape, device, repeat_noise)
+ # no noise when t == 0
+ nonzero_mask = (1 - (t == 0).float()).reshape(b, *((1,) * (len(x.shape) - 1)))
+ return model_mean + nonzero_mask * (0.5 * model_log_variance).exp() * noise
+
+ @torch.no_grad()
+ def p_sample_loop(self, shape, return_intermediates=False):
+ device = self.betas.device
+ b = shape[0]
+ img = torch.randn(shape, device=device)
+ intermediates = [img]
+ for i in tqdm(reversed(range(0, self.num_timesteps)), desc='Sampling t', total=self.num_timesteps):
+ img = self.p_sample(img, torch.full((b,), i, device=device, dtype=torch.long),
+ clip_denoised=self.clip_denoised)
+ if i % self.log_every_t == 0 or i == self.num_timesteps - 1:
+ intermediates.append(img)
+ if return_intermediates:
+ return img, intermediates
+ return img
+
+ @torch.no_grad()
+ def sample(self, batch_size=16, return_intermediates=False):
+ image_size = self.image_size
+ channels = self.channels
+ return self.p_sample_loop((batch_size, channels, image_size, image_size),
+ return_intermediates=return_intermediates)
+
+ def q_sample(self, x_start, t, noise=None):
+ noise = default(noise, lambda: torch.randn_like(x_start))
+ return (extract_into_tensor(self.sqrt_alphas_cumprod, t, x_start.shape) * x_start +
+ extract_into_tensor(self.sqrt_one_minus_alphas_cumprod, t, x_start.shape) * noise)
+
+ def get_v(self, x, noise, t):
+ return (
+ extract_into_tensor(self.sqrt_alphas_cumprod, t, x.shape) * noise -
+ extract_into_tensor(self.sqrt_one_minus_alphas_cumprod, t, x.shape) * x
+ )
+
+ def get_loss(self, pred, target, mean=True):
+ if self.loss_type == 'l1':
+ loss = (target - pred).abs()
+ if mean:
+ loss = loss.mean()
+ elif self.loss_type == 'l2':
+ if mean:
+ loss = torch.nn.functional.mse_loss(target, pred)
+ else:
+ loss = torch.nn.functional.mse_loss(target, pred, reduction='none')
+ else:
+ raise NotImplementedError("unknown loss type '{loss_type}'")
+
+ return loss
+
+ def p_losses(self, x_start, t, noise=None):
+ noise = default(noise, lambda: torch.randn_like(x_start))
+ x_noisy = self.q_sample(x_start=x_start, t=t, noise=noise)
+ model_out = self.model(x_noisy, t)
+
+ loss_dict = {}
+ if self.parameterization == "eps":
+ target = noise
+ elif self.parameterization == "x0":
+ target = x_start
+ elif self.parameterization == "v":
+ target = self.get_v(x_start, noise, t)
+ else:
+ raise NotImplementedError(f"Parameterization {self.parameterization} not yet supported")
+
+ loss = self.get_loss(model_out, target, mean=False).mean(dim=[1, 2, 3])
+
+ log_prefix = 'train' if self.training else 'val'
+
+ loss_dict.update({f'{log_prefix}/loss_simple': loss.mean()})
+ loss_simple = loss.mean() * self.l_simple_weight
+
+ loss_vlb = (self.lvlb_weights[t] * loss).mean()
+ loss_dict.update({f'{log_prefix}/loss_vlb': loss_vlb})
+
+ loss = loss_simple + self.original_elbo_weight * loss_vlb
+
+ loss_dict.update({f'{log_prefix}/loss': loss})
+
+ return loss, loss_dict
+
+ def forward(self, x, *args, **kwargs):
+ # b, c, h, w, device, img_size, = *x.shape, x.device, self.image_size
+ # assert h == img_size and w == img_size, f'height and width of image must be {img_size}'
+ t = torch.randint(0, self.num_timesteps, (x.shape[0],), device=self.device).long()
+ return self.p_losses(x, t, *args, **kwargs)
+
+ def get_input(self, batch, k):
+ x = batch[k]
+ if len(x.shape) == 3:
+ x = x[..., None]
+ x = rearrange(x, 'b h w c -> b c h w')
+ x = x.to(memory_format=torch.contiguous_format).float()
+ return x
+
+ def shared_step(self, batch):
+ x = self.get_input(batch, self.first_stage_key)
+ loss, loss_dict = self(x)
+ return loss, loss_dict
+
+ def training_step(self, batch, batch_idx):
+ for k in self.ucg_training:
+ p = self.ucg_training[k]["p"]
+ val = self.ucg_training[k]["val"]
+ if val is None:
+ val = ""
+ for i in range(len(batch[k])):
+ if self.ucg_prng.choice(2, p=[1 - p, p]):
+ batch[k][i] = val
+
+ loss, loss_dict = self.shared_step(batch)
+
+ self.log_dict(loss_dict, prog_bar=True,
+ logger=True, on_step=True, on_epoch=True)
+
+ self.log("global_step", self.global_step,
+ prog_bar=True, logger=True, on_step=True, on_epoch=False)
+
+ if self.use_scheduler:
+ lr = self.optimizers().param_groups[0]['lr']
+ self.log('lr_abs', lr, prog_bar=True, logger=True, on_step=True, on_epoch=False)
+
+ return loss
+
+ @torch.no_grad()
+ def validation_step(self, batch, batch_idx):
+ _, loss_dict_no_ema = self.shared_step(batch)
+ with self.ema_scope():
+ _, loss_dict_ema = self.shared_step(batch)
+ loss_dict_ema = {key + '_ema': loss_dict_ema[key] for key in loss_dict_ema}
+ self.log_dict(loss_dict_no_ema, prog_bar=False, logger=True, on_step=False, on_epoch=True)
+ self.log_dict(loss_dict_ema, prog_bar=False, logger=True, on_step=False, on_epoch=True)
+
+ def on_train_batch_end(self, *args, **kwargs):
+ if self.use_ema:
+ self.model_ema(self.model)
+
+ def _get_rows_from_list(self, samples):
+ n_imgs_per_row = len(samples)
+ denoise_grid = rearrange(samples, 'n b c h w -> b n c h w')
+ denoise_grid = rearrange(denoise_grid, 'b n c h w -> (b n) c h w')
+ denoise_grid = make_grid(denoise_grid, nrow=n_imgs_per_row)
+ return denoise_grid
+
+ @torch.no_grad()
+ def log_images(self, batch, N=8, n_row=2, sample=True, return_keys=None, **kwargs):
+ log = dict()
+ x = self.get_input(batch, self.first_stage_key)
+ N = min(x.shape[0], N)
+ n_row = min(x.shape[0], n_row)
+ x = x.to(self.device)[:N]
+ log["inputs"] = x
+
+ # get diffusion row
+ diffusion_row = list()
+ x_start = x[:n_row]
+
+ for t in range(self.num_timesteps):
+ if t % self.log_every_t == 0 or t == self.num_timesteps - 1:
+ t = repeat(torch.tensor([t]), '1 -> b', b=n_row)
+ t = t.to(self.device).long()
+ noise = torch.randn_like(x_start)
+ x_noisy = self.q_sample(x_start=x_start, t=t, noise=noise)
+ diffusion_row.append(x_noisy)
+
+ log["diffusion_row"] = self._get_rows_from_list(diffusion_row)
+
+ if sample:
+ # get denoise row
+ with self.ema_scope("Plotting"):
+ samples, denoise_row = self.sample(batch_size=N, return_intermediates=True)
+
+ log["samples"] = samples
+ log["denoise_row"] = self._get_rows_from_list(denoise_row)
+
+ if return_keys:
+ if np.intersect1d(list(log.keys()), return_keys).shape[0] == 0:
+ return log
+ else:
+ return {key: log[key] for key in return_keys}
+ return log
+
+ def configure_optimizers(self):
+ lr = self.learning_rate
+ params = list(self.model.parameters())
+ if self.learn_logvar:
+ params = params + [self.logvar]
+ opt = torch.optim.AdamW(params, lr=lr)
+ return opt
+
+
+class LatentDiffusion(DDPM):
+ """main class"""
+
+ def __init__(self,
+ first_stage_config,
+ cond_stage_config,
+ num_timesteps_cond=None,
+ cond_stage_key="image",
+ cond_stage_trainable=False,
+ concat_mode=True,
+ cond_stage_forward=None,
+ conditioning_key=None,
+ scale_factor=1.0,
+ scale_by_std=False,
+ force_null_conditioning=False,
+ *args, **kwargs):
+ self.force_null_conditioning = force_null_conditioning
+ self.num_timesteps_cond = default(num_timesteps_cond, 1)
+ self.scale_by_std = scale_by_std
+ assert self.num_timesteps_cond <= kwargs['timesteps']
+ # for backwards compatibility after implementation of DiffusionWrapper
+ if conditioning_key is None:
+ conditioning_key = 'concat' if concat_mode else 'crossattn'
+ if cond_stage_config == '__is_unconditional__' and not self.force_null_conditioning:
+ conditioning_key = None
+ ckpt_path = kwargs.pop("ckpt_path", None)
+ reset_ema = kwargs.pop("reset_ema", False)
+ reset_num_ema_updates = kwargs.pop("reset_num_ema_updates", False)
+ ignore_keys = kwargs.pop("ignore_keys", [])
+ super().__init__(conditioning_key=conditioning_key, *args, **kwargs)
+ self.concat_mode = concat_mode
+ self.cond_stage_trainable = cond_stage_trainable
+ self.cond_stage_key = cond_stage_key
+ try:
+ self.num_downs = len(first_stage_config.params.ddconfig.ch_mult) - 1
+ except:
+ self.num_downs = 0
+ if not scale_by_std:
+ self.scale_factor = scale_factor
+ else:
+ self.register_buffer('scale_factor', torch.tensor(scale_factor))
+ self.instantiate_first_stage(first_stage_config)
+ self.instantiate_cond_stage(cond_stage_config)
+ self.cond_stage_forward = cond_stage_forward
+ self.clip_denoised = False
+ self.bbox_tokenizer = None
+
+ self.restarted_from_ckpt = False
+ if ckpt_path is not None:
+ self.init_from_ckpt(ckpt_path, ignore_keys)
+ self.restarted_from_ckpt = True
+ if reset_ema:
+ assert self.use_ema
+ print(
+ f"Resetting ema to pure model weights. This is useful when restoring from an ema-only checkpoint.")
+ self.model_ema = LitEma(self.model)
+ if reset_num_ema_updates:
+ print(" +++++++++++ WARNING: RESETTING NUM_EMA UPDATES TO ZERO +++++++++++ ")
+ assert self.use_ema
+ self.model_ema.reset_num_updates()
+
+ def make_cond_schedule(self, ):
+ self.cond_ids = torch.full(size=(self.num_timesteps,), fill_value=self.num_timesteps - 1, dtype=torch.long)
+ ids = torch.round(torch.linspace(0, self.num_timesteps - 1, self.num_timesteps_cond)).long()
+ self.cond_ids[:self.num_timesteps_cond] = ids
+
+ @rank_zero_only
+ @torch.no_grad()
+ def on_train_batch_start(self, batch, batch_idx, dataloader_idx):
+ # only for very first batch
+ if self.scale_by_std and self.current_epoch == 0 and self.global_step == 0 and batch_idx == 0 and not self.restarted_from_ckpt:
+ assert self.scale_factor == 1., 'rather not use custom rescaling and std-rescaling simultaneously'
+ # set rescale weight to 1./std of encodings
+ print("### USING STD-RESCALING ###")
+ x = super().get_input(batch, self.first_stage_key)
+ x = x.to(self.device)
+ encoder_posterior = self.encode_first_stage(x)
+ z = self.get_first_stage_encoding(encoder_posterior).detach()
+ del self.scale_factor
+ self.register_buffer('scale_factor', 1. / z.flatten().std())
+ print(f"setting self.scale_factor to {self.scale_factor}")
+ print("### USING STD-RESCALING ###")
+
+ def register_schedule(self,
+ given_betas=None, beta_schedule="linear", timesteps=1000,
+ linear_start=1e-4, linear_end=2e-2, cosine_s=8e-3):
+ super().register_schedule(given_betas, beta_schedule, timesteps, linear_start, linear_end, cosine_s)
+
+ self.shorten_cond_schedule = self.num_timesteps_cond > 1
+ if self.shorten_cond_schedule:
+ self.make_cond_schedule()
+
+ def instantiate_first_stage(self, config):
+ model = instantiate_from_config(config)
+ self.first_stage_model = model.eval()
+ self.first_stage_model.train = disabled_train
+ for param in self.first_stage_model.parameters():
+ param.requires_grad = False
+
+ def instantiate_cond_stage(self, config):
+ if not self.cond_stage_trainable:
+ if config == "__is_first_stage__":
+ print("Using first stage also as cond stage.")
+ self.cond_stage_model = self.first_stage_model
+ elif config == "__is_unconditional__":
+ print(f"Training {self.__class__.__name__} as an unconditional model.")
+ self.cond_stage_model = None
+ # self.be_unconditional = True
+ else:
+ model = instantiate_from_config(config)
+ self.cond_stage_model = model.eval()
+ self.cond_stage_model.train = disabled_train
+ for param in self.cond_stage_model.parameters():
+ param.requires_grad = False
+ else:
+ assert config != '__is_first_stage__'
+ assert config != '__is_unconditional__'
+ model = instantiate_from_config(config)
+ self.cond_stage_model = model
+
+ def _get_denoise_row_from_list(self, samples, desc='', force_no_decoder_quantization=False):
+ denoise_row = []
+ for zd in tqdm(samples, desc=desc):
+ denoise_row.append(self.decode_first_stage(zd.to(self.device),
+ force_not_quantize=force_no_decoder_quantization))
+ n_imgs_per_row = len(denoise_row)
+ denoise_row = torch.stack(denoise_row) # n_log_step, n_row, C, H, W
+ denoise_grid = rearrange(denoise_row, 'n b c h w -> b n c h w')
+ denoise_grid = rearrange(denoise_grid, 'b n c h w -> (b n) c h w')
+ denoise_grid = make_grid(denoise_grid, nrow=n_imgs_per_row)
+ return denoise_grid
+
+ def get_first_stage_encoding(self, encoder_posterior):
+ if isinstance(encoder_posterior, DiagonalGaussianDistribution):
+ z = encoder_posterior.sample()
+ elif isinstance(encoder_posterior, torch.Tensor):
+ z = encoder_posterior
+ else:
+ raise NotImplementedError(f"encoder_posterior of type '{type(encoder_posterior)}' not yet implemented")
+ return self.scale_factor * z
+
+ def get_learned_conditioning(self, c):
+ if self.cond_stage_forward is None:
+ if hasattr(self.cond_stage_model, 'encode') and callable(self.cond_stage_model.encode):
+ c = self.cond_stage_model.encode(c)
+ if isinstance(c, DiagonalGaussianDistribution):
+ c = c.mode()
+ else:
+ c = self.cond_stage_model(c)
+ else:
+ assert hasattr(self.cond_stage_model, self.cond_stage_forward)
+ c = getattr(self.cond_stage_model, self.cond_stage_forward)(c)
+ return c
+
+ def meshgrid(self, h, w):
+ y = torch.arange(0, h).view(h, 1, 1).repeat(1, w, 1)
+ x = torch.arange(0, w).view(1, w, 1).repeat(h, 1, 1)
+
+ arr = torch.cat([y, x], dim=-1)
+ return arr
+
+ def delta_border(self, h, w):
+ """
+ :param h: height
+ :param w: width
+ :return: normalized distance to image border,
+ wtith min distance = 0 at border and max dist = 0.5 at image center
+ """
+ lower_right_corner = torch.tensor([h - 1, w - 1]).view(1, 1, 2)
+ arr = self.meshgrid(h, w) / lower_right_corner
+ dist_left_up = torch.min(arr, dim=-1, keepdims=True)[0]
+ dist_right_down = torch.min(1 - arr, dim=-1, keepdims=True)[0]
+ edge_dist = torch.min(torch.cat([dist_left_up, dist_right_down], dim=-1), dim=-1)[0]
+ return edge_dist
+
+ def get_weighting(self, h, w, Ly, Lx, device):
+ weighting = self.delta_border(h, w)
+ weighting = torch.clip(weighting, self.split_input_params["clip_min_weight"],
+ self.split_input_params["clip_max_weight"], )
+ weighting = weighting.view(1, h * w, 1).repeat(1, 1, Ly * Lx).to(device)
+
+ if self.split_input_params["tie_braker"]:
+ L_weighting = self.delta_border(Ly, Lx)
+ L_weighting = torch.clip(L_weighting,
+ self.split_input_params["clip_min_tie_weight"],
+ self.split_input_params["clip_max_tie_weight"])
+
+ L_weighting = L_weighting.view(1, 1, Ly * Lx).to(device)
+ weighting = weighting * L_weighting
+ return weighting
+
+ def get_fold_unfold(self, x, kernel_size, stride, uf=1, df=1): # todo load once not every time, shorten code
+ """
+ :param x: img of size (bs, c, h, w)
+ :return: n img crops of size (n, bs, c, kernel_size[0], kernel_size[1])
+ """
+ bs, nc, h, w = x.shape
+
+ # number of crops in image
+ Ly = (h - kernel_size[0]) // stride[0] + 1
+ Lx = (w - kernel_size[1]) // stride[1] + 1
+
+ if uf == 1 and df == 1:
+ fold_params = dict(kernel_size=kernel_size, dilation=1, padding=0, stride=stride)
+ unfold = torch.nn.Unfold(**fold_params)
+
+ fold = torch.nn.Fold(output_size=x.shape[2:], **fold_params)
+
+ weighting = self.get_weighting(kernel_size[0], kernel_size[1], Ly, Lx, x.device).to(x.dtype)
+ normalization = fold(weighting).view(1, 1, h, w) # normalizes the overlap
+ weighting = weighting.view((1, 1, kernel_size[0], kernel_size[1], Ly * Lx))
+
+ elif uf > 1 and df == 1:
+ fold_params = dict(kernel_size=kernel_size, dilation=1, padding=0, stride=stride)
+ unfold = torch.nn.Unfold(**fold_params)
+
+ fold_params2 = dict(kernel_size=(kernel_size[0] * uf, kernel_size[0] * uf),
+ dilation=1, padding=0,
+ stride=(stride[0] * uf, stride[1] * uf))
+ fold = torch.nn.Fold(output_size=(x.shape[2] * uf, x.shape[3] * uf), **fold_params2)
+
+ weighting = self.get_weighting(kernel_size[0] * uf, kernel_size[1] * uf, Ly, Lx, x.device).to(x.dtype)
+ normalization = fold(weighting).view(1, 1, h * uf, w * uf) # normalizes the overlap
+ weighting = weighting.view((1, 1, kernel_size[0] * uf, kernel_size[1] * uf, Ly * Lx))
+
+ elif df > 1 and uf == 1:
+ fold_params = dict(kernel_size=kernel_size, dilation=1, padding=0, stride=stride)
+ unfold = torch.nn.Unfold(**fold_params)
+
+ fold_params2 = dict(kernel_size=(kernel_size[0] // df, kernel_size[0] // df),
+ dilation=1, padding=0,
+ stride=(stride[0] // df, stride[1] // df))
+ fold = torch.nn.Fold(output_size=(x.shape[2] // df, x.shape[3] // df), **fold_params2)
+
+ weighting = self.get_weighting(kernel_size[0] // df, kernel_size[1] // df, Ly, Lx, x.device).to(x.dtype)
+ normalization = fold(weighting).view(1, 1, h // df, w // df) # normalizes the overlap
+ weighting = weighting.view((1, 1, kernel_size[0] // df, kernel_size[1] // df, Ly * Lx))
+
+ else:
+ raise NotImplementedError
+
+ return fold, unfold, normalization, weighting
+
+ @torch.no_grad()
+ def get_input(self, batch, k, return_first_stage_outputs=False, force_c_encode=False,
+ cond_key=None, return_original_cond=False, bs=None, return_x=False):
+ x = super().get_input(batch, k)
+ if bs is not None:
+ x = x[:bs]
+ x = x.to(self.device)
+ encoder_posterior = self.encode_first_stage(x)
+ z = self.get_first_stage_encoding(encoder_posterior).detach()
+
+ if self.model.conditioning_key is not None and not self.force_null_conditioning:
+ if cond_key is None:
+ cond_key = self.cond_stage_key
+ if cond_key != self.first_stage_key:
+ if cond_key in ['caption', 'coordinates_bbox', "txt"]:
+ xc = batch[cond_key]
+ elif cond_key in ['class_label', 'cls']:
+ xc = batch
+ else:
+ xc = super().get_input(batch, cond_key).to(self.device)
+ else:
+ xc = x
+ if not self.cond_stage_trainable or force_c_encode:
+ if isinstance(xc, dict) or isinstance(xc, list):
+ c = self.get_learned_conditioning(xc)
+ else:
+ c = self.get_learned_conditioning(xc.to(self.device))
+ else:
+ c = xc
+ if bs is not None:
+ c = c[:bs]
+
+ if self.use_positional_encodings:
+ pos_x, pos_y = self.compute_latent_shifts(batch)
+ ckey = __conditioning_keys__[self.model.conditioning_key]
+ c = {ckey: c, 'pos_x': pos_x, 'pos_y': pos_y}
+
+ else:
+ c = None
+ xc = None
+ if self.use_positional_encodings:
+ pos_x, pos_y = self.compute_latent_shifts(batch)
+ c = {'pos_x': pos_x, 'pos_y': pos_y}
+ out = [z, c]
+ if return_first_stage_outputs:
+ xrec = self.decode_first_stage(z)
+ out.extend([x, xrec])
+ if return_x:
+ out.extend([x])
+ if return_original_cond:
+ out.append(xc)
+ return out
+
+ @torch.no_grad()
+ def decode_first_stage(self, z, predict_cids=False, force_not_quantize=False):
+ if predict_cids:
+ if z.dim() == 4:
+ z = torch.argmax(z.exp(), dim=1).long()
+ z = self.first_stage_model.quantize.get_codebook_entry(z, shape=None)
+ z = rearrange(z, 'b h w c -> b c h w').contiguous()
+
+ z = 1. / self.scale_factor * z
+ return self.first_stage_model.decode(z)
+
+ @torch.no_grad()
+ def encode_first_stage(self, x):
+ return self.first_stage_model.encode(x)
+
+ def shared_step(self, batch, **kwargs):
+ x, c = self.get_input(batch, self.first_stage_key)
+ loss = self(x, c)
+ return loss
+
+ def forward(self, x, c, *args, **kwargs):
+ t = torch.randint(0, self.num_timesteps, (x.shape[0],), device=self.device).long()
+ if self.model.conditioning_key is not None:
+ assert c is not None
+ if self.cond_stage_trainable:
+ c = self.get_learned_conditioning(c)
+ if self.shorten_cond_schedule: # TODO: drop this option
+ tc = self.cond_ids[t].to(self.device)
+ c = self.q_sample(x_start=c, t=tc, noise=torch.randn_like(c.float()))
+ return self.p_losses(x, c, t, *args, **kwargs)
+
+ def apply_model(self, x_noisy, t, cond, return_ids=False):
+ if isinstance(cond, dict):
+ # hybrid case, cond is expected to be a dict
+ pass
+ else:
+ if not isinstance(cond, list):
+ cond = [cond]
+ key = 'c_concat' if self.model.conditioning_key == 'concat' else 'c_crossattn'
+ cond = {key: cond}
+
+ x_recon = self.model(x_noisy, t, **cond)
+
+ if isinstance(x_recon, tuple) and not return_ids:
+ return x_recon[0]
+ else:
+ return x_recon
+
+ def _predict_eps_from_xstart(self, x_t, t, pred_xstart):
+ return (extract_into_tensor(self.sqrt_recip_alphas_cumprod, t, x_t.shape) * x_t - pred_xstart) / \
+ extract_into_tensor(self.sqrt_recipm1_alphas_cumprod, t, x_t.shape)
+
+ def _prior_bpd(self, x_start):
+ """
+ Get the prior KL term for the variational lower-bound, measured in
+ bits-per-dim.
+ This term can't be optimized, as it only depends on the encoder.
+ :param x_start: the [N x C x ...] tensor of inputs.
+ :return: a batch of [N] KL values (in bits), one per batch element.
+ """
+ batch_size = x_start.shape[0]
+ t = torch.tensor([self.num_timesteps - 1] * batch_size, device=x_start.device)
+ qt_mean, _, qt_log_variance = self.q_mean_variance(x_start, t)
+ kl_prior = normal_kl(mean1=qt_mean, logvar1=qt_log_variance, mean2=0.0, logvar2=0.0)
+ return mean_flat(kl_prior) / np.log(2.0)
+
+ def p_losses(self, x_start, cond, t, noise=None):
+ noise = default(noise, lambda: torch.randn_like(x_start))
+ x_noisy = self.q_sample(x_start=x_start, t=t, noise=noise)
+ model_output = self.apply_model(x_noisy, t, cond)
+
+ loss_dict = {}
+ prefix = 'train' if self.training else 'val'
+
+ if self.parameterization == "x0":
+ target = x_start
+ elif self.parameterization == "eps":
+ target = noise
+ elif self.parameterization == "v":
+ target = self.get_v(x_start, noise, t)
+ else:
+ raise NotImplementedError()
+
+ loss_simple = self.get_loss(model_output, target, mean=False).mean([1, 2, 3])
+ loss_dict.update({f'{prefix}/loss_simple': loss_simple.mean()})
+
+ logvar_t = self.logvar[t].to(self.device)
+ loss = loss_simple / torch.exp(logvar_t) + logvar_t
+ # loss = loss_simple / torch.exp(self.logvar) + self.logvar
+ if self.learn_logvar:
+ loss_dict.update({f'{prefix}/loss_gamma': loss.mean()})
+ loss_dict.update({'logvar': self.logvar.data.mean()})
+
+ loss = self.l_simple_weight * loss.mean()
+
+ loss_vlb = self.get_loss(model_output, target, mean=False).mean(dim=(1, 2, 3))
+ loss_vlb = (self.lvlb_weights[t] * loss_vlb).mean()
+ loss_dict.update({f'{prefix}/loss_vlb': loss_vlb})
+ loss += (self.original_elbo_weight * loss_vlb)
+ loss_dict.update({f'{prefix}/loss': loss})
+
+ return loss, loss_dict
+
+ def p_mean_variance(self, x, c, t, clip_denoised: bool, return_codebook_ids=False, quantize_denoised=False,
+ return_x0=False, score_corrector=None, corrector_kwargs=None):
+ t_in = t
+ model_out = self.apply_model(x, t_in, c, return_ids=return_codebook_ids)
+
+ if score_corrector is not None:
+ assert self.parameterization == "eps"
+ model_out = score_corrector.modify_score(self, model_out, x, t, c, **corrector_kwargs)
+
+ if return_codebook_ids:
+ model_out, logits = model_out
+
+ if self.parameterization == "eps":
+ x_recon = self.predict_start_from_noise(x, t=t, noise=model_out)
+ elif self.parameterization == "x0":
+ x_recon = model_out
+ else:
+ raise NotImplementedError()
+
+ if clip_denoised:
+ x_recon.clamp_(-1., 1.)
+ if quantize_denoised:
+ x_recon, _, [_, _, indices] = self.first_stage_model.quantize(x_recon)
+ model_mean, posterior_variance, posterior_log_variance = self.q_posterior(x_start=x_recon, x_t=x, t=t)
+ if return_codebook_ids:
+ return model_mean, posterior_variance, posterior_log_variance, logits
+ elif return_x0:
+ return model_mean, posterior_variance, posterior_log_variance, x_recon
+ else:
+ return model_mean, posterior_variance, posterior_log_variance
+
+ @torch.no_grad()
+ def p_sample(self, x, c, t, clip_denoised=False, repeat_noise=False,
+ return_codebook_ids=False, quantize_denoised=False, return_x0=False,
+ temperature=1., noise_dropout=0., score_corrector=None, corrector_kwargs=None):
+ b, *_, device = *x.shape, x.device
+ outputs = self.p_mean_variance(x=x, c=c, t=t, clip_denoised=clip_denoised,
+ return_codebook_ids=return_codebook_ids,
+ quantize_denoised=quantize_denoised,
+ return_x0=return_x0,
+ score_corrector=score_corrector, corrector_kwargs=corrector_kwargs)
+ if return_codebook_ids:
+ raise DeprecationWarning("Support dropped.")
+ model_mean, _, model_log_variance, logits = outputs
+ elif return_x0:
+ model_mean, _, model_log_variance, x0 = outputs
+ else:
+ model_mean, _, model_log_variance = outputs
+
+ noise = noise_like(x.shape, device, repeat_noise) * temperature
+ if noise_dropout > 0.:
+ noise = torch.nn.functional.dropout(noise, p=noise_dropout)
+ # no noise when t == 0
+ nonzero_mask = (1 - (t == 0).float()).reshape(b, *((1,) * (len(x.shape) - 1)))
+
+ if return_codebook_ids:
+ return model_mean + nonzero_mask * (0.5 * model_log_variance).exp() * noise, logits.argmax(dim=1)
+ if return_x0:
+ return model_mean + nonzero_mask * (0.5 * model_log_variance).exp() * noise, x0
+ else:
+ return model_mean + nonzero_mask * (0.5 * model_log_variance).exp() * noise
+
+ @torch.no_grad()
+ def progressive_denoising(self, cond, shape, verbose=True, callback=None, quantize_denoised=False,
+ img_callback=None, mask=None, x0=None, temperature=1., noise_dropout=0.,
+ score_corrector=None, corrector_kwargs=None, batch_size=None, x_T=None, start_T=None,
+ log_every_t=None):
+ if not log_every_t:
+ log_every_t = self.log_every_t
+ timesteps = self.num_timesteps
+ if batch_size is not None:
+ b = batch_size if batch_size is not None else shape[0]
+ shape = [batch_size] + list(shape)
+ else:
+ b = batch_size = shape[0]
+ if x_T is None:
+ img = torch.randn(shape, device=self.device)
+ else:
+ img = x_T
+ intermediates = []
+ if cond is not None:
+ if isinstance(cond, dict):
+ cond = {key: cond[key][:batch_size] if not isinstance(cond[key], list) else
+ list(map(lambda x: x[:batch_size], cond[key])) for key in cond}
+ else:
+ cond = [c[:batch_size] for c in cond] if isinstance(cond, list) else cond[:batch_size]
+
+ if start_T is not None:
+ timesteps = min(timesteps, start_T)
+ iterator = tqdm(reversed(range(0, timesteps)), desc='Progressive Generation',
+ total=timesteps) if verbose else reversed(
+ range(0, timesteps))
+ if type(temperature) == float:
+ temperature = [temperature] * timesteps
+
+ for i in iterator:
+ ts = torch.full((b,), i, device=self.device, dtype=torch.long)
+ if self.shorten_cond_schedule:
+ assert self.model.conditioning_key != 'hybrid'
+ tc = self.cond_ids[ts].to(cond.device)
+ cond = self.q_sample(x_start=cond, t=tc, noise=torch.randn_like(cond))
+
+ img, x0_partial = self.p_sample(img, cond, ts,
+ clip_denoised=self.clip_denoised,
+ quantize_denoised=quantize_denoised, return_x0=True,
+ temperature=temperature[i], noise_dropout=noise_dropout,
+ score_corrector=score_corrector, corrector_kwargs=corrector_kwargs)
+ if mask is not None:
+ assert x0 is not None
+ img_orig = self.q_sample(x0, ts)
+ img = img_orig * mask + (1. - mask) * img
+
+ if i % log_every_t == 0 or i == timesteps - 1:
+ intermediates.append(x0_partial)
+ if callback: callback(i)
+ if img_callback: img_callback(img, i)
+ return img, intermediates
+
+ @torch.no_grad()
+ def p_sample_loop(self, cond, shape, return_intermediates=False,
+ x_T=None, verbose=True, callback=None, timesteps=None, quantize_denoised=False,
+ mask=None, x0=None, img_callback=None, start_T=None,
+ log_every_t=None):
+
+ if not log_every_t:
+ log_every_t = self.log_every_t
+ device = self.betas.device
+ b = shape[0]
+ if x_T is None:
+ img = torch.randn(shape, device=device)
+ else:
+ img = x_T
+
+ intermediates = [img]
+ if timesteps is None:
+ timesteps = self.num_timesteps
+
+ if start_T is not None:
+ timesteps = min(timesteps, start_T)
+ iterator = tqdm(reversed(range(0, timesteps)), desc='Sampling t', total=timesteps) if verbose else reversed(
+ range(0, timesteps))
+
+ if mask is not None:
+ assert x0 is not None
+ assert x0.shape[2:3] == mask.shape[2:3] # spatial size has to match
+
+ for i in iterator:
+ ts = torch.full((b,), i, device=device, dtype=torch.long)
+ if self.shorten_cond_schedule:
+ assert self.model.conditioning_key != 'hybrid'
+ tc = self.cond_ids[ts].to(cond.device)
+ cond = self.q_sample(x_start=cond, t=tc, noise=torch.randn_like(cond))
+
+ img = self.p_sample(img, cond, ts,
+ clip_denoised=self.clip_denoised,
+ quantize_denoised=quantize_denoised)
+ if mask is not None:
+ img_orig = self.q_sample(x0, ts)
+ img = img_orig * mask + (1. - mask) * img
+
+ if i % log_every_t == 0 or i == timesteps - 1:
+ intermediates.append(img)
+ if callback: callback(i)
+ if img_callback: img_callback(img, i)
+
+ if return_intermediates:
+ return img, intermediates
+ return img
+
+ @torch.no_grad()
+ def sample(self, cond, batch_size=16, return_intermediates=False, x_T=None,
+ verbose=True, timesteps=None, quantize_denoised=False,
+ mask=None, x0=None, shape=None, **kwargs):
+ if shape is None:
+ shape = (batch_size, self.channels, self.image_size, self.image_size)
+ if cond is not None:
+ if isinstance(cond, dict):
+ cond = {key: cond[key][:batch_size] if not isinstance(cond[key], list) else
+ list(map(lambda x: x[:batch_size], cond[key])) for key in cond}
+ else:
+ cond = [c[:batch_size] for c in cond] if isinstance(cond, list) else cond[:batch_size]
+ return self.p_sample_loop(cond,
+ shape,
+ return_intermediates=return_intermediates, x_T=x_T,
+ verbose=verbose, timesteps=timesteps, quantize_denoised=quantize_denoised,
+ mask=mask, x0=x0)
+
+ @torch.no_grad()
+ def sample_log(self, cond, batch_size, ddim, ddim_steps, **kwargs):
+ if ddim:
+ ddim_sampler = DDIMSampler(self)
+ shape = (self.channels, self.image_size, self.image_size)
+ samples, intermediates = ddim_sampler.sample(ddim_steps, batch_size,
+ shape, cond, verbose=False, **kwargs)
+
+ else:
+ samples, intermediates = self.sample(cond=cond, batch_size=batch_size,
+ return_intermediates=True, **kwargs)
+
+ return samples, intermediates
+
+ @torch.no_grad()
+ def get_unconditional_conditioning(self, batch_size, null_label=None):
+ if null_label is not None:
+ xc = null_label
+ if isinstance(xc, ListConfig):
+ xc = list(xc)
+ if isinstance(xc, dict) or isinstance(xc, list):
+ c = self.get_learned_conditioning(xc)
+ else:
+ if hasattr(xc, "to"):
+ xc = xc.to(self.device)
+ c = self.get_learned_conditioning(xc)
+ else:
+ if self.cond_stage_key in ["class_label", "cls"]:
+ xc = self.cond_stage_model.get_unconditional_conditioning(batch_size, device=self.device)
+ return self.get_learned_conditioning(xc)
+ else:
+ raise NotImplementedError("todo")
+ if isinstance(c, list): # in case the encoder gives us a list
+ for i in range(len(c)):
+ c[i] = repeat(c[i], '1 ... -> b ...', b=batch_size).to(self.device)
+ else:
+ c = repeat(c, '1 ... -> b ...', b=batch_size).to(self.device)
+ return c
+
+ @torch.no_grad()
+ def log_images(self, batch, N=8, n_row=4, sample=True, ddim_steps=50, ddim_eta=0., return_keys=None,
+ quantize_denoised=True, inpaint=True, plot_denoise_rows=False, plot_progressive_rows=True,
+ plot_diffusion_rows=True, unconditional_guidance_scale=1., unconditional_guidance_label=None,
+ use_ema_scope=True,
+ **kwargs):
+ ema_scope = self.ema_scope if use_ema_scope else nullcontext
+ use_ddim = ddim_steps is not None
+
+ log = dict()
+ z, c, x, xrec, xc = self.get_input(batch, self.first_stage_key,
+ return_first_stage_outputs=True,
+ force_c_encode=True,
+ return_original_cond=True,
+ bs=N)
+ N = min(x.shape[0], N)
+ n_row = min(x.shape[0], n_row)
+ log["inputs"] = x
+ log["reconstruction"] = xrec
+ if self.model.conditioning_key is not None:
+ if hasattr(self.cond_stage_model, "decode"):
+ xc = self.cond_stage_model.decode(c)
+ log["conditioning"] = xc
+ elif self.cond_stage_key in ["caption", "txt"]:
+ xc = log_txt_as_img((x.shape[2], x.shape[3]), batch[self.cond_stage_key], size=x.shape[2] // 25)
+ log["conditioning"] = xc
+ elif self.cond_stage_key in ['class_label', "cls"]:
+ try:
+ xc = log_txt_as_img((x.shape[2], x.shape[3]), batch["human_label"], size=x.shape[2] // 25)
+ log['conditioning'] = xc
+ except KeyError:
+ # probably no "human_label" in batch
+ pass
+ elif isimage(xc):
+ log["conditioning"] = xc
+ if ismap(xc):
+ log["original_conditioning"] = self.to_rgb(xc)
+
+ if plot_diffusion_rows:
+ # get diffusion row
+ diffusion_row = list()
+ z_start = z[:n_row]
+ for t in range(self.num_timesteps):
+ if t % self.log_every_t == 0 or t == self.num_timesteps - 1:
+ t = repeat(torch.tensor([t]), '1 -> b', b=n_row)
+ t = t.to(self.device).long()
+ noise = torch.randn_like(z_start)
+ z_noisy = self.q_sample(x_start=z_start, t=t, noise=noise)
+ diffusion_row.append(self.decode_first_stage(z_noisy))
+
+ diffusion_row = torch.stack(diffusion_row) # n_log_step, n_row, C, H, W
+ diffusion_grid = rearrange(diffusion_row, 'n b c h w -> b n c h w')
+ diffusion_grid = rearrange(diffusion_grid, 'b n c h w -> (b n) c h w')
+ diffusion_grid = make_grid(diffusion_grid, nrow=diffusion_row.shape[0])
+ log["diffusion_row"] = diffusion_grid
+
+ if sample:
+ # get denoise row
+ with ema_scope("Sampling"):
+ samples, z_denoise_row = self.sample_log(cond=c, batch_size=N, ddim=use_ddim,
+ ddim_steps=ddim_steps, eta=ddim_eta)
+ # samples, z_denoise_row = self.sample(cond=c, batch_size=N, return_intermediates=True)
+ x_samples = self.decode_first_stage(samples)
+ log["samples"] = x_samples
+ if plot_denoise_rows:
+ denoise_grid = self._get_denoise_row_from_list(z_denoise_row)
+ log["denoise_row"] = denoise_grid
+
+ if quantize_denoised and not isinstance(self.first_stage_model, AutoencoderKL) and not isinstance(
+ self.first_stage_model, IdentityFirstStage):
+ # also display when quantizing x0 while sampling
+ with ema_scope("Plotting Quantized Denoised"):
+ samples, z_denoise_row = self.sample_log(cond=c, batch_size=N, ddim=use_ddim,
+ ddim_steps=ddim_steps, eta=ddim_eta,
+ quantize_denoised=True)
+ # samples, z_denoise_row = self.sample(cond=c, batch_size=N, return_intermediates=True,
+ # quantize_denoised=True)
+ x_samples = self.decode_first_stage(samples.to(self.device))
+ log["samples_x0_quantized"] = x_samples
+
+ if unconditional_guidance_scale > 1.0:
+ uc = self.get_unconditional_conditioning(N, unconditional_guidance_label)
+ if self.model.conditioning_key == "crossattn-adm":
+ uc = {"c_crossattn": [uc], "c_adm": c["c_adm"]}
+ with ema_scope("Sampling with classifier-free guidance"):
+ samples_cfg, _ = self.sample_log(cond=c, batch_size=N, ddim=use_ddim,
+ ddim_steps=ddim_steps, eta=ddim_eta,
+ unconditional_guidance_scale=unconditional_guidance_scale,
+ unconditional_conditioning=uc,
+ )
+ x_samples_cfg = self.decode_first_stage(samples_cfg)
+ log[f"samples_cfg_scale_{unconditional_guidance_scale:.2f}"] = x_samples_cfg
+
+ if inpaint:
+ # make a simple center square
+ b, h, w = z.shape[0], z.shape[2], z.shape[3]
+ mask = torch.ones(N, h, w).to(self.device)
+ # zeros will be filled in
+ mask[:, h // 4:3 * h // 4, w // 4:3 * w // 4] = 0.
+ mask = mask[:, None, ...]
+ with ema_scope("Plotting Inpaint"):
+ samples, _ = self.sample_log(cond=c, batch_size=N, ddim=use_ddim, eta=ddim_eta,
+ ddim_steps=ddim_steps, x0=z[:N], mask=mask)
+ x_samples = self.decode_first_stage(samples.to(self.device))
+ log["samples_inpainting"] = x_samples
+ log["mask"] = mask
+
+ # outpaint
+ mask = 1. - mask
+ with ema_scope("Plotting Outpaint"):
+ samples, _ = self.sample_log(cond=c, batch_size=N, ddim=use_ddim, eta=ddim_eta,
+ ddim_steps=ddim_steps, x0=z[:N], mask=mask)
+ x_samples = self.decode_first_stage(samples.to(self.device))
+ log["samples_outpainting"] = x_samples
+
+ if plot_progressive_rows:
+ with ema_scope("Plotting Progressives"):
+ img, progressives = self.progressive_denoising(c,
+ shape=(self.channels, self.image_size, self.image_size),
+ batch_size=N)
+ prog_row = self._get_denoise_row_from_list(progressives, desc="Progressive Generation")
+ log["progressive_row"] = prog_row
+
+ if return_keys:
+ if np.intersect1d(list(log.keys()), return_keys).shape[0] == 0:
+ return log
+ else:
+ return {key: log[key] for key in return_keys}
+ return log
+
+ def configure_optimizers(self):
+ lr = self.learning_rate
+ params = list(self.model.parameters())
+ if self.cond_stage_trainable:
+ print(f"{self.__class__.__name__}: Also optimizing conditioner params!")
+ params = params + list(self.cond_stage_model.parameters())
+ if self.learn_logvar:
+ print('Diffusion model optimizing logvar')
+ params.append(self.logvar)
+ opt = torch.optim.AdamW(params, lr=lr)
+ if self.use_scheduler:
+ assert 'target' in self.scheduler_config
+ scheduler = instantiate_from_config(self.scheduler_config)
+
+ print("Setting up LambdaLR scheduler...")
+ scheduler = [
+ {
+ 'scheduler': LambdaLR(opt, lr_lambda=scheduler.schedule),
+ 'interval': 'step',
+ 'frequency': 1
+ }]
+ return [opt], scheduler
+ return opt
+
+ @torch.no_grad()
+ def to_rgb(self, x):
+ x = x.float()
+ if not hasattr(self, "colorize"):
+ self.colorize = torch.randn(3, x.shape[1], 1, 1).to(x)
+ x = nn.functional.conv2d(x, weight=self.colorize)
+ x = 2. * (x - x.min()) / (x.max() - x.min()) - 1.
+ return x
+
+
+class DiffusionWrapper(pl.LightningModule):
+ def __init__(self, diff_model_config, conditioning_key):
+ super().__init__()
+ self.sequential_cross_attn = diff_model_config.pop("sequential_crossattn", False)
+ self.diffusion_model = instantiate_from_config(diff_model_config)
+ self.conditioning_key = conditioning_key
+ assert self.conditioning_key in [None, 'concat', 'crossattn', 'hybrid', 'adm', 'hybrid-adm', 'crossattn-adm']
+
+ def forward(self, x, t, c_concat: list = None, c_crossattn: list = None, c_adm=None):
+ if self.conditioning_key is None:
+ out = self.diffusion_model(x, t)
+ elif self.conditioning_key == 'concat':
+ xc = torch.cat([x] + c_concat, dim=1)
+ out = self.diffusion_model(xc, t)
+ elif self.conditioning_key == 'crossattn':
+ if not self.sequential_cross_attn:
+ cc = torch.cat(c_crossattn, 1)
+ else:
+ cc = c_crossattn
+ out = self.diffusion_model(x, t, context=cc)
+ elif self.conditioning_key == 'hybrid':
+ xc = torch.cat([x] + c_concat, dim=1)
+ cc = torch.cat(c_crossattn, 1)
+ out = self.diffusion_model(xc, t, context=cc)
+ elif self.conditioning_key == 'hybrid-adm':
+ assert c_adm is not None
+ xc = torch.cat([x] + c_concat, dim=1)
+ cc = torch.cat(c_crossattn, 1)
+ out = self.diffusion_model(xc, t, context=cc, y=c_adm)
+ elif self.conditioning_key == 'crossattn-adm':
+ assert c_adm is not None
+ cc = torch.cat(c_crossattn, 1)
+ out = self.diffusion_model(x, t, context=cc, y=c_adm)
+ elif self.conditioning_key == 'adm':
+ cc = c_crossattn[0]
+ out = self.diffusion_model(x, t, y=cc)
+ else:
+ raise NotImplementedError()
+
+ return out
+
+
+class LatentUpscaleDiffusion(LatentDiffusion):
+ def __init__(self, *args, low_scale_config, low_scale_key="LR", noise_level_key=None, **kwargs):
+ super().__init__(*args, **kwargs)
+ # assumes that neither the cond_stage nor the low_scale_model contain trainable params
+ assert not self.cond_stage_trainable
+ self.instantiate_low_stage(low_scale_config)
+ self.low_scale_key = low_scale_key
+ self.noise_level_key = noise_level_key
+
+ def instantiate_low_stage(self, config):
+ model = instantiate_from_config(config)
+ self.low_scale_model = model.eval()
+ self.low_scale_model.train = disabled_train
+ for param in self.low_scale_model.parameters():
+ param.requires_grad = False
+
+ @torch.no_grad()
+ def get_input(self, batch, k, cond_key=None, bs=None, log_mode=False):
+ if not log_mode:
+ z, c = super().get_input(batch, k, force_c_encode=True, bs=bs)
+ else:
+ z, c, x, xrec, xc = super().get_input(batch, self.first_stage_key, return_first_stage_outputs=True,
+ force_c_encode=True, return_original_cond=True, bs=bs)
+ x_low = batch[self.low_scale_key][:bs]
+ x_low = rearrange(x_low, 'b h w c -> b c h w')
+ x_low = x_low.to(memory_format=torch.contiguous_format).float()
+ zx, noise_level = self.low_scale_model(x_low)
+ if self.noise_level_key is not None:
+ # get noise level from batch instead, e.g. when extracting a custom noise level for bsr
+ raise NotImplementedError('TODO')
+
+ all_conds = {"c_concat": [zx], "c_crossattn": [c], "c_adm": noise_level}
+ if log_mode:
+ # TODO: maybe disable if too expensive
+ x_low_rec = self.low_scale_model.decode(zx)
+ return z, all_conds, x, xrec, xc, x_low, x_low_rec, noise_level
+ return z, all_conds
+
+ @torch.no_grad()
+ def log_images(self, batch, N=8, n_row=4, sample=True, ddim_steps=200, ddim_eta=1., return_keys=None,
+ plot_denoise_rows=False, plot_progressive_rows=True, plot_diffusion_rows=True,
+ unconditional_guidance_scale=1., unconditional_guidance_label=None, use_ema_scope=True,
+ **kwargs):
+ ema_scope = self.ema_scope if use_ema_scope else nullcontext
+ use_ddim = ddim_steps is not None
+
+ log = dict()
+ z, c, x, xrec, xc, x_low, x_low_rec, noise_level = self.get_input(batch, self.first_stage_key, bs=N,
+ log_mode=True)
+ N = min(x.shape[0], N)
+ n_row = min(x.shape[0], n_row)
+ log["inputs"] = x
+ log["reconstruction"] = xrec
+ log["x_lr"] = x_low
+ log[f"x_lr_rec_@noise_levels{'-'.join(map(lambda x: str(x), list(noise_level.cpu().numpy())))}"] = x_low_rec
+ if self.model.conditioning_key is not None:
+ if hasattr(self.cond_stage_model, "decode"):
+ xc = self.cond_stage_model.decode(c)
+ log["conditioning"] = xc
+ elif self.cond_stage_key in ["caption", "txt"]:
+ xc = log_txt_as_img((x.shape[2], x.shape[3]), batch[self.cond_stage_key], size=x.shape[2] // 25)
+ log["conditioning"] = xc
+ elif self.cond_stage_key in ['class_label', 'cls']:
+ xc = log_txt_as_img((x.shape[2], x.shape[3]), batch["human_label"], size=x.shape[2] // 25)
+ log['conditioning'] = xc
+ elif isimage(xc):
+ log["conditioning"] = xc
+ if ismap(xc):
+ log["original_conditioning"] = self.to_rgb(xc)
+
+ if plot_diffusion_rows:
+ # get diffusion row
+ diffusion_row = list()
+ z_start = z[:n_row]
+ for t in range(self.num_timesteps):
+ if t % self.log_every_t == 0 or t == self.num_timesteps - 1:
+ t = repeat(torch.tensor([t]), '1 -> b', b=n_row)
+ t = t.to(self.device).long()
+ noise = torch.randn_like(z_start)
+ z_noisy = self.q_sample(x_start=z_start, t=t, noise=noise)
+ diffusion_row.append(self.decode_first_stage(z_noisy))
+
+ diffusion_row = torch.stack(diffusion_row) # n_log_step, n_row, C, H, W
+ diffusion_grid = rearrange(diffusion_row, 'n b c h w -> b n c h w')
+ diffusion_grid = rearrange(diffusion_grid, 'b n c h w -> (b n) c h w')
+ diffusion_grid = make_grid(diffusion_grid, nrow=diffusion_row.shape[0])
+ log["diffusion_row"] = diffusion_grid
+
+ if sample:
+ # get denoise row
+ with ema_scope("Sampling"):
+ samples, z_denoise_row = self.sample_log(cond=c, batch_size=N, ddim=use_ddim,
+ ddim_steps=ddim_steps, eta=ddim_eta)
+ # samples, z_denoise_row = self.sample(cond=c, batch_size=N, return_intermediates=True)
+ x_samples = self.decode_first_stage(samples)
+ log["samples"] = x_samples
+ if plot_denoise_rows:
+ denoise_grid = self._get_denoise_row_from_list(z_denoise_row)
+ log["denoise_row"] = denoise_grid
+
+ if unconditional_guidance_scale > 1.0:
+ uc_tmp = self.get_unconditional_conditioning(N, unconditional_guidance_label)
+ # TODO explore better "unconditional" choices for the other keys
+ # maybe guide away from empty text label and highest noise level and maximally degraded zx?
+ uc = dict()
+ for k in c:
+ if k == "c_crossattn":
+ assert isinstance(c[k], list) and len(c[k]) == 1
+ uc[k] = [uc_tmp]
+ elif k == "c_adm": # todo: only run with text-based guidance?
+ assert isinstance(c[k], torch.Tensor)
+ #uc[k] = torch.ones_like(c[k]) * self.low_scale_model.max_noise_level
+ uc[k] = c[k]
+ elif isinstance(c[k], list):
+ uc[k] = [c[k][i] for i in range(len(c[k]))]
+ else:
+ uc[k] = c[k]
+
+ with ema_scope("Sampling with classifier-free guidance"):
+ samples_cfg, _ = self.sample_log(cond=c, batch_size=N, ddim=use_ddim,
+ ddim_steps=ddim_steps, eta=ddim_eta,
+ unconditional_guidance_scale=unconditional_guidance_scale,
+ unconditional_conditioning=uc,
+ )
+ x_samples_cfg = self.decode_first_stage(samples_cfg)
+ log[f"samples_cfg_scale_{unconditional_guidance_scale:.2f}"] = x_samples_cfg
+
+ if plot_progressive_rows:
+ with ema_scope("Plotting Progressives"):
+ img, progressives = self.progressive_denoising(c,
+ shape=(self.channels, self.image_size, self.image_size),
+ batch_size=N)
+ prog_row = self._get_denoise_row_from_list(progressives, desc="Progressive Generation")
+ log["progressive_row"] = prog_row
+
+ return log
+
+
+class LatentFinetuneDiffusion(LatentDiffusion):
+ """
+ Basis for different finetunas, such as inpainting or depth2image
+ To disable finetuning mode, set finetune_keys to None
+ """
+
+ def __init__(self,
+ concat_keys: tuple,
+ finetune_keys=("model.diffusion_model.input_blocks.0.0.weight",
+ "model_ema.diffusion_modelinput_blocks00weight"
+ ),
+ keep_finetune_dims=4,
+ # if model was trained without concat mode before and we would like to keep these channels
+ c_concat_log_start=None, # to log reconstruction of c_concat codes
+ c_concat_log_end=None,
+ *args, **kwargs
+ ):
+ ckpt_path = kwargs.pop("ckpt_path", None)
+ ignore_keys = kwargs.pop("ignore_keys", list())
+ super().__init__(*args, **kwargs)
+ self.finetune_keys = finetune_keys
+ self.concat_keys = concat_keys
+ self.keep_dims = keep_finetune_dims
+ self.c_concat_log_start = c_concat_log_start
+ self.c_concat_log_end = c_concat_log_end
+ if exists(self.finetune_keys): assert exists(ckpt_path), 'can only finetune from a given checkpoint'
+ if exists(ckpt_path):
+ self.init_from_ckpt(ckpt_path, ignore_keys)
+
+ def init_from_ckpt(self, path, ignore_keys=list(), only_model=False):
+ sd = torch.load(path, map_location="cpu")
+ if "state_dict" in list(sd.keys()):
+ sd = sd["state_dict"]
+ keys = list(sd.keys())
+ for k in keys:
+ for ik in ignore_keys:
+ if k.startswith(ik):
+ print("Deleting key {} from state_dict.".format(k))
+ del sd[k]
+
+ # make it explicit, finetune by including extra input channels
+ if exists(self.finetune_keys) and k in self.finetune_keys:
+ new_entry = None
+ for name, param in self.named_parameters():
+ if name in self.finetune_keys:
+ print(
+ f"modifying key '{name}' and keeping its original {self.keep_dims} (channels) dimensions only")
+ new_entry = torch.zeros_like(param) # zero init
+ assert exists(new_entry), 'did not find matching parameter to modify'
+ new_entry[:, :self.keep_dims, ...] = sd[k]
+ sd[k] = new_entry
+
+ missing, unexpected = self.load_state_dict(sd, strict=False) if not only_model else self.model.load_state_dict(
+ sd, strict=False)
+ print(f"Restored from {path} with {len(missing)} missing and {len(unexpected)} unexpected keys")
+ if len(missing) > 0:
+ print(f"Missing Keys: {missing}")
+ if len(unexpected) > 0:
+ print(f"Unexpected Keys: {unexpected}")
+
+ @torch.no_grad()
+ def log_images(self, batch, N=8, n_row=4, sample=True, ddim_steps=200, ddim_eta=1., return_keys=None,
+ quantize_denoised=True, inpaint=True, plot_denoise_rows=False, plot_progressive_rows=True,
+ plot_diffusion_rows=True, unconditional_guidance_scale=1., unconditional_guidance_label=None,
+ use_ema_scope=True,
+ **kwargs):
+ ema_scope = self.ema_scope if use_ema_scope else nullcontext
+ use_ddim = ddim_steps is not None
+
+ log = dict()
+ z, c, x, xrec, xc = self.get_input(batch, self.first_stage_key, bs=N, return_first_stage_outputs=True)
+ c_cat, c = c["c_concat"][0], c["c_crossattn"][0]
+ N = min(x.shape[0], N)
+ n_row = min(x.shape[0], n_row)
+ log["inputs"] = x
+ log["reconstruction"] = xrec
+ if self.model.conditioning_key is not None:
+ if hasattr(self.cond_stage_model, "decode"):
+ xc = self.cond_stage_model.decode(c)
+ log["conditioning"] = xc
+ elif self.cond_stage_key in ["caption", "txt"]:
+ xc = log_txt_as_img((x.shape[2], x.shape[3]), batch[self.cond_stage_key], size=x.shape[2] // 25)
+ log["conditioning"] = xc
+ elif self.cond_stage_key in ['class_label', 'cls']:
+ xc = log_txt_as_img((x.shape[2], x.shape[3]), batch["human_label"], size=x.shape[2] // 25)
+ log['conditioning'] = xc
+ elif isimage(xc):
+ log["conditioning"] = xc
+ if ismap(xc):
+ log["original_conditioning"] = self.to_rgb(xc)
+
+ if not (self.c_concat_log_start is None and self.c_concat_log_end is None):
+ log["c_concat_decoded"] = self.decode_first_stage(c_cat[:, self.c_concat_log_start:self.c_concat_log_end])
+
+ if plot_diffusion_rows:
+ # get diffusion row
+ diffusion_row = list()
+ z_start = z[:n_row]
+ for t in range(self.num_timesteps):
+ if t % self.log_every_t == 0 or t == self.num_timesteps - 1:
+ t = repeat(torch.tensor([t]), '1 -> b', b=n_row)
+ t = t.to(self.device).long()
+ noise = torch.randn_like(z_start)
+ z_noisy = self.q_sample(x_start=z_start, t=t, noise=noise)
+ diffusion_row.append(self.decode_first_stage(z_noisy))
+
+ diffusion_row = torch.stack(diffusion_row) # n_log_step, n_row, C, H, W
+ diffusion_grid = rearrange(diffusion_row, 'n b c h w -> b n c h w')
+ diffusion_grid = rearrange(diffusion_grid, 'b n c h w -> (b n) c h w')
+ diffusion_grid = make_grid(diffusion_grid, nrow=diffusion_row.shape[0])
+ log["diffusion_row"] = diffusion_grid
+
+ if sample:
+ # get denoise row
+ with ema_scope("Sampling"):
+ samples, z_denoise_row = self.sample_log(cond={"c_concat": [c_cat], "c_crossattn": [c]},
+ batch_size=N, ddim=use_ddim,
+ ddim_steps=ddim_steps, eta=ddim_eta)
+ # samples, z_denoise_row = self.sample(cond=c, batch_size=N, return_intermediates=True)
+ x_samples = self.decode_first_stage(samples)
+ log["samples"] = x_samples
+ if plot_denoise_rows:
+ denoise_grid = self._get_denoise_row_from_list(z_denoise_row)
+ log["denoise_row"] = denoise_grid
+
+ if unconditional_guidance_scale > 1.0:
+ uc_cross = self.get_unconditional_conditioning(N, unconditional_guidance_label)
+ uc_cat = c_cat
+ uc_full = {"c_concat": [uc_cat], "c_crossattn": [uc_cross]}
+ with ema_scope("Sampling with classifier-free guidance"):
+ samples_cfg, _ = self.sample_log(cond={"c_concat": [c_cat], "c_crossattn": [c]},
+ batch_size=N, ddim=use_ddim,
+ ddim_steps=ddim_steps, eta=ddim_eta,
+ unconditional_guidance_scale=unconditional_guidance_scale,
+ unconditional_conditioning=uc_full,
+ )
+ x_samples_cfg = self.decode_first_stage(samples_cfg)
+ log[f"samples_cfg_scale_{unconditional_guidance_scale:.2f}"] = x_samples_cfg
+
+ return log
+
+
+class LatentInpaintDiffusion(LatentFinetuneDiffusion):
+ """
+ can either run as pure inpainting model (only concat mode) or with mixed conditionings,
+ e.g. mask as concat and text via cross-attn.
+ To disable finetuning mode, set finetune_keys to None
+ """
+
+ def __init__(self,
+ concat_keys=("mask", "masked_image"),
+ masked_image_key="masked_image",
+ *args, **kwargs
+ ):
+ super().__init__(concat_keys, *args, **kwargs)
+ self.masked_image_key = masked_image_key
+ assert self.masked_image_key in concat_keys
+
+ @torch.no_grad()
+ def get_input(self, batch, k, cond_key=None, bs=None, return_first_stage_outputs=False):
+ # note: restricted to non-trainable encoders currently
+ assert not self.cond_stage_trainable, 'trainable cond stages not yet supported for inpainting'
+ z, c, x, xrec, xc = super().get_input(batch, self.first_stage_key, return_first_stage_outputs=True,
+ force_c_encode=True, return_original_cond=True, bs=bs)
+
+ assert exists(self.concat_keys)
+ c_cat = list()
+ for ck in self.concat_keys:
+ cc = rearrange(batch[ck], 'b h w c -> b c h w').to(memory_format=torch.contiguous_format).float()
+ if bs is not None:
+ cc = cc[:bs]
+ cc = cc.to(self.device)
+ bchw = z.shape
+ if ck != self.masked_image_key:
+ cc = torch.nn.functional.interpolate(cc, size=bchw[-2:])
+ else:
+ cc = self.get_first_stage_encoding(self.encode_first_stage(cc))
+ c_cat.append(cc)
+ c_cat = torch.cat(c_cat, dim=1)
+ all_conds = {"c_concat": [c_cat], "c_crossattn": [c]}
+ if return_first_stage_outputs:
+ return z, all_conds, x, xrec, xc
+ return z, all_conds
+
+ @torch.no_grad()
+ def log_images(self, *args, **kwargs):
+ log = super(LatentInpaintDiffusion, self).log_images(*args, **kwargs)
+ log["masked_image"] = rearrange(args[0]["masked_image"],
+ 'b h w c -> b c h w').to(memory_format=torch.contiguous_format).float()
+ return log
+
+
+class LatentDepth2ImageDiffusion(LatentFinetuneDiffusion):
+ """
+ condition on monocular depth estimation
+ """
+
+ def __init__(self, depth_stage_config, concat_keys=("midas_in",), *args, **kwargs):
+ super().__init__(concat_keys=concat_keys, *args, **kwargs)
+ self.depth_model = instantiate_from_config(depth_stage_config)
+ self.depth_stage_key = concat_keys[0]
+
+ @torch.no_grad()
+ def get_input(self, batch, k, cond_key=None, bs=None, return_first_stage_outputs=False):
+ # note: restricted to non-trainable encoders currently
+ assert not self.cond_stage_trainable, 'trainable cond stages not yet supported for depth2img'
+ z, c, x, xrec, xc = super().get_input(batch, self.first_stage_key, return_first_stage_outputs=True,
+ force_c_encode=True, return_original_cond=True, bs=bs)
+
+ assert exists(self.concat_keys)
+ assert len(self.concat_keys) == 1
+ c_cat = list()
+ for ck in self.concat_keys:
+ cc = batch[ck]
+ if bs is not None:
+ cc = cc[:bs]
+ cc = cc.to(self.device)
+ cc = self.depth_model(cc)
+ cc = torch.nn.functional.interpolate(
+ cc,
+ size=z.shape[2:],
+ mode="bicubic",
+ align_corners=False,
+ )
+
+ depth_min, depth_max = torch.amin(cc, dim=[1, 2, 3], keepdim=True), torch.amax(cc, dim=[1, 2, 3],
+ keepdim=True)
+ cc = 2. * (cc - depth_min) / (depth_max - depth_min + 0.001) - 1.
+ c_cat.append(cc)
+ c_cat = torch.cat(c_cat, dim=1)
+ all_conds = {"c_concat": [c_cat], "c_crossattn": [c]}
+ if return_first_stage_outputs:
+ return z, all_conds, x, xrec, xc
+ return z, all_conds
+
+ @torch.no_grad()
+ def log_images(self, *args, **kwargs):
+ log = super().log_images(*args, **kwargs)
+ depth = self.depth_model(args[0][self.depth_stage_key])
+ depth_min, depth_max = torch.amin(depth, dim=[1, 2, 3], keepdim=True), \
+ torch.amax(depth, dim=[1, 2, 3], keepdim=True)
+ log["depth"] = 2. * (depth - depth_min) / (depth_max - depth_min) - 1.
+ return log
+
+
+class LatentUpscaleFinetuneDiffusion(LatentFinetuneDiffusion):
+ """
+ condition on low-res image (and optionally on some spatial noise augmentation)
+ """
+ def __init__(self, concat_keys=("lr",), reshuffle_patch_size=None,
+ low_scale_config=None, low_scale_key=None, *args, **kwargs):
+ super().__init__(concat_keys=concat_keys, *args, **kwargs)
+ self.reshuffle_patch_size = reshuffle_patch_size
+ self.low_scale_model = None
+ if low_scale_config is not None:
+ print("Initializing a low-scale model")
+ assert exists(low_scale_key)
+ self.instantiate_low_stage(low_scale_config)
+ self.low_scale_key = low_scale_key
+
+ def instantiate_low_stage(self, config):
+ model = instantiate_from_config(config)
+ self.low_scale_model = model.eval()
+ self.low_scale_model.train = disabled_train
+ for param in self.low_scale_model.parameters():
+ param.requires_grad = False
+
+ @torch.no_grad()
+ def get_input(self, batch, k, cond_key=None, bs=None, return_first_stage_outputs=False):
+ # note: restricted to non-trainable encoders currently
+ assert not self.cond_stage_trainable, 'trainable cond stages not yet supported for upscaling-ft'
+ z, c, x, xrec, xc = super().get_input(batch, self.first_stage_key, return_first_stage_outputs=True,
+ force_c_encode=True, return_original_cond=True, bs=bs)
+
+ assert exists(self.concat_keys)
+ assert len(self.concat_keys) == 1
+ # optionally make spatial noise_level here
+ c_cat = list()
+ noise_level = None
+ for ck in self.concat_keys:
+ cc = batch[ck]
+ cc = rearrange(cc, 'b h w c -> b c h w')
+ if exists(self.reshuffle_patch_size):
+ assert isinstance(self.reshuffle_patch_size, int)
+ cc = rearrange(cc, 'b c (p1 h) (p2 w) -> b (p1 p2 c) h w',
+ p1=self.reshuffle_patch_size, p2=self.reshuffle_patch_size)
+ if bs is not None:
+ cc = cc[:bs]
+ cc = cc.to(self.device)
+ if exists(self.low_scale_model) and ck == self.low_scale_key:
+ cc, noise_level = self.low_scale_model(cc)
+ c_cat.append(cc)
+ c_cat = torch.cat(c_cat, dim=1)
+ if exists(noise_level):
+ all_conds = {"c_concat": [c_cat], "c_crossattn": [c], "c_adm": noise_level}
+ else:
+ all_conds = {"c_concat": [c_cat], "c_crossattn": [c]}
+ if return_first_stage_outputs:
+ return z, all_conds, x, xrec, xc
+ return z, all_conds
+
+ @torch.no_grad()
+ def log_images(self, *args, **kwargs):
+ log = super().log_images(*args, **kwargs)
+ log["lr"] = rearrange(args[0]["lr"], 'b h w c -> b c h w')
+ return log
diff --git a/ldm/models/diffusion/dpm_solver/__init__.py b/ldm/models/diffusion/dpm_solver/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..7427f38c07530afbab79154ea8aaf88c4bf70a08
--- /dev/null
+++ b/ldm/models/diffusion/dpm_solver/__init__.py
@@ -0,0 +1 @@
+from .sampler import DPMSolverSampler
\ No newline at end of file
diff --git a/ldm/models/diffusion/dpm_solver/dpm_solver.py b/ldm/models/diffusion/dpm_solver/dpm_solver.py
new file mode 100644
index 0000000000000000000000000000000000000000..095e5ba3ce0b1aa7f4b3f1e2e5d8fff7cfe6dc8c
--- /dev/null
+++ b/ldm/models/diffusion/dpm_solver/dpm_solver.py
@@ -0,0 +1,1154 @@
+import torch
+import torch.nn.functional as F
+import math
+from tqdm import tqdm
+
+
+class NoiseScheduleVP:
+ def __init__(
+ self,
+ schedule='discrete',
+ betas=None,
+ alphas_cumprod=None,
+ continuous_beta_0=0.1,
+ continuous_beta_1=20.,
+ ):
+ """Create a wrapper class for the forward SDE (VP type).
+ ***
+ Update: We support discrete-time diffusion models by implementing a picewise linear interpolation for log_alpha_t.
+ We recommend to use schedule='discrete' for the discrete-time diffusion models, especially for high-resolution images.
+ ***
+ The forward SDE ensures that the condition distribution q_{t|0}(x_t | x_0) = N ( alpha_t * x_0, sigma_t^2 * I ).
+ We further define lambda_t = log(alpha_t) - log(sigma_t), which is the half-logSNR (described in the DPM-Solver paper).
+ Therefore, we implement the functions for computing alpha_t, sigma_t and lambda_t. For t in [0, T], we have:
+ log_alpha_t = self.marginal_log_mean_coeff(t)
+ sigma_t = self.marginal_std(t)
+ lambda_t = self.marginal_lambda(t)
+ Moreover, as lambda(t) is an invertible function, we also support its inverse function:
+ t = self.inverse_lambda(lambda_t)
+ ===============================================================
+ We support both discrete-time DPMs (trained on n = 0, 1, ..., N-1) and continuous-time DPMs (trained on t in [t_0, T]).
+ 1. For discrete-time DPMs:
+ For discrete-time DPMs trained on n = 0, 1, ..., N-1, we convert the discrete steps to continuous time steps by:
+ t_i = (i + 1) / N
+ e.g. for N = 1000, we have t_0 = 1e-3 and T = t_{N-1} = 1.
+ We solve the corresponding diffusion ODE from time T = 1 to time t_0 = 1e-3.
+ Args:
+ betas: A `torch.Tensor`. The beta array for the discrete-time DPM. (See the original DDPM paper for details)
+ alphas_cumprod: A `torch.Tensor`. The cumprod alphas for the discrete-time DPM. (See the original DDPM paper for details)
+ Note that we always have alphas_cumprod = cumprod(betas). Therefore, we only need to set one of `betas` and `alphas_cumprod`.
+ **Important**: Please pay special attention for the args for `alphas_cumprod`:
+ The `alphas_cumprod` is the \hat{alpha_n} arrays in the notations of DDPM. Specifically, DDPMs assume that
+ q_{t_n | 0}(x_{t_n} | x_0) = N ( \sqrt{\hat{alpha_n}} * x_0, (1 - \hat{alpha_n}) * I ).
+ Therefore, the notation \hat{alpha_n} is different from the notation alpha_t in DPM-Solver. In fact, we have
+ alpha_{t_n} = \sqrt{\hat{alpha_n}},
+ and
+ log(alpha_{t_n}) = 0.5 * log(\hat{alpha_n}).
+ 2. For continuous-time DPMs:
+ We support two types of VPSDEs: linear (DDPM) and cosine (improved-DDPM). The hyperparameters for the noise
+ schedule are the default settings in DDPM and improved-DDPM:
+ Args:
+ beta_min: A `float` number. The smallest beta for the linear schedule.
+ beta_max: A `float` number. The largest beta for the linear schedule.
+ cosine_s: A `float` number. The hyperparameter in the cosine schedule.
+ cosine_beta_max: A `float` number. The hyperparameter in the cosine schedule.
+ T: A `float` number. The ending time of the forward process.
+ ===============================================================
+ Args:
+ schedule: A `str`. The noise schedule of the forward SDE. 'discrete' for discrete-time DPMs,
+ 'linear' or 'cosine' for continuous-time DPMs.
+ Returns:
+ A wrapper object of the forward SDE (VP type).
+
+ ===============================================================
+ Example:
+ # For discrete-time DPMs, given betas (the beta array for n = 0, 1, ..., N - 1):
+ >>> ns = NoiseScheduleVP('discrete', betas=betas)
+ # For discrete-time DPMs, given alphas_cumprod (the \hat{alpha_n} array for n = 0, 1, ..., N - 1):
+ >>> ns = NoiseScheduleVP('discrete', alphas_cumprod=alphas_cumprod)
+ # For continuous-time DPMs (VPSDE), linear schedule:
+ >>> ns = NoiseScheduleVP('linear', continuous_beta_0=0.1, continuous_beta_1=20.)
+ """
+
+ if schedule not in ['discrete', 'linear', 'cosine']:
+ raise ValueError(
+ "Unsupported noise schedule {}. The schedule needs to be 'discrete' or 'linear' or 'cosine'".format(
+ schedule))
+
+ self.schedule = schedule
+ if schedule == 'discrete':
+ if betas is not None:
+ log_alphas = 0.5 * torch.log(1 - betas).cumsum(dim=0)
+ else:
+ assert alphas_cumprod is not None
+ log_alphas = 0.5 * torch.log(alphas_cumprod)
+ self.total_N = len(log_alphas)
+ self.T = 1.
+ self.t_array = torch.linspace(0., 1., self.total_N + 1)[1:].reshape((1, -1))
+ self.log_alpha_array = log_alphas.reshape((1, -1,))
+ else:
+ self.total_N = 1000
+ self.beta_0 = continuous_beta_0
+ self.beta_1 = continuous_beta_1
+ self.cosine_s = 0.008
+ self.cosine_beta_max = 999.
+ self.cosine_t_max = math.atan(self.cosine_beta_max * (1. + self.cosine_s) / math.pi) * 2. * (
+ 1. + self.cosine_s) / math.pi - self.cosine_s
+ self.cosine_log_alpha_0 = math.log(math.cos(self.cosine_s / (1. + self.cosine_s) * math.pi / 2.))
+ self.schedule = schedule
+ if schedule == 'cosine':
+ # For the cosine schedule, T = 1 will have numerical issues. So we manually set the ending time T.
+ # Note that T = 0.9946 may be not the optimal setting. However, we find it works well.
+ self.T = 0.9946
+ else:
+ self.T = 1.
+
+ def marginal_log_mean_coeff(self, t):
+ """
+ Compute log(alpha_t) of a given continuous-time label t in [0, T].
+ """
+ if self.schedule == 'discrete':
+ return interpolate_fn(t.reshape((-1, 1)), self.t_array.to(t.device),
+ self.log_alpha_array.to(t.device)).reshape((-1))
+ elif self.schedule == 'linear':
+ return -0.25 * t ** 2 * (self.beta_1 - self.beta_0) - 0.5 * t * self.beta_0
+ elif self.schedule == 'cosine':
+ log_alpha_fn = lambda s: torch.log(torch.cos((s + self.cosine_s) / (1. + self.cosine_s) * math.pi / 2.))
+ log_alpha_t = log_alpha_fn(t) - self.cosine_log_alpha_0
+ return log_alpha_t
+
+ def marginal_alpha(self, t):
+ """
+ Compute alpha_t of a given continuous-time label t in [0, T].
+ """
+ return torch.exp(self.marginal_log_mean_coeff(t))
+
+ def marginal_std(self, t):
+ """
+ Compute sigma_t of a given continuous-time label t in [0, T].
+ """
+ return torch.sqrt(1. - torch.exp(2. * self.marginal_log_mean_coeff(t)))
+
+ def marginal_lambda(self, t):
+ """
+ Compute lambda_t = log(alpha_t) - log(sigma_t) of a given continuous-time label t in [0, T].
+ """
+ log_mean_coeff = self.marginal_log_mean_coeff(t)
+ log_std = 0.5 * torch.log(1. - torch.exp(2. * log_mean_coeff))
+ return log_mean_coeff - log_std
+
+ def inverse_lambda(self, lamb):
+ """
+ Compute the continuous-time label t in [0, T] of a given half-logSNR lambda_t.
+ """
+ if self.schedule == 'linear':
+ tmp = 2. * (self.beta_1 - self.beta_0) * torch.logaddexp(-2. * lamb, torch.zeros((1,)).to(lamb))
+ Delta = self.beta_0 ** 2 + tmp
+ return tmp / (torch.sqrt(Delta) + self.beta_0) / (self.beta_1 - self.beta_0)
+ elif self.schedule == 'discrete':
+ log_alpha = -0.5 * torch.logaddexp(torch.zeros((1,)).to(lamb.device), -2. * lamb)
+ t = interpolate_fn(log_alpha.reshape((-1, 1)), torch.flip(self.log_alpha_array.to(lamb.device), [1]),
+ torch.flip(self.t_array.to(lamb.device), [1]))
+ return t.reshape((-1,))
+ else:
+ log_alpha = -0.5 * torch.logaddexp(-2. * lamb, torch.zeros((1,)).to(lamb))
+ t_fn = lambda log_alpha_t: torch.arccos(torch.exp(log_alpha_t + self.cosine_log_alpha_0)) * 2. * (
+ 1. + self.cosine_s) / math.pi - self.cosine_s
+ t = t_fn(log_alpha)
+ return t
+
+
+def model_wrapper(
+ model,
+ noise_schedule,
+ model_type="noise",
+ model_kwargs={},
+ guidance_type="uncond",
+ condition=None,
+ unconditional_condition=None,
+ guidance_scale=1.,
+ classifier_fn=None,
+ classifier_kwargs={},
+):
+ """Create a wrapper function for the noise prediction model.
+ DPM-Solver needs to solve the continuous-time diffusion ODEs. For DPMs trained on discrete-time labels, we need to
+ firstly wrap the model function to a noise prediction model that accepts the continuous time as the input.
+ We support four types of the diffusion model by setting `model_type`:
+ 1. "noise": noise prediction model. (Trained by predicting noise).
+ 2. "x_start": data prediction model. (Trained by predicting the data x_0 at time 0).
+ 3. "v": velocity prediction model. (Trained by predicting the velocity).
+ The "v" prediction is derivation detailed in Appendix D of [1], and is used in Imagen-Video [2].
+ [1] Salimans, Tim, and Jonathan Ho. "Progressive distillation for fast sampling of diffusion models."
+ arXiv preprint arXiv:2202.00512 (2022).
+ [2] Ho, Jonathan, et al. "Imagen Video: High Definition Video Generation with Diffusion Models."
+ arXiv preprint arXiv:2210.02303 (2022).
+
+ 4. "score": marginal score function. (Trained by denoising score matching).
+ Note that the score function and the noise prediction model follows a simple relationship:
+ ```
+ noise(x_t, t) = -sigma_t * score(x_t, t)
+ ```
+ We support three types of guided sampling by DPMs by setting `guidance_type`:
+ 1. "uncond": unconditional sampling by DPMs.
+ The input `model` has the following format:
+ ``
+ model(x, t_input, **model_kwargs) -> noise | x_start | v | score
+ ``
+ 2. "classifier": classifier guidance sampling [3] by DPMs and another classifier.
+ The input `model` has the following format:
+ ``
+ model(x, t_input, **model_kwargs) -> noise | x_start | v | score
+ ``
+ The input `classifier_fn` has the following format:
+ ``
+ classifier_fn(x, t_input, cond, **classifier_kwargs) -> logits(x, t_input, cond)
+ ``
+ [3] P. Dhariwal and A. Q. Nichol, "Diffusion models beat GANs on image synthesis,"
+ in Advances in Neural Information Processing Systems, vol. 34, 2021, pp. 8780-8794.
+ 3. "classifier-free": classifier-free guidance sampling by conditional DPMs.
+ The input `model` has the following format:
+ ``
+ model(x, t_input, cond, **model_kwargs) -> noise | x_start | v | score
+ ``
+ And if cond == `unconditional_condition`, the model output is the unconditional DPM output.
+ [4] Ho, Jonathan, and Tim Salimans. "Classifier-free diffusion guidance."
+ arXiv preprint arXiv:2207.12598 (2022).
+
+ The `t_input` is the time label of the model, which may be discrete-time labels (i.e. 0 to 999)
+ or continuous-time labels (i.e. epsilon to T).
+ We wrap the model function to accept only `x` and `t_continuous` as inputs, and outputs the predicted noise:
+ ``
+ def model_fn(x, t_continuous) -> noise:
+ t_input = get_model_input_time(t_continuous)
+ return noise_pred(model, x, t_input, **model_kwargs)
+ ``
+ where `t_continuous` is the continuous time labels (i.e. epsilon to T). And we use `model_fn` for DPM-Solver.
+ ===============================================================
+ Args:
+ model: A diffusion model with the corresponding format described above.
+ noise_schedule: A noise schedule object, such as NoiseScheduleVP.
+ model_type: A `str`. The parameterization type of the diffusion model.
+ "noise" or "x_start" or "v" or "score".
+ model_kwargs: A `dict`. A dict for the other inputs of the model function.
+ guidance_type: A `str`. The type of the guidance for sampling.
+ "uncond" or "classifier" or "classifier-free".
+ condition: A pytorch tensor. The condition for the guided sampling.
+ Only used for "classifier" or "classifier-free" guidance type.
+ unconditional_condition: A pytorch tensor. The condition for the unconditional sampling.
+ Only used for "classifier-free" guidance type.
+ guidance_scale: A `float`. The scale for the guided sampling.
+ classifier_fn: A classifier function. Only used for the classifier guidance.
+ classifier_kwargs: A `dict`. A dict for the other inputs of the classifier function.
+ Returns:
+ A noise prediction model that accepts the noised data and the continuous time as the inputs.
+ """
+
+ def get_model_input_time(t_continuous):
+ """
+ Convert the continuous-time `t_continuous` (in [epsilon, T]) to the model input time.
+ For discrete-time DPMs, we convert `t_continuous` in [1 / N, 1] to `t_input` in [0, 1000 * (N - 1) / N].
+ For continuous-time DPMs, we just use `t_continuous`.
+ """
+ if noise_schedule.schedule == 'discrete':
+ return (t_continuous - 1. / noise_schedule.total_N) * 1000.
+ else:
+ return t_continuous
+
+ def noise_pred_fn(x, t_continuous, cond=None):
+ if t_continuous.reshape((-1,)).shape[0] == 1:
+ t_continuous = t_continuous.expand((x.shape[0]))
+ t_input = get_model_input_time(t_continuous)
+ if cond is None:
+ output = model(x, t_input, **model_kwargs)
+ else:
+ output = model(x, t_input, cond, **model_kwargs)
+ if model_type == "noise":
+ return output
+ elif model_type == "x_start":
+ alpha_t, sigma_t = noise_schedule.marginal_alpha(t_continuous), noise_schedule.marginal_std(t_continuous)
+ dims = x.dim()
+ return (x - expand_dims(alpha_t, dims) * output) / expand_dims(sigma_t, dims)
+ elif model_type == "v":
+ alpha_t, sigma_t = noise_schedule.marginal_alpha(t_continuous), noise_schedule.marginal_std(t_continuous)
+ dims = x.dim()
+ return expand_dims(alpha_t, dims) * output + expand_dims(sigma_t, dims) * x
+ elif model_type == "score":
+ sigma_t = noise_schedule.marginal_std(t_continuous)
+ dims = x.dim()
+ return -expand_dims(sigma_t, dims) * output
+
+ def cond_grad_fn(x, t_input):
+ """
+ Compute the gradient of the classifier, i.e. nabla_{x} log p_t(cond | x_t).
+ """
+ with torch.enable_grad():
+ x_in = x.detach().requires_grad_(True)
+ log_prob = classifier_fn(x_in, t_input, condition, **classifier_kwargs)
+ return torch.autograd.grad(log_prob.sum(), x_in)[0]
+
+ def model_fn(x, t_continuous):
+ """
+ The noise predicition model function that is used for DPM-Solver.
+ """
+ if t_continuous.reshape((-1,)).shape[0] == 1:
+ t_continuous = t_continuous.expand((x.shape[0]))
+ if guidance_type == "uncond":
+ return noise_pred_fn(x, t_continuous)
+ elif guidance_type == "classifier":
+ assert classifier_fn is not None
+ t_input = get_model_input_time(t_continuous)
+ cond_grad = cond_grad_fn(x, t_input)
+ sigma_t = noise_schedule.marginal_std(t_continuous)
+ noise = noise_pred_fn(x, t_continuous)
+ return noise - guidance_scale * expand_dims(sigma_t, dims=cond_grad.dim()) * cond_grad
+ elif guidance_type == "classifier-free":
+ if guidance_scale == 1. or unconditional_condition is None:
+ return noise_pred_fn(x, t_continuous, cond=condition)
+ else:
+ x_in = torch.cat([x] * 2)
+ t_in = torch.cat([t_continuous] * 2)
+ c_in = torch.cat([unconditional_condition, condition])
+ noise_uncond, noise = noise_pred_fn(x_in, t_in, cond=c_in).chunk(2)
+ return noise_uncond + guidance_scale * (noise - noise_uncond)
+
+ assert model_type in ["noise", "x_start", "v"]
+ assert guidance_type in ["uncond", "classifier", "classifier-free"]
+ return model_fn
+
+
+class DPM_Solver:
+ def __init__(self, model_fn, noise_schedule, predict_x0=False, thresholding=False, max_val=1.):
+ """Construct a DPM-Solver.
+ We support both the noise prediction model ("predicting epsilon") and the data prediction model ("predicting x0").
+ If `predict_x0` is False, we use the solver for the noise prediction model (DPM-Solver).
+ If `predict_x0` is True, we use the solver for the data prediction model (DPM-Solver++).
+ In such case, we further support the "dynamic thresholding" in [1] when `thresholding` is True.
+ The "dynamic thresholding" can greatly improve the sample quality for pixel-space DPMs with large guidance scales.
+ Args:
+ model_fn: A noise prediction model function which accepts the continuous-time input (t in [epsilon, T]):
+ ``
+ def model_fn(x, t_continuous):
+ return noise
+ ``
+ noise_schedule: A noise schedule object, such as NoiseScheduleVP.
+ predict_x0: A `bool`. If true, use the data prediction model; else, use the noise prediction model.
+ thresholding: A `bool`. Valid when `predict_x0` is True. Whether to use the "dynamic thresholding" in [1].
+ max_val: A `float`. Valid when both `predict_x0` and `thresholding` are True. The max value for thresholding.
+
+ [1] Chitwan Saharia, William Chan, Saurabh Saxena, Lala Li, Jay Whang, Emily Denton, Seyed Kamyar Seyed Ghasemipour, Burcu Karagol Ayan, S Sara Mahdavi, Rapha Gontijo Lopes, et al. Photorealistic text-to-image diffusion models with deep language understanding. arXiv preprint arXiv:2205.11487, 2022b.
+ """
+ self.model = model_fn
+ self.noise_schedule = noise_schedule
+ self.predict_x0 = predict_x0
+ self.thresholding = thresholding
+ self.max_val = max_val
+
+ def noise_prediction_fn(self, x, t):
+ """
+ Return the noise prediction model.
+ """
+ return self.model(x, t)
+
+ def data_prediction_fn(self, x, t):
+ """
+ Return the data prediction model (with thresholding).
+ """
+ noise = self.noise_prediction_fn(x, t)
+ dims = x.dim()
+ alpha_t, sigma_t = self.noise_schedule.marginal_alpha(t), self.noise_schedule.marginal_std(t)
+ x0 = (x - expand_dims(sigma_t, dims) * noise) / expand_dims(alpha_t, dims)
+ if self.thresholding:
+ p = 0.995 # A hyperparameter in the paper of "Imagen" [1].
+ s = torch.quantile(torch.abs(x0).reshape((x0.shape[0], -1)), p, dim=1)
+ s = expand_dims(torch.maximum(s, self.max_val * torch.ones_like(s).to(s.device)), dims)
+ x0 = torch.clamp(x0, -s, s) / s
+ return x0
+
+ def model_fn(self, x, t):
+ """
+ Convert the model to the noise prediction model or the data prediction model.
+ """
+ if self.predict_x0:
+ return self.data_prediction_fn(x, t)
+ else:
+ return self.noise_prediction_fn(x, t)
+
+ def get_time_steps(self, skip_type, t_T, t_0, N, device):
+ """Compute the intermediate time steps for sampling.
+ Args:
+ skip_type: A `str`. The type for the spacing of the time steps. We support three types:
+ - 'logSNR': uniform logSNR for the time steps.
+ - 'time_uniform': uniform time for the time steps. (**Recommended for high-resolutional data**.)
+ - 'time_quadratic': quadratic time for the time steps. (Used in DDIM for low-resolutional data.)
+ t_T: A `float`. The starting time of the sampling (default is T).
+ t_0: A `float`. The ending time of the sampling (default is epsilon).
+ N: A `int`. The total number of the spacing of the time steps.
+ device: A torch device.
+ Returns:
+ A pytorch tensor of the time steps, with the shape (N + 1,).
+ """
+ if skip_type == 'logSNR':
+ lambda_T = self.noise_schedule.marginal_lambda(torch.tensor(t_T).to(device))
+ lambda_0 = self.noise_schedule.marginal_lambda(torch.tensor(t_0).to(device))
+ logSNR_steps = torch.linspace(lambda_T.cpu().item(), lambda_0.cpu().item(), N + 1).to(device)
+ return self.noise_schedule.inverse_lambda(logSNR_steps)
+ elif skip_type == 'time_uniform':
+ return torch.linspace(t_T, t_0, N + 1).to(device)
+ elif skip_type == 'time_quadratic':
+ t_order = 2
+ t = torch.linspace(t_T ** (1. / t_order), t_0 ** (1. / t_order), N + 1).pow(t_order).to(device)
+ return t
+ else:
+ raise ValueError(
+ "Unsupported skip_type {}, need to be 'logSNR' or 'time_uniform' or 'time_quadratic'".format(skip_type))
+
+ def get_orders_and_timesteps_for_singlestep_solver(self, steps, order, skip_type, t_T, t_0, device):
+ """
+ Get the order of each step for sampling by the singlestep DPM-Solver.
+ We combine both DPM-Solver-1,2,3 to use all the function evaluations, which is named as "DPM-Solver-fast".
+ Given a fixed number of function evaluations by `steps`, the sampling procedure by DPM-Solver-fast is:
+ - If order == 1:
+ We take `steps` of DPM-Solver-1 (i.e. DDIM).
+ - If order == 2:
+ - Denote K = (steps // 2). We take K or (K + 1) intermediate time steps for sampling.
+ - If steps % 2 == 0, we use K steps of DPM-Solver-2.
+ - If steps % 2 == 1, we use K steps of DPM-Solver-2 and 1 step of DPM-Solver-1.
+ - If order == 3:
+ - Denote K = (steps // 3 + 1). We take K intermediate time steps for sampling.
+ - If steps % 3 == 0, we use (K - 2) steps of DPM-Solver-3, and 1 step of DPM-Solver-2 and 1 step of DPM-Solver-1.
+ - If steps % 3 == 1, we use (K - 1) steps of DPM-Solver-3 and 1 step of DPM-Solver-1.
+ - If steps % 3 == 2, we use (K - 1) steps of DPM-Solver-3 and 1 step of DPM-Solver-2.
+ ============================================
+ Args:
+ order: A `int`. The max order for the solver (2 or 3).
+ steps: A `int`. The total number of function evaluations (NFE).
+ skip_type: A `str`. The type for the spacing of the time steps. We support three types:
+ - 'logSNR': uniform logSNR for the time steps.
+ - 'time_uniform': uniform time for the time steps. (**Recommended for high-resolutional data**.)
+ - 'time_quadratic': quadratic time for the time steps. (Used in DDIM for low-resolutional data.)
+ t_T: A `float`. The starting time of the sampling (default is T).
+ t_0: A `float`. The ending time of the sampling (default is epsilon).
+ device: A torch device.
+ Returns:
+ orders: A list of the solver order of each step.
+ """
+ if order == 3:
+ K = steps // 3 + 1
+ if steps % 3 == 0:
+ orders = [3, ] * (K - 2) + [2, 1]
+ elif steps % 3 == 1:
+ orders = [3, ] * (K - 1) + [1]
+ else:
+ orders = [3, ] * (K - 1) + [2]
+ elif order == 2:
+ if steps % 2 == 0:
+ K = steps // 2
+ orders = [2, ] * K
+ else:
+ K = steps // 2 + 1
+ orders = [2, ] * (K - 1) + [1]
+ elif order == 1:
+ K = 1
+ orders = [1, ] * steps
+ else:
+ raise ValueError("'order' must be '1' or '2' or '3'.")
+ if skip_type == 'logSNR':
+ # To reproduce the results in DPM-Solver paper
+ timesteps_outer = self.get_time_steps(skip_type, t_T, t_0, K, device)
+ else:
+ timesteps_outer = self.get_time_steps(skip_type, t_T, t_0, steps, device)[
+ torch.cumsum(torch.tensor([0, ] + orders)).to(device)]
+ return timesteps_outer, orders
+
+ def denoise_to_zero_fn(self, x, s):
+ """
+ Denoise at the final step, which is equivalent to solve the ODE from lambda_s to infty by first-order discretization.
+ """
+ return self.data_prediction_fn(x, s)
+
+ def dpm_solver_first_update(self, x, s, t, model_s=None, return_intermediate=False):
+ """
+ DPM-Solver-1 (equivalent to DDIM) from time `s` to time `t`.
+ Args:
+ x: A pytorch tensor. The initial value at time `s`.
+ s: A pytorch tensor. The starting time, with the shape (x.shape[0],).
+ t: A pytorch tensor. The ending time, with the shape (x.shape[0],).
+ model_s: A pytorch tensor. The model function evaluated at time `s`.
+ If `model_s` is None, we evaluate the model by `x` and `s`; otherwise we directly use it.
+ return_intermediate: A `bool`. If true, also return the model value at time `s`.
+ Returns:
+ x_t: A pytorch tensor. The approximated solution at time `t`.
+ """
+ ns = self.noise_schedule
+ dims = x.dim()
+ lambda_s, lambda_t = ns.marginal_lambda(s), ns.marginal_lambda(t)
+ h = lambda_t - lambda_s
+ log_alpha_s, log_alpha_t = ns.marginal_log_mean_coeff(s), ns.marginal_log_mean_coeff(t)
+ sigma_s, sigma_t = ns.marginal_std(s), ns.marginal_std(t)
+ alpha_t = torch.exp(log_alpha_t)
+
+ if self.predict_x0:
+ phi_1 = torch.expm1(-h)
+ if model_s is None:
+ model_s = self.model_fn(x, s)
+ x_t = (
+ expand_dims(sigma_t / sigma_s, dims) * x
+ - expand_dims(alpha_t * phi_1, dims) * model_s
+ )
+ if return_intermediate:
+ return x_t, {'model_s': model_s}
+ else:
+ return x_t
+ else:
+ phi_1 = torch.expm1(h)
+ if model_s is None:
+ model_s = self.model_fn(x, s)
+ x_t = (
+ expand_dims(torch.exp(log_alpha_t - log_alpha_s), dims) * x
+ - expand_dims(sigma_t * phi_1, dims) * model_s
+ )
+ if return_intermediate:
+ return x_t, {'model_s': model_s}
+ else:
+ return x_t
+
+ def singlestep_dpm_solver_second_update(self, x, s, t, r1=0.5, model_s=None, return_intermediate=False,
+ solver_type='dpm_solver'):
+ """
+ Singlestep solver DPM-Solver-2 from time `s` to time `t`.
+ Args:
+ x: A pytorch tensor. The initial value at time `s`.
+ s: A pytorch tensor. The starting time, with the shape (x.shape[0],).
+ t: A pytorch tensor. The ending time, with the shape (x.shape[0],).
+ r1: A `float`. The hyperparameter of the second-order solver.
+ model_s: A pytorch tensor. The model function evaluated at time `s`.
+ If `model_s` is None, we evaluate the model by `x` and `s`; otherwise we directly use it.
+ return_intermediate: A `bool`. If true, also return the model value at time `s` and `s1` (the intermediate time).
+ solver_type: either 'dpm_solver' or 'taylor'. The type for the high-order solvers.
+ The type slightly impacts the performance. We recommend to use 'dpm_solver' type.
+ Returns:
+ x_t: A pytorch tensor. The approximated solution at time `t`.
+ """
+ if solver_type not in ['dpm_solver', 'taylor']:
+ raise ValueError("'solver_type' must be either 'dpm_solver' or 'taylor', got {}".format(solver_type))
+ if r1 is None:
+ r1 = 0.5
+ ns = self.noise_schedule
+ dims = x.dim()
+ lambda_s, lambda_t = ns.marginal_lambda(s), ns.marginal_lambda(t)
+ h = lambda_t - lambda_s
+ lambda_s1 = lambda_s + r1 * h
+ s1 = ns.inverse_lambda(lambda_s1)
+ log_alpha_s, log_alpha_s1, log_alpha_t = ns.marginal_log_mean_coeff(s), ns.marginal_log_mean_coeff(
+ s1), ns.marginal_log_mean_coeff(t)
+ sigma_s, sigma_s1, sigma_t = ns.marginal_std(s), ns.marginal_std(s1), ns.marginal_std(t)
+ alpha_s1, alpha_t = torch.exp(log_alpha_s1), torch.exp(log_alpha_t)
+
+ if self.predict_x0:
+ phi_11 = torch.expm1(-r1 * h)
+ phi_1 = torch.expm1(-h)
+
+ if model_s is None:
+ model_s = self.model_fn(x, s)
+ x_s1 = (
+ expand_dims(sigma_s1 / sigma_s, dims) * x
+ - expand_dims(alpha_s1 * phi_11, dims) * model_s
+ )
+ model_s1 = self.model_fn(x_s1, s1)
+ if solver_type == 'dpm_solver':
+ x_t = (
+ expand_dims(sigma_t / sigma_s, dims) * x
+ - expand_dims(alpha_t * phi_1, dims) * model_s
+ - (0.5 / r1) * expand_dims(alpha_t * phi_1, dims) * (model_s1 - model_s)
+ )
+ elif solver_type == 'taylor':
+ x_t = (
+ expand_dims(sigma_t / sigma_s, dims) * x
+ - expand_dims(alpha_t * phi_1, dims) * model_s
+ + (1. / r1) * expand_dims(alpha_t * ((torch.exp(-h) - 1.) / h + 1.), dims) * (
+ model_s1 - model_s)
+ )
+ else:
+ phi_11 = torch.expm1(r1 * h)
+ phi_1 = torch.expm1(h)
+
+ if model_s is None:
+ model_s = self.model_fn(x, s)
+ x_s1 = (
+ expand_dims(torch.exp(log_alpha_s1 - log_alpha_s), dims) * x
+ - expand_dims(sigma_s1 * phi_11, dims) * model_s
+ )
+ model_s1 = self.model_fn(x_s1, s1)
+ if solver_type == 'dpm_solver':
+ x_t = (
+ expand_dims(torch.exp(log_alpha_t - log_alpha_s), dims) * x
+ - expand_dims(sigma_t * phi_1, dims) * model_s
+ - (0.5 / r1) * expand_dims(sigma_t * phi_1, dims) * (model_s1 - model_s)
+ )
+ elif solver_type == 'taylor':
+ x_t = (
+ expand_dims(torch.exp(log_alpha_t - log_alpha_s), dims) * x
+ - expand_dims(sigma_t * phi_1, dims) * model_s
+ - (1. / r1) * expand_dims(sigma_t * ((torch.exp(h) - 1.) / h - 1.), dims) * (model_s1 - model_s)
+ )
+ if return_intermediate:
+ return x_t, {'model_s': model_s, 'model_s1': model_s1}
+ else:
+ return x_t
+
+ def singlestep_dpm_solver_third_update(self, x, s, t, r1=1. / 3., r2=2. / 3., model_s=None, model_s1=None,
+ return_intermediate=False, solver_type='dpm_solver'):
+ """
+ Singlestep solver DPM-Solver-3 from time `s` to time `t`.
+ Args:
+ x: A pytorch tensor. The initial value at time `s`.
+ s: A pytorch tensor. The starting time, with the shape (x.shape[0],).
+ t: A pytorch tensor. The ending time, with the shape (x.shape[0],).
+ r1: A `float`. The hyperparameter of the third-order solver.
+ r2: A `float`. The hyperparameter of the third-order solver.
+ model_s: A pytorch tensor. The model function evaluated at time `s`.
+ If `model_s` is None, we evaluate the model by `x` and `s`; otherwise we directly use it.
+ model_s1: A pytorch tensor. The model function evaluated at time `s1` (the intermediate time given by `r1`).
+ If `model_s1` is None, we evaluate the model at `s1`; otherwise we directly use it.
+ return_intermediate: A `bool`. If true, also return the model value at time `s`, `s1` and `s2` (the intermediate times).
+ solver_type: either 'dpm_solver' or 'taylor'. The type for the high-order solvers.
+ The type slightly impacts the performance. We recommend to use 'dpm_solver' type.
+ Returns:
+ x_t: A pytorch tensor. The approximated solution at time `t`.
+ """
+ if solver_type not in ['dpm_solver', 'taylor']:
+ raise ValueError("'solver_type' must be either 'dpm_solver' or 'taylor', got {}".format(solver_type))
+ if r1 is None:
+ r1 = 1. / 3.
+ if r2 is None:
+ r2 = 2. / 3.
+ ns = self.noise_schedule
+ dims = x.dim()
+ lambda_s, lambda_t = ns.marginal_lambda(s), ns.marginal_lambda(t)
+ h = lambda_t - lambda_s
+ lambda_s1 = lambda_s + r1 * h
+ lambda_s2 = lambda_s + r2 * h
+ s1 = ns.inverse_lambda(lambda_s1)
+ s2 = ns.inverse_lambda(lambda_s2)
+ log_alpha_s, log_alpha_s1, log_alpha_s2, log_alpha_t = ns.marginal_log_mean_coeff(
+ s), ns.marginal_log_mean_coeff(s1), ns.marginal_log_mean_coeff(s2), ns.marginal_log_mean_coeff(t)
+ sigma_s, sigma_s1, sigma_s2, sigma_t = ns.marginal_std(s), ns.marginal_std(s1), ns.marginal_std(
+ s2), ns.marginal_std(t)
+ alpha_s1, alpha_s2, alpha_t = torch.exp(log_alpha_s1), torch.exp(log_alpha_s2), torch.exp(log_alpha_t)
+
+ if self.predict_x0:
+ phi_11 = torch.expm1(-r1 * h)
+ phi_12 = torch.expm1(-r2 * h)
+ phi_1 = torch.expm1(-h)
+ phi_22 = torch.expm1(-r2 * h) / (r2 * h) + 1.
+ phi_2 = phi_1 / h + 1.
+ phi_3 = phi_2 / h - 0.5
+
+ if model_s is None:
+ model_s = self.model_fn(x, s)
+ if model_s1 is None:
+ x_s1 = (
+ expand_dims(sigma_s1 / sigma_s, dims) * x
+ - expand_dims(alpha_s1 * phi_11, dims) * model_s
+ )
+ model_s1 = self.model_fn(x_s1, s1)
+ x_s2 = (
+ expand_dims(sigma_s2 / sigma_s, dims) * x
+ - expand_dims(alpha_s2 * phi_12, dims) * model_s
+ + r2 / r1 * expand_dims(alpha_s2 * phi_22, dims) * (model_s1 - model_s)
+ )
+ model_s2 = self.model_fn(x_s2, s2)
+ if solver_type == 'dpm_solver':
+ x_t = (
+ expand_dims(sigma_t / sigma_s, dims) * x
+ - expand_dims(alpha_t * phi_1, dims) * model_s
+ + (1. / r2) * expand_dims(alpha_t * phi_2, dims) * (model_s2 - model_s)
+ )
+ elif solver_type == 'taylor':
+ D1_0 = (1. / r1) * (model_s1 - model_s)
+ D1_1 = (1. / r2) * (model_s2 - model_s)
+ D1 = (r2 * D1_0 - r1 * D1_1) / (r2 - r1)
+ D2 = 2. * (D1_1 - D1_0) / (r2 - r1)
+ x_t = (
+ expand_dims(sigma_t / sigma_s, dims) * x
+ - expand_dims(alpha_t * phi_1, dims) * model_s
+ + expand_dims(alpha_t * phi_2, dims) * D1
+ - expand_dims(alpha_t * phi_3, dims) * D2
+ )
+ else:
+ phi_11 = torch.expm1(r1 * h)
+ phi_12 = torch.expm1(r2 * h)
+ phi_1 = torch.expm1(h)
+ phi_22 = torch.expm1(r2 * h) / (r2 * h) - 1.
+ phi_2 = phi_1 / h - 1.
+ phi_3 = phi_2 / h - 0.5
+
+ if model_s is None:
+ model_s = self.model_fn(x, s)
+ if model_s1 is None:
+ x_s1 = (
+ expand_dims(torch.exp(log_alpha_s1 - log_alpha_s), dims) * x
+ - expand_dims(sigma_s1 * phi_11, dims) * model_s
+ )
+ model_s1 = self.model_fn(x_s1, s1)
+ x_s2 = (
+ expand_dims(torch.exp(log_alpha_s2 - log_alpha_s), dims) * x
+ - expand_dims(sigma_s2 * phi_12, dims) * model_s
+ - r2 / r1 * expand_dims(sigma_s2 * phi_22, dims) * (model_s1 - model_s)
+ )
+ model_s2 = self.model_fn(x_s2, s2)
+ if solver_type == 'dpm_solver':
+ x_t = (
+ expand_dims(torch.exp(log_alpha_t - log_alpha_s), dims) * x
+ - expand_dims(sigma_t * phi_1, dims) * model_s
+ - (1. / r2) * expand_dims(sigma_t * phi_2, dims) * (model_s2 - model_s)
+ )
+ elif solver_type == 'taylor':
+ D1_0 = (1. / r1) * (model_s1 - model_s)
+ D1_1 = (1. / r2) * (model_s2 - model_s)
+ D1 = (r2 * D1_0 - r1 * D1_1) / (r2 - r1)
+ D2 = 2. * (D1_1 - D1_0) / (r2 - r1)
+ x_t = (
+ expand_dims(torch.exp(log_alpha_t - log_alpha_s), dims) * x
+ - expand_dims(sigma_t * phi_1, dims) * model_s
+ - expand_dims(sigma_t * phi_2, dims) * D1
+ - expand_dims(sigma_t * phi_3, dims) * D2
+ )
+
+ if return_intermediate:
+ return x_t, {'model_s': model_s, 'model_s1': model_s1, 'model_s2': model_s2}
+ else:
+ return x_t
+
+ def multistep_dpm_solver_second_update(self, x, model_prev_list, t_prev_list, t, solver_type="dpm_solver"):
+ """
+ Multistep solver DPM-Solver-2 from time `t_prev_list[-1]` to time `t`.
+ Args:
+ x: A pytorch tensor. The initial value at time `s`.
+ model_prev_list: A list of pytorch tensor. The previous computed model values.
+ t_prev_list: A list of pytorch tensor. The previous times, each time has the shape (x.shape[0],)
+ t: A pytorch tensor. The ending time, with the shape (x.shape[0],).
+ solver_type: either 'dpm_solver' or 'taylor'. The type for the high-order solvers.
+ The type slightly impacts the performance. We recommend to use 'dpm_solver' type.
+ Returns:
+ x_t: A pytorch tensor. The approximated solution at time `t`.
+ """
+ if solver_type not in ['dpm_solver', 'taylor']:
+ raise ValueError("'solver_type' must be either 'dpm_solver' or 'taylor', got {}".format(solver_type))
+ ns = self.noise_schedule
+ dims = x.dim()
+ model_prev_1, model_prev_0 = model_prev_list
+ t_prev_1, t_prev_0 = t_prev_list
+ lambda_prev_1, lambda_prev_0, lambda_t = ns.marginal_lambda(t_prev_1), ns.marginal_lambda(
+ t_prev_0), ns.marginal_lambda(t)
+ log_alpha_prev_0, log_alpha_t = ns.marginal_log_mean_coeff(t_prev_0), ns.marginal_log_mean_coeff(t)
+ sigma_prev_0, sigma_t = ns.marginal_std(t_prev_0), ns.marginal_std(t)
+ alpha_t = torch.exp(log_alpha_t)
+
+ h_0 = lambda_prev_0 - lambda_prev_1
+ h = lambda_t - lambda_prev_0
+ r0 = h_0 / h
+ D1_0 = expand_dims(1. / r0, dims) * (model_prev_0 - model_prev_1)
+ if self.predict_x0:
+ if solver_type == 'dpm_solver':
+ x_t = (
+ expand_dims(sigma_t / sigma_prev_0, dims) * x
+ - expand_dims(alpha_t * (torch.exp(-h) - 1.), dims) * model_prev_0
+ - 0.5 * expand_dims(alpha_t * (torch.exp(-h) - 1.), dims) * D1_0
+ )
+ elif solver_type == 'taylor':
+ x_t = (
+ expand_dims(sigma_t / sigma_prev_0, dims) * x
+ - expand_dims(alpha_t * (torch.exp(-h) - 1.), dims) * model_prev_0
+ + expand_dims(alpha_t * ((torch.exp(-h) - 1.) / h + 1.), dims) * D1_0
+ )
+ else:
+ if solver_type == 'dpm_solver':
+ x_t = (
+ expand_dims(torch.exp(log_alpha_t - log_alpha_prev_0), dims) * x
+ - expand_dims(sigma_t * (torch.exp(h) - 1.), dims) * model_prev_0
+ - 0.5 * expand_dims(sigma_t * (torch.exp(h) - 1.), dims) * D1_0
+ )
+ elif solver_type == 'taylor':
+ x_t = (
+ expand_dims(torch.exp(log_alpha_t - log_alpha_prev_0), dims) * x
+ - expand_dims(sigma_t * (torch.exp(h) - 1.), dims) * model_prev_0
+ - expand_dims(sigma_t * ((torch.exp(h) - 1.) / h - 1.), dims) * D1_0
+ )
+ return x_t
+
+ def multistep_dpm_solver_third_update(self, x, model_prev_list, t_prev_list, t, solver_type='dpm_solver'):
+ """
+ Multistep solver DPM-Solver-3 from time `t_prev_list[-1]` to time `t`.
+ Args:
+ x: A pytorch tensor. The initial value at time `s`.
+ model_prev_list: A list of pytorch tensor. The previous computed model values.
+ t_prev_list: A list of pytorch tensor. The previous times, each time has the shape (x.shape[0],)
+ t: A pytorch tensor. The ending time, with the shape (x.shape[0],).
+ solver_type: either 'dpm_solver' or 'taylor'. The type for the high-order solvers.
+ The type slightly impacts the performance. We recommend to use 'dpm_solver' type.
+ Returns:
+ x_t: A pytorch tensor. The approximated solution at time `t`.
+ """
+ ns = self.noise_schedule
+ dims = x.dim()
+ model_prev_2, model_prev_1, model_prev_0 = model_prev_list
+ t_prev_2, t_prev_1, t_prev_0 = t_prev_list
+ lambda_prev_2, lambda_prev_1, lambda_prev_0, lambda_t = ns.marginal_lambda(t_prev_2), ns.marginal_lambda(
+ t_prev_1), ns.marginal_lambda(t_prev_0), ns.marginal_lambda(t)
+ log_alpha_prev_0, log_alpha_t = ns.marginal_log_mean_coeff(t_prev_0), ns.marginal_log_mean_coeff(t)
+ sigma_prev_0, sigma_t = ns.marginal_std(t_prev_0), ns.marginal_std(t)
+ alpha_t = torch.exp(log_alpha_t)
+
+ h_1 = lambda_prev_1 - lambda_prev_2
+ h_0 = lambda_prev_0 - lambda_prev_1
+ h = lambda_t - lambda_prev_0
+ r0, r1 = h_0 / h, h_1 / h
+ D1_0 = expand_dims(1. / r0, dims) * (model_prev_0 - model_prev_1)
+ D1_1 = expand_dims(1. / r1, dims) * (model_prev_1 - model_prev_2)
+ D1 = D1_0 + expand_dims(r0 / (r0 + r1), dims) * (D1_0 - D1_1)
+ D2 = expand_dims(1. / (r0 + r1), dims) * (D1_0 - D1_1)
+ if self.predict_x0:
+ x_t = (
+ expand_dims(sigma_t / sigma_prev_0, dims) * x
+ - expand_dims(alpha_t * (torch.exp(-h) - 1.), dims) * model_prev_0
+ + expand_dims(alpha_t * ((torch.exp(-h) - 1.) / h + 1.), dims) * D1
+ - expand_dims(alpha_t * ((torch.exp(-h) - 1. + h) / h ** 2 - 0.5), dims) * D2
+ )
+ else:
+ x_t = (
+ expand_dims(torch.exp(log_alpha_t - log_alpha_prev_0), dims) * x
+ - expand_dims(sigma_t * (torch.exp(h) - 1.), dims) * model_prev_0
+ - expand_dims(sigma_t * ((torch.exp(h) - 1.) / h - 1.), dims) * D1
+ - expand_dims(sigma_t * ((torch.exp(h) - 1. - h) / h ** 2 - 0.5), dims) * D2
+ )
+ return x_t
+
+ def singlestep_dpm_solver_update(self, x, s, t, order, return_intermediate=False, solver_type='dpm_solver', r1=None,
+ r2=None):
+ """
+ Singlestep DPM-Solver with the order `order` from time `s` to time `t`.
+ Args:
+ x: A pytorch tensor. The initial value at time `s`.
+ s: A pytorch tensor. The starting time, with the shape (x.shape[0],).
+ t: A pytorch tensor. The ending time, with the shape (x.shape[0],).
+ order: A `int`. The order of DPM-Solver. We only support order == 1 or 2 or 3.
+ return_intermediate: A `bool`. If true, also return the model value at time `s`, `s1` and `s2` (the intermediate times).
+ solver_type: either 'dpm_solver' or 'taylor'. The type for the high-order solvers.
+ The type slightly impacts the performance. We recommend to use 'dpm_solver' type.
+ r1: A `float`. The hyperparameter of the second-order or third-order solver.
+ r2: A `float`. The hyperparameter of the third-order solver.
+ Returns:
+ x_t: A pytorch tensor. The approximated solution at time `t`.
+ """
+ if order == 1:
+ return self.dpm_solver_first_update(x, s, t, return_intermediate=return_intermediate)
+ elif order == 2:
+ return self.singlestep_dpm_solver_second_update(x, s, t, return_intermediate=return_intermediate,
+ solver_type=solver_type, r1=r1)
+ elif order == 3:
+ return self.singlestep_dpm_solver_third_update(x, s, t, return_intermediate=return_intermediate,
+ solver_type=solver_type, r1=r1, r2=r2)
+ else:
+ raise ValueError("Solver order must be 1 or 2 or 3, got {}".format(order))
+
+ def multistep_dpm_solver_update(self, x, model_prev_list, t_prev_list, t, order, solver_type='dpm_solver'):
+ """
+ Multistep DPM-Solver with the order `order` from time `t_prev_list[-1]` to time `t`.
+ Args:
+ x: A pytorch tensor. The initial value at time `s`.
+ model_prev_list: A list of pytorch tensor. The previous computed model values.
+ t_prev_list: A list of pytorch tensor. The previous times, each time has the shape (x.shape[0],)
+ t: A pytorch tensor. The ending time, with the shape (x.shape[0],).
+ order: A `int`. The order of DPM-Solver. We only support order == 1 or 2 or 3.
+ solver_type: either 'dpm_solver' or 'taylor'. The type for the high-order solvers.
+ The type slightly impacts the performance. We recommend to use 'dpm_solver' type.
+ Returns:
+ x_t: A pytorch tensor. The approximated solution at time `t`.
+ """
+ if order == 1:
+ return self.dpm_solver_first_update(x, t_prev_list[-1], t, model_s=model_prev_list[-1])
+ elif order == 2:
+ return self.multistep_dpm_solver_second_update(x, model_prev_list, t_prev_list, t, solver_type=solver_type)
+ elif order == 3:
+ return self.multistep_dpm_solver_third_update(x, model_prev_list, t_prev_list, t, solver_type=solver_type)
+ else:
+ raise ValueError("Solver order must be 1 or 2 or 3, got {}".format(order))
+
+ def dpm_solver_adaptive(self, x, order, t_T, t_0, h_init=0.05, atol=0.0078, rtol=0.05, theta=0.9, t_err=1e-5,
+ solver_type='dpm_solver'):
+ """
+ The adaptive step size solver based on singlestep DPM-Solver.
+ Args:
+ x: A pytorch tensor. The initial value at time `t_T`.
+ order: A `int`. The (higher) order of the solver. We only support order == 2 or 3.
+ t_T: A `float`. The starting time of the sampling (default is T).
+ t_0: A `float`. The ending time of the sampling (default is epsilon).
+ h_init: A `float`. The initial step size (for logSNR).
+ atol: A `float`. The absolute tolerance of the solver. For image data, the default setting is 0.0078, followed [1].
+ rtol: A `float`. The relative tolerance of the solver. The default setting is 0.05.
+ theta: A `float`. The safety hyperparameter for adapting the step size. The default setting is 0.9, followed [1].
+ t_err: A `float`. The tolerance for the time. We solve the diffusion ODE until the absolute error between the
+ current time and `t_0` is less than `t_err`. The default setting is 1e-5.
+ solver_type: either 'dpm_solver' or 'taylor'. The type for the high-order solvers.
+ The type slightly impacts the performance. We recommend to use 'dpm_solver' type.
+ Returns:
+ x_0: A pytorch tensor. The approximated solution at time `t_0`.
+ [1] A. Jolicoeur-Martineau, K. Li, R. Piché-Taillefer, T. Kachman, and I. Mitliagkas, "Gotta go fast when generating data with score-based models," arXiv preprint arXiv:2105.14080, 2021.
+ """
+ ns = self.noise_schedule
+ s = t_T * torch.ones((x.shape[0],)).to(x)
+ lambda_s = ns.marginal_lambda(s)
+ lambda_0 = ns.marginal_lambda(t_0 * torch.ones_like(s).to(x))
+ h = h_init * torch.ones_like(s).to(x)
+ x_prev = x
+ nfe = 0
+ if order == 2:
+ r1 = 0.5
+ lower_update = lambda x, s, t: self.dpm_solver_first_update(x, s, t, return_intermediate=True)
+ higher_update = lambda x, s, t, **kwargs: self.singlestep_dpm_solver_second_update(x, s, t, r1=r1,
+ solver_type=solver_type,
+ **kwargs)
+ elif order == 3:
+ r1, r2 = 1. / 3., 2. / 3.
+ lower_update = lambda x, s, t: self.singlestep_dpm_solver_second_update(x, s, t, r1=r1,
+ return_intermediate=True,
+ solver_type=solver_type)
+ higher_update = lambda x, s, t, **kwargs: self.singlestep_dpm_solver_third_update(x, s, t, r1=r1, r2=r2,
+ solver_type=solver_type,
+ **kwargs)
+ else:
+ raise ValueError("For adaptive step size solver, order must be 2 or 3, got {}".format(order))
+ while torch.abs((s - t_0)).mean() > t_err:
+ t = ns.inverse_lambda(lambda_s + h)
+ x_lower, lower_noise_kwargs = lower_update(x, s, t)
+ x_higher = higher_update(x, s, t, **lower_noise_kwargs)
+ delta = torch.max(torch.ones_like(x).to(x) * atol, rtol * torch.max(torch.abs(x_lower), torch.abs(x_prev)))
+ norm_fn = lambda v: torch.sqrt(torch.square(v.reshape((v.shape[0], -1))).mean(dim=-1, keepdim=True))
+ E = norm_fn((x_higher - x_lower) / delta).max()
+ if torch.all(E <= 1.):
+ x = x_higher
+ s = t
+ x_prev = x_lower
+ lambda_s = ns.marginal_lambda(s)
+ h = torch.min(theta * h * torch.float_power(E, -1. / order).float(), lambda_0 - lambda_s)
+ nfe += order
+ print('adaptive solver nfe', nfe)
+ return x
+
+ def sample(self, x, steps=20, t_start=None, t_end=None, order=3, skip_type='time_uniform',
+ method='singlestep', lower_order_final=True, denoise_to_zero=False, solver_type='dpm_solver',
+ atol=0.0078, rtol=0.05,
+ ):
+ """
+ Compute the sample at time `t_end` by DPM-Solver, given the initial `x` at time `t_start`.
+ =====================================================
+ We support the following algorithms for both noise prediction model and data prediction model:
+ - 'singlestep':
+ Singlestep DPM-Solver (i.e. "DPM-Solver-fast" in the paper), which combines different orders of singlestep DPM-Solver.
+ We combine all the singlestep solvers with order <= `order` to use up all the function evaluations (steps).
+ The total number of function evaluations (NFE) == `steps`.
+ Given a fixed NFE == `steps`, the sampling procedure is:
+ - If `order` == 1:
+ - Denote K = steps. We use K steps of DPM-Solver-1 (i.e. DDIM).
+ - If `order` == 2:
+ - Denote K = (steps // 2) + (steps % 2). We take K intermediate time steps for sampling.
+ - If steps % 2 == 0, we use K steps of singlestep DPM-Solver-2.
+ - If steps % 2 == 1, we use (K - 1) steps of singlestep DPM-Solver-2 and 1 step of DPM-Solver-1.
+ - If `order` == 3:
+ - Denote K = (steps // 3 + 1). We take K intermediate time steps for sampling.
+ - If steps % 3 == 0, we use (K - 2) steps of singlestep DPM-Solver-3, and 1 step of singlestep DPM-Solver-2 and 1 step of DPM-Solver-1.
+ - If steps % 3 == 1, we use (K - 1) steps of singlestep DPM-Solver-3 and 1 step of DPM-Solver-1.
+ - If steps % 3 == 2, we use (K - 1) steps of singlestep DPM-Solver-3 and 1 step of singlestep DPM-Solver-2.
+ - 'multistep':
+ Multistep DPM-Solver with the order of `order`. The total number of function evaluations (NFE) == `steps`.
+ We initialize the first `order` values by lower order multistep solvers.
+ Given a fixed NFE == `steps`, the sampling procedure is:
+ Denote K = steps.
+ - If `order` == 1:
+ - We use K steps of DPM-Solver-1 (i.e. DDIM).
+ - If `order` == 2:
+ - We firstly use 1 step of DPM-Solver-1, then use (K - 1) step of multistep DPM-Solver-2.
+ - If `order` == 3:
+ - We firstly use 1 step of DPM-Solver-1, then 1 step of multistep DPM-Solver-2, then (K - 2) step of multistep DPM-Solver-3.
+ - 'singlestep_fixed':
+ Fixed order singlestep DPM-Solver (i.e. DPM-Solver-1 or singlestep DPM-Solver-2 or singlestep DPM-Solver-3).
+ We use singlestep DPM-Solver-`order` for `order`=1 or 2 or 3, with total [`steps` // `order`] * `order` NFE.
+ - 'adaptive':
+ Adaptive step size DPM-Solver (i.e. "DPM-Solver-12" and "DPM-Solver-23" in the paper).
+ We ignore `steps` and use adaptive step size DPM-Solver with a higher order of `order`.
+ You can adjust the absolute tolerance `atol` and the relative tolerance `rtol` to balance the computatation costs
+ (NFE) and the sample quality.
+ - If `order` == 2, we use DPM-Solver-12 which combines DPM-Solver-1 and singlestep DPM-Solver-2.
+ - If `order` == 3, we use DPM-Solver-23 which combines singlestep DPM-Solver-2 and singlestep DPM-Solver-3.
+ =====================================================
+ Some advices for choosing the algorithm:
+ - For **unconditional sampling** or **guided sampling with small guidance scale** by DPMs:
+ Use singlestep DPM-Solver ("DPM-Solver-fast" in the paper) with `order = 3`.
+ e.g.
+ >>> dpm_solver = DPM_Solver(model_fn, noise_schedule, predict_x0=False)
+ >>> x_sample = dpm_solver.sample(x, steps=steps, t_start=t_start, t_end=t_end, order=3,
+ skip_type='time_uniform', method='singlestep')
+ - For **guided sampling with large guidance scale** by DPMs:
+ Use multistep DPM-Solver with `predict_x0 = True` and `order = 2`.
+ e.g.
+ >>> dpm_solver = DPM_Solver(model_fn, noise_schedule, predict_x0=True)
+ >>> x_sample = dpm_solver.sample(x, steps=steps, t_start=t_start, t_end=t_end, order=2,
+ skip_type='time_uniform', method='multistep')
+ We support three types of `skip_type`:
+ - 'logSNR': uniform logSNR for the time steps. **Recommended for low-resolutional images**
+ - 'time_uniform': uniform time for the time steps. **Recommended for high-resolutional images**.
+ - 'time_quadratic': quadratic time for the time steps.
+ =====================================================
+ Args:
+ x: A pytorch tensor. The initial value at time `t_start`
+ e.g. if `t_start` == T, then `x` is a sample from the standard normal distribution.
+ steps: A `int`. The total number of function evaluations (NFE).
+ t_start: A `float`. The starting time of the sampling.
+ If `T` is None, we use self.noise_schedule.T (default is 1.0).
+ t_end: A `float`. The ending time of the sampling.
+ If `t_end` is None, we use 1. / self.noise_schedule.total_N.
+ e.g. if total_N == 1000, we have `t_end` == 1e-3.
+ For discrete-time DPMs:
+ - We recommend `t_end` == 1. / self.noise_schedule.total_N.
+ For continuous-time DPMs:
+ - We recommend `t_end` == 1e-3 when `steps` <= 15; and `t_end` == 1e-4 when `steps` > 15.
+ order: A `int`. The order of DPM-Solver.
+ skip_type: A `str`. The type for the spacing of the time steps. 'time_uniform' or 'logSNR' or 'time_quadratic'.
+ method: A `str`. The method for sampling. 'singlestep' or 'multistep' or 'singlestep_fixed' or 'adaptive'.
+ denoise_to_zero: A `bool`. Whether to denoise to time 0 at the final step.
+ Default is `False`. If `denoise_to_zero` is `True`, the total NFE is (`steps` + 1).
+ This trick is firstly proposed by DDPM (https://arxiv.org/abs/2006.11239) and
+ score_sde (https://arxiv.org/abs/2011.13456). Such trick can improve the FID
+ for diffusion models sampling by diffusion SDEs for low-resolutional images
+ (such as CIFAR-10). However, we observed that such trick does not matter for
+ high-resolutional images. As it needs an additional NFE, we do not recommend
+ it for high-resolutional images.
+ lower_order_final: A `bool`. Whether to use lower order solvers at the final steps.
+ Only valid for `method=multistep` and `steps < 15`. We empirically find that
+ this trick is a key to stabilizing the sampling by DPM-Solver with very few steps
+ (especially for steps <= 10). So we recommend to set it to be `True`.
+ solver_type: A `str`. The taylor expansion type for the solver. `dpm_solver` or `taylor`. We recommend `dpm_solver`.
+ atol: A `float`. The absolute tolerance of the adaptive step size solver. Valid when `method` == 'adaptive'.
+ rtol: A `float`. The relative tolerance of the adaptive step size solver. Valid when `method` == 'adaptive'.
+ Returns:
+ x_end: A pytorch tensor. The approximated solution at time `t_end`.
+ """
+ t_0 = 1. / self.noise_schedule.total_N if t_end is None else t_end
+ t_T = self.noise_schedule.T if t_start is None else t_start
+ device = x.device
+ if method == 'adaptive':
+ with torch.no_grad():
+ x = self.dpm_solver_adaptive(x, order=order, t_T=t_T, t_0=t_0, atol=atol, rtol=rtol,
+ solver_type=solver_type)
+ elif method == 'multistep':
+ assert steps >= order
+ timesteps = self.get_time_steps(skip_type=skip_type, t_T=t_T, t_0=t_0, N=steps, device=device)
+ assert timesteps.shape[0] - 1 == steps
+ with torch.no_grad():
+ vec_t = timesteps[0].expand((x.shape[0]))
+ model_prev_list = [self.model_fn(x, vec_t)]
+ t_prev_list = [vec_t]
+ # Init the first `order` values by lower order multistep DPM-Solver.
+ for init_order in tqdm(range(1, order), desc="DPM init order"):
+ vec_t = timesteps[init_order].expand(x.shape[0])
+ x = self.multistep_dpm_solver_update(x, model_prev_list, t_prev_list, vec_t, init_order,
+ solver_type=solver_type)
+ model_prev_list.append(self.model_fn(x, vec_t))
+ t_prev_list.append(vec_t)
+ # Compute the remaining values by `order`-th order multistep DPM-Solver.
+ for step in tqdm(range(order, steps + 1), desc="DPM multistep"):
+ vec_t = timesteps[step].expand(x.shape[0])
+ if lower_order_final and steps < 15:
+ step_order = min(order, steps + 1 - step)
+ else:
+ step_order = order
+ x = self.multistep_dpm_solver_update(x, model_prev_list, t_prev_list, vec_t, step_order,
+ solver_type=solver_type)
+ for i in range(order - 1):
+ t_prev_list[i] = t_prev_list[i + 1]
+ model_prev_list[i] = model_prev_list[i + 1]
+ t_prev_list[-1] = vec_t
+ # We do not need to evaluate the final model value.
+ if step < steps:
+ model_prev_list[-1] = self.model_fn(x, vec_t)
+ elif method in ['singlestep', 'singlestep_fixed']:
+ if method == 'singlestep':
+ timesteps_outer, orders = self.get_orders_and_timesteps_for_singlestep_solver(steps=steps, order=order,
+ skip_type=skip_type,
+ t_T=t_T, t_0=t_0,
+ device=device)
+ elif method == 'singlestep_fixed':
+ K = steps // order
+ orders = [order, ] * K
+ timesteps_outer = self.get_time_steps(skip_type=skip_type, t_T=t_T, t_0=t_0, N=K, device=device)
+ for i, order in enumerate(orders):
+ t_T_inner, t_0_inner = timesteps_outer[i], timesteps_outer[i + 1]
+ timesteps_inner = self.get_time_steps(skip_type=skip_type, t_T=t_T_inner.item(), t_0=t_0_inner.item(),
+ N=order, device=device)
+ lambda_inner = self.noise_schedule.marginal_lambda(timesteps_inner)
+ vec_s, vec_t = t_T_inner.tile(x.shape[0]), t_0_inner.tile(x.shape[0])
+ h = lambda_inner[-1] - lambda_inner[0]
+ r1 = None if order <= 1 else (lambda_inner[1] - lambda_inner[0]) / h
+ r2 = None if order <= 2 else (lambda_inner[2] - lambda_inner[0]) / h
+ x = self.singlestep_dpm_solver_update(x, vec_s, vec_t, order, solver_type=solver_type, r1=r1, r2=r2)
+ if denoise_to_zero:
+ x = self.denoise_to_zero_fn(x, torch.ones((x.shape[0],)).to(device) * t_0)
+ return x
+
+
+#############################################################
+# other utility functions
+#############################################################
+
+def interpolate_fn(x, xp, yp):
+ """
+ A piecewise linear function y = f(x), using xp and yp as keypoints.
+ We implement f(x) in a differentiable way (i.e. applicable for autograd).
+ The function f(x) is well-defined for all x-axis. (For x beyond the bounds of xp, we use the outmost points of xp to define the linear function.)
+ Args:
+ x: PyTorch tensor with shape [N, C], where N is the batch size, C is the number of channels (we use C = 1 for DPM-Solver).
+ xp: PyTorch tensor with shape [C, K], where K is the number of keypoints.
+ yp: PyTorch tensor with shape [C, K].
+ Returns:
+ The function values f(x), with shape [N, C].
+ """
+ N, K = x.shape[0], xp.shape[1]
+ all_x = torch.cat([x.unsqueeze(2), xp.unsqueeze(0).repeat((N, 1, 1))], dim=2)
+ sorted_all_x, x_indices = torch.sort(all_x, dim=2)
+ x_idx = torch.argmin(x_indices, dim=2)
+ cand_start_idx = x_idx - 1
+ start_idx = torch.where(
+ torch.eq(x_idx, 0),
+ torch.tensor(1, device=x.device),
+ torch.where(
+ torch.eq(x_idx, K), torch.tensor(K - 2, device=x.device), cand_start_idx,
+ ),
+ )
+ end_idx = torch.where(torch.eq(start_idx, cand_start_idx), start_idx + 2, start_idx + 1)
+ start_x = torch.gather(sorted_all_x, dim=2, index=start_idx.unsqueeze(2)).squeeze(2)
+ end_x = torch.gather(sorted_all_x, dim=2, index=end_idx.unsqueeze(2)).squeeze(2)
+ start_idx2 = torch.where(
+ torch.eq(x_idx, 0),
+ torch.tensor(0, device=x.device),
+ torch.where(
+ torch.eq(x_idx, K), torch.tensor(K - 2, device=x.device), cand_start_idx,
+ ),
+ )
+ y_positions_expanded = yp.unsqueeze(0).expand(N, -1, -1)
+ start_y = torch.gather(y_positions_expanded, dim=2, index=start_idx2.unsqueeze(2)).squeeze(2)
+ end_y = torch.gather(y_positions_expanded, dim=2, index=(start_idx2 + 1).unsqueeze(2)).squeeze(2)
+ cand = start_y + (x - start_x) * (end_y - start_y) / (end_x - start_x)
+ return cand
+
+
+def expand_dims(v, dims):
+ """
+ Expand the tensor `v` to the dim `dims`.
+ Args:
+ `v`: a PyTorch tensor with shape [N].
+ `dim`: a `int`.
+ Returns:
+ a PyTorch tensor with shape [N, 1, 1, ..., 1] and the total dimension is `dims`.
+ """
+ return v[(...,) + (None,) * (dims - 1)]
\ No newline at end of file
diff --git a/ldm/models/diffusion/dpm_solver/sampler.py b/ldm/models/diffusion/dpm_solver/sampler.py
new file mode 100644
index 0000000000000000000000000000000000000000..7d137b8cf36718c1c58faa09f9dd919e5fb2977b
--- /dev/null
+++ b/ldm/models/diffusion/dpm_solver/sampler.py
@@ -0,0 +1,87 @@
+"""SAMPLING ONLY."""
+import torch
+
+from .dpm_solver import NoiseScheduleVP, model_wrapper, DPM_Solver
+
+
+MODEL_TYPES = {
+ "eps": "noise",
+ "v": "v"
+}
+
+
+class DPMSolverSampler(object):
+ def __init__(self, model, **kwargs):
+ super().__init__()
+ self.model = model
+ to_torch = lambda x: x.clone().detach().to(torch.float32).to(model.device)
+ self.register_buffer('alphas_cumprod', to_torch(model.alphas_cumprod))
+
+ def register_buffer(self, name, attr):
+ if type(attr) == torch.Tensor:
+ if attr.device != torch.device("cuda"):
+ attr = attr.to(torch.device("cuda"))
+ setattr(self, name, attr)
+
+ @torch.no_grad()
+ def sample(self,
+ S,
+ batch_size,
+ shape,
+ conditioning=None,
+ callback=None,
+ normals_sequence=None,
+ img_callback=None,
+ quantize_x0=False,
+ eta=0.,
+ mask=None,
+ x0=None,
+ temperature=1.,
+ noise_dropout=0.,
+ score_corrector=None,
+ corrector_kwargs=None,
+ verbose=True,
+ x_T=None,
+ log_every_t=100,
+ unconditional_guidance_scale=1.,
+ unconditional_conditioning=None,
+ # this has to come in the same format as the conditioning, # e.g. as encoded tokens, ...
+ **kwargs
+ ):
+ if conditioning is not None:
+ if isinstance(conditioning, dict):
+ cbs = conditioning[list(conditioning.keys())[0]].shape[0]
+ if cbs != batch_size:
+ print(f"Warning: Got {cbs} conditionings but batch-size is {batch_size}")
+ else:
+ if conditioning.shape[0] != batch_size:
+ print(f"Warning: Got {conditioning.shape[0]} conditionings but batch-size is {batch_size}")
+
+ # sampling
+ C, H, W = shape
+ size = (batch_size, C, H, W)
+
+ print(f'Data shape for DPM-Solver sampling is {size}, sampling steps {S}')
+
+ device = self.model.betas.device
+ if x_T is None:
+ img = torch.randn(size, device=device)
+ else:
+ img = x_T
+
+ ns = NoiseScheduleVP('discrete', alphas_cumprod=self.alphas_cumprod)
+
+ model_fn = model_wrapper(
+ lambda x, t, c: self.model.apply_model(x, t, c),
+ ns,
+ model_type=MODEL_TYPES[self.model.parameterization],
+ guidance_type="classifier-free",
+ condition=conditioning,
+ unconditional_condition=unconditional_conditioning,
+ guidance_scale=unconditional_guidance_scale,
+ )
+
+ dpm_solver = DPM_Solver(model_fn, ns, predict_x0=True, thresholding=False)
+ x = dpm_solver.sample(img, steps=S, skip_type="time_uniform", method="multistep", order=2, lower_order_final=True)
+
+ return x.to(device), None
\ No newline at end of file
diff --git a/ldm/models/diffusion/plms.py b/ldm/models/diffusion/plms.py
new file mode 100644
index 0000000000000000000000000000000000000000..7002a365d27168ced0a04e9a4d83e088f8284eae
--- /dev/null
+++ b/ldm/models/diffusion/plms.py
@@ -0,0 +1,244 @@
+"""SAMPLING ONLY."""
+
+import torch
+import numpy as np
+from tqdm import tqdm
+from functools import partial
+
+from ldm.modules.diffusionmodules.util import make_ddim_sampling_parameters, make_ddim_timesteps, noise_like
+from ldm.models.diffusion.sampling_util import norm_thresholding
+
+
+class PLMSSampler(object):
+ def __init__(self, model, schedule="linear", **kwargs):
+ super().__init__()
+ self.model = model
+ self.ddpm_num_timesteps = model.num_timesteps
+ self.schedule = schedule
+
+ def register_buffer(self, name, attr):
+ if type(attr) == torch.Tensor:
+ if attr.device != torch.device("cuda"):
+ attr = attr.to(torch.device("cuda"))
+ setattr(self, name, attr)
+
+ def make_schedule(self, ddim_num_steps, ddim_discretize="uniform", ddim_eta=0., verbose=True):
+ if ddim_eta != 0:
+ raise ValueError('ddim_eta must be 0 for PLMS')
+ self.ddim_timesteps = make_ddim_timesteps(ddim_discr_method=ddim_discretize, num_ddim_timesteps=ddim_num_steps,
+ num_ddpm_timesteps=self.ddpm_num_timesteps,verbose=verbose)
+ alphas_cumprod = self.model.alphas_cumprod
+ assert alphas_cumprod.shape[0] == self.ddpm_num_timesteps, 'alphas have to be defined for each timestep'
+ to_torch = lambda x: x.clone().detach().to(torch.float32).to(self.model.device)
+
+ self.register_buffer('betas', to_torch(self.model.betas))
+ self.register_buffer('alphas_cumprod', to_torch(alphas_cumprod))
+ self.register_buffer('alphas_cumprod_prev', to_torch(self.model.alphas_cumprod_prev))
+
+ # calculations for diffusion q(x_t | x_{t-1}) and others
+ self.register_buffer('sqrt_alphas_cumprod', to_torch(np.sqrt(alphas_cumprod.cpu())))
+ self.register_buffer('sqrt_one_minus_alphas_cumprod', to_torch(np.sqrt(1. - alphas_cumprod.cpu())))
+ self.register_buffer('log_one_minus_alphas_cumprod', to_torch(np.log(1. - alphas_cumprod.cpu())))
+ self.register_buffer('sqrt_recip_alphas_cumprod', to_torch(np.sqrt(1. / alphas_cumprod.cpu())))
+ self.register_buffer('sqrt_recipm1_alphas_cumprod', to_torch(np.sqrt(1. / alphas_cumprod.cpu() - 1)))
+
+ # ddim sampling parameters
+ ddim_sigmas, ddim_alphas, ddim_alphas_prev = make_ddim_sampling_parameters(alphacums=alphas_cumprod.cpu(),
+ ddim_timesteps=self.ddim_timesteps,
+ eta=ddim_eta,verbose=verbose)
+ self.register_buffer('ddim_sigmas', ddim_sigmas)
+ self.register_buffer('ddim_alphas', ddim_alphas)
+ self.register_buffer('ddim_alphas_prev', ddim_alphas_prev)
+ self.register_buffer('ddim_sqrt_one_minus_alphas', np.sqrt(1. - ddim_alphas))
+ sigmas_for_original_sampling_steps = ddim_eta * torch.sqrt(
+ (1 - self.alphas_cumprod_prev) / (1 - self.alphas_cumprod) * (
+ 1 - self.alphas_cumprod / self.alphas_cumprod_prev))
+ self.register_buffer('ddim_sigmas_for_original_num_steps', sigmas_for_original_sampling_steps)
+
+ @torch.no_grad()
+ def sample(self,
+ S,
+ batch_size,
+ shape,
+ conditioning=None,
+ callback=None,
+ normals_sequence=None,
+ img_callback=None,
+ quantize_x0=False,
+ eta=0.,
+ mask=None,
+ x0=None,
+ temperature=1.,
+ noise_dropout=0.,
+ score_corrector=None,
+ corrector_kwargs=None,
+ verbose=True,
+ x_T=None,
+ log_every_t=100,
+ unconditional_guidance_scale=1.,
+ unconditional_conditioning=None,
+ # this has to come in the same format as the conditioning, # e.g. as encoded tokens, ...
+ dynamic_threshold=None,
+ **kwargs
+ ):
+ if conditioning is not None:
+ if isinstance(conditioning, dict):
+ cbs = conditioning[list(conditioning.keys())[0]].shape[0]
+ if cbs != batch_size:
+ print(f"Warning: Got {cbs} conditionings but batch-size is {batch_size}")
+ else:
+ if conditioning.shape[0] != batch_size:
+ print(f"Warning: Got {conditioning.shape[0]} conditionings but batch-size is {batch_size}")
+
+ self.make_schedule(ddim_num_steps=S, ddim_eta=eta, verbose=verbose)
+ # sampling
+ C, H, W = shape
+ size = (batch_size, C, H, W)
+ print(f'Data shape for PLMS sampling is {size}')
+
+ samples, intermediates = self.plms_sampling(conditioning, size,
+ callback=callback,
+ img_callback=img_callback,
+ quantize_denoised=quantize_x0,
+ mask=mask, x0=x0,
+ ddim_use_original_steps=False,
+ noise_dropout=noise_dropout,
+ temperature=temperature,
+ score_corrector=score_corrector,
+ corrector_kwargs=corrector_kwargs,
+ x_T=x_T,
+ log_every_t=log_every_t,
+ unconditional_guidance_scale=unconditional_guidance_scale,
+ unconditional_conditioning=unconditional_conditioning,
+ dynamic_threshold=dynamic_threshold,
+ )
+ return samples, intermediates
+
+ @torch.no_grad()
+ def plms_sampling(self, cond, shape,
+ x_T=None, ddim_use_original_steps=False,
+ callback=None, timesteps=None, quantize_denoised=False,
+ mask=None, x0=None, img_callback=None, log_every_t=100,
+ temperature=1., noise_dropout=0., score_corrector=None, corrector_kwargs=None,
+ unconditional_guidance_scale=1., unconditional_conditioning=None,
+ dynamic_threshold=None):
+ device = self.model.betas.device
+ b = shape[0]
+ if x_T is None:
+ img = torch.randn(shape, device=device)
+ else:
+ img = x_T
+
+ if timesteps is None:
+ timesteps = self.ddpm_num_timesteps if ddim_use_original_steps else self.ddim_timesteps
+ elif timesteps is not None and not ddim_use_original_steps:
+ subset_end = int(min(timesteps / self.ddim_timesteps.shape[0], 1) * self.ddim_timesteps.shape[0]) - 1
+ timesteps = self.ddim_timesteps[:subset_end]
+
+ intermediates = {'x_inter': [img], 'pred_x0': [img]}
+ time_range = list(reversed(range(0,timesteps))) if ddim_use_original_steps else np.flip(timesteps)
+ total_steps = timesteps if ddim_use_original_steps else timesteps.shape[0]
+ print(f"Running PLMS Sampling with {total_steps} timesteps")
+
+ iterator = tqdm(time_range, desc='PLMS Sampler', total=total_steps)
+ old_eps = []
+
+ for i, step in enumerate(iterator):
+ index = total_steps - i - 1
+ ts = torch.full((b,), step, device=device, dtype=torch.long)
+ ts_next = torch.full((b,), time_range[min(i + 1, len(time_range) - 1)], device=device, dtype=torch.long)
+
+ if mask is not None:
+ assert x0 is not None
+ img_orig = self.model.q_sample(x0, ts) # TODO: deterministic forward pass?
+ img = img_orig * mask + (1. - mask) * img
+
+ outs = self.p_sample_plms(img, cond, ts, index=index, use_original_steps=ddim_use_original_steps,
+ quantize_denoised=quantize_denoised, temperature=temperature,
+ noise_dropout=noise_dropout, score_corrector=score_corrector,
+ corrector_kwargs=corrector_kwargs,
+ unconditional_guidance_scale=unconditional_guidance_scale,
+ unconditional_conditioning=unconditional_conditioning,
+ old_eps=old_eps, t_next=ts_next,
+ dynamic_threshold=dynamic_threshold)
+ img, pred_x0, e_t = outs
+ old_eps.append(e_t)
+ if len(old_eps) >= 4:
+ old_eps.pop(0)
+ if callback: callback(i)
+ if img_callback: img_callback(pred_x0, i)
+
+ if index % log_every_t == 0 or index == total_steps - 1:
+ intermediates['x_inter'].append(img)
+ intermediates['pred_x0'].append(pred_x0)
+
+ return img, intermediates
+
+ @torch.no_grad()
+ def p_sample_plms(self, x, c, t, index, repeat_noise=False, use_original_steps=False, quantize_denoised=False,
+ temperature=1., noise_dropout=0., score_corrector=None, corrector_kwargs=None,
+ unconditional_guidance_scale=1., unconditional_conditioning=None, old_eps=None, t_next=None,
+ dynamic_threshold=None):
+ b, *_, device = *x.shape, x.device
+
+ def get_model_output(x, t):
+ if unconditional_conditioning is None or unconditional_guidance_scale == 1.:
+ e_t = self.model.apply_model(x, t, c)
+ else:
+ x_in = torch.cat([x] * 2)
+ t_in = torch.cat([t] * 2)
+ c_in = torch.cat([unconditional_conditioning, c])
+ e_t_uncond, e_t = self.model.apply_model(x_in, t_in, c_in).chunk(2)
+ e_t = e_t_uncond + unconditional_guidance_scale * (e_t - e_t_uncond)
+
+ if score_corrector is not None:
+ assert self.model.parameterization == "eps"
+ e_t = score_corrector.modify_score(self.model, e_t, x, t, c, **corrector_kwargs)
+
+ return e_t
+
+ alphas = self.model.alphas_cumprod if use_original_steps else self.ddim_alphas
+ alphas_prev = self.model.alphas_cumprod_prev if use_original_steps else self.ddim_alphas_prev
+ sqrt_one_minus_alphas = self.model.sqrt_one_minus_alphas_cumprod if use_original_steps else self.ddim_sqrt_one_minus_alphas
+ sigmas = self.model.ddim_sigmas_for_original_num_steps if use_original_steps else self.ddim_sigmas
+
+ def get_x_prev_and_pred_x0(e_t, index):
+ # select parameters corresponding to the currently considered timestep
+ a_t = torch.full((b, 1, 1, 1), alphas[index], device=device)
+ a_prev = torch.full((b, 1, 1, 1), alphas_prev[index], device=device)
+ sigma_t = torch.full((b, 1, 1, 1), sigmas[index], device=device)
+ sqrt_one_minus_at = torch.full((b, 1, 1, 1), sqrt_one_minus_alphas[index],device=device)
+
+ # current prediction for x_0
+ pred_x0 = (x - sqrt_one_minus_at * e_t) / a_t.sqrt()
+ if quantize_denoised:
+ pred_x0, _, *_ = self.model.first_stage_model.quantize(pred_x0)
+ if dynamic_threshold is not None:
+ pred_x0 = norm_thresholding(pred_x0, dynamic_threshold)
+ # direction pointing to x_t
+ dir_xt = (1. - a_prev - sigma_t**2).sqrt() * e_t
+ noise = sigma_t * noise_like(x.shape, device, repeat_noise) * temperature
+ if noise_dropout > 0.:
+ noise = torch.nn.functional.dropout(noise, p=noise_dropout)
+ x_prev = a_prev.sqrt() * pred_x0 + dir_xt + noise
+ return x_prev, pred_x0
+
+ e_t = get_model_output(x, t)
+ if len(old_eps) == 0:
+ # Pseudo Improved Euler (2nd order)
+ x_prev, pred_x0 = get_x_prev_and_pred_x0(e_t, index)
+ e_t_next = get_model_output(x_prev, t_next)
+ e_t_prime = (e_t + e_t_next) / 2
+ elif len(old_eps) == 1:
+ # 2nd order Pseudo Linear Multistep (Adams-Bashforth)
+ e_t_prime = (3 * e_t - old_eps[-1]) / 2
+ elif len(old_eps) == 2:
+ # 3nd order Pseudo Linear Multistep (Adams-Bashforth)
+ e_t_prime = (23 * e_t - 16 * old_eps[-1] + 5 * old_eps[-2]) / 12
+ elif len(old_eps) >= 3:
+ # 4nd order Pseudo Linear Multistep (Adams-Bashforth)
+ e_t_prime = (55 * e_t - 59 * old_eps[-1] + 37 * old_eps[-2] - 9 * old_eps[-3]) / 24
+
+ x_prev, pred_x0 = get_x_prev_and_pred_x0(e_t_prime, index)
+
+ return x_prev, pred_x0, e_t
diff --git a/ldm/models/diffusion/sampling_util.py b/ldm/models/diffusion/sampling_util.py
new file mode 100644
index 0000000000000000000000000000000000000000..7eff02be6d7c54d43ee6680636ac0698dd3b3f33
--- /dev/null
+++ b/ldm/models/diffusion/sampling_util.py
@@ -0,0 +1,22 @@
+import torch
+import numpy as np
+
+
+def append_dims(x, target_dims):
+ """Appends dimensions to the end of a tensor until it has target_dims dimensions.
+ From https://github.com/crowsonkb/k-diffusion/blob/master/k_diffusion/utils.py"""
+ dims_to_append = target_dims - x.ndim
+ if dims_to_append < 0:
+ raise ValueError(f'input has {x.ndim} dims but target_dims is {target_dims}, which is less')
+ return x[(...,) + (None,) * dims_to_append]
+
+
+def norm_thresholding(x0, value):
+ s = append_dims(x0.pow(2).flatten(1).mean(1).sqrt().clamp(min=value), x0.ndim)
+ return x0 * (value / s)
+
+
+def spatial_norm_thresholding(x0, value):
+ # b c h w
+ s = x0.pow(2).mean(1, keepdim=True).sqrt().clamp(min=value)
+ return x0 * (value / s)
\ No newline at end of file
diff --git a/ldm/modules/attention.py b/ldm/modules/attention.py
new file mode 100644
index 0000000000000000000000000000000000000000..509cd873768f0dd75a75ab3fcdd652822b12b59f
--- /dev/null
+++ b/ldm/modules/attention.py
@@ -0,0 +1,341 @@
+from inspect import isfunction
+import math
+import torch
+import torch.nn.functional as F
+from torch import nn, einsum
+from einops import rearrange, repeat
+from typing import Optional, Any
+
+from ldm.modules.diffusionmodules.util import checkpoint
+
+
+try:
+ import xformers
+ import xformers.ops
+ XFORMERS_IS_AVAILBLE = True
+except:
+ XFORMERS_IS_AVAILBLE = False
+
+# CrossAttn precision handling
+import os
+_ATTN_PRECISION = os.environ.get("ATTN_PRECISION", "fp32")
+
+def exists(val):
+ return val is not None
+
+
+def uniq(arr):
+ return{el: True for el in arr}.keys()
+
+
+def default(val, d):
+ if exists(val):
+ return val
+ return d() if isfunction(d) else d
+
+
+def max_neg_value(t):
+ return -torch.finfo(t.dtype).max
+
+
+def init_(tensor):
+ dim = tensor.shape[-1]
+ std = 1 / math.sqrt(dim)
+ tensor.uniform_(-std, std)
+ return tensor
+
+
+# feedforward
+class GEGLU(nn.Module):
+ def __init__(self, dim_in, dim_out):
+ super().__init__()
+ self.proj = nn.Linear(dim_in, dim_out * 2)
+
+ def forward(self, x):
+ x, gate = self.proj(x).chunk(2, dim=-1)
+ return x * F.gelu(gate)
+
+
+class FeedForward(nn.Module):
+ def __init__(self, dim, dim_out=None, mult=4, glu=False, dropout=0.):
+ super().__init__()
+ inner_dim = int(dim * mult)
+ dim_out = default(dim_out, dim)
+ project_in = nn.Sequential(
+ nn.Linear(dim, inner_dim),
+ nn.GELU()
+ ) if not glu else GEGLU(dim, inner_dim)
+
+ self.net = nn.Sequential(
+ project_in,
+ nn.Dropout(dropout),
+ nn.Linear(inner_dim, dim_out)
+ )
+
+ def forward(self, x):
+ return self.net(x)
+
+
+def zero_module(module):
+ """
+ Zero out the parameters of a module and return it.
+ """
+ for p in module.parameters():
+ p.detach().zero_()
+ return module
+
+
+def Normalize(in_channels):
+ return torch.nn.GroupNorm(num_groups=32, num_channels=in_channels, eps=1e-6, affine=True)
+
+
+class SpatialSelfAttention(nn.Module):
+ def __init__(self, in_channels):
+ super().__init__()
+ self.in_channels = in_channels
+
+ self.norm = Normalize(in_channels)
+ self.q = torch.nn.Conv2d(in_channels,
+ in_channels,
+ kernel_size=1,
+ stride=1,
+ padding=0)
+ self.k = torch.nn.Conv2d(in_channels,
+ in_channels,
+ kernel_size=1,
+ stride=1,
+ padding=0)
+ self.v = torch.nn.Conv2d(in_channels,
+ in_channels,
+ kernel_size=1,
+ stride=1,
+ padding=0)
+ self.proj_out = torch.nn.Conv2d(in_channels,
+ in_channels,
+ kernel_size=1,
+ stride=1,
+ padding=0)
+
+ def forward(self, x):
+ h_ = x
+ h_ = self.norm(h_)
+ q = self.q(h_)
+ k = self.k(h_)
+ v = self.v(h_)
+
+ # compute attention
+ b,c,h,w = q.shape
+ q = rearrange(q, 'b c h w -> b (h w) c')
+ k = rearrange(k, 'b c h w -> b c (h w)')
+ w_ = torch.einsum('bij,bjk->bik', q, k)
+
+ w_ = w_ * (int(c)**(-0.5))
+ w_ = torch.nn.functional.softmax(w_, dim=2)
+
+ # attend to values
+ v = rearrange(v, 'b c h w -> b c (h w)')
+ w_ = rearrange(w_, 'b i j -> b j i')
+ h_ = torch.einsum('bij,bjk->bik', v, w_)
+ h_ = rearrange(h_, 'b c (h w) -> b c h w', h=h)
+ h_ = self.proj_out(h_)
+
+ return x+h_
+
+
+class CrossAttention(nn.Module):
+ def __init__(self, query_dim, context_dim=None, heads=8, dim_head=64, dropout=0.):
+ super().__init__()
+ inner_dim = dim_head * heads
+ context_dim = default(context_dim, query_dim)
+
+ self.scale = dim_head ** -0.5
+ self.heads = heads
+
+ self.to_q = nn.Linear(query_dim, inner_dim, bias=False)
+ self.to_k = nn.Linear(context_dim, inner_dim, bias=False)
+ self.to_v = nn.Linear(context_dim, inner_dim, bias=False)
+
+ self.to_out = nn.Sequential(
+ nn.Linear(inner_dim, query_dim),
+ nn.Dropout(dropout)
+ )
+
+ def forward(self, x, context=None, mask=None):
+ h = self.heads
+
+ q = self.to_q(x)
+ context = default(context, x)
+ k = self.to_k(context)
+ v = self.to_v(context)
+
+ q, k, v = map(lambda t: rearrange(t, 'b n (h d) -> (b h) n d', h=h), (q, k, v))
+
+ # force cast to fp32 to avoid overflowing
+ if _ATTN_PRECISION =="fp32":
+ with torch.autocast(enabled=False, device_type = 'cuda'):
+ q, k = q.float(), k.float()
+ sim = einsum('b i d, b j d -> b i j', q, k) * self.scale
+ else:
+ sim = einsum('b i d, b j d -> b i j', q, k) * self.scale
+
+ del q, k
+
+ if exists(mask):
+ mask = rearrange(mask, 'b ... -> b (...)')
+ max_neg_value = -torch.finfo(sim.dtype).max
+ mask = repeat(mask, 'b j -> (b h) () j', h=h)
+ sim.masked_fill_(~mask, max_neg_value)
+
+ # attention, what we cannot get enough of
+ sim = sim.softmax(dim=-1)
+
+ out = einsum('b i j, b j d -> b i d', sim, v)
+ out = rearrange(out, '(b h) n d -> b n (h d)', h=h)
+ return self.to_out(out)
+
+
+class MemoryEfficientCrossAttention(nn.Module):
+ # https://github.com/MatthieuTPHR/diffusers/blob/d80b531ff8060ec1ea982b65a1b8df70f73aa67c/src/diffusers/models/attention.py#L223
+ def __init__(self, query_dim, context_dim=None, heads=8, dim_head=64, dropout=0.0):
+ super().__init__()
+ print(f"Setting up {self.__class__.__name__}. Query dim is {query_dim}, context_dim is {context_dim} and using "
+ f"{heads} heads.")
+ inner_dim = dim_head * heads
+ context_dim = default(context_dim, query_dim)
+
+ self.heads = heads
+ self.dim_head = dim_head
+
+ self.to_q = nn.Linear(query_dim, inner_dim, bias=False)
+ self.to_k = nn.Linear(context_dim, inner_dim, bias=False)
+ self.to_v = nn.Linear(context_dim, inner_dim, bias=False)
+
+ self.to_out = nn.Sequential(nn.Linear(inner_dim, query_dim), nn.Dropout(dropout))
+ self.attention_op: Optional[Any] = None
+
+ def forward(self, x, context=None, mask=None):
+ q = self.to_q(x)
+ context = default(context, x)
+ k = self.to_k(context)
+ v = self.to_v(context)
+
+ b, _, _ = q.shape
+ q, k, v = map(
+ lambda t: t.unsqueeze(3)
+ .reshape(b, t.shape[1], self.heads, self.dim_head)
+ .permute(0, 2, 1, 3)
+ .reshape(b * self.heads, t.shape[1], self.dim_head)
+ .contiguous(),
+ (q, k, v),
+ )
+
+ # actually compute the attention, what we cannot get enough of
+ out = xformers.ops.memory_efficient_attention(q, k, v, attn_bias=None, op=self.attention_op)
+
+ if exists(mask):
+ raise NotImplementedError
+ out = (
+ out.unsqueeze(0)
+ .reshape(b, self.heads, out.shape[1], self.dim_head)
+ .permute(0, 2, 1, 3)
+ .reshape(b, out.shape[1], self.heads * self.dim_head)
+ )
+ return self.to_out(out)
+
+
+class BasicTransformerBlock(nn.Module):
+ ATTENTION_MODES = {
+ "softmax": CrossAttention, # vanilla attention
+ "softmax-xformers": MemoryEfficientCrossAttention
+ }
+ def __init__(self, dim, n_heads, d_head, dropout=0., context_dim=None, gated_ff=True, checkpoint=True,
+ disable_self_attn=False):
+ super().__init__()
+ attn_mode = "softmax-xformers" if XFORMERS_IS_AVAILBLE else "softmax"
+ assert attn_mode in self.ATTENTION_MODES
+ attn_cls = self.ATTENTION_MODES[attn_mode]
+ self.disable_self_attn = disable_self_attn
+ self.attn1 = attn_cls(query_dim=dim, heads=n_heads, dim_head=d_head, dropout=dropout,
+ context_dim=context_dim if self.disable_self_attn else None) # is a self-attention if not self.disable_self_attn
+ self.ff = FeedForward(dim, dropout=dropout, glu=gated_ff)
+ self.attn2 = attn_cls(query_dim=dim, context_dim=context_dim,
+ heads=n_heads, dim_head=d_head, dropout=dropout) # is self-attn if context is none
+ self.norm1 = nn.LayerNorm(dim)
+ self.norm2 = nn.LayerNorm(dim)
+ self.norm3 = nn.LayerNorm(dim)
+ self.checkpoint = checkpoint
+
+ def forward(self, x, context=None):
+ return checkpoint(self._forward, (x, context), self.parameters(), self.checkpoint)
+
+ def _forward(self, x, context=None):
+ x = self.attn1(self.norm1(x), context=context if self.disable_self_attn else None) + x
+ x = self.attn2(self.norm2(x), context=context) + x
+ x = self.ff(self.norm3(x)) + x
+ return x
+
+
+class SpatialTransformer(nn.Module):
+ """
+ Transformer block for image-like data.
+ First, project the input (aka embedding)
+ and reshape to b, t, d.
+ Then apply standard transformer action.
+ Finally, reshape to image
+ NEW: use_linear for more efficiency instead of the 1x1 convs
+ """
+ def __init__(self, in_channels, n_heads, d_head,
+ depth=1, dropout=0., context_dim=None,
+ disable_self_attn=False, use_linear=False,
+ use_checkpoint=True):
+ super().__init__()
+ if exists(context_dim) and not isinstance(context_dim, list):
+ context_dim = [context_dim]
+ self.in_channels = in_channels
+ inner_dim = n_heads * d_head
+ self.norm = Normalize(in_channels)
+ if not use_linear:
+ self.proj_in = nn.Conv2d(in_channels,
+ inner_dim,
+ kernel_size=1,
+ stride=1,
+ padding=0)
+ else:
+ self.proj_in = nn.Linear(in_channels, inner_dim)
+
+ self.transformer_blocks = nn.ModuleList(
+ [BasicTransformerBlock(inner_dim, n_heads, d_head, dropout=dropout, context_dim=context_dim[d],
+ disable_self_attn=disable_self_attn, checkpoint=use_checkpoint)
+ for d in range(depth)]
+ )
+ if not use_linear:
+ self.proj_out = zero_module(nn.Conv2d(inner_dim,
+ in_channels,
+ kernel_size=1,
+ stride=1,
+ padding=0))
+ else:
+ self.proj_out = zero_module(nn.Linear(in_channels, inner_dim))
+ self.use_linear = use_linear
+
+ def forward(self, x, context=None):
+ # note: if no context is given, cross-attention defaults to self-attention
+ if not isinstance(context, list):
+ context = [context]
+ b, c, h, w = x.shape
+ x_in = x
+ x = self.norm(x)
+ if not self.use_linear:
+ x = self.proj_in(x)
+ x = rearrange(x, 'b c h w -> b (h w) c').contiguous()
+ if self.use_linear:
+ x = self.proj_in(x)
+ for i, block in enumerate(self.transformer_blocks):
+ x = block(x, context=context[i])
+ if self.use_linear:
+ x = self.proj_out(x)
+ x = rearrange(x, 'b (h w) c -> b c h w', h=h, w=w).contiguous()
+ if not self.use_linear:
+ x = self.proj_out(x)
+ return x + x_in
+
diff --git a/ldm/modules/diffusionmodules/__init__.py b/ldm/modules/diffusionmodules/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/ldm/modules/diffusionmodules/model.py b/ldm/modules/diffusionmodules/model.py
new file mode 100644
index 0000000000000000000000000000000000000000..b089eebbe1676d8249005bb9def002ff5180715b
--- /dev/null
+++ b/ldm/modules/diffusionmodules/model.py
@@ -0,0 +1,852 @@
+# pytorch_diffusion + derived encoder decoder
+import math
+import torch
+import torch.nn as nn
+import numpy as np
+from einops import rearrange
+from typing import Optional, Any
+
+from ldm.modules.attention import MemoryEfficientCrossAttention
+
+try:
+ import xformers
+ import xformers.ops
+ XFORMERS_IS_AVAILBLE = True
+except:
+ XFORMERS_IS_AVAILBLE = False
+ print("No module 'xformers'. Proceeding without it.")
+
+
+def get_timestep_embedding(timesteps, embedding_dim):
+ """
+ This matches the implementation in Denoising Diffusion Probabilistic Models:
+ From Fairseq.
+ Build sinusoidal embeddings.
+ This matches the implementation in tensor2tensor, but differs slightly
+ from the description in Section 3.5 of "Attention Is All You Need".
+ """
+ assert len(timesteps.shape) == 1
+
+ half_dim = embedding_dim // 2
+ emb = math.log(10000) / (half_dim - 1)
+ emb = torch.exp(torch.arange(half_dim, dtype=torch.float32) * -emb)
+ emb = emb.to(device=timesteps.device)
+ emb = timesteps.float()[:, None] * emb[None, :]
+ emb = torch.cat([torch.sin(emb), torch.cos(emb)], dim=1)
+ if embedding_dim % 2 == 1: # zero pad
+ emb = torch.nn.functional.pad(emb, (0,1,0,0))
+ return emb
+
+
+def nonlinearity(x):
+ # swish
+ return x*torch.sigmoid(x)
+
+
+def Normalize(in_channels, num_groups=32):
+ return torch.nn.GroupNorm(num_groups=num_groups, num_channels=in_channels, eps=1e-6, affine=True)
+
+
+class Upsample(nn.Module):
+ def __init__(self, in_channels, with_conv):
+ super().__init__()
+ self.with_conv = with_conv
+ if self.with_conv:
+ self.conv = torch.nn.Conv2d(in_channels,
+ in_channels,
+ kernel_size=3,
+ stride=1,
+ padding=1)
+
+ def forward(self, x):
+ x = torch.nn.functional.interpolate(x, scale_factor=2.0, mode="nearest")
+ if self.with_conv:
+ x = self.conv(x)
+ return x
+
+
+class Downsample(nn.Module):
+ def __init__(self, in_channels, with_conv):
+ super().__init__()
+ self.with_conv = with_conv
+ if self.with_conv:
+ # no asymmetric padding in torch conv, must do it ourselves
+ self.conv = torch.nn.Conv2d(in_channels,
+ in_channels,
+ kernel_size=3,
+ stride=2,
+ padding=0)
+
+ def forward(self, x):
+ if self.with_conv:
+ pad = (0,1,0,1)
+ x = torch.nn.functional.pad(x, pad, mode="constant", value=0)
+ x = self.conv(x)
+ else:
+ x = torch.nn.functional.avg_pool2d(x, kernel_size=2, stride=2)
+ return x
+
+
+class ResnetBlock(nn.Module):
+ def __init__(self, *, in_channels, out_channels=None, conv_shortcut=False,
+ dropout, temb_channels=512):
+ super().__init__()
+ self.in_channels = in_channels
+ out_channels = in_channels if out_channels is None else out_channels
+ self.out_channels = out_channels
+ self.use_conv_shortcut = conv_shortcut
+
+ self.norm1 = Normalize(in_channels)
+ self.conv1 = torch.nn.Conv2d(in_channels,
+ out_channels,
+ kernel_size=3,
+ stride=1,
+ padding=1)
+ if temb_channels > 0:
+ self.temb_proj = torch.nn.Linear(temb_channels,
+ out_channels)
+ self.norm2 = Normalize(out_channels)
+ self.dropout = torch.nn.Dropout(dropout)
+ self.conv2 = torch.nn.Conv2d(out_channels,
+ out_channels,
+ kernel_size=3,
+ stride=1,
+ padding=1)
+ if self.in_channels != self.out_channels:
+ if self.use_conv_shortcut:
+ self.conv_shortcut = torch.nn.Conv2d(in_channels,
+ out_channels,
+ kernel_size=3,
+ stride=1,
+ padding=1)
+ else:
+ self.nin_shortcut = torch.nn.Conv2d(in_channels,
+ out_channels,
+ kernel_size=1,
+ stride=1,
+ padding=0)
+
+ def forward(self, x, temb):
+ h = x
+ h = self.norm1(h)
+ h = nonlinearity(h)
+ h = self.conv1(h)
+
+ if temb is not None:
+ h = h + self.temb_proj(nonlinearity(temb))[:,:,None,None]
+
+ h = self.norm2(h)
+ h = nonlinearity(h)
+ h = self.dropout(h)
+ h = self.conv2(h)
+
+ if self.in_channels != self.out_channels:
+ if self.use_conv_shortcut:
+ x = self.conv_shortcut(x)
+ else:
+ x = self.nin_shortcut(x)
+
+ return x+h
+
+
+class AttnBlock(nn.Module):
+ def __init__(self, in_channels):
+ super().__init__()
+ self.in_channels = in_channels
+
+ self.norm = Normalize(in_channels)
+ self.q = torch.nn.Conv2d(in_channels,
+ in_channels,
+ kernel_size=1,
+ stride=1,
+ padding=0)
+ self.k = torch.nn.Conv2d(in_channels,
+ in_channels,
+ kernel_size=1,
+ stride=1,
+ padding=0)
+ self.v = torch.nn.Conv2d(in_channels,
+ in_channels,
+ kernel_size=1,
+ stride=1,
+ padding=0)
+ self.proj_out = torch.nn.Conv2d(in_channels,
+ in_channels,
+ kernel_size=1,
+ stride=1,
+ padding=0)
+
+ def forward(self, x):
+ h_ = x
+ h_ = self.norm(h_)
+ q = self.q(h_)
+ k = self.k(h_)
+ v = self.v(h_)
+
+ # compute attention
+ b,c,h,w = q.shape
+ q = q.reshape(b,c,h*w)
+ q = q.permute(0,2,1) # b,hw,c
+ k = k.reshape(b,c,h*w) # b,c,hw
+ w_ = torch.bmm(q,k) # b,hw,hw w[b,i,j]=sum_c q[b,i,c]k[b,c,j]
+ w_ = w_ * (int(c)**(-0.5))
+ w_ = torch.nn.functional.softmax(w_, dim=2)
+
+ # attend to values
+ v = v.reshape(b,c,h*w)
+ w_ = w_.permute(0,2,1) # b,hw,hw (first hw of k, second of q)
+ h_ = torch.bmm(v,w_) # b, c,hw (hw of q) h_[b,c,j] = sum_i v[b,c,i] w_[b,i,j]
+ h_ = h_.reshape(b,c,h,w)
+
+ h_ = self.proj_out(h_)
+
+ return x+h_
+
+class MemoryEfficientAttnBlock(nn.Module):
+ """
+ Uses xformers efficient implementation,
+ see https://github.com/MatthieuTPHR/diffusers/blob/d80b531ff8060ec1ea982b65a1b8df70f73aa67c/src/diffusers/models/attention.py#L223
+ Note: this is a single-head self-attention operation
+ """
+ #
+ def __init__(self, in_channels):
+ super().__init__()
+ self.in_channels = in_channels
+
+ self.norm = Normalize(in_channels)
+ self.q = torch.nn.Conv2d(in_channels,
+ in_channels,
+ kernel_size=1,
+ stride=1,
+ padding=0)
+ self.k = torch.nn.Conv2d(in_channels,
+ in_channels,
+ kernel_size=1,
+ stride=1,
+ padding=0)
+ self.v = torch.nn.Conv2d(in_channels,
+ in_channels,
+ kernel_size=1,
+ stride=1,
+ padding=0)
+ self.proj_out = torch.nn.Conv2d(in_channels,
+ in_channels,
+ kernel_size=1,
+ stride=1,
+ padding=0)
+ self.attention_op: Optional[Any] = None
+
+ def forward(self, x):
+ h_ = x
+ h_ = self.norm(h_)
+ q = self.q(h_)
+ k = self.k(h_)
+ v = self.v(h_)
+
+ # compute attention
+ B, C, H, W = q.shape
+ q, k, v = map(lambda x: rearrange(x, 'b c h w -> b (h w) c'), (q, k, v))
+
+ q, k, v = map(
+ lambda t: t.unsqueeze(3)
+ .reshape(B, t.shape[1], 1, C)
+ .permute(0, 2, 1, 3)
+ .reshape(B * 1, t.shape[1], C)
+ .contiguous(),
+ (q, k, v),
+ )
+ out = xformers.ops.memory_efficient_attention(q, k, v, attn_bias=None, op=self.attention_op)
+
+ out = (
+ out.unsqueeze(0)
+ .reshape(B, 1, out.shape[1], C)
+ .permute(0, 2, 1, 3)
+ .reshape(B, out.shape[1], C)
+ )
+ out = rearrange(out, 'b (h w) c -> b c h w', b=B, h=H, w=W, c=C)
+ out = self.proj_out(out)
+ return x+out
+
+
+class MemoryEfficientCrossAttentionWrapper(MemoryEfficientCrossAttention):
+ def forward(self, x, context=None, mask=None):
+ b, c, h, w = x.shape
+ x = rearrange(x, 'b c h w -> b (h w) c')
+ out = super().forward(x, context=context, mask=mask)
+ out = rearrange(out, 'b (h w) c -> b c h w', h=h, w=w, c=c)
+ return x + out
+
+
+def make_attn(in_channels, attn_type="vanilla", attn_kwargs=None):
+ assert attn_type in ["vanilla", "vanilla-xformers", "memory-efficient-cross-attn", "linear", "none"], f'attn_type {attn_type} unknown'
+ if XFORMERS_IS_AVAILBLE and attn_type == "vanilla":
+ attn_type = "vanilla-xformers"
+ print(f"making attention of type '{attn_type}' with {in_channels} in_channels")
+ if attn_type == "vanilla":
+ assert attn_kwargs is None
+ return AttnBlock(in_channels)
+ elif attn_type == "vanilla-xformers":
+ print(f"building MemoryEfficientAttnBlock with {in_channels} in_channels...")
+ return MemoryEfficientAttnBlock(in_channels)
+ elif type == "memory-efficient-cross-attn":
+ attn_kwargs["query_dim"] = in_channels
+ return MemoryEfficientCrossAttentionWrapper(**attn_kwargs)
+ elif attn_type == "none":
+ return nn.Identity(in_channels)
+ else:
+ raise NotImplementedError()
+
+
+class Model(nn.Module):
+ def __init__(self, *, ch, out_ch, ch_mult=(1,2,4,8), num_res_blocks,
+ attn_resolutions, dropout=0.0, resamp_with_conv=True, in_channels,
+ resolution, use_timestep=True, use_linear_attn=False, attn_type="vanilla"):
+ super().__init__()
+ if use_linear_attn: attn_type = "linear"
+ self.ch = ch
+ self.temb_ch = self.ch*4
+ self.num_resolutions = len(ch_mult)
+ self.num_res_blocks = num_res_blocks
+ self.resolution = resolution
+ self.in_channels = in_channels
+
+ self.use_timestep = use_timestep
+ if self.use_timestep:
+ # timestep embedding
+ self.temb = nn.Module()
+ self.temb.dense = nn.ModuleList([
+ torch.nn.Linear(self.ch,
+ self.temb_ch),
+ torch.nn.Linear(self.temb_ch,
+ self.temb_ch),
+ ])
+
+ # downsampling
+ self.conv_in = torch.nn.Conv2d(in_channels,
+ self.ch,
+ kernel_size=3,
+ stride=1,
+ padding=1)
+
+ curr_res = resolution
+ in_ch_mult = (1,)+tuple(ch_mult)
+ self.down = nn.ModuleList()
+ for i_level in range(self.num_resolutions):
+ block = nn.ModuleList()
+ attn = nn.ModuleList()
+ block_in = ch*in_ch_mult[i_level]
+ block_out = ch*ch_mult[i_level]
+ for i_block in range(self.num_res_blocks):
+ block.append(ResnetBlock(in_channels=block_in,
+ out_channels=block_out,
+ temb_channels=self.temb_ch,
+ dropout=dropout))
+ block_in = block_out
+ if curr_res in attn_resolutions:
+ attn.append(make_attn(block_in, attn_type=attn_type))
+ down = nn.Module()
+ down.block = block
+ down.attn = attn
+ if i_level != self.num_resolutions-1:
+ down.downsample = Downsample(block_in, resamp_with_conv)
+ curr_res = curr_res // 2
+ self.down.append(down)
+
+ # middle
+ self.mid = nn.Module()
+ self.mid.block_1 = ResnetBlock(in_channels=block_in,
+ out_channels=block_in,
+ temb_channels=self.temb_ch,
+ dropout=dropout)
+ self.mid.attn_1 = make_attn(block_in, attn_type=attn_type)
+ self.mid.block_2 = ResnetBlock(in_channels=block_in,
+ out_channels=block_in,
+ temb_channels=self.temb_ch,
+ dropout=dropout)
+
+ # upsampling
+ self.up = nn.ModuleList()
+ for i_level in reversed(range(self.num_resolutions)):
+ block = nn.ModuleList()
+ attn = nn.ModuleList()
+ block_out = ch*ch_mult[i_level]
+ skip_in = ch*ch_mult[i_level]
+ for i_block in range(self.num_res_blocks+1):
+ if i_block == self.num_res_blocks:
+ skip_in = ch*in_ch_mult[i_level]
+ block.append(ResnetBlock(in_channels=block_in+skip_in,
+ out_channels=block_out,
+ temb_channels=self.temb_ch,
+ dropout=dropout))
+ block_in = block_out
+ if curr_res in attn_resolutions:
+ attn.append(make_attn(block_in, attn_type=attn_type))
+ up = nn.Module()
+ up.block = block
+ up.attn = attn
+ if i_level != 0:
+ up.upsample = Upsample(block_in, resamp_with_conv)
+ curr_res = curr_res * 2
+ self.up.insert(0, up) # prepend to get consistent order
+
+ # end
+ self.norm_out = Normalize(block_in)
+ self.conv_out = torch.nn.Conv2d(block_in,
+ out_ch,
+ kernel_size=3,
+ stride=1,
+ padding=1)
+
+ def forward(self, x, t=None, context=None):
+ #assert x.shape[2] == x.shape[3] == self.resolution
+ if context is not None:
+ # assume aligned context, cat along channel axis
+ x = torch.cat((x, context), dim=1)
+ if self.use_timestep:
+ # timestep embedding
+ assert t is not None
+ temb = get_timestep_embedding(t, self.ch)
+ temb = self.temb.dense[0](temb)
+ temb = nonlinearity(temb)
+ temb = self.temb.dense[1](temb)
+ else:
+ temb = None
+
+ # downsampling
+ hs = [self.conv_in(x)]
+ for i_level in range(self.num_resolutions):
+ for i_block in range(self.num_res_blocks):
+ h = self.down[i_level].block[i_block](hs[-1], temb)
+ if len(self.down[i_level].attn) > 0:
+ h = self.down[i_level].attn[i_block](h)
+ hs.append(h)
+ if i_level != self.num_resolutions-1:
+ hs.append(self.down[i_level].downsample(hs[-1]))
+
+ # middle
+ h = hs[-1]
+ h = self.mid.block_1(h, temb)
+ h = self.mid.attn_1(h)
+ h = self.mid.block_2(h, temb)
+
+ # upsampling
+ for i_level in reversed(range(self.num_resolutions)):
+ for i_block in range(self.num_res_blocks+1):
+ h = self.up[i_level].block[i_block](
+ torch.cat([h, hs.pop()], dim=1), temb)
+ if len(self.up[i_level].attn) > 0:
+ h = self.up[i_level].attn[i_block](h)
+ if i_level != 0:
+ h = self.up[i_level].upsample(h)
+
+ # end
+ h = self.norm_out(h)
+ h = nonlinearity(h)
+ h = self.conv_out(h)
+ return h
+
+ def get_last_layer(self):
+ return self.conv_out.weight
+
+
+class Encoder(nn.Module):
+ def __init__(self, *, ch, out_ch, ch_mult=(1,2,4,8), num_res_blocks,
+ attn_resolutions, dropout=0.0, resamp_with_conv=True, in_channels,
+ resolution, z_channels, double_z=True, use_linear_attn=False, attn_type="vanilla",
+ **ignore_kwargs):
+ super().__init__()
+ if use_linear_attn: attn_type = "linear"
+ self.ch = ch
+ self.temb_ch = 0
+ self.num_resolutions = len(ch_mult)
+ self.num_res_blocks = num_res_blocks
+ self.resolution = resolution
+ self.in_channels = in_channels
+
+ # downsampling
+ self.conv_in = torch.nn.Conv2d(in_channels,
+ self.ch,
+ kernel_size=3,
+ stride=1,
+ padding=1)
+
+ curr_res = resolution
+ in_ch_mult = (1,)+tuple(ch_mult)
+ self.in_ch_mult = in_ch_mult
+ self.down = nn.ModuleList()
+ for i_level in range(self.num_resolutions):
+ block = nn.ModuleList()
+ attn = nn.ModuleList()
+ block_in = ch*in_ch_mult[i_level]
+ block_out = ch*ch_mult[i_level]
+ for i_block in range(self.num_res_blocks):
+ block.append(ResnetBlock(in_channels=block_in,
+ out_channels=block_out,
+ temb_channels=self.temb_ch,
+ dropout=dropout))
+ block_in = block_out
+ if curr_res in attn_resolutions:
+ attn.append(make_attn(block_in, attn_type=attn_type))
+ down = nn.Module()
+ down.block = block
+ down.attn = attn
+ if i_level != self.num_resolutions-1:
+ down.downsample = Downsample(block_in, resamp_with_conv)
+ curr_res = curr_res // 2
+ self.down.append(down)
+
+ # middle
+ self.mid = nn.Module()
+ self.mid.block_1 = ResnetBlock(in_channels=block_in,
+ out_channels=block_in,
+ temb_channels=self.temb_ch,
+ dropout=dropout)
+ self.mid.attn_1 = make_attn(block_in, attn_type=attn_type)
+ self.mid.block_2 = ResnetBlock(in_channels=block_in,
+ out_channels=block_in,
+ temb_channels=self.temb_ch,
+ dropout=dropout)
+
+ # end
+ self.norm_out = Normalize(block_in)
+ self.conv_out = torch.nn.Conv2d(block_in,
+ 2*z_channels if double_z else z_channels,
+ kernel_size=3,
+ stride=1,
+ padding=1)
+
+ def forward(self, x):
+ # timestep embedding
+ temb = None
+
+ # downsampling
+ hs = [self.conv_in(x)]
+ for i_level in range(self.num_resolutions):
+ for i_block in range(self.num_res_blocks):
+ h = self.down[i_level].block[i_block](hs[-1], temb)
+ if len(self.down[i_level].attn) > 0:
+ h = self.down[i_level].attn[i_block](h)
+ hs.append(h)
+ if i_level != self.num_resolutions-1:
+ hs.append(self.down[i_level].downsample(hs[-1]))
+
+ # middle
+ h = hs[-1]
+ h = self.mid.block_1(h, temb)
+ h = self.mid.attn_1(h)
+ h = self.mid.block_2(h, temb)
+
+ # end
+ h = self.norm_out(h)
+ h = nonlinearity(h)
+ h = self.conv_out(h)
+ return h
+
+
+class Decoder(nn.Module):
+ def __init__(self, *, ch, out_ch, ch_mult=(1,2,4,8), num_res_blocks,
+ attn_resolutions, dropout=0.0, resamp_with_conv=True, in_channels,
+ resolution, z_channels, give_pre_end=False, tanh_out=False, use_linear_attn=False,
+ attn_type="vanilla", **ignorekwargs):
+ super().__init__()
+ if use_linear_attn: attn_type = "linear"
+ self.ch = ch
+ self.temb_ch = 0
+ self.num_resolutions = len(ch_mult)
+ self.num_res_blocks = num_res_blocks
+ self.resolution = resolution
+ self.in_channels = in_channels
+ self.give_pre_end = give_pre_end
+ self.tanh_out = tanh_out
+
+ # compute in_ch_mult, block_in and curr_res at lowest res
+ in_ch_mult = (1,)+tuple(ch_mult)
+ block_in = ch*ch_mult[self.num_resolutions-1]
+ curr_res = resolution // 2**(self.num_resolutions-1)
+ self.z_shape = (1,z_channels,curr_res,curr_res)
+ print("Working with z of shape {} = {} dimensions.".format(
+ self.z_shape, np.prod(self.z_shape)))
+
+ # z to block_in
+ self.conv_in = torch.nn.Conv2d(z_channels,
+ block_in,
+ kernel_size=3,
+ stride=1,
+ padding=1)
+
+ # middle
+ self.mid = nn.Module()
+ self.mid.block_1 = ResnetBlock(in_channels=block_in,
+ out_channels=block_in,
+ temb_channels=self.temb_ch,
+ dropout=dropout)
+ self.mid.attn_1 = make_attn(block_in, attn_type=attn_type)
+ self.mid.block_2 = ResnetBlock(in_channels=block_in,
+ out_channels=block_in,
+ temb_channels=self.temb_ch,
+ dropout=dropout)
+
+ # upsampling
+ self.up = nn.ModuleList()
+ for i_level in reversed(range(self.num_resolutions)):
+ block = nn.ModuleList()
+ attn = nn.ModuleList()
+ block_out = ch*ch_mult[i_level]
+ for i_block in range(self.num_res_blocks+1):
+ block.append(ResnetBlock(in_channels=block_in,
+ out_channels=block_out,
+ temb_channels=self.temb_ch,
+ dropout=dropout))
+ block_in = block_out
+ if curr_res in attn_resolutions:
+ attn.append(make_attn(block_in, attn_type=attn_type))
+ up = nn.Module()
+ up.block = block
+ up.attn = attn
+ if i_level != 0:
+ up.upsample = Upsample(block_in, resamp_with_conv)
+ curr_res = curr_res * 2
+ self.up.insert(0, up) # prepend to get consistent order
+
+ # end
+ self.norm_out = Normalize(block_in)
+ self.conv_out = torch.nn.Conv2d(block_in,
+ out_ch,
+ kernel_size=3,
+ stride=1,
+ padding=1)
+
+ def forward(self, z):
+ #assert z.shape[1:] == self.z_shape[1:]
+ self.last_z_shape = z.shape
+
+ # timestep embedding
+ temb = None
+
+ # z to block_in
+ h = self.conv_in(z)
+
+ # middle
+ h = self.mid.block_1(h, temb)
+ h = self.mid.attn_1(h)
+ h = self.mid.block_2(h, temb)
+
+ # upsampling
+ for i_level in reversed(range(self.num_resolutions)):
+ for i_block in range(self.num_res_blocks+1):
+ h = self.up[i_level].block[i_block](h, temb)
+ if len(self.up[i_level].attn) > 0:
+ h = self.up[i_level].attn[i_block](h)
+ if i_level != 0:
+ h = self.up[i_level].upsample(h)
+
+ # end
+ if self.give_pre_end:
+ return h
+
+ h = self.norm_out(h)
+ h = nonlinearity(h)
+ h = self.conv_out(h)
+ if self.tanh_out:
+ h = torch.tanh(h)
+ return h
+
+
+class SimpleDecoder(nn.Module):
+ def __init__(self, in_channels, out_channels, *args, **kwargs):
+ super().__init__()
+ self.model = nn.ModuleList([nn.Conv2d(in_channels, in_channels, 1),
+ ResnetBlock(in_channels=in_channels,
+ out_channels=2 * in_channels,
+ temb_channels=0, dropout=0.0),
+ ResnetBlock(in_channels=2 * in_channels,
+ out_channels=4 * in_channels,
+ temb_channels=0, dropout=0.0),
+ ResnetBlock(in_channels=4 * in_channels,
+ out_channels=2 * in_channels,
+ temb_channels=0, dropout=0.0),
+ nn.Conv2d(2*in_channels, in_channels, 1),
+ Upsample(in_channels, with_conv=True)])
+ # end
+ self.norm_out = Normalize(in_channels)
+ self.conv_out = torch.nn.Conv2d(in_channels,
+ out_channels,
+ kernel_size=3,
+ stride=1,
+ padding=1)
+
+ def forward(self, x):
+ for i, layer in enumerate(self.model):
+ if i in [1,2,3]:
+ x = layer(x, None)
+ else:
+ x = layer(x)
+
+ h = self.norm_out(x)
+ h = nonlinearity(h)
+ x = self.conv_out(h)
+ return x
+
+
+class UpsampleDecoder(nn.Module):
+ def __init__(self, in_channels, out_channels, ch, num_res_blocks, resolution,
+ ch_mult=(2,2), dropout=0.0):
+ super().__init__()
+ # upsampling
+ self.temb_ch = 0
+ self.num_resolutions = len(ch_mult)
+ self.num_res_blocks = num_res_blocks
+ block_in = in_channels
+ curr_res = resolution // 2 ** (self.num_resolutions - 1)
+ self.res_blocks = nn.ModuleList()
+ self.upsample_blocks = nn.ModuleList()
+ for i_level in range(self.num_resolutions):
+ res_block = []
+ block_out = ch * ch_mult[i_level]
+ for i_block in range(self.num_res_blocks + 1):
+ res_block.append(ResnetBlock(in_channels=block_in,
+ out_channels=block_out,
+ temb_channels=self.temb_ch,
+ dropout=dropout))
+ block_in = block_out
+ self.res_blocks.append(nn.ModuleList(res_block))
+ if i_level != self.num_resolutions - 1:
+ self.upsample_blocks.append(Upsample(block_in, True))
+ curr_res = curr_res * 2
+
+ # end
+ self.norm_out = Normalize(block_in)
+ self.conv_out = torch.nn.Conv2d(block_in,
+ out_channels,
+ kernel_size=3,
+ stride=1,
+ padding=1)
+
+ def forward(self, x):
+ # upsampling
+ h = x
+ for k, i_level in enumerate(range(self.num_resolutions)):
+ for i_block in range(self.num_res_blocks + 1):
+ h = self.res_blocks[i_level][i_block](h, None)
+ if i_level != self.num_resolutions - 1:
+ h = self.upsample_blocks[k](h)
+ h = self.norm_out(h)
+ h = nonlinearity(h)
+ h = self.conv_out(h)
+ return h
+
+
+class LatentRescaler(nn.Module):
+ def __init__(self, factor, in_channels, mid_channels, out_channels, depth=2):
+ super().__init__()
+ # residual block, interpolate, residual block
+ self.factor = factor
+ self.conv_in = nn.Conv2d(in_channels,
+ mid_channels,
+ kernel_size=3,
+ stride=1,
+ padding=1)
+ self.res_block1 = nn.ModuleList([ResnetBlock(in_channels=mid_channels,
+ out_channels=mid_channels,
+ temb_channels=0,
+ dropout=0.0) for _ in range(depth)])
+ self.attn = AttnBlock(mid_channels)
+ self.res_block2 = nn.ModuleList([ResnetBlock(in_channels=mid_channels,
+ out_channels=mid_channels,
+ temb_channels=0,
+ dropout=0.0) for _ in range(depth)])
+
+ self.conv_out = nn.Conv2d(mid_channels,
+ out_channels,
+ kernel_size=1,
+ )
+
+ def forward(self, x):
+ x = self.conv_in(x)
+ for block in self.res_block1:
+ x = block(x, None)
+ x = torch.nn.functional.interpolate(x, size=(int(round(x.shape[2]*self.factor)), int(round(x.shape[3]*self.factor))))
+ x = self.attn(x)
+ for block in self.res_block2:
+ x = block(x, None)
+ x = self.conv_out(x)
+ return x
+
+
+class MergedRescaleEncoder(nn.Module):
+ def __init__(self, in_channels, ch, resolution, out_ch, num_res_blocks,
+ attn_resolutions, dropout=0.0, resamp_with_conv=True,
+ ch_mult=(1,2,4,8), rescale_factor=1.0, rescale_module_depth=1):
+ super().__init__()
+ intermediate_chn = ch * ch_mult[-1]
+ self.encoder = Encoder(in_channels=in_channels, num_res_blocks=num_res_blocks, ch=ch, ch_mult=ch_mult,
+ z_channels=intermediate_chn, double_z=False, resolution=resolution,
+ attn_resolutions=attn_resolutions, dropout=dropout, resamp_with_conv=resamp_with_conv,
+ out_ch=None)
+ self.rescaler = LatentRescaler(factor=rescale_factor, in_channels=intermediate_chn,
+ mid_channels=intermediate_chn, out_channels=out_ch, depth=rescale_module_depth)
+
+ def forward(self, x):
+ x = self.encoder(x)
+ x = self.rescaler(x)
+ return x
+
+
+class MergedRescaleDecoder(nn.Module):
+ def __init__(self, z_channels, out_ch, resolution, num_res_blocks, attn_resolutions, ch, ch_mult=(1,2,4,8),
+ dropout=0.0, resamp_with_conv=True, rescale_factor=1.0, rescale_module_depth=1):
+ super().__init__()
+ tmp_chn = z_channels*ch_mult[-1]
+ self.decoder = Decoder(out_ch=out_ch, z_channels=tmp_chn, attn_resolutions=attn_resolutions, dropout=dropout,
+ resamp_with_conv=resamp_with_conv, in_channels=None, num_res_blocks=num_res_blocks,
+ ch_mult=ch_mult, resolution=resolution, ch=ch)
+ self.rescaler = LatentRescaler(factor=rescale_factor, in_channels=z_channels, mid_channels=tmp_chn,
+ out_channels=tmp_chn, depth=rescale_module_depth)
+
+ def forward(self, x):
+ x = self.rescaler(x)
+ x = self.decoder(x)
+ return x
+
+
+class Upsampler(nn.Module):
+ def __init__(self, in_size, out_size, in_channels, out_channels, ch_mult=2):
+ super().__init__()
+ assert out_size >= in_size
+ num_blocks = int(np.log2(out_size//in_size))+1
+ factor_up = 1.+ (out_size % in_size)
+ print(f"Building {self.__class__.__name__} with in_size: {in_size} --> out_size {out_size} and factor {factor_up}")
+ self.rescaler = LatentRescaler(factor=factor_up, in_channels=in_channels, mid_channels=2*in_channels,
+ out_channels=in_channels)
+ self.decoder = Decoder(out_ch=out_channels, resolution=out_size, z_channels=in_channels, num_res_blocks=2,
+ attn_resolutions=[], in_channels=None, ch=in_channels,
+ ch_mult=[ch_mult for _ in range(num_blocks)])
+
+ def forward(self, x):
+ x = self.rescaler(x)
+ x = self.decoder(x)
+ return x
+
+
+class Resize(nn.Module):
+ def __init__(self, in_channels=None, learned=False, mode="bilinear"):
+ super().__init__()
+ self.with_conv = learned
+ self.mode = mode
+ if self.with_conv:
+ print(f"Note: {self.__class__.__name} uses learned downsampling and will ignore the fixed {mode} mode")
+ raise NotImplementedError()
+ assert in_channels is not None
+ # no asymmetric padding in torch conv, must do it ourselves
+ self.conv = torch.nn.Conv2d(in_channels,
+ in_channels,
+ kernel_size=4,
+ stride=2,
+ padding=1)
+
+ def forward(self, x, scale_factor=1.0):
+ if scale_factor==1.0:
+ return x
+ else:
+ x = torch.nn.functional.interpolate(x, mode=self.mode, align_corners=False, scale_factor=scale_factor)
+ return x
diff --git a/ldm/modules/diffusionmodules/openaimodel.py b/ldm/modules/diffusionmodules/openaimodel.py
new file mode 100644
index 0000000000000000000000000000000000000000..73650df5102fb79dab4a268b4fad3573607e5b34
--- /dev/null
+++ b/ldm/modules/diffusionmodules/openaimodel.py
@@ -0,0 +1,786 @@
+from abc import abstractmethod
+import math
+
+import numpy as np
+import torch as th
+import torch.nn as nn
+import torch.nn.functional as F
+
+from ldm.modules.diffusionmodules.util import (
+ checkpoint,
+ conv_nd,
+ linear,
+ avg_pool_nd,
+ zero_module,
+ normalization,
+ timestep_embedding,
+)
+from ldm.modules.attention import SpatialTransformer
+from ldm.util import exists
+
+
+# dummy replace
+def convert_module_to_f16(x):
+ pass
+
+def convert_module_to_f32(x):
+ pass
+
+
+## go
+class AttentionPool2d(nn.Module):
+ """
+ Adapted from CLIP: https://github.com/openai/CLIP/blob/main/clip/model.py
+ """
+
+ def __init__(
+ self,
+ spacial_dim: int,
+ embed_dim: int,
+ num_heads_channels: int,
+ output_dim: int = None,
+ ):
+ super().__init__()
+ self.positional_embedding = nn.Parameter(th.randn(embed_dim, spacial_dim ** 2 + 1) / embed_dim ** 0.5)
+ self.qkv_proj = conv_nd(1, embed_dim, 3 * embed_dim, 1)
+ self.c_proj = conv_nd(1, embed_dim, output_dim or embed_dim, 1)
+ self.num_heads = embed_dim // num_heads_channels
+ self.attention = QKVAttention(self.num_heads)
+
+ def forward(self, x):
+ b, c, *_spatial = x.shape
+ x = x.reshape(b, c, -1) # NC(HW)
+ x = th.cat([x.mean(dim=-1, keepdim=True), x], dim=-1) # NC(HW+1)
+ x = x + self.positional_embedding[None, :, :].to(x.dtype) # NC(HW+1)
+ x = self.qkv_proj(x)
+ x = self.attention(x)
+ x = self.c_proj(x)
+ return x[:, :, 0]
+
+
+class TimestepBlock(nn.Module):
+ """
+ Any module where forward() takes timestep embeddings as a second argument.
+ """
+
+ @abstractmethod
+ def forward(self, x, emb):
+ """
+ Apply the module to `x` given `emb` timestep embeddings.
+ """
+
+
+class TimestepEmbedSequential(nn.Sequential, TimestepBlock):
+ """
+ A sequential module that passes timestep embeddings to the children that
+ support it as an extra input.
+ """
+
+ def forward(self, x, emb, context=None, *args):
+ for layer in self:
+ if isinstance(layer, TimestepBlock):
+ x = layer(x, emb)
+ elif isinstance(layer, SpatialTransformer):
+ x = layer(x, context)
+ else:
+ x = layer(x)
+ return x
+
+
+class Upsample(nn.Module):
+ """
+ An upsampling layer with an optional convolution.
+ :param channels: channels in the inputs and outputs.
+ :param use_conv: a bool determining if a convolution is applied.
+ :param dims: determines if the signal is 1D, 2D, or 3D. If 3D, then
+ upsampling occurs in the inner-two dimensions.
+ """
+
+ def __init__(self, channels, use_conv, dims=2, out_channels=None, padding=1):
+ super().__init__()
+ self.channels = channels
+ self.out_channels = out_channels or channels
+ self.use_conv = use_conv
+ self.dims = dims
+ if use_conv:
+ self.conv = conv_nd(dims, self.channels, self.out_channels, 3, padding=padding)
+
+ def forward(self, x):
+ assert x.shape[1] == self.channels
+ if self.dims == 3:
+ x = F.interpolate(
+ x, (x.shape[2], x.shape[3] * 2, x.shape[4] * 2), mode="nearest"
+ )
+ else:
+ x = F.interpolate(x, scale_factor=2, mode="nearest")
+ if self.use_conv:
+ x = self.conv(x)
+ return x
+
+class TransposedUpsample(nn.Module):
+ 'Learned 2x upsampling without padding'
+ def __init__(self, channels, out_channels=None, ks=5):
+ super().__init__()
+ self.channels = channels
+ self.out_channels = out_channels or channels
+
+ self.up = nn.ConvTranspose2d(self.channels,self.out_channels,kernel_size=ks,stride=2)
+
+ def forward(self,x):
+ return self.up(x)
+
+
+class Downsample(nn.Module):
+ """
+ A downsampling layer with an optional convolution.
+ :param channels: channels in the inputs and outputs.
+ :param use_conv: a bool determining if a convolution is applied.
+ :param dims: determines if the signal is 1D, 2D, or 3D. If 3D, then
+ downsampling occurs in the inner-two dimensions.
+ """
+
+ def __init__(self, channels, use_conv, dims=2, out_channels=None,padding=1):
+ super().__init__()
+ self.channels = channels
+ self.out_channels = out_channels or channels
+ self.use_conv = use_conv
+ self.dims = dims
+ stride = 2 if dims != 3 else (1, 2, 2)
+ if use_conv:
+ self.op = conv_nd(
+ dims, self.channels, self.out_channels, 3, stride=stride, padding=padding
+ )
+ else:
+ assert self.channels == self.out_channels
+ self.op = avg_pool_nd(dims, kernel_size=stride, stride=stride)
+
+ def forward(self, x):
+ assert x.shape[1] == self.channels
+ return self.op(x)
+
+
+class ResBlock(TimestepBlock):
+ """
+ A residual block that can optionally change the number of channels.
+ :param channels: the number of input channels.
+ :param emb_channels: the number of timestep embedding channels.
+ :param dropout: the rate of dropout.
+ :param out_channels: if specified, the number of out channels.
+ :param use_conv: if True and out_channels is specified, use a spatial
+ convolution instead of a smaller 1x1 convolution to change the
+ channels in the skip connection.
+ :param dims: determines if the signal is 1D, 2D, or 3D.
+ :param use_checkpoint: if True, use gradient checkpointing on this module.
+ :param up: if True, use this block for upsampling.
+ :param down: if True, use this block for downsampling.
+ """
+
+ def __init__(
+ self,
+ channels,
+ emb_channels,
+ dropout,
+ out_channels=None,
+ use_conv=False,
+ use_scale_shift_norm=False,
+ dims=2,
+ use_checkpoint=False,
+ up=False,
+ down=False,
+ ):
+ super().__init__()
+ self.channels = channels
+ self.emb_channels = emb_channels
+ self.dropout = dropout
+ self.out_channels = out_channels or channels
+ self.use_conv = use_conv
+ self.use_checkpoint = use_checkpoint
+ self.use_scale_shift_norm = use_scale_shift_norm
+
+ self.in_layers = nn.Sequential(
+ normalization(channels),
+ nn.SiLU(),
+ conv_nd(dims, channels, self.out_channels, 3, padding=1),
+ )
+
+ self.updown = up or down
+
+ if up:
+ self.h_upd = Upsample(channels, False, dims)
+ self.x_upd = Upsample(channels, False, dims)
+ elif down:
+ self.h_upd = Downsample(channels, False, dims)
+ self.x_upd = Downsample(channels, False, dims)
+ else:
+ self.h_upd = self.x_upd = nn.Identity()
+
+ self.emb_layers = nn.Sequential(
+ nn.SiLU(),
+ linear(
+ emb_channels,
+ 2 * self.out_channels if use_scale_shift_norm else self.out_channels,
+ ),
+ )
+ self.out_layers = nn.Sequential(
+ normalization(self.out_channels),
+ nn.SiLU(),
+ nn.Dropout(p=dropout),
+ zero_module(
+ conv_nd(dims, self.out_channels, self.out_channels, 3, padding=1)
+ ),
+ )
+
+ if self.out_channels == channels:
+ self.skip_connection = nn.Identity()
+ elif use_conv:
+ self.skip_connection = conv_nd(
+ dims, channels, self.out_channels, 3, padding=1
+ )
+ else:
+ self.skip_connection = conv_nd(dims, channels, self.out_channels, 1)
+
+ def forward(self, x, emb):
+ """
+ Apply the block to a Tensor, conditioned on a timestep embedding.
+ :param x: an [N x C x ...] Tensor of features.
+ :param emb: an [N x emb_channels] Tensor of timestep embeddings.
+ :return: an [N x C x ...] Tensor of outputs.
+ """
+ return checkpoint(
+ self._forward, (x, emb), self.parameters(), self.use_checkpoint
+ )
+
+
+ def _forward(self, x, emb):
+ if self.updown:
+ in_rest, in_conv = self.in_layers[:-1], self.in_layers[-1]
+ h = in_rest(x)
+ h = self.h_upd(h)
+ x = self.x_upd(x)
+ h = in_conv(h)
+ else:
+ h = self.in_layers(x)
+ emb_out = self.emb_layers(emb).type(h.dtype)
+ while len(emb_out.shape) < len(h.shape):
+ emb_out = emb_out[..., None]
+ if self.use_scale_shift_norm:
+ out_norm, out_rest = self.out_layers[0], self.out_layers[1:]
+ scale, shift = th.chunk(emb_out, 2, dim=1)
+ h = out_norm(h) * (1 + scale) + shift
+ h = out_rest(h)
+ else:
+ h = h + emb_out
+ h = self.out_layers(h)
+ return self.skip_connection(x) + h
+
+
+class AttentionBlock(nn.Module):
+ """
+ An attention block that allows spatial positions to attend to each other.
+ Originally ported from here, but adapted to the N-d case.
+ https://github.com/hojonathanho/diffusion/blob/1e0dceb3b3495bbe19116a5e1b3596cd0706c543/diffusion_tf/models/unet.py#L66.
+ """
+
+ def __init__(
+ self,
+ channels,
+ num_heads=1,
+ num_head_channels=-1,
+ use_checkpoint=False,
+ use_new_attention_order=False,
+ ):
+ super().__init__()
+ self.channels = channels
+ if num_head_channels == -1:
+ self.num_heads = num_heads
+ else:
+ assert (
+ channels % num_head_channels == 0
+ ), f"q,k,v channels {channels} is not divisible by num_head_channels {num_head_channels}"
+ self.num_heads = channels // num_head_channels
+ self.use_checkpoint = use_checkpoint
+ self.norm = normalization(channels)
+ self.qkv = conv_nd(1, channels, channels * 3, 1)
+ if use_new_attention_order:
+ # split qkv before split heads
+ self.attention = QKVAttention(self.num_heads)
+ else:
+ # split heads before split qkv
+ self.attention = QKVAttentionLegacy(self.num_heads)
+
+ self.proj_out = zero_module(conv_nd(1, channels, channels, 1))
+
+ def forward(self, x):
+ return checkpoint(self._forward, (x,), self.parameters(), True) # TODO: check checkpoint usage, is True # TODO: fix the .half call!!!
+ #return pt_checkpoint(self._forward, x) # pytorch
+
+ def _forward(self, x):
+ b, c, *spatial = x.shape
+ x = x.reshape(b, c, -1)
+ qkv = self.qkv(self.norm(x))
+ h = self.attention(qkv)
+ h = self.proj_out(h)
+ return (x + h).reshape(b, c, *spatial)
+
+
+def count_flops_attn(model, _x, y):
+ """
+ A counter for the `thop` package to count the operations in an
+ attention operation.
+ Meant to be used like:
+ macs, params = thop.profile(
+ model,
+ inputs=(inputs, timestamps),
+ custom_ops={QKVAttention: QKVAttention.count_flops},
+ )
+ """
+ b, c, *spatial = y[0].shape
+ num_spatial = int(np.prod(spatial))
+ # We perform two matmuls with the same number of ops.
+ # The first computes the weight matrix, the second computes
+ # the combination of the value vectors.
+ matmul_ops = 2 * b * (num_spatial ** 2) * c
+ model.total_ops += th.DoubleTensor([matmul_ops])
+
+
+class QKVAttentionLegacy(nn.Module):
+ """
+ A module which performs QKV attention. Matches legacy QKVAttention + input/ouput heads shaping
+ """
+
+ def __init__(self, n_heads):
+ super().__init__()
+ self.n_heads = n_heads
+
+ def forward(self, qkv):
+ """
+ Apply QKV attention.
+ :param qkv: an [N x (H * 3 * C) x T] tensor of Qs, Ks, and Vs.
+ :return: an [N x (H * C) x T] tensor after attention.
+ """
+ bs, width, length = qkv.shape
+ assert width % (3 * self.n_heads) == 0
+ ch = width // (3 * self.n_heads)
+ q, k, v = qkv.reshape(bs * self.n_heads, ch * 3, length).split(ch, dim=1)
+ scale = 1 / math.sqrt(math.sqrt(ch))
+ weight = th.einsum(
+ "bct,bcs->bts", q * scale, k * scale
+ ) # More stable with f16 than dividing afterwards
+ weight = th.softmax(weight.float(), dim=-1).type(weight.dtype)
+ a = th.einsum("bts,bcs->bct", weight, v)
+ return a.reshape(bs, -1, length)
+
+ @staticmethod
+ def count_flops(model, _x, y):
+ return count_flops_attn(model, _x, y)
+
+
+class QKVAttention(nn.Module):
+ """
+ A module which performs QKV attention and splits in a different order.
+ """
+
+ def __init__(self, n_heads):
+ super().__init__()
+ self.n_heads = n_heads
+
+ def forward(self, qkv):
+ """
+ Apply QKV attention.
+ :param qkv: an [N x (3 * H * C) x T] tensor of Qs, Ks, and Vs.
+ :return: an [N x (H * C) x T] tensor after attention.
+ """
+ bs, width, length = qkv.shape
+ assert width % (3 * self.n_heads) == 0
+ ch = width // (3 * self.n_heads)
+ q, k, v = qkv.chunk(3, dim=1)
+ scale = 1 / math.sqrt(math.sqrt(ch))
+ weight = th.einsum(
+ "bct,bcs->bts",
+ (q * scale).view(bs * self.n_heads, ch, length),
+ (k * scale).view(bs * self.n_heads, ch, length),
+ ) # More stable with f16 than dividing afterwards
+ weight = th.softmax(weight.float(), dim=-1).type(weight.dtype)
+ a = th.einsum("bts,bcs->bct", weight, v.reshape(bs * self.n_heads, ch, length))
+ return a.reshape(bs, -1, length)
+
+ @staticmethod
+ def count_flops(model, _x, y):
+ return count_flops_attn(model, _x, y)
+
+
+class UNetModel(nn.Module):
+ """
+ The full UNet model with attention and timestep embedding.
+ :param in_channels: channels in the input Tensor.
+ :param model_channels: base channel count for the model.
+ :param out_channels: channels in the output Tensor.
+ :param num_res_blocks: number of residual blocks per downsample.
+ :param attention_resolutions: a collection of downsample rates at which
+ attention will take place. May be a set, list, or tuple.
+ For example, if this contains 4, then at 4x downsampling, attention
+ will be used.
+ :param dropout: the dropout probability.
+ :param channel_mult: channel multiplier for each level of the UNet.
+ :param conv_resample: if True, use learned convolutions for upsampling and
+ downsampling.
+ :param dims: determines if the signal is 1D, 2D, or 3D.
+ :param num_classes: if specified (as an int), then this model will be
+ class-conditional with `num_classes` classes.
+ :param use_checkpoint: use gradient checkpointing to reduce memory usage.
+ :param num_heads: the number of attention heads in each attention layer.
+ :param num_heads_channels: if specified, ignore num_heads and instead use
+ a fixed channel width per attention head.
+ :param num_heads_upsample: works with num_heads to set a different number
+ of heads for upsampling. Deprecated.
+ :param use_scale_shift_norm: use a FiLM-like conditioning mechanism.
+ :param resblock_updown: use residual blocks for up/downsampling.
+ :param use_new_attention_order: use a different attention pattern for potentially
+ increased efficiency.
+ """
+
+ def __init__(
+ self,
+ image_size,
+ in_channels,
+ model_channels,
+ out_channels,
+ num_res_blocks,
+ attention_resolutions,
+ dropout=0,
+ channel_mult=(1, 2, 4, 8),
+ conv_resample=True,
+ dims=2,
+ num_classes=None,
+ use_checkpoint=False,
+ use_fp16=False,
+ num_heads=-1,
+ num_head_channels=-1,
+ num_heads_upsample=-1,
+ use_scale_shift_norm=False,
+ resblock_updown=False,
+ use_new_attention_order=False,
+ use_spatial_transformer=False, # custom transformer support
+ transformer_depth=1, # custom transformer support
+ context_dim=None, # custom transformer support
+ n_embed=None, # custom support for prediction of discrete ids into codebook of first stage vq model
+ legacy=True,
+ disable_self_attentions=None,
+ num_attention_blocks=None,
+ disable_middle_self_attn=False,
+ use_linear_in_transformer=False,
+ ):
+ super().__init__()
+ if use_spatial_transformer:
+ assert context_dim is not None, 'Fool!! You forgot to include the dimension of your cross-attention conditioning...'
+
+ if context_dim is not None:
+ assert use_spatial_transformer, 'Fool!! You forgot to use the spatial transformer for your cross-attention conditioning...'
+ from omegaconf.listconfig import ListConfig
+ if type(context_dim) == ListConfig:
+ context_dim = list(context_dim)
+
+ if num_heads_upsample == -1:
+ num_heads_upsample = num_heads
+
+ if num_heads == -1:
+ assert num_head_channels != -1, 'Either num_heads or num_head_channels has to be set'
+
+ if num_head_channels == -1:
+ assert num_heads != -1, 'Either num_heads or num_head_channels has to be set'
+
+ self.image_size = image_size
+ self.in_channels = in_channels
+ self.model_channels = model_channels
+ self.out_channels = out_channels
+ if isinstance(num_res_blocks, int):
+ self.num_res_blocks = len(channel_mult) * [num_res_blocks]
+ else:
+ if len(num_res_blocks) != len(channel_mult):
+ raise ValueError("provide num_res_blocks either as an int (globally constant) or "
+ "as a list/tuple (per-level) with the same length as channel_mult")
+ self.num_res_blocks = num_res_blocks
+ if disable_self_attentions is not None:
+ # should be a list of booleans, indicating whether to disable self-attention in TransformerBlocks or not
+ assert len(disable_self_attentions) == len(channel_mult)
+ if num_attention_blocks is not None:
+ assert len(num_attention_blocks) == len(self.num_res_blocks)
+ assert all(map(lambda i: self.num_res_blocks[i] >= num_attention_blocks[i], range(len(num_attention_blocks))))
+ print(f"Constructor of UNetModel received num_attention_blocks={num_attention_blocks}. "
+ f"This option has LESS priority than attention_resolutions {attention_resolutions}, "
+ f"i.e., in cases where num_attention_blocks[i] > 0 but 2**i not in attention_resolutions, "
+ f"attention will still not be set.")
+
+ self.attention_resolutions = attention_resolutions
+ self.dropout = dropout
+ self.channel_mult = channel_mult
+ self.conv_resample = conv_resample
+ self.num_classes = num_classes
+ self.use_checkpoint = use_checkpoint
+ self.dtype = th.float16 if use_fp16 else th.float32
+ self.num_heads = num_heads
+ self.num_head_channels = num_head_channels
+ self.num_heads_upsample = num_heads_upsample
+ self.predict_codebook_ids = n_embed is not None
+
+ time_embed_dim = model_channels * 4
+ self.time_embed = nn.Sequential(
+ linear(model_channels, time_embed_dim),
+ nn.SiLU(),
+ linear(time_embed_dim, time_embed_dim),
+ )
+
+ if self.num_classes is not None:
+ if isinstance(self.num_classes, int):
+ self.label_emb = nn.Embedding(num_classes, time_embed_dim)
+ elif self.num_classes == "continuous":
+ print("setting up linear c_adm embedding layer")
+ self.label_emb = nn.Linear(1, time_embed_dim)
+ else:
+ raise ValueError()
+
+ self.input_blocks = nn.ModuleList(
+ [
+ TimestepEmbedSequential(
+ conv_nd(dims, in_channels, model_channels, 3, padding=1)
+ )
+ ]
+ )
+ self._feature_size = model_channels
+ input_block_chans = [model_channels]
+ ch = model_channels
+ ds = 1
+ for level, mult in enumerate(channel_mult):
+ for nr in range(self.num_res_blocks[level]):
+ layers = [
+ ResBlock(
+ ch,
+ time_embed_dim,
+ dropout,
+ out_channels=mult * model_channels,
+ dims=dims,
+ use_checkpoint=use_checkpoint,
+ use_scale_shift_norm=use_scale_shift_norm,
+ )
+ ]
+ ch = mult * model_channels
+ if ds in attention_resolutions:
+ if num_head_channels == -1:
+ dim_head = ch // num_heads
+ else:
+ num_heads = ch // num_head_channels
+ dim_head = num_head_channels
+ if legacy:
+ #num_heads = 1
+ dim_head = ch // num_heads if use_spatial_transformer else num_head_channels
+ if exists(disable_self_attentions):
+ disabled_sa = disable_self_attentions[level]
+ else:
+ disabled_sa = False
+
+ if not exists(num_attention_blocks) or nr < num_attention_blocks[level]:
+ layers.append(
+ AttentionBlock(
+ ch,
+ use_checkpoint=use_checkpoint,
+ num_heads=num_heads,
+ num_head_channels=dim_head,
+ use_new_attention_order=use_new_attention_order,
+ ) if not use_spatial_transformer else SpatialTransformer(
+ ch, num_heads, dim_head, depth=transformer_depth, context_dim=context_dim,
+ disable_self_attn=disabled_sa, use_linear=use_linear_in_transformer,
+ use_checkpoint=use_checkpoint
+ )
+ )
+ self.input_blocks.append(TimestepEmbedSequential(*layers))
+ self._feature_size += ch
+ input_block_chans.append(ch)
+ if level != len(channel_mult) - 1:
+ out_ch = ch
+ self.input_blocks.append(
+ TimestepEmbedSequential(
+ ResBlock(
+ ch,
+ time_embed_dim,
+ dropout,
+ out_channels=out_ch,
+ dims=dims,
+ use_checkpoint=use_checkpoint,
+ use_scale_shift_norm=use_scale_shift_norm,
+ down=True,
+ )
+ if resblock_updown
+ else Downsample(
+ ch, conv_resample, dims=dims, out_channels=out_ch
+ )
+ )
+ )
+ ch = out_ch
+ input_block_chans.append(ch)
+ ds *= 2
+ self._feature_size += ch
+
+ if num_head_channels == -1:
+ dim_head = ch // num_heads
+ else:
+ num_heads = ch // num_head_channels
+ dim_head = num_head_channels
+ if legacy:
+ #num_heads = 1
+ dim_head = ch // num_heads if use_spatial_transformer else num_head_channels
+ self.middle_block = TimestepEmbedSequential(
+ ResBlock(
+ ch,
+ time_embed_dim,
+ dropout,
+ dims=dims,
+ use_checkpoint=use_checkpoint,
+ use_scale_shift_norm=use_scale_shift_norm,
+ ),
+ AttentionBlock(
+ ch,
+ use_checkpoint=use_checkpoint,
+ num_heads=num_heads,
+ num_head_channels=dim_head,
+ use_new_attention_order=use_new_attention_order,
+ ) if not use_spatial_transformer else SpatialTransformer( # always uses a self-attn
+ ch, num_heads, dim_head, depth=transformer_depth, context_dim=context_dim,
+ disable_self_attn=disable_middle_self_attn, use_linear=use_linear_in_transformer,
+ use_checkpoint=use_checkpoint
+ ),
+ ResBlock(
+ ch,
+ time_embed_dim,
+ dropout,
+ dims=dims,
+ use_checkpoint=use_checkpoint,
+ use_scale_shift_norm=use_scale_shift_norm,
+ ),
+ )
+ self._feature_size += ch
+
+ self.output_blocks = nn.ModuleList([])
+ for level, mult in list(enumerate(channel_mult))[::-1]:
+ for i in range(self.num_res_blocks[level] + 1):
+ ich = input_block_chans.pop()
+ layers = [
+ ResBlock(
+ ch + ich,
+ time_embed_dim,
+ dropout,
+ out_channels=model_channels * mult,
+ dims=dims,
+ use_checkpoint=use_checkpoint,
+ use_scale_shift_norm=use_scale_shift_norm,
+ )
+ ]
+ ch = model_channels * mult
+ if ds in attention_resolutions:
+ if num_head_channels == -1:
+ dim_head = ch // num_heads
+ else:
+ num_heads = ch // num_head_channels
+ dim_head = num_head_channels
+ if legacy:
+ #num_heads = 1
+ dim_head = ch // num_heads if use_spatial_transformer else num_head_channels
+ if exists(disable_self_attentions):
+ disabled_sa = disable_self_attentions[level]
+ else:
+ disabled_sa = False
+
+ if not exists(num_attention_blocks) or i < num_attention_blocks[level]:
+ layers.append(
+ AttentionBlock(
+ ch,
+ use_checkpoint=use_checkpoint,
+ num_heads=num_heads_upsample,
+ num_head_channels=dim_head,
+ use_new_attention_order=use_new_attention_order,
+ ) if not use_spatial_transformer else SpatialTransformer(
+ ch, num_heads, dim_head, depth=transformer_depth, context_dim=context_dim,
+ disable_self_attn=disabled_sa, use_linear=use_linear_in_transformer,
+ use_checkpoint=use_checkpoint
+ )
+ )
+ if level and i == self.num_res_blocks[level]:
+ out_ch = ch
+ layers.append(
+ ResBlock(
+ ch,
+ time_embed_dim,
+ dropout,
+ out_channels=out_ch,
+ dims=dims,
+ use_checkpoint=use_checkpoint,
+ use_scale_shift_norm=use_scale_shift_norm,
+ up=True,
+ )
+ if resblock_updown
+ else Upsample(ch, conv_resample, dims=dims, out_channels=out_ch)
+ )
+ ds //= 2
+ self.output_blocks.append(TimestepEmbedSequential(*layers))
+ self._feature_size += ch
+
+ self.out = nn.Sequential(
+ normalization(ch),
+ nn.SiLU(),
+ zero_module(conv_nd(dims, model_channels, out_channels, 3, padding=1)),
+ )
+ if self.predict_codebook_ids:
+ self.id_predictor = nn.Sequential(
+ normalization(ch),
+ conv_nd(dims, model_channels, n_embed, 1),
+ #nn.LogSoftmax(dim=1) # change to cross_entropy and produce non-normalized logits
+ )
+
+ def convert_to_fp16(self):
+ """
+ Convert the torso of the model to float16.
+ """
+ self.input_blocks.apply(convert_module_to_f16)
+ self.middle_block.apply(convert_module_to_f16)
+ self.output_blocks.apply(convert_module_to_f16)
+
+ def convert_to_fp32(self):
+ """
+ Convert the torso of the model to float32.
+ """
+ self.input_blocks.apply(convert_module_to_f32)
+ self.middle_block.apply(convert_module_to_f32)
+ self.output_blocks.apply(convert_module_to_f32)
+
+ def forward(self, x, timesteps=None, context=None, y=None,**kwargs):
+ """
+ Apply the model to an input batch.
+ :param x: an [N x C x ...] Tensor of inputs.
+ :param timesteps: a 1-D batch of timesteps.
+ :param context: conditioning plugged in via crossattn
+ :param y: an [N] Tensor of labels, if class-conditional.
+ :return: an [N x C x ...] Tensor of outputs.
+ """
+ assert (y is not None) == (
+ self.num_classes is not None
+ ), "must specify y if and only if the model is class-conditional"
+ hs = []
+ t_emb = timestep_embedding(timesteps, self.model_channels, repeat_only=False)
+ emb = self.time_embed(t_emb)
+
+ if self.num_classes is not None:
+ assert y.shape[0] == x.shape[0]
+ emb = emb + self.label_emb(y)
+
+ h = x.type(self.dtype)
+ for module in self.input_blocks:
+ h = module(h, emb, context)
+ hs.append(h)
+ h = self.middle_block(h, emb, context)
+ for module in self.output_blocks:
+ h = th.cat([h, hs.pop()], dim=1)
+ h = module(h, emb, context)
+ h = h.type(x.dtype)
+ if self.predict_codebook_ids:
+ return self.id_predictor(h)
+ else:
+ return self.out(h)
diff --git a/ldm/modules/diffusionmodules/upscaling.py b/ldm/modules/diffusionmodules/upscaling.py
new file mode 100644
index 0000000000000000000000000000000000000000..03816662098ce1ffac79bd939b892e867ab91988
--- /dev/null
+++ b/ldm/modules/diffusionmodules/upscaling.py
@@ -0,0 +1,81 @@
+import torch
+import torch.nn as nn
+import numpy as np
+from functools import partial
+
+from ldm.modules.diffusionmodules.util import extract_into_tensor, make_beta_schedule
+from ldm.util import default
+
+
+class AbstractLowScaleModel(nn.Module):
+ # for concatenating a downsampled image to the latent representation
+ def __init__(self, noise_schedule_config=None):
+ super(AbstractLowScaleModel, self).__init__()
+ if noise_schedule_config is not None:
+ self.register_schedule(**noise_schedule_config)
+
+ def register_schedule(self, beta_schedule="linear", timesteps=1000,
+ linear_start=1e-4, linear_end=2e-2, cosine_s=8e-3):
+ betas = make_beta_schedule(beta_schedule, timesteps, linear_start=linear_start, linear_end=linear_end,
+ cosine_s=cosine_s)
+ alphas = 1. - betas
+ alphas_cumprod = np.cumprod(alphas, axis=0)
+ alphas_cumprod_prev = np.append(1., alphas_cumprod[:-1])
+
+ timesteps, = betas.shape
+ self.num_timesteps = int(timesteps)
+ self.linear_start = linear_start
+ self.linear_end = linear_end
+ assert alphas_cumprod.shape[0] == self.num_timesteps, 'alphas have to be defined for each timestep'
+
+ to_torch = partial(torch.tensor, dtype=torch.float32)
+
+ self.register_buffer('betas', to_torch(betas))
+ self.register_buffer('alphas_cumprod', to_torch(alphas_cumprod))
+ self.register_buffer('alphas_cumprod_prev', to_torch(alphas_cumprod_prev))
+
+ # calculations for diffusion q(x_t | x_{t-1}) and others
+ self.register_buffer('sqrt_alphas_cumprod', to_torch(np.sqrt(alphas_cumprod)))
+ self.register_buffer('sqrt_one_minus_alphas_cumprod', to_torch(np.sqrt(1. - alphas_cumprod)))
+ self.register_buffer('log_one_minus_alphas_cumprod', to_torch(np.log(1. - alphas_cumprod)))
+ self.register_buffer('sqrt_recip_alphas_cumprod', to_torch(np.sqrt(1. / alphas_cumprod)))
+ self.register_buffer('sqrt_recipm1_alphas_cumprod', to_torch(np.sqrt(1. / alphas_cumprod - 1)))
+
+ def q_sample(self, x_start, t, noise=None):
+ noise = default(noise, lambda: torch.randn_like(x_start))
+ return (extract_into_tensor(self.sqrt_alphas_cumprod, t, x_start.shape) * x_start +
+ extract_into_tensor(self.sqrt_one_minus_alphas_cumprod, t, x_start.shape) * noise)
+
+ def forward(self, x):
+ return x, None
+
+ def decode(self, x):
+ return x
+
+
+class SimpleImageConcat(AbstractLowScaleModel):
+ # no noise level conditioning
+ def __init__(self):
+ super(SimpleImageConcat, self).__init__(noise_schedule_config=None)
+ self.max_noise_level = 0
+
+ def forward(self, x):
+ # fix to constant noise level
+ return x, torch.zeros(x.shape[0], device=x.device).long()
+
+
+class ImageConcatWithNoiseAugmentation(AbstractLowScaleModel):
+ def __init__(self, noise_schedule_config, max_noise_level=1000, to_cuda=False):
+ super().__init__(noise_schedule_config=noise_schedule_config)
+ self.max_noise_level = max_noise_level
+
+ def forward(self, x, noise_level=None):
+ if noise_level is None:
+ noise_level = torch.randint(0, self.max_noise_level, (x.shape[0],), device=x.device).long()
+ else:
+ assert isinstance(noise_level, torch.Tensor)
+ z = self.q_sample(x, noise_level)
+ return z, noise_level
+
+
+
diff --git a/ldm/modules/diffusionmodules/util.py b/ldm/modules/diffusionmodules/util.py
new file mode 100644
index 0000000000000000000000000000000000000000..637363dfe34799e70cfdbcd11445212df9d9ca1f
--- /dev/null
+++ b/ldm/modules/diffusionmodules/util.py
@@ -0,0 +1,270 @@
+# adopted from
+# https://github.com/openai/improved-diffusion/blob/main/improved_diffusion/gaussian_diffusion.py
+# and
+# https://github.com/lucidrains/denoising-diffusion-pytorch/blob/7706bdfc6f527f58d33f84b7b522e61e6e3164b3/denoising_diffusion_pytorch/denoising_diffusion_pytorch.py
+# and
+# https://github.com/openai/guided-diffusion/blob/0ba878e517b276c45d1195eb29f6f5f72659a05b/guided_diffusion/nn.py
+#
+# thanks!
+
+
+import os
+import math
+import torch
+import torch.nn as nn
+import numpy as np
+from einops import repeat
+
+from ldm.util import instantiate_from_config
+
+
+def make_beta_schedule(schedule, n_timestep, linear_start=1e-4, linear_end=2e-2, cosine_s=8e-3):
+ if schedule == "linear":
+ betas = (
+ torch.linspace(linear_start ** 0.5, linear_end ** 0.5, n_timestep, dtype=torch.float64) ** 2
+ )
+
+ elif schedule == "cosine":
+ timesteps = (
+ torch.arange(n_timestep + 1, dtype=torch.float64) / n_timestep + cosine_s
+ )
+ alphas = timesteps / (1 + cosine_s) * np.pi / 2
+ alphas = torch.cos(alphas).pow(2)
+ alphas = alphas / alphas[0]
+ betas = 1 - alphas[1:] / alphas[:-1]
+ betas = np.clip(betas, a_min=0, a_max=0.999)
+
+ elif schedule == "sqrt_linear":
+ betas = torch.linspace(linear_start, linear_end, n_timestep, dtype=torch.float64)
+ elif schedule == "sqrt":
+ betas = torch.linspace(linear_start, linear_end, n_timestep, dtype=torch.float64) ** 0.5
+ else:
+ raise ValueError(f"schedule '{schedule}' unknown.")
+ return betas.numpy()
+
+
+def make_ddim_timesteps(ddim_discr_method, num_ddim_timesteps, num_ddpm_timesteps, verbose=True):
+ if ddim_discr_method == 'uniform':
+ c = num_ddpm_timesteps // num_ddim_timesteps
+ ddim_timesteps = np.asarray(list(range(0, num_ddpm_timesteps, c)))
+ elif ddim_discr_method == 'quad':
+ ddim_timesteps = ((np.linspace(0, np.sqrt(num_ddpm_timesteps * .8), num_ddim_timesteps)) ** 2).astype(int)
+ else:
+ raise NotImplementedError(f'There is no ddim discretization method called "{ddim_discr_method}"')
+
+ # assert ddim_timesteps.shape[0] == num_ddim_timesteps
+ # add one to get the final alpha values right (the ones from first scale to data during sampling)
+ steps_out = ddim_timesteps + 1
+ if verbose:
+ print(f'Selected timesteps for ddim sampler: {steps_out}')
+ return steps_out
+
+
+def make_ddim_sampling_parameters(alphacums, ddim_timesteps, eta, verbose=True):
+ # select alphas for computing the variance schedule
+ alphas = alphacums[ddim_timesteps]
+ alphas_prev = np.asarray([alphacums[0]] + alphacums[ddim_timesteps[:-1]].tolist())
+
+ # according the the formula provided in https://arxiv.org/abs/2010.02502
+ sigmas = eta * np.sqrt((1 - alphas_prev) / (1 - alphas) * (1 - alphas / alphas_prev))
+ if verbose:
+ print(f'Selected alphas for ddim sampler: a_t: {alphas}; a_(t-1): {alphas_prev}')
+ print(f'For the chosen value of eta, which is {eta}, '
+ f'this results in the following sigma_t schedule for ddim sampler {sigmas}')
+ return sigmas, alphas, alphas_prev
+
+
+def betas_for_alpha_bar(num_diffusion_timesteps, alpha_bar, max_beta=0.999):
+ """
+ Create a beta schedule that discretizes the given alpha_t_bar function,
+ which defines the cumulative product of (1-beta) over time from t = [0,1].
+ :param num_diffusion_timesteps: the number of betas to produce.
+ :param alpha_bar: a lambda that takes an argument t from 0 to 1 and
+ produces the cumulative product of (1-beta) up to that
+ part of the diffusion process.
+ :param max_beta: the maximum beta to use; use values lower than 1 to
+ prevent singularities.
+ """
+ betas = []
+ for i in range(num_diffusion_timesteps):
+ t1 = i / num_diffusion_timesteps
+ t2 = (i + 1) / num_diffusion_timesteps
+ betas.append(min(1 - alpha_bar(t2) / alpha_bar(t1), max_beta))
+ return np.array(betas)
+
+
+def extract_into_tensor(a, t, x_shape):
+ b, *_ = t.shape
+ out = a.gather(-1, t)
+ return out.reshape(b, *((1,) * (len(x_shape) - 1)))
+
+
+def checkpoint(func, inputs, params, flag):
+ """
+ Evaluate a function without caching intermediate activations, allowing for
+ reduced memory at the expense of extra compute in the backward pass.
+ :param func: the function to evaluate.
+ :param inputs: the argument sequence to pass to `func`.
+ :param params: a sequence of parameters `func` depends on but does not
+ explicitly take as arguments.
+ :param flag: if False, disable gradient checkpointing.
+ """
+ if flag:
+ args = tuple(inputs) + tuple(params)
+ return CheckpointFunction.apply(func, len(inputs), *args)
+ else:
+ return func(*inputs)
+
+
+class CheckpointFunction(torch.autograd.Function):
+ @staticmethod
+ def forward(ctx, run_function, length, *args):
+ ctx.run_function = run_function
+ ctx.input_tensors = list(args[:length])
+ ctx.input_params = list(args[length:])
+ ctx.gpu_autocast_kwargs = {"enabled": torch.is_autocast_enabled(),
+ "dtype": torch.get_autocast_gpu_dtype(),
+ "cache_enabled": torch.is_autocast_cache_enabled()}
+ with torch.no_grad():
+ output_tensors = ctx.run_function(*ctx.input_tensors)
+ return output_tensors
+
+ @staticmethod
+ def backward(ctx, *output_grads):
+ ctx.input_tensors = [x.detach().requires_grad_(True) for x in ctx.input_tensors]
+ with torch.enable_grad(), \
+ torch.cuda.amp.autocast(**ctx.gpu_autocast_kwargs):
+ # Fixes a bug where the first op in run_function modifies the
+ # Tensor storage in place, which is not allowed for detach()'d
+ # Tensors.
+ shallow_copies = [x.view_as(x) for x in ctx.input_tensors]
+ output_tensors = ctx.run_function(*shallow_copies)
+ input_grads = torch.autograd.grad(
+ output_tensors,
+ ctx.input_tensors + ctx.input_params,
+ output_grads,
+ allow_unused=True,
+ )
+ del ctx.input_tensors
+ del ctx.input_params
+ del output_tensors
+ return (None, None) + input_grads
+
+
+def timestep_embedding(timesteps, dim, max_period=10000, repeat_only=False):
+ """
+ Create sinusoidal timestep embeddings.
+ :param timesteps: a 1-D Tensor of N indices, one per batch element.
+ These may be fractional.
+ :param dim: the dimension of the output.
+ :param max_period: controls the minimum frequency of the embeddings.
+ :return: an [N x dim] Tensor of positional embeddings.
+ """
+ if not repeat_only:
+ half = dim // 2
+ freqs = torch.exp(
+ -math.log(max_period) * torch.arange(start=0, end=half, dtype=torch.float32) / half
+ ).to(device=timesteps.device)
+ args = timesteps[:, None].float() * freqs[None]
+ embedding = torch.cat([torch.cos(args), torch.sin(args)], dim=-1)
+ if dim % 2:
+ embedding = torch.cat([embedding, torch.zeros_like(embedding[:, :1])], dim=-1)
+ else:
+ embedding = repeat(timesteps, 'b -> b d', d=dim)
+ return embedding
+
+
+def zero_module(module):
+ """
+ Zero out the parameters of a module and return it.
+ """
+ for p in module.parameters():
+ p.detach().zero_()
+ return module
+
+
+def scale_module(module, scale):
+ """
+ Scale the parameters of a module and return it.
+ """
+ for p in module.parameters():
+ p.detach().mul_(scale)
+ return module
+
+
+def mean_flat(tensor):
+ """
+ Take the mean over all non-batch dimensions.
+ """
+ return tensor.mean(dim=list(range(1, len(tensor.shape))))
+
+
+def normalization(channels):
+ """
+ Make a standard normalization layer.
+ :param channels: number of input channels.
+ :return: an nn.Module for normalization.
+ """
+ return GroupNorm32(32, channels)
+
+
+# PyTorch 1.7 has SiLU, but we support PyTorch 1.5.
+class SiLU(nn.Module):
+ def forward(self, x):
+ return x * torch.sigmoid(x)
+
+
+class GroupNorm32(nn.GroupNorm):
+ def forward(self, x):
+ return super().forward(x.float()).type(x.dtype)
+
+def conv_nd(dims, *args, **kwargs):
+ """
+ Create a 1D, 2D, or 3D convolution module.
+ """
+ if dims == 1:
+ return nn.Conv1d(*args, **kwargs)
+ elif dims == 2:
+ return nn.Conv2d(*args, **kwargs)
+ elif dims == 3:
+ return nn.Conv3d(*args, **kwargs)
+ raise ValueError(f"unsupported dimensions: {dims}")
+
+
+def linear(*args, **kwargs):
+ """
+ Create a linear module.
+ """
+ return nn.Linear(*args, **kwargs)
+
+
+def avg_pool_nd(dims, *args, **kwargs):
+ """
+ Create a 1D, 2D, or 3D average pooling module.
+ """
+ if dims == 1:
+ return nn.AvgPool1d(*args, **kwargs)
+ elif dims == 2:
+ return nn.AvgPool2d(*args, **kwargs)
+ elif dims == 3:
+ return nn.AvgPool3d(*args, **kwargs)
+ raise ValueError(f"unsupported dimensions: {dims}")
+
+
+class HybridConditioner(nn.Module):
+
+ def __init__(self, c_concat_config, c_crossattn_config):
+ super().__init__()
+ self.concat_conditioner = instantiate_from_config(c_concat_config)
+ self.crossattn_conditioner = instantiate_from_config(c_crossattn_config)
+
+ def forward(self, c_concat, c_crossattn):
+ c_concat = self.concat_conditioner(c_concat)
+ c_crossattn = self.crossattn_conditioner(c_crossattn)
+ return {'c_concat': [c_concat], 'c_crossattn': [c_crossattn]}
+
+
+def noise_like(shape, device, repeat=False):
+ repeat_noise = lambda: torch.randn((1, *shape[1:]), device=device).repeat(shape[0], *((1,) * (len(shape) - 1)))
+ noise = lambda: torch.randn(shape, device=device)
+ return repeat_noise() if repeat else noise()
\ No newline at end of file
diff --git a/ldm/modules/distributions/__init__.py b/ldm/modules/distributions/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/ldm/modules/distributions/distributions.py b/ldm/modules/distributions/distributions.py
new file mode 100644
index 0000000000000000000000000000000000000000..f2b8ef901130efc171aa69742ca0244d94d3f2e9
--- /dev/null
+++ b/ldm/modules/distributions/distributions.py
@@ -0,0 +1,92 @@
+import torch
+import numpy as np
+
+
+class AbstractDistribution:
+ def sample(self):
+ raise NotImplementedError()
+
+ def mode(self):
+ raise NotImplementedError()
+
+
+class DiracDistribution(AbstractDistribution):
+ def __init__(self, value):
+ self.value = value
+
+ def sample(self):
+ return self.value
+
+ def mode(self):
+ return self.value
+
+
+class DiagonalGaussianDistribution(object):
+ def __init__(self, parameters, deterministic=False):
+ self.parameters = parameters
+ self.mean, self.logvar = torch.chunk(parameters, 2, dim=1)
+ self.logvar = torch.clamp(self.logvar, -30.0, 20.0)
+ self.deterministic = deterministic
+ self.std = torch.exp(0.5 * self.logvar)
+ self.var = torch.exp(self.logvar)
+ if self.deterministic:
+ self.var = self.std = torch.zeros_like(self.mean).to(device=self.parameters.device)
+
+ def sample(self):
+ x = self.mean + self.std * torch.randn(self.mean.shape).to(device=self.parameters.device)
+ return x
+
+ def kl(self, other=None):
+ if self.deterministic:
+ return torch.Tensor([0.])
+ else:
+ if other is None:
+ return 0.5 * torch.sum(torch.pow(self.mean, 2)
+ + self.var - 1.0 - self.logvar,
+ dim=[1, 2, 3])
+ else:
+ return 0.5 * torch.sum(
+ torch.pow(self.mean - other.mean, 2) / other.var
+ + self.var / other.var - 1.0 - self.logvar + other.logvar,
+ dim=[1, 2, 3])
+
+ def nll(self, sample, dims=[1,2,3]):
+ if self.deterministic:
+ return torch.Tensor([0.])
+ logtwopi = np.log(2.0 * np.pi)
+ return 0.5 * torch.sum(
+ logtwopi + self.logvar + torch.pow(sample - self.mean, 2) / self.var,
+ dim=dims)
+
+ def mode(self):
+ return self.mean
+
+
+def normal_kl(mean1, logvar1, mean2, logvar2):
+ """
+ source: https://github.com/openai/guided-diffusion/blob/27c20a8fab9cb472df5d6bdd6c8d11c8f430b924/guided_diffusion/losses.py#L12
+ Compute the KL divergence between two gaussians.
+ Shapes are automatically broadcasted, so batches can be compared to
+ scalars, among other use cases.
+ """
+ tensor = None
+ for obj in (mean1, logvar1, mean2, logvar2):
+ if isinstance(obj, torch.Tensor):
+ tensor = obj
+ break
+ assert tensor is not None, "at least one argument must be a Tensor"
+
+ # Force variances to be Tensors. Broadcasting helps convert scalars to
+ # Tensors, but it does not work for torch.exp().
+ logvar1, logvar2 = [
+ x if isinstance(x, torch.Tensor) else torch.tensor(x).to(tensor)
+ for x in (logvar1, logvar2)
+ ]
+
+ return 0.5 * (
+ -1.0
+ + logvar2
+ - logvar1
+ + torch.exp(logvar1 - logvar2)
+ + ((mean1 - mean2) ** 2) * torch.exp(-logvar2)
+ )
diff --git a/ldm/modules/ema.py b/ldm/modules/ema.py
new file mode 100644
index 0000000000000000000000000000000000000000..bded25019b9bcbcd0260f0b8185f8c7859ca58c4
--- /dev/null
+++ b/ldm/modules/ema.py
@@ -0,0 +1,80 @@
+import torch
+from torch import nn
+
+
+class LitEma(nn.Module):
+ def __init__(self, model, decay=0.9999, use_num_upates=True):
+ super().__init__()
+ if decay < 0.0 or decay > 1.0:
+ raise ValueError('Decay must be between 0 and 1')
+
+ self.m_name2s_name = {}
+ self.register_buffer('decay', torch.tensor(decay, dtype=torch.float32))
+ self.register_buffer('num_updates', torch.tensor(0, dtype=torch.int) if use_num_upates
+ else torch.tensor(-1, dtype=torch.int))
+
+ for name, p in model.named_parameters():
+ if p.requires_grad:
+ # remove as '.'-character is not allowed in buffers
+ s_name = name.replace('.', '')
+ self.m_name2s_name.update({name: s_name})
+ self.register_buffer(s_name, p.clone().detach().data)
+
+ self.collected_params = []
+
+ def reset_num_updates(self):
+ del self.num_updates
+ self.register_buffer('num_updates', torch.tensor(0, dtype=torch.int))
+
+ def forward(self, model):
+ decay = self.decay
+
+ if self.num_updates >= 0:
+ self.num_updates += 1
+ decay = min(self.decay, (1 + self.num_updates) / (10 + self.num_updates))
+
+ one_minus_decay = 1.0 - decay
+
+ with torch.no_grad():
+ m_param = dict(model.named_parameters())
+ shadow_params = dict(self.named_buffers())
+
+ for key in m_param:
+ if m_param[key].requires_grad:
+ sname = self.m_name2s_name[key]
+ shadow_params[sname] = shadow_params[sname].type_as(m_param[key])
+ shadow_params[sname].sub_(one_minus_decay * (shadow_params[sname] - m_param[key]))
+ else:
+ assert not key in self.m_name2s_name
+
+ def copy_to(self, model):
+ m_param = dict(model.named_parameters())
+ shadow_params = dict(self.named_buffers())
+ for key in m_param:
+ if m_param[key].requires_grad:
+ m_param[key].data.copy_(shadow_params[self.m_name2s_name[key]].data)
+ else:
+ assert not key in self.m_name2s_name
+
+ def store(self, parameters):
+ """
+ Save the current parameters for restoring later.
+ Args:
+ parameters: Iterable of `torch.nn.Parameter`; the parameters to be
+ temporarily stored.
+ """
+ self.collected_params = [param.clone() for param in parameters]
+
+ def restore(self, parameters):
+ """
+ Restore the parameters stored with the `store` method.
+ Useful to validate the model with EMA parameters without affecting the
+ original optimization process. Store the parameters before the
+ `copy_to` method. After validation (or model saving), use this to
+ restore the former parameters.
+ Args:
+ parameters: Iterable of `torch.nn.Parameter`; the parameters to be
+ updated with the stored parameters.
+ """
+ for c_param, param in zip(self.collected_params, parameters):
+ param.data.copy_(c_param.data)
diff --git a/ldm/modules/encoders/__init__.py b/ldm/modules/encoders/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/ldm/modules/encoders/modules.py b/ldm/modules/encoders/modules.py
new file mode 100644
index 0000000000000000000000000000000000000000..4edd5496b9e668ea72a5be39db9cca94b6a42f9b
--- /dev/null
+++ b/ldm/modules/encoders/modules.py
@@ -0,0 +1,213 @@
+import torch
+import torch.nn as nn
+from torch.utils.checkpoint import checkpoint
+
+from transformers import T5Tokenizer, T5EncoderModel, CLIPTokenizer, CLIPTextModel
+
+import open_clip
+from ldm.util import default, count_params
+
+
+class AbstractEncoder(nn.Module):
+ def __init__(self):
+ super().__init__()
+
+ def encode(self, *args, **kwargs):
+ raise NotImplementedError
+
+
+class IdentityEncoder(AbstractEncoder):
+
+ def encode(self, x):
+ return x
+
+
+class ClassEmbedder(nn.Module):
+ def __init__(self, embed_dim, n_classes=1000, key='class', ucg_rate=0.1):
+ super().__init__()
+ self.key = key
+ self.embedding = nn.Embedding(n_classes, embed_dim)
+ self.n_classes = n_classes
+ self.ucg_rate = ucg_rate
+
+ def forward(self, batch, key=None, disable_dropout=False):
+ if key is None:
+ key = self.key
+ # this is for use in crossattn
+ c = batch[key][:, None]
+ if self.ucg_rate > 0. and not disable_dropout:
+ mask = 1. - torch.bernoulli(torch.ones_like(c) * self.ucg_rate)
+ c = mask * c + (1-mask) * torch.ones_like(c)*(self.n_classes-1)
+ c = c.long()
+ c = self.embedding(c)
+ return c
+
+ def get_unconditional_conditioning(self, bs, device="cuda"):
+ uc_class = self.n_classes - 1 # 1000 classes --> 0 ... 999, one extra class for ucg (class 1000)
+ uc = torch.ones((bs,), device=device) * uc_class
+ uc = {self.key: uc}
+ return uc
+
+
+def disabled_train(self, mode=True):
+ """Overwrite model.train with this function to make sure train/eval mode
+ does not change anymore."""
+ return self
+
+
+class FrozenT5Embedder(AbstractEncoder):
+ """Uses the T5 transformer encoder for text"""
+ def __init__(self, version="google/t5-v1_1-large", device="cuda", max_length=77, freeze=True): # others are google/t5-v1_1-xl and google/t5-v1_1-xxl
+ super().__init__()
+ self.tokenizer = T5Tokenizer.from_pretrained(version)
+ self.transformer = T5EncoderModel.from_pretrained(version)
+ self.device = device
+ self.max_length = max_length # TODO: typical value?
+ if freeze:
+ self.freeze()
+
+ def freeze(self):
+ self.transformer = self.transformer.eval()
+ #self.train = disabled_train
+ for param in self.parameters():
+ param.requires_grad = False
+
+ def forward(self, text):
+ batch_encoding = self.tokenizer(text, truncation=True, max_length=self.max_length, return_length=True,
+ return_overflowing_tokens=False, padding="max_length", return_tensors="pt")
+ tokens = batch_encoding["input_ids"].to(self.device)
+ outputs = self.transformer(input_ids=tokens)
+
+ z = outputs.last_hidden_state
+ return z
+
+ def encode(self, text):
+ return self(text)
+
+
+class FrozenCLIPEmbedder(AbstractEncoder):
+ """Uses the CLIP transformer encoder for text (from huggingface)"""
+ LAYERS = [
+ "last",
+ "pooled",
+ "hidden"
+ ]
+ def __init__(self, version="openai/clip-vit-large-patch14", device="cuda", max_length=77,
+ freeze=True, layer="last", layer_idx=None): # clip-vit-base-patch32
+ super().__init__()
+ assert layer in self.LAYERS
+ self.tokenizer = CLIPTokenizer.from_pretrained(version)
+ self.transformer = CLIPTextModel.from_pretrained(version)
+ self.device = device
+ self.max_length = max_length
+ if freeze:
+ self.freeze()
+ self.layer = layer
+ self.layer_idx = layer_idx
+ if layer == "hidden":
+ assert layer_idx is not None
+ assert 0 <= abs(layer_idx) <= 12
+
+ def freeze(self):
+ self.transformer = self.transformer.eval()
+ #self.train = disabled_train
+ for param in self.parameters():
+ param.requires_grad = False
+
+ def forward(self, text):
+ batch_encoding = self.tokenizer(text, truncation=True, max_length=self.max_length, return_length=True,
+ return_overflowing_tokens=False, padding="max_length", return_tensors="pt")
+ tokens = batch_encoding["input_ids"].to(self.device)
+ outputs = self.transformer(input_ids=tokens, output_hidden_states=self.layer=="hidden")
+ if self.layer == "last":
+ z = outputs.last_hidden_state
+ elif self.layer == "pooled":
+ z = outputs.pooler_output[:, None, :]
+ else:
+ z = outputs.hidden_states[self.layer_idx]
+ return z
+
+ def encode(self, text):
+ return self(text)
+
+
+class FrozenOpenCLIPEmbedder(AbstractEncoder):
+ """
+ Uses the OpenCLIP transformer encoder for text
+ """
+ LAYERS = [
+ #"pooled",
+ "last",
+ "penultimate"
+ ]
+ def __init__(self, arch="ViT-H-14", version="laion2b_s32b_b79k", device="cuda", max_length=77,
+ freeze=True, layer="last"):
+ super().__init__()
+ assert layer in self.LAYERS
+ model, _, _ = open_clip.create_model_and_transforms(arch, device=torch.device('cpu'), pretrained=version)
+ del model.visual
+ self.model = model
+
+ self.device = device
+ self.max_length = max_length
+ if freeze:
+ self.freeze()
+ self.layer = layer
+ if self.layer == "last":
+ self.layer_idx = 0
+ elif self.layer == "penultimate":
+ self.layer_idx = 1
+ else:
+ raise NotImplementedError()
+
+ def freeze(self):
+ self.model = self.model.eval()
+ for param in self.parameters():
+ param.requires_grad = False
+
+ def forward(self, text):
+ tokens = open_clip.tokenize(text)
+ z = self.encode_with_transformer(tokens.to(self.device))
+ return z
+
+ def encode_with_transformer(self, text):
+ x = self.model.token_embedding(text) # [batch_size, n_ctx, d_model]
+ x = x + self.model.positional_embedding
+ x = x.permute(1, 0, 2) # NLD -> LND
+ x = self.text_transformer_forward(x, attn_mask=self.model.attn_mask)
+ x = x.permute(1, 0, 2) # LND -> NLD
+ x = self.model.ln_final(x)
+ return x
+
+ def text_transformer_forward(self, x: torch.Tensor, attn_mask = None):
+ for i, r in enumerate(self.model.transformer.resblocks):
+ if i == len(self.model.transformer.resblocks) - self.layer_idx:
+ break
+ if self.model.transformer.grad_checkpointing and not torch.jit.is_scripting():
+ x = checkpoint(r, x, attn_mask)
+ else:
+ x = r(x, attn_mask=attn_mask)
+ return x
+
+ def encode(self, text):
+ return self(text)
+
+
+class FrozenCLIPT5Encoder(AbstractEncoder):
+ def __init__(self, clip_version="openai/clip-vit-large-patch14", t5_version="google/t5-v1_1-xl", device="cuda",
+ clip_max_length=77, t5_max_length=77):
+ super().__init__()
+ self.clip_encoder = FrozenCLIPEmbedder(clip_version, device, max_length=clip_max_length)
+ self.t5_encoder = FrozenT5Embedder(t5_version, device, max_length=t5_max_length)
+ print(f"{self.clip_encoder.__class__.__name__} has {count_params(self.clip_encoder)*1.e-6:.2f} M parameters, "
+ f"{self.t5_encoder.__class__.__name__} comes with {count_params(self.t5_encoder)*1.e-6:.2f} M params.")
+
+ def encode(self, text):
+ return self(text)
+
+ def forward(self, text):
+ clip_z = self.clip_encoder.encode(text)
+ t5_z = self.t5_encoder.encode(text)
+ return [clip_z, t5_z]
+
+
diff --git a/ldm/modules/image_degradation/__init__.py b/ldm/modules/image_degradation/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..7836cada81f90ded99c58d5942eea4c3477f58fc
--- /dev/null
+++ b/ldm/modules/image_degradation/__init__.py
@@ -0,0 +1,2 @@
+from ldm.modules.image_degradation.bsrgan import degradation_bsrgan_variant as degradation_fn_bsr
+from ldm.modules.image_degradation.bsrgan_light import degradation_bsrgan_variant as degradation_fn_bsr_light
diff --git a/ldm/modules/image_degradation/bsrgan.py b/ldm/modules/image_degradation/bsrgan.py
new file mode 100644
index 0000000000000000000000000000000000000000..32ef56169978e550090261cddbcf5eb611a6173b
--- /dev/null
+++ b/ldm/modules/image_degradation/bsrgan.py
@@ -0,0 +1,730 @@
+# -*- coding: utf-8 -*-
+"""
+# --------------------------------------------
+# Super-Resolution
+# --------------------------------------------
+#
+# Kai Zhang (cskaizhang@gmail.com)
+# https://github.com/cszn
+# From 2019/03--2021/08
+# --------------------------------------------
+"""
+
+import numpy as np
+import cv2
+import torch
+
+from functools import partial
+import random
+from scipy import ndimage
+import scipy
+import scipy.stats as ss
+from scipy.interpolate import interp2d
+from scipy.linalg import orth
+import albumentations
+
+import ldm.modules.image_degradation.utils_image as util
+
+
+def modcrop_np(img, sf):
+ '''
+ Args:
+ img: numpy image, WxH or WxHxC
+ sf: scale factor
+ Return:
+ cropped image
+ '''
+ w, h = img.shape[:2]
+ im = np.copy(img)
+ return im[:w - w % sf, :h - h % sf, ...]
+
+
+"""
+# --------------------------------------------
+# anisotropic Gaussian kernels
+# --------------------------------------------
+"""
+
+
+def analytic_kernel(k):
+ """Calculate the X4 kernel from the X2 kernel (for proof see appendix in paper)"""
+ k_size = k.shape[0]
+ # Calculate the big kernels size
+ big_k = np.zeros((3 * k_size - 2, 3 * k_size - 2))
+ # Loop over the small kernel to fill the big one
+ for r in range(k_size):
+ for c in range(k_size):
+ big_k[2 * r:2 * r + k_size, 2 * c:2 * c + k_size] += k[r, c] * k
+ # Crop the edges of the big kernel to ignore very small values and increase run time of SR
+ crop = k_size // 2
+ cropped_big_k = big_k[crop:-crop, crop:-crop]
+ # Normalize to 1
+ return cropped_big_k / cropped_big_k.sum()
+
+
+def anisotropic_Gaussian(ksize=15, theta=np.pi, l1=6, l2=6):
+ """ generate an anisotropic Gaussian kernel
+ Args:
+ ksize : e.g., 15, kernel size
+ theta : [0, pi], rotation angle range
+ l1 : [0.1,50], scaling of eigenvalues
+ l2 : [0.1,l1], scaling of eigenvalues
+ If l1 = l2, will get an isotropic Gaussian kernel.
+ Returns:
+ k : kernel
+ """
+
+ v = np.dot(np.array([[np.cos(theta), -np.sin(theta)], [np.sin(theta), np.cos(theta)]]), np.array([1., 0.]))
+ V = np.array([[v[0], v[1]], [v[1], -v[0]]])
+ D = np.array([[l1, 0], [0, l2]])
+ Sigma = np.dot(np.dot(V, D), np.linalg.inv(V))
+ k = gm_blur_kernel(mean=[0, 0], cov=Sigma, size=ksize)
+
+ return k
+
+
+def gm_blur_kernel(mean, cov, size=15):
+ center = size / 2.0 + 0.5
+ k = np.zeros([size, size])
+ for y in range(size):
+ for x in range(size):
+ cy = y - center + 1
+ cx = x - center + 1
+ k[y, x] = ss.multivariate_normal.pdf([cx, cy], mean=mean, cov=cov)
+
+ k = k / np.sum(k)
+ return k
+
+
+def shift_pixel(x, sf, upper_left=True):
+ """shift pixel for super-resolution with different scale factors
+ Args:
+ x: WxHxC or WxH
+ sf: scale factor
+ upper_left: shift direction
+ """
+ h, w = x.shape[:2]
+ shift = (sf - 1) * 0.5
+ xv, yv = np.arange(0, w, 1.0), np.arange(0, h, 1.0)
+ if upper_left:
+ x1 = xv + shift
+ y1 = yv + shift
+ else:
+ x1 = xv - shift
+ y1 = yv - shift
+
+ x1 = np.clip(x1, 0, w - 1)
+ y1 = np.clip(y1, 0, h - 1)
+
+ if x.ndim == 2:
+ x = interp2d(xv, yv, x)(x1, y1)
+ if x.ndim == 3:
+ for i in range(x.shape[-1]):
+ x[:, :, i] = interp2d(xv, yv, x[:, :, i])(x1, y1)
+
+ return x
+
+
+def blur(x, k):
+ '''
+ x: image, NxcxHxW
+ k: kernel, Nx1xhxw
+ '''
+ n, c = x.shape[:2]
+ p1, p2 = (k.shape[-2] - 1) // 2, (k.shape[-1] - 1) // 2
+ x = torch.nn.functional.pad(x, pad=(p1, p2, p1, p2), mode='replicate')
+ k = k.repeat(1, c, 1, 1)
+ k = k.view(-1, 1, k.shape[2], k.shape[3])
+ x = x.view(1, -1, x.shape[2], x.shape[3])
+ x = torch.nn.functional.conv2d(x, k, bias=None, stride=1, padding=0, groups=n * c)
+ x = x.view(n, c, x.shape[2], x.shape[3])
+
+ return x
+
+
+def gen_kernel(k_size=np.array([15, 15]), scale_factor=np.array([4, 4]), min_var=0.6, max_var=10., noise_level=0):
+ """"
+ # modified version of https://github.com/assafshocher/BlindSR_dataset_generator
+ # Kai Zhang
+ # min_var = 0.175 * sf # variance of the gaussian kernel will be sampled between min_var and max_var
+ # max_var = 2.5 * sf
+ """
+ # Set random eigen-vals (lambdas) and angle (theta) for COV matrix
+ lambda_1 = min_var + np.random.rand() * (max_var - min_var)
+ lambda_2 = min_var + np.random.rand() * (max_var - min_var)
+ theta = np.random.rand() * np.pi # random theta
+ noise = -noise_level + np.random.rand(*k_size) * noise_level * 2
+
+ # Set COV matrix using Lambdas and Theta
+ LAMBDA = np.diag([lambda_1, lambda_2])
+ Q = np.array([[np.cos(theta), -np.sin(theta)],
+ [np.sin(theta), np.cos(theta)]])
+ SIGMA = Q @ LAMBDA @ Q.T
+ INV_SIGMA = np.linalg.inv(SIGMA)[None, None, :, :]
+
+ # Set expectation position (shifting kernel for aligned image)
+ MU = k_size // 2 - 0.5 * (scale_factor - 1) # - 0.5 * (scale_factor - k_size % 2)
+ MU = MU[None, None, :, None]
+
+ # Create meshgrid for Gaussian
+ [X, Y] = np.meshgrid(range(k_size[0]), range(k_size[1]))
+ Z = np.stack([X, Y], 2)[:, :, :, None]
+
+ # Calcualte Gaussian for every pixel of the kernel
+ ZZ = Z - MU
+ ZZ_t = ZZ.transpose(0, 1, 3, 2)
+ raw_kernel = np.exp(-0.5 * np.squeeze(ZZ_t @ INV_SIGMA @ ZZ)) * (1 + noise)
+
+ # shift the kernel so it will be centered
+ # raw_kernel_centered = kernel_shift(raw_kernel, scale_factor)
+
+ # Normalize the kernel and return
+ # kernel = raw_kernel_centered / np.sum(raw_kernel_centered)
+ kernel = raw_kernel / np.sum(raw_kernel)
+ return kernel
+
+
+def fspecial_gaussian(hsize, sigma):
+ hsize = [hsize, hsize]
+ siz = [(hsize[0] - 1.0) / 2.0, (hsize[1] - 1.0) / 2.0]
+ std = sigma
+ [x, y] = np.meshgrid(np.arange(-siz[1], siz[1] + 1), np.arange(-siz[0], siz[0] + 1))
+ arg = -(x * x + y * y) / (2 * std * std)
+ h = np.exp(arg)
+ h[h < scipy.finfo(float).eps * h.max()] = 0
+ sumh = h.sum()
+ if sumh != 0:
+ h = h / sumh
+ return h
+
+
+def fspecial_laplacian(alpha):
+ alpha = max([0, min([alpha, 1])])
+ h1 = alpha / (alpha + 1)
+ h2 = (1 - alpha) / (alpha + 1)
+ h = [[h1, h2, h1], [h2, -4 / (alpha + 1), h2], [h1, h2, h1]]
+ h = np.array(h)
+ return h
+
+
+def fspecial(filter_type, *args, **kwargs):
+ '''
+ python code from:
+ https://github.com/ronaldosena/imagens-medicas-2/blob/40171a6c259edec7827a6693a93955de2bd39e76/Aulas/aula_2_-_uniform_filter/matlab_fspecial.py
+ '''
+ if filter_type == 'gaussian':
+ return fspecial_gaussian(*args, **kwargs)
+ if filter_type == 'laplacian':
+ return fspecial_laplacian(*args, **kwargs)
+
+
+"""
+# --------------------------------------------
+# degradation models
+# --------------------------------------------
+"""
+
+
+def bicubic_degradation(x, sf=3):
+ '''
+ Args:
+ x: HxWxC image, [0, 1]
+ sf: down-scale factor
+ Return:
+ bicubicly downsampled LR image
+ '''
+ x = util.imresize_np(x, scale=1 / sf)
+ return x
+
+
+def srmd_degradation(x, k, sf=3):
+ ''' blur + bicubic downsampling
+ Args:
+ x: HxWxC image, [0, 1]
+ k: hxw, double
+ sf: down-scale factor
+ Return:
+ downsampled LR image
+ Reference:
+ @inproceedings{zhang2018learning,
+ title={Learning a single convolutional super-resolution network for multiple degradations},
+ author={Zhang, Kai and Zuo, Wangmeng and Zhang, Lei},
+ booktitle={IEEE Conference on Computer Vision and Pattern Recognition},
+ pages={3262--3271},
+ year={2018}
+ }
+ '''
+ x = ndimage.filters.convolve(x, np.expand_dims(k, axis=2), mode='wrap') # 'nearest' | 'mirror'
+ x = bicubic_degradation(x, sf=sf)
+ return x
+
+
+def dpsr_degradation(x, k, sf=3):
+ ''' bicubic downsampling + blur
+ Args:
+ x: HxWxC image, [0, 1]
+ k: hxw, double
+ sf: down-scale factor
+ Return:
+ downsampled LR image
+ Reference:
+ @inproceedings{zhang2019deep,
+ title={Deep Plug-and-Play Super-Resolution for Arbitrary Blur Kernels},
+ author={Zhang, Kai and Zuo, Wangmeng and Zhang, Lei},
+ booktitle={IEEE Conference on Computer Vision and Pattern Recognition},
+ pages={1671--1681},
+ year={2019}
+ }
+ '''
+ x = bicubic_degradation(x, sf=sf)
+ x = ndimage.filters.convolve(x, np.expand_dims(k, axis=2), mode='wrap')
+ return x
+
+
+def classical_degradation(x, k, sf=3):
+ ''' blur + downsampling
+ Args:
+ x: HxWxC image, [0, 1]/[0, 255]
+ k: hxw, double
+ sf: down-scale factor
+ Return:
+ downsampled LR image
+ '''
+ x = ndimage.filters.convolve(x, np.expand_dims(k, axis=2), mode='wrap')
+ # x = filters.correlate(x, np.expand_dims(np.flip(k), axis=2))
+ st = 0
+ return x[st::sf, st::sf, ...]
+
+
+def add_sharpening(img, weight=0.5, radius=50, threshold=10):
+ """USM sharpening. borrowed from real-ESRGAN
+ Input image: I; Blurry image: B.
+ 1. K = I + weight * (I - B)
+ 2. Mask = 1 if abs(I - B) > threshold, else: 0
+ 3. Blur mask:
+ 4. Out = Mask * K + (1 - Mask) * I
+ Args:
+ img (Numpy array): Input image, HWC, BGR; float32, [0, 1].
+ weight (float): Sharp weight. Default: 1.
+ radius (float): Kernel size of Gaussian blur. Default: 50.
+ threshold (int):
+ """
+ if radius % 2 == 0:
+ radius += 1
+ blur = cv2.GaussianBlur(img, (radius, radius), 0)
+ residual = img - blur
+ mask = np.abs(residual) * 255 > threshold
+ mask = mask.astype('float32')
+ soft_mask = cv2.GaussianBlur(mask, (radius, radius), 0)
+
+ K = img + weight * residual
+ K = np.clip(K, 0, 1)
+ return soft_mask * K + (1 - soft_mask) * img
+
+
+def add_blur(img, sf=4):
+ wd2 = 4.0 + sf
+ wd = 2.0 + 0.2 * sf
+ if random.random() < 0.5:
+ l1 = wd2 * random.random()
+ l2 = wd2 * random.random()
+ k = anisotropic_Gaussian(ksize=2 * random.randint(2, 11) + 3, theta=random.random() * np.pi, l1=l1, l2=l2)
+ else:
+ k = fspecial('gaussian', 2 * random.randint(2, 11) + 3, wd * random.random())
+ img = ndimage.filters.convolve(img, np.expand_dims(k, axis=2), mode='mirror')
+
+ return img
+
+
+def add_resize(img, sf=4):
+ rnum = np.random.rand()
+ if rnum > 0.8: # up
+ sf1 = random.uniform(1, 2)
+ elif rnum < 0.7: # down
+ sf1 = random.uniform(0.5 / sf, 1)
+ else:
+ sf1 = 1.0
+ img = cv2.resize(img, (int(sf1 * img.shape[1]), int(sf1 * img.shape[0])), interpolation=random.choice([1, 2, 3]))
+ img = np.clip(img, 0.0, 1.0)
+
+ return img
+
+
+# def add_Gaussian_noise(img, noise_level1=2, noise_level2=25):
+# noise_level = random.randint(noise_level1, noise_level2)
+# rnum = np.random.rand()
+# if rnum > 0.6: # add color Gaussian noise
+# img += np.random.normal(0, noise_level / 255.0, img.shape).astype(np.float32)
+# elif rnum < 0.4: # add grayscale Gaussian noise
+# img += np.random.normal(0, noise_level / 255.0, (*img.shape[:2], 1)).astype(np.float32)
+# else: # add noise
+# L = noise_level2 / 255.
+# D = np.diag(np.random.rand(3))
+# U = orth(np.random.rand(3, 3))
+# conv = np.dot(np.dot(np.transpose(U), D), U)
+# img += np.random.multivariate_normal([0, 0, 0], np.abs(L ** 2 * conv), img.shape[:2]).astype(np.float32)
+# img = np.clip(img, 0.0, 1.0)
+# return img
+
+def add_Gaussian_noise(img, noise_level1=2, noise_level2=25):
+ noise_level = random.randint(noise_level1, noise_level2)
+ rnum = np.random.rand()
+ if rnum > 0.6: # add color Gaussian noise
+ img = img + np.random.normal(0, noise_level / 255.0, img.shape).astype(np.float32)
+ elif rnum < 0.4: # add grayscale Gaussian noise
+ img = img + np.random.normal(0, noise_level / 255.0, (*img.shape[:2], 1)).astype(np.float32)
+ else: # add noise
+ L = noise_level2 / 255.
+ D = np.diag(np.random.rand(3))
+ U = orth(np.random.rand(3, 3))
+ conv = np.dot(np.dot(np.transpose(U), D), U)
+ img = img + np.random.multivariate_normal([0, 0, 0], np.abs(L ** 2 * conv), img.shape[:2]).astype(np.float32)
+ img = np.clip(img, 0.0, 1.0)
+ return img
+
+
+def add_speckle_noise(img, noise_level1=2, noise_level2=25):
+ noise_level = random.randint(noise_level1, noise_level2)
+ img = np.clip(img, 0.0, 1.0)
+ rnum = random.random()
+ if rnum > 0.6:
+ img += img * np.random.normal(0, noise_level / 255.0, img.shape).astype(np.float32)
+ elif rnum < 0.4:
+ img += img * np.random.normal(0, noise_level / 255.0, (*img.shape[:2], 1)).astype(np.float32)
+ else:
+ L = noise_level2 / 255.
+ D = np.diag(np.random.rand(3))
+ U = orth(np.random.rand(3, 3))
+ conv = np.dot(np.dot(np.transpose(U), D), U)
+ img += img * np.random.multivariate_normal([0, 0, 0], np.abs(L ** 2 * conv), img.shape[:2]).astype(np.float32)
+ img = np.clip(img, 0.0, 1.0)
+ return img
+
+
+def add_Poisson_noise(img):
+ img = np.clip((img * 255.0).round(), 0, 255) / 255.
+ vals = 10 ** (2 * random.random() + 2.0) # [2, 4]
+ if random.random() < 0.5:
+ img = np.random.poisson(img * vals).astype(np.float32) / vals
+ else:
+ img_gray = np.dot(img[..., :3], [0.299, 0.587, 0.114])
+ img_gray = np.clip((img_gray * 255.0).round(), 0, 255) / 255.
+ noise_gray = np.random.poisson(img_gray * vals).astype(np.float32) / vals - img_gray
+ img += noise_gray[:, :, np.newaxis]
+ img = np.clip(img, 0.0, 1.0)
+ return img
+
+
+def add_JPEG_noise(img):
+ quality_factor = random.randint(30, 95)
+ img = cv2.cvtColor(util.single2uint(img), cv2.COLOR_RGB2BGR)
+ result, encimg = cv2.imencode('.jpg', img, [int(cv2.IMWRITE_JPEG_QUALITY), quality_factor])
+ img = cv2.imdecode(encimg, 1)
+ img = cv2.cvtColor(util.uint2single(img), cv2.COLOR_BGR2RGB)
+ return img
+
+
+def random_crop(lq, hq, sf=4, lq_patchsize=64):
+ h, w = lq.shape[:2]
+ rnd_h = random.randint(0, h - lq_patchsize)
+ rnd_w = random.randint(0, w - lq_patchsize)
+ lq = lq[rnd_h:rnd_h + lq_patchsize, rnd_w:rnd_w + lq_patchsize, :]
+
+ rnd_h_H, rnd_w_H = int(rnd_h * sf), int(rnd_w * sf)
+ hq = hq[rnd_h_H:rnd_h_H + lq_patchsize * sf, rnd_w_H:rnd_w_H + lq_patchsize * sf, :]
+ return lq, hq
+
+
+def degradation_bsrgan(img, sf=4, lq_patchsize=72, isp_model=None):
+ """
+ This is the degradation model of BSRGAN from the paper
+ "Designing a Practical Degradation Model for Deep Blind Image Super-Resolution"
+ ----------
+ img: HXWXC, [0, 1], its size should be large than (lq_patchsizexsf)x(lq_patchsizexsf)
+ sf: scale factor
+ isp_model: camera ISP model
+ Returns
+ -------
+ img: low-quality patch, size: lq_patchsizeXlq_patchsizeXC, range: [0, 1]
+ hq: corresponding high-quality patch, size: (lq_patchsizexsf)X(lq_patchsizexsf)XC, range: [0, 1]
+ """
+ isp_prob, jpeg_prob, scale2_prob = 0.25, 0.9, 0.25
+ sf_ori = sf
+
+ h1, w1 = img.shape[:2]
+ img = img.copy()[:w1 - w1 % sf, :h1 - h1 % sf, ...] # mod crop
+ h, w = img.shape[:2]
+
+ if h < lq_patchsize * sf or w < lq_patchsize * sf:
+ raise ValueError(f'img size ({h1}X{w1}) is too small!')
+
+ hq = img.copy()
+
+ if sf == 4 and random.random() < scale2_prob: # downsample1
+ if np.random.rand() < 0.5:
+ img = cv2.resize(img, (int(1 / 2 * img.shape[1]), int(1 / 2 * img.shape[0])),
+ interpolation=random.choice([1, 2, 3]))
+ else:
+ img = util.imresize_np(img, 1 / 2, True)
+ img = np.clip(img, 0.0, 1.0)
+ sf = 2
+
+ shuffle_order = random.sample(range(7), 7)
+ idx1, idx2 = shuffle_order.index(2), shuffle_order.index(3)
+ if idx1 > idx2: # keep downsample3 last
+ shuffle_order[idx1], shuffle_order[idx2] = shuffle_order[idx2], shuffle_order[idx1]
+
+ for i in shuffle_order:
+
+ if i == 0:
+ img = add_blur(img, sf=sf)
+
+ elif i == 1:
+ img = add_blur(img, sf=sf)
+
+ elif i == 2:
+ a, b = img.shape[1], img.shape[0]
+ # downsample2
+ if random.random() < 0.75:
+ sf1 = random.uniform(1, 2 * sf)
+ img = cv2.resize(img, (int(1 / sf1 * img.shape[1]), int(1 / sf1 * img.shape[0])),
+ interpolation=random.choice([1, 2, 3]))
+ else:
+ k = fspecial('gaussian', 25, random.uniform(0.1, 0.6 * sf))
+ k_shifted = shift_pixel(k, sf)
+ k_shifted = k_shifted / k_shifted.sum() # blur with shifted kernel
+ img = ndimage.filters.convolve(img, np.expand_dims(k_shifted, axis=2), mode='mirror')
+ img = img[0::sf, 0::sf, ...] # nearest downsampling
+ img = np.clip(img, 0.0, 1.0)
+
+ elif i == 3:
+ # downsample3
+ img = cv2.resize(img, (int(1 / sf * a), int(1 / sf * b)), interpolation=random.choice([1, 2, 3]))
+ img = np.clip(img, 0.0, 1.0)
+
+ elif i == 4:
+ # add Gaussian noise
+ img = add_Gaussian_noise(img, noise_level1=2, noise_level2=25)
+
+ elif i == 5:
+ # add JPEG noise
+ if random.random() < jpeg_prob:
+ img = add_JPEG_noise(img)
+
+ elif i == 6:
+ # add processed camera sensor noise
+ if random.random() < isp_prob and isp_model is not None:
+ with torch.no_grad():
+ img, hq = isp_model.forward(img.copy(), hq)
+
+ # add final JPEG compression noise
+ img = add_JPEG_noise(img)
+
+ # random crop
+ img, hq = random_crop(img, hq, sf_ori, lq_patchsize)
+
+ return img, hq
+
+
+# todo no isp_model?
+def degradation_bsrgan_variant(image, sf=4, isp_model=None):
+ """
+ This is the degradation model of BSRGAN from the paper
+ "Designing a Practical Degradation Model for Deep Blind Image Super-Resolution"
+ ----------
+ sf: scale factor
+ isp_model: camera ISP model
+ Returns
+ -------
+ img: low-quality patch, size: lq_patchsizeXlq_patchsizeXC, range: [0, 1]
+ hq: corresponding high-quality patch, size: (lq_patchsizexsf)X(lq_patchsizexsf)XC, range: [0, 1]
+ """
+ image = util.uint2single(image)
+ isp_prob, jpeg_prob, scale2_prob = 0.25, 0.9, 0.25
+ sf_ori = sf
+
+ h1, w1 = image.shape[:2]
+ image = image.copy()[:w1 - w1 % sf, :h1 - h1 % sf, ...] # mod crop
+ h, w = image.shape[:2]
+
+ hq = image.copy()
+
+ if sf == 4 and random.random() < scale2_prob: # downsample1
+ if np.random.rand() < 0.5:
+ image = cv2.resize(image, (int(1 / 2 * image.shape[1]), int(1 / 2 * image.shape[0])),
+ interpolation=random.choice([1, 2, 3]))
+ else:
+ image = util.imresize_np(image, 1 / 2, True)
+ image = np.clip(image, 0.0, 1.0)
+ sf = 2
+
+ shuffle_order = random.sample(range(7), 7)
+ idx1, idx2 = shuffle_order.index(2), shuffle_order.index(3)
+ if idx1 > idx2: # keep downsample3 last
+ shuffle_order[idx1], shuffle_order[idx2] = shuffle_order[idx2], shuffle_order[idx1]
+
+ for i in shuffle_order:
+
+ if i == 0:
+ image = add_blur(image, sf=sf)
+
+ elif i == 1:
+ image = add_blur(image, sf=sf)
+
+ elif i == 2:
+ a, b = image.shape[1], image.shape[0]
+ # downsample2
+ if random.random() < 0.75:
+ sf1 = random.uniform(1, 2 * sf)
+ image = cv2.resize(image, (int(1 / sf1 * image.shape[1]), int(1 / sf1 * image.shape[0])),
+ interpolation=random.choice([1, 2, 3]))
+ else:
+ k = fspecial('gaussian', 25, random.uniform(0.1, 0.6 * sf))
+ k_shifted = shift_pixel(k, sf)
+ k_shifted = k_shifted / k_shifted.sum() # blur with shifted kernel
+ image = ndimage.filters.convolve(image, np.expand_dims(k_shifted, axis=2), mode='mirror')
+ image = image[0::sf, 0::sf, ...] # nearest downsampling
+ image = np.clip(image, 0.0, 1.0)
+
+ elif i == 3:
+ # downsample3
+ image = cv2.resize(image, (int(1 / sf * a), int(1 / sf * b)), interpolation=random.choice([1, 2, 3]))
+ image = np.clip(image, 0.0, 1.0)
+
+ elif i == 4:
+ # add Gaussian noise
+ image = add_Gaussian_noise(image, noise_level1=2, noise_level2=25)
+
+ elif i == 5:
+ # add JPEG noise
+ if random.random() < jpeg_prob:
+ image = add_JPEG_noise(image)
+
+ # elif i == 6:
+ # # add processed camera sensor noise
+ # if random.random() < isp_prob and isp_model is not None:
+ # with torch.no_grad():
+ # img, hq = isp_model.forward(img.copy(), hq)
+
+ # add final JPEG compression noise
+ image = add_JPEG_noise(image)
+ image = util.single2uint(image)
+ example = {"image":image}
+ return example
+
+
+# TODO incase there is a pickle error one needs to replace a += x with a = a + x in add_speckle_noise etc...
+def degradation_bsrgan_plus(img, sf=4, shuffle_prob=0.5, use_sharp=True, lq_patchsize=64, isp_model=None):
+ """
+ This is an extended degradation model by combining
+ the degradation models of BSRGAN and Real-ESRGAN
+ ----------
+ img: HXWXC, [0, 1], its size should be large than (lq_patchsizexsf)x(lq_patchsizexsf)
+ sf: scale factor
+ use_shuffle: the degradation shuffle
+ use_sharp: sharpening the img
+ Returns
+ -------
+ img: low-quality patch, size: lq_patchsizeXlq_patchsizeXC, range: [0, 1]
+ hq: corresponding high-quality patch, size: (lq_patchsizexsf)X(lq_patchsizexsf)XC, range: [0, 1]
+ """
+
+ h1, w1 = img.shape[:2]
+ img = img.copy()[:w1 - w1 % sf, :h1 - h1 % sf, ...] # mod crop
+ h, w = img.shape[:2]
+
+ if h < lq_patchsize * sf or w < lq_patchsize * sf:
+ raise ValueError(f'img size ({h1}X{w1}) is too small!')
+
+ if use_sharp:
+ img = add_sharpening(img)
+ hq = img.copy()
+
+ if random.random() < shuffle_prob:
+ shuffle_order = random.sample(range(13), 13)
+ else:
+ shuffle_order = list(range(13))
+ # local shuffle for noise, JPEG is always the last one
+ shuffle_order[2:6] = random.sample(shuffle_order[2:6], len(range(2, 6)))
+ shuffle_order[9:13] = random.sample(shuffle_order[9:13], len(range(9, 13)))
+
+ poisson_prob, speckle_prob, isp_prob = 0.1, 0.1, 0.1
+
+ for i in shuffle_order:
+ if i == 0:
+ img = add_blur(img, sf=sf)
+ elif i == 1:
+ img = add_resize(img, sf=sf)
+ elif i == 2:
+ img = add_Gaussian_noise(img, noise_level1=2, noise_level2=25)
+ elif i == 3:
+ if random.random() < poisson_prob:
+ img = add_Poisson_noise(img)
+ elif i == 4:
+ if random.random() < speckle_prob:
+ img = add_speckle_noise(img)
+ elif i == 5:
+ if random.random() < isp_prob and isp_model is not None:
+ with torch.no_grad():
+ img, hq = isp_model.forward(img.copy(), hq)
+ elif i == 6:
+ img = add_JPEG_noise(img)
+ elif i == 7:
+ img = add_blur(img, sf=sf)
+ elif i == 8:
+ img = add_resize(img, sf=sf)
+ elif i == 9:
+ img = add_Gaussian_noise(img, noise_level1=2, noise_level2=25)
+ elif i == 10:
+ if random.random() < poisson_prob:
+ img = add_Poisson_noise(img)
+ elif i == 11:
+ if random.random() < speckle_prob:
+ img = add_speckle_noise(img)
+ elif i == 12:
+ if random.random() < isp_prob and isp_model is not None:
+ with torch.no_grad():
+ img, hq = isp_model.forward(img.copy(), hq)
+ else:
+ print('check the shuffle!')
+
+ # resize to desired size
+ img = cv2.resize(img, (int(1 / sf * hq.shape[1]), int(1 / sf * hq.shape[0])),
+ interpolation=random.choice([1, 2, 3]))
+
+ # add final JPEG compression noise
+ img = add_JPEG_noise(img)
+
+ # random crop
+ img, hq = random_crop(img, hq, sf, lq_patchsize)
+
+ return img, hq
+
+
+if __name__ == '__main__':
+ print("hey")
+ img = util.imread_uint('utils/test.png', 3)
+ print(img)
+ img = util.uint2single(img)
+ print(img)
+ img = img[:448, :448]
+ h = img.shape[0] // 4
+ print("resizing to", h)
+ sf = 4
+ deg_fn = partial(degradation_bsrgan_variant, sf=sf)
+ for i in range(20):
+ print(i)
+ img_lq = deg_fn(img)
+ print(img_lq)
+ img_lq_bicubic = albumentations.SmallestMaxSize(max_size=h, interpolation=cv2.INTER_CUBIC)(image=img)["image"]
+ print(img_lq.shape)
+ print("bicubic", img_lq_bicubic.shape)
+ print(img_hq.shape)
+ lq_nearest = cv2.resize(util.single2uint(img_lq), (int(sf * img_lq.shape[1]), int(sf * img_lq.shape[0])),
+ interpolation=0)
+ lq_bicubic_nearest = cv2.resize(util.single2uint(img_lq_bicubic), (int(sf * img_lq.shape[1]), int(sf * img_lq.shape[0])),
+ interpolation=0)
+ img_concat = np.concatenate([lq_bicubic_nearest, lq_nearest, util.single2uint(img_hq)], axis=1)
+ util.imsave(img_concat, str(i) + '.png')
+
+
diff --git a/ldm/modules/image_degradation/bsrgan_light.py b/ldm/modules/image_degradation/bsrgan_light.py
new file mode 100644
index 0000000000000000000000000000000000000000..808c7f882cb75e2ba2340d5b55881d11927351f0
--- /dev/null
+++ b/ldm/modules/image_degradation/bsrgan_light.py
@@ -0,0 +1,651 @@
+# -*- coding: utf-8 -*-
+import numpy as np
+import cv2
+import torch
+
+from functools import partial
+import random
+from scipy import ndimage
+import scipy
+import scipy.stats as ss
+from scipy.interpolate import interp2d
+from scipy.linalg import orth
+import albumentations
+
+import ldm.modules.image_degradation.utils_image as util
+
+"""
+# --------------------------------------------
+# Super-Resolution
+# --------------------------------------------
+#
+# Kai Zhang (cskaizhang@gmail.com)
+# https://github.com/cszn
+# From 2019/03--2021/08
+# --------------------------------------------
+"""
+
+def modcrop_np(img, sf):
+ '''
+ Args:
+ img: numpy image, WxH or WxHxC
+ sf: scale factor
+ Return:
+ cropped image
+ '''
+ w, h = img.shape[:2]
+ im = np.copy(img)
+ return im[:w - w % sf, :h - h % sf, ...]
+
+
+"""
+# --------------------------------------------
+# anisotropic Gaussian kernels
+# --------------------------------------------
+"""
+
+
+def analytic_kernel(k):
+ """Calculate the X4 kernel from the X2 kernel (for proof see appendix in paper)"""
+ k_size = k.shape[0]
+ # Calculate the big kernels size
+ big_k = np.zeros((3 * k_size - 2, 3 * k_size - 2))
+ # Loop over the small kernel to fill the big one
+ for r in range(k_size):
+ for c in range(k_size):
+ big_k[2 * r:2 * r + k_size, 2 * c:2 * c + k_size] += k[r, c] * k
+ # Crop the edges of the big kernel to ignore very small values and increase run time of SR
+ crop = k_size // 2
+ cropped_big_k = big_k[crop:-crop, crop:-crop]
+ # Normalize to 1
+ return cropped_big_k / cropped_big_k.sum()
+
+
+def anisotropic_Gaussian(ksize=15, theta=np.pi, l1=6, l2=6):
+ """ generate an anisotropic Gaussian kernel
+ Args:
+ ksize : e.g., 15, kernel size
+ theta : [0, pi], rotation angle range
+ l1 : [0.1,50], scaling of eigenvalues
+ l2 : [0.1,l1], scaling of eigenvalues
+ If l1 = l2, will get an isotropic Gaussian kernel.
+ Returns:
+ k : kernel
+ """
+
+ v = np.dot(np.array([[np.cos(theta), -np.sin(theta)], [np.sin(theta), np.cos(theta)]]), np.array([1., 0.]))
+ V = np.array([[v[0], v[1]], [v[1], -v[0]]])
+ D = np.array([[l1, 0], [0, l2]])
+ Sigma = np.dot(np.dot(V, D), np.linalg.inv(V))
+ k = gm_blur_kernel(mean=[0, 0], cov=Sigma, size=ksize)
+
+ return k
+
+
+def gm_blur_kernel(mean, cov, size=15):
+ center = size / 2.0 + 0.5
+ k = np.zeros([size, size])
+ for y in range(size):
+ for x in range(size):
+ cy = y - center + 1
+ cx = x - center + 1
+ k[y, x] = ss.multivariate_normal.pdf([cx, cy], mean=mean, cov=cov)
+
+ k = k / np.sum(k)
+ return k
+
+
+def shift_pixel(x, sf, upper_left=True):
+ """shift pixel for super-resolution with different scale factors
+ Args:
+ x: WxHxC or WxH
+ sf: scale factor
+ upper_left: shift direction
+ """
+ h, w = x.shape[:2]
+ shift = (sf - 1) * 0.5
+ xv, yv = np.arange(0, w, 1.0), np.arange(0, h, 1.0)
+ if upper_left:
+ x1 = xv + shift
+ y1 = yv + shift
+ else:
+ x1 = xv - shift
+ y1 = yv - shift
+
+ x1 = np.clip(x1, 0, w - 1)
+ y1 = np.clip(y1, 0, h - 1)
+
+ if x.ndim == 2:
+ x = interp2d(xv, yv, x)(x1, y1)
+ if x.ndim == 3:
+ for i in range(x.shape[-1]):
+ x[:, :, i] = interp2d(xv, yv, x[:, :, i])(x1, y1)
+
+ return x
+
+
+def blur(x, k):
+ '''
+ x: image, NxcxHxW
+ k: kernel, Nx1xhxw
+ '''
+ n, c = x.shape[:2]
+ p1, p2 = (k.shape[-2] - 1) // 2, (k.shape[-1] - 1) // 2
+ x = torch.nn.functional.pad(x, pad=(p1, p2, p1, p2), mode='replicate')
+ k = k.repeat(1, c, 1, 1)
+ k = k.view(-1, 1, k.shape[2], k.shape[3])
+ x = x.view(1, -1, x.shape[2], x.shape[3])
+ x = torch.nn.functional.conv2d(x, k, bias=None, stride=1, padding=0, groups=n * c)
+ x = x.view(n, c, x.shape[2], x.shape[3])
+
+ return x
+
+
+def gen_kernel(k_size=np.array([15, 15]), scale_factor=np.array([4, 4]), min_var=0.6, max_var=10., noise_level=0):
+ """"
+ # modified version of https://github.com/assafshocher/BlindSR_dataset_generator
+ # Kai Zhang
+ # min_var = 0.175 * sf # variance of the gaussian kernel will be sampled between min_var and max_var
+ # max_var = 2.5 * sf
+ """
+ # Set random eigen-vals (lambdas) and angle (theta) for COV matrix
+ lambda_1 = min_var + np.random.rand() * (max_var - min_var)
+ lambda_2 = min_var + np.random.rand() * (max_var - min_var)
+ theta = np.random.rand() * np.pi # random theta
+ noise = -noise_level + np.random.rand(*k_size) * noise_level * 2
+
+ # Set COV matrix using Lambdas and Theta
+ LAMBDA = np.diag([lambda_1, lambda_2])
+ Q = np.array([[np.cos(theta), -np.sin(theta)],
+ [np.sin(theta), np.cos(theta)]])
+ SIGMA = Q @ LAMBDA @ Q.T
+ INV_SIGMA = np.linalg.inv(SIGMA)[None, None, :, :]
+
+ # Set expectation position (shifting kernel for aligned image)
+ MU = k_size // 2 - 0.5 * (scale_factor - 1) # - 0.5 * (scale_factor - k_size % 2)
+ MU = MU[None, None, :, None]
+
+ # Create meshgrid for Gaussian
+ [X, Y] = np.meshgrid(range(k_size[0]), range(k_size[1]))
+ Z = np.stack([X, Y], 2)[:, :, :, None]
+
+ # Calcualte Gaussian for every pixel of the kernel
+ ZZ = Z - MU
+ ZZ_t = ZZ.transpose(0, 1, 3, 2)
+ raw_kernel = np.exp(-0.5 * np.squeeze(ZZ_t @ INV_SIGMA @ ZZ)) * (1 + noise)
+
+ # shift the kernel so it will be centered
+ # raw_kernel_centered = kernel_shift(raw_kernel, scale_factor)
+
+ # Normalize the kernel and return
+ # kernel = raw_kernel_centered / np.sum(raw_kernel_centered)
+ kernel = raw_kernel / np.sum(raw_kernel)
+ return kernel
+
+
+def fspecial_gaussian(hsize, sigma):
+ hsize = [hsize, hsize]
+ siz = [(hsize[0] - 1.0) / 2.0, (hsize[1] - 1.0) / 2.0]
+ std = sigma
+ [x, y] = np.meshgrid(np.arange(-siz[1], siz[1] + 1), np.arange(-siz[0], siz[0] + 1))
+ arg = -(x * x + y * y) / (2 * std * std)
+ h = np.exp(arg)
+ h[h < scipy.finfo(float).eps * h.max()] = 0
+ sumh = h.sum()
+ if sumh != 0:
+ h = h / sumh
+ return h
+
+
+def fspecial_laplacian(alpha):
+ alpha = max([0, min([alpha, 1])])
+ h1 = alpha / (alpha + 1)
+ h2 = (1 - alpha) / (alpha + 1)
+ h = [[h1, h2, h1], [h2, -4 / (alpha + 1), h2], [h1, h2, h1]]
+ h = np.array(h)
+ return h
+
+
+def fspecial(filter_type, *args, **kwargs):
+ '''
+ python code from:
+ https://github.com/ronaldosena/imagens-medicas-2/blob/40171a6c259edec7827a6693a93955de2bd39e76/Aulas/aula_2_-_uniform_filter/matlab_fspecial.py
+ '''
+ if filter_type == 'gaussian':
+ return fspecial_gaussian(*args, **kwargs)
+ if filter_type == 'laplacian':
+ return fspecial_laplacian(*args, **kwargs)
+
+
+"""
+# --------------------------------------------
+# degradation models
+# --------------------------------------------
+"""
+
+
+def bicubic_degradation(x, sf=3):
+ '''
+ Args:
+ x: HxWxC image, [0, 1]
+ sf: down-scale factor
+ Return:
+ bicubicly downsampled LR image
+ '''
+ x = util.imresize_np(x, scale=1 / sf)
+ return x
+
+
+def srmd_degradation(x, k, sf=3):
+ ''' blur + bicubic downsampling
+ Args:
+ x: HxWxC image, [0, 1]
+ k: hxw, double
+ sf: down-scale factor
+ Return:
+ downsampled LR image
+ Reference:
+ @inproceedings{zhang2018learning,
+ title={Learning a single convolutional super-resolution network for multiple degradations},
+ author={Zhang, Kai and Zuo, Wangmeng and Zhang, Lei},
+ booktitle={IEEE Conference on Computer Vision and Pattern Recognition},
+ pages={3262--3271},
+ year={2018}
+ }
+ '''
+ x = ndimage.convolve(x, np.expand_dims(k, axis=2), mode='wrap') # 'nearest' | 'mirror'
+ x = bicubic_degradation(x, sf=sf)
+ return x
+
+
+def dpsr_degradation(x, k, sf=3):
+ ''' bicubic downsampling + blur
+ Args:
+ x: HxWxC image, [0, 1]
+ k: hxw, double
+ sf: down-scale factor
+ Return:
+ downsampled LR image
+ Reference:
+ @inproceedings{zhang2019deep,
+ title={Deep Plug-and-Play Super-Resolution for Arbitrary Blur Kernels},
+ author={Zhang, Kai and Zuo, Wangmeng and Zhang, Lei},
+ booktitle={IEEE Conference on Computer Vision and Pattern Recognition},
+ pages={1671--1681},
+ year={2019}
+ }
+ '''
+ x = bicubic_degradation(x, sf=sf)
+ x = ndimage.convolve(x, np.expand_dims(k, axis=2), mode='wrap')
+ return x
+
+
+def classical_degradation(x, k, sf=3):
+ ''' blur + downsampling
+ Args:
+ x: HxWxC image, [0, 1]/[0, 255]
+ k: hxw, double
+ sf: down-scale factor
+ Return:
+ downsampled LR image
+ '''
+ x = ndimage.convolve(x, np.expand_dims(k, axis=2), mode='wrap')
+ # x = filters.correlate(x, np.expand_dims(np.flip(k), axis=2))
+ st = 0
+ return x[st::sf, st::sf, ...]
+
+
+def add_sharpening(img, weight=0.5, radius=50, threshold=10):
+ """USM sharpening. borrowed from real-ESRGAN
+ Input image: I; Blurry image: B.
+ 1. K = I + weight * (I - B)
+ 2. Mask = 1 if abs(I - B) > threshold, else: 0
+ 3. Blur mask:
+ 4. Out = Mask * K + (1 - Mask) * I
+ Args:
+ img (Numpy array): Input image, HWC, BGR; float32, [0, 1].
+ weight (float): Sharp weight. Default: 1.
+ radius (float): Kernel size of Gaussian blur. Default: 50.
+ threshold (int):
+ """
+ if radius % 2 == 0:
+ radius += 1
+ blur = cv2.GaussianBlur(img, (radius, radius), 0)
+ residual = img - blur
+ mask = np.abs(residual) * 255 > threshold
+ mask = mask.astype('float32')
+ soft_mask = cv2.GaussianBlur(mask, (radius, radius), 0)
+
+ K = img + weight * residual
+ K = np.clip(K, 0, 1)
+ return soft_mask * K + (1 - soft_mask) * img
+
+
+def add_blur(img, sf=4):
+ wd2 = 4.0 + sf
+ wd = 2.0 + 0.2 * sf
+
+ wd2 = wd2/4
+ wd = wd/4
+
+ if random.random() < 0.5:
+ l1 = wd2 * random.random()
+ l2 = wd2 * random.random()
+ k = anisotropic_Gaussian(ksize=random.randint(2, 11) + 3, theta=random.random() * np.pi, l1=l1, l2=l2)
+ else:
+ k = fspecial('gaussian', random.randint(2, 4) + 3, wd * random.random())
+ img = ndimage.convolve(img, np.expand_dims(k, axis=2), mode='mirror')
+
+ return img
+
+
+def add_resize(img, sf=4):
+ rnum = np.random.rand()
+ if rnum > 0.8: # up
+ sf1 = random.uniform(1, 2)
+ elif rnum < 0.7: # down
+ sf1 = random.uniform(0.5 / sf, 1)
+ else:
+ sf1 = 1.0
+ img = cv2.resize(img, (int(sf1 * img.shape[1]), int(sf1 * img.shape[0])), interpolation=random.choice([1, 2, 3]))
+ img = np.clip(img, 0.0, 1.0)
+
+ return img
+
+
+# def add_Gaussian_noise(img, noise_level1=2, noise_level2=25):
+# noise_level = random.randint(noise_level1, noise_level2)
+# rnum = np.random.rand()
+# if rnum > 0.6: # add color Gaussian noise
+# img += np.random.normal(0, noise_level / 255.0, img.shape).astype(np.float32)
+# elif rnum < 0.4: # add grayscale Gaussian noise
+# img += np.random.normal(0, noise_level / 255.0, (*img.shape[:2], 1)).astype(np.float32)
+# else: # add noise
+# L = noise_level2 / 255.
+# D = np.diag(np.random.rand(3))
+# U = orth(np.random.rand(3, 3))
+# conv = np.dot(np.dot(np.transpose(U), D), U)
+# img += np.random.multivariate_normal([0, 0, 0], np.abs(L ** 2 * conv), img.shape[:2]).astype(np.float32)
+# img = np.clip(img, 0.0, 1.0)
+# return img
+
+def add_Gaussian_noise(img, noise_level1=2, noise_level2=25):
+ noise_level = random.randint(noise_level1, noise_level2)
+ rnum = np.random.rand()
+ if rnum > 0.6: # add color Gaussian noise
+ img = img + np.random.normal(0, noise_level / 255.0, img.shape).astype(np.float32)
+ elif rnum < 0.4: # add grayscale Gaussian noise
+ img = img + np.random.normal(0, noise_level / 255.0, (*img.shape[:2], 1)).astype(np.float32)
+ else: # add noise
+ L = noise_level2 / 255.
+ D = np.diag(np.random.rand(3))
+ U = orth(np.random.rand(3, 3))
+ conv = np.dot(np.dot(np.transpose(U), D), U)
+ img = img + np.random.multivariate_normal([0, 0, 0], np.abs(L ** 2 * conv), img.shape[:2]).astype(np.float32)
+ img = np.clip(img, 0.0, 1.0)
+ return img
+
+
+def add_speckle_noise(img, noise_level1=2, noise_level2=25):
+ noise_level = random.randint(noise_level1, noise_level2)
+ img = np.clip(img, 0.0, 1.0)
+ rnum = random.random()
+ if rnum > 0.6:
+ img += img * np.random.normal(0, noise_level / 255.0, img.shape).astype(np.float32)
+ elif rnum < 0.4:
+ img += img * np.random.normal(0, noise_level / 255.0, (*img.shape[:2], 1)).astype(np.float32)
+ else:
+ L = noise_level2 / 255.
+ D = np.diag(np.random.rand(3))
+ U = orth(np.random.rand(3, 3))
+ conv = np.dot(np.dot(np.transpose(U), D), U)
+ img += img * np.random.multivariate_normal([0, 0, 0], np.abs(L ** 2 * conv), img.shape[:2]).astype(np.float32)
+ img = np.clip(img, 0.0, 1.0)
+ return img
+
+
+def add_Poisson_noise(img):
+ img = np.clip((img * 255.0).round(), 0, 255) / 255.
+ vals = 10 ** (2 * random.random() + 2.0) # [2, 4]
+ if random.random() < 0.5:
+ img = np.random.poisson(img * vals).astype(np.float32) / vals
+ else:
+ img_gray = np.dot(img[..., :3], [0.299, 0.587, 0.114])
+ img_gray = np.clip((img_gray * 255.0).round(), 0, 255) / 255.
+ noise_gray = np.random.poisson(img_gray * vals).astype(np.float32) / vals - img_gray
+ img += noise_gray[:, :, np.newaxis]
+ img = np.clip(img, 0.0, 1.0)
+ return img
+
+
+def add_JPEG_noise(img):
+ quality_factor = random.randint(80, 95)
+ img = cv2.cvtColor(util.single2uint(img), cv2.COLOR_RGB2BGR)
+ result, encimg = cv2.imencode('.jpg', img, [int(cv2.IMWRITE_JPEG_QUALITY), quality_factor])
+ img = cv2.imdecode(encimg, 1)
+ img = cv2.cvtColor(util.uint2single(img), cv2.COLOR_BGR2RGB)
+ return img
+
+
+def random_crop(lq, hq, sf=4, lq_patchsize=64):
+ h, w = lq.shape[:2]
+ rnd_h = random.randint(0, h - lq_patchsize)
+ rnd_w = random.randint(0, w - lq_patchsize)
+ lq = lq[rnd_h:rnd_h + lq_patchsize, rnd_w:rnd_w + lq_patchsize, :]
+
+ rnd_h_H, rnd_w_H = int(rnd_h * sf), int(rnd_w * sf)
+ hq = hq[rnd_h_H:rnd_h_H + lq_patchsize * sf, rnd_w_H:rnd_w_H + lq_patchsize * sf, :]
+ return lq, hq
+
+
+def degradation_bsrgan(img, sf=4, lq_patchsize=72, isp_model=None):
+ """
+ This is the degradation model of BSRGAN from the paper
+ "Designing a Practical Degradation Model for Deep Blind Image Super-Resolution"
+ ----------
+ img: HXWXC, [0, 1], its size should be large than (lq_patchsizexsf)x(lq_patchsizexsf)
+ sf: scale factor
+ isp_model: camera ISP model
+ Returns
+ -------
+ img: low-quality patch, size: lq_patchsizeXlq_patchsizeXC, range: [0, 1]
+ hq: corresponding high-quality patch, size: (lq_patchsizexsf)X(lq_patchsizexsf)XC, range: [0, 1]
+ """
+ isp_prob, jpeg_prob, scale2_prob = 0.25, 0.9, 0.25
+ sf_ori = sf
+
+ h1, w1 = img.shape[:2]
+ img = img.copy()[:w1 - w1 % sf, :h1 - h1 % sf, ...] # mod crop
+ h, w = img.shape[:2]
+
+ if h < lq_patchsize * sf or w < lq_patchsize * sf:
+ raise ValueError(f'img size ({h1}X{w1}) is too small!')
+
+ hq = img.copy()
+
+ if sf == 4 and random.random() < scale2_prob: # downsample1
+ if np.random.rand() < 0.5:
+ img = cv2.resize(img, (int(1 / 2 * img.shape[1]), int(1 / 2 * img.shape[0])),
+ interpolation=random.choice([1, 2, 3]))
+ else:
+ img = util.imresize_np(img, 1 / 2, True)
+ img = np.clip(img, 0.0, 1.0)
+ sf = 2
+
+ shuffle_order = random.sample(range(7), 7)
+ idx1, idx2 = shuffle_order.index(2), shuffle_order.index(3)
+ if idx1 > idx2: # keep downsample3 last
+ shuffle_order[idx1], shuffle_order[idx2] = shuffle_order[idx2], shuffle_order[idx1]
+
+ for i in shuffle_order:
+
+ if i == 0:
+ img = add_blur(img, sf=sf)
+
+ elif i == 1:
+ img = add_blur(img, sf=sf)
+
+ elif i == 2:
+ a, b = img.shape[1], img.shape[0]
+ # downsample2
+ if random.random() < 0.75:
+ sf1 = random.uniform(1, 2 * sf)
+ img = cv2.resize(img, (int(1 / sf1 * img.shape[1]), int(1 / sf1 * img.shape[0])),
+ interpolation=random.choice([1, 2, 3]))
+ else:
+ k = fspecial('gaussian', 25, random.uniform(0.1, 0.6 * sf))
+ k_shifted = shift_pixel(k, sf)
+ k_shifted = k_shifted / k_shifted.sum() # blur with shifted kernel
+ img = ndimage.convolve(img, np.expand_dims(k_shifted, axis=2), mode='mirror')
+ img = img[0::sf, 0::sf, ...] # nearest downsampling
+ img = np.clip(img, 0.0, 1.0)
+
+ elif i == 3:
+ # downsample3
+ img = cv2.resize(img, (int(1 / sf * a), int(1 / sf * b)), interpolation=random.choice([1, 2, 3]))
+ img = np.clip(img, 0.0, 1.0)
+
+ elif i == 4:
+ # add Gaussian noise
+ img = add_Gaussian_noise(img, noise_level1=2, noise_level2=8)
+
+ elif i == 5:
+ # add JPEG noise
+ if random.random() < jpeg_prob:
+ img = add_JPEG_noise(img)
+
+ elif i == 6:
+ # add processed camera sensor noise
+ if random.random() < isp_prob and isp_model is not None:
+ with torch.no_grad():
+ img, hq = isp_model.forward(img.copy(), hq)
+
+ # add final JPEG compression noise
+ img = add_JPEG_noise(img)
+
+ # random crop
+ img, hq = random_crop(img, hq, sf_ori, lq_patchsize)
+
+ return img, hq
+
+
+# todo no isp_model?
+def degradation_bsrgan_variant(image, sf=4, isp_model=None, up=False):
+ """
+ This is the degradation model of BSRGAN from the paper
+ "Designing a Practical Degradation Model for Deep Blind Image Super-Resolution"
+ ----------
+ sf: scale factor
+ isp_model: camera ISP model
+ Returns
+ -------
+ img: low-quality patch, size: lq_patchsizeXlq_patchsizeXC, range: [0, 1]
+ hq: corresponding high-quality patch, size: (lq_patchsizexsf)X(lq_patchsizexsf)XC, range: [0, 1]
+ """
+ image = util.uint2single(image)
+ isp_prob, jpeg_prob, scale2_prob = 0.25, 0.9, 0.25
+ sf_ori = sf
+
+ h1, w1 = image.shape[:2]
+ image = image.copy()[:w1 - w1 % sf, :h1 - h1 % sf, ...] # mod crop
+ h, w = image.shape[:2]
+
+ hq = image.copy()
+
+ if sf == 4 and random.random() < scale2_prob: # downsample1
+ if np.random.rand() < 0.5:
+ image = cv2.resize(image, (int(1 / 2 * image.shape[1]), int(1 / 2 * image.shape[0])),
+ interpolation=random.choice([1, 2, 3]))
+ else:
+ image = util.imresize_np(image, 1 / 2, True)
+ image = np.clip(image, 0.0, 1.0)
+ sf = 2
+
+ shuffle_order = random.sample(range(7), 7)
+ idx1, idx2 = shuffle_order.index(2), shuffle_order.index(3)
+ if idx1 > idx2: # keep downsample3 last
+ shuffle_order[idx1], shuffle_order[idx2] = shuffle_order[idx2], shuffle_order[idx1]
+
+ for i in shuffle_order:
+
+ if i == 0:
+ image = add_blur(image, sf=sf)
+
+ # elif i == 1:
+ # image = add_blur(image, sf=sf)
+
+ if i == 0:
+ pass
+
+ elif i == 2:
+ a, b = image.shape[1], image.shape[0]
+ # downsample2
+ if random.random() < 0.8:
+ sf1 = random.uniform(1, 2 * sf)
+ image = cv2.resize(image, (int(1 / sf1 * image.shape[1]), int(1 / sf1 * image.shape[0])),
+ interpolation=random.choice([1, 2, 3]))
+ else:
+ k = fspecial('gaussian', 25, random.uniform(0.1, 0.6 * sf))
+ k_shifted = shift_pixel(k, sf)
+ k_shifted = k_shifted / k_shifted.sum() # blur with shifted kernel
+ image = ndimage.convolve(image, np.expand_dims(k_shifted, axis=2), mode='mirror')
+ image = image[0::sf, 0::sf, ...] # nearest downsampling
+
+ image = np.clip(image, 0.0, 1.0)
+
+ elif i == 3:
+ # downsample3
+ image = cv2.resize(image, (int(1 / sf * a), int(1 / sf * b)), interpolation=random.choice([1, 2, 3]))
+ image = np.clip(image, 0.0, 1.0)
+
+ elif i == 4:
+ # add Gaussian noise
+ image = add_Gaussian_noise(image, noise_level1=1, noise_level2=2)
+
+ elif i == 5:
+ # add JPEG noise
+ if random.random() < jpeg_prob:
+ image = add_JPEG_noise(image)
+ #
+ # elif i == 6:
+ # # add processed camera sensor noise
+ # if random.random() < isp_prob and isp_model is not None:
+ # with torch.no_grad():
+ # img, hq = isp_model.forward(img.copy(), hq)
+
+ # add final JPEG compression noise
+ image = add_JPEG_noise(image)
+ image = util.single2uint(image)
+ if up:
+ image = cv2.resize(image, (w1, h1), interpolation=cv2.INTER_CUBIC) # todo: random, as above? want to condition on it then
+ example = {"image": image}
+ return example
+
+
+
+
+if __name__ == '__main__':
+ print("hey")
+ img = util.imread_uint('utils/test.png', 3)
+ img = img[:448, :448]
+ h = img.shape[0] // 4
+ print("resizing to", h)
+ sf = 4
+ deg_fn = partial(degradation_bsrgan_variant, sf=sf)
+ for i in range(20):
+ print(i)
+ img_hq = img
+ img_lq = deg_fn(img)["image"]
+ img_hq, img_lq = util.uint2single(img_hq), util.uint2single(img_lq)
+ print(img_lq)
+ img_lq_bicubic = albumentations.SmallestMaxSize(max_size=h, interpolation=cv2.INTER_CUBIC)(image=img_hq)["image"]
+ print(img_lq.shape)
+ print("bicubic", img_lq_bicubic.shape)
+ print(img_hq.shape)
+ lq_nearest = cv2.resize(util.single2uint(img_lq), (int(sf * img_lq.shape[1]), int(sf * img_lq.shape[0])),
+ interpolation=0)
+ lq_bicubic_nearest = cv2.resize(util.single2uint(img_lq_bicubic),
+ (int(sf * img_lq.shape[1]), int(sf * img_lq.shape[0])),
+ interpolation=0)
+ img_concat = np.concatenate([lq_bicubic_nearest, lq_nearest, util.single2uint(img_hq)], axis=1)
+ util.imsave(img_concat, str(i) + '.png')
diff --git a/ldm/modules/image_degradation/utils/test.png b/ldm/modules/image_degradation/utils/test.png
new file mode 100644
index 0000000000000000000000000000000000000000..4249b43de0f22707758d13c240268a401642f6e6
Binary files /dev/null and b/ldm/modules/image_degradation/utils/test.png differ
diff --git a/ldm/modules/image_degradation/utils_image.py b/ldm/modules/image_degradation/utils_image.py
new file mode 100644
index 0000000000000000000000000000000000000000..0175f155ad900ae33c3c46ed87f49b352e3faf98
--- /dev/null
+++ b/ldm/modules/image_degradation/utils_image.py
@@ -0,0 +1,916 @@
+import os
+import math
+import random
+import numpy as np
+import torch
+import cv2
+from torchvision.utils import make_grid
+from datetime import datetime
+#import matplotlib.pyplot as plt # TODO: check with Dominik, also bsrgan.py vs bsrgan_light.py
+
+
+os.environ["KMP_DUPLICATE_LIB_OK"]="TRUE"
+
+
+'''
+# --------------------------------------------
+# Kai Zhang (github: https://github.com/cszn)
+# 03/Mar/2019
+# --------------------------------------------
+# https://github.com/twhui/SRGAN-pyTorch
+# https://github.com/xinntao/BasicSR
+# --------------------------------------------
+'''
+
+
+IMG_EXTENSIONS = ['.jpg', '.JPG', '.jpeg', '.JPEG', '.png', '.PNG', '.ppm', '.PPM', '.bmp', '.BMP', '.tif']
+
+
+def is_image_file(filename):
+ return any(filename.endswith(extension) for extension in IMG_EXTENSIONS)
+
+
+def get_timestamp():
+ return datetime.now().strftime('%y%m%d-%H%M%S')
+
+
+def imshow(x, title=None, cbar=False, figsize=None):
+ plt.figure(figsize=figsize)
+ plt.imshow(np.squeeze(x), interpolation='nearest', cmap='gray')
+ if title:
+ plt.title(title)
+ if cbar:
+ plt.colorbar()
+ plt.show()
+
+
+def surf(Z, cmap='rainbow', figsize=None):
+ plt.figure(figsize=figsize)
+ ax3 = plt.axes(projection='3d')
+
+ w, h = Z.shape[:2]
+ xx = np.arange(0,w,1)
+ yy = np.arange(0,h,1)
+ X, Y = np.meshgrid(xx, yy)
+ ax3.plot_surface(X,Y,Z,cmap=cmap)
+ #ax3.contour(X,Y,Z, zdim='z',offset=-2,cmap=cmap)
+ plt.show()
+
+
+'''
+# --------------------------------------------
+# get image pathes
+# --------------------------------------------
+'''
+
+
+def get_image_paths(dataroot):
+ paths = None # return None if dataroot is None
+ if dataroot is not None:
+ paths = sorted(_get_paths_from_images(dataroot))
+ return paths
+
+
+def _get_paths_from_images(path):
+ assert os.path.isdir(path), '{:s} is not a valid directory'.format(path)
+ images = []
+ for dirpath, _, fnames in sorted(os.walk(path)):
+ for fname in sorted(fnames):
+ if is_image_file(fname):
+ img_path = os.path.join(dirpath, fname)
+ images.append(img_path)
+ assert images, '{:s} has no valid image file'.format(path)
+ return images
+
+
+'''
+# --------------------------------------------
+# split large images into small images
+# --------------------------------------------
+'''
+
+
+def patches_from_image(img, p_size=512, p_overlap=64, p_max=800):
+ w, h = img.shape[:2]
+ patches = []
+ if w > p_max and h > p_max:
+ w1 = list(np.arange(0, w-p_size, p_size-p_overlap, dtype=np.int))
+ h1 = list(np.arange(0, h-p_size, p_size-p_overlap, dtype=np.int))
+ w1.append(w-p_size)
+ h1.append(h-p_size)
+# print(w1)
+# print(h1)
+ for i in w1:
+ for j in h1:
+ patches.append(img[i:i+p_size, j:j+p_size,:])
+ else:
+ patches.append(img)
+
+ return patches
+
+
+def imssave(imgs, img_path):
+ """
+ imgs: list, N images of size WxHxC
+ """
+ img_name, ext = os.path.splitext(os.path.basename(img_path))
+
+ for i, img in enumerate(imgs):
+ if img.ndim == 3:
+ img = img[:, :, [2, 1, 0]]
+ new_path = os.path.join(os.path.dirname(img_path), img_name+str('_s{:04d}'.format(i))+'.png')
+ cv2.imwrite(new_path, img)
+
+
+def split_imageset(original_dataroot, taget_dataroot, n_channels=3, p_size=800, p_overlap=96, p_max=1000):
+ """
+ split the large images from original_dataroot into small overlapped images with size (p_size)x(p_size),
+ and save them into taget_dataroot; only the images with larger size than (p_max)x(p_max)
+ will be splitted.
+ Args:
+ original_dataroot:
+ taget_dataroot:
+ p_size: size of small images
+ p_overlap: patch size in training is a good choice
+ p_max: images with smaller size than (p_max)x(p_max) keep unchanged.
+ """
+ paths = get_image_paths(original_dataroot)
+ for img_path in paths:
+ # img_name, ext = os.path.splitext(os.path.basename(img_path))
+ img = imread_uint(img_path, n_channels=n_channels)
+ patches = patches_from_image(img, p_size, p_overlap, p_max)
+ imssave(patches, os.path.join(taget_dataroot,os.path.basename(img_path)))
+ #if original_dataroot == taget_dataroot:
+ #del img_path
+
+'''
+# --------------------------------------------
+# makedir
+# --------------------------------------------
+'''
+
+
+def mkdir(path):
+ if not os.path.exists(path):
+ os.makedirs(path)
+
+
+def mkdirs(paths):
+ if isinstance(paths, str):
+ mkdir(paths)
+ else:
+ for path in paths:
+ mkdir(path)
+
+
+def mkdir_and_rename(path):
+ if os.path.exists(path):
+ new_name = path + '_archived_' + get_timestamp()
+ print('Path already exists. Rename it to [{:s}]'.format(new_name))
+ os.rename(path, new_name)
+ os.makedirs(path)
+
+
+'''
+# --------------------------------------------
+# read image from path
+# opencv is fast, but read BGR numpy image
+# --------------------------------------------
+'''
+
+
+# --------------------------------------------
+# get uint8 image of size HxWxn_channles (RGB)
+# --------------------------------------------
+def imread_uint(path, n_channels=3):
+ # input: path
+ # output: HxWx3(RGB or GGG), or HxWx1 (G)
+ if n_channels == 1:
+ img = cv2.imread(path, 0) # cv2.IMREAD_GRAYSCALE
+ img = np.expand_dims(img, axis=2) # HxWx1
+ elif n_channels == 3:
+ img = cv2.imread(path, cv2.IMREAD_UNCHANGED) # BGR or G
+ if img.ndim == 2:
+ img = cv2.cvtColor(img, cv2.COLOR_GRAY2RGB) # GGG
+ else:
+ img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB) # RGB
+ return img
+
+
+# --------------------------------------------
+# matlab's imwrite
+# --------------------------------------------
+def imsave(img, img_path):
+ img = np.squeeze(img)
+ if img.ndim == 3:
+ img = img[:, :, [2, 1, 0]]
+ cv2.imwrite(img_path, img)
+
+def imwrite(img, img_path):
+ img = np.squeeze(img)
+ if img.ndim == 3:
+ img = img[:, :, [2, 1, 0]]
+ cv2.imwrite(img_path, img)
+
+
+
+# --------------------------------------------
+# get single image of size HxWxn_channles (BGR)
+# --------------------------------------------
+def read_img(path):
+ # read image by cv2
+ # return: Numpy float32, HWC, BGR, [0,1]
+ img = cv2.imread(path, cv2.IMREAD_UNCHANGED) # cv2.IMREAD_GRAYSCALE
+ img = img.astype(np.float32) / 255.
+ if img.ndim == 2:
+ img = np.expand_dims(img, axis=2)
+ # some images have 4 channels
+ if img.shape[2] > 3:
+ img = img[:, :, :3]
+ return img
+
+
+'''
+# --------------------------------------------
+# image format conversion
+# --------------------------------------------
+# numpy(single) <---> numpy(unit)
+# numpy(single) <---> tensor
+# numpy(unit) <---> tensor
+# --------------------------------------------
+'''
+
+
+# --------------------------------------------
+# numpy(single) [0, 1] <---> numpy(unit)
+# --------------------------------------------
+
+
+def uint2single(img):
+
+ return np.float32(img/255.)
+
+
+def single2uint(img):
+
+ return np.uint8((img.clip(0, 1)*255.).round())
+
+
+def uint162single(img):
+
+ return np.float32(img/65535.)
+
+
+def single2uint16(img):
+
+ return np.uint16((img.clip(0, 1)*65535.).round())
+
+
+# --------------------------------------------
+# numpy(unit) (HxWxC or HxW) <---> tensor
+# --------------------------------------------
+
+
+# convert uint to 4-dimensional torch tensor
+def uint2tensor4(img):
+ if img.ndim == 2:
+ img = np.expand_dims(img, axis=2)
+ return torch.from_numpy(np.ascontiguousarray(img)).permute(2, 0, 1).float().div(255.).unsqueeze(0)
+
+
+# convert uint to 3-dimensional torch tensor
+def uint2tensor3(img):
+ if img.ndim == 2:
+ img = np.expand_dims(img, axis=2)
+ return torch.from_numpy(np.ascontiguousarray(img)).permute(2, 0, 1).float().div(255.)
+
+
+# convert 2/3/4-dimensional torch tensor to uint
+def tensor2uint(img):
+ img = img.data.squeeze().float().clamp_(0, 1).cpu().numpy()
+ if img.ndim == 3:
+ img = np.transpose(img, (1, 2, 0))
+ return np.uint8((img*255.0).round())
+
+
+# --------------------------------------------
+# numpy(single) (HxWxC) <---> tensor
+# --------------------------------------------
+
+
+# convert single (HxWxC) to 3-dimensional torch tensor
+def single2tensor3(img):
+ return torch.from_numpy(np.ascontiguousarray(img)).permute(2, 0, 1).float()
+
+
+# convert single (HxWxC) to 4-dimensional torch tensor
+def single2tensor4(img):
+ return torch.from_numpy(np.ascontiguousarray(img)).permute(2, 0, 1).float().unsqueeze(0)
+
+
+# convert torch tensor to single
+def tensor2single(img):
+ img = img.data.squeeze().float().cpu().numpy()
+ if img.ndim == 3:
+ img = np.transpose(img, (1, 2, 0))
+
+ return img
+
+# convert torch tensor to single
+def tensor2single3(img):
+ img = img.data.squeeze().float().cpu().numpy()
+ if img.ndim == 3:
+ img = np.transpose(img, (1, 2, 0))
+ elif img.ndim == 2:
+ img = np.expand_dims(img, axis=2)
+ return img
+
+
+def single2tensor5(img):
+ return torch.from_numpy(np.ascontiguousarray(img)).permute(2, 0, 1, 3).float().unsqueeze(0)
+
+
+def single32tensor5(img):
+ return torch.from_numpy(np.ascontiguousarray(img)).float().unsqueeze(0).unsqueeze(0)
+
+
+def single42tensor4(img):
+ return torch.from_numpy(np.ascontiguousarray(img)).permute(2, 0, 1, 3).float()
+
+
+# from skimage.io import imread, imsave
+def tensor2img(tensor, out_type=np.uint8, min_max=(0, 1)):
+ '''
+ Converts a torch Tensor into an image Numpy array of BGR channel order
+ Input: 4D(B,(3/1),H,W), 3D(C,H,W), or 2D(H,W), any range, RGB channel order
+ Output: 3D(H,W,C) or 2D(H,W), [0,255], np.uint8 (default)
+ '''
+ tensor = tensor.squeeze().float().cpu().clamp_(*min_max) # squeeze first, then clamp
+ tensor = (tensor - min_max[0]) / (min_max[1] - min_max[0]) # to range [0,1]
+ n_dim = tensor.dim()
+ if n_dim == 4:
+ n_img = len(tensor)
+ img_np = make_grid(tensor, nrow=int(math.sqrt(n_img)), normalize=False).numpy()
+ img_np = np.transpose(img_np[[2, 1, 0], :, :], (1, 2, 0)) # HWC, BGR
+ elif n_dim == 3:
+ img_np = tensor.numpy()
+ img_np = np.transpose(img_np[[2, 1, 0], :, :], (1, 2, 0)) # HWC, BGR
+ elif n_dim == 2:
+ img_np = tensor.numpy()
+ else:
+ raise TypeError(
+ 'Only support 4D, 3D and 2D tensor. But received with dimension: {:d}'.format(n_dim))
+ if out_type == np.uint8:
+ img_np = (img_np * 255.0).round()
+ # Important. Unlike matlab, numpy.unit8() WILL NOT round by default.
+ return img_np.astype(out_type)
+
+
+'''
+# --------------------------------------------
+# Augmentation, flipe and/or rotate
+# --------------------------------------------
+# The following two are enough.
+# (1) augmet_img: numpy image of WxHxC or WxH
+# (2) augment_img_tensor4: tensor image 1xCxWxH
+# --------------------------------------------
+'''
+
+
+def augment_img(img, mode=0):
+ '''Kai Zhang (github: https://github.com/cszn)
+ '''
+ if mode == 0:
+ return img
+ elif mode == 1:
+ return np.flipud(np.rot90(img))
+ elif mode == 2:
+ return np.flipud(img)
+ elif mode == 3:
+ return np.rot90(img, k=3)
+ elif mode == 4:
+ return np.flipud(np.rot90(img, k=2))
+ elif mode == 5:
+ return np.rot90(img)
+ elif mode == 6:
+ return np.rot90(img, k=2)
+ elif mode == 7:
+ return np.flipud(np.rot90(img, k=3))
+
+
+def augment_img_tensor4(img, mode=0):
+ '''Kai Zhang (github: https://github.com/cszn)
+ '''
+ if mode == 0:
+ return img
+ elif mode == 1:
+ return img.rot90(1, [2, 3]).flip([2])
+ elif mode == 2:
+ return img.flip([2])
+ elif mode == 3:
+ return img.rot90(3, [2, 3])
+ elif mode == 4:
+ return img.rot90(2, [2, 3]).flip([2])
+ elif mode == 5:
+ return img.rot90(1, [2, 3])
+ elif mode == 6:
+ return img.rot90(2, [2, 3])
+ elif mode == 7:
+ return img.rot90(3, [2, 3]).flip([2])
+
+
+def augment_img_tensor(img, mode=0):
+ '''Kai Zhang (github: https://github.com/cszn)
+ '''
+ img_size = img.size()
+ img_np = img.data.cpu().numpy()
+ if len(img_size) == 3:
+ img_np = np.transpose(img_np, (1, 2, 0))
+ elif len(img_size) == 4:
+ img_np = np.transpose(img_np, (2, 3, 1, 0))
+ img_np = augment_img(img_np, mode=mode)
+ img_tensor = torch.from_numpy(np.ascontiguousarray(img_np))
+ if len(img_size) == 3:
+ img_tensor = img_tensor.permute(2, 0, 1)
+ elif len(img_size) == 4:
+ img_tensor = img_tensor.permute(3, 2, 0, 1)
+
+ return img_tensor.type_as(img)
+
+
+def augment_img_np3(img, mode=0):
+ if mode == 0:
+ return img
+ elif mode == 1:
+ return img.transpose(1, 0, 2)
+ elif mode == 2:
+ return img[::-1, :, :]
+ elif mode == 3:
+ img = img[::-1, :, :]
+ img = img.transpose(1, 0, 2)
+ return img
+ elif mode == 4:
+ return img[:, ::-1, :]
+ elif mode == 5:
+ img = img[:, ::-1, :]
+ img = img.transpose(1, 0, 2)
+ return img
+ elif mode == 6:
+ img = img[:, ::-1, :]
+ img = img[::-1, :, :]
+ return img
+ elif mode == 7:
+ img = img[:, ::-1, :]
+ img = img[::-1, :, :]
+ img = img.transpose(1, 0, 2)
+ return img
+
+
+def augment_imgs(img_list, hflip=True, rot=True):
+ # horizontal flip OR rotate
+ hflip = hflip and random.random() < 0.5
+ vflip = rot and random.random() < 0.5
+ rot90 = rot and random.random() < 0.5
+
+ def _augment(img):
+ if hflip:
+ img = img[:, ::-1, :]
+ if vflip:
+ img = img[::-1, :, :]
+ if rot90:
+ img = img.transpose(1, 0, 2)
+ return img
+
+ return [_augment(img) for img in img_list]
+
+
+'''
+# --------------------------------------------
+# modcrop and shave
+# --------------------------------------------
+'''
+
+
+def modcrop(img_in, scale):
+ # img_in: Numpy, HWC or HW
+ img = np.copy(img_in)
+ if img.ndim == 2:
+ H, W = img.shape
+ H_r, W_r = H % scale, W % scale
+ img = img[:H - H_r, :W - W_r]
+ elif img.ndim == 3:
+ H, W, C = img.shape
+ H_r, W_r = H % scale, W % scale
+ img = img[:H - H_r, :W - W_r, :]
+ else:
+ raise ValueError('Wrong img ndim: [{:d}].'.format(img.ndim))
+ return img
+
+
+def shave(img_in, border=0):
+ # img_in: Numpy, HWC or HW
+ img = np.copy(img_in)
+ h, w = img.shape[:2]
+ img = img[border:h-border, border:w-border]
+ return img
+
+
+'''
+# --------------------------------------------
+# image processing process on numpy image
+# channel_convert(in_c, tar_type, img_list):
+# rgb2ycbcr(img, only_y=True):
+# bgr2ycbcr(img, only_y=True):
+# ycbcr2rgb(img):
+# --------------------------------------------
+'''
+
+
+def rgb2ycbcr(img, only_y=True):
+ '''same as matlab rgb2ycbcr
+ only_y: only return Y channel
+ Input:
+ uint8, [0, 255]
+ float, [0, 1]
+ '''
+ in_img_type = img.dtype
+ img.astype(np.float32)
+ if in_img_type != np.uint8:
+ img *= 255.
+ # convert
+ if only_y:
+ rlt = np.dot(img, [65.481, 128.553, 24.966]) / 255.0 + 16.0
+ else:
+ rlt = np.matmul(img, [[65.481, -37.797, 112.0], [128.553, -74.203, -93.786],
+ [24.966, 112.0, -18.214]]) / 255.0 + [16, 128, 128]
+ if in_img_type == np.uint8:
+ rlt = rlt.round()
+ else:
+ rlt /= 255.
+ return rlt.astype(in_img_type)
+
+
+def ycbcr2rgb(img):
+ '''same as matlab ycbcr2rgb
+ Input:
+ uint8, [0, 255]
+ float, [0, 1]
+ '''
+ in_img_type = img.dtype
+ img.astype(np.float32)
+ if in_img_type != np.uint8:
+ img *= 255.
+ # convert
+ rlt = np.matmul(img, [[0.00456621, 0.00456621, 0.00456621], [0, -0.00153632, 0.00791071],
+ [0.00625893, -0.00318811, 0]]) * 255.0 + [-222.921, 135.576, -276.836]
+ if in_img_type == np.uint8:
+ rlt = rlt.round()
+ else:
+ rlt /= 255.
+ return rlt.astype(in_img_type)
+
+
+def bgr2ycbcr(img, only_y=True):
+ '''bgr version of rgb2ycbcr
+ only_y: only return Y channel
+ Input:
+ uint8, [0, 255]
+ float, [0, 1]
+ '''
+ in_img_type = img.dtype
+ img.astype(np.float32)
+ if in_img_type != np.uint8:
+ img *= 255.
+ # convert
+ if only_y:
+ rlt = np.dot(img, [24.966, 128.553, 65.481]) / 255.0 + 16.0
+ else:
+ rlt = np.matmul(img, [[24.966, 112.0, -18.214], [128.553, -74.203, -93.786],
+ [65.481, -37.797, 112.0]]) / 255.0 + [16, 128, 128]
+ if in_img_type == np.uint8:
+ rlt = rlt.round()
+ else:
+ rlt /= 255.
+ return rlt.astype(in_img_type)
+
+
+def channel_convert(in_c, tar_type, img_list):
+ # conversion among BGR, gray and y
+ if in_c == 3 and tar_type == 'gray': # BGR to gray
+ gray_list = [cv2.cvtColor(img, cv2.COLOR_BGR2GRAY) for img in img_list]
+ return [np.expand_dims(img, axis=2) for img in gray_list]
+ elif in_c == 3 and tar_type == 'y': # BGR to y
+ y_list = [bgr2ycbcr(img, only_y=True) for img in img_list]
+ return [np.expand_dims(img, axis=2) for img in y_list]
+ elif in_c == 1 and tar_type == 'RGB': # gray/y to BGR
+ return [cv2.cvtColor(img, cv2.COLOR_GRAY2BGR) for img in img_list]
+ else:
+ return img_list
+
+
+'''
+# --------------------------------------------
+# metric, PSNR and SSIM
+# --------------------------------------------
+'''
+
+
+# --------------------------------------------
+# PSNR
+# --------------------------------------------
+def calculate_psnr(img1, img2, border=0):
+ # img1 and img2 have range [0, 255]
+ #img1 = img1.squeeze()
+ #img2 = img2.squeeze()
+ if not img1.shape == img2.shape:
+ raise ValueError('Input images must have the same dimensions.')
+ h, w = img1.shape[:2]
+ img1 = img1[border:h-border, border:w-border]
+ img2 = img2[border:h-border, border:w-border]
+
+ img1 = img1.astype(np.float64)
+ img2 = img2.astype(np.float64)
+ mse = np.mean((img1 - img2)**2)
+ if mse == 0:
+ return float('inf')
+ return 20 * math.log10(255.0 / math.sqrt(mse))
+
+
+# --------------------------------------------
+# SSIM
+# --------------------------------------------
+def calculate_ssim(img1, img2, border=0):
+ '''calculate SSIM
+ the same outputs as MATLAB's
+ img1, img2: [0, 255]
+ '''
+ #img1 = img1.squeeze()
+ #img2 = img2.squeeze()
+ if not img1.shape == img2.shape:
+ raise ValueError('Input images must have the same dimensions.')
+ h, w = img1.shape[:2]
+ img1 = img1[border:h-border, border:w-border]
+ img2 = img2[border:h-border, border:w-border]
+
+ if img1.ndim == 2:
+ return ssim(img1, img2)
+ elif img1.ndim == 3:
+ if img1.shape[2] == 3:
+ ssims = []
+ for i in range(3):
+ ssims.append(ssim(img1[:,:,i], img2[:,:,i]))
+ return np.array(ssims).mean()
+ elif img1.shape[2] == 1:
+ return ssim(np.squeeze(img1), np.squeeze(img2))
+ else:
+ raise ValueError('Wrong input image dimensions.')
+
+
+def ssim(img1, img2):
+ C1 = (0.01 * 255)**2
+ C2 = (0.03 * 255)**2
+
+ img1 = img1.astype(np.float64)
+ img2 = img2.astype(np.float64)
+ kernel = cv2.getGaussianKernel(11, 1.5)
+ window = np.outer(kernel, kernel.transpose())
+
+ mu1 = cv2.filter2D(img1, -1, window)[5:-5, 5:-5] # valid
+ mu2 = cv2.filter2D(img2, -1, window)[5:-5, 5:-5]
+ mu1_sq = mu1**2
+ mu2_sq = mu2**2
+ mu1_mu2 = mu1 * mu2
+ sigma1_sq = cv2.filter2D(img1**2, -1, window)[5:-5, 5:-5] - mu1_sq
+ sigma2_sq = cv2.filter2D(img2**2, -1, window)[5:-5, 5:-5] - mu2_sq
+ sigma12 = cv2.filter2D(img1 * img2, -1, window)[5:-5, 5:-5] - mu1_mu2
+
+ ssim_map = ((2 * mu1_mu2 + C1) * (2 * sigma12 + C2)) / ((mu1_sq + mu2_sq + C1) *
+ (sigma1_sq + sigma2_sq + C2))
+ return ssim_map.mean()
+
+
+'''
+# --------------------------------------------
+# matlab's bicubic imresize (numpy and torch) [0, 1]
+# --------------------------------------------
+'''
+
+
+# matlab 'imresize' function, now only support 'bicubic'
+def cubic(x):
+ absx = torch.abs(x)
+ absx2 = absx**2
+ absx3 = absx**3
+ return (1.5*absx3 - 2.5*absx2 + 1) * ((absx <= 1).type_as(absx)) + \
+ (-0.5*absx3 + 2.5*absx2 - 4*absx + 2) * (((absx > 1)*(absx <= 2)).type_as(absx))
+
+
+def calculate_weights_indices(in_length, out_length, scale, kernel, kernel_width, antialiasing):
+ if (scale < 1) and (antialiasing):
+ # Use a modified kernel to simultaneously interpolate and antialias- larger kernel width
+ kernel_width = kernel_width / scale
+
+ # Output-space coordinates
+ x = torch.linspace(1, out_length, out_length)
+
+ # Input-space coordinates. Calculate the inverse mapping such that 0.5
+ # in output space maps to 0.5 in input space, and 0.5+scale in output
+ # space maps to 1.5 in input space.
+ u = x / scale + 0.5 * (1 - 1 / scale)
+
+ # What is the left-most pixel that can be involved in the computation?
+ left = torch.floor(u - kernel_width / 2)
+
+ # What is the maximum number of pixels that can be involved in the
+ # computation? Note: it's OK to use an extra pixel here; if the
+ # corresponding weights are all zero, it will be eliminated at the end
+ # of this function.
+ P = math.ceil(kernel_width) + 2
+
+ # The indices of the input pixels involved in computing the k-th output
+ # pixel are in row k of the indices matrix.
+ indices = left.view(out_length, 1).expand(out_length, P) + torch.linspace(0, P - 1, P).view(
+ 1, P).expand(out_length, P)
+
+ # The weights used to compute the k-th output pixel are in row k of the
+ # weights matrix.
+ distance_to_center = u.view(out_length, 1).expand(out_length, P) - indices
+ # apply cubic kernel
+ if (scale < 1) and (antialiasing):
+ weights = scale * cubic(distance_to_center * scale)
+ else:
+ weights = cubic(distance_to_center)
+ # Normalize the weights matrix so that each row sums to 1.
+ weights_sum = torch.sum(weights, 1).view(out_length, 1)
+ weights = weights / weights_sum.expand(out_length, P)
+
+ # If a column in weights is all zero, get rid of it. only consider the first and last column.
+ weights_zero_tmp = torch.sum((weights == 0), 0)
+ if not math.isclose(weights_zero_tmp[0], 0, rel_tol=1e-6):
+ indices = indices.narrow(1, 1, P - 2)
+ weights = weights.narrow(1, 1, P - 2)
+ if not math.isclose(weights_zero_tmp[-1], 0, rel_tol=1e-6):
+ indices = indices.narrow(1, 0, P - 2)
+ weights = weights.narrow(1, 0, P - 2)
+ weights = weights.contiguous()
+ indices = indices.contiguous()
+ sym_len_s = -indices.min() + 1
+ sym_len_e = indices.max() - in_length
+ indices = indices + sym_len_s - 1
+ return weights, indices, int(sym_len_s), int(sym_len_e)
+
+
+# --------------------------------------------
+# imresize for tensor image [0, 1]
+# --------------------------------------------
+def imresize(img, scale, antialiasing=True):
+ # Now the scale should be the same for H and W
+ # input: img: pytorch tensor, CHW or HW [0,1]
+ # output: CHW or HW [0,1] w/o round
+ need_squeeze = True if img.dim() == 2 else False
+ if need_squeeze:
+ img.unsqueeze_(0)
+ in_C, in_H, in_W = img.size()
+ out_C, out_H, out_W = in_C, math.ceil(in_H * scale), math.ceil(in_W * scale)
+ kernel_width = 4
+ kernel = 'cubic'
+
+ # Return the desired dimension order for performing the resize. The
+ # strategy is to perform the resize first along the dimension with the
+ # smallest scale factor.
+ # Now we do not support this.
+
+ # get weights and indices
+ weights_H, indices_H, sym_len_Hs, sym_len_He = calculate_weights_indices(
+ in_H, out_H, scale, kernel, kernel_width, antialiasing)
+ weights_W, indices_W, sym_len_Ws, sym_len_We = calculate_weights_indices(
+ in_W, out_W, scale, kernel, kernel_width, antialiasing)
+ # process H dimension
+ # symmetric copying
+ img_aug = torch.FloatTensor(in_C, in_H + sym_len_Hs + sym_len_He, in_W)
+ img_aug.narrow(1, sym_len_Hs, in_H).copy_(img)
+
+ sym_patch = img[:, :sym_len_Hs, :]
+ inv_idx = torch.arange(sym_patch.size(1) - 1, -1, -1).long()
+ sym_patch_inv = sym_patch.index_select(1, inv_idx)
+ img_aug.narrow(1, 0, sym_len_Hs).copy_(sym_patch_inv)
+
+ sym_patch = img[:, -sym_len_He:, :]
+ inv_idx = torch.arange(sym_patch.size(1) - 1, -1, -1).long()
+ sym_patch_inv = sym_patch.index_select(1, inv_idx)
+ img_aug.narrow(1, sym_len_Hs + in_H, sym_len_He).copy_(sym_patch_inv)
+
+ out_1 = torch.FloatTensor(in_C, out_H, in_W)
+ kernel_width = weights_H.size(1)
+ for i in range(out_H):
+ idx = int(indices_H[i][0])
+ for j in range(out_C):
+ out_1[j, i, :] = img_aug[j, idx:idx + kernel_width, :].transpose(0, 1).mv(weights_H[i])
+
+ # process W dimension
+ # symmetric copying
+ out_1_aug = torch.FloatTensor(in_C, out_H, in_W + sym_len_Ws + sym_len_We)
+ out_1_aug.narrow(2, sym_len_Ws, in_W).copy_(out_1)
+
+ sym_patch = out_1[:, :, :sym_len_Ws]
+ inv_idx = torch.arange(sym_patch.size(2) - 1, -1, -1).long()
+ sym_patch_inv = sym_patch.index_select(2, inv_idx)
+ out_1_aug.narrow(2, 0, sym_len_Ws).copy_(sym_patch_inv)
+
+ sym_patch = out_1[:, :, -sym_len_We:]
+ inv_idx = torch.arange(sym_patch.size(2) - 1, -1, -1).long()
+ sym_patch_inv = sym_patch.index_select(2, inv_idx)
+ out_1_aug.narrow(2, sym_len_Ws + in_W, sym_len_We).copy_(sym_patch_inv)
+
+ out_2 = torch.FloatTensor(in_C, out_H, out_W)
+ kernel_width = weights_W.size(1)
+ for i in range(out_W):
+ idx = int(indices_W[i][0])
+ for j in range(out_C):
+ out_2[j, :, i] = out_1_aug[j, :, idx:idx + kernel_width].mv(weights_W[i])
+ if need_squeeze:
+ out_2.squeeze_()
+ return out_2
+
+
+# --------------------------------------------
+# imresize for numpy image [0, 1]
+# --------------------------------------------
+def imresize_np(img, scale, antialiasing=True):
+ # Now the scale should be the same for H and W
+ # input: img: Numpy, HWC or HW [0,1]
+ # output: HWC or HW [0,1] w/o round
+ img = torch.from_numpy(img)
+ need_squeeze = True if img.dim() == 2 else False
+ if need_squeeze:
+ img.unsqueeze_(2)
+
+ in_H, in_W, in_C = img.size()
+ out_C, out_H, out_W = in_C, math.ceil(in_H * scale), math.ceil(in_W * scale)
+ kernel_width = 4
+ kernel = 'cubic'
+
+ # Return the desired dimension order for performing the resize. The
+ # strategy is to perform the resize first along the dimension with the
+ # smallest scale factor.
+ # Now we do not support this.
+
+ # get weights and indices
+ weights_H, indices_H, sym_len_Hs, sym_len_He = calculate_weights_indices(
+ in_H, out_H, scale, kernel, kernel_width, antialiasing)
+ weights_W, indices_W, sym_len_Ws, sym_len_We = calculate_weights_indices(
+ in_W, out_W, scale, kernel, kernel_width, antialiasing)
+ # process H dimension
+ # symmetric copying
+ img_aug = torch.FloatTensor(in_H + sym_len_Hs + sym_len_He, in_W, in_C)
+ img_aug.narrow(0, sym_len_Hs, in_H).copy_(img)
+
+ sym_patch = img[:sym_len_Hs, :, :]
+ inv_idx = torch.arange(sym_patch.size(0) - 1, -1, -1).long()
+ sym_patch_inv = sym_patch.index_select(0, inv_idx)
+ img_aug.narrow(0, 0, sym_len_Hs).copy_(sym_patch_inv)
+
+ sym_patch = img[-sym_len_He:, :, :]
+ inv_idx = torch.arange(sym_patch.size(0) - 1, -1, -1).long()
+ sym_patch_inv = sym_patch.index_select(0, inv_idx)
+ img_aug.narrow(0, sym_len_Hs + in_H, sym_len_He).copy_(sym_patch_inv)
+
+ out_1 = torch.FloatTensor(out_H, in_W, in_C)
+ kernel_width = weights_H.size(1)
+ for i in range(out_H):
+ idx = int(indices_H[i][0])
+ for j in range(out_C):
+ out_1[i, :, j] = img_aug[idx:idx + kernel_width, :, j].transpose(0, 1).mv(weights_H[i])
+
+ # process W dimension
+ # symmetric copying
+ out_1_aug = torch.FloatTensor(out_H, in_W + sym_len_Ws + sym_len_We, in_C)
+ out_1_aug.narrow(1, sym_len_Ws, in_W).copy_(out_1)
+
+ sym_patch = out_1[:, :sym_len_Ws, :]
+ inv_idx = torch.arange(sym_patch.size(1) - 1, -1, -1).long()
+ sym_patch_inv = sym_patch.index_select(1, inv_idx)
+ out_1_aug.narrow(1, 0, sym_len_Ws).copy_(sym_patch_inv)
+
+ sym_patch = out_1[:, -sym_len_We:, :]
+ inv_idx = torch.arange(sym_patch.size(1) - 1, -1, -1).long()
+ sym_patch_inv = sym_patch.index_select(1, inv_idx)
+ out_1_aug.narrow(1, sym_len_Ws + in_W, sym_len_We).copy_(sym_patch_inv)
+
+ out_2 = torch.FloatTensor(out_H, out_W, in_C)
+ kernel_width = weights_W.size(1)
+ for i in range(out_W):
+ idx = int(indices_W[i][0])
+ for j in range(out_C):
+ out_2[:, i, j] = out_1_aug[:, idx:idx + kernel_width, j].mv(weights_W[i])
+ if need_squeeze:
+ out_2.squeeze_()
+
+ return out_2.numpy()
+
+
+if __name__ == '__main__':
+ print('---')
+# img = imread_uint('test.bmp', 3)
+# img = uint2single(img)
+# img_bicubic = imresize_np(img, 1/4)
\ No newline at end of file
diff --git a/ldm/modules/midas/__init__.py b/ldm/modules/midas/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/ldm/modules/midas/api.py b/ldm/modules/midas/api.py
new file mode 100644
index 0000000000000000000000000000000000000000..b58ebbffd942a2fc22264f0ab47e400c26b9f41c
--- /dev/null
+++ b/ldm/modules/midas/api.py
@@ -0,0 +1,170 @@
+# based on https://github.com/isl-org/MiDaS
+
+import cv2
+import torch
+import torch.nn as nn
+from torchvision.transforms import Compose
+
+from ldm.modules.midas.midas.dpt_depth import DPTDepthModel
+from ldm.modules.midas.midas.midas_net import MidasNet
+from ldm.modules.midas.midas.midas_net_custom import MidasNet_small
+from ldm.modules.midas.midas.transforms import Resize, NormalizeImage, PrepareForNet
+
+
+ISL_PATHS = {
+ "dpt_large": "midas_models/dpt_large-midas-2f21e586.pt",
+ "dpt_hybrid": "midas_models/dpt_hybrid-midas-501f0c75.pt",
+ "midas_v21": "",
+ "midas_v21_small": "",
+}
+
+
+def disabled_train(self, mode=True):
+ """Overwrite model.train with this function to make sure train/eval mode
+ does not change anymore."""
+ return self
+
+
+def load_midas_transform(model_type):
+ # https://github.com/isl-org/MiDaS/blob/master/run.py
+ # load transform only
+ if model_type == "dpt_large": # DPT-Large
+ net_w, net_h = 384, 384
+ resize_mode = "minimal"
+ normalization = NormalizeImage(mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5])
+
+ elif model_type == "dpt_hybrid": # DPT-Hybrid
+ net_w, net_h = 384, 384
+ resize_mode = "minimal"
+ normalization = NormalizeImage(mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5])
+
+ elif model_type == "midas_v21":
+ net_w, net_h = 384, 384
+ resize_mode = "upper_bound"
+ normalization = NormalizeImage(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
+
+ elif model_type == "midas_v21_small":
+ net_w, net_h = 256, 256
+ resize_mode = "upper_bound"
+ normalization = NormalizeImage(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
+
+ else:
+ assert False, f"model_type '{model_type}' not implemented, use: --model_type large"
+
+ transform = Compose(
+ [
+ Resize(
+ net_w,
+ net_h,
+ resize_target=None,
+ keep_aspect_ratio=True,
+ ensure_multiple_of=32,
+ resize_method=resize_mode,
+ image_interpolation_method=cv2.INTER_CUBIC,
+ ),
+ normalization,
+ PrepareForNet(),
+ ]
+ )
+
+ return transform
+
+
+def load_model(model_type):
+ # https://github.com/isl-org/MiDaS/blob/master/run.py
+ # load network
+ model_path = ISL_PATHS[model_type]
+ if model_type == "dpt_large": # DPT-Large
+ model = DPTDepthModel(
+ path=model_path,
+ backbone="vitl16_384",
+ non_negative=True,
+ )
+ net_w, net_h = 384, 384
+ resize_mode = "minimal"
+ normalization = NormalizeImage(mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5])
+
+ elif model_type == "dpt_hybrid": # DPT-Hybrid
+ model = DPTDepthModel(
+ path=model_path,
+ backbone="vitb_rn50_384",
+ non_negative=True,
+ )
+ net_w, net_h = 384, 384
+ resize_mode = "minimal"
+ normalization = NormalizeImage(mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5])
+
+ elif model_type == "midas_v21":
+ model = MidasNet(model_path, non_negative=True)
+ net_w, net_h = 384, 384
+ resize_mode = "upper_bound"
+ normalization = NormalizeImage(
+ mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]
+ )
+
+ elif model_type == "midas_v21_small":
+ model = MidasNet_small(model_path, features=64, backbone="efficientnet_lite3", exportable=True,
+ non_negative=True, blocks={'expand': True})
+ net_w, net_h = 256, 256
+ resize_mode = "upper_bound"
+ normalization = NormalizeImage(
+ mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]
+ )
+
+ else:
+ print(f"model_type '{model_type}' not implemented, use: --model_type large")
+ assert False
+
+ transform = Compose(
+ [
+ Resize(
+ net_w,
+ net_h,
+ resize_target=None,
+ keep_aspect_ratio=True,
+ ensure_multiple_of=32,
+ resize_method=resize_mode,
+ image_interpolation_method=cv2.INTER_CUBIC,
+ ),
+ normalization,
+ PrepareForNet(),
+ ]
+ )
+
+ return model.eval(), transform
+
+
+class MiDaSInference(nn.Module):
+ MODEL_TYPES_TORCH_HUB = [
+ "DPT_Large",
+ "DPT_Hybrid",
+ "MiDaS_small"
+ ]
+ MODEL_TYPES_ISL = [
+ "dpt_large",
+ "dpt_hybrid",
+ "midas_v21",
+ "midas_v21_small",
+ ]
+
+ def __init__(self, model_type):
+ super().__init__()
+ assert (model_type in self.MODEL_TYPES_ISL)
+ model, _ = load_model(model_type)
+ self.model = model
+ self.model.train = disabled_train
+
+ def forward(self, x):
+ # x in 0..1 as produced by calling self.transform on a 0..1 float64 numpy array
+ # NOTE: we expect that the correct transform has been called during dataloading.
+ with torch.no_grad():
+ prediction = self.model(x)
+ prediction = torch.nn.functional.interpolate(
+ prediction.unsqueeze(1),
+ size=x.shape[2:],
+ mode="bicubic",
+ align_corners=False,
+ )
+ assert prediction.shape == (x.shape[0], 1, x.shape[2], x.shape[3])
+ return prediction
+
diff --git a/ldm/modules/midas/midas/__init__.py b/ldm/modules/midas/midas/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/ldm/modules/midas/midas/base_model.py b/ldm/modules/midas/midas/base_model.py
new file mode 100644
index 0000000000000000000000000000000000000000..5cf430239b47ec5ec07531263f26f5c24a2311cd
--- /dev/null
+++ b/ldm/modules/midas/midas/base_model.py
@@ -0,0 +1,16 @@
+import torch
+
+
+class BaseModel(torch.nn.Module):
+ def load(self, path):
+ """Load model from file.
+
+ Args:
+ path (str): file path
+ """
+ parameters = torch.load(path, map_location=torch.device('cpu'))
+
+ if "optimizer" in parameters:
+ parameters = parameters["model"]
+
+ self.load_state_dict(parameters)
diff --git a/ldm/modules/midas/midas/blocks.py b/ldm/modules/midas/midas/blocks.py
new file mode 100644
index 0000000000000000000000000000000000000000..2145d18fa98060a618536d9a64fe6589e9be4f78
--- /dev/null
+++ b/ldm/modules/midas/midas/blocks.py
@@ -0,0 +1,342 @@
+import torch
+import torch.nn as nn
+
+from .vit import (
+ _make_pretrained_vitb_rn50_384,
+ _make_pretrained_vitl16_384,
+ _make_pretrained_vitb16_384,
+ forward_vit,
+)
+
+def _make_encoder(backbone, features, use_pretrained, groups=1, expand=False, exportable=True, hooks=None, use_vit_only=False, use_readout="ignore",):
+ if backbone == "vitl16_384":
+ pretrained = _make_pretrained_vitl16_384(
+ use_pretrained, hooks=hooks, use_readout=use_readout
+ )
+ scratch = _make_scratch(
+ [256, 512, 1024, 1024], features, groups=groups, expand=expand
+ ) # ViT-L/16 - 85.0% Top1 (backbone)
+ elif backbone == "vitb_rn50_384":
+ pretrained = _make_pretrained_vitb_rn50_384(
+ use_pretrained,
+ hooks=hooks,
+ use_vit_only=use_vit_only,
+ use_readout=use_readout,
+ )
+ scratch = _make_scratch(
+ [256, 512, 768, 768], features, groups=groups, expand=expand
+ ) # ViT-H/16 - 85.0% Top1 (backbone)
+ elif backbone == "vitb16_384":
+ pretrained = _make_pretrained_vitb16_384(
+ use_pretrained, hooks=hooks, use_readout=use_readout
+ )
+ scratch = _make_scratch(
+ [96, 192, 384, 768], features, groups=groups, expand=expand
+ ) # ViT-B/16 - 84.6% Top1 (backbone)
+ elif backbone == "resnext101_wsl":
+ pretrained = _make_pretrained_resnext101_wsl(use_pretrained)
+ scratch = _make_scratch([256, 512, 1024, 2048], features, groups=groups, expand=expand) # efficientnet_lite3
+ elif backbone == "efficientnet_lite3":
+ pretrained = _make_pretrained_efficientnet_lite3(use_pretrained, exportable=exportable)
+ scratch = _make_scratch([32, 48, 136, 384], features, groups=groups, expand=expand) # efficientnet_lite3
+ else:
+ print(f"Backbone '{backbone}' not implemented")
+ assert False
+
+ return pretrained, scratch
+
+
+def _make_scratch(in_shape, out_shape, groups=1, expand=False):
+ scratch = nn.Module()
+
+ out_shape1 = out_shape
+ out_shape2 = out_shape
+ out_shape3 = out_shape
+ out_shape4 = out_shape
+ if expand==True:
+ out_shape1 = out_shape
+ out_shape2 = out_shape*2
+ out_shape3 = out_shape*4
+ out_shape4 = out_shape*8
+
+ scratch.layer1_rn = nn.Conv2d(
+ in_shape[0], out_shape1, kernel_size=3, stride=1, padding=1, bias=False, groups=groups
+ )
+ scratch.layer2_rn = nn.Conv2d(
+ in_shape[1], out_shape2, kernel_size=3, stride=1, padding=1, bias=False, groups=groups
+ )
+ scratch.layer3_rn = nn.Conv2d(
+ in_shape[2], out_shape3, kernel_size=3, stride=1, padding=1, bias=False, groups=groups
+ )
+ scratch.layer4_rn = nn.Conv2d(
+ in_shape[3], out_shape4, kernel_size=3, stride=1, padding=1, bias=False, groups=groups
+ )
+
+ return scratch
+
+
+def _make_pretrained_efficientnet_lite3(use_pretrained, exportable=False):
+ efficientnet = torch.hub.load(
+ "rwightman/gen-efficientnet-pytorch",
+ "tf_efficientnet_lite3",
+ pretrained=use_pretrained,
+ exportable=exportable
+ )
+ return _make_efficientnet_backbone(efficientnet)
+
+
+def _make_efficientnet_backbone(effnet):
+ pretrained = nn.Module()
+
+ pretrained.layer1 = nn.Sequential(
+ effnet.conv_stem, effnet.bn1, effnet.act1, *effnet.blocks[0:2]
+ )
+ pretrained.layer2 = nn.Sequential(*effnet.blocks[2:3])
+ pretrained.layer3 = nn.Sequential(*effnet.blocks[3:5])
+ pretrained.layer4 = nn.Sequential(*effnet.blocks[5:9])
+
+ return pretrained
+
+
+def _make_resnet_backbone(resnet):
+ pretrained = nn.Module()
+ pretrained.layer1 = nn.Sequential(
+ resnet.conv1, resnet.bn1, resnet.relu, resnet.maxpool, resnet.layer1
+ )
+
+ pretrained.layer2 = resnet.layer2
+ pretrained.layer3 = resnet.layer3
+ pretrained.layer4 = resnet.layer4
+
+ return pretrained
+
+
+def _make_pretrained_resnext101_wsl(use_pretrained):
+ resnet = torch.hub.load("facebookresearch/WSL-Images", "resnext101_32x8d_wsl")
+ return _make_resnet_backbone(resnet)
+
+
+
+class Interpolate(nn.Module):
+ """Interpolation module.
+ """
+
+ def __init__(self, scale_factor, mode, align_corners=False):
+ """Init.
+
+ Args:
+ scale_factor (float): scaling
+ mode (str): interpolation mode
+ """
+ super(Interpolate, self).__init__()
+
+ self.interp = nn.functional.interpolate
+ self.scale_factor = scale_factor
+ self.mode = mode
+ self.align_corners = align_corners
+
+ def forward(self, x):
+ """Forward pass.
+
+ Args:
+ x (tensor): input
+
+ Returns:
+ tensor: interpolated data
+ """
+
+ x = self.interp(
+ x, scale_factor=self.scale_factor, mode=self.mode, align_corners=self.align_corners
+ )
+
+ return x
+
+
+class ResidualConvUnit(nn.Module):
+ """Residual convolution module.
+ """
+
+ def __init__(self, features):
+ """Init.
+
+ Args:
+ features (int): number of features
+ """
+ super().__init__()
+
+ self.conv1 = nn.Conv2d(
+ features, features, kernel_size=3, stride=1, padding=1, bias=True
+ )
+
+ self.conv2 = nn.Conv2d(
+ features, features, kernel_size=3, stride=1, padding=1, bias=True
+ )
+
+ self.relu = nn.ReLU(inplace=True)
+
+ def forward(self, x):
+ """Forward pass.
+
+ Args:
+ x (tensor): input
+
+ Returns:
+ tensor: output
+ """
+ out = self.relu(x)
+ out = self.conv1(out)
+ out = self.relu(out)
+ out = self.conv2(out)
+
+ return out + x
+
+
+class FeatureFusionBlock(nn.Module):
+ """Feature fusion block.
+ """
+
+ def __init__(self, features):
+ """Init.
+
+ Args:
+ features (int): number of features
+ """
+ super(FeatureFusionBlock, self).__init__()
+
+ self.resConfUnit1 = ResidualConvUnit(features)
+ self.resConfUnit2 = ResidualConvUnit(features)
+
+ def forward(self, *xs):
+ """Forward pass.
+
+ Returns:
+ tensor: output
+ """
+ output = xs[0]
+
+ if len(xs) == 2:
+ output += self.resConfUnit1(xs[1])
+
+ output = self.resConfUnit2(output)
+
+ output = nn.functional.interpolate(
+ output, scale_factor=2, mode="bilinear", align_corners=True
+ )
+
+ return output
+
+
+
+
+class ResidualConvUnit_custom(nn.Module):
+ """Residual convolution module.
+ """
+
+ def __init__(self, features, activation, bn):
+ """Init.
+
+ Args:
+ features (int): number of features
+ """
+ super().__init__()
+
+ self.bn = bn
+
+ self.groups=1
+
+ self.conv1 = nn.Conv2d(
+ features, features, kernel_size=3, stride=1, padding=1, bias=True, groups=self.groups
+ )
+
+ self.conv2 = nn.Conv2d(
+ features, features, kernel_size=3, stride=1, padding=1, bias=True, groups=self.groups
+ )
+
+ if self.bn==True:
+ self.bn1 = nn.BatchNorm2d(features)
+ self.bn2 = nn.BatchNorm2d(features)
+
+ self.activation = activation
+
+ self.skip_add = nn.quantized.FloatFunctional()
+
+ def forward(self, x):
+ """Forward pass.
+
+ Args:
+ x (tensor): input
+
+ Returns:
+ tensor: output
+ """
+
+ out = self.activation(x)
+ out = self.conv1(out)
+ if self.bn==True:
+ out = self.bn1(out)
+
+ out = self.activation(out)
+ out = self.conv2(out)
+ if self.bn==True:
+ out = self.bn2(out)
+
+ if self.groups > 1:
+ out = self.conv_merge(out)
+
+ return self.skip_add.add(out, x)
+
+ # return out + x
+
+
+class FeatureFusionBlock_custom(nn.Module):
+ """Feature fusion block.
+ """
+
+ def __init__(self, features, activation, deconv=False, bn=False, expand=False, align_corners=True):
+ """Init.
+
+ Args:
+ features (int): number of features
+ """
+ super(FeatureFusionBlock_custom, self).__init__()
+
+ self.deconv = deconv
+ self.align_corners = align_corners
+
+ self.groups=1
+
+ self.expand = expand
+ out_features = features
+ if self.expand==True:
+ out_features = features//2
+
+ self.out_conv = nn.Conv2d(features, out_features, kernel_size=1, stride=1, padding=0, bias=True, groups=1)
+
+ self.resConfUnit1 = ResidualConvUnit_custom(features, activation, bn)
+ self.resConfUnit2 = ResidualConvUnit_custom(features, activation, bn)
+
+ self.skip_add = nn.quantized.FloatFunctional()
+
+ def forward(self, *xs):
+ """Forward pass.
+
+ Returns:
+ tensor: output
+ """
+ output = xs[0]
+
+ if len(xs) == 2:
+ res = self.resConfUnit1(xs[1])
+ output = self.skip_add.add(output, res)
+ # output += res
+
+ output = self.resConfUnit2(output)
+
+ output = nn.functional.interpolate(
+ output, scale_factor=2, mode="bilinear", align_corners=self.align_corners
+ )
+
+ output = self.out_conv(output)
+
+ return output
+
diff --git a/ldm/modules/midas/midas/dpt_depth.py b/ldm/modules/midas/midas/dpt_depth.py
new file mode 100644
index 0000000000000000000000000000000000000000..4e9aab5d2767dffea39da5b3f30e2798688216f1
--- /dev/null
+++ b/ldm/modules/midas/midas/dpt_depth.py
@@ -0,0 +1,109 @@
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+
+from .base_model import BaseModel
+from .blocks import (
+ FeatureFusionBlock,
+ FeatureFusionBlock_custom,
+ Interpolate,
+ _make_encoder,
+ forward_vit,
+)
+
+
+def _make_fusion_block(features, use_bn):
+ return FeatureFusionBlock_custom(
+ features,
+ nn.ReLU(False),
+ deconv=False,
+ bn=use_bn,
+ expand=False,
+ align_corners=True,
+ )
+
+
+class DPT(BaseModel):
+ def __init__(
+ self,
+ head,
+ features=256,
+ backbone="vitb_rn50_384",
+ readout="project",
+ channels_last=False,
+ use_bn=False,
+ ):
+
+ super(DPT, self).__init__()
+
+ self.channels_last = channels_last
+
+ hooks = {
+ "vitb_rn50_384": [0, 1, 8, 11],
+ "vitb16_384": [2, 5, 8, 11],
+ "vitl16_384": [5, 11, 17, 23],
+ }
+
+ # Instantiate backbone and reassemble blocks
+ self.pretrained, self.scratch = _make_encoder(
+ backbone,
+ features,
+ False, # Set to true of you want to train from scratch, uses ImageNet weights
+ groups=1,
+ expand=False,
+ exportable=False,
+ hooks=hooks[backbone],
+ use_readout=readout,
+ )
+
+ self.scratch.refinenet1 = _make_fusion_block(features, use_bn)
+ self.scratch.refinenet2 = _make_fusion_block(features, use_bn)
+ self.scratch.refinenet3 = _make_fusion_block(features, use_bn)
+ self.scratch.refinenet4 = _make_fusion_block(features, use_bn)
+
+ self.scratch.output_conv = head
+
+
+ def forward(self, x):
+ if self.channels_last == True:
+ x.contiguous(memory_format=torch.channels_last)
+
+ layer_1, layer_2, layer_3, layer_4 = forward_vit(self.pretrained, x)
+
+ layer_1_rn = self.scratch.layer1_rn(layer_1)
+ layer_2_rn = self.scratch.layer2_rn(layer_2)
+ layer_3_rn = self.scratch.layer3_rn(layer_3)
+ layer_4_rn = self.scratch.layer4_rn(layer_4)
+
+ path_4 = self.scratch.refinenet4(layer_4_rn)
+ path_3 = self.scratch.refinenet3(path_4, layer_3_rn)
+ path_2 = self.scratch.refinenet2(path_3, layer_2_rn)
+ path_1 = self.scratch.refinenet1(path_2, layer_1_rn)
+
+ out = self.scratch.output_conv(path_1)
+
+ return out
+
+
+class DPTDepthModel(DPT):
+ def __init__(self, path=None, non_negative=True, **kwargs):
+ features = kwargs["features"] if "features" in kwargs else 256
+
+ head = nn.Sequential(
+ nn.Conv2d(features, features // 2, kernel_size=3, stride=1, padding=1),
+ Interpolate(scale_factor=2, mode="bilinear", align_corners=True),
+ nn.Conv2d(features // 2, 32, kernel_size=3, stride=1, padding=1),
+ nn.ReLU(True),
+ nn.Conv2d(32, 1, kernel_size=1, stride=1, padding=0),
+ nn.ReLU(True) if non_negative else nn.Identity(),
+ nn.Identity(),
+ )
+
+ super().__init__(head, **kwargs)
+
+ if path is not None:
+ self.load(path)
+
+ def forward(self, x):
+ return super().forward(x).squeeze(dim=1)
+
diff --git a/ldm/modules/midas/midas/midas_net.py b/ldm/modules/midas/midas/midas_net.py
new file mode 100644
index 0000000000000000000000000000000000000000..8a954977800b0a0f48807e80fa63041910e33c1f
--- /dev/null
+++ b/ldm/modules/midas/midas/midas_net.py
@@ -0,0 +1,76 @@
+"""MidashNet: Network for monocular depth estimation trained by mixing several datasets.
+This file contains code that is adapted from
+https://github.com/thomasjpfan/pytorch_refinenet/blob/master/pytorch_refinenet/refinenet/refinenet_4cascade.py
+"""
+import torch
+import torch.nn as nn
+
+from .base_model import BaseModel
+from .blocks import FeatureFusionBlock, Interpolate, _make_encoder
+
+
+class MidasNet(BaseModel):
+ """Network for monocular depth estimation.
+ """
+
+ def __init__(self, path=None, features=256, non_negative=True):
+ """Init.
+
+ Args:
+ path (str, optional): Path to saved model. Defaults to None.
+ features (int, optional): Number of features. Defaults to 256.
+ backbone (str, optional): Backbone network for encoder. Defaults to resnet50
+ """
+ print("Loading weights: ", path)
+
+ super(MidasNet, self).__init__()
+
+ use_pretrained = False if path is None else True
+
+ self.pretrained, self.scratch = _make_encoder(backbone="resnext101_wsl", features=features, use_pretrained=use_pretrained)
+
+ self.scratch.refinenet4 = FeatureFusionBlock(features)
+ self.scratch.refinenet3 = FeatureFusionBlock(features)
+ self.scratch.refinenet2 = FeatureFusionBlock(features)
+ self.scratch.refinenet1 = FeatureFusionBlock(features)
+
+ self.scratch.output_conv = nn.Sequential(
+ nn.Conv2d(features, 128, kernel_size=3, stride=1, padding=1),
+ Interpolate(scale_factor=2, mode="bilinear"),
+ nn.Conv2d(128, 32, kernel_size=3, stride=1, padding=1),
+ nn.ReLU(True),
+ nn.Conv2d(32, 1, kernel_size=1, stride=1, padding=0),
+ nn.ReLU(True) if non_negative else nn.Identity(),
+ )
+
+ if path:
+ self.load(path)
+
+ def forward(self, x):
+ """Forward pass.
+
+ Args:
+ x (tensor): input data (image)
+
+ Returns:
+ tensor: depth
+ """
+
+ layer_1 = self.pretrained.layer1(x)
+ layer_2 = self.pretrained.layer2(layer_1)
+ layer_3 = self.pretrained.layer3(layer_2)
+ layer_4 = self.pretrained.layer4(layer_3)
+
+ layer_1_rn = self.scratch.layer1_rn(layer_1)
+ layer_2_rn = self.scratch.layer2_rn(layer_2)
+ layer_3_rn = self.scratch.layer3_rn(layer_3)
+ layer_4_rn = self.scratch.layer4_rn(layer_4)
+
+ path_4 = self.scratch.refinenet4(layer_4_rn)
+ path_3 = self.scratch.refinenet3(path_4, layer_3_rn)
+ path_2 = self.scratch.refinenet2(path_3, layer_2_rn)
+ path_1 = self.scratch.refinenet1(path_2, layer_1_rn)
+
+ out = self.scratch.output_conv(path_1)
+
+ return torch.squeeze(out, dim=1)
diff --git a/ldm/modules/midas/midas/midas_net_custom.py b/ldm/modules/midas/midas/midas_net_custom.py
new file mode 100644
index 0000000000000000000000000000000000000000..50e4acb5e53d5fabefe3dde16ab49c33c2b7797c
--- /dev/null
+++ b/ldm/modules/midas/midas/midas_net_custom.py
@@ -0,0 +1,128 @@
+"""MidashNet: Network for monocular depth estimation trained by mixing several datasets.
+This file contains code that is adapted from
+https://github.com/thomasjpfan/pytorch_refinenet/blob/master/pytorch_refinenet/refinenet/refinenet_4cascade.py
+"""
+import torch
+import torch.nn as nn
+
+from .base_model import BaseModel
+from .blocks import FeatureFusionBlock, FeatureFusionBlock_custom, Interpolate, _make_encoder
+
+
+class MidasNet_small(BaseModel):
+ """Network for monocular depth estimation.
+ """
+
+ def __init__(self, path=None, features=64, backbone="efficientnet_lite3", non_negative=True, exportable=True, channels_last=False, align_corners=True,
+ blocks={'expand': True}):
+ """Init.
+
+ Args:
+ path (str, optional): Path to saved model. Defaults to None.
+ features (int, optional): Number of features. Defaults to 256.
+ backbone (str, optional): Backbone network for encoder. Defaults to resnet50
+ """
+ print("Loading weights: ", path)
+
+ super(MidasNet_small, self).__init__()
+
+ use_pretrained = False if path else True
+
+ self.channels_last = channels_last
+ self.blocks = blocks
+ self.backbone = backbone
+
+ self.groups = 1
+
+ features1=features
+ features2=features
+ features3=features
+ features4=features
+ self.expand = False
+ if "expand" in self.blocks and self.blocks['expand'] == True:
+ self.expand = True
+ features1=features
+ features2=features*2
+ features3=features*4
+ features4=features*8
+
+ self.pretrained, self.scratch = _make_encoder(self.backbone, features, use_pretrained, groups=self.groups, expand=self.expand, exportable=exportable)
+
+ self.scratch.activation = nn.ReLU(False)
+
+ self.scratch.refinenet4 = FeatureFusionBlock_custom(features4, self.scratch.activation, deconv=False, bn=False, expand=self.expand, align_corners=align_corners)
+ self.scratch.refinenet3 = FeatureFusionBlock_custom(features3, self.scratch.activation, deconv=False, bn=False, expand=self.expand, align_corners=align_corners)
+ self.scratch.refinenet2 = FeatureFusionBlock_custom(features2, self.scratch.activation, deconv=False, bn=False, expand=self.expand, align_corners=align_corners)
+ self.scratch.refinenet1 = FeatureFusionBlock_custom(features1, self.scratch.activation, deconv=False, bn=False, align_corners=align_corners)
+
+
+ self.scratch.output_conv = nn.Sequential(
+ nn.Conv2d(features, features//2, kernel_size=3, stride=1, padding=1, groups=self.groups),
+ Interpolate(scale_factor=2, mode="bilinear"),
+ nn.Conv2d(features//2, 32, kernel_size=3, stride=1, padding=1),
+ self.scratch.activation,
+ nn.Conv2d(32, 1, kernel_size=1, stride=1, padding=0),
+ nn.ReLU(True) if non_negative else nn.Identity(),
+ nn.Identity(),
+ )
+
+ if path:
+ self.load(path)
+
+
+ def forward(self, x):
+ """Forward pass.
+
+ Args:
+ x (tensor): input data (image)
+
+ Returns:
+ tensor: depth
+ """
+ if self.channels_last==True:
+ print("self.channels_last = ", self.channels_last)
+ x.contiguous(memory_format=torch.channels_last)
+
+
+ layer_1 = self.pretrained.layer1(x)
+ layer_2 = self.pretrained.layer2(layer_1)
+ layer_3 = self.pretrained.layer3(layer_2)
+ layer_4 = self.pretrained.layer4(layer_3)
+
+ layer_1_rn = self.scratch.layer1_rn(layer_1)
+ layer_2_rn = self.scratch.layer2_rn(layer_2)
+ layer_3_rn = self.scratch.layer3_rn(layer_3)
+ layer_4_rn = self.scratch.layer4_rn(layer_4)
+
+
+ path_4 = self.scratch.refinenet4(layer_4_rn)
+ path_3 = self.scratch.refinenet3(path_4, layer_3_rn)
+ path_2 = self.scratch.refinenet2(path_3, layer_2_rn)
+ path_1 = self.scratch.refinenet1(path_2, layer_1_rn)
+
+ out = self.scratch.output_conv(path_1)
+
+ return torch.squeeze(out, dim=1)
+
+
+
+def fuse_model(m):
+ prev_previous_type = nn.Identity()
+ prev_previous_name = ''
+ previous_type = nn.Identity()
+ previous_name = ''
+ for name, module in m.named_modules():
+ if prev_previous_type == nn.Conv2d and previous_type == nn.BatchNorm2d and type(module) == nn.ReLU:
+ # print("FUSED ", prev_previous_name, previous_name, name)
+ torch.quantization.fuse_modules(m, [prev_previous_name, previous_name, name], inplace=True)
+ elif prev_previous_type == nn.Conv2d and previous_type == nn.BatchNorm2d:
+ # print("FUSED ", prev_previous_name, previous_name)
+ torch.quantization.fuse_modules(m, [prev_previous_name, previous_name], inplace=True)
+ # elif previous_type == nn.Conv2d and type(module) == nn.ReLU:
+ # print("FUSED ", previous_name, name)
+ # torch.quantization.fuse_modules(m, [previous_name, name], inplace=True)
+
+ prev_previous_type = previous_type
+ prev_previous_name = previous_name
+ previous_type = type(module)
+ previous_name = name
\ No newline at end of file
diff --git a/ldm/modules/midas/midas/transforms.py b/ldm/modules/midas/midas/transforms.py
new file mode 100644
index 0000000000000000000000000000000000000000..350cbc11662633ad7f8968eb10be2e7de6e384e9
--- /dev/null
+++ b/ldm/modules/midas/midas/transforms.py
@@ -0,0 +1,234 @@
+import numpy as np
+import cv2
+import math
+
+
+def apply_min_size(sample, size, image_interpolation_method=cv2.INTER_AREA):
+ """Rezise the sample to ensure the given size. Keeps aspect ratio.
+
+ Args:
+ sample (dict): sample
+ size (tuple): image size
+
+ Returns:
+ tuple: new size
+ """
+ shape = list(sample["disparity"].shape)
+
+ if shape[0] >= size[0] and shape[1] >= size[1]:
+ return sample
+
+ scale = [0, 0]
+ scale[0] = size[0] / shape[0]
+ scale[1] = size[1] / shape[1]
+
+ scale = max(scale)
+
+ shape[0] = math.ceil(scale * shape[0])
+ shape[1] = math.ceil(scale * shape[1])
+
+ # resize
+ sample["image"] = cv2.resize(
+ sample["image"], tuple(shape[::-1]), interpolation=image_interpolation_method
+ )
+
+ sample["disparity"] = cv2.resize(
+ sample["disparity"], tuple(shape[::-1]), interpolation=cv2.INTER_NEAREST
+ )
+ sample["mask"] = cv2.resize(
+ sample["mask"].astype(np.float32),
+ tuple(shape[::-1]),
+ interpolation=cv2.INTER_NEAREST,
+ )
+ sample["mask"] = sample["mask"].astype(bool)
+
+ return tuple(shape)
+
+
+class Resize(object):
+ """Resize sample to given size (width, height).
+ """
+
+ def __init__(
+ self,
+ width,
+ height,
+ resize_target=True,
+ keep_aspect_ratio=False,
+ ensure_multiple_of=1,
+ resize_method="lower_bound",
+ image_interpolation_method=cv2.INTER_AREA,
+ ):
+ """Init.
+
+ Args:
+ width (int): desired output width
+ height (int): desired output height
+ resize_target (bool, optional):
+ True: Resize the full sample (image, mask, target).
+ False: Resize image only.
+ Defaults to True.
+ keep_aspect_ratio (bool, optional):
+ True: Keep the aspect ratio of the input sample.
+ Output sample might not have the given width and height, and
+ resize behaviour depends on the parameter 'resize_method'.
+ Defaults to False.
+ ensure_multiple_of (int, optional):
+ Output width and height is constrained to be multiple of this parameter.
+ Defaults to 1.
+ resize_method (str, optional):
+ "lower_bound": Output will be at least as large as the given size.
+ "upper_bound": Output will be at max as large as the given size. (Output size might be smaller than given size.)
+ "minimal": Scale as least as possible. (Output size might be smaller than given size.)
+ Defaults to "lower_bound".
+ """
+ self.__width = width
+ self.__height = height
+
+ self.__resize_target = resize_target
+ self.__keep_aspect_ratio = keep_aspect_ratio
+ self.__multiple_of = ensure_multiple_of
+ self.__resize_method = resize_method
+ self.__image_interpolation_method = image_interpolation_method
+
+ def constrain_to_multiple_of(self, x, min_val=0, max_val=None):
+ y = (np.round(x / self.__multiple_of) * self.__multiple_of).astype(int)
+
+ if max_val is not None and y > max_val:
+ y = (np.floor(x / self.__multiple_of) * self.__multiple_of).astype(int)
+
+ if y < min_val:
+ y = (np.ceil(x / self.__multiple_of) * self.__multiple_of).astype(int)
+
+ return y
+
+ def get_size(self, width, height):
+ # determine new height and width
+ scale_height = self.__height / height
+ scale_width = self.__width / width
+
+ if self.__keep_aspect_ratio:
+ if self.__resize_method == "lower_bound":
+ # scale such that output size is lower bound
+ if scale_width > scale_height:
+ # fit width
+ scale_height = scale_width
+ else:
+ # fit height
+ scale_width = scale_height
+ elif self.__resize_method == "upper_bound":
+ # scale such that output size is upper bound
+ if scale_width < scale_height:
+ # fit width
+ scale_height = scale_width
+ else:
+ # fit height
+ scale_width = scale_height
+ elif self.__resize_method == "minimal":
+ # scale as least as possbile
+ if abs(1 - scale_width) < abs(1 - scale_height):
+ # fit width
+ scale_height = scale_width
+ else:
+ # fit height
+ scale_width = scale_height
+ else:
+ raise ValueError(
+ f"resize_method {self.__resize_method} not implemented"
+ )
+
+ if self.__resize_method == "lower_bound":
+ new_height = self.constrain_to_multiple_of(
+ scale_height * height, min_val=self.__height
+ )
+ new_width = self.constrain_to_multiple_of(
+ scale_width * width, min_val=self.__width
+ )
+ elif self.__resize_method == "upper_bound":
+ new_height = self.constrain_to_multiple_of(
+ scale_height * height, max_val=self.__height
+ )
+ new_width = self.constrain_to_multiple_of(
+ scale_width * width, max_val=self.__width
+ )
+ elif self.__resize_method == "minimal":
+ new_height = self.constrain_to_multiple_of(scale_height * height)
+ new_width = self.constrain_to_multiple_of(scale_width * width)
+ else:
+ raise ValueError(f"resize_method {self.__resize_method} not implemented")
+
+ return (new_width, new_height)
+
+ def __call__(self, sample):
+ width, height = self.get_size(
+ sample["image"].shape[1], sample["image"].shape[0]
+ )
+
+ # resize sample
+ sample["image"] = cv2.resize(
+ sample["image"],
+ (width, height),
+ interpolation=self.__image_interpolation_method,
+ )
+
+ if self.__resize_target:
+ if "disparity" in sample:
+ sample["disparity"] = cv2.resize(
+ sample["disparity"],
+ (width, height),
+ interpolation=cv2.INTER_NEAREST,
+ )
+
+ if "depth" in sample:
+ sample["depth"] = cv2.resize(
+ sample["depth"], (width, height), interpolation=cv2.INTER_NEAREST
+ )
+
+ sample["mask"] = cv2.resize(
+ sample["mask"].astype(np.float32),
+ (width, height),
+ interpolation=cv2.INTER_NEAREST,
+ )
+ sample["mask"] = sample["mask"].astype(bool)
+
+ return sample
+
+
+class NormalizeImage(object):
+ """Normlize image by given mean and std.
+ """
+
+ def __init__(self, mean, std):
+ self.__mean = mean
+ self.__std = std
+
+ def __call__(self, sample):
+ sample["image"] = (sample["image"] - self.__mean) / self.__std
+
+ return sample
+
+
+class PrepareForNet(object):
+ """Prepare sample for usage as network input.
+ """
+
+ def __init__(self):
+ pass
+
+ def __call__(self, sample):
+ image = np.transpose(sample["image"], (2, 0, 1))
+ sample["image"] = np.ascontiguousarray(image).astype(np.float32)
+
+ if "mask" in sample:
+ sample["mask"] = sample["mask"].astype(np.float32)
+ sample["mask"] = np.ascontiguousarray(sample["mask"])
+
+ if "disparity" in sample:
+ disparity = sample["disparity"].astype(np.float32)
+ sample["disparity"] = np.ascontiguousarray(disparity)
+
+ if "depth" in sample:
+ depth = sample["depth"].astype(np.float32)
+ sample["depth"] = np.ascontiguousarray(depth)
+
+ return sample
diff --git a/ldm/modules/midas/midas/vit.py b/ldm/modules/midas/midas/vit.py
new file mode 100644
index 0000000000000000000000000000000000000000..ea46b1be88b261b0dec04f3da0256f5f66f88a74
--- /dev/null
+++ b/ldm/modules/midas/midas/vit.py
@@ -0,0 +1,491 @@
+import torch
+import torch.nn as nn
+import timm
+import types
+import math
+import torch.nn.functional as F
+
+
+class Slice(nn.Module):
+ def __init__(self, start_index=1):
+ super(Slice, self).__init__()
+ self.start_index = start_index
+
+ def forward(self, x):
+ return x[:, self.start_index :]
+
+
+class AddReadout(nn.Module):
+ def __init__(self, start_index=1):
+ super(AddReadout, self).__init__()
+ self.start_index = start_index
+
+ def forward(self, x):
+ if self.start_index == 2:
+ readout = (x[:, 0] + x[:, 1]) / 2
+ else:
+ readout = x[:, 0]
+ return x[:, self.start_index :] + readout.unsqueeze(1)
+
+
+class ProjectReadout(nn.Module):
+ def __init__(self, in_features, start_index=1):
+ super(ProjectReadout, self).__init__()
+ self.start_index = start_index
+
+ self.project = nn.Sequential(nn.Linear(2 * in_features, in_features), nn.GELU())
+
+ def forward(self, x):
+ readout = x[:, 0].unsqueeze(1).expand_as(x[:, self.start_index :])
+ features = torch.cat((x[:, self.start_index :], readout), -1)
+
+ return self.project(features)
+
+
+class Transpose(nn.Module):
+ def __init__(self, dim0, dim1):
+ super(Transpose, self).__init__()
+ self.dim0 = dim0
+ self.dim1 = dim1
+
+ def forward(self, x):
+ x = x.transpose(self.dim0, self.dim1)
+ return x
+
+
+def forward_vit(pretrained, x):
+ b, c, h, w = x.shape
+
+ glob = pretrained.model.forward_flex(x)
+
+ layer_1 = pretrained.activations["1"]
+ layer_2 = pretrained.activations["2"]
+ layer_3 = pretrained.activations["3"]
+ layer_4 = pretrained.activations["4"]
+
+ layer_1 = pretrained.act_postprocess1[0:2](layer_1)
+ layer_2 = pretrained.act_postprocess2[0:2](layer_2)
+ layer_3 = pretrained.act_postprocess3[0:2](layer_3)
+ layer_4 = pretrained.act_postprocess4[0:2](layer_4)
+
+ unflatten = nn.Sequential(
+ nn.Unflatten(
+ 2,
+ torch.Size(
+ [
+ h // pretrained.model.patch_size[1],
+ w // pretrained.model.patch_size[0],
+ ]
+ ),
+ )
+ )
+
+ if layer_1.ndim == 3:
+ layer_1 = unflatten(layer_1)
+ if layer_2.ndim == 3:
+ layer_2 = unflatten(layer_2)
+ if layer_3.ndim == 3:
+ layer_3 = unflatten(layer_3)
+ if layer_4.ndim == 3:
+ layer_4 = unflatten(layer_4)
+
+ layer_1 = pretrained.act_postprocess1[3 : len(pretrained.act_postprocess1)](layer_1)
+ layer_2 = pretrained.act_postprocess2[3 : len(pretrained.act_postprocess2)](layer_2)
+ layer_3 = pretrained.act_postprocess3[3 : len(pretrained.act_postprocess3)](layer_3)
+ layer_4 = pretrained.act_postprocess4[3 : len(pretrained.act_postprocess4)](layer_4)
+
+ return layer_1, layer_2, layer_3, layer_4
+
+
+def _resize_pos_embed(self, posemb, gs_h, gs_w):
+ posemb_tok, posemb_grid = (
+ posemb[:, : self.start_index],
+ posemb[0, self.start_index :],
+ )
+
+ gs_old = int(math.sqrt(len(posemb_grid)))
+
+ posemb_grid = posemb_grid.reshape(1, gs_old, gs_old, -1).permute(0, 3, 1, 2)
+ posemb_grid = F.interpolate(posemb_grid, size=(gs_h, gs_w), mode="bilinear")
+ posemb_grid = posemb_grid.permute(0, 2, 3, 1).reshape(1, gs_h * gs_w, -1)
+
+ posemb = torch.cat([posemb_tok, posemb_grid], dim=1)
+
+ return posemb
+
+
+def forward_flex(self, x):
+ b, c, h, w = x.shape
+
+ pos_embed = self._resize_pos_embed(
+ self.pos_embed, h // self.patch_size[1], w // self.patch_size[0]
+ )
+
+ B = x.shape[0]
+
+ if hasattr(self.patch_embed, "backbone"):
+ x = self.patch_embed.backbone(x)
+ if isinstance(x, (list, tuple)):
+ x = x[-1] # last feature if backbone outputs list/tuple of features
+
+ x = self.patch_embed.proj(x).flatten(2).transpose(1, 2)
+
+ if getattr(self, "dist_token", None) is not None:
+ cls_tokens = self.cls_token.expand(
+ B, -1, -1
+ ) # stole cls_tokens impl from Phil Wang, thanks
+ dist_token = self.dist_token.expand(B, -1, -1)
+ x = torch.cat((cls_tokens, dist_token, x), dim=1)
+ else:
+ cls_tokens = self.cls_token.expand(
+ B, -1, -1
+ ) # stole cls_tokens impl from Phil Wang, thanks
+ x = torch.cat((cls_tokens, x), dim=1)
+
+ x = x + pos_embed
+ x = self.pos_drop(x)
+
+ for blk in self.blocks:
+ x = blk(x)
+
+ x = self.norm(x)
+
+ return x
+
+
+activations = {}
+
+
+def get_activation(name):
+ def hook(model, input, output):
+ activations[name] = output
+
+ return hook
+
+
+def get_readout_oper(vit_features, features, use_readout, start_index=1):
+ if use_readout == "ignore":
+ readout_oper = [Slice(start_index)] * len(features)
+ elif use_readout == "add":
+ readout_oper = [AddReadout(start_index)] * len(features)
+ elif use_readout == "project":
+ readout_oper = [
+ ProjectReadout(vit_features, start_index) for out_feat in features
+ ]
+ else:
+ assert (
+ False
+ ), "wrong operation for readout token, use_readout can be 'ignore', 'add', or 'project'"
+
+ return readout_oper
+
+
+def _make_vit_b16_backbone(
+ model,
+ features=[96, 192, 384, 768],
+ size=[384, 384],
+ hooks=[2, 5, 8, 11],
+ vit_features=768,
+ use_readout="ignore",
+ start_index=1,
+):
+ pretrained = nn.Module()
+
+ pretrained.model = model
+ pretrained.model.blocks[hooks[0]].register_forward_hook(get_activation("1"))
+ pretrained.model.blocks[hooks[1]].register_forward_hook(get_activation("2"))
+ pretrained.model.blocks[hooks[2]].register_forward_hook(get_activation("3"))
+ pretrained.model.blocks[hooks[3]].register_forward_hook(get_activation("4"))
+
+ pretrained.activations = activations
+
+ readout_oper = get_readout_oper(vit_features, features, use_readout, start_index)
+
+ # 32, 48, 136, 384
+ pretrained.act_postprocess1 = nn.Sequential(
+ readout_oper[0],
+ Transpose(1, 2),
+ nn.Unflatten(2, torch.Size([size[0] // 16, size[1] // 16])),
+ nn.Conv2d(
+ in_channels=vit_features,
+ out_channels=features[0],
+ kernel_size=1,
+ stride=1,
+ padding=0,
+ ),
+ nn.ConvTranspose2d(
+ in_channels=features[0],
+ out_channels=features[0],
+ kernel_size=4,
+ stride=4,
+ padding=0,
+ bias=True,
+ dilation=1,
+ groups=1,
+ ),
+ )
+
+ pretrained.act_postprocess2 = nn.Sequential(
+ readout_oper[1],
+ Transpose(1, 2),
+ nn.Unflatten(2, torch.Size([size[0] // 16, size[1] // 16])),
+ nn.Conv2d(
+ in_channels=vit_features,
+ out_channels=features[1],
+ kernel_size=1,
+ stride=1,
+ padding=0,
+ ),
+ nn.ConvTranspose2d(
+ in_channels=features[1],
+ out_channels=features[1],
+ kernel_size=2,
+ stride=2,
+ padding=0,
+ bias=True,
+ dilation=1,
+ groups=1,
+ ),
+ )
+
+ pretrained.act_postprocess3 = nn.Sequential(
+ readout_oper[2],
+ Transpose(1, 2),
+ nn.Unflatten(2, torch.Size([size[0] // 16, size[1] // 16])),
+ nn.Conv2d(
+ in_channels=vit_features,
+ out_channels=features[2],
+ kernel_size=1,
+ stride=1,
+ padding=0,
+ ),
+ )
+
+ pretrained.act_postprocess4 = nn.Sequential(
+ readout_oper[3],
+ Transpose(1, 2),
+ nn.Unflatten(2, torch.Size([size[0] // 16, size[1] // 16])),
+ nn.Conv2d(
+ in_channels=vit_features,
+ out_channels=features[3],
+ kernel_size=1,
+ stride=1,
+ padding=0,
+ ),
+ nn.Conv2d(
+ in_channels=features[3],
+ out_channels=features[3],
+ kernel_size=3,
+ stride=2,
+ padding=1,
+ ),
+ )
+
+ pretrained.model.start_index = start_index
+ pretrained.model.patch_size = [16, 16]
+
+ # We inject this function into the VisionTransformer instances so that
+ # we can use it with interpolated position embeddings without modifying the library source.
+ pretrained.model.forward_flex = types.MethodType(forward_flex, pretrained.model)
+ pretrained.model._resize_pos_embed = types.MethodType(
+ _resize_pos_embed, pretrained.model
+ )
+
+ return pretrained
+
+
+def _make_pretrained_vitl16_384(pretrained, use_readout="ignore", hooks=None):
+ model = timm.create_model("vit_large_patch16_384", pretrained=pretrained)
+
+ hooks = [5, 11, 17, 23] if hooks == None else hooks
+ return _make_vit_b16_backbone(
+ model,
+ features=[256, 512, 1024, 1024],
+ hooks=hooks,
+ vit_features=1024,
+ use_readout=use_readout,
+ )
+
+
+def _make_pretrained_vitb16_384(pretrained, use_readout="ignore", hooks=None):
+ model = timm.create_model("vit_base_patch16_384", pretrained=pretrained)
+
+ hooks = [2, 5, 8, 11] if hooks == None else hooks
+ return _make_vit_b16_backbone(
+ model, features=[96, 192, 384, 768], hooks=hooks, use_readout=use_readout
+ )
+
+
+def _make_pretrained_deitb16_384(pretrained, use_readout="ignore", hooks=None):
+ model = timm.create_model("vit_deit_base_patch16_384", pretrained=pretrained)
+
+ hooks = [2, 5, 8, 11] if hooks == None else hooks
+ return _make_vit_b16_backbone(
+ model, features=[96, 192, 384, 768], hooks=hooks, use_readout=use_readout
+ )
+
+
+def _make_pretrained_deitb16_distil_384(pretrained, use_readout="ignore", hooks=None):
+ model = timm.create_model(
+ "vit_deit_base_distilled_patch16_384", pretrained=pretrained
+ )
+
+ hooks = [2, 5, 8, 11] if hooks == None else hooks
+ return _make_vit_b16_backbone(
+ model,
+ features=[96, 192, 384, 768],
+ hooks=hooks,
+ use_readout=use_readout,
+ start_index=2,
+ )
+
+
+def _make_vit_b_rn50_backbone(
+ model,
+ features=[256, 512, 768, 768],
+ size=[384, 384],
+ hooks=[0, 1, 8, 11],
+ vit_features=768,
+ use_vit_only=False,
+ use_readout="ignore",
+ start_index=1,
+):
+ pretrained = nn.Module()
+
+ pretrained.model = model
+
+ if use_vit_only == True:
+ pretrained.model.blocks[hooks[0]].register_forward_hook(get_activation("1"))
+ pretrained.model.blocks[hooks[1]].register_forward_hook(get_activation("2"))
+ else:
+ pretrained.model.patch_embed.backbone.stages[0].register_forward_hook(
+ get_activation("1")
+ )
+ pretrained.model.patch_embed.backbone.stages[1].register_forward_hook(
+ get_activation("2")
+ )
+
+ pretrained.model.blocks[hooks[2]].register_forward_hook(get_activation("3"))
+ pretrained.model.blocks[hooks[3]].register_forward_hook(get_activation("4"))
+
+ pretrained.activations = activations
+
+ readout_oper = get_readout_oper(vit_features, features, use_readout, start_index)
+
+ if use_vit_only == True:
+ pretrained.act_postprocess1 = nn.Sequential(
+ readout_oper[0],
+ Transpose(1, 2),
+ nn.Unflatten(2, torch.Size([size[0] // 16, size[1] // 16])),
+ nn.Conv2d(
+ in_channels=vit_features,
+ out_channels=features[0],
+ kernel_size=1,
+ stride=1,
+ padding=0,
+ ),
+ nn.ConvTranspose2d(
+ in_channels=features[0],
+ out_channels=features[0],
+ kernel_size=4,
+ stride=4,
+ padding=0,
+ bias=True,
+ dilation=1,
+ groups=1,
+ ),
+ )
+
+ pretrained.act_postprocess2 = nn.Sequential(
+ readout_oper[1],
+ Transpose(1, 2),
+ nn.Unflatten(2, torch.Size([size[0] // 16, size[1] // 16])),
+ nn.Conv2d(
+ in_channels=vit_features,
+ out_channels=features[1],
+ kernel_size=1,
+ stride=1,
+ padding=0,
+ ),
+ nn.ConvTranspose2d(
+ in_channels=features[1],
+ out_channels=features[1],
+ kernel_size=2,
+ stride=2,
+ padding=0,
+ bias=True,
+ dilation=1,
+ groups=1,
+ ),
+ )
+ else:
+ pretrained.act_postprocess1 = nn.Sequential(
+ nn.Identity(), nn.Identity(), nn.Identity()
+ )
+ pretrained.act_postprocess2 = nn.Sequential(
+ nn.Identity(), nn.Identity(), nn.Identity()
+ )
+
+ pretrained.act_postprocess3 = nn.Sequential(
+ readout_oper[2],
+ Transpose(1, 2),
+ nn.Unflatten(2, torch.Size([size[0] // 16, size[1] // 16])),
+ nn.Conv2d(
+ in_channels=vit_features,
+ out_channels=features[2],
+ kernel_size=1,
+ stride=1,
+ padding=0,
+ ),
+ )
+
+ pretrained.act_postprocess4 = nn.Sequential(
+ readout_oper[3],
+ Transpose(1, 2),
+ nn.Unflatten(2, torch.Size([size[0] // 16, size[1] // 16])),
+ nn.Conv2d(
+ in_channels=vit_features,
+ out_channels=features[3],
+ kernel_size=1,
+ stride=1,
+ padding=0,
+ ),
+ nn.Conv2d(
+ in_channels=features[3],
+ out_channels=features[3],
+ kernel_size=3,
+ stride=2,
+ padding=1,
+ ),
+ )
+
+ pretrained.model.start_index = start_index
+ pretrained.model.patch_size = [16, 16]
+
+ # We inject this function into the VisionTransformer instances so that
+ # we can use it with interpolated position embeddings without modifying the library source.
+ pretrained.model.forward_flex = types.MethodType(forward_flex, pretrained.model)
+
+ # We inject this function into the VisionTransformer instances so that
+ # we can use it with interpolated position embeddings without modifying the library source.
+ pretrained.model._resize_pos_embed = types.MethodType(
+ _resize_pos_embed, pretrained.model
+ )
+
+ return pretrained
+
+
+def _make_pretrained_vitb_rn50_384(
+ pretrained, use_readout="ignore", hooks=None, use_vit_only=False
+):
+ model = timm.create_model("vit_base_resnet50_384", pretrained=pretrained)
+
+ hooks = [0, 1, 8, 11] if hooks == None else hooks
+ return _make_vit_b_rn50_backbone(
+ model,
+ features=[256, 512, 768, 768],
+ size=[384, 384],
+ hooks=hooks,
+ use_vit_only=use_vit_only,
+ use_readout=use_readout,
+ )
diff --git a/ldm/modules/midas/utils.py b/ldm/modules/midas/utils.py
new file mode 100644
index 0000000000000000000000000000000000000000..9a9d3b5b66370fa98da9e067ba53ead848ea9a59
--- /dev/null
+++ b/ldm/modules/midas/utils.py
@@ -0,0 +1,189 @@
+"""Utils for monoDepth."""
+import sys
+import re
+import numpy as np
+import cv2
+import torch
+
+
+def read_pfm(path):
+ """Read pfm file.
+
+ Args:
+ path (str): path to file
+
+ Returns:
+ tuple: (data, scale)
+ """
+ with open(path, "rb") as file:
+
+ color = None
+ width = None
+ height = None
+ scale = None
+ endian = None
+
+ header = file.readline().rstrip()
+ if header.decode("ascii") == "PF":
+ color = True
+ elif header.decode("ascii") == "Pf":
+ color = False
+ else:
+ raise Exception("Not a PFM file: " + path)
+
+ dim_match = re.match(r"^(\d+)\s(\d+)\s$", file.readline().decode("ascii"))
+ if dim_match:
+ width, height = list(map(int, dim_match.groups()))
+ else:
+ raise Exception("Malformed PFM header.")
+
+ scale = float(file.readline().decode("ascii").rstrip())
+ if scale < 0:
+ # little-endian
+ endian = "<"
+ scale = -scale
+ else:
+ # big-endian
+ endian = ">"
+
+ data = np.fromfile(file, endian + "f")
+ shape = (height, width, 3) if color else (height, width)
+
+ data = np.reshape(data, shape)
+ data = np.flipud(data)
+
+ return data, scale
+
+
+def write_pfm(path, image, scale=1):
+ """Write pfm file.
+
+ Args:
+ path (str): pathto file
+ image (array): data
+ scale (int, optional): Scale. Defaults to 1.
+ """
+
+ with open(path, "wb") as file:
+ color = None
+
+ if image.dtype.name != "float32":
+ raise Exception("Image dtype must be float32.")
+
+ image = np.flipud(image)
+
+ if len(image.shape) == 3 and image.shape[2] == 3: # color image
+ color = True
+ elif (
+ len(image.shape) == 2 or len(image.shape) == 3 and image.shape[2] == 1
+ ): # greyscale
+ color = False
+ else:
+ raise Exception("Image must have H x W x 3, H x W x 1 or H x W dimensions.")
+
+ file.write("PF\n" if color else "Pf\n".encode())
+ file.write("%d %d\n".encode() % (image.shape[1], image.shape[0]))
+
+ endian = image.dtype.byteorder
+
+ if endian == "<" or endian == "=" and sys.byteorder == "little":
+ scale = -scale
+
+ file.write("%f\n".encode() % scale)
+
+ image.tofile(file)
+
+
+def read_image(path):
+ """Read image and output RGB image (0-1).
+
+ Args:
+ path (str): path to file
+
+ Returns:
+ array: RGB image (0-1)
+ """
+ img = cv2.imread(path)
+
+ if img.ndim == 2:
+ img = cv2.cvtColor(img, cv2.COLOR_GRAY2BGR)
+
+ img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB) / 255.0
+
+ return img
+
+
+def resize_image(img):
+ """Resize image and make it fit for network.
+
+ Args:
+ img (array): image
+
+ Returns:
+ tensor: data ready for network
+ """
+ height_orig = img.shape[0]
+ width_orig = img.shape[1]
+
+ if width_orig > height_orig:
+ scale = width_orig / 384
+ else:
+ scale = height_orig / 384
+
+ height = (np.ceil(height_orig / scale / 32) * 32).astype(int)
+ width = (np.ceil(width_orig / scale / 32) * 32).astype(int)
+
+ img_resized = cv2.resize(img, (width, height), interpolation=cv2.INTER_AREA)
+
+ img_resized = (
+ torch.from_numpy(np.transpose(img_resized, (2, 0, 1))).contiguous().float()
+ )
+ img_resized = img_resized.unsqueeze(0)
+
+ return img_resized
+
+
+def resize_depth(depth, width, height):
+ """Resize depth map and bring to CPU (numpy).
+
+ Args:
+ depth (tensor): depth
+ width (int): image width
+ height (int): image height
+
+ Returns:
+ array: processed depth
+ """
+ depth = torch.squeeze(depth[0, :, :, :]).to("cpu")
+
+ depth_resized = cv2.resize(
+ depth.numpy(), (width, height), interpolation=cv2.INTER_CUBIC
+ )
+
+ return depth_resized
+
+def write_depth(path, depth, bits=1):
+ """Write depth map to pfm and png file.
+
+ Args:
+ path (str): filepath without extension
+ depth (array): depth
+ """
+ write_pfm(path + ".pfm", depth.astype(np.float32))
+
+ depth_min = depth.min()
+ depth_max = depth.max()
+
+ max_val = (2**(8*bits))-1
+
+ if depth_max - depth_min > np.finfo("float").eps:
+ out = max_val * (depth - depth_min) / (depth_max - depth_min)
+ else:
+ out = np.zeros(depth.shape, dtype=depth.type)
+
+ if bits == 1:
+ cv2.imwrite(path + ".png", out.astype("uint8"))
+ elif bits == 2:
+ cv2.imwrite(path + ".png", out.astype("uint16"))
+
+ return
diff --git a/ldm/util.py b/ldm/util.py
new file mode 100644
index 0000000000000000000000000000000000000000..45cb050ece6f401a22dde098ce3f1ff663c5eb6a
--- /dev/null
+++ b/ldm/util.py
@@ -0,0 +1,197 @@
+import importlib
+
+import torch
+from torch import optim
+import numpy as np
+
+from inspect import isfunction
+from PIL import Image, ImageDraw, ImageFont
+
+
+def log_txt_as_img(wh, xc, size=10):
+ # wh a tuple of (width, height)
+ # xc a list of captions to plot
+ b = len(xc)
+ txts = list()
+ for bi in range(b):
+ txt = Image.new("RGB", wh, color="white")
+ draw = ImageDraw.Draw(txt)
+ font = ImageFont.truetype('font/DejaVuSans.ttf', size=size)
+ nc = int(40 * (wh[0] / 256))
+ lines = "\n".join(xc[bi][start:start + nc] for start in range(0, len(xc[bi]), nc))
+
+ try:
+ draw.text((0, 0), lines, fill="black", font=font)
+ except UnicodeEncodeError:
+ print("Cant encode string for logging. Skipping.")
+
+ txt = np.array(txt).transpose(2, 0, 1) / 127.5 - 1.0
+ txts.append(txt)
+ txts = np.stack(txts)
+ txts = torch.tensor(txts)
+ return txts
+
+
+def ismap(x):
+ if not isinstance(x, torch.Tensor):
+ return False
+ return (len(x.shape) == 4) and (x.shape[1] > 3)
+
+
+def isimage(x):
+ if not isinstance(x,torch.Tensor):
+ return False
+ return (len(x.shape) == 4) and (x.shape[1] == 3 or x.shape[1] == 1)
+
+
+def exists(x):
+ return x is not None
+
+
+def default(val, d):
+ if exists(val):
+ return val
+ return d() if isfunction(d) else d
+
+
+def mean_flat(tensor):
+ """
+ https://github.com/openai/guided-diffusion/blob/27c20a8fab9cb472df5d6bdd6c8d11c8f430b924/guided_diffusion/nn.py#L86
+ Take the mean over all non-batch dimensions.
+ """
+ return tensor.mean(dim=list(range(1, len(tensor.shape))))
+
+
+def count_params(model, verbose=False):
+ total_params = sum(p.numel() for p in model.parameters())
+ if verbose:
+ print(f"{model.__class__.__name__} has {total_params*1.e-6:.2f} M params.")
+ return total_params
+
+
+def instantiate_from_config(config):
+ if not "target" in config:
+ if config == '__is_first_stage__':
+ return None
+ elif config == "__is_unconditional__":
+ return None
+ raise KeyError("Expected key `target` to instantiate.")
+ return get_obj_from_str(config["target"])(**config.get("params", dict()))
+
+
+def get_obj_from_str(string, reload=False):
+ module, cls = string.rsplit(".", 1)
+ if reload:
+ module_imp = importlib.import_module(module)
+ importlib.reload(module_imp)
+ return getattr(importlib.import_module(module, package=None), cls)
+
+
+class AdamWwithEMAandWings(optim.Optimizer):
+ # credit to https://gist.github.com/crowsonkb/65f7265353f403714fce3b2595e0b298
+ def __init__(self, params, lr=1.e-3, betas=(0.9, 0.999), eps=1.e-8, # TODO: check hyperparameters before using
+ weight_decay=1.e-2, amsgrad=False, ema_decay=0.9999, # ema decay to match previous code
+ ema_power=1., param_names=()):
+ """AdamW that saves EMA versions of the parameters."""
+ if not 0.0 <= lr:
+ raise ValueError("Invalid learning rate: {}".format(lr))
+ if not 0.0 <= eps:
+ raise ValueError("Invalid epsilon value: {}".format(eps))
+ if not 0.0 <= betas[0] < 1.0:
+ raise ValueError("Invalid beta parameter at index 0: {}".format(betas[0]))
+ if not 0.0 <= betas[1] < 1.0:
+ raise ValueError("Invalid beta parameter at index 1: {}".format(betas[1]))
+ if not 0.0 <= weight_decay:
+ raise ValueError("Invalid weight_decay value: {}".format(weight_decay))
+ if not 0.0 <= ema_decay <= 1.0:
+ raise ValueError("Invalid ema_decay value: {}".format(ema_decay))
+ defaults = dict(lr=lr, betas=betas, eps=eps,
+ weight_decay=weight_decay, amsgrad=amsgrad, ema_decay=ema_decay,
+ ema_power=ema_power, param_names=param_names)
+ super().__init__(params, defaults)
+
+ def __setstate__(self, state):
+ super().__setstate__(state)
+ for group in self.param_groups:
+ group.setdefault('amsgrad', False)
+
+ @torch.no_grad()
+ def step(self, closure=None):
+ """Performs a single optimization step.
+ Args:
+ closure (callable, optional): A closure that reevaluates the model
+ and returns the loss.
+ """
+ loss = None
+ if closure is not None:
+ with torch.enable_grad():
+ loss = closure()
+
+ for group in self.param_groups:
+ params_with_grad = []
+ grads = []
+ exp_avgs = []
+ exp_avg_sqs = []
+ ema_params_with_grad = []
+ state_sums = []
+ max_exp_avg_sqs = []
+ state_steps = []
+ amsgrad = group['amsgrad']
+ beta1, beta2 = group['betas']
+ ema_decay = group['ema_decay']
+ ema_power = group['ema_power']
+
+ for p in group['params']:
+ if p.grad is None:
+ continue
+ params_with_grad.append(p)
+ if p.grad.is_sparse:
+ raise RuntimeError('AdamW does not support sparse gradients')
+ grads.append(p.grad)
+
+ state = self.state[p]
+
+ # State initialization
+ if len(state) == 0:
+ state['step'] = 0
+ # Exponential moving average of gradient values
+ state['exp_avg'] = torch.zeros_like(p, memory_format=torch.preserve_format)
+ # Exponential moving average of squared gradient values
+ state['exp_avg_sq'] = torch.zeros_like(p, memory_format=torch.preserve_format)
+ if amsgrad:
+ # Maintains max of all exp. moving avg. of sq. grad. values
+ state['max_exp_avg_sq'] = torch.zeros_like(p, memory_format=torch.preserve_format)
+ # Exponential moving average of parameter values
+ state['param_exp_avg'] = p.detach().float().clone()
+
+ exp_avgs.append(state['exp_avg'])
+ exp_avg_sqs.append(state['exp_avg_sq'])
+ ema_params_with_grad.append(state['param_exp_avg'])
+
+ if amsgrad:
+ max_exp_avg_sqs.append(state['max_exp_avg_sq'])
+
+ # update the steps for each param group update
+ state['step'] += 1
+ # record the step after step update
+ state_steps.append(state['step'])
+
+ optim._functional.adamw(params_with_grad,
+ grads,
+ exp_avgs,
+ exp_avg_sqs,
+ max_exp_avg_sqs,
+ state_steps,
+ amsgrad=amsgrad,
+ beta1=beta1,
+ beta2=beta2,
+ lr=group['lr'],
+ weight_decay=group['weight_decay'],
+ eps=group['eps'],
+ maximize=False)
+
+ cur_ema_decay = min(ema_decay, 1 - state['step'] ** -ema_power)
+ for param, ema_param in zip(params_with_grad, ema_params_with_grad):
+ ema_param.mul_(cur_ema_decay).add_(param.float(), alpha=1 - cur_ema_decay)
+
+ return loss
\ No newline at end of file
diff --git a/requirements.txt b/requirements.txt
new file mode 100644
index 0000000000000000000000000000000000000000..8ae0ecccee94d025644d1848f54f4ad758daaeea
--- /dev/null
+++ b/requirements.txt
@@ -0,0 +1,47 @@
+addict==2.4.0
+albumentations==1.3.0
+einops==0.3.0
+gradio==3.16.2
+imageio==2.9.0
+imageio-ffmpeg==0.4.2
+kornia==0.6.0
+omegaconf==2.3.0
+open-clip-torch==2.0.2
+opencv-contrib-python==4.3.0.36
+opencv-python-headless==4.7.0.72
+prettytable==3.6.0
+pytorch-lightning==1.5.0
+safetensors==0.2.7
+timm==0.6.12
+transformers==4.19.2
+# torch
+yapf==0.32.0
+--extra-index-url https://download.pytorch.org/whl
+torch==1.10.0+cu113
+torchvision==0.11.0+cu113
+torchaudio==0.10.0
+numpy==1.23
+-f https://dl.fbaipublicfiles.com/detectron2/wheels/cu113/torch1.10/index.html
+detectron2==0.6
+opencv-python
+git+https://github.com/cocodataset/panopticapi.git
+git+https://github.com/mcordts/cityscapesScripts.git
+imutils==0.5.4
+# natten==0.14.4
+cython
+setuptools==59.5.0
+scipy
+shapely
+h5py
+submitit
+scikit-image
+icecream
+wandb
+ftfy
+regex
+inflect
+diffdist
+gdown
+huggingface_hub
+tqdm
+wget
\ No newline at end of file
diff --git a/share.py b/share.py
new file mode 100644
index 0000000000000000000000000000000000000000..d7da9c5fc6e197dd3e2a92a37dfb330580b0e06c
--- /dev/null
+++ b/share.py
@@ -0,0 +1,3 @@
+from cldm.hack import disable_verbosity, enable_sliced_attention
+
+disable_verbosity()