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| # -*- coding = utf-8 -*- | |
| # Copyright (c) Facebook, Inc. and its affiliates. | |
| # pyre-ignore-all-errors | |
| from detectron2.config import CfgNode as CN | |
| def add_dataset_category_config(cfg: CN) -> None: | |
| """ | |
| Add config for additional category-related dataset options | |
| - category whitelisting | |
| - category mapping | |
| """ | |
| _C = cfg | |
| _C.DATASETS.CATEGORY_MAPS = CN(new_allowed=True) | |
| _C.DATASETS.WHITELISTED_CATEGORIES = CN(new_allowed=True) | |
| # class to mesh mapping | |
| _C.DATASETS.CLASS_TO_MESH_NAME_MAPPING = CN(new_allowed=True) | |
| def add_evaluation_config(cfg: CN) -> None: | |
| _C = cfg | |
| _C.DENSEPOSE_EVALUATION = CN() | |
| # evaluator type, possible values: | |
| # - "iou": evaluator for models that produce iou data | |
| # - "cse": evaluator for models that produce cse data | |
| _C.DENSEPOSE_EVALUATION.TYPE = "iou" | |
| # storage for DensePose results, possible values: | |
| # - "none": no explicit storage, all the results are stored in the | |
| # dictionary with predictions, memory intensive; | |
| # historically the default storage type | |
| # - "ram": RAM storage, uses per-process RAM storage, which is | |
| # reduced to a single process storage on later stages, | |
| # less memory intensive | |
| # - "file": file storage, uses per-process file-based storage, | |
| # the least memory intensive, but may create bottlenecks | |
| # on file system accesses | |
| _C.DENSEPOSE_EVALUATION.STORAGE = "none" | |
| # minimum threshold for IOU values: the lower its values is, | |
| # the more matches are produced (and the higher the AP score) | |
| _C.DENSEPOSE_EVALUATION.MIN_IOU_THRESHOLD = 0.5 | |
| # Non-distributed inference is slower (at inference time) but can avoid RAM OOM | |
| _C.DENSEPOSE_EVALUATION.DISTRIBUTED_INFERENCE = True | |
| # evaluate mesh alignment based on vertex embeddings, only makes sense in CSE context | |
| _C.DENSEPOSE_EVALUATION.EVALUATE_MESH_ALIGNMENT = False | |
| # meshes to compute mesh alignment for | |
| _C.DENSEPOSE_EVALUATION.MESH_ALIGNMENT_MESH_NAMES = [] | |
| def add_bootstrap_config(cfg: CN) -> None: | |
| """ """ | |
| _C = cfg | |
| _C.BOOTSTRAP_DATASETS = [] | |
| _C.BOOTSTRAP_MODEL = CN() | |
| _C.BOOTSTRAP_MODEL.WEIGHTS = "" | |
| _C.BOOTSTRAP_MODEL.DEVICE = "cuda" | |
| def get_bootstrap_dataset_config() -> CN: | |
| _C = CN() | |
| _C.DATASET = "" | |
| # ratio used to mix data loaders | |
| _C.RATIO = 0.1 | |
| # image loader | |
| _C.IMAGE_LOADER = CN(new_allowed=True) | |
| _C.IMAGE_LOADER.TYPE = "" | |
| _C.IMAGE_LOADER.BATCH_SIZE = 4 | |
| _C.IMAGE_LOADER.NUM_WORKERS = 4 | |
| _C.IMAGE_LOADER.CATEGORIES = [] | |
| _C.IMAGE_LOADER.MAX_COUNT_PER_CATEGORY = 1_000_000 | |
| _C.IMAGE_LOADER.CATEGORY_TO_CLASS_MAPPING = CN(new_allowed=True) | |
| # inference | |
| _C.INFERENCE = CN() | |
| # batch size for model inputs | |
| _C.INFERENCE.INPUT_BATCH_SIZE = 4 | |
| # batch size to group model outputs | |
| _C.INFERENCE.OUTPUT_BATCH_SIZE = 2 | |
| # sampled data | |
| _C.DATA_SAMPLER = CN(new_allowed=True) | |
| _C.DATA_SAMPLER.TYPE = "" | |
| _C.DATA_SAMPLER.USE_GROUND_TRUTH_CATEGORIES = False | |
| # filter | |
| _C.FILTER = CN(new_allowed=True) | |
| _C.FILTER.TYPE = "" | |
| return _C | |
| def load_bootstrap_config(cfg: CN) -> None: | |
| """ | |
| Bootstrap datasets are given as a list of `dict` that are not automatically | |
| converted into CfgNode. This method processes all bootstrap dataset entries | |
| and ensures that they are in CfgNode format and comply with the specification | |
| """ | |
| if not cfg.BOOTSTRAP_DATASETS: | |
| return | |
| bootstrap_datasets_cfgnodes = [] | |
| for dataset_cfg in cfg.BOOTSTRAP_DATASETS: | |
| _C = get_bootstrap_dataset_config().clone() | |
| _C.merge_from_other_cfg(CN(dataset_cfg)) | |
| bootstrap_datasets_cfgnodes.append(_C) | |
| cfg.BOOTSTRAP_DATASETS = bootstrap_datasets_cfgnodes | |
| def add_densepose_head_cse_config(cfg: CN) -> None: | |
| """ | |
| Add configuration options for Continuous Surface Embeddings (CSE) | |
| """ | |
| _C = cfg | |
| _C.MODEL.ROI_DENSEPOSE_HEAD.CSE = CN() | |
| # Dimensionality D of the embedding space | |
| _C.MODEL.ROI_DENSEPOSE_HEAD.CSE.EMBED_SIZE = 16 | |
| # Embedder specifications for various mesh IDs | |
| _C.MODEL.ROI_DENSEPOSE_HEAD.CSE.EMBEDDERS = CN(new_allowed=True) | |
| # normalization coefficient for embedding distances | |
| _C.MODEL.ROI_DENSEPOSE_HEAD.CSE.EMBEDDING_DIST_GAUSS_SIGMA = 0.01 | |
| # normalization coefficient for geodesic distances | |
| _C.MODEL.ROI_DENSEPOSE_HEAD.CSE.GEODESIC_DIST_GAUSS_SIGMA = 0.01 | |
| # embedding loss weight | |
| _C.MODEL.ROI_DENSEPOSE_HEAD.CSE.EMBED_LOSS_WEIGHT = 0.6 | |
| # embedding loss name, currently the following options are supported: | |
| # - EmbeddingLoss: cross-entropy on vertex labels | |
| # - SoftEmbeddingLoss: cross-entropy on vertex label combined with | |
| # Gaussian penalty on distance between vertices | |
| _C.MODEL.ROI_DENSEPOSE_HEAD.CSE.EMBED_LOSS_NAME = "EmbeddingLoss" | |
| # optimizer hyperparameters | |
| _C.MODEL.ROI_DENSEPOSE_HEAD.CSE.FEATURES_LR_FACTOR = 1.0 | |
| _C.MODEL.ROI_DENSEPOSE_HEAD.CSE.EMBEDDING_LR_FACTOR = 1.0 | |
| # Shape to shape cycle consistency loss parameters: | |
| _C.MODEL.ROI_DENSEPOSE_HEAD.CSE.SHAPE_TO_SHAPE_CYCLE_LOSS = CN({"ENABLED": False}) | |
| # shape to shape cycle consistency loss weight | |
| _C.MODEL.ROI_DENSEPOSE_HEAD.CSE.SHAPE_TO_SHAPE_CYCLE_LOSS.WEIGHT = 0.025 | |
| # norm type used for loss computation | |
| _C.MODEL.ROI_DENSEPOSE_HEAD.CSE.SHAPE_TO_SHAPE_CYCLE_LOSS.NORM_P = 2 | |
| # normalization term for embedding similarity matrices | |
| _C.MODEL.ROI_DENSEPOSE_HEAD.CSE.SHAPE_TO_SHAPE_CYCLE_LOSS.TEMPERATURE = 0.05 | |
| # maximum number of vertices to include into shape to shape cycle loss | |
| # if negative or zero, all vertices are considered | |
| # if positive, random subset of vertices of given size is considered | |
| _C.MODEL.ROI_DENSEPOSE_HEAD.CSE.SHAPE_TO_SHAPE_CYCLE_LOSS.MAX_NUM_VERTICES = 4936 | |
| # Pixel to shape cycle consistency loss parameters: | |
| _C.MODEL.ROI_DENSEPOSE_HEAD.CSE.PIX_TO_SHAPE_CYCLE_LOSS = CN({"ENABLED": False}) | |
| # pixel to shape cycle consistency loss weight | |
| _C.MODEL.ROI_DENSEPOSE_HEAD.CSE.PIX_TO_SHAPE_CYCLE_LOSS.WEIGHT = 0.0001 | |
| # norm type used for loss computation | |
| _C.MODEL.ROI_DENSEPOSE_HEAD.CSE.PIX_TO_SHAPE_CYCLE_LOSS.NORM_P = 2 | |
| # map images to all meshes and back (if false, use only gt meshes from the batch) | |
| _C.MODEL.ROI_DENSEPOSE_HEAD.CSE.PIX_TO_SHAPE_CYCLE_LOSS.USE_ALL_MESHES_NOT_GT_ONLY = False | |
| # Randomly select at most this number of pixels from every instance | |
| # if negative or zero, all vertices are considered | |
| _C.MODEL.ROI_DENSEPOSE_HEAD.CSE.PIX_TO_SHAPE_CYCLE_LOSS.NUM_PIXELS_TO_SAMPLE = 100 | |
| # normalization factor for pixel to pixel distances (higher value = smoother distribution) | |
| _C.MODEL.ROI_DENSEPOSE_HEAD.CSE.PIX_TO_SHAPE_CYCLE_LOSS.PIXEL_SIGMA = 5.0 | |
| _C.MODEL.ROI_DENSEPOSE_HEAD.CSE.PIX_TO_SHAPE_CYCLE_LOSS.TEMPERATURE_PIXEL_TO_VERTEX = 0.05 | |
| _C.MODEL.ROI_DENSEPOSE_HEAD.CSE.PIX_TO_SHAPE_CYCLE_LOSS.TEMPERATURE_VERTEX_TO_PIXEL = 0.05 | |
| def add_densepose_head_config(cfg: CN) -> None: | |
| """ | |
| Add config for densepose head. | |
| """ | |
| _C = cfg | |
| _C.MODEL.DENSEPOSE_ON = True | |
| _C.MODEL.ROI_DENSEPOSE_HEAD = CN() | |
| _C.MODEL.ROI_DENSEPOSE_HEAD.NAME = "" | |
| _C.MODEL.ROI_DENSEPOSE_HEAD.NUM_STACKED_CONVS = 8 | |
| # Number of parts used for point labels | |
| _C.MODEL.ROI_DENSEPOSE_HEAD.NUM_PATCHES = 24 | |
| _C.MODEL.ROI_DENSEPOSE_HEAD.DECONV_KERNEL = 4 | |
| _C.MODEL.ROI_DENSEPOSE_HEAD.CONV_HEAD_DIM = 512 | |
| _C.MODEL.ROI_DENSEPOSE_HEAD.CONV_HEAD_KERNEL = 3 | |
| _C.MODEL.ROI_DENSEPOSE_HEAD.UP_SCALE = 2 | |
| _C.MODEL.ROI_DENSEPOSE_HEAD.HEATMAP_SIZE = 112 | |
| _C.MODEL.ROI_DENSEPOSE_HEAD.POOLER_TYPE = "ROIAlignV2" | |
| _C.MODEL.ROI_DENSEPOSE_HEAD.POOLER_RESOLUTION = 28 | |
| _C.MODEL.ROI_DENSEPOSE_HEAD.POOLER_SAMPLING_RATIO = 2 | |
| _C.MODEL.ROI_DENSEPOSE_HEAD.NUM_COARSE_SEGM_CHANNELS = 2 # 15 or 2 | |
| # Overlap threshold for an RoI to be considered foreground (if >= FG_IOU_THRESHOLD) | |
| _C.MODEL.ROI_DENSEPOSE_HEAD.FG_IOU_THRESHOLD = 0.7 | |
| # Loss weights for annotation masks.(14 Parts) | |
| _C.MODEL.ROI_DENSEPOSE_HEAD.INDEX_WEIGHTS = 5.0 | |
| # Loss weights for surface parts. (24 Parts) | |
| _C.MODEL.ROI_DENSEPOSE_HEAD.PART_WEIGHTS = 1.0 | |
| # Loss weights for UV regression. | |
| _C.MODEL.ROI_DENSEPOSE_HEAD.POINT_REGRESSION_WEIGHTS = 0.01 | |
| # Coarse segmentation is trained using instance segmentation task data | |
| _C.MODEL.ROI_DENSEPOSE_HEAD.COARSE_SEGM_TRAINED_BY_MASKS = False | |
| # For Decoder | |
| _C.MODEL.ROI_DENSEPOSE_HEAD.DECODER_ON = True | |
| _C.MODEL.ROI_DENSEPOSE_HEAD.DECODER_NUM_CLASSES = 256 | |
| _C.MODEL.ROI_DENSEPOSE_HEAD.DECODER_CONV_DIMS = 256 | |
| _C.MODEL.ROI_DENSEPOSE_HEAD.DECODER_NORM = "" | |
| _C.MODEL.ROI_DENSEPOSE_HEAD.DECODER_COMMON_STRIDE = 4 | |
| # For DeepLab head | |
| _C.MODEL.ROI_DENSEPOSE_HEAD.DEEPLAB = CN() | |
| _C.MODEL.ROI_DENSEPOSE_HEAD.DEEPLAB.NORM = "GN" | |
| _C.MODEL.ROI_DENSEPOSE_HEAD.DEEPLAB.NONLOCAL_ON = 0 | |
| # Predictor class name, must be registered in DENSEPOSE_PREDICTOR_REGISTRY | |
| # Some registered predictors: | |
| # "DensePoseChartPredictor": predicts segmentation and UV coordinates for predefined charts | |
| # "DensePoseChartWithConfidencePredictor": predicts segmentation, UV coordinates | |
| # and associated confidences for predefined charts (default) | |
| # "DensePoseEmbeddingWithConfidencePredictor": predicts segmentation, embeddings | |
| # and associated confidences for CSE | |
| _C.MODEL.ROI_DENSEPOSE_HEAD.PREDICTOR_NAME = "DensePoseChartWithConfidencePredictor" | |
| # Loss class name, must be registered in DENSEPOSE_LOSS_REGISTRY | |
| # Some registered losses: | |
| # "DensePoseChartLoss": loss for chart-based models that estimate | |
| # segmentation and UV coordinates | |
| # "DensePoseChartWithConfidenceLoss": loss for chart-based models that estimate | |
| # segmentation, UV coordinates and the corresponding confidences (default) | |
| _C.MODEL.ROI_DENSEPOSE_HEAD.LOSS_NAME = "DensePoseChartWithConfidenceLoss" | |
| # Confidences | |
| # Enable learning UV confidences (variances) along with the actual values | |
| _C.MODEL.ROI_DENSEPOSE_HEAD.UV_CONFIDENCE = CN({"ENABLED": False}) | |
| # UV confidence lower bound | |
| _C.MODEL.ROI_DENSEPOSE_HEAD.UV_CONFIDENCE.EPSILON = 0.01 | |
| # Enable learning segmentation confidences (variances) along with the actual values | |
| _C.MODEL.ROI_DENSEPOSE_HEAD.SEGM_CONFIDENCE = CN({"ENABLED": False}) | |
| # Segmentation confidence lower bound | |
| _C.MODEL.ROI_DENSEPOSE_HEAD.SEGM_CONFIDENCE.EPSILON = 0.01 | |
| # Statistical model type for confidence learning, possible values: | |
| # - "iid_iso": statistically independent identically distributed residuals | |
| # with isotropic covariance | |
| # - "indep_aniso": statistically independent residuals with anisotropic | |
| # covariances | |
| _C.MODEL.ROI_DENSEPOSE_HEAD.UV_CONFIDENCE.TYPE = "iid_iso" | |
| # List of angles for rotation in data augmentation during training | |
| _C.INPUT.ROTATION_ANGLES = [0] | |
| _C.TEST.AUG.ROTATION_ANGLES = () # Rotation TTA | |
| add_densepose_head_cse_config(cfg) | |
| def add_hrnet_config(cfg: CN) -> None: | |
| """ | |
| Add config for HRNet backbone. | |
| """ | |
| _C = cfg | |
| # For HigherHRNet w32 | |
| _C.MODEL.HRNET = CN() | |
| _C.MODEL.HRNET.STEM_INPLANES = 64 | |
| _C.MODEL.HRNET.STAGE2 = CN() | |
| _C.MODEL.HRNET.STAGE2.NUM_MODULES = 1 | |
| _C.MODEL.HRNET.STAGE2.NUM_BRANCHES = 2 | |
| _C.MODEL.HRNET.STAGE2.BLOCK = "BASIC" | |
| _C.MODEL.HRNET.STAGE2.NUM_BLOCKS = [4, 4] | |
| _C.MODEL.HRNET.STAGE2.NUM_CHANNELS = [32, 64] | |
| _C.MODEL.HRNET.STAGE2.FUSE_METHOD = "SUM" | |
| _C.MODEL.HRNET.STAGE3 = CN() | |
| _C.MODEL.HRNET.STAGE3.NUM_MODULES = 4 | |
| _C.MODEL.HRNET.STAGE3.NUM_BRANCHES = 3 | |
| _C.MODEL.HRNET.STAGE3.BLOCK = "BASIC" | |
| _C.MODEL.HRNET.STAGE3.NUM_BLOCKS = [4, 4, 4] | |
| _C.MODEL.HRNET.STAGE3.NUM_CHANNELS = [32, 64, 128] | |
| _C.MODEL.HRNET.STAGE3.FUSE_METHOD = "SUM" | |
| _C.MODEL.HRNET.STAGE4 = CN() | |
| _C.MODEL.HRNET.STAGE4.NUM_MODULES = 3 | |
| _C.MODEL.HRNET.STAGE4.NUM_BRANCHES = 4 | |
| _C.MODEL.HRNET.STAGE4.BLOCK = "BASIC" | |
| _C.MODEL.HRNET.STAGE4.NUM_BLOCKS = [4, 4, 4, 4] | |
| _C.MODEL.HRNET.STAGE4.NUM_CHANNELS = [32, 64, 128, 256] | |
| _C.MODEL.HRNET.STAGE4.FUSE_METHOD = "SUM" | |
| _C.MODEL.HRNET.HRFPN = CN() | |
| _C.MODEL.HRNET.HRFPN.OUT_CHANNELS = 256 | |
| def add_densepose_config(cfg: CN) -> None: | |
| add_densepose_head_config(cfg) | |
| add_hrnet_config(cfg) | |
| add_bootstrap_config(cfg) | |
| add_dataset_category_config(cfg) | |
| add_evaluation_config(cfg) | |