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README.md

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+---
+tags:
+- monai
+- medical
+library_name: monai
+license: apache-2.0
+---
+# MedNIST Hand Image Registration
+
+Based on [the tutorial of 2D registration](https://github.com/Project-MONAI/tutorials/tree/main/2d_registration)
+
+## Downloading the Dataset
+Download the dataset [from here](https://github.com/Project-MONAI/MONAI-extra-test-data/releases/download/0.8.1/MedNIST.tar.gz) and extract the contents to a convenient location.
+
+The MedNIST dataset was gathered from several sets from [TCIA](https://wiki.cancerimagingarchive.net/display/Public/Data+Usage+Policies+and+Restrictions),
+[the RSNA Bone Age Challenge](http://rsnachallenges.cloudapp.net/competitions/4),
+and [the NIH Chest X-ray dataset](https://cloud.google.com/healthcare/docs/resources/public-datasets/nih-chest).
+
+The dataset is kindly made available by [Dr. Bradley J. Erickson M.D., Ph.D.](https://www.mayo.edu/research/labs/radiology-informatics/overview) (Department of Radiology, Mayo Clinic)
+under the Creative Commons [CC BY-SA 4.0 license](https://creativecommons.org/licenses/by-sa/4.0/).
+
+If you use the MedNIST dataset, please acknowledge the source.
+
+## Training
+
+Training with same-subject image inputs
+```bash
+python -m monai.bundle run training --config_file configs/train.yaml --dataset_dir "/workspace/data/MedNIST/Hand"
+```
+
+Training with cross-subject image inputs
+```bash
+python -m monai.bundle run training \
+  --config_file configs/train.yaml \
+  --dataset_dir "/workspace/data/MedNIST/Hand" \
+  --cross_subjects True
+```
+
+Training from an existing checkpoint file, for example, `models/model_key_metric=-0.0734.pt`:
+```bash
+python -m monai.bundle run training --config_file configs/train.yaml [...omitting other args] --ckpt "models/model_key_metric=-0.0734.pt"
+```
+
+## Inference
+
+The following figure shows an intra-subject (`--cross_subjects False`) model inference results (Fixed, moving and predicted images from left to right)
+
+![fixed](./examples/008502_fixed_6.png)
+![moving](./examples/008502_moving_6.png)
+![predicted](./examples/008502_pred_6.png)
+
+The command shows an inference workflow with the checkpoint `"models/model_key_metric=-0.0890.pt"` and using device `"cuda:1"`:
+```bash
+python -m monai.bundle run eval \
+  --config_file configs/inference.yaml \
+  --ckpt "models/model_key_metric=-0.0890.pt" \
+  --logging_file configs/logging.conf \
+  --device "cuda:1"
+```
+
+## Fine-tuning for cross-subject alignments
+
+The following commands starts a finetuning workflow based on the checkpoint `"models/model_key_metric=-0.0065.pt"`
+for `5` epochs using the global mutual information loss.
+
+```bash
+python -m monai.bundle run training \
+  --config_file configs/train.yaml \
+  --cross_subjects True \
+  --ckpt "models/model_key_metric=-0.0065.pt" \
+  --lr 0.000001 \
+  --trainer#loss_function "@mutual_info_loss" \
+  --max_epochs 5
+```
+The following figure shows an inter-subject (`--cross_subjects True`) model inference results (Fixed, moving and predicted images from left to right)
+
+![fixed](./examples/008501_fixed_7.png)
+![moving](./examples/008504_moving_7.png)
+![predicted](./examples/008504_pred_7.png)
+
+## Visualize the first pair of images for debugging (requires `matplotlib`)
+
+```bash
+python -m monai.bundle run display --config_file configs/train.yaml
+```
+
+```bash
+python -m monai.bundle run display --config_file configs/train.yaml --cross_subjects True
+```
+
+# License
+Copyright (c) MONAI Consortium
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.

configs/inference.yaml

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+---
+imports:
+  - $import glob
+  - $import matplotlib.pyplot as plt
+dataset_dir: "../MedNIST/Hand"
+# inference with 10 images, modify the indices to run it with different image inputs
+datalist: "$list(sorted(glob.glob(@dataset_dir + '/*.jpeg')))[8500:8510]"
+
+bundle_root: "./"
+device: "$torch.device('cuda:0' if torch.cuda.is_available() else 'cpu')"
+output_dir: "$@bundle_root + '/eval'"
+ckpt: "$@bundle_root + '/models/model.pt'"
+cross_subjects: false  # whether the input images are from the same subject
+
+image_load:
+  - _target_: LoadImage
+    image_only: True
+    ensure_channel_first: True
+
+  - _target_: ScaleIntensityRange
+    a_min: 0.0
+    a_max: 255.0
+    b_min: 0.0
+    b_max: 1.0
+
+  - _target_: EnsureType
+    device: "@device"
+
+image_aug:
+  - _target_: RandAffine
+    spatial_size: [64, 64]
+    translate_range: 5
+    scale_range: [-0.15, 0.15]
+    prob: 1.0
+    rotate_range: $np.pi / 8
+    mode: bilinear
+    padding_mode: border
+    cache_grid: True
+    device: "@device"
+
+preprocessing:
+  _target_: Compose
+  transforms: "$@image_load + @image_aug"
+
+datasets:
+  - _target_: ShuffleBuffer
+    data:
+      _target_: Dataset
+      data: "@datalist"
+      transform: {_target_: Compose, transforms: "@image_load"}
+    seed: "$int(3) if @cross_subjects else int(2)"
+  - _target_: ShuffleBuffer
+    data:
+      _target_: Dataset
+      data: "@datalist"
+      transform: $@preprocessing.set_random_state(3)
+    seed: 2
+
+zip_dataset:
+  _target_: IterableDataset
+  data: "$map(lambda t: dict(image=monai.transforms.concatenate(t), m_img=t[0], label=t[1]), zip(*@datasets))"
+
+data_loader:
+  _target_: ThreadDataLoader
+  dataset: "@zip_dataset"
+  batch_size: 1
+  num_workers: 0
+
+
+# components for debugging
+first_pair: $monai.utils.misc.first(@data_loader)
+display:
+  - $monai.utils.set_determinism(seed=23)
+  - $print(@first_pair.keys())
+  - $plt.subplot(1,3,1)
+  - $plt.imshow(@first_pair['image'][0, 0], cmap="gray")
+  - $plt.subplot(1,3,2)
+  - $plt.imshow(@first_pair['image'][0, 1], cmap="gray")
+  - $plt.subplot(1,3,3)
+  - $plt.imshow(np.abs(@first_pair['image'][0, 0] - @first_pair['image'][0, 1]), cmap="gray")
+  - $plt.show()
+
+# network definition
+network_def:
+  _target_: scripts.net.RegResNet
+  image_size: [64, 64]
+  spatial_dims: 2
+  mode: "bilinear"
+  padding_mode: "border"
+
+# create the primary evaluator
+handlers:
+  - _target_: CheckpointLoader
+    load_path: "@ckpt"
+    load_dict: {model: "@network_def"}
+  - _target_: StatsHandler
+    iteration_log: false
+
+inferer: {_target_: SimpleInferer}
+
+evaluator:
+  _target_: SupervisedEvaluator
+  device: "@device"
+  val_data_loader: "@data_loader"
+  network: "@network_def"
+  epoch_length: $len(@datalist) // @data_loader#batch_size
+  inferer: "@inferer"
+  val_handlers: "@handlers"
+  postprocessing:
+    _target_: Compose
+    transforms:
+      - _target_: SaveImaged
+        keys: [m_img]
+        resample: False
+        output_dir: "@output_dir"
+        output_ext: "png"
+        output_postfix: "moving"
+        output_dtype: "$np.uint8"
+        scale: 255
+        separate_folder: False
+        writer: "PILWriter"
+        output_name_formatter: "$lambda x, s: dict(idx=s._data_index, subject=x['filename_or_obj'])"
+      - _target_: SaveImaged
+        keys: [label]
+        resample: False
+        output_dir: "@output_dir"
+        output_ext: "png"
+        output_postfix: "fixed"
+        output_dtype: "$np.uint8"
+        scale: 255
+        separate_folder: False
+        writer: "PILWriter"
+        output_name_formatter: "$lambda x, s: dict(idx=s._data_index, subject=x['filename_or_obj'])"
+      - _target_: SaveImaged
+        keys: [pred]
+        resample: False
+        output_dir: "@output_dir"
+        output_ext: "png"
+        output_postfix: "pred"
+        output_dtype: "$np.uint8"
+        scale: 255
+        separate_folder: False
+        writer: "PILWriter"
+        output_name_formatter: "$lambda x, s: dict(idx=s._data_index, subject=x['filename_or_obj'])"
+
+eval:
+  - $monai.utils.set_determinism(seed=123)
+  - "$setattr(torch.backends.cudnn, 'benchmark', True)"
+  - $@evaluator.run()

configs/logging.conf

@@ -0,0 +1,21 @@
+[loggers]
+keys=root
+
+[handlers]
+keys=consoleHandler
+
+[formatters]
+keys=fullFormatter
+
+[logger_root]
+level=INFO
+handlers=consoleHandler
+
+[handler_consoleHandler]
+class=StreamHandler
+level=INFO
+formatter=fullFormatter
+args=(sys.stdout,)
+
+[formatter_fullFormatter]
+format=%(asctime)s - %(name)s - %(levelname)s - %(message)s

configs/metadata.json

@@ -0,0 +1,62 @@
+{
+    "schema": "https://github.com/Project-MONAI/MONAI-extra-test-data/releases/download/0.8.1/meta_schema_20220324.json",
+    "version": "0.0.1",
+    "changelog": {
+        "0.0.1": "Initial version"
+    },
+    "monai_version": "1.0.1",
+    "pytorch_version": "1.13.0",
+    "numpy_version": "1.22.2",
+    "optional_packages_version": {
+        "pytorch-ignite": "0.4.8"
+    },
+    "task": "Spatial transformer for hand image registration from the MedNIST dataset",
+    "description": "This is an example of a ResNet and spatial transformer for hand xray image registration",
+    "authors": "MONAI team",
+    "copyright": "Copyright (c) MONAI Consortium",
+    "intended_use": "This is an example of image registration using MONAI, suitable for demonstration purposes only.",
+    "data_type": "jpeg",
+    "network_data_format": {
+        "inputs": {
+            "image": {
+                "type": "image",
+                "format": "magnitude",
+                "num_channels": 2,
+                "spatial_shape": [
+                    64,
+                    64
+                ],
+                "dtype": "float32",
+                "value_range": [
+                    0,
+                    1
+                ],
+                "is_patch_data": false,
+                "channel_def": {
+                    "0": "moving image",
+                    "1": "fixed image"
+                }
+            }
+        },
+        "outputs": {
+            "pred": {
+                "type": "image",
+                "format": "magnitude",
+                "num_channels": 1,
+                "spatial_shape": [
+                    64,
+                    64
+                ],
+                "dtype": "float32",
+                "value_range": [
+                    0,
+                    1
+                ],
+                "is_patch_data": false,
+                "channel_def": {
+                    "0": "image"
+                }
+            }
+        }
+    }
+}

configs/train.yaml

@@ -0,0 +1,223 @@
+---
+imports:
+  - $import glob
+  - $import matplotlib.pyplot as plt
+
+# workflow parameters
+bundle_root: "./"
+device: "$torch.device('cuda:0' if torch.cuda.is_available() else 'cpu')"
+ckpt_dir: "$@bundle_root + '/models'"  # folder to save new checkpoints
+ckpt: ""  # path to load an existing checkpoint
+val_interval: 1  # every epoch
+max_epochs: 300
+cross_subjects: false  # whether the input images are from the same subject
+
+# construct the moving and fixed datasets
+dataset_dir: "../MedNIST/Hand"
+datalist: "$list(sorted(glob.glob(@dataset_dir + '/*.jpeg')))[:7000]"  # training with 7000 images
+val_datalist: "$list(sorted(glob.glob(@dataset_dir + '/*.jpeg')))[7000:8500]"  # validation with 1500 images
+
+image_load:
+  - _target_: LoadImage
+    image_only: True
+    ensure_channel_first: True
+
+  - _target_: ScaleIntensityRange
+    a_min: 0.0
+    a_max: 255.0
+    b_min: 0.0
+    b_max: 1.0
+
+  - _target_: EnsureType
+    device: "@device"
+
+image_aug:
+  - _target_: RandAffine
+    spatial_size: [64, 64]
+    translate_range: 5
+    scale_range: [-0.15, 0.15]
+    prob: 1.0
+    rotate_range: $np.pi / 8
+    mode: bilinear
+    padding_mode: border
+    cache_grid: True
+    device: "@device"
+
+  - _target_: RandGridDistortion
+    prob: 0.2
+    num_cells: 8
+    device: "@device"
+    distort_limit: 0.1
+
+preprocessing:
+  _target_: Compose
+  transforms: "$@image_load + @image_aug"
+
+cache_datasets:
+  - _target_: ShuffleBuffer
+    data:
+      _target_: CacheDataset
+      data: "@datalist"
+      transform: $@preprocessing.set_random_state(123)
+      hash_as_key: true
+      runtime_cache: true
+    epochs: "@max_epochs"
+    seed: "$int(3) if @cross_subjects else int(2)"
+  - _target_: ShuffleBuffer
+    data:
+      _target_: CacheDataset
+      data: "@datalist"
+      transform: $@preprocessing.set_random_state(234)
+      hash_as_key: true
+      runtime_cache: true
+    epochs: "@max_epochs"
+    seed: 2
+
+zip_dataset:
+  _target_: IterableDataset
+  data: "$map(lambda t: dict(image=monai.transforms.concatenate(t), label=t[1]), zip(*@cache_datasets))"
+
+data_loader:
+  _requires_:
+    - $@cache_datasets[0].data.disable_share_memory_cache()  # to cache on GPU
+    - $@cache_datasets[1].data.disable_share_memory_cache()
+  _target_: ThreadDataLoader
+  dataset: "@zip_dataset"
+  batch_size: 64
+  num_workers: 0
+
+
+# components for debugging
+first_pair: $monai.utils.misc.first(@data_loader)
+display:
+  - $monai.utils.set_determinism(seed=123)
+  - $print(@first_pair.keys(), @first_pair['image'].meta['filename_or_obj'])
+  - "$print(@trainer#loss_function(@first_pair['image'][:, 0:1], @first_pair['image'][:, 1:2]))"  # print loss
+  - $plt.subplot(1,2,1)
+  - $plt.imshow(@first_pair['image'][0, 0], cmap="gray")
+  - $plt.subplot(1,2,2)
+  - $plt.imshow(@first_pair['image'][0, 1], cmap="gray")
+  - $plt.show()
+
+
+# network definition
+net:
+  _target_: scripts.net.RegResNet
+  image_size: [64, 64]
+  spatial_dims: 2
+  mode: "bilinear"
+  padding_mode: "border"
+
+optimizer:
+  _target_: torch.optim.Adam
+  params: $@net.parameters()
+  lr: 0.00001
+
+# create a validation evaluator
+val:
+  cache_datasets:
+    - _target_: ShuffleBuffer
+      data:
+        _target_: CacheDataset
+        data: "@val_datalist"
+        transform: $@preprocessing.set_random_state(123)
+        hash_as_key: true
+        runtime_cache: true
+      epochs: -1  # infinite
+      seed: "$int(3) if @cross_subjects else int(2)"
+    - _target_: ShuffleBuffer
+      data:
+        _target_: CacheDataset
+        data: "@val_datalist"
+        transform: $@preprocessing.set_random_state(234)
+        hash_as_key: true
+        runtime_cache: true
+      epochs: -1  # infinite
+      seed: 2
+
+  zip_dataset:
+    _target_: IterableDataset
+    data: "$map(lambda t: dict(image=monai.transforms.concatenate(t), label=t[1]), zip(*@val#cache_datasets))"
+
+  data_loader:
+    _requires_:
+      - $@val#cache_datasets[0].data.disable_share_memory_cache()
+      - $@val#cache_datasets[1].data.disable_share_memory_cache()
+    _target_: ThreadDataLoader
+    dataset: "@val#zip_dataset"
+    batch_size: 64
+    num_workers: 0
+
+  evaluator:
+    _target_: SupervisedEvaluator
+    device: "@device"
+    val_data_loader: "@val#data_loader"
+    network: "@net"
+    epoch_length: $len(@val_datalist) // @val#data_loader#batch_size
+    inferer: "$monai.inferers.SimpleInferer()"
+    metric_cmp_fn: "$lambda x, y: x < y"
+    key_val_metric:
+      val_mse:
+        _target_: MeanSquaredError
+        output_transform: "$monai.handlers.from_engine(['pred', 'label'])"
+    additional_metrics: {"mutual info loss": "@loss_metric#metric_handler"}
+    val_handlers:
+      - _target_: StatsHandler
+        iteration_log: false
+      - _target_: CheckpointSaver
+        save_dir: "@ckpt_dir"
+        save_dict: {model: "@net"}
+        save_key_metric: true
+        key_metric_negative_sign: true
+        # key_metric_filename: "model.pt"
+
+# training handlers
+handlers:
+  - _target_: StatsHandler
+    tag_name: "train_loss"
+    output_transform: "$monai.handlers.from_engine(['loss'], first=True)"
+  - _target_: ValidationHandler
+    validator: "@val#evaluator"
+    epoch_level: true
+    interval: "@val_interval"
+
+loss_metric:
+  metric_handler:
+    _target_: IgniteMetric
+    output_transform: "$monai.handlers.from_engine(['pred', 'label'])"
+    metric_fn:
+      _target_: LossMetric
+      loss_fn: "@mutual_info_loss"
+      get_not_nans: true
+
+ckpt_loader:
+  - _target_: CheckpointLoader
+    load_path: "@ckpt"
+    load_dict: {model: "@net"}
+
+lncc_loss:
+  _target_: LocalNormalizedCrossCorrelationLoss
+  spatial_dims: 2
+  kernel_size: 5
+  kernel_type: rectangular
+  reduction: mean
+
+mutual_info_loss:
+  _target_: GlobalMutualInformationLoss
+
+# create the primary trainer
+trainer:
+  _target_: SupervisedTrainer
+  device: "@device"
+  train_data_loader: "@data_loader"
+  network: "@net"
+  max_epochs: "@max_epochs"
+  epoch_length: $len(@datalist) // @data_loader#batch_size
+  loss_function: "@lncc_loss"
+  optimizer: "@optimizer"
+  train_handlers: "$@handlers + @ckpt_loader if @ckpt else @handlers"
+
+training:
+  - $monai.utils.set_determinism(seed=23)
+  - "$setattr(torch.backends.cudnn, 'benchmark', True)"
+  - $@trainer.run()

docs/README.md

@@ -0,0 +1,97 @@
+# MedNIST Hand Image Registration
+
+Based on [the tutorial of 2D registration](https://github.com/Project-MONAI/tutorials/tree/main/2d_registration)
+
+## Downloading the Dataset
+Download the dataset [from here](https://github.com/Project-MONAI/MONAI-extra-test-data/releases/download/0.8.1/MedNIST.tar.gz) and extract the contents to a convenient location.
+
+The MedNIST dataset was gathered from several sets from [TCIA](https://wiki.cancerimagingarchive.net/display/Public/Data+Usage+Policies+and+Restrictions),
+[the RSNA Bone Age Challenge](http://rsnachallenges.cloudapp.net/competitions/4),
+and [the NIH Chest X-ray dataset](https://cloud.google.com/healthcare/docs/resources/public-datasets/nih-chest).
+
+The dataset is kindly made available by [Dr. Bradley J. Erickson M.D., Ph.D.](https://www.mayo.edu/research/labs/radiology-informatics/overview) (Department of Radiology, Mayo Clinic)
+under the Creative Commons [CC BY-SA 4.0 license](https://creativecommons.org/licenses/by-sa/4.0/).
+
+If you use the MedNIST dataset, please acknowledge the source.
+
+## Training
+
+Training with same-subject image inputs
+```bash
+python -m monai.bundle run training --config_file configs/train.yaml --dataset_dir "/workspace/data/MedNIST/Hand"
+```
+
+Training with cross-subject image inputs
+```bash
+python -m monai.bundle run training \
+  --config_file configs/train.yaml \
+  --dataset_dir "/workspace/data/MedNIST/Hand" \
+  --cross_subjects True
+```
+
+Training from an existing checkpoint file, for example, `models/model_key_metric=-0.0734.pt`:
+```bash
+python -m monai.bundle run training --config_file configs/train.yaml [...omitting other args] --ckpt "models/model_key_metric=-0.0734.pt"
+```
+
+## Inference
+
+The following figure shows an intra-subject (`--cross_subjects False`) model inference results (Fixed, moving and predicted images from left to right)
+
+![fixed](./examples/008502_fixed_6.png)
+![moving](./examples/008502_moving_6.png)
+![predicted](./examples/008502_pred_6.png)
+
+The command shows an inference workflow with the checkpoint `"models/model_key_metric=-0.0890.pt"` and using device `"cuda:1"`:
+```bash
+python -m monai.bundle run eval \
+  --config_file configs/inference.yaml \
+  --ckpt "models/model_key_metric=-0.0890.pt" \
+  --logging_file configs/logging.conf \
+  --device "cuda:1"
+```
+
+## Fine-tuning for cross-subject alignments
+
+The following commands starts a finetuning workflow based on the checkpoint `"models/model_key_metric=-0.0065.pt"`
+for `5` epochs using the global mutual information loss.
+
+```bash
+python -m monai.bundle run training \
+  --config_file configs/train.yaml \
+  --cross_subjects True \
+  --ckpt "models/model_key_metric=-0.0065.pt" \
+  --lr 0.000001 \
+  --trainer#loss_function "@mutual_info_loss" \
+  --max_epochs 5
+```
+The following figure shows an inter-subject (`--cross_subjects True`) model inference results (Fixed, moving and predicted images from left to right)
+
+![fixed](./examples/008501_fixed_7.png)
+![moving](./examples/008504_moving_7.png)
+![predicted](./examples/008504_pred_7.png)
+
+## Visualize the first pair of images for debugging (requires `matplotlib`)
+
+```bash
+python -m monai.bundle run display --config_file configs/train.yaml
+```
+
+```bash
+python -m monai.bundle run display --config_file configs/train.yaml --cross_subjects True
+```
+
+# License
+Copyright (c) MONAI Consortium
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.

docs/data_license.txt

@@ -0,0 +1,24 @@
+Copyright 2022 MONAI Consortium
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+    http://www.apache.org/licenses/LICENSE-2.0
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+
+Third Party Licenses
+-----------------------------------------------------------------------
+
+/*********************************************************************/
+i. MedNIST Dataset
+The dataset is kindly made available by Dr. Bradley J. Erickson M.D., Ph.D. (https://www.mayo.edu/research/labs/radiology-informatics/overview), Department of Radiology, Mayo Clinic under the Creative Commons [CC BY-SA 4.0 license](https://creativecommons.org/licenses/by-sa/4.0/).
+
+The MedNIST dataset was gathered from several sets from:
+ * TCIA (https://wiki.cancerimagingarchive.net/display/Public/Data+Usage+Policies+and+Restrictions)
+ * the RSNA Bone Age Challenge (http://rsnachallenges.cloudapp.net/competitions/4),
+ * the NIH Chest X-ray dataset (https://cloud.google.com/healthcare/docs/resources/public-datasets/nih-chest).
+
+If you use the MedNIST dataset, please acknowledge the source. For the license and usage conditions of the source datasets, please see their respective sites.

models/model.pt

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:1b997c0803bdfcde07394522809e09b1d7b41c38ccfc909f5d80ab5b7de8aed4
+size 45611604

scripts/__init__.py

@@ -0,0 +1,10 @@
+# Copyright (c) MONAI Consortium
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#     http://www.apache.org/licenses/LICENSE-2.0
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.

scripts/net.py

@@ -0,0 +1,42 @@
+# Copyright (c) MONAI Consortium
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#     http://www.apache.org/licenses/LICENSE-2.0
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import torch.nn as nn
+from monai.networks.blocks import Warp
+from monai.networks.nets import resnet18
+from monai.networks.nets.regunet import AffineHead
+
+
+class RegResNet(nn.Module):
+    def __init__(
+        self,
+        image_size=(64, 64),
+        spatial_dims=2,
+        mod=None,
+        mode="bilinear",
+        padding_mode="border",
+        features=400,  # feature dimension of `mod`
+    ):
+        super().__init__()
+        self.features = resnet18(n_input_channels=2, spatial_dims=spatial_dims) if mod is None else mod
+        self.affine_head = AffineHead(
+            spatial_dims=spatial_dims, image_size=image_size, decode_size=[1] * spatial_dims, in_channels=features
+        )
+        self.warp = Warp(mode=mode, padding_mode=padding_mode)
+        self.image_size = image_size
+
+    def forward(self, x):
+        self.features.to(device=x.device)
+        self.affine_head.to(device=x.device)
+        out = self.features(x)
+        ddf = self.affine_head([out], self.image_size)
+        f = self.warp(x[:, :1], ddf)  # warp the first channel
+        return f