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update tutorials/modules for metatensor (#771)
* update load image demo Signed-off-by: Wenqi Li <wenqil@nvidia.com> * update 2d_slices_from_3d_training.ipynb Signed-off-by: Wenqi Li <wenqil@nvidia.com> * image_dataset.ipynb Signed-off-by: Wenqi Li <wenqil@nvidia.com> * fixes fasttraining Signed-off-by: Wenqi Li <wenqil@nvidia.com> * update tta Signed-off-by: Wenqi Li <wenqil@nvidia.com> * nifti read Signed-off-by: Wenqi Li <wenqil@nvidia.com> * update tta Signed-off-by: Wenqi Li <wenqil@nvidia.com> * fixes typos Signed-off-by: Wenqi Li <wenqil@nvidia.com> * fixes interpretability/cats_and_dogs.ipynb Signed-off-by: Wenqi Li <wenqil@nvidia.com> * fixes pep8 Signed-off-by: Wenqi Li <wenqil@nvidia.com> * pep8 Signed-off-by: Wenqi Li <wenqil@nvidia.com> * update 2d_slices_from_3d_sampling.ipynb Signed-off-by: Wenqi Li <wenqil@nvidia.com> * modules/resample_benchmark.ipynb Signed-off-by: Wenqi Li <wenqil@nvidia.com> * modules/benchmark_global_mutual_information.ipynb Signed-off-by: Wenqi Li <wenqil@nvidia.com> * update modules/nifti_read_example.ipynb Signed-off-by: Wenqi Li <wenqil@nvidia.com> * modules/integrate_3rd_party_transforms.ipynb Signed-off-by: Wenqi Li <wenqil@nvidia.com> * fixes pep8 Signed-off-by: Wenqi Li <wenqil@nvidia.com> * update based on comments Signed-off-by: Wenqi Li <wenqil@nvidia.com>
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‎acceleration/fast_training_tutorial.ipynb‎

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@@ -614,7 +614,7 @@
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" with torch.no_grad():\n",
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" val_loader_iterator = iter(val_loader)\n",
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"\n",
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" for val_step in range(len(val_loader)):\n",
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" for _ in range(len(val_loader)):\n",
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" # profiling: val dataload\n",
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" with nvtx.annotate(\"dataload\", color=\"red\") if profiling else no_profiling:\n",
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" val_data = next(val_loader_iterator)\n",

‎modules/2d_slices_from_3d_sampling.ipynb‎

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@@ -519,7 +519,7 @@
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" device=torch.device(\"cpu\"),\n",
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" padding_mode=\"replicate\",\n",
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" )\n",
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" val_output = slice_inferer(val_images, model)\n",
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" val_output = slice_inferer(val_images, model).cpu()\n",
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" dice_metric(y_pred=val_output > 0.5, y=val_data[\"seg\"])\n",
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" print(\"Dice: \", dice_metric.get_buffer()[-1][0])\n",
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" fig = plt.figure(figsize=(10, 4))\n",

‎modules/2d_slices_from_3d_training.ipynb‎

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@@ -501,7 +501,7 @@
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" device=torch.device(\"cpu\"),\n",
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" padding_mode=\"replicate\",\n",
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" )\n",
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" val_output = slice_inferer(val_images, model)\n",
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" val_output = slice_inferer(val_images, model).to(\"cpu\")\n",
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" dice_metric(y_pred=val_output > 0.5, y=val_data[\"seg\"])\n",
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" print(\"Dice: \", dice_metric.get_buffer()[-1][0])\n",
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" fig = plt.figure(figsize=(10, 4))\n",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.8.12"
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"version": "3.8.0"
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}
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},
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"nbformat": 4,

‎modules/3d_image_transforms.ipynb‎

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‎modules/benchmark_global_mutual_information.ipynb‎

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@@ -232,8 +232,8 @@
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" a2: numpy array of shape HWD\n",
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" \"\"\"\n",
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" antspyx_result = ants.image_mutual_information(\n",
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" ants.from_numpy(a1),\n",
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" ants.from_numpy(a2)\n",
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" ants.from_numpy(a1.detach().cpu().numpy()),\n",
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" ants.from_numpy(a2.detach().cpu().numpy())\n",
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" )\n",
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" monai_result = GlobalMutualInformationLoss(\n",
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" kernel_type=\"b-spline\",\n",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.8.12"
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"version": "3.8.0"
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}
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},
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"nbformat": 4,

‎modules/image_dataset.ipynb‎

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},
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{
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"cell_type": "code",
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"execution_count": 1,
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"execution_count": 2,
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"metadata": {
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"execution": {
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"iopub.execute_input": "2021-01-14T12:34:04.909819Z",
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"MONAI version: 0.6.0rc1+23.gc6793fd0\n",
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"Numpy version: 1.20.3\n",
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"Pytorch version: 1.9.0a0+c3d40fd\n",
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"MONAI flags: HAS_EXT = True, USE_COMPILED = False\n",
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"MONAI rev id: c6793fd0f316a448778d0047664aaf8c1895fe1c\n",
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"MONAI version: 0.9.1rc1+6.gcbe16eb3\n",
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"Numpy version: 1.23.0\n",
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"Pytorch version: 1.12.0+cu102\n",
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"MONAI flags: HAS_EXT = False, USE_COMPILED = False, USE_META_DICT = False\n",
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"MONAI rev id: cbe16eb326830ef31479fd34ae1e44d27fc2d64d\n",
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"MONAI __file__: /home/wenqil/Documents/MONAI/monai/__init__.py\n",
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"\n",
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"Optional dependencies:\n",
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"Pytorch Ignite version: 0.4.5\n",
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"Nibabel version: 3.2.1\n",
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"scikit-image version: 0.15.0\n",
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"Pillow version: 8.2.0\n",
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"Tensorboard version: 2.5.0\n",
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"gdown version: 3.13.0\n",
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"TorchVision version: 0.10.0a0\n",
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"ITK version: 5.1.2\n",
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"tqdm version: 4.53.0\n",
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"lmdb version: 1.2.1\n",
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"psutil version: 5.8.0\n",
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"pandas version: 1.1.4\n",
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"einops version: 0.3.0\n",
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"Pytorch Ignite version: 0.4.9\n",
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"Nibabel version: 4.0.1\n",
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"scikit-image version: 0.19.3\n",
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"Pillow version: 9.2.0\n",
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"Tensorboard version: 2.9.1\n",
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"gdown version: 4.5.1\n",
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"TorchVision version: 0.13.0+cu102\n",
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"tqdm version: 4.64.0\n",
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"lmdb version: 1.3.0\n",
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"psutil version: 5.9.1\n",
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"pandas version: 1.4.3\n",
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"einops version: 0.4.1\n",
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"transformers version: 4.20.1\n",
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"mlflow version: 1.27.0\n",
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"pynrrd version: 0.4.3\n",
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"\n",
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"For details about installing the optional dependencies, please visit:\n",
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" https://docs.monai.io/en/latest/installation.html#installing-the-recommended-dependencies\n",
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},
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{
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"cell_type": "code",
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"execution_count": 2,
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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},
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"cell_type": "code",
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"execution_count": 4,
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"metadata": {
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"execution": {
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"iopub.execute_input": "2021-01-14T12:34:07.530773Z",
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"source": [
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"class TestCompose(Compose):\n",
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" def __call__(self, data, meta):\n",
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" data = self.transforms[0](data, meta) # ensure channel first\n",
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" data, _, meta[\"affine\"] = self.transforms[1](data, meta[\"affine\"]) # spacing\n",
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" data = self.transforms[0](data) # ensure channel first\n",
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" data = self.transforms[1](data) # spacing\n",
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" meta = data.meta\n",
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" if len(self.transforms) == 3:\n",
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" return self.transforms[2](data), meta # image contrast\n",
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" return data, meta\n",
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python 3",
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"display_name": "Python 3 (ipykernel)",
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"language": "python",
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"name": "python3"
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.8.10"
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"version": "3.8.0"
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"nbformat": 4,

‎modules/integrate_3rd_party_transforms.ipynb‎

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" ScaleIntensityRanged(keys=[\"image\"], a_min=-57,\n",
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" a_max=164, b_min=0.0, b_max=1.0, clip=True),\n",
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" CropForegroundd(keys=[\"image\", \"label\"], source_key=\"image\"),\n",
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" EnsureTyped(keys=[\"image\", \"label\"], data_type=\"numpy\"),\n",
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"]"
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"metadata": {
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"display_name": "Python 3 (ipykernel)",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.7.10"
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"version": "3.8.0"
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"nbformat": 4,

‎modules/interpretability/cats_and_dogs.ipynb‎

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" axes = np.asarray(axes) if nims == 1 else axes\n",
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" for d, ax in zip(data, axes.ravel()):\n",
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" # channel last for matplotlib\n",
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" im = np.moveaxis(d[\"image\"], 0, -1)\n",
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" im = np.moveaxis(d[\"image\"].detach().cpu().numpy(), 0, -1)\n",
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" ax.imshow(im, cmap='gray')\n",
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" ax.set_title(Animals(d['label']).name, fontsize=25)\n",
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" ax.axis(\"off\")\n",
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"version": "3.8.0"
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‎modules/inverse_transforms_and_test_time_augmentations.ipynb‎

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‎modules/nifti_read_example.ipynb‎

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"MONAI version: 0.8.1\n",
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"Numpy version: 1.21.5\n",
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"Pytorch version: 1.10.2\n",
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"MONAI flags: HAS_EXT = False, USE_COMPILED = False\n",
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"MONAI rev id: 71ff399a3ea07aef667b23653620a290364095b1\n",
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"MONAI version: 0.9.1rc2+2.gc0268266.dirty\n",
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"Numpy version: 1.23.0\n",
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"Pytorch version: 1.12.0+cu102\n",
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"MONAI flags: HAS_EXT = False, USE_COMPILED = False, USE_META_DICT = False\n",
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"MONAI rev id: c0268266f688c3bacd9c92ef7458f54b6afdf609\n",
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"MONAI __file__: /home/wenqil/Documents/MONAI/monai/__init__.py\n",
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"\n",
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"Optional dependencies:\n",
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"Pytorch Ignite version: 0.4.8\n",
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"Nibabel version: 3.2.1\n",
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"scikit-image version: 0.19.1\n",
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"Pillow version: 9.0.0\n",
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"Tensorboard version: 2.8.0\n",
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"gdown version: 4.2.0\n",
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"TorchVision version: 0.11.3\n",
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"tqdm version: 4.62.3\n",
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"Pytorch Ignite version: 0.4.9\n",
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"Nibabel version: 4.0.1\n",
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"scikit-image version: 0.19.3\n",
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"Pillow version: 9.2.0\n",
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"Tensorboard version: 2.9.1\n",
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"gdown version: 4.5.1\n",
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"TorchVision version: 0.13.0+cu102\n",
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"tqdm version: 4.64.0\n",
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"lmdb version: 1.3.0\n",
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"psutil version: 5.9.0\n",
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"pandas version: 1.1.5\n",
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"einops version: 0.4.0\n",
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"transformers version: 4.16.1\n",
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"mlflow version: 1.23.1\n",
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"psutil version: 5.9.1\n",
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"pandas version: 1.4.3\n",
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"einops version: 0.4.1\n",
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"transformers version: 4.20.1\n",
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"mlflow version: 1.27.0\n",
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"pynrrd version: 0.4.3\n",
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"\n",
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"For details about installing the optional dependencies, please visit:\n",
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" https://docs.monai.io/en/latest/installation.html#installing-the-recommended-dependencies\n",
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"from monai.data import (\n",
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" ArrayDataset, GridPatchDataset, create_test_image_3d, PatchIter)\n",
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"from monai.transforms import (\n",
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" AddChannel,\n",
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" Compose,\n",
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" LoadImage,\n",
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" RandSpatialCrop,\n",
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"execution_count": 3,
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"metadata": {
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"tags": []
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"outputs": [],
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"/tmp/tmpkzde8m5b\n"
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]
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}
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],
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"source": [
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"directory = os.environ.get(\"MONAI_DATA_DIRECTORY\")\n",
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"root_dir = tempfile.mkdtemp() if directory is None else directory\n",
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"text": [
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"torch.Size([5, 1, 64, 64, 64]) torch.Size([5, 1, 64, 64, 64])\n"
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"(5, 1, 64, 64, 64) (5, 1, 64, 64, 64)\n"
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"imtrans = Compose(\n",
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" LoadImage(image_only=True, ensure_channel_first=True),\n",
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" ScaleIntensity(),\n",
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" AddChannel(),\n",
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" RandSpatialCrop((64, 64, 64), random_size=False),\n",
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" EnsureType(),\n",
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" LoadImage(image_only=True, ensure_channel_first=True),\n",
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"imtrans = Compose([LoadImage(image_only=True, ensure_channel_first=True),\n",
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" ScaleIntensity(), EnsureType()])\n",
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"segtrans = Compose([LoadImage(image_only=True, ensure_channel_first=True), EnsureType()])\n",
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"patch_iter = PatchIter(patch_size=(64, 64, 64), start_pos=(0, 0, 0))\n",
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"version": "3.8.0"
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"nbformat": 4,

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