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Yuchen Xu
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updated for 0.9.1rc2 compatibility
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-164
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5 files changed

+45
-164
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‎acceleration/automatic_mixed_precision.ipynb

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" RandCropByPosNegLabeld,\n",
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" ScaleIntensityRanged,\n",
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" Spacingd,\n",
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" EnsureTyped,\n",
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" EnsureType,\n",
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")\n",
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"from monai.utils import get_torch_version_tuple, set_determinism\n",
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"from torch.utils.data import DataLoader\n",

‎acceleration/dataset_type_performance.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": null,
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"metadata": {
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"tags": []
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},
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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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"MONAI version: 0.6.0rc1+15.gf3d436a0\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: f3d436a09deefcf905ece2faeec37f55ab030003\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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"\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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"\n"
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]
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}
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],
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"outputs": [],
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"source": [
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"# Copyright 2020 MONAI Consortium\n",
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"# Licensed under the Apache License, Version 2.0 (the \"License\");\n",
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" RandCropByPosNegLabeld,\n",
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" ScaleIntensityRanged,\n",
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" Spacingd,\n",
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" EnsureTyped,\n",
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" EnsureType,\n",
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")\n",
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"from monai.utils import set_determinism\n",
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"\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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"tags": []
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},
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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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"/workspace/data/medical\n"
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]
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}
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],
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"outputs": [],
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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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" loss_function = DiceLoss(to_onehot_y=True, softmax=True)\n",
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" optimizer = torch.optim.Adam(model.parameters(), 1e-4)\n",
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"\n",
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" post_pred = Compose([EnsureType(), AsDiscrete(argmax=True, to_onehot=2)])\n",
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" post_label = Compose([EnsureType(), AsDiscrete(to_onehot=2)])\n",
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" post_pred = Compose([AsDiscrete(argmax=True, to_onehot=2)])\n",
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" post_label = Compose([AsDiscrete(to_onehot=2)])\n",
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"\n",
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" dice_metric = DiceMetric(include_background=True, reduction=\"mean\", get_not_nans=False)\n",
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"\n",
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" val_outputs = sliding_window_inference(\n",
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" val_inputs, roi_size, sw_batch_size, model\n",
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" )\n",
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" val_outputs = [post_pred(i) for i in decollate_batch(val_outputs)]\n",
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" val_labels = [post_label(i) for i in decollate_batch(val_labels)]\n",
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" val_outputs = [post_pred(i) for i in decollate_batch(val_outputs.as_tensor())]\n",
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" val_labels = [post_label(i) for i in decollate_batch(val_labels.as_tensor())]\n",
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" dice_metric(y_pred=val_outputs, y=val_labels)\n",
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"\n",
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" metric = dice_metric.aggregate().item()\n",
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"\n",
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"Non-deterministic transforms:\n",
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"* RandCropByPosNegLabeld\n",
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"* EnsureTyped\n",
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"\n",
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"All the validation transforms are deterministic.\n",
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"The results of all the deterministic transforms will be cached to accelerate training."
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" image_key=\"image\",\n",
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" image_threshold=0,\n",
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" ),\n",
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" EnsureTyped(keys=[\"image\", \"label\"]),\n",
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" ]\n",
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" )\n",
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"\n",
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" clip=True,\n",
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" ),\n",
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" CropForegroundd(keys=[\"image\", \"label\"], source_key=\"image\"),\n",
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" EnsureTyped(keys=[\"image\", \"label\"]),\n",
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" ]\n",
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" )\n",
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" return train_transforms, val_transforms"
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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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},
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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": "3.8.13"
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}
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},
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"nbformat": 4,

‎acceleration/fast_training_tutorial.ipynb

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},
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{
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"cell_type": "code",
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"metadata": {
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"vscode": {
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"languageId": "python"
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" ScaleIntensityRanged,\n",
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" Spacingd,\n",
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" ToDeviced,\n",
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" EnsureTyped,\n",
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" EnsureType,\n",
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")\n",
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"from monai.utils import set_determinism\n",
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"\n",
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},
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"languageId": "python"
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},
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},
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},
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" bg_postfix=\"_bg\",\n",
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" image_key=\"image\",\n",
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" )),\n",
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" # change to execute transforms with Tensor data\n",
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" range_func(\"EnsureType\", EnsureTyped(keys=[\"image\", \"label\"])),\n",
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" ]\n",
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"\n",
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" if fast:\n",
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" clip=True,\n",
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" ),\n",
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" CropForegroundd(keys=[\"image\", \"label\"], source_key=\"image\"),\n",
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" EnsureTyped(keys=[\"image\", \"label\"]),\n",
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" ]\n",
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" if fast:\n",
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" # move the data to GPU and cache to avoid CPU -> GPU sync in every epoch\n",
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},
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" norm=Norm.BATCH,\n",
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" ).to(device)\n",
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" post_pred = Compose([EnsureType(), AsDiscrete(argmax=True, to_onehot=2)])\n",
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" post_label = Compose([EnsureType(), AsDiscrete(to_onehot=2)])\n",
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" post_pred = Compose([AsDiscrete(argmax=True, to_onehot=2)])\n",
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" post_label = Compose([AsDiscrete(to_onehot=2)])\n",
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" dice_metric = DiceMetric(include_background=False, reduction=\"mean\", get_not_nans=False)\n",
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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",
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"\n",
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" # profiling: decollate batch\n",
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" with nvtx.annotate(\"decollate batch\", color=\"blue\") if profiling else no_profiling:\n",
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" val_outputs = [post_pred(i) for i in decollate_batch(val_outputs)]\n",
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" val_labels = [post_label(i) for i in decollate_batch(val_labels)]\n",
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" val_outputs = [post_pred(i) for i in decollate_batch(val_outputs.as_tensor())]\n",
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" val_labels = [post_label(i) for i in decollate_batch(val_labels.as_tensor())]\n",
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"\n",
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" # profiling: compute metric\n",
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" with nvtx.annotate(\"compute metric\", color=\"yellow\") if profiling else no_profiling:\n",
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},
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"languageId": "python"

‎acceleration/threadbuffer_performance.ipynb

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"source": [
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"## Setup Environment\n",
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"\n",
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"The current MONAI main branch must be installed for this feature (as of release 0.3.0), skip this step if already installed:"
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"The current MONAI main branch must be installed for this feature (as of release 0.9.1), skip this step if already installed:"
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]
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{
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},
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"text": [
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"MONAI version: 0.4.0\n",
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"Numpy version: 1.19.1\n",
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"Pytorch version: 1.7.0a0+7036e91\n",
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"MONAI flags: HAS_EXT = False, USE_COMPILED = False\n",
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"MONAI rev id: 0563a4467fa602feca92d91c7f47261868d171a1\n",
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"\n",
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"Optional dependencies:\n",
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"Pytorch Ignite version: 0.4.2\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: 8.0.1\n",
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"Tensorboard version: 2.2.0\n",
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"gdown version: 3.12.2\n",
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"TorchVision version: 0.8.0a0\n",
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"ITK version: 5.1.2\n",
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"tqdm version: 4.54.1\n",
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"lmdb version: 1.0.0\n",
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"psutil version: 5.7.2\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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"\n"
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]
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}
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],
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"outputs": [],
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"source": [
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"import monai\n",
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"import numpy as np\n",
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"import torch\n",
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"from monai.data import DataLoader, Dataset, ThreadBuffer, create_test_image_2d\n",
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"from monai.losses import Dice\n",
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"from monai.networks.nets import UNet\n",
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"from monai.transforms import AddChanneld, Compose, MapTransform, EnsureTyped\n",
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"from monai.transforms import AddChanneld, Compose, MapTransform\n",
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"\n",
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"monai.utils.set_determinism(seed=0)\n",
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"source": [
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" [\n",
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" RandomGenerator(keys=(\"im\", \"seg\")),\n",
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" AddChanneld(keys=(\"im\", \"seg\")),\n",
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" EnsureTyped(keys=(\"im\", \"seg\")),\n",
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"metadata": {
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"scrolled": true
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"name": "python",
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"version": "3.8.13"
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}
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},
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"nbformat": 4,

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