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Remove deprecated feature for v1.5 (#1989)
Part of Project-MONAI/MONAI#8421 ### Description Remove deprecated feature for v1.5 ### Checks <!--- Put an `x` in all the boxes that apply, and remove the not applicable items --> - [ ] Avoid including large-size files in the PR. - [ ] Clean up long text outputs from code cells in the notebook. - [ ] For security purposes, please check the contents and remove any sensitive info such as user names and private key. - [ ] Ensure (1) hyperlinks and markdown anchors are working (2) use relative paths for tutorial repo files (3) put figure and graphs in the `./figure` folder - [ ] Notebook runs automatically `./runner.sh -t <path to .ipynb file>` --------- Signed-off-by: YunLiu <55491388+KumoLiu@users.noreply.github.com> Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
1 parent fd86def commit 211cfd8

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-66
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‎3d_segmentation/spleen_segmentation_3d.ipynb‎

Lines changed: 4 additions & 4 deletions
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@@ -284,7 +284,7 @@
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" b_max=1.0,\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",
287+
" CropForegroundd(keys=[\"image\", \"label\"], source_key=\"image\", allow_smaller=True),\n",
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" Orientationd(keys=[\"image\", \"label\"], axcodes=\"RAS\"),\n",
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" Spacingd(keys=[\"image\", \"label\"], pixdim=(1.5, 1.5, 2.0), mode=(\"bilinear\", \"nearest\")),\n",
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" RandCropByPosNegLabeld(\n",
@@ -318,7 +318,7 @@
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" b_max=1.0,\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",
321+
" CropForegroundd(keys=[\"image\", \"label\"], source_key=\"image\", allow_smaller=True),\n",
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" Orientationd(keys=[\"image\", \"label\"], axcodes=\"RAS\"),\n",
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" Spacingd(keys=[\"image\", \"label\"], pixdim=(1.5, 1.5, 2.0), mode=(\"bilinear\", \"nearest\")),\n",
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" ]\n",
@@ -690,7 +690,7 @@
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" b_max=1.0,\n",
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" clip=True,\n",
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" ),\n",
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" CropForegroundd(keys=[\"image\"], source_key=\"image\"),\n",
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" CropForegroundd(keys=[\"image\"], source_key=\"image\", allow_smaller=True),\n",
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" ]\n",
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")\n",
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"\n",
@@ -784,7 +784,7 @@
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" b_max=1.0,\n",
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" clip=True,\n",
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" ),\n",
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" CropForegroundd(keys=[\"image\"], source_key=\"image\"),\n",
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" CropForegroundd(keys=[\"image\"], source_key=\"image\", allow_smaller=True),\n",
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" ]\n",
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")\n",
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"\n",

‎3d_segmentation/spleen_segmentation_3d_lightning.ipynb‎

Lines changed: 2 additions & 2 deletions
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@@ -278,7 +278,7 @@
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" b_max=1.0,\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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" CropForegroundd(keys=[\"image\", \"label\"], source_key=\"image\", allow_smaller=True),\n",
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" # randomly crop out patch samples from\n",
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" # big image based on pos / neg ratio\n",
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" # the image centers of negative samples\n",
@@ -321,7 +321,7 @@
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" b_max=1.0,\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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" CropForegroundd(keys=[\"image\", \"label\"], source_key=\"image\", allow_smaller=True),\n",
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" ]\n",
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" )\n",
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"\n",

‎3d_segmentation/spleen_segmentation_3d_visualization_basic.ipynb‎

Lines changed: 3 additions & 3 deletions
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@@ -317,7 +317,7 @@
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" b_max=1.0,\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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" CropForegroundd(keys=[\"image\", \"label\"], source_key=\"image\", allow_smaller=True),\n",
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" Orientationd(keys=[\"image\", \"label\"], axcodes=\"RAS\"),\n",
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" Spacingd(keys=[\"image\", \"label\"], pixdim=(1.5, 1.5, 2.0), mode=(\"bilinear\", \"nearest\")),\n",
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" RandCropByPosNegLabeld(\n",
@@ -351,7 +351,7 @@
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" b_max=1.0,\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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" CropForegroundd(keys=[\"image\", \"label\"], source_key=\"image\", allow_smaller=True),\n",
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" Orientationd(keys=[\"image\", \"label\"], axcodes=\"RAS\"),\n",
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" Spacingd(keys=[\"image\", \"label\"], pixdim=(1.5, 1.5, 2.0), mode=(\"bilinear\", \"nearest\")),\n",
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" ]\n",
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" b_max=1.0,\n",
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" clip=True,\n",
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" ),\n",
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" CropForegroundd(keys=[\"image\"], source_key=\"image\"),\n",
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" CropForegroundd(keys=[\"image\"], source_key=\"image\", allow_smaller=True),\n",
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" ]\n",
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")\n",
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"\n",

‎3d_segmentation/swin_unetr_brats21_segmentation_3d.ipynb‎

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@@ -326,6 +326,7 @@
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" keys=[\"image\", \"label\"],\n",
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" source_key=\"image\",\n",
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" k_divisible=[roi[0], roi[1], roi[2]],\n",
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" allow_smaller=True,\n",
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" ),\n",
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" transforms.RandSpatialCropd(\n",
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" keys=[\"image\", \"label\"],\n",
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"device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n",
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"\n",
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"model = SwinUNETR(\n",
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" img_size=roi,\n",
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" in_channels=4,\n",
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" out_channels=3,\n",
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" feature_size=48,\n",

‎3d_segmentation/swin_unetr_btcv_segmentation_3d.ipynb‎

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" b_max=1.0,\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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" CropForegroundd(keys=[\"image\", \"label\"], source_key=\"image\", allow_smaller=True),\n",
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" Orientationd(keys=[\"image\", \"label\"], axcodes=\"RAS\"),\n",
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" Spacingd(\n",
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" keys=[\"image\", \"label\"],\n",
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" [\n",
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" LoadImaged(keys=[\"image\", \"label\"], ensure_channel_first=True),\n",
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" ScaleIntensityRanged(keys=[\"image\"], a_min=-175, a_max=250, 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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" CropForegroundd(keys=[\"image\", \"label\"], source_key=\"image\", allow_smaller=True),\n",
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" Orientationd(keys=[\"image\", \"label\"], axcodes=\"RAS\"),\n",
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" Spacingd(\n",
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" keys=[\"image\", \"label\"],\n",
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"device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n",
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"\n",
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"model = SwinUNETR(\n",
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" img_size=(96, 96, 96),\n",
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" in_channels=1,\n",
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" out_channels=14,\n",
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" feature_size=48,\n",

‎3d_segmentation/unetr_btcv_segmentation_3d.ipynb‎

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" b_max=1.0,\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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" CropForegroundd(keys=[\"image\", \"label\"], source_key=\"image\", allow_smaller=True),\n",
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" RandCropByPosNegLabeld(\n",
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" keys=[\"image\", \"label\"],\n",
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" label_key=\"label\",\n",
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" mode=(\"bilinear\", \"nearest\"),\n",
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" ),\n",
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" ScaleIntensityRanged(keys=[\"image\"], a_min=-175, a_max=250, 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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" CropForegroundd(keys=[\"image\", \"label\"], source_key=\"image\", allow_smaller=True),\n",
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" ]\n",
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")"
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]

‎3d_segmentation/unetr_btcv_segmentation_3d_lightning.ipynb‎

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@@ -466,7 +466,7 @@
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" b_max=1.0,\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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" CropForegroundd(keys=[\"image\", \"label\"], source_key=\"image\", allow_smaller=True),\n",
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" Orientationd(keys=[\"image\", \"label\"], axcodes=\"RAS\"),\n",
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" Spacingd(\n",
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" keys=[\"image\", \"label\"],\n",
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" b_max=1.0,\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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" CropForegroundd(keys=[\"image\", \"label\"], source_key=\"image\", allow_smaller=True),\n",
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" Orientationd(keys=[\"image\", \"label\"], axcodes=\"RAS\"),\n",
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" Spacingd(\n",
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" keys=[\"image\", \"label\"],\n",

‎acceleration/automatic_mixed_precision.ipynb‎

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@@ -199,7 +199,7 @@
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" b_max=1.0,\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",
202+
" CropForegroundd(keys=[\"image\", \"label\"], source_key=\"image\", allow_smaller=True),\n",
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" # pre-compute foreground and background indexes\n",
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" # and cache them to accelerate training\n",
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" FgBgToIndicesd(\n",
@@ -241,7 +241,7 @@
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" b_max=1.0,\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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" CropForegroundd(keys=[\"image\", \"label\"], source_key=\"image\", allow_smaller=True),\n",
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" ]\n",
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" )\n",
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" return train_transforms, val_transforms"

‎acceleration/dataset_type_performance.ipynb‎

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@@ -380,7 +380,7 @@
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" b_max=1.0,\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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" CropForegroundd(keys=[\"image\", \"label\"], source_key=\"image\", allow_smaller=True),\n",
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" # randomly crop out patch samples from big\n",
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" # image based on pos / neg ratio\n",
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" # the image centers of negative samples\n",
@@ -420,7 +420,7 @@
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" b_max=1.0,\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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" CropForegroundd(keys=[\"image\", \"label\"], source_key=\"image\", allow_smaller=True),\n",
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" ]\n",
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" )\n",
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" return train_transforms, val_transforms"

‎acceleration/fast_training_tutorial.ipynb‎

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@@ -311,7 +311,7 @@
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" clip=True,\n",
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" ),\n",
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" ),\n",
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" range_func(\"CropForeground\", CropForegroundd(keys=[\"image\", \"label\"], source_key=\"image\")),\n",
314+
" range_func(\"CropForeground\", CropForegroundd(keys=[\"image\", \"label\"], source_key=\"image\", allow_smaller=True)),\n",
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" # pre-compute foreground and background indexes\n",
316316
" # and cache them to accelerate training\n",
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" range_func(\n",
@@ -368,7 +368,7 @@
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" b_max=1.0,\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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" CropForegroundd(keys=[\"image\", \"label\"], source_key=\"image\", allow_smaller=True),\n",
372372
" ]\n",
373373
" if fast:\n",
374374
" # convert the data to Tensor without meta, move to GPU and cache to avoid CPU -> GPU sync in every epoch\n",

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