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Commit 211cfd8

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KumoLiupre-commit-ci[bot]
KumoLiu
and
pre-commit-ci[bot]
authored
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>
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‎3d_segmentation/spleen_segmentation_3d.ipynb

Copy file name to clipboardExpand all lines: 3d_segmentation/spleen_segmentation_3d.ipynb
+4-4Lines 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",
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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",
@@ -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

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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

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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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" 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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@@ -228,7 +228,7 @@
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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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" 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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+2-2Lines changed: 2 additions & 2 deletions
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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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+2-2Lines changed: 2 additions & 2 deletions
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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",
244+
" CropForegroundd(keys=[\"image\", \"label\"], source_key=\"image\", allow_smaller=True),\n",
245245
" ]\n",
246246
" )\n",
247247
" return train_transforms, val_transforms"

‎acceleration/dataset_type_performance.ipynb

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+2-2Lines changed: 2 additions & 2 deletions
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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",
383+
" 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 @@
420420
" 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",
424424
" ]\n",
425425
" )\n",
426426
" return train_transforms, val_transforms"

‎acceleration/fast_training_tutorial.ipynb

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+2-2Lines changed: 2 additions & 2 deletions
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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",
314-
" 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",
315315
" # pre-compute foreground and background indexes\n",
316316
" # and cache them to accelerate training\n",
317317
" range_func(\n",
@@ -368,7 +368,7 @@
368368
" b_max=1.0,\n",
369369
" clip=True,\n",
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" ),\n",
371-
" CropForegroundd(keys=[\"image\", \"label\"], source_key=\"image\"),\n",
371+
" 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",

‎auto3dseg/docs/algorithm_generation.md

Copy file name to clipboardExpand all lines: auto3dseg/docs/algorithm_generation.md
+6-1Lines changed: 6 additions & 1 deletion
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@@ -143,7 +143,12 @@ class DintsAlgo(BundleAlgo):
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"b_max": 1.0,
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"clip": True,
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},
146-
{"_target_": "CropForegroundd", "keys": ["@image_key", "@label_key"], "source_key": "@image_key"},
146+
{
147+
"_target_": "CropForegroundd",
148+
"keys": ["@image_key", "@label_key"],
149+
"source_key": "@image_key",
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"allow_smaller:" True,
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},
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],
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}
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‎auto3dseg/tasks/hecktor22/hecktor_crop_neck_region.py

Copy file name to clipboardExpand all lines: auto3dseg/tasks/hecktor22/hecktor_crop_neck_region.py
+8-1Lines changed: 8 additions & 1 deletion
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@@ -31,9 +31,16 @@ def __init__(
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source_key="image",
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box_size=[200, 200, 310],
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allow_missing_keys=True,
34+
allow_smaller=True,
3435
**kwargs,
3536
) -> None:
36-
super().__init__(keys=keys, source_key=source_key, allow_missing_keys=allow_missing_keys, **kwargs)
37+
super().__init__(
38+
keys=keys,
39+
source_key=source_key,
40+
allow_missing_keys=allow_missing_keys,
41+
allow_smaller=allow_smaller,
42+
**kwargs,
43+
)
3744
self.box_size = box_size
3845

3946
def __call__(self, data, **kwargs):

‎bundle/python_bundle_workflow/scripts/inference.py

Copy file name to clipboardExpand all lines: bundle/python_bundle_workflow/scripts/inference.py
+1-1Lines changed: 1 addition & 1 deletion
Original file line numberDiff line numberDiff line change
@@ -48,7 +48,7 @@ class InferenceWorkflow(BundleWorkflow):
4848
"""
4949

5050
def __init__(self, dataset_dir: str = "./infer"):
51-
super().__init__(workflow="inference")
51+
super().__init__(workflow_type="inference")
5252
print_config()
5353
# set root log level to INFO and init a evaluation logger, will be used in `StatsHandler`
5454
logging.basicConfig(stream=sys.stdout, level=logging.INFO)

‎bundle/python_bundle_workflow/scripts/train.py

Copy file name to clipboardExpand all lines: bundle/python_bundle_workflow/scripts/train.py
+1-1Lines changed: 1 addition & 1 deletion
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@@ -67,7 +67,7 @@ class TrainWorkflow(BundleWorkflow):
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"""
6868

6969
def __init__(self, dataset_dir: str = "./train"):
70-
super().__init__(workflow="train")
70+
super().__init__(workflow_type="train")
7171
print_config()
7272
# set root log level to INFO and init a train logger, will be used in `StatsHandler`
7373
logging.basicConfig(stream=sys.stdout, level=logging.INFO)

‎bundle/pythonic_usage_guidance/pythonic_bundle_access.ipynb

Copy file name to clipboardExpand all lines: bundle/pythonic_usage_guidance/pythonic_bundle_access.ipynb
+1-5Lines changed: 1 addition & 5 deletions
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@@ -397,9 +397,7 @@
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"metadata": {},
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"outputs": [],
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"source": [
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"# Here we specify `return_state_dict=False` to return an instantiated model only for compatibility, will remove after MONAI v1.5.\n",
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"# directly get an instantiated network that loaded the weights.\n",
402-
"model = load(name=\"brats_mri_segmentation\", bundle_dir=root_dir, source=\"monaihosting\", return_state_dict=False)\n",
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"model = load(name=\"brats_mri_segmentation\", bundle_dir=root_dir, source=\"monaihosting\")\n",
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"\n",
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"# directly update the parameters for the model from the bundle.\n",
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"model = load(\n",
@@ -408,7 +406,6 @@
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" source=\"monaihosting\",\n",
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" in_channels=3,\n",
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" out_channels=1,\n",
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" return_state_dict=False,\n",
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")\n",
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"\n",
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"# using `exclude_vars` to filter loading weights.\n",
@@ -417,7 +414,6 @@
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" bundle_dir=root_dir,\n",
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" source=\"monaihosting\",\n",
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" copy_model_args={\"exclude_vars\": \"convInit|conv_final\"},\n",
420-
" return_state_dict=False,\n",
421417
")\n",
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"\n",
423419
"# pass model and return an instantiated network that loaded the weights.\n",

‎deepgrow/ignite/train.py

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@@ -92,7 +92,7 @@ def get_pre_transforms(roi_size, model_size, dimensions):
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t = [
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LoadImaged(keys=("image", "label")),
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EnsureChannelFirstd(keys=("image", "label"), channel_dim="no_channel"),
95-
SpatialCropForegroundd(keys=("image", "label"), source_key="label", spatial_size=roi_size),
95+
SpatialCropForegroundd(keys=("image", "label"), source_key="label", spatial_size=roi_size, allow_smaller=True),
9696
Resized(keys=("image", "label"), spatial_size=model_size, mode=("area", "nearest")),
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NormalizeIntensityd(keys="image", subtrahend=208.0, divisor=388.0),
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]

‎deployment/Triton/models/monai_covid/1/model.py

Copy file name to clipboardExpand all lines: deployment/Triton/models/monai_covid/1/model.py
+1-1Lines changed: 1 addition & 1 deletion
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@@ -112,7 +112,7 @@ def initialize(self, args):
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LoadImage(reader="NibabelReader", image_only=True, dtype=np.float32),
113113
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114114
ScaleIntensityRange(a_min=-1000, a_max=500, b_min=0.0, b_max=1.0, clip=True),
115-
CropForeground(margin=5),
115+
CropForeground(margin=5, allow_smaller=True),
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Resize([192, 192, 64], mode="area"),
117117
EnsureChannelFirst(channel_dim="no_channel"),
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ToTensor(),

‎experiment_management/spleen_segmentation_aim.ipynb

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+2-2Lines changed: 2 additions & 2 deletions
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@@ -251,7 +251,7 @@
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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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" RandCropByPosNegLabeld(\n",
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" keys=[\"image\", \"label\"],\n",
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" label_key=\"label\",\n",
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" b_max=1.0,\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",
289289
" ]\n",
290290
")"
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]

‎experiment_management/spleen_segmentation_mlflow.ipynb

Copy file name to clipboardExpand all lines: experiment_management/spleen_segmentation_mlflow.ipynb
+2-2Lines changed: 2 additions & 2 deletions
Original file line numberDiff line numberDiff line change
@@ -249,7 +249,7 @@
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" b_max=1.0,\n",
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" clip=True,\n",
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" ),\n",
252-
" CropForegroundd(keys=[\"image\", \"label\"], source_key=\"image\"),\n",
252+
" 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",
@@ -283,7 +283,7 @@
283283
" b_max=1.0,\n",
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" clip=True,\n",
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" ),\n",
286-
" CropForegroundd(keys=[\"image\", \"label\"], source_key=\"image\"),\n",
286+
" CropForegroundd(keys=[\"image\", \"label\"], source_key=\"image\", allow_smaller=True),\n",
287287
" ]\n",
288288
")"
289289
]

‎model_zoo/transfer_learning_with_bundle/evaluate.py

Copy file name to clipboardExpand all lines: model_zoo/transfer_learning_with_bundle/evaluate.py
-1Lines changed: 0 additions & 1 deletion
Original file line numberDiff line numberDiff line change
@@ -37,7 +37,6 @@
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AsDiscrete,
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AsDiscreted,
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Compose,
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CropForegroundd,
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EnsureChannelFirstd,
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Invertd,
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LoadImaged,

‎model_zoo/transfer_learning_with_bundle/train.py

Copy file name to clipboardExpand all lines: model_zoo/transfer_learning_with_bundle/train.py
-4Lines changed: 0 additions & 4 deletions
Original file line numberDiff line numberDiff line change
@@ -35,15 +35,11 @@
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from monai.transforms import (
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Activations,
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AsDiscrete,
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AsDiscreted,
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Compose,
40-
CropForegroundd,
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EnsureChannelFirstd,
42-
Invertd,
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LoadImaged,
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Orientationd,
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RandCropByPosNegLabeld,
46-
SaveImaged,
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ScaleIntensityRanged,
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Spacingd,
4945
)

‎modules/dynunet_pipeline/transforms.py

Copy file name to clipboardExpand all lines: modules/dynunet_pipeline/transforms.py
+2-2Lines changed: 2 additions & 2 deletions
Original file line numberDiff line numberDiff line change
@@ -277,7 +277,7 @@ def __init__(
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self.mean = normalize_values[0]
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self.std = normalize_values[1]
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self.training = False
280-
self.crop_foreg = CropForegroundd(keys=["image", "label"], source_key="image")
280+
self.crop_foreg = CropForegroundd(keys=["image", "label"], source_key="image", allow_smaller=True)
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self.normalize_intensity = NormalizeIntensity(nonzero=True, channel_wise=True)
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if model_mode in ["train"]:
283283
self.training = True
@@ -310,7 +310,7 @@ def __call__(self, data):
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image, label = cropped_data["image"], cropped_data["label"]
311311
else:
312312
d["original_shape"] = np.array(image.shape[1:])
313-
box_start, box_end = generate_spatial_bounding_box(image)
313+
box_start, box_end = generate_spatial_bounding_box(image, allow_smaller=True)
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image = SpatialCrop(roi_start=box_start, roi_end=box_end)(image)
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d["bbox"] = np.vstack([box_start, box_end])
316316
d["crop_shape"] = np.array(image.shape[1:])

‎modules/integrate_3rd_party_transforms.ipynb

Copy file name to clipboardExpand all lines: modules/integrate_3rd_party_transforms.ipynb
+1-1Lines changed: 1 addition & 1 deletion
Original file line numberDiff line numberDiff line change
@@ -210,7 +210,7 @@
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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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" ScaleIntensityRanged(keys=[\"image\"], a_min=-57, a_max=164, b_min=0.0, b_max=1.0, clip=True),\n",
213-
" CropForegroundd(keys=[\"image\", \"label\"], source_key=\"image\"),\n",
213+
" CropForegroundd(keys=[\"image\", \"label\"], source_key=\"image\", allow_smaller=True),\n",
214214
" EnsureTyped(keys=[\"image\", \"label\"], data_type=\"numpy\"),\n",
215215
"]"
216216
]

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