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>

Author: KumoLiu

3d_segmentation/spleen_segmentation_3d.ipynb

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

3d_segmentation/spleen_segmentation_3d_lightning.ipynb

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

3d_segmentation/spleen_segmentation_3d_visualization_basic.ipynb

@@ -317,7 +317,7 @@
     "            b_max=1.0,\n",
     "            clip=True,\n",
     "        ),\n",
-    "        CropForegroundd(keys=[\"image\", \"label\"], source_key=\"image\"),\n",
+    "        CropForegroundd(keys=[\"image\", \"label\"], source_key=\"image\", allow_smaller=True),\n",
     "        Orientationd(keys=[\"image\", \"label\"], axcodes=\"RAS\"),\n",
     "        Spacingd(keys=[\"image\", \"label\"], pixdim=(1.5, 1.5, 2.0), mode=(\"bilinear\", \"nearest\")),\n",
     "        RandCropByPosNegLabeld(\n",
@@ -351,7 +351,7 @@
     "            b_max=1.0,\n",
     "            clip=True,\n",
     "        ),\n",
-    "        CropForegroundd(keys=[\"image\", \"label\"], source_key=\"image\"),\n",
+    "        CropForegroundd(keys=[\"image\", \"label\"], source_key=\"image\", allow_smaller=True),\n",
     "        Orientationd(keys=[\"image\", \"label\"], axcodes=\"RAS\"),\n",
     "        Spacingd(keys=[\"image\", \"label\"], pixdim=(1.5, 1.5, 2.0), mode=(\"bilinear\", \"nearest\")),\n",
     "    ]\n",
@@ -782,7 +782,7 @@
     "            b_max=1.0,\n",
     "            clip=True,\n",
     "        ),\n",
-    "        CropForegroundd(keys=[\"image\"], source_key=\"image\"),\n",
+    "        CropForegroundd(keys=[\"image\"], source_key=\"image\", allow_smaller=True),\n",
     "    ]\n",
     ")\n",
     "\n",

3d_segmentation/swin_unetr_brats21_segmentation_3d.ipynb

@@ -326,6 +326,7 @@
     "                keys=[\"image\", \"label\"],\n",
     "                source_key=\"image\",\n",
     "                k_divisible=[roi[0], roi[1], roi[2]],\n",
+    "                allow_smaller=True,\n",
     "            ),\n",
     "            transforms.RandSpatialCropd(\n",
     "                keys=[\"image\", \"label\"],\n",
@@ -467,7 +468,6 @@
     "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n",
     "\n",
     "model = SwinUNETR(\n",
-    "    img_size=roi,\n",
     "    in_channels=4,\n",
     "    out_channels=3,\n",
     "    feature_size=48,\n",

3d_segmentation/swin_unetr_btcv_segmentation_3d.ipynb

@@ -243,7 +243,7 @@
     "            b_max=1.0,\n",
     "            clip=True,\n",
     "        ),\n",
-    "        CropForegroundd(keys=[\"image\", \"label\"], source_key=\"image\"),\n",
+    "        CropForegroundd(keys=[\"image\", \"label\"], source_key=\"image\", allow_smaller=True),\n",
     "        Orientationd(keys=[\"image\", \"label\"], axcodes=\"RAS\"),\n",
     "        Spacingd(\n",
     "            keys=[\"image\", \"label\"],\n",
@@ -292,7 +292,7 @@
     "    [\n",
     "        LoadImaged(keys=[\"image\", \"label\"], ensure_channel_first=True),\n",
     "        ScaleIntensityRanged(keys=[\"image\"], a_min=-175, a_max=250, b_min=0.0, b_max=1.0, clip=True),\n",
-    "        CropForegroundd(keys=[\"image\", \"label\"], source_key=\"image\"),\n",
+    "        CropForegroundd(keys=[\"image\", \"label\"], source_key=\"image\", allow_smaller=True),\n",
     "        Orientationd(keys=[\"image\", \"label\"], axcodes=\"RAS\"),\n",
     "        Spacingd(\n",
     "            keys=[\"image\", \"label\"],\n",
@@ -439,7 +439,6 @@
     "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n",
     "\n",
     "model = SwinUNETR(\n",
-    "    img_size=(96, 96, 96),\n",
     "    in_channels=1,\n",
     "    out_channels=14,\n",
     "    feature_size=48,\n",

3d_segmentation/unetr_btcv_segmentation_3d.ipynb

@@ -228,7 +228,7 @@
     "            b_max=1.0,\n",
     "            clip=True,\n",
     "        ),\n",
-    "        CropForegroundd(keys=[\"image\", \"label\"], source_key=\"image\"),\n",
+    "        CropForegroundd(keys=[\"image\", \"label\"], source_key=\"image\", allow_smaller=True),\n",
     "        RandCropByPosNegLabeld(\n",
     "            keys=[\"image\", \"label\"],\n",
     "            label_key=\"label\",\n",
@@ -277,7 +277,7 @@
     "            mode=(\"bilinear\", \"nearest\"),\n",
     "        ),\n",
     "        ScaleIntensityRanged(keys=[\"image\"], a_min=-175, a_max=250, b_min=0.0, b_max=1.0, clip=True),\n",
-    "        CropForegroundd(keys=[\"image\", \"label\"], source_key=\"image\"),\n",
+    "        CropForegroundd(keys=[\"image\", \"label\"], source_key=\"image\", allow_smaller=True),\n",
     "    ]\n",
     ")"
    ]

3d_segmentation/unetr_btcv_segmentation_3d_lightning.ipynb

@@ -466,7 +466,7 @@
     "                    b_max=1.0,\n",
     "                    clip=True,\n",
     "                ),\n",
-    "                CropForegroundd(keys=[\"image\", \"label\"], source_key=\"image\"),\n",
+    "                CropForegroundd(keys=[\"image\", \"label\"], source_key=\"image\", allow_smaller=True),\n",
     "                Orientationd(keys=[\"image\", \"label\"], axcodes=\"RAS\"),\n",
     "                Spacingd(\n",
     "                    keys=[\"image\", \"label\"],\n",
@@ -522,7 +522,7 @@
     "                    b_max=1.0,\n",
     "                    clip=True,\n",
     "                ),\n",
-    "                CropForegroundd(keys=[\"image\", \"label\"], source_key=\"image\"),\n",
+    "                CropForegroundd(keys=[\"image\", \"label\"], source_key=\"image\", allow_smaller=True),\n",
     "                Orientationd(keys=[\"image\", \"label\"], axcodes=\"RAS\"),\n",
     "                Spacingd(\n",
     "                    keys=[\"image\", \"label\"],\n",

acceleration/automatic_mixed_precision.ipynb

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

acceleration/dataset_type_performance.ipynb

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

acceleration/fast_training_tutorial.ipynb

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