Remove deprecated feature for v1.5

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",

auto3dseg/docs/algorithm_generation.md

@@ -143,7 +143,12 @@ class DintsAlgo(BundleAlgo):
                         "b_max": 1.0,
                         "clip": True,
                     },
-                    {"_target_": "CropForegroundd", "keys": ["@image_key", "@label_key"], "source_key": "@image_key"},
+                    {
+                        "_target_": "CropForegroundd",
+                        "keys": ["@image_key", "@label_key"],
+                        "source_key": "@image_key",
+                        "allow_smaller:" True,
+                    },
                 ],
             }
 

auto3dseg/tasks/hecktor22/hecktor_crop_neck_region.py

@@ -31,9 +31,16 @@ def __init__(
         source_key="image",
         box_size=[200, 200, 310],
         allow_missing_keys=True,
+        allow_smaller=True,
         **kwargs,
     ) -> None:
-        super().__init__(keys=keys, source_key=source_key, allow_missing_keys=allow_missing_keys, **kwargs)
+        super().__init__(
+            keys=keys,
+            source_key=source_key,
+            allow_missing_keys=allow_missing_keys,
+            allow_smaller=allow_smaller,
+            **kwargs,
+        )
         self.box_size = box_size
 
     def __call__(self, data, **kwargs):

bundle/python_bundle_workflow/scripts/inference.py

@@ -48,7 +48,7 @@ class InferenceWorkflow(BundleWorkflow):
     """
 
     def __init__(self, dataset_dir: str = "./infer"):
-        super().__init__(workflow="inference")
+        super().__init__(workflow_type="inference")
         print_config()
         # set root log level to INFO and init a evaluation logger, will be used in `StatsHandler`
         logging.basicConfig(stream=sys.stdout, level=logging.INFO)

bundle/python_bundle_workflow/scripts/train.py

@@ -67,7 +67,7 @@ class TrainWorkflow(BundleWorkflow):
     """
 
     def __init__(self, dataset_dir: str = "./train"):
-        super().__init__(workflow="train")
+        super().__init__(workflow_type="train")
         print_config()
         # set root log level to INFO and init a train logger, will be used in `StatsHandler`
         logging.basicConfig(stream=sys.stdout, level=logging.INFO)

bundle/pythonic_usage_guidance/pythonic_bundle_access.ipynb

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

deepgrow/ignite/train.py

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

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

@@ -112,7 +112,7 @@ def initialize(self, args):
                 LoadImage(reader="NibabelReader", image_only=True, dtype=np.float32),
                 EnsureChannelFirst(channel_dim="no_channel"),
                 ScaleIntensityRange(a_min=-1000, a_max=500, b_min=0.0, b_max=1.0, clip=True),
-                CropForeground(margin=5),
+                CropForeground(margin=5, allow_smaller=True),
                 Resize([192, 192, 64], mode="area"),
                 EnsureChannelFirst(channel_dim="no_channel"),
                 ToTensor(),

experiment_management/spleen_segmentation_aim.ipynb

@@ -251,7 +251,7 @@
     "            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",
@@ -285,7 +285,7 @@
     "            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",
     ")"
    ]

experiment_management/spleen_segmentation_mlflow.ipynb

@@ -249,7 +249,7 @@
     "            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",
@@ -283,7 +283,7 @@
     "            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",
     ")"
    ]

model_zoo/transfer_learning_with_bundle/evaluate.py

@@ -37,7 +37,6 @@
     AsDiscrete,
     AsDiscreted,
     Compose,
-    CropForegroundd,
     EnsureChannelFirstd,
     Invertd,
     LoadImaged,

model_zoo/transfer_learning_with_bundle/train.py

@@ -35,15 +35,11 @@
 from monai.transforms import (
     Activations,
     AsDiscrete,
-    AsDiscreted,
     Compose,
-    CropForegroundd,
     EnsureChannelFirstd,
-    Invertd,
     LoadImaged,
     Orientationd,
     RandCropByPosNegLabeld,
-    SaveImaged,
     ScaleIntensityRanged,
     Spacingd,
 )

modules/dynunet_pipeline/transforms.py

@@ -277,7 +277,7 @@ def __init__(
         self.mean = normalize_values[0]
         self.std = normalize_values[1]
         self.training = False
-        self.crop_foreg = CropForegroundd(keys=["image", "label"], source_key="image")
+        self.crop_foreg = CropForegroundd(keys=["image", "label"], source_key="image", allow_smaller=True)
         self.normalize_intensity = NormalizeIntensity(nonzero=True, channel_wise=True)
         if model_mode in ["train"]:
             self.training = True
@@ -310,7 +310,7 @@ def __call__(self, data):
             image, label = cropped_data["image"], cropped_data["label"]
         else:
             d["original_shape"] = np.array(image.shape[1:])
-            box_start, box_end = generate_spatial_bounding_box(image)
+            box_start, box_end = generate_spatial_bounding_box(image, allow_smaller=True)
             image = SpatialCrop(roi_start=box_start, roi_end=box_end)(image)
             d["bbox"] = np.vstack([box_start, box_end])
             d["crop_shape"] = np.array(image.shape[1:])

modules/integrate_3rd_party_transforms.ipynb

@@ -210,7 +210,7 @@
     "    Orientationd(keys=[\"image\", \"label\"], axcodes=\"RAS\"),\n",
     "    Spacingd(keys=[\"image\", \"label\"], pixdim=(1.5, 1.5, 2.0), mode=(\"bilinear\", \"nearest\")),\n",
     "    ScaleIntensityRanged(keys=[\"image\"], a_min=-57, a_max=164, 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",
     "    EnsureTyped(keys=[\"image\", \"label\"], data_type=\"numpy\"),\n",
     "]"
    ]