Merge branch 'dev' into randfgcrop

docs/source/apps.rst

@@ -126,6 +126,11 @@ Applications
 `Detection`
 -----------
 
+`Hard Negative Sampler`
+~~~~~~~~~~~~~~~~~~~~~~~
+.. automodule:: monai.apps.detection.utils.hard_negative_sampler
+    :members:
+
 `RetinaNet`
 ~~~~~~~~~~~
 .. automodule:: monai.apps.detection.networks.retinanet_network

docs/source/bundle.rst

@@ -41,3 +41,4 @@ Model Bundle
 .. autofunction:: run
 .. autofunction:: verify_metadata
 .. autofunction:: verify_net_in_out
+.. autofunction:: init_bundle

docs/source/mb_specification.rst

@@ -104,21 +104,22 @@ The format for tensors used as inputs and outputs can be used to specify semanti
 * **latent**: ND tensor of data from the latent space from some layer of a network
 * **gradient**: ND tensor of gradients from some layer of a network
 
-Spatial shape definition can be complex for models accepting inputs of varying shapes, especially if there are specific conditions on what those shapes can be. Shapes are specified as lists of either positive integers for fixed sizes or strings containing expressions defining the condition a size depends on. This can be "*" to mean any size, or use an expression with Python mathematical operators and one character variables to represent dependence on an unknown quantity. For example, "2**n" represents a size which must be a power of 2, "2**n*m" must be a multiple of a power of 2. Variables are shared between dimension expressions, so a spatial shape of `["2**n", "2**n"]` states that the dimensions must be the same powers of 2 given by `n`.
+Spatial shape definition can be complex for models accepting inputs of varying shapes, especially if there are specific conditions on what those shapes can be. Shapes are specified as lists of either positive integers for fixed sizes or strings containing expressions defining the condition a size depends on. This can be "*" to mean any size, or use an expression with Python mathematical operators and one character variables to represent dependence on an unknown quantity. For example, "2**p" represents a size which must be a power of 2, "2**p*n" must be a multiple of a power of 2. Variables are shared between dimension expressions, a spatial shape example: `["*", "16*n", "2**p*n"]`.
 
-A JSON schema for this file can be found at https://github.com/Project-MONAI/MONAI/blob/3049e280f2424962bb2a69261389fcc0b98e0036/monai/apps/mmars/schema/metadata.json
+The download link of a JSON schema to verify this file can be found within it with key "schema".
 
 An example JSON metadata file:
 
 ::
 
   {
+      "schema": "https://github.com/Project-MONAI/MONAI-extra-test-data/releases/download/0.8.1/meta_schema_20220324.json",
       "version": "0.1.0",
       "changelog": {
           "0.1.0": "complete the model package",
           "0.0.1": "initialize the model package structure"
       },
-      "monai_version": "0.8.0",
+      "monai_version": "0.9.0",
       "pytorch_version": "1.10.0",
       "numpy_version": "1.21.2",
       "optional_packages_version": {"nibabel": "3.2.1"},

docs/source/transforms.rst

@@ -446,6 +446,15 @@ Intensity
     :members:
     :special-members: __call__
 
+
+`ForegroundMask`
+""""""""""""""""
+.. image:: https://github.com/Project-MONAI/DocImages/raw/main/transforms/ForegroundMask.png
+    :alt: example of ForegroundMask
+.. autoclass:: ForegroundMask
+    :members:
+    :special-members: __call__
+
 IO
 ^^
 
@@ -737,6 +746,18 @@ Spatial
     :members:
     :special-members: __call__
 
+`GridPatch`
+"""""""""""
+.. autoclass:: GridPatch
+    :members:
+    :special-members: __call__
+
+`RandGridPatch`
+"""""""""""""""
+.. autoclass:: RandGridPatch
+    :members:
+    :special-members: __call__
+
 `GridSplit`
 """""""""""
 .. autoclass:: GridSplit
@@ -1327,6 +1348,13 @@ Intensity (Dict)
     :members:
     :special-members: __call__
 
+`ForegroundMaskd`
+"""""""""""""""""
+.. image:: https://github.com/Project-MONAI/DocImages/raw/main/transforms/ForegroundMaskd.png
+    :alt: example of ForegroundMaskd
+.. autoclass:: ForegroundMaskd
+    :members:
+    :special-members: __call__
 
 IO (Dict)
 ^^^^^^^^^
@@ -1513,6 +1541,18 @@ Spatial (Dict)
     :members:
     :special-members: __call__
 
+`GridPatchd`
+""""""""""""
+.. autoclass:: GridPatchd
+    :members:
+    :special-members: __call__
+
+`RandGridPatchd`
+""""""""""""""""
+.. autoclass:: RandGridPatchd
+    :members:
+    :special-members: __call__
+
 `GridSplitd`
 """"""""""""
 .. autoclass:: GridSplitd

monai/apps/detection/networks/retinanet_network.py

@@ -66,7 +66,7 @@ def __init__(
         self.num_classes = num_classes
         self.num_anchors = num_anchors
 
-    def forward(self, x: Union[List[Tensor], Tensor]) -> List[Tensor]:
+    def forward(self, x: List[Tensor]) -> List[Tensor]:
         """
         It takes a list of feature maps as inputs, and outputs a list of classification maps.
         Each output classification map has same spatial size with the corresponding input feature map,
@@ -135,7 +135,7 @@ def __init__(self, in_channels: int, num_anchors: int, spatial_dims: int):
                 torch.nn.init.normal_(layer.weight, std=0.01)  # type: ignore
                 torch.nn.init.zeros_(layer.bias)  # type: ignore
 
-    def forward(self, x: Union[List[Tensor], Tensor]) -> List[Tensor]:
+    def forward(self, x: List[Tensor]) -> List[Tensor]:
         """
         It takes a list of feature maps as inputs, and outputs a list of box regression maps.
         Each output box regression map has same spatial size with the corresponding input feature map,

monai/apps/detection/utils/anchor_utils.py

@@ -234,7 +234,8 @@ def grid_anchors(self, grid_sizes: List[List[int]], strides: List[List[Tensor]])
         """
         anchors = []
         cell_anchors = self.cell_anchors
-        assert cell_anchors is not None
+        if cell_anchors is None:
+            raise AssertionError
 
         if not (len(grid_sizes) == len(strides) == len(cell_anchors)):
             raise ValueError(