1973 - docstring update convs example

.github/workflows/docker.yml

@@ -76,6 +76,7 @@ jobs:
         echo "${{ secrets.DOCKER_PW }}" | docker login -u projectmonai --password-stdin
         docker push projectmonai/monai:latest
         docker logout
+        docker image prune -f
 
   docker_test_latest:
     if: github.repository == 'Project-MONAI/MONAI'

monai/networks/blocks/convolutions.py

@@ -30,6 +30,34 @@ class Convolution(nn.Sequential):
 
         -- (Conv|ConvTrans) --
 
+    For example:
+
+    .. code-block:: python
+
+        from monai.networks.blocks import Convolution
+
+        conv = Convolution(
+            dimensions=3,
+            in_channels=1,
+            out_channels=1,
+            adn_ordering="ADN",
+            act=("prelu", {"init": 0.2}),
+            dropout=0.1,
+            norm=("layer", {"normalized_shape": (10, 10, 10)}),
+        )
+        print(conv)
+
+    output::
+
+        Convolution(
+          (conv): Conv3d(1, 1, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))
+          (adn): ADN(
+            (A): PReLU(num_parameters=1)
+            (D): Dropout(p=0.1, inplace=False)
+            (N): LayerNorm((10, 10, 10), eps=1e-05, elementwise_affine=True)
+          )
+        )
+
     Args:
         dimensions: number of spatial dimensions.
         in_channels: number of input channels.
@@ -142,6 +170,44 @@ class ResidualUnit(nn.Module):
     """
     Residual module with multiple convolutions and a residual connection.
 
+    For example:
+
+    .. code-block:: python
+
+        from monai.networks.blocks import ResidualUnit
+
+        convs = ResidualUnit(
+            dimensions=3,
+            in_channels=1,
+            out_channels=1,
+            adn_ordering="AN",
+            act=("prelu", {"init": 0.2}),
+            norm=("layer", {"normalized_shape": (10, 10, 10)}),
+        )
+        print(convs)
+
+    output::
+
+        ResidualUnit(
+          (conv): Sequential(
+            (unit0): Convolution(
+              (conv): Conv3d(1, 1, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))
+              (adn): ADN(
+                (A): PReLU(num_parameters=1)
+                (N): LayerNorm((10, 10, 10), eps=1e-05, elementwise_affine=True)
+              )
+            )
+            (unit1): Convolution(
+              (conv): Conv3d(1, 1, kernel_size=(3, 3, 3), stride=(1, 1, 1), padding=(1, 1, 1))
+              (adn): ADN(
+                (A): PReLU(num_parameters=1)
+                (N): LayerNorm((10, 10, 10), eps=1e-05, elementwise_affine=True)
+              )
+            )
+          )
+          (residual): Identity()
+        )
+
     Args:
         dimensions: number of spatial dimensions.
         in_channels: number of input channels.

requirements-dev.txt

@@ -26,9 +26,9 @@ mypy>=0.790
 ninja
 torchvision
 psutil
-Sphinx==3.3.0
+Sphinx==3.5.3
 recommonmark==0.6.0
 sphinx-autodoc-typehints==1.11.1
-sphinx-rtd-theme==0.5.0
+sphinx-rtd-theme==0.5.2
 cucim==0.18.2
 openslide-python==1.1.2

tests/test_crf_cuda.py

@@ -444,7 +444,7 @@ def test(self, test_case_description, params, input, features, expected):
         output = crf(input_tensor, feature_tensor).cpu().numpy()
 
         # Ensure result are as expected
-        np.testing.assert_allclose(output, expected, atol=1e-4, rtol=1e-4)
+        np.testing.assert_allclose(output, expected, atol=1e-3, rtol=1e-3)
 
 
 if __name__ == "__main__":