solve problem in unittests (two files with the same name)

aramis-lab · Jul 22, 2024 · 03ec7b9 · 03ec7b9
1 parent 456431d
commit 03ec7b9
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Showing 8 changed files with 5 additions and 14 deletions.
diff --git a/tests/unittests/nn/__init__.py b/tests/unittests/nn/__init__.py
diff --git a/tests/unittests/nn/blocks/__init__.py b/tests/unittests/nn/blocks/__init__.py
diff --git a/tests/unittests/nn/blocks/test_se.py b/tests/unittests/nn/blocks/test_se.py
@@ -15,7 +15,7 @@ def test_SE_Block(input_3d):
     assert out.shape == input_3d.shape
 
 
-def test_ResBlock_SE(input_3d, helpers):
+def test_ResBlock_SE(input_3d):
     from clinicadl.nn.blocks import ResBlock_SE
 
     layer = ResBlock_SE(
@@ -25,18 +25,9 @@ def test_ResBlock_SE(input_3d, helpers):
         ratio_channel=4,
     )
     out = layer(input_3d)
-    expected_out_shape = helpers.compute_conv_output_size(
-        in_size=input_3d.shape[-1], kernel_size=3, stride=1, padding=1
-    )
-    expected_out_shape = helpers.compute_conv_output_size(
-        in_size=expected_out_shape, kernel_size=3, stride=1, padding=1
-    )
-    assert out.shape == torch.Size(
+    assert out.shape[:2] == torch.Size(
         (
             input_3d.shape[0],
             2**3,
-            expected_out_shape,
-            expected_out_shape,
-            expected_out_shape,
         )
     )
diff --git a/...s/unittests/nn/blocks/test_unet_blocks.py → tests/unittests/nn/blocks/test_unet.py b/...s/unittests/nn/blocks/test_unet_blocks.py → tests/unittests/nn/blocks/test_unet.py
@@ -12,23 +12,23 @@ def skip_input():
     return torch.randn(2, 4, 10, 10, 10)
 
 
-def test_UNetDown(input_3d, helpers):
+def test_UNetDown(input_3d):
     from clinicadl.nn.blocks import UNetDown
 
     layer = UNetDown(in_size=input_3d.shape[1], out_size=8)
     out = layer(input_3d)
     assert out.shape[:2] == torch.Size((input_3d.shape[0], 8))
 
 
-def test_UNetUp(input_3d, skip_input, helpers):
+def test_UNetUp(input_3d, skip_input):
     from clinicadl.nn.blocks import UNetUp
 
     layer = UNetUp(in_size=input_3d.shape[1] * 2, out_size=2)
     out = layer(input_3d, skip_input=skip_input)
     assert out.shape[:2] == torch.Size((input_3d.shape[0], 2))
 
 
-def test_UNetFinalLayer(input_3d, skip_input, helpers):
+def test_UNetFinalLayer(input_3d, skip_input):
     from clinicadl.nn.blocks import UNetFinalLayer
 
     layer = UNetFinalLayer(in_size=input_3d.shape[1] * 2, out_size=2)

diff --git a/tests/unittests/nn/layers/__init__.py b/tests/unittests/nn/layers/__init__.py
diff --git a/tests/unittests/nn/layers/factory/__init__.py b/tests/unittests/nn/layers/factory/__init__.py
diff --git a/tests/unittests/nn/networks/__init__.py b/tests/unittests/nn/networks/__init__.py
diff --git a/tests/unittests/nn/networks/factory/__init__.py b/tests/unittests/nn/networks/factory/__init__.py