fix/tests(convblock): fix skip connections with same skip_dest, stric…

…ter tests

tests/unit/convnet/architecture/test_convblock.py

@@ -110,12 +110,38 @@ def not_test_forward_naive(mocker):
     )
 
 
-def test_forward_skips(mocker):
-    mocker.patch("torch.nn.Conv2d.forward", lambda _, x: x)
-    block = convnet.architecture.ConvBlock(
-        kernel_sizes=[2, 2], num_channels=[1, 2, 3, 4, 5], skips={"0": 3, "1": 3, "2": 4}
-    )
-    result = block(torch.ones([1, 1, 1, 1]))
-    assert_array_equal(
-        result.cpu().detach().numpy(), 6 * torch.ones([1, 1, 1, 1]).cpu().detach().numpy()
-    )
+@pytest.mark.parametrize(
+    "skips, expected",
+    [
+        # fmt: off
+        [
+            None,
+            1 * 2 * 2 * 2 * 2           # 4 convolutions
+        ],
+        [
+            {"0": 2, "1": 2, "2": 3},
+            (((1 * 2 * 2)               # first 2 convolutions
+            + 1                         # Skip content "0": 2
+            + 1 * 2                     # Skip content "1": 2
+            ) * 2                       # third convolution
+            + (1 * 2 * 2)               # Skip content "2": 3
+            + 1                         # also includes the skip content "0": 2
+            + 1 * 2                     # also includes the skip content "1": 2
+            ) * 2                       # fourth convolution
+        ],
+        [
+            {"0": 3, "1": 3, "2": 4},
+            ((1 * 2 * 2 * 2)            # first 3 convolutions
+            + 1                         # Skip content "0": 3
+            + 1 * 2                     # Skip content "1": 3
+            ) * 2                       # fourth convolution
+            + (1 * 2 * 2)               # Skip content "2": 4
+        ],
+        # fmt: on
+    ],
+)
+def test_forward_skips(mocker, skips, expected):
+    mocker.patch("torch.nn.Conv2d.forward", lambda _, x: 2 * x)
+    block = convnet.architecture.ConvBlock(kernel_sizes=[1], num_channels=[1] * 5, skips=skips)
+    result = block(torch.ones((1, 1, 1, 1)))
+    assert_array_equal(result.detach().numpy(), torch.full((1, 1, 1, 1), expected).numpy())

tests/unit/convnet/architecture/test_unet.py

@@ -185,19 +185,46 @@ def not_test_forward_naive(mocker):
     )
 
 
-def test_forward_skips(mocker):
-    mocker.patch("torch.nn.Conv2d.forward", lambda _, x: x)
+@pytest.mark.parametrize(
+    "skips, expected",
+    [
+        # fmt: off
+        [
+            None,
+            (1 * 2 * 2          # L0 convblock, left
+            * 2 * 2 * 2         # L1 convblock
+            + 1 * 2 * 2         # UNet skip connection
+            ) * 2 * 2           # L0 convblock, right
+        ],
+        [
+            {"0": 1},
+            ((((1 * 2 + 1) * 2  # L0 convblock, left   --> 6
+            * 2 + 6) * 2 * 2    # L1 convblock         --> 72
+            + 6                 # UNet skip connection --> 78
+            ) * 2 + 78) * 2     # L0 convblock, right  --> 468
+        ],
+        [
+            {"0": 2},
+            ((((1 * 2 * 2 + 1)  # L0 convblock, left   --> 5
+            * 2 * 2 + 5) * 2    # L1 convblock         --> 50
+            + 5                 # UNet skip connection --> 55
+            ) * 2 * 2 + 55)     # L0 convblock, right  --> 275
+        ],
+        # fmt: on
+    ],
+)
+def test_forward_skips(mocker, skips, expected):
+    mocker.patch("torch.nn.Conv2d.forward", lambda _, x: 2 * x)
     unet = convnet.architecture.UNet(
-        kernel_sizes=[3, 3],
+        kernel_sizes=[1],
         list_num_channels=[[1, 1, 1], [1, 1, 1, 1], [1, 1, 1]],
-        downsample=partial(torch.nn.AvgPool2d, kernel_size=2),
-        upsample=partial(torch.nn.Upsample, scale_factor=2),
-        skips={"0": 2},
-    )
-    result = unet.forward(torch.ones([1, 1, 2, 2]))
-    assert_array_equal(
-        result.cpu().detach().numpy(), 12 * torch.ones([1, 1, 2, 2]).cpu().detach().numpy()
+        downsample=partial(torch.nn.AvgPool2d, kernel_size=1),
+        upsample=partial(torch.nn.Upsample, scale_factor=1),
+        skips=skips,
+        unet_skip_mode="sum",
     )
+    result = unet.forward(torch.ones((1, 1, 1, 1)))
+    assert_array_equal(result.detach().numpy(), torch.full((1, 1, 1, 1), expected).numpy())
 
 
 @pytest.mark.parametrize(

zetta_utils/convnet/architecture/convblock.py

@@ -168,7 +168,7 @@ def forward(self, data: torch.Tensor) -> torch.Tensor:
                 skip_dest = self.skips[str(conv_count)]
                 if skip_dest in skip_data_for:
                     size = _get_size(result)
-                    skip_data_for[skip_dest] += crop_center(skip_data_for[skip_dest], size)
+                    skip_data_for[skip_dest] += crop_center(result, size)
                 else:
                     skip_data_for[skip_dest] = result