Merge branch 'main' into update-changelog

.github/workflows/test.yaml

@@ -44,10 +44,3 @@ jobs:
       if: contains('refs/heads/main refs/heads/development refs/heads/release', github.ref) != 1
       run: |
         make test-light
-
-    - name: Test coveralls - python ${{ matrix.python-version }}
-      run: coveralls --service=github
-      env:
-        GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }}
-        flag-name: run-${{ matrix.python-version }}
-        parallel: true

makefile

@@ -47,13 +47,13 @@ uninstall:
 .PHONY: install-dev
 
 install-dev:
-	@pip install -e .[test]
-	@pip install -e .[lint]
+	@pip install -e ."[test]"
+	@pip install -e ."[lint]"
 
 .PHONY: install-test
 
 install-test:
-	@pip install -e .[test]
+	@pip install -e ."[test]"
 
 .PHONY: test test-light
 
@@ -66,7 +66,7 @@ test-light:
 .PHONY: install-lint
 
 install-lint:
-	@pip install -e .[lint]
+	@pip install -e ."[lint]"
 
 .PHONY: isort isort-check
 

setup.cfg

@@ -79,6 +79,7 @@ ignore =
 	W291, # trailing whitespace
 	W503, # line break before binary operator
 	W504, # line break after binary operator
+	B905, # `zip()` without an explicit `strict=` parameter
 exclude = docs, docs_src, build, .git, .eggs
 
 [isort]

test/fold_settings.py

@@ -16,6 +16,16 @@
             "kernel_size": (2, 2),
         },
     },
+    {
+        "seed": 0,
+        "input_fn": lambda: torch.rand(2, 3 * 2 * 2, 5 * 9),
+        "fold_kwargs": {
+            "output_size": (4, 8),
+            "kernel_size": 2,
+            "padding": 1,
+        },
+        "id": "bug30-fold-with-padding",
+    },
 ]
 PROBLEMS_2D_IDS = [make_id(problem) for problem in PROBLEMS_2D]
 

unfoldNd/fold.py

@@ -56,34 +56,48 @@ def foldNd(input, output_size, kernel_size, dilation=1, padding=0, stride=1):
     """
     device = input.device
 
-    if isinstance(output_size, tuple):
-        N = len(output_size)
-        output_size_numel = _get_numel_from_shape(output_size)
-    else:
+    if not isinstance(output_size, tuple):
         raise ValueError(f"'output_size' must be tuple. Got {type(output_size)}.")
 
+    N = len(output_size)
     kernel_size = _tuple(kernel_size, N)
     kernel_size_numel = _get_kernel_size_numel(kernel_size)
 
     batch_size = input.shape[0]
     in_channels_kernel_size_numel = input.shape[1]
     in_channels = in_channels_kernel_size_numel // kernel_size_numel
 
-    _check_output_size(output_size_numel)
-    idx = torch.arange(output_size_numel, dtype=torch.float32, device=device).reshape(
-        1, 1, *output_size
-    )
-    idx = unfoldNd(idx, kernel_size, dilation=dilation, padding=padding, stride=stride)
+    # Set up an array containing the locations on the padded image
+    padding = _tuple(padding, N)
+    padded_output_size = tuple(o + 2 * p for o, p in zip(output_size, padding))
+    padded_output_size_numel = _get_numel_from_shape(padded_output_size)
+    _check_output_size(padded_output_size_numel)
+
+    idx = torch.arange(
+        padded_output_size_numel, dtype=torch.float32, device=device
+    ).reshape(1, 1, *padded_output_size)
+    idx = unfoldNd(idx, kernel_size, dilation=dilation, padding=0, stride=stride)
 
+    # Replicate indices over batch and channels, then scatter the patch values
+    # back to the padded image
     input = input.reshape(batch_size, in_channels, -1)
     idx = idx.reshape(1, 1, -1).long().expand(batch_size, in_channels, -1)
 
     output = torch.zeros(
-        batch_size, in_channels, output_size_numel, device=device, dtype=input.dtype
+        batch_size,
+        in_channels,
+        padded_output_size_numel,
+        device=device,
+        dtype=input.dtype,
     )
     output.scatter_add_(2, idx, input)
+    output = output.reshape(batch_size, in_channels, *padded_output_size)
+
+    # Remove the pixels that correspond to padding
+    for n, (out_n, padding_n) in enumerate(zip(output_size, padding), start=2):
+        output = output.narrow(n, padding_n, out_n)
 
-    return output.reshape(batch_size, in_channels, *output_size)
+    return output
 
 
 def _check_output_size(output_size_numel):