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Fix nn.PixelUnshuffle wrong channel ordering (replace SpaceToDepth with Reshape→Transpose→Reshape) #2892

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Fix nn.PixelUnshuffle wrong channel ordering (replace SpaceToDepth with Reshape→Transpose→Reshape) #2892

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Copilot Apr 17, 2026
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Fix nn.PixelUnshuffle to use Reshape->Transpose->Reshape instead of S…
Copilot Apr 17, 2026
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34 changes: 26 additions & 8 deletions onnxscript/function_libs/torch_lib/ops/core.py
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Original file line number Diff line number Diff line change
Expand Up @@ -7618,20 +7618,38 @@ def aten_pixel_shuffle(self: TReal, upscale_factor: int) -> TReal:
@torch_op("aten::pixel_unshuffle", trace_only=True)
def aten_pixel_unshuffle(self: TReal, downscale_factor: int) -> TReal:
"""pixel_unshuffle(Tensor self, int downscale_factor) -> Tensor"""
if len(self.shape) == 4:
return op.SpaceToDepth(self, blocksize=downscale_factor)
# pixel_unshuffle is the inverse of pixel_shuffle (which uses DepthToSpace with mode="CRD").
# SpaceToDepth only supports DCR channel ordering, so we implement via Reshape->Transpose->Reshape
# to get the correct CRD ordering.
# Input: [..., C, H*r, W*r] -> Output: [..., C*r*r, H, W]

# Reshaping input by collapsing all leading dimensions to match ONNX op requirement (4D)
batch_dims = op.Shape(self, end=-3)
chw_in_dims = op.Shape(self, start=-3)
self_4d = op.Reshape(self, op.Concat(op.Constant(value_ints=[-1]), chw_in_dims, axis=0))

reshaped_self = op.Reshape(
self, op.Concat(op.Constant(value_ints=[-1]), chw_in_dims, axis=0)
)
space_to_depth = op.SpaceToDepth(reshaped_self, blocksize=downscale_factor)
final_dims = op.Shape(space_to_depth, start=1)
r = op.Constant(value_ints=[downscale_factor])
c = op.Shape(self_4d, start=1, end=2)
h_r = op.Shape(self_4d, start=2, end=3)
w_r = op.Shape(self_4d, start=3, end=4)
h = op.Div(h_r, r)
w = op.Div(w_r, r)

# Step 1: Reshape to [batch, C, H, r, W, r]
shape_6d = op.Concat(op.Constant(value_ints=[-1]), c, h, r, w, r, axis=0)
tmp = op.Reshape(self_4d, shape_6d)

# Step 2: Transpose to [batch, C, r, r, H, W] (inverse of CRD DepthToSpace transpose)
tmp = op.Transpose(tmp, perm=[0, 1, 3, 5, 2, 4])

# Step 3: Reshape to [batch, C*r*r, H, W]
c_out = op.Mul(c, op.Mul(r, r))
shape_4d_out = op.Concat(op.Constant(value_ints=[-1]), c_out, h, w, axis=0)
pixel_unshuffled = op.Reshape(tmp, shape_4d_out)

final_dims = op.Shape(pixel_unshuffled, start=1)
output_shape = op.Concat(batch_dims, final_dims, axis=0)
return op.Reshape(space_to_depth, output_shape, allowzero=True)
return op.Reshape(pixel_unshuffled, output_shape, allowzero=True)


def aten_poisson(self: TensorType, generator: Optional[str] = None) -> TensorType:
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