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Make pixelshuffle scriptable #4109

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Merged
merged 3 commits into from
Apr 12, 2022
Merged

Make pixelshuffle scriptable #4109

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09f8559
Update the existing functionality to comply with the `torchscript.jit…
ramonemiliani93 Apr 12, 2022
4ab097c
Add test to verify `pixelshuffle` is scriptable.
ramonemiliani93 Apr 12, 2022
026f840
Merge branch 'dev' into 4108-pixelshuffle-scriptable
wyli Apr 12, 2022
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12 changes: 7 additions & 5 deletions monai/networks/utils.py
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Original file line number Diff line number Diff line change
Expand Up @@ -281,14 +281,16 @@ def pixelshuffle(
f"divisible by scale_factor ** dimensions ({factor}**{dim}={scale_divisor})."
)

org_channels = channels // scale_divisor
org_channels = int(channels // scale_divisor)
output_size = [batch_size, org_channels] + [d * factor for d in input_size[2:]]

indices = tuple(range(2, 2 + 2 * dim))
indices_factor, indices_dim = indices[:dim], indices[dim:]
permute_indices = (0, 1) + sum(zip(indices_dim, indices_factor), ())
indices = list(range(2, 2 + 2 * dim))
indices = indices[dim:] + indices[:dim]
permute_indices = [0, 1]
for idx in range(dim):
permute_indices.extend(indices[idx::dim])

x = x.reshape(batch_size, org_channels, *([factor] * dim + input_size[2:]))
x = x.reshape([batch_size, org_channels] + [factor] * dim + input_size[2:])
x = x.permute(permute_indices).reshape(output_size)
return x

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8 changes: 8 additions & 0 deletions tests/test_subpixel_upsample.py
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Original file line number Diff line number Diff line change
Expand Up @@ -18,6 +18,7 @@
from monai.networks import eval_mode
from monai.networks.blocks import SubpixelUpsample
from monai.networks.layers.factories import Conv
from tests.utils import SkipIfBeforePyTorchVersion, test_script_save

TEST_CASE_SUBPIXEL = []
for inch in range(1, 5):
Expand Down Expand Up @@ -73,6 +74,13 @@ def test_subpixel_shape(self, input_param, input_shape, expected_shape):
result = net.forward(torch.randn(input_shape))
self.assertEqual(result.shape, expected_shape)

@SkipIfBeforePyTorchVersion((1, 8, 1))
def test_script(self):
input_param, input_shape, _ = TEST_CASE_SUBPIXEL[0]
net = SubpixelUpsample(**input_param)
test_data = torch.randn(input_shape)
test_script_save(net, test_data)


if __name__ == "__main__":
unittest.main()