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Fix pytorch upsample parsing #1186

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Merged
merged 2 commits into from
Feb 7, 2025
Merged

Fix pytorch upsample parsing #1186

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49ab8c0
Fix parsing Pytorch Upsample layer in 2D
vloncar Feb 6, 2025
489f1f1
Add test case for Upsample to have a layer after to ensure proper par…
vloncar Feb 6, 2025
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2 changes: 1 addition & 1 deletion hls4ml/converters/pytorch/reshape.py
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Original file line number Diff line number Diff line change
Expand Up @@ -152,7 +152,7 @@ def handle_upsample(operation, layer_name, input_names, input_shapes, node, clas
layer['out_height'] = int(layer['in_height'] * scale_height)
layer['out_width'] = int(layer['in_width'] * scale_width)

output_shape = [layer['n_chan'], layer['out_height'], layer['out_width']]
output_shape = [input_shapes[0][0], layer['n_chan'], layer['out_height'], layer['out_width']]
else:
raise Exception(f'Parsing "Upsample" with {len(input_shape)}-dimensional tensors is not yet supported.')

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6 changes: 4 additions & 2 deletions test/pytest/test_upsampling_pytorch.py
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Expand Up @@ -33,19 +33,21 @@ class Upsample1DModel(nn.Module):
def __init__(self):
super().__init__()
self.upsample = nn.Upsample(scale_factor=2)
self.relu = nn.ReLU()

def forward(self, x):
return self.upsample(x)
return self.relu(self.upsample(x))


class Upsample2DModel(nn.Module):
def __init__(self):
super().__init__()
# this scale_factor tests proper output shape calculation with fractional scaling and parsing per-axis scales
self.upsample = nn.UpsamplingNearest2d(scale_factor=(1, 2.4)) # Would also work with Upsample(mode='nearest')
self.relu = nn.ReLU()

def forward(self, x):
return self.upsample(x)
return self.relu(self.upsample(x))


@pytest.mark.parametrize('io_type', ['io_stream', 'io_parallel'])
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