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Improve test_conv1d layer checks for io_stream + same padding #1479

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Improve test_conv1d layer checks for io_stream + same padding #1479

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51d47e9
adjusted test_conv1d to check layers also for combinatio of Vitis/Viv…
frabazz May 22, 2026
6c9abe3
added comments, formatted
frabazz May 22, 2026
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60 changes: 31 additions & 29 deletions test/pytest/test_keras_api.py
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Original file line number Diff line number Diff line change
Expand Up @@ -175,36 +175,38 @@ def test_conv1d(test_case_id, padds, backend, strategy, io_type, synthesis_confi
# 5e-2 might be too high
np.testing.assert_allclose(hls_prediction, keras_prediction, rtol=0, atol=5e-2)

if not (backend in ['Vivado', 'Vitis'] and io_type == 'io_stream' and padds == 'same'):
# Vivado/Vitis inserts and additional layer for 'same' padding in io_stream
assert len(model.layers) + 2 == len(hls_model.get_layers())
assert list(hls_model.get_layers())[1].attributes['name'] == model.layers[0]._name
assert list(hls_model.get_layers())[1].attributes['class_name'] == 'Conv1D'
assert list(hls_model.get_layers())[1].attributes['activation'] == str(model.layers[0].activation).split()[1]
assert list(hls_model.get_layers())[1].attributes['in_width'] == model.layers[0]._batch_input_shape[1]
assert list(hls_model.get_layers())[1].attributes['filt_width'] == model.layers[0].kernel_size[0]
assert list(hls_model.get_layers())[1].attributes['n_chan'] == model.layers[0].input_shape[2]
assert list(hls_model.get_layers())[1].attributes['n_filt'] == model.layers[0].filters
assert list(hls_model.get_layers())[1].attributes['stride_width'] == model.layers[0].strides[0]
assert list(hls_model.get_layers())[1].attributes['data_format'] == model.layers[0].data_format
assert list(hls_model.get_layers())[1].attributes['out_width'] == list(model.layers[0].output_shape)[1]

out_width = math.ceil(float(model.layers[0]._batch_input_shape[2]) / float(model.layers[0].strides[0]))
pad_along_width = max(
(out_width - 1) * model.layers[0].strides[0]
+ model.layers[0].kernel_size[0]
- model.layers[0]._batch_input_shape[2],
0,
)
pad_left = pad_along_width // 2
pad_right = pad_along_width - pad_left
off = 0
# Vitis/Vivado adds a padding layer after the input in the io_stream case
if backend in ['Vitis', 'Vivado'] and io_type == 'io_stream' and padds == 'same':
off = 1
assert len(model.layers) + 2 + off == len(hls_model.get_layers())
assert list(hls_model.get_layers())[1 + off].attributes['name'] == model.layers[0]._name
assert list(hls_model.get_layers())[1 + off].attributes['class_name'] == 'Conv1D'
assert list(hls_model.get_layers())[1 + off].attributes['activation'] == str(model.layers[0].activation).split()[1]
assert list(hls_model.get_layers())[1].attributes['in_width'] == model.layers[0]._batch_input_shape[1]
assert list(hls_model.get_layers())[1 + off].attributes['filt_width'] == model.layers[0].kernel_size[0]
assert list(hls_model.get_layers())[1 + off].attributes['n_chan'] == model.layers[0].input_shape[2]
assert list(hls_model.get_layers())[1 + off].attributes['n_filt'] == model.layers[0].filters
assert list(hls_model.get_layers())[1 + off].attributes['stride_width'] == model.layers[0].strides[0]
assert list(hls_model.get_layers())[1 + off].attributes['data_format'] == model.layers[0].data_format
assert list(hls_model.get_layers())[1 + off].attributes['out_width'] == list(model.layers[0].output_shape)[1]

out_width = math.ceil(float(model.layers[0]._batch_input_shape[2]) / float(model.layers[0].strides[0]))
pad_along_width = max(
(out_width - 1) * model.layers[0].strides[0]
+ model.layers[0].kernel_size[0]
- model.layers[0]._batch_input_shape[2],
0,
)
pad_left = pad_along_width // 2
pad_right = pad_along_width - pad_left

if model.layers[0].padding == 'same':
assert list(hls_model.get_layers())[1].attributes['pad_left'] == pad_left
assert list(hls_model.get_layers())[1].attributes['pad_right'] == pad_right
elif model.layers[0].padding == 'valid':
assert list(hls_model.get_layers())[1].attributes['pad_left'] == 0
assert list(hls_model.get_layers())[1].attributes['pad_right'] == 0
if model.layers[0].padding == 'same':
assert list(hls_model.get_layers())[1].attributes['pad_left'] == pad_left
assert list(hls_model.get_layers())[1].attributes['pad_right'] == pad_right
elif model.layers[0].padding == 'valid':
assert list(hls_model.get_layers())[1 + off].attributes['pad_left'] == 0
assert list(hls_model.get_layers())[1 + off].attributes['pad_right'] == 0


chans_options = ['channels_last']
Expand Down