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Uniform test root path #1435

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Merged
JanFSchulte merged 4 commits into from
Feb 12, 2026
Merged

Uniform test root path #1435

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fa1a428
make test_root_path consistent for all the tests
jmitrevs Feb 10, 2026
c72faf4
prevent folder name conflicts, make uniform
jmitrevs Feb 10, 2026
ea9948a
pre-commit fixes
jmitrevs Feb 10, 2026
c022f9c
make sure the test output directories are unique
jmitrevs Feb 10, 2026
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19 changes: 10 additions & 9 deletions test/pytest/test_keras_v3_api.py
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Original file line number Diff line number Diff line change
Expand Up @@ -26,7 +26,7 @@

import hls4ml

test_root_path = Path('/tmp/tests')
test_root_path = Path(__file__).parent


@pytest.mark.parametrize('backend', ['Vivado', 'Vitis', 'Quartus', 'oneAPI', 'Catapult'])
Expand Down Expand Up @@ -57,7 +57,7 @@ def test_dense(backend, io_type):
keras_prediction = model.predict(X_input, verbose=0) # type: ignore

config = hls4ml.utils.config_from_keras_model(model)
output_dir = str(test_root_path / f'hls4mlprj_keras_api_dense_{backend}_{io_type}')
output_dir = str(test_root_path / f'hls4mlprj_keras_v3_api_dense_{backend}_{io_type}')

hls_model = hls4ml.converters.convert_from_keras_model(
model, hls_config=config, output_dir=output_dir, backend=backend, io_type=io_type
Expand Down Expand Up @@ -105,7 +105,7 @@ def test_activations(activation_function, backend, io_type):
X_input = np.random.rand(1000, 1)
keras_prediction = model.predict(X_input, verbose=0) # type: ignore
config = hls4ml.utils.config_from_keras_model(model)
output_dir = str(test_root_path / f'hls4mlprj_keras_api_activations_{activation_function.name}_{backend}_{io_type}')
output_dir = str(test_root_path / f'hls4mlprj_keras_v3_api_activations_{activation_function.name}_{backend}_{io_type}')
hls_model = hls4ml.converters.convert_from_keras_model(
model, hls_config=config, output_dir=output_dir, backend=backend, io_type=io_type
)
Expand Down Expand Up @@ -151,7 +151,7 @@ def test_conv1d(padds, backend, io_type):
keras_prediction = model.predict(X_input, verbose=0) # type: ignore

config = hls4ml.utils.config_from_keras_model(model)
output_dir = str(test_root_path / f'hls4mlprj_keras_api_conv1d_{padds}_{backend}_{io_type}')
output_dir = str(test_root_path / f'hls4mlprj_keras_v3_api_conv1d_{padds}_{backend}_{io_type}')
hls_model = hls4ml.converters.convert_from_keras_model(
model, hls_config=config, output_dir=output_dir, backend=backend, io_type=io_type
)
Expand Down Expand Up @@ -228,7 +228,7 @@ def test_conv2d(chans, padds, backend, io_type):
keras_prediction = model.predict(X_input)

config = hls4ml.utils.config_from_keras_model(model)
output_dir = str(test_root_path / f'hls4ml_project_keras_api_conv2d_{backend}_{chans}_{padds}_{io_type}')
output_dir = str(test_root_path / f'hls4mlprj_keras_v3_api_conv2d_{backend}_{chans}_{padds}_{io_type}')
hls_model = hls4ml.converters.convert_from_keras_model(
model, hls_config=config, output_dir=output_dir, backend=backend, io_type=io_type
)
Expand Down Expand Up @@ -311,7 +311,7 @@ def test_depthwise2d(backend, io_type):
config = hls4ml.utils.config_from_keras_model(
model, granularity='name', default_precision='fixed<32,12>', backend=backend
)
output_dir = str(test_root_path / f'hls4mlprj_keras_api_depthwiseconv2d_{backend}_{io_type}')
output_dir = str(test_root_path / f'hls4mlprj_keras_v3_api_depthwiseconv2d_{backend}_{io_type}')
hls_model = hls4ml.converters.convert_from_keras_model(
model, hls_config=config, output_dir=output_dir, backend=backend, io_type=io_type
)
Expand All @@ -336,7 +336,7 @@ def test_depthwise1d(backend, io_type):
model.compile()

config = hls4ml.utils.config_from_keras_model(model, granularity='name', backend=backend)
output_dir = str(test_root_path / f'hls4mlprj_keras_api_depthwiseconv1d_{backend}_{io_type}')
output_dir = str(test_root_path / f'hls4mlprj_keras_v3_api_depthwiseconv1d_{backend}_{io_type}')
hls_model = hls4ml.converters.convert_from_keras_model(
model, hls_config=config, output_dir=output_dir, backend=backend, io_type=io_type
)
Expand Down Expand Up @@ -368,7 +368,8 @@ def test_pooling(pooling, padds, chans, backend):

hls_cfg = hls4ml.utils.config_from_keras_model(keras_model)
output_dir = str(
test_root_path / f'hls4mlprj_keras_api_pooling_{pooling.__name__}_channels_{chans}_padds_{padds}_backend_{backend}'
test_root_path
/ f'hls4mlprj_keras_v3_api_pooling_{pooling.__name__}_channels_{chans}_padds_{padds}_backend_{backend}'
)
hls_model = hls4ml.converters.convert_from_keras_model(
keras_model, hls_config=hls_cfg, output_dir=output_dir, backend=backend
Expand Down Expand Up @@ -497,7 +498,7 @@ def test_reused_layer(backend, io_type):
_ = model([inp1, inp1])

hls_config = {'Model': {'Precision': 'ap_fixed<32,8>', 'ReuseFactor': 1}}
output_dir = str(test_root_path / f'hls4mlprj_keras_api_conv1d_{backend}_{io_type}')
output_dir = str(test_root_path / f'hls4mlprj_keras_v3_api_reused_{backend}_{io_type}')

model_hls = hls4ml.converters.convert_from_keras_model(
model, backend=backend, io_type=io_type, hls_config=hls_config, output_dir=output_dir
Expand Down
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