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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,32 @@ | ||
| import ast | ||
| import io | ||
| from contextlib import redirect_stdout | ||
| from pathlib import Path | ||
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| import pytest | ||
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| import hls4ml | ||
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| test_root_path = Path(__file__).parent | ||
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| @pytest.mark.parametrize('backend', ['Vivado', 'Vitis', 'Quartus']) | ||
| def test_fetch_example_utils(backend): | ||
| f = io.StringIO() | ||
| with redirect_stdout(f): | ||
| hls4ml.utils.fetch_example_list() | ||
| out = f.getvalue() | ||
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| model_list = ast.literal_eval(out) # Check if we indeed got a dictionary back | ||
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| assert 'qkeras_mnist_cnn.json' in model_list['keras'] | ||
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| # This model has an example config that is also downloaded. Stored configurations don't set "Backend" value. | ||
| config = hls4ml.utils.fetch_example_model('qkeras_mnist_cnn.json', backend=backend) | ||
| config['KerasJson'] = 'qkeras_mnist_cnn.json' | ||
| config['KerasH5'] | ||
| config['Backend'] = backend | ||
| config['OutputDir'] = str(test_root_path / f'hls4mlprj_fetch_example_{backend}') | ||
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| hls_model = hls4ml.converters.keras_to_hls(config) | ||
| hls_model.compile() # For now, it is enough if it compiles, we're only testing downloading works as expected |
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,27 @@ | ||
| from tensorflow import keras | ||
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| from hls4ml.converters import convert_from_keras_model | ||
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| def test_repack_precision(): | ||
| inp = keras.Input(shape=(3, 3), name='inp') | ||
| out = keras.layers.Reshape((3, 3), name='reshape')(inp) | ||
| out = keras.layers.Conv1D(2, 2, name='conv')(out) | ||
| model = keras.Model(inp, out) | ||
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| layer_conf = { | ||
| 'inp': {'Precision': 'fixed<20,10>'}, | ||
| 'reshape': {'Precision': 'fixed<20,10>'}, | ||
| 'conv': {'Precision': 'fixed<20,10>'}, | ||
| } | ||
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| hls_config = {'Model': {'Precision': 'fixed<2,1>', 'ReuseFactor': 1}, 'LayerName': layer_conf} | ||
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| # Repack only happens in io_stream | ||
| model_hls = convert_from_keras_model(model, hls_config=hls_config, io_type='io_stream') | ||
| assert 'repack_reshape' in model_hls.graph, 'repack_reshape not found in graph' | ||
| repack_precision = model_hls.graph['repack_reshape'].attributes['result_t'].precision | ||
| assert repack_precision.integer == 10, 'Precision mismatch' | ||
| assert repack_precision.fractional == 10, 'Precision mismatch' | ||
| assert repack_precision.width == 20, 'Precision mismatch' | ||
| assert repack_precision.signed is True, 'Precision mismatch' |
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,59 @@ | ||
| import os | ||
| import random | ||
| from pathlib import Path | ||
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| import numpy as np | ||
| import pytest | ||
| import tensorflow as tf | ||
| from keras.layers import Add, Dense | ||
| from tensorflow import keras | ||
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| from hls4ml.converters import convert_from_keras_model | ||
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| test_root_path = Path(__file__).parent | ||
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| @pytest.fixture(scope='module') | ||
| def model(): | ||
| seed = 42 | ||
| os.environ['RANDOM_SEED'] = f'{seed}' | ||
| np.random.seed(seed) | ||
| tf.random.set_seed(seed) | ||
| tf.get_logger().setLevel('ERROR') | ||
| random.seed(seed) | ||
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| inp = keras.Input(shape=(10,)) | ||
| x = Dense(10)(inp) | ||
| y = Dense(10)(inp) | ||
| z = Dense(10)(inp) | ||
| xy = Add()([x, y]) # 5 | ||
| xy = Add()([xy, y]) # 5 | ||
| out = Add()([xy, z]) # 5 | ||
| model = keras.Model(inp, out) | ||
| return model | ||
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| @pytest.fixture(scope='module') | ||
| def data(): | ||
| rng = np.random.RandomState(42) | ||
| X = rng.normal(0, 1, (1000, 10)) | ||
| X = np.clip(X, -16, 15) | ||
| return X | ||
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| @pytest.mark.parametrize('backend', ['Vivado', 'Quartus', 'Vitis']) | ||
| def test_multi_clone(model, data, backend: str): | ||
| output_dir = str(test_root_path / f'hls4mlprj_stream_multi_clone_{backend}') | ||
| hls_config = {'Model': {'Precision': 'fixed<32,5>', 'ReuseFactor': 1}} | ||
| model_hls = convert_from_keras_model( | ||
| model, | ||
| backend=backend, | ||
| output_dir=output_dir, | ||
| hls_config=hls_config, | ||
| io_type='io_stream', # clone only happens with stream io. | ||
| ) | ||
| model_hls.compile() | ||
| r_hls = model_hls.predict(data) | ||
| r_keras = model(data).numpy() | ||
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| assert np.allclose(r_hls, r_keras, atol=1e-5, rtol=0) |