Test case for pointwise conv1d/2d

Author: vloncar

test/pytest/test_pointwiseconv.py

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+import pytest
+import hls4ml
+import tensorflow as tf
+import numpy as np
+from pathlib import Path
+from tensorflow.keras.layers import Conv1D, Conv2D
+from tensorflow.keras import backend as K
+
+test_root_path = Path(__file__).parent
+
+padds_options = ['same', 'valid']
+chans_options = ['channels_last']
+io_type_options = ['io_parallel', 'io_stream']
+strides1d_options = [(1,), (2,)]
+strides2d_options = [(1, 1), (2, 2)]
+strategy_options = ['Latency', 'Resource']
+
+@pytest.mark.parametrize("chans", chans_options)
+@pytest.mark.parametrize("padds", padds_options)
+@pytest.mark.parametrize("strides", strides1d_options)
+@pytest.mark.parametrize("io_type", io_type_options)
+@pytest.mark.parametrize("strategy", strategy_options)
+def test_pointwiseconv1d(chans, padds, strides, io_type, strategy):
+    model = tf.keras.models.Sequential()
+    input_shape = (28, 3)
+    model.add(Conv1D(filters=32,
+                     kernel_size=(1,),
+                     strides=strides,
+                     padding=padds,
+                     input_shape=input_shape,
+                     kernel_initializer='normal',
+                     use_bias=False,
+                     data_format=chans
+                     ))
+
+    model.compile(optimizer='adam', loss='mse')
+    X_input = np.random.rand(100, *input_shape)
+    keras_prediction = model.predict(X_input)
+    config = hls4ml.utils.config_from_keras_model(model, default_precision='ap_fixed<32,16>')
+    config['Model']['Strategy'] = strategy
+    output_dir = str(test_root_path / 'hls4mlprj_pointwise1d_{}_strides_{}_{}_padding_{}_{}'.format(chans, strides[0], padds, io_type, strategy))
+    hls_model = hls4ml.converters.convert_from_keras_model(model, hls_config=config, output_dir=output_dir, io_type=io_type)
+    hls_model.compile()
+    hls_prediction = hls_model.predict(X_input).reshape(keras_prediction.shape)
+
+    assert 'Pointwise' in list(hls_model.graph.values())[1].class_name
+    np.testing.assert_allclose(hls_prediction, keras_prediction, rtol=0, atol=0.001)
+
+@pytest.mark.parametrize("chans", chans_options)
+@pytest.mark.parametrize("padds", padds_options)
+@pytest.mark.parametrize("strides", strides2d_options)
+@pytest.mark.parametrize("io_type", io_type_options)
+@pytest.mark.parametrize("strategy", strategy_options)
+def test_pointwiseconv2d(chans, padds, strides, io_type, strategy):
+    model = tf.keras.models.Sequential()
+    input_shape = (28, 28, 3)
+    model.add(Conv2D(filters=32,
+                     kernel_size=(1, 1),
+                     strides=strides,
+                     padding=padds,
+                     input_shape=input_shape,
+                     kernel_initializer='normal',
+                     use_bias=False,
+                     data_format=chans
+                     ))
+
+    model.compile(optimizer='adam', loss='mse')
+    X_input = np.random.rand(100, *input_shape)
+    keras_prediction = model.predict(X_input)
+    config = hls4ml.utils.config_from_keras_model(model, default_precision='ap_fixed<32,16>')
+    config['Model']['Strategy'] = strategy
+    stride_cfg = str(strides).replace(', ', '_').replace('(', '').replace(')', '')
+    output_dir = str(test_root_path / 'hls4mlprj_pointwise2d_{}_strides_{}_{}_padding_{}_{}'.format(chans, stride_cfg, padds, io_type, strategy))
+    hls_model = hls4ml.converters.convert_from_keras_model(model, hls_config=config, output_dir=output_dir, io_type=io_type)
+    hls_model.compile()
+    hls_prediction = hls_model.predict(X_input).reshape(keras_prediction.shape)
+
+    assert 'Pointwise' in list(hls_model.graph.values())[1].class_name
+    np.testing.assert_allclose(hls_prediction, keras_prediction, rtol=0, atol=0.001)