Add explicit DepthwiseConv tests and simpligy SepConv tests

Author: vloncar

test/pytest/test_depthconv1d.py

@@ -0,0 +1,66 @@
+from pathlib import Path
+
+import numpy as np
+import pytest
+import tensorflow as tf
+
+import hls4ml
+
+test_root_path = Path(__file__).parent
+
+padds_options = ['same', 'valid']
+chans_options = ['channels_last']
+strides_options = [(1), (2)]
+kernel_options = [(2), (3)]
+bias_options = [False]
+
+
+@pytest.mark.parametrize('chans', chans_options)
+@pytest.mark.parametrize('padds', padds_options)
+@pytest.mark.parametrize('strides', strides_options)
+@pytest.mark.parametrize('kernels', kernel_options)
+@pytest.mark.parametrize('bias', bias_options)
+@pytest.mark.parametrize(
+    'backend, io_type',
+    [
+        ('Vivado', 'io_parallel'),
+        ('Vitis', 'io_parallel'),
+        ('Vivado', 'io_stream'),
+        ('Vitis', 'io_stream'),
+        ('Catapult', 'io_stream'),
+    ],
+)
+def test_depthconv1d(chans, padds, strides, kernels, bias, io_type, backend):
+    model = tf.keras.models.Sequential()
+    input_shape = (16, 3)
+    model.add(
+        tf.keras.layers.DepthwiseConv1D(
+            kernel_size=kernels,
+            strides=strides,
+            padding=padds,
+            input_shape=input_shape,
+            kernel_initializer='normal',
+            use_bias=bias,
+            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>')
+    stride_cfg = str(strides).replace(', ', '_').replace('(', '').replace(')', '')
+    kernel_cfg = str(kernels).replace(', ', '_').replace('(', '').replace(')', '')
+    output_dir = str(
+        test_root_path
+        / 'hls4mlprj_depthconv1d_{}_strides_{}_kernels_{}_{}_padding_{}_{}'.format(
+            chans, stride_cfg, kernel_cfg, padds, backend, io_type
+        )
+    )
+    hls_model = hls4ml.converters.convert_from_keras_model(
+        model, hls_config=config, output_dir=output_dir, io_type=io_type, backend=backend
+    )
+    hls_model.compile()
+    hls_prediction = hls_model.predict(X_input).reshape(keras_prediction.shape)
+
+    np.testing.assert_allclose(hls_prediction, keras_prediction, rtol=0, atol=0.001)

test/pytest/test_depthconv2d.py

@@ -0,0 +1,67 @@
+from pathlib import Path
+
+import numpy as np
+import pytest
+import tensorflow as tf
+
+import hls4ml
+
+test_root_path = Path(__file__).parent
+
+padds_options = ['same', 'valid']
+chans_options = ['channels_last']
+io_type_options = ['io_parallel', 'io_stream']
+strides_options = [(1, 1), (2, 2)]
+kernel_options = [(2, 2), (3, 3)]
+bias_options = [False]
+
+
+@pytest.mark.parametrize('chans', chans_options)
+@pytest.mark.parametrize('padds', padds_options)
+@pytest.mark.parametrize('strides', strides_options)
+@pytest.mark.parametrize('kernels', kernel_options)
+@pytest.mark.parametrize('bias', bias_options)
+@pytest.mark.parametrize(
+    'backend, io_type',
+    [
+        ('Vivado', 'io_parallel'),
+        ('Vitis', 'io_parallel'),
+        ('Vivado', 'io_stream'),
+        ('Vitis', 'io_stream'),
+        ('Catapult', 'io_stream'),
+    ],
+)
+def test_depthconv2d(chans, padds, strides, kernels, bias, io_type, backend):
+    model = tf.keras.models.Sequential()
+    input_shape = (16, 16, 3)
+    model.add(
+        tf.keras.layers.DepthwiseConv2D(
+            kernel_size=kernels,
+            strides=strides,
+            padding=padds,
+            input_shape=input_shape,
+            kernel_initializer='normal',
+            use_bias=bias,
+            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>')
+    stride_cfg = str(strides).replace(', ', '_').replace('(', '').replace(')', '')
+    kernel_cfg = str(kernels).replace(', ', '_').replace('(', '').replace(')', '')
+    output_dir = str(
+        test_root_path
+        / 'hls4mlprj_depthconv2d_{}_strides_{}_kernels_{}_{}_padding_{}_{}'.format(
+            chans, stride_cfg, kernel_cfg, padds, backend, io_type
+        )
+    )
+    hls_model = hls4ml.converters.convert_from_keras_model(
+        model, hls_config=config, output_dir=output_dir, io_type=io_type, backend=backend
+    )
+    hls_model.compile()
+    hls_prediction = hls_model.predict(X_input).reshape(keras_prediction.shape)
+
+    np.testing.assert_allclose(hls_prediction, keras_prediction, rtol=0, atol=0.001)

test/pytest/test_sepconv1d.py

@@ -3,21 +3,18 @@
 import numpy as np
 import pytest
 import tensorflow as tf
-from tensorflow.keras.layers import SeparableConv1D
 
 import hls4ml
 
 test_root_path = Path(__file__).parent
 
-keras_conv1d = [SeparableConv1D]
 padds_options = ['same', 'valid']
 chans_options = ['channels_last']
 strides_options = [(1), (2)]
 kernel_options = [(2), (3)]
 bias_options = [False]
 
 
-@pytest.mark.parametrize('conv1d', keras_conv1d)
 @pytest.mark.parametrize('chans', chans_options)
 @pytest.mark.parametrize('padds', padds_options)
 @pytest.mark.parametrize('strides', strides_options)
@@ -33,11 +30,11 @@
         ('Catapult', 'io_stream'),
     ],
 )
-def test_sepconv1d(conv1d, chans, padds, strides, kernels, bias, io_type, backend):
+def test_sepconv1d(chans, padds, strides, kernels, bias, io_type, backend):
     model = tf.keras.models.Sequential()
     input_shape = (16, 3)
     model.add(
-        conv1d(
+        tf.keras.layers.SeparableConv1D(
             filters=8,
             kernel_size=kernels,
             strides=strides,
@@ -57,8 +54,8 @@ def test_sepconv1d(conv1d, chans, padds, strides, kernels, bias, io_type, backen
     kernel_cfg = str(kernels).replace(', ', '_').replace('(', '').replace(')', '')
     output_dir = str(
         test_root_path
-        / 'hls4mlprj_{}_{}_strides_{}_kernels_{}_{}_padding_{}_{}'.format(
-            conv1d.__name__.lower(), chans, stride_cfg, kernel_cfg, padds, backend, io_type
+        / 'hls4mlprj_sepconv1d_{}_strides_{}_kernels_{}_{}_padding_{}_{}'.format(
+            chans, stride_cfg, kernel_cfg, padds, backend, io_type
         )
     )
     hls_model = hls4ml.converters.convert_from_keras_model(

test/pytest/test_sepconv2d.py

@@ -3,13 +3,11 @@
 import numpy as np
 import pytest
 import tensorflow as tf
-from tensorflow.keras.layers import SeparableConv2D
 
 import hls4ml
 
 test_root_path = Path(__file__).parent
 
-keras_conv2d = [SeparableConv2D]
 padds_options = ['same', 'valid']
 chans_options = ['channels_last']
 io_type_options = ['io_parallel', 'io_stream']
@@ -18,7 +16,6 @@
 bias_options = [False]
 
 
-@pytest.mark.parametrize('conv2d', keras_conv2d)
 @pytest.mark.parametrize('chans', chans_options)
 @pytest.mark.parametrize('padds', padds_options)
 @pytest.mark.parametrize('strides', strides_options)
@@ -34,11 +31,11 @@
         ('Catapult', 'io_stream'),
     ],
 )
-def test_sepconv2d(conv2d, chans, padds, strides, kernels, bias, io_type, backend):
+def test_sepconv2d(chans, padds, strides, kernels, bias, io_type, backend):
     model = tf.keras.models.Sequential()
     input_shape = (16, 16, 3)
     model.add(
-        conv2d(
+        tf.keras.layers.SeparableConv2D(
             filters=8,
             kernel_size=kernels,
             strides=strides,
@@ -58,8 +55,8 @@ def test_sepconv2d(conv2d, chans, padds, strides, kernels, bias, io_type, backen
     kernel_cfg = str(kernels).replace(', ', '_').replace('(', '').replace(')', '')
     output_dir = str(
         test_root_path
-        / 'hls4mlprj_{}_{}_strides_{}_kernels_{}_{}_padding_{}_{}'.format(
-            conv2d.__name__.lower(), chans, stride_cfg, kernel_cfg, padds, backend, io_type
+        / 'hls4mlprj_sepconv2d_{}_strides_{}_kernels_{}_{}_padding_{}_{}'.format(
+            chans, stride_cfg, kernel_cfg, padds, backend, io_type
         )
     )
     hls_model = hls4ml.converters.convert_from_keras_model(