Merge pull request #802 from vloncar/conv_bn_fix

Fix parsing of QConv2DBatchnorm weights

hls4ml/converters/keras/convolution.py

@@ -44,7 +44,7 @@ def parse_conv2d_layer(keras_layer, input_names, input_shapes, data_reader):
 
     (layer['in_height'], layer['in_width'], layer['n_chan']) = parse_data_format(input_shapes[0], layer['data_format'])
 
-    if layer['class_name'] in ['Conv2D', 'QConv2D']:
+    if layer['class_name'] in ['Conv2D', 'QConv2D', 'QConv2DBatchnorm']:
         layer['weight_data'] = get_weights_data(data_reader, layer['name'], 'kernel')
     elif layer['class_name'] in ['SeparableConv2D', 'QSeparableConv2D']:
         layer['depthwise_data'], layer['pointwise_data'] = get_weights_data(

test/pytest/test_qkeras.py

@@ -3,6 +3,7 @@
 
 import numpy as np
 import pytest
+from qkeras.qconv2d_batchnorm import QConv2DBatchnorm
 from qkeras.qlayers import QActivation, QDense
 from qkeras.quantizers import binary, quantized_bits, quantized_relu, quantized_sigmoid, quantized_tanh, ternary
 from qkeras.utils import _add_supported_quantized_objects
@@ -348,3 +349,44 @@ def test_qactivation_kwarg(randX_100_10, activation_quantizer, weight_quantizer)
             y_hls4ml = np.where(y_hls4ml == 0, -1, 1)
         wrong = (y_hls4ml != y_qkeras).ravel()
         assert sum(wrong) / len(wrong) <= 0.005
+
+
+@pytest.fixture(scope='module')
+def randX_100_8_8_1():
+    return np.random.rand(100, 8, 8, 1)
+
+
+@pytest.mark.parametrize('backend', ['Vivado', 'Vitis', 'Quartus'])
+@pytest.mark.parametrize('io_type', ['io_parallel', 'io_stream'])
+def test_qconv2dbn(randX_100_8_8_1, backend, io_type):
+    '''
+    Test proper handling of QConv2DBatchnorm.
+    '''
+    X = randX_100_8_8_1
+    X = np.round(X * 2**10) * 2**-10  # make it an exact ap_fixed<16,6>
+    model = Sequential()
+    model.add(
+        QConv2DBatchnorm(
+            4,
+            kernel_size=(3, 3),
+            input_shape=(8, 8, 1),
+            kernel_quantizer='quantized_bits(8, 0, alpha=1)',
+            kernel_initializer='ones',
+            bias_quantizer='quantized_bits(8, 0, alpha=1)',
+            bias_initializer='zeros',
+            activation='quantized_relu(8, 0)',
+        )
+    )
+    model.compile()
+
+    config = hls4ml.utils.config_from_keras_model(model, granularity='name')
+    output_dir = str(test_root_path / f'hls4mlprj_qkeras_qconv2dbn_{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
+    )
+    hls_model.compile()
+
+    y_qkeras = model.predict(X)
+    y_hls4ml = hls_model.predict(X)
+
+    np.testing.assert_array_equal(y_qkeras, y_hls4ml.reshape(y_qkeras.shape))