Add (bit accurate) tests for QSeparableConv1D/2D

Author: vloncar

test/pytest/test_qkeras.py

@@ -4,7 +4,7 @@
 import numpy as np
 import pytest
 from qkeras.qconv2d_batchnorm import QConv2DBatchnorm
-from qkeras.qconvolutional import QDepthwiseConv2D
+from qkeras.qconvolutional import QDepthwiseConv2D, QSeparableConv1D, QSeparableConv2D
 from qkeras.qlayers import QActivation, QDense
 from qkeras.quantizers import (
     binary,
@@ -478,3 +478,95 @@ def test_quantised_po2_bit_width(backend, io_type, strategy):
     y_hls = hls_model.predict(np.ascontiguousarray(X))
 
     np.testing.assert_allclose(y_hls.flatten(), y_keras.flatten(), rtol=2e-2)
+
+
+@pytest.mark.parametrize('backend', ['Vivado', 'Vitis'])
+@pytest.mark.parametrize('io_type', ['io_stream'])
+def test_qseparableconv1d(backend, io_type):
+    '''
+    Test proper handling of QSeparableConv1D.
+    '''
+    x_in = Input((13, 20), name='input_layer')
+    x = QSeparableConv1D(
+        5,
+        3,
+        depthwise_quantizer=quantized_bits(8, 3, alpha=1),
+        pointwise_quantizer=quantized_bits(8, 3, alpha=1),
+        bias_quantizer=quantized_bits(8, 3, alpha=1),
+        name='qsepconv_1',
+    )(x_in)
+    model = Model(inputs=x_in, outputs=x)
+
+    config = hls4ml.utils.config_from_keras_model(
+        model, granularity='name', backend=backend, default_precision='fixed<23,7>'
+    )
+
+    # Use 8 bits for input
+    config['LayerName']['input_layer']['Precision']['result'] = 'fixed<8,1>'
+    # default_precision is will be used for accum_t and result_t of the conv layer, so we don't need to set them here
+    # We need <15,4> for the result of depthwise step
+    config['LayerName']['qsepconv_1']['Precision']['dw_output'] = 'fixed<15,4>'
+
+    output_dir = str(test_root_path / f'hls4mlprj_qsepconv1d_{backend}_{io_type}')
+    hls_model = hls4ml.converters.convert_from_keras_model(
+        model,
+        hls_config=config,
+        output_dir=output_dir,
+        io_type=io_type,
+    )
+    hls_model.compile()
+
+    data = np.random.rand(100, 13, 20)
+    input_quantizer = quantized_bits(8, 0, alpha=1)
+    dataq = input_quantizer(data).numpy()
+
+    y_qkeras = model.predict(dataq)
+    y_hls4ml = hls_model.predict(dataq)
+
+    np.testing.assert_allclose(y_qkeras, y_hls4ml.reshape(y_qkeras.shape), rtol=0, atol=0)
+
+
+@pytest.mark.parametrize('backend', ['Vivado', 'Vitis'])
+@pytest.mark.parametrize('io_type', ['io_stream'])
+def test_qseparableconv2d(backend, io_type):
+    '''
+    Test proper handling of QSeparableConv2D.
+    '''
+    x_in = Input((13, 21, 20), name='input_layer')
+    x = QSeparableConv2D(
+        5,
+        3,
+        depthwise_quantizer=quantized_bits(8, 3, alpha=1),
+        pointwise_quantizer=quantized_bits(8, 3, alpha=1),
+        bias_quantizer=quantized_bits(8, 3, alpha=1),
+        name='qsepconv_1',
+    )(x_in)
+    model = Model(inputs=x_in, outputs=x)
+
+    config = hls4ml.utils.config_from_keras_model(
+        model, granularity='name', backend=backend, default_precision='fixed<23,7>'
+    )
+
+    # Use 8 bits for input
+    config['LayerName']['input_layer']['Precision']['result'] = 'fixed<8,1>'
+    # default_precision is will be used for accum_t and result_t of the conv layer, so we don't need to set them here
+    # We need <15,4> for the result of depthwise step
+    config['LayerName']['qsepconv_1']['Precision']['dw_output'] = 'fixed<15,4>'
+
+    output_dir = str(test_root_path / f'hls4mlprj_qsepconv2d_{backend}_{io_type}')
+    hls_model = hls4ml.converters.convert_from_keras_model(
+        model,
+        hls_config=config,
+        output_dir=output_dir,
+        io_type=io_type,
+    )
+    hls_model.compile()
+
+    data = np.random.rand(100, 13, 21, 20)
+    input_quantizer = quantized_bits(8, 0, alpha=1)
+    dataq = input_quantizer(data).numpy()
+
+    y_qkeras = model.predict(dataq)
+    y_hls4ml = hls_model.predict(dataq)
+
+    np.testing.assert_allclose(y_qkeras, y_hls4ml.reshape(y_qkeras.shape), rtol=0, atol=0)