add pytest for binary cnn

Author: jmitrevs

test/pytest/test_binary_cnn.py

@@ -0,0 +1,100 @@
+import numpy as np
+import pytest
+from qkeras import QActivation, QBatchNormalization, QConv2D, QDense
+from tensorflow.keras.layers import Flatten, Input, MaxPooling2D
+from tensorflow.keras.models import Model
+from tensorflow.keras.regularizers import l2
+
+import hls4ml
+
+
+@pytest.mark.parametrize('backend', ['Vivado', 'Vitis', 'Quartus'])
+@pytest.mark.parametrize('io_type', ['io_parallel', 'io_stream'])
+def test_model2(backend, io_type):
+    x_in = Input(shape=(28, 28, 1))
+
+    x = QConv2D(
+        4,
+        (3, 3),
+        kernel_quantizer="binary",
+        bias_quantizer="binary",
+        name="conv2d_1",
+        kernel_regularizer=l2(0.0001),
+    )(x_in)
+    x = QBatchNormalization()(x)
+    x = QActivation("binary", name="act1")(x)
+
+    x = QConv2D(
+        8,
+        (3, 3),
+        kernel_quantizer="binary",
+        bias_quantizer="binary",
+        name="conv2d_2",
+        kernel_regularizer=l2(0.0001),
+    )(x)
+    x = QBatchNormalization()(x)
+    x = QActivation("binary", name="act2")(x)
+    x = MaxPooling2D(pool_size=(2, 2))(x)
+
+    x = QConv2D(
+        8,
+        (3, 3),
+        kernel_quantizer="binary",
+        bias_quantizer="binary",
+        name="conv2d_3",
+        kernel_regularizer=l2(0.0001),
+    )(x)
+    x = QBatchNormalization()(x)
+    x = QActivation("binary", name="act3")(x)
+    x = MaxPooling2D(pool_size=(2, 2))(x)
+
+    x = Flatten()(x)
+
+    x = QDense(10, kernel_quantizer="binary", bias_quantizer="binary", name="q_dense_6")(x)
+    x = QBatchNormalization()(x)
+    x = QActivation("binary_tanh", name="act4")(x)
+
+    x = QDense(
+        10,
+        kernel_quantizer="binary",
+        bias_quantizer="binary",
+        activation="softmax",
+        name="q_dense_7",
+    )(x)
+
+    model2 = Model(inputs=x_in, outputs=x)
+
+    model2.compile(optimizer="adam", loss="categorical_crossentropy", metrics=["accuracy"])
+
+    model2.summary()
+
+    hls_config = hls4ml.utils.config_from_keras_model(model2, granularity="name")
+    hls_config["Model"]["Strategy"] = "Resource"
+    hls_config["IOTtype"] = "io_stream"
+
+    hls_config["LayerName"]["conv2d_1"]["ReuseFactor"] = 36
+    hls_config["LayerName"]["conv2d_2"]["ReuseFactor"] = 288
+    hls_config["LayerName"]["conv2d_3"]["ReuseFactor"] = 576
+    hls_config["LayerName"]["q_dense_6"]["ReuseFactor"] = 2000
+    hls_config["LayerName"]["q_dense_7"]["ReuseFactor"] = 100
+
+    hls_model = hls4ml.converters.convert_from_keras_model(
+        model2,
+        hls_config=hls_config,
+        output_dir=f"hls4mlprj_binary_cnn_{backend}_{io_type}",
+        backend=backend,
+        io_type=io_type,
+    )
+
+    hls_model.compile()
+    y = model2.predict(np.zeros((1, 28, 28, 1)))
+    y_hls = hls_model.predict(np.zeros((1, 28, 28, 1)))
+
+    print(f"{y_hls=}")
+    print(f"{y=}")
+
+    np.testing.assert_allclose(np.squeeze(y_hls), np.squeeze(y), rtol=1e-2, atol=0.01)
+
+
+if __name__ == "__main__":
+    test_model2("Vivado", "io_stream")