Add a test for QKeras activations passed as an argument

test/pytest/test_qkeras.py

@@ -6,10 +6,10 @@
 from sklearn.datasets import fetch_openml
 from sklearn.model_selection import train_test_split
 from sklearn.preprocessing import LabelEncoder, StandardScaler
-from tensorflow.keras.models import Sequential, model_from_json
+from tensorflow.keras.models import Sequential, Model, model_from_json
 from tensorflow.keras.optimizers import Adam
 from tensorflow.keras.regularizers import l1
-from tensorflow.keras.layers import Activation, BatchNormalization
+from tensorflow.keras.layers import Activation, BatchNormalization, Input
 from qkeras.qlayers import QDense, QActivation
 from qkeras.quantizers import quantized_bits, quantized_relu, ternary, binary
 from qkeras.utils import _add_supported_quantized_objects; co = {}; _add_supported_quantized_objects(co)
@@ -228,3 +228,83 @@ def test_quantizer(randX_1000_1, quantizer, backend):
   y_hls4ml = hls_model.predict(X)
   # Goal is to get it passing with all equal
   np.testing.assert_array_equal(y_qkeras, y_hls4ml)
+
+
+@pytest.mark.parametrize(
+    'weight_quantizer,activation_quantizer,', [
+        ('binary', 'binary'),
+        ('ternary', 'ternary'),
+        ('quantized_bits(4, 0, alpha=1)', 'quantized_relu(2, 0)'),
+        ('quantized_bits(4, 0, alpha=1)', 'quantized_relu(4, 0)'),
+        ('quantized_bits(4, 0, alpha=1)', 'quantized_relu(8, 0)')
+    ]
+)
+def test_qactivation_kwarg(randX_100_10,
+                           activation_quantizer,
+                           weight_quantizer):
+    if activation_quantizer in ['binary', 'ternary']:
+        name = 'bnbt_qdense_alpha'
+    else:
+        name = 'qdense_{}'.format(
+            eval(activation_quantizer).__class__.__name__)
+
+    inputs = Input(shape=(10,))
+
+    outputs = QDense(
+        10,
+        activation=activation_quantizer,
+        name='qdense',
+        kernel_quantizer=weight_quantizer,
+        bias_quantizer=weight_quantizer,
+        kernel_initializer='lecun_uniform'
+    )(inputs)
+    model = Model(inputs, outputs)
+
+    hls4ml.model.optimizer.get_optimizer(
+        'output_rounding_saturation_mode'
+    ).configure(
+        layers=[name],
+        rounding_mode='AP_RND_CONV',
+        saturation_mode='AP_SAT'
+    )
+    config = hls4ml.utils.config_from_keras_model(
+        model,
+        granularity='name'
+    )
+
+    out_dir = str(
+        test_root_path / 'hls4mlprj_qactivation_kwarg_{}'.format(
+            activation_quantizer
+        )
+    )
+
+    hls_model = hls4ml.converters.convert_from_keras_model(
+        model,
+        hls_config=config,
+        output_dir=out_dir
+    )
+    hls4ml.model.optimizer.get_optimizer(
+        'output_rounding_saturation_mode'
+    ).configure(layers=[])
+    hls_model.compile()
+
+    # Verify if activation in hls_model
+    assert name in [layer.name for layer in hls_model.get_layers()]
+
+    # Output tests
+    X = randX_100_10
+    X = np.round(X * 2**10) * 2**-10
+    y_qkeras = model.predict(X)
+    y_hls4ml = hls_model.predict(X)
+    if hasattr(eval(activation_quantizer), 'bits'):
+        np.testing.assert_allclose(
+            y_qkeras.ravel(),
+            y_hls4ml.ravel(),
+            atol=2**-eval(activation_quantizer).bits,
+            rtol=1.0
+        )
+    else:
+        if activation_quantizer == 'binary':
+            y_hls4ml = np.where(y_hls4ml == 0, -1, 1)
+        wrong = (y_hls4ml != y_qkeras).ravel()
+        assert sum(wrong) / len(wrong) <= 0.005