Speed up Keras profiling

hls4ml/model/profiling.py

@@ -343,21 +343,22 @@ def activations_keras(model, X, fmt='longform', plot='boxplot'):
         # return summary statistics for matplotlib.axes.Axes.bxp
         # or histogram bin edges and heights
         data = []
-
-    for layer in model.layers:
-        print(f"   {layer.name}")
-        if not isinstance(layer, keras.layers.InputLayer):
-            y = _get_output(layer, X, model.input).flatten()
-            y = abs(y[y != 0])
-            if len(y) == 0:
-                print(f'Activations for {layer.name} are only zeros, ignoring.')
-                continue
-            if fmt == 'longform':
-                data['x'].extend(y.tolist())
-                data['weight'].extend([layer.name for i in range(len(y))])
-            elif fmt == 'summary':
-                data.append(array_to_summary(y, fmt=plot))
-                data[-1]['weight'] = layer.name
+    outputs = _get_outputs(
+        [layer for layer in model.layers if not isinstance(layer, keras.layers.InputLayer)], X, model.input
+    )
+    for layer_name, y in outputs.items():
+        print(f"   {layer_name}")
+        y = y.flatten()
+        y = abs(y[y != 0])
+        if len(y) == 0:
+            print(f'Activations for {layer_name} are only zeros, ignoring.')
+            continue
+        if fmt == 'longform':
+            data['x'].extend(y.tolist())
+            data['weight'].extend([layer_name for i in range(len(y))])
+        elif fmt == 'summary':
+            data.append(array_to_summary(y, fmt=plot))
+            data[-1]['weight'] = layer_name
 
     if fmt == 'longform':
         data = pandas.DataFrame(data)
@@ -544,10 +545,10 @@ def _is_ignored_layer(layer):
     return False
 
 
-def _get_output(layer, X, model_input):
-    """Get output of partial model"""
-    partial_model = keras.models.Model(inputs=model_input, outputs=layer.output)
-    y = partial_model.predict(X)
+def _get_outputs(layers, X, model_input):
+    """Get outputs of intermediate layers"""
+    partial_models = keras.models.Model(inputs=model_input, outputs=[layer.output for layer in layers])
+    y = partial_models.predict(X)
     return y
 
 
@@ -562,37 +563,30 @@ def get_ymodel_keras(keras_model, X):
     Returns:
         dict: A dictionary in the form {"layer_name": ouput array of layer}.
     """
-
     ymodel = {}
-
+    traced_layers = []
+    layer_names = []
     for layer in keras_model.layers:
-        print(f"Processing {layer.name} in Keras model...")
-        if not _is_ignored_layer(layer):
-            # If the layer has activation integrated then separate them
-            # Note that if the layer is a standalone activation layer then skip this
-            if hasattr(layer, 'activation') and not (
-                isinstance(layer, keras.layers.Activation) or isinstance(layer, qkeras.qlayers.QActivation)
-            ):
-                if layer.activation:
-                    if layer.activation.__class__.__name__ == "linear":
-                        ymodel[layer.name] = _get_output(layer, X, keras_model.input)
-
-                    else:
-                        temp_activation = layer.activation
-                        layer.activation = None
-                        # Get output for layer without activation
-                        ymodel[layer.name] = _get_output(layer, X, keras_model.input)
-
-                        # Add the activation back
-                        layer.activation = temp_activation
-                        # Get ouput for activation
-                        ymodel[layer.name + f"_{temp_activation.__class__.__name__}"] = _get_output(
-                            layer, X, keras_model.input
-                        )
-                else:
-                    ymodel[layer.name] = _get_output(layer, X, keras_model.input)
-            else:
-                ymodel[layer.name] = _get_output(layer, X, keras_model.input)
+        if _is_ignored_layer(layer):
+            continue
+        # If the layer has activation integrated then separate them
+        # Note that if the layer is a standalone activation layer then skip this
+        name = layer.name
+        if (
+            hasattr(layer, "activation")
+            and layer.activation.__name__ != "linear"
+            and not isinstance(layer, (keras.layers.Activation, qkeras.qlayers.QActivation))
+        ):
+            tmp_activation = layer.activation
+            layer.activation = None
+            ymodel.update({layer.name: _get_outputs([layer], X, keras_model.input)})
+            layer.activation = tmp_activation
+            name = layer.name + f"_{tmp_activation.__name__}"
+        traced_layers.append(layer)
+        layer_names.append(name)
+    outputs = _get_outputs(traced_layers, X, keras_model.input)
+    for name, output in zip(layer_names, outputs):
+        ymodel[name] = output
     print("Done taking outputs for Keras model.")
     return ymodel
 

test/pytest/test_trace.py

@@ -12,14 +12,16 @@
 
 
 @pytest.mark.parametrize('backend', ['Vivado', 'Vitis', 'Quartus'])
-def test_trace(backend):
+@pytest.mark.parametrize('activation', ['relu', None])
+def test_trace(backend, activation):
     '''Test the tracing feature with a simple Keras model.'''
     model = tf.keras.models.Sequential()
     model.add(
         Dense(
             2,
             input_shape=(1,),
             name='Dense',
+            activation=activation,
             use_bias=True,
             kernel_initializer=tf.keras.initializers.RandomUniform(minval=1, maxval=10),
             bias_initializer='zeros',
@@ -48,6 +50,7 @@ def test_trace(backend):
     hls_model.compile()
     hls4ml_pred, hls4ml_trace = hls_model.trace(X_input)
     keras_trace = hls4ml.model.profiling.get_ymodel_keras(model, X_input)
-
-    np.testing.assert_allclose(hls4ml_trace['Dense'], keras_trace['Dense'], rtol=1e-2, atol=0.01)
+    assert keras_trace.keys() == hls4ml_trace.keys()
+    for key in hls4ml_trace.keys():
+        np.testing.assert_allclose(hls4ml_trace[key], keras_trace[key], rtol=1e-2, atol=0.01)
     np.testing.assert_allclose(hls4ml_pred, keras_prediction, rtol=1e-2, atol=0.01)