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Handle y_true as class targets for GradCAM #143

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Handle y_true as class targets for GradCAM #143

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27 changes: 17 additions & 10 deletions tf_explain/core/grad_cam.py
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Original file line number Diff line number Diff line change
Expand Up @@ -22,7 +22,7 @@ def explain(
self,
validation_data,
model,
class_index,
class_index=None,
layer_name=None,
use_guided_grads=True,
colormap=cv2.COLORMAP_VIRIDIS,
Expand All @@ -38,21 +38,27 @@ def explain(
class_index (int): Index of targeted class
layer_name (str): Targeted layer for GradCAM. If no layer is provided, it is
automatically infered from the model architecture.
use_guided_grads (boolean): Whether to use guided grads or raw gradients
colormap (int): OpenCV Colormap to use for heatmap visualization
image_weight (float): An optional `float` value in range [0,1] indicating the weight of
the input image to be overlaying the calculated attribution maps. Defaults to `0.7`.
use_guided_grads (boolean): Whether to use guided grads or raw gradients

Returns:
numpy.ndarray: Grid of all the GradCAM
"""
images, _ = validation_data
images, targets = validation_data
is_target_categorical = len(np.array(targets, dtype="uint8").shape) == 2
if is_target_categorical:
targets = list(np.argmax(targets, axis=1))

if layer_name is None:
layer_name = self.infer_grad_cam_target_layer(model)

outputs, grads = GradCAM.get_gradients_and_filters(
model, images, layer_name, class_index, use_guided_grads
if class_index is not None:
targets = [class_index] * len(images)

outputs, grads = GradCAM.get_gradients_and_filters_from_targets(
model, images, layer_name, targets, use_guided_grads
)

cams = GradCAM.generate_ponderated_output(outputs, grads)
Expand Down Expand Up @@ -92,8 +98,8 @@ def infer_grad_cam_target_layer(model):

@staticmethod
@tf.function
def get_gradients_and_filters(
model, images, layer_name, class_index, use_guided_grads
def get_gradients_and_filters_from_targets(
model, images, layer_name, targets, use_guided_grads
):
"""
Generate guided gradients and convolutional outputs with an inference.
Expand All @@ -102,7 +108,7 @@ def get_gradients_and_filters(
model (tf.keras.Model): tf.keras model to inspect
images (numpy.ndarray): 4D-Tensor with shape (batch_size, H, W, 3)
layer_name (str): Targeted layer for GradCAM
class_index (int): Index of targeted class
targets (List[int]): List of class targets
use_guided_grads (boolean): Whether to use guided grads or raw gradients

Returns:
Expand All @@ -115,8 +121,9 @@ def get_gradients_and_filters(
with tf.GradientTape() as tape:
inputs = tf.cast(images, tf.float32)
conv_outputs, predictions = grad_model(inputs)
loss = predictions[:, class_index]

loss = tf.gather_nd(
predictions, [[index, target] for index, target in enumerate(targets)]
)
grads = tape.gradient(loss, conv_outputs)

if use_guided_grads:
Expand Down