Skip to content
New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

Sign up for GitHub

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

Added minor checks #9

Open
wants to merge 1 commit into
base: main
Choose a base branch
from
Open

Added minor checks #9

Changes from all commits
Commits
Show all changes
1 commit
Select commit
File filter

Filter by extension

Filter by extension
Conversations
Failed to load comments.
Loading
Jump to
Jump to file
Failed to load files.
Loading
Diff view
Diff view
11 changes: 7 additions & 4 deletions myoquant/src/gradcam.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -27,11 +27,11 @@ def get_img_array(img_path, size):
return array


def make_gradcam_heatmap(img_array, _model, last_conv_layer_name, pred_index=None):
def make_gradcam_heatmap(img_array, model, last_conv_layer_name, pred_index=None):
# First, we create a model that maps the input image to the activations
# of the last conv layer as well as the output predictions
grad_model = tf.keras.models.Model(
[_model.inputs], [_model.get_layer(last_conv_layer_name).output, _model.output]
[model.inputs], [model.get_layer(last_conv_layer_name).output, model.output]
)

# Then, we compute the gradient of the top predicted class for our input image
Expand All @@ -53,8 +53,11 @@ def make_gradcam_heatmap(img_array, _model, last_conv_layer_name, pred_index=Non
# We multiply each channel in the feature map array
# by "how important this channel is" with regard to the top predicted class
# then sum all the channels to obtain the heatmap class activation
last_conv_layer_output = last_conv_layer_output[0]
heatmap = last_conv_layer_output @ pooled_grads[..., tf.newaxis]
if isinstance(last_conv_layer_output, np.ndarray):
last_conv_layer_output_np = last_conv_layer_output
else:
last_conv_layer_output_np = last_conv_layer_output.numpy()
heatmap = last_conv_layer_output_np @ pooled_grads[..., tf.newaxis]
heatmap = tf.squeeze(heatmap)

# For visualization purpose, we will also normalize the heatmap between 0 & 1
Expand Down