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cam updated to save cam per class #8

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cam updated to save cam per class #8

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dfa0f9a
cam updated to save cam per class
edithDenim Jun 24, 2024
9fcdea2
bugfix, alternative cam saving
edithDenim Jul 10, 2024
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94 changes: 64 additions & 30 deletions src/models/prediction.py
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Original file line number Diff line number Diff line change
Expand Up @@ -171,45 +171,79 @@ def save_cam(model, data, normalize_transform, classes, valid_dataset, is_regres

# create cam
activations = activation_hook.activation[0]
cam_map = torch.einsum('ck,kij->cij', out_weights, activations)
cam_map = torch.einsum('ck,kij->cij', out_weights, activations).cpu()

text = 'validation_data: {}\nprediction: {}\nvalue: {:.3f}'.format('True' if image_id in valid_dataset_ids else 'False', pred_class, pred_value)

n_classes = 1 if is_regression else len(classes)

fig, ax = plt.subplots(1, n_classes+1, figsize=((n_classes+1)*2.5, 2.5))
# fig, ax = plt.subplots(1, n_classes+1, figsize=((n_classes+1)*2.5, 2.5))

ax[0].imshow(image.permute(1, 2, 0))
ax[0].axis('off')
# ax[0].imshow(image.permute(1, 2, 0))
# ax[0].axis('off')

for i in range(1, n_classes+1):
# for i in range(1, n_classes+1):

# merge original image with cam
# # merge original image with cam

ax[i].imshow(image.permute(1, 2, 0))

ax[i].imshow(cam_map[i-1].detach(), alpha=0.75, extent=(0, image.shape[2], image.shape[1], 0),
# ax[i].imshow(image.permute(1, 2, 0))

# ax[i].imshow(cam_map[i-1].detach(), alpha=0.75, extent=(0, image.shape[2], image.shape[1], 0),
# interpolation='bicubic', cmap='magma')

# ax[i].axis('off')

# # if i - 1 == idx:
# # # draw prediction on image
# # ax[i].text(10, 80, text, color='white', fontsize=6)
# # else:
# # t = '\n\nprediction: {}\nvalue: {:.3f}'.format(classes[i - 1], output[i - 1].item())
# # ax[i].text(10, 80, t, color='white', fontsize=6)

# t = '\n\nprediction: {}\nvalue: {:.3f}'.format(classes[i - 1], output[i - 1].item())
# ax[i].text(10, 60, t, color='white', fontsize=6)

# # save image
# # image_path = os.path.join(image_folder, "{}_cam.png".format(image_id))
# # plt.savefig(image_path)

# # show image
# plt.show()
# plt.close()

for i in range(n_classes):
class_name = classes[i]
# Erstelle eine neue Figur für jedes Bild
fig, ax = plt.subplots()
# ax.imshow(image.permute(1, 2, 0))
ax.imshow(cam_map[i].detach().cpu(), alpha=1.0, extent=(0, 48, 48, 0),
interpolation='bicubic', cmap='magma')

ax[i].axis('off')

# if i - 1 == idx:
# # draw prediction on image
# ax[i].text(10, 80, text, color='white', fontsize=6)
# else:
# t = '\n\nprediction: {}\nvalue: {:.3f}'.format(classes[i - 1], output[i - 1].item())
# ax[i].text(10, 80, t, color='white', fontsize=6)

t = '\n\nprediction: {}\nvalue: {:.3f}'.format(classes[i - 1], output[i - 1].item())
ax[i].text(10, 60, t, color='white', fontsize=6)

# save image
# image_path = os.path.join(image_folder, "{}_cam.png".format(image_id))
# plt.savefig(image_path)

# show image
plt.show()
plt.close()
ax.axis('off')

# Speichere das Bild mit dem Klassennamen als Präfix
class_image_path = os.path.join(image_folder, f"{image_id}_{class_name}_cam.jpg")
plt.savefig(class_image_path, bbox_inches='tight', pad_inches=0)
plt.close()

print(f"Images saved in {image_folder}")
# break

# for i in range(n_classes):
# if (i == idx) or (classes[i] == "paving_stones"):
# class_name = classes[i]
# if (i == idx):
# class_name = class_name + '_max'
# # Erstelle eine neue Figur für jedes Bild
# fig, ax = plt.subplots()
# ax.imshow(image.permute(1, 2, 0))
# ax.imshow(cam_map[i].detach().cpu(), alpha=0.75, extent=(0, image.shape[2], image.shape[1], 0),
# interpolation='bicubic', cmap='magma')
# ax.axis('off')
#
# # Speichere das Bild mit dem Klassennamen als Präfix
# class_image_path = os.path.join(image_folder, f"{image_id}_cam_{class_name}.jpg")
# plt.savefig(class_image_path, bbox_inches='tight', pad_inches=0)
# plt.close()


def prepare_data(data_root, dataset, transform):
Expand Down Expand Up @@ -353,4 +387,4 @@ def main():
print('no valid saving format')

if __name__ == "__main__":
main()
main()