def conditional_save_img_comparison_with_intensity(self, mode, i, ele,
pred, pred_intensity,
epoch):
# save 8 images for visualization
if mode == 'val' or mode == 'eval':
skip = 100
if i == 0:
self.img_merge = vis_utils.merge_into_row_with_intensity(
ele, pred, pred_intensity)
# self.img_merge = vis_utils.merge_into_row(ele, pred)
elif i % skip == 0 and i < 8 * skip:
# row = vis_utils.merge_into_row(ele, pred)
row = vis_utils.merge_into_row_with_intensity(
ele, pred, pred_intensity)
self.img_merge = vis_utils.add_row(self.img_merge, row)
elif i == 8 * skip:
filename = self._get_img_comparison_name(mode, epoch)
vis_utils.save_image(self.img_merge, filename) #HWC
# input C x H x W
img_np_rescale = skimage.transform.rescale(np.array(
self.img_merge, dtype='float64'),
0.5,
order=0)
img_np_rescale_CHW = np.transpose(img_np_rescale, (2, 0, 1))
self.writer.add_image('comparison', img_np_rescale_CHW, i)
def conditional_save_img_comparison(self, mode, i, ele, pred, epoch):
# save 8 images for visualization
if mode == 'val' or mode == 'eval':
skip = 100
if i == 0:
self.img_merge = vis_utils.merge_into_row(ele, pred)
elif i % skip == 0 and i < 8 * skip:
row = vis_utils.merge_into_row(ele, pred)
self.img_merge = vis_utils.add_row(self.img_merge, row)
elif i == 8 * skip:
filename = self._get_img_comparison_name(mode, epoch)
vis_utils.save_image(self.img_merge, filename)
def visualise_layer_without_hooks(self):
# Process image and return variable
# Generate a random image
random_image = np.uint8(np.random.uniform(150, 180, (1, 10, 100, 100)))
# Process image and return variable
processed_image = preprocess_clip(random_image)
print(processed_image.shape)
# Define optimizer for the image
optimizer = Adam([processed_image], lr=0.5, weight_decay=1e-6)
for i in range(1, 101):
optimizer.zero_grad()
# Assign create image to a variable to move forward in the model
x = processed_image
for index, layer in enumerate(self.model):
# Forward pass layer by layer
x = layer(x)
if index == self.selected_layer:
# Only need to forward until the selected layer is reached
# Now, x is the output of the selected layer
break
# Here, we get the specific filter from the output of the convolution operation
# x is a tensor of shape 1x512x28x28.(For layer 17)
# So there are 512 unique filter outputs
# Following line selects a filter from 512 filters so self.conv_output will become
# a tensor of shape 28x28
self.conv_output = x[0, self.selected_filter]
# Loss function is the mean of the output of the selected layer/filter
# We try to minimize the mean of the output of that specific filter
loss = -torch.mean(self.conv_output)
print('Iteration:', str(i), 'Loss:', "{0:.6f}".format(loss.data.numpy()))
# Backward
loss.backward()
# Update image
optimizer.step()
# TODO: check if we need to recreate image
#self.created_image = recreate_image(processed_image)
# Save image
if i % 10 == 0:
im_path = '../generated/layer_vis_l' + str(self.selected_layer) + \
'_f' + str(self.selected_filter) + '_iter' + str(i) + '.jpg'
save_image(self.created_image, im_path)
def save_img_comparison_as_best(self, mode, epoch):
if mode == 'val':
filename = self._get_img_comparison_name(mode, epoch, is_best=True)
vis_utils.save_image(self.img_merge, filename)
'float32') # cv2 only works with 32 bit floating point
input_data_img = cv2.cvtColor(input_data_img, cv2.COLOR_GRAY2RGB)
heatmap = cv2.applyColorMap(cv2.bitwise_not(np.uint8(255 * mask[i])),
cv2.COLORMAP_JET)
if RESIZE_FLAG:
input_data_img = cv2.resize(input_data_img, (RESIZE_SIZE, RESIZE_SIZE))
heatmap = cv2.resize(heatmap, (RESIZE_SIZE, RESIZE_SIZE))
heatmap = np.float32(heatmap) / 255
cam = heatmap + np.float32(input_data_img)
cam = cam / np.max(cam)
combined_img = np.concatenate(
(np.uint8(255 * input_data_img), np.uint8(255 * cam)), axis=1)
# these always need to be saved so we can make the gif
save_image(
combined_img,
os.path.join(output_images_folder_cam_combined,
"img%02d.jpg" % (i + 1)))
if SAVE_INDIVIDUALS:
save_image(
np.uint8(255 * cam),
os.path.join(output_images_folder_cam, "img%02d.jpg" % (i + 1)))
save_image(
np.uint8(255 * input_data_img),
os.path.join(output_images_folder_original,
"img%02d.jpg" % (i + 1)))
path_to_combined_gif = os.path.join(output_images_folder_cam_combined,
"mygif.gif")
os.system("convert -delay 20 -loop 0 {}.jpg {}".format(
os.path.join(output_images_folder_cam_combined, "*"),
def save_img_comparison_as_best(self, mode, epoch):
if mode == 'val':
filename = self._get_img_comparison_name(mode, epoch, is_best=True)
vis_utils.save_image(self.img_merge, filename)
self.writer.add_image(f"img_merge_{mode}", self.img_merge,
self.current_step)
def save_img_comparison_as_best(self, mode, epoch):
if mode == 'val':
filename = self._get_img_comparison_name(mode, epoch, is_best=True)
vis_utils.save_image(self.img_merge, filename)
self.writer.add_image('val_comparison_best',
np.transpose(self.img_merge, (2, 0, 1)))