# In[19]:
print("Class Likelihood Bad: {} Good: {}".format(logit[0, 0], logit[0, 1]))
# ### Visualize It
# Take the images and create a nice tiled image to look at. This will created a tiled image of:
#
# (1) The input image.
# (2) The saliency map.
# (3) The saliency map overlaid on the input image.
# (4) The raw image enhanced with the most salient locations.
# (5) The top 10% most salient locations.
# In[20]:
images = misc.TileOutput(raw_tensor, cam_map, getMask)
# We now put all the images into a nice grid for display.
# In[21]:
images = make_grid(torch.cat(images, 0), nrow=5)
# ... save and look at it.
# In[22]:
output_name = "{}.FASTCAM.jpg".format(save_prefix)
output_path = os.path.join(output_dir, output_name)
save_image(images, output_path)
# Get Forward sal map
csmap,smaps,_ = get_salmap(in_tensor)
# Let's get our original input image back. We will just use this one for visualization.
raw_tensor = misc.LoadImageToTensor(load_image_name, device, norm=False)
raw_tensor = F.interpolate(raw_tensor, size=(in_height, in_width), mode='bilinear', align_corners=False)
# We create an object to get back the mask of the saliency map
getMask = mask.SaliencyMaskDropout(keep_percent = 0.1, scale_map=False)
# Now we will create illustrations of the combined saliency map.
images = []
images = misc.TileOutput(raw_tensor,csmap,getMask,images)
# Let's double check and make sure it's picking the correct class
too_logit = logit.max(1)
print("Network Class Output: {} : Value {} ".format(too_logit[1][0],too_logit[0][0]))
# Now visualize the results
images = misc.TileOutput(raw_tensor, cam_map.squeeze(0), getMask, images)
# ### Combined CAM and SMOE Scale Maps
# Now we combine the Grad-CAM map and the SMOE Scale saliency maps in the same way we would combine Grad-CAM with Guided Backprop.
fastcam_map = csmap*cam_map