# Use the inverse of the squared norm of the CNN features as the weight for each layer
weights = 1. / (feat_norm**2)
# Normalise the weights such that the sum of the weights = 1
weights = weights / weights.sum()
layer_weight = dict(zip(layers, weights))
opts.update({'layer_weight': layer_weight})
# Reconstruction
snapshots_dir = os.path.join(save_dir, 'snapshots',
'image-%s' % image_label)
recon_img, loss_list = reconstruct_image(
features,
net,
net_gen,
save_intermediate=True,
save_intermediate_path=snapshots_dir,
**opts)
# Save the results
# Save the raw reconstructed image
save_name = 'recon_img' + '-' + image_label + '.mat'
sio.savemat(os.path.join(save_dir, save_name), {'recon_img': recon_img})
# To better display the image, clip pixels with extreme values (0.02% of
# pixels with extreme low values and 0.02% of the pixels with extreme high
# values). And then normalise the image by mapping the pixel value to be
# within [0,255].
save_name = 'recon_img_normalized' + '-' + image_label + '.jpg'
# Weight of each layer in the total loss function
num_of_layer = len(layers)
feat_norm_list = np.zeros(num_of_layer, dtype='float32')
for j, layer in enumerate(layers):
# Norm of the CNN features for each layer
feat_norm_list[j] = np.linalg.norm(features[layer])
# Use the inverse of the squared norm of the CNN features as the weight for each layer
weights = 1. / (feat_norm_list**2)
# Normalise the weights such that the sum of the weights = 1
weights = weights / weights.sum()
layer_weight = {}
for j, layer in enumerate(layers):
layer_weight[layer] = weights[j]
opts['layer_weight'] = layer_weight
# Reconstruction
recon_img, loss_list = reconstruct_image(features, net, net_gen, **opts)
# Save the results
save_name = 'recon_img' + '_' + subject + '_' + roi + '_Img%04d.mat' % image_label
# Save the raw reconstructed image
sio.savemat(os.path.join(save_path, save_name), {'recon_img': recon_img})
# To better display the image, clip pixels with extreme values (0.02% of
# pixels with extreme low values and 0.02% of the pixels with extreme high
# values). And then normalise the image by mapping the pixel value to be
# within [0,255].
save_name = 'recon_img' + '_' + subject + '_' + roi + '_Img%04d.jpg' % image_label
PIL.Image.fromarray(normalise_img(clip_extreme_value(
recon_img, pct=0.04))).save(os.path.join(save_path, save_name))