'channel': None,
# A python dictionary consists of masks for the traget CNN features,
# arranged in pairs of layer name (key) and mask (value); the mask selects
# units for each layer to be used in the loss function (1: using the uint;
# 0: excluding the unit); mask can be 3D or 2D numpy array; use all the
# units if some layer not in the dictionary; setting to None for using all
#units for all layers;
'mask': None,
}
# Save the optional parameters
save_name = 'options.pkl'
with open(os.path.join(save_path, save_name), 'w') as f:
pickle.dump(opts, f)
# Reconstruction
recon_img, loss_list = reconstruct_image(features, net, net_gen, **opts)
# Save the results ------------------------------------------------------------
save_name = 'recon_img' + '.mat'
sio.savemat(os.path.join(save_path, save_name), {'recon_img': recon_img})
save_name = 'recon_img' + '.jpg'
PIL.Image.fromarray(normalise_img(recon_img)).save(
os.path.join(save_path, save_name))
save_name = 'loss_list' + '.mat'
sio.savemat(os.path.join(save_path, save_name), {'loss_list': loss_list})
# 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,
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'
PIL.Image.fromarray(normalise_img(clip_extreme_value(recon_img, pct=0.04))).save(os.path.join(save_dir, save_name))
print('Done')
# for using all channels for all layers;
'channel': None,
# A python dictionary consists of masks for the traget CNN features,
# arranged in pairs of layer name (key) and mask (value); the mask selects
# units for each layer to be used in the loss function (1: using the uint;
# 0: excluding the unit); mask can be 3D or 2D numpy array; use all the
# units if some layer not in the dictionary; setting to None for using all
#units for all layers;
'mask': None,
}
# Save the optional parameters
save_name = 'options.pkl'
with open(os.path.join(save_path, save_name), 'w') as f:
pickle.dump(opts, f)
# Reconstruction
recon_img, loss_list = reconstruct_image(features, net, **opts)
# Save the results ------------------------------------------------------------
save_name = 'recon_img' + '.mat'
sio.savemat(os.path.join(save_path, save_name), {'recon_img': recon_img})
save_name = 'recon_img' + '.jpg'
PIL.Image.fromarray(normalise_img(recon_img)).save(os.path.join(save_path, save_name))
save_name = 'loss_list' + '.mat'
sio.savemat(os.path.join(save_path, save_name), {'loss_list': loss_list})
