y_index = int(feat_size[1] / 2) # the unit in the center of feature map
x_index = int(feat_size[2] / 2) # the unit in the center of feature map
feature_mask = np.zeros(feat_size)
feature_mask[channel, y_index, x_index] = 1
# weights for the target units
feature_weight = np.zeros(feat_size, dtype=np.float32)
feature_weight[:] = 1.
#
preferred_img = generate_image(net,
layer,
feature_mask,
feature_weight=feature_weight,
**opts)
# save the results
save_name = 'preferred_img' + '_layer_' + str(layer) + '_channel_' + str(
channel) + '.mat'
sio.savemat(os.path.join(save_path, save_name),
{'preferred_img': preferred_img})
save_name = 'preferred_img' + '_layer_' + str(layer) + '_channel_' + str(
channel) + '.jpg'
PIL.Image.fromarray(
normalise_img(clip_extreme_pixel(preferred_img, pct=0.04))).save(
os.path.join(save_path, save_name))
# end
print('Done!')
feat_size = net.blobs[layer].data.shape[1:]
feature_mask = np.zeros(feat_size)
feature_mask[channel] = 1
# weights for the target units
feature_weight = np.zeros(feat_size, dtype=np.float32)
feature_weight[:] = 10.
# create image mask for the receptive fields of the target units
img_mask = create_receptive_field_mask(net, layer, feature_mask)
#
preferred_img = generate_image(net_gen, net, layer, feature_mask, feature_weight=feature_weight, **opts)
# save the results
save_name = 'preferred_img' + '_layer_' + str(layer) + '_channel_' + str(channel) + '.mat'
sio.savemat(os.path.join(save_path,save_name),{'preferred_img':preferred_img})
save_name = 'preferred_img' + '_layer_' + str(layer) + '_channel_' + str(channel) + '.jpg'
PIL.Image.fromarray(normalise_img(clip_extreme_pixel(preferred_img,pct=0.04))).save(os.path.join(save_path,save_name))
save_name = 'preferred_img_masked' + '_layer_' + str(layer) + '_channel_' + str(channel) + '.mat'
sio.savemat(os.path.join(save_path,save_name),{'preferred_img':preferred_img * img_mask})
save_name = 'preferred_img_masked' + '_layer_' + str(layer) + '_channel_' + str(channel) + '.jpg'
PIL.Image.fromarray(normalise_img(clip_extreme_pixel(preferred_img,pct=0.04)) * img_mask).save(os.path.join(save_path,save_name))
# end
print('Done!')