print(out_fname)
print(np.min(out_file[..., 0]), np.max(out_file[..., 0]))
Image.fromarray(out_file.astype(np.uint8)).save(out_fname)
if __name__ == '__main__':
args = get_arguments()
params = Params()
params = load_json_to_params(params, args.json_path)
params.num_steps_predict = params.num_steps_eval
params.save_predictions = args.save_predictions
params.save_dir = args.save_dir
params.is_training = False
params.batch_norm_istraining = False
params.height_input = 512
params.width_input = 1024
params.height_orig = 604
params.width_orig = 960
params.Nb = 1
filenames_list = list()
for file in os.listdir(args.image_dir):
if file.endswith(".png") or file.endswith(".jpg"):
filenames_list.append(os.path.join(args.image_dir, file))
predict(params, filenames_list)
# Create the Timeline object, and write it to a json file
fetched_timeline = timeline.Timeline(run_metadata.step_stats)
chrome_trace = fetched_timeline.generate_chrome_trace_format()
with open(os.path.join(params.log_dir, 'timeline_01.json'), 'w') as f:
f.write(chrome_trace)
with open(os.path.join(params.log_dir, 'mem_info.json'), 'w') as f:
f.write(str(run_metadata))
else:
loss_value, _ = sess.run([loss, train_op], feed_dict=feed_dict)
if step % params.ckpt_save_steps == 0:
model.save(saver, sess, params.log_dir, step)
duration = time.time() - start_time
print('step {:d} \t loss = {:.3f}, ({:.3f} sec/step)'.format(step, loss_value, duration))
coord.request_stop()
coord.join(threads)
if __name__ == '__main__':
args = get_arguments()
params = Params()
params = load_json_to_params(params, args.json_path)
params.dataset_directory = '/home/ddegeus/datasets/Cityscapes/training/'
params.filelist_filepath = '/home/ddegeus/datasets/Cityscapes/training/panoptic/filenames.lst'
params.is_training = True
params.batch_norm_istraining = True
print(params)
train(params)