# 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)
class_colors = class_colors.astype(np.uint8) * np.expand_dims(edges_invert, axis=2) + np.expand_dims(
edges_total, axis=2)
img_obj = Image.fromarray(np.uint8(class_colors))
ax.imshow(img_obj)
plt.waitforbuttonpress(timeout=5)
if __name__ == '__main__':
args = get_arguments()
params = Params()
params = load_json_to_params(params, args.json_path)
params.dataset_directory = '/home/ddegeus/datasets/Cityscapes/validation/'
params.filelist_filepath = '/home/ddegeus/datasets/Cityscapes/validation/panoptic/filenames.lst'
params.is_training = False
params.batch_norm_istraining = False
params.num_steps_predict = params.num_steps_eval
params.height_input = 512
params.width_input = 1024
params.Nb = 1
predict(params)