def main(_): pp.pprint(FLAGS.__flags) if FLAGS.height is None: FLAGS.height = FLAGS.width unet = Unet(width=FLAGS.width, height=FLAGS.height, learning_rate=FLAGS.learning_rate, data_set=FLAGS.data_set, test_set=FLAGS.test_set, result_name=FLAGS.result_name, ckpt_dir=FLAGS.ckpt_dir, logs_step=FLAGS.logs_step, restore_step=FLAGS.restore_step, hidden_num=FLAGS.hidden_num, epoch_num=FLAGS.epoch_num, batch_size=FLAGS.batch_size, num_gpu=FLAGS.num_gpu, is_train=FLAGS.is_train, w_bn=FLAGS.w_bn) show_all_variables() if FLAGS.is_train: unet.train() else: unet.test()
train_gen = make_image_gen(balanced_df) train_x, train_y = next(train_gen) print('x', train_x.shape, train_x.min(), train_x.max()) print('y', train_y.shape, train_y.min(), train_y.max()) # valid data check valid_x, valid_y = next(make_image_gen(balanced_df, batch_size=VALID_IMG_COUNT)) print(valid_x.shape, valid_y.shape) # augment data check cur_gen = create_aug_gen(train_gen) t_x, t_y = next(cur_gen) print('x', t_x.shape, t_x.dtype, t_x.min(), t_x.max()) print('y', t_y.shape, t_y.dtype, t_y.min(), t_y.max()) # fig, (ax1, ax2) = plt.subplots(1, 2, figsize = (20, 10)) # ax1.imshow(montage_rgb(t_x), cmap='gray') # ax1.set_title('images') # ax2.imshow(montage(t_y[:, :, :, 0]), cmap='gray_r') # ax2.set_title('ships') # plt.show() model = Unet(t_x.shape[1:]) # loss_history = model.train(balanced_train_df=balanced_df, # valid_x=valid_x, valid_y=valid_y, # make_image_gen=make_image_gen, # create_aug_gen=create_aug_gen) # # show_loss(loss_history) model.test()