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()
