def exec(model_dir, chips_dir, eval_size):
tf.logging.set_verbosity(tf.logging.INFO)
start_time = time.time()
param_path = dl_utils.new_filepath('train_params.dat', directory=model_dir)
params = dl_utils.load_object(param_path)
tf.set_random_seed(params['seed'])
if eval_size <= 0:
eval_size = params['eval_size']
if chips_dir is None:
chips_dir = params['chips_dir']
dat_path, exp_path, mtd_path = dl_utils.chips_data_files(chips_dir)
train_data, eval_data, train_expect, eval_expect, chips_info = dl_utils.train_test_split(
dat_path, exp_path, mtd_path, eval_size)
print("Evaluating the model stored into " + model_dir)
estimator = tf.estimator.Estimator(model_fn=md.description,
params=params,
model_dir=model_dir)
do_evaluation(estimator, eval_data, eval_expect, 'EVALUATING', params)
def exec(img_path,
output_dir,
chip_size,
pad_size,
flip,
rotate,
shuffle=True,
offset_list=[[0, 0]],
nodata_value=-50.0,
discard_nodata=False):
print("Analyzing " + img_path + " image.")
dat_path, exp_path, mtd_path = dl_utils.chips_data_files(output_dir)
chips_info = dl_utils.chips_info(img_path, nodata_value, chip_size,
pad_size, offset_list, rotate, flip,
discard_nodata)
dl_utils.save_object(mtd_path, chips_info)
dat_ndarray = np.memmap(dat_path,
dtype=chips_info['dat_dtype'],
mode='w+',
shape=chips_info['dat_shape'])
exp_ndarray = np.memmap(exp_path,
dtype=chips_info['exp_dtype'],
mode='w+',
shape=chips_info['exp_shape'])
print("Generating " + str(chips_info['dat_shape'][0]) + " chips into " +
output_dir + " directory.")
dl_utils.generate_chips(img_path, dat_ndarray, exp_ndarray, nodata_value,
chip_size, pad_size, offset_list, rotate, flip,
discard_nodata)
if shuffle:
print("Shuffling generated chips.")
shuffle_chips(dat_ndarray, exp_ndarray, chips_info['n_chips'])
dat_ndarray.flush()
exp_ndarray.flush()
return dat_ndarray, exp_ndarray
if __name__ == "__main__":
args = parse_args()
chips_dir = args.chips_dir
output_dir = args.output_dir
eval_size = args.eval_size
batch_size = args.batch_size
epochs = args.epochs
seed = args.seed
params = vars(args)
tf.set_random_seed(seed)
tf.logging.set_verbosity(tf.logging.INFO)
dat_path, exp_path, mtd_path = dl_utils.chips_data_files(chips_dir)
train_data, test_data, train_expect, test_expect, chips_info = dl_utils.train_test_split(dat_path, exp_path, mtd_path, eval_size)
print("Memory size: %d Mb" % ( ((train_data.size * train_data.itemsize) + (test_data.size * test_data.itemsize))*0.000001 ))
print("Train data shape: " + str(train_data.shape))
print("Train label shape: " + str(train_expect.shape))
print("Train params: " + str(params))
dl_utils.mkdirp(output_dir)
param_path = dl_utils.new_filepath('train_params.dat', directory=output_dir)
chips_info_path = dl_utils.new_filepath('chips_info.dat', directory=output_dir)
dl_utils.save_object(param_path, params)
dl_utils.save_object(chips_info_path, chips_info)
estimator = tf.estimator.Estimator(model_fn=md.description, params=params, model_dir=output_dir)