def main(argv): parser = dan_run_loop.DANArgParser() parser.set_defaults(data_dir='./data_dir', model_dir='/output', data_format='channels_last', train_epochs=20, epochs_per_eval=10, batch_size=64) flags = parser.parse_args(args=argv[1:]) mean_shape = None imgs_mean = None imgs_std = None flags_trans = { 'train': tf.estimator.ModeKeys.TRAIN, 'eval': tf.estimator.ModeKeys.EVAL, 'predict': tf.estimator.ModeKeys.PREDICT } flags.mode = flags_trans[flags.mode] if flags.mode == tf.estimator.ModeKeys.TRAIN: mean_shape, imgs_mean, imgs_std = read_dataset_info(flags.data_dir) def vgg16_model_fn(features, labels, mode, params): return dan_run_loop.dan_model_fn(features=features, groundtruth=labels, mode=mode, stage=params['dan_stage'], num_lmark=params['num_lmark'], model_class=VGG16Model, mean_shape=mean_shape, imgs_mean=imgs_mean, imgs_std=imgs_std, data_format=params['data_format'], multi_gpu=params['multi_gpu']) input_function = flags.use_synthetic_data and get_synth_input_fn( ) or vgg16_input_fn if flags.mode == tf.estimator.ModeKeys.PREDICT: faceset = '/media/morzh/ext4_volume/data/Faces/all_in_one/set_004_rect/' file_img = '2UiNSKC3sGw.jpg' file_rect = faceset + file_img + '.rect' # rect = np.loadtxt(file_rect) input_function = img_input_fn(faceset + file_img, (0, 0, 112, 112), 112, 74) dan_run_loop.dan_main(flags, vgg16_model_fn, input_function)
def main(argv): parser = dan_run_loop.DANArgParser() parser.set_defaults(data_dir='./data_dir', model_dir='./model_dirl2loss', data_format='channels_last', train_epochs=20, epochs_per_eval=10, batch_size=64) flags = parser.parse_args(args=argv[1:]) mean_shape = None imgs_mean = None imgs_std = None flags_trans = { 'train': tf.estimator.ModeKeys.TRAIN, 'eval': tf.estimator.ModeKeys.EVAL, 'predict': tf.estimator.ModeKeys.PREDICT } flags.mode = flags_trans[flags.mode] if flags.mode == tf.estimator.ModeKeys.TRAIN: mean_shape, imgs_mean, imgs_std = read_dataset_info(flags.data_dir) def vgg16_model_fn(features, labels, mode, params): return dan_run_loop.dan_model_fn(features=features, groundtruth=labels, mode=mode, stage=params['dan_stage'], num_lmark=params['num_lmark'], model_class=VGG16Model, mean_shape=mean_shape, imgs_mean=imgs_mean, imgs_std=imgs_std, data_format=params['data_format'], multi_gpu=params['multi_gpu']) input_function = flags.use_synthetic_data and get_synth_input_fn( ) or vgg16_input_fn #use vgg16_input_fn print(flags.use_synthetic_data) if flags.mode == tf.estimator.ModeKeys.PREDICT: input_function = video_input_fn(flags.data_dir, 112, flags.num_lmark) dan_run_loop.dan_main(flags, vgg16_model_fn, input_function)