def init_dataset(self): dataset = Flags.dataset if dataset == 'UCF101': self.dataset = ucf_ts.ucf_dataset( split_number=split, is_training_split=False, batch_size=self.batch_size, epoch=1, test_crop=test_crop, eval_type=eval_type, video_split=self.video_split, new_length=new_length, image_size=self._IMAGE_SIZE, frame_counts=self._FRAME_COUNTS, prefetch_buffer_size=self.batch_size).test_dataset() elif dataset == 'hmdb51': self.dataset = hmdb_ts.hmdb_dataset( split_number=split, is_training_split=False, batch_size=self.batch_size, epoch=1, test_crop=test_crop, eval_type=eval_type, video_split=self.video_split, new_length=new_length, image_size=self._IMAGE_SIZE, frame_counts=self._FRAME_COUNTS, prefetch_buffer_size=self.batch_size).test_dataset() iter = tf.data.Iterator.from_structure(self.dataset.output_types, self.dataset.output_shapes) self.next_element = iter.get_next() self.training_init_op = iter.make_initializer(self.dataset)
flow_train = tf.placeholder (tf.float32, [None, _FRAME_COUNTS * factor, _IMAGE_SIZE, _IMAGE_SIZE, 20]) y_ = tf.placeholder (tf.float32, [None, _NUM_CLASSES]) dataset = Flags.dataset if dataset == 'UCF101': dataset = ucf_ts.ucf_dataset (split_number=split, is_training_split=False, batch_size=batch_size, epoch=1,test_crop = test_crop, eval_type=eval_type, frame_counts=_FRAME_COUNTS, image_size=_IMAGE_SIZE, prefetch_buffer_size=batch_size).test_dataset () elif dataset == 'hmdb51': dataset = hmdb_ts.hmdb_dataset (split_number=split, is_training_split=False, batch_size=batch_size, epoch=1,test_crop = test_crop, eval_type=eval_type, frame_counts=_FRAME_COUNTS, image_size=_IMAGE_SIZE, prefetch_buffer_size=batch_size).test_dataset () iter = dataset.make_initializable_iterator () next_element = iter.get_next () if model_type == 'resnet50': base_net = 'TS_resnet50' elif model_type == 'resnet101': base_net = 'ts_resnet101' elif model_type == 'inception_v1': base_net = 'ts_inception_v1' with tf.variable_scope ('RGB', reuse=tf.AUTO_REUSE):