def build_pose_test_graph(self, input_uint8): input_mc = self.select_tensor_or_placeholder_input(input_uint8) loader = DataLoader() tgt_image, src_image_stack = \ loader.batch_unpack_image_sequence( input_mc, self.img_height, self.img_width, self.num_source) with tf.name_scope("pose_prediction"): pred_poses, _ = pose_net(tgt_image, src_image_stack, is_training=False) self.pred_poses = pred_poses
def build_pose_test_graph(self): input_uint8 = tf.placeholder(tf.uint8, [self.batch_size, self.img_height, self.img_width * self.seq_length, 3], name='raw_input') input_mc = self.preprocess_image(input_uint8) loader = DataLoader() tgt_image, src_image_stack = \ loader.batch_unpack_image_sequence( input_mc, self.img_height, self.img_width, self.num_source) with tf.name_scope("pose_prediction"): pred_poses, _, _ = pose_exp_net( tgt_image, src_image_stack, do_exp=False, is_training=False) self.inputs = input_uint8 self.pred_poses = pred_poses
def build_pose_test_graph(self): input_uint8 = tf.placeholder(tf.uint8, [ self.batch_size, self.img_height, self.img_width * self.seq_length, 3 ], name='raw_input') #shape(1,128,416*3,3) input_mc = self.preprocess_image(input_uint8) #shape(1,128,416*3,3) # print('input_mc.shape:',input_mc.shape) # input_mc = tf.Print(input_mc,[input_mc.shape],message='input_mc') loader = DataLoader() tgt_image, src_image_stack = \ loader.batch_unpack_image_sequence( input_mc, self.img_height, self.img_width, self.num_source) with tf.name_scope("pose_prediction"): pred_poses, _, _ = pose_exp_net(tgt_image, src_image_stack, do_exp=False, is_training=False) # print('pred_poses:',pred_poses) # tf.Print(pred_poses,[pred_poses.shape],message='pred_poses') #shape(1,2,6) self.inputs = input_uint8 self.pred_poses = pred_poses