def image_decode_job(images_def: oft.ListListNumpy.Placeholder( shape=static_shape, dtype=flow.int8)): images_buffer = flow.tensor_list_to_tensor_buffer(images_def) decoded_images_buffer = flow.image_decode(images_buffer) return flow.tensor_buffer_to_tensor_list(decoded_images_buffer, shape=(640, 640, 3), dtype=flow.uint8)
def coco_load_fn(): with flow.scope.placement("cpu", "0:0-{}".format(nthread - 1)): ( image, image_id, image_size, gt_bbox, gt_label, gt_segm, gt_segm_index, ) = flow.data.coco_reader( annotation_file=anno_file, image_dir=image_dir, batch_size=batch_size, shuffle=shuffle_after_epoch, stride_partition=stride_partition, name="COCOReader", ) if ret_image_id_only: return image_id decoded_image = flow.image_decode(image, dtype=flow.float) image_list = flow.tensor_buffer_to_tensor_list(decoded_image, shape=(800, 1333, 3), dtype=flow.float) bbox_list = flow.tensor_buffer_to_tensor_list(gt_bbox, shape=(128, 4), dtype=flow.float) label_list = flow.tensor_buffer_to_tensor_list(gt_label, shape=(128, ), dtype=flow.int32) segm_list = flow.tensor_buffer_to_tensor_list(gt_segm, shape=(1024, 2), dtype=flow.float) segm_index_list = flow.tensor_buffer_to_tensor_list( gt_segm_index, shape=(1024, 3), dtype=flow.int32) return ( image_id, image_size, image_list, bbox_list, label_list, segm_list, segm_index_list, )