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,
)