def test_box_encoder_cpu():
pipe = Pipeline(batch_size=batch_size, num_threads=4, device_id=None)
test_box_shape = [20, 4]
def get_boxes():
out = [
(np.random.randint(0, 255, size=test_box_shape, dtype=np.uint8) /
255).astype(dtype=np.float32) for _ in range(batch_size)
]
return out
test_lables_shape = [20, 1]
def get_lables():
out = [
np.random.randint(0, 255, size=test_lables_shape, dtype=np.int32)
for _ in range(batch_size)
]
return out
boxes = fn.external_source(source=get_boxes)
lables = fn.external_source(source=get_lables)
processed, _ = fn.box_encoder(boxes, lables, anchors=coco_anchors())
pipe.set_outputs(processed)
pipe.build()
for _ in range(3):
pipe.run()
def test_box_encoder():
get_boxes = GetData([[(rng.integers(0, 255, size=[20, 4], dtype=np.uint8) / 255)
.astype(dtype=np.float32) for _ in range(batch_size)]
for _ in range(data_size)])
get_labels = GetData([[rng.integers(0, 255, size=[20, 1], dtype=np.int32)
for _ in range(batch_size)] for _ in range(data_size)])
def eager_source(i, _):
return get_boxes.eager_source(i), get_labels.eager_source(i)
pipe = box_encoder_pipeline(get_boxes.fn_source, get_labels.fn_source)
compare_eager_with_pipeline(pipe, eager.box_encoder,
eager_source=eager_source, anchors=coco_anchors())
def __init__(self, args, data_path = test_data_path):
super(COCODetectionPipeline, self).__init__(
args.batch_size, args.num_workers, 0, 0)
self.input = ops.readers.COCO(
file_root=os.path.join(data_path, 'images'),
annotations_file=os.path.join(data_path, 'instances.json'),
shard_id=0,
num_shards=1,
ratio=True,
ltrb=True,
random_shuffle=False)
self.decode_gpu = ops.decoders.Image(device="mixed", output_type=types.RGB)
self.box_encoder = ops.BoxEncoder(
device="cpu",
criteria=0.5,
anchors=coco_anchors())
def __init__(self, args):
super(TFRecordDetectionPipeline, self).__init__(
args.batch_size, args.num_workers, 0, 0)
self.input = ops.readers.TFRecord(
path = os.path.join(test_dummy_data_path, 'small_coco.tfrecord'),
index_path = os.path.join(test_dummy_data_path, 'small_coco_index.idx'),
features = {
'image/encoded' : tfrec.FixedLenFeature((), tfrec.string, ""),
'image/object/class/label': tfrec.VarLenFeature([], tfrec.int64, 0),
'image/object/bbox': tfrec.VarLenFeature([4], tfrec.float32, 0.0),
},
shard_id=0,
num_shards=1,
random_shuffle=False)
self.decode_gpu = ops.decoders.Image(device="mixed", output_type=types.RGB)
self.cast = ops.Cast(dtype = types.INT32)
self.box_encoder = ops.BoxEncoder(
device="cpu",
criteria=0.5,
anchors=coco_anchors())
def box_encoder_pipeline(get_boxes, get_labels):
boxes = fn.external_source(source=get_boxes)
labels = fn.external_source(source=get_labels)
out = fn.box_encoder(boxes, labels, anchors=coco_anchors())
return tuple(out)