def bricon_pipe(data_iterator, contrast_center, bri, con, dtype, dev='cpu'):
if bri:
brightness, brightness_shift = brightness_params()
if con:
contrast = contrast_param()
inp = fn.external_source(source=data_iterator)
if dev == 'gpu':
inp = inp.gpu()
if bri and con:
return fn.brightness_contrast(inp,
brightness=brightness,
brightness_shift=brightness_shift,
contrast=contrast,
contrast_center=contrast_center,
dtype=dtype)
elif bri:
return fn.brightness_contrast(inp,
brightness=brightness,
brightness_shift=brightness_shift,
dtype=dtype)
elif con:
return fn.brightness_contrast(inp,
contrast=contrast,
contrast_center=contrast_center,
dtype=dtype)
def get_pipeline(folder="train", custom_reader=None):
pipe = Pipeline(batch_size=64, num_threads=1, device_id=1)
if custom_reader:
raw_files, labels = custom_reader
else:
raw_files, labels = fn.file_reader(file_root="%s" % folder,
random_shuffle=True)
decode = fn.image_decoder(raw_files, device="mixed", output_type=types.GRAY)
resize = fn.resize(decode, device="gpu", image_type=types.RGB,
interp_type=types.INTERP_LINEAR, resize_x=WIDTH, resize_y=HEIGHT)
hsv = fn.hsv(resize, hue=fn.uniform(range=(-10, 10)), saturation=fn.uniform(range=(-.5, .5)),
value=fn.uniform(range=(0.9, 1.2)), device="gpu", dtype=types.UINT8)
bc = fn.brightness_contrast(hsv, device="gpu", brightness=fn.uniform(range=(.9, 1.1)))
cmn = fn.crop_mirror_normalize(bc, device="gpu", output_dtype=types.FLOAT,
output_layout=types.NHWC,
image_type=types.GRAY,
mean=[255 // 2],
std=[255 // 2])
rot = fn.rotate(cmn, angle=fn.uniform(range=(-40, 40)), device="gpu", keep_size=True)
tpose = fn.transpose(rot, perm=(2, 0, 1), device="gpu") # Reshaping to a format PyTorch likes
pipe.set_outputs(tpose, labels)
pipe.build()
dali_iter = DALIClassificationIterator([pipe], -1)
return dali_iter
def create_coco_pipeline(file_root,
annotations_file,
batch_size=1,
device_id=0,
num_threads=4,
local_rank=0,
world_size=1):
pipeline = Pipeline(batch_size, num_threads,
local_rank, seed=42 + device_id)
with pipeline:
images, bboxes, labels = fn.coco_reader(file_root=file_root,
annotations_file=annotations_file,
skip_empty=True,
shard_id=local_rank,
num_shards=world_size,
ratio=True,
ltrb=True,
random_shuffle=False,
shuffle_after_epoch=True,
name="Reader")
crop_begin, crop_size, bboxes, labels = fn.random_bbox_crop(bboxes, labels,
device="cpu",
aspect_ratio=[0.5, 2.0],
thresholds=[0, 0.1, 0.3, 0.5, 0.7, 0.9],
scaling=[0.3, 1.0],
bbox_layout="xyXY",
allow_no_crop=True,
num_attempts=50)
images = fn.image_decoder_slice(images, crop_begin, crop_size, device="mixed", output_type=types.RGB)
flip_coin = fn.coin_flip(probability=0.5)
images = fn.resize(images,
resize_x=300,
resize_y=300,
min_filter=types.DALIInterpType.INTERP_TRIANGULAR)
# use float to avoid clipping and quantizing the intermediate result
images = fn.hsv(images, dtype=types.FLOAT, hue=fn.uniform(range=[-0.5, 0.5]),
saturation=fn.uniform(range=[0.5, 1.5]))
images = fn.brightness_contrast(images,
contrast_center = 128, # input is in float, but in 0..255 range
dtype = types.UINT8,
brightness = fn.uniform(range=[0.875, 1.125]),
contrast = fn.uniform(range=[0.5, 1.5]))
bboxes = fn.bb_flip(bboxes, ltrb=True, horizontal=flip_coin)
images = fn.crop_mirror_normalize(images,
mean=[104., 117., 123.],
std=[1., 1., 1.],
mirror=flip_coin,
dtype=types.FLOAT,
output_layout="CHW",
pad_output=False)
pipeline.set_outputs(images, bboxes, labels)
return pipeline
def create_coco_pipeline(default_boxes, args):
try:
shard_id = torch.distributed.get_rank()
num_shards = torch.distributed.get_world_size()
except RuntimeError:
shard_id = 0
num_shards = 1
images, bboxes, labels = fn.readers.coco(
file_root=args.train_coco_root,
annotations_file=args.train_annotate,
skip_empty=True,
shard_id=shard_id,
num_shards=num_shards,
ratio=True,
ltrb=True,
random_shuffle=False,
shuffle_after_epoch=True,
name="Reader")
crop_begin, crop_size, bboxes, labels = fn.random_bbox_crop(
bboxes,
labels,
device="cpu",
aspect_ratio=[0.5, 2.0],
thresholds=[0, 0.1, 0.3, 0.5, 0.7, 0.9],
scaling=[0.3, 1.0],
bbox_layout="xyXY",
allow_no_crop=True,
num_attempts=50)
images = fn.image_decoder_slice(images,
crop_begin,
crop_size,
device="mixed",
output_type=types.RGB)
flip_coin = fn.random.coin_flip(probability=0.5)
images = fn.resize(images,
resize_x=300,
resize_y=300,
min_filter=types.DALIInterpType.INTERP_TRIANGULAR)
saturation = fn.uniform(range=[0.5, 1.5])
contrast = fn.uniform(range=[0.5, 1.5])
brightness = fn.uniform(range=[0.875, 1.125])
hue = fn.uniform(range=[-0.5, 0.5])
images = fn.hsv(images, dtype=types.FLOAT, hue=hue,
saturation=saturation) # use float to avoid clipping and
# quantizing the intermediate result
images = fn.brightness_contrast(
images,
contrast_center=128, # input is in float, but in 0..255 range
dtype=types.UINT8,
brightness=brightness,
contrast=contrast)
dtype = types.FLOAT16 if args.fp16 else types.FLOAT
bboxes = fn.bb_flip(bboxes, ltrb=True, horizontal=flip_coin)
images = fn.crop_mirror_normalize(
images,
crop=(300, 300),
mean=[0.485 * 255, 0.456 * 255, 0.406 * 255],
std=[0.229 * 255, 0.224 * 255, 0.225 * 255],
mirror=flip_coin,
dtype=dtype,
output_layout="CHW",
pad_output=False)
bboxes, labels = fn.box_encoder(bboxes,
labels,
criteria=0.5,
anchors=default_boxes.as_ltrb_list())
labels = labels.gpu()
bboxes = bboxes.gpu()
return images, bboxes, labels
