def __init__(self, batch_size, num_threads, device_id = 0, seed = 0):
super(TestPipeline, self).__init__(batch_size, num_threads, device_id, seed)
self.input = ops.COCOReader(
file_root = file_root,
annotations_file = annotations_file,
shard_id = 0,
num_shards = 1,
ratio=False,
save_img_ids=True)
self.decode = ops.ImageDecoder(device = "cpu", output_type = types.RGB)
self.resize = ops.Resize(
device = "cpu",
image_type = types.RGB,
interp_type = types.INTERP_LINEAR)
self.cmn = ops.CropMirrorNormalize(
device = "cpu",
output_dtype = types.FLOAT,
crop = (224, 224),
image_type = types.RGB,
mean = [128., 128., 128.],
std = [1., 1., 1.])
self.res_uniform = ops.Uniform(range = (256.,480.))
self.uniform = ops.Uniform(range = (0.0, 1.0))
self.cast = ops.Cast(
device = "cpu",
dtype = types.FLOAT16)
def __init__(self,
batch_size,
num_threads,
device,
device_id=0,
shard_id=0,
num_shards=1,
seed=0):
super(TestPipeline, self).__init__(batch_size, num_threads, device_id,
seed)
self.device = device
self.input = ops.COCOReader(file_root=file_root,
annotations_file=annotations_file,
shard_id=shard_id,
num_shards=num_shards,
ratio=False,
save_img_ids=True)
self.decode = ops.ImageDecoder(
device='mixed' if device is 'gpu' else 'cpu',
output_type=types.RGB)
self.resize = ops.Resize(device=device,
image_type=types.RGB,
resize_x=224,
resize_y=224,
interp_type=types.INTERP_LINEAR)
self.cmn = ops.CropMirrorNormalize(device=device,
output_dtype=types.FLOAT,
image_type=types.RGB,
mean=[128., 128., 128.],
std=[1., 1., 1.])
self.cast = ops.Cast(device=device, dtype=types.INT16)
def __init__(self,
data_paths,
batch_size,
num_threads,
shard_id,
num_gpus,
random_shuffle,
stick_to_shard,
shuffle_after_epoch,
pad_last_batch,
initial_fill=1024):
# use only 1 GPU, as we care only about shard_id
super(COCOReaderPipeline, self).__init__(batch_size,
num_threads,
0,
prefetch_queue_depth=1)
self.input = ops.COCOReader(file_root=data_paths[0],
annotations_file=data_paths[1],
shard_id=shard_id,
num_shards=num_gpus,
random_shuffle=random_shuffle,
save_img_ids=True,
stick_to_shard=stick_to_shard,
shuffle_after_epoch=shuffle_after_epoch,
pad_last_batch=pad_last_batch,
initial_fill=initial_fill)
def __init__(self,
image_dir,
annotations_file,
cache_path,
batch_size,
target_size,
preproc_param,
num_threads,
num_shards,
device_ids,
training=False):
Pipeline.__init__(self,
batch_size=batch_size,
num_threads=num_threads,
device_id=device_ids,
prefetch_queue_depth=num_threads,
seed=42)
DaliPipeline.__init__(self,
target_size=target_size,
preproc_param=preproc_param,
training=training)
self.reader = ops.COCOReader(annotations_file=annotations_file,
file_root=image_dir,
num_shards=num_shards,
shard_id=0,
ltrb=True,
ratio=True,
shuffle_after_epoch=training,
save_img_ids=True,
dump_meta_files=True,
dump_meta_files_path=cache_path)
def __init__(self, batch_size, num_threads, path, training, annotations, world, device_id, mean, std, resize, max_size, stride):
super().__init__(batch_size=batch_size, num_threads=num_threads, device_id = device_id, prefetch_queue_depth=num_threads, seed=42)
self.path = path
self.training = training
self.stride = stride
self.iter = 0
self.reader = ops.COCOReader(annotations_file=annotations, file_root=path, num_shards=world,shard_id=torch.cuda.current_device(),
ltrb=True, ratio=True, shuffle_after_epoch=True, save_img_ids=True)
self.decode_train = ops.ImageDecoderSlice(device="mixed", output_type=types.RGB)
self.decode_infer = ops.ImageDecoder(device="mixed", output_type=types.RGB)
self.bbox_crop = ops.RandomBBoxCrop(device='cpu', ltrb=True, scaling=[0.3, 1.0], thresholds=[0.1,0.3,0.5,0.7,0.9])
self.bbox_flip = ops.BbFlip(device='cpu', ltrb=True)
self.img_flip = ops.Flip(device='gpu')
self.coin_flip = ops.CoinFlip(probability=0.5)
if isinstance(resize, list): resize = max(resize)
self.rand_resize = ops.Uniform(range=[resize, float(max_size)])
self.resize_train = ops.Resize(device='gpu', interp_type=types.DALIInterpType.INTERP_CUBIC, save_attrs=True)
self.resize_infer = ops.Resize(device='gpu', interp_type=types.DALIInterpType.INTERP_CUBIC, resize_longer=max_size, save_attrs=True)
padded_size = max_size + ((self.stride - max_size % self.stride) % self.stride)
self.pad = ops.Paste(device='gpu', fill_value = 0, ratio=1.1, min_canvas_size=padded_size, paste_x=0, paste_y=0)
self.normalize = ops.CropMirrorNormalize(device='gpu', mean=mean, std=std, crop=padded_size, crop_pos_x=0, crop_pos_y=0)
def __init__(self, batch_size, num_threads, device_id, num_gpus,
data_paths):
super(COCOReaderPipeline, self).__init__(batch_size, num_threads,
device_id)
self.input = ops.COCOReader(file_root=data_paths[0],
annotations_file=data_paths[1],
shard_id=device_id,
num_shards=num_gpus)
def __init__(self,
file_root,
annotations_file,
batch_size=1,
device_id=0,
num_threads=4,
local_rank=0,
world_size=1):
super(HybridTrainPipe, self).__init__(batch_size,
num_threads,
device_id,
seed=42 + device_id)
self.reader = ops.COCOReader(
file_root=file_root,
annotations_file=annotations_file,
skip_empty=True,
shard_id=local_rank,
num_shards=world_size,
ratio=True,
ltrb=True,
shuffle_after_epoch=True,
pad_last_batch=True)
self.crop = ops.RandomBBoxCrop(
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],
ltrb=True,
allow_no_crop=True,
num_attempts=50)
self.bbflip = ops.BbFlip(device="cpu", ltrb=True)
self.roi_decode = ops.ImageDecoderSlice(device="mixed")
self.resize = ops.Resize(
device="gpu",
resize_x=300,
resize_y=300,
min_filter=types.DALIInterpType.INTERP_TRIANGULAR)
self.hsv = ops.Hsv(device="gpu", dtype=types.FLOAT) # use float to avoid clipping and
# quantizing the intermediate result
self.bc = ops.BrightnessContrast(device="gpu",
contrast_center=128, # input is in float, but in 0..255 range
dtype=types.UINT8)
self.cmnp = ops.CropMirrorNormalize(
device="gpu",
mean=[104., 117., 123.],
std=[1., 1., 1.],
dtype=types.FLOAT,
output_layout=types.NCHW,
pad_output=False)
self.rng1 = ops.Uniform(range=[0.5, 1.5])
self.rng2 = ops.Uniform(range=[0.875, 1.125])
self.rng3 = ops.Uniform(range=[-0.5, 0.5])
self.coin = ops.CoinFlip(probability=0.5)
self.build()
def __init__(self, batch_size, device_id, file_root, annotations_file):
super(DetectionPipeline, self).__init__(batch_size, 2, device_id, True,
12)
# Reading COCO dataset
self.input = ops.COCOReader(file_root=file_root,
annotations_file=annotations_file,
shard_id=device_id,
num_shards=1,
ratio=True,
ltrb=True)
def __init__(self, batch_size, num_threads, device_id, num_gpus,
data_paths, prefetch, seed):
super(COCOReaderPipeline, self).__init__(batch_size, num_threads,
device_id, prefetch, seed)
self.input = ops.COCOReader(file_root=data_paths[0],
annotations_file=data_paths[1],
shard_id=device_id,
num_shards=num_gpus,
ratio=True,
ltrb=True,
random_shuffle=True)
def __init__(self, batch_size, num_threads, path, training, annotations, world, device_id, mean, std, resize,
max_size, stride, rotate_augment=False,
augment_brightness=0.0,
augment_contrast=0.0, augment_hue=0.0,
augment_saturation=0.0):
super().__init__(batch_size=batch_size, num_threads=num_threads, device_id=device_id,
prefetch_queue_depth=num_threads, seed=42)
self.path = path
self.training = training
self.stride = stride
self.iter = 0
self.rotate_augment = rotate_augment
self.augment_brightness = augment_brightness
self.augment_contrast = augment_contrast
self.augment_hue = augment_hue
self.augment_saturation = augment_saturation
self.reader = ops.COCOReader(annotations_file=annotations, file_root=path, num_shards=world,
shard_id=torch.cuda.current_device(),
ltrb=True, ratio=True, shuffle_after_epoch=True, save_img_ids=True)
self.decode_train = ops.ImageDecoderSlice(device="mixed", output_type=types.RGB)
self.decode_infer = ops.ImageDecoder(device="mixed", output_type=types.RGB)
self.bbox_crop = ops.RandomBBoxCrop(device='cpu', ltrb=True, scaling=[0.3, 1.0],
thresholds=[0.1, 0.3, 0.5, 0.7, 0.9])
self.bbox_flip = ops.BbFlip(device='cpu', ltrb=True)
self.img_flip = ops.Flip(device='gpu')
self.coin_flip = ops.CoinFlip(probability=0.5)
self.bc = ops.BrightnessContrast(device='gpu')
self.hsv = ops.Hsv(device='gpu')
# Random number generation for augmentation
self.brightness_dist = ops.NormalDistribution(mean=1.0, stddev=augment_brightness)
self.contrast_dist = ops.NormalDistribution(mean=1.0, stddev=augment_contrast)
self.hue_dist = ops.NormalDistribution(mean=0.0, stddev=augment_hue)
self.saturation_dist = ops.NormalDistribution(mean=1.0, stddev=augment_saturation)
if rotate_augment:
raise RuntimeWarning("--augment-rotate current has no effect when using the DALI data loader.")
if isinstance(resize, list): resize = max(resize)
self.rand_resize = ops.Uniform(range=[resize, float(max_size)])
self.resize_train = ops.Resize(device='gpu', interp_type=types.DALIInterpType.INTERP_CUBIC, save_attrs=True)
self.resize_infer = ops.Resize(device='gpu', interp_type=types.DALIInterpType.INTERP_CUBIC,
resize_longer=max_size, save_attrs=True)
padded_size = max_size + ((self.stride - max_size % self.stride) % self.stride)
self.pad = ops.Paste(device='gpu', fill_value=0, ratio=1.1, min_canvas_size=padded_size, paste_x=0, paste_y=0)
self.normalize = ops.CropMirrorNormalize(device='gpu', mean=mean, std=std, crop=(padded_size, padded_size),
crop_pos_x=0, crop_pos_y=0)
def __init__(self,
batch_size,
device_id,
file_root,
annotations_file,
num_gpus,
output_fp16=False,
output_nhwc=False,
pad_output=False,
num_threads=1,
seed=15):
super(COCOPipeline, self).__init__(batch_size=batch_size,
device_id=device_id,
num_threads=num_threads,
seed=seed)
if torch.distributed.is_initialized():
shard_id = torch.distributed.get_rank()
else:
shard_id = 0
self.input = ops.COCOReader(file_root=file_root,
annotations_file=annotations_file,
shard_id=shard_id,
num_shards=num_gpus,
ratio=True,
ltrb=True,
random_shuffle=True,
skip_empty=True)
self.decode = ops.ImageDecoder(device="cpu", output_type=types.RGB)
# Augumentation techniques
self.crop = ops.SSDRandomCrop(device="cpu", num_attempts=1)
self.twist = ops.ColorTwist(device="gpu")
self.resize = ops.Resize(device="gpu", resize_x=300, resize_y=300)
output_dtype = types.FLOAT16 if output_fp16 else types.FLOAT
output_layout = types.NHWC if output_nhwc else types.NCHW
self.normalize = ops.CropMirrorNormalize(device="gpu",
crop=(300, 300),
mean=[0.0, 0.0, 0.0],
std=[255.0, 255.0, 255.0],
mirror=0,
output_dtype=output_dtype,
output_layout=output_layout,
pad_output=pad_output)
# Random variables
self.rng1 = ops.Uniform(range=[0.5, 1.5])
self.rng2 = ops.Uniform(range=[0.875, 1.125])
self.rng3 = ops.Uniform(range=[-0.5, 0.5])
def __init__(self, batch_size, num_threads, device_id):
super(COCOPipeline, self).__init__(batch_size,
num_threads,
device_id,
exec_async=False,
exec_pipelined=False,
seed=15)
self.input = ops.COCOReader(file_root=file_root,
annotations_file=annotations_file,
shard_id=device_id,
num_shards=num_gpus,
ratio=True,
ltrb=True)
self.decode = ops.ImageDecoder(device="mixed", output_type=types.RGB)
self.flip = ops.Flip(device="gpu")
self.bbflip = ops.BbFlip(device="cpu", ltrb=True)
self.paste_pos = ops.Uniform(range=(0, 1))
self.paste_ratio = ops.Uniform(range=(1, 2))
self.coin = ops.CoinFlip(probability=0.5)
self.coin2 = ops.CoinFlip(probability=0.5)
self.paste = ops.Paste(device="gpu", fill_value=(32, 64, 128))
self.bbpaste = ops.BBoxPaste(device="cpu", ltrb=True)
self.prospective_crop = ops.RandomBBoxCrop(device="cpu",
aspect_ratio=[0.5, 2.0],
thresholds=[0.1, 0.3, 0.5],
scaling=[0.8, 1.0],
ltrb=True)
self.slice = ops.Slice(device="gpu")
# resize
self.resize = ops.Resize(device="gpu",
interp_type=types.INTERP_LINEAR,
resize_shorter=800,
max_size=1200)
self.shape = ops.Shapes(device="gpu")
# normalize and convert hwc to chw
self.cmnp = ops.CropMirrorNormalize(
device="gpu",
output_dtype=types.FLOAT,
output_layout=types.NCHW,
image_type=types.RGB,
mean=[0.485 * 255, 0.456 * 255, 0.406 * 255],
std=[0.229 * 255, 0.224 * 255, 0.225 * 255])
# padding axes=(0,1) -> hwc, axes=(1,2) -> chw
self.padding = ops.Pad(device="gpu",
fill_value=0,
axes=(1, 2),
shape=(800, 1200))
def __init__(self,
batch_size,
file_root,
annotations_file,
default_boxes,
seed,
device_id=0,
num_threads=4):
super(COCOPipeline, self).__init__(batch_size=batch_size,
device_id=device_id,
num_threads=num_threads,
seed=seed)
self.input = ops.COCOReader(file_root=file_root,
annotations_file=annotations_file,
ratio=True,
ltrb=True,
random_shuffle=True)
self.decode = ops.nvJPEGDecoder(device="mixed", output_type=types.RGB)
# Augumentation techniques
self.crop = ops.RandomBBoxCrop(device="cpu",
aspect_ratio=[0.5, 2.0],
thresholds=[0.1, 0.3, 0.5, 0.7, 0.9],
scaling=[0.8, 1.0],
ltrb=True)
self.slice = ops.Slice(device="gpu")
self.twist = ops.ColorTwist(device="gpu")
self.resize = ops.Resize(device="gpu", resize_x=300, resize_y=300)
self.normalize = ops.CropMirrorNormalize(
device="gpu",
crop=(300, 300),
mean=[0.485 * 255., 0.456 * 255., 0.406 * 255.],
std=[0.229 * 255., 0.224 * 255., 0.225 * 255.])
# Random variables
self.rng1 = ops.Uniform(range=[0.5, 1.5])
self.rng2 = ops.Uniform(range=[0.875, 1.125])
self.rng3 = ops.Uniform(range=[-0.5, 0.5])
self.flip = ops.Flip(device="gpu")
self.bbflip = ops.BbFlip(device="cpu", ltrb=True)
self.flip_coin = ops.CoinFlip(probability=0.5)
self.box_encoder = ops.BoxEncoder(device="cpu",
criteria=0.5,
anchors=default_boxes.as_ltrb_list())
def __init__(self, args, data_path = test_data_path):
super(COCODetectionPipeline, self).__init__(
args.batch_size, args.num_workers, 0, 0)
self.input = ops.COCOReader(
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.ImageDecoder(device="mixed", output_type=types.RGB)
self.box_encoder = ops.BoxEncoder(
device="cpu",
criteria=0.5,
anchors=coco_anchors())
def __init__(self, file_root, annotations_file,
batch_size, num_threads, device_id=0, num_gpus=1,
mean=(123.675, 116.28, 103.53), stddev=(1., 1., 1.),
random_shuffle=True):
super(COCOPipeline, self).__init__(batch_size, num_threads, device_id, seed = 15)
self.input = ops.COCOReader(file_root=file_root, annotations_file=annotations_file,
shard_id=device_id, num_shards=num_gpus, ratio=True,
skip_empty=True, prefetch_queue_depth=32, random_shuffle=True)
self.decode = ops.ImageDecoder(device='mixed', output_type=types.BGR)
self.resize = ops.Resize(device='gpu', max_size=1216, resize_shorter=800)
self.flip = ops.CoinFlip(device='cpu')
self.bbox_flip = ops.BbFlip(device='gpu')
self.CMN = ops.CropMirrorNormalize(device='gpu', mean=mean, std=stddev, output_layout='HWC')
self.image_pad = ops.Pad(device='gpu', fill_value=0, axes=(0,1), shape=(1216, 1216))
self.bbox_pad = ops.Pad(device='gpu', fill_value=0, axes=(0,), shape=(100,))
self.label_pad = ops.Pad(device='gpu', fill_value=-1, axes=(0,), shape=(100,))
self.get_shape = ops.Shapes(device='gpu')
self.float_cast = ops.Cast(device='gpu', dtype=types.FLOAT)
def __init__(self, args, device_id, file_root, annotations_file):
super(DetectionPipeline,
self).__init__(args.batch_size, args.num_workers, device_id,
args.prefetch, args.seed)
# Reading COCO dataset
self.input = ops.COCOReader(file_root=file_root,
annotations_file=annotations_file,
shard_id=device_id,
num_shards=args.num_gpus,
ratio=True,
ltrb=True,
random_shuffle=True)
self.decode_cpu = ops.HostDecoder(device="cpu", output_type=types.RGB)
self.decode_crop = ops.HostDecoderSlice(device="cpu",
output_type=types.RGB)
self.decode_gpu = ops.nvJPEGDecoder(device="mixed",
output_type=types.RGB)
self.decode_gpu_crop = ops.nvJPEGDecoderSlice(device="mixed",
output_type=types.RGB)
self.ssd_crop = ops.SSDRandomCrop(device="cpu",
num_attempts=1,
seed=args.seed)
self.random_bbox_crop = ops.RandomBBoxCrop(
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],
ltrb=True,
seed=args.seed)
self.slice_cpu = ops.Slice(device="cpu")
self.slice_gpu = ops.Slice(device="gpu")
self.resize_cpu = ops.Resize(
device="cpu",
resize_x=300,
resize_y=300,
min_filter=types.DALIInterpType.INTERP_TRIANGULAR)
self.resize_gpu = ops.Resize(
device="gpu",
resize_x=300,
resize_y=300,
min_filter=types.DALIInterpType.INTERP_TRIANGULAR)
mean = [0.485 * 255, 0.456 * 255, 0.406 * 255]
std = [0.229 * 255, 0.224 * 255, 0.225 * 255]
crop_size = (300, 300)
self.normalize_cpu = ops.CropMirrorNormalize(device="cpu",
crop=crop_size,
mean=mean,
std=std,
mirror=0,
output_dtype=types.FLOAT)
self.normalize_gpu = ops.CropMirrorNormalize(device="gpu",
crop=crop_size,
mean=mean,
std=std,
mirror=0,
output_dtype=types.FLOAT)
self.twist_cpu = ops.ColorTwist(device="cpu")
self.twist_gpu = ops.ColorTwist(device="gpu")
self.flip_cpu = ops.Flip(device="cpu")
self.bbox_flip_cpu = ops.BbFlip(device="cpu", ltrb=True)
self.flip_gpu = ops.Flip(device="gpu")
self.bbox_flip_gpu = ops.BbFlip(device="gpu", ltrb=True)
default_boxes = coco_anchors()
self.box_encoder_cpu = ops.BoxEncoder(device="cpu",
criteria=0.5,
anchors=default_boxes)
self.box_encoder_gpu = ops.BoxEncoder(device="gpu",
criteria=0.5,
anchors=default_boxes)
self.box_encoder_cpu_offsets = ops.BoxEncoder(
device="cpu",
criteria=0.5,
offset=True,
scale=2,
stds=[0.1, 0.1, 0.2, 0.2],
anchors=default_boxes)
self.box_encoder_gpu_offsets = ops.BoxEncoder(
device="gpu",
criteria=0.5,
offset=True,
scale=2,
stds=[0.1, 0.1, 0.2, 0.2],
anchors=default_boxes)
# Random variables
self.rng1 = ops.Uniform(range=[0.5, 1.5])
self.rng2 = ops.Uniform(range=[0.875, 1.125])
self.rng3 = ops.Uniform(range=[-0.5, 0.5])
def __init__(self, train=False, batch_size=16, workers=4, size=512):
# TODO: support size as tuple
local_rank, world_size = env_rank(), env_world_size()
super().__init__(batch_size, workers, local_rank, seed=42)
split_str = "train" if train else "val"
self.input = ops.COCOReader(
file_root=f"{DATA_DIR}/{split_str}2017",
annotations_file=
f"{DATA_DIR}/annotations/instances_{split_str}2017.json",
shard_id=local_rank,
num_shards=world_size,
ratio=True, # want bbox in [0, 1]
ltrb=True, #
random_shuffle=train,
save_img_ids=True, # Need ids for evaluation
skip_empty=
True, # skips images without objects. not sure if we want to do so
)
self.bbox_crop = ops.RandomBBoxCrop(
device="cpu", # gpu is not supported (and not needed actually)
bbox_layout="xyXY", # same as 'ltrb'
scaling=[0.3, 1.0],
# adding 0.0 to thr instead of `allow_no_crop`
thresholds=[0.0, 0.1, 0.3, 0.5, 0.7, 0.9],
)
if train:
self.decode = ops.ImageDecoderSlice(device="mixed",
output_type=types.RGB)
else:
self.decode = ops.ImageDecoder(device="mixed",
output_type=types.RGB)
self.resize = ops.Resize(device="gpu",
interp_type=types.INTERP_CUBIC,
resize_longer=size,
save_attrs=True)
self.resize = ops.Resize(device="gpu",
interp_type=types.INTERP_CUBIC,
resize_longer=size)
self.bbox_flip = ops.BbFlip(device="cpu", ltrb=True)
self.img_flip = ops.Flip(device="gpu")
# color augmentations
self.bc = ops.BrightnessContrast(device="gpu")
self.hsv = ops.Hsv(device="gpu")
# pad to match output stride
self.pad = ops.Pad(device="gpu",
fill_value=0,
axes=(1, 2),
shape=(size, size))
self.normalize = ops.CropMirrorNormalize(
device="gpu",
# Imagenet mean and std
mean=[0.485 * 255, 0.456 * 255, 0.406 * 255],
std=[0.229 * 255, 0.224 * 255, 0.225 * 255],
# mean=[0, 0, 0],
# std=[1, 1, 1],
output_dtype=types.FLOAT,
output_layout=types.NCHW,
)
# TODO: add Jitter aug
# Random number generation for augmentation
self.coin_flip = ops.CoinFlip(probability=0.5)
self.rng1 = ops.Uniform(range=[0, 1])
self.rng2 = ops.Uniform(range=[0.85, 1.15])
self.rng3 = ops.Uniform(range=[-15, 15])
self.train = train
def __init__(self, default_boxes, root, annFile, batch_size, mean, std,
local_rank, num_workers, seed):
super(COCOPipeline, self).__init__(batch_size=batch_size,
device_id=local_rank,
num_threads=num_workers,
seed=seed)
# try:
# shard_id = torch.distributed.get_rank()
# num_shards = torch.distributed.get_world_size()
# except RuntimeError:
shard_id = 0
num_shards = 1
self.input = ops.COCOReader(file_root=root,
annotations_file=annFile,
skip_empty=True,
shard_id=shard_id,
num_shards=num_shards,
ratio=True,
ltrb=True,
random_shuffle=False,
shuffle_after_epoch=True)
self.decode = ops.nvJPEGDecoder(device="mixed", output_type=types.RGB)
# Augumentation techniques
# expand 1~2
self.paste_ratio = ops.Uniform(range=[1, 2])
self.paste_pos = ops.Uniform(range=[0, 1])
self.paste = ops.Paste(device="gpu", fill_value=tuple(mean))
self.bbpaste = ops.BBoxPaste(device="cpu", ltrb=True)
# random crop
self.crop = ops.RandomBBoxCrop(device="cpu",
aspect_ratio=[0.5, 2.0],
thresholds=[0.1, 0.3, 0.5, 0.7, 0.9],
scaling=[0.3, 1.0],
ltrb=True,
allow_no_crop=True,
num_attempts=50)
self.slice = ops.Slice(device="gpu")
self.twist = ops.ColorTwist(device="gpu")
self.resize = ops.Resize(
device="gpu",
resize_x=320,
resize_y=320,
min_filter=types.DALIInterpType.INTERP_TRIANGULAR)
self.normalize = ops.CropMirrorNormalize(device="gpu",
crop=(320, 320),
mean=mean,
std=std,
mirror=0,
output_dtype=types.FLOAT,
output_layout=types.NCHW,
pad_output=False)
# Random variables
self.rng1 = ops.Uniform(range=[0.5, 1.5])
self.rng2 = ops.Uniform(range=[0.875, 1.125])
self.rng3 = ops.Uniform(range=[-0.5, 0.5])
self.flip = ops.Flip(device="gpu")
self.bbflip = ops.BbFlip(device="cpu", ltrb=True)
self.flip_coin = ops.CoinFlip(probability=0.5)
self.box_encoder = ops.BoxEncoder(device="cpu",
criteria=0.5,
anchors=default_boxes.as_ltrb_list())
def __init__(self, default_boxes, args, seed):
super(COCOPipeline, self).__init__(batch_size=args.batch_size,
device_id=args.local_rank,
num_threads=args.num_workers,
seed=seed)
try:
shard_id = torch.distributed.get_rank()
num_shards = torch.distributed.get_world_size()
except RuntimeError:
shard_id = 0
num_shards = 1
self.input = ops.COCOReader(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)
self.decode = ops.ImageDecoder(device="cpu", output_type=types.RGB)
# Augumentation techniques
self.crop = ops.RandomBBoxCrop(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],
ltrb=True,
allow_no_crop=True,
num_attempts=1)
self.slice = ops.Slice(device="cpu")
self.hsv = ops.Hsv(
device="gpu", dtype=types.FLOAT) # use float to avoid clipping and
# quantizing the intermediate result
self.bc = ops.BrightnessContrast(
device="gpu",
contrast_center=128, # input is in float, but in 0..255 range
dtype=types.UINT8)
self.resize = ops.Resize(
device="cpu",
resize_x=300,
resize_y=300,
min_filter=types.DALIInterpType.INTERP_TRIANGULAR)
dtype = types.FLOAT16 if args.fp16 else types.FLOAT
self.normalize = ops.CropMirrorNormalize(
device="gpu",
crop=(300, 300),
mean=[0.485 * 255, 0.456 * 255, 0.406 * 255],
std=[0.229 * 255, 0.224 * 255, 0.225 * 255],
mirror=0,
dtype=dtype,
output_layout=types.NCHW,
pad_output=False)
# Random variables
self.rng1 = ops.Uniform(range=[0.5, 1.5])
self.rng2 = ops.Uniform(range=[0.875, 1.125])
self.rng3 = ops.Uniform(range=[-0.5, 0.5])
self.flip = ops.Flip(device="cpu")
self.bbflip = ops.BbFlip(device="cpu", ltrb=True)
self.flip_coin = ops.CoinFlip(probability=0.5)
self.box_encoder = ops.BoxEncoder(device="cpu",
criteria=0.5,
anchors=default_boxes.as_ltrb_list())
def __init__(self, batch_size, device_id, file_root, annotations_file, num_gpus,
output_fp16=False, output_nhwc=False, pad_output=False, num_threads=1, seed=15):
super(COCOPipeline, self).__init__(batch_size=batch_size, device_id=device_id,
num_threads=num_threads, seed=seed)
if torch.distributed.is_initialized():
shard_id = torch.distributed.get_rank()
else:
shard_id = 0
self.input = ops.COCOReader(file_root=file_root, annotations_file=annotations_file,
shard_id=shard_id, num_shards=num_gpus, ratio=True, ltrb=True, random_shuffle=True,
skip_empty=True)
self.decode = ops.ImageDecoder(device="cpu", output_type=types.RGB)
# Augumentation techniques
self.rotate = ops.Rotate(device="gpu", angle=30, interp_type=types.INTERP_LINEAR, fill_value=0)
self.crop = ops.SSDRandomCrop(device="cpu", num_attempts=1)
self.twist = ops.ColorTwist(device="gpu")
self.resize = ops.Resize(device="gpu", resize_x=300, resize_y=300)
# Will flip with probability provided in CoinFlip
self.flip = ops.Flip(device='gpu')
self.coin_flip_v = ops.CoinFlip(probability=0.1)
self.coin_flip_h = ops.CoinFlip(probability=0.1)
# bbox flipping
self.bbflip = ops.BbFlip(device='gpu', ltrb=True)
# paste
self.paste = ops.Paste(device='gpu', fill_value=0)
self.paste_pos = ops.Uniform(range=(0, 1))
self.paste_ratio = ops.Uniform(range=(1, 2))
self.bbpaste = ops.BBoxPaste(device='cpu', ltrb=True)
# prospective
self.prospective_crop = ops.RandomBBoxCrop(
device='cpu',
aspect_ratio=[0.5, 2.0],
thresholds=[0.1, 0.3, 0.5],
scaling=[0.8, 1.0],
ltrb=True
)
# slice (after prospective crop)
self.slice = ops.Slice(device='gpu')
# color
self.water = ops.Water(device='gpu')
# self.contrast = ops.BrightnessContrast(device="gpu", brightness=0.5, contrast=1.5)
# self.hsv = ops.Hsv(device="gpu", hue=45., saturation=0.2)
self.sphere = ops.Sphere(device='gpu')
self.warpaffine = ops.WarpAffine(device="gpu", matrix=[1.0, 0.8, 0.0, 0.0, 1.2, 0.0],
interp_type=types.INTERP_LINEAR)
output_dtype = types.FLOAT16 if output_fp16 else types.FLOAT
output_layout = types.NHWC if output_nhwc else types.NCHW
self.normalize = ops.CropMirrorNormalize(device="gpu", crop=(300, 300),
mean=[0.0, 0.0, 0.0],
std=[255.0, 255.0, 255.0],
mirror=0,
output_dtype=output_dtype,
output_layout=output_layout,
pad_output=pad_output)
# Random variables
self.rng1 = ops.Uniform(range=[0.5, 1.5])
self.rng2 = ops.Uniform(range=[0.875, 1.125])
self.rng3 = ops.Uniform(range=[-0.5, 0.5])
def __init__(self,
batch_size,
device_id,
file_root,
annotations_file,
num_gpus,
output_fp16=False,
output_nhwc=False,
pad_output=False,
num_threads=1,
seed=15,
dali_cache=-1,
dali_async=True,
use_nvjpeg=False,
use_roi=False):
super(COCOPipeline, self).__init__(batch_size=batch_size,
device_id=device_id,
num_threads=num_threads,
seed=seed,
exec_pipelined=dali_async,
exec_async=dali_async)
self.use_roi = use_roi
self.use_nvjpeg = use_nvjpeg
try:
shard_id = torch.distributed.get_rank()
except RuntimeError:
shard_id = 0
self.input = ops.COCOReader(file_root=file_root,
annotations_file=annotations_file,
shard_id=shard_id,
num_shards=num_gpus,
ratio=True,
ltrb=True,
skip_empty=True,
random_shuffle=(dali_cache > 0),
stick_to_shard=(dali_cache > 0),
shuffle_after_epoch=(dali_cache <= 0))
if use_nvjpeg:
if use_roi:
self.decode = ops.nvJPEGDecoderSlice(device="mixed",
output_type=types.RGB)
# handled in ROI decoder
self.slice = None
else:
if dali_cache > 0:
self.decode = ops.nvJPEGDecoder(device="mixed",
output_type=types.RGB,
cache_size=dali_cache *
1024,
cache_type="threshold",
cache_threshold=10000)
else:
self.decode = ops.nvJPEGDecoder(device="mixed",
output_type=types.RGB)
self.slice = ops.Slice(device="gpu")
self.crop = ops.RandomBBoxCrop(
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],
ltrb=True,
allow_no_crop=True,
num_attempts=1)
else:
self.decode = ops.HostDecoder(device="cpu", output_type=types.RGB)
# handled in the cropper
self.slice = None
self.crop = ops.SSDRandomCrop(device="cpu", num_attempts=1)
# Augumentation techniques (in addition to random crop)
self.twist = ops.ColorTwist(device="gpu")
self.resize = ops.Resize(
device="gpu",
resize_x=300,
resize_y=300,
min_filter=types.DALIInterpType.INTERP_TRIANGULAR)
output_dtype = types.FLOAT16 if output_fp16 else types.FLOAT
output_layout = types.NHWC if output_nhwc else types.NCHW
mean_val = list(np.array([0.485, 0.456, 0.406]) * 255.)
std_val = list(np.array([0.229, 0.224, 0.225]) * 255.)
self.normalize = ops.CropMirrorNormalize(device="gpu",
crop=(300, 300),
mean=mean_val,
std=std_val,
mirror=0,
output_dtype=output_dtype,
output_layout=output_layout,
pad_output=pad_output)
# Random variables
self.rng1 = ops.Uniform(range=[0.5, 1.5])
self.rng2 = ops.Uniform(range=[0.875, 1.125])
self.rng3 = ops.Uniform(range=[-0.5, 0.5])
def __init__(self,
batch_size,
device_id,
file_root,
meta_files_path,
annotations_file,
num_gpus,
anchors_ltrb_list,
output_fp16=False,
output_nhwc=False,
pad_output=False,
num_threads=1,
seed=15,
dali_cache=-1,
dali_async=True,
use_nvjpeg=False):
super(COCOPipeline, self).__init__(batch_size=batch_size,
device_id=device_id,
num_threads=num_threads,
seed=seed,
exec_pipelined=dali_async,
exec_async=dali_async)
self.use_nvjpeg = use_nvjpeg
try:
shard_id = torch.distributed.get_rank()
# Note: <= 19.05 was a RuntimeError, 19.06 is now throwing AssertionError
except (RuntimeError, AssertionError):
shard_id = 0
if meta_files_path == None:
self.c_input = ops.COCOReader(
file_root=file_root,
annotations_file=annotations_file,
shard_id=shard_id,
num_shards=num_gpus,
ratio=True,
ltrb=True,
skip_empty=True,
random_shuffle=(dali_cache > 0),
stick_to_shard=(dali_cache > 0),
lazy_init=True,
shuffle_after_epoch=(dali_cache <= 0))
else:
self.c_input = ops.COCOReader(
file_root=file_root,
meta_files_path=meta_files_path,
shard_id=shard_id,
num_shards=num_gpus,
random_shuffle=(dali_cache > 0),
stick_to_shard=(dali_cache > 0),
lazy_init=True,
shuffle_after_epoch=(dali_cache <= 0))
self.c_crop = ops.RandomBBoxCrop(
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],
ltrb=True,
allow_no_crop=True,
num_attempts=1)
decoder_device = 'mixed' if use_nvjpeg else 'cpu'
# fused decode and slice. This is "region-of-interest" (roi) decoding
self.m_decode = ops.ImageDecoderSlice(device=decoder_device,
output_type=types.RGB)
self.g_slice = None
# special case for using dali decode caching: the caching decoder can't
# be fused with slicing (because we need to slice the decoded image
# differently every epoch), so we need to unfuse decode and slice:
if dali_cache > 0 and use_nvjpeg:
self.m_decode = ops.ImageDecoder(device='mixed',
output_type=types.RGB,
cache_size=dali_cache * 1024,
cache_type="threshold",
cache_threshold=10000)
self.g_slice = ops.Slice(device="gpu")
# Augumentation techniques (in addition to random crop)
self.g_twist = ops.ColorTwist(device="gpu")
self.g_resize = ops.Resize(
device="gpu",
resize_x=300,
resize_y=300,
min_filter=types.DALIInterpType.INTERP_TRIANGULAR)
output_dtype = types.FLOAT16 if output_fp16 else types.FLOAT
output_layout = types.NHWC if output_nhwc else types.NCHW
mean_val = list(np.array([0.485, 0.456, 0.406]) * 255.)
std_val = list(np.array([0.229, 0.224, 0.225]) * 255.)
self.g_normalize = ops.CropMirrorNormalize(device="gpu",
crop=(300, 300),
mean=mean_val,
std=std_val,
output_dtype=output_dtype,
output_layout=output_layout,
pad_output=pad_output)
# Random variables
self.c_rng1 = ops.Uniform(range=[0.5, 1.5])
self.c_rng2 = ops.Uniform(range=[0.875, 1.125])
self.c_rng3 = ops.Uniform(range=[-0.5, 0.5])
flip_probability = 0.5
self.c_flip_coin = ops.CoinFlip(
probability=flip_probability) # coin_rnd
self.c_bbflip = ops.BbFlip(device="cpu", ltrb=True)
self.g_box_encoder = ops.BoxEncoder(device="gpu",
criteria=0.5,
anchors=anchors_ltrb_list,
offset=True,
stds=[0.1, 0.1, 0.2, 0.2],
scale=300)
self.g_cast = ops.Cast(device="gpu", dtype=types.FLOAT)
