def __init__(self, batch_size, num_threads, device_id, prefetch, seed):
super(CommonPipeline, self).__init__(batch_size,
num_threads,
device_id,
prefetch_queue_depth=prefetch)
self.decode_cpu = ops.HostDecoder(device="cpu", output_type=types.RGB)
self.decode_crop = ops.HostDecoderSlice(device="cpu",
output_type=types.RGB)
self.crop = ops.SSDRandomCrop(device="cpu", num_attempts=1, seed=seed)
self.crop2 = 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=seed)
self.slice_cpu = ops.Slice(device="cpu")
self.slice_gpu = ops.Slice(device="gpu")
self.flip_cpu = ops.Flip(device="cpu")
self.bb_flip_cpu = ops.BbFlip(device="cpu", ltrb=True)
self.flip_gpu = ops.Flip(device="gpu")
self.bb_flip_gpu = ops.BbFlip(device="gpu", ltrb=True)
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, 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, args, device_id, file_root, annotations_file):
super(DetectionPipeline,
self).__init__(batch_size=args.batch_size,
num_threads=args.num_workers,
device_id=device_id,
prefetch_queue_depth=args.prefetch,
seed=args.seed)
# Reading COCO dataset
self.input = ops.readers.COCO(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.decoders.Image(device="cpu",
output_type=types.RGB)
self.decode_crop = ops.decoders.ImageSlice(device="cpu",
output_type=types.RGB)
self.decode_gpu = ops.decoders.Image(device="mixed",
output_type=types.RGB,
hw_decoder_load=0)
self.decode_gpu_crop = ops.decoders.ImageSlice(device="mixed",
output_type=types.RGB,
hw_decoder_load=0)
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],
bbox_layout="xyXY",
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,
dtype=types.FLOAT)
self.normalize_gpu = ops.CropMirrorNormalize(device="gpu",
crop=crop_size,
mean=mean,
std=std,
mirror=0,
dtype=types.FLOAT)
self.twist_cpu = ops.ColorTwist(device="cpu")
self.twist_gpu = ops.ColorTwist(device="gpu")
self.hsv_cpu = ops.Hsv(device="cpu", dtype=types.FLOAT)
self.hsv_gpu = ops.Hsv(device="gpu", dtype=types.FLOAT)
self.bc_cpu = ops.BrightnessContrast(device="cpu",
dtype=types.UINT8,
contrast_center=128)
self.bc_gpu = ops.BrightnessContrast(device="gpu",
dtype=types.UINT8,
contrast_center=128)
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.saturation_rng = ops.random.Uniform(range=[0.8, 1.2])
self.contrast_rng = ops.random.Uniform(range=[0.5, 1.5])
self.brighness_rng = ops.random.Uniform(range=[0.875, 1.125])
self.hue_rng = ops.random.Uniform(range=[-45, 45])
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, 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])
