def __init__(self, args):
super(TFRecordDetectionPipeline,
self).__init__(args.batch_size, args.num_workers, 0, 0)
self.input = ops.TFRecordReader(
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([1], 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.ImageDecoder(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 __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.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, 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, 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, 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,
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)
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())
