def create_video_pipe(video_files,
sequence_length=8,
target_size=224,
stride=30):
pipeline = Pipeline(1, 4, 0, seed=42)
with pipeline:
images = fn.readers.video(device="gpu",
filenames=video_files,
sequence_length=sequence_length,
stride=stride,
shard_id=0,
num_shards=1,
random_shuffle=False,
pad_last_batch=True,
name="Reader")
images = fn.crop_mirror_normalize(
images,
dtype=types.FLOAT,
output_layout="FCHW",
crop=(target_size, target_size),
mean=[0.485 * 255, 0.456 * 255, 0.406 * 255],
std=[0.229 * 255, 0.224 * 255, 0.225 * 255])
pipeline.set_outputs(images)
return pipeline
def val_pipeline(cfg: ValLoaderConfig):
jpeg, label = fn.readers.file(
file_root=ROOT_DATA_DIR + "/val/",
shard_id=env_rank(),
num_shards=env_world_size(),
name="Reader",
)
image = fn.decoders.image(jpeg, device="mixed", output_type=types.RGB)
crop_size = cfg.image_size if cfg.full_crop else math.ceil(
(cfg.image_size * 1.14 + 8) // 16 * 16)
image = fn.resize(image,
device="gpu",
interp_type=types.INTERP_TRIANGULAR,
resize_shorter=crop_size)
image = fn.crop_mirror_normalize(
image,
device="gpu",
crop=(cfg.image_size, cfg.image_size),
mean=DATA_MEAN,
std=DATA_STD,
dtype=types.FLOAT,
output_layout=types.NCHW,
)
label = fn.one_hot(label, num_classes=cfg.num_classes).gpu()
return image, label
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 check_gaussian_blur_output(batch_size, sigma, window_size, op_type="cpu"):
decoder_device = "cpu" if op_type == "cpu" else "mixed"
pipe = Pipeline(batch_size=batch_size, num_threads=4, device_id=0)
with pipe:
input, _ = fn.file_reader(file_root=images_dir,
shard_id=0,
num_shards=1)
decoded = fn.image_decoder(input,
device=decoder_device,
output_type=types.RGB)
blurred = fn.gaussian_blur(decoded,
device=op_type,
sigma=sigma,
window_size=window_size)
normalized = fn.crop_mirror_normalize(blurred,
device=op_type,
dtype=types.FLOAT,
output_layout="HWC",
mean=[128.0, 128.0, 128.0],
std=[100.0, 100.0, 100.0])
pipe.set_outputs(normalized)
pipe.build()
for _ in range(3):
result = pipe.run()
def mnist_pipeline(num_threads,
path,
device,
device_id=0,
shard_id=0,
num_shards=1,
seed=0):
pipeline = Pipeline(BATCH_SIZE, num_threads, device_id, seed)
with pipeline:
jpegs, labels = fn.readers.caffe2(path=path,
random_shuffle=True,
shard_id=shard_id,
num_shards=num_shards)
images = fn.image_decoder(jpegs,
device='mixed' if device == 'gpu' else 'cpu',
output_type=types.GRAY)
if device == 'gpu':
labels = labels.gpu()
images = fn.crop_mirror_normalize(images,
dtype=types.FLOAT,
mean=[0.],
std=[255.],
output_layout="CHW")
pipeline.set_outputs(images, labels)
return pipeline
def order_change_pipeline():
if order_change_pipeline.change:
rng = 0
else:
order_change_pipeline.change = True
rng = fn.random.coin_flip(probability=0.5, seed=47)
jpegs, labels = fn.readers.file(file_root=file_root,
shard_id=0,
num_shards=2)
images = fn.decoders.image(jpegs, device='mixed', output_type=types.RGB)
resized_images = fn.random_resized_crop(images,
device="gpu",
size=(224, 224),
seed=27)
out_type = types.FLOAT16
output = fn.crop_mirror_normalize(
resized_images.gpu(),
mirror=rng,
device="gpu",
dtype=out_type,
crop=(224, 224),
mean=[0.485 * 255, 0.456 * 255, 0.406 * 255],
std=[0.229 * 255, 0.224 * 255, 0.225 * 255])
return rng, jpegs, labels, images, resized_images, output
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 dali_data_iter(batch_size: int,
rec_file: str,
idx_file: str,
num_threads: int,
initial_fill=32768,
random_shuffle=True,
prefetch_queue_depth=1,
local_rank=0,
name="reader",
mean=(127.5, 127.5, 127.5),
std=(127.5, 127.5, 127.5)):
"""
Parameters:
----------
initial_fill: int
Size of the buffer that is used for shuffling. If random_shuffle is False, this parameter is ignored.
"""
rank: int = distributed.get_rank()
world_size: int = distributed.get_world_size()
import nvidia.dali.fn as fn
import nvidia.dali.types as types
from nvidia.dali.pipeline import Pipeline
from nvidia.dali.plugin.pytorch import DALIClassificationIterator
pipe = Pipeline(
batch_size=batch_size,
num_threads=num_threads,
device_id=local_rank,
prefetch_queue_depth=prefetch_queue_depth,
)
condition_flip = fn.random.coin_flip(probability=0.5)
with pipe:
jpegs, labels = fn.readers.mxnet(path=rec_file,
index_path=idx_file,
initial_fill=initial_fill,
num_shards=world_size,
shard_id=rank,
random_shuffle=random_shuffle,
pad_last_batch=False,
name=name)
images = fn.decoders.image(jpegs,
device="mixed",
output_type=types.RGB)
images = fn.crop_mirror_normalize(images,
dtype=types.FLOAT,
mean=mean,
std=std,
mirror=condition_flip)
pipe.set_outputs(images, labels)
pipe.build()
return DALIWarper(
DALIClassificationIterator(
pipelines=[pipe],
reader_name=name,
))
def get_pipe():
def get_data():
out = [
np.zeros(input_shape, dtype=np.uint8)
for _ in range(batch_size)
]
return out
data = fn.external_source(source=get_data, device=device)
return fn.crop_mirror_normalize(data, crop_h=10, crop_w=20)
def create_dali_pipeline(data_dir,
crop,
size,
shard_id,
num_shards,
dali_cpu=False,
is_training=True):
images, labels = fn.readers.file(file_root=data_dir,
shard_id=shard_id,
num_shards=num_shards,
random_shuffle=is_training,
pad_last_batch=True,
name="Reader")
dali_device = 'cpu' if dali_cpu else 'gpu'
decoder_device = 'cpu' if dali_cpu else 'mixed'
device_memory_padding = 211025920 if decoder_device == 'mixed' else 0
host_memory_padding = 140544512 if decoder_device == 'mixed' else 0
if is_training:
images = fn.decoders.image_random_crop(
images,
device=decoder_device,
output_type=types.RGB,
device_memory_padding=device_memory_padding,
host_memory_padding=host_memory_padding,
random_aspect_ratio=[0.8, 1.25],
random_area=[0.1, 1.0],
num_attempts=100)
images = fn.resize(images,
device=dali_device,
resize_x=crop,
resize_y=crop,
interp_type=types.INTERP_TRIANGULAR)
mirror = fn.random.coin_flip(probability=0.5)
else:
images = fn.decoders.image(images,
device=decoder_device,
output_type=types.RGB)
images = fn.resize(images,
device=dali_device,
size=size,
mode="not_smaller",
interp_type=types.INTERP_TRIANGULAR)
mirror = False
images = fn.crop_mirror_normalize(
images.gpu(),
dtype=types.FLOAT,
output_layout="CHW",
crop=(crop, crop),
mean=[0.485 * 255, 0.456 * 255, 0.406 * 255],
std=[0.229 * 255, 0.224 * 255, 0.225 * 255],
mirror=mirror)
labels = labels.gpu()
return images, labels
def create_dali_pipeline(batch_size, num_threads, device_id, data_dir):
files = []
with open(join(data_dir, "file_list.txt"), "r") as f:
files = [line.rstrip() for line in f if line is not '']
shuffle(files)
img_files = []
seg_files = []
for prefix in files:
img_files.append(join(data_dir, "leftImg8bit/train", prefix + "_leftImg8bit.png"))
seg_files.append(join(data_dir, "gtFine/train", prefix + "_gtFine_labelIds.png"))
pipeline = Pipeline(batch_size, num_threads, device_id, seed=12 + device_id)
with pipeline:
imgs, _ = fn.file_reader(files=img_files,
shard_id=0, num_shards=1,
random_shuffle=False, pad_last_batch=True)
segs, _ = fn.file_reader(files=seg_files,
shard_id=0, num_shards=1,
random_shuffle=False, pad_last_batch=True)
dali_device = 'gpu'
decoder_device = 'mixed'
# device_memory_padding = 211025920 if decoder_device == 'mixed' else 0
# host_memory_padding = 140544512 if decoder_device == 'mixed' else 0
device_memory_padding = 0
host_memory_padding = 0
imgs = fn.image_decoder(imgs, device=decoder_device, output_type=types.RGB,
device_memory_padding=device_memory_padding,
host_memory_padding=host_memory_padding,
hybrid_huffman_threshold=250000)
segs = fn.image_decoder(segs, device=decoder_device, output_type=types.GRAY,
device_memory_padding=device_memory_padding,
host_memory_padding=host_memory_padding,
hybrid_huffman_threshold=250000)
imgs = fn.crop_mirror_normalize(imgs, device=dali_device,
crop=(512, 512),
dtype=types.FLOAT,
mean=[0.485 * 255, 0.456 * 255, 0.406 * 255],
std=[0.229 * 255, 0.224 * 255, 0.225 * 255],
output_layout="CHW")
segs = fn.crop(segs, device=dali_device,
dtype=types.UINT8, crop=(512, 512))
pipeline.set_outputs(imgs, segs)
return pipeline
def rn50_pipeline_2(data_path):
uniform = fn.random.uniform(range=(0., 1.), shape=2)
resize_uniform = fn.random.uniform(range=(256., 480.))
mirror = fn.random.coin_flip(probability=0.5)
jpegs, _ = fn.readers.file(file_root=data_path)
images = fn.decoders.image(jpegs, device='mixed', output_type=types.RGB)
resized_images = fn.resize(images, device='gpu', interp_type=types.INTERP_LINEAR,
resize_shorter=resize_uniform)
output = fn.crop_mirror_normalize(resized_images, device='gpu', dtype=types.FLOAT16,
crop=(224, 224), mean=[128., 128., 128.], std=[1., 1., 1.],
mirror=mirror, crop_pos_x=uniform[0], crop_pos_y=uniform[1])
return output
def create_dali_pipeline(data_dir, crop, size, shard_id, num_shards, dali_cpu=False, is_training=True):
images, labels = fn.readers.file(file_root=data_dir,
shard_id=shard_id,
num_shards=num_shards,
random_shuffle=is_training,
pad_last_batch=True,
name="Reader")
dali_device = 'cpu' if dali_cpu else 'gpu'
decoder_device = 'cpu' if dali_cpu else 'mixed'
# ask nvJPEG to preallocate memory for the biggest sample in ImageNet for CPU and GPU to avoid reallocations in runtime
device_memory_padding = 211025920 if decoder_device == 'mixed' else 0
host_memory_padding = 140544512 if decoder_device == 'mixed' else 0
# ask HW NVJPEG to allocate memory ahead for the biggest image in the data set to avoid reallocations in runtime
preallocate_width_hint = 5980 if decoder_device == 'mixed' else 0
preallocate_height_hint = 6430 if decoder_device == 'mixed' else 0
if is_training:
images = fn.decoders.image_random_crop(images,
device=decoder_device, output_type=types.RGB,
device_memory_padding=device_memory_padding,
host_memory_padding=host_memory_padding,
preallocate_width_hint=preallocate_width_hint,
preallocate_height_hint=preallocate_height_hint,
random_aspect_ratio=[0.8, 1.25],
random_area=[0.1, 1.0],
num_attempts=100)
images = fn.resize(images,
device=dali_device,
resize_x=crop,
resize_y=crop,
interp_type=types.INTERP_TRIANGULAR)
mirror = fn.random.coin_flip(probability=0.5)
else:
images = fn.decoders.image(images,
device=decoder_device,
output_type=types.RGB)
images = fn.resize(images,
device=dali_device,
size=size,
mode="not_smaller",
interp_type=types.INTERP_TRIANGULAR)
mirror = False
images = fn.crop_mirror_normalize(images.gpu(),
dtype=types.FLOAT,
output_layout="CHW",
crop=(crop, crop),
mean=[0.485 * 255,0.456 * 255,0.406 * 255],
std=[0.229 * 255,0.224 * 255,0.225 * 255],
mirror=mirror)
labels = labels.gpu()
return images, labels
def es_pipeline_debug():
images = fn.external_source(name='input')
labels = fn.external_source(name='labels')
rng = fn.random.coin_flip(probability=0.5, seed=47)
images = fn.random_resized_crop(images, size=(224, 224), seed=27)
out_type = types.FLOAT16
output = fn.crop_mirror_normalize(
images.gpu(),
mirror=rng,
device="gpu",
dtype=out_type,
crop=(224, 224),
mean=[0.485 * 255, 0.456 * 255, 0.406 * 255],
std=[0.229 * 255, 0.224 * 255, 0.225 * 255])
return rng, images, output, labels
def get_image_pipeline(batch_size, num_threads, device, device_id=0, shard_id=0, num_shards=1,
def_for_dataset=False):
test_data_root = get_dali_extra_path()
file_root = os.path.join(test_data_root, 'db', 'coco_dummy', 'images')
annotations_file = os.path.join(
test_data_root, 'db', 'coco_dummy', 'instances.json')
pipe = Pipeline(batch_size, num_threads, device_id)
with pipe:
jpegs, _, _, image_ids = fn.readers.coco(
file_root=file_root,
annotations_file=annotations_file,
shard_id=shard_id,
num_shards=num_shards,
ratio=False,
image_ids=True)
images = fn.decoders.image(
jpegs,
device=('mixed' if device == 'gpu' else 'cpu'),
output_type=types.RGB)
images = fn.resize(
images,
resize_x=224,
resize_y=224,
interp_type=types.INTERP_LINEAR)
images = fn.crop_mirror_normalize(
images,
dtype=types.FLOAT,
mean=[128., 128., 128.],
std=[1., 1., 1.])
if device == 'gpu':
image_ids = image_ids.gpu()
ids_reshaped = fn.reshape(image_ids, shape=[1, 1])
ids_int16 = fn.cast(image_ids, dtype=types.INT16)
pipe.set_outputs(images, ids_reshaped, ids_int16)
shapes = (
(batch_size, 3, 224, 224),
(batch_size, 1, 1),
(batch_size, 1))
dtypes = (
tf.float32,
tf.int32,
tf.int16)
return pipe, shapes, dtypes
def injection_pipeline(callback, device='cpu'):
rng = fn.random.coin_flip(probability=0.5, seed=47)
images = fn.random_resized_crop(callback(),
device=device,
size=(224, 224),
seed=27)
out_type = types.FLOAT16
output = fn.crop_mirror_normalize(
images.gpu(),
mirror=rng,
device="gpu",
dtype=out_type,
crop=(224, 224),
mean=[0.485 * 255, 0.456 * 255, 0.406 * 255],
std=[0.229 * 255, 0.224 * 255, 0.225 * 255])
return rng, images, output
def es_pipeline_standard():
jpegs, labels = fn.readers.file(file_root=file_root,
shard_id=0,
num_shards=2)
images = fn.decoders.image(jpegs, output_type=types.RGB)
rng = fn.random.coin_flip(probability=0.5, seed=47)
images = fn.random_resized_crop(images, size=(224, 224), seed=27)
out_type = types.FLOAT16
output = fn.crop_mirror_normalize(
images.gpu(),
mirror=rng,
device="gpu",
dtype=out_type,
crop=(224, 224),
mean=[0.485 * 255, 0.456 * 255, 0.406 * 255],
std=[0.229 * 255, 0.224 * 255, 0.225 * 255])
return rng, images, output, labels
def pipe():
image_like = fn.random.uniform(device=device,
range=(0, 255),
shape=(80, 120, 3))
image_like = fn.reshape(image_like, layout="HWC")
mean = [0.485 * 255, 0.456 * 255, 0.406 * 255]
std = [0.229 * 255, 0.224 * 255, 0.225 * 255]
if rand_mean:
mean = fn.random.uniform(range=(100, 125), shape=(3, ))
if rand_stdev:
std = fn.random.uniform(range=(55, 60), shape=(3, ))
out = fn.crop_mirror_normalize(image_like,
dtype=types.FLOAT,
output_layout="HWC",
mean=mean,
std=std,
scale=scale,
shift=shift,
pad_output=False)
return out, image_like, mean, std
def rn50_pipeline():
rng = fn.random.coin_flip(probability=0.5, seed=47)
print(f'rng: {rng.get().as_array()}')
tmp = rng ^ 1
print(f'rng xor: {tmp.get().as_array()}')
jpegs, labels = fn.readers.file(file_root=file_root,
shard_id=0,
num_shards=2)
if jpegs.get().is_dense_tensor():
print(f'jpegs: {jpegs.get().as_array()}')
else:
print('jpegs shapes:')
for j in jpegs.get():
print(j.shape())
print(f'labels: {labels.get().as_array()}')
images = fn.decoders.image(jpegs, device='mixed', output_type=types.RGB)
for i in images.get().as_cpu():
print(i)
for i in images.get():
print(i.shape())
images = fn.random_resized_crop(images,
device="gpu",
size=(224, 224),
seed=27)
for i in images.get():
print(i.shape())
print(np.array(images.get().as_cpu()[0]))
images += 1
print(np.array(images.get().as_cpu()[0]))
out_type = types.FLOAT16
output = fn.crop_mirror_normalize(
images.gpu(),
mirror=rng,
device="gpu",
dtype=out_type,
crop=(224, 224),
mean=[0.485 * 255, 0.456 * 255, 0.406 * 255],
std=[0.229 * 255, 0.224 * 255, 0.225 * 255])
return (output, labels.gpu())
def get_pipeline(batch_size,
num_threads,
device,
device_id=0,
shard_id=0,
num_shards=1):
test_data_root = os.environ['DALI_EXTRA_PATH']
file_root = os.path.join(test_data_root, 'db', 'coco_dummy', 'images')
annotations_file = os.path.join(test_data_root, 'db', 'coco_dummy',
'instances.json')
pipe = Pipeline(batch_size, num_threads, device_id)
with pipe:
jpegs, _, _, image_ids = fn.coco_reader(
file_root=file_root,
annotations_file=annotations_file,
shard_id=shard_id,
num_shards=num_shards,
ratio=False,
image_ids=True)
images = fn.image_decoder(
jpegs,
device=('mixed' if device == 'gpu' else 'cpu'),
output_type=types.RGB)
images = fn.resize(images,
resize_x=224,
resize_y=224,
interp_type=types.INTERP_LINEAR)
images = fn.crop_mirror_normalize(images,
dtype=types.FLOAT,
mean=[128., 128., 128.],
std=[1., 1., 1.])
if device == 'gpu':
image_ids = image_ids.gpu()
ids_reshaped = fn.reshape(image_ids, shape=[1, 1])
ids_int16 = fn.cast(image_ids, dtype=types.INT16)
pipe.set_outputs(images, ids_reshaped, ids_int16)
return pipe
def RN50Pipeline():
device = 'mixed' if args.device == 'gpu' else 'cpu'
jpegs, _ = fn.readers.file(file_root=args.images_dir)
images = fn.decoders.image_random_crop(
jpegs,
device=device,
output_type=types.RGB,
hw_decoder_load=args.hw_load,
preallocate_width_hint=args.width_hint,
preallocate_height_hint=args.height_hint)
images = fn.resize(images, resize_x=224, resize_y=224)
layout = types.NCHW
out_type = types.FLOAT16
coin_flip = fn.random.coin_flip(probability=0.5)
images = fn.crop_mirror_normalize(
images,
dtype=out_type,
output_layout=layout,
crop=(224, 224),
mean=[0.485 * 255, 0.456 * 255, 0.406 * 255],
std=[0.229 * 255, 0.224 * 255, 0.225 * 255],
mirror=coin_flip)
return images
def create_image_pipeline(
batch_size,
num_threads,
device_id,
image0_list,
image1_list,
flow_list,
valBool,
):
pipeline = Pipeline(batch_size, num_threads, device_id, seed=2)
with pipeline:
if valBool:
shuffleBool = False
else:
shuffleBool = True
""" READ FILES """
image0, _ = fn.readers.file(
file_root=args.data,
files=image0_list,
random_shuffle=shuffleBool,
name="Reader",
seed=1,
)
image1, _ = fn.readers.file(
file_root=args.data,
files=image1_list,
random_shuffle=shuffleBool,
seed=1,
)
flo = fn.readers.numpy(
file_root=args.data,
files=flow_list,
random_shuffle=shuffleBool,
seed=1,
)
""" DECODE AND RESHAPE """
image0 = fn.decoders.image(image0, device="cpu")
image0 = fn.reshape(image0, layout="HWC")
image1 = fn.decoders.image(image1, device="cpu")
image1 = fn.reshape(image1, layout="HWC")
images = fn.cat(image0, image1, axis=2)
flo = fn.reshape(flo, layout="HWC")
if valBool:
images = fn.resize(images, resize_x=162, resize_y=122)
else:
""" CO-TRANSFORM """
# random translate
# angle_rng = fn.random.uniform(range=(-90, 90))
# images = fn.rotate(images, angle=angle_rng, fill_value=0)
# flo = fn.rotate(flo, angle=angle_rng, fill_value=0)
images = fn.random_resized_crop(
images,
size=[122, 162], # 122, 162
random_aspect_ratio=[1.3, 1.4],
random_area=[0.8, 0.9],
seed=1,
)
flo = fn.random_resized_crop(
flo,
size=[122, 162],
random_aspect_ratio=[1.3, 1.4],
random_area=[0.8, 0.9],
seed=1,
)
# coin1 = fn.random.coin_flip(dtype=types.DALIDataType.BOOL, seed=10)
# coin1_n = coin1 ^ True
# coin2 = fn.random.coin_flip(dtype=types.DALIDataType.BOOL, seed=20)
# coin2_n = coin2 ^ True
# images = (
# fn.flip(images, horizontal=1, vertical=1) * coin1 * coin2
# + fn.flip(images, horizontal=1) * coin1 * coin2_n
# + fn.flip(images, vertical=1) * coin1_n * coin2
# + images * coin1_n * coin2_n
# )
# flo = (
# fn.flip(flo, horizontal=1, vertical=1) * coin1 * coin2
# + fn.flip(flo, horizontal=1) * coin1 * coin2_n
# + fn.flip(flo, vertical=1) * coin1_n * coin2
# + flo * coin1_n * coin2_n
# )
# _flo = flo
# flo_0 = fn.slice(_flo, axis_names="C", start=0, shape=1)
# flo_1 = fn.slice(_flo, axis_names="C", start=1, shape=1)
# flo_0 = flo_0 * coin1 * -1 + flo_0 * coin1_n
# flo_1 = flo_1 * coin2 * -1 + flo_1 * coin2_n
# # flo = noflip + vertical flip + horizontal flip + both_flip
# # A horizontal flip is around the vertical axis (switch left and right)
# # So for a vertical flip coin1 is activated and needs to give +1, coin2 is activated needs to give -1
# # for a horizontal flip coin1 is activated and needs to be -1, coin2_n needs +1
# # no flip coin coin1_n +1, coin2_n +1
# flo = fn.cat(flo_0, flo_1, axis_name="C")
""" NORMALIZE """
images = fn.crop_mirror_normalize(
images,
mean=[0, 0, 0, 0, 0, 0],
std=[255, 255, 255, 255, 255, 255])
images = fn.crop_mirror_normalize(
images,
mean=[0.45, 0.432, 0.411, 0.45, 0.432, 0.411],
std=[1, 1, 1, 1, 1, 1],
)
flo = fn.crop_mirror_normalize(
flo, mean=[0, 0], std=[args.div_flow, args.div_flow])
pipeline.set_outputs(images, flo)
return pipeline
import nvidia.dali.types as types
import matplotlib.pylab as plt
import nvidia.dali.fn as fn
import nvidia.dali.types as types
pipe = Pipeline(batch_size = 64, num_threads = 1, device_id = 0)
raw_files, labels = fn.file_reader(file_root = "data/resized", random_shuffle = True)
decode = fn.image_decoder(raw_files, device = "mixed", output_type = types.GRAY)
resize = fn.resize(decode, device = "gpu", image_type = types.GRAY,
interp_type = types.INTERP_LINEAR, resize_x=WIDTH, resize_y=HEIGHT)
cmn = fn.crop_mirror_normalize(resize, device="gpu",output_dtype=types.FLOAT,
output_layout=types.NCHW,
image_type=types.GRAY,
mean=[ 255//2],
std=[255//2])
pipe.set_outputs(cmn, labels)
pipe.build()
from nvidia.dali.plugin.pytorch import DALIClassificationIterator
dali_iter = DALIClassificationIterator([pipe], -1)
output = next(dali_iter)[0]
output["data"].shape, output["label"].shape
def get_dali_pipeline(tfrec_filenames,
tfrec_idx_filenames,
height,
width,
shard_id,
num_gpus,
dali_cpu=True,
training=True):
inputs = fn.readers.tfrecord(path=tfrec_filenames,
index_path=tfrec_idx_filenames,
random_shuffle=training,
shard_id=shard_id,
num_shards=num_gpus,
initial_fill=10000,
features={
'image/encoded':
tfrec.FixedLenFeature((), tfrec.string,
""),
'image/class/label':
tfrec.FixedLenFeature([1], tfrec.int64,
-1),
'image/class/text':
tfrec.FixedLenFeature([], tfrec.string,
''),
'image/object/bbox/xmin':
tfrec.VarLenFeature(tfrec.float32, 0.0),
'image/object/bbox/ymin':
tfrec.VarLenFeature(tfrec.float32, 0.0),
'image/object/bbox/xmax':
tfrec.VarLenFeature(tfrec.float32, 0.0),
'image/object/bbox/ymax':
tfrec.VarLenFeature(tfrec.float32, 0.0)
})
decode_device = "cpu" if dali_cpu else "mixed"
resize_device = "cpu" if dali_cpu else "gpu"
if training:
images = fn.decoders.image_random_crop(
inputs["image/encoded"],
device=decode_device,
output_type=types.RGB,
random_aspect_ratio=[0.75, 1.25],
random_area=[0.05, 1.0],
num_attempts=100)
images = fn.resize(images,
device=resize_device,
resize_x=width,
resize_y=height)
else:
images = fn.decoders.image(inputs["image/encoded"],
device=decode_device,
output_type=types.RGB)
# Make sure that every image > 224 for CropMirrorNormalize
images = fn.resize(images, device=resize_device, resize_shorter=256)
images = fn.crop_mirror_normalize(images.gpu(),
dtype=types.FLOAT,
crop=(height, width),
mean=[123.68, 116.78, 103.94],
std=[58.4, 57.12, 57.3],
output_layout="HWC",
mirror=fn.random.coin_flip())
labels = inputs["image/class/label"].gpu()
labels -= 1 # Change to 0-based (don't use background class)
return images, labels
def dali_dataloader(
tfrec_filenames,
tfrec_idx_filenames,
shard_id=0, num_shards=1,
batch_size=128, num_threads=os.cpu_count(),
image_size=224, num_workers=1, training=True):
pipe = Pipeline(batch_size=batch_size,
num_threads=num_threads, device_id=0)
with pipe:
inputs = fn.readers.tfrecord(
path=tfrec_filenames,
index_path=tfrec_idx_filenames,
random_shuffle=training,
shard_id=shard_id,
num_shards=num_shards,
initial_fill=10000,
read_ahead=True,
pad_last_batch=True,
prefetch_queue_depth=num_workers,
name='Reader',
features={
'image/encoded': tfrec.FixedLenFeature((), tfrec.string, ""),
'image/class/label': tfrec.FixedLenFeature([1], tfrec.int64, -1),
})
jpegs = inputs["image/encoded"]
if training:
images = fn.decoders.image_random_crop(
jpegs,
device="mixed",
output_type=types.RGB,
random_aspect_ratio=[0.8, 1.25],
random_area=[0.1, 1.0],
num_attempts=100)
images = fn.resize(images,
device='gpu',
resize_x=image_size,
resize_y=image_size,
interp_type=types.INTERP_TRIANGULAR)
mirror = fn.random.coin_flip(probability=0.5)
else:
images = fn.decoders.image(jpegs,
device='mixed',
output_type=types.RGB)
images = fn.resize(images,
device='gpu',
size=int(image_size / 0.875),
mode="not_smaller",
interp_type=types.INTERP_TRIANGULAR)
mirror = False
images = fn.crop_mirror_normalize(
images.gpu(),
dtype=types.FLOAT,
crop=(image_size, image_size),
mean=[0.485 * 255, 0.456 * 255, 0.406 * 255],
std=[0.229 * 255, 0.224 * 255, 0.225 * 255],
mirror=mirror)
label = inputs["image/class/label"] - 1 # 0-999
label = fn.element_extract(label, element_map=0) # Flatten
label = label.gpu()
pipe.set_outputs(images, label)
pipe.build()
last_batch_policy = LastBatchPolicy.DROP if training else LastBatchPolicy.PARTIAL
loader = DALIClassificationIterator(
pipe, reader_name="Reader", auto_reset=True, last_batch_policy=last_batch_policy)
return loader
def train_pipeline(cfg: TrainLoaderConfig):
jpeg, label = fn.readers.file(
file_root=ROOT_DATA_DIR + "/train/",
random_shuffle=True,
shard_id=env_rank(),
num_shards=env_world_size(),
name="Reader",
)
image = fn.decoders.image_random_crop(
jpeg,
device="mixed",
random_aspect_ratio=[0.75, 1.25],
random_area=[cfg.min_area, 1.0],
num_attempts=100,
output_type=types.RGB,
)
image_tr = fn.resize(image,
device="gpu",
size=cfg.image_size,
interp_type=types.INTERP_TRIANGULAR)
if cfg.random_interpolation:
image_cub = fn.resize(image,
device="gpu",
size=cfg.image_size,
interp_type=types.INTERP_CUBIC)
image = mix(fn.random.coin_flip(probability=0.5), image_cub, image_tr)
else:
image = image_tr
if cfg.blur_prob > 0:
blur_image = fn.gaussian_blur(
image,
device="gpu",
window_size=11,
sigma=fn.random.uniform(range=[0.5, 1.1]))
image = mix(
fn.random.coin_flip(probability=cfg.blur_prob, dtype=types.BOOL),
blur_image, image)
if cfg.color_twist_prob > 0:
image_ct = fn.color_twist(
image,
device="gpu",
contrast=fn.random.uniform(range=[0.7, 1.3]),
brightness=fn.random.uniform(range=[0.7, 1.3]),
hue=fn.random.uniform(range=[-20, 20]), # in degrees
saturation=fn.random.uniform(range=[0.7, 1.3]),
)
image = mix(
fn.random.coin_flip(probability=cfg.color_twist_prob,
dtype=types.BOOL), image_ct, image)
if cfg.gray_prob > 0:
grayscale_coin = fn.cast(
fn.random.coin_flip(probability=cfg.gray_prob), dtype=types.FLOAT)
image = fn.hsv(image, device="gpu", saturation=grayscale_coin)
if cfg.re_prob: # random erasing
image_re = fn.erase(
image,
device="gpu",
anchor=fn.random.uniform(range=(0.0, 1), shape=cfg.re_count * 2),
shape=fn.random.uniform(range=(0.05, 0.25),
shape=cfg.re_count * 2),
axis_names="HW",
fill_value=DATA_MEAN,
normalized_anchor=True,
normalized_shape=True,
)
image = mix(
fn.random.coin_flip(probability=cfg.re_prob, dtype=types.BOOL),
image_re, image)
image = fn.crop_mirror_normalize(
image,
device="gpu",
crop=(cfg.image_size, cfg.image_size),
mirror=fn.random.coin_flip(probability=0.5),
mean=DATA_MEAN,
std=DATA_STD,
dtype=types.FLOAT,
output_layout=types.NCHW,
)
label = fn.one_hot(label, num_classes=cfg.num_classes).gpu()
return image, label
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
def load_tfrecord(directory, batch_size, training):
tfrecord = []
tfrecord_idx = []
for f in os.listdir(directory):
fullpath = os.path.join(directory, f)
if not os.path.isfile(fullpath):
continue
if f.endswith(".tfrecord"):
tfrecord.append(fullpath)
if f.endswith(".idx"):
tfrecord_idx.append(fullpath)
tfrecord.sort()
tfrecord_idx.sort()
pipe = Pipeline(batch_size=batch_size, num_threads=32, device_id=0)
with pipe:
inputs = fn.tfrecord_reader(
path=tfrecord,
index_path=tfrecord_idx,
features={
"frame_one":
tfrec.FixedLenFeature((), tfrec.string, ""),
"frame_two":
tfrec.FixedLenFeature((), tfrec.string, ""),
"frame_three":
tfrec.FixedLenFeature((), tfrec.string, ""),
"frame_four":
tfrec.FixedLenFeature((), tfrec.string, ""),
"plus_one_position":
tfrec.FixedLenFeature([3], tfrec.float32, 0.0),
"plus_one_orientation":
tfrec.FixedLenFeature([3], tfrec.float32, 0.0),
"plus_two_position":
tfrec.FixedLenFeature([3], tfrec.float32, 0.0),
"plus_two_orientation":
tfrec.FixedLenFeature([3], tfrec.float32, 0.0),
"plus_three_position":
tfrec.FixedLenFeature([3], tfrec.float32, 0.0),
"plus_three_orientation":
tfrec.FixedLenFeature([3], tfrec.float32, 0.0),
"speed":
tfrec.FixedLenFeature([], tfrec.float32, 0.0),
})
frame1 = inputs["frame_one"]
frame1 = fn.image_decoder(frame1,
device="mixed",
output_type=types.RGB)
# frame1 = fn.resize(frame1, device="gpu", resize_shorter=256.)
frame1 = fn.crop_mirror_normalize(frame1,
device="gpu",
dtype=types.FLOAT,
mean=[0., 0., 0.],
std=[1., 1., 1.])
frame1 = fn.transpose(frame1, device="gpu", perm=[1, 2, 0])
frame2 = inputs["frame_two"]
frame2 = fn.image_decoder(frame2,
device="mixed",
output_type=types.RGB)
# frame2 = fn.resize(frame2, device="gpu", resize_shorter=256.)
frame2 = fn.crop_mirror_normalize(frame2,
device="gpu",
dtype=types.FLOAT,
mean=[0., 0., 0.],
std=[1., 1., 1.])
frame2 = fn.transpose(frame2, device="gpu", perm=[1, 2, 0])
position = inputs["plus_one_position"].gpu()
orientation = inputs["plus_one_orientation"].gpu()
speed = inputs["speed"].gpu()
image = fn.cat(frame1, frame2, device="gpu", axis=2)
pose = fn.cat(position, orientation, device="gpu", axis=0)
pipe.set_outputs(image, pose, speed)
# Define shapes and types of the outputs
shapes = ((batch_size, 480, 640), (batch_size, 6), (batch_size))
dtypes = (tf.float32, tf.float32)
# Create dataset
return dali_tf.DALIDataset(pipeline=pipe,
batch_size=batch_size,
output_shapes=shapes,
output_dtypes=dtypes,
device_id=0)
