本文整理匯總了Python中nvidia.dali.ops.Uniform方法的典型用法代碼示例。如果您正苦於以下問題：Python ops.Uniform方法的具體用法？Python ops.Uniform怎麽用？Python ops.Uniform使用的例子？那麽, 這裏精選的方法代碼示例或許可以為您提供幫助。您也可以進一步了解該方法所在類nvidia.dali.ops的用法示例。
在下文中一共展示了ops.Uniform方法的7個代碼示例，這些例子默認根據受歡迎程度排序。您可以為喜歡或者感覺有用的代碼點讚，您的評價將有助於係統推薦出更棒的Python代碼示例。
示例1: __init__
# 需要導入模塊: from nvidia.dali import ops [as 別名]
# 或者: from nvidia.dali.ops import Uniform [as 別名]
def __init__(self, batch_size, num_threads, device_id, data_dir, crop, seed=12, local_rank=0, world_size=1,
spos_pre=False):
super(HybridTrainPipe, self).__init__(batch_size, num_threads, device_id, seed=seed + device_id)
color_space_type = types.BGR if spos_pre else types.RGB
self.input = ops.FileReader(file_root=data_dir, shard_id=local_rank, num_shards=world_size, random_shuffle=True)
self.decode = ops.ImageDecoder(device="mixed", output_type=color_space_type)
self.res = ops.RandomResizedCrop(device="gpu", size=crop,
interp_type=types.INTERP_LINEAR if spos_pre else types.INTERP_TRIANGULAR)
self.twist = ops.ColorTwist(device="gpu")
self.jitter_rng = ops.Uniform(range=[0.6, 1.4])
self.cmnp = ops.CropMirrorNormalize(device="gpu",
output_dtype=types.FLOAT,
output_layout=types.NCHW,
image_type=color_space_type,
mean=0. if spos_pre else [0.485 * 255, 0.456 * 255, 0.406 * 255],
std=1. if spos_pre else [0.229 * 255, 0.224 * 255, 0.225 * 255])
self.coin = ops.CoinFlip(probability=0.5) 示例2: __init__
# 需要導入模塊: from nvidia.dali import ops [as 別名]
# 或者: from nvidia.dali.ops import Uniform [as 別名]
def __init__(self, name, batch_size, num_workers, device_id, num_gpu,
root=os.path.expanduser('~/.mxnet/datasets/face')):
super().__init__(batch_size, num_workers, device_id, seed=12 + device_id)
idx_files = [os.path.join(root, name, "train.idx")]
rec_files = [os.path.join(root, name, "train.rec")]
prop = open(os.path.join(root, name, "property"), "r").read().strip().split(',')
assert len(prop) == 3
self.num_classes = int(prop[0])
self.image_size = [int(prop[1]), int(prop[2])]
self._input = ops.MXNetReader(path=rec_files, index_path=idx_files, random_shuffle=True,
num_shards=num_gpu, tensor_init_bytes=self.image_size[0] * self.image_size[1] * 8)
self._decode = ops.nvJPEGDecoder(device="mixed", output_type=types.RGB)
self._cmnp = ops.CropMirrorNormalize(device="gpu", output_dtype=types.FLOAT, output_layout=types.NCHW,
crop=self.image_size, image_type=types.RGB,
mean=[127.5, 127.5, 127.5], std=[127.5, 127.5, 127.5])
self._contrast = ops.Contrast(device="gpu", )
self._saturation = ops.Saturation(device="gpu", )
self._brightness = ops.Brightness(device="gpu", )
self._uniform = ops.Uniform(range=(0.7, 1.3))
self._coin = ops.CoinFlip(probability=0.5)
self.iter = 0 示例3: define_graph
# 需要導入模塊: from nvidia.dali import ops [as 別名]
# 或者: from nvidia.dali.ops import Uniform [as 別名]
def define_graph(self):
jpegs, labels = self.loader()
images = self.decode(jpegs)
images = self.resize(images.gpu())
# images = self.hue(images, hue=ops.Uniform(range=(-3.0, 3.0))())
# images = self.bright(images,
# brightness=ops.Uniform(range=(0.9, 1.1))())
# images = self.cntrst(images,
# contrast=ops.Uniform(range=(0.9, 1.1))())
# images = self.rotate(images,
# angle=ops.Uniform(range=(-5.0, 5.0))())
# images = self.jitter(images)
images = self.rrcrop(images)
images = self.cmnorm(images, mirror=self.coin())
return images, labels 示例4: __init__
# 需要導入模塊: from nvidia.dali import ops [as 別名]
# 或者: from nvidia.dali.ops import Uniform [as 別名]
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)
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, random_shuffle=True)
self.decode = ops.HostDecoder(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]) 示例5: __init__
# 需要導入模塊: from nvidia.dali import ops [as 別名]
# 或者: from nvidia.dali.ops import Uniform [as 別名]
def __init__(self, name, batch_size, num_threads, device_id, num_shards, shard_id,
root=os.path.expanduser('~/.mxnet/datasets/face'), ):
super().__init__(batch_size, num_threads, device_id, seed=12)
idx_files = [os.path.join(root, name, "train.idx")]
rec_files = [os.path.join(root, name, "train.rec")]
prop = open(os.path.join(root, name, "property"), "r").read().strip().split(',')
assert len(prop) == 3
self.num_classes = int(prop[0])
self.image_size = [int(prop[1]), int(prop[2])]
self.size = 0
for idx_file in idx_files:
with open(idx_file, "r") as f:
self.size += len(list(f.readlines()))
self._input = ops.MXNetReader(path=rec_files, index_path=idx_files, random_shuffle=True,
num_shards=num_shards, shard_id=shard_id, seed=12,
tensor_init_bytes=self.image_size[0] * self.image_size[1] * 8)
self._decode = ops.ImageDecoder(device="mixed", output_type=types.RGB)
self._cmnp = ops.CropMirrorNormalize(device="gpu",
output_dtype=types.FLOAT,
output_layout=types.NCHW,
crop=self.image_size,
image_type=types.RGB,
mean=[0., 0., 0.],
std=[255., 255., 255.])
self._contrast = ops.Contrast(device="gpu")
self._saturation = ops.Saturation(device="gpu")
self._brightness = ops.Brightness(device="gpu")
self._uniform = ops.Uniform(range=(0.7, 1.3))
self._coin = ops.CoinFlip(probability=0.5) 示例6: __new__
# 需要導入模塊: from nvidia.dali import ops [as 別名]
# 或者: from nvidia.dali.ops import Uniform [as 別名]
def __new__(cls, range=(-1., 1.)):
"""Create an ``Uniform`` operator.
Parameters
----------
range : Tuple[float, float], optional
The lower and upper bound of distribution.
Returns
-------
nvidia.dali.ops.Uniform
The operator.
"""
return ops.Uniform(range=range) 示例7: __init__
# 需要導入模塊: from nvidia.dali import ops [as 別名]
# 或者: from nvidia.dali.ops import Uniform [as 別名]
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', bbox_layout="xyXY", 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)