本文整理汇总了Python中nvidia.dali.types.FLOAT16属性的典型用法代码示例。如果您正苦于以下问题：Python types.FLOAT16属性的具体用法？Python types.FLOAT16怎么用？Python types.FLOAT16使用的例子？那么恭喜您, 这里精选的属性代码示例或许可以为您提供帮助。您也可以进一步了解该属性所在类nvidia.dali.types的用法示例。
在下文中一共展示了types.FLOAT16属性的5个代码示例，这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞，您的评价将有助于系统推荐出更棒的Python代码示例。
示例1: __init__
# 需要导入模块: from nvidia.dali import types [as 别名]
# 或者: from nvidia.dali.types import FLOAT16 [as 别名]
def __init__(self, batch_size, num_threads, device_id, rec_path, idx_path,
shard_id, num_shards, crop_shape,
nvjpeg_padding, prefetch_queue=3,
output_layout=types.NCHW, pad_output=True, dtype='float16'):
super(HybridTrainPipe, self).__init__(batch_size, num_threads, device_id, seed = 12 + device_id, prefetch_queue_depth = prefetch_queue)
self.input = ops.MXNetReader(path = [rec_path], index_path=[idx_path],
random_shuffle=True, shard_id=shard_id, num_shards=num_shards)
self.decode = ops.nvJPEGDecoder(device = "mixed", output_type = types.RGB,
device_memory_padding = nvjpeg_padding,
host_memory_padding = nvjpeg_padding)
self.rrc = ops.RandomResizedCrop(device = "gpu", size = crop_shape)
self.cmnp = ops.CropMirrorNormalize(device = "gpu",
output_dtype = types.FLOAT16 if dtype == 'float16' else types.FLOAT,
output_layout = output_layout,
crop = crop_shape,
pad_output = pad_output,
image_type = types.RGB,
mean = _mean_pixel,
std = _std_pixel)
self.coin = ops.CoinFlip(probability = 0.5) 示例2: __init__
# 需要导入模块: from nvidia.dali import types [as 别名]
# 或者: from nvidia.dali.types import FLOAT16 [as 别名]
def __init__(self, batch_size, num_threads, shard_id, image_dir, file_list, nvjpeg_padding,
prefetch_queue=3, seed=1, num_shards=1, channel_last=True,
spatial_size=(224, 224), dtype="half",
mean=_pixel_mean, std=_pixel_std, pad_output=True):
super(TrainPipeline, self).__init__(
batch_size, num_threads, shard_id, seed=seed, prefetch_queue_depth=prefetch_queue)
self.input = ops.FileReader(file_root=image_dir, file_list=file_list,
random_shuffle=True, num_shards=num_shards, shard_id=shard_id)
self.decode = ops.ImageDecoder(device="mixed", output_type=types.RGB,
device_memory_padding=nvjpeg_padding,
host_memory_padding=nvjpeg_padding)
self.rrc = ops.RandomResizedCrop(device="gpu", size=spatial_size)
self.cmnp = ops.CropMirrorNormalize(device="gpu",
output_dtype=types.FLOAT16 if dtype == "half" else types.FLOAT,
output_layout=types.NHWC if channel_last else types.NCHW,
crop=spatial_size,
image_type=types.RGB,
mean=mean,
std=std,
pad_output=pad_output)
self.coin = ops.CoinFlip(probability=0.5) 示例3: __init__
# 需要导入模块: from nvidia.dali import types [as 别名]
# 或者: from nvidia.dali.types import FLOAT16 [as 别名]
def __init__(self, batch_size, num_threads, device_id, rec_path, idx_path,
shard_id, num_shards, crop_shape,
nvjpeg_padding, prefetch_queue=3,
seed=12, resize_shp=None,
output_layout=types.NCHW, pad_output=True, dtype='float16',
mlperf_print=True):
super(HybridValPipe, self).__init__(
batch_size, num_threads, device_id,
seed = seed + device_id,
prefetch_queue_depth = prefetch_queue)
self.input = ops.MXNetReader(path = [rec_path], index_path=[idx_path],
random_shuffle=False, shard_id=shard_id, num_shards=num_shards)
self.decode = ops.nvJPEGDecoder(device = "mixed", output_type = types.RGB,
device_memory_padding = nvjpeg_padding,
host_memory_padding = nvjpeg_padding)
self.resize = ops.Resize(device = "gpu", resize_shorter=resize_shp) if resize_shp else None
self.cmnp = ops.CropMirrorNormalize(device = "gpu",
output_dtype = types.FLOAT16 if dtype == 'float16' else types.FLOAT,
output_layout = output_layout,
crop = crop_shape,
pad_output = pad_output,
image_type = types.RGB,
mean = _mean_pixel,
std = _std_pixel)
if mlperf_print:
mx_resnet_print(
key=mlperf_log.INPUT_MEAN_SUBTRACTION,
val=_mean_pixel)
mx_resnet_print(
key=mlperf_log.INPUT_RESIZE_ASPECT_PRESERVING)
mx_resnet_print(
key=mlperf_log.INPUT_CENTRAL_CROP) 示例4: __init__
# 需要导入模块: from nvidia.dali import types [as 别名]
# 或者: from nvidia.dali.types import FLOAT16 [as 别名]
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__
# 需要导入模块: from nvidia.dali import types [as 别名]
# 或者: from nvidia.dali.types import FLOAT16 [as 别名]
def __init__(self, batch_size, num_threads, device_id, data_dir, crop, size,
mean, std, local_rank=0, world_size=1, dali_cpu=False, shuffle=False, fp16=False):
# As we're recreating the Pipeline at every epoch, the seed must be -1 (random seed)
super(HybridValPipe, self).__init__(batch_size, num_threads, device_id, seed=-1)
# Enabling read_ahead slowed down processing ~40%
# Note: initial_fill is for the shuffle buffer. As we only want to see every example once, this is set to 1
self.input = ops.FileReader(file_root=data_dir, shard_id=local_rank, num_shards=world_size, random_shuffle=shuffle, initial_fill=1)
if dali_cpu:
decode_device = "cpu"
self.dali_device = "cpu"
self.crop = ops.Crop(device="cpu", crop=(crop, crop))
else:
decode_device = "mixed"
self.dali_device = "gpu"
output_dtype = types.FLOAT
if fp16:
output_dtype = types.FLOAT16
self.cmnp = ops.CropMirrorNormalize(device="gpu",
output_dtype=output_dtype,
output_layout=types.NCHW,
crop=(crop, crop),
image_type=types.RGB,
mean=mean,
std=std)
self.decode = ops.ImageDecoder(device=decode_device, output_type=types.RGB)
# Resize to desired size. To match torchvision dataloader, use triangular interpolation
self.res = ops.Resize(device=self.dali_device, resize_shorter=size, interp_type=types.INTERP_TRIANGULAR)