def __init__(self, data_path, batch_size, num_thread, crop, rali_cpu=True):
super(trainPipeline, self).__init__(batch_size,
num_thread,
rali_cpu=rali_cpu)
world_size = 1
local_rank = 0
self.input = ops.FileReader(file_root=data_path,
shard_id=local_rank,
num_shards=world_size,
random_shuffle=True)
rali_device = 'cpu' if rali_cpu else 'gpu'
decoder_device = 'cpu' if rali_cpu else 'mixed'
device_memory_padding = 211025920 if decoder_device == 'mixed' else 0
host_memory_padding = 140544512 if decoder_device == 'mixed' else 0
self.decode = ops.ImageDecoderRandomCrop(
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)
self.res = ops.Resize(device=rali_device, resize_x=crop, resize_y=crop)
self.cmnp = ops.CropMirrorNormalize(
device="gpu",
output_dtype=types.FLOAT,
output_layout=types.NCHW,
crop=(crop, crop),
image_type=types.RGB,
mean=[0.485 * 255, 0.456 * 255, 0.406 * 255],
std=[0.229 * 255, 0.224 * 255, 0.225 * 255])
self.coin = ops.CoinFlip(probability=0.5)
print('rali "{0}" variant'.format(rali_device))
def __init__(self, data_path, batch_size, num_thread, crop, rali_cpu=True):
super(valPipeline, self).__init__(batch_size,
num_thread,
rali_cpu=rali_cpu)
world_size = 1
local_rank = 0
self.input = ops.FileReader(file_root=data_path,
shard_id=local_rank,
num_shards=world_size,
random_shuffle=True)
rali_device = 'cpu' if rali_cpu else 'gpu'
decoder_device = 'cpu' if rali_cpu else 'mixed'
device_memory_padding = 211025920 if decoder_device == 'mixed' else 0
host_memory_padding = 140544512 if decoder_device == 'mixed' else 0
self.decode = ops.ImageDecoder(device=decoder_device,
output_type=types.RGB)
self.res = ops.Resize(device=rali_device, resize_x=256, resize_y=256)
self.centrecrop = ops.CentreCrop(crop=(crop, crop))
self.cmnp = ops.CropMirrorNormalize(
device="gpu",
output_dtype=types.FLOAT,
output_layout=types.NCHW,
crop=(crop, crop),
image_type=types.RGB,
mean=[0.485 * 255, 0.456 * 255, 0.406 * 255],
std=[0.229 * 255, 0.224 * 255, 0.225 * 255])
print('rali "{0}" variant'.format(rali_device))
def __init__(self,
batch_size,
num_threads,
device_id,
data_dir,
rali_cpu=True,
prefetch_queue_depth=2):
super(HybridTrainPipe,
self).__init__(batch_size,
num_threads,
device_id,
rali_cpu=rali_cpu,
prefetch_queue_depth=prefetch_queue_depth)
world_size = 1
local_rank = 0
resize_width = 300
resize_height = 300
self.input = ops.FileReader(file_root=data_dir,
shard_id=local_rank,
num_shards=world_size,
random_shuffle=True)
rali_device = 'cpu' if rali_cpu else 'gpu'
decoder_device = 'cpu' if rali_cpu else 'mixed'
self.decode = ops.ImageDecoder(device=decoder_device,
output_type=types.RGB)
self.res = ops.Resize(device=rali_device,
resize_x=resize_width,
resize_y=resize_height)
self.rain = ops.Rain(rain=0.5)
def __init__(self,
batch_size,
num_threads,
device_id,
data_dir,
crop,
rali_cpu=True):
super(HybridTrainPipe, self).__init__(batch_size,
num_threads,
device_id,
seed=12 + device_id,
rali_cpu=rali_cpu)
world_size = 1
local_rank = 0
self.input = ops.FileReader(file_root=data_dir,
shard_id=local_rank,
num_shards=world_size,
random_shuffle=True)
rali_device = 'cpu' if rali_cpu else 'gpu'
decoder_device = 'cpu' if rali_cpu else 'mixed'
device_memory_padding = 211025920 if decoder_device == 'mixed' else 0
host_memory_padding = 140544512 if decoder_device == 'mixed' else 0
self.decode = ops.ImageDecoderRandomCrop(
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)
self.res = ops.Resize(device=rali_device, resize_x=crop, resize_y=crop)
# #self.res = ops.Crop(crop=(crop, crop))
# self.rain = ops.Rain(rain=0.5)
# self.blur = ops.Blur(blur=0.5)
# self.jitter = ops.Jitter()
# self.contrast =ops.Rotate(angle=20)
# self.hue = ops.Hue()
# self.blend = ops.Blend(blend=0.5)
# self.snp = ops.SnPNoise(snpNoise = 0.5)
# self.ving = ops.Vignette(vignette = 0.2)
# self.exp = ops.Exposure(exposure = 0.2)
# #self.wf = ops.WarpAffine()
# self.sat = ops.Saturation()
self.cmnp = ops.CropMirrorNormalize(
device="gpu",
output_dtype=types.FLOAT,
output_layout=types.NCHW,
crop=(crop, crop),
image_type=types.RGB,
mean=[0.485 * 255, 0.456 * 255, 0.406 * 255],
std=[0.229 * 255, 0.224 * 255, 0.225 * 255])
self.coin = ops.CoinFlip(probability=0.5)
print('rali "{0}" variant'.format(rali_device))