def __init__(
self,
device,
batch_size,
num_threads=1,
device_id=0,
num_gpus=1,
crop_shape=(224, 224),
crop_x=0.3,
crop_y=0.2,
is_fused_decoder=False,
):
super(CropPipeline, self).__init__(batch_size, num_threads, device_id)
self.is_fused_decoder = is_fused_decoder
self.device = device
self.input = ops.CaffeReader(path=caffe_db_folder,
shard_id=device_id,
num_shards=num_gpus)
if self.is_fused_decoder:
self.decode = ops.ImageDecoderCrop(device="cpu",
crop=crop_shape,
crop_pos_x=crop_x,
crop_pos_y=crop_y,
output_type=types.RGB)
else:
self.decode = ops.ImageDecoder(device="cpu", output_type=types.RGB)
self.crop = ops.Crop(device=self.device,
crop=crop_shape,
crop_pos_x=crop_x,
crop_pos_y=crop_y)
def __init__(self, path, batch_size, num_threads=1, device_id=0, num_gpus=1):
super(CaffeReaderPipeline, self).__init__(batch_size,
num_threads,
device_id)
self.input = ops.CaffeReader(path = path, shard_id = device_id, num_shards = num_gpus)
self.decode = ops.ImageDecoderCrop(device = "cpu",
crop = (224, 224),
crop_pos_x = 0.3,
crop_pos_y = 0.2,
output_type = types.RGB)
def __init__(
self,
train=False,
batch_size=16,
size=384,
num_threads=4,
device_id=0
):
super(ExternalSourcePipeline, self).__init__(
batch_size, num_threads, device_id, seed=42
)
self.eii = iter(
ExternalInputIterator(train, batch_size)
)
self.images_input = ops.ExternalSource()
self.masks_input = ops.ExternalSource()
if train:
fixed_area = (size / 784)**2
self.decode = ops.ImageDecoderRandomCrop(
device="mixed",
random_area=[fixed_area*0.7, fixed_area*1.3],
random_aspect_ratio=[0.7, 1.3],
)
else:
self.decode = ops.ImageDecoderCrop(
device="mixed",
crop=(size, size)
)
self.resize = ops.Resize(
device="gpu",
interp_type=types.INTERP_TRIANGULAR,
resize_x=size,
resize_y=size
)
self.mask_resize = ops.Resize(
device="gpu",
interp_type=types.INTERP_NN,
resize_x=size,
resize_y=size
)
self.normalize = ops.CropMirrorNormalize(
device="gpu",
mean=[0.5 * 255], # 0.456 * 255, 0.406 * 255],
std=[0.5 * 255], # , 0.224 * 255, 0.225 * 255],
output_layout=types.NCHW
)
self.mask_normalize = ops.CropMirrorNormalize(
device="gpu",
mean=[0],
std=[255],
output_layout=types.NCHW,
image_type=types.GRAY,
)
# extra augmentations
self.to_gray = ops.ColorSpaceConversion(
device="gpu", image_type=types.RGB, output_type=types.GRAY
)
self.contrast = ops.BrightnessContrast(device="gpu")
self.hsv = ops.Hsv(device="gpu")
self.jitter = ops.Jitter(device ="gpu")
# self.rng1 = ops.Uniform(range=[0, 1])
self.rng2 = ops.Uniform(range=[0.8,1.2])
self.rng3 = ops.Uniform(range=[-30, 30]) # for hue
self.coin03 = ops.CoinFlip(probability=0.3)
self.train = train