def get_data(name, batch):
isTrain = name == 'train'
if isTrain:
augmentors = [
GoogleNetResize(crop_area_fraction=0.49),
imgaug.RandomOrderAug([
imgaug.BrightnessScale((0.6, 1.4), clip=False),
imgaug.Contrast((0.6, 1.4), clip=False),
imgaug.Saturation(0.4, rgb=False),
# rgb-bgr conversion for the constants copied from fb.resnet.torch
imgaug.Lighting(
0.1,
eigval=np.asarray([0.2175, 0.0188, 0.0045][::-1]) * 255.0,
eigvec=np.array([[-0.5675, 0.7192, 0.4009],
[-0.5808, -0.0045, -0.8140],
[-0.5836, -0.6948, 0.4203]],
dtype='float32')[::-1, ::-1])
]),
imgaug.Flip(horiz=True),
]
else:
augmentors = [
imgaug.ResizeShortestEdge(256, cv2.INTER_CUBIC),
imgaug.CenterCrop((224, 224)),
]
return get_imagenet_dataflow(args.data, name, batch, augmentors)
def get_data(name, batch):
isTrain = name == 'train'
image_shape = 224
if isTrain:
augmentors = [
# use lighter augs if model is too small
GoogleNetResize(
crop_area_fraction=0.49 if args.width_ratio < 1 else 0.08,
target_shape=image_shape),
imgaug.RandomOrderAug([
imgaug.BrightnessScale((0.6, 1.4), clip=False),
imgaug.Contrast((0.6, 1.4), clip=False),
imgaug.Saturation(0.4, rgb=False),
]),
imgaug.Flip(horiz=True),
]
else:
augmentors = [
imgaug.ResizeShortestEdge(int(image_shape * 256 / 224),
cv2.INTER_CUBIC),
imgaug.CenterCrop((image_shape, image_shape)),
]
return get_imagenet_dataflow(args.data_dir,
name,
batch,
augmentors,
meta_dir=args.meta_dir)