def preprocess(img, size=224): transform = T.Compose([ T.Scale(size), T.ToTensor(), T.Normalize(SQUEEZENET_MEAN.tolist(), SQUEEZENET_STD.tolist()), T.Lambda(lambda x: x[None]), ]) return transform(img)
def deprocess(img): transform = T.Compose([ T.Lambda(lambda x: x[0]), T.Normalize(mean=[0, 0, 0], std=[1.0 / s for s in SQUEEZENET_STD.tolist()]), T.Normalize(mean=[-m for m in SQUEEZENET_MEAN.tolist()], std=[1, 1, 1]), T.Lambda(rescale), T.ToPILImage(), ]) return transform(img)
def preprocess(img, size=512): transform = T.Compose([ T.Resize(size), T.ToTensor(), T.Normalize(mean=SQUEEZENET_MEAN.tolist(), std=SQUEEZENET_STD.tolist()), T.Lambda(lambda x: x[None]), ]) return transform(img)