(self)
return transform
Creates a basic transformation that was used to train the models
Creates a basic transformation that was used to train the models
Creates a basic transformation that was used to train the models
def build_transform(self):
        """
        Creates a basic transformation that was used to train the models
        """
        cfg = self.cfg

        # we are loading images with OpenCV, so we don't need to convert them
        # to BGR, they are already! So all we need to do is to normalize
        # by 255 if we want to convert to BGR255 format, or flip the channels
        # if we want it to be in RGB in [0-1] range.
        if cfg.INPUT.TO_BGR255:
            to_bgr_transform = T.Lambda(lambda x: x * 255)
        else:
            to_bgr_transform = T.Lambda(lambda x: x[[2, 1, 0]])

        normalize_transform = T.Normalize(
            mean=cfg.INPUT.PIXEL_MEAN, std=cfg.INPUT.PIXEL_STD
        )

        transform = T.Compose(
            [
                T.ToPILImage(),
                T.Resize(self.min_image_size),
                T.ToTensor(),
                to_bgr_transform,
                normalize_transform,
            ]
        )
        return transform
def build_transform ( self ) : cfg = self . cfg # we are loading images with OpenCV, so we don't need to convert them # to BGR, they are already! So all we need to do is to normalize # by 255 if we want to convert to BGR255 format, or flip the channels # if we want it to be in RGB in [0-1] range. if cfg . INPUT . TO_BGR255 : to_bgr_transform = T . Lambda ( lambda x : x * 255 ) else : to_bgr_transform = T . Lambda ( lambda x : x [ [ 2 , 1 , 0 ] ] ) normalize_transform = T . Normalize ( mean = cfg . INPUT . PIXEL_MEAN , std = cfg . INPUT . PIXEL_STD ) transform = T . Compose ( [ T . ToPILImage ( ) , T . Resize ( self . min_image_size ) , T . ToTensor ( ) , to_bgr_transform , normalize_transform , ] ) return transform