def pre_process(self, images): mean = config.image_mean.reshape(1, 3, 1, 1).astype(np.float32) std = config.image_std.reshape(1, 3, 1, 1).astype(np.float32) mean = mge.tensor(mean).to(images.device) std = mge.tensor(std).to(images.device) normed_images = (images - mean) / std normed_images = get_padded_tensor(normed_images, 64) return normed_images
def forward(self, inputs): images = inputs['image'] im_info = inputs['im_info'] gt_boxes = inputs['gt_boxes'] # process the images normed_images = (images - config.image_mean[None, :, None, None] ) / config.image_std[None, :, None, None] normed_images = get_padded_tensor(normed_images, 64) if self.training: return self._forward_train(normed_images, im_info, gt_boxes) else: return self._forward_test(normed_images, im_info)