Open skming666 opened 10 months ago
将yolox利用onnxruntime推理时模型输出后结果需要利用下面代码映射到原图上，但是直接利用pth进行推理时，没有看到这一步，这个是为什么？ def demo_postprocess(outputs, img_size, p6=False): grids = [] expanded_strides = [] if not p6: strides = [8, 16, 32] else: strides = [8, 16, 32, 64] hsizes = [img_size[0] // stride for stride in strides] wsizes = [img_size[1] // stride for stride in strides] for hsize, wsize, stride in zip(hsizes, wsizes, strides): xv, yv = np.meshgrid(np.arange(wsize), np.arange(hsize)) grid = np.stack((xv, yv), 2).reshape(1, -1, 2) grids.append(grid) shape = grid.shape[:2] expanded_strides.append(np.full((shape, 1), stride)) grids = np.concatenate(grids, 1) expanded_strides = np.concatenate(expanded_strides, 1) outputs[..., :2] = (outputs[..., :2] + grids) expanded_strides outputs[..., 2:4] = np.exp(outputs[..., 2:4]) * expanded_strides return outputs
将yolox利用onnxruntime推理时模型输出后结果需要利用下面代码映射到原图上，但是直接利用pth进行推理时，没有看到这一步，这个是为什么？ def demo_postprocess(outputs, img_size, p6=False): grids = [] expanded_strides = [] if not p6: strides = [8, 16, 32] else: strides = [8, 16, 32, 64] hsizes = [img_size[0] // stride for stride in strides] wsizes = [img_size[1] // stride for stride in strides] for hsize, wsize, stride in zip(hsizes, wsizes, strides): xv, yv = np.meshgrid(np.arange(wsize), np.arange(hsize)) grid = np.stack((xv, yv), 2).reshape(1, -1, 2) grids.append(grid) shape = grid.shape[:2] expanded_strides.append(np.full((shape, 1), stride)) grids = np.concatenate(grids, 1) expanded_strides = np.concatenate(expanded_strides, 1) outputs[..., :2] = (outputs[..., :2] + grids) expanded_strides outputs[..., 2:4] = np.exp(outputs[..., 2:4]) * expanded_strides return outputs