base.py

import copy
import mmcv
import torch
from mmcv.parallel import DataContainer as DC
from mmcv.runner import auto_fp16
from os import path as osp

from mmdet3d.core import Box3DMode, show_result
from mmdet.models.detectors import BaseDetector


class Base3DDetector(BaseDetector):
    """Base class for detectors."""

    def forward_test(self, points, img_metas, img=None, **kwargs):
        """
        Args:
            points (list[torch.Tensor]): the outer list indicates test-time
                augmentations and inner torch.Tensor should have a shape NxC,
                which contains all points in the batch.
            img_metas (list[list[dict]]): the outer list indicates test-time
                augs (multiscale, flip, etc.) and the inner list indicates
                images in a batch
            img (list[torch.Tensor], optional): the outer
                list indicates test-time augmentations and inner
                torch.Tensor should have a shape NxCxHxW, which contains
                all images in the batch. Defaults to None.
        """
        for var, name in [(points, 'points'), (img_metas, 'img_metas')]:
            if not isinstance(var, list):
                raise TypeError('{} must be a list, but got {}'.format(
                    name, type(var)))

        num_augs = len(points)
        if num_augs != len(img_metas):
            raise ValueError(
                'num of augmentations ({}) != num of image meta ({})'.format(
                    len(points), len(img_metas)))

        if num_augs == 1:
            img = [img] if img is None else img
            return self.simple_test(points[0], img_metas[0], img[0], **kwargs)
        else:
            return self.aug_test(points, img_metas, img, **kwargs)

    @auto_fp16(apply_to=('img', 'points'))
    def forward(self, return_loss=True, **kwargs):
        """Calls either forward_train or forward_test depending on whether
        return_loss=True.

        Note this setting will change the expected inputs. When
        `return_loss=True`, img and img_metas are single-nested (i.e.
        torch.Tensor and list[dict]), and when `resturn_loss=False`, img and
        img_metas should be double nested (i.e.  list[torch.Tensor],
        list[list[dict]]), with the outer list indicating test time
        augmentations.
        """
        if return_loss:
            return self.forward_train(**kwargs)
        else:
            return self.forward_test(**kwargs)

    def show_results(self, data, result, out_dir):
        """Results visualization.

        Args:
            data (list[dict]): Input points and the information of the sample.
            result (list[dict]): Prediction results.
            out_dir (str): Output directory of visualization result.
        """
        for batch_id in range(len(result)):
            if isinstance(data['points'][0], DC):
                points = data['points'][0]._data[0][batch_id].numpy()
            elif mmcv.is_list_of(data['points'][0], torch.Tensor):
                points = data['points'][0][batch_id]
            else:
                ValueError(f"Unsupported data type {type(data['points'][0])} "
                           f'for visualization!')
            if isinstance(data['img_metas'][0], DC):
                pts_filename = data['img_metas'][0]._data[0][batch_id][
                    'pts_filename']
                box_mode_3d = data['img_metas'][0]._data[0][batch_id][
                    'box_mode_3d']
            elif mmcv.is_list_of(data['img_metas'][0], dict):
                pts_filename = data['img_metas'][0][batch_id]['pts_filename']
                box_mode_3d = data['img_metas'][0][batch_id]['box_mode_3d']
            else:
                ValueError(
                    f"Unsupported data type {type(data['img_metas'][0])} "
                    f'for visualization!')
            file_name = osp.split(pts_filename)[-1].split('.')[0]

            assert out_dir is not None, 'Expect out_dir, got none.'

            pred_bboxes = copy.deepcopy(
                result[batch_id]['boxes_3d'].tensor.numpy())
            # for now we convert points into depth mode
            if box_mode_3d == Box3DMode.DEPTH:
                pred_bboxes[..., 2] += pred_bboxes[..., 5] / 2
            elif (box_mode_3d == Box3DMode.CAM) or (box_mode_3d
                                                    == Box3DMode.LIDAR):
                points = points[..., [1, 0, 2]]
                points[..., 0] *= -1
                pred_bboxes = Box3DMode.convert(pred_bboxes, box_mode_3d,
                                                Box3DMode.DEPTH)
                pred_bboxes[..., 2] += pred_bboxes[..., 5] / 2
            else:
                ValueError(
                    f'Unsupported box_mode_3d {box_mode_3d} for convertion!')

            show_result(points, None, pred_bboxes, out_dir, file_name)