Reformat Eval logs to support tensorboard

.gitlab-ci.yml

 variables:
-  PYTORCH_IMAGE: registry.sensetime.com/eig-research/pytorch:pytorch1.3.1-cuda10.1-devel
+  PYTORCH_IMAGE: registry.sensetime.com/eig-research/pytorch:1.3.1-cuda10.1-cudnn7-devel
 
 stages:
   - linting

mmdet3d/core/evaluation/kitti_utils/eval.py

@@ -681,7 +681,6 @@ def kitti_eval(gt_annos,
         # mAP threshold array: [num_minoverlap, metric, class]
         # mAP result: [num_class, num_diff, num_minoverlap]
         curcls_name = class_to_name[curcls]
-        ret_dict[curcls_name] = {}
         for i in range(min_overlaps.shape[0]):
             # prepare results for print
             result += ('{} AP@{:.2f}, {:.2f}, {:.2f}:\n'.format(
@@ -702,18 +701,17 @@ def kitti_eval(gt_annos,
 
             # prepare results for logger
             for idx in range(3):
-                postfix = '{}_{}'.format(difficulty[idx], min_overlaps[i, idx,
-                                                                       j])
+                if i == 0:
+                    postfix = f'{difficulty[idx]}_strict'
+                else:
+                    postfix = f'{difficulty[idx]}_loose'
+                prefix = f'KITTI/{curcls_name}'
                 if mAP3d is not None:
-                    ret_dict[curcls_name]['3D_{}'.format(postfix)] = mAP3d[j,
-                                                                           idx,
-                                                                           i]
+                    ret_dict[f'{prefix}_3D_{postfix}'] = mAP3d[j, idx, i]
                 if mAPbev is not None:
-                    ret_dict[curcls_name]['BEV_{}'.format(postfix)] = mAPbev[
-                        j, idx, i]
+                    ret_dict[f'{prefix}_BEV_{postfix}'] = mAPbev[j, idx, i]
                 if mAPbbox is not None:
-                    ret_dict[curcls_name]['2D_{}'.format(postfix)] = mAPbbox[
-                        j, idx, i]
+                    ret_dict[f'{prefix}_2D_{postfix}'] = mAPbbox[j, idx, i]
 
     # calculate mAP over all classes if there are multiple classes
     if len(current_classes) > 1:
@@ -735,14 +733,14 @@ def kitti_eval(gt_annos,
         # prepare results for logger
         ret_dict['Overall'] = dict()
         for idx in range(3):
-            postfix = '{}'.format(difficulty[idx])
+            postfix = f'{difficulty[idx]}'
             if mAP3d is not None:
-                ret_dict['Overall']['3D_{}'.format(postfix)] = mAP3d[idx, 0]
+                ret_dict[f'KITTI/Overall_3D_{postfix}'] = mAP3d[idx, 0]
             if mAPbev is not None:
-                ret_dict['Overall']['BEV_{}'.format(postfix)] = mAPbev[idx, 0]
+                ret_dict[f'KITTI/Overall_BEV_{postfix}'] = mAPbev[idx, 0]
             if mAPbbox is not None:
-                ret_dict['Overall']['2D_{}'.format(postfix)] = mAPbbox[idx, 0]
-    print(result)
+                ret_dict[f'KITTI/Overall_2D_{postfix}'] = mAPbbox[idx, 0]
+
     return result, ret_dict
 
 

mmdet3d/datasets/kitti_dataset.py

@@ -9,6 +9,7 @@ import torch
 import torch.utils.data as torch_data
 
 from mmdet.datasets import DATASETS
+from mmdet.utils import print_log
 from ..core.bbox import box_np_ops
 from .pipelines import Compose
 from .utils import remove_dontcare
@@ -317,10 +318,10 @@ class KittiDataset(torch_data.Dataset):
         else:
             ap_result_str, ap_dict = kitti_eval(gt_annos, result_files,
                                                 self.class_names)
-
+        print_log(ap_result_str, logger=logger)
         if tmp_dir is not None:
             tmp_dir.cleanup()
-        return ap_dict, tmp_dir
+        return ap_dict
 
     def bbox2result_kitti(self,
                           net_outputs,

tools/train.py

 from __future__ import division
 import argparse
 import copy
+import logging
 import os
 import os.path as osp
 import time
@@ -103,6 +104,10 @@ def main():
     log_file = osp.join(cfg.work_dir, '{}.log'.format(timestamp))
     logger = get_root_logger(log_file=log_file, log_level=cfg.log_level)
 
+    # add a logging filter
+    logging_filter = logging.Filter('mmdet')
+    logging_filter.filter = lambda record: record.find('mmdet') != -1
+
     # init the meta dict to record some important information such as
     # environment info and seed, which will be logged
     meta = dict()