[Enhance] Show instance statistics before and after through pipeline (#1863)

* add instance statistics before and after through pipeline

* add docstring

* support showing cat-wise instance statistics

* show all statistics of the dataset

* small fix

* polish code

* show table

* small fix

* rename some varibles

mmdet3d/datasets/det3d_dataset.py

@@ -5,7 +5,10 @@ from typing import Callable, List, Optional, Union
 
 import mmengine
 import numpy as np
+import torch
 from mmengine.dataset import BaseDataset
+from mmengine.logging import print_log
+from terminaltables import AsciiTable
 
 from mmdet3d.datasets import DATASETS
 from mmdet3d.structures import get_box_type
@@ -58,6 +61,9 @@ class Det3DDataset(BaseDataset):
             which can be used in Evaluator. Defaults to True.
         file_client_args (dict, optional): Configuration of file client.
             Defaults to dict(backend='disk').
+        show_ins_var (bool, optional): For debug purpose. Whether to show
+            variation of the number of instances before and after through
+            pipeline. Defaults to False.
     """
 
     def __init__(self,
@@ -73,6 +79,7 @@ class Det3DDataset(BaseDataset):
                  test_mode: bool = False,
                  load_eval_anns=True,
                  file_client_args: dict = dict(backend='disk'),
+                 show_ins_var: bool = False,
                  **kwargs) -> None:
         # init file client
         self.file_client = mmengine.FileClient(**file_client_args)
@@ -105,6 +112,8 @@ class Det3DDataset(BaseDataset):
             for label_idx, name in enumerate(metainfo['CLASSES']):
                 ori_label = self.METAINFO['CLASSES'].index(name)
                 self.label_mapping[ori_label] = label_idx
+
+            self.num_ins_per_cat = {name: 0 for name in metainfo['CLASSES']}
         else:
             self.label_mapping = {
                 i: i
@@ -112,6 +121,11 @@ class Det3DDataset(BaseDataset):
             }
             self.label_mapping[-1] = -1
 
+            self.num_ins_per_cat = {
+                name: 0
+                for name in self.METAINFO['CLASSES']
+            }
+
         super().__init__(
             ann_file=ann_file,
             metainfo=metainfo,
@@ -125,7 +139,22 @@ class Det3DDataset(BaseDataset):
         self.metainfo['box_type_3d'] = box_type_3d
         self.metainfo['label_mapping'] = self.label_mapping
 
-    def _remove_dontcare(self, ann_info: dict) -> dict:
+        # used for showing variation of the number of instances before and
+        # after through the pipeline
+        self.show_ins_var = show_ins_var
+
+        # show statistics of this dataset
+        print_log('-' * 30, 'current')
+        print_log(f'The length of the dataset: {len(self)}', 'current')
+        content_show = [['category', 'number']]
+        for cat_name, num in self.num_ins_per_cat.items():
+            content_show.append([cat_name, num])
+        table = AsciiTable(content_show)
+        print_log(
+            f'The number of instances per category in the dataset:\n{table.table}',  # noqa: E501
+            'current')
+
+    def _remove_dontcare(self, ann_info):
         """Remove annotations that do not need to be cared.
 
         -1 indicate dontcare in MMDet3d.
@@ -223,6 +252,11 @@ class Det3DDataset(BaseDataset):
 
                 ann_info[mapped_ann_name] = temp_anns
             ann_info['instances'] = info['instances']
+
+            for label in ann_info['gt_labels_3d']:
+                cat_name = self.metainfo['CLASSES'][label]
+                self.num_ins_per_cat[cat_name] += 1
+
         return ann_info
 
     def parse_data_info(self, info: dict) -> dict:
@@ -291,6 +325,31 @@ class Det3DDataset(BaseDataset):
 
         return info
 
+    def _show_ins_var(self, old_labels: np.ndarray, new_labels: torch.Tensor):
+        """Show variation of the number of instances before and after through
+        the pipeline.
+
+        Args:
+            old_labels (np.ndarray): The labels before through the pipeline.
+            new_labels (torch.Tensor): The labels after through the pipeline.
+        """
+        ori_num_per_cat = dict()
+        for label in old_labels:
+            cat_name = self.metainfo['CLASSES'][label]
+            ori_num_per_cat[cat_name] = ori_num_per_cat.get(cat_name, 0) + 1
+        new_num_per_cat = dict()
+        for label in new_labels:
+            cat_name = self.metainfo['CLASSES'][label]
+            new_num_per_cat[cat_name] = new_num_per_cat.get(cat_name, 0) + 1
+        content_show = [['category', 'new number', 'ori number']]
+        for cat_name, num in ori_num_per_cat.items():
+            new_num = new_num_per_cat.get(cat_name, 0)
+            content_show.append([cat_name, new_num, num])
+        table = AsciiTable(content_show)
+        print_log(
+            'The number of instances per category after and before '
+            f'through pipeline:\n{table.table}', 'current')
+
     def prepare_data(self, index: int) -> Optional[dict]:
         """Data preparation for both training and testing stage.
 
@@ -302,10 +361,10 @@ class Det3DDataset(BaseDataset):
         Returns:
             dict | None: Data dict of the corresponding index.
         """
-        input_dict = self.get_data_info(index)
+        ori_input_dict = self.get_data_info(index)
 
         # deepcopy here to avoid inplace modification in pipeline.
-        input_dict = copy.deepcopy(input_dict)
+        input_dict = copy.deepcopy(ori_input_dict)
 
         # box_type_3d (str): 3D box type.
         input_dict['box_type_3d'] = self.box_type_3d
@@ -318,12 +377,19 @@ class Det3DDataset(BaseDataset):
                 return None
 
         example = self.pipeline(input_dict)
+
         if not self.test_mode and self.filter_empty_gt:
             # after pipeline drop the example with empty annotations
             # return None to random another in `__getitem__`
             if example is None or len(
                     example['data_samples'].gt_instances_3d.labels_3d) == 0:
                 return None
+
+        if self.show_ins_var:
+            self._show_ins_var(
+                ori_input_dict['ann_info']['gt_labels_3d'],
+                example['data_samples'].gt_instances_3d.labels_3d)
+
         return example
 
     def get_cat_ids(self, idx: int) -> List[int]:

mmdet3d/datasets/transforms/dbsampler.py

@@ -133,12 +133,12 @@ class DataBaseSampler(object):
         from mmengine.logging import MMLogger
         logger: MMLogger = MMLogger.get_current_instance()
         for k, v in db_infos.items():
-            logger.info(f'load {len(v)} {k} database infos')
+            logger.info(f'load {len(v)} {k} database infos in DataBaseSampler')
         for prep_func, val in prepare.items():
             db_infos = getattr(self, prep_func)(db_infos, val)
         logger.info('After filter database:')
         for k, v in db_infos.items():
-            logger.info(f'load {len(v)} {k} database infos')
+            logger.info(f'load {len(v)} {k} database infos in DataBaseSampler')
 
         self.db_infos = db_infos