* Rewrite relative unittest based on dataset_info

* Add bc-breaking test for functions related to dataset_info
* Rename DatasetInfo.dataset_info as DatasetInfo._dataset_info
* Fix dataset_info of h36m dataset

configs/_base_/datasets/h36m.py

@@ -148,4 +148,5 @@
         dict(link=('right_elbow', 'right_wrist'), id=15, color=[255, 128, 0])
     },
     joint_weights=[1.] * 17,
-    sigmas=[])
+    sigmas=[],
+    stats_info=dict(bbox_center=(528., 427.), bbox_scale=400.))

mmpose/apis/inference_3d.py

@@ -204,7 +204,7 @@ def _collate_pose_sequence(pose_results, with_track_id=True, target_frame=-1):
 
 def inference_pose_lifter_model(model,
                                 pose_results_2d,
-                                dataset,
+                                dataset=None,
                                 dataset_info=None,
                                 with_track_id=True,
                                 image_size=None,
@@ -245,8 +245,9 @@ def inference_pose_lifter_model(model,
 
     if dataset_info is not None:
         flip_pairs = dataset_info.flip_pairs
-        bbox_center = dataset_info.bbox_center
-        bbox_scale = dataset_info.bbox_scale
+        assert 'stats_info' in dataset_info._dataset_info
+        bbox_center = dataset_info._dataset_info['stats_info']['bbox_center']
+        bbox_scale = dataset_info._dataset_info['stats_info']['bbox_scale']
     else:
         warnings.warn(
             'dataset is deprecated.'
@@ -283,6 +284,7 @@ def inference_pose_lifter_model(model,
         else:
             input_2d_visible = np.ones((T, K, 1), dtype=np.float32)
 
+        # TODO: Will be removed in the later versions
         # Dummy 3D input
         # This is for compatibility with configs in mmpose<=v0.14.0, where a
         # 3D input is required to generate denormalization parameters. This
@@ -348,11 +350,11 @@ def vis_3d_pose_result(model,
                        result,
                        img=None,
                        dataset='Body3DH36MDataset',
+                       dataset_info=None,
                        kpt_score_thr=0.3,
                        radius=8,
                        thickness=2,
                        num_instances=-1,
-                       dataset_info=None,
                        show=False,
                        out_file=None):
     """Visualize the 3D pose estimation results.

mmpose/datasets/dataset_info.py

@@ -4,21 +4,21 @@
 class DatasetInfo:
 
     def __init__(self, dataset_info):
-        self.dataset_info = dataset_info
-        self.dataset_name = self.dataset_info['dataset_name']
-        self.paper_info = self.dataset_info['paper_info']
-        self.keypoint_info = self.dataset_info['keypoint_info']
-        self.skeleton_info = self.dataset_info['skeleton_info']
+        self._dataset_info = dataset_info
+        self.dataset_name = self._dataset_info['dataset_name']
+        self.paper_info = self._dataset_info['paper_info']
+        self.keypoint_info = self._dataset_info['keypoint_info']
+        self.skeleton_info = self._dataset_info['skeleton_info']
         self.joint_weights = np.array(
-            self.dataset_info['joint_weights'], dtype=np.float32)[:, None]
+            self._dataset_info['joint_weights'], dtype=np.float32)[:, None]
 
-        self.sigmas = np.array(self.dataset_info['sigmas'])
+        self.sigmas = np.array(self._dataset_info['sigmas'])
 
         self._parse_keypoint_info()
         self._parse_skeleton_info()
 
     def _parse_skeleton_info(self):
-        """Parse skeleton info.
+        """Parse skeleton information.
 
         - link_num (int): number of links.
         - skeleton (list((2,))): list of links (id).
@@ -42,7 +42,7 @@ def _parse_skeleton_info(self):
         self.pose_link_color = np.array(self.pose_link_color)
 
     def _parse_keypoint_info(self):
-        """Parse keypoint info.
+        """Parse keypoint information.
 
         - keypoint_num (int): number of keypoints.
         - keypoint_id2name (dict): mapping keypoint id to keypoint name.

tests/test_apis/test_inference.py

@@ -1,9 +1,9 @@
 import numpy as np
-import pytest
 
 from mmpose.apis import (inference_bottom_up_pose_model,
                          inference_top_down_pose_model, init_pose_model,
                          process_mmdet_results, vis_pose_result)
+from mmpose.datasets import DatasetInfo
 
 
 def test_top_down_demo():
@@ -15,14 +15,21 @@ def test_top_down_demo():
         None,
         device='cpu')
     image_name = 'tests/data/coco/000000000785.jpg'
+    dataset_info = DatasetInfo(pose_model.cfg.data['test'].get(
+        'dataset_info', None))
 
     person_result = []
     person_result.append({'bbox': [50, 50, 50, 100]})
     # test a single image, with a list of bboxes.
     pose_results, _ = inference_top_down_pose_model(
-        pose_model, image_name, person_result, format='xywh')
+        pose_model,
+        image_name,
+        person_result,
+        format='xywh',
+        dataset_info=dataset_info)
     # show the results
-    vis_pose_result(pose_model, image_name, pose_results)
+    vis_pose_result(
+        pose_model, image_name, pose_results, dataset_info=dataset_info)
 
     # AIC demo
     pose_model = init_pose_model(
@@ -31,16 +38,18 @@ def test_top_down_demo():
         None,
         device='cpu')
     image_name = 'tests/data/aic/054d9ce9201beffc76e5ff2169d2af2f027002ca.jpg'
+    dataset_info = DatasetInfo(pose_model.cfg.data['test'].get(
+        'dataset_info', None))
     # test a single image, with a list of bboxes.
     pose_results, _ = inference_top_down_pose_model(
         pose_model,
         image_name,
         person_result,
         format='xywh',
-        dataset='TopDownAicDataset')
+        dataset_info=dataset_info)
     # show the results
     vis_pose_result(
-        pose_model, image_name, pose_results, dataset='TopDownAicDataset')
+        pose_model, image_name, pose_results, dataset_info=dataset_info)
 
     # OneHand10K demo
     # build the pose model from a config file and a checkpoint file
@@ -50,16 +59,18 @@ def test_top_down_demo():
         None,
         device='cpu')
     image_name = 'tests/data/onehand10k/9.jpg'
+    dataset_info = DatasetInfo(pose_model.cfg.data['test'].get(
+        'dataset_info', None))
     # test a single image, with a list of bboxes.
     pose_results, _ = inference_top_down_pose_model(
         pose_model,
         image_name,
         person_result,
         format='xywh',
-        dataset='OneHand10KDataset')
+        dataset_info=dataset_info)
     # show the results
     vis_pose_result(
-        pose_model, image_name, pose_results, dataset='OneHand10KDataset')
+        pose_model, image_name, pose_results, dataset_info=dataset_info)
 
     # InterHand2DDataset demo
     # build the pose model from a config file and a checkpoint file
@@ -69,16 +80,18 @@ def test_top_down_demo():
         None,
         device='cpu')
     image_name = 'tests/data/interhand2.6m/image2017.jpg'
+    dataset_info = DatasetInfo(pose_model.cfg.data['test'].get(
+        'dataset_info', None))
     # test a single image, with a list of bboxes.
     pose_results, _ = inference_top_down_pose_model(
         pose_model,
         image_name,
         person_result,
         format='xywh',
-        dataset='InterHand2DDataset')
+        dataset_info=dataset_info)
     # show the results
     vis_pose_result(
-        pose_model, image_name, pose_results, dataset='InterHand2DDataset')
+        pose_model, image_name, pose_results, dataset_info=dataset_info)
 
     # Face300WDataset demo
     # build the pose model from a config file and a checkpoint file
@@ -88,16 +101,18 @@ def test_top_down_demo():
         None,
         device='cpu')
     image_name = 'tests/data/300w/indoor_020.png'
+    dataset_info = DatasetInfo(pose_model.cfg.data['test'].get(
+        'dataset_info', None))
     # test a single image, with a list of bboxes.
     pose_results, _ = inference_top_down_pose_model(
         pose_model,
         image_name,
         person_result,
         format='xywh',
-        dataset='Face300WDataset')
+        dataset_info=dataset_info)
     # show the results
     vis_pose_result(
-        pose_model, image_name, pose_results, dataset='Face300WDataset')
+        pose_model, image_name, pose_results, dataset_info=dataset_info)
 
     # FaceAFLWDataset demo
     # build the pose model from a config file and a checkpoint file
@@ -107,16 +122,18 @@ def test_top_down_demo():
         None,
         device='cpu')
     image_name = 'tests/data/aflw/image04476.jpg'
+    dataset_info = DatasetInfo(pose_model.cfg.data['test'].get(
+        'dataset_info', None))
     # test a single image, with a list of bboxes.
     pose_results, _ = inference_top_down_pose_model(
         pose_model,
         image_name,
         person_result,
         format='xywh',
-        dataset='FaceAFLWDataset')
+        dataset_info=dataset_info)
     # show the results
     vis_pose_result(
-        pose_model, image_name, pose_results, dataset='FaceAFLWDataset')
+        pose_model, image_name, pose_results, dataset_info=dataset_info)
 
     # FaceCOFWDataset demo
     # build the pose model from a config file and a checkpoint file
@@ -126,24 +143,18 @@ def test_top_down_demo():
         None,
         device='cpu')
     image_name = 'tests/data/cofw/001766.jpg'
+    dataset_info = DatasetInfo(pose_model.cfg.data['test'].get(
+        'dataset_info', None))
     # test a single image, with a list of bboxes.
     pose_results, _ = inference_top_down_pose_model(
         pose_model,
         image_name,
         person_result,
         format='xywh',
-        dataset='FaceCOFWDataset')
+        dataset_info=dataset_info)
     # show the results
     vis_pose_result(
-        pose_model, image_name, pose_results, dataset='FaceCOFWDataset')
-
-    with pytest.raises(NotImplementedError):
-        pose_results, _ = inference_top_down_pose_model(
-            pose_model,
-            image_name,
-            person_result,
-            format='xywh',
-            dataset='test')
+        pose_model, image_name, pose_results, dataset_info=dataset_info)
 
 
 def test_bottom_up_demo():
@@ -156,12 +167,15 @@ def test_bottom_up_demo():
         device='cpu')
 
     image_name = 'tests/data/coco/000000000785.jpg'
+    dataset_info = DatasetInfo(pose_model.cfg.data['test'].get(
+        'dataset_info', None))
 
-    pose_results, _ = inference_bottom_up_pose_model(pose_model, image_name)
+    pose_results, _ = inference_bottom_up_pose_model(
+        pose_model, image_name, dataset_info=dataset_info)
 
     # show the results
     vis_pose_result(
-        pose_model, image_name, pose_results, dataset='BottomUpCocoDataset')
+        pose_model, image_name, pose_results, dataset_info=dataset_info)
 
 
 def test_process_mmdet_results():