add transforms and full config

configs/body_3d_keypoint/pose_lift/h36m/pose-lift_motionbert-243frm_8xb32-120e_h36m.py

@@ -7,11 +7,16 @@
     type='Pose3dLocalVisualizer', vis_backends=vis_backends, name='visualizer')
 
 # runtime
-train_cfg = None
+train_cfg = dict(max_epochs=120, val_interval=10)
 
 # optimizer
+optim_wrapper = dict(
+    optimizer=dict(type='AdamW', lr=0.0002, weight_decay=0.01))
 
 # learning policy
+param_scheduler = [
+    dict(type='ExponentialLR', gamma=0.99, end=120, by_epoch=True)
+]
 
 auto_scale_lr = dict(base_batch_size=512)
 
@@ -57,6 +62,18 @@
 data_root = 'data/h36m/'
 
 # pipelines
+train_pipeline = [
+    dict(
+        type='RandomFlipAroundRoot',
+        keypoints_flip_cfg={},
+        target_flip_cfg={},
+        flip_image=True),
+    dict(type='GenerateTarget', encoder=codec),
+    dict(
+        type='PackPoseInputs',
+        meta_keys=('id', 'category_id', 'target_img_path', 'flip_indices',
+                   'factor', 'camera_param'))
+]
 val_pipeline = [
     dict(type='GenerateTarget', encoder=codec),
     dict(
@@ -66,9 +83,27 @@
 ]
 
 # data loaders
+train_dataloader = dict(
+    batch_size=32,
+    prefetch_factor=4,
+    pin_memory=True,
+    num_workers=2,
+    persistent_workers=True,
+    sampler=dict(type='DefaultSampler', shuffle=True),
+    dataset=dict(
+        type=dataset_type,
+        ann_file='annotation_body3d/fps50/h36m_train.npz',
+        seq_len=1,
+        multiple_target=243,
+        multiple_target_step=81,
+        camera_param_file='annotation_body3d/cameras.pkl',
+        data_root=data_root,
+        data_prefix=dict(img='images/'),
+        pipeline=train_pipeline,
+    ))
+
 val_dataloader = dict(
     batch_size=32,
-    shuffle=False,
     prefetch_factor=4,
     pin_memory=True,
     num_workers=2,
@@ -78,8 +113,8 @@
         type=dataset_type,
         ann_file='annotation_body3d/fps50/h36m_test.npz',
         seq_len=1,
-        multiple_target=243,
         seq_step=1,
+        multiple_target=243,
         camera_param_file='annotation_body3d/cameras.pkl',
         data_root=data_root,
         data_prefix=dict(img='images/'),

mmpose/datasets/datasets/body3d/h36m_dataset.py

@@ -47,6 +47,8 @@ class Human36mDataset(BaseMocapDataset):
             Default: 1.
         multiple_target (int): If larger than 0, merge every
             ``multiple_target`` sequence together. Default: 0.
+        multiple_target_step (int): The interval for merging sequence. Only
+            valid when ``multiple_target`` is larger than 0. Default: 0.
         pad_video_seq (bool): Whether to pad the video so that poses will be
             predicted for every frame in the video. Default: ``False``.
         causal (bool): If set to ``True``, the rightmost input frame will be
@@ -110,6 +112,7 @@ def __init__(self,
                  seq_len: int = 1,
                  seq_step: int = 1,
                  multiple_target: int = 0,
+                 multiple_target_step: int = 0,
                  pad_video_seq: bool = False,
                  causal: bool = True,
                  subset_frac: float = 1.0,
@@ -151,6 +154,10 @@ def __init__(self,
             assert exists(factor_file), 'Annotation file does not exist.'
         self.factor_file = factor_file
 
+        if multiple_target > 0 and multiple_target_step == 0:
+            multiple_target_step = multiple_target
+        self.multiple_target_step = multiple_target_step
+
         super().__init__(
             ann_file=ann_file,
             seq_len=seq_len,
@@ -191,8 +198,9 @@ def get_sequence_indices(self) -> List[List[int]]:
                 n_frame = len(_indices)
                 seqs_from_video = [
                     _indices[i:(i + self.multiple_target):_step]
-                    for i in range(0, n_frame, self.multiple_target)
-                ][:n_frame // self.multiple_target]
+                    for i in range(0, n_frame, self.multiple_target_step)
+                ][:(n_frame + self.multiple_target_step -
+                    self.multiple_target) // self.multiple_target_step]
                 sequence_indices.extend(seqs_from_video)
 
         else:

mmpose/datasets/transforms/pose3d_transforms.py

@@ -25,6 +25,8 @@ class RandomFlipAroundRoot(BaseTransform):
         flip_prob (float): Probability of flip. Default: 0.5.
         flip_camera (bool): Whether to flip horizontal distortion coefficients.
             Default: ``False``.
+        flip_image (bool): Whether to flip keypoints horizontally according
+            to image size. Default: ``False``.
 
     Required keys:
         keypoints
@@ -39,14 +41,16 @@ def __init__(self,
                  keypoints_flip_cfg,
                  target_flip_cfg,
                  flip_prob=0.5,
-                 flip_camera=False):
+                 flip_camera=False,
+                 flip_image=False):
         self.keypoints_flip_cfg = keypoints_flip_cfg
         self.target_flip_cfg = target_flip_cfg
         self.flip_prob = flip_prob
         self.flip_camera = flip_camera
+        self.flip_image = flip_image
 
     def transform(self, results: Dict) -> dict:
-        """The transform function of :class:`ZeroCenterPose`.
+        """The transform function of :class:`RandomFlipAroundRoot`.
 
         See ``transform()`` method of :class:`BaseTransform` for details.
 
@@ -76,6 +80,15 @@ def transform(self, results: Dict) -> dict:
                 flip_indices = results['flip_indices']
 
             # flip joint coordinates
+            _camera_param = deepcopy(results['camera_param'])
+            if self.flip_image:
+                assert 'camera_param' in results, \
+                    'Camera parameters are missing.'
+                assert 'w' in _camera_param
+                w = _camera_param['w'] / 2
+                self.keypoints_flip_cfg['center_x'] = w
+                self.target_flip_cfg['center_x'] = w
+
             keypoints, keypoints_visible = flip_keypoints_custom_center(
                 keypoints, keypoints_visible, flip_indices,
                 **self.keypoints_flip_cfg)
@@ -92,7 +105,6 @@ def transform(self, results: Dict) -> dict:
             if self.flip_camera:
                 assert 'camera_param' in results, \
                     'Camera parameters are missing.'
-                _camera_param = deepcopy(results['camera_param'])
 
                 assert 'c' in _camera_param
                 _camera_param['c'][0] *= -1

tests/test_datasets/test_transforms/test_pose3d_transforms.py

@@ -153,3 +153,23 @@ def test_transform(self):
                 -self.data_info['camera_param']['p'][0],
                 camera2['p'][0],
                 atol=4.))
+
+        # test flipping w.r.t. image
+        transform = RandomFlipAroundRoot({}, {}, flip_prob=1, flip_image=True)
+        results = deepcopy(self.data_info)
+        results = transform(results)
+        kpts2 = results['keypoints']
+        tar2 = results['lifting_target']
+
+        camera_param = results['camera_param']
+        for left, right in enumerate(flip_indices):
+            self.assertTrue(
+                np.allclose(
+                    camera_param['w'] - kpts1[0][left][:1],
+                    kpts2[0][right][:1],
+                    atol=4.))
+            self.assertTrue(
+                np.allclose(kpts1[0][left][1:], kpts2[0][right][1:], atol=4.))
+            self.assertTrue(
+                np.allclose(
+                    tar1[..., left, 1:], tar2[..., right, 1:], atol=4.))