MMPose 0.X 兼容性说明¶
MMPose 1.0 经过了大规模重构并解决了许多遗留问题，对于 0.x 版本的大部分代码 MMPose 1.0 将不兼容。
数据变换¶
平移、旋转和缩放¶
旧版的数据变换方法 TopDownRandomShiftBboxCenter 和 TopDownGetRandomScaleRotation，将被合并为 RandomBBoxTransform：
@TRANSFORMS.register_module()
class RandomBBoxTransform(BaseTransform):
r"""Rnadomly shift, resize and rotate the bounding boxes.
Required Keys:
- bbox_center
- bbox_scale
Modified Keys:
- bbox_center
- bbox_scale
Added Keys:
- bbox_rotation
Args:
shift_factor (float): Randomly shift the bbox in range
:math:`[-dx, dx]` and :math:`[-dy, dy]` in X and Y directions,
where :math:`dx(y) = x(y)_scale \cdot shift_factor` in pixels.
Defaults to 0.16
shift_prob (float): Probability of applying random shift. Defaults to
0.3
scale_factor (Tuple[float, float]): Randomly resize the bbox in range
:math:`[scale_factor[0], scale_factor[1]]`. Defaults to (0.5, 1.5)
scale_prob (float): Probability of applying random resizing. Defaults
to 1.0
rotate_factor (float): Randomly rotate the bbox in
:math:`[-rotate_factor, rotate_factor]` in degrees. Defaults
to 80.0
rotate_prob (float): Probability of applying random rotation. Defaults
to 0.6
"""
def __init__(self,
shift_factor: float = 0.16,
shift_prob: float = 0.3,
scale_factor: Tuple[float, float] = (0.5, 1.5),
scale_prob: float = 1.0,
rotate_factor: float = 80.0,
rotate_prob: float = 0.6) -> None:
标签生成¶
旧版用于训练标签生成的方法 TopDownGenerateTarget 、TopDownGenerateTargetRegression、BottomUpGenerateHeatmapTarget、BottomUpGenerateTarget 等将被合并为 GenerateTarget，而实际的生成方法由编解码器 提供：
@TRANSFORMS.register_module()
class GenerateTarget(BaseTransform):
"""Encode keypoints into Target.
The generated target is usually the supervision signal of the model
learning, e.g. heatmaps or regression labels.
Required Keys:
- keypoints
- keypoints_visible
- dataset_keypoint_weights
Added Keys:
- The keys of the encoded items from the codec will be updated into
the results, e.g. ``'heatmaps'`` or ``'keypoint_weights'``. See
the specific codec for more details.
Args:
encoder (dict | list[dict]): The codec config for keypoint encoding.
Both single encoder and multiple encoders (given as a list) are
supported
multilevel (bool): Determine the method to handle multiple encoders.
If ``multilevel==True``, generate multilevel targets from a group
of encoders of the same type (e.g. multiple :class:`MSRAHeatmap`
encoders with different sigma values); If ``multilevel==False``,
generate combined targets from a group of different encoders. This
argument will have no effect in case of single encoder. Defaults
to ``False``
use_dataset_keypoint_weights (bool): Whether use the keypoint weights
from the dataset meta information. Defaults to ``False``
"""
def __init__(self,
encoder: MultiConfig,
multilevel: bool = False,
use_dataset_keypoint_weights: bool = False) -> None:
数据归一化¶
旧版的数据归一化操作 NormalizeTensor 和 ToTensor 方法将由 DataPreprocessor 模块替代，不再作为流水线的一部分，而是作为模块加入到模型前向传播中。
旧版用于 3D 人类姿态数据变换的方法 GetRootCenteredPose, ImageCoordinateNormalization 和 NormalizeJointCoordinate 等，将被合并入编码器，比如 ImagePoseLifting 和 VideoPoseLifting 等。
数据转换和重构操作 PoseSequenceToTensor 将在相应的编解码器和 PackPoseInputs 中实现。
模型兼容¶
我们对 model zoo 提供的模型权重进行了兼容性处理，确保相同的模型权重测试精度能够与 0.x 版本保持同等水平，但由于在这两个版本中存在大量处理细节的差异，推理结果可能会产生轻微的不同（精度误差小于 0.05%）。
对于使用 0.x 版本训练保存的模型权重，我们在预测头中提供了 _load_state_dict_pre_hook() 方法来将旧版的权重字典替换为新版，如果你希望将在旧版上开发的模型兼容到新版，可以参考我们的实现。
@MODELS.register_module()
class YourHead(BaseHead):
def __init__(self):
## omitted
# Register the hook to automatically convert old version state dicts
self._register_load_state_dict_pre_hook(self._load_state_dict_pre_hook)
Heatmap-based 方法¶
对于基于SimpleBaseline方法的模型，主要需要注意最后一层卷积层的兼容：
def _load_state_dict_pre_hook(self, state_dict, prefix, local_meta, *args,
**kwargs):
version = local_meta.get('version', None)
if version and version >= self._version:
return
# convert old-version state dict
keys = list(state_dict.keys())
for _k in keys:
if not _k.startswith(prefix):
continue
v = state_dict.pop(_k)
k = _k[len(prefix):]
# In old version, "final_layer" includes both intermediate
# conv layers (new "conv_layers") and final conv layers (new
# "final_layer").
#
# If there is no intermediate conv layer, old "final_layer" will
# have keys like "final_layer.xxx", which should be still
# named "final_layer.xxx";
#
# If there are intermediate conv layers, old "final_layer" will
# have keys like "final_layer.n.xxx", where the weights of the last
# one should be renamed "final_layer.xxx", and others should be
# renamed "conv_layers.n.xxx"
k_parts = k.split('.')
if k_parts[0] == 'final_layer':
if len(k_parts) == 3:
assert isinstance(self.conv_layers, nn.Sequential)
idx = int(k_parts[1])
if idx < len(self.conv_layers):
# final_layer.n.xxx -> conv_layers.n.xxx
k_new = 'conv_layers.' + '.'.join(k_parts[1:])
else:
# final_layer.n.xxx -> final_layer.xxx
k_new = 'final_layer.' + k_parts[2]
else:
# final_layer.xxx remains final_layer.xxx
k_new = k
else:
k_new = k
state_dict[prefix + k_new] = v
RLE-based 方法¶
对于基于 RLE 的模型，由于新版的 loss 模块更名为 loss_module，且 flow 模型归属在 loss 模块下，因此需要对权重字典中 loss 字段进行更改：
def _load_state_dict_pre_hook(self, state_dict, prefix, local_meta, *args,
**kwargs):
version = local_meta.get('version', None)
if version and version >= self._version:
return
# convert old-version state dict
keys = list(state_dict.keys())
for _k in keys:
v = state_dict.pop(_k)
k = _k.lstrip(prefix)
# In old version, "loss" includes the instances of loss,
# now it should be renamed "loss_module"
k_parts = k.split('.')
if k_parts[0] == 'loss':
# loss.xxx -> loss_module.xxx
k_new = prefix + 'loss_module.' + '.'.join(k_parts[1:])
else:
k_new = _k
state_dict[k_new] = v