mmpose.models.heads.coord_cls_heads.rtmcc_head 源代码
# Copyright (c) OpenMMLab. All rights reserved.
import warnings
from typing import Optional, Sequence, Tuple, Union
import torch
from mmengine.dist import get_dist_info
from mmengine.structures import PixelData
from torch import Tensor, nn
from mmpose.codecs.utils import get_simcc_normalized
from mmpose.evaluation.functional import simcc_pck_accuracy
from mmpose.models.utils.rtmcc_block import RTMCCBlock, ScaleNorm
from mmpose.models.utils.tta import flip_vectors
from mmpose.registry import KEYPOINT_CODECS, MODELS
from mmpose.utils.tensor_utils import to_numpy
from mmpose.utils.typing import (ConfigType, InstanceList, OptConfigType,
OptSampleList)
from ..base_head import BaseHead
OptIntSeq = Optional[Sequence[int]]
[文档]@MODELS.register_module()
class RTMCCHead(BaseHead):
"""Top-down head introduced in RTMPose (2023). The head is composed of a
large-kernel convolutional layer, a fully-connected layer and a Gated
Attention Unit to generate 1d representation from low-resolution feature
maps.
Args:
in_channels (int | sequence[int]): Number of channels in the input
feature map.
out_channels (int): Number of channels in the output heatmap.
input_size (tuple): Size of input image in shape [w, h].
in_featuremap_size (int | sequence[int]): Size of input feature map.
simcc_split_ratio (float): Split ratio of pixels.
Default: 2.0.
final_layer_kernel_size (int): Kernel size of the convolutional layer.
Default: 1.
gau_cfg (Config): Config dict for the Gated Attention Unit.
Default: dict(
hidden_dims=256,
s=128,
expansion_factor=2,
dropout_rate=0.,
drop_path=0.,
act_fn='ReLU',
use_rel_bias=False,
pos_enc=False).
loss (Config): Config of the keypoint loss. Defaults to use
:class:`KLDiscretLoss`
decoder (Config, optional): The decoder config that controls decoding
keypoint coordinates from the network output. Defaults to ``None``
init_cfg (Config, optional): Config to control the initialization. See
:attr:`default_init_cfg` for default settings
"""
def __init__(
self,
in_channels: Union[int, Sequence[int]],
out_channels: int,
input_size: Tuple[int, int],
in_featuremap_size: Tuple[int, int],
simcc_split_ratio: float = 2.0,
final_layer_kernel_size: int = 1,
gau_cfg: ConfigType = dict(
hidden_dims=256,
s=128,
expansion_factor=2,
dropout_rate=0.,
drop_path=0.,
act_fn='ReLU',
use_rel_bias=False,
pos_enc=False),
loss: ConfigType = dict(type='KLDiscretLoss', use_target_weight=True),
decoder: OptConfigType = None,
init_cfg: OptConfigType = None,
):
if init_cfg is None:
init_cfg = self.default_init_cfg
super().__init__(init_cfg)
self.in_channels = in_channels
self.out_channels = out_channels
self.input_size = input_size
self.in_featuremap_size = in_featuremap_size
self.simcc_split_ratio = simcc_split_ratio
self.loss_module = MODELS.build(loss)
if decoder is not None:
self.decoder = KEYPOINT_CODECS.build(decoder)
else:
self.decoder = None
if isinstance(in_channels, (tuple, list)):
raise ValueError(
f'{self.__class__.__name__} does not support selecting '
'multiple input features.')
# Define SimCC layers
flatten_dims = self.in_featuremap_size[0] * self.in_featuremap_size[1]
self.final_layer = nn.Conv2d(
in_channels,
out_channels,
kernel_size=final_layer_kernel_size,
stride=1,
padding=final_layer_kernel_size // 2)
self.mlp = nn.Sequential(
ScaleNorm(flatten_dims),
nn.Linear(flatten_dims, gau_cfg['hidden_dims'], bias=False))
W = int(self.input_size[0] * self.simcc_split_ratio)
H = int(self.input_size[1] * self.simcc_split_ratio)
self.gau = RTMCCBlock(
self.out_channels,
gau_cfg['hidden_dims'],
gau_cfg['hidden_dims'],
s=gau_cfg['s'],
expansion_factor=gau_cfg['expansion_factor'],
dropout_rate=gau_cfg['dropout_rate'],
drop_path=gau_cfg['drop_path'],
attn_type='self-attn',
act_fn=gau_cfg['act_fn'],
use_rel_bias=gau_cfg['use_rel_bias'],
pos_enc=gau_cfg['pos_enc'])
self.cls_x = nn.Linear(gau_cfg['hidden_dims'], W, bias=False)
self.cls_y = nn.Linear(gau_cfg['hidden_dims'], H, bias=False)
[文档] def forward(self, feats: Tuple[Tensor]) -> Tuple[Tensor, Tensor]:
"""Forward the network.
The input is the featuremap extracted by backbone and the
output is the simcc representation.
Args:
feats (Tuple[Tensor]): Multi scale feature maps.
Returns:
pred_x (Tensor): 1d representation of x.
pred_y (Tensor): 1d representation of y.
"""
feats = feats[-1]
feats = self.final_layer(feats) # -> B, K, H, W
# flatten the output heatmap
feats = torch.flatten(feats, 2)
feats = self.mlp(feats) # -> B, K, hidden
feats = self.gau(feats)
pred_x = self.cls_x(feats)
pred_y = self.cls_y(feats)
return pred_x, pred_y
[文档] def predict(
self,
feats: Tuple[Tensor],
batch_data_samples: OptSampleList,
test_cfg: OptConfigType = {},
) -> InstanceList:
"""Predict results from features.
Args:
feats (Tuple[Tensor] | List[Tuple[Tensor]]): The multi-stage
features (or multiple multi-stage features in TTA)
batch_data_samples (List[:obj:`PoseDataSample`]): The batch
data samples
test_cfg (dict): The runtime config for testing process. Defaults
to {}
Returns:
List[InstanceData]: The pose predictions, each contains
the following fields:
- keypoints (np.ndarray): predicted keypoint coordinates in
shape (num_instances, K, D) where K is the keypoint number
and D is the keypoint dimension
- keypoint_scores (np.ndarray): predicted keypoint scores in
shape (num_instances, K)
- keypoint_x_labels (np.ndarray, optional): The predicted 1-D
intensity distribution in the x direction
- keypoint_y_labels (np.ndarray, optional): The predicted 1-D
intensity distribution in the y direction
"""
if test_cfg.get('flip_test', False):
# TTA: flip test -> feats = [orig, flipped]
assert isinstance(feats, list) and len(feats) == 2
flip_indices = batch_data_samples[0].metainfo['flip_indices']
_feats, _feats_flip = feats
_batch_pred_x, _batch_pred_y = self.forward(_feats)
_batch_pred_x_flip, _batch_pred_y_flip = self.forward(_feats_flip)
_batch_pred_x_flip, _batch_pred_y_flip = flip_vectors(
_batch_pred_x_flip,
_batch_pred_y_flip,
flip_indices=flip_indices)
batch_pred_x = (_batch_pred_x + _batch_pred_x_flip) * 0.5
batch_pred_y = (_batch_pred_y + _batch_pred_y_flip) * 0.5
else:
batch_pred_x, batch_pred_y = self.forward(feats)
preds = self.decode((batch_pred_x, batch_pred_y))
if test_cfg.get('output_heatmaps', False):
rank, _ = get_dist_info()
if rank == 0:
warnings.warn('The predicted simcc values are normalized for '
'visualization. This may cause discrepancy '
'between the keypoint scores and the 1D heatmaps'
'.')
# normalize the predicted 1d distribution
batch_pred_x = get_simcc_normalized(batch_pred_x)
batch_pred_y = get_simcc_normalized(batch_pred_y)
B, K, _ = batch_pred_x.shape
# B, K, Wx -> B, K, Wx, 1
x = batch_pred_x.reshape(B, K, 1, -1)
# B, K, Wy -> B, K, 1, Wy
y = batch_pred_y.reshape(B, K, -1, 1)
# B, K, Wx, Wy
batch_heatmaps = torch.matmul(y, x)
pred_fields = [
PixelData(heatmaps=hm) for hm in batch_heatmaps.detach()
]
for pred_instances, pred_x, pred_y in zip(preds,
to_numpy(batch_pred_x),
to_numpy(batch_pred_y)):
pred_instances.keypoint_x_labels = pred_x[None]
pred_instances.keypoint_y_labels = pred_y[None]
return preds, pred_fields
else:
return preds
[文档] def loss(
self,
feats: Tuple[Tensor],
batch_data_samples: OptSampleList,
train_cfg: OptConfigType = {},
) -> dict:
"""Calculate losses from a batch of inputs and data samples."""
pred_x, pred_y = self.forward(feats)
gt_x = torch.cat([
d.gt_instance_labels.keypoint_x_labels for d in batch_data_samples
],
dim=0)
gt_y = torch.cat([
d.gt_instance_labels.keypoint_y_labels for d in batch_data_samples
],
dim=0)
keypoint_weights = torch.cat(
[
d.gt_instance_labels.keypoint_weights
for d in batch_data_samples
],
dim=0,
)
pred_simcc = (pred_x, pred_y)
gt_simcc = (gt_x, gt_y)
# calculate losses
losses = dict()
loss = self.loss_module(pred_simcc, gt_simcc, keypoint_weights)
losses.update(loss_kpt=loss)
# calculate accuracy
_, avg_acc, _ = simcc_pck_accuracy(
output=to_numpy(pred_simcc),
target=to_numpy(gt_simcc),
simcc_split_ratio=self.simcc_split_ratio,
mask=to_numpy(keypoint_weights) > 0,
)
acc_pose = torch.tensor(avg_acc, device=gt_x.device)
losses.update(acc_pose=acc_pose)
return losses
@property
def default_init_cfg(self):
init_cfg = [
dict(type='Normal', layer=['Conv2d'], std=0.001),
dict(type='Constant', layer='BatchNorm2d', val=1),
dict(type='Normal', layer=['Linear'], std=0.01, bias=0),
]
return init_cfg