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,
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 = if decoder is not None: self.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 =[ d.gt_instance_labels.keypoint_x_labels for d in batch_data_samples ], dim=0) gt_y =[ d.gt_instance_labels.keypoint_y_labels for d in batch_data_samples ], dim=0) keypoint_weights = [ 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
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