mmpose.models.heads.regression_heads.regression_head 源代码

# Copyright (c) OpenMMLab. All rights reserved.
from typing import Optional, Sequence, Tuple, Union

import numpy as np
import torch
from torch import Tensor, nn

from mmpose.evaluation.functional import keypoint_pck_accuracy
from mmpose.models.utils.tta import flip_coordinates
from mmpose.registry import KEYPOINT_CODECS, MODELS
from mmpose.utils.tensor_utils import to_numpy
from mmpose.utils.typing import (ConfigType, OptConfigType, OptSampleList,
from ..base_head import BaseHead

OptIntSeq = Optional[Sequence[int]]

[文档]@MODELS.register_module() class RegressionHead(BaseHead): """Top-down regression head introduced in `Deeppose`_ by Toshev et al (2014). The head is composed of fully-connected layers to predict the coordinates directly. Args: in_channels (int | sequence[int]): Number of input channels num_joints (int): Number of joints loss (Config): Config for keypoint loss. Defaults to use :class:`SmoothL1Loss` 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 .. _`Deeppose`: """ _version = 2 def __init__(self, in_channels: Union[int, Sequence[int]], num_joints: int, loss: ConfigType = dict( type='SmoothL1Loss', 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.num_joints = num_joints self.loss_module = if decoder is not None: self.decoder = else: self.decoder = None # Define fully-connected layers self.fc = nn.Linear(in_channels, self.num_joints * 2)
[文档] def forward(self, feats: Tuple[Tensor]) -> Tensor: """Forward the network. The input is multi scale feature maps and the output is the coordinates. Args: feats (Tuple[Tensor]): Multi scale feature maps. Returns: Tensor: output coordinates(and sigmas[optional]). """ x = feats[-1] x = torch.flatten(x, 1) x = self.fc(x) return x.reshape(-1, self.num_joints, 2)
[文档] def predict(self, feats: Tuple[Tensor], batch_data_samples: OptSampleList, test_cfg: ConfigType = {}) -> Predictions: """Predict results from outputs.""" 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'] input_size = batch_data_samples[0].metainfo['input_size'] _feats, _feats_flip = feats _batch_coords = self.forward(_feats) _batch_coords_flip = flip_coordinates( self.forward(_feats_flip), flip_indices=flip_indices, shift_coords=test_cfg.get('shift_coords', True), input_size=input_size) batch_coords = (_batch_coords + _batch_coords_flip) * 0.5 else: batch_coords = self.forward(feats) # (B, K, D) batch_coords.unsqueeze_(dim=1) # (B, N, K, D) preds = self.decode(batch_coords) return preds
[文档] def loss(self, inputs: Tuple[Tensor], batch_data_samples: OptSampleList, train_cfg: ConfigType = {}) -> dict: """Calculate losses from a batch of inputs and data samples.""" pred_outputs = self.forward(inputs) keypoint_labels = [d.gt_instance_labels.keypoint_labels for d in batch_data_samples]) keypoint_weights =[ d.gt_instance_labels.keypoint_weights for d in batch_data_samples ]) # calculate losses losses = dict() loss = self.loss_module(pred_outputs, keypoint_labels, keypoint_weights.unsqueeze(-1)) losses.update(loss_kpt=loss) # calculate accuracy _, avg_acc, _ = keypoint_pck_accuracy( pred=to_numpy(pred_outputs), gt=to_numpy(keypoint_labels), mask=to_numpy(keypoint_weights) > 0, thr=0.05, norm_factor=np.ones((pred_outputs.size(0), 2), dtype=np.float32)) acc_pose = torch.tensor(avg_acc, device=keypoint_labels.device) losses.update(acc_pose=acc_pose) return losses
@property def default_init_cfg(self): init_cfg = [dict(type='Normal', layer=['Linear'], std=0.01, bias=0)] return init_cfg
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