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mmpretrain.models.heads.conformer_head 源代码

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

import torch
import torch.nn as nn

from mmpretrain.evaluation.metrics import Accuracy
from mmpretrain.registry import MODELS
from mmpretrain.structures import DataSample
from .cls_head import ClsHead


[文档]@MODELS.register_module() class ConformerHead(ClsHead): """Linear classifier head. Args: num_classes (int): Number of categories excluding the background category. in_channels (Sequence[int]): Number of channels in the input feature map. init_cfg (dict | optional): The extra init config of layers. Defaults to use ``dict(type='Normal', layer='Linear', std=0.01)``. """ def __init__( self, num_classes: int, in_channels: Sequence[int], # [conv_dim, trans_dim] init_cfg: dict = dict(type='TruncNormal', layer='Linear', std=.02), **kwargs): super(ConformerHead, self).__init__(init_cfg=init_cfg, **kwargs) self.in_channels = in_channels self.num_classes = num_classes self.init_cfg = init_cfg if self.num_classes <= 0: raise ValueError( f'num_classes={num_classes} must be a positive integer') self.conv_cls_head = nn.Linear(self.in_channels[0], num_classes) self.trans_cls_head = nn.Linear(self.in_channels[1], num_classes)
[文档] def pre_logits(self, feats: Tuple[List[torch.Tensor]]) -> torch.Tensor: """The process before the final classification head. The input ``feats`` is a tuple of tensor, and each tensor is the feature of a backbone stage. In ``ConformerHead``, we just obtain the feature of the last stage. """ # The ConformerHead doesn't have other module, # just return after unpacking. return feats[-1]
[文档] def forward(self, feats: Tuple[List[torch.Tensor]]) -> Tuple[torch.Tensor]: """The forward process.""" x = self.pre_logits(feats) # There are two outputs in the Conformer model assert len(x) == 2 conv_cls_score = self.conv_cls_head(x[0]) tran_cls_score = self.trans_cls_head(x[1]) return conv_cls_score, tran_cls_score
[文档] def predict(self, feats: Tuple[List[torch.Tensor]], data_samples: List[DataSample] = None) -> List[DataSample]: """Inference without augmentation. Args: feats (tuple[Tensor]): The features extracted from the backbone. Multiple stage inputs are acceptable but only the last stage will be used to classify. The shape of every item should be ``(num_samples, num_classes)``. data_samples (List[DataSample], optional): The annotation data of every samples. If not None, set ``pred_label`` of the input data samples. Defaults to None. Returns: List[DataSample]: A list of data samples which contains the predicted results. """ # The part can be traced by torch.fx conv_cls_score, tran_cls_score = self(feats) cls_score = conv_cls_score + tran_cls_score # The part can not be traced by torch.fx predictions = self._get_predictions(cls_score, data_samples) return predictions
def _get_loss(self, cls_score: Tuple[torch.Tensor], data_samples: List[DataSample], **kwargs) -> dict: """Unpack data samples and compute loss.""" # Unpack data samples and pack targets if 'gt_score' in data_samples[0]: # Batch augmentation may convert labels to one-hot format scores. target = torch.stack([i.gt_score for i in data_samples]) else: target = torch.cat([i.gt_label for i in data_samples]) # compute loss losses = dict() loss = sum([ self.loss_module( score, target, avg_factor=score.size(0), **kwargs) for score in cls_score ]) losses['loss'] = loss # compute accuracy if self.cal_acc: assert target.ndim == 1, 'If you enable batch augmentation ' \ 'like mixup during training, `cal_acc` is pointless.' acc = Accuracy.calculate( cls_score[0] + cls_score[1], target, topk=self.topk) losses.update( {f'accuracy_top-{k}': a for k, a in zip(self.topk, acc)}) return losses
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