cls_head.py

import torch
import torch.nn.functional as F

from mmcls.models.losses import Accuracy
from ..builder import HEADS, build_loss
from .base_head import BaseHead


@HEADS.register_module()
class ClsHead(BaseHead):
    """classification head.

    Args:
        loss (dict): Config of classification loss.
        topk (int | tuple): Top-k accuracy.
        cal_acc (bool): Whether to calculate accuracy during training.
            If you use Mixup/CutMix or something like that during training,
            it is not reasonable to calculate accuracy. Defaults to False.
    """

    def __init__(self,
                 loss=dict(type='CrossEntropyLoss', loss_weight=1.0),
                 topk=(1, ),
                 cal_acc=False,
                 init_cfg=None):
        super(ClsHead, self).__init__(init_cfg=init_cfg)

        assert isinstance(loss, dict)
        assert isinstance(topk, (int, tuple))
        if isinstance(topk, int):
            topk = (topk, )
        for _topk in topk:
            assert _topk > 0, 'Top-k should be larger than 0'
        self.topk = topk

        self.compute_loss = build_loss(loss)
        self.compute_accuracy = Accuracy(topk=self.topk)
        self.cal_acc = cal_acc

    def loss(self, cls_score, gt_label):
        num_samples = len(cls_score)
        losses = dict()
        # compute loss
        loss = self.compute_loss(cls_score, gt_label, avg_factor=num_samples)
        if self.cal_acc:
            # compute accuracy
            acc = self.compute_accuracy(cls_score, gt_label)
            assert len(acc) == len(self.topk)
            losses['accuracy'] = {
                f'top-{k}': a
                for k, a in zip(self.topk, acc)
            }
        losses['loss'] = loss
        return losses

    def forward_train(self, cls_score, gt_label):
        losses = self.loss(cls_score, gt_label)
        return losses

    def simple_test(self, cls_score):
        """Test without augmentation."""
        if isinstance(cls_score, list):
            cls_score = sum(cls_score) / float(len(cls_score))
        pred = F.softmax(cls_score, dim=1) if cls_score is not None else None

        on_trace = hasattr(torch.jit, 'is_tracing') and torch.jit.is_tracing()
        if torch.onnx.is_in_onnx_export() or on_trace:
            return pred
        pred = list(pred.detach().cpu().numpy())
        return pred