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- import copy
- import paddle
- import paddle.nn as nn
- from paddlex.ppcls.utils import logger
- from .celoss import CELoss, MixCELoss
- from .googlenetloss import GoogLeNetLoss
- from .centerloss import CenterLoss
- from .emlloss import EmlLoss
- from .msmloss import MSMLoss
- from .npairsloss import NpairsLoss
- from .trihardloss import TriHardLoss
- from .triplet import TripletLoss, TripletLossV2
- from .supconloss import SupConLoss
- from .pairwisecosface import PairwiseCosface
- from .dmlloss import DMLLoss
- from .distanceloss import DistanceLoss
- from .distillationloss import DistillationCELoss
- from .distillationloss import DistillationGTCELoss
- from .distillationloss import DistillationDMLLoss
- from .multilabelloss import MultiLabelLoss
- from .deephashloss import DSHSDLoss, LCDSHLoss
- class CombinedLoss(nn.Layer):
- def __init__(self, config_list):
- super().__init__()
- self.loss_func = []
- self.loss_weight = []
- assert isinstance(config_list, list), (
- 'operator config should be a list')
- for config in config_list:
- assert isinstance(config,
- dict) and len(config) == 1, "yaml format error"
- name = list(config)[0]
- param = config[name]
- assert "weight" in param, "weight must be in param, but param just contains {}".format(
- param.keys())
- self.loss_weight.append(param.pop("weight"))
- self.loss_func.append(eval(name)(**param))
- def __call__(self, input, batch):
- loss_dict = {}
- # just for accelerate classification traing speed
- if len(self.loss_func) == 1:
- loss = self.loss_func[0](input, batch)
- loss_dict.update(loss)
- loss_dict["loss"] = list(loss.values())[0]
- else:
- for idx, loss_func in enumerate(self.loss_func):
- loss = loss_func(input, batch)
- weight = self.loss_weight[idx]
- loss = {key: loss[key] * weight for key in loss}
- loss_dict.update(loss)
- loss_dict["loss"] = paddle.add_n(list(loss_dict.values()))
- return loss_dict
- def build_loss(config):
- module_class = CombinedLoss(copy.deepcopy(config))
- logger.debug("build loss {} success.".format(module_class))
- return module_class
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