106 lines
4.1 KiB
Python
106 lines
4.1 KiB
Python
import copy
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import paddle
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import paddle.nn as nn
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from ppcls.utils import logger
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from .celoss import CELoss, MixCELoss
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from .googlenetloss import GoogLeNetLoss
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from .centerloss import CenterLoss
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from .contrasiveloss import ContrastiveLoss
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from .contrasiveloss import ContrastiveLoss_XBM
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from .emlloss import EmlLoss
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from .msmloss import MSMLoss
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from .npairsloss import NpairsLoss
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from .trihardloss import TriHardLoss
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from .triplet import TripletLoss, TripletLossV2
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from .tripletangularmarginloss import TripletAngularMarginLoss, TripletAngularMarginLoss_XBM
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from .supconloss import SupConLoss
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from .softsuploss import SoftSupConLoss
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from .ccssl_loss import CCSSLCELoss
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from .pairwisecosface import PairwiseCosface
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from .dmlloss import DMLLoss
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from .distanceloss import DistanceLoss
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from .softtargetceloss import SoftTargetCrossEntropy
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from .distillationloss import DistillationCELoss
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from .distillationloss import DistillationGTCELoss
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from .distillationloss import DistillationDMLLoss
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from .distillationloss import DistillationDistanceLoss
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from .distillationloss import DistillationRKDLoss
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from .distillationloss import DistillationKLDivLoss
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from .distillationloss import DistillationDKDLoss
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from .distillationloss import DistillationWSLLoss
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from .distillationloss import DistillationSKDLoss
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from .distillationloss import DistillationMultiLabelLoss
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from .distillationloss import DistillationDISTLoss
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from .distillationloss import DistillationPairLoss
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from .multilabelloss import MultiLabelLoss
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from .afdloss import AFDLoss
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from .deephashloss import DSHSDLoss
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from .deephashloss import LCDSHLoss
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from .deephashloss import DCHLoss
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from .metabinloss import CELossForMetaBIN
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from .metabinloss import TripletLossForMetaBIN
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from .metabinloss import InterDomainShuffleLoss
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from .metabinloss import IntraDomainScatterLoss
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class CombinedLoss(nn.Layer):
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def __init__(self, config_list):
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super().__init__()
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loss_func = []
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self.loss_weight = []
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assert isinstance(config_list, list), (
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'operator config should be a list')
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for config in config_list:
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assert isinstance(config,
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dict) and len(config) == 1, "yaml format error"
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name = list(config)[0]
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param = config[name]
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assert "weight" in param, "weight must be in param, but param just contains {}".format(
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param.keys())
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self.loss_weight.append(param.pop("weight"))
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loss_func.append(eval(name)(**param))
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self.loss_func = nn.LayerList(loss_func)
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logger.debug("build loss {} success.".format(loss_func))
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def __call__(self, input, batch):
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loss_dict = {}
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# just for accelerate classification traing speed
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if len(self.loss_func) == 1:
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loss = self.loss_func[0](input, batch)
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loss_dict.update(loss)
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loss_dict["loss"] = list(loss.values())[0]
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else:
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for idx, loss_func in enumerate(self.loss_func):
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loss = loss_func(input, batch)
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weight = self.loss_weight[idx]
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loss = {key: loss[key] * weight for key in loss}
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loss_dict.update(loss)
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loss_dict["loss"] = paddle.add_n(list(loss_dict.values()))
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return loss_dict
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def build_loss(config, mode="train"):
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train_loss_func, unlabel_train_loss_func, eval_loss_func = None, None, None
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if mode == "train":
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label_loss_info = config["Loss"]["Train"]
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if label_loss_info:
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train_loss_func = CombinedLoss(copy.deepcopy(label_loss_info))
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unlabel_loss_info = config.get("UnLabelLoss", {}).get("Train", None)
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if unlabel_loss_info:
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unlabel_train_loss_func = CombinedLoss(
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copy.deepcopy(unlabel_loss_info))
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if mode == "eval" or (mode == "train" and
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config["Global"]["eval_during_train"]):
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loss_config = config.get("Loss", None)
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if loss_config is not None:
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loss_config = loss_config.get("Eval")
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if loss_config is not None:
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eval_loss_func = CombinedLoss(copy.deepcopy(loss_config))
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return train_loss_func, unlabel_train_loss_func, eval_loss_func
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