55 lines
1.7 KiB
Python
55 lines
1.7 KiB
Python
import copy
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from torch.nn import functional as F
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from dassl.engine import TRAINER_REGISTRY, TrainerXU
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from dassl.metrics import compute_accuracy
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from dassl.modeling.ops.utils import sigmoid_rampup, ema_model_update
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@TRAINER_REGISTRY.register()
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class MeanTeacher(TrainerXU):
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"""Mean teacher.
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https://arxiv.org/abs/1703.01780.
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"""
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def __init__(self, cfg):
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super().__init__(cfg)
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self.weight_u = cfg.TRAINER.MEANTEA.WEIGHT_U
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self.ema_alpha = cfg.TRAINER.MEANTEA.EMA_ALPHA
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self.rampup = cfg.TRAINER.MEANTEA.RAMPUP
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self.teacher = copy.deepcopy(self.model)
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self.teacher.train()
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for param in self.teacher.parameters():
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param.requires_grad_(False)
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def forward_backward(self, batch_x, batch_u):
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input_x, label_x, input_u = self.parse_batch_train(batch_x, batch_u)
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logit_x = self.model(input_x)
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loss_x = F.cross_entropy(logit_x, label_x)
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target_u = F.softmax(self.teacher(input_u), 1)
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prob_u = F.softmax(self.model(input_u), 1)
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loss_u = ((prob_u - target_u)**2).sum(1).mean()
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weight_u = self.weight_u * sigmoid_rampup(self.epoch, self.rampup)
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loss = loss_x + loss_u*weight_u
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self.model_backward_and_update(loss)
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global_step = self.batch_idx + self.epoch * self.num_batches
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ema_alpha = min(1 - 1 / (global_step+1), self.ema_alpha)
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ema_model_update(self.model, self.teacher, ema_alpha)
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loss_summary = {
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"loss_x": loss_x.item(),
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"acc_x": compute_accuracy(logit_x, label_x)[0].item(),
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"loss_u": loss_u.item(),
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}
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if (self.batch_idx + 1) == self.num_batches:
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self.update_lr()
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return loss_summary
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