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- import copy
- import torch
- import torch.nn as nn
-
- from dassl.optim import build_optimizer, build_lr_scheduler
- from dassl.utils import check_isfile, count_num_param, open_specified_layers
- from dassl.engine import TRAINER_REGISTRY, TrainerXU
- from dassl.modeling import build_head
-
-
- @TRAINER_REGISTRY.register()
- class ADDA(TrainerXU):
- """Adversarial Discriminative Domain Adaptation.
-
- https://arxiv.org/abs/1702.05464.
- """
-
- def __init__(self, cfg):
- super().__init__(cfg)
- self.open_layers = ["backbone"]
- if isinstance(self.model.head, nn.Module):
- self.open_layers.append("head")
-
- self.source_model = copy.deepcopy(self.model)
- self.source_model.eval()
- for param in self.source_model.parameters():
- param.requires_grad_(False)
-
- self.build_critic()
-
- self.bce = nn.BCEWithLogitsLoss()
-
- def check_cfg(self, cfg):
- assert check_isfile(
- cfg.MODEL.INIT_WEIGHTS
- ), "The weights of source model must be provided"
-
- def build_critic(self):
- cfg = self.cfg
-
- print("Building critic network")
- fdim = self.model.fdim
- critic_body = build_head(
- "mlp",
- verbose=cfg.VERBOSE,
- in_features=fdim,
- hidden_layers=[fdim, fdim // 2],
- activation="leaky_relu",
- )
- self.critic = nn.Sequential(critic_body, nn.Linear(fdim // 2, 1))
- print("# params: {:,}".format(count_num_param(self.critic)))
- self.critic.to(self.device)
- self.optim_c = build_optimizer(self.critic, cfg.OPTIM)
- self.sched_c = build_lr_scheduler(self.optim_c, cfg.OPTIM)
- self.register_model("critic", self.critic, self.optim_c, self.sched_c)
-
- def forward_backward(self, batch_x, batch_u):
- open_specified_layers(self.model, self.open_layers)
- input_x, _, input_u = self.parse_batch_train(batch_x, batch_u)
- domain_x = torch.ones(input_x.shape[0], 1).to(self.device)
- domain_u = torch.zeros(input_u.shape[0], 1).to(self.device)
-
- _, feat_x = self.source_model(input_x, return_feature=True)
- _, feat_u = self.model(input_u, return_feature=True)
-
- logit_xd = self.critic(feat_x)
- logit_ud = self.critic(feat_u.detach())
-
- loss_critic = self.bce(logit_xd, domain_x)
- loss_critic += self.bce(logit_ud, domain_u)
- self.model_backward_and_update(loss_critic, "critic")
-
- logit_ud = self.critic(feat_u)
- loss_model = self.bce(logit_ud, 1 - domain_u)
- self.model_backward_and_update(loss_model, "model")
-
- loss_summary = {
- "loss_critic": loss_critic.item(),
- "loss_model": loss_model.item(),
- }
-
- if (self.batch_idx + 1) == self.num_batches:
- self.update_lr()
-
- return loss_summary
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