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