release code

Dassl.ProGrad.pytorch/dassl/engine/da/dann.py

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+import numpy as np
+import torch
+import torch.nn as nn
+
+from dassl.optim import build_optimizer, build_lr_scheduler
+from dassl.utils import count_num_param
+from dassl.engine import TRAINER_REGISTRY, TrainerXU
+from dassl.metrics import compute_accuracy
+from dassl.modeling import build_head
+from dassl.modeling.ops import ReverseGrad
+
+
+@TRAINER_REGISTRY.register()
+class DANN(TrainerXU):
+    """Domain-Adversarial Neural Networks.
+
+    https://arxiv.org/abs/1505.07818.
+    """
+
+    def __init__(self, cfg):
+        super().__init__(cfg)
+        self.build_critic()
+        self.ce = nn.CrossEntropyLoss()
+        self.bce = nn.BCEWithLogitsLoss()
+
+    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],
+            activation="leaky_relu",
+        )
+        self.critic = nn.Sequential(critic_body, nn.Linear(fdim, 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)
+        self.revgrad = ReverseGrad()
+
+    def forward_backward(self, batch_x, batch_u):
+        input_x, label_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)
+
+        global_step = self.batch_idx + self.epoch * self.num_batches
+        progress = global_step / (self.max_epoch * self.num_batches)
+        lmda = 2 / (1 + np.exp(-10 * progress)) - 1
+
+        logit_x, feat_x = self.model(input_x, return_feature=True)
+        _, feat_u = self.model(input_u, return_feature=True)
+
+        loss_x = self.ce(logit_x, label_x)
+
+        feat_x = self.revgrad(feat_x, grad_scaling=lmda)
+        feat_u = self.revgrad(feat_u, grad_scaling=lmda)
+        output_xd = self.critic(feat_x)
+        output_ud = self.critic(feat_u)
+        loss_d = self.bce(output_xd, domain_x) + self.bce(output_ud, domain_u)
+
+        loss = loss_x + loss_d
+        self.model_backward_and_update(loss)
+
+        loss_summary = {
+            "loss_x": loss_x.item(),
+            "acc_x": compute_accuracy(logit_x, label_x)[0].item(),
+            "loss_d": loss_d.item(),
+        }
+
+        if (self.batch_idx + 1) == self.num_batches:
+            self.update_lr()
+
+        return loss_summary