release code

Dassl.ProGrad.pytorch/dassl/engine/dg/vanilla.py

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+from torch.nn import functional as F
+
+from dassl.engine import TRAINER_REGISTRY, TrainerX
+from dassl.metrics import compute_accuracy
+
+
+@TRAINER_REGISTRY.register()
+class Vanilla(TrainerX):
+    """Vanilla baseline."""
+
+    def forward_backward(self, batch):
+        input, label = self.parse_batch_train(batch)
+        output = self.model(input)
+        loss = F.cross_entropy(output, label)
+        self.model_backward_and_update(loss)
+
+        loss_summary = {
+            "loss": loss.item(),
+            "acc": compute_accuracy(output, label)[0].item(),
+        }
+
+        if (self.batch_idx + 1) == self.num_batches:
+            self.update_lr()
+
+        return loss_summary
+
+    def parse_batch_train(self, batch):
+        input = batch["img"]
+        label = batch["label"]
+        input = input.to(self.device)
+        label = label.to(self.device)
+        return input, label