Make epoch scheduler step after epoch instead of iter

1 files changed, 11 insertions, 12 deletions

diff --git a/simclr/evaluate.py b/simclr/evaluate.py
index a8005c4..0f26e16 100644
--- a/simclr/evaluate.py
+++ b/simclr/evaluate.py
@@ -222,18 +222,17 @@ class SimCLREvalTrainer(SimCLRTrainer):
                     batch, num_batches, global_batch, iter_, num_iters,
                     optim_c.param_groups[0]['lr'], train_loss.item()
                 ))
-                if batch == loader_size - 1:
-                    metrics = torch.Tensor(list(self.eval(loss_fn, device))).mean(0)
-                    eval_loss = metrics[0].item()
-                    eval_accuracy = metrics[1].item()
-                    epoch_log = self.EpochLogRecord(iter_, num_iters,
-                                                    eval_loss, eval_accuracy)
-                    self.log(logger, epoch_log)
-                    self.save_checkpoint(epoch_log)
-                if sched_b is not None and self.finetune:
-                    sched_b.step()
-                if sched_c is not None:
-                    sched_c.step()
+            metrics = torch.Tensor(list(self.eval(loss_fn, device))).mean(0)
+            eval_loss = metrics[0].item()
+            eval_accuracy = metrics[1].item()
+            epoch_log = self.EpochLogRecord(iter_, num_iters,
+                                            eval_loss, eval_accuracy)
+            self.log(logger, epoch_log)
+            self.save_checkpoint(epoch_log)
+            if sched_b is not None and self.finetune:
+                sched_b.step()
+            if sched_c is not None:
+                sched_c.step()
 
     def eval(self, loss_fn: Callable, device: torch.device):
         backbone, classifier = self.models.values()