From e7313d916d783744012ac7bb3011469d72803d25 Mon Sep 17 00:00:00 2001
From: Jordan Gong <jordan.gong@protonmail.com>
Date: Wed, 10 Aug 2022 19:54:37 +0800
Subject: Fix epoch scheduler problem

---
 libs/utils.py          |  1 +
 simclr/evaluate.py     | 22 ++++++++++++----------
 supervised/baseline.py | 21 ++++++++++++---------
 3 files changed, 25 insertions(+), 19 deletions(-)

diff --git a/libs/utils.py b/libs/utils.py
index c019ba9..2f73705 100644
--- a/libs/utils.py
+++ b/libs/utils.py
@@ -91,6 +91,7 @@ class Trainer(ABC):
             raise NotImplementedError(f"Unknown log type: '{type(last_metrics)}'")
         if not inf_mode:
             num_iters *= len(train_loader)
+            config.sched_config.warmup_iters *= len(train_loader)  # FIXME: a little bit hacky here
         scheds = dict(self._configure_scheduler(
             optims.items(), last_iter, num_iters, config.sched_config,
         ))
diff --git a/simclr/evaluate.py b/simclr/evaluate.py
index f4a8fda..a8005c4 100644
--- a/simclr/evaluate.py
+++ b/simclr/evaluate.py
@@ -222,16 +222,18 @@ class SimCLREvalTrainer(SimCLRTrainer):
                     batch, num_batches, global_batch, iter_, num_iters,
                     optim_c.param_groups[0]['lr'], train_loss.item()
                 ))
-            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()
+                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()
 
     def eval(self, loss_fn: Callable, device: torch.device):
         backbone, classifier = self.models.values()
diff --git a/supervised/baseline.py b/supervised/baseline.py
index db93304..6072c10 100644
--- a/supervised/baseline.py
+++ b/supervised/baseline.py
@@ -231,15 +231,18 @@ class SupBaselineTrainer(Trainer):
                     batch, num_batches, global_batch, iter_, num_iters,
                     optim.param_groups[0]['lr'], train_loss.item()
                 ))
-            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)
-            # Step after save checkpoint, otherwise the schedular will one iter ahead after restore
-            if sched is not None:
-                sched.step()
+                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)
+                # Step after save checkpoint, otherwise the schedular will
+                # one iter ahead after restore
+                if sched is not None:
+                    sched.step()
 
     def eval(self, loss_fn: Callable, device: torch.device):
         model = self.models['model']
-- 
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