Merge pull request #27 from debrekXuHan/fix_log_bug

fix log bug

data/samplers/triplet_sampler.py

@@ -68,6 +68,7 @@ class RandomIdentitySampler(Sampler):
                 if len(batch_idxs_dict[pid]) == 0:
                     avai_pids.remove(pid)
 
+        self.length = len(final_idxs)
         return iter(final_idxs)
 
     def __len__(self):

engine/trainer.py

@@ -14,6 +14,8 @@ from ignite.metrics import RunningAverage
 
 from utils.reid_metric import R1_mAP
 
+global ITER
+ITER = 0
 
 def create_supervised_trainer(model, optimizer, loss_fn,
                               device=None):
@@ -173,13 +175,16 @@ def do_train(
 
     @trainer.on(Events.ITERATION_COMPLETED)
     def log_training_loss(engine):
-        iter = (engine.state.iteration - 1) % len(train_loader) + 1
+        global ITER
+        ITER += 1
 
-        if iter % log_period == 0:
+        if ITER % log_period == 0:
             logger.info("Epoch[{}] Iteration[{}/{}] Loss: {:.3f}, Acc: {:.3f}, Base Lr: {:.2e}"
-                        .format(engine.state.epoch, iter, len(train_loader),
+                        .format(engine.state.epoch, ITER, len(train_loader),
                                 engine.state.metrics['avg_loss'], engine.state.metrics['avg_acc'],
                                 scheduler.get_lr()[0]))
+        if len(train_loader) == ITER:
+            ITER = 0
 
     # adding handlers using `trainer.on` decorator API
     @trainer.on(Events.EPOCH_COMPLETED)
@@ -251,13 +256,16 @@ def do_train_with_center(
 
     @trainer.on(Events.ITERATION_COMPLETED)
     def log_training_loss(engine):
-        iter = (engine.state.iteration - 1) % len(train_loader) + 1
+        global ITER
+        ITER += 1
 
-        if iter % log_period == 0:
+        if ITER % log_period == 0:
             logger.info("Epoch[{}] Iteration[{}/{}] Loss: {:.3f}, Acc: {:.3f}, Base Lr: {:.2e}"
-                        .format(engine.state.epoch, iter, len(train_loader),
+                        .format(engine.state.epoch, ITER, len(train_loader),
                                 engine.state.metrics['avg_loss'], engine.state.metrics['avg_acc'],
                                 scheduler.get_lr()[0]))
+        if len(train_loader) == ITER:
+            ITER = 0
 
     # adding handlers using `trainer.on` decorator API
     @trainer.on(Events.EPOCH_COMPLETED)