Merge branch 'flush_logger' into 'VTM-11.0_nnvc'

avoid flush with None file

See merge request jvet-ahg-nnvc/VVCSoftware_VTM!117

training/training_scripts/NN_Filtering_HOP/training/logger.py

@@ -144,12 +144,16 @@ class PrintLogger(BaseLogger):
     def on_train_iter_end(
         self, epoch: int, iteration: int, train_metrics: Dict[str, Any]
     ) -> None:
-        if self.log_train_interval > 0 and (iteration + 1) % self.log_train_interval == 0:
+        if (
+            self.log_train_interval > 0
+            and (iteration + 1) % self.log_train_interval == 0
+        ):
             print(
                 f"Epoch {epoch}, iteration {iteration}: {self.format_metrics(train_metrics)}",
                 file=self.out_file,
             )
-            self.out_file.flush()
+            if self.out_file is not None:
+                self.out_file.flush()
 
     def on_val_epoch_end(
         self, epoch: int, val_metrics: Dict[str, Dict[str, Any]]
@@ -161,7 +165,8 @@ class PrintLogger(BaseLogger):
                     f"\t{val_tag}: {self.format_metrics(val_tag_metrics)}",
                     file=self.out_file,
                 )
-                self.out_file.flush()
+                if self.out_file is not None:
+                    self.out_file.flush()
 
 
 class ProgressLogger(BaseLogger):
@@ -197,12 +202,14 @@ class ProgressLogger(BaseLogger):
 
     def on_train_start(self):
         print(f"{datetime.datetime.now()}: Training started", file=self.out_file)
-        self.out_file.flush()
+        if self.out_file is not None:
+            self.out_file.flush()
 
     def on_train_end(self):
         if self.log_stage_ends:
             print(f"{datetime.datetime.now()}: Training finished", file=self.out_file)
-            self.out_file.flush()
+            if self.out_file is not None:
+                self.out_file.flush()
 
     def on_train_epoch_start(self, epoch: int):
         if self.log_train_epochs:
@@ -210,7 +217,8 @@ class ProgressLogger(BaseLogger):
                 f"{datetime.datetime.now()}: Training started epoch {epoch}",
                 file=self.out_file,
             )
-            self.out_file.flush()
+            if self.out_file is not None:
+                self.out_file.flush()
 
     def on_train_epoch_end(self, epoch: int):
         if self.log_train_epochs and self.log_stage_ends:
@@ -218,7 +226,8 @@ class ProgressLogger(BaseLogger):
                 f"{datetime.datetime.now()}: Training finished epoch {epoch}",
                 file=self.out_file,
             )
-            self.out_file.flush()
+            if self.out_file is not None:
+                self.out_file.flush()
 
     def on_train_iter_start(self, epoch: int, iteration: int):
         if self.log_train_iterations:
@@ -226,7 +235,8 @@ class ProgressLogger(BaseLogger):
                 f"{datetime.datetime.now()}: Training starting epoch {epoch}, iteration {iteration}",
                 file=self.out_file,
             )
-            self.out_file.flush()
+            if self.out_file is not None:
+                self.out_file.flush()
 
     def on_train_iter_end(
         self, epoch: int, iteration: int, train_metrics: Dict[str, Any]
@@ -236,7 +246,8 @@ class ProgressLogger(BaseLogger):
                 f"{datetime.datetime.now()}: Training finished epoch {epoch}, iteration {iteration}",
                 file=self.out_file,
             )
-            self.out_file.flush()
+            if self.out_file is not None:
+                self.out_file.flush()
 
     def on_val_epoch_start(self, epoch: int):
         if self.log_val_epochs:
@@ -244,7 +255,8 @@ class ProgressLogger(BaseLogger):
                 f"{datetime.datetime.now()}: Validation starting epoch {epoch}",
                 file=self.out_file,
             )
-            self.out_file.flush()
+            if self.out_file is not None:
+                self.out_file.flush()
 
     def on_val_epoch_end(self, epoch: int, val_metrics: Dict[str, Dict[str, Any]]):
         if self.log_val_epochs and self.log_stage_ends:
@@ -252,7 +264,8 @@ class ProgressLogger(BaseLogger):
                 f"{datetime.datetime.now()}: Validation finished epoch {epoch}",
                 file=self.out_file,
             )
-            self.out_file.flush()
+            if self.out_file is not None:
+                self.out_file.flush()
 
 
 from torch.utils.tensorboard import SummaryWriter  # noqa: E402
@@ -290,7 +303,10 @@ class TensorboardLogger(BaseLogger):
     def on_train_iter_end(
         self, epoch: int, iteration: int, train_metrics: Dict[str, Any]
     ) -> None:
-        if self.log_train_interval > 0 and (iteration + 1) % self.log_train_interval == 0:
+        if (
+            self.log_train_interval > 0
+            and (iteration + 1) % self.log_train_interval == 0
+        ):
             self.global_iteration += self.log_train_interval
             for metric, value in train_metrics.items():
                 self.writer.add_scalar(