Way to fix bug with validation frequency

src/super_gradients/training/sg_trainer/sg_trainer.py

@@ -756,6 +756,22 @@ def _init_arch_params(self) -> None:
         if arch_params is not None:
             self.arch_params.override(**arch_params.to_dict())
 
+    def _should_run_validation_for_epoch(self, epoch: int) -> bool:
+        """
+        Method returns true if the validation should to be calculated on this epoch (starting from 0).
+
+        We need to calculate validation if
+        1) the epoch is divisible by #run_validation_freq
+        2) if epoch is last
+        3) if epoch is in self.save_ckpt_epoch_list
+        """
+
+        is_run_val_freq_divisible = ((epoch + 1) % self.run_validation_freq) == 0
+        is_last_epoch = (epoch + 1) == self.max_epochs
+        is_in_checkpoint_list = (epoch + 1) in self.training_params.save_ckpt_epoch_list
+
+        return is_run_val_freq_divisible or is_last_epoch or is_in_checkpoint_list
+
     # FIXME - we need to resolve flake8's 'function is too complex' for this function
     def train(
         self,
@@ -1262,9 +1278,14 @@ def forward(self, inputs, targets):
                     logger.warning("[Warning] Checkpoint does not include EMA weights, continuing training without EMA.")
 
         self.run_validation_freq = self.training_params.run_validation_freq
+
+        if self.max_epochs % self.run_validation_freq != 0:
+            logger.warning(
+                "max_epochs is not divisible by run_validation_freq. "
+                "Please check the training parameters and ensure that run_validation_freq has been set correctly."
+            )
         self.run_test_freq = self.training_params.run_test_freq
 
-        inf_time = 0
         timer = core_utils.Timer(device_config.device)
 
         # IF THE LR MODE IS NOT DEFAULT TAKE IT FROM THE TRAINING PARAMS
@@ -1438,6 +1459,7 @@ def forward(self, inputs, targets):
             for epoch in range(self.start_epoch, self.max_epochs):
                 # broadcast_from_master is necessary here, since in DDP mode, only the master node will
                 # receive the Ctrl-C signal, and we want all nodes to stop training.
+                timer.start()
                 if broadcast_from_master(context.stop_training):
                     logger.info("Request to stop training has been received, stopping training")
                     break
@@ -1485,13 +1507,18 @@ def forward(self, inputs, targets):
                     keep_model = self.net
                     self.net = self.ema_model.ema
 
+                train_inf_time = timer.stop()
+                self._write_scalars_to_logger(metrics=train_metrics_dict, epoch=1 + epoch, inference_time=train_inf_time, tag="Train")
+
                 # RUN TEST ON VALIDATION SET EVERY self.run_validation_freq EPOCHS
                 valid_metrics_dict = {}
-                if (epoch + 1) % self.run_validation_freq == 0:
+                should_run_validation = self._should_run_validation_for_epoch(epoch)
+
+                if should_run_validation:
                     self.phase_callback_handler.on_validation_loader_start(context)
                     timer.start()
                     valid_metrics_dict = self._validate_epoch(context=context, silent_mode=silent_mode)
-                    inf_time = timer.stop()
+                    val_inf_time = timer.stop()
 
                     self.valid_monitored_values = sg_trainer_utils.update_monitored_values_dict(
                         monitored_values_dict=self.valid_monitored_values,
@@ -1502,11 +1529,16 @@ def forward(self, inputs, targets):
                     context.update_context(metrics_dict=valid_metrics_dict)
                     self.phase_callback_handler.on_validation_loader_end(context)
 
+                    self._write_scalars_to_logger(metrics=valid_metrics_dict, epoch=1 + epoch, inference_time=val_inf_time, tag="Valid")
+
                 test_metrics_dict = {}
                 if (epoch + 1) % self.run_test_freq == 0:
                     self.phase_callback_handler.on_test_loader_start(context)
+                    test_inf_time = 0.0
                     for dataset_name, dataloader in self.test_loaders.items():
+                        timer.start()
                         dataset_metrics_dict = self._test_epoch(data_loader=dataloader, context=context, silent_mode=silent_mode, dataset_name=dataset_name)
+                        test_inf_time += timer.stop()
                         dataset_metrics_dict_with_name = {
                             f"{dataset_name}:{metric_name}": metric_value for metric_name, metric_value in dataset_metrics_dict.items()
                         }
@@ -1519,20 +1551,23 @@ def forward(self, inputs, targets):
                     context.update_context(metrics_dict=test_metrics_dict)
                     self.phase_callback_handler.on_test_loader_end(context)
 
+                    self._write_scalars_to_logger(metrics=test_metrics_dict, epoch=1 + epoch, inference_time=test_inf_time, tag="Test")
+
                 if self.ema:
                     self.net = keep_model
 
                 if not self.ddp_silent_mode:
+                    self.sg_logger.add_scalars(tag_scalar_dict=self._epoch_start_logging_values, global_step=1 + epoch)
+
                     # SAVING AND LOGGING OCCURS ONLY IN THE MAIN PROCESS (IN CASES THERE ARE SEVERAL PROCESSES - DDP)
-                    self._write_to_disk_operations(
-                        train_metrics_dict=train_metrics_dict,
-                        validation_results_dict=valid_metrics_dict,
-                        test_metrics_dict=test_metrics_dict,
-                        lr_dict=self._epoch_start_logging_values,
-                        inf_time=inf_time,
-                        epoch=epoch,
-                        context=context,
-                    )
+                    if should_run_validation and self.training_params.save_model:
+                        self._save_checkpoint(
+                            optimizer=self.optimizer,
+                            epoch=epoch + 1,
+                            train_metrics_dict=train_metrics_dict,
+                            validation_results_dict=valid_metrics_dict,
+                            context=context,
+                        )
                     self.sg_logger.upload()
 
                 if not silent_mode:
@@ -1938,35 +1973,20 @@ def _get_hyper_param_config(self):
         }
         return hyper_param_config
 
-    def _write_to_disk_operations(
-        self,
-        train_metrics_dict: dict,
-        validation_results_dict: dict,
-        test_metrics_dict: dict,
-        lr_dict: dict,
-        inf_time: float,
-        epoch: int,
-        context: PhaseContext,
-    ):
-        """Run the various logging operations, e.g.: log file, Tensorboard, save checkpoint etc."""
-        result_dict = {
-            "Inference Time": inf_time,
-            **{f"Train_{k}": v for k, v in train_metrics_dict.items()},
-            **{f"Valid_{k}": v for k, v in validation_results_dict.items()},
-            **{f"Test_{k}": v for k, v in test_metrics_dict.items()},
-        }
-        self.sg_logger.add_scalars(tag_scalar_dict=result_dict, global_step=epoch)
-        self.sg_logger.add_scalars(tag_scalar_dict=lr_dict, global_step=epoch)
+    def _write_scalars_to_logger(self, metrics: dict, epoch: int, inference_time: float, tag: str) -> None:
+        """
+        Method for writing metrics and LR info to logger.
 
-        # SAVE THE CHECKPOINT
-        if self.training_params.save_model:
-            self._save_checkpoint(
-                optimizer=self.optimizer,
-                epoch=epoch + 1,
-                train_metrics_dict=train_metrics_dict,
-                validation_results_dict=validation_results_dict,
-                context=context,
-            )
+        :param metrics:         (dict) dict of metrics..
+        :param epoch:           (inf) 1-based number of epoch.
+        :param inference_time:  (float) time of inference.
+        :param tag:             (str) tag for writing to logger (rule of thumb: Train/Test/Valid)
+        """
+
+        if not self.ddp_silent_mode:
+            info_dict = {f"{tag} Inference Time": inference_time, **{f"{tag}_{k}": v for k, v in metrics.items()}}
+
+            self.sg_logger.add_scalars(tag_scalar_dict=info_dict, global_step=epoch)
 
     def _get_epoch_start_logging_values(self) -> dict:
         """Get all the values that should be logged at the start of each epoch.

tests/end_to_end_tests/trainer_test.py

@@ -93,6 +93,37 @@ def test_checkpoint_content(self):
         weights_only = torch.load(os.path.join(trainer.checkpoints_dir_path, "ckpt_latest_weights_only.pth"))
         self.assertListEqual(["net"], list(weights_only.keys()))
 
+    def test_validation_frequency_divisible(self):
+        trainer, model = self.get_classification_trainer(self.experiment_names[0])
+        training_params = self.training_params.copy()
+        training_params["max_epochs"] = 4
+        training_params["run_validation_freq"] = 2
+        trainer.train(
+            model=model, training_params=training_params, train_loader=classification_test_dataloader(), valid_loader=classification_test_dataloader()
+        )
+        ckpt_filename = ["ckpt_best.pth", "ckpt_latest.pth"]
+        ckpt_paths = [os.path.join(trainer.checkpoints_dir_path, suf) for suf in ckpt_filename]
+        metrics = {}
+        for ckpt_path in ckpt_paths:
+            ckpt = torch.load(ckpt_path)
+            metrics[ckpt_path] = ckpt["metrics"]
+        self.assertTrue(metrics[ckpt_paths[0]] == metrics[ckpt_paths[1]])
+
+    def test_validation_frequency_and_save_ckpt_list(self):
+        trainer, model = self.get_classification_trainer(self.experiment_names[0])
+        training_params = self.training_params.copy()
+        training_params["max_epochs"] = 5
+        training_params["run_validation_freq"] = 3
+        training_params["save_ckpt_epoch_list"] = [1]
+        trainer.train(
+            model=model, training_params=training_params, train_loader=classification_test_dataloader(), valid_loader=classification_test_dataloader()
+        )
+        ckpt_filename = ["ckpt_epoch_1.pth", "ckpt_latest.pth"]
+        ckpt_paths = [os.path.join(trainer.checkpoints_dir_path, suf) for suf in ckpt_filename]
+        for ckpt_path in ckpt_paths:
+            ckpt = torch.load(ckpt_path)
+            self.assertTrue("valid" in ckpt["metrics"].keys())
+
 
 if __name__ == "__main__":
     unittest.main()