Getting rid of "module." heritage (#1184)

* Getting rid of "module." heritage

* Remove import of     "WrappedModel",

* Remove remaining usages of .net.module

* Remove remaining usages of .net.module

* Remove remaining usages of .net.module

* Remove remaining usages of .net.module

* Put back WrappedModel class in place, but add deprecation warning when someone is about to use it to indicate it is deprecated.

* Fix _yolox_ckpt_solver (updating condition to account missing "module.")

* Change python3.8  to python

* Reorder tests

* Add missing unwrap_model after merge with master

---------

Co-authored-by: Shay Aharon <[email protected]>

Author: BloodAxe

Makefile

@@ -8,9 +8,9 @@ yolo_nas_integration_tests:
 	python -m unittest tests/integration_tests/yolo_nas_integration_test.py
 
 recipe_accuracy_tests:
-	python3.8 src/super_gradients/examples/convert_recipe_example/convert_recipe_example.py --config-name=cifar10_conversion_params experiment_name=shortened_cifar10_resnet_accuracy_test
-	python3.8 src/super_gradients/train_from_recipe.py --config-name=coco2017_pose_dekr_w32_no_dc experiment_name=shortened_coco2017_pose_dekr_w32_ap_test epochs=1 batch_size=4 val_batch_size=8 training_hyperparams.lr_warmup_steps=0 training_hyperparams.average_best_models=False training_hyperparams.max_train_batches=1000 training_hyperparams.max_valid_batches=100 multi_gpu=DDP num_gpus=4
-	python3.8 src/super_gradients/train_from_recipe.py --config-name=cifar10_resnet               experiment_name=shortened_cifar10_resnet_accuracy_test   epochs=100 training_hyperparams.average_best_models=False multi_gpu=DDP num_gpus=4
-	python3.8 src/super_gradients/train_from_recipe.py --config-name=coco2017_yolox               experiment_name=shortened_coco2017_yolox_n_map_test      epochs=10  architecture=yolox_n training_hyperparams.loss=yolox_fast_loss training_hyperparams.average_best_models=False multi_gpu=DDP num_gpus=4
-	python3.8 src/super_gradients/train_from_recipe.py --config-name=cityscapes_regseg48          experiment_name=shortened_cityscapes_regseg48_iou_test   epochs=10 training_hyperparams.average_best_models=False multi_gpu=DDP num_gpus=4
+	python src/super_gradients/train_from_recipe.py --config-name=coco2017_pose_dekr_w32_no_dc experiment_name=shortened_coco2017_pose_dekr_w32_ap_test epochs=1 batch_size=4 val_batch_size=8 training_hyperparams.lr_warmup_steps=0 training_hyperparams.average_best_models=False training_hyperparams.max_train_batches=1000 training_hyperparams.max_valid_batches=100 multi_gpu=DDP num_gpus=4
+	python src/super_gradients/train_from_recipe.py --config-name=cifar10_resnet               experiment_name=shortened_cifar10_resnet_accuracy_test   epochs=100 training_hyperparams.average_best_models=False multi_gpu=DDP num_gpus=4
+	python src/super_gradients/train_from_recipe.py --config-name=coco2017_yolox               experiment_name=shortened_coco2017_yolox_n_map_test      epochs=10  architecture=yolox_n training_hyperparams.loss=yolox_fast_loss training_hyperparams.average_best_models=False multi_gpu=DDP num_gpus=4
+	python src/super_gradients/train_from_recipe.py --config-name=cityscapes_regseg48          experiment_name=shortened_cityscapes_regseg48_iou_test   epochs=10 training_hyperparams.average_best_models=False multi_gpu=DDP num_gpus=4
+	python src/super_gradients/examples/convert_recipe_example/convert_recipe_example.py --config-name=cifar10_conversion_params experiment_name=shortened_cifar10_resnet_accuracy_test
 	coverage run --source=super_gradients -m unittest tests/deci_core_recipe_test_suite_runner.py

src/super_gradients/training/kd_trainer/kd_trainer.py

@@ -27,6 +27,7 @@
 from super_gradients.training.utils.checkpoint_utils import read_ckpt_state_dict, load_checkpoint_to_model
 from super_gradients.training.utils.distributed_training_utils import setup_device
 from super_gradients.training.utils.ema import KDModelEMA
+from super_gradients.training.utils.utils import unwrap_model
 
 logger = get_logger(__name__)
 
@@ -211,7 +212,7 @@ def _load_checkpoint_to_model(self):
          the entire KD network following the same logic as in Trainer.
         """
         teacher_checkpoint_path = get_param(self.checkpoint_params, "teacher_checkpoint_path")
-        teacher_net = self.net.module.teacher
+        teacher_net = unwrap_model(self.net).teacher
 
         if teacher_checkpoint_path is not None:
 
@@ -271,12 +272,12 @@ def _save_best_checkpoint(self, epoch, state):
         Overrides parent best_ckpt saving to modify the state dict so that we only save the student.
         """
         if self.ema:
-            best_net = core_utils.WrappedModel(self.ema_model.ema.module.student)
+            best_net = self.ema_model.ema.student
             state.pop("ema_net")
         else:
-            best_net = core_utils.WrappedModel(self.net.module.student)
+            best_net = self.net.student
 
-        state["net"] = best_net.state_dict()
+        state["net"] = unwrap_model(best_net).state_dict()
         self.sg_logger.add_checkpoint(tag=self.ckpt_best_name, state_dict=state, global_step=epoch)
 
     def train(

src/super_gradients/training/sg_trainer/sg_trainer.py

@@ -66,7 +66,7 @@
 from super_gradients.training.utils.ema import ModelEMA
 from super_gradients.training.utils.optimizer_utils import build_optimizer
 from super_gradients.training.utils.sg_trainer_utils import MonitoredValue, log_main_training_params
-from super_gradients.training.utils.utils import fuzzy_idx_in_list
+from super_gradients.training.utils.utils import fuzzy_idx_in_list, unwrap_model
 from super_gradients.training.utils.weight_averaging_utils import ModelWeightAveraging
 from super_gradients.training.metrics import Accuracy, Top5
 from super_gradients.training.utils import random_seed
@@ -396,9 +396,6 @@ def _net_to_device(self):
             local_rank = int(device_config.device.split(":")[1])
             self.net = torch.nn.parallel.DistributedDataParallel(self.net, device_ids=[local_rank], output_device=local_rank, find_unused_parameters=True)
 
-        else:
-            self.net = core_utils.WrappedModel(self.net)
-
     def _train_epoch(self, epoch: int, silent_mode: bool = False) -> tuple:
         """
         train_epoch - A single epoch training procedure
@@ -601,15 +598,16 @@ def _save_checkpoint(
         metric = validation_results_dict[self.metric_to_watch]
 
         # BUILD THE state_dict
-        state = {"net": self.net.state_dict(), "acc": metric, "epoch": epoch}
+        state = {"net": unwrap_model(self.net).state_dict(), "acc": metric, "epoch": epoch}
+
         if optimizer is not None:
             state["optimizer_state_dict"] = optimizer.state_dict()
 
         if self.scaler is not None:
             state["scaler_state_dict"] = self.scaler.state_dict()
 
         if self.ema:
-            state["ema_net"] = self.ema_model.ema.state_dict()
+            state["ema_net"] = unwrap_model(self.ema_model.ema).state_dict()
 
         processing_params = self._get_preprocessing_from_valid_loader()
         if processing_params is not None:
@@ -636,7 +634,7 @@ def _save_checkpoint(
             logger.info("Best checkpoint overriden: validation " + self.metric_to_watch + ": " + str(metric))
 
         if self.training_params.average_best_models:
-            net_for_averaging = self.ema_model.ema if self.ema else self.net
+            net_for_averaging = unwrap_model(self.ema_model.ema if self.ema else self.net)
 
             state["net"] = self.model_weight_averaging.get_average_model(net_for_averaging, validation_results_dict=validation_results_dict)
             self.sg_logger.add_checkpoint(tag=self.average_model_checkpoint_filename, state_dict=state, global_step=epoch)
@@ -652,7 +650,7 @@ def _prep_net_for_train(self) -> None:
         self._net_to_device()
 
         # SET THE FLAG FOR DIFFERENT PARAMETER GROUP OPTIMIZER UPDATE
-        self.update_param_groups = hasattr(self.net.module, "update_param_groups")
+        self.update_param_groups = hasattr(unwrap_model(self.net), "update_param_groups")
 
         self.checkpoint = {}
         self.strict_load = core_utils.get_param(self.training_params, "resume_strict_load", StrictLoad.ON)
@@ -1161,7 +1159,9 @@ def forward(self, inputs, targets):
         if not self.ddp_silent_mode:
             if self.training_params.dataset_statistics:
                 dataset_statistics_logger = DatasetStatisticsTensorboardLogger(self.sg_logger)
-                dataset_statistics_logger.analyze(self.train_loader, all_classes=self.classes, title="Train-set", anchors=self.net.module.arch_params.anchors)
+                dataset_statistics_logger.analyze(
+                    self.train_loader, all_classes=self.classes, title="Train-set", anchors=unwrap_model(self.net).arch_params.anchors
+                )
                 dataset_statistics_logger.analyze(self.valid_loader, all_classes=self.classes, title="val-set")
 
         sg_trainer_utils.log_uncaught_exceptions(logger)
@@ -1175,7 +1175,7 @@ def forward(self, inputs, targets):
         if isinstance(self.training_params.optimizer, str) or (
             inspect.isclass(self.training_params.optimizer) and issubclass(self.training_params.optimizer, torch.optim.Optimizer)
         ):
-            self.optimizer = build_optimizer(net=self.net, lr=self.training_params.initial_lr, training_params=self.training_params)
+            self.optimizer = build_optimizer(net=unwrap_model(self.net), lr=self.training_params.initial_lr, training_params=self.training_params)
         elif isinstance(self.training_params.optimizer, torch.optim.Optimizer):
             self.optimizer = self.training_params.optimizer
         else:
@@ -1248,7 +1248,7 @@ def forward(self, inputs, targets):
 
         processing_params = self._get_preprocessing_from_valid_loader()
         if processing_params is not None:
-            self.net.module.set_dataset_processing_params(**processing_params)
+            unwrap_model(self.net).set_dataset_processing_params(**processing_params)
 
         try:
             # HEADERS OF THE TRAINING PROGRESS
@@ -1295,7 +1295,7 @@ def forward(self, inputs, targets):
                             num_gpus=get_world_size(),
                         )
 
-                # model switch - we replace self.net.module with the ema model for the testing and saving part
+                # model switch - we replace self.net with the ema model for the testing and saving part
                 # and then switch it back before the next training epoch
                 if self.ema:
                     self.ema_model.update_attr(self.net)
@@ -1355,7 +1355,7 @@ def forward(self, inputs, targets):
     def _get_preprocessing_from_valid_loader(self) -> Optional[dict]:
         valid_loader = self.valid_loader
 
-        if isinstance(self.net.module, HasPredict) and isinstance(valid_loader.dataset, HasPreprocessingParams):
+        if isinstance(unwrap_model(self.net), HasPredict) and isinstance(valid_loader.dataset, HasPreprocessingParams):
             try:
                 return valid_loader.dataset.get_dataset_preprocessing_params()
             except Exception as e:
@@ -1413,7 +1413,7 @@ def _initialize_mixed_precision(self, mixed_precision_enabled: bool):
                 def hook(module, _):
                     module.forward = MultiGPUModeAutocastWrapper(module.forward)
 
-                self.net.module.register_forward_pre_hook(hook=hook)
+                unwrap_model(self.net).register_forward_pre_hook(hook=hook)
 
             if self.load_checkpoint:
                 scaler_state_dict = core_utils.get_param(self.checkpoint, "scaler_state_dict")
@@ -1439,7 +1439,7 @@ def _validate_final_average_model(self, cleanup_snapshots_pkl_file=False):
         with wait_for_the_master(local_rank):
             average_model_sd = read_ckpt_state_dict(average_model_ckpt_path)["net"]
 
-        self.net.load_state_dict(average_model_sd)
+        unwrap_model(self.net).load_state_dict(average_model_sd)
         # testing the averaged model and save instead of best model if needed
         averaged_model_results_dict = self._validate_epoch(epoch=self.max_epochs)
 
@@ -1462,7 +1462,7 @@ def get_arch_params(self):
 
     @property
     def get_structure(self):
-        return self.net.module.structure
+        return unwrap_model(self.net).structure
 
     @property
     def get_architecture(self):
@@ -1494,7 +1494,8 @@ def _re_build_model(self, arch_params={}):
 
         if device_config.multi_gpu == MultiGPUMode.DISTRIBUTED_DATA_PARALLEL:
             logger.warning("Warning: distributed training is not supported in re_build_model()")
-        self.net = torch.nn.DataParallel(self.net, device_ids=get_device_ids()) if device_config.multi_gpu else core_utils.WrappedModel(self.net)
+        if device_config.multi_gpu == MultiGPUMode.DATA_PARALLEL:
+            self.net = torch.nn.DataParallel(self.net, device_ids=get_device_ids())
 
     @property
     def get_module(self):
@@ -1635,7 +1636,7 @@ def _initialize_sg_logger_objects(self, additional_configs_to_log: Dict = None):
             if "model_name" in get_callable_param_names(sg_logger_cls.__init__):
                 if sg_logger_params.get("model_name") is None:
                     # Use the model name used in `models.get(...)` if relevant
-                    sg_logger_params["model_name"] = get_model_name(self.net.module)
+                    sg_logger_params["model_name"] = get_model_name(unwrap_model(self.net))
 
                 if sg_logger_params["model_name"] is None:
                     raise ValueError(

src/super_gradients/training/utils/__init__.py

@@ -1,7 +1,6 @@
 from super_gradients.training.utils.utils import (
     Timer,
     HpmStruct,
-    WrappedModel,
     convert_to_tensor,
     get_param,
     tensor_container_to_device,
@@ -17,7 +16,6 @@
 __all__ = [
     "Timer",
     "HpmStruct",
-    "WrappedModel",
     "convert_to_tensor",
     "get_param",
     "tensor_container_to_device",

src/super_gradients/training/utils/callbacks/callbacks.py

@@ -23,7 +23,7 @@
 from super_gradients.training.utils.segmentation_utils import BinarySegmentationVisualization
 from super_gradients.common.environment.ddp_utils import multi_process_safe
 from super_gradients.common.environment.checkpoints_dir_utils import get_project_checkpoints_dir_path
-
+from super_gradients.training.utils.utils import unwrap_model
 
 logger = get_logger(__name__)
 
@@ -75,7 +75,7 @@ def __init__(self, model_name: str, input_dimensions: Sequence[int], primary_bat
         self.atol = kwargs.get("atol", 1e-05)
 
     def __call__(self, context: PhaseContext):
-        model = copy.deepcopy(context.net.module)
+        model = copy.deepcopy(unwrap_model(context.net))
         model = model.cpu()
         model.eval()  # Put model into eval mode
 
@@ -204,12 +204,12 @@ def __call__(self, context: PhaseContext) -> None:
         :param context: Training phase context
         """
         try:
-            model = copy.deepcopy(context.net)
+            model = copy.deepcopy(unwrap_model(context.net))
             model_state_dict_path = os.path.join(context.ckpt_dir, self.ckpt_name)
             model_state_dict = torch.load(model_state_dict_path)["net"]
             model.load_state_dict(state_dict=model_state_dict)
 
-            model = model.module.cpu()
+            model = model.cpu()
             if hasattr(model, "prep_model_for_conversion"):
                 model.prep_model_for_conversion(input_size=self.input_dimensions)
 
@@ -267,7 +267,9 @@ def perform_scheduling(self, context: PhaseContext):
 
     def update_lr(self, optimizer, epoch, batch_idx=None):
         if self.update_param_groups:
-            param_groups = self.net.module.update_param_groups(optimizer.param_groups, self.lr, epoch, batch_idx, self.training_params, self.train_loader_len)
+            param_groups = unwrap_model(self.net).update_param_groups(
+                optimizer.param_groups, self.lr, epoch, batch_idx, self.training_params, self.train_loader_len
+            )
             optimizer.param_groups = param_groups
         else:
             # UPDATE THE OPTIMIZERS PARAMETER
@@ -373,7 +375,9 @@ def update_lr(self, optimizer, epoch, batch_idx=None):
         :return:
         """
         if self.update_param_groups:
-            param_groups = self.net.module.update_param_groups(optimizer.param_groups, self.lr, epoch, batch_idx, self.training_params, self.train_loader_len)
+            param_groups = unwrap_model(self.net).update_param_groups(
+                optimizer.param_groups, self.lr, epoch, batch_idx, self.training_params, self.train_loader_len
+            )
             optimizer.param_groups = param_groups
         else:
             # UPDATE THE OPTIMIZERS PARAMETER

src/super_gradients/training/utils/checkpoint_utils.py

@@ -14,6 +14,7 @@
 from super_gradients.module_interfaces import HasPredict
 from super_gradients.training.pretrained_models import MODEL_URLS
 from super_gradients.training.utils.distributed_training_utils import get_local_rank, wait_for_the_master
+from super_gradients.training.utils.utils import unwrap_model
 
 try:
     from torch.hub import download_url_to_file, load_state_dict_from_url
@@ -54,6 +55,13 @@ def adaptive_load_state_dict(net: torch.nn.Module, state_dict: dict, strict: Uni
     :return:
     """
     state_dict = state_dict["net"] if "net" in state_dict else state_dict
+
+    # This is a backward compatibility fix for checkpoints that were saved with DataParallel/DistributedDataParallel wrapper
+    # and contains "module." prefix in all keys
+    # If all keys start with "module.", then we remove it.
+    if all([key.startswith("module.") for key in state_dict.keys()]):
+        state_dict = collections.OrderedDict([(key[7:], value) for key, value in state_dict.items()])
+
     try:
         strict_bool = strict if isinstance(strict, bool) else strict != StrictLoad.OFF
         net.load_state_dict(state_dict, strict=strict_bool)
@@ -217,6 +225,8 @@ def load_checkpoint_to_model(
     if isinstance(strict, str):
         strict = StrictLoad(strict)
 
+    net = unwrap_model(net)
+
     if load_backbone and not hasattr(net, "backbone"):
         raise ValueError("No backbone attribute in net - Can't load backbone weights")
 
@@ -239,7 +249,7 @@ def load_checkpoint_to_model(
     message_model = "model" if not load_backbone else "model's backbone"
     logger.info("Successfully loaded " + message_model + " weights from " + ckpt_local_path + message_suffix)
 
-    if (isinstance(net, HasPredict) or (hasattr(net, "module") and isinstance(net.module, HasPredict))) and load_processing_params:
+    if (isinstance(net, HasPredict)) and load_processing_params:
         if "processing_params" not in checkpoint.keys():
             raise ValueError("Can't load processing params - could not find any stored in checkpoint file.")
         try:
@@ -275,7 +285,7 @@ def _yolox_ckpt_solver(ckpt_key, ckpt_val, model_key, model_val):
 
     if (
         ckpt_val.shape != model_val.shape
-        and ckpt_key == "module._backbone._modules_list.0.conv.conv.weight"
+        and (ckpt_key == "module._backbone._modules_list.0.conv.conv.weight" or ckpt_key == "_backbone._modules_list.0.conv.conv.weight")
         and model_key == "_backbone._modules_list.0.conv.weight"
     ):
         model_val.data[:, :, ::2, ::2] = ckpt_val.data[:, :3]