Merge remote-tracking branch 'origin/master' into bugfix/SG-000_fix_c…

…yclic_lr_state_dict

# Conflicts:
#	src/super_gradients/training/utils/checkpoint_utils.py

src/super_gradients/training/models/model_factory.py

@@ -153,6 +153,13 @@ def instantiate_model(
             net = architecture_cls(arch_params=arch_params)
 
         if pretrained_weights:
+            # The logic is follows - first we initialize the preprocessing params using default hard-coded params
+            # If pretrained checkpoint contains preprocessing params, new params will be loaded and override the ones from
+            # this step in load_pretrained_weights_local/load_pretrained_weights
+            if isinstance(net, HasPredict):
+                processing_params = get_pretrained_processing_params(model_name, pretrained_weights)
+                net.set_dataset_processing_params(**processing_params)
+
             if is_remote and pretrained_weights_path:
                 load_pretrained_weights_local(net, model_name, pretrained_weights_path)
             else:
@@ -162,11 +169,6 @@ def instantiate_model(
                 net.replace_head(new_num_classes=num_classes_new_head)
                 arch_params.num_classes = num_classes_new_head
 
-            # STILL NEED TO GET PREPROCESSING PARAMS IN CASE CHECKPOINT HAS NO RECIPE
-            if isinstance(net, HasPredict):
-                processing_params = get_pretrained_processing_params(model_name, pretrained_weights)
-                net.set_dataset_processing_params(**processing_params)
-
     _add_model_name_attribute(net, model_name)
 
     return net

src/super_gradients/training/utils/checkpoint_utils.py

@@ -1,12 +1,11 @@
 import collections
 import os
 import tempfile
-from typing import Union, Mapping, Dict
+from typing import Union, Mapping
 
 import pkg_resources
 import torch
 from torch import nn, Tensor
-from torch.optim.lr_scheduler import CyclicLR
 
 from super_gradients.common.abstractions.abstract_logger import get_logger
 from super_gradients.common.data_interface.adnn_model_repository_data_interface import ADNNModelRepositoryDataInterfaces
@@ -23,6 +22,7 @@
 except (ModuleNotFoundError, ImportError, NameError):
     from torch.hub import _download_url_to_file as download_url_to_file
 
+
 logger = get_logger(__name__)
 
 
@@ -1517,16 +1517,7 @@ 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)) 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:
-            net.set_dataset_processing_params(**checkpoint["processing_params"])
-        except Exception as e:
-            logger.warning(
-                f"Could not set preprocessing pipeline from the checkpoint dataset: {e}. Before calling"
-                "predict make sure to call set_dataset_processing_params."
-            )
+    _maybe_load_preprocessing_params(net, checkpoint)
 
     if load_weights_only or load_backbone:
         # DISCARD ALL THE DATA STORED IN CHECKPOINT OTHER THAN THE WEIGHTS
@@ -1549,10 +1540,12 @@ def __init__(self, desc):
 def load_pretrained_weights(model: torch.nn.Module, architecture: str, pretrained_weights: str):
     """
     Loads pretrained weights from the MODEL_URLS dictionary to model
-    :param architecture: name of the model's architecture
-    :param model: model to load pretrinaed weights for
-    :param pretrained_weights: name for the pretrianed weights (i.e imagenet)
-    :return: None
+
+    :param architecture:        name of the model's architecture
+    :param model:               model to load pretrinaed weights for
+    :param pretrained_weights:  name for the pretrianed weights (i.e imagenet)
+
+    :return:                    None
     """
     from super_gradients.common.object_names import Models
 
@@ -1569,22 +1562,23 @@ def load_pretrained_weights(model: torch.nn.Module, architecture: str, pretraine
             "By downloading the pre-trained weight files you agree to comply with these terms."
         )
 
-    unique_filename = url.split("https://sghub.deci.ai/models/")[1].replace("/", "_").replace(" ", "_")
-    map_location = torch.device("cpu")
-    with wait_for_the_master(get_local_rank()):
-        pretrained_state_dict = load_state_dict_from_url(url=url, map_location=map_location, file_name=unique_filename)
-    _load_weights(architecture, model, pretrained_state_dict)
-
+    # Basically this check allows settings pretrained weights from local path using file:///path/to/weights scheme
+    # which is a valid URI scheme for local files
+    # Supporting local files and file URI allows us modification of pretrained weights dics in unit tests
+    if url.startswith("file://") or os.path.exists(url):
+        pretrained_state_dict = torch.load(url.replace("file://", ""), map_location="cpu")
+    else:
+        unique_filename = url.split("https://sghub.deci.ai/models/")[1].replace("/", "_").replace(" ", "_")
+        map_location = torch.device("cpu")
+        with wait_for_the_master(get_local_rank()):
+            pretrained_state_dict = load_state_dict_from_url(url=url, map_location=map_location, file_name=unique_filename)
 
-def _load_weights(architecture, model, pretrained_state_dict):
-    if "ema_net" in pretrained_state_dict.keys():
-        pretrained_state_dict["net"] = pretrained_state_dict["ema_net"]
-    solver = YoloXCheckpointSolver() if "yolox" in architecture else DefaultCheckpointSolver()
-    adaptive_load_state_dict(net=model, state_dict=pretrained_state_dict, strict=StrictLoad.NO_KEY_MATCHING, solver=solver)
-    logger.info(f"Successfully loaded pretrained weights for architecture {architecture}")
+    _load_weights(architecture, model, pretrained_state_dict)
+    _maybe_load_preprocessing_params(model, pretrained_state_dict)
 
 
 def load_pretrained_weights_local(model: torch.nn.Module, architecture: str, pretrained_weights: str):
+
     """
     Loads pretrained weights from the MODEL_URLS dictionary to model
     :param architecture: name of the model's architecture
@@ -1597,18 +1591,41 @@ def load_pretrained_weights_local(model: torch.nn.Module, architecture: str, pre
 
     pretrained_state_dict = torch.load(pretrained_weights, map_location=map_location)
     _load_weights(architecture, model, pretrained_state_dict)
+    _maybe_load_preprocessing_params(model, pretrained_state_dict)
+
+
+def _load_weights(architecture, model, pretrained_state_dict):
+    if "ema_net" in pretrained_state_dict.keys():
+        pretrained_state_dict["net"] = pretrained_state_dict["ema_net"]
+    solver = YoloXCheckpointSolver() if "yolox" in architecture else DefaultCheckpointSolver()
+    adaptive_load_state_dict(net=model, state_dict=pretrained_state_dict, strict=StrictLoad.NO_KEY_MATCHING, solver=solver)
+    logger.info(f"Successfully loaded pretrained weights for architecture {architecture}")
 
 
-def get_scheduler_state(scheduler) -> Dict:
+def _maybe_load_preprocessing_params(model: Union[nn.Module, HasPredict], checkpoint: Mapping[str, Tensor]) -> bool:
     """
-    Wrapper for getting a torch lr scheduler state dict, resolving some issues with CyclicLR
-    (see https://github.com/pytorch/pytorch/pull/91400)
-    :param scheduler: torch.optim.lr_scheduler._LRScheduler, the scheduler
-    :return: the scheduler's state_dict
+    Tries to load preprocessing params from the checkpoint to the model.
+    The function does not crash, and raises a warning if the loading fails.
+    :param model:      Instance of nn.Module
+    :param checkpoint: Entire checkpoint dict (not state_dict with model weights)
+    :return:           True if the loading was successful, False otherwise.
     """
-    from super_gradients.training.utils import torch_version_is_greater_or_equal
-
-    state = scheduler.state_dict()
-    if isinstance(scheduler, CyclicLR) and not torch_version_is_greater_or_equal(2, 0):
-        del state["_scale_fn_ref"]
-    return state
+    model = unwrap_model(model)
+    checkpoint_has_preprocessing_params = "processing_params" in checkpoint.keys()
+    model_has_predict = isinstance(model, HasPredict)
+    logger.debug(
+        f"Trying to load preprocessing params from checkpoint. Preprocessing params in checkpoint: {checkpoint_has_preprocessing_params}. "
+        f"Model {model.__class__.__name__} inherit HasPredict: {model_has_predict}"
+    )
+
+    if model_has_predict and checkpoint_has_preprocessing_params:
+        try:
+            model.set_dataset_processing_params(**checkpoint["processing_params"])
+            logger.debug(f"Successfully loaded preprocessing params from checkpoint {checkpoint['processing_params']}")
+            return True
+        except Exception as e:
+            logger.warning(
+                f"Could not set preprocessing pipeline from the checkpoint dataset: {e}. Before calling"
+                "predict make sure to call set_dataset_processing_params."
+            )
+    return False

tests/unit_tests/pretrained_models_unit_test.py

@@ -1,12 +1,19 @@
+import os
+import shutil
+import tempfile
 import unittest
+
+import numpy as np
+import torch
+
 import super_gradients
 from super_gradients.common.object_names import Models
-from super_gradients.training import models
 from super_gradients.training import Trainer
+from super_gradients.training import models
 from super_gradients.training.dataloaders.dataloaders import classification_test_dataloader
 from super_gradients.training.metrics import Accuracy
-import os
-import shutil
+from super_gradients.training.pretrained_models import MODEL_URLS, PRETRAINED_NUM_CLASSES
+from super_gradients.training.processing.processing import default_yolo_nas_coco_processing_params
 
 
 class PretrainedModelsUnitTest(unittest.TestCase):
@@ -29,6 +36,22 @@ def test_pretrained_repvgg_a0_imagenet(self):
         model = models.get(Models.REPVGG_A0, pretrained_weights="imagenet", arch_params={"build_residual_branches": True})
         trainer.test(model=model, test_loader=classification_test_dataloader(), test_metrics_list=[Accuracy()], metrics_progress_verbose=True)
 
+    def test_pretrained_models_load_preprocessing_params(self):
+        """
+        Test that checks whether preprocessing params from pretrained model load correctly.
+        """
+        state = {"net": models.get(Models.YOLO_NAS_S, num_classes=80).state_dict(), "processing_params": default_yolo_nas_coco_processing_params()}
+        with tempfile.TemporaryDirectory() as td:
+            checkpoint_path = os.path.join(td, "yolo_nas_s_coco.pth")
+            torch.save(state, checkpoint_path)
+
+            MODEL_URLS[Models.YOLO_NAS_S + "_test"] = checkpoint_path
+            PRETRAINED_NUM_CLASSES["test"] = 80
+
+            model = models.get(Models.YOLO_NAS_S, pretrained_weights="test")
+            # .predict() would fail it model has no preprocessing params
+            self.assertIsNotNone(model.predict(np.zeros(shape=(512, 512, 3), dtype=np.uint8)))
+
     def tearDown(self) -> None:
         if os.path.exists("~/.cache/torch/hub/"):
             shutil.rmtree("~/.cache/torch/hub/")