Redirect logs based on env var

documentation/source/qat_ptq_yolo_nas.md

@@ -20,27 +20,27 @@ Now, let's get to it.
 
 ## Step 0: Installations and Dataset Setup
 
-Follow the setup instructions for RF100:
+Follow the [official instructions](https://github.com/roboflow/roboflow-100-benchmark?ref=roboflow-blog) to download Roboflow100: 
+
+To use this dataset, you **must** download the "coco" format, **NOT** the yolov5.
+
 ```
-        - Follow the official instructions to download Roboflow100: https://github.com/roboflow/roboflow-100-benchmark?ref=roboflow-blog
-            //!\\ To use this dataset, you must download the "coco" format, NOT the yolov5.
-
-        - Your dataset should look like this:
-            rf100
-            ├── 4-fold-defect
-            │      ├─ train
-            │      │    ├─ 000000000001.jpg
-            │      │    ├─ ...
-            │      │    └─ _annotations.coco.json
-            │      ├─ valid
-            │      │    └─ ...
-            │      └─ test
-            │           └─ ...
-            ├── abdomen-mri
-            │      └─ ...
-            └── ...
-
-        - Install CoCo API: https://github.com/pdollar/coco/tree/master/PythonAPI
+- Your dataset should look like this:
+    rf100
+    ├── 4-fold-defect
+    │      ├─ train
+    │      │    ├─ 000000000001.jpg
+    │      │    ├─ ...
+    │      │    └─ _annotations.coco.json
+    │      ├─ valid
+    │      │    └─ ...
+    │      └─ test
+    │           └─ ...
+    ├── abdomen-mri
+    │      └─ ...
+    └── ...
+
+- Install CoCo API: https://github.com/pdollar/coco/tree/master/PythonAPI
 ```
 
 Install the latest version of SG:

src/super_gradients/common/auto_logging/auto_logger.py

@@ -32,7 +32,7 @@ def _setup_default_logging(self, log_level: str = None) -> None:
         # Therefore the log file will have the parent PID to being able to discriminate the logs corresponding to a single run.
         timestamp = time.strftime("%Y_%m_%d_%H_%M_%S", time.localtime())
         self._setup_logging(
-            filename=os.path.expanduser(f"~/sg_logs/logs_{os.getppid()}_{timestamp}.log"),
+            filename=os.path.join(env_variables.SUPER_GRADIENTS_LOG_DIR, f"logs_{os.getppid()}_{timestamp}.log"),
             copy_already_logged_messages=False,
             filemode="w",
             log_level=log_level,

src/super_gradients/common/auto_logging/console_logging.py

@@ -7,6 +7,7 @@
 from threading import Lock
 
 from super_gradients.common.environment.ddp_utils import multi_process_safe, is_main_process
+from super_gradients.common.environment.env_variables import env_variables
 
 
 class BufferWriter:
@@ -117,7 +118,7 @@ def __init__(self):
     @multi_process_safe
     def _setup(self):
         """On instantiation, setup the default sink file."""
-        filename = Path.home() / "sg_logs" / "console.log"
+        filename = Path(env_variables.SUPER_GRADIENTS_LOG_DIR) / "console.log"
         filename.parent.mkdir(exist_ok=True)
         self.filename = str(filename)
         os.makedirs(os.path.dirname(self.filename), exist_ok=True)

src/super_gradients/common/environment/env_variables.py

@@ -1,4 +1,5 @@
 import os
+from pathlib import Path
 from typing import Optional
 
 
@@ -45,5 +46,9 @@ def HYDRA_FULL_ERROR(self) -> Optional[str]:
     def HYDRA_FULL_ERROR(self, value: str):
         os.environ["HYDRA_FULL_ERROR"] = value
 
+    @property
+    def SUPER_GRADIENTS_LOG_DIR(self) -> str:
+        return os.getenv("SUPER_GRADIENTS_LOG_DIR", default=str(Path.home() / "sg_logs"))
+
 
 env_variables = EnvironmentVariables()

src/super_gradients/training/losses/label_smoothing_cross_entropy_loss.py

@@ -113,5 +113,6 @@ def forward(self, input, target, smooth_dist=None):
 
 
 @deprecated(deprecated_since="3.2.1", removed_from="3.5.0", target=CrossEntropyLoss)
+@register_loss("LabelSmoothingCrossEntropyLoss")
 class LabelSmoothingCrossEntropyLoss(CrossEntropyLoss):
     ...

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

@@ -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
 
@@ -1569,19 +1562,19 @@ 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):
@@ -1598,3 +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 _maybe_load_preprocessing_params(model: Union[nn.Module, HasPredict], checkpoint: Mapping[str, Tensor]) -> bool:
+    """
+    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.
+    """
+    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/")