Feature/sg 1386 granular control over export in ptq and qat (#1879)

* (Work in progress) Adding a granular yet uniform control on exporting a model after PTQ/QAT

* Adding granular control on model.export during PTQ & QAT

* Intermediate fixes for ptq()

* Added additional properties to export params

* Update trainer

* Improve signature of ptq() and qat() methods

* Moving dataclasses to separate files

* Update notebook

* Move export params to super_gradients.conversion

src/super_gradients/common/deprecate.py

@@ -109,7 +109,12 @@ def deprecated_parameter(parameter_name: str, deprecated_since: str, removed_fro
 
     def decorator(func: callable) -> callable:
         argspec = inspect.getfullargspec(func)
-        argument_index = argspec.args.index(parameter_name)
+        # This check is necessary for methods with star-signature foo(*, a,b,c)
+        # For such methods argspec.args is actually empty and argspec.kwonlyargs contains the parameter names
+        if "parameter_name" in argspec.args:
+            argument_index = argspec.args.index(parameter_name)
+        else:
+            argument_index = None
 
         default_value = None
         sig = inspect.signature(func)
@@ -126,9 +131,13 @@ def wrapper(*args, **kwargs):
 
             # Try to get the actual value from the arguments
             # Have to check both positional and keyword arguments
-            try:
-                value = args[argument_index]
-            except IndexError:
+            if argument_index is not None:
+                try:
+                    value = args[argument_index]
+                except IndexError:
+                    if parameter_name in kwargs:
+                        value = kwargs[parameter_name]
+            else:
                 if parameter_name in kwargs:
                     value = kwargs[parameter_name]
 

src/super_gradients/conversion/__init__.py

@@ -1,3 +1,4 @@
 from .conversion_enums import ExportTargetBackend, ExportQuantizationMode, DetectionOutputFormatMode
+from .export_params import ExportParams
 
-__all__ = ["ExportQuantizationMode", "DetectionOutputFormatMode", "ExportTargetBackend"]
+__all__ = ["ExportQuantizationMode", "DetectionOutputFormatMode", "ExportTargetBackend", "ExportParams"]

src/super_gradients/conversion/export_params.py

@@ -0,0 +1,74 @@
+import dataclasses
+from typing import Optional, Tuple
+
+from super_gradients.conversion.conversion_enums import ExportTargetBackend, DetectionOutputFormatMode
+
+
+@dataclasses.dataclass
+class ExportParams:
+    """
+    Parameters for exporting a model to ONNX format in PTQ/QAT methods of Trainer.
+    Most of the parameters are related ot ExportableObjectDetectionModel.export method.
+
+    :param output_onnx_path: The path to save the ONNX model.
+           If None, the ONNX filename will use current experiment dir folder
+           and the output filename will reflect model input shape & whether it's a PTQ or QAT model.
+
+    :param batch_size: The batch size for the ONNX model. Default is 1.
+
+    :param input_image_shape: The input image shape (rows, cols) for the ONNX model.
+           If None, the input shape will be inferred from the model or validation dataloader.
+
+    :param preprocessing: If True, the preprocessing will be included in the ONNX model.
+           This option is only available for models that support model.export() syntax.
+
+    :param postprocessing: If True, the postprocessing will be included in the ONNX model.
+           This option is only available for models that support model.export() syntax.
+
+    :param confidence_threshold: The confidence threshold for object detection models.
+           This option is only available for models that support model.export() syntax.
+           If None, the default confidence threshold for a given model will be used.
+    :param onnx_export_kwargs: (dict) Optional keyword arguments for torch.onnx.export() function.
+    :param onnx_simplify: (bool) If True, apply onnx-simplifier to the exported model.
+
+    :param detection_nms_iou_threshold: (float) A IoU threshold for the NMS step.
+           Relevant only for object detection models and only if postprocessing is True.
+           Default to None, which means the default value for a given model will be used.
+
+    :param detection_max_predictions_per_image: (int) Maximum number of predictions per image.
+           Relevant only for object detection models and only if postprocessing is True.
+
+    :param detection_num_pre_nms_predictions: (int) Number of predictions to keep before NMS.
+           Relevant only for object detection models and only if postprocessing is True.
+
+    :param detection_predictions_format: (DetectionOutputFormatMode) Format of the output predictions of detection models.
+           Possible values:
+           DetectionOutputFormatMode.BATCH_FORMAT - A tuple of 4 tensors will be returned
+           (num_detections, detection_boxes, detection_scores, detection_classes)
+           - A tensor of [batch_size, 1] containing the image indices for each detection.
+           - A tensor of [batch_size, max_output_boxes, 4] containing the bounding box coordinates for each detection in [x1, y1, x2, y2] format.
+           - A tensor of [batch_size, max_output_boxes] containing the confidence scores for each detection.
+           - A tensor of [batch_size, max_output_boxes] containing the class indices for each detection.
+
+           DetectionOutputFormatMode.FLAT_FORMAT - Tensor of shape [N, 7], where N is the total number of
+           predictions in the entire batch.
+           Each row will contain [image_index, x1, y1, x2, y2, class confidence, class index] values.
+           Relevant only for object detection models and only if postprocessing is True.
+    """
+
+    output_onnx_path: Optional[str] = None
+    engine: Optional[ExportTargetBackend] = None
+    batch_size: int = 1
+    input_image_shape: Optional[Tuple[int, int]] = None
+    preprocessing: bool = True
+    postprocessing: bool = True
+    confidence_threshold: Optional[float] = None
+
+    onnx_export_kwargs: Optional[dict] = None
+    onnx_simplify: bool = True
+
+    # These are only relevant for object detection and pose estimation models
+    detection_nms_iou_threshold: Optional[float] = None
+    detection_max_predictions_per_image: Optional[int] = None
+    detection_predictions_format: DetectionOutputFormatMode = DetectionOutputFormatMode.BATCH_FORMAT
+    detection_num_pre_nms_predictions: int = 1000

src/super_gradients/module_interfaces/__init__.py

@@ -1,9 +1,11 @@
 from .module_interfaces import HasPredict, HasPreprocessingParams, SupportsReplaceNumClasses, SupportsReplaceInputChannels, SupportsFineTune
 from .exceptions import ModelHasNoPreprocessingParamsException
-from .exportable_detector import ExportableObjectDetectionModel, AbstractObjectDetectionDecodingModule
+from .exportable_detector import ExportableObjectDetectionModel, AbstractObjectDetectionDecodingModule, ObjectDetectionModelExportResult
 from .exportable_pose_estimation import ExportablePoseEstimationModel, PoseEstimationModelExportResult, AbstractPoseEstimationDecodingModule
 from .pose_estimation_post_prediction_callback import AbstractPoseEstimationPostPredictionCallback, PoseEstimationPredictions
 from .supports_input_shape_check import SupportsInputShapeCheck
+from .quantization_result import QuantizationResult
+
 
 __all__ = [
     "HasPredict",
@@ -20,4 +22,6 @@
     "AbstractPoseEstimationDecodingModule",
     "SupportsFineTune",
     "SupportsInputShapeCheck",
+    "ObjectDetectionModelExportResult",
+    "QuantizationResult",
 ]

src/super_gradients/module_interfaces/exportable_detector.py

@@ -22,7 +22,13 @@
 
 logger = get_logger(__name__)
 
-__all__ = ["ExportableObjectDetectionModel", "AbstractObjectDetectionDecodingModule", "ModelExportResult", "ModelHasNoPreprocessingParamsException"]
+__all__ = [
+    "ExportableObjectDetectionModel",
+    "AbstractObjectDetectionDecodingModule",
+    "ModelExportResult",
+    "ModelHasNoPreprocessingParamsException",
+    "ObjectDetectionModelExportResult",
+]
 
 
 class ModelHasNoPreprocessingParamsException(Exception):
@@ -92,7 +98,7 @@ def get_num_pre_nms_predictions(self) -> int:
 
 
 @dataclasses.dataclass
-class ModelExportResult:
+class ObjectDetectionModelExportResult:
     """
     A dataclass that holds the result of model export.
     """
@@ -113,6 +119,9 @@ def __repr__(self):
         return self.usage_instructions
 
 
+ModelExportResult = ObjectDetectionModelExportResult  # Alias for backward compatibility
+
+
 class ExportableObjectDetectionModel:
     """
     A mixin class that adds export functionality to the object detection models.

src/super_gradients/module_interfaces/quantization_result.py

@@ -0,0 +1,16 @@
+import dataclasses
+from typing import Union, Dict
+
+from torch import nn
+
+from super_gradients.module_interfaces import PoseEstimationModelExportResult, ObjectDetectionModelExportResult
+
+__all__ = ["QuantizationResult"]
+
+
+@dataclasses.dataclass
+class QuantizationResult:
+    quantized_model: nn.Module
+    output_onnx_path: str
+    valid_metrics_dict: Dict[str, float]
+    export_result: Union[None, ObjectDetectionModelExportResult, PoseEstimationModelExportResult] = None