Feature/quantizer factory refactoring

documentation/source/ptq_qat.md

@@ -259,17 +259,14 @@ If you prefer more of a DIY approach, here is the code sample:
 
 ```python
 import torch
-from super_gradients.training.utils.quantization.export import export_quantized_module_to_onnx
-
+from super_gradients.conversion.onnx import export_to_onnx
 onnx_filename = f"qat_model_1x3x224x224.onnx"
 
 dummy_input = torch.randn([1, 3, 224, 224], device="cpu")
-export_quantized_module_to_onnx(
-    model=quantized_model.cpu(),
+export_to_onnx(
+    model=quantized_model.eval(),
+    model_input=dummy_input,
     onnx_filename=onnx_filename,
-    input_shape=[1, 3, 224, 224],
-    input_size=[1, 3, 224, 224],
-    train=False,
 )
 ```
 

notebooks/DEKR_PoseEstimationFineTuning.ipynb

@@ -22,7 +22,7 @@
   {
    "cell_type": "code",
    "source": [
-    "!pip install -q super_gradients==3.7.1 scikit-learn"
+    "!pip install -q super_gradients==3.8.0 scikit-learn"
    ],
    "metadata": {
     "id": "3UZJqTehg0On",

notebooks/PTQ_and_QAT_for_classification.ipynb

@@ -63,7 +63,7 @@
    },
    "outputs": [],
    "source": [
-    "!pip -qq install super-gradients==3.7.1 pytorch-quantization==2.1.2 --extra-index-url https://pypi.ngc.nvidia.com"
+    "!pip -qq install super-gradients==3.8.0 pytorch-quantization==2.1.2 --extra-index-url https://pypi.ngc.nvidia.com"
    ]
   },
   {

notebooks/Segmentation_Model_Export.ipynb

@@ -52,7 +52,7 @@
    "execution_count": 30,
    "outputs": [],
    "source": [
-    "!pip install -qq super_gradients==3.7.1"
+    "!pip install -qq super_gradients==3.8.0"
    ],
    "metadata": {
     "colab": {

notebooks/YoloNAS_Inference_using_TensorRT.ipynb

@@ -149,7 +149,7 @@
     "# This hack is to prevent occasional issue in Colab https://github.com/googlecolab/colabtools/issues/3409\n",
     "locale.getpreferredencoding = lambda *args: \"UTF-8\"\n",
     "\n",
-    "!pip install -qq super-gradients==3.7.1\n",
+    "!pip install -qq super-gradients==3.8.0\n",
     "!pip install -qq tensorrt~=8.6 pycuda pytorch-quantization==2.1.2 --extra-index-url https://pypi.ngc.nvidia.com"
    ],
    "metadata": {
@@ -337,7 +337,7 @@
     "from super_gradients.conversion.conversion_enums import ExportTargetBackend, ExportQuantizationMode, DetectionOutputFormatMode\n",
     "\n",
     "yolonas = models.get(Models.YOLO_NAS_M, pretrained_weights=\"coco\")\n",
-    "yolonas.export(\"yolonas_m.onnx\", preprocessing=True, postprocessing=True, engine=ExportTargetBackend.TENSORRT)"
+    "yolonas.export(\"yolonas_m.onnx\", preprocessing=True, postprocessing=True, postprocessing_use_tensorrt_nms=True)"
    ],
    "metadata": {
     "colab": {
@@ -647,7 +647,7 @@
     "def convert_onnx_to_trt_engine(onnx_file, trt_output_file, enable_int8_quantization:bool = False):\n",
     "  EXPLICIT_BATCH = 1 << int(trt.NetworkDefinitionCreationFlag.EXPLICIT_BATCH)\n",
     "\n",
-    "  with trt.Builder(trt_logger) as builder, builder.create_network(EXPLICIT_BATCH) as network, builder.create_builder_config() as config:\n",
+    "  with trt.Builder(trt_logger) as builder, builder.create_network(EXPLICIT_BATCH) as network:\n",
     "\n",
     "    config = builder.create_builder_config()\n",
     "    config.profiling_verbosity = trt.ProfilingVerbosity.DETAILED\n",
@@ -1068,7 +1068,7 @@
     "from super_gradients.conversion.conversion_enums import ExportQuantizationMode\n",
     "\n",
     "yolonas = models.get(Models.YOLO_NAS_M, pretrained_weights=\"coco\")\n",
-    "yolonas.export(\"yolonas_m_int8.onnx\", preprocessing=True, postprocessing=True, engine=ExportTargetBackend.TENSORRT, quantization_mode=ExportQuantizationMode.INT8)\n",
+    "yolonas.export(\"yolonas_m_int8.onnx\", preprocessing=True, postprocessing=True, postprocessing_use_tensorrt_nms=True, quantization_mode=ExportQuantizationMode.INT8)\n",
     "convert_onnx_to_trt_engine(\"yolonas_m_int8.onnx\", \"yolonas_m_int8.trt\", enable_int8_quantization=True)"
    ],
    "metadata": {

notebooks/YoloNAS_Pose_Fine_Tuning_Animals_Pose_Dataset.ipynb

@@ -57,7 +57,7 @@
    },
    "outputs": [],
    "source": [
-    "!pip install -qq super-gradients==3.7.1 gdown"
+    "!pip install -qq super-gradients==3.8.0 gdown"
    ]
   },
   {

notebooks/albumentations_tutorial.ipynb

@@ -145,7 +145,7 @@
     }
    ],
    "source": [
-    "! pip install -qq super-gradients==3.7.1\n",
+    "! pip install -qq super-gradients==3.8.0\n",
     "! pip install -qq albumentations == 1.3.1\n",
     "! pip install -qq datasets[vision]~=2.1 pandas~=2.0"
    ]

notebooks/dataloader_adapter.ipynb

@@ -51,7 +51,7 @@
   {
    "cell_type": "code",
    "source": [
-    "!pip install -q super-gradients==3.7.1"
+    "!pip install -q super-gradients==3.8.0"
    ],
    "metadata": {
     "id": "0puCRQGZSP8r",

notebooks/detection_how_to_connect_custom_dataset.ipynb

@@ -90,7 +90,7 @@
    },
    "outputs": [],
    "source": [
-    "! pip install -q super_gradients==3.7.1"
+    "! pip install -q super_gradients==3.8.0"
    ]
   },
   {

notebooks/detection_transfer_learning.ipynb

@@ -141,7 +141,7 @@
     }
    ],
    "source": [
-    "! pip install -qq super_gradients==3.7.1"
+    "! pip install -qq super_gradients==3.8.0"
    ]
   },
   {

notebooks/how_to_run_model_predict.ipynb

@@ -51,7 +51,7 @@
     }
    ],
    "source": [
-    "!pip install -qq super-gradients==3.7.1"
+    "!pip install -qq super-gradients==3.8.0"
    ]
   },
   {

notebooks/how_to_use_knowledge_distillation_for_classification.ipynb

@@ -34,7 +34,7 @@
    },
    "outputs": [],
    "source": [
-    "!pip install -qq super_gradients==3.7.1"
+    "!pip install -qq super_gradients==3.8.0"
    ]
   },
   {

notebooks/quickstart_segmentation.ipynb

@@ -55,7 +55,7 @@
    },
    "outputs": [],
    "source": [
-    "! pip install -qq super-gradients==3.7.1"
+    "! pip install -qq super-gradients==3.8.0"
    ]
   },
   {

notebooks/segmentation_connect_custom_dataset.ipynb

@@ -141,7 +141,7 @@
     }
    ],
    "source": [
-    "! pip install -qq super-gradients==3.7.1"
+    "! pip install -qq super-gradients==3.8.0"
    ]
   },
   {

notebooks/transfer_learning_classification.ipynb

@@ -59,7 +59,7 @@
     }
    ],
    "source": [
-    "! pip install -qq super-gradients==3.7.1"
+    "! pip install -qq super-gradients==3.8.0"
    ]
   },
   {

notebooks/transfer_learning_semantic_segmentation.ipynb

@@ -71,7 +71,7 @@
    },
    "outputs": [],
    "source": [
-    "! pip install -qq super-gradients==3.7.1"
+    "! pip install -qq super-gradients==3.8.0"
    ]
   },
   {

notebooks/what_are_recipes_and_how_to_use.ipynb

@@ -41,7 +41,7 @@
   {
    "cell_type": "code",
    "source": [
-    "!pip install -q super-gradients==3.7.1"
+    "!pip install -q super-gradients==3.8.0"
    ],
    "metadata": {
     "id": "8uZM-4va5Rpu",