Merge remote-tracking branch 'origin/feature/SG-000_add_dataset_licen…

…se_message' into feature/SG-000_add_dataset_license_message

.circleci/config.yml

@@ -142,6 +142,7 @@ commands:
 
 jobs:
   build:
+    parallelism: 16  # Adjust based on your needs
     environment:
       CIRCLE_COMPARE_URL: << pipeline.project.git_url >>/compare/<< pipeline.git.base_revision >>..<<pipeline.git.revision>>
     parameters:
@@ -186,6 +187,7 @@ jobs:
             . venv/bin/activate
             python3 -m pip install pip==23.1.2
             python3 -m pip install -r requirements.txt
+            python3 -m pip install -r requirements.dev.txt
       - run:
           name: edit package version
           command: |
@@ -210,14 +212,23 @@ jobs:
             . venv/bin/activate
             python3 -m pip install pytorch-quantization==2.1.2 --extra-index-url https://pypi.ngc.nvidia.com
             python3 -m pip install onnx_graphsurgeon==0.3.27   --extra-index-url https://pypi.ngc.nvidia.com
+
       - run:
-          name: run tests with coverage
+          name: run tests with coverage in parallel
           no_output_timeout: 30m
           command: |
             . venv/bin/activate
-            coverage run --source=super_gradients -m unittest tests/deci_core_unit_test_suite_runner.py
-            coverage report
-            coverage html  # open htmlcov/index.html in a browser
+            # Split test files across parallel containers
+            TEST_FILES=$(circleci tests glob "tests/unit_tests/*test*.py" | circleci tests split --split-by=timings)
+            # Run tests with coverage for the assigned subset of files
+            echo "Running tests on the following files: $TEST_FILES"
+            for file in $TEST_FILES; do
+                echo "Running $file"
+                coverage run --source=super_gradients -m unittest $file
+            done
+            # If needed, each container can generate a partial coverage report
+            coverage report -m
+
 
       - store_artifacts:
           path: htmlcov
@@ -529,6 +540,7 @@ jobs:
             source << parameters.sg_new_env_name >>/bin/activate
             python3.8 -m pip install --upgrade setuptools pip wheel
             python3.8 -m pip install -r requirements.txt
+            python3.8 -m pip install -r requirements.dev.txt
             python3.8 -m pip install .
             python3.8 -m pip install torch torchvision torchaudio
             make sweeper_test
@@ -566,6 +578,7 @@ jobs:
             source << parameters.sg_new_env_name >>/bin/activate
             python3.8 -m pip install --upgrade setuptools pip wheel
             python3.8 -m pip install -r requirements.txt
+            python3.8 -m pip install -r requirements.dev.txt
             python3.8 -m pip install .
             python3.8 -m pip install torch torchvision torchaudio
             python3.8 -m pip install pytorch-quantization==2.1.2 --extra-index-url https://pypi.ngc.nvidia.com

README.md

@@ -218,7 +218,8 @@ model = models.get("model-name", pretrained_weights="pretrained-model-name")
 
 ### Pose Estimation
 
-* [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/Deci-AI/super-gradients/blob/master/notebooks/DEKR_PoseEstimationFineTuning.ipynb) [Pose Estimation Transfer Learning](https://github.com/Deci-AI/super-gradients/blob/master/notebooks/DEKR_PoseEstimationFineTuning.ipynb)
+* [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/Deci-AI/super-gradients/blob/master/notebooks/YoloNAS_Pose_Fine_Tuning_Animals_Pose_Dataset.ipynb) [Fine Tuning YoloNAS-Pose on AnimalPose dataset](https://github.com/Deci-AI/super-gradients/blob/master/notebooks/YoloNAS_Pose_Fine_Tuning_Animals_Pose_Dataset.ipynb)
+* [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/Deci-AI/super-gradients/blob/master/notebooks/DEKR_PoseEstimationFineTuning.ipynb) [Fine Tuning DEKR on AnimalPose dataset](https://github.com/Deci-AI/super-gradients/blob/master/notebooks/DEKR_PoseEstimationFineTuning.ipynb)
 
 
 ###  Object Detection
@@ -489,7 +490,8 @@ train_params = { ... # training parameters
 <details markdown="1">
 
 <summary><h3> Integration to ClearML </h3></summary>    
-
+
+
 ```python
 from super_gradients import Trainer
 
@@ -508,7 +510,45 @@ train_params = { ... # training parameters
                }
 ```
 
+
   </details>
+<details markdown="1">
+
+  <summary><h3> Integration to Voxel51 </h3></summary>    
+
+You can apply SuperGradients YOLO-NAS models directly to your FiftyOne dataset using the apply_model() method:
+
+```python
+import fiftyone as fo
+import fiftyone.zoo as foz
+
+from super_gradients.training import models
+
+dataset = foz.load_zoo_dataset("quickstart", max_samples=25)
+dataset.select_fields().keep_fields()
+
+model = models.get("yolo_nas_m", pretrained_weights="coco")
+
+dataset.apply_model(model, label_field="yolo_nas", confidence_thresh=0.7)
+
+session = fo.launch_app(dataset)
+```
+
+The SuperGradients YOLO-NAS model can be accessed directly from the FiftyOne Model Zoo:
+
+```python
+import fiftyone as fo
+import fiftyone.zoo as foz
+
+model = foz.load_zoo_model("yolo-nas-torch")
+
+dataset = foz.load_zoo_dataset("quickstart")
+dataset.apply_model(model, label_field="yolo_nas")
+
+session = fo.launch_app(dataset)
+```
+
+</details>
 
 
 ## Installation Methods

YOLONAS-POSE.md

@@ -72,9 +72,12 @@ We provide training recipies for training YOLO-NAS-POSE on COCO, CrowdPose and A
     </td>
 </tr>
 <tr>
-    <td>   
-        <a target="_blank" href="https://colab.research.google.com/drive/1agLj0aGx48C_rZPrTkeA18kuncack6lF">
-            <img src="./documentation/assets/SG_img/colab_logo.png" /> Fine-Tune YoloNAS Pose on AnimalPose dataset Notebook
+    <td>
+        <a href="https://colab.research.google.com/github/Deci-AI/super-gradients/blob/master/notebooks/YoloNAS_Pose_Fine_Tuning_Animals_Pose_Dataset.ipynb">
+            <img src="./documentation/assets/SG_img/colab_logo.png" alt="Open In Colab" />
+        </a>
+        <a href="https://github.com/Deci-AI/super-gradients/blob/master/notebooks/YoloNAS_Pose_Fine_Tuning_Animals_Pose_Dataset.ipynb">
+            Fine Tuning YoloNAS-Pose on AnimalPose dataset
         </a>
     </td>
 </tr>

documentation/source/ptq_qat.md

@@ -22,6 +22,8 @@ In this tutorial, we’ll compare post-training quantization (PTQ) to quantizati
 
 For mode detailed information and theoretical background, refer to this [NVIDIA whitepaper](https://arxiv.org/pdf/2004.09602.pdf) and [this practical guide from PyTorch](https://pytorch.org/blog/quantization-in-practice/).
 
+**Note: quantization is currently supported exclusively for GPU and TensorRT environments.**
+
 
 ## Quantization: FP32 vs FP16 vs INT8
 Quantization is a model size reduction technique that converts model weights from high-precision floating-point representation (32-bit float) to low-precision floating-point (FP) representation, such as 16-bit or 8-bit.

documentation/source/qat_ptq_yolo_nas.md

@@ -15,6 +15,7 @@ Pre-requisites:
 - [Training with configuration files](https://github.com/Deci-AI/super-gradients/blob/master/documentation/source/configuration_files.md)
 - [PTQ and QAT](https://github.com/Deci-AI/super-gradients/blob/master/documentation/source/ptq_qat.md)
 
+**Note: quantization is currently supported exclusively for GPU and TensorRT environments.**
 
 Now, let's get to it.
 

documentation/source/welcome.md

@@ -35,6 +35,22 @@ You can also start from our tutorial on [Detection](ObjectDetection.md), [Segmen
 ## What's New
 
 __________________________________________________________________________________________________________
+Version 3.6.0 (Jan 25, 2024)
+
+* Added segmentation samples and support for albumentation transforms for segmentation
+* Implemented distance-based detection matching in `DetectionMetrics` as an enhancement (by @DimaBir)
+* New training hyperparameter - finetune, and multiple LR assignment read about it [https://github.com/Deci-AI/super-gradients/blob/master/documentation/source/LRAssignment.md](here)
+* Enhanced `ImagePermute` processing inclusion
+* Improved dataset plotting and plot functionality
+* A new API for checking model input compatibility
+* Extended `predict()` support for segmentation models
+
+Version 3.5.0 (November 23, 2023)
+
+* Support for long videos in `model.predict()` (by @hakuryuu96)
+* Added support for multiple test loaders in `train_from_config`
+* Added skip_resize to `model.predict()` to support large images and small objects
+
 Version 3.4.0 (November 6, 2023)
 
 * [YoloNAS-Pose](YOLONAS-POSE.md) model released - a new frontier in pose estimation

mkdocs.yml

@@ -22,6 +22,7 @@ nav:
     - Computer Vision Datasets: ./src/super_gradients/training/datasets/Dataset_Setup_Instructions.md
     - Dataset Adapter: ./documentation/source/dataloader_adapter.md
   - Loss functions: ./documentation/source/Losses.md
+  - LR Assignment: ./documentation/source/LRAssignment.md
   - LR schedulers: ./documentation/source/LRScheduling.md
   - Metrics: ./documentation/source/Metrics.md
   - Optimizers: ./documentation/source/optimizers.md

notebooks/PTQ_and_QAT_for_classification.ipynb

@@ -32,7 +32,9 @@
     "\n",
     "Full tutorial can be found [here](https://github.com/Deci-AI/super-gradients/blob/master/documentation/source/ptq_qat.md)\n",
     "\n",
-    "SuperGradient's Recipes for PTQ/QAT can be found [here](https://github.com/Deci-AI/super-gradients/tree/master/src/super_gradients/recipes/quantization_params)\n"
+    "SuperGradient's Recipes for PTQ/QAT can be found [here](https://github.com/Deci-AI/super-gradients/tree/master/src/super_gradients/recipes/quantization_params)\n",
+    "\n",
+    "**Note: quantization is currently supported exclusively for GPU and TensorRT environments.**"
    ]
   },
   {
@@ -1854,4 +1856,4 @@
  },
  "nbformat": 4,
  "nbformat_minor": 0
-}
+}