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103 lines
8.1 KiB
103 lines
8.1 KiB
2 years ago
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📚 This guide explains how to apply **pruning** to YOLOv5 🚀 models.
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UPDATED 25 September 2022.
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## Before You Start
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Clone repo and install [requirements.txt](https://github.com/ultralytics/yolov5/blob/master/requirements.txt) in a [**Python>=3.7.0**](https://www.python.org/) environment, including [**PyTorch>=1.7**](https://pytorch.org/get-started/locally/). [Models](https://github.com/ultralytics/yolov5/tree/master/models) and [datasets](https://github.com/ultralytics/yolov5/tree/master/data) download automatically from the latest YOLOv5 [release](https://github.com/ultralytics/yolov5/releases).
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```bash
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git clone https://github.com/ultralytics/yolov5 # clone
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cd yolov5
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pip install -r requirements.txt # install
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```
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## Test Normally
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Before pruning we want to establish a baseline performance to compare to. This command tests YOLOv5x on COCO val2017 at image size 640 pixels. `yolov5x.pt` is the largest and most accurate model available. Other options are `yolov5s.pt`, `yolov5m.pt` and `yolov5l.pt`, or you own checkpoint from training a custom dataset `./weights/best.pt`. For details on all available models please see our README [table](https://github.com/ultralytics/yolov5#pretrained-checkpoints).
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```bash
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python val.py --weights yolov5x.pt --data coco.yaml --img 640 --half
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```
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Output:
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```shell
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val: data=/content/yolov5/data/coco.yaml, weights=['yolov5x.pt'], batch_size=32, imgsz=640, conf_thres=0.001, iou_thres=0.65, task=val, device=, workers=8, single_cls=False, augment=False, verbose=False, save_txt=False, save_hybrid=False, save_conf=False, save_json=True, project=runs/val, name=exp, exist_ok=False, half=True, dnn=False
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YOLOv5 🚀 v6.0-224-g4c40933 torch 1.10.0+cu111 CUDA:0 (Tesla V100-SXM2-16GB, 16160MiB)
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Fusing layers...
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Model Summary: 444 layers, 86705005 parameters, 0 gradients
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val: Scanning '/content/datasets/coco/val2017.cache' images and labels... 4952 found, 48 missing, 0 empty, 0 corrupt: 100% 5000/5000 [00:00<?, ?it/s]
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Class Images Labels P R mAP@.5 mAP@.5:.95: 100% 157/157 [01:12<00:00, 2.16it/s]
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all 5000 36335 0.732 0.628 0.683 0.496
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Speed: 0.1ms pre-process, 5.2ms inference, 1.7ms NMS per image at shape (32, 3, 640, 640) # <--- base speed
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Evaluating pycocotools mAP... saving runs/val/exp2/yolov5x_predictions.json...
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...
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Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.507 # <--- base mAP
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Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.689
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Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.552
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Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.345
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Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.559
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Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.652
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Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.381
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Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.630
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Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.682
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Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.526
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Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.731
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Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.829
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Results saved to runs/val/exp
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```
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## Test YOLOv5x on COCO (0.30 sparsity)
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We repeat the above test with a pruned model by using the `torch_utils.prune()` command. We update `val.py` to prune YOLOv5x to 0.3 sparsity:
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<img width="894" alt="Screenshot 2022-02-02 at 22 54 18" src="https://user-images.githubusercontent.com/26833433/152243799-b0ac2777-b1a8-47b1-801a-2e4c93c06ead.png">
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30% pruned output:
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```bash
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val: data=/content/yolov5/data/coco.yaml, weights=['yolov5x.pt'], batch_size=32, imgsz=640, conf_thres=0.001, iou_thres=0.65, task=val, device=, workers=8, single_cls=False, augment=False, verbose=False, save_txt=False, save_hybrid=False, save_conf=False, save_json=True, project=runs/val, name=exp, exist_ok=False, half=True, dnn=False
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YOLOv5 🚀 v6.0-224-g4c40933 torch 1.10.0+cu111 CUDA:0 (Tesla V100-SXM2-16GB, 16160MiB)
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Fusing layers...
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Model Summary: 444 layers, 86705005 parameters, 0 gradients
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Pruning model... 0.3 global sparsity
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val: Scanning '/content/datasets/coco/val2017.cache' images and labels... 4952 found, 48 missing, 0 empty, 0 corrupt: 100% 5000/5000 [00:00<?, ?it/s]
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Class Images Labels P R mAP@.5 mAP@.5:.95: 100% 157/157 [01:11<00:00, 2.19it/s]
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all 5000 36335 0.724 0.614 0.671 0.478
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Speed: 0.1ms pre-process, 5.2ms inference, 1.7ms NMS per image at shape (32, 3, 640, 640) # <--- prune mAP
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Evaluating pycocotools mAP... saving runs/val/exp3/yolov5x_predictions.json...
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...
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Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.489 # <--- prune mAP
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Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.677
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Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.537
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Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.334
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Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.542
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Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.635
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Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.370
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Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.612
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Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.664
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Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.496
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Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.722
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Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.803
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Results saved to runs/val/exp3
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```
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In the results we can observe that we have achieved a **sparsity of 30%** in our model after pruning, which means that 30% of the model's weight parameters in `nn.Conv2d` layers are equal to 0. **Inference time is essentially unchanged**, while the model's **AP and AR scores a slightly reduced**.
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## Environments
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YOLOv5 may be run in any of the following up-to-date verified environments (with all dependencies including [CUDA](https://developer.nvidia.com/cuda)/[CUDNN](https://developer.nvidia.com/cudnn), [Python](https://www.python.org/) and [PyTorch](https://pytorch.org/) preinstalled):
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- **Notebooks** with free GPU: <a href="https://bit.ly/yolov5-paperspace-notebook"><img src="https://assets.paperspace.io/img/gradient-badge.svg" alt="Run on Gradient"></a> <a href="https://colab.research.google.com/github/ultralytics/yolov5/blob/master/tutorial.ipynb"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"></a> <a href="https://www.kaggle.com/ultralytics/yolov5"><img src="https://kaggle.com/static/images/open-in-kaggle.svg" alt="Open In Kaggle"></a>
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- **Google Cloud** Deep Learning VM. See [GCP Quickstart Guide](https://github.com/ultralytics/yolov5/wiki/GCP-Quickstart)
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- **Amazon** Deep Learning AMI. See [AWS Quickstart Guide](https://github.com/ultralytics/yolov5/wiki/AWS-Quickstart)
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- **Docker Image**. See [Docker Quickstart Guide](https://github.com/ultralytics/yolov5/wiki/Docker-Quickstart) <a href="https://hub.docker.com/r/ultralytics/yolov5"><img src="https://img.shields.io/docker/pulls/ultralytics/yolov5?logo=docker" alt="Docker Pulls"></a>
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## Status
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<a href="https://github.com/ultralytics/yolov5/actions/workflows/ci-testing.yml"><img src="https://github.com/ultralytics/yolov5/actions/workflows/ci-testing.yml/badge.svg" alt="YOLOv5 CI"></a>
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If this badge is green, all [YOLOv5 GitHub Actions](https://github.com/ultralytics/yolov5/actions) Continuous Integration (CI) tests are currently passing. CI tests verify correct operation of YOLOv5 [training](https://github.com/ultralytics/yolov5/blob/master/train.py), [validation](https://github.com/ultralytics/yolov5/blob/master/val.py), [inference](https://github.com/ultralytics/yolov5/blob/master/detect.py), [export](https://github.com/ultralytics/yolov5/blob/master/export.py) and [benchmarks](https://github.com/ultralytics/yolov5/blob/master/benchmarks.py) on macOS, Windows, and Ubuntu every 24 hours and on every commit.
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