yolo with runs.zip file exp 14 is the best weight
This commit is contained in:
parent
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@ -1,6 +1,6 @@
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## Contributing to YOLOv3 🚀
|
||||
|
||||
We love your input! We want to make contributing to as easy and transparent as possible, whether it's:
|
||||
We love your input! We want to make contributing to YOLOv3 as easy and transparent as possible, whether it's:
|
||||
|
||||
- Reporting a bug
|
||||
- Discussing the current state of the code
|
||||
@ -8,7 +8,7 @@ We love your input! We want to make contributing to as easy and transparent as p
|
||||
- Proposing a new feature
|
||||
- Becoming a maintainer
|
||||
|
||||
works so well due to our combined community effort, and for every small improvement you contribute you will be
|
||||
YOLOv3 works so well due to our combined community effort, and for every small improvement you contribute you will be
|
||||
helping push the frontiers of what's possible in AI 😃!
|
||||
|
||||
## Submitting a Pull Request (PR) 🛠️
|
||||
@ -18,72 +18,73 @@ Submitting a PR is easy! This example shows how to submit a PR for updating `req
|
||||
### 1. Select File to Update
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|
||||
Select `requirements.txt` to update by clicking on it in GitHub.
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|
||||
<p align="center"><img width="800" alt="PR_step1" src="https://user-images.githubusercontent.com/26833433/122260847-08be2600-ced4-11eb-828b-8287ace4136c.png"></p>
|
||||
|
||||
### 2. Click 'Edit this file'
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||||
|
||||
The button is in the top-right corner.
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||||
|
||||
Button is in top-right corner.
|
||||
<p align="center"><img width="800" alt="PR_step2" src="https://user-images.githubusercontent.com/26833433/122260844-06f46280-ced4-11eb-9eec-b8a24be519ca.png"></p>
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### 3. Make Changes
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||||
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Change the `matplotlib` version from `3.2.2` to `3.3`.
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|
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Change `matplotlib` version from `3.2.2` to `3.3`.
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<p align="center"><img width="800" alt="PR_step3" src="https://user-images.githubusercontent.com/26833433/122260853-0a87e980-ced4-11eb-9fd2-3650fb6e0842.png"></p>
|
||||
|
||||
### 4. Preview Changes and Submit PR
|
||||
|
||||
Click on the **Preview changes** tab to verify your updates. At the bottom of the screen select 'Create a **new branch**
|
||||
for this commit', assign your branch a descriptive name such as `fix/matplotlib_version` and click the green **Propose
|
||||
changes** button. All done, your PR is now submitted to for review and approval 😃!
|
||||
|
||||
changes** button. All done, your PR is now submitted to YOLOv3 for review and approval 😃!
|
||||
<p align="center"><img width="800" alt="PR_step4" src="https://user-images.githubusercontent.com/26833433/122260856-0b208000-ced4-11eb-8e8e-77b6151cbcc3.png"></p>
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|
||||
### PR recommendations
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||||
|
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To allow your work to be integrated as seamlessly as possible, we advise you to:
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|
||||
- ✅ Verify your PR is **up-to-date** with `ultralytics/yolov5` `master` branch. If your PR is behind you can update
|
||||
your code by clicking the 'Update branch' button or by running `git pull` and `git merge master` locally.
|
||||
- ✅ Verify your PR is **up-to-date with upstream/master.** If your PR is behind upstream/master an
|
||||
automatic [GitHub actions](https://github.com/ultralytics/yolov3/blob/master/.github/workflows/rebase.yml) rebase may
|
||||
be attempted by including the /rebase command in a comment body, or by running the following code, replacing 'feature'
|
||||
with the name of your local branch:
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|
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<p align="center"><img width="751" alt="Screenshot 2022-08-29 at 22 47 15" src="https://user-images.githubusercontent.com/26833433/187295893-50ed9f44-b2c9-4138-a614-de69bd1753d7.png"></p>
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```bash
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git remote add upstream https://github.com/ultralytics/yolov3.git
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git fetch upstream
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git checkout feature # <----- replace 'feature' with local branch name
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git merge upstream/master
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git push -u origin -f
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```
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|
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- ✅ Verify all Continuous Integration (CI) **checks are passing**.
|
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|
||||
<p align="center"><img width="751" alt="Screenshot 2022-08-29 at 22 47 03" src="https://user-images.githubusercontent.com/26833433/187296922-545c5498-f64a-4d8c-8300-5fa764360da6.png"></p>
|
||||
|
||||
- ✅ Reduce changes to the absolute **minimum** required for your bug fix or feature addition. _"It is not daily increase
|
||||
but daily decrease, hack away the unessential. The closer to the source, the less wastage there is."_ — Bruce Lee
|
||||
|
||||
## Submitting a Bug Report 🐛
|
||||
|
||||
If you spot a problem with please submit a Bug Report!
|
||||
If you spot a problem with YOLOv3 please submit a Bug Report!
|
||||
|
||||
For us to start investigating a possible problem we need to be able to reproduce it ourselves first. We've created a few
|
||||
short guidelines below to help users provide what we need to get started.
|
||||
short guidelines below to help users provide what we need in order to get started.
|
||||
|
||||
When asking a question, people will be better able to provide help if you provide **code** that they can easily
|
||||
understand and use to **reproduce** the problem. This is referred to by community members as creating
|
||||
a [minimum reproducible example](https://stackoverflow.com/help/minimal-reproducible-example). Your code that reproduces
|
||||
the problem should be:
|
||||
|
||||
- ✅ **Minimal** – Use as little code as possible that still produces the same problem
|
||||
- ✅ **Complete** – Provide **all** parts someone else needs to reproduce your problem in the question itself
|
||||
- ✅ **Reproducible** – Test the code you're about to provide to make sure it reproduces the problem
|
||||
* ✅ **Minimal** – Use as little code as possible that still produces the same problem
|
||||
* ✅ **Complete** – Provide **all** parts someone else needs to reproduce your problem in the question itself
|
||||
* ✅ **Reproducible** – Test the code you're about to provide to make sure it reproduces the problem
|
||||
|
||||
In addition to the above requirements, for [Ultralytics](https://ultralytics.com/) to provide assistance your code
|
||||
should be:
|
||||
|
||||
- ✅ **Current** – Verify that your code is up-to-date with the current
|
||||
GitHub [master](https://github.com/ultralytics/yolov5/tree/master), and if necessary `git pull` or `git clone` a new
|
||||
* ✅ **Current** – Verify that your code is up-to-date with current
|
||||
GitHub [master](https://github.com/ultralytics/yolov3/tree/master), and if necessary `git pull` or `git clone` a new
|
||||
copy to ensure your problem has not already been resolved by previous commits.
|
||||
- ✅ **Unmodified** – Your problem must be reproducible without any modifications to the codebase in this
|
||||
* ✅ **Unmodified** – Your problem must be reproducible without any modifications to the codebase in this
|
||||
repository. [Ultralytics](https://ultralytics.com/) does not provide support for custom code ⚠️.
|
||||
|
||||
If you believe your problem meets all of the above criteria, please close this issue and raise a new one using the 🐛
|
||||
**Bug Report** [template](https://github.com/ultralytics/yolov5/issues/new/choose) and provide
|
||||
If you believe your problem meets all of the above criteria, please close this issue and raise a new one using the 🐛 **
|
||||
Bug Report** [template](https://github.com/ultralytics/yolov3/issues/new/choose) and providing
|
||||
a [minimum reproducible example](https://stackoverflow.com/help/minimal-reproducible-example) to help us better
|
||||
understand and diagnose your problem.
|
||||
|
||||
|
||||
602
yolov3/README.md
602
yolov3/README.md
@ -1,103 +1,94 @@
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<div align="center">
|
||||
<p>
|
||||
<a align="center" href="https://ultralytics.com/yolov3" target="_blank">
|
||||
<img width="100%" src="https://raw.githubusercontent.com/ultralytics/assets/main/yolov3/banner-yolov3.png"></a>
|
||||
</p>
|
||||
|
||||
[English](README.md) | [简体中文](README.zh-CN.md)
|
||||
<p>
|
||||
<a align="left" href="https://ultralytics.com/yolov3" target="_blank">
|
||||
<img width="850" src="https://user-images.githubusercontent.com/26833433/99805965-8f2ca800-2b3d-11eb-8fad-13a96b222a23.jpg"></a>
|
||||
</p>
|
||||
<br>
|
||||
|
||||
<div>
|
||||
<a href="https://github.com/ultralytics/yolov3/actions/workflows/ci-testing.yml"><img src="https://github.com/ultralytics/yolov3/actions/workflows/ci-testing.yml/badge.svg" alt="YOLOv3 CI"></a>
|
||||
<a href="https://zenodo.org/badge/latestdoi/264818686"><img src="https://zenodo.org/badge/264818686.svg" alt="YOLOv3 Citation"></a>
|
||||
<a href="https://hub.docker.com/r/ultralytics/yolov3"><img src="https://img.shields.io/docker/pulls/ultralytics/yolov3?logo=docker" alt="Docker Pulls"></a>
|
||||
<br>
|
||||
<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>
|
||||
</div>
|
||||
<br>
|
||||
|
||||
YOLOv3 🚀 is the world's most loved vision AI, representing <a href="https://ultralytics.com">Ultralytics</a> open-source research into future vision AI methods, incorporating lessons learned and best practices evolved over thousands of hours of research and development.
|
||||
|
||||
To request an Enterprise License please complete the form at <a href="https://ultralytics.com/license">Ultralytics Licensing</a>.
|
||||
|
||||
<div align="center">
|
||||
<a href="https://github.com/ultralytics" style="text-decoration:none;">
|
||||
<img src="https://github.com/ultralytics/assets/raw/main/social/logo-social-github.png" width="2%" alt="" /></a>
|
||||
<img src="https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png" width="2%" alt="" />
|
||||
<a href="https://www.linkedin.com/company/ultralytics" style="text-decoration:none;">
|
||||
<img src="https://github.com/ultralytics/assets/raw/main/social/logo-social-linkedin.png" width="2%" alt="" /></a>
|
||||
<img src="https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png" width="2%" alt="" />
|
||||
<a href="https://twitter.com/ultralytics" style="text-decoration:none;">
|
||||
<img src="https://github.com/ultralytics/assets/raw/main/social/logo-social-twitter.png" width="2%" alt="" /></a>
|
||||
<img src="https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png" width="2%" alt="" />
|
||||
<a href="https://www.producthunt.com/@glenn_jocher" style="text-decoration:none;">
|
||||
<img src="https://github.com/ultralytics/assets/raw/main/social/logo-social-producthunt.png" width="2%" alt="" /></a>
|
||||
<img src="https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png" width="2%" alt="" />
|
||||
<a href="https://youtube.com/ultralytics" style="text-decoration:none;">
|
||||
<img src="https://github.com/ultralytics/assets/raw/main/social/logo-social-youtube.png" width="2%" alt="" /></a>
|
||||
<img src="https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png" width="2%" alt="" />
|
||||
<a href="https://www.facebook.com/ultralytics" style="text-decoration:none;">
|
||||
<img src="https://github.com/ultralytics/assets/raw/main/social/logo-social-facebook.png" width="2%" alt="" /></a>
|
||||
<img src="https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png" width="2%" alt="" />
|
||||
<a href="https://www.instagram.com/ultralytics/" style="text-decoration:none;">
|
||||
<img src="https://github.com/ultralytics/assets/raw/main/social/logo-social-instagram.png" width="2%" alt="" /></a>
|
||||
</div>
|
||||
<a href="https://github.com/ultralytics/yolov3/actions"><img src="https://github.com/ultralytics/yolov3/workflows/CI%20CPU%20testing/badge.svg" alt="CI CPU testing"></a>
|
||||
<a href="https://zenodo.org/badge/latestdoi/264818686"><img src="https://zenodo.org/badge/264818686.svg" alt="YOLOv3 Citation"></a>
|
||||
<a href="https://hub.docker.com/r/ultralytics/yolov3"><img src="https://img.shields.io/docker/pulls/ultralytics/yolov3?logo=docker" alt="Docker Pulls"></a>
|
||||
<br>
|
||||
<a href="https://colab.research.google.com/github/ultralytics/yolov3/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/yolov3"><img src="https://kaggle.com/static/images/open-in-kaggle.svg" alt="Open In Kaggle"></a>
|
||||
<a href="https://join.slack.com/t/ultralytics/shared_invite/zt-w29ei8bp-jczz7QYUmDtgo6r6KcMIAg"><img src="https://img.shields.io/badge/Slack-Join_Forum-blue.svg?logo=slack" alt="Join Forum"></a>
|
||||
</div>
|
||||
<br>
|
||||
|
||||
## <div align="center">YOLOv8 🚀 NEW</div>
|
||||
|
||||
We are thrilled to announce the launch of Ultralytics YOLOv8 🚀, our NEW cutting-edge, state-of-the-art (SOTA) model
|
||||
released at **[https://github.com/ultralytics/ultralytics](https://github.com/ultralytics/ultralytics)**.
|
||||
YOLOv8 is designed to be fast, accurate, and easy to use, making it an excellent choice for a wide range of
|
||||
object detection, image segmentation and image classification tasks.
|
||||
|
||||
See the [YOLOv8 Docs](https://docs.ultralytics.com) for details and get started with:
|
||||
|
||||
```commandline
|
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pip install ultralytics
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||||
```
|
||||
|
||||
<div align="center">
|
||||
<a href="https://ultralytics.com/yolov8" target="_blank">
|
||||
<img width="100%" src="https://raw.githubusercontent.com/ultralytics/assets/main/yolov8/yolo-comparison-plots.png"></a>
|
||||
<a href="https://github.com/ultralytics">
|
||||
<img src="https://github.com/ultralytics/yolov5/releases/download/v1.0/logo-social-github.png" width="2%"/>
|
||||
</a>
|
||||
<img width="2%" />
|
||||
<a href="https://www.linkedin.com/company/ultralytics">
|
||||
<img src="https://github.com/ultralytics/yolov5/releases/download/v1.0/logo-social-linkedin.png" width="2%"/>
|
||||
</a>
|
||||
<img width="2%" />
|
||||
<a href="https://twitter.com/ultralytics">
|
||||
<img src="https://github.com/ultralytics/yolov5/releases/download/v1.0/logo-social-twitter.png" width="2%"/>
|
||||
</a>
|
||||
<img width="2%" />
|
||||
<a href="https://youtube.com/ultralytics">
|
||||
<img src="https://github.com/ultralytics/yolov5/releases/download/v1.0/logo-social-youtube.png" width="2%"/>
|
||||
</a>
|
||||
<img width="2%" />
|
||||
<a href="https://www.facebook.com/ultralytics">
|
||||
<img src="https://github.com/ultralytics/yolov5/releases/download/v1.0/logo-social-facebook.png" width="2%"/>
|
||||
</a>
|
||||
<img width="2%" />
|
||||
<a href="https://www.instagram.com/ultralytics/">
|
||||
<img src="https://github.com/ultralytics/yolov5/releases/download/v1.0/logo-social-instagram.png" width="2%"/>
|
||||
</a>
|
||||
</div>
|
||||
|
||||
<br>
|
||||
<p>
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YOLOv3 🚀 is a family of object detection architectures and models pretrained on the COCO dataset, and represents <a href="https://ultralytics.com">Ultralytics</a>
|
||||
open-source research into future vision AI methods, incorporating lessons learned and best practices evolved over thousands of hours of research and development.
|
||||
</p>
|
||||
|
||||
<!--
|
||||
<a align="center" href="https://ultralytics.com/yolov3" target="_blank">
|
||||
<img width="800" src="https://github.com/ultralytics/yolov5/releases/download/v1.0/banner-api.png"></a>
|
||||
-->
|
||||
|
||||
</div>
|
||||
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## <div align="center">Documentation</div>
|
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|
||||
See the [YOLOv3 Docs](https://docs.ultralytics.com) for full documentation on training, testing and deployment. See below for quickstart examples.
|
||||
See the [YOLOv3 Docs](https://docs.ultralytics.com) for full documentation on training, testing and deployment.
|
||||
|
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## <div align="center">Quick Start Examples</div>
|
||||
|
||||
<details open>
|
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<summary>Install</summary>
|
||||
|
||||
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/).
|
||||
[**Python>=3.6.0**](https://www.python.org/) is required with all
|
||||
[requirements.txt](https://github.com/ultralytics/yolov3/blob/master/requirements.txt) installed including
|
||||
[**PyTorch>=1.7**](https://pytorch.org/get-started/locally/):
|
||||
<!-- $ sudo apt update && apt install -y libgl1-mesa-glx libsm6 libxext6 libxrender-dev -->
|
||||
|
||||
```bash
|
||||
git clone https://github.com/ultralytics/yolov3 # clone
|
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cd yolov3
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pip install -r requirements.txt # install
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$ git clone https://github.com/ultralytics/yolov3
|
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$ cd yolov3
|
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$ pip install -r requirements.txt
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```
|
||||
|
||||
</details>
|
||||
|
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<details>
|
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<details open>
|
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<summary>Inference</summary>
|
||||
|
||||
YOLOv3 [PyTorch Hub](https://github.com/ultralytics/yolov5/issues/36) inference. [Models](https://github.com/ultralytics/yolov5/tree/master/models) download automatically from the latest
|
||||
YOLOv3 [release](https://github.com/ultralytics/yolov5/releases).
|
||||
Inference with YOLOv3 and [PyTorch Hub](https://github.com/ultralytics/yolov5/issues/36). Models automatically download
|
||||
from the [latest YOLOv3 release](https://github.com/ultralytics/yolov3/releases).
|
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|
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```python
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import torch
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# Model
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model = torch.hub.load("ultralytics/yolov3", "yolov3") # or yolov5n - yolov5x6, custom
|
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model = torch.hub.load('ultralytics/yolov3', 'yolov3') # or yolov3-spp, yolov3-tiny, custom
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# Images
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img = "https://ultralytics.com/images/zidane.jpg" # or file, Path, PIL, OpenCV, numpy, list
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img = 'https://ultralytics.com/images/zidane.jpg' # or file, Path, PIL, OpenCV, numpy, list
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# Inference
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results = model(img)
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@ -108,23 +99,22 @@ results.print() # or .show(), .save(), .crop(), .pandas(), etc.
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|
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</details>
|
||||
|
||||
|
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|
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<details>
|
||||
<summary>Inference with detect.py</summary>
|
||||
|
||||
`detect.py` runs inference on a variety of sources, downloading [models](https://github.com/ultralytics/yolov5/tree/master/models) automatically from
|
||||
the latest YOLOv3 [release](https://github.com/ultralytics/yolov5/releases) and saving results to `runs/detect`.
|
||||
`detect.py` runs inference on a variety of sources, downloading models automatically from
|
||||
the [latest YOLOv3 release](https://github.com/ultralytics/yolov3/releases) and saving results to `runs/detect`.
|
||||
|
||||
```bash
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python detect.py --weights yolov5s.pt --source 0 # webcam
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img.jpg # image
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vid.mp4 # video
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screen # screenshot
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path/ # directory
|
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list.txt # list of images
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list.streams # list of streams
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'path/*.jpg' # glob
|
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'https://youtu.be/Zgi9g1ksQHc' # YouTube
|
||||
'rtsp://example.com/media.mp4' # RTSP, RTMP, HTTP stream
|
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$ python detect.py --source 0 # webcam
|
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img.jpg # image
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vid.mp4 # video
|
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path/ # directory
|
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path/*.jpg # glob
|
||||
'https://youtu.be/Zgi9g1ksQHc' # YouTube
|
||||
'rtsp://example.com/media.mp4' # RTSP, RTMP, HTTP stream
|
||||
```
|
||||
|
||||
</details>
|
||||
@ -132,21 +122,7 @@ python detect.py --weights yolov5s.pt --source 0 #
|
||||
<details>
|
||||
<summary>Training</summary>
|
||||
|
||||
The commands below reproduce YOLOv3 [COCO](https://github.com/ultralytics/yolov5/blob/master/data/scripts/get_coco.sh)
|
||||
results. [Models](https://github.com/ultralytics/yolov5/tree/master/models)
|
||||
and [datasets](https://github.com/ultralytics/yolov5/tree/master/data) download automatically from the latest
|
||||
YOLOv3 [release](https://github.com/ultralytics/yolov5/releases). Training times for YOLOv5n/s/m/l/x are
|
||||
1/2/4/6/8 days on a V100 GPU ([Multi-GPU](https://github.com/ultralytics/yolov5/issues/475) times faster). Use the
|
||||
largest `--batch-size` possible, or pass `--batch-size -1` for
|
||||
YOLOv3 [AutoBatch](https://github.com/ultralytics/yolov5/pull/5092). Batch sizes shown for V100-16GB.
|
||||
|
||||
```bash
|
||||
python train.py --data coco.yaml --epochs 300 --weights '' --cfg yolov5n.yaml --batch-size 128
|
||||
yolov5s 64
|
||||
yolov5m 40
|
||||
yolov5l 24
|
||||
yolov5x 16
|
||||
```
|
||||
|
||||
<img width="800" src="https://user-images.githubusercontent.com/26833433/90222759-949d8800-ddc1-11ea-9fa1-1c97eed2b963.png">
|
||||
|
||||
@ -155,270 +131,20 @@ python train.py --data coco.yaml --epochs 300 --weights '' --cfg yolov5n.yaml -
|
||||
<details open>
|
||||
<summary>Tutorials</summary>
|
||||
|
||||
- [Train Custom Data](https://github.com/ultralytics/yolov5/wiki/Train-Custom-Data) 🚀 RECOMMENDED
|
||||
- [Tips for Best Training Results](https://github.com/ultralytics/yolov5/wiki/Tips-for-Best-Training-Results) ☘️
|
||||
* [Train Custom Data](https://github.com/ultralytics/yolov3/wiki/Train-Custom-Data) 🚀 RECOMMENDED
|
||||
* [Tips for Best Training Results](https://github.com/ultralytics/yolov3/wiki/Tips-for-Best-Training-Results) ☘️
|
||||
RECOMMENDED
|
||||
- [Multi-GPU Training](https://github.com/ultralytics/yolov5/issues/475)
|
||||
- [PyTorch Hub](https://github.com/ultralytics/yolov5/issues/36) 🌟 NEW
|
||||
- [TFLite, ONNX, CoreML, TensorRT Export](https://github.com/ultralytics/yolov5/issues/251) 🚀
|
||||
- [NVIDIA Jetson Nano Deployment](https://github.com/ultralytics/yolov5/issues/9627) 🌟 NEW
|
||||
- [Test-Time Augmentation (TTA)](https://github.com/ultralytics/yolov5/issues/303)
|
||||
- [Model Ensembling](https://github.com/ultralytics/yolov5/issues/318)
|
||||
- [Model Pruning/Sparsity](https://github.com/ultralytics/yolov5/issues/304)
|
||||
- [Hyperparameter Evolution](https://github.com/ultralytics/yolov5/issues/607)
|
||||
- [Transfer Learning with Frozen Layers](https://github.com/ultralytics/yolov5/issues/1314)
|
||||
- [Architecture Summary](https://github.com/ultralytics/yolov5/issues/6998) 🌟 NEW
|
||||
- [Roboflow for Datasets, Labeling, and Active Learning](https://github.com/ultralytics/yolov5/issues/4975) 🌟 NEW
|
||||
- [ClearML Logging](https://github.com/ultralytics/yolov5/tree/master/utils/loggers/clearml) 🌟 NEW
|
||||
- [YOLOv3 with Neural Magic's Deepsparse](https://bit.ly/yolov5-neuralmagic) 🌟 NEW
|
||||
- [Comet Logging](https://github.com/ultralytics/yolov5/tree/master/utils/loggers/comet) 🌟 NEW
|
||||
|
||||
</details>
|
||||
|
||||
## <div align="center">Integrations</div>
|
||||
|
||||
<br>
|
||||
<a align="center" href="https://bit.ly/ultralytics_hub" target="_blank">
|
||||
<img width="100%" src="https://github.com/ultralytics/assets/raw/main/yolov3/banner-integrations.png"></a>
|
||||
<br>
|
||||
<br>
|
||||
|
||||
<div align="center">
|
||||
<a href="https://roboflow.com/?ref=ultralytics">
|
||||
<img src="https://github.com/ultralytics/assets/raw/main/partners/logo-roboflow.png" width="10%" /></a>
|
||||
<img src="https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png" width="15%" height="0" alt="" />
|
||||
<a href="https://cutt.ly/yolov5-readme-clearml">
|
||||
<img src="https://github.com/ultralytics/assets/raw/main/partners/logo-clearml.png" width="10%" /></a>
|
||||
<img src="https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png" width="15%" height="0" alt="" />
|
||||
<a href="https://bit.ly/yolov5-readme-comet2">
|
||||
<img src="https://github.com/ultralytics/assets/raw/main/partners/logo-comet.png" width="10%" /></a>
|
||||
<img src="https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png" width="15%" height="0" alt="" />
|
||||
<a href="https://bit.ly/yolov5-neuralmagic">
|
||||
<img src="https://github.com/ultralytics/assets/raw/main/partners/logo-neuralmagic.png" width="10%" /></a>
|
||||
</div>
|
||||
|
||||
| Roboflow | ClearML ⭐ NEW | Comet ⭐ NEW | Neural Magic ⭐ NEW |
|
||||
| :--------------------------------------------------------------------------------------------------------------------------: | :---------------------------------------------------------------------------------------------------------------------------------: | :--------------------------------------------------------------------------------------------------------------------------------------------------------: | :----------------------------------------------------------------------------------------------------: |
|
||||
| Label and export your custom datasets directly to YOLOv3 for training with [Roboflow](https://roboflow.com/?ref=ultralytics) | Automatically track, visualize and even remotely train YOLOv3 using [ClearML](https://cutt.ly/yolov5-readme-clearml) (open-source!) | Free forever, [Comet](https://bit.ly/yolov5-readme-comet2) lets you save YOLOv3 models, resume training, and interactively visualise and debug predictions | Run YOLOv3 inference up to 6x faster with [Neural Magic DeepSparse](https://bit.ly/yolov5-neuralmagic) |
|
||||
|
||||
## <div align="center">Ultralytics HUB</div>
|
||||
|
||||
Experience seamless AI with [Ultralytics HUB](https://bit.ly/ultralytics_hub) ⭐, the all-in-one solution for data visualization, YOLO 🚀 model training and deployment, without any coding. Transform images into actionable insights and bring your AI visions to life with ease using our cutting-edge platform and user-friendly [Ultralytics App](https://ultralytics.com/app_install). Start your journey for **Free** now!
|
||||
|
||||
<a align="center" href="https://bit.ly/ultralytics_hub" target="_blank">
|
||||
<img width="100%" src="https://github.com/ultralytics/assets/raw/main/im/ultralytics-hub.png"></a>
|
||||
|
||||
## <div align="center">Why YOLOv3</div>
|
||||
|
||||
YOLOv3 has been designed to be super easy to get started and simple to learn. We prioritize real-world results.
|
||||
|
||||
<p align="left"><img width="800" src="https://user-images.githubusercontent.com/26833433/155040763-93c22a27-347c-4e3c-847a-8094621d3f4e.png"></p>
|
||||
<details>
|
||||
<summary>YOLOv3-P5 640 Figure</summary>
|
||||
|
||||
<p align="left"><img width="800" src="https://user-images.githubusercontent.com/26833433/155040757-ce0934a3-06a6-43dc-a979-2edbbd69ea0e.png"></p>
|
||||
</details>
|
||||
<details>
|
||||
<summary>Figure Notes</summary>
|
||||
|
||||
- **COCO AP val** denotes mAP@0.5:0.95 metric measured on the 5000-image [COCO val2017](http://cocodataset.org) dataset over various inference sizes from 256 to 1536.
|
||||
- **GPU Speed** measures average inference time per image on [COCO val2017](http://cocodataset.org) dataset using a [AWS p3.2xlarge](https://aws.amazon.com/ec2/instance-types/p3/) V100 instance at batch-size 32.
|
||||
- **EfficientDet** data from [google/automl](https://github.com/google/automl) at batch size 8.
|
||||
- **Reproduce** by `python val.py --task study --data coco.yaml --iou 0.7 --weights yolov5n6.pt yolov5s6.pt yolov5m6.pt yolov5l6.pt yolov5x6.pt`
|
||||
|
||||
</details>
|
||||
|
||||
### Pretrained Checkpoints
|
||||
|
||||
| Model | size<br><sup>(pixels) | mAP<sup>val<br>50-95 | mAP<sup>val<br>50 | Speed<br><sup>CPU b1<br>(ms) | Speed<br><sup>V100 b1<br>(ms) | Speed<br><sup>V100 b32<br>(ms) | params<br><sup>(M) | FLOPs<br><sup>@640 (B) |
|
||||
| ----------------------------------------------------------------------------------------------- | --------------------- | -------------------- | ----------------- | ---------------------------- | ----------------------------- | ------------------------------ | ------------------ | ---------------------- |
|
||||
| [YOLOv5n](https://github.com/ultralytics/yolov5/releases/download/v7.0/yolov5n.pt) | 640 | 28.0 | 45.7 | **45** | **6.3** | **0.6** | **1.9** | **4.5** |
|
||||
| [YOLOv5s](https://github.com/ultralytics/yolov5/releases/download/v7.0/yolov5s.pt) | 640 | 37.4 | 56.8 | 98 | 6.4 | 0.9 | 7.2 | 16.5 |
|
||||
| [YOLOv5m](https://github.com/ultralytics/yolov5/releases/download/v7.0/yolov5m.pt) | 640 | 45.4 | 64.1 | 224 | 8.2 | 1.7 | 21.2 | 49.0 |
|
||||
| [YOLOv5l](https://github.com/ultralytics/yolov5/releases/download/v7.0/yolov5l.pt) | 640 | 49.0 | 67.3 | 430 | 10.1 | 2.7 | 46.5 | 109.1 |
|
||||
| [YOLOv5x](https://github.com/ultralytics/yolov5/releases/download/v7.0/yolov5x.pt) | 640 | 50.7 | 68.9 | 766 | 12.1 | 4.8 | 86.7 | 205.7 |
|
||||
| | | | | | | | | |
|
||||
| [YOLOv5n6](https://github.com/ultralytics/yolov5/releases/download/v7.0/yolov5n6.pt) | 1280 | 36.0 | 54.4 | 153 | 8.1 | 2.1 | 3.2 | 4.6 |
|
||||
| [YOLOv5s6](https://github.com/ultralytics/yolov5/releases/download/v7.0/yolov5s6.pt) | 1280 | 44.8 | 63.7 | 385 | 8.2 | 3.6 | 12.6 | 16.8 |
|
||||
| [YOLOv5m6](https://github.com/ultralytics/yolov5/releases/download/v7.0/yolov5m6.pt) | 1280 | 51.3 | 69.3 | 887 | 11.1 | 6.8 | 35.7 | 50.0 |
|
||||
| [YOLOv5l6](https://github.com/ultralytics/yolov5/releases/download/v7.0/yolov5l6.pt) | 1280 | 53.7 | 71.3 | 1784 | 15.8 | 10.5 | 76.8 | 111.4 |
|
||||
| [YOLOv5x6](https://github.com/ultralytics/yolov5/releases/download/v7.0/yolov5x6.pt)<br>+ [TTA] | 1280<br>1536 | 55.0<br>**55.8** | 72.7<br>**72.7** | 3136<br>- | 26.2<br>- | 19.4<br>- | 140.7<br>- | 209.8<br>- |
|
||||
|
||||
<details>
|
||||
<summary>Table Notes</summary>
|
||||
|
||||
- All checkpoints are trained to 300 epochs with default settings. Nano and Small models use [hyp.scratch-low.yaml](https://github.com/ultralytics/yolov5/blob/master/data/hyps/hyp.scratch-low.yaml) hyps, all others use [hyp.scratch-high.yaml](https://github.com/ultralytics/yolov5/blob/master/data/hyps/hyp.scratch-high.yaml).
|
||||
- **mAP<sup>val</sup>** values are for single-model single-scale on [COCO val2017](http://cocodataset.org) dataset.<br>Reproduce by `python val.py --data coco.yaml --img 640 --conf 0.001 --iou 0.65`
|
||||
- **Speed** averaged over COCO val images using a [AWS p3.2xlarge](https://aws.amazon.com/ec2/instance-types/p3/) instance. NMS times (~1 ms/img) not included.<br>Reproduce by `python val.py --data coco.yaml --img 640 --task speed --batch 1`
|
||||
- **TTA** [Test Time Augmentation](https://github.com/ultralytics/yolov5/issues/303) includes reflection and scale augmentations.<br>Reproduce by `python val.py --data coco.yaml --img 1536 --iou 0.7 --augment`
|
||||
|
||||
</details>
|
||||
|
||||
## <div align="center">Segmentation</div>
|
||||
|
||||
Our new YOLOv5 [release v7.0](https://github.com/ultralytics/yolov5/releases/v7.0) instance segmentation models are the fastest and most accurate in the world, beating all current [SOTA benchmarks](https://paperswithcode.com/sota/real-time-instance-segmentation-on-mscoco). We've made them super simple to train, validate and deploy. See full details in our [Release Notes](https://github.com/ultralytics/yolov5/releases/v7.0) and visit our [YOLOv5 Segmentation Colab Notebook](https://github.com/ultralytics/yolov5/blob/master/segment/tutorial.ipynb) for quickstart tutorials.
|
||||
|
||||
<details>
|
||||
<summary>Segmentation Checkpoints</summary>
|
||||
|
||||
<div align="center">
|
||||
<a align="center" href="https://ultralytics.com/yolov5" target="_blank">
|
||||
<img width="800" src="https://user-images.githubusercontent.com/61612323/204180385-84f3aca9-a5e9-43d8-a617-dda7ca12e54a.png"></a>
|
||||
</div>
|
||||
|
||||
We trained YOLOv5 segmentations models on COCO for 300 epochs at image size 640 using A100 GPUs. We exported all models to ONNX FP32 for CPU speed tests and to TensorRT FP16 for GPU speed tests. We ran all speed tests on Google [Colab Pro](https://colab.research.google.com/signup) notebooks for easy reproducibility.
|
||||
|
||||
| Model | size<br><sup>(pixels) | mAP<sup>box<br>50-95 | mAP<sup>mask<br>50-95 | Train time<br><sup>300 epochs<br>A100 (hours) | Speed<br><sup>ONNX CPU<br>(ms) | Speed<br><sup>TRT A100<br>(ms) | params<br><sup>(M) | FLOPs<br><sup>@640 (B) |
|
||||
| ------------------------------------------------------------------------------------------ | --------------------- | -------------------- | --------------------- | --------------------------------------------- | ------------------------------ | ------------------------------ | ------------------ | ---------------------- |
|
||||
| [YOLOv5n-seg](https://github.com/ultralytics/yolov5/releases/download/v7.0/yolov5n-seg.pt) | 640 | 27.6 | 23.4 | 80:17 | **62.7** | **1.2** | **2.0** | **7.1** |
|
||||
| [YOLOv5s-seg](https://github.com/ultralytics/yolov5/releases/download/v7.0/yolov5s-seg.pt) | 640 | 37.6 | 31.7 | 88:16 | 173.3 | 1.4 | 7.6 | 26.4 |
|
||||
| [YOLOv5m-seg](https://github.com/ultralytics/yolov5/releases/download/v7.0/yolov5m-seg.pt) | 640 | 45.0 | 37.1 | 108:36 | 427.0 | 2.2 | 22.0 | 70.8 |
|
||||
| [YOLOv5l-seg](https://github.com/ultralytics/yolov5/releases/download/v7.0/yolov5l-seg.pt) | 640 | 49.0 | 39.9 | 66:43 (2x) | 857.4 | 2.9 | 47.9 | 147.7 |
|
||||
| [YOLOv5x-seg](https://github.com/ultralytics/yolov5/releases/download/v7.0/yolov5x-seg.pt) | 640 | **50.7** | **41.4** | 62:56 (3x) | 1579.2 | 4.5 | 88.8 | 265.7 |
|
||||
|
||||
- All checkpoints are trained to 300 epochs with SGD optimizer with `lr0=0.01` and `weight_decay=5e-5` at image size 640 and all default settings.<br>Runs logged to https://wandb.ai/glenn-jocher/YOLOv5_v70_official
|
||||
- **Accuracy** values are for single-model single-scale on COCO dataset.<br>Reproduce by `python segment/val.py --data coco.yaml --weights yolov5s-seg.pt`
|
||||
- **Speed** averaged over 100 inference images using a [Colab Pro](https://colab.research.google.com/signup) A100 High-RAM instance. Values indicate inference speed only (NMS adds about 1ms per image). <br>Reproduce by `python segment/val.py --data coco.yaml --weights yolov5s-seg.pt --batch 1`
|
||||
- **Export** to ONNX at FP32 and TensorRT at FP16 done with `export.py`. <br>Reproduce by `python export.py --weights yolov5s-seg.pt --include engine --device 0 --half`
|
||||
|
||||
</details>
|
||||
|
||||
<details>
|
||||
<summary>Segmentation Usage Examples <a href="https://colab.research.google.com/github/ultralytics/yolov5/blob/master/segment/tutorial.ipynb"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"></a></summary>
|
||||
|
||||
### Train
|
||||
|
||||
YOLOv5 segmentation training supports auto-download COCO128-seg segmentation dataset with `--data coco128-seg.yaml` argument and manual download of COCO-segments dataset with `bash data/scripts/get_coco.sh --train --val --segments` and then `python train.py --data coco.yaml`.
|
||||
|
||||
```bash
|
||||
# Single-GPU
|
||||
python segment/train.py --data coco128-seg.yaml --weights yolov5s-seg.pt --img 640
|
||||
|
||||
# Multi-GPU DDP
|
||||
python -m torch.distributed.run --nproc_per_node 4 --master_port 1 segment/train.py --data coco128-seg.yaml --weights yolov5s-seg.pt --img 640 --device 0,1,2,3
|
||||
```
|
||||
|
||||
### Val
|
||||
|
||||
Validate YOLOv5s-seg mask mAP on COCO dataset:
|
||||
|
||||
```bash
|
||||
bash data/scripts/get_coco.sh --val --segments # download COCO val segments split (780MB, 5000 images)
|
||||
python segment/val.py --weights yolov5s-seg.pt --data coco.yaml --img 640 # validate
|
||||
```
|
||||
|
||||
### Predict
|
||||
|
||||
Use pretrained YOLOv5m-seg.pt to predict bus.jpg:
|
||||
|
||||
```bash
|
||||
python segment/predict.py --weights yolov5m-seg.pt --data data/images/bus.jpg
|
||||
```
|
||||
|
||||
```python
|
||||
model = torch.hub.load(
|
||||
"ultralytics/yolov5", "custom", "yolov5m-seg.pt"
|
||||
) # load from PyTorch Hub (WARNING: inference not yet supported)
|
||||
```
|
||||
|
||||
|  |  |
|
||||
| ---------------------------------------------------------------------------------------------------------------- | ------------------------------------------------------------------------------------------------------------- |
|
||||
|
||||
### Export
|
||||
|
||||
Export YOLOv5s-seg model to ONNX and TensorRT:
|
||||
|
||||
```bash
|
||||
python export.py --weights yolov5s-seg.pt --include onnx engine --img 640 --device 0
|
||||
```
|
||||
|
||||
</details>
|
||||
|
||||
## <div align="center">Classification</div>
|
||||
|
||||
YOLOv5 [release v6.2](https://github.com/ultralytics/yolov5/releases) brings support for classification model training, validation and deployment! See full details in our [Release Notes](https://github.com/ultralytics/yolov5/releases/v6.2) and visit our [YOLOv5 Classification Colab Notebook](https://github.com/ultralytics/yolov5/blob/master/classify/tutorial.ipynb) for quickstart tutorials.
|
||||
|
||||
<details>
|
||||
<summary>Classification Checkpoints</summary>
|
||||
|
||||
<br>
|
||||
|
||||
We trained YOLOv5-cls classification models on ImageNet for 90 epochs using a 4xA100 instance, and we trained ResNet and EfficientNet models alongside with the same default training settings to compare. We exported all models to ONNX FP32 for CPU speed tests and to TensorRT FP16 for GPU speed tests. We ran all speed tests on Google [Colab Pro](https://colab.research.google.com/signup) for easy reproducibility.
|
||||
|
||||
| Model | size<br><sup>(pixels) | acc<br><sup>top1 | acc<br><sup>top5 | Training<br><sup>90 epochs<br>4xA100 (hours) | Speed<br><sup>ONNX CPU<br>(ms) | Speed<br><sup>TensorRT V100<br>(ms) | params<br><sup>(M) | FLOPs<br><sup>@224 (B) |
|
||||
| -------------------------------------------------------------------------------------------------- | --------------------- | ---------------- | ---------------- | -------------------------------------------- | ------------------------------ | ----------------------------------- | ------------------ | ---------------------- |
|
||||
| [YOLOv5n-cls](https://github.com/ultralytics/yolov5/releases/download/v7.0/yolov5n-cls.pt) | 224 | 64.6 | 85.4 | 7:59 | **3.3** | **0.5** | **2.5** | **0.5** |
|
||||
| [YOLOv5s-cls](https://github.com/ultralytics/yolov5/releases/download/v7.0/yolov5s-cls.pt) | 224 | 71.5 | 90.2 | 8:09 | 6.6 | 0.6 | 5.4 | 1.4 |
|
||||
| [YOLOv5m-cls](https://github.com/ultralytics/yolov5/releases/download/v7.0/yolov5m-cls.pt) | 224 | 75.9 | 92.9 | 10:06 | 15.5 | 0.9 | 12.9 | 3.9 |
|
||||
| [YOLOv5l-cls](https://github.com/ultralytics/yolov5/releases/download/v7.0/yolov5l-cls.pt) | 224 | 78.0 | 94.0 | 11:56 | 26.9 | 1.4 | 26.5 | 8.5 |
|
||||
| [YOLOv5x-cls](https://github.com/ultralytics/yolov5/releases/download/v7.0/yolov5x-cls.pt) | 224 | **79.0** | **94.4** | 15:04 | 54.3 | 1.8 | 48.1 | 15.9 |
|
||||
| | | | | | | | | |
|
||||
| [ResNet18](https://github.com/ultralytics/yolov5/releases/download/v7.0/resnet18.pt) | 224 | 70.3 | 89.5 | **6:47** | 11.2 | 0.5 | 11.7 | 3.7 |
|
||||
| [ResNet34](https://github.com/ultralytics/yolov5/releases/download/v7.0/resnet34.pt) | 224 | 73.9 | 91.8 | 8:33 | 20.6 | 0.9 | 21.8 | 7.4 |
|
||||
| [ResNet50](https://github.com/ultralytics/yolov5/releases/download/v7.0/resnet50.pt) | 224 | 76.8 | 93.4 | 11:10 | 23.4 | 1.0 | 25.6 | 8.5 |
|
||||
| [ResNet101](https://github.com/ultralytics/yolov5/releases/download/v7.0/resnet101.pt) | 224 | 78.5 | 94.3 | 17:10 | 42.1 | 1.9 | 44.5 | 15.9 |
|
||||
| | | | | | | | | |
|
||||
| [EfficientNet_b0](https://github.com/ultralytics/yolov5/releases/download/v7.0/efficientnet_b0.pt) | 224 | 75.1 | 92.4 | 13:03 | 12.5 | 1.3 | 5.3 | 1.0 |
|
||||
| [EfficientNet_b1](https://github.com/ultralytics/yolov5/releases/download/v7.0/efficientnet_b1.pt) | 224 | 76.4 | 93.2 | 17:04 | 14.9 | 1.6 | 7.8 | 1.5 |
|
||||
| [EfficientNet_b2](https://github.com/ultralytics/yolov5/releases/download/v7.0/efficientnet_b2.pt) | 224 | 76.6 | 93.4 | 17:10 | 15.9 | 1.6 | 9.1 | 1.7 |
|
||||
| [EfficientNet_b3](https://github.com/ultralytics/yolov5/releases/download/v7.0/efficientnet_b3.pt) | 224 | 77.7 | 94.0 | 19:19 | 18.9 | 1.9 | 12.2 | 2.4 |
|
||||
|
||||
<details>
|
||||
<summary>Table Notes (click to expand)</summary>
|
||||
|
||||
- All checkpoints are trained to 90 epochs with SGD optimizer with `lr0=0.001` and `weight_decay=5e-5` at image size 224 and all default settings.<br>Runs logged to https://wandb.ai/glenn-jocher/YOLOv5-Classifier-v6-2
|
||||
- **Accuracy** values are for single-model single-scale on [ImageNet-1k](https://www.image-net.org/index.php) dataset.<br>Reproduce by `python classify/val.py --data ../datasets/imagenet --img 224`
|
||||
- **Speed** averaged over 100 inference images using a Google [Colab Pro](https://colab.research.google.com/signup) V100 High-RAM instance.<br>Reproduce by `python classify/val.py --data ../datasets/imagenet --img 224 --batch 1`
|
||||
- **Export** to ONNX at FP32 and TensorRT at FP16 done with `export.py`. <br>Reproduce by `python export.py --weights yolov5s-cls.pt --include engine onnx --imgsz 224`
|
||||
|
||||
</details>
|
||||
</details>
|
||||
|
||||
<details>
|
||||
<summary>Classification Usage Examples <a href="https://colab.research.google.com/github/ultralytics/yolov5/blob/master/classify/tutorial.ipynb"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"></a></summary>
|
||||
|
||||
### Train
|
||||
|
||||
YOLOv5 classification training supports auto-download of MNIST, Fashion-MNIST, CIFAR10, CIFAR100, Imagenette, Imagewoof, and ImageNet datasets with the `--data` argument. To start training on MNIST for example use `--data mnist`.
|
||||
|
||||
```bash
|
||||
# Single-GPU
|
||||
python classify/train.py --model yolov5s-cls.pt --data cifar100 --epochs 5 --img 224 --batch 128
|
||||
|
||||
# Multi-GPU DDP
|
||||
python -m torch.distributed.run --nproc_per_node 4 --master_port 1 classify/train.py --model yolov5s-cls.pt --data imagenet --epochs 5 --img 224 --device 0,1,2,3
|
||||
```
|
||||
|
||||
### Val
|
||||
|
||||
Validate YOLOv5m-cls accuracy on ImageNet-1k dataset:
|
||||
|
||||
```bash
|
||||
bash data/scripts/get_imagenet.sh --val # download ImageNet val split (6.3G, 50000 images)
|
||||
python classify/val.py --weights yolov5m-cls.pt --data ../datasets/imagenet --img 224 # validate
|
||||
```
|
||||
|
||||
### Predict
|
||||
|
||||
Use pretrained YOLOv5s-cls.pt to predict bus.jpg:
|
||||
|
||||
```bash
|
||||
python classify/predict.py --weights yolov5s-cls.pt --data data/images/bus.jpg
|
||||
```
|
||||
|
||||
```python
|
||||
model = torch.hub.load(
|
||||
"ultralytics/yolov5", "custom", "yolov5s-cls.pt"
|
||||
) # load from PyTorch Hub
|
||||
```
|
||||
|
||||
### Export
|
||||
|
||||
Export a group of trained YOLOv5s-cls, ResNet and EfficientNet models to ONNX and TensorRT:
|
||||
|
||||
```bash
|
||||
python export.py --weights yolov5s-cls.pt resnet50.pt efficientnet_b0.pt --include onnx engine --img 224
|
||||
```
|
||||
* [Weights & Biases Logging](https://github.com/ultralytics/yolov5/issues/1289) 🌟 NEW
|
||||
* [Roboflow for Datasets, Labeling, and Active Learning](https://github.com/ultralytics/yolov5/issues/4975) 🌟 NEW
|
||||
* [Multi-GPU Training](https://github.com/ultralytics/yolov5/issues/475)
|
||||
* [PyTorch Hub](https://github.com/ultralytics/yolov5/issues/36) ⭐ NEW
|
||||
* [TorchScript, ONNX, CoreML Export](https://github.com/ultralytics/yolov5/issues/251) 🚀
|
||||
* [Test-Time Augmentation (TTA)](https://github.com/ultralytics/yolov5/issues/303)
|
||||
* [Model Ensembling](https://github.com/ultralytics/yolov5/issues/318)
|
||||
* [Model Pruning/Sparsity](https://github.com/ultralytics/yolov5/issues/304)
|
||||
* [Hyperparameter Evolution](https://github.com/ultralytics/yolov5/issues/607)
|
||||
* [Transfer Learning with Frozen Layers](https://github.com/ultralytics/yolov5/issues/1314) ⭐ NEW
|
||||
* [TensorRT Deployment](https://github.com/wang-xinyu/tensorrtx)
|
||||
|
||||
</details>
|
||||
|
||||
@ -427,67 +153,121 @@ python export.py --weights yolov5s-cls.pt resnet50.pt efficientnet_b0.pt --inclu
|
||||
Get started in seconds with our verified environments. Click each icon below for details.
|
||||
|
||||
<div align="center">
|
||||
<a href="https://bit.ly/yolov5-paperspace-notebook">
|
||||
<img src="https://github.com/ultralytics/yolov5/releases/download/v1.0/logo-gradient.png" width="10%" /></a>
|
||||
<img src="https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png" width="5%" alt="" />
|
||||
<a href="https://colab.research.google.com/github/ultralytics/yolov5/blob/master/tutorial.ipynb">
|
||||
<img src="https://github.com/ultralytics/yolov5/releases/download/v1.0/logo-colab-small.png" width="10%" /></a>
|
||||
<img src="https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png" width="5%" alt="" />
|
||||
<a href="https://www.kaggle.com/ultralytics/yolov5">
|
||||
<img src="https://github.com/ultralytics/yolov5/releases/download/v1.0/logo-kaggle-small.png" width="10%" /></a>
|
||||
<img src="https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png" width="5%" alt="" />
|
||||
<a href="https://hub.docker.com/r/ultralytics/yolov5">
|
||||
<img src="https://github.com/ultralytics/yolov5/releases/download/v1.0/logo-docker-small.png" width="10%" /></a>
|
||||
<img src="https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png" width="5%" alt="" />
|
||||
<a href="https://github.com/ultralytics/yolov5/wiki/AWS-Quickstart">
|
||||
<img src="https://github.com/ultralytics/yolov5/releases/download/v1.0/logo-aws-small.png" width="10%" /></a>
|
||||
<img src="https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png" width="5%" alt="" />
|
||||
<a href="https://github.com/ultralytics/yolov5/wiki/GCP-Quickstart">
|
||||
<img src="https://github.com/ultralytics/yolov5/releases/download/v1.0/logo-gcp-small.png" width="10%" /></a>
|
||||
<a href="https://colab.research.google.com/github/ultralytics/yolov3/blob/master/tutorial.ipynb">
|
||||
<img src="https://github.com/ultralytics/yolov5/releases/download/v1.0/logo-colab-small.png" width="15%"/>
|
||||
</a>
|
||||
<a href="https://www.kaggle.com/ultralytics/yolov3">
|
||||
<img src="https://github.com/ultralytics/yolov5/releases/download/v1.0/logo-kaggle-small.png" width="15%"/>
|
||||
</a>
|
||||
<a href="https://hub.docker.com/r/ultralytics/yolov3">
|
||||
<img src="https://github.com/ultralytics/yolov5/releases/download/v1.0/logo-docker-small.png" width="15%"/>
|
||||
</a>
|
||||
<a href="https://github.com/ultralytics/yolov3/wiki/AWS-Quickstart">
|
||||
<img src="https://github.com/ultralytics/yolov5/releases/download/v1.0/logo-aws-small.png" width="15%"/>
|
||||
</a>
|
||||
<a href="https://github.com/ultralytics/yolov3/wiki/GCP-Quickstart">
|
||||
<img src="https://github.com/ultralytics/yolov5/releases/download/v1.0/logo-gcp-small.png" width="15%"/>
|
||||
</a>
|
||||
</div>
|
||||
|
||||
## <div align="center">Integrations</div>
|
||||
|
||||
<div align="center">
|
||||
<a href="https://wandb.ai/site?utm_campaign=repo_yolo_readme">
|
||||
<img src="https://github.com/ultralytics/yolov5/releases/download/v1.0/logo-wb-long.png" width="49%"/>
|
||||
</a>
|
||||
<a href="https://roboflow.com/?ref=ultralytics">
|
||||
<img src="https://github.com/ultralytics/yolov5/releases/download/v1.0/logo-roboflow-long.png" width="49%"/>
|
||||
</a>
|
||||
</div>
|
||||
|
||||
|Weights and Biases|Roboflow ⭐ NEW|
|
||||
|:-:|:-:|
|
||||
|Automatically track and visualize all your YOLOv3 training runs in the cloud with [Weights & Biases](https://wandb.ai/site?utm_campaign=repo_yolo_readme)|Label and export your custom datasets directly to YOLOv3 for training with [Roboflow](https://roboflow.com/?ref=ultralytics) |
|
||||
|
||||
|
||||
## <div align="center">Why YOLOv5</div>
|
||||
|
||||
<p align="left"><img width="800" src="https://user-images.githubusercontent.com/26833433/136901921-abcfcd9d-f978-4942-9b97-0e3f202907df.png"></p>
|
||||
<details>
|
||||
<summary>YOLOv3-P5 640 Figure (click to expand)</summary>
|
||||
|
||||
<p align="left"><img width="800" src="https://user-images.githubusercontent.com/26833433/136763877-b174052b-c12f-48d2-8bc4-545e3853398e.png"></p>
|
||||
</details>
|
||||
<details>
|
||||
<summary>Figure Notes (click to expand)</summary>
|
||||
|
||||
* **COCO AP val** denotes mAP@0.5:0.95 metric measured on the 5000-image [COCO val2017](http://cocodataset.org) dataset over various inference sizes from 256 to 1536.
|
||||
* **GPU Speed** measures average inference time per image on [COCO val2017](http://cocodataset.org) dataset using a [AWS p3.2xlarge](https://aws.amazon.com/ec2/instance-types/p3/) V100 instance at batch-size 32.
|
||||
* **EfficientDet** data from [google/automl](https://github.com/google/automl) at batch size 8.
|
||||
* **Reproduce** by `python val.py --task study --data coco.yaml --iou 0.7 --weights yolov5n6.pt yolov5s6.pt yolov5m6.pt yolov5l6.pt yolov5x6.pt`
|
||||
</details>
|
||||
|
||||
### Pretrained Checkpoints
|
||||
|
||||
[assets]: https://github.com/ultralytics/yolov5/releases
|
||||
[TTA]: https://github.com/ultralytics/yolov5/issues/303
|
||||
|
||||
|Model |size<br><sup>(pixels) |mAP<sup>val<br>0.5:0.95 |mAP<sup>val<br>0.5 |Speed<br><sup>CPU b1<br>(ms) |Speed<br><sup>V100 b1<br>(ms) |Speed<br><sup>V100 b32<br>(ms) |params<br><sup>(M) |FLOPs<br><sup>@640 (B)
|
||||
|--- |--- |--- |--- |--- |--- |--- |--- |---
|
||||
|[YOLOv5n][assets] |640 |28.4 |46.0 |**45** |**6.3**|**0.6**|**1.9**|**4.5**
|
||||
|[YOLOv5s][assets] |640 |37.2 |56.0 |98 |6.4 |0.9 |7.2 |16.5
|
||||
|[YOLOv5m][assets] |640 |45.2 |63.9 |224 |8.2 |1.7 |21.2 |49.0
|
||||
|[YOLOv5l][assets] |640 |48.8 |67.2 |430 |10.1 |2.7 |46.5 |109.1
|
||||
|[YOLOv5x][assets] |640 |50.7 |68.9 |766 |12.1 |4.8 |86.7 |205.7
|
||||
| | | | | | | | |
|
||||
|[YOLOv5n6][assets] |1280 |34.0 |50.7 |153 |8.1 |2.1 |3.2 |4.6
|
||||
|[YOLOv5s6][assets] |1280 |44.5 |63.0 |385 |8.2 |3.6 |16.8 |12.6
|
||||
|[YOLOv5m6][assets] |1280 |51.0 |69.0 |887 |11.1 |6.8 |35.7 |50.0
|
||||
|[YOLOv5l6][assets] |1280 |53.6 |71.6 |1784 |15.8 |10.5 |76.8 |111.4
|
||||
|[YOLOv5x6][assets]<br>+ [TTA][TTA]|1280<br>1536 |54.7<br>**55.4** |**72.4**<br>72.3 |3136<br>- |26.2<br>- |19.4<br>- |140.7<br>- |209.8<br>-
|
||||
|
||||
<details>
|
||||
<summary>Table Notes (click to expand)</summary>
|
||||
|
||||
* All checkpoints are trained to 300 epochs with default settings and hyperparameters.
|
||||
* **mAP<sup>val</sup>** values are for single-model single-scale on [COCO val2017](http://cocodataset.org) dataset.<br>Reproduce by `python val.py --data coco.yaml --img 640 --conf 0.001 --iou 0.65`
|
||||
* **Speed** averaged over COCO val images using a [AWS p3.2xlarge](https://aws.amazon.com/ec2/instance-types/p3/) instance. NMS times (~1 ms/img) not included.<br>Reproduce by `python val.py --data coco.yaml --img 640 --conf 0.25 --iou 0.45`
|
||||
* **TTA** [Test Time Augmentation](https://github.com/ultralytics/yolov5/issues/303) includes reflection and scale augmentations.<br>Reproduce by `python val.py --data coco.yaml --img 1536 --iou 0.7 --augment`
|
||||
|
||||
</details>
|
||||
|
||||
## <div align="center">Contribute</div>
|
||||
|
||||
We love your input! We want to make contributing to YOLOv3 as easy and transparent as possible. Please see our [Contributing Guide](CONTRIBUTING.md) to get started, and fill out the [YOLOv3 Survey](https://ultralytics.com/survey?utm_source=github&utm_medium=social&utm_campaign=Survey) to send us feedback on your experiences. Thank you to all our contributors!
|
||||
|
||||
<!-- SVG image from https://opencollective.com/ultralytics/contributors.svg?width=990 -->
|
||||
<a href="https://github.com/ultralytics/yolov3/graphs/contributors"><img src="https://opencollective.com/ultralytics/contributors.svg?width=990" /></a>
|
||||
|
||||
<a href="https://github.com/ultralytics/yolov5/graphs/contributors">
|
||||
<img src="https://github.com/ultralytics/assets/raw/main/im/image-contributors.png" /></a>
|
||||
|
||||
## <div align="center">License</div>
|
||||
|
||||
YOLOv3 is available under two different licenses:
|
||||
|
||||
- **GPL-3.0 License**: See [LICENSE](https://github.com/ultralytics/yolov5/blob/master/LICENSE) file for details.
|
||||
- **Enterprise License**: Provides greater flexibility for commercial product development without the open-source requirements of GPL-3.0. Typical use cases are embedding Ultralytics software and AI models in commercial products and applications. Request an Enterprise License at [Ultralytics Licensing](https://ultralytics.com/license).
|
||||
|
||||
## <div align="center">Contact</div>
|
||||
|
||||
For YOLOv3 bug reports and feature requests please visit [GitHub Issues](https://github.com/ultralytics/yolov5/issues) or the [Ultralytics Community Forum](https://community.ultralytics.com/).
|
||||
For YOLOv3 bugs and feature requests please visit [GitHub Issues](https://github.com/ultralytics/yolov3/issues). For business inquiries or
|
||||
professional support requests please visit [https://ultralytics.com/contact](https://ultralytics.com/contact).
|
||||
|
||||
<br>
|
||||
<div align="center">
|
||||
<a href="https://github.com/ultralytics" style="text-decoration:none;">
|
||||
<img src="https://github.com/ultralytics/assets/raw/main/social/logo-social-github.png" width="3%" alt="" /></a>
|
||||
<img src="https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png" width="3%" alt="" />
|
||||
<a href="https://www.linkedin.com/company/ultralytics" style="text-decoration:none;">
|
||||
<img src="https://github.com/ultralytics/assets/raw/main/social/logo-social-linkedin.png" width="3%" alt="" /></a>
|
||||
<img src="https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png" width="3%" alt="" />
|
||||
<a href="https://twitter.com/ultralytics" style="text-decoration:none;">
|
||||
<img src="https://github.com/ultralytics/assets/raw/main/social/logo-social-twitter.png" width="3%" alt="" /></a>
|
||||
<img src="https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png" width="3%" alt="" />
|
||||
<a href="https://www.producthunt.com/@glenn_jocher" style="text-decoration:none;">
|
||||
<img src="https://github.com/ultralytics/assets/raw/main/social/logo-social-producthunt.png" width="3%" alt="" /></a>
|
||||
<img src="https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png" width="3%" alt="" />
|
||||
<a href="https://youtube.com/ultralytics" style="text-decoration:none;">
|
||||
<img src="https://github.com/ultralytics/assets/raw/main/social/logo-social-youtube.png" width="3%" alt="" /></a>
|
||||
<img src="https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png" width="3%" alt="" />
|
||||
<a href="https://www.facebook.com/ultralytics" style="text-decoration:none;">
|
||||
<img src="https://github.com/ultralytics/assets/raw/main/social/logo-social-facebook.png" width="3%" alt="" /></a>
|
||||
<img src="https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png" width="3%" alt="" />
|
||||
<a href="https://www.instagram.com/ultralytics/" style="text-decoration:none;">
|
||||
<img src="https://github.com/ultralytics/assets/raw/main/social/logo-social-instagram.png" width="3%" alt="" /></a>
|
||||
</div>
|
||||
|
||||
[tta]: https://github.com/ultralytics/yolov5/issues/303
|
||||
<div align="center">
|
||||
<a href="https://github.com/ultralytics">
|
||||
<img src="https://github.com/ultralytics/yolov5/releases/download/v1.0/logo-social-github.png" width="3%"/>
|
||||
</a>
|
||||
<img width="3%" />
|
||||
<a href="https://www.linkedin.com/company/ultralytics">
|
||||
<img src="https://github.com/ultralytics/yolov5/releases/download/v1.0/logo-social-linkedin.png" width="3%"/>
|
||||
</a>
|
||||
<img width="3%" />
|
||||
<a href="https://twitter.com/ultralytics">
|
||||
<img src="https://github.com/ultralytics/yolov5/releases/download/v1.0/logo-social-twitter.png" width="3%"/>
|
||||
</a>
|
||||
<img width="3%" />
|
||||
<a href="https://youtube.com/ultralytics">
|
||||
<img src="https://github.com/ultralytics/yolov5/releases/download/v1.0/logo-social-youtube.png" width="3%"/>
|
||||
</a>
|
||||
<img width="3%" />
|
||||
<a href="https://www.facebook.com/ultralytics">
|
||||
<img src="https://github.com/ultralytics/yolov5/releases/download/v1.0/logo-social-facebook.png" width="3%"/>
|
||||
</a>
|
||||
<img width="3%" />
|
||||
<a href="https://www.instagram.com/ultralytics/">
|
||||
<img src="https://github.com/ultralytics/yolov5/releases/download/v1.0/logo-social-instagram.png" width="3%"/>
|
||||
</a>
|
||||
</div>
|
||||
|
||||
@ -1,10 +1,10 @@
|
||||
# YOLOv3 🚀 by Ultralytics, GPL-3.0 license
|
||||
# Argoverse-HD dataset (ring-front-center camera) http://www.cs.cmu.edu/~mengtial/proj/streaming/ by Argo AI
|
||||
# Argoverse-HD dataset (ring-front-center camera) http://www.cs.cmu.edu/~mengtial/proj/streaming/
|
||||
# Example usage: python train.py --data Argoverse.yaml
|
||||
# parent
|
||||
# ├── yolov5
|
||||
# ├── yolov3
|
||||
# └── datasets
|
||||
# └── Argoverse ← downloads here (31.3 GB)
|
||||
# └── Argoverse ← downloads here
|
||||
|
||||
|
||||
# Train/val/test sets as 1) dir: path/to/imgs, 2) file: path/to/imgs.txt, or 3) list: [path/to/imgs1, path/to/imgs2, ..]
|
||||
@ -14,15 +14,8 @@ val: Argoverse-1.1/images/val/ # val images (relative to 'path') 15062 images
|
||||
test: Argoverse-1.1/images/test/ # test images (optional) https://eval.ai/web/challenges/challenge-page/800/overview
|
||||
|
||||
# Classes
|
||||
names:
|
||||
0: person
|
||||
1: bicycle
|
||||
2: car
|
||||
3: motorcycle
|
||||
4: bus
|
||||
5: truck
|
||||
6: traffic_light
|
||||
7: stop_sign
|
||||
nc: 8 # number of classes
|
||||
names: ['person', 'bicycle', 'car', 'motorcycle', 'bus', 'truck', 'traffic_light', 'stop_sign'] # class names
|
||||
|
||||
|
||||
# Download script/URL (optional) ---------------------------------------------------------------------------------------
|
||||
@ -39,7 +32,7 @@ download: |
|
||||
for annot in tqdm(a['annotations'], desc=f"Converting {set} to YOLOv3 format..."):
|
||||
img_id = annot['image_id']
|
||||
img_name = a['images'][img_id]['name']
|
||||
img_label_name = f'{img_name[:-3]}txt'
|
||||
img_label_name = img_name[:-3] + "txt"
|
||||
|
||||
cls = annot['category_id'] # instance class id
|
||||
x_center, y_center, width, height = annot['bbox']
|
||||
@ -63,7 +56,7 @@ download: |
|
||||
|
||||
|
||||
# Download
|
||||
dir = Path(yaml['path']) # dataset root dir
|
||||
dir = Path('../datasets/Argoverse') # dataset root dir
|
||||
urls = ['https://argoverse-hd.s3.us-east-2.amazonaws.com/Argoverse-HD-Full.zip']
|
||||
download(urls, dir=dir, delete=False)
|
||||
|
||||
|
||||
@ -1,10 +1,10 @@
|
||||
# YOLOv3 🚀 by Ultralytics, GPL-3.0 license
|
||||
# Global Wheat 2020 dataset http://www.global-wheat.com/ by University of Saskatchewan
|
||||
# Global Wheat 2020 dataset http://www.global-wheat.com/
|
||||
# Example usage: python train.py --data GlobalWheat2020.yaml
|
||||
# parent
|
||||
# ├── yolov5
|
||||
# ├── yolov3
|
||||
# └── datasets
|
||||
# └── GlobalWheat2020 ← downloads here (7.0 GB)
|
||||
# └── GlobalWheat2020 ← downloads here
|
||||
|
||||
|
||||
# Train/val/test sets as 1) dir: path/to/imgs, 2) file: path/to/imgs.txt, or 3) list: [path/to/imgs1, path/to/imgs2, ..]
|
||||
@ -26,15 +26,14 @@ test: # test images (optional) 1276 images
|
||||
- images/uq_1
|
||||
|
||||
# Classes
|
||||
names:
|
||||
0: wheat_head
|
||||
nc: 1 # number of classes
|
||||
names: ['wheat_head'] # class names
|
||||
|
||||
|
||||
# Download script/URL (optional) ---------------------------------------------------------------------------------------
|
||||
download: |
|
||||
from utils.general import download, Path
|
||||
|
||||
|
||||
# Download
|
||||
dir = Path(yaml['path']) # dataset root dir
|
||||
urls = ['https://zenodo.org/record/4298502/files/global-wheat-codalab-official.zip',
|
||||
|
||||
@ -1,10 +1,10 @@
|
||||
# YOLOv3 🚀 by Ultralytics, GPL-3.0 license
|
||||
# SKU-110K retail items dataset https://github.com/eg4000/SKU110K_CVPR19 by Trax Retail
|
||||
# SKU-110K retail items dataset https://github.com/eg4000/SKU110K_CVPR19
|
||||
# Example usage: python train.py --data SKU-110K.yaml
|
||||
# parent
|
||||
# ├── yolov5
|
||||
# ├── yolov3
|
||||
# └── datasets
|
||||
# └── SKU-110K ← downloads here (13.6 GB)
|
||||
# └── SKU-110K ← downloads here
|
||||
|
||||
|
||||
# Train/val/test sets as 1) dir: path/to/imgs, 2) file: path/to/imgs.txt, or 3) list: [path/to/imgs1, path/to/imgs2, ..]
|
||||
@ -14,8 +14,8 @@ val: val.txt # val images (relative to 'path') 588 images
|
||||
test: test.txt # test images (optional) 2936 images
|
||||
|
||||
# Classes
|
||||
names:
|
||||
0: object
|
||||
nc: 1 # number of classes
|
||||
names: ['object'] # class names
|
||||
|
||||
|
||||
# Download script/URL (optional) ---------------------------------------------------------------------------------------
|
||||
@ -24,7 +24,6 @@ download: |
|
||||
from tqdm import tqdm
|
||||
from utils.general import np, pd, Path, download, xyxy2xywh
|
||||
|
||||
|
||||
# Download
|
||||
dir = Path(yaml['path']) # dataset root dir
|
||||
parent = Path(dir.parent) # download dir
|
||||
|
||||
@ -1,10 +1,10 @@
|
||||
# YOLOv3 🚀 by Ultralytics, GPL-3.0 license
|
||||
# VisDrone2019-DET dataset https://github.com/VisDrone/VisDrone-Dataset by Tianjin University
|
||||
# VisDrone2019-DET dataset https://github.com/VisDrone/VisDrone-Dataset
|
||||
# Example usage: python train.py --data VisDrone.yaml
|
||||
# parent
|
||||
# ├── yolov5
|
||||
# ├── yolov3
|
||||
# └── datasets
|
||||
# └── VisDrone ← downloads here (2.3 GB)
|
||||
# └── VisDrone ← downloads here
|
||||
|
||||
|
||||
# Train/val/test sets as 1) dir: path/to/imgs, 2) file: path/to/imgs.txt, or 3) list: [path/to/imgs1, path/to/imgs2, ..]
|
||||
@ -14,17 +14,8 @@ val: VisDrone2019-DET-val/images # val images (relative to 'path') 548 images
|
||||
test: VisDrone2019-DET-test-dev/images # test images (optional) 1610 images
|
||||
|
||||
# Classes
|
||||
names:
|
||||
0: pedestrian
|
||||
1: people
|
||||
2: bicycle
|
||||
3: car
|
||||
4: van
|
||||
5: truck
|
||||
6: tricycle
|
||||
7: awning-tricycle
|
||||
8: bus
|
||||
9: motor
|
||||
nc: 10 # number of classes
|
||||
names: ['pedestrian', 'people', 'bicycle', 'car', 'van', 'truck', 'tricycle', 'awning-tricycle', 'bus', 'motor']
|
||||
|
||||
|
||||
# Download script/URL (optional) ---------------------------------------------------------------------------------------
|
||||
@ -63,7 +54,7 @@ download: |
|
||||
'https://github.com/ultralytics/yolov5/releases/download/v1.0/VisDrone2019-DET-val.zip',
|
||||
'https://github.com/ultralytics/yolov5/releases/download/v1.0/VisDrone2019-DET-test-dev.zip',
|
||||
'https://github.com/ultralytics/yolov5/releases/download/v1.0/VisDrone2019-DET-test-challenge.zip']
|
||||
download(urls, dir=dir, curl=True, threads=4)
|
||||
download(urls, dir=dir)
|
||||
|
||||
# Convert
|
||||
for d in 'VisDrone2019-DET-train', 'VisDrone2019-DET-val', 'VisDrone2019-DET-test-dev':
|
||||
|
||||
@ -1,107 +1,35 @@
|
||||
# YOLOv3 🚀 by Ultralytics, GPL-3.0 license
|
||||
# COCO 2017 dataset http://cocodataset.org by Microsoft
|
||||
# COCO 2017 dataset http://cocodataset.org
|
||||
# Example usage: python train.py --data coco.yaml
|
||||
# parent
|
||||
# ├── yolov5
|
||||
# ├── yolov3
|
||||
# └── datasets
|
||||
# └── coco ← downloads here (20.1 GB)
|
||||
# └── coco ← downloads here
|
||||
|
||||
|
||||
# Train/val/test sets as 1) dir: path/to/imgs, 2) file: path/to/imgs.txt, or 3) list: [path/to/imgs1, path/to/imgs2, ..]
|
||||
path: ../datasets/coco # dataset root dir
|
||||
train: train2017.txt # train images (relative to 'path') 118287 images
|
||||
val: val2017.txt # val images (relative to 'path') 5000 images
|
||||
val: val2017.txt # train images (relative to 'path') 5000 images
|
||||
test: test-dev2017.txt # 20288 of 40670 images, submit to https://competitions.codalab.org/competitions/20794
|
||||
|
||||
# Classes
|
||||
names:
|
||||
0: person
|
||||
1: bicycle
|
||||
2: car
|
||||
3: motorcycle
|
||||
4: airplane
|
||||
5: bus
|
||||
6: train
|
||||
7: truck
|
||||
8: boat
|
||||
9: traffic light
|
||||
10: fire hydrant
|
||||
11: stop sign
|
||||
12: parking meter
|
||||
13: bench
|
||||
14: bird
|
||||
15: cat
|
||||
16: dog
|
||||
17: horse
|
||||
18: sheep
|
||||
19: cow
|
||||
20: elephant
|
||||
21: bear
|
||||
22: zebra
|
||||
23: giraffe
|
||||
24: backpack
|
||||
25: umbrella
|
||||
26: handbag
|
||||
27: tie
|
||||
28: suitcase
|
||||
29: frisbee
|
||||
30: skis
|
||||
31: snowboard
|
||||
32: sports ball
|
||||
33: kite
|
||||
34: baseball bat
|
||||
35: baseball glove
|
||||
36: skateboard
|
||||
37: surfboard
|
||||
38: tennis racket
|
||||
39: bottle
|
||||
40: wine glass
|
||||
41: cup
|
||||
42: fork
|
||||
43: knife
|
||||
44: spoon
|
||||
45: bowl
|
||||
46: banana
|
||||
47: apple
|
||||
48: sandwich
|
||||
49: orange
|
||||
50: broccoli
|
||||
51: carrot
|
||||
52: hot dog
|
||||
53: pizza
|
||||
54: donut
|
||||
55: cake
|
||||
56: chair
|
||||
57: couch
|
||||
58: potted plant
|
||||
59: bed
|
||||
60: dining table
|
||||
61: toilet
|
||||
62: tv
|
||||
63: laptop
|
||||
64: mouse
|
||||
65: remote
|
||||
66: keyboard
|
||||
67: cell phone
|
||||
68: microwave
|
||||
69: oven
|
||||
70: toaster
|
||||
71: sink
|
||||
72: refrigerator
|
||||
73: book
|
||||
74: clock
|
||||
75: vase
|
||||
76: scissors
|
||||
77: teddy bear
|
||||
78: hair drier
|
||||
79: toothbrush
|
||||
nc: 80 # number of classes
|
||||
names: ['person', 'bicycle', 'car', 'motorcycle', 'airplane', 'bus', 'train', 'truck', 'boat', 'traffic light',
|
||||
'fire hydrant', 'stop sign', 'parking meter', 'bench', 'bird', 'cat', 'dog', 'horse', 'sheep', 'cow',
|
||||
'elephant', 'bear', 'zebra', 'giraffe', 'backpack', 'umbrella', 'handbag', 'tie', 'suitcase', 'frisbee',
|
||||
'skis', 'snowboard', 'sports ball', 'kite', 'baseball bat', 'baseball glove', 'skateboard', 'surfboard',
|
||||
'tennis racket', 'bottle', 'wine glass', 'cup', 'fork', 'knife', 'spoon', 'bowl', 'banana', 'apple',
|
||||
'sandwich', 'orange', 'broccoli', 'carrot', 'hot dog', 'pizza', 'donut', 'cake', 'chair', 'couch',
|
||||
'potted plant', 'bed', 'dining table', 'toilet', 'tv', 'laptop', 'mouse', 'remote', 'keyboard', 'cell phone',
|
||||
'microwave', 'oven', 'toaster', 'sink', 'refrigerator', 'book', 'clock', 'vase', 'scissors', 'teddy bear',
|
||||
'hair drier', 'toothbrush'] # class names
|
||||
|
||||
|
||||
# Download script/URL (optional)
|
||||
download: |
|
||||
from utils.general import download, Path
|
||||
|
||||
|
||||
# Download labels
|
||||
segments = False # segment or box labels
|
||||
dir = Path(yaml['path']) # dataset root dir
|
||||
|
||||
@ -1,10 +1,10 @@
|
||||
# YOLOv3 🚀 by Ultralytics, GPL-3.0 license
|
||||
# COCO128 dataset https://www.kaggle.com/ultralytics/coco128 (first 128 images from COCO train2017) by Ultralytics
|
||||
# COCO128 dataset https://www.kaggle.com/ultralytics/coco128 (first 128 images from COCO train2017)
|
||||
# Example usage: python train.py --data coco128.yaml
|
||||
# parent
|
||||
# ├── yolov5
|
||||
# ├── yolov3
|
||||
# └── datasets
|
||||
# └── coco128 ← downloads here (7 MB)
|
||||
# └── coco128 ← downloads here
|
||||
|
||||
|
||||
# Train/val/test sets as 1) dir: path/to/imgs, 2) file: path/to/imgs.txt, or 3) list: [path/to/imgs1, path/to/imgs2, ..]
|
||||
@ -14,87 +14,16 @@ val: images/train2017 # val images (relative to 'path') 128 images
|
||||
test: # test images (optional)
|
||||
|
||||
# Classes
|
||||
names:
|
||||
0: person
|
||||
1: bicycle
|
||||
2: car
|
||||
3: motorcycle
|
||||
4: airplane
|
||||
5: bus
|
||||
6: train
|
||||
7: truck
|
||||
8: boat
|
||||
9: traffic light
|
||||
10: fire hydrant
|
||||
11: stop sign
|
||||
12: parking meter
|
||||
13: bench
|
||||
14: bird
|
||||
15: cat
|
||||
16: dog
|
||||
17: horse
|
||||
18: sheep
|
||||
19: cow
|
||||
20: elephant
|
||||
21: bear
|
||||
22: zebra
|
||||
23: giraffe
|
||||
24: backpack
|
||||
25: umbrella
|
||||
26: handbag
|
||||
27: tie
|
||||
28: suitcase
|
||||
29: frisbee
|
||||
30: skis
|
||||
31: snowboard
|
||||
32: sports ball
|
||||
33: kite
|
||||
34: baseball bat
|
||||
35: baseball glove
|
||||
36: skateboard
|
||||
37: surfboard
|
||||
38: tennis racket
|
||||
39: bottle
|
||||
40: wine glass
|
||||
41: cup
|
||||
42: fork
|
||||
43: knife
|
||||
44: spoon
|
||||
45: bowl
|
||||
46: banana
|
||||
47: apple
|
||||
48: sandwich
|
||||
49: orange
|
||||
50: broccoli
|
||||
51: carrot
|
||||
52: hot dog
|
||||
53: pizza
|
||||
54: donut
|
||||
55: cake
|
||||
56: chair
|
||||
57: couch
|
||||
58: potted plant
|
||||
59: bed
|
||||
60: dining table
|
||||
61: toilet
|
||||
62: tv
|
||||
63: laptop
|
||||
64: mouse
|
||||
65: remote
|
||||
66: keyboard
|
||||
67: cell phone
|
||||
68: microwave
|
||||
69: oven
|
||||
70: toaster
|
||||
71: sink
|
||||
72: refrigerator
|
||||
73: book
|
||||
74: clock
|
||||
75: vase
|
||||
76: scissors
|
||||
77: teddy bear
|
||||
78: hair drier
|
||||
79: toothbrush
|
||||
nc: 80 # number of classes
|
||||
names: ['person', 'bicycle', 'car', 'motorcycle', 'airplane', 'bus', 'train', 'truck', 'boat', 'traffic light',
|
||||
'fire hydrant', 'stop sign', 'parking meter', 'bench', 'bird', 'cat', 'dog', 'horse', 'sheep', 'cow',
|
||||
'elephant', 'bear', 'zebra', 'giraffe', 'backpack', 'umbrella', 'handbag', 'tie', 'suitcase', 'frisbee',
|
||||
'skis', 'snowboard', 'sports ball', 'kite', 'baseball bat', 'baseball glove', 'skateboard', 'surfboard',
|
||||
'tennis racket', 'bottle', 'wine glass', 'cup', 'fork', 'knife', 'spoon', 'bowl', 'banana', 'apple',
|
||||
'sandwich', 'orange', 'broccoli', 'carrot', 'hot dog', 'pizza', 'donut', 'cake', 'chair', 'couch',
|
||||
'potted plant', 'bed', 'dining table', 'toilet', 'tv', 'laptop', 'mouse', 'remote', 'keyboard', 'cell phone',
|
||||
'microwave', 'oven', 'toaster', 'sink', 'refrigerator', 'book', 'clock', 'vase', 'scissors', 'teddy bear',
|
||||
'hair drier', 'toothbrush'] # class names
|
||||
|
||||
|
||||
# Download script/URL (optional)
|
||||
|
||||
18
yolov3/data/custom-yolov3.yaml
Normal file
18
yolov3/data/custom-yolov3.yaml
Normal file
@ -0,0 +1,18 @@
|
||||
# YOLOv3 🚀 by Ultralytics, GPL-3.0 license
|
||||
# COCO 2017 dataset http://cocodataset.org
|
||||
# Example usage: python train.py --data coco.yaml
|
||||
# parent
|
||||
# ├── yolov3
|
||||
# └── datasets
|
||||
# └── coco ← downloads here
|
||||
|
||||
|
||||
# Train/val/test sets as 1) dir: path/to/imgs, 2) file: path/to/imgs.txt, or 3) list: [path/to/imgs1, path/to/imgs2, ..]
|
||||
path: ../pipe-dataset/ # dataset root dir
|
||||
train: train/images # train images (relative to 'path') 118287 images
|
||||
val: val/images # train images (relative to 'path') 5000 images
|
||||
# test: test-dev2017.txt # 20288 of 40670 images, submit to https://competitions.codalab.org/competitions/20794
|
||||
|
||||
# Classes
|
||||
nc: 1 # number of classes
|
||||
names: ['pipe'] # class names
|
||||
@ -4,7 +4,7 @@
|
||||
# See tutorials for hyperparameter evolution https://github.com/ultralytics/yolov5#tutorials
|
||||
|
||||
lr0: 0.01 # initial learning rate (SGD=1E-2, Adam=1E-3)
|
||||
lrf: 0.1 # final OneCycleLR learning rate (lr0 * lrf)
|
||||
lrf: 0.2 # final OneCycleLR learning rate (lr0 * lrf)
|
||||
momentum: 0.937 # SGD momentum/Adam beta1
|
||||
weight_decay: 0.0005 # optimizer weight decay 5e-4
|
||||
warmup_epochs: 3.0 # warmup epochs (fractions ok)
|
||||
|
||||
34
yolov3/data/hyps/hyp.scratch.yaml
Normal file
34
yolov3/data/hyps/hyp.scratch.yaml
Normal file
@ -0,0 +1,34 @@
|
||||
# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
|
||||
# Hyperparameters for COCO training from scratch
|
||||
# python train.py --batch 40 --cfg yolov5m.yaml --weights '' --data coco.yaml --img 640 --epochs 300
|
||||
# See tutorials for hyperparameter evolution https://github.com/ultralytics/yolov5#tutorials
|
||||
|
||||
lr0: 0.01 # initial learning rate (SGD=1E-2, Adam=1E-3)
|
||||
lrf: 0.1 # final OneCycleLR learning rate (lr0 * lrf)
|
||||
momentum: 0.937 # SGD momentum/Adam beta1
|
||||
weight_decay: 0.0005 # optimizer weight decay 5e-4
|
||||
warmup_epochs: 3.0 # warmup epochs (fractions ok)
|
||||
warmup_momentum: 0.8 # warmup initial momentum
|
||||
warmup_bias_lr: 0.1 # warmup initial bias lr
|
||||
box: 0.05 # box loss gain
|
||||
cls: 0.5 # cls loss gain
|
||||
cls_pw: 1.0 # cls BCELoss positive_weight
|
||||
obj: 1.0 # obj loss gain (scale with pixels)
|
||||
obj_pw: 1.0 # obj BCELoss positive_weight
|
||||
iou_t: 0.20 # IoU training threshold
|
||||
anchor_t: 4.0 # anchor-multiple threshold
|
||||
# anchors: 3 # anchors per output layer (0 to ignore)
|
||||
fl_gamma: 0.0 # focal loss gamma (efficientDet default gamma=1.5)
|
||||
hsv_h: 0.015 # image HSV-Hue augmentation (fraction)
|
||||
hsv_s: 0.7 # image HSV-Saturation augmentation (fraction)
|
||||
hsv_v: 0.4 # image HSV-Value augmentation (fraction)
|
||||
degrees: 0.0 # image rotation (+/- deg)
|
||||
translate: 0.1 # image translation (+/- fraction)
|
||||
scale: 0.5 # image scale (+/- gain)
|
||||
shear: 0.0 # image shear (+/- deg)
|
||||
perspective: 0.0 # image perspective (+/- fraction), range 0-0.001
|
||||
flipud: 0.0 # image flip up-down (probability)
|
||||
fliplr: 0.5 # image flip left-right (probability)
|
||||
mosaic: 1.0 # image mosaic (probability)
|
||||
mixup: 0.0 # image mixup (probability)
|
||||
copy_paste: 0.0 # segment copy-paste (probability)
|
||||
@ -1,10 +1,10 @@
|
||||
# YOLOv3 🚀 by Ultralytics, GPL-3.0 license
|
||||
# Objects365 dataset https://www.objects365.org/ by Megvii
|
||||
# Objects365 dataset https://www.objects365.org/
|
||||
# Example usage: python train.py --data Objects365.yaml
|
||||
# parent
|
||||
# ├── yolov5
|
||||
# ├── yolov3
|
||||
# └── datasets
|
||||
# └── Objects365 ← downloads here (712 GB = 367G data + 345G zips)
|
||||
# └── Objects365 ← downloads here
|
||||
|
||||
|
||||
# Train/val/test sets as 1) dir: path/to/imgs, 2) file: path/to/imgs.txt, or 3) list: [path/to/imgs1, path/to/imgs2, ..]
|
||||
@ -14,382 +14,56 @@ val: images/val # val images (relative to 'path') 80000 images
|
||||
test: # test images (optional)
|
||||
|
||||
# Classes
|
||||
names:
|
||||
0: Person
|
||||
1: Sneakers
|
||||
2: Chair
|
||||
3: Other Shoes
|
||||
4: Hat
|
||||
5: Car
|
||||
6: Lamp
|
||||
7: Glasses
|
||||
8: Bottle
|
||||
9: Desk
|
||||
10: Cup
|
||||
11: Street Lights
|
||||
12: Cabinet/shelf
|
||||
13: Handbag/Satchel
|
||||
14: Bracelet
|
||||
15: Plate
|
||||
16: Picture/Frame
|
||||
17: Helmet
|
||||
18: Book
|
||||
19: Gloves
|
||||
20: Storage box
|
||||
21: Boat
|
||||
22: Leather Shoes
|
||||
23: Flower
|
||||
24: Bench
|
||||
25: Potted Plant
|
||||
26: Bowl/Basin
|
||||
27: Flag
|
||||
28: Pillow
|
||||
29: Boots
|
||||
30: Vase
|
||||
31: Microphone
|
||||
32: Necklace
|
||||
33: Ring
|
||||
34: SUV
|
||||
35: Wine Glass
|
||||
36: Belt
|
||||
37: Monitor/TV
|
||||
38: Backpack
|
||||
39: Umbrella
|
||||
40: Traffic Light
|
||||
41: Speaker
|
||||
42: Watch
|
||||
43: Tie
|
||||
44: Trash bin Can
|
||||
45: Slippers
|
||||
46: Bicycle
|
||||
47: Stool
|
||||
48: Barrel/bucket
|
||||
49: Van
|
||||
50: Couch
|
||||
51: Sandals
|
||||
52: Basket
|
||||
53: Drum
|
||||
54: Pen/Pencil
|
||||
55: Bus
|
||||
56: Wild Bird
|
||||
57: High Heels
|
||||
58: Motorcycle
|
||||
59: Guitar
|
||||
60: Carpet
|
||||
61: Cell Phone
|
||||
62: Bread
|
||||
63: Camera
|
||||
64: Canned
|
||||
65: Truck
|
||||
66: Traffic cone
|
||||
67: Cymbal
|
||||
68: Lifesaver
|
||||
69: Towel
|
||||
70: Stuffed Toy
|
||||
71: Candle
|
||||
72: Sailboat
|
||||
73: Laptop
|
||||
74: Awning
|
||||
75: Bed
|
||||
76: Faucet
|
||||
77: Tent
|
||||
78: Horse
|
||||
79: Mirror
|
||||
80: Power outlet
|
||||
81: Sink
|
||||
82: Apple
|
||||
83: Air Conditioner
|
||||
84: Knife
|
||||
85: Hockey Stick
|
||||
86: Paddle
|
||||
87: Pickup Truck
|
||||
88: Fork
|
||||
89: Traffic Sign
|
||||
90: Balloon
|
||||
91: Tripod
|
||||
92: Dog
|
||||
93: Spoon
|
||||
94: Clock
|
||||
95: Pot
|
||||
96: Cow
|
||||
97: Cake
|
||||
98: Dinning Table
|
||||
99: Sheep
|
||||
100: Hanger
|
||||
101: Blackboard/Whiteboard
|
||||
102: Napkin
|
||||
103: Other Fish
|
||||
104: Orange/Tangerine
|
||||
105: Toiletry
|
||||
106: Keyboard
|
||||
107: Tomato
|
||||
108: Lantern
|
||||
109: Machinery Vehicle
|
||||
110: Fan
|
||||
111: Green Vegetables
|
||||
112: Banana
|
||||
113: Baseball Glove
|
||||
114: Airplane
|
||||
115: Mouse
|
||||
116: Train
|
||||
117: Pumpkin
|
||||
118: Soccer
|
||||
119: Skiboard
|
||||
120: Luggage
|
||||
121: Nightstand
|
||||
122: Tea pot
|
||||
123: Telephone
|
||||
124: Trolley
|
||||
125: Head Phone
|
||||
126: Sports Car
|
||||
127: Stop Sign
|
||||
128: Dessert
|
||||
129: Scooter
|
||||
130: Stroller
|
||||
131: Crane
|
||||
132: Remote
|
||||
133: Refrigerator
|
||||
134: Oven
|
||||
135: Lemon
|
||||
136: Duck
|
||||
137: Baseball Bat
|
||||
138: Surveillance Camera
|
||||
139: Cat
|
||||
140: Jug
|
||||
141: Broccoli
|
||||
142: Piano
|
||||
143: Pizza
|
||||
144: Elephant
|
||||
145: Skateboard
|
||||
146: Surfboard
|
||||
147: Gun
|
||||
148: Skating and Skiing shoes
|
||||
149: Gas stove
|
||||
150: Donut
|
||||
151: Bow Tie
|
||||
152: Carrot
|
||||
153: Toilet
|
||||
154: Kite
|
||||
155: Strawberry
|
||||
156: Other Balls
|
||||
157: Shovel
|
||||
158: Pepper
|
||||
159: Computer Box
|
||||
160: Toilet Paper
|
||||
161: Cleaning Products
|
||||
162: Chopsticks
|
||||
163: Microwave
|
||||
164: Pigeon
|
||||
165: Baseball
|
||||
166: Cutting/chopping Board
|
||||
167: Coffee Table
|
||||
168: Side Table
|
||||
169: Scissors
|
||||
170: Marker
|
||||
171: Pie
|
||||
172: Ladder
|
||||
173: Snowboard
|
||||
174: Cookies
|
||||
175: Radiator
|
||||
176: Fire Hydrant
|
||||
177: Basketball
|
||||
178: Zebra
|
||||
179: Grape
|
||||
180: Giraffe
|
||||
181: Potato
|
||||
182: Sausage
|
||||
183: Tricycle
|
||||
184: Violin
|
||||
185: Egg
|
||||
186: Fire Extinguisher
|
||||
187: Candy
|
||||
188: Fire Truck
|
||||
189: Billiards
|
||||
190: Converter
|
||||
191: Bathtub
|
||||
192: Wheelchair
|
||||
193: Golf Club
|
||||
194: Briefcase
|
||||
195: Cucumber
|
||||
196: Cigar/Cigarette
|
||||
197: Paint Brush
|
||||
198: Pear
|
||||
199: Heavy Truck
|
||||
200: Hamburger
|
||||
201: Extractor
|
||||
202: Extension Cord
|
||||
203: Tong
|
||||
204: Tennis Racket
|
||||
205: Folder
|
||||
206: American Football
|
||||
207: earphone
|
||||
208: Mask
|
||||
209: Kettle
|
||||
210: Tennis
|
||||
211: Ship
|
||||
212: Swing
|
||||
213: Coffee Machine
|
||||
214: Slide
|
||||
215: Carriage
|
||||
216: Onion
|
||||
217: Green beans
|
||||
218: Projector
|
||||
219: Frisbee
|
||||
220: Washing Machine/Drying Machine
|
||||
221: Chicken
|
||||
222: Printer
|
||||
223: Watermelon
|
||||
224: Saxophone
|
||||
225: Tissue
|
||||
226: Toothbrush
|
||||
227: Ice cream
|
||||
228: Hot-air balloon
|
||||
229: Cello
|
||||
230: French Fries
|
||||
231: Scale
|
||||
232: Trophy
|
||||
233: Cabbage
|
||||
234: Hot dog
|
||||
235: Blender
|
||||
236: Peach
|
||||
237: Rice
|
||||
238: Wallet/Purse
|
||||
239: Volleyball
|
||||
240: Deer
|
||||
241: Goose
|
||||
242: Tape
|
||||
243: Tablet
|
||||
244: Cosmetics
|
||||
245: Trumpet
|
||||
246: Pineapple
|
||||
247: Golf Ball
|
||||
248: Ambulance
|
||||
249: Parking meter
|
||||
250: Mango
|
||||
251: Key
|
||||
252: Hurdle
|
||||
253: Fishing Rod
|
||||
254: Medal
|
||||
255: Flute
|
||||
256: Brush
|
||||
257: Penguin
|
||||
258: Megaphone
|
||||
259: Corn
|
||||
260: Lettuce
|
||||
261: Garlic
|
||||
262: Swan
|
||||
263: Helicopter
|
||||
264: Green Onion
|
||||
265: Sandwich
|
||||
266: Nuts
|
||||
267: Speed Limit Sign
|
||||
268: Induction Cooker
|
||||
269: Broom
|
||||
270: Trombone
|
||||
271: Plum
|
||||
272: Rickshaw
|
||||
273: Goldfish
|
||||
274: Kiwi fruit
|
||||
275: Router/modem
|
||||
276: Poker Card
|
||||
277: Toaster
|
||||
278: Shrimp
|
||||
279: Sushi
|
||||
280: Cheese
|
||||
281: Notepaper
|
||||
282: Cherry
|
||||
283: Pliers
|
||||
284: CD
|
||||
285: Pasta
|
||||
286: Hammer
|
||||
287: Cue
|
||||
288: Avocado
|
||||
289: Hamimelon
|
||||
290: Flask
|
||||
291: Mushroom
|
||||
292: Screwdriver
|
||||
293: Soap
|
||||
294: Recorder
|
||||
295: Bear
|
||||
296: Eggplant
|
||||
297: Board Eraser
|
||||
298: Coconut
|
||||
299: Tape Measure/Ruler
|
||||
300: Pig
|
||||
301: Showerhead
|
||||
302: Globe
|
||||
303: Chips
|
||||
304: Steak
|
||||
305: Crosswalk Sign
|
||||
306: Stapler
|
||||
307: Camel
|
||||
308: Formula 1
|
||||
309: Pomegranate
|
||||
310: Dishwasher
|
||||
311: Crab
|
||||
312: Hoverboard
|
||||
313: Meat ball
|
||||
314: Rice Cooker
|
||||
315: Tuba
|
||||
316: Calculator
|
||||
317: Papaya
|
||||
318: Antelope
|
||||
319: Parrot
|
||||
320: Seal
|
||||
321: Butterfly
|
||||
322: Dumbbell
|
||||
323: Donkey
|
||||
324: Lion
|
||||
325: Urinal
|
||||
326: Dolphin
|
||||
327: Electric Drill
|
||||
328: Hair Dryer
|
||||
329: Egg tart
|
||||
330: Jellyfish
|
||||
331: Treadmill
|
||||
332: Lighter
|
||||
333: Grapefruit
|
||||
334: Game board
|
||||
335: Mop
|
||||
336: Radish
|
||||
337: Baozi
|
||||
338: Target
|
||||
339: French
|
||||
340: Spring Rolls
|
||||
341: Monkey
|
||||
342: Rabbit
|
||||
343: Pencil Case
|
||||
344: Yak
|
||||
345: Red Cabbage
|
||||
346: Binoculars
|
||||
347: Asparagus
|
||||
348: Barbell
|
||||
349: Scallop
|
||||
350: Noddles
|
||||
351: Comb
|
||||
352: Dumpling
|
||||
353: Oyster
|
||||
354: Table Tennis paddle
|
||||
355: Cosmetics Brush/Eyeliner Pencil
|
||||
356: Chainsaw
|
||||
357: Eraser
|
||||
358: Lobster
|
||||
359: Durian
|
||||
360: Okra
|
||||
361: Lipstick
|
||||
362: Cosmetics Mirror
|
||||
363: Curling
|
||||
364: Table Tennis
|
||||
nc: 365 # number of classes
|
||||
names: ['Person', 'Sneakers', 'Chair', 'Other Shoes', 'Hat', 'Car', 'Lamp', 'Glasses', 'Bottle', 'Desk', 'Cup',
|
||||
'Street Lights', 'Cabinet/shelf', 'Handbag/Satchel', 'Bracelet', 'Plate', 'Picture/Frame', 'Helmet', 'Book',
|
||||
'Gloves', 'Storage box', 'Boat', 'Leather Shoes', 'Flower', 'Bench', 'Potted Plant', 'Bowl/Basin', 'Flag',
|
||||
'Pillow', 'Boots', 'Vase', 'Microphone', 'Necklace', 'Ring', 'SUV', 'Wine Glass', 'Belt', 'Monitor/TV',
|
||||
'Backpack', 'Umbrella', 'Traffic Light', 'Speaker', 'Watch', 'Tie', 'Trash bin Can', 'Slippers', 'Bicycle',
|
||||
'Stool', 'Barrel/bucket', 'Van', 'Couch', 'Sandals', 'Basket', 'Drum', 'Pen/Pencil', 'Bus', 'Wild Bird',
|
||||
'High Heels', 'Motorcycle', 'Guitar', 'Carpet', 'Cell Phone', 'Bread', 'Camera', 'Canned', 'Truck',
|
||||
'Traffic cone', 'Cymbal', 'Lifesaver', 'Towel', 'Stuffed Toy', 'Candle', 'Sailboat', 'Laptop', 'Awning',
|
||||
'Bed', 'Faucet', 'Tent', 'Horse', 'Mirror', 'Power outlet', 'Sink', 'Apple', 'Air Conditioner', 'Knife',
|
||||
'Hockey Stick', 'Paddle', 'Pickup Truck', 'Fork', 'Traffic Sign', 'Balloon', 'Tripod', 'Dog', 'Spoon', 'Clock',
|
||||
'Pot', 'Cow', 'Cake', 'Dinning Table', 'Sheep', 'Hanger', 'Blackboard/Whiteboard', 'Napkin', 'Other Fish',
|
||||
'Orange/Tangerine', 'Toiletry', 'Keyboard', 'Tomato', 'Lantern', 'Machinery Vehicle', 'Fan',
|
||||
'Green Vegetables', 'Banana', 'Baseball Glove', 'Airplane', 'Mouse', 'Train', 'Pumpkin', 'Soccer', 'Skiboard',
|
||||
'Luggage', 'Nightstand', 'Tea pot', 'Telephone', 'Trolley', 'Head Phone', 'Sports Car', 'Stop Sign',
|
||||
'Dessert', 'Scooter', 'Stroller', 'Crane', 'Remote', 'Refrigerator', 'Oven', 'Lemon', 'Duck', 'Baseball Bat',
|
||||
'Surveillance Camera', 'Cat', 'Jug', 'Broccoli', 'Piano', 'Pizza', 'Elephant', 'Skateboard', 'Surfboard',
|
||||
'Gun', 'Skating and Skiing shoes', 'Gas stove', 'Donut', 'Bow Tie', 'Carrot', 'Toilet', 'Kite', 'Strawberry',
|
||||
'Other Balls', 'Shovel', 'Pepper', 'Computer Box', 'Toilet Paper', 'Cleaning Products', 'Chopsticks',
|
||||
'Microwave', 'Pigeon', 'Baseball', 'Cutting/chopping Board', 'Coffee Table', 'Side Table', 'Scissors',
|
||||
'Marker', 'Pie', 'Ladder', 'Snowboard', 'Cookies', 'Radiator', 'Fire Hydrant', 'Basketball', 'Zebra', 'Grape',
|
||||
'Giraffe', 'Potato', 'Sausage', 'Tricycle', 'Violin', 'Egg', 'Fire Extinguisher', 'Candy', 'Fire Truck',
|
||||
'Billiards', 'Converter', 'Bathtub', 'Wheelchair', 'Golf Club', 'Briefcase', 'Cucumber', 'Cigar/Cigarette',
|
||||
'Paint Brush', 'Pear', 'Heavy Truck', 'Hamburger', 'Extractor', 'Extension Cord', 'Tong', 'Tennis Racket',
|
||||
'Folder', 'American Football', 'earphone', 'Mask', 'Kettle', 'Tennis', 'Ship', 'Swing', 'Coffee Machine',
|
||||
'Slide', 'Carriage', 'Onion', 'Green beans', 'Projector', 'Frisbee', 'Washing Machine/Drying Machine',
|
||||
'Chicken', 'Printer', 'Watermelon', 'Saxophone', 'Tissue', 'Toothbrush', 'Ice cream', 'Hot-air balloon',
|
||||
'Cello', 'French Fries', 'Scale', 'Trophy', 'Cabbage', 'Hot dog', 'Blender', 'Peach', 'Rice', 'Wallet/Purse',
|
||||
'Volleyball', 'Deer', 'Goose', 'Tape', 'Tablet', 'Cosmetics', 'Trumpet', 'Pineapple', 'Golf Ball',
|
||||
'Ambulance', 'Parking meter', 'Mango', 'Key', 'Hurdle', 'Fishing Rod', 'Medal', 'Flute', 'Brush', 'Penguin',
|
||||
'Megaphone', 'Corn', 'Lettuce', 'Garlic', 'Swan', 'Helicopter', 'Green Onion', 'Sandwich', 'Nuts',
|
||||
'Speed Limit Sign', 'Induction Cooker', 'Broom', 'Trombone', 'Plum', 'Rickshaw', 'Goldfish', 'Kiwi fruit',
|
||||
'Router/modem', 'Poker Card', 'Toaster', 'Shrimp', 'Sushi', 'Cheese', 'Notepaper', 'Cherry', 'Pliers', 'CD',
|
||||
'Pasta', 'Hammer', 'Cue', 'Avocado', 'Hamimelon', 'Flask', 'Mushroom', 'Screwdriver', 'Soap', 'Recorder',
|
||||
'Bear', 'Eggplant', 'Board Eraser', 'Coconut', 'Tape Measure/Ruler', 'Pig', 'Showerhead', 'Globe', 'Chips',
|
||||
'Steak', 'Crosswalk Sign', 'Stapler', 'Camel', 'Formula 1', 'Pomegranate', 'Dishwasher', 'Crab',
|
||||
'Hoverboard', 'Meat ball', 'Rice Cooker', 'Tuba', 'Calculator', 'Papaya', 'Antelope', 'Parrot', 'Seal',
|
||||
'Butterfly', 'Dumbbell', 'Donkey', 'Lion', 'Urinal', 'Dolphin', 'Electric Drill', 'Hair Dryer', 'Egg tart',
|
||||
'Jellyfish', 'Treadmill', 'Lighter', 'Grapefruit', 'Game board', 'Mop', 'Radish', 'Baozi', 'Target', 'French',
|
||||
'Spring Rolls', 'Monkey', 'Rabbit', 'Pencil Case', 'Yak', 'Red Cabbage', 'Binoculars', 'Asparagus', 'Barbell',
|
||||
'Scallop', 'Noddles', 'Comb', 'Dumpling', 'Oyster', 'Table Tennis paddle', 'Cosmetics Brush/Eyeliner Pencil',
|
||||
'Chainsaw', 'Eraser', 'Lobster', 'Durian', 'Okra', 'Lipstick', 'Cosmetics Mirror', 'Curling', 'Table Tennis']
|
||||
|
||||
|
||||
# Download script/URL (optional) ---------------------------------------------------------------------------------------
|
||||
download: |
|
||||
from pycocotools.coco import COCO
|
||||
from tqdm import tqdm
|
||||
|
||||
from utils.general import Path, check_requirements, download, np, xyxy2xywhn
|
||||
|
||||
check_requirements(('pycocotools>=2.0',))
|
||||
from pycocotools.coco import COCO
|
||||
from utils.general import Path, download, np, xyxy2xywhn
|
||||
|
||||
# Make Directories
|
||||
dir = Path(yaml['path']) # dataset root dir
|
||||
|
||||
@ -1,22 +1,18 @@
|
||||
#!/bin/bash
|
||||
# YOLOv3 🚀 by Ultralytics, GPL-3.0 license
|
||||
# Download latest models from https://github.com/ultralytics/yolov5/releases
|
||||
# Example usage: bash data/scripts/download_weights.sh
|
||||
# Download latest models from https://github.com/ultralytics/yolov3/releases
|
||||
# Example usage: bash path/to/download_weights.sh
|
||||
# parent
|
||||
# └── yolov5
|
||||
# ├── yolov5s.pt ← downloads here
|
||||
# ├── yolov5m.pt
|
||||
# └── yolov3
|
||||
# ├── yolov3.pt ← downloads here
|
||||
# ├── yolov3-spp.pt
|
||||
# └── ...
|
||||
|
||||
python - <<EOF
|
||||
from utils.downloads import attempt_download
|
||||
|
||||
p5 = list('nsmlx') # P5 models
|
||||
p6 = [f'{x}6' for x in p5] # P6 models
|
||||
cls = [f'{x}-cls' for x in p5] # classification models
|
||||
seg = [f'{x}-seg' for x in p5] # classification models
|
||||
|
||||
for x in p5 + p6 + cls + seg:
|
||||
attempt_download(f'weights/yolov5{x}.pt')
|
||||
models = ['yolov3', 'yolov3-spp', 'yolov3-tiny']
|
||||
for x in models:
|
||||
attempt_download(f'{x}.pt')
|
||||
|
||||
EOF
|
||||
|
||||
@ -3,54 +3,25 @@
|
||||
# Download COCO 2017 dataset http://cocodataset.org
|
||||
# Example usage: bash data/scripts/get_coco.sh
|
||||
# parent
|
||||
# ├── yolov5
|
||||
# ├── yolov3
|
||||
# └── datasets
|
||||
# └── coco ← downloads here
|
||||
|
||||
# Arguments (optional) Usage: bash data/scripts/get_coco.sh --train --val --test --segments
|
||||
if [ "$#" -gt 0 ]; then
|
||||
for opt in "$@"; do
|
||||
case "${opt}" in
|
||||
--train) train=true ;;
|
||||
--val) val=true ;;
|
||||
--test) test=true ;;
|
||||
--segments) segments=true ;;
|
||||
esac
|
||||
done
|
||||
else
|
||||
train=true
|
||||
val=true
|
||||
test=false
|
||||
segments=false
|
||||
fi
|
||||
|
||||
# Download/unzip labels
|
||||
d='../datasets' # unzip directory
|
||||
url=https://github.com/ultralytics/yolov5/releases/download/v1.0/
|
||||
if [ "$segments" == "true" ]; then
|
||||
f='coco2017labels-segments.zip' # 168 MB
|
||||
else
|
||||
f='coco2017labels.zip' # 46 MB
|
||||
fi
|
||||
f='coco2017labels.zip' # or 'coco2017labels-segments.zip', 68 MB
|
||||
echo 'Downloading' $url$f ' ...'
|
||||
curl -L $url$f -o $f -# && unzip -q $f -d $d && rm $f &
|
||||
curl -L $url$f -o $f && unzip -q $f -d $d && rm $f &
|
||||
|
||||
# Download/unzip images
|
||||
d='../datasets/coco/images' # unzip directory
|
||||
url=http://images.cocodataset.org/zips/
|
||||
if [ "$train" == "true" ]; then
|
||||
f='train2017.zip' # 19G, 118k images
|
||||
f1='train2017.zip' # 19G, 118k images
|
||||
f2='val2017.zip' # 1G, 5k images
|
||||
f3='test2017.zip' # 7G, 41k images (optional)
|
||||
for f in $f1 $f2; do
|
||||
echo 'Downloading' $url$f '...'
|
||||
curl -L $url$f -o $f -# && unzip -q $f -d $d && rm $f &
|
||||
fi
|
||||
if [ "$val" == "true" ]; then
|
||||
f='val2017.zip' # 1G, 5k images
|
||||
echo 'Downloading' $url$f '...'
|
||||
curl -L $url$f -o $f -# && unzip -q $f -d $d && rm $f &
|
||||
fi
|
||||
if [ "$test" == "true" ]; then
|
||||
f='test2017.zip' # 7G, 41k images (optional)
|
||||
echo 'Downloading' $url$f '...'
|
||||
curl -L $url$f -o $f -# && unzip -q $f -d $d && rm $f &
|
||||
fi
|
||||
curl -L $url$f -o $f && unzip -q $f -d $d && rm $f &
|
||||
done
|
||||
wait # finish background tasks
|
||||
|
||||
4
yolov3/data/scripts/get_coco128.sh
Executable file → Normal file
4
yolov3/data/scripts/get_coco128.sh
Executable file → Normal file
@ -3,7 +3,7 @@
|
||||
# Download COCO128 dataset https://www.kaggle.com/ultralytics/coco128 (first 128 images from COCO train2017)
|
||||
# Example usage: bash data/scripts/get_coco128.sh
|
||||
# parent
|
||||
# ├── yolov5
|
||||
# ├── yolov3
|
||||
# └── datasets
|
||||
# └── coco128 ← downloads here
|
||||
|
||||
@ -12,6 +12,6 @@ d='../datasets' # unzip directory
|
||||
url=https://github.com/ultralytics/yolov5/releases/download/v1.0/
|
||||
f='coco128.zip' # or 'coco128-segments.zip', 68 MB
|
||||
echo 'Downloading' $url$f ' ...'
|
||||
curl -L $url$f -o $f -# && unzip -q $f -d $d && rm $f &
|
||||
curl -L $url$f -o $f && unzip -q $f -d $d && rm $f &
|
||||
|
||||
wait # finish background tasks
|
||||
|
||||
@ -1,10 +1,10 @@
|
||||
# YOLOv3 🚀 by Ultralytics, GPL-3.0 license
|
||||
# PASCAL VOC dataset http://host.robots.ox.ac.uk/pascal/VOC by University of Oxford
|
||||
# PASCAL VOC dataset http://host.robots.ox.ac.uk/pascal/VOC
|
||||
# Example usage: python train.py --data VOC.yaml
|
||||
# parent
|
||||
# ├── yolov5
|
||||
# ├── yolov3
|
||||
# └── datasets
|
||||
# └── VOC ← downloads here (2.8 GB)
|
||||
# └── VOC ← downloads here
|
||||
|
||||
|
||||
# Train/val/test sets as 1) dir: path/to/imgs, 2) file: path/to/imgs.txt, or 3) list: [path/to/imgs1, path/to/imgs2, ..]
|
||||
@ -20,27 +20,9 @@ test: # test images (optional)
|
||||
- images/test2007
|
||||
|
||||
# Classes
|
||||
names:
|
||||
0: aeroplane
|
||||
1: bicycle
|
||||
2: bird
|
||||
3: boat
|
||||
4: bottle
|
||||
5: bus
|
||||
6: car
|
||||
7: cat
|
||||
8: chair
|
||||
9: cow
|
||||
10: diningtable
|
||||
11: dog
|
||||
12: horse
|
||||
13: motorbike
|
||||
14: person
|
||||
15: pottedplant
|
||||
16: sheep
|
||||
17: sofa
|
||||
18: train
|
||||
19: tvmonitor
|
||||
nc: 20 # number of classes
|
||||
names: ['aeroplane', 'bicycle', 'bird', 'boat', 'bottle', 'bus', 'car', 'cat', 'chair', 'cow', 'diningtable', 'dog',
|
||||
'horse', 'motorbike', 'person', 'pottedplant', 'sheep', 'sofa', 'train', 'tvmonitor'] # class names
|
||||
|
||||
|
||||
# Download script/URL (optional) ---------------------------------------------------------------------------------------
|
||||
@ -65,34 +47,32 @@ download: |
|
||||
w = int(size.find('width').text)
|
||||
h = int(size.find('height').text)
|
||||
|
||||
names = list(yaml['names'].values()) # names list
|
||||
for obj in root.iter('object'):
|
||||
cls = obj.find('name').text
|
||||
if cls in names and int(obj.find('difficult').text) != 1:
|
||||
if cls in yaml['names'] and not int(obj.find('difficult').text) == 1:
|
||||
xmlbox = obj.find('bndbox')
|
||||
bb = convert_box((w, h), [float(xmlbox.find(x).text) for x in ('xmin', 'xmax', 'ymin', 'ymax')])
|
||||
cls_id = names.index(cls) # class id
|
||||
cls_id = yaml['names'].index(cls) # class id
|
||||
out_file.write(" ".join([str(a) for a in (cls_id, *bb)]) + '\n')
|
||||
|
||||
|
||||
# Download
|
||||
dir = Path(yaml['path']) # dataset root dir
|
||||
url = 'https://github.com/ultralytics/yolov5/releases/download/v1.0/'
|
||||
urls = [f'{url}VOCtrainval_06-Nov-2007.zip', # 446MB, 5012 images
|
||||
f'{url}VOCtest_06-Nov-2007.zip', # 438MB, 4953 images
|
||||
f'{url}VOCtrainval_11-May-2012.zip'] # 1.95GB, 17126 images
|
||||
download(urls, dir=dir / 'images', delete=False, curl=True, threads=3)
|
||||
urls = [url + 'VOCtrainval_06-Nov-2007.zip', # 446MB, 5012 images
|
||||
url + 'VOCtest_06-Nov-2007.zip', # 438MB, 4953 images
|
||||
url + 'VOCtrainval_11-May-2012.zip'] # 1.95GB, 17126 images
|
||||
download(urls, dir=dir / 'images', delete=False)
|
||||
|
||||
# Convert
|
||||
path = dir / 'images/VOCdevkit'
|
||||
path = dir / f'images/VOCdevkit'
|
||||
for year, image_set in ('2012', 'train'), ('2012', 'val'), ('2007', 'train'), ('2007', 'val'), ('2007', 'test'):
|
||||
imgs_path = dir / 'images' / f'{image_set}{year}'
|
||||
lbs_path = dir / 'labels' / f'{image_set}{year}'
|
||||
imgs_path.mkdir(exist_ok=True, parents=True)
|
||||
lbs_path.mkdir(exist_ok=True, parents=True)
|
||||
|
||||
with open(path / f'VOC{year}/ImageSets/Main/{image_set}.txt') as f:
|
||||
image_ids = f.read().strip().split()
|
||||
image_ids = open(path / f'VOC{year}/ImageSets/Main/{image_set}.txt').read().strip().split()
|
||||
for id in tqdm(image_ids, desc=f'{image_set}{year}'):
|
||||
f = path / f'VOC{year}/JPEGImages/{id}.jpg' # old img path
|
||||
lb_path = (lbs_path / f.name).with_suffix('.txt') # new label path
|
||||
|
||||
@ -1,11 +1,11 @@
|
||||
# YOLOv3 🚀 by Ultralytics, GPL-3.0 license
|
||||
# DIUx xView 2018 Challenge https://challenge.xviewdataset.org by U.S. National Geospatial-Intelligence Agency (NGA)
|
||||
# -------- DOWNLOAD DATA MANUALLY and jar xf val_images.zip to 'datasets/xView' before running train command! --------
|
||||
# xView 2018 dataset https://challenge.xviewdataset.org
|
||||
# -------- DOWNLOAD DATA MANUALLY from URL above and unzip to 'datasets/xView' before running train command! --------
|
||||
# Example usage: python train.py --data xView.yaml
|
||||
# parent
|
||||
# ├── yolov5
|
||||
# ├── yolov3
|
||||
# └── datasets
|
||||
# └── xView ← downloads here (20.7 GB)
|
||||
# └── xView ← downloads here
|
||||
|
||||
|
||||
# Train/val/test sets as 1) dir: path/to/imgs, 2) file: path/to/imgs.txt, or 3) list: [path/to/imgs1, path/to/imgs2, ..]
|
||||
@ -14,67 +14,16 @@ train: images/autosplit_train.txt # train images (relative to 'path') 90% of 84
|
||||
val: images/autosplit_val.txt # train images (relative to 'path') 10% of 847 train images
|
||||
|
||||
# Classes
|
||||
names:
|
||||
0: Fixed-wing Aircraft
|
||||
1: Small Aircraft
|
||||
2: Cargo Plane
|
||||
3: Helicopter
|
||||
4: Passenger Vehicle
|
||||
5: Small Car
|
||||
6: Bus
|
||||
7: Pickup Truck
|
||||
8: Utility Truck
|
||||
9: Truck
|
||||
10: Cargo Truck
|
||||
11: Truck w/Box
|
||||
12: Truck Tractor
|
||||
13: Trailer
|
||||
14: Truck w/Flatbed
|
||||
15: Truck w/Liquid
|
||||
16: Crane Truck
|
||||
17: Railway Vehicle
|
||||
18: Passenger Car
|
||||
19: Cargo Car
|
||||
20: Flat Car
|
||||
21: Tank car
|
||||
22: Locomotive
|
||||
23: Maritime Vessel
|
||||
24: Motorboat
|
||||
25: Sailboat
|
||||
26: Tugboat
|
||||
27: Barge
|
||||
28: Fishing Vessel
|
||||
29: Ferry
|
||||
30: Yacht
|
||||
31: Container Ship
|
||||
32: Oil Tanker
|
||||
33: Engineering Vehicle
|
||||
34: Tower crane
|
||||
35: Container Crane
|
||||
36: Reach Stacker
|
||||
37: Straddle Carrier
|
||||
38: Mobile Crane
|
||||
39: Dump Truck
|
||||
40: Haul Truck
|
||||
41: Scraper/Tractor
|
||||
42: Front loader/Bulldozer
|
||||
43: Excavator
|
||||
44: Cement Mixer
|
||||
45: Ground Grader
|
||||
46: Hut/Tent
|
||||
47: Shed
|
||||
48: Building
|
||||
49: Aircraft Hangar
|
||||
50: Damaged Building
|
||||
51: Facility
|
||||
52: Construction Site
|
||||
53: Vehicle Lot
|
||||
54: Helipad
|
||||
55: Storage Tank
|
||||
56: Shipping container lot
|
||||
57: Shipping Container
|
||||
58: Pylon
|
||||
59: Tower
|
||||
nc: 60 # number of classes
|
||||
names: ['Fixed-wing Aircraft', 'Small Aircraft', 'Cargo Plane', 'Helicopter', 'Passenger Vehicle', 'Small Car', 'Bus',
|
||||
'Pickup Truck', 'Utility Truck', 'Truck', 'Cargo Truck', 'Truck w/Box', 'Truck Tractor', 'Trailer',
|
||||
'Truck w/Flatbed', 'Truck w/Liquid', 'Crane Truck', 'Railway Vehicle', 'Passenger Car', 'Cargo Car',
|
||||
'Flat Car', 'Tank car', 'Locomotive', 'Maritime Vessel', 'Motorboat', 'Sailboat', 'Tugboat', 'Barge',
|
||||
'Fishing Vessel', 'Ferry', 'Yacht', 'Container Ship', 'Oil Tanker', 'Engineering Vehicle', 'Tower crane',
|
||||
'Container Crane', 'Reach Stacker', 'Straddle Carrier', 'Mobile Crane', 'Dump Truck', 'Haul Truck',
|
||||
'Scraper/Tractor', 'Front loader/Bulldozer', 'Excavator', 'Cement Mixer', 'Ground Grader', 'Hut/Tent', 'Shed',
|
||||
'Building', 'Aircraft Hangar', 'Damaged Building', 'Facility', 'Construction Site', 'Vehicle Lot', 'Helipad',
|
||||
'Storage Tank', 'Shipping container lot', 'Shipping Container', 'Pylon', 'Tower'] # class names
|
||||
|
||||
|
||||
# Download script/URL (optional) ---------------------------------------------------------------------------------------
|
||||
@ -87,7 +36,7 @@ download: |
|
||||
from PIL import Image
|
||||
from tqdm import tqdm
|
||||
|
||||
from utils.dataloaders import autosplit
|
||||
from utils.datasets import autosplit
|
||||
from utils.general import download, xyxy2xywhn
|
||||
|
||||
|
||||
|
||||
157
yolov3/detect.py
157
yolov3/detect.py
@ -1,61 +1,44 @@
|
||||
# YOLOv3 🚀 by Ultralytics, GPL-3.0 license
|
||||
"""
|
||||
Run YOLOv3 detection inference on images, videos, directories, globs, YouTube, webcam, streams, etc.
|
||||
Run inference on images, videos, directories, streams, etc.
|
||||
|
||||
Usage - sources:
|
||||
$ python detect.py --weights yolov5s.pt --source 0 # webcam
|
||||
img.jpg # image
|
||||
vid.mp4 # video
|
||||
screen # screenshot
|
||||
path/ # directory
|
||||
list.txt # list of images
|
||||
list.streams # list of streams
|
||||
'path/*.jpg' # glob
|
||||
'https://youtu.be/Zgi9g1ksQHc' # YouTube
|
||||
'rtsp://example.com/media.mp4' # RTSP, RTMP, HTTP stream
|
||||
|
||||
Usage - formats:
|
||||
$ python detect.py --weights yolov5s.pt # PyTorch
|
||||
yolov5s.torchscript # TorchScript
|
||||
yolov5s.onnx # ONNX Runtime or OpenCV DNN with --dnn
|
||||
yolov5s_openvino_model # OpenVINO
|
||||
yolov5s.engine # TensorRT
|
||||
yolov5s.mlmodel # CoreML (macOS-only)
|
||||
yolov5s_saved_model # TensorFlow SavedModel
|
||||
yolov5s.pb # TensorFlow GraphDef
|
||||
yolov5s.tflite # TensorFlow Lite
|
||||
yolov5s_edgetpu.tflite # TensorFlow Edge TPU
|
||||
yolov5s_paddle_model # PaddlePaddle
|
||||
Usage:
|
||||
$ python path/to/detect.py --weights yolov3.pt --source 0 # webcam
|
||||
img.jpg # image
|
||||
vid.mp4 # video
|
||||
path/ # directory
|
||||
path/*.jpg # glob
|
||||
'https://youtu.be/Zgi9g1ksQHc' # YouTube
|
||||
'rtsp://example.com/media.mp4' # RTSP, RTMP, HTTP stream
|
||||
"""
|
||||
|
||||
import argparse
|
||||
import os
|
||||
import platform
|
||||
import sys
|
||||
from pathlib import Path
|
||||
|
||||
import cv2
|
||||
import torch
|
||||
import torch.backends.cudnn as cudnn
|
||||
|
||||
FILE = Path(__file__).resolve()
|
||||
ROOT = FILE.parents[0] # YOLOv3 root directory
|
||||
ROOT = FILE.parents[0] # root directory
|
||||
if str(ROOT) not in sys.path:
|
||||
sys.path.append(str(ROOT)) # add ROOT to PATH
|
||||
ROOT = Path(os.path.relpath(ROOT, Path.cwd())) # relative
|
||||
|
||||
from models.common import DetectMultiBackend
|
||||
from utils.dataloaders import IMG_FORMATS, VID_FORMATS, LoadImages, LoadScreenshots, LoadStreams
|
||||
from utils.general import (LOGGER, Profile, check_file, check_img_size, check_imshow, check_requirements, colorstr, cv2,
|
||||
increment_path, non_max_suppression, print_args, scale_boxes, strip_optimizer, xyxy2xywh)
|
||||
from utils.datasets import IMG_FORMATS, VID_FORMATS, LoadImages, LoadStreams
|
||||
from utils.general import (LOGGER, check_file, check_img_size, check_imshow, check_requirements, colorstr,
|
||||
increment_path, non_max_suppression, print_args, scale_coords, strip_optimizer, xyxy2xywh)
|
||||
from utils.plots import Annotator, colors, save_one_box
|
||||
from utils.torch_utils import select_device, smart_inference_mode
|
||||
from utils.torch_utils import select_device, time_sync
|
||||
|
||||
|
||||
@smart_inference_mode()
|
||||
def run(
|
||||
weights=ROOT / 'yolov5s.pt', # model path or triton URL
|
||||
source=ROOT / 'data/images', # file/dir/URL/glob/screen/0(webcam)
|
||||
data=ROOT / 'data/coco128.yaml', # dataset.yaml path
|
||||
imgsz=(640, 640), # inference size (height, width)
|
||||
@torch.no_grad()
|
||||
def run(weights=ROOT / 'yolov3.pt', # model.pt path(s)
|
||||
source=ROOT / 'data/images', # file/dir/URL/glob, 0 for webcam
|
||||
imgsz=640, # inference size (pixels)
|
||||
conf_thres=0.25, # confidence threshold
|
||||
iou_thres=0.45, # NMS IOU threshold
|
||||
max_det=1000, # maximum detections per image
|
||||
@ -78,14 +61,12 @@ def run(
|
||||
hide_conf=False, # hide confidences
|
||||
half=False, # use FP16 half-precision inference
|
||||
dnn=False, # use OpenCV DNN for ONNX inference
|
||||
vid_stride=1, # video frame-rate stride
|
||||
):
|
||||
):
|
||||
source = str(source)
|
||||
save_img = not nosave and not source.endswith('.txt') # save inference images
|
||||
is_file = Path(source).suffix[1:] in (IMG_FORMATS + VID_FORMATS)
|
||||
is_url = source.lower().startswith(('rtsp://', 'rtmp://', 'http://', 'https://'))
|
||||
webcam = source.isnumeric() or source.endswith('.streams') or (is_url and not is_file)
|
||||
screenshot = source.lower().startswith('screen')
|
||||
webcam = source.isnumeric() or source.endswith('.txt') or (is_url and not is_file)
|
||||
if is_url and is_file:
|
||||
source = check_file(source) # download
|
||||
|
||||
@ -95,41 +76,49 @@ def run(
|
||||
|
||||
# Load model
|
||||
device = select_device(device)
|
||||
model = DetectMultiBackend(weights, device=device, dnn=dnn, data=data, fp16=half)
|
||||
stride, names, pt = model.stride, model.names, model.pt
|
||||
model = DetectMultiBackend(weights, device=device, dnn=dnn)
|
||||
stride, names, pt, jit, onnx = model.stride, model.names, model.pt, model.jit, model.onnx
|
||||
imgsz = check_img_size(imgsz, s=stride) # check image size
|
||||
|
||||
# Half
|
||||
half &= pt and device.type != 'cpu' # half precision only supported by PyTorch on CUDA
|
||||
if pt:
|
||||
model.model.half() if half else model.model.float()
|
||||
|
||||
# Dataloader
|
||||
bs = 1 # batch_size
|
||||
if webcam:
|
||||
view_img = check_imshow(warn=True)
|
||||
dataset = LoadStreams(source, img_size=imgsz, stride=stride, auto=pt, vid_stride=vid_stride)
|
||||
bs = len(dataset)
|
||||
elif screenshot:
|
||||
dataset = LoadScreenshots(source, img_size=imgsz, stride=stride, auto=pt)
|
||||
view_img = check_imshow()
|
||||
cudnn.benchmark = True # set True to speed up constant image size inference
|
||||
dataset = LoadStreams(source, img_size=imgsz, stride=stride, auto=pt and not jit)
|
||||
bs = len(dataset) # batch_size
|
||||
else:
|
||||
dataset = LoadImages(source, img_size=imgsz, stride=stride, auto=pt, vid_stride=vid_stride)
|
||||
dataset = LoadImages(source, img_size=imgsz, stride=stride, auto=pt and not jit)
|
||||
bs = 1 # batch_size
|
||||
vid_path, vid_writer = [None] * bs, [None] * bs
|
||||
|
||||
# Run inference
|
||||
model.warmup(imgsz=(1 if pt or model.triton else bs, 3, *imgsz)) # warmup
|
||||
seen, windows, dt = 0, [], (Profile(), Profile(), Profile())
|
||||
if pt and device.type != 'cpu':
|
||||
model(torch.zeros(1, 3, *imgsz).to(device).type_as(next(model.model.parameters()))) # warmup
|
||||
dt, seen = [0.0, 0.0, 0.0], 0
|
||||
for path, im, im0s, vid_cap, s in dataset:
|
||||
with dt[0]:
|
||||
im = torch.from_numpy(im).to(model.device)
|
||||
im = im.half() if model.fp16 else im.float() # uint8 to fp16/32
|
||||
im /= 255 # 0 - 255 to 0.0 - 1.0
|
||||
if len(im.shape) == 3:
|
||||
im = im[None] # expand for batch dim
|
||||
t1 = time_sync()
|
||||
im = torch.from_numpy(im).to(device)
|
||||
im = im.half() if half else im.float() # uint8 to fp16/32
|
||||
im /= 255 # 0 - 255 to 0.0 - 1.0
|
||||
if len(im.shape) == 3:
|
||||
im = im[None] # expand for batch dim
|
||||
t2 = time_sync()
|
||||
dt[0] += t2 - t1
|
||||
|
||||
# Inference
|
||||
with dt[1]:
|
||||
visualize = increment_path(save_dir / Path(path).stem, mkdir=True) if visualize else False
|
||||
pred = model(im, augment=augment, visualize=visualize)
|
||||
visualize = increment_path(save_dir / Path(path).stem, mkdir=True) if visualize else False
|
||||
pred = model(im, augment=augment, visualize=visualize)
|
||||
t3 = time_sync()
|
||||
dt[1] += t3 - t2
|
||||
|
||||
# NMS
|
||||
with dt[2]:
|
||||
pred = non_max_suppression(pred, conf_thres, iou_thres, classes, agnostic_nms, max_det=max_det)
|
||||
pred = non_max_suppression(pred, conf_thres, iou_thres, classes, agnostic_nms, max_det=max_det)
|
||||
dt[2] += time_sync() - t3
|
||||
|
||||
# Second-stage classifier (optional)
|
||||
# pred = utils.general.apply_classifier(pred, classifier_model, im, im0s)
|
||||
@ -152,11 +141,11 @@ def run(
|
||||
annotator = Annotator(im0, line_width=line_thickness, example=str(names))
|
||||
if len(det):
|
||||
# Rescale boxes from img_size to im0 size
|
||||
det[:, :4] = scale_boxes(im.shape[2:], det[:, :4], im0.shape).round()
|
||||
det[:, :4] = scale_coords(im.shape[2:], det[:, :4], im0.shape).round()
|
||||
|
||||
# Print results
|
||||
for c in det[:, 5].unique():
|
||||
n = (det[:, 5] == c).sum() # detections per class
|
||||
for c in det[:, -1].unique():
|
||||
n = (det[:, -1] == c).sum() # detections per class
|
||||
s += f"{n} {names[int(c)]}{'s' * (n > 1)}, " # add to string
|
||||
|
||||
# Write results
|
||||
@ -164,23 +153,22 @@ def run(
|
||||
if save_txt: # Write to file
|
||||
xywh = (xyxy2xywh(torch.tensor(xyxy).view(1, 4)) / gn).view(-1).tolist() # normalized xywh
|
||||
line = (cls, *xywh, conf) if save_conf else (cls, *xywh) # label format
|
||||
with open(f'{txt_path}.txt', 'a') as f:
|
||||
with open(txt_path + '.txt', 'a') as f:
|
||||
f.write(('%g ' * len(line)).rstrip() % line + '\n')
|
||||
|
||||
if save_img or save_crop or view_img: # Add bbox to image
|
||||
c = int(cls) # integer class
|
||||
label = None if hide_labels else (names[c] if hide_conf else f'{names[c]} {conf:.2f}')
|
||||
annotator.box_label(xyxy, label, color=colors(c, True))
|
||||
if save_crop:
|
||||
save_one_box(xyxy, imc, file=save_dir / 'crops' / names[c] / f'{p.stem}.jpg', BGR=True)
|
||||
if save_crop:
|
||||
save_one_box(xyxy, imc, file=save_dir / 'crops' / names[c] / f'{p.stem}.jpg', BGR=True)
|
||||
|
||||
# Print time (inference-only)
|
||||
LOGGER.info(f'{s}Done. ({t3 - t2:.3f}s)')
|
||||
|
||||
# Stream results
|
||||
im0 = annotator.result()
|
||||
if view_img:
|
||||
if platform.system() == 'Linux' and p not in windows:
|
||||
windows.append(p)
|
||||
cv2.namedWindow(str(p), cv2.WINDOW_NORMAL | cv2.WINDOW_KEEPRATIO) # allow window resize (Linux)
|
||||
cv2.resizeWindow(str(p), im0.shape[1], im0.shape[0])
|
||||
cv2.imshow(str(p), im0)
|
||||
cv2.waitKey(1) # 1 millisecond
|
||||
|
||||
@ -199,32 +187,24 @@ def run(
|
||||
h = int(vid_cap.get(cv2.CAP_PROP_FRAME_HEIGHT))
|
||||
else: # stream
|
||||
fps, w, h = 30, im0.shape[1], im0.shape[0]
|
||||
save_path = str(Path(save_path).with_suffix('.mp4')) # force *.mp4 suffix on results videos
|
||||
save_path += '.mp4'
|
||||
vid_writer[i] = cv2.VideoWriter(save_path, cv2.VideoWriter_fourcc(*'mp4v'), fps, (w, h))
|
||||
vid_writer[i].write(im0)
|
||||
|
||||
# Print time (inference-only)
|
||||
LOGGER.info(f"{s}{'' if len(det) else '(no detections), '}{dt[1].dt * 1E3:.1f}ms")
|
||||
|
||||
# Print results
|
||||
t = tuple(x.t / seen * 1E3 for x in dt) # speeds per image
|
||||
t = tuple(x / seen * 1E3 for x in dt) # speeds per image
|
||||
LOGGER.info(f'Speed: %.1fms pre-process, %.1fms inference, %.1fms NMS per image at shape {(1, 3, *imgsz)}' % t)
|
||||
if save_txt or save_img:
|
||||
s = f"\n{len(list(save_dir.glob('labels/*.txt')))} labels saved to {save_dir / 'labels'}" if save_txt else ''
|
||||
LOGGER.info(f"Results saved to {colorstr('bold', save_dir)}{s}")
|
||||
if update:
|
||||
strip_optimizer(weights[0]) # update model (to fix SourceChangeWarning)
|
||||
strip_optimizer(weights) # update model (to fix SourceChangeWarning)
|
||||
|
||||
|
||||
def parse_opt():
|
||||
parser = argparse.ArgumentParser()
|
||||
parser.add_argument('--weights',
|
||||
nargs='+',
|
||||
type=str,
|
||||
default=ROOT / 'yolov3-tiny.pt',
|
||||
help='model path or triton URL')
|
||||
parser.add_argument('--source', type=str, default=ROOT / 'data/images', help='file/dir/URL/glob/screen/0(webcam)')
|
||||
parser.add_argument('--data', type=str, default=ROOT / 'data/coco128.yaml', help='(optional) dataset.yaml path')
|
||||
parser.add_argument('--weights', nargs='+', type=str, default=ROOT / 'yolov3.pt', help='model path(s)')
|
||||
parser.add_argument('--source', type=str, default=ROOT / 'data/images', help='file/dir/URL/glob, 0 for webcam')
|
||||
parser.add_argument('--imgsz', '--img', '--img-size', nargs='+', type=int, default=[640], help='inference size h,w')
|
||||
parser.add_argument('--conf-thres', type=float, default=0.25, help='confidence threshold')
|
||||
parser.add_argument('--iou-thres', type=float, default=0.45, help='NMS IoU threshold')
|
||||
@ -248,10 +228,9 @@ def parse_opt():
|
||||
parser.add_argument('--hide-conf', default=False, action='store_true', help='hide confidences')
|
||||
parser.add_argument('--half', action='store_true', help='use FP16 half-precision inference')
|
||||
parser.add_argument('--dnn', action='store_true', help='use OpenCV DNN for ONNX inference')
|
||||
parser.add_argument('--vid-stride', type=int, default=1, help='video frame-rate stride')
|
||||
opt = parser.parse_args()
|
||||
opt.imgsz *= 2 if len(opt.imgsz) == 1 else 1 # expand
|
||||
print_args(vars(opt))
|
||||
print_args(FILE.stem, opt)
|
||||
return opt
|
||||
|
||||
|
||||
@ -260,6 +239,6 @@ def main(opt):
|
||||
run(**vars(opt))
|
||||
|
||||
|
||||
if __name__ == '__main__':
|
||||
if __name__ == "__main__":
|
||||
opt = parse_opt()
|
||||
main(opt)
|
||||
|
||||
800
yolov3/export.py
800
yolov3/export.py
@ -1,538 +1,287 @@
|
||||
# YOLOv3 🚀 by Ultralytics, GPL-3.0 license
|
||||
"""
|
||||
Export a YOLOv3 PyTorch model to other formats. TensorFlow exports authored by https://github.com/zldrobit
|
||||
|
||||
Format | `export.py --include` | Model
|
||||
--- | --- | ---
|
||||
PyTorch | - | yolov5s.pt
|
||||
TorchScript | `torchscript` | yolov5s.torchscript
|
||||
ONNX | `onnx` | yolov5s.onnx
|
||||
OpenVINO | `openvino` | yolov5s_openvino_model/
|
||||
TensorRT | `engine` | yolov5s.engine
|
||||
CoreML | `coreml` | yolov5s.mlmodel
|
||||
TensorFlow SavedModel | `saved_model` | yolov5s_saved_model/
|
||||
TensorFlow GraphDef | `pb` | yolov5s.pb
|
||||
TensorFlow Lite | `tflite` | yolov5s.tflite
|
||||
TensorFlow Edge TPU | `edgetpu` | yolov5s_edgetpu.tflite
|
||||
TensorFlow.js | `tfjs` | yolov5s_web_model/
|
||||
PaddlePaddle | `paddle` | yolov5s_paddle_model/
|
||||
|
||||
Requirements:
|
||||
$ pip install -r requirements.txt coremltools onnx onnx-simplifier onnxruntime openvino-dev tensorflow-cpu # CPU
|
||||
$ pip install -r requirements.txt coremltools onnx onnx-simplifier onnxruntime-gpu openvino-dev tensorflow # GPU
|
||||
Export a PyTorch model to TorchScript, ONNX, CoreML, TensorFlow (saved_model, pb, TFLite, TF.js,) formats
|
||||
TensorFlow exports authored by https://github.com/zldrobit
|
||||
|
||||
Usage:
|
||||
$ python export.py --weights yolov5s.pt --include torchscript onnx openvino engine coreml tflite ...
|
||||
$ python path/to/export.py --weights yolov3.pt --include torchscript onnx coreml saved_model pb tflite tfjs
|
||||
|
||||
Inference:
|
||||
$ python detect.py --weights yolov5s.pt # PyTorch
|
||||
yolov5s.torchscript # TorchScript
|
||||
yolov5s.onnx # ONNX Runtime or OpenCV DNN with --dnn
|
||||
yolov5s_openvino_model # OpenVINO
|
||||
yolov5s.engine # TensorRT
|
||||
yolov5s.mlmodel # CoreML (macOS-only)
|
||||
yolov5s_saved_model # TensorFlow SavedModel
|
||||
yolov5s.pb # TensorFlow GraphDef
|
||||
yolov5s.tflite # TensorFlow Lite
|
||||
yolov5s_edgetpu.tflite # TensorFlow Edge TPU
|
||||
yolov5s_paddle_model # PaddlePaddle
|
||||
$ python path/to/detect.py --weights yolov3.pt
|
||||
yolov3.onnx (must export with --dynamic)
|
||||
yolov3_saved_model
|
||||
yolov3.pb
|
||||
yolov3.tflite
|
||||
|
||||
TensorFlow.js:
|
||||
$ cd .. && git clone https://github.com/zldrobit/tfjs-yolov5-example.git && cd tfjs-yolov5-example
|
||||
$ npm install
|
||||
$ ln -s ../../yolov5/yolov5s_web_model public/yolov5s_web_model
|
||||
$ ln -s ../../yolov5/yolov3_web_model public/yolov3_web_model
|
||||
$ npm start
|
||||
"""
|
||||
|
||||
import argparse
|
||||
import contextlib
|
||||
import json
|
||||
import os
|
||||
import platform
|
||||
import re
|
||||
import subprocess
|
||||
import sys
|
||||
import time
|
||||
import warnings
|
||||
from pathlib import Path
|
||||
|
||||
import pandas as pd
|
||||
import torch
|
||||
import torch.nn as nn
|
||||
from torch.utils.mobile_optimizer import optimize_for_mobile
|
||||
|
||||
FILE = Path(__file__).resolve()
|
||||
ROOT = FILE.parents[0] # YOLOv3 root directory
|
||||
ROOT = FILE.parents[0] # root directory
|
||||
if str(ROOT) not in sys.path:
|
||||
sys.path.append(str(ROOT)) # add ROOT to PATH
|
||||
if platform.system() != 'Windows':
|
||||
ROOT = Path(os.path.relpath(ROOT, Path.cwd())) # relative
|
||||
ROOT = Path(os.path.relpath(ROOT, Path.cwd())) # relative
|
||||
|
||||
from models.common import Conv
|
||||
from models.experimental import attempt_load
|
||||
from models.yolo import ClassificationModel, Detect, DetectionModel, SegmentationModel
|
||||
from utils.dataloaders import LoadImages
|
||||
from utils.general import (LOGGER, Profile, check_dataset, check_img_size, check_requirements, check_version,
|
||||
check_yaml, colorstr, file_size, get_default_args, print_args, url2file, yaml_save)
|
||||
from utils.torch_utils import select_device, smart_inference_mode
|
||||
|
||||
MACOS = platform.system() == 'Darwin' # macOS environment
|
||||
from models.yolo import Detect
|
||||
from utils.activations import SiLU
|
||||
from utils.datasets import LoadImages
|
||||
from utils.general import (LOGGER, check_dataset, check_img_size, check_requirements, colorstr, file_size, print_args,
|
||||
url2file)
|
||||
from utils.torch_utils import select_device
|
||||
|
||||
|
||||
def export_formats():
|
||||
# YOLOv3 export formats
|
||||
x = [
|
||||
['PyTorch', '-', '.pt', True, True],
|
||||
['TorchScript', 'torchscript', '.torchscript', True, True],
|
||||
['ONNX', 'onnx', '.onnx', True, True],
|
||||
['OpenVINO', 'openvino', '_openvino_model', True, False],
|
||||
['TensorRT', 'engine', '.engine', False, True],
|
||||
['CoreML', 'coreml', '.mlmodel', True, False],
|
||||
['TensorFlow SavedModel', 'saved_model', '_saved_model', True, True],
|
||||
['TensorFlow GraphDef', 'pb', '.pb', True, True],
|
||||
['TensorFlow Lite', 'tflite', '.tflite', True, False],
|
||||
['TensorFlow Edge TPU', 'edgetpu', '_edgetpu.tflite', False, False],
|
||||
['TensorFlow.js', 'tfjs', '_web_model', False, False],
|
||||
['PaddlePaddle', 'paddle', '_paddle_model', True, True],]
|
||||
return pd.DataFrame(x, columns=['Format', 'Argument', 'Suffix', 'CPU', 'GPU'])
|
||||
|
||||
|
||||
def try_export(inner_func):
|
||||
# YOLOv3 export decorator, i..e @try_export
|
||||
inner_args = get_default_args(inner_func)
|
||||
|
||||
def outer_func(*args, **kwargs):
|
||||
prefix = inner_args['prefix']
|
||||
try:
|
||||
with Profile() as dt:
|
||||
f, model = inner_func(*args, **kwargs)
|
||||
LOGGER.info(f'{prefix} export success ✅ {dt.t:.1f}s, saved as {f} ({file_size(f):.1f} MB)')
|
||||
return f, model
|
||||
except Exception as e:
|
||||
LOGGER.info(f'{prefix} export failure ❌ {dt.t:.1f}s: {e}')
|
||||
return None, None
|
||||
|
||||
return outer_func
|
||||
|
||||
|
||||
@try_export
|
||||
def export_torchscript(model, im, file, optimize, prefix=colorstr('TorchScript:')):
|
||||
# YOLOv3 TorchScript model export
|
||||
LOGGER.info(f'\n{prefix} starting export with torch {torch.__version__}...')
|
||||
f = file.with_suffix('.torchscript')
|
||||
|
||||
ts = torch.jit.trace(model, im, strict=False)
|
||||
d = {'shape': im.shape, 'stride': int(max(model.stride)), 'names': model.names}
|
||||
extra_files = {'config.txt': json.dumps(d)} # torch._C.ExtraFilesMap()
|
||||
if optimize: # https://pytorch.org/tutorials/recipes/mobile_interpreter.html
|
||||
optimize_for_mobile(ts)._save_for_lite_interpreter(str(f), _extra_files=extra_files)
|
||||
else:
|
||||
ts.save(str(f), _extra_files=extra_files)
|
||||
return f, None
|
||||
|
||||
|
||||
@try_export
|
||||
def export_onnx(model, im, file, opset, dynamic, simplify, prefix=colorstr('ONNX:')):
|
||||
# YOLOv3 ONNX export
|
||||
check_requirements('onnx>=1.12.0')
|
||||
import onnx
|
||||
|
||||
LOGGER.info(f'\n{prefix} starting export with onnx {onnx.__version__}...')
|
||||
f = file.with_suffix('.onnx')
|
||||
|
||||
output_names = ['output0', 'output1'] if isinstance(model, SegmentationModel) else ['output0']
|
||||
if dynamic:
|
||||
dynamic = {'images': {0: 'batch', 2: 'height', 3: 'width'}} # shape(1,3,640,640)
|
||||
if isinstance(model, SegmentationModel):
|
||||
dynamic['output0'] = {0: 'batch', 1: 'anchors'} # shape(1,25200,85)
|
||||
dynamic['output1'] = {0: 'batch', 2: 'mask_height', 3: 'mask_width'} # shape(1,32,160,160)
|
||||
elif isinstance(model, DetectionModel):
|
||||
dynamic['output0'] = {0: 'batch', 1: 'anchors'} # shape(1,25200,85)
|
||||
|
||||
torch.onnx.export(
|
||||
model.cpu() if dynamic else model, # --dynamic only compatible with cpu
|
||||
im.cpu() if dynamic else im,
|
||||
f,
|
||||
verbose=False,
|
||||
opset_version=opset,
|
||||
do_constant_folding=True, # WARNING: DNN inference with torch>=1.12 may require do_constant_folding=False
|
||||
input_names=['images'],
|
||||
output_names=output_names,
|
||||
dynamic_axes=dynamic or None)
|
||||
|
||||
# Checks
|
||||
model_onnx = onnx.load(f) # load onnx model
|
||||
onnx.checker.check_model(model_onnx) # check onnx model
|
||||
|
||||
# Metadata
|
||||
d = {'stride': int(max(model.stride)), 'names': model.names}
|
||||
for k, v in d.items():
|
||||
meta = model_onnx.metadata_props.add()
|
||||
meta.key, meta.value = k, str(v)
|
||||
onnx.save(model_onnx, f)
|
||||
|
||||
# Simplify
|
||||
if simplify:
|
||||
try:
|
||||
cuda = torch.cuda.is_available()
|
||||
check_requirements(('onnxruntime-gpu' if cuda else 'onnxruntime', 'onnx-simplifier>=0.4.1'))
|
||||
import onnxsim
|
||||
|
||||
LOGGER.info(f'{prefix} simplifying with onnx-simplifier {onnxsim.__version__}...')
|
||||
model_onnx, check = onnxsim.simplify(model_onnx)
|
||||
assert check, 'assert check failed'
|
||||
onnx.save(model_onnx, f)
|
||||
except Exception as e:
|
||||
LOGGER.info(f'{prefix} simplifier failure: {e}')
|
||||
return f, model_onnx
|
||||
|
||||
|
||||
@try_export
|
||||
def export_openvino(file, metadata, half, prefix=colorstr('OpenVINO:')):
|
||||
# YOLOv3 OpenVINO export
|
||||
check_requirements('openvino-dev') # requires openvino-dev: https://pypi.org/project/openvino-dev/
|
||||
import openvino.inference_engine as ie
|
||||
|
||||
LOGGER.info(f'\n{prefix} starting export with openvino {ie.__version__}...')
|
||||
f = str(file).replace('.pt', f'_openvino_model{os.sep}')
|
||||
|
||||
cmd = f"mo --input_model {file.with_suffix('.onnx')} --output_dir {f} --data_type {'FP16' if half else 'FP32'}"
|
||||
subprocess.run(cmd.split(), check=True, env=os.environ) # export
|
||||
yaml_save(Path(f) / file.with_suffix('.yaml').name, metadata) # add metadata.yaml
|
||||
return f, None
|
||||
|
||||
|
||||
@try_export
|
||||
def export_paddle(model, im, file, metadata, prefix=colorstr('PaddlePaddle:')):
|
||||
# YOLOv3 Paddle export
|
||||
check_requirements(('paddlepaddle', 'x2paddle'))
|
||||
import x2paddle
|
||||
from x2paddle.convert import pytorch2paddle
|
||||
|
||||
LOGGER.info(f'\n{prefix} starting export with X2Paddle {x2paddle.__version__}...')
|
||||
f = str(file).replace('.pt', f'_paddle_model{os.sep}')
|
||||
|
||||
pytorch2paddle(module=model, save_dir=f, jit_type='trace', input_examples=[im]) # export
|
||||
yaml_save(Path(f) / file.with_suffix('.yaml').name, metadata) # add metadata.yaml
|
||||
return f, None
|
||||
|
||||
|
||||
@try_export
|
||||
def export_coreml(model, im, file, int8, half, prefix=colorstr('CoreML:')):
|
||||
# YOLOv3 CoreML export
|
||||
check_requirements('coremltools')
|
||||
import coremltools as ct
|
||||
|
||||
LOGGER.info(f'\n{prefix} starting export with coremltools {ct.__version__}...')
|
||||
f = file.with_suffix('.mlmodel')
|
||||
|
||||
ts = torch.jit.trace(model, im, strict=False) # TorchScript model
|
||||
ct_model = ct.convert(ts, inputs=[ct.ImageType('image', shape=im.shape, scale=1 / 255, bias=[0, 0, 0])])
|
||||
bits, mode = (8, 'kmeans_lut') if int8 else (16, 'linear') if half else (32, None)
|
||||
if bits < 32:
|
||||
if MACOS: # quantization only supported on macOS
|
||||
with warnings.catch_warnings():
|
||||
warnings.filterwarnings('ignore', category=DeprecationWarning) # suppress numpy==1.20 float warning
|
||||
ct_model = ct.models.neural_network.quantization_utils.quantize_weights(ct_model, bits, mode)
|
||||
else:
|
||||
print(f'{prefix} quantization only supported on macOS, skipping...')
|
||||
ct_model.save(f)
|
||||
return f, ct_model
|
||||
|
||||
|
||||
@try_export
|
||||
def export_engine(model, im, file, half, dynamic, simplify, workspace=4, verbose=False, prefix=colorstr('TensorRT:')):
|
||||
# YOLOv3 TensorRT export https://developer.nvidia.com/tensorrt
|
||||
assert im.device.type != 'cpu', 'export running on CPU but must be on GPU, i.e. `python export.py --device 0`'
|
||||
# TorchScript model export
|
||||
try:
|
||||
import tensorrt as trt
|
||||
except Exception:
|
||||
if platform.system() == 'Linux':
|
||||
check_requirements('nvidia-tensorrt', cmds='-U --index-url https://pypi.ngc.nvidia.com')
|
||||
import tensorrt as trt
|
||||
LOGGER.info(f'\n{prefix} starting export with torch {torch.__version__}...')
|
||||
f = file.with_suffix('.torchscript.pt')
|
||||
|
||||
if trt.__version__[0] == '7': # TensorRT 7 handling https://github.com/ultralytics/yolov5/issues/6012
|
||||
grid = model.model[-1].anchor_grid
|
||||
model.model[-1].anchor_grid = [a[..., :1, :1, :] for a in grid]
|
||||
export_onnx(model, im, file, 12, dynamic, simplify) # opset 12
|
||||
model.model[-1].anchor_grid = grid
|
||||
else: # TensorRT >= 8
|
||||
check_version(trt.__version__, '8.0.0', hard=True) # require tensorrt>=8.0.0
|
||||
export_onnx(model, im, file, 12, dynamic, simplify) # opset 12
|
||||
onnx = file.with_suffix('.onnx')
|
||||
ts = torch.jit.trace(model, im, strict=False)
|
||||
d = {"shape": im.shape, "stride": int(max(model.stride)), "names": model.names}
|
||||
extra_files = {'config.txt': json.dumps(d)} # torch._C.ExtraFilesMap()
|
||||
(optimize_for_mobile(ts) if optimize else ts).save(f, _extra_files=extra_files)
|
||||
|
||||
LOGGER.info(f'\n{prefix} starting export with TensorRT {trt.__version__}...')
|
||||
assert onnx.exists(), f'failed to export ONNX file: {onnx}'
|
||||
f = file.with_suffix('.engine') # TensorRT engine file
|
||||
logger = trt.Logger(trt.Logger.INFO)
|
||||
if verbose:
|
||||
logger.min_severity = trt.Logger.Severity.VERBOSE
|
||||
|
||||
builder = trt.Builder(logger)
|
||||
config = builder.create_builder_config()
|
||||
config.max_workspace_size = workspace * 1 << 30
|
||||
# config.set_memory_pool_limit(trt.MemoryPoolType.WORKSPACE, workspace << 30) # fix TRT 8.4 deprecation notice
|
||||
|
||||
flag = (1 << int(trt.NetworkDefinitionCreationFlag.EXPLICIT_BATCH))
|
||||
network = builder.create_network(flag)
|
||||
parser = trt.OnnxParser(network, logger)
|
||||
if not parser.parse_from_file(str(onnx)):
|
||||
raise RuntimeError(f'failed to load ONNX file: {onnx}')
|
||||
|
||||
inputs = [network.get_input(i) for i in range(network.num_inputs)]
|
||||
outputs = [network.get_output(i) for i in range(network.num_outputs)]
|
||||
for inp in inputs:
|
||||
LOGGER.info(f'{prefix} input "{inp.name}" with shape{inp.shape} {inp.dtype}')
|
||||
for out in outputs:
|
||||
LOGGER.info(f'{prefix} output "{out.name}" with shape{out.shape} {out.dtype}')
|
||||
|
||||
if dynamic:
|
||||
if im.shape[0] <= 1:
|
||||
LOGGER.warning(f'{prefix} WARNING ⚠️ --dynamic model requires maximum --batch-size argument')
|
||||
profile = builder.create_optimization_profile()
|
||||
for inp in inputs:
|
||||
profile.set_shape(inp.name, (1, *im.shape[1:]), (max(1, im.shape[0] // 2), *im.shape[1:]), im.shape)
|
||||
config.add_optimization_profile(profile)
|
||||
|
||||
LOGGER.info(f'{prefix} building FP{16 if builder.platform_has_fast_fp16 and half else 32} engine as {f}')
|
||||
if builder.platform_has_fast_fp16 and half:
|
||||
config.set_flag(trt.BuilderFlag.FP16)
|
||||
with builder.build_engine(network, config) as engine, open(f, 'wb') as t:
|
||||
t.write(engine.serialize())
|
||||
return f, None
|
||||
LOGGER.info(f'{prefix} export success, saved as {f} ({file_size(f):.1f} MB)')
|
||||
except Exception as e:
|
||||
LOGGER.info(f'{prefix} export failure: {e}')
|
||||
|
||||
|
||||
@try_export
|
||||
def export_saved_model(model,
|
||||
im,
|
||||
file,
|
||||
dynamic,
|
||||
tf_nms=False,
|
||||
agnostic_nms=False,
|
||||
topk_per_class=100,
|
||||
topk_all=100,
|
||||
iou_thres=0.45,
|
||||
conf_thres=0.25,
|
||||
keras=False,
|
||||
prefix=colorstr('TensorFlow SavedModel:')):
|
||||
# YOLOv3 TensorFlow SavedModel export
|
||||
def export_onnx(model, im, file, opset, train, dynamic, simplify, prefix=colorstr('ONNX:')):
|
||||
# ONNX export
|
||||
try:
|
||||
check_requirements(('onnx',))
|
||||
import onnx
|
||||
|
||||
LOGGER.info(f'\n{prefix} starting export with onnx {onnx.__version__}...')
|
||||
f = file.with_suffix('.onnx')
|
||||
|
||||
torch.onnx.export(model, im, f, verbose=False, opset_version=opset,
|
||||
training=torch.onnx.TrainingMode.TRAINING if train else torch.onnx.TrainingMode.EVAL,
|
||||
do_constant_folding=not train,
|
||||
input_names=['images'],
|
||||
output_names=['output'],
|
||||
dynamic_axes={'images': {0: 'batch', 2: 'height', 3: 'width'}, # shape(1,3,640,640)
|
||||
'output': {0: 'batch', 1: 'anchors'} # shape(1,25200,85)
|
||||
} if dynamic else None)
|
||||
|
||||
# Checks
|
||||
model_onnx = onnx.load(f) # load onnx model
|
||||
onnx.checker.check_model(model_onnx) # check onnx model
|
||||
# LOGGER.info(onnx.helper.printable_graph(model_onnx.graph)) # print
|
||||
|
||||
# Simplify
|
||||
if simplify:
|
||||
try:
|
||||
check_requirements(('onnx-simplifier',))
|
||||
import onnxsim
|
||||
|
||||
LOGGER.info(f'{prefix} simplifying with onnx-simplifier {onnxsim.__version__}...')
|
||||
model_onnx, check = onnxsim.simplify(
|
||||
model_onnx,
|
||||
dynamic_input_shape=dynamic,
|
||||
input_shapes={'images': list(im.shape)} if dynamic else None)
|
||||
assert check, 'assert check failed'
|
||||
onnx.save(model_onnx, f)
|
||||
except Exception as e:
|
||||
LOGGER.info(f'{prefix} simplifier failure: {e}')
|
||||
LOGGER.info(f'{prefix} export success, saved as {f} ({file_size(f):.1f} MB)')
|
||||
LOGGER.info(f"{prefix} run --dynamic ONNX model inference with: 'python detect.py --weights {f}'")
|
||||
except Exception as e:
|
||||
LOGGER.info(f'{prefix} export failure: {e}')
|
||||
|
||||
|
||||
def export_coreml(model, im, file, prefix=colorstr('CoreML:')):
|
||||
# CoreML export
|
||||
ct_model = None
|
||||
try:
|
||||
check_requirements(('coremltools',))
|
||||
import coremltools as ct
|
||||
|
||||
LOGGER.info(f'\n{prefix} starting export with coremltools {ct.__version__}...')
|
||||
f = file.with_suffix('.mlmodel')
|
||||
|
||||
model.train() # CoreML exports should be placed in model.train() mode
|
||||
ts = torch.jit.trace(model, im, strict=False) # TorchScript model
|
||||
ct_model = ct.convert(ts, inputs=[ct.ImageType('image', shape=im.shape, scale=1 / 255, bias=[0, 0, 0])])
|
||||
ct_model.save(f)
|
||||
|
||||
LOGGER.info(f'{prefix} export success, saved as {f} ({file_size(f):.1f} MB)')
|
||||
except Exception as e:
|
||||
LOGGER.info(f'\n{prefix} export failure: {e}')
|
||||
|
||||
return ct_model
|
||||
|
||||
|
||||
def export_saved_model(model, im, file, dynamic,
|
||||
tf_nms=False, agnostic_nms=False, topk_per_class=100, topk_all=100, iou_thres=0.45,
|
||||
conf_thres=0.25, prefix=colorstr('TensorFlow saved_model:')):
|
||||
# TensorFlow saved_model export
|
||||
keras_model = None
|
||||
try:
|
||||
import tensorflow as tf
|
||||
except Exception:
|
||||
check_requirements(f"tensorflow{'' if torch.cuda.is_available() else '-macos' if MACOS else '-cpu'}")
|
||||
import tensorflow as tf
|
||||
from tensorflow.python.framework.convert_to_constants import convert_variables_to_constants_v2
|
||||
from tensorflow import keras
|
||||
|
||||
from models.tf import TFModel
|
||||
from models.tf import TFDetect, TFModel
|
||||
|
||||
LOGGER.info(f'\n{prefix} starting export with tensorflow {tf.__version__}...')
|
||||
f = str(file).replace('.pt', '_saved_model')
|
||||
batch_size, ch, *imgsz = list(im.shape) # BCHW
|
||||
LOGGER.info(f'\n{prefix} starting export with tensorflow {tf.__version__}...')
|
||||
f = str(file).replace('.pt', '_saved_model')
|
||||
batch_size, ch, *imgsz = list(im.shape) # BCHW
|
||||
|
||||
tf_model = TFModel(cfg=model.yaml, model=model, nc=model.nc, imgsz=imgsz)
|
||||
im = tf.zeros((batch_size, *imgsz, ch)) # BHWC order for TensorFlow
|
||||
_ = tf_model.predict(im, tf_nms, agnostic_nms, topk_per_class, topk_all, iou_thres, conf_thres)
|
||||
inputs = tf.keras.Input(shape=(*imgsz, ch), batch_size=None if dynamic else batch_size)
|
||||
outputs = tf_model.predict(inputs, tf_nms, agnostic_nms, topk_per_class, topk_all, iou_thres, conf_thres)
|
||||
keras_model = tf.keras.Model(inputs=inputs, outputs=outputs)
|
||||
keras_model.trainable = False
|
||||
keras_model.summary()
|
||||
if keras:
|
||||
tf_model = TFModel(cfg=model.yaml, model=model, nc=model.nc, imgsz=imgsz)
|
||||
im = tf.zeros((batch_size, *imgsz, 3)) # BHWC order for TensorFlow
|
||||
y = tf_model.predict(im, tf_nms, agnostic_nms, topk_per_class, topk_all, iou_thres, conf_thres)
|
||||
inputs = keras.Input(shape=(*imgsz, 3), batch_size=None if dynamic else batch_size)
|
||||
outputs = tf_model.predict(inputs, tf_nms, agnostic_nms, topk_per_class, topk_all, iou_thres, conf_thres)
|
||||
keras_model = keras.Model(inputs=inputs, outputs=outputs)
|
||||
keras_model.trainable = False
|
||||
keras_model.summary()
|
||||
keras_model.save(f, save_format='tf')
|
||||
else:
|
||||
spec = tf.TensorSpec(keras_model.inputs[0].shape, keras_model.inputs[0].dtype)
|
||||
|
||||
LOGGER.info(f'{prefix} export success, saved as {f} ({file_size(f):.1f} MB)')
|
||||
except Exception as e:
|
||||
LOGGER.info(f'\n{prefix} export failure: {e}')
|
||||
|
||||
return keras_model
|
||||
|
||||
|
||||
def export_pb(keras_model, im, file, prefix=colorstr('TensorFlow GraphDef:')):
|
||||
# TensorFlow GraphDef *.pb export https://github.com/leimao/Frozen_Graph_TensorFlow
|
||||
try:
|
||||
import tensorflow as tf
|
||||
from tensorflow.python.framework.convert_to_constants import convert_variables_to_constants_v2
|
||||
|
||||
LOGGER.info(f'\n{prefix} starting export with tensorflow {tf.__version__}...')
|
||||
f = file.with_suffix('.pb')
|
||||
|
||||
m = tf.function(lambda x: keras_model(x)) # full model
|
||||
m = m.get_concrete_function(spec)
|
||||
m = m.get_concrete_function(tf.TensorSpec(keras_model.inputs[0].shape, keras_model.inputs[0].dtype))
|
||||
frozen_func = convert_variables_to_constants_v2(m)
|
||||
tfm = tf.Module()
|
||||
tfm.__call__ = tf.function(lambda x: frozen_func(x)[:4] if tf_nms else frozen_func(x), [spec])
|
||||
tfm.__call__(im)
|
||||
tf.saved_model.save(tfm,
|
||||
f,
|
||||
options=tf.saved_model.SaveOptions(experimental_custom_gradients=False) if check_version(
|
||||
tf.__version__, '2.6') else tf.saved_model.SaveOptions())
|
||||
return f, keras_model
|
||||
frozen_func.graph.as_graph_def()
|
||||
tf.io.write_graph(graph_or_graph_def=frozen_func.graph, logdir=str(f.parent), name=f.name, as_text=False)
|
||||
|
||||
LOGGER.info(f'{prefix} export success, saved as {f} ({file_size(f):.1f} MB)')
|
||||
except Exception as e:
|
||||
LOGGER.info(f'\n{prefix} export failure: {e}')
|
||||
|
||||
|
||||
@try_export
|
||||
def export_pb(keras_model, file, prefix=colorstr('TensorFlow GraphDef:')):
|
||||
# YOLOv3 TensorFlow GraphDef *.pb export https://github.com/leimao/Frozen_Graph_TensorFlow
|
||||
import tensorflow as tf
|
||||
from tensorflow.python.framework.convert_to_constants import convert_variables_to_constants_v2
|
||||
def export_tflite(keras_model, im, file, int8, data, ncalib, prefix=colorstr('TensorFlow Lite:')):
|
||||
# TensorFlow Lite export
|
||||
try:
|
||||
import tensorflow as tf
|
||||
|
||||
LOGGER.info(f'\n{prefix} starting export with tensorflow {tf.__version__}...')
|
||||
f = file.with_suffix('.pb')
|
||||
|
||||
m = tf.function(lambda x: keras_model(x)) # full model
|
||||
m = m.get_concrete_function(tf.TensorSpec(keras_model.inputs[0].shape, keras_model.inputs[0].dtype))
|
||||
frozen_func = convert_variables_to_constants_v2(m)
|
||||
frozen_func.graph.as_graph_def()
|
||||
tf.io.write_graph(graph_or_graph_def=frozen_func.graph, logdir=str(f.parent), name=f.name, as_text=False)
|
||||
return f, None
|
||||
|
||||
|
||||
@try_export
|
||||
def export_tflite(keras_model, im, file, int8, data, nms, agnostic_nms, prefix=colorstr('TensorFlow Lite:')):
|
||||
# YOLOv3 TensorFlow Lite export
|
||||
import tensorflow as tf
|
||||
|
||||
LOGGER.info(f'\n{prefix} starting export with tensorflow {tf.__version__}...')
|
||||
batch_size, ch, *imgsz = list(im.shape) # BCHW
|
||||
f = str(file).replace('.pt', '-fp16.tflite')
|
||||
|
||||
converter = tf.lite.TFLiteConverter.from_keras_model(keras_model)
|
||||
converter.target_spec.supported_ops = [tf.lite.OpsSet.TFLITE_BUILTINS]
|
||||
converter.target_spec.supported_types = [tf.float16]
|
||||
converter.optimizations = [tf.lite.Optimize.DEFAULT]
|
||||
if int8:
|
||||
from models.tf import representative_dataset_gen
|
||||
dataset = LoadImages(check_dataset(check_yaml(data))['train'], img_size=imgsz, auto=False)
|
||||
converter.representative_dataset = lambda: representative_dataset_gen(dataset, ncalib=100)
|
||||
converter.target_spec.supported_ops = [tf.lite.OpsSet.TFLITE_BUILTINS_INT8]
|
||||
converter.target_spec.supported_types = []
|
||||
converter.inference_input_type = tf.uint8 # or tf.int8
|
||||
converter.inference_output_type = tf.uint8 # or tf.int8
|
||||
converter.experimental_new_quantizer = True
|
||||
f = str(file).replace('.pt', '-int8.tflite')
|
||||
if nms or agnostic_nms:
|
||||
converter.target_spec.supported_ops.append(tf.lite.OpsSet.SELECT_TF_OPS)
|
||||
|
||||
tflite_model = converter.convert()
|
||||
open(f, 'wb').write(tflite_model)
|
||||
return f, None
|
||||
LOGGER.info(f'\n{prefix} starting export with tensorflow {tf.__version__}...')
|
||||
batch_size, ch, *imgsz = list(im.shape) # BCHW
|
||||
f = str(file).replace('.pt', '-fp16.tflite')
|
||||
|
||||
converter = tf.lite.TFLiteConverter.from_keras_model(keras_model)
|
||||
converter.target_spec.supported_ops = [tf.lite.OpsSet.TFLITE_BUILTINS]
|
||||
converter.target_spec.supported_types = [tf.float16]
|
||||
converter.optimizations = [tf.lite.Optimize.DEFAULT]
|
||||
if int8:
|
||||
dataset = LoadImages(check_dataset(data)['train'], img_size=imgsz, auto=False) # representative data
|
||||
converter.representative_dataset = lambda: representative_dataset_gen(dataset, ncalib)
|
||||
converter.target_spec.supported_ops = [tf.lite.OpsSet.TFLITE_BUILTINS_INT8]
|
||||
converter.target_spec.supported_types = []
|
||||
converter.inference_input_type = tf.uint8 # or tf.int8
|
||||
converter.inference_output_type = tf.uint8 # or tf.int8
|
||||
converter.experimental_new_quantizer = False
|
||||
f = str(file).replace('.pt', '-int8.tflite')
|
||||
|
||||
tflite_model = converter.convert()
|
||||
open(f, "wb").write(tflite_model)
|
||||
LOGGER.info(f'{prefix} export success, saved as {f} ({file_size(f):.1f} MB)')
|
||||
|
||||
except Exception as e:
|
||||
LOGGER.info(f'\n{prefix} export failure: {e}')
|
||||
|
||||
|
||||
@try_export
|
||||
def export_edgetpu(file, prefix=colorstr('Edge TPU:')):
|
||||
# YOLOv3 Edge TPU export https://coral.ai/docs/edgetpu/models-intro/
|
||||
cmd = 'edgetpu_compiler --version'
|
||||
help_url = 'https://coral.ai/docs/edgetpu/compiler/'
|
||||
assert platform.system() == 'Linux', f'export only supported on Linux. See {help_url}'
|
||||
if subprocess.run(f'{cmd} >/dev/null', shell=True).returncode != 0:
|
||||
LOGGER.info(f'\n{prefix} export requires Edge TPU compiler. Attempting install from {help_url}')
|
||||
sudo = subprocess.run('sudo --version >/dev/null', shell=True).returncode == 0 # sudo installed on system
|
||||
for c in (
|
||||
'curl https://packages.cloud.google.com/apt/doc/apt-key.gpg | sudo apt-key add -',
|
||||
'echo "deb https://packages.cloud.google.com/apt coral-edgetpu-stable main" | sudo tee /etc/apt/sources.list.d/coral-edgetpu.list',
|
||||
'sudo apt-get update', 'sudo apt-get install edgetpu-compiler'):
|
||||
subprocess.run(c if sudo else c.replace('sudo ', ''), shell=True, check=True)
|
||||
ver = subprocess.run(cmd, shell=True, capture_output=True, check=True).stdout.decode().split()[-1]
|
||||
def export_tfjs(keras_model, im, file, prefix=colorstr('TensorFlow.js:')):
|
||||
# TensorFlow.js export
|
||||
try:
|
||||
check_requirements(('tensorflowjs',))
|
||||
import re
|
||||
|
||||
LOGGER.info(f'\n{prefix} starting export with Edge TPU compiler {ver}...')
|
||||
f = str(file).replace('.pt', '-int8_edgetpu.tflite') # Edge TPU model
|
||||
f_tfl = str(file).replace('.pt', '-int8.tflite') # TFLite model
|
||||
import tensorflowjs as tfjs
|
||||
|
||||
cmd = f'edgetpu_compiler -s -d -k 10 --out_dir {file.parent} {f_tfl}'
|
||||
subprocess.run(cmd.split(), check=True)
|
||||
return f, None
|
||||
LOGGER.info(f'\n{prefix} starting export with tensorflowjs {tfjs.__version__}...')
|
||||
f = str(file).replace('.pt', '_web_model') # js dir
|
||||
f_pb = file.with_suffix('.pb') # *.pb path
|
||||
f_json = f + '/model.json' # *.json path
|
||||
|
||||
cmd = f"tensorflowjs_converter --input_format=tf_frozen_model " \
|
||||
f"--output_node_names='Identity,Identity_1,Identity_2,Identity_3' {f_pb} {f}"
|
||||
subprocess.run(cmd, shell=True)
|
||||
|
||||
json = open(f_json).read()
|
||||
with open(f_json, 'w') as j: # sort JSON Identity_* in ascending order
|
||||
subst = re.sub(
|
||||
r'{"outputs": {"Identity.?.?": {"name": "Identity.?.?"}, '
|
||||
r'"Identity.?.?": {"name": "Identity.?.?"}, '
|
||||
r'"Identity.?.?": {"name": "Identity.?.?"}, '
|
||||
r'"Identity.?.?": {"name": "Identity.?.?"}}}',
|
||||
r'{"outputs": {"Identity": {"name": "Identity"}, '
|
||||
r'"Identity_1": {"name": "Identity_1"}, '
|
||||
r'"Identity_2": {"name": "Identity_2"}, '
|
||||
r'"Identity_3": {"name": "Identity_3"}}}',
|
||||
json)
|
||||
j.write(subst)
|
||||
|
||||
LOGGER.info(f'{prefix} export success, saved as {f} ({file_size(f):.1f} MB)')
|
||||
except Exception as e:
|
||||
LOGGER.info(f'\n{prefix} export failure: {e}')
|
||||
|
||||
|
||||
@try_export
|
||||
def export_tfjs(file, int8, prefix=colorstr('TensorFlow.js:')):
|
||||
# YOLOv3 TensorFlow.js export
|
||||
check_requirements('tensorflowjs')
|
||||
import tensorflowjs as tfjs
|
||||
|
||||
LOGGER.info(f'\n{prefix} starting export with tensorflowjs {tfjs.__version__}...')
|
||||
f = str(file).replace('.pt', '_web_model') # js dir
|
||||
f_pb = file.with_suffix('.pb') # *.pb path
|
||||
f_json = f'{f}/model.json' # *.json path
|
||||
|
||||
int8_export = ' --quantize_uint8 ' if int8 else ''
|
||||
|
||||
cmd = f'tensorflowjs_converter --input_format=tf_frozen_model {int8_export}' \
|
||||
f'--output_node_names=Identity,Identity_1,Identity_2,Identity_3 {f_pb} {f}'
|
||||
subprocess.run(cmd.split())
|
||||
|
||||
json = Path(f_json).read_text()
|
||||
with open(f_json, 'w') as j: # sort JSON Identity_* in ascending order
|
||||
subst = re.sub(
|
||||
r'{"outputs": {"Identity.?.?": {"name": "Identity.?.?"}, '
|
||||
r'"Identity.?.?": {"name": "Identity.?.?"}, '
|
||||
r'"Identity.?.?": {"name": "Identity.?.?"}, '
|
||||
r'"Identity.?.?": {"name": "Identity.?.?"}}}', r'{"outputs": {"Identity": {"name": "Identity"}, '
|
||||
r'"Identity_1": {"name": "Identity_1"}, '
|
||||
r'"Identity_2": {"name": "Identity_2"}, '
|
||||
r'"Identity_3": {"name": "Identity_3"}}}', json)
|
||||
j.write(subst)
|
||||
return f, None
|
||||
|
||||
|
||||
def add_tflite_metadata(file, metadata, num_outputs):
|
||||
# Add metadata to *.tflite models per https://www.tensorflow.org/lite/models/convert/metadata
|
||||
with contextlib.suppress(ImportError):
|
||||
# check_requirements('tflite_support')
|
||||
from tflite_support import flatbuffers
|
||||
from tflite_support import metadata as _metadata
|
||||
from tflite_support import metadata_schema_py_generated as _metadata_fb
|
||||
|
||||
tmp_file = Path('/tmp/meta.txt')
|
||||
with open(tmp_file, 'w') as meta_f:
|
||||
meta_f.write(str(metadata))
|
||||
|
||||
model_meta = _metadata_fb.ModelMetadataT()
|
||||
label_file = _metadata_fb.AssociatedFileT()
|
||||
label_file.name = tmp_file.name
|
||||
model_meta.associatedFiles = [label_file]
|
||||
|
||||
subgraph = _metadata_fb.SubGraphMetadataT()
|
||||
subgraph.inputTensorMetadata = [_metadata_fb.TensorMetadataT()]
|
||||
subgraph.outputTensorMetadata = [_metadata_fb.TensorMetadataT()] * num_outputs
|
||||
model_meta.subgraphMetadata = [subgraph]
|
||||
|
||||
b = flatbuffers.Builder(0)
|
||||
b.Finish(model_meta.Pack(b), _metadata.MetadataPopulator.METADATA_FILE_IDENTIFIER)
|
||||
metadata_buf = b.Output()
|
||||
|
||||
populator = _metadata.MetadataPopulator.with_model_file(file)
|
||||
populator.load_metadata_buffer(metadata_buf)
|
||||
populator.load_associated_files([str(tmp_file)])
|
||||
populator.populate()
|
||||
tmp_file.unlink()
|
||||
|
||||
|
||||
@smart_inference_mode()
|
||||
def run(
|
||||
data=ROOT / 'data/coco128.yaml', # 'dataset.yaml path'
|
||||
weights=ROOT / 'yolov5s.pt', # weights path
|
||||
@torch.no_grad()
|
||||
def run(data=ROOT / 'data/coco128.yaml', # 'dataset.yaml path'
|
||||
weights=ROOT / 'yolov3.pt', # weights path
|
||||
imgsz=(640, 640), # image (height, width)
|
||||
batch_size=1, # batch size
|
||||
device='cpu', # cuda device, i.e. 0 or 0,1,2,3 or cpu
|
||||
include=('torchscript', 'onnx'), # include formats
|
||||
include=('torchscript', 'onnx', 'coreml'), # include formats
|
||||
half=False, # FP16 half-precision export
|
||||
inplace=False, # set YOLOv3 Detect() inplace=True
|
||||
keras=False, # use Keras
|
||||
inplace=False, # set Detect() inplace=True
|
||||
train=False, # model.train() mode
|
||||
optimize=False, # TorchScript: optimize for mobile
|
||||
int8=False, # CoreML/TF INT8 quantization
|
||||
dynamic=False, # ONNX/TF/TensorRT: dynamic axes
|
||||
dynamic=False, # ONNX/TF: dynamic axes
|
||||
simplify=False, # ONNX: simplify model
|
||||
opset=12, # ONNX: opset version
|
||||
verbose=False, # TensorRT: verbose log
|
||||
workspace=4, # TensorRT: workspace size (GB)
|
||||
nms=False, # TF: add NMS to model
|
||||
agnostic_nms=False, # TF: add agnostic NMS to model
|
||||
topk_per_class=100, # TF.js NMS: topk per class to keep
|
||||
topk_all=100, # TF.js NMS: topk for all classes to keep
|
||||
iou_thres=0.45, # TF.js NMS: IoU threshold
|
||||
conf_thres=0.25, # TF.js NMS: confidence threshold
|
||||
):
|
||||
conf_thres=0.25 # TF.js NMS: confidence threshold
|
||||
):
|
||||
t = time.time()
|
||||
include = [x.lower() for x in include] # to lowercase
|
||||
fmts = tuple(export_formats()['Argument'][1:]) # --include arguments
|
||||
flags = [x in include for x in fmts]
|
||||
assert sum(flags) == len(include), f'ERROR: Invalid --include {include}, valid --include arguments are {fmts}'
|
||||
jit, onnx, xml, engine, coreml, saved_model, pb, tflite, edgetpu, tfjs, paddle = flags # export booleans
|
||||
file = Path(url2file(weights) if str(weights).startswith(('http:/', 'https:/')) else weights) # PyTorch weights
|
||||
include = [x.lower() for x in include]
|
||||
tf_exports = list(x in include for x in ('saved_model', 'pb', 'tflite', 'tfjs')) # TensorFlow exports
|
||||
imgsz *= 2 if len(imgsz) == 1 else 1 # expand
|
||||
file = Path(url2file(weights) if str(weights).startswith(('http:/', 'https:/')) else weights)
|
||||
|
||||
# Load PyTorch model
|
||||
device = select_device(device)
|
||||
if half:
|
||||
assert device.type != 'cpu' or coreml, '--half only compatible with GPU export, i.e. use --device 0'
|
||||
assert not dynamic, '--half not compatible with --dynamic, i.e. use either --half or --dynamic but not both'
|
||||
model = attempt_load(weights, device=device, inplace=True, fuse=True) # load FP32 model
|
||||
|
||||
# Checks
|
||||
imgsz *= 2 if len(imgsz) == 1 else 1 # expand
|
||||
if optimize:
|
||||
assert device.type == 'cpu', '--optimize not compatible with cuda devices, i.e. use --device cpu'
|
||||
assert not (device.type == 'cpu' and half), '--half only compatible with GPU export, i.e. use --device 0'
|
||||
model = attempt_load(weights, map_location=device, inplace=True, fuse=True) # load FP32 model
|
||||
nc, names = model.nc, model.names # number of classes, class names
|
||||
|
||||
# Input
|
||||
gs = int(max(model.stride)) # grid size (max stride)
|
||||
@ -540,116 +289,81 @@ def run(
|
||||
im = torch.zeros(batch_size, 3, *imgsz).to(device) # image size(1,3,320,192) BCHW iDetection
|
||||
|
||||
# Update model
|
||||
model.eval()
|
||||
if half:
|
||||
im, model = im.half(), model.half() # to FP16
|
||||
model.train() if train else model.eval() # training mode = no Detect() layer grid construction
|
||||
for k, m in model.named_modules():
|
||||
if isinstance(m, Detect):
|
||||
if isinstance(m, Conv): # assign export-friendly activations
|
||||
if isinstance(m.act, nn.SiLU):
|
||||
m.act = SiLU()
|
||||
elif isinstance(m, Detect):
|
||||
m.inplace = inplace
|
||||
m.dynamic = dynamic
|
||||
m.export = True
|
||||
m.onnx_dynamic = dynamic
|
||||
# m.forward = m.forward_export # assign forward (optional)
|
||||
|
||||
for _ in range(2):
|
||||
y = model(im) # dry runs
|
||||
if half and not coreml:
|
||||
im, model = im.half(), model.half() # to FP16
|
||||
shape = tuple((y[0] if isinstance(y, tuple) else y).shape) # model output shape
|
||||
metadata = {'stride': int(max(model.stride)), 'names': model.names} # model metadata
|
||||
LOGGER.info(f"\n{colorstr('PyTorch:')} starting from {file} with output shape {shape} ({file_size(file):.1f} MB)")
|
||||
LOGGER.info(f"\n{colorstr('PyTorch:')} starting from {file} ({file_size(file):.1f} MB)")
|
||||
|
||||
# Exports
|
||||
f = [''] * len(fmts) # exported filenames
|
||||
warnings.filterwarnings(action='ignore', category=torch.jit.TracerWarning) # suppress TracerWarning
|
||||
if jit: # TorchScript
|
||||
f[0], _ = export_torchscript(model, im, file, optimize)
|
||||
if engine: # TensorRT required before ONNX
|
||||
f[1], _ = export_engine(model, im, file, half, dynamic, simplify, workspace, verbose)
|
||||
if onnx or xml: # OpenVINO requires ONNX
|
||||
f[2], _ = export_onnx(model, im, file, opset, dynamic, simplify)
|
||||
if xml: # OpenVINO
|
||||
f[3], _ = export_openvino(file, metadata, half)
|
||||
if coreml: # CoreML
|
||||
f[4], _ = export_coreml(model, im, file, int8, half)
|
||||
if any((saved_model, pb, tflite, edgetpu, tfjs)): # TensorFlow formats
|
||||
assert not tflite or not tfjs, 'TFLite and TF.js models must be exported separately, please pass only one type.'
|
||||
assert not isinstance(model, ClassificationModel), 'ClassificationModel export to TF formats not yet supported.'
|
||||
f[5], s_model = export_saved_model(model.cpu(),
|
||||
im,
|
||||
file,
|
||||
dynamic,
|
||||
tf_nms=nms or agnostic_nms or tfjs,
|
||||
agnostic_nms=agnostic_nms or tfjs,
|
||||
topk_per_class=topk_per_class,
|
||||
topk_all=topk_all,
|
||||
iou_thres=iou_thres,
|
||||
conf_thres=conf_thres,
|
||||
keras=keras)
|
||||
if 'torchscript' in include:
|
||||
export_torchscript(model, im, file, optimize)
|
||||
if 'onnx' in include:
|
||||
export_onnx(model, im, file, opset, train, dynamic, simplify)
|
||||
if 'coreml' in include:
|
||||
export_coreml(model, im, file)
|
||||
|
||||
# TensorFlow Exports
|
||||
if any(tf_exports):
|
||||
pb, tflite, tfjs = tf_exports[1:]
|
||||
assert not (tflite and tfjs), 'TFLite and TF.js models must be exported separately, please pass only one type.'
|
||||
model = export_saved_model(model.cpu(), im, file, dynamic, tf_nms=tfjs, agnostic_nms=tfjs,
|
||||
topk_per_class=topk_per_class, topk_all=topk_all, conf_thres=conf_thres,
|
||||
iou_thres=iou_thres) # keras model
|
||||
if pb or tfjs: # pb prerequisite to tfjs
|
||||
f[6], _ = export_pb(s_model, file)
|
||||
if tflite or edgetpu:
|
||||
f[7], _ = export_tflite(s_model, im, file, int8 or edgetpu, data=data, nms=nms, agnostic_nms=agnostic_nms)
|
||||
if edgetpu:
|
||||
f[8], _ = export_edgetpu(file)
|
||||
add_tflite_metadata(f[8] or f[7], metadata, num_outputs=len(s_model.outputs))
|
||||
export_pb(model, im, file)
|
||||
if tflite:
|
||||
export_tflite(model, im, file, int8=int8, data=data, ncalib=100)
|
||||
if tfjs:
|
||||
f[9], _ = export_tfjs(file, int8)
|
||||
if paddle: # PaddlePaddle
|
||||
f[10], _ = export_paddle(model, im, file, metadata)
|
||||
export_tfjs(model, im, file)
|
||||
|
||||
# Finish
|
||||
f = [str(x) for x in f if x] # filter out '' and None
|
||||
if any(f):
|
||||
cls, det, seg = (isinstance(model, x) for x in (ClassificationModel, DetectionModel, SegmentationModel)) # type
|
||||
det &= not seg # segmentation models inherit from SegmentationModel(DetectionModel)
|
||||
dir = Path('segment' if seg else 'classify' if cls else '')
|
||||
h = '--half' if half else '' # --half FP16 inference arg
|
||||
s = '# WARNING ⚠️ ClassificationModel not yet supported for PyTorch Hub AutoShape inference' if cls else \
|
||||
'# WARNING ⚠️ SegmentationModel not yet supported for PyTorch Hub AutoShape inference' if seg else ''
|
||||
LOGGER.info(f'\nExport complete ({time.time() - t:.1f}s)'
|
||||
f"\nResults saved to {colorstr('bold', file.parent.resolve())}"
|
||||
f"\nDetect: python {dir / ('detect.py' if det else 'predict.py')} --weights {f[-1]} {h}"
|
||||
f"\nValidate: python {dir / 'val.py'} --weights {f[-1]} {h}"
|
||||
f"\nPyTorch Hub: model = torch.hub.load('ultralytics/yolov5', 'custom', '{f[-1]}') {s}"
|
||||
f'\nVisualize: https://netron.app')
|
||||
return f # return list of exported files/dirs
|
||||
LOGGER.info(f'\nExport complete ({time.time() - t:.2f}s)'
|
||||
f"\nResults saved to {colorstr('bold', file.parent.resolve())}"
|
||||
f'\nVisualize with https://netron.app')
|
||||
|
||||
|
||||
def parse_opt(known=False):
|
||||
def parse_opt():
|
||||
parser = argparse.ArgumentParser()
|
||||
parser.add_argument('--data', type=str, default=ROOT / 'data/coco128.yaml', help='dataset.yaml path')
|
||||
parser.add_argument('--weights', nargs='+', type=str, default=ROOT / 'yolov3-tiny.pt', help='model.pt path(s)')
|
||||
parser.add_argument('--weights', type=str, default=ROOT / 'yolov3.pt', help='weights path')
|
||||
parser.add_argument('--imgsz', '--img', '--img-size', nargs='+', type=int, default=[640, 640], help='image (h, w)')
|
||||
parser.add_argument('--batch-size', type=int, default=1, help='batch size')
|
||||
parser.add_argument('--device', default='cpu', help='cuda device, i.e. 0 or 0,1,2,3 or cpu')
|
||||
parser.add_argument('--half', action='store_true', help='FP16 half-precision export')
|
||||
parser.add_argument('--inplace', action='store_true', help='set Detect() inplace=True')
|
||||
parser.add_argument('--keras', action='store_true', help='TF: use Keras')
|
||||
parser.add_argument('--inplace', action='store_true', help='set YOLOv3 Detect() inplace=True')
|
||||
parser.add_argument('--train', action='store_true', help='model.train() mode')
|
||||
parser.add_argument('--optimize', action='store_true', help='TorchScript: optimize for mobile')
|
||||
parser.add_argument('--int8', action='store_true', help='CoreML/TF INT8 quantization')
|
||||
parser.add_argument('--dynamic', action='store_true', help='ONNX/TF/TensorRT: dynamic axes')
|
||||
parser.add_argument('--dynamic', action='store_true', help='ONNX/TF: dynamic axes')
|
||||
parser.add_argument('--simplify', action='store_true', help='ONNX: simplify model')
|
||||
parser.add_argument('--opset', type=int, default=17, help='ONNX: opset version')
|
||||
parser.add_argument('--verbose', action='store_true', help='TensorRT: verbose log')
|
||||
parser.add_argument('--workspace', type=int, default=4, help='TensorRT: workspace size (GB)')
|
||||
parser.add_argument('--nms', action='store_true', help='TF: add NMS to model')
|
||||
parser.add_argument('--agnostic-nms', action='store_true', help='TF: add agnostic NMS to model')
|
||||
parser.add_argument('--opset', type=int, default=13, help='ONNX: opset version')
|
||||
parser.add_argument('--topk-per-class', type=int, default=100, help='TF.js NMS: topk per class to keep')
|
||||
parser.add_argument('--topk-all', type=int, default=100, help='TF.js NMS: topk for all classes to keep')
|
||||
parser.add_argument('--iou-thres', type=float, default=0.45, help='TF.js NMS: IoU threshold')
|
||||
parser.add_argument('--conf-thres', type=float, default=0.25, help='TF.js NMS: confidence threshold')
|
||||
parser.add_argument(
|
||||
'--include',
|
||||
nargs='+',
|
||||
default=['torchscript'],
|
||||
help='torchscript, onnx, openvino, engine, coreml, saved_model, pb, tflite, edgetpu, tfjs, paddle')
|
||||
opt = parser.parse_known_args()[0] if known else parser.parse_args()
|
||||
print_args(vars(opt))
|
||||
parser.add_argument('--include', nargs='+',
|
||||
default=['torchscript', 'onnx'],
|
||||
help='available formats are (torchscript, onnx, coreml, saved_model, pb, tflite, tfjs)')
|
||||
opt = parser.parse_args()
|
||||
print_args(FILE.stem, opt)
|
||||
return opt
|
||||
|
||||
|
||||
def main(opt):
|
||||
for opt.weights in (opt.weights if isinstance(opt.weights, list) else [opt.weights]):
|
||||
run(**vars(opt))
|
||||
run(**vars(opt))
|
||||
|
||||
|
||||
if __name__ == '__main__':
|
||||
if __name__ == "__main__":
|
||||
opt = parse_opt()
|
||||
main(opt)
|
||||
|
||||
@ -1,66 +1,52 @@
|
||||
# YOLOv3 🚀 by Ultralytics, GPL-3.0 license
|
||||
"""
|
||||
PyTorch Hub models https://pytorch.org/hub/ultralytics_yolov5
|
||||
PyTorch Hub models https://pytorch.org/hub/ultralytics_yolov5/
|
||||
|
||||
Usage:
|
||||
import torch
|
||||
model = torch.hub.load('ultralytics/yolov5', 'yolov5s') # official model
|
||||
model = torch.hub.load('ultralytics/yolov5:master', 'yolov5s') # from branch
|
||||
model = torch.hub.load('ultralytics/yolov5', 'custom', 'yolov5s.pt') # custom/local model
|
||||
model = torch.hub.load('.', 'custom', 'yolov5s.pt', source='local') # local repo
|
||||
model = torch.hub.load('ultralytics/yolov3', 'yolov3')
|
||||
"""
|
||||
|
||||
import torch
|
||||
|
||||
|
||||
def _create(name, pretrained=True, channels=3, classes=80, autoshape=True, verbose=True, device=None):
|
||||
"""Creates or loads a YOLOv3 model
|
||||
"""Creates a specified model
|
||||
|
||||
Arguments:
|
||||
name (str): model name 'yolov5s' or path 'path/to/best.pt'
|
||||
name (str): name of model, i.e. 'yolov3'
|
||||
pretrained (bool): load pretrained weights into the model
|
||||
channels (int): number of input channels
|
||||
classes (int): number of model classes
|
||||
autoshape (bool): apply YOLOv3 .autoshape() wrapper to model
|
||||
autoshape (bool): apply .autoshape() wrapper to model
|
||||
verbose (bool): print all information to screen
|
||||
device (str, torch.device, None): device to use for model parameters
|
||||
|
||||
Returns:
|
||||
YOLOv3 model
|
||||
pytorch model
|
||||
"""
|
||||
from pathlib import Path
|
||||
|
||||
from models.common import AutoShape, DetectMultiBackend
|
||||
from models.experimental import attempt_load
|
||||
from models.yolo import ClassificationModel, DetectionModel, SegmentationModel
|
||||
from models.yolo import Model
|
||||
from utils.downloads import attempt_download
|
||||
from utils.general import LOGGER, check_requirements, intersect_dicts, logging
|
||||
from utils.general import check_requirements, intersect_dicts, set_logging
|
||||
from utils.torch_utils import select_device
|
||||
|
||||
if not verbose:
|
||||
LOGGER.setLevel(logging.WARNING)
|
||||
check_requirements(exclude=('opencv-python', 'tensorboard', 'thop'))
|
||||
name = Path(name)
|
||||
path = name.with_suffix('.pt') if name.suffix == '' and not name.is_dir() else name # checkpoint path
|
||||
file = Path(__file__).resolve()
|
||||
check_requirements(exclude=('tensorboard', 'thop', 'opencv-python'))
|
||||
set_logging(verbose=verbose)
|
||||
|
||||
save_dir = Path('') if str(name).endswith('.pt') else file.parent
|
||||
path = (save_dir / name).with_suffix('.pt') # checkpoint path
|
||||
try:
|
||||
device = select_device(device)
|
||||
device = select_device(('0' if torch.cuda.is_available() else 'cpu') if device is None else device)
|
||||
|
||||
if pretrained and channels == 3 and classes == 80:
|
||||
try:
|
||||
model = DetectMultiBackend(path, device=device, fuse=autoshape) # detection model
|
||||
if autoshape:
|
||||
if model.pt and isinstance(model.model, ClassificationModel):
|
||||
LOGGER.warning('WARNING ⚠️ YOLOv3 ClassificationModel is not yet AutoShape compatible. '
|
||||
'You must pass torch tensors in BCHW to this model, i.e. shape(1,3,224,224).')
|
||||
elif model.pt and isinstance(model.model, SegmentationModel):
|
||||
LOGGER.warning('WARNING ⚠️ YOLOv3 SegmentationModel is not yet AutoShape compatible. '
|
||||
'You will not be able to run inference with this model.')
|
||||
else:
|
||||
model = AutoShape(model) # for file/URI/PIL/cv2/np inputs and NMS
|
||||
except Exception:
|
||||
model = attempt_load(path, device=device, fuse=False) # arbitrary model
|
||||
model = attempt_load(path, map_location=device) # download/load FP32 model
|
||||
else:
|
||||
cfg = list((Path(__file__).parent / 'models').rglob(f'{path.stem}.yaml'))[0] # model.yaml path
|
||||
model = DetectionModel(cfg, channels, classes) # create model
|
||||
cfg = list((Path(__file__).parent / 'models').rglob(f'{name}.yaml'))[0] # model.yaml path
|
||||
model = Model(cfg, channels, classes) # create model
|
||||
if pretrained:
|
||||
ckpt = torch.load(attempt_download(path), map_location=device) # load
|
||||
csd = ckpt['model'].float().state_dict() # checkpoint state_dict as FP32
|
||||
@ -68,102 +54,54 @@ def _create(name, pretrained=True, channels=3, classes=80, autoshape=True, verbo
|
||||
model.load_state_dict(csd, strict=False) # load
|
||||
if len(ckpt['model'].names) == classes:
|
||||
model.names = ckpt['model'].names # set class names attribute
|
||||
if not verbose:
|
||||
LOGGER.setLevel(logging.INFO) # reset to default
|
||||
if autoshape:
|
||||
model = model.autoshape() # for file/URI/PIL/cv2/np inputs and NMS
|
||||
return model.to(device)
|
||||
|
||||
except Exception as e:
|
||||
help_url = 'https://github.com/ultralytics/yolov5/issues/36'
|
||||
s = f'{e}. Cache may be out of date, try `force_reload=True` or see {help_url} for help.'
|
||||
s = 'Cache may be out of date, try `force_reload=True`. See %s for help.' % help_url
|
||||
raise Exception(s) from e
|
||||
|
||||
|
||||
def custom(path='path/to/model.pt', autoshape=True, _verbose=True, device=None):
|
||||
# YOLOv3 custom or local model
|
||||
return _create(path, autoshape=autoshape, verbose=_verbose, device=device)
|
||||
def custom(path='path/to/model.pt', autoshape=True, verbose=True, device=None):
|
||||
# custom or local model
|
||||
return _create(path, autoshape=autoshape, verbose=verbose, device=device)
|
||||
|
||||
|
||||
def yolov5n(pretrained=True, channels=3, classes=80, autoshape=True, _verbose=True, device=None):
|
||||
# YOLOv3-nano model https://github.com/ultralytics/yolov5
|
||||
return _create('yolov5n', pretrained, channels, classes, autoshape, _verbose, device)
|
||||
def yolov3(pretrained=True, channels=3, classes=80, autoshape=True, verbose=True, device=None):
|
||||
# YOLOv3 model https://github.com/ultralytics/yolov3
|
||||
return _create('yolov3', pretrained, channels, classes, autoshape, verbose, device)
|
||||
|
||||
|
||||
def yolov5s(pretrained=True, channels=3, classes=80, autoshape=True, _verbose=True, device=None):
|
||||
# YOLOv3-small model https://github.com/ultralytics/yolov5
|
||||
return _create('yolov5s', pretrained, channels, classes, autoshape, _verbose, device)
|
||||
def yolov3_spp(pretrained=True, channels=3, classes=80, autoshape=True, verbose=True, device=None):
|
||||
# YOLOv3-SPP model https://github.com/ultralytics/yolov3
|
||||
return _create('yolov3-spp', pretrained, channels, classes, autoshape, verbose, device)
|
||||
|
||||
|
||||
def yolov5m(pretrained=True, channels=3, classes=80, autoshape=True, _verbose=True, device=None):
|
||||
# YOLOv3-medium model https://github.com/ultralytics/yolov5
|
||||
return _create('yolov5m', pretrained, channels, classes, autoshape, _verbose, device)
|
||||
|
||||
|
||||
def yolov5l(pretrained=True, channels=3, classes=80, autoshape=True, _verbose=True, device=None):
|
||||
# YOLOv3-large model https://github.com/ultralytics/yolov5
|
||||
return _create('yolov5l', pretrained, channels, classes, autoshape, _verbose, device)
|
||||
|
||||
|
||||
def yolov5x(pretrained=True, channels=3, classes=80, autoshape=True, _verbose=True, device=None):
|
||||
# YOLOv3-xlarge model https://github.com/ultralytics/yolov5
|
||||
return _create('yolov5x', pretrained, channels, classes, autoshape, _verbose, device)
|
||||
|
||||
|
||||
def yolov5n6(pretrained=True, channels=3, classes=80, autoshape=True, _verbose=True, device=None):
|
||||
# YOLOv3-nano-P6 model https://github.com/ultralytics/yolov5
|
||||
return _create('yolov5n6', pretrained, channels, classes, autoshape, _verbose, device)
|
||||
|
||||
|
||||
def yolov5s6(pretrained=True, channels=3, classes=80, autoshape=True, _verbose=True, device=None):
|
||||
# YOLOv3-small-P6 model https://github.com/ultralytics/yolov5
|
||||
return _create('yolov5s6', pretrained, channels, classes, autoshape, _verbose, device)
|
||||
|
||||
|
||||
def yolov5m6(pretrained=True, channels=3, classes=80, autoshape=True, _verbose=True, device=None):
|
||||
# YOLOv3-medium-P6 model https://github.com/ultralytics/yolov5
|
||||
return _create('yolov5m6', pretrained, channels, classes, autoshape, _verbose, device)
|
||||
|
||||
|
||||
def yolov5l6(pretrained=True, channels=3, classes=80, autoshape=True, _verbose=True, device=None):
|
||||
# YOLOv3-large-P6 model https://github.com/ultralytics/yolov5
|
||||
return _create('yolov5l6', pretrained, channels, classes, autoshape, _verbose, device)
|
||||
|
||||
|
||||
def yolov5x6(pretrained=True, channels=3, classes=80, autoshape=True, _verbose=True, device=None):
|
||||
# YOLOv3-xlarge-P6 model https://github.com/ultralytics/yolov5
|
||||
return _create('yolov5x6', pretrained, channels, classes, autoshape, _verbose, device)
|
||||
def yolov3_tiny(pretrained=True, channels=3, classes=80, autoshape=True, verbose=True, device=None):
|
||||
# YOLOv3-tiny model https://github.com/ultralytics/yolov3
|
||||
return _create('yolov3-tiny', pretrained, channels, classes, autoshape, verbose, device)
|
||||
|
||||
|
||||
if __name__ == '__main__':
|
||||
import argparse
|
||||
model = _create(name='yolov3-tiny', pretrained=True, channels=3, classes=80, autoshape=True, verbose=True) # pretrained
|
||||
# model = custom(path='path/to/model.pt') # custom
|
||||
|
||||
# Verify inference
|
||||
from pathlib import Path
|
||||
|
||||
import cv2
|
||||
import numpy as np
|
||||
from PIL import Image
|
||||
|
||||
from utils.general import cv2, print_args
|
||||
imgs = ['data/images/zidane.jpg', # filename
|
||||
Path('data/images/zidane.jpg'), # Path
|
||||
'https://ultralytics.com/images/zidane.jpg', # URI
|
||||
cv2.imread('data/images/bus.jpg')[:, :, ::-1], # OpenCV
|
||||
Image.open('data/images/bus.jpg'), # PIL
|
||||
np.zeros((320, 640, 3))] # numpy
|
||||
|
||||
# Argparser
|
||||
parser = argparse.ArgumentParser()
|
||||
parser.add_argument('--model', type=str, default='yolov5s', help='model name')
|
||||
opt = parser.parse_args()
|
||||
print_args(vars(opt))
|
||||
|
||||
# Model
|
||||
model = _create(name=opt.model, pretrained=True, channels=3, classes=80, autoshape=True, verbose=True)
|
||||
# model = custom(path='path/to/model.pt') # custom
|
||||
|
||||
# Images
|
||||
imgs = [
|
||||
'data/images/zidane.jpg', # filename
|
||||
Path('data/images/zidane.jpg'), # Path
|
||||
'https://ultralytics.com/images/zidane.jpg', # URI
|
||||
cv2.imread('data/images/bus.jpg')[:, :, ::-1], # OpenCV
|
||||
Image.open('data/images/bus.jpg'), # PIL
|
||||
np.zeros((320, 640, 3))] # numpy
|
||||
|
||||
# Inference
|
||||
results = model(imgs, size=320) # batched inference
|
||||
|
||||
# Results
|
||||
results = model(imgs) # batched inference
|
||||
results.print()
|
||||
results.save()
|
||||
|
||||
@ -3,17 +3,12 @@
|
||||
Common modules
|
||||
"""
|
||||
|
||||
import ast
|
||||
import contextlib
|
||||
import json
|
||||
import math
|
||||
import platform
|
||||
import warnings
|
||||
import zipfile
|
||||
from collections import OrderedDict, namedtuple
|
||||
from copy import copy
|
||||
from pathlib import Path
|
||||
from urllib.parse import urlparse
|
||||
|
||||
import cv2
|
||||
import numpy as np
|
||||
@ -21,37 +16,30 @@ import pandas as pd
|
||||
import requests
|
||||
import torch
|
||||
import torch.nn as nn
|
||||
from IPython.display import display
|
||||
from PIL import Image
|
||||
from torch.cuda import amp
|
||||
|
||||
from utils import TryExcept
|
||||
from utils.dataloaders import exif_transpose, letterbox
|
||||
from utils.general import (LOGGER, ROOT, Profile, check_requirements, check_suffix, check_version, colorstr,
|
||||
increment_path, is_notebook, make_divisible, non_max_suppression, scale_boxes, xywh2xyxy,
|
||||
xyxy2xywh, yaml_load)
|
||||
from utils.datasets import exif_transpose, letterbox
|
||||
from utils.general import (LOGGER, check_requirements, check_suffix, colorstr, increment_path, make_divisible,
|
||||
non_max_suppression, scale_coords, xywh2xyxy, xyxy2xywh)
|
||||
from utils.plots import Annotator, colors, save_one_box
|
||||
from utils.torch_utils import copy_attr, smart_inference_mode
|
||||
from utils.torch_utils import time_sync
|
||||
|
||||
|
||||
def autopad(k, p=None, d=1): # kernel, padding, dilation
|
||||
# Pad to 'same' shape outputs
|
||||
if d > 1:
|
||||
k = d * (k - 1) + 1 if isinstance(k, int) else [d * (x - 1) + 1 for x in k] # actual kernel-size
|
||||
def autopad(k, p=None): # kernel, padding
|
||||
# Pad to 'same'
|
||||
if p is None:
|
||||
p = k // 2 if isinstance(k, int) else [x // 2 for x in k] # auto-pad
|
||||
return p
|
||||
|
||||
|
||||
class Conv(nn.Module):
|
||||
# Standard convolution with args(ch_in, ch_out, kernel, stride, padding, groups, dilation, activation)
|
||||
default_act = nn.SiLU() # default activation
|
||||
|
||||
def __init__(self, c1, c2, k=1, s=1, p=None, g=1, d=1, act=True):
|
||||
# Standard convolution
|
||||
def __init__(self, c1, c2, k=1, s=1, p=None, g=1, act=True): # ch_in, ch_out, kernel, stride, padding, groups
|
||||
super().__init__()
|
||||
self.conv = nn.Conv2d(c1, c2, k, s, autopad(k, p, d), groups=g, dilation=d, bias=False)
|
||||
self.conv = nn.Conv2d(c1, c2, k, s, autopad(k, p), groups=g, bias=False)
|
||||
self.bn = nn.BatchNorm2d(c2)
|
||||
self.act = self.default_act if act is True else act if isinstance(act, nn.Module) else nn.Identity()
|
||||
self.act = nn.SiLU() if act is True else (act if isinstance(act, nn.Module) else nn.Identity())
|
||||
|
||||
def forward(self, x):
|
||||
return self.act(self.bn(self.conv(x)))
|
||||
@ -61,15 +49,9 @@ class Conv(nn.Module):
|
||||
|
||||
|
||||
class DWConv(Conv):
|
||||
# Depth-wise convolution
|
||||
def __init__(self, c1, c2, k=1, s=1, d=1, act=True): # ch_in, ch_out, kernel, stride, dilation, activation
|
||||
super().__init__(c1, c2, k, s, g=math.gcd(c1, c2), d=d, act=act)
|
||||
|
||||
|
||||
class DWConvTranspose2d(nn.ConvTranspose2d):
|
||||
# Depth-wise transpose convolution
|
||||
def __init__(self, c1, c2, k=1, s=1, p1=0, p2=0): # ch_in, ch_out, kernel, stride, padding, padding_out
|
||||
super().__init__(c1, c2, k, s, p1, p2, groups=math.gcd(c1, c2))
|
||||
# Depth-wise convolution class
|
||||
def __init__(self, c1, c2, k=1, s=1, act=True): # ch_in, ch_out, kernel, stride, padding, groups
|
||||
super().__init__(c1, c2, k, s, g=math.gcd(c1, c2), act=act)
|
||||
|
||||
|
||||
class TransformerLayer(nn.Module):
|
||||
@ -104,8 +86,8 @@ class TransformerBlock(nn.Module):
|
||||
if self.conv is not None:
|
||||
x = self.conv(x)
|
||||
b, _, w, h = x.shape
|
||||
p = x.flatten(2).permute(2, 0, 1)
|
||||
return self.tr(p + self.linear(p)).permute(1, 2, 0).reshape(b, self.c2, w, h)
|
||||
p = x.flatten(2).unsqueeze(0).transpose(0, 3).squeeze(3)
|
||||
return self.tr(p + self.linear(p)).unsqueeze(3).transpose(0, 3).reshape(b, self.c2, w, h)
|
||||
|
||||
|
||||
class Bottleneck(nn.Module):
|
||||
@ -137,21 +119,7 @@ class BottleneckCSP(nn.Module):
|
||||
def forward(self, x):
|
||||
y1 = self.cv3(self.m(self.cv1(x)))
|
||||
y2 = self.cv2(x)
|
||||
return self.cv4(self.act(self.bn(torch.cat((y1, y2), 1))))
|
||||
|
||||
|
||||
class CrossConv(nn.Module):
|
||||
# Cross Convolution Downsample
|
||||
def __init__(self, c1, c2, k=3, s=1, g=1, e=1.0, shortcut=False):
|
||||
# ch_in, ch_out, kernel, stride, groups, expansion, shortcut
|
||||
super().__init__()
|
||||
c_ = int(c2 * e) # hidden channels
|
||||
self.cv1 = Conv(c1, c_, (1, k), (1, s))
|
||||
self.cv2 = Conv(c_, c2, (k, 1), (s, 1), g=g)
|
||||
self.add = shortcut and c1 == c2
|
||||
|
||||
def forward(self, x):
|
||||
return x + self.cv2(self.cv1(x)) if self.add else self.cv2(self.cv1(x))
|
||||
return self.cv4(self.act(self.bn(torch.cat((y1, y2), dim=1))))
|
||||
|
||||
|
||||
class C3(nn.Module):
|
||||
@ -161,19 +129,12 @@ class C3(nn.Module):
|
||||
c_ = int(c2 * e) # hidden channels
|
||||
self.cv1 = Conv(c1, c_, 1, 1)
|
||||
self.cv2 = Conv(c1, c_, 1, 1)
|
||||
self.cv3 = Conv(2 * c_, c2, 1) # optional act=FReLU(c2)
|
||||
self.cv3 = Conv(2 * c_, c2, 1) # act=FReLU(c2)
|
||||
self.m = nn.Sequential(*(Bottleneck(c_, c_, shortcut, g, e=1.0) for _ in range(n)))
|
||||
# self.m = nn.Sequential(*[CrossConv(c_, c_, 3, 1, g, 1.0, shortcut) for _ in range(n)])
|
||||
|
||||
def forward(self, x):
|
||||
return self.cv3(torch.cat((self.m(self.cv1(x)), self.cv2(x)), 1))
|
||||
|
||||
|
||||
class C3x(C3):
|
||||
# C3 module with cross-convolutions
|
||||
def __init__(self, c1, c2, n=1, shortcut=True, g=1, e=0.5):
|
||||
super().__init__(c1, c2, n, shortcut, g, e)
|
||||
c_ = int(c2 * e)
|
||||
self.m = nn.Sequential(*(CrossConv(c_, c_, 3, 1, g, 1.0, shortcut) for _ in range(n)))
|
||||
return self.cv3(torch.cat((self.m(self.cv1(x)), self.cv2(x)), dim=1))
|
||||
|
||||
|
||||
class C3TR(C3):
|
||||
@ -217,7 +178,7 @@ class SPP(nn.Module):
|
||||
|
||||
|
||||
class SPPF(nn.Module):
|
||||
# Spatial Pyramid Pooling - Fast (SPPF) layer for YOLOv3 by Glenn Jocher
|
||||
# Spatial Pyramid Pooling - Fast (SPPF) layer for by Glenn Jocher
|
||||
def __init__(self, c1, c2, k=5): # equivalent to SPP(k=(5, 9, 13))
|
||||
super().__init__()
|
||||
c_ = c1 // 2 # hidden channels
|
||||
@ -231,18 +192,18 @@ class SPPF(nn.Module):
|
||||
warnings.simplefilter('ignore') # suppress torch 1.9.0 max_pool2d() warning
|
||||
y1 = self.m(x)
|
||||
y2 = self.m(y1)
|
||||
return self.cv2(torch.cat((x, y1, y2, self.m(y2)), 1))
|
||||
return self.cv2(torch.cat([x, y1, y2, self.m(y2)], 1))
|
||||
|
||||
|
||||
class Focus(nn.Module):
|
||||
# Focus wh information into c-space
|
||||
def __init__(self, c1, c2, k=1, s=1, p=None, g=1, act=True): # ch_in, ch_out, kernel, stride, padding, groups
|
||||
super().__init__()
|
||||
self.conv = Conv(c1 * 4, c2, k, s, p, g, act=act)
|
||||
self.conv = Conv(c1 * 4, c2, k, s, p, g, act)
|
||||
# self.contract = Contract(gain=2)
|
||||
|
||||
def forward(self, x): # x(b,c,w,h) -> y(b,4c,w/2,h/2)
|
||||
return self.conv(torch.cat((x[..., ::2, ::2], x[..., 1::2, ::2], x[..., ::2, 1::2], x[..., 1::2, 1::2]), 1))
|
||||
return self.conv(torch.cat([x[..., ::2, ::2], x[..., 1::2, ::2], x[..., ::2, 1::2], x[..., 1::2, 1::2]], 1))
|
||||
# return self.conv(self.contract(x))
|
||||
|
||||
|
||||
@ -251,12 +212,12 @@ class GhostConv(nn.Module):
|
||||
def __init__(self, c1, c2, k=1, s=1, g=1, act=True): # ch_in, ch_out, kernel, stride, groups
|
||||
super().__init__()
|
||||
c_ = c2 // 2 # hidden channels
|
||||
self.cv1 = Conv(c1, c_, k, s, None, g, act=act)
|
||||
self.cv2 = Conv(c_, c_, 5, 1, None, c_, act=act)
|
||||
self.cv1 = Conv(c1, c_, k, s, None, g, act)
|
||||
self.cv2 = Conv(c_, c_, 5, 1, None, c_, act)
|
||||
|
||||
def forward(self, x):
|
||||
y = self.cv1(x)
|
||||
return torch.cat((y, self.cv2(y)), 1)
|
||||
return torch.cat([y, self.cv2(y)], 1)
|
||||
|
||||
|
||||
class GhostBottleneck(nn.Module):
|
||||
@ -264,12 +225,11 @@ class GhostBottleneck(nn.Module):
|
||||
def __init__(self, c1, c2, k=3, s=1): # ch_in, ch_out, kernel, stride
|
||||
super().__init__()
|
||||
c_ = c2 // 2
|
||||
self.conv = nn.Sequential(
|
||||
GhostConv(c1, c_, 1, 1), # pw
|
||||
DWConv(c_, c_, k, s, act=False) if s == 2 else nn.Identity(), # dw
|
||||
GhostConv(c_, c2, 1, 1, act=False)) # pw-linear
|
||||
self.shortcut = nn.Sequential(DWConv(c1, c1, k, s, act=False), Conv(c1, c2, 1, 1,
|
||||
act=False)) if s == 2 else nn.Identity()
|
||||
self.conv = nn.Sequential(GhostConv(c1, c_, 1, 1), # pw
|
||||
DWConv(c_, c_, k, s, act=False) if s == 2 else nn.Identity(), # dw
|
||||
GhostConv(c_, c2, 1, 1, act=False)) # pw-linear
|
||||
self.shortcut = nn.Sequential(DWConv(c1, c1, k, s, act=False),
|
||||
Conv(c1, c2, 1, 1, act=False)) if s == 2 else nn.Identity()
|
||||
|
||||
def forward(self, x):
|
||||
return self.conv(x) + self.shortcut(x)
|
||||
@ -314,350 +274,159 @@ class Concat(nn.Module):
|
||||
|
||||
|
||||
class DetectMultiBackend(nn.Module):
|
||||
# YOLOv3 MultiBackend class for python inference on various backends
|
||||
def __init__(self, weights='yolov5s.pt', device=torch.device('cpu'), dnn=False, data=None, fp16=False, fuse=True):
|
||||
# MultiBackend class for python inference on various backends
|
||||
def __init__(self, weights='yolov3.pt', device=None, dnn=True):
|
||||
# Usage:
|
||||
# PyTorch: weights = *.pt
|
||||
# TorchScript: *.torchscript
|
||||
# ONNX Runtime: *.onnx
|
||||
# ONNX OpenCV DNN: *.onnx --dnn
|
||||
# OpenVINO: *_openvino_model
|
||||
# CoreML: *.mlmodel
|
||||
# TensorRT: *.engine
|
||||
# TensorFlow SavedModel: *_saved_model
|
||||
# TensorFlow GraphDef: *.pb
|
||||
# TensorFlow Lite: *.tflite
|
||||
# TensorFlow Edge TPU: *_edgetpu.tflite
|
||||
# PaddlePaddle: *_paddle_model
|
||||
from models.experimental import attempt_download, attempt_load # scoped to avoid circular import
|
||||
|
||||
# PyTorch: weights = *.pt
|
||||
# TorchScript: *.torchscript.pt
|
||||
# CoreML: *.mlmodel
|
||||
# TensorFlow: *_saved_model
|
||||
# TensorFlow: *.pb
|
||||
# TensorFlow Lite: *.tflite
|
||||
# ONNX Runtime: *.onnx
|
||||
# OpenCV DNN: *.onnx with dnn=True
|
||||
super().__init__()
|
||||
w = str(weights[0] if isinstance(weights, list) else weights)
|
||||
pt, jit, onnx, xml, engine, coreml, saved_model, pb, tflite, edgetpu, tfjs, paddle, triton = self._model_type(w)
|
||||
fp16 &= pt or jit or onnx or engine # FP16
|
||||
nhwc = coreml or saved_model or pb or tflite or edgetpu # BHWC formats (vs torch BCWH)
|
||||
stride = 32 # default stride
|
||||
cuda = torch.cuda.is_available() and device.type != 'cpu' # use CUDA
|
||||
if not (pt or triton):
|
||||
w = attempt_download(w) # download if not local
|
||||
suffix, suffixes = Path(w).suffix.lower(), ['.pt', '.onnx', '.tflite', '.pb', '', '.mlmodel']
|
||||
check_suffix(w, suffixes) # check weights have acceptable suffix
|
||||
pt, onnx, tflite, pb, saved_model, coreml = (suffix == x for x in suffixes) # backend booleans
|
||||
jit = pt and 'torchscript' in w.lower()
|
||||
stride, names = 64, [f'class{i}' for i in range(1000)] # assign defaults
|
||||
|
||||
if pt: # PyTorch
|
||||
model = attempt_load(weights if isinstance(weights, list) else w, device=device, inplace=True, fuse=fuse)
|
||||
stride = max(int(model.stride.max()), 32) # model stride
|
||||
names = model.module.names if hasattr(model, 'module') else model.names # get class names
|
||||
model.half() if fp16 else model.float()
|
||||
self.model = model # explicitly assign for to(), cpu(), cuda(), half()
|
||||
elif jit: # TorchScript
|
||||
if jit: # TorchScript
|
||||
LOGGER.info(f'Loading {w} for TorchScript inference...')
|
||||
extra_files = {'config.txt': ''} # model metadata
|
||||
model = torch.jit.load(w, _extra_files=extra_files, map_location=device)
|
||||
model.half() if fp16 else model.float()
|
||||
if extra_files['config.txt']: # load metadata dict
|
||||
d = json.loads(extra_files['config.txt'],
|
||||
object_hook=lambda d: {int(k) if k.isdigit() else k: v
|
||||
for k, v in d.items()})
|
||||
model = torch.jit.load(w, _extra_files=extra_files)
|
||||
if extra_files['config.txt']:
|
||||
d = json.loads(extra_files['config.txt']) # extra_files dict
|
||||
stride, names = int(d['stride']), d['names']
|
||||
elif pt: # PyTorch
|
||||
from models.experimental import attempt_load # scoped to avoid circular import
|
||||
model = torch.jit.load(w) if 'torchscript' in w else attempt_load(weights, map_location=device)
|
||||
stride = int(model.stride.max()) # model stride
|
||||
names = model.module.names if hasattr(model, 'module') else model.names # get class names
|
||||
elif coreml: # CoreML *.mlmodel
|
||||
import coremltools as ct
|
||||
model = ct.models.MLModel(w)
|
||||
elif dnn: # ONNX OpenCV DNN
|
||||
LOGGER.info(f'Loading {w} for ONNX OpenCV DNN inference...')
|
||||
check_requirements('opencv-python>=4.5.4')
|
||||
check_requirements(('opencv-python>=4.5.4',))
|
||||
net = cv2.dnn.readNetFromONNX(w)
|
||||
elif onnx: # ONNX Runtime
|
||||
LOGGER.info(f'Loading {w} for ONNX Runtime inference...')
|
||||
cuda = torch.cuda.is_available()
|
||||
check_requirements(('onnx', 'onnxruntime-gpu' if cuda else 'onnxruntime'))
|
||||
import onnxruntime
|
||||
providers = ['CUDAExecutionProvider', 'CPUExecutionProvider'] if cuda else ['CPUExecutionProvider']
|
||||
session = onnxruntime.InferenceSession(w, providers=providers)
|
||||
output_names = [x.name for x in session.get_outputs()]
|
||||
meta = session.get_modelmeta().custom_metadata_map # metadata
|
||||
if 'stride' in meta:
|
||||
stride, names = int(meta['stride']), eval(meta['names'])
|
||||
elif xml: # OpenVINO
|
||||
LOGGER.info(f'Loading {w} for OpenVINO inference...')
|
||||
check_requirements('openvino') # requires openvino-dev: https://pypi.org/project/openvino-dev/
|
||||
from openvino.runtime import Core, Layout, get_batch
|
||||
ie = Core()
|
||||
if not Path(w).is_file(): # if not *.xml
|
||||
w = next(Path(w).glob('*.xml')) # get *.xml file from *_openvino_model dir
|
||||
network = ie.read_model(model=w, weights=Path(w).with_suffix('.bin'))
|
||||
if network.get_parameters()[0].get_layout().empty:
|
||||
network.get_parameters()[0].set_layout(Layout('NCHW'))
|
||||
batch_dim = get_batch(network)
|
||||
if batch_dim.is_static:
|
||||
batch_size = batch_dim.get_length()
|
||||
executable_network = ie.compile_model(network, device_name='CPU') # device_name="MYRIAD" for Intel NCS2
|
||||
stride, names = self._load_metadata(Path(w).with_suffix('.yaml')) # load metadata
|
||||
elif engine: # TensorRT
|
||||
LOGGER.info(f'Loading {w} for TensorRT inference...')
|
||||
import tensorrt as trt # https://developer.nvidia.com/nvidia-tensorrt-download
|
||||
check_version(trt.__version__, '7.0.0', hard=True) # require tensorrt>=7.0.0
|
||||
if device.type == 'cpu':
|
||||
device = torch.device('cuda:0')
|
||||
Binding = namedtuple('Binding', ('name', 'dtype', 'shape', 'data', 'ptr'))
|
||||
logger = trt.Logger(trt.Logger.INFO)
|
||||
with open(w, 'rb') as f, trt.Runtime(logger) as runtime:
|
||||
model = runtime.deserialize_cuda_engine(f.read())
|
||||
context = model.create_execution_context()
|
||||
bindings = OrderedDict()
|
||||
output_names = []
|
||||
fp16 = False # default updated below
|
||||
dynamic = False
|
||||
for i in range(model.num_bindings):
|
||||
name = model.get_binding_name(i)
|
||||
dtype = trt.nptype(model.get_binding_dtype(i))
|
||||
if model.binding_is_input(i):
|
||||
if -1 in tuple(model.get_binding_shape(i)): # dynamic
|
||||
dynamic = True
|
||||
context.set_binding_shape(i, tuple(model.get_profile_shape(0, i)[2]))
|
||||
if dtype == np.float16:
|
||||
fp16 = True
|
||||
else: # output
|
||||
output_names.append(name)
|
||||
shape = tuple(context.get_binding_shape(i))
|
||||
im = torch.from_numpy(np.empty(shape, dtype=dtype)).to(device)
|
||||
bindings[name] = Binding(name, dtype, shape, im, int(im.data_ptr()))
|
||||
binding_addrs = OrderedDict((n, d.ptr) for n, d in bindings.items())
|
||||
batch_size = bindings['images'].shape[0] # if dynamic, this is instead max batch size
|
||||
elif coreml: # CoreML
|
||||
LOGGER.info(f'Loading {w} for CoreML inference...')
|
||||
import coremltools as ct
|
||||
model = ct.models.MLModel(w)
|
||||
elif saved_model: # TF SavedModel
|
||||
LOGGER.info(f'Loading {w} for TensorFlow SavedModel inference...')
|
||||
import tensorflow as tf
|
||||
keras = False # assume TF1 saved_model
|
||||
model = tf.keras.models.load_model(w) if keras else tf.saved_model.load(w)
|
||||
elif pb: # GraphDef https://www.tensorflow.org/guide/migrate#a_graphpb_or_graphpbtxt
|
||||
LOGGER.info(f'Loading {w} for TensorFlow GraphDef inference...')
|
||||
else: # TensorFlow model (TFLite, pb, saved_model)
|
||||
import tensorflow as tf
|
||||
if pb: # https://www.tensorflow.org/guide/migrate#a_graphpb_or_graphpbtxt
|
||||
def wrap_frozen_graph(gd, inputs, outputs):
|
||||
x = tf.compat.v1.wrap_function(lambda: tf.compat.v1.import_graph_def(gd, name=""), []) # wrapped
|
||||
return x.prune(tf.nest.map_structure(x.graph.as_graph_element, inputs),
|
||||
tf.nest.map_structure(x.graph.as_graph_element, outputs))
|
||||
|
||||
def wrap_frozen_graph(gd, inputs, outputs):
|
||||
x = tf.compat.v1.wrap_function(lambda: tf.compat.v1.import_graph_def(gd, name=''), []) # wrapped
|
||||
ge = x.graph.as_graph_element
|
||||
return x.prune(tf.nest.map_structure(ge, inputs), tf.nest.map_structure(ge, outputs))
|
||||
|
||||
def gd_outputs(gd):
|
||||
name_list, input_list = [], []
|
||||
for node in gd.node: # tensorflow.core.framework.node_def_pb2.NodeDef
|
||||
name_list.append(node.name)
|
||||
input_list.extend(node.input)
|
||||
return sorted(f'{x}:0' for x in list(set(name_list) - set(input_list)) if not x.startswith('NoOp'))
|
||||
|
||||
gd = tf.Graph().as_graph_def() # TF GraphDef
|
||||
with open(w, 'rb') as f:
|
||||
gd.ParseFromString(f.read())
|
||||
frozen_func = wrap_frozen_graph(gd, inputs='x:0', outputs=gd_outputs(gd))
|
||||
elif tflite or edgetpu: # https://www.tensorflow.org/lite/guide/python#install_tensorflow_lite_for_python
|
||||
try: # https://coral.ai/docs/edgetpu/tflite-python/#update-existing-tf-lite-code-for-the-edge-tpu
|
||||
from tflite_runtime.interpreter import Interpreter, load_delegate
|
||||
except ImportError:
|
||||
import tensorflow as tf
|
||||
Interpreter, load_delegate = tf.lite.Interpreter, tf.lite.experimental.load_delegate,
|
||||
if edgetpu: # TF Edge TPU https://coral.ai/software/#edgetpu-runtime
|
||||
LOGGER.info(f'Loading {w} for TensorFlow Lite Edge TPU inference...')
|
||||
delegate = {
|
||||
'Linux': 'libedgetpu.so.1',
|
||||
'Darwin': 'libedgetpu.1.dylib',
|
||||
'Windows': 'edgetpu.dll'}[platform.system()]
|
||||
interpreter = Interpreter(model_path=w, experimental_delegates=[load_delegate(delegate)])
|
||||
else: # TFLite
|
||||
LOGGER.info(f'Loading {w} for TensorFlow Lite inference...')
|
||||
interpreter = Interpreter(model_path=w) # load TFLite model
|
||||
interpreter.allocate_tensors() # allocate
|
||||
input_details = interpreter.get_input_details() # inputs
|
||||
output_details = interpreter.get_output_details() # outputs
|
||||
# load metadata
|
||||
with contextlib.suppress(zipfile.BadZipFile):
|
||||
with zipfile.ZipFile(w, 'r') as model:
|
||||
meta_file = model.namelist()[0]
|
||||
meta = ast.literal_eval(model.read(meta_file).decode('utf-8'))
|
||||
stride, names = int(meta['stride']), meta['names']
|
||||
elif tfjs: # TF.js
|
||||
raise NotImplementedError('ERROR: YOLOv3 TF.js inference is not supported')
|
||||
elif paddle: # PaddlePaddle
|
||||
LOGGER.info(f'Loading {w} for PaddlePaddle inference...')
|
||||
check_requirements('paddlepaddle-gpu' if cuda else 'paddlepaddle')
|
||||
import paddle.inference as pdi
|
||||
if not Path(w).is_file(): # if not *.pdmodel
|
||||
w = next(Path(w).rglob('*.pdmodel')) # get *.pdmodel file from *_paddle_model dir
|
||||
weights = Path(w).with_suffix('.pdiparams')
|
||||
config = pdi.Config(str(w), str(weights))
|
||||
if cuda:
|
||||
config.enable_use_gpu(memory_pool_init_size_mb=2048, device_id=0)
|
||||
predictor = pdi.create_predictor(config)
|
||||
input_handle = predictor.get_input_handle(predictor.get_input_names()[0])
|
||||
output_names = predictor.get_output_names()
|
||||
elif triton: # NVIDIA Triton Inference Server
|
||||
LOGGER.info(f'Using {w} as Triton Inference Server...')
|
||||
check_requirements('tritonclient[all]')
|
||||
from utils.triton import TritonRemoteModel
|
||||
model = TritonRemoteModel(url=w)
|
||||
nhwc = model.runtime.startswith('tensorflow')
|
||||
else:
|
||||
raise NotImplementedError(f'ERROR: {w} is not a supported format')
|
||||
|
||||
# class names
|
||||
if 'names' not in locals():
|
||||
names = yaml_load(data)['names'] if data else {i: f'class{i}' for i in range(999)}
|
||||
if names[0] == 'n01440764' and len(names) == 1000: # ImageNet
|
||||
names = yaml_load(ROOT / 'data/ImageNet.yaml')['names'] # human-readable names
|
||||
|
||||
LOGGER.info(f'Loading {w} for TensorFlow *.pb inference...')
|
||||
graph_def = tf.Graph().as_graph_def()
|
||||
graph_def.ParseFromString(open(w, 'rb').read())
|
||||
frozen_func = wrap_frozen_graph(gd=graph_def, inputs="x:0", outputs="Identity:0")
|
||||
elif saved_model:
|
||||
LOGGER.info(f'Loading {w} for TensorFlow saved_model inference...')
|
||||
model = tf.keras.models.load_model(w)
|
||||
elif tflite: # https://www.tensorflow.org/lite/guide/python#install_tensorflow_lite_for_python
|
||||
if 'edgetpu' in w.lower():
|
||||
LOGGER.info(f'Loading {w} for TensorFlow Edge TPU inference...')
|
||||
import tflite_runtime.interpreter as tfli
|
||||
delegate = {'Linux': 'libedgetpu.so.1', # install https://coral.ai/software/#edgetpu-runtime
|
||||
'Darwin': 'libedgetpu.1.dylib',
|
||||
'Windows': 'edgetpu.dll'}[platform.system()]
|
||||
interpreter = tfli.Interpreter(model_path=w, experimental_delegates=[tfli.load_delegate(delegate)])
|
||||
else:
|
||||
LOGGER.info(f'Loading {w} for TensorFlow Lite inference...')
|
||||
interpreter = tf.lite.Interpreter(model_path=w) # load TFLite model
|
||||
interpreter.allocate_tensors() # allocate
|
||||
input_details = interpreter.get_input_details() # inputs
|
||||
output_details = interpreter.get_output_details() # outputs
|
||||
self.__dict__.update(locals()) # assign all variables to self
|
||||
|
||||
def forward(self, im, augment=False, visualize=False):
|
||||
# YOLOv3 MultiBackend inference
|
||||
def forward(self, im, augment=False, visualize=False, val=False):
|
||||
# MultiBackend inference
|
||||
b, ch, h, w = im.shape # batch, channel, height, width
|
||||
if self.fp16 and im.dtype != torch.float16:
|
||||
im = im.half() # to FP16
|
||||
if self.nhwc:
|
||||
im = im.permute(0, 2, 3, 1) # torch BCHW to numpy BHWC shape(1,320,192,3)
|
||||
|
||||
if self.pt: # PyTorch
|
||||
y = self.model(im, augment=augment, visualize=visualize) if augment or visualize else self.model(im)
|
||||
elif self.jit: # TorchScript
|
||||
y = self.model(im)
|
||||
elif self.dnn: # ONNX OpenCV DNN
|
||||
im = im.cpu().numpy() # torch to numpy
|
||||
self.net.setInput(im)
|
||||
y = self.net.forward()
|
||||
elif self.onnx: # ONNX Runtime
|
||||
im = im.cpu().numpy() # torch to numpy
|
||||
y = self.session.run(self.output_names, {self.session.get_inputs()[0].name: im})
|
||||
elif self.xml: # OpenVINO
|
||||
im = im.cpu().numpy() # FP32
|
||||
y = list(self.executable_network([im]).values())
|
||||
elif self.engine: # TensorRT
|
||||
if self.dynamic and im.shape != self.bindings['images'].shape:
|
||||
i = self.model.get_binding_index('images')
|
||||
self.context.set_binding_shape(i, im.shape) # reshape if dynamic
|
||||
self.bindings['images'] = self.bindings['images']._replace(shape=im.shape)
|
||||
for name in self.output_names:
|
||||
i = self.model.get_binding_index(name)
|
||||
self.bindings[name].data.resize_(tuple(self.context.get_binding_shape(i)))
|
||||
s = self.bindings['images'].shape
|
||||
assert im.shape == s, f"input size {im.shape} {'>' if self.dynamic else 'not equal to'} max model size {s}"
|
||||
self.binding_addrs['images'] = int(im.data_ptr())
|
||||
self.context.execute_v2(list(self.binding_addrs.values()))
|
||||
y = [self.bindings[x].data for x in sorted(self.output_names)]
|
||||
elif self.coreml: # CoreML
|
||||
im = im.cpu().numpy()
|
||||
y = self.model(im) if self.jit else self.model(im, augment=augment, visualize=visualize)
|
||||
return y if val else y[0]
|
||||
elif self.coreml: # CoreML *.mlmodel
|
||||
im = im.permute(0, 2, 3, 1).cpu().numpy() # torch BCHW to numpy BHWC shape(1,320,192,3)
|
||||
im = Image.fromarray((im[0] * 255).astype('uint8'))
|
||||
# im = im.resize((192, 320), Image.ANTIALIAS)
|
||||
y = self.model.predict({'image': im}) # coordinates are xywh normalized
|
||||
if 'confidence' in y:
|
||||
box = xywh2xyxy(y['coordinates'] * [[w, h, w, h]]) # xyxy pixels
|
||||
conf, cls = y['confidence'].max(1), y['confidence'].argmax(1).astype(np.float)
|
||||
y = np.concatenate((box, conf.reshape(-1, 1), cls.reshape(-1, 1)), 1)
|
||||
else:
|
||||
y = list(reversed(y.values())) # reversed for segmentation models (pred, proto)
|
||||
elif self.paddle: # PaddlePaddle
|
||||
im = im.cpu().numpy().astype(np.float32)
|
||||
self.input_handle.copy_from_cpu(im)
|
||||
self.predictor.run()
|
||||
y = [self.predictor.get_output_handle(x).copy_to_cpu() for x in self.output_names]
|
||||
elif self.triton: # NVIDIA Triton Inference Server
|
||||
y = self.model(im)
|
||||
else: # TensorFlow (SavedModel, GraphDef, Lite, Edge TPU)
|
||||
im = im.cpu().numpy()
|
||||
if self.saved_model: # SavedModel
|
||||
y = self.model(im, training=False) if self.keras else self.model(im)
|
||||
elif self.pb: # GraphDef
|
||||
y = self.frozen_func(x=self.tf.constant(im))
|
||||
else: # Lite or Edge TPU
|
||||
input = self.input_details[0]
|
||||
box = xywh2xyxy(y['coordinates'] * [[w, h, w, h]]) # xyxy pixels
|
||||
conf, cls = y['confidence'].max(1), y['confidence'].argmax(1).astype(np.float)
|
||||
y = np.concatenate((box, conf.reshape(-1, 1), cls.reshape(-1, 1)), 1)
|
||||
elif self.onnx: # ONNX
|
||||
im = im.cpu().numpy() # torch to numpy
|
||||
if self.dnn: # ONNX OpenCV DNN
|
||||
self.net.setInput(im)
|
||||
y = self.net.forward()
|
||||
else: # ONNX Runtime
|
||||
y = self.session.run([self.session.get_outputs()[0].name], {self.session.get_inputs()[0].name: im})[0]
|
||||
else: # TensorFlow model (TFLite, pb, saved_model)
|
||||
im = im.permute(0, 2, 3, 1).cpu().numpy() # torch BCHW to numpy BHWC shape(1,320,192,3)
|
||||
if self.pb:
|
||||
y = self.frozen_func(x=self.tf.constant(im)).numpy()
|
||||
elif self.saved_model:
|
||||
y = self.model(im, training=False).numpy()
|
||||
elif self.tflite:
|
||||
input, output = self.input_details[0], self.output_details[0]
|
||||
int8 = input['dtype'] == np.uint8 # is TFLite quantized uint8 model
|
||||
if int8:
|
||||
scale, zero_point = input['quantization']
|
||||
im = (im / scale + zero_point).astype(np.uint8) # de-scale
|
||||
self.interpreter.set_tensor(input['index'], im)
|
||||
self.interpreter.invoke()
|
||||
y = []
|
||||
for output in self.output_details:
|
||||
x = self.interpreter.get_tensor(output['index'])
|
||||
if int8:
|
||||
scale, zero_point = output['quantization']
|
||||
x = (x.astype(np.float32) - zero_point) * scale # re-scale
|
||||
y.append(x)
|
||||
y = [x if isinstance(x, np.ndarray) else x.numpy() for x in y]
|
||||
y[0][..., :4] *= [w, h, w, h] # xywh normalized to pixels
|
||||
|
||||
if isinstance(y, (list, tuple)):
|
||||
return self.from_numpy(y[0]) if len(y) == 1 else [self.from_numpy(x) for x in y]
|
||||
else:
|
||||
return self.from_numpy(y)
|
||||
|
||||
def from_numpy(self, x):
|
||||
return torch.from_numpy(x).to(self.device) if isinstance(x, np.ndarray) else x
|
||||
|
||||
def warmup(self, imgsz=(1, 3, 640, 640)):
|
||||
# Warmup model by running inference once
|
||||
warmup_types = self.pt, self.jit, self.onnx, self.engine, self.saved_model, self.pb, self.triton
|
||||
if any(warmup_types) and (self.device.type != 'cpu' or self.triton):
|
||||
im = torch.empty(*imgsz, dtype=torch.half if self.fp16 else torch.float, device=self.device) # input
|
||||
for _ in range(2 if self.jit else 1): #
|
||||
self.forward(im) # warmup
|
||||
|
||||
@staticmethod
|
||||
def _model_type(p='path/to/model.pt'):
|
||||
# Return model type from model path, i.e. path='path/to/model.onnx' -> type=onnx
|
||||
# types = [pt, jit, onnx, xml, engine, coreml, saved_model, pb, tflite, edgetpu, tfjs, paddle]
|
||||
from export import export_formats
|
||||
from utils.downloads import is_url
|
||||
sf = list(export_formats().Suffix) # export suffixes
|
||||
if not is_url(p, check=False):
|
||||
check_suffix(p, sf) # checks
|
||||
url = urlparse(p) # if url may be Triton inference server
|
||||
types = [s in Path(p).name for s in sf]
|
||||
types[8] &= not types[9] # tflite &= not edgetpu
|
||||
triton = not any(types) and all([any(s in url.scheme for s in ['http', 'grpc']), url.netloc])
|
||||
return types + [triton]
|
||||
|
||||
@staticmethod
|
||||
def _load_metadata(f=Path('path/to/meta.yaml')):
|
||||
# Load metadata from meta.yaml if it exists
|
||||
if f.exists():
|
||||
d = yaml_load(f)
|
||||
return d['stride'], d['names'] # assign stride, names
|
||||
return None, None
|
||||
y = self.interpreter.get_tensor(output['index'])
|
||||
if int8:
|
||||
scale, zero_point = output['quantization']
|
||||
y = (y.astype(np.float32) - zero_point) * scale # re-scale
|
||||
y[..., 0] *= w # x
|
||||
y[..., 1] *= h # y
|
||||
y[..., 2] *= w # w
|
||||
y[..., 3] *= h # h
|
||||
y = torch.tensor(y)
|
||||
return (y, []) if val else y
|
||||
|
||||
|
||||
class AutoShape(nn.Module):
|
||||
# YOLOv3 input-robust model wrapper for passing cv2/np/PIL/torch inputs. Includes preprocessing, inference and NMS
|
||||
# input-robust model wrapper for passing cv2/np/PIL/torch inputs. Includes preprocessing, inference and NMS
|
||||
conf = 0.25 # NMS confidence threshold
|
||||
iou = 0.45 # NMS IoU threshold
|
||||
agnostic = False # NMS class-agnostic
|
||||
multi_label = False # NMS multiple labels per box
|
||||
classes = None # (optional list) filter by class, i.e. = [0, 15, 16] for COCO persons, cats and dogs
|
||||
multi_label = False # NMS multiple labels per box
|
||||
max_det = 1000 # maximum number of detections per image
|
||||
amp = False # Automatic Mixed Precision (AMP) inference
|
||||
|
||||
def __init__(self, model, verbose=True):
|
||||
def __init__(self, model):
|
||||
super().__init__()
|
||||
if verbose:
|
||||
LOGGER.info('Adding AutoShape... ')
|
||||
copy_attr(self, model, include=('yaml', 'nc', 'hyp', 'names', 'stride', 'abc'), exclude=()) # copy attributes
|
||||
self.dmb = isinstance(model, DetectMultiBackend) # DetectMultiBackend() instance
|
||||
self.pt = not self.dmb or model.pt # PyTorch model
|
||||
self.model = model.eval()
|
||||
if self.pt:
|
||||
m = self.model.model.model[-1] if self.dmb else self.model.model[-1] # Detect()
|
||||
m.inplace = False # Detect.inplace=False for safe multithread inference
|
||||
m.export = True # do not output loss values
|
||||
|
||||
def autoshape(self):
|
||||
LOGGER.info('AutoShape already enabled, skipping... ') # model already converted to model.autoshape()
|
||||
return self
|
||||
|
||||
def _apply(self, fn):
|
||||
# Apply to(), cpu(), cuda(), half() to model tensors that are not parameters or registered buffers
|
||||
self = super()._apply(fn)
|
||||
if self.pt:
|
||||
m = self.model.model.model[-1] if self.dmb else self.model.model[-1] # Detect()
|
||||
m.stride = fn(m.stride)
|
||||
m.grid = list(map(fn, m.grid))
|
||||
if isinstance(m.anchor_grid, list):
|
||||
m.anchor_grid = list(map(fn, m.anchor_grid))
|
||||
m = self.model.model[-1] # Detect()
|
||||
m.stride = fn(m.stride)
|
||||
m.grid = list(map(fn, m.grid))
|
||||
if isinstance(m.anchor_grid, list):
|
||||
m.anchor_grid = list(map(fn, m.anchor_grid))
|
||||
return self
|
||||
|
||||
@smart_inference_mode()
|
||||
def forward(self, ims, size=640, augment=False, profile=False):
|
||||
# Inference from various sources. For size(height=640, width=1280), RGB images example inputs are:
|
||||
# file: ims = 'data/images/zidane.jpg' # str or PosixPath
|
||||
@torch.no_grad()
|
||||
def forward(self, imgs, size=640, augment=False, profile=False):
|
||||
# Inference from various sources. For height=640, width=1280, RGB images example inputs are:
|
||||
# file: imgs = 'data/images/zidane.jpg' # str or PosixPath
|
||||
# URI: = 'https://ultralytics.com/images/zidane.jpg'
|
||||
# OpenCV: = cv2.imread('image.jpg')[:,:,::-1] # HWC BGR to RGB x(640,1280,3)
|
||||
# PIL: = Image.open('image.jpg') or ImageGrab.grab() # HWC x(640,1280,3)
|
||||
@ -665,139 +434,129 @@ class AutoShape(nn.Module):
|
||||
# torch: = torch.zeros(16,3,320,640) # BCHW (scaled to size=640, 0-1 values)
|
||||
# multiple: = [Image.open('image1.jpg'), Image.open('image2.jpg'), ...] # list of images
|
||||
|
||||
dt = (Profile(), Profile(), Profile())
|
||||
with dt[0]:
|
||||
if isinstance(size, int): # expand
|
||||
size = (size, size)
|
||||
p = next(self.model.parameters()) if self.pt else torch.empty(1, device=self.model.device) # param
|
||||
autocast = self.amp and (p.device.type != 'cpu') # Automatic Mixed Precision (AMP) inference
|
||||
if isinstance(ims, torch.Tensor): # torch
|
||||
with amp.autocast(autocast):
|
||||
return self.model(ims.to(p.device).type_as(p), augment=augment) # inference
|
||||
t = [time_sync()]
|
||||
p = next(self.model.parameters()) # for device and type
|
||||
if isinstance(imgs, torch.Tensor): # torch
|
||||
with amp.autocast(enabled=p.device.type != 'cpu'):
|
||||
return self.model(imgs.to(p.device).type_as(p), augment, profile) # inference
|
||||
|
||||
# Pre-process
|
||||
n, ims = (len(ims), list(ims)) if isinstance(ims, (list, tuple)) else (1, [ims]) # number, list of images
|
||||
shape0, shape1, files = [], [], [] # image and inference shapes, filenames
|
||||
for i, im in enumerate(ims):
|
||||
f = f'image{i}' # filename
|
||||
if isinstance(im, (str, Path)): # filename or uri
|
||||
im, f = Image.open(requests.get(im, stream=True).raw if str(im).startswith('http') else im), im
|
||||
im = np.asarray(exif_transpose(im))
|
||||
elif isinstance(im, Image.Image): # PIL Image
|
||||
im, f = np.asarray(exif_transpose(im)), getattr(im, 'filename', f) or f
|
||||
files.append(Path(f).with_suffix('.jpg').name)
|
||||
if im.shape[0] < 5: # image in CHW
|
||||
im = im.transpose((1, 2, 0)) # reverse dataloader .transpose(2, 0, 1)
|
||||
im = im[..., :3] if im.ndim == 3 else cv2.cvtColor(im, cv2.COLOR_GRAY2BGR) # enforce 3ch input
|
||||
s = im.shape[:2] # HWC
|
||||
shape0.append(s) # image shape
|
||||
g = max(size) / max(s) # gain
|
||||
shape1.append([int(y * g) for y in s])
|
||||
ims[i] = im if im.data.contiguous else np.ascontiguousarray(im) # update
|
||||
shape1 = [make_divisible(x, self.stride) for x in np.array(shape1).max(0)] # inf shape
|
||||
x = [letterbox(im, shape1, auto=False)[0] for im in ims] # pad
|
||||
x = np.ascontiguousarray(np.array(x).transpose((0, 3, 1, 2))) # stack and BHWC to BCHW
|
||||
x = torch.from_numpy(x).to(p.device).type_as(p) / 255 # uint8 to fp16/32
|
||||
# Pre-process
|
||||
n, imgs = (len(imgs), imgs) if isinstance(imgs, list) else (1, [imgs]) # number of images, list of images
|
||||
shape0, shape1, files = [], [], [] # image and inference shapes, filenames
|
||||
for i, im in enumerate(imgs):
|
||||
f = f'image{i}' # filename
|
||||
if isinstance(im, (str, Path)): # filename or uri
|
||||
im, f = Image.open(requests.get(im, stream=True).raw if str(im).startswith('http') else im), im
|
||||
im = np.asarray(exif_transpose(im))
|
||||
elif isinstance(im, Image.Image): # PIL Image
|
||||
im, f = np.asarray(exif_transpose(im)), getattr(im, 'filename', f) or f
|
||||
files.append(Path(f).with_suffix('.jpg').name)
|
||||
if im.shape[0] < 5: # image in CHW
|
||||
im = im.transpose((1, 2, 0)) # reverse dataloader .transpose(2, 0, 1)
|
||||
im = im[..., :3] if im.ndim == 3 else np.tile(im[..., None], 3) # enforce 3ch input
|
||||
s = im.shape[:2] # HWC
|
||||
shape0.append(s) # image shape
|
||||
g = (size / max(s)) # gain
|
||||
shape1.append([y * g for y in s])
|
||||
imgs[i] = im if im.data.contiguous else np.ascontiguousarray(im) # update
|
||||
shape1 = [make_divisible(x, int(self.stride.max())) for x in np.stack(shape1, 0).max(0)] # inference shape
|
||||
x = [letterbox(im, new_shape=shape1, auto=False)[0] for im in imgs] # pad
|
||||
x = np.stack(x, 0) if n > 1 else x[0][None] # stack
|
||||
x = np.ascontiguousarray(x.transpose((0, 3, 1, 2))) # BHWC to BCHW
|
||||
x = torch.from_numpy(x).to(p.device).type_as(p) / 255 # uint8 to fp16/32
|
||||
t.append(time_sync())
|
||||
|
||||
with amp.autocast(autocast):
|
||||
with amp.autocast(enabled=p.device.type != 'cpu'):
|
||||
# Inference
|
||||
with dt[1]:
|
||||
y = self.model(x, augment=augment) # forward
|
||||
y = self.model(x, augment, profile)[0] # forward
|
||||
t.append(time_sync())
|
||||
|
||||
# Post-process
|
||||
with dt[2]:
|
||||
y = non_max_suppression(y if self.dmb else y[0],
|
||||
self.conf,
|
||||
self.iou,
|
||||
self.classes,
|
||||
self.agnostic,
|
||||
self.multi_label,
|
||||
max_det=self.max_det) # NMS
|
||||
for i in range(n):
|
||||
scale_boxes(shape1, y[i][:, :4], shape0[i])
|
||||
y = non_max_suppression(y, self.conf, iou_thres=self.iou, classes=self.classes,
|
||||
multi_label=self.multi_label, max_det=self.max_det) # NMS
|
||||
for i in range(n):
|
||||
scale_coords(shape1, y[i][:, :4], shape0[i])
|
||||
|
||||
return Detections(ims, y, files, dt, self.names, x.shape)
|
||||
t.append(time_sync())
|
||||
return Detections(imgs, y, files, t, self.names, x.shape)
|
||||
|
||||
|
||||
class Detections:
|
||||
# YOLOv3 detections class for inference results
|
||||
def __init__(self, ims, pred, files, times=(0, 0, 0), names=None, shape=None):
|
||||
# detections class for inference results
|
||||
def __init__(self, imgs, pred, files, times=None, names=None, shape=None):
|
||||
super().__init__()
|
||||
d = pred[0].device # device
|
||||
gn = [torch.tensor([*(im.shape[i] for i in [1, 0, 1, 0]), 1, 1], device=d) for im in ims] # normalizations
|
||||
self.ims = ims # list of images as numpy arrays
|
||||
gn = [torch.tensor([*(im.shape[i] for i in [1, 0, 1, 0]), 1, 1], device=d) for im in imgs] # normalizations
|
||||
self.imgs = imgs # list of images as numpy arrays
|
||||
self.pred = pred # list of tensors pred[0] = (xyxy, conf, cls)
|
||||
self.names = names # class names
|
||||
self.files = files # image filenames
|
||||
self.times = times # profiling times
|
||||
self.xyxy = pred # xyxy pixels
|
||||
self.xywh = [xyxy2xywh(x) for x in pred] # xywh pixels
|
||||
self.xyxyn = [x / g for x, g in zip(self.xyxy, gn)] # xyxy normalized
|
||||
self.xywhn = [x / g for x, g in zip(self.xywh, gn)] # xywh normalized
|
||||
self.n = len(self.pred) # number of images (batch size)
|
||||
self.t = tuple(x.t / self.n * 1E3 for x in times) # timestamps (ms)
|
||||
self.s = tuple(shape) # inference BCHW shape
|
||||
self.t = tuple((times[i + 1] - times[i]) * 1000 / self.n for i in range(3)) # timestamps (ms)
|
||||
self.s = shape # inference BCHW shape
|
||||
|
||||
def _run(self, pprint=False, show=False, save=False, crop=False, render=False, labels=True, save_dir=Path('')):
|
||||
s, crops = '', []
|
||||
for i, (im, pred) in enumerate(zip(self.ims, self.pred)):
|
||||
s += f'\nimage {i + 1}/{len(self.pred)}: {im.shape[0]}x{im.shape[1]} ' # string
|
||||
def display(self, pprint=False, show=False, save=False, crop=False, render=False, save_dir=Path('')):
|
||||
crops = []
|
||||
for i, (im, pred) in enumerate(zip(self.imgs, self.pred)):
|
||||
s = f'image {i + 1}/{len(self.pred)}: {im.shape[0]}x{im.shape[1]} ' # string
|
||||
if pred.shape[0]:
|
||||
for c in pred[:, -1].unique():
|
||||
n = (pred[:, -1] == c).sum() # detections per class
|
||||
s += f"{n} {self.names[int(c)]}{'s' * (n > 1)}, " # add to string
|
||||
s = s.rstrip(', ')
|
||||
if show or save or render or crop:
|
||||
annotator = Annotator(im, example=str(self.names))
|
||||
for *box, conf, cls in reversed(pred): # xyxy, confidence, class
|
||||
label = f'{self.names[int(cls)]} {conf:.2f}'
|
||||
if crop:
|
||||
file = save_dir / 'crops' / self.names[int(cls)] / self.files[i] if save else None
|
||||
crops.append({
|
||||
'box': box,
|
||||
'conf': conf,
|
||||
'cls': cls,
|
||||
'label': label,
|
||||
'im': save_one_box(box, im, file=file, save=save)})
|
||||
crops.append({'box': box, 'conf': conf, 'cls': cls, 'label': label,
|
||||
'im': save_one_box(box, im, file=file, save=save)})
|
||||
else: # all others
|
||||
annotator.box_label(box, label if labels else '', color=colors(cls))
|
||||
annotator.box_label(box, label, color=colors(cls))
|
||||
im = annotator.im
|
||||
else:
|
||||
s += '(no detections)'
|
||||
|
||||
im = Image.fromarray(im.astype(np.uint8)) if isinstance(im, np.ndarray) else im # from np
|
||||
if pprint:
|
||||
LOGGER.info(s.rstrip(', '))
|
||||
if show:
|
||||
display(im) if is_notebook() else im.show(self.files[i])
|
||||
im.show(self.files[i]) # show
|
||||
if save:
|
||||
f = self.files[i]
|
||||
im.save(save_dir / f) # save
|
||||
if i == self.n - 1:
|
||||
LOGGER.info(f"Saved {self.n} image{'s' * (self.n > 1)} to {colorstr('bold', save_dir)}")
|
||||
if render:
|
||||
self.ims[i] = np.asarray(im)
|
||||
if pprint:
|
||||
s = s.lstrip('\n')
|
||||
return f'{s}\nSpeed: %.1fms pre-process, %.1fms inference, %.1fms NMS per image at shape {self.s}' % self.t
|
||||
self.imgs[i] = np.asarray(im)
|
||||
if crop:
|
||||
if save:
|
||||
LOGGER.info(f'Saved results to {save_dir}\n')
|
||||
return crops
|
||||
|
||||
@TryExcept('Showing images is not supported in this environment')
|
||||
def show(self, labels=True):
|
||||
self._run(show=True, labels=labels) # show results
|
||||
def print(self):
|
||||
self.display(pprint=True) # print results
|
||||
LOGGER.info(f'Speed: %.1fms pre-process, %.1fms inference, %.1fms NMS per image at shape {tuple(self.s)}' %
|
||||
self.t)
|
||||
|
||||
def save(self, labels=True, save_dir='runs/detect/exp', exist_ok=False):
|
||||
save_dir = increment_path(save_dir, exist_ok, mkdir=True) # increment save_dir
|
||||
self._run(save=True, labels=labels, save_dir=save_dir) # save results
|
||||
def show(self):
|
||||
self.display(show=True) # show results
|
||||
|
||||
def crop(self, save=True, save_dir='runs/detect/exp', exist_ok=False):
|
||||
save_dir = increment_path(save_dir, exist_ok, mkdir=True) if save else None
|
||||
return self._run(crop=True, save=save, save_dir=save_dir) # crop results
|
||||
def save(self, save_dir='runs/detect/exp'):
|
||||
save_dir = increment_path(save_dir, exist_ok=save_dir != 'runs/detect/exp', mkdir=True) # increment save_dir
|
||||
self.display(save=True, save_dir=save_dir) # save results
|
||||
|
||||
def render(self, labels=True):
|
||||
self._run(render=True, labels=labels) # render results
|
||||
return self.ims
|
||||
def crop(self, save=True, save_dir='runs/detect/exp'):
|
||||
save_dir = increment_path(save_dir, exist_ok=save_dir != 'runs/detect/exp', mkdir=True) if save else None
|
||||
return self.display(crop=True, save=save, save_dir=save_dir) # crop results
|
||||
|
||||
def render(self):
|
||||
self.display(render=True) # render results
|
||||
return self.imgs
|
||||
|
||||
def pandas(self):
|
||||
# return detections as pandas DataFrames, i.e. print(results.pandas().xyxy[0])
|
||||
@ -811,57 +570,24 @@ class Detections:
|
||||
|
||||
def tolist(self):
|
||||
# return a list of Detections objects, i.e. 'for result in results.tolist():'
|
||||
r = range(self.n) # iterable
|
||||
x = [Detections([self.ims[i]], [self.pred[i]], [self.files[i]], self.times, self.names, self.s) for i in r]
|
||||
# for d in x:
|
||||
# for k in ['ims', 'pred', 'xyxy', 'xyxyn', 'xywh', 'xywhn']:
|
||||
# setattr(d, k, getattr(d, k)[0]) # pop out of list
|
||||
x = [Detections([self.imgs[i]], [self.pred[i]], self.names, self.s) for i in range(self.n)]
|
||||
for d in x:
|
||||
for k in ['imgs', 'pred', 'xyxy', 'xyxyn', 'xywh', 'xywhn']:
|
||||
setattr(d, k, getattr(d, k)[0]) # pop out of list
|
||||
return x
|
||||
|
||||
def print(self):
|
||||
LOGGER.info(self.__str__())
|
||||
|
||||
def __len__(self): # override len(results)
|
||||
def __len__(self):
|
||||
return self.n
|
||||
|
||||
def __str__(self): # override print(results)
|
||||
return self._run(pprint=True) # print results
|
||||
|
||||
def __repr__(self):
|
||||
return f'YOLOv3 {self.__class__} instance\n' + self.__str__()
|
||||
|
||||
|
||||
class Proto(nn.Module):
|
||||
# YOLOv3 mask Proto module for segmentation models
|
||||
def __init__(self, c1, c_=256, c2=32): # ch_in, number of protos, number of masks
|
||||
super().__init__()
|
||||
self.cv1 = Conv(c1, c_, k=3)
|
||||
self.upsample = nn.Upsample(scale_factor=2, mode='nearest')
|
||||
self.cv2 = Conv(c_, c_, k=3)
|
||||
self.cv3 = Conv(c_, c2)
|
||||
|
||||
def forward(self, x):
|
||||
return self.cv3(self.cv2(self.upsample(self.cv1(x))))
|
||||
|
||||
|
||||
class Classify(nn.Module):
|
||||
# YOLOv3 classification head, i.e. x(b,c1,20,20) to x(b,c2)
|
||||
def __init__(self,
|
||||
c1,
|
||||
c2,
|
||||
k=1,
|
||||
s=1,
|
||||
p=None,
|
||||
g=1,
|
||||
dropout_p=0.0): # ch_in, ch_out, kernel, stride, padding, groups, dropout probability
|
||||
# Classification head, i.e. x(b,c1,20,20) to x(b,c2)
|
||||
def __init__(self, c1, c2, k=1, s=1, p=None, g=1): # ch_in, ch_out, kernel, stride, padding, groups
|
||||
super().__init__()
|
||||
c_ = 1280 # efficientnet_b0 size
|
||||
self.conv = Conv(c1, c_, k, s, autopad(k, p), g)
|
||||
self.pool = nn.AdaptiveAvgPool2d(1) # to x(b,c_,1,1)
|
||||
self.drop = nn.Dropout(p=dropout_p, inplace=True)
|
||||
self.linear = nn.Linear(c_, c2) # to x(b,c2)
|
||||
self.aap = nn.AdaptiveAvgPool2d(1) # to x(b,c1,1,1)
|
||||
self.conv = nn.Conv2d(c1, c2, k, s, autopad(k, p), groups=g) # to x(b,c2,1,1)
|
||||
self.flat = nn.Flatten()
|
||||
|
||||
def forward(self, x):
|
||||
if isinstance(x, list):
|
||||
x = torch.cat(x, 1)
|
||||
return self.linear(self.drop(self.pool(self.conv(x)).flatten(1)))
|
||||
z = torch.cat([self.aap(y) for y in (x if isinstance(x, list) else [x])], 1) # cat if list
|
||||
return self.flat(self.conv(z)) # flatten to x(b,c2)
|
||||
|
||||
@ -8,9 +8,24 @@ import numpy as np
|
||||
import torch
|
||||
import torch.nn as nn
|
||||
|
||||
from models.common import Conv
|
||||
from utils.downloads import attempt_download
|
||||
|
||||
|
||||
class CrossConv(nn.Module):
|
||||
# Cross Convolution Downsample
|
||||
def __init__(self, c1, c2, k=3, s=1, g=1, e=1.0, shortcut=False):
|
||||
# ch_in, ch_out, kernel, stride, groups, expansion, shortcut
|
||||
super().__init__()
|
||||
c_ = int(c2 * e) # hidden channels
|
||||
self.cv1 = Conv(c1, c_, (1, k), (1, s))
|
||||
self.cv2 = Conv(c_, c2, (k, 1), (s, 1), g=g)
|
||||
self.add = shortcut and c1 == c2
|
||||
|
||||
def forward(self, x):
|
||||
return x + self.cv2(self.cv1(x)) if self.add else self.cv2(self.cv1(x))
|
||||
|
||||
|
||||
class Sum(nn.Module):
|
||||
# Weighted sum of 2 or more layers https://arxiv.org/abs/1911.09070
|
||||
def __init__(self, n, weight=False): # n: number of inputs
|
||||
@ -48,8 +63,8 @@ class MixConv2d(nn.Module):
|
||||
a[0] = 1
|
||||
c_ = np.linalg.lstsq(a, b, rcond=None)[0].round() # solve for equal weight indices, ax = b
|
||||
|
||||
self.m = nn.ModuleList([
|
||||
nn.Conv2d(c1, int(c_), k, s, k // 2, groups=math.gcd(c1, int(c_)), bias=False) for k, c_ in zip(k, c_)])
|
||||
self.m = nn.ModuleList(
|
||||
[nn.Conv2d(c1, int(c_), k, s, k // 2, groups=math.gcd(c1, int(c_)), bias=False) for k, c_ in zip(k, c_)])
|
||||
self.bn = nn.BatchNorm2d(c2)
|
||||
self.act = nn.SiLU()
|
||||
|
||||
@ -63,49 +78,44 @@ class Ensemble(nn.ModuleList):
|
||||
super().__init__()
|
||||
|
||||
def forward(self, x, augment=False, profile=False, visualize=False):
|
||||
y = [module(x, augment, profile, visualize)[0] for module in self]
|
||||
y = []
|
||||
for module in self:
|
||||
y.append(module(x, augment, profile, visualize)[0])
|
||||
# y = torch.stack(y).max(0)[0] # max ensemble
|
||||
# y = torch.stack(y).mean(0) # mean ensemble
|
||||
y = torch.cat(y, 1) # nms ensemble
|
||||
return y, None # inference, train output
|
||||
|
||||
|
||||
def attempt_load(weights, device=None, inplace=True, fuse=True):
|
||||
# Loads an ensemble of models weights=[a,b,c] or a single model weights=[a] or weights=a
|
||||
def attempt_load(weights, map_location=None, inplace=True, fuse=True):
|
||||
from models.yolo import Detect, Model
|
||||
|
||||
# Loads an ensemble of models weights=[a,b,c] or a single model weights=[a] or weights=a
|
||||
model = Ensemble()
|
||||
for w in weights if isinstance(weights, list) else [weights]:
|
||||
ckpt = torch.load(attempt_download(w), map_location='cpu') # load
|
||||
ckpt = (ckpt.get('ema') or ckpt['model']).to(device).float() # FP32 model
|
||||
ckpt = torch.load(attempt_download(w), map_location=map_location) # load
|
||||
ckpt = (ckpt['ema'] or ckpt['model']).float() # FP32 model
|
||||
model.append(ckpt.fuse().eval() if fuse else ckpt.eval()) # fused or un-fused model in eval mode
|
||||
|
||||
# Model compatibility updates
|
||||
if not hasattr(ckpt, 'stride'):
|
||||
ckpt.stride = torch.tensor([32.])
|
||||
if hasattr(ckpt, 'names') and isinstance(ckpt.names, (list, tuple)):
|
||||
ckpt.names = dict(enumerate(ckpt.names)) # convert to dict
|
||||
|
||||
model.append(ckpt.fuse().eval() if fuse and hasattr(ckpt, 'fuse') else ckpt.eval()) # model in eval mode
|
||||
|
||||
# Module compatibility updates
|
||||
# Compatibility updates
|
||||
for m in model.modules():
|
||||
t = type(m)
|
||||
if t in (nn.Hardswish, nn.LeakyReLU, nn.ReLU, nn.ReLU6, nn.SiLU, Detect, Model):
|
||||
m.inplace = inplace # torch 1.7.0 compatibility
|
||||
if t is Detect and not isinstance(m.anchor_grid, list):
|
||||
delattr(m, 'anchor_grid')
|
||||
setattr(m, 'anchor_grid', [torch.zeros(1)] * m.nl)
|
||||
if t is Detect:
|
||||
if not isinstance(m.anchor_grid, list): # new Detect Layer compatibility
|
||||
delattr(m, 'anchor_grid')
|
||||
setattr(m, 'anchor_grid', [torch.zeros(1)] * m.nl)
|
||||
elif t is Conv:
|
||||
m._non_persistent_buffers_set = set() # torch 1.6.0 compatibility
|
||||
elif t is nn.Upsample and not hasattr(m, 'recompute_scale_factor'):
|
||||
m.recompute_scale_factor = None # torch 1.11.0 compatibility
|
||||
|
||||
# Return model
|
||||
if len(model) == 1:
|
||||
return model[-1]
|
||||
|
||||
# Return detection ensemble
|
||||
print(f'Ensemble created with {weights}\n')
|
||||
for k in 'names', 'nc', 'yaml':
|
||||
setattr(model, k, getattr(model[0], k))
|
||||
model.stride = model[torch.argmax(torch.tensor([m.stride.max() for m in model])).int()].stride # max stride
|
||||
assert all(model[0].nc == m.nc for m in model), f'Models have different class counts: {[m.nc for m in model]}'
|
||||
return model
|
||||
return model[-1] # return model
|
||||
else:
|
||||
print(f'Ensemble created with {weights}\n')
|
||||
for k in ['names']:
|
||||
setattr(model, k, getattr(model[-1], k))
|
||||
model.stride = model[torch.argmax(torch.tensor([m.stride.max() for m in model])).int()].stride # max stride
|
||||
return model # return ensemble
|
||||
|
||||
@ -1,22 +1,25 @@
|
||||
# YOLOv3 🚀 by Ultralytics, GPL-3.0 license
|
||||
"""
|
||||
TensorFlow, Keras and TFLite versions of YOLOv3
|
||||
TensorFlow, Keras and TFLite versions of
|
||||
Authored by https://github.com/zldrobit in PR https://github.com/ultralytics/yolov5/pull/1127
|
||||
|
||||
Usage:
|
||||
$ python models/tf.py --weights yolov5s.pt
|
||||
$ python models/tf.py --weights yolov3.pt
|
||||
|
||||
Export:
|
||||
$ python export.py --weights yolov5s.pt --include saved_model pb tflite tfjs
|
||||
$ python path/to/export.py --weights yolov3.pt --include saved_model pb tflite tfjs
|
||||
"""
|
||||
|
||||
import argparse
|
||||
import logging
|
||||
import sys
|
||||
from copy import deepcopy
|
||||
from pathlib import Path
|
||||
|
||||
from packaging import version
|
||||
|
||||
FILE = Path(__file__).resolve()
|
||||
ROOT = FILE.parents[1] # YOLOv3 root directory
|
||||
ROOT = FILE.parents[1] # root directory
|
||||
if str(ROOT) not in sys.path:
|
||||
sys.path.append(str(ROOT)) # add ROOT to PATH
|
||||
# ROOT = ROOT.relative_to(Path.cwd()) # relative
|
||||
@ -25,15 +28,21 @@ import numpy as np
|
||||
import tensorflow as tf
|
||||
import torch
|
||||
import torch.nn as nn
|
||||
from keras import backend
|
||||
from keras.engine.base_layer import Layer
|
||||
from keras.engine.input_spec import InputSpec
|
||||
from keras.utils import conv_utils
|
||||
from tensorflow import keras
|
||||
|
||||
from models.common import (C3, SPP, SPPF, Bottleneck, BottleneckCSP, C3x, Concat, Conv, CrossConv, DWConv,
|
||||
DWConvTranspose2d, Focus, autopad)
|
||||
from models.experimental import MixConv2d, attempt_load
|
||||
from models.yolo import Detect, Segment
|
||||
from models.common import C3, SPP, SPPF, Bottleneck, BottleneckCSP, Concat, Conv, DWConv, Focus, autopad
|
||||
from models.experimental import CrossConv, MixConv2d, attempt_load
|
||||
from models.yolo import Detect
|
||||
from utils.activations import SiLU
|
||||
from utils.general import LOGGER, make_divisible, print_args
|
||||
|
||||
# isort: off
|
||||
from tensorflow.python.util.tf_export import keras_export
|
||||
|
||||
|
||||
class TFBN(keras.layers.Layer):
|
||||
# TensorFlow BatchNormalization wrapper
|
||||
@ -50,14 +59,33 @@ class TFBN(keras.layers.Layer):
|
||||
return self.bn(inputs)
|
||||
|
||||
|
||||
class TFMaxPool2d(keras.layers.Layer):
|
||||
# TensorFlow MAX Pooling
|
||||
def __init__(self, k, s, p, w=None):
|
||||
super().__init__()
|
||||
self.pool = keras.layers.MaxPool2D(pool_size=k, strides=s, padding='valid')
|
||||
|
||||
def call(self, inputs):
|
||||
return self.pool(inputs)
|
||||
|
||||
|
||||
class TFZeroPad2d(keras.layers.Layer):
|
||||
# TensorFlow MAX Pooling
|
||||
def __init__(self, p, w=None):
|
||||
super().__init__()
|
||||
if version.parse(tf.__version__) < version.parse('2.11.0'):
|
||||
self.zero_pad = ZeroPadding2D(padding=p)
|
||||
else:
|
||||
self.zero_pad = keras.layers.ZeroPadding2D(padding=((p[0], p[1]), (p[2], p[3])))
|
||||
|
||||
def call(self, inputs):
|
||||
return self.zero_pad(inputs)
|
||||
|
||||
|
||||
class TFPad(keras.layers.Layer):
|
||||
# Pad inputs in spatial dimensions 1 and 2
|
||||
def __init__(self, pad):
|
||||
super().__init__()
|
||||
if isinstance(pad, int):
|
||||
self.pad = tf.constant([[0, 0], [pad, pad], [pad, pad], [0, 0]])
|
||||
else: # tuple/list
|
||||
self.pad = tf.constant([[0, 0], [pad[0], pad[0]], [pad[1], pad[1]], [0, 0]])
|
||||
self.pad = tf.constant([[0, 0], [pad, pad], [pad, pad], [0, 0]])
|
||||
|
||||
def call(self, inputs):
|
||||
return tf.pad(inputs, self.pad, mode='constant', constant_values=0)
|
||||
@ -69,69 +97,31 @@ class TFConv(keras.layers.Layer):
|
||||
# ch_in, ch_out, weights, kernel, stride, padding, groups
|
||||
super().__init__()
|
||||
assert g == 1, "TF v2.2 Conv2D does not support 'groups' argument"
|
||||
assert isinstance(k, int), "Convolution with multiple kernels are not allowed."
|
||||
# TensorFlow convolution padding is inconsistent with PyTorch (e.g. k=3 s=2 'SAME' padding)
|
||||
# see https://stackoverflow.com/questions/52975843/comparing-conv2d-with-padding-between-tensorflow-and-pytorch
|
||||
|
||||
conv = keras.layers.Conv2D(
|
||||
filters=c2,
|
||||
kernel_size=k,
|
||||
strides=s,
|
||||
padding='SAME' if s == 1 else 'VALID',
|
||||
use_bias=not hasattr(w, 'bn'),
|
||||
c2, k, s, 'SAME' if s == 1 else 'VALID', use_bias=False if hasattr(w, 'bn') else True,
|
||||
kernel_initializer=keras.initializers.Constant(w.conv.weight.permute(2, 3, 1, 0).numpy()),
|
||||
bias_initializer='zeros' if hasattr(w, 'bn') else keras.initializers.Constant(w.conv.bias.numpy()))
|
||||
self.conv = conv if s == 1 else keras.Sequential([TFPad(autopad(k, p)), conv])
|
||||
self.bn = TFBN(w.bn) if hasattr(w, 'bn') else tf.identity
|
||||
self.act = activations(w.act) if act else tf.identity
|
||||
|
||||
# activations
|
||||
if isinstance(w.act, nn.LeakyReLU):
|
||||
self.act = (lambda x: keras.activations.relu(x, alpha=0.1)) if act else tf.identity
|
||||
elif isinstance(w.act, nn.Hardswish):
|
||||
self.act = (lambda x: x * tf.nn.relu6(x + 3) * 0.166666667) if act else tf.identity
|
||||
elif isinstance(w.act, (nn.SiLU, SiLU)):
|
||||
self.act = (lambda x: keras.activations.swish(x)) if act else tf.identity
|
||||
else:
|
||||
raise Exception(f'no matching TensorFlow activation found for {w.act}')
|
||||
|
||||
def call(self, inputs):
|
||||
return self.act(self.bn(self.conv(inputs)))
|
||||
|
||||
|
||||
class TFDWConv(keras.layers.Layer):
|
||||
# Depthwise convolution
|
||||
def __init__(self, c1, c2, k=1, s=1, p=None, act=True, w=None):
|
||||
# ch_in, ch_out, weights, kernel, stride, padding, groups
|
||||
super().__init__()
|
||||
assert c2 % c1 == 0, f'TFDWConv() output={c2} must be a multiple of input={c1} channels'
|
||||
conv = keras.layers.DepthwiseConv2D(
|
||||
kernel_size=k,
|
||||
depth_multiplier=c2 // c1,
|
||||
strides=s,
|
||||
padding='SAME' if s == 1 else 'VALID',
|
||||
use_bias=not hasattr(w, 'bn'),
|
||||
depthwise_initializer=keras.initializers.Constant(w.conv.weight.permute(2, 3, 1, 0).numpy()),
|
||||
bias_initializer='zeros' if hasattr(w, 'bn') else keras.initializers.Constant(w.conv.bias.numpy()))
|
||||
self.conv = conv if s == 1 else keras.Sequential([TFPad(autopad(k, p)), conv])
|
||||
self.bn = TFBN(w.bn) if hasattr(w, 'bn') else tf.identity
|
||||
self.act = activations(w.act) if act else tf.identity
|
||||
|
||||
def call(self, inputs):
|
||||
return self.act(self.bn(self.conv(inputs)))
|
||||
|
||||
|
||||
class TFDWConvTranspose2d(keras.layers.Layer):
|
||||
# Depthwise ConvTranspose2d
|
||||
def __init__(self, c1, c2, k=1, s=1, p1=0, p2=0, w=None):
|
||||
# ch_in, ch_out, weights, kernel, stride, padding, groups
|
||||
super().__init__()
|
||||
assert c1 == c2, f'TFDWConv() output={c2} must be equal to input={c1} channels'
|
||||
assert k == 4 and p1 == 1, 'TFDWConv() only valid for k=4 and p1=1'
|
||||
weight, bias = w.weight.permute(2, 3, 1, 0).numpy(), w.bias.numpy()
|
||||
self.c1 = c1
|
||||
self.conv = [
|
||||
keras.layers.Conv2DTranspose(filters=1,
|
||||
kernel_size=k,
|
||||
strides=s,
|
||||
padding='VALID',
|
||||
output_padding=p2,
|
||||
use_bias=True,
|
||||
kernel_initializer=keras.initializers.Constant(weight[..., i:i + 1]),
|
||||
bias_initializer=keras.initializers.Constant(bias[i])) for i in range(c1)]
|
||||
|
||||
def call(self, inputs):
|
||||
return tf.concat([m(x) for m, x in zip(self.conv, tf.split(inputs, self.c1, 3))], 3)[:, 1:-1, 1:-1]
|
||||
|
||||
|
||||
class TFFocus(keras.layers.Layer):
|
||||
# Focus wh information into c-space
|
||||
def __init__(self, c1, c2, k=1, s=1, p=None, g=1, act=True, w=None):
|
||||
@ -141,8 +131,10 @@ class TFFocus(keras.layers.Layer):
|
||||
|
||||
def call(self, inputs): # x(b,w,h,c) -> y(b,w/2,h/2,4c)
|
||||
# inputs = inputs / 255 # normalize 0-255 to 0-1
|
||||
inputs = [inputs[:, ::2, ::2, :], inputs[:, 1::2, ::2, :], inputs[:, ::2, 1::2, :], inputs[:, 1::2, 1::2, :]]
|
||||
return self.conv(tf.concat(inputs, 3))
|
||||
return self.conv(tf.concat([inputs[:, ::2, ::2, :],
|
||||
inputs[:, 1::2, ::2, :],
|
||||
inputs[:, ::2, 1::2, :],
|
||||
inputs[:, 1::2, 1::2, :]], 3))
|
||||
|
||||
|
||||
class TFBottleneck(keras.layers.Layer):
|
||||
@ -158,32 +150,15 @@ class TFBottleneck(keras.layers.Layer):
|
||||
return inputs + self.cv2(self.cv1(inputs)) if self.add else self.cv2(self.cv1(inputs))
|
||||
|
||||
|
||||
class TFCrossConv(keras.layers.Layer):
|
||||
# Cross Convolution
|
||||
def __init__(self, c1, c2, k=3, s=1, g=1, e=1.0, shortcut=False, w=None):
|
||||
super().__init__()
|
||||
c_ = int(c2 * e) # hidden channels
|
||||
self.cv1 = TFConv(c1, c_, (1, k), (1, s), w=w.cv1)
|
||||
self.cv2 = TFConv(c_, c2, (k, 1), (s, 1), g=g, w=w.cv2)
|
||||
self.add = shortcut and c1 == c2
|
||||
|
||||
def call(self, inputs):
|
||||
return inputs + self.cv2(self.cv1(inputs)) if self.add else self.cv2(self.cv1(inputs))
|
||||
|
||||
|
||||
class TFConv2d(keras.layers.Layer):
|
||||
# Substitution for PyTorch nn.Conv2D
|
||||
def __init__(self, c1, c2, k, s=1, g=1, bias=True, w=None):
|
||||
super().__init__()
|
||||
assert g == 1, "TF v2.2 Conv2D does not support 'groups' argument"
|
||||
self.conv = keras.layers.Conv2D(filters=c2,
|
||||
kernel_size=k,
|
||||
strides=s,
|
||||
padding='VALID',
|
||||
use_bias=bias,
|
||||
kernel_initializer=keras.initializers.Constant(
|
||||
w.weight.permute(2, 3, 1, 0).numpy()),
|
||||
bias_initializer=keras.initializers.Constant(w.bias.numpy()) if bias else None)
|
||||
self.conv = keras.layers.Conv2D(
|
||||
c2, k, s, 'VALID', use_bias=bias,
|
||||
kernel_initializer=keras.initializers.Constant(w.weight.permute(2, 3, 1, 0).numpy()),
|
||||
bias_initializer=keras.initializers.Constant(w.bias.numpy()) if bias else None, )
|
||||
|
||||
def call(self, inputs):
|
||||
return self.conv(inputs)
|
||||
@ -200,7 +175,7 @@ class TFBottleneckCSP(keras.layers.Layer):
|
||||
self.cv3 = TFConv2d(c_, c_, 1, 1, bias=False, w=w.cv3)
|
||||
self.cv4 = TFConv(2 * c_, c2, 1, 1, w=w.cv4)
|
||||
self.bn = TFBN(w.bn)
|
||||
self.act = lambda x: keras.activations.swish(x)
|
||||
self.act = lambda x: keras.activations.relu(x, alpha=0.1)
|
||||
self.m = keras.Sequential([TFBottleneck(c_, c_, shortcut, g, e=1.0, w=w.m[j]) for j in range(n)])
|
||||
|
||||
def call(self, inputs):
|
||||
@ -224,22 +199,6 @@ class TFC3(keras.layers.Layer):
|
||||
return self.cv3(tf.concat((self.m(self.cv1(inputs)), self.cv2(inputs)), axis=3))
|
||||
|
||||
|
||||
class TFC3x(keras.layers.Layer):
|
||||
# 3 module with cross-convolutions
|
||||
def __init__(self, c1, c2, n=1, shortcut=True, g=1, e=0.5, w=None):
|
||||
# ch_in, ch_out, number, shortcut, groups, expansion
|
||||
super().__init__()
|
||||
c_ = int(c2 * e) # hidden channels
|
||||
self.cv1 = TFConv(c1, c_, 1, 1, w=w.cv1)
|
||||
self.cv2 = TFConv(c1, c_, 1, 1, w=w.cv2)
|
||||
self.cv3 = TFConv(2 * c_, c2, 1, 1, w=w.cv3)
|
||||
self.m = keras.Sequential([
|
||||
TFCrossConv(c_, c_, k=3, s=1, g=g, e=1.0, shortcut=shortcut, w=w.m[j]) for j in range(n)])
|
||||
|
||||
def call(self, inputs):
|
||||
return self.cv3(tf.concat((self.m(self.cv1(inputs)), self.cv2(inputs)), axis=3))
|
||||
|
||||
|
||||
class TFSPP(keras.layers.Layer):
|
||||
# Spatial pyramid pooling layer used in YOLOv3-SPP
|
||||
def __init__(self, c1, c2, k=(5, 9, 13), w=None):
|
||||
@ -271,7 +230,6 @@ class TFSPPF(keras.layers.Layer):
|
||||
|
||||
|
||||
class TFDetect(keras.layers.Layer):
|
||||
# TF YOLOv3 Detect layer
|
||||
def __init__(self, nc=80, anchors=(), ch=(), imgsz=(640, 640), w=None): # detection layer
|
||||
super().__init__()
|
||||
self.stride = tf.convert_to_tensor(w.stride.numpy(), dtype=tf.float32)
|
||||
@ -281,7 +239,8 @@ class TFDetect(keras.layers.Layer):
|
||||
self.na = len(anchors[0]) // 2 # number of anchors
|
||||
self.grid = [tf.zeros(1)] * self.nl # init grid
|
||||
self.anchors = tf.convert_to_tensor(w.anchors.numpy(), dtype=tf.float32)
|
||||
self.anchor_grid = tf.reshape(self.anchors * tf.reshape(self.stride, [self.nl, 1, 1]), [self.nl, 1, -1, 1, 2])
|
||||
self.anchor_grid = tf.reshape(self.anchors * tf.reshape(self.stride, [self.nl, 1, 1]),
|
||||
[self.nl, 1, -1, 1, 2])
|
||||
self.m = [TFConv2d(x, self.no * self.na, 1, w=w.m[i]) for i, x in enumerate(ch)]
|
||||
self.training = False # set to False after building model
|
||||
self.imgsz = imgsz
|
||||
@ -296,21 +255,19 @@ class TFDetect(keras.layers.Layer):
|
||||
x.append(self.m[i](inputs[i]))
|
||||
# x(bs,20,20,255) to x(bs,3,20,20,85)
|
||||
ny, nx = self.imgsz[0] // self.stride[i], self.imgsz[1] // self.stride[i]
|
||||
x[i] = tf.reshape(x[i], [-1, ny * nx, self.na, self.no])
|
||||
x[i] = tf.transpose(tf.reshape(x[i], [-1, ny * nx, self.na, self.no]), [0, 2, 1, 3])
|
||||
|
||||
if not self.training: # inference
|
||||
y = x[i]
|
||||
grid = tf.transpose(self.grid[i], [0, 2, 1, 3]) - 0.5
|
||||
anchor_grid = tf.transpose(self.anchor_grid[i], [0, 2, 1, 3]) * 4
|
||||
xy = (tf.sigmoid(y[..., 0:2]) * 2 + grid) * self.stride[i] # xy
|
||||
wh = tf.sigmoid(y[..., 2:4]) ** 2 * anchor_grid
|
||||
y = tf.sigmoid(x[i])
|
||||
xy = (y[..., 0:2] * 2 - 0.5 + self.grid[i]) * self.stride[i] # xy
|
||||
wh = (y[..., 2:4] * 2) ** 2 * self.anchor_grid[i]
|
||||
# Normalize xywh to 0-1 to reduce calibration error
|
||||
xy /= tf.constant([[self.imgsz[1], self.imgsz[0]]], dtype=tf.float32)
|
||||
wh /= tf.constant([[self.imgsz[1], self.imgsz[0]]], dtype=tf.float32)
|
||||
y = tf.concat([xy, wh, tf.sigmoid(y[..., 4:5 + self.nc]), y[..., 5 + self.nc:]], -1)
|
||||
z.append(tf.reshape(y, [-1, self.na * ny * nx, self.no]))
|
||||
y = tf.concat([xy, wh, y[..., 4:]], -1)
|
||||
z.append(tf.reshape(y, [-1, 3 * ny * nx, self.no]))
|
||||
|
||||
return tf.transpose(x, [0, 2, 1, 3]) if self.training else (tf.concat(z, 1),)
|
||||
return x if self.training else (tf.concat(z, 1), x)
|
||||
|
||||
@staticmethod
|
||||
def _make_grid(nx=20, ny=20):
|
||||
@ -320,44 +277,11 @@ class TFDetect(keras.layers.Layer):
|
||||
return tf.cast(tf.reshape(tf.stack([xv, yv], 2), [1, 1, ny * nx, 2]), dtype=tf.float32)
|
||||
|
||||
|
||||
class TFSegment(TFDetect):
|
||||
# YOLOv3 Segment head for segmentation models
|
||||
def __init__(self, nc=80, anchors=(), nm=32, npr=256, ch=(), imgsz=(640, 640), w=None):
|
||||
super().__init__(nc, anchors, ch, imgsz, w)
|
||||
self.nm = nm # number of masks
|
||||
self.npr = npr # number of protos
|
||||
self.no = 5 + nc + self.nm # number of outputs per anchor
|
||||
self.m = [TFConv2d(x, self.no * self.na, 1, w=w.m[i]) for i, x in enumerate(ch)] # output conv
|
||||
self.proto = TFProto(ch[0], self.npr, self.nm, w=w.proto) # protos
|
||||
self.detect = TFDetect.call
|
||||
|
||||
def call(self, x):
|
||||
p = self.proto(x[0])
|
||||
# p = TFUpsample(None, scale_factor=4, mode='nearest')(self.proto(x[0])) # (optional) full-size protos
|
||||
p = tf.transpose(p, [0, 3, 1, 2]) # from shape(1,160,160,32) to shape(1,32,160,160)
|
||||
x = self.detect(self, x)
|
||||
return (x, p) if self.training else (x[0], p)
|
||||
|
||||
|
||||
class TFProto(keras.layers.Layer):
|
||||
|
||||
def __init__(self, c1, c_=256, c2=32, w=None):
|
||||
super().__init__()
|
||||
self.cv1 = TFConv(c1, c_, k=3, w=w.cv1)
|
||||
self.upsample = TFUpsample(None, scale_factor=2, mode='nearest')
|
||||
self.cv2 = TFConv(c_, c_, k=3, w=w.cv2)
|
||||
self.cv3 = TFConv(c_, c2, w=w.cv3)
|
||||
|
||||
def call(self, inputs):
|
||||
return self.cv3(self.cv2(self.upsample(self.cv1(inputs))))
|
||||
|
||||
|
||||
class TFUpsample(keras.layers.Layer):
|
||||
# TF version of torch.nn.Upsample()
|
||||
def __init__(self, size, scale_factor, mode, w=None): # warning: all arguments needed including 'w'
|
||||
super().__init__()
|
||||
assert scale_factor % 2 == 0, 'scale_factor must be multiple of 2'
|
||||
self.upsample = lambda x: tf.image.resize(x, (x.shape[1] * scale_factor, x.shape[2] * scale_factor), mode)
|
||||
assert scale_factor == 2, "scale_factor must be 2"
|
||||
self.upsample = lambda x: tf.image.resize(x, (x.shape[1] * 2, x.shape[2] * 2), method=mode)
|
||||
# self.upsample = keras.layers.UpSampling2D(size=scale_factor, interpolation=mode)
|
||||
# with default arguments: align_corners=False, half_pixel_centers=False
|
||||
# self.upsample = lambda x: tf.raw_ops.ResizeNearestNeighbor(images=x,
|
||||
@ -368,10 +292,9 @@ class TFUpsample(keras.layers.Layer):
|
||||
|
||||
|
||||
class TFConcat(keras.layers.Layer):
|
||||
# TF version of torch.concat()
|
||||
def __init__(self, dimension=1, w=None):
|
||||
super().__init__()
|
||||
assert dimension == 1, 'convert only NCHW to NHWC concat'
|
||||
assert dimension == 1, "convert only NCHW to NHWC concat"
|
||||
self.d = 3
|
||||
|
||||
def call(self, inputs):
|
||||
@ -395,26 +318,22 @@ def parse_model(d, ch, model, imgsz): # model_dict, input_channels(3)
|
||||
pass
|
||||
|
||||
n = max(round(n * gd), 1) if n > 1 else n # depth gain
|
||||
if m in [
|
||||
nn.Conv2d, Conv, DWConv, DWConvTranspose2d, Bottleneck, SPP, SPPF, MixConv2d, Focus, CrossConv,
|
||||
BottleneckCSP, C3, C3x]:
|
||||
if m in [nn.Conv2d, Conv, Bottleneck, SPP, SPPF, DWConv, MixConv2d, Focus, CrossConv, BottleneckCSP, C3]:
|
||||
c1, c2 = ch[f], args[0]
|
||||
c2 = make_divisible(c2 * gw, 8) if c2 != no else c2
|
||||
|
||||
args = [c1, c2, *args[1:]]
|
||||
if m in [BottleneckCSP, C3, C3x]:
|
||||
if m in [BottleneckCSP, C3]:
|
||||
args.insert(2, n)
|
||||
n = 1
|
||||
elif m is nn.BatchNorm2d:
|
||||
args = [ch[f]]
|
||||
elif m is Concat:
|
||||
c2 = sum(ch[-1 if x == -1 else x + 1] for x in f)
|
||||
elif m in [Detect, Segment]:
|
||||
elif m is Detect:
|
||||
args.append([ch[x + 1] for x in f])
|
||||
if isinstance(args[1], int): # number of anchors
|
||||
args[1] = [list(range(args[1] * 2))] * len(f)
|
||||
if m is Segment:
|
||||
args[3] = make_divisible(args[3] * gw, 8)
|
||||
args.append(imgsz)
|
||||
else:
|
||||
c2 = ch[f]
|
||||
@ -435,8 +354,7 @@ def parse_model(d, ch, model, imgsz): # model_dict, input_channels(3)
|
||||
|
||||
|
||||
class TFModel:
|
||||
# TF YOLOv3 model
|
||||
def __init__(self, cfg='yolov5s.yaml', ch=3, nc=None, model=None, imgsz=(640, 640)): # model, channels, classes
|
||||
def __init__(self, cfg='yolov3.yaml', ch=3, nc=None, model=None, imgsz=(640, 640)): # model, channels, classes
|
||||
super().__init__()
|
||||
if isinstance(cfg, dict):
|
||||
self.yaml = cfg # model dict
|
||||
@ -452,17 +370,11 @@ class TFModel:
|
||||
self.yaml['nc'] = nc # override yaml value
|
||||
self.model, self.savelist = parse_model(deepcopy(self.yaml), ch=[ch], model=model, imgsz=imgsz)
|
||||
|
||||
def predict(self,
|
||||
inputs,
|
||||
tf_nms=False,
|
||||
agnostic_nms=False,
|
||||
topk_per_class=100,
|
||||
topk_all=100,
|
||||
iou_thres=0.45,
|
||||
def predict(self, inputs, tf_nms=False, agnostic_nms=False, topk_per_class=100, topk_all=100, iou_thres=0.45,
|
||||
conf_thres=0.25):
|
||||
y = [] # outputs
|
||||
x = inputs
|
||||
for m in self.model.layers:
|
||||
for i, m in enumerate(self.model.layers):
|
||||
if m.f != -1: # if not from previous layer
|
||||
x = y[m.f] if isinstance(m.f, int) else [x if j == -1 else y[j] for j in m.f] # from earlier layers
|
||||
|
||||
@ -477,18 +389,15 @@ class TFModel:
|
||||
scores = probs * classes
|
||||
if agnostic_nms:
|
||||
nms = AgnosticNMS()((boxes, classes, scores), topk_all, iou_thres, conf_thres)
|
||||
return nms, x[1]
|
||||
else:
|
||||
boxes = tf.expand_dims(boxes, 2)
|
||||
nms = tf.image.combined_non_max_suppression(boxes,
|
||||
scores,
|
||||
topk_per_class,
|
||||
topk_all,
|
||||
iou_thres,
|
||||
conf_thres,
|
||||
clip_boxes=False)
|
||||
return (nms,)
|
||||
return x # output [1,6300,85] = [xywh, conf, class0, class1, ...]
|
||||
# x = x[0] # [x(1,6300,85), ...] to x(6300,85)
|
||||
nms = tf.image.combined_non_max_suppression(
|
||||
boxes, scores, topk_per_class, topk_all, iou_thres, conf_thres, clip_boxes=False)
|
||||
return nms, x[1]
|
||||
|
||||
return x[0] # output only first tensor [1,6300,85] = [xywh, conf, class0, class1, ...]
|
||||
# x = x[0][0] # [x(1,6300,85), ...] to x(6300,85)
|
||||
# xywh = x[..., :4] # x(6300,4) boxes
|
||||
# conf = x[..., 4:5] # x(6300,1) confidences
|
||||
# cls = tf.reshape(tf.cast(tf.argmax(x[..., 5:], axis=1), tf.float32), (-1, 1)) # x(6300,1) classes
|
||||
@ -505,8 +414,7 @@ class AgnosticNMS(keras.layers.Layer):
|
||||
# TF Agnostic NMS
|
||||
def call(self, input, topk_all, iou_thres, conf_thres):
|
||||
# wrap map_fn to avoid TypeSpec related error https://stackoverflow.com/a/65809989/3036450
|
||||
return tf.map_fn(lambda x: self._nms(x, topk_all, iou_thres, conf_thres),
|
||||
input,
|
||||
return tf.map_fn(lambda x: self._nms(x, topk_all, iou_thres, conf_thres), input,
|
||||
fn_output_signature=(tf.float32, tf.float32, tf.float32, tf.int32),
|
||||
name='agnostic_nms')
|
||||
|
||||
@ -515,69 +423,50 @@ class AgnosticNMS(keras.layers.Layer):
|
||||
boxes, classes, scores = x
|
||||
class_inds = tf.cast(tf.argmax(classes, axis=-1), tf.float32)
|
||||
scores_inp = tf.reduce_max(scores, -1)
|
||||
selected_inds = tf.image.non_max_suppression(boxes,
|
||||
scores_inp,
|
||||
max_output_size=topk_all,
|
||||
iou_threshold=iou_thres,
|
||||
score_threshold=conf_thres)
|
||||
selected_inds = tf.image.non_max_suppression(
|
||||
boxes, scores_inp, max_output_size=topk_all, iou_threshold=iou_thres, score_threshold=conf_thres)
|
||||
selected_boxes = tf.gather(boxes, selected_inds)
|
||||
padded_boxes = tf.pad(selected_boxes,
|
||||
paddings=[[0, topk_all - tf.shape(selected_boxes)[0]], [0, 0]],
|
||||
mode='CONSTANT',
|
||||
constant_values=0.0)
|
||||
mode="CONSTANT", constant_values=0.0)
|
||||
selected_scores = tf.gather(scores_inp, selected_inds)
|
||||
padded_scores = tf.pad(selected_scores,
|
||||
paddings=[[0, topk_all - tf.shape(selected_boxes)[0]]],
|
||||
mode='CONSTANT',
|
||||
constant_values=-1.0)
|
||||
mode="CONSTANT", constant_values=-1.0)
|
||||
selected_classes = tf.gather(class_inds, selected_inds)
|
||||
padded_classes = tf.pad(selected_classes,
|
||||
paddings=[[0, topk_all - tf.shape(selected_boxes)[0]]],
|
||||
mode='CONSTANT',
|
||||
constant_values=-1.0)
|
||||
mode="CONSTANT", constant_values=-1.0)
|
||||
valid_detections = tf.shape(selected_inds)[0]
|
||||
return padded_boxes, padded_scores, padded_classes, valid_detections
|
||||
|
||||
|
||||
def activations(act=nn.SiLU):
|
||||
# Returns TF activation from input PyTorch activation
|
||||
if isinstance(act, nn.LeakyReLU):
|
||||
return lambda x: keras.activations.relu(x, alpha=0.1)
|
||||
elif isinstance(act, nn.Hardswish):
|
||||
return lambda x: x * tf.nn.relu6(x + 3) * 0.166666667
|
||||
elif isinstance(act, (nn.SiLU, SiLU)):
|
||||
return lambda x: keras.activations.swish(x)
|
||||
else:
|
||||
raise Exception(f'no matching TensorFlow activation found for PyTorch activation {act}')
|
||||
|
||||
|
||||
def representative_dataset_gen(dataset, ncalib=100):
|
||||
# Representative dataset generator for use with converter.representative_dataset, returns a generator of np arrays
|
||||
for n, (path, img, im0s, vid_cap, string) in enumerate(dataset):
|
||||
im = np.transpose(img, [1, 2, 0])
|
||||
im = np.expand_dims(im, axis=0).astype(np.float32)
|
||||
im /= 255
|
||||
yield [im]
|
||||
input = np.transpose(img, [1, 2, 0])
|
||||
input = np.expand_dims(input, axis=0).astype(np.float32)
|
||||
input /= 255
|
||||
yield [input]
|
||||
if n >= ncalib:
|
||||
break
|
||||
|
||||
|
||||
def run(
|
||||
weights=ROOT / 'yolov5s.pt', # weights path
|
||||
def run(weights=ROOT / 'yolov3.pt', # weights path
|
||||
imgsz=(640, 640), # inference size h,w
|
||||
batch_size=1, # batch size
|
||||
dynamic=False, # dynamic batch size
|
||||
):
|
||||
):
|
||||
# PyTorch model
|
||||
im = torch.zeros((batch_size, 3, *imgsz)) # BCHW image
|
||||
model = attempt_load(weights, device=torch.device('cpu'), inplace=True, fuse=False)
|
||||
_ = model(im) # inference
|
||||
model = attempt_load(weights, map_location=torch.device('cpu'), inplace=True, fuse=False)
|
||||
y = model(im) # inference
|
||||
model.info()
|
||||
|
||||
# TensorFlow model
|
||||
im = tf.zeros((batch_size, *imgsz, 3)) # BHWC image
|
||||
tf_model = TFModel(cfg=model.yaml, model=model, nc=model.nc, imgsz=imgsz)
|
||||
_ = tf_model.predict(im) # inference
|
||||
y = tf_model.predict(im) # inference
|
||||
|
||||
# Keras model
|
||||
im = keras.Input(shape=(*imgsz, 3), batch_size=None if dynamic else batch_size)
|
||||
@ -587,15 +476,146 @@ def run(
|
||||
LOGGER.info('PyTorch, TensorFlow and Keras models successfully verified.\nUse export.py for TF model export.')
|
||||
|
||||
|
||||
@keras_export("keras.layers.ZeroPadding2D")
|
||||
class ZeroPadding2D(Layer):
|
||||
"""Zero-padding layer for 2D input (e.g. picture).
|
||||
|
||||
This layer can add rows and columns of zeros
|
||||
at the top, bottom, left and right side of an image tensor.
|
||||
|
||||
Examples:
|
||||
|
||||
>>> input_shape = (1, 1, 2, 2)
|
||||
>>> x = np.arange(np.prod(input_shape)).reshape(input_shape)
|
||||
>>> print(x)
|
||||
[[[[0 1]
|
||||
[2 3]]]]
|
||||
>>> y = tf.keras.layers.ZeroPadding2D(padding=1)(x)
|
||||
>>> print(y)
|
||||
tf.Tensor(
|
||||
[[[[0 0]
|
||||
[0 0]
|
||||
[0 0]
|
||||
[0 0]]
|
||||
[[0 0]
|
||||
[0 1]
|
||||
[2 3]
|
||||
[0 0]]
|
||||
[[0 0]
|
||||
[0 0]
|
||||
[0 0]
|
||||
[0 0]]]], shape=(1, 3, 4, 2), dtype=int64)
|
||||
|
||||
Args:
|
||||
padding: Int, or tuple of 2 ints, or tuple of 2 tuples of 2 ints.
|
||||
- If int: the same symmetric padding
|
||||
is applied to height and width.
|
||||
- If tuple of 2 ints:
|
||||
interpreted as two different
|
||||
symmetric padding values for height and width:
|
||||
`(symmetric_height_pad, symmetric_width_pad)`.
|
||||
- If tuple of 2 tuples of 2 ints:
|
||||
interpreted as
|
||||
`((top_pad, bottom_pad), (left_pad, right_pad))`
|
||||
data_format: A string,
|
||||
one of `channels_last` (default) or `channels_first`.
|
||||
The ordering of the dimensions in the inputs.
|
||||
`channels_last` corresponds to inputs with shape
|
||||
`(batch_size, height, width, channels)` while `channels_first`
|
||||
corresponds to inputs with shape
|
||||
`(batch_size, channels, height, width)`.
|
||||
It defaults to the `image_data_format` value found in your
|
||||
Keras config file at `~/.keras/keras.json`.
|
||||
If you never set it, then it will be "channels_last".
|
||||
|
||||
Input shape:
|
||||
4D tensor with shape:
|
||||
- If `data_format` is `"channels_last"`:
|
||||
`(batch_size, rows, cols, channels)`
|
||||
- If `data_format` is `"channels_first"`:
|
||||
`(batch_size, channels, rows, cols)`
|
||||
|
||||
Output shape:
|
||||
4D tensor with shape:
|
||||
- If `data_format` is `"channels_last"`:
|
||||
`(batch_size, padded_rows, padded_cols, channels)`
|
||||
- If `data_format` is `"channels_first"`:
|
||||
`(batch_size, channels, padded_rows, padded_cols)`
|
||||
"""
|
||||
|
||||
def __init__(self, padding=(1, 1), data_format=None, **kwargs):
|
||||
super().__init__(**kwargs)
|
||||
self.data_format = conv_utils.normalize_data_format(data_format)
|
||||
if isinstance(padding, int):
|
||||
self.padding = ((padding, padding), (padding, padding))
|
||||
elif hasattr(padding, "__len__"):
|
||||
if len(padding) == 4:
|
||||
padding = ((padding[0], padding[1]), (padding[2], padding[3]))
|
||||
if len(padding) != 2:
|
||||
raise ValueError(
|
||||
f"`padding` should have two elements. Received: {padding}."
|
||||
)
|
||||
height_padding = conv_utils.normalize_tuple(
|
||||
padding[0], 2, "1st entry of padding", allow_zero=True
|
||||
)
|
||||
width_padding = conv_utils.normalize_tuple(
|
||||
padding[1], 2, "2nd entry of padding", allow_zero=True
|
||||
)
|
||||
self.padding = (height_padding, width_padding)
|
||||
else:
|
||||
raise ValueError(
|
||||
"`padding` should be either an int, "
|
||||
"a tuple of 2 ints "
|
||||
"(symmetric_height_pad, symmetric_width_pad), "
|
||||
"or a tuple of 2 tuples of 2 ints "
|
||||
"((top_pad, bottom_pad), (left_pad, right_pad)). "
|
||||
f"Received: {padding}."
|
||||
)
|
||||
self.input_spec = InputSpec(ndim=4)
|
||||
|
||||
def compute_output_shape(self, input_shape):
|
||||
input_shape = tf.TensorShape(input_shape).as_list()
|
||||
if self.data_format == "channels_first":
|
||||
if input_shape[2] is not None:
|
||||
rows = input_shape[2] + self.padding[0][0] + self.padding[0][1]
|
||||
else:
|
||||
rows = None
|
||||
if input_shape[3] is not None:
|
||||
cols = input_shape[3] + self.padding[1][0] + self.padding[1][1]
|
||||
else:
|
||||
cols = None
|
||||
return tf.TensorShape([input_shape[0], input_shape[1], rows, cols])
|
||||
elif self.data_format == "channels_last":
|
||||
if input_shape[1] is not None:
|
||||
rows = input_shape[1] + self.padding[0][0] + self.padding[0][1]
|
||||
else:
|
||||
rows = None
|
||||
if input_shape[2] is not None:
|
||||
cols = input_shape[2] + self.padding[1][0] + self.padding[1][1]
|
||||
else:
|
||||
cols = None
|
||||
return tf.TensorShape([input_shape[0], rows, cols, input_shape[3]])
|
||||
|
||||
def call(self, inputs):
|
||||
return backend.spatial_2d_padding(
|
||||
inputs, padding=self.padding, data_format=self.data_format
|
||||
)
|
||||
|
||||
def get_config(self):
|
||||
config = {"padding": self.padding, "data_format": self.data_format}
|
||||
base_config = super().get_config()
|
||||
return dict(list(base_config.items()) + list(config.items()))
|
||||
|
||||
|
||||
def parse_opt():
|
||||
parser = argparse.ArgumentParser()
|
||||
parser.add_argument('--weights', type=str, default=ROOT / 'yolov5s.pt', help='weights path')
|
||||
parser.add_argument('--weights', type=str, default=ROOT / 'yolov3.pt', help='weights path')
|
||||
parser.add_argument('--imgsz', '--img', '--img-size', nargs='+', type=int, default=[640], help='inference size h,w')
|
||||
parser.add_argument('--batch-size', type=int, default=1, help='batch size')
|
||||
parser.add_argument('--dynamic', action='store_true', help='dynamic batch size')
|
||||
opt = parser.parse_args()
|
||||
opt.imgsz *= 2 if len(opt.imgsz) == 1 else 1 # expand
|
||||
print_args(vars(opt))
|
||||
print_args(FILE.stem, opt)
|
||||
return opt
|
||||
|
||||
|
||||
@ -603,6 +623,6 @@ def main(opt):
|
||||
run(**vars(opt))
|
||||
|
||||
|
||||
if __name__ == '__main__':
|
||||
if __name__ == "__main__":
|
||||
opt = parse_opt()
|
||||
main(opt)
|
||||
|
||||
@ -3,29 +3,26 @@
|
||||
YOLO-specific modules
|
||||
|
||||
Usage:
|
||||
$ python models/yolo.py --cfg yolov5s.yaml
|
||||
$ python path/to/models/yolo.py --cfg yolov3.yaml
|
||||
"""
|
||||
|
||||
import argparse
|
||||
import os
|
||||
import platform
|
||||
import sys
|
||||
from copy import deepcopy
|
||||
from pathlib import Path
|
||||
|
||||
FILE = Path(__file__).resolve()
|
||||
ROOT = FILE.parents[1] # YOLOv3 root directory
|
||||
ROOT = FILE.parents[1] # root directory
|
||||
if str(ROOT) not in sys.path:
|
||||
sys.path.append(str(ROOT)) # add ROOT to PATH
|
||||
if platform.system() != 'Windows':
|
||||
ROOT = Path(os.path.relpath(ROOT, Path.cwd())) # relative
|
||||
# ROOT = ROOT.relative_to(Path.cwd()) # relative
|
||||
|
||||
from models.common import *
|
||||
from models.experimental import *
|
||||
from utils.autoanchor import check_anchor_order
|
||||
from utils.general import LOGGER, check_version, check_yaml, make_divisible, print_args
|
||||
from utils.plots import feature_visualization
|
||||
from utils.torch_utils import (fuse_conv_and_bn, initialize_weights, model_info, profile, scale_img, select_device,
|
||||
from utils.torch_utils import (copy_attr, fuse_conv_and_bn, initialize_weights, model_info, scale_img, select_device,
|
||||
time_sync)
|
||||
|
||||
try:
|
||||
@ -35,10 +32,8 @@ except ImportError:
|
||||
|
||||
|
||||
class Detect(nn.Module):
|
||||
# YOLOv3 Detect head for detection models
|
||||
stride = None # strides computed during build
|
||||
dynamic = False # force grid reconstruction
|
||||
export = False # export mode
|
||||
onnx_dynamic = False # ONNX export parameter
|
||||
|
||||
def __init__(self, nc=80, anchors=(), ch=(), inplace=True): # detection layer
|
||||
super().__init__()
|
||||
@ -46,11 +41,11 @@ class Detect(nn.Module):
|
||||
self.no = nc + 5 # number of outputs per anchor
|
||||
self.nl = len(anchors) # number of detection layers
|
||||
self.na = len(anchors[0]) // 2 # number of anchors
|
||||
self.grid = [torch.empty(0) for _ in range(self.nl)] # init grid
|
||||
self.anchor_grid = [torch.empty(0) for _ in range(self.nl)] # init anchor grid
|
||||
self.grid = [torch.zeros(1)] * self.nl # init grid
|
||||
self.anchor_grid = [torch.zeros(1)] * self.nl # init anchor grid
|
||||
self.register_buffer('anchors', torch.tensor(anchors).float().view(self.nl, -1, 2)) # shape(nl,na,2)
|
||||
self.m = nn.ModuleList(nn.Conv2d(x, self.no * self.na, 1) for x in ch) # output conv
|
||||
self.inplace = inplace # use inplace ops (e.g. slice assignment)
|
||||
self.inplace = inplace # use in-place ops (e.g. slice assignment)
|
||||
|
||||
def forward(self, x):
|
||||
z = [] # inference output
|
||||
@ -60,110 +55,35 @@ class Detect(nn.Module):
|
||||
x[i] = x[i].view(bs, self.na, self.no, ny, nx).permute(0, 1, 3, 4, 2).contiguous()
|
||||
|
||||
if not self.training: # inference
|
||||
if self.dynamic or self.grid[i].shape[2:4] != x[i].shape[2:4]:
|
||||
if self.onnx_dynamic or self.grid[i].shape[2:4] != x[i].shape[2:4]:
|
||||
self.grid[i], self.anchor_grid[i] = self._make_grid(nx, ny, i)
|
||||
|
||||
if isinstance(self, Segment): # (boxes + masks)
|
||||
xy, wh, conf, mask = x[i].split((2, 2, self.nc + 1, self.no - self.nc - 5), 4)
|
||||
xy = (xy.sigmoid() * 2 + self.grid[i]) * self.stride[i] # xy
|
||||
wh = (wh.sigmoid() * 2) ** 2 * self.anchor_grid[i] # wh
|
||||
y = torch.cat((xy, wh, conf.sigmoid(), mask), 4)
|
||||
else: # Detect (boxes only)
|
||||
xy, wh, conf = x[i].sigmoid().split((2, 2, self.nc + 1), 4)
|
||||
xy = (xy * 2 + self.grid[i]) * self.stride[i] # xy
|
||||
wh = (wh * 2) ** 2 * self.anchor_grid[i] # wh
|
||||
y = torch.cat((xy, wh, conf), 4)
|
||||
z.append(y.view(bs, self.na * nx * ny, self.no))
|
||||
y = x[i].sigmoid()
|
||||
if self.inplace:
|
||||
y[..., 0:2] = (y[..., 0:2] * 2 - 0.5 + self.grid[i]) * self.stride[i] # xy
|
||||
y[..., 2:4] = (y[..., 2:4] * 2) ** 2 * self.anchor_grid[i] # wh
|
||||
else: # for on AWS Inferentia https://github.com/ultralytics/yolov5/pull/2953
|
||||
xy = (y[..., 0:2] * 2 - 0.5 + self.grid[i]) * self.stride[i] # xy
|
||||
wh = (y[..., 2:4] * 2) ** 2 * self.anchor_grid[i] # wh
|
||||
y = torch.cat((xy, wh, y[..., 4:]), -1)
|
||||
z.append(y.view(bs, -1, self.no))
|
||||
|
||||
return x if self.training else (torch.cat(z, 1),) if self.export else (torch.cat(z, 1), x)
|
||||
return x if self.training else (torch.cat(z, 1), x)
|
||||
|
||||
def _make_grid(self, nx=20, ny=20, i=0, torch_1_10=check_version(torch.__version__, '1.10.0')):
|
||||
def _make_grid(self, nx=20, ny=20, i=0):
|
||||
d = self.anchors[i].device
|
||||
t = self.anchors[i].dtype
|
||||
shape = 1, self.na, ny, nx, 2 # grid shape
|
||||
y, x = torch.arange(ny, device=d, dtype=t), torch.arange(nx, device=d, dtype=t)
|
||||
yv, xv = torch.meshgrid(y, x, indexing='ij') if torch_1_10 else torch.meshgrid(y, x) # torch>=0.7 compatibility
|
||||
grid = torch.stack((xv, yv), 2).expand(shape) - 0.5 # add grid offset, i.e. y = 2.0 * x - 0.5
|
||||
anchor_grid = (self.anchors[i] * self.stride[i]).view((1, self.na, 1, 1, 2)).expand(shape)
|
||||
if check_version(torch.__version__, '1.10.0'): # torch>=1.10.0 meshgrid workaround for torch>=0.7 compatibility
|
||||
yv, xv = torch.meshgrid([torch.arange(ny).to(d), torch.arange(nx).to(d)], indexing='ij')
|
||||
else:
|
||||
yv, xv = torch.meshgrid([torch.arange(ny).to(d), torch.arange(nx).to(d)])
|
||||
grid = torch.stack((xv, yv), 2).expand((1, self.na, ny, nx, 2)).float()
|
||||
anchor_grid = (self.anchors[i].clone() * self.stride[i]) \
|
||||
.view((1, self.na, 1, 1, 2)).expand((1, self.na, ny, nx, 2)).float()
|
||||
return grid, anchor_grid
|
||||
|
||||
|
||||
class Segment(Detect):
|
||||
# YOLOv3 Segment head for segmentation models
|
||||
def __init__(self, nc=80, anchors=(), nm=32, npr=256, ch=(), inplace=True):
|
||||
super().__init__(nc, anchors, ch, inplace)
|
||||
self.nm = nm # number of masks
|
||||
self.npr = npr # number of protos
|
||||
self.no = 5 + nc + self.nm # number of outputs per anchor
|
||||
self.m = nn.ModuleList(nn.Conv2d(x, self.no * self.na, 1) for x in ch) # output conv
|
||||
self.proto = Proto(ch[0], self.npr, self.nm) # protos
|
||||
self.detect = Detect.forward
|
||||
|
||||
def forward(self, x):
|
||||
p = self.proto(x[0])
|
||||
x = self.detect(self, x)
|
||||
return (x, p) if self.training else (x[0], p) if self.export else (x[0], p, x[1])
|
||||
|
||||
|
||||
class BaseModel(nn.Module):
|
||||
# YOLOv3 base model
|
||||
def forward(self, x, profile=False, visualize=False):
|
||||
return self._forward_once(x, profile, visualize) # single-scale inference, train
|
||||
|
||||
def _forward_once(self, x, profile=False, visualize=False):
|
||||
y, dt = [], [] # outputs
|
||||
for m in self.model:
|
||||
if m.f != -1: # if not from previous layer
|
||||
x = y[m.f] if isinstance(m.f, int) else [x if j == -1 else y[j] for j in m.f] # from earlier layers
|
||||
if profile:
|
||||
self._profile_one_layer(m, x, dt)
|
||||
x = m(x) # run
|
||||
y.append(x if m.i in self.save else None) # save output
|
||||
if visualize:
|
||||
feature_visualization(x, m.type, m.i, save_dir=visualize)
|
||||
return x
|
||||
|
||||
def _profile_one_layer(self, m, x, dt):
|
||||
c = m == self.model[-1] # is final layer, copy input as inplace fix
|
||||
o = thop.profile(m, inputs=(x.copy() if c else x,), verbose=False)[0] / 1E9 * 2 if thop else 0 # FLOPs
|
||||
t = time_sync()
|
||||
for _ in range(10):
|
||||
m(x.copy() if c else x)
|
||||
dt.append((time_sync() - t) * 100)
|
||||
if m == self.model[0]:
|
||||
LOGGER.info(f"{'time (ms)':>10s} {'GFLOPs':>10s} {'params':>10s} module")
|
||||
LOGGER.info(f'{dt[-1]:10.2f} {o:10.2f} {m.np:10.0f} {m.type}')
|
||||
if c:
|
||||
LOGGER.info(f"{sum(dt):10.2f} {'-':>10s} {'-':>10s} Total")
|
||||
|
||||
def fuse(self): # fuse model Conv2d() + BatchNorm2d() layers
|
||||
LOGGER.info('Fusing layers... ')
|
||||
for m in self.model.modules():
|
||||
if isinstance(m, (Conv, DWConv)) and hasattr(m, 'bn'):
|
||||
m.conv = fuse_conv_and_bn(m.conv, m.bn) # update conv
|
||||
delattr(m, 'bn') # remove batchnorm
|
||||
m.forward = m.forward_fuse # update forward
|
||||
self.info()
|
||||
return self
|
||||
|
||||
def info(self, verbose=False, img_size=640): # print model information
|
||||
model_info(self, verbose, img_size)
|
||||
|
||||
def _apply(self, fn):
|
||||
# Apply to(), cpu(), cuda(), half() to model tensors that are not parameters or registered buffers
|
||||
self = super()._apply(fn)
|
||||
m = self.model[-1] # Detect()
|
||||
if isinstance(m, (Detect, Segment)):
|
||||
m.stride = fn(m.stride)
|
||||
m.grid = list(map(fn, m.grid))
|
||||
if isinstance(m.anchor_grid, list):
|
||||
m.anchor_grid = list(map(fn, m.anchor_grid))
|
||||
return self
|
||||
|
||||
|
||||
class DetectionModel(BaseModel):
|
||||
# YOLOv3 detection model
|
||||
def __init__(self, cfg='yolov5s.yaml', ch=3, nc=None, anchors=None): # model, input channels, number of classes
|
||||
class Model(nn.Module):
|
||||
def __init__(self, cfg='yolov3.yaml', ch=3, nc=None, anchors=None): # model, input channels, number of classes
|
||||
super().__init__()
|
||||
if isinstance(cfg, dict):
|
||||
self.yaml = cfg # model dict
|
||||
@ -187,13 +107,12 @@ class DetectionModel(BaseModel):
|
||||
|
||||
# Build strides, anchors
|
||||
m = self.model[-1] # Detect()
|
||||
if isinstance(m, (Detect, Segment)):
|
||||
if isinstance(m, Detect):
|
||||
s = 256 # 2x min stride
|
||||
m.inplace = self.inplace
|
||||
forward = lambda x: self.forward(x)[0] if isinstance(m, Segment) else self.forward(x)
|
||||
m.stride = torch.tensor([s / x.shape[-2] for x in forward(torch.zeros(1, ch, s, s))]) # forward
|
||||
check_anchor_order(m)
|
||||
m.stride = torch.tensor([s / x.shape[-2] for x in self.forward(torch.zeros(1, ch, s, s))]) # forward
|
||||
m.anchors /= m.stride.view(-1, 1, 1)
|
||||
check_anchor_order(m)
|
||||
self.stride = m.stride
|
||||
self._initialize_biases() # only run once
|
||||
|
||||
@ -221,6 +140,19 @@ class DetectionModel(BaseModel):
|
||||
y = self._clip_augmented(y) # clip augmented tails
|
||||
return torch.cat(y, 1), None # augmented inference, train
|
||||
|
||||
def _forward_once(self, x, profile=False, visualize=False):
|
||||
y, dt = [], [] # outputs
|
||||
for m in self.model:
|
||||
if m.f != -1: # if not from previous layer
|
||||
x = y[m.f] if isinstance(m.f, int) else [x if j == -1 else y[j] for j in m.f] # from earlier layers
|
||||
if profile:
|
||||
self._profile_one_layer(m, x, dt)
|
||||
x = m(x) # run
|
||||
y.append(x if m.i in self.save else None) # save output
|
||||
if visualize:
|
||||
feature_visualization(x, m.type, m.i, save_dir=visualize)
|
||||
return x
|
||||
|
||||
def _descale_pred(self, p, flips, scale, img_size):
|
||||
# de-scale predictions following augmented inference (inverse operation)
|
||||
if self.inplace:
|
||||
@ -249,6 +181,19 @@ class DetectionModel(BaseModel):
|
||||
y[-1] = y[-1][:, i:] # small
|
||||
return y
|
||||
|
||||
def _profile_one_layer(self, m, x, dt):
|
||||
c = isinstance(m, Detect) # is final layer, copy input as inplace fix
|
||||
o = thop.profile(m, inputs=(x.copy() if c else x,), verbose=False)[0] / 1E9 * 2 if thop else 0 # FLOPs
|
||||
t = time_sync()
|
||||
for _ in range(10):
|
||||
m(x.copy() if c else x)
|
||||
dt.append((time_sync() - t) * 100)
|
||||
if m == self.model[0]:
|
||||
LOGGER.info(f"{'time (ms)':>10s} {'GFLOPs':>10s} {'params':>10s} {'module'}")
|
||||
LOGGER.info(f'{dt[-1]:10.2f} {o:10.2f} {m.np:10.0f} {m.type}')
|
||||
if c:
|
||||
LOGGER.info(f"{sum(dt):10.2f} {'-':>10s} {'-':>10s} Total")
|
||||
|
||||
def _initialize_biases(self, cf=None): # initialize biases into Detect(), cf is class frequency
|
||||
# https://arxiv.org/abs/1708.02002 section 3.3
|
||||
# cf = torch.bincount(torch.tensor(np.concatenate(dataset.labels, 0)[:, 0]).long(), minlength=nc) + 1.
|
||||
@ -256,52 +201,55 @@ class DetectionModel(BaseModel):
|
||||
for mi, s in zip(m.m, m.stride): # from
|
||||
b = mi.bias.view(m.na, -1) # conv.bias(255) to (3,85)
|
||||
b.data[:, 4] += math.log(8 / (640 / s) ** 2) # obj (8 objects per 640 image)
|
||||
b.data[:, 5:5 + m.nc] += math.log(0.6 / (m.nc - 0.99999)) if cf is None else torch.log(cf / cf.sum()) # cls
|
||||
b.data[:, 5:] += math.log(0.6 / (m.nc - 0.999999)) if cf is None else torch.log(cf / cf.sum()) # cls
|
||||
mi.bias = torch.nn.Parameter(b.view(-1), requires_grad=True)
|
||||
|
||||
def _print_biases(self):
|
||||
m = self.model[-1] # Detect() module
|
||||
for mi in m.m: # from
|
||||
b = mi.bias.detach().view(m.na, -1).T # conv.bias(255) to (3,85)
|
||||
LOGGER.info(
|
||||
('%6g Conv2d.bias:' + '%10.3g' * 6) % (mi.weight.shape[1], *b[:5].mean(1).tolist(), b[5:].mean()))
|
||||
|
||||
Model = DetectionModel # retain 'Model' class for backwards compatibility
|
||||
# def _print_weights(self):
|
||||
# for m in self.model.modules():
|
||||
# if type(m) is Bottleneck:
|
||||
# LOGGER.info('%10.3g' % (m.w.detach().sigmoid() * 2)) # shortcut weights
|
||||
|
||||
def fuse(self): # fuse model Conv2d() + BatchNorm2d() layers
|
||||
LOGGER.info('Fusing layers... ')
|
||||
for m in self.model.modules():
|
||||
if isinstance(m, (Conv, DWConv)) and hasattr(m, 'bn'):
|
||||
m.conv = fuse_conv_and_bn(m.conv, m.bn) # update conv
|
||||
delattr(m, 'bn') # remove batchnorm
|
||||
m.forward = m.forward_fuse # update forward
|
||||
self.info()
|
||||
return self
|
||||
|
||||
class SegmentationModel(DetectionModel):
|
||||
# segmentation model
|
||||
def __init__(self, cfg='yolov5s-seg.yaml', ch=3, nc=None, anchors=None):
|
||||
super().__init__(cfg, ch, nc, anchors)
|
||||
def autoshape(self): # add AutoShape module
|
||||
LOGGER.info('Adding AutoShape... ')
|
||||
m = AutoShape(self) # wrap model
|
||||
copy_attr(m, self, include=('yaml', 'nc', 'hyp', 'names', 'stride'), exclude=()) # copy attributes
|
||||
return m
|
||||
|
||||
def info(self, verbose=False, img_size=640): # print model information
|
||||
model_info(self, verbose, img_size)
|
||||
|
||||
class ClassificationModel(BaseModel):
|
||||
# classification model
|
||||
def __init__(self, cfg=None, model=None, nc=1000, cutoff=10): # yaml, model, number of classes, cutoff index
|
||||
super().__init__()
|
||||
self._from_detection_model(model, nc, cutoff) if model is not None else self._from_yaml(cfg)
|
||||
|
||||
def _from_detection_model(self, model, nc=1000, cutoff=10):
|
||||
# Create a classification model from a detection model
|
||||
if isinstance(model, DetectMultiBackend):
|
||||
model = model.model # unwrap DetectMultiBackend
|
||||
model.model = model.model[:cutoff] # backbone
|
||||
m = model.model[-1] # last layer
|
||||
ch = m.conv.in_channels if hasattr(m, 'conv') else m.cv1.conv.in_channels # ch into module
|
||||
c = Classify(ch, nc) # Classify()
|
||||
c.i, c.f, c.type = m.i, m.f, 'models.common.Classify' # index, from, type
|
||||
model.model[-1] = c # replace
|
||||
self.model = model.model
|
||||
self.stride = model.stride
|
||||
self.save = []
|
||||
self.nc = nc
|
||||
|
||||
def _from_yaml(self, cfg):
|
||||
# Create a classification model from a *.yaml file
|
||||
self.model = None
|
||||
def _apply(self, fn):
|
||||
# Apply to(), cpu(), cuda(), half() to model tensors that are not parameters or registered buffers
|
||||
self = super()._apply(fn)
|
||||
m = self.model[-1] # Detect()
|
||||
if isinstance(m, Detect):
|
||||
m.stride = fn(m.stride)
|
||||
m.grid = list(map(fn, m.grid))
|
||||
if isinstance(m.anchor_grid, list):
|
||||
m.anchor_grid = list(map(fn, m.anchor_grid))
|
||||
return self
|
||||
|
||||
|
||||
def parse_model(d, ch): # model_dict, input_channels(3)
|
||||
# Parse a model.yaml dictionary
|
||||
LOGGER.info(f"\n{'':>3}{'from':>18}{'n':>3}{'params':>10} {'module':<40}{'arguments':<30}")
|
||||
anchors, nc, gd, gw, act = d['anchors'], d['nc'], d['depth_multiple'], d['width_multiple'], d.get('activation')
|
||||
if act:
|
||||
Conv.default_act = eval(act) # redefine default activation, i.e. Conv.default_act = nn.SiLU()
|
||||
LOGGER.info(f"{colorstr('activation:')} {act}") # print
|
||||
anchors, nc, gd, gw = d['anchors'], d['nc'], d['depth_multiple'], d['width_multiple']
|
||||
na = (len(anchors[0]) // 2) if isinstance(anchors, list) else anchors # number of anchors
|
||||
no = na * (nc + 5) # number of outputs = anchors * (classes + 5)
|
||||
|
||||
@ -309,32 +257,30 @@ def parse_model(d, ch): # model_dict, input_channels(3)
|
||||
for i, (f, n, m, args) in enumerate(d['backbone'] + d['head']): # from, number, module, args
|
||||
m = eval(m) if isinstance(m, str) else m # eval strings
|
||||
for j, a in enumerate(args):
|
||||
with contextlib.suppress(NameError):
|
||||
try:
|
||||
args[j] = eval(a) if isinstance(a, str) else a # eval strings
|
||||
except NameError:
|
||||
pass
|
||||
|
||||
n = n_ = max(round(n * gd), 1) if n > 1 else n # depth gain
|
||||
if m in {
|
||||
Conv, GhostConv, Bottleneck, GhostBottleneck, SPP, SPPF, DWConv, MixConv2d, Focus, CrossConv,
|
||||
BottleneckCSP, C3, C3TR, C3SPP, C3Ghost, nn.ConvTranspose2d, DWConvTranspose2d, C3x}:
|
||||
if m in [Conv, GhostConv, Bottleneck, GhostBottleneck, SPP, SPPF, DWConv, MixConv2d, Focus, CrossConv,
|
||||
BottleneckCSP, C3, C3TR, C3SPP, C3Ghost]:
|
||||
c1, c2 = ch[f], args[0]
|
||||
if c2 != no: # if not output
|
||||
c2 = make_divisible(c2 * gw, 8)
|
||||
|
||||
args = [c1, c2, *args[1:]]
|
||||
if m in {BottleneckCSP, C3, C3TR, C3Ghost, C3x}:
|
||||
if m in [BottleneckCSP, C3, C3TR, C3Ghost]:
|
||||
args.insert(2, n) # number of repeats
|
||||
n = 1
|
||||
elif m is nn.BatchNorm2d:
|
||||
args = [ch[f]]
|
||||
elif m is Concat:
|
||||
c2 = sum(ch[x] for x in f)
|
||||
# TODO: channel, gw, gd
|
||||
elif m in {Detect, Segment}:
|
||||
elif m is Detect:
|
||||
args.append([ch[x] for x in f])
|
||||
if isinstance(args[1], int): # number of anchors
|
||||
args[1] = [list(range(args[1] * 2))] * len(f)
|
||||
if m is Segment:
|
||||
args[3] = make_divisible(args[3] * gw, 8)
|
||||
elif m is Contract:
|
||||
c2 = ch[f] * args[0] ** 2
|
||||
elif m is Expand:
|
||||
@ -357,34 +303,34 @@ def parse_model(d, ch): # model_dict, input_channels(3)
|
||||
|
||||
if __name__ == '__main__':
|
||||
parser = argparse.ArgumentParser()
|
||||
parser.add_argument('--cfg', type=str, default='yolov5s.yaml', help='model.yaml')
|
||||
parser.add_argument('--batch-size', type=int, default=1, help='total batch size for all GPUs')
|
||||
parser.add_argument('--cfg', type=str, default='yolov3yaml', help='model.yaml')
|
||||
parser.add_argument('--device', default='', help='cuda device, i.e. 0 or 0,1,2,3 or cpu')
|
||||
parser.add_argument('--profile', action='store_true', help='profile model speed')
|
||||
parser.add_argument('--line-profile', action='store_true', help='profile model speed layer by layer')
|
||||
parser.add_argument('--test', action='store_true', help='test all yolo*.yaml')
|
||||
opt = parser.parse_args()
|
||||
opt.cfg = check_yaml(opt.cfg) # check YAML
|
||||
print_args(vars(opt))
|
||||
print_args(FILE.stem, opt)
|
||||
device = select_device(opt.device)
|
||||
|
||||
# Create model
|
||||
im = torch.rand(opt.batch_size, 3, 640, 640).to(device)
|
||||
model = Model(opt.cfg).to(device)
|
||||
model.train()
|
||||
|
||||
# Options
|
||||
if opt.line_profile: # profile layer by layer
|
||||
model(im, profile=True)
|
||||
# Profile
|
||||
if opt.profile:
|
||||
img = torch.rand(8 if torch.cuda.is_available() else 1, 3, 640, 640).to(device)
|
||||
y = model(img, profile=True)
|
||||
|
||||
elif opt.profile: # profile forward-backward
|
||||
results = profile(input=im, ops=[model], n=3)
|
||||
|
||||
elif opt.test: # test all models
|
||||
# Test all models
|
||||
if opt.test:
|
||||
for cfg in Path(ROOT / 'models').rglob('yolo*.yaml'):
|
||||
try:
|
||||
_ = Model(cfg)
|
||||
except Exception as e:
|
||||
print(f'Error in {cfg}: {e}')
|
||||
|
||||
else: # report fused model summary
|
||||
model.fuse()
|
||||
# Tensorboard (not working https://github.com/ultralytics/yolov5/issues/2898)
|
||||
# from torch.utils.tensorboard import SummaryWriter
|
||||
# tb_writer = SummaryWriter('.')
|
||||
# LOGGER.info("Run 'tensorboard --logdir=models' to view tensorboard at http://localhost:6006/")
|
||||
# tb_writer.add_graph(torch.jit.trace(model, img, strict=False), []) # add model graph
|
||||
|
||||
30
yolov3/requirements.txt
Normal file → Executable file
30
yolov3/requirements.txt
Normal file → Executable file
@ -1,35 +1,31 @@
|
||||
# YOLOv3 requirements
|
||||
# Usage: pip install -r requirements.txt
|
||||
|
||||
# Base ------------------------------------------------------------------------
|
||||
gitpython
|
||||
ipython # interactive notebook
|
||||
# Base ----------------------------------------
|
||||
matplotlib>=3.2.2
|
||||
numpy>=1.18.5
|
||||
opencv-python>=4.1.1
|
||||
Pillow>=7.1.2
|
||||
psutil # system resources
|
||||
PyYAML>=5.3.1
|
||||
requests>=2.23.0
|
||||
scipy>=1.4.1
|
||||
thop>=0.1.1 # FLOPs computation
|
||||
torch>=1.7.0 # see https://pytorch.org/get-started/locally (recommended)
|
||||
torch>=1.7.0 # see https://pytorch.org/get-started/locally/ (recommended)
|
||||
torchvision>=0.8.1
|
||||
tqdm>=4.64.0
|
||||
# protobuf<=3.20.1 # https://github.com/ultralytics/yolov5/issues/8012
|
||||
|
||||
# Logging ---------------------------------------------------------------------
|
||||
# Logging -------------------------------------
|
||||
tensorboard>=2.4.1
|
||||
# clearml>=1.2.0
|
||||
# clearml
|
||||
# comet
|
||||
|
||||
# Plotting --------------------------------------------------------------------
|
||||
# Plotting ------------------------------------
|
||||
pandas>=1.1.4
|
||||
seaborn>=0.11.0
|
||||
|
||||
# Export ----------------------------------------------------------------------
|
||||
# Export --------------------------------------
|
||||
# coremltools>=6.0 # CoreML export
|
||||
# onnx>=1.12.0 # ONNX export
|
||||
# onnx>=1.9.0 # ONNX export
|
||||
# onnx-simplifier>=0.4.1 # ONNX simplifier
|
||||
# nvidia-pyindex # TensorRT export
|
||||
# nvidia-tensorrt # TensorRT export
|
||||
@ -38,14 +34,14 @@ seaborn>=0.11.0
|
||||
# tensorflowjs>=3.9.0 # TF.js export
|
||||
# openvino-dev # OpenVINO export
|
||||
|
||||
# Deploy ----------------------------------------------------------------------
|
||||
setuptools>=65.5.1 # Snyk vulnerability fix
|
||||
wheel>=0.38.0 # Snyk vulnerability fix
|
||||
# Deploy --------------------------------------
|
||||
# tritonclient[all]~=2.24.0
|
||||
|
||||
# Extras ----------------------------------------------------------------------
|
||||
# Extras --------------------------------------
|
||||
ipython # interactive notebook
|
||||
psutil # system utilization
|
||||
thop>=0.1.1 # FLOPs computation
|
||||
# mss # screenshots
|
||||
# albumentations>=1.0.3
|
||||
# pycocotools>=2.0.6 # COCO mAP
|
||||
# pycocotools>=2.0 # COCO mAP
|
||||
# roboflow
|
||||
# ultralytics # HUB https://hub.ultralytics.com
|
||||
|
||||
BIN
yolov3/runs-20230212T095309Z-001.zip
Normal file
BIN
yolov3/runs-20230212T095309Z-001.zip
Normal file
Binary file not shown.
@ -1,10 +1,10 @@
|
||||
# Project-wide configuration file, can be used for package metadata and other toll configurations
|
||||
# Example usage: global configuration for PEP8 (via flake8) setting or default pytest arguments
|
||||
# Local usage: pip install pre-commit, pre-commit run --all-files
|
||||
|
||||
[metadata]
|
||||
license_file = LICENSE
|
||||
description_file = README.md
|
||||
description-file = README.md
|
||||
|
||||
|
||||
[tool:pytest]
|
||||
norecursedirs =
|
||||
@ -16,6 +16,7 @@ addopts =
|
||||
--durations=25
|
||||
--color=yes
|
||||
|
||||
|
||||
[flake8]
|
||||
max-line-length = 120
|
||||
exclude = .tox,*.egg,build,temp
|
||||
@ -25,30 +26,26 @@ verbose = 2
|
||||
# https://pep8.readthedocs.io/en/latest/intro.html#error-codes
|
||||
format = pylint
|
||||
# see: https://www.flake8rules.com/
|
||||
ignore = E731,F405,E402,F401,W504,E127,E231,E501,F403
|
||||
# E731: Do not assign a lambda expression, use a def
|
||||
# F405: name may be undefined, or defined from star imports: module
|
||||
# E402: module level import not at top of file
|
||||
# F401: module imported but unused
|
||||
# W504: line break after binary operator
|
||||
# E127: continuation line over-indented for visual indent
|
||||
# E231: missing whitespace after ‘,’, ‘;’, or ‘:’
|
||||
# E501: line too long
|
||||
# F403: ‘from module import *’ used; unable to detect undefined names
|
||||
ignore =
|
||||
E731 # Do not assign a lambda expression, use a def
|
||||
F405
|
||||
E402
|
||||
F841
|
||||
E741
|
||||
F821
|
||||
E722
|
||||
F401
|
||||
W504
|
||||
E127
|
||||
W504
|
||||
E231
|
||||
E501
|
||||
F403
|
||||
E302
|
||||
F541
|
||||
|
||||
|
||||
[isort]
|
||||
# https://pycqa.github.io/isort/docs/configuration/options.html
|
||||
line_length = 120
|
||||
# see: https://pycqa.github.io/isort/docs/configuration/multi_line_output_modes.html
|
||||
multi_line_output = 0
|
||||
|
||||
[yapf]
|
||||
based_on_style = pep8
|
||||
spaces_before_comment = 2
|
||||
COLUMN_LIMIT = 120
|
||||
COALESCE_BRACKETS = True
|
||||
SPACES_AROUND_POWER_OPERATOR = True
|
||||
SPACE_BETWEEN_ENDING_COMMA_AND_CLOSING_BRACKET = False
|
||||
SPLIT_BEFORE_CLOSING_BRACKET = False
|
||||
SPLIT_BEFORE_FIRST_ARGUMENT = False
|
||||
# EACH_DICT_ENTRY_ON_SEPARATE_LINE = False
|
||||
|
||||
492
yolov3/train.py
492
yolov3/train.py
@ -1,25 +1,14 @@
|
||||
# YOLOv3 🚀 by Ultralytics, GPL-3.0 license
|
||||
"""
|
||||
Train a YOLOv3 model on a custom dataset.
|
||||
Models and datasets download automatically from the latest YOLOv3 release.
|
||||
Train a model on a custom dataset
|
||||
|
||||
Usage - Single-GPU training:
|
||||
$ python train.py --data coco128.yaml --weights yolov5s.pt --img 640 # from pretrained (recommended)
|
||||
$ python train.py --data coco128.yaml --weights '' --cfg yolov5s.yaml --img 640 # from scratch
|
||||
|
||||
Usage - Multi-GPU DDP training:
|
||||
$ python -m torch.distributed.run --nproc_per_node 4 --master_port 1 train.py --data coco128.yaml --weights yolov5s.pt --img 640 --device 0,1,2,3
|
||||
|
||||
Models: https://github.com/ultralytics/yolov5/tree/master/models
|
||||
Datasets: https://github.com/ultralytics/yolov5/tree/master/data
|
||||
Tutorial: https://github.com/ultralytics/yolov5/wiki/Train-Custom-Data
|
||||
Usage:
|
||||
$ python path/to/train.py --data coco128.yaml --weights yolov3.pt --img 640
|
||||
"""
|
||||
|
||||
import argparse
|
||||
import math
|
||||
import os
|
||||
import random
|
||||
import subprocess
|
||||
import sys
|
||||
import time
|
||||
from copy import deepcopy
|
||||
@ -31,46 +20,49 @@ import torch
|
||||
import torch.distributed as dist
|
||||
import torch.nn as nn
|
||||
import yaml
|
||||
from torch.optim import lr_scheduler
|
||||
from torch.cuda import amp
|
||||
from torch.nn.parallel import DistributedDataParallel as DDP
|
||||
from torch.optim import SGD, Adam, lr_scheduler
|
||||
from tqdm import tqdm
|
||||
|
||||
FILE = Path(__file__).resolve()
|
||||
ROOT = FILE.parents[0] # YOLOv3 root directory
|
||||
ROOT = FILE.parents[0] # root directory
|
||||
if str(ROOT) not in sys.path:
|
||||
sys.path.append(str(ROOT)) # add ROOT to PATH
|
||||
ROOT = Path(os.path.relpath(ROOT, Path.cwd())) # relative
|
||||
|
||||
import val as validate # for end-of-epoch mAP
|
||||
import val # for end-of-epoch mAP
|
||||
from models.experimental import attempt_load
|
||||
from models.yolo import Model
|
||||
from utils.autoanchor import check_anchors
|
||||
from utils.autobatch import check_train_batch_size
|
||||
from utils.callbacks import Callbacks
|
||||
from utils.dataloaders import create_dataloader
|
||||
from utils.downloads import attempt_download, is_url
|
||||
from utils.general import (LOGGER, TQDM_BAR_FORMAT, check_amp, check_dataset, check_file, check_git_info,
|
||||
check_git_status, check_img_size, check_requirements, check_suffix, check_yaml, colorstr,
|
||||
get_latest_run, increment_path, init_seeds, intersect_dicts, labels_to_class_weights,
|
||||
labels_to_image_weights, methods, one_cycle, print_args, print_mutation, strip_optimizer,
|
||||
yaml_save)
|
||||
from utils.datasets import create_dataloader
|
||||
from utils.downloads import attempt_download
|
||||
from utils.general import (LOGGER, NCOLS, check_dataset, check_file, check_git_status, check_img_size,
|
||||
check_requirements, check_suffix, check_yaml, colorstr, get_latest_run, increment_path,
|
||||
init_seeds, intersect_dicts, labels_to_class_weights, labels_to_image_weights, methods,
|
||||
one_cycle, print_args, print_mutation, strip_optimizer)
|
||||
from utils.loggers import Loggers
|
||||
from utils.loggers.comet.comet_utils import check_comet_resume
|
||||
from utils.loggers.wandb.wandb_utils import check_wandb_resume
|
||||
from utils.loss import ComputeLoss
|
||||
from utils.metrics import fitness
|
||||
from utils.plots import plot_evolve
|
||||
from utils.torch_utils import (EarlyStopping, ModelEMA, de_parallel, select_device, smart_DDP, smart_optimizer,
|
||||
smart_resume, torch_distributed_zero_first)
|
||||
from utils.plots import plot_evolve, plot_labels
|
||||
from utils.torch_utils import EarlyStopping, ModelEMA, de_parallel, select_device, torch_distributed_zero_first
|
||||
|
||||
LOCAL_RANK = int(os.getenv('LOCAL_RANK', -1)) # https://pytorch.org/docs/stable/elastic/run.html
|
||||
RANK = int(os.getenv('RANK', -1))
|
||||
WORLD_SIZE = int(os.getenv('WORLD_SIZE', 1))
|
||||
GIT_INFO = check_git_info()
|
||||
|
||||
|
||||
def train(hyp, opt, device, callbacks): # hyp is path/to/hyp.yaml or hyp dictionary
|
||||
save_dir, epochs, batch_size, weights, single_cls, evolve, data, cfg, resume, noval, nosave, workers, freeze = \
|
||||
Path(opt.save_dir), opt.epochs, opt.batch_size, opt.weights, opt.single_cls, opt.evolve, opt.data, opt.cfg, opt.resume, opt.noval, opt.nosave, opt.workers, opt.freeze
|
||||
callbacks.run('on_pretrain_routine_start')
|
||||
def train(hyp, # path/to/hyp.yaml or hyp dictionary
|
||||
opt,
|
||||
device,
|
||||
callbacks
|
||||
):
|
||||
save_dir, epochs, batch_size, weights, single_cls, evolve, data, cfg, resume, noval, nosave, workers, freeze, = \
|
||||
Path(opt.save_dir), opt.epochs, opt.batch_size, opt.weights, opt.single_cls, opt.evolve, opt.data, opt.cfg, \
|
||||
opt.resume, opt.noval, opt.nosave, opt.workers, opt.freeze
|
||||
|
||||
# Directories
|
||||
w = save_dir / 'weights' # weights dir
|
||||
@ -82,36 +74,36 @@ def train(hyp, opt, device, callbacks): # hyp is path/to/hyp.yaml or hyp dictio
|
||||
with open(hyp, errors='ignore') as f:
|
||||
hyp = yaml.safe_load(f) # load hyps dict
|
||||
LOGGER.info(colorstr('hyperparameters: ') + ', '.join(f'{k}={v}' for k, v in hyp.items()))
|
||||
opt.hyp = hyp.copy() # for saving hyps to checkpoints
|
||||
|
||||
# Save run settings
|
||||
if not evolve:
|
||||
yaml_save(save_dir / 'hyp.yaml', hyp)
|
||||
yaml_save(save_dir / 'opt.yaml', vars(opt))
|
||||
with open(save_dir / 'hyp.yaml', 'w') as f:
|
||||
yaml.safe_dump(hyp, f, sort_keys=False)
|
||||
with open(save_dir / 'opt.yaml', 'w') as f:
|
||||
yaml.safe_dump(vars(opt), f, sort_keys=False)
|
||||
data_dict = None
|
||||
|
||||
# Loggers
|
||||
data_dict = None
|
||||
if RANK in {-1, 0}:
|
||||
if RANK in [-1, 0]:
|
||||
loggers = Loggers(save_dir, weights, opt, hyp, LOGGER) # loggers instance
|
||||
if loggers.wandb:
|
||||
data_dict = loggers.wandb.data_dict
|
||||
if resume:
|
||||
weights, epochs, hyp = opt.weights, opt.epochs, opt.hyp
|
||||
|
||||
# Register actions
|
||||
for k in methods(loggers):
|
||||
callbacks.register_action(k, callback=getattr(loggers, k))
|
||||
|
||||
# Process custom dataset artifact link
|
||||
data_dict = loggers.remote_dataset
|
||||
if resume: # If resuming runs from remote artifact
|
||||
weights, epochs, hyp, batch_size = opt.weights, opt.epochs, opt.hyp, opt.batch_size
|
||||
|
||||
# Config
|
||||
plots = not evolve and not opt.noplots # create plots
|
||||
plots = not evolve # create plots
|
||||
cuda = device.type != 'cpu'
|
||||
init_seeds(opt.seed + 1 + RANK, deterministic=True)
|
||||
init_seeds(1 + RANK)
|
||||
with torch_distributed_zero_first(LOCAL_RANK):
|
||||
data_dict = data_dict or check_dataset(data) # check if None
|
||||
train_path, val_path = data_dict['train'], data_dict['val']
|
||||
nc = 1 if single_cls else int(data_dict['nc']) # number of classes
|
||||
names = {0: 'item'} if single_cls and len(data_dict['names']) != 1 else data_dict['names'] # class names
|
||||
names = ['item'] if single_cls and len(data_dict['names']) != 1 else data_dict['names'] # class names
|
||||
assert len(names) == nc, f'{len(names)} names found for nc={nc} dataset in {data}' # check
|
||||
is_coco = isinstance(val_path, str) and val_path.endswith('coco/val2017.txt') # COCO dataset
|
||||
|
||||
# Model
|
||||
@ -120,7 +112,7 @@ def train(hyp, opt, device, callbacks): # hyp is path/to/hyp.yaml or hyp dictio
|
||||
if pretrained:
|
||||
with torch_distributed_zero_first(LOCAL_RANK):
|
||||
weights = attempt_download(weights) # download if not found locally
|
||||
ckpt = torch.load(weights, map_location='cpu') # load checkpoint to CPU to avoid CUDA memory leak
|
||||
ckpt = torch.load(weights, map_location=device) # load checkpoint
|
||||
model = Model(cfg or ckpt['model'].yaml, ch=3, nc=nc, anchors=hyp.get('anchors')).to(device) # create
|
||||
exclude = ['anchor'] if (cfg or hyp.get('anchors')) and not resume else [] # exclude keys
|
||||
csd = ckpt['model'].float().state_dict() # checkpoint state_dict as FP32
|
||||
@ -129,13 +121,11 @@ def train(hyp, opt, device, callbacks): # hyp is path/to/hyp.yaml or hyp dictio
|
||||
LOGGER.info(f'Transferred {len(csd)}/{len(model.state_dict())} items from {weights}') # report
|
||||
else:
|
||||
model = Model(cfg, ch=3, nc=nc, anchors=hyp.get('anchors')).to(device) # create
|
||||
amp = check_amp(model) # check AMP
|
||||
|
||||
# Freeze
|
||||
freeze = [f'model.{x}.' for x in (freeze if len(freeze) > 1 else range(freeze[0]))] # layers to freeze
|
||||
freeze = [f'model.{x}.' for x in range(freeze)] # layers to freeze
|
||||
for k, v in model.named_parameters():
|
||||
v.requires_grad = True # train all layers
|
||||
# v.register_hook(lambda x: torch.nan_to_num(x)) # NaN to 0 (commented for erratic training results)
|
||||
if any(x in k for x in freeze):
|
||||
LOGGER.info(f'freezing {k}')
|
||||
v.requires_grad = False
|
||||
@ -146,35 +136,70 @@ def train(hyp, opt, device, callbacks): # hyp is path/to/hyp.yaml or hyp dictio
|
||||
|
||||
# Batch size
|
||||
if RANK == -1 and batch_size == -1: # single-GPU only, estimate best batch size
|
||||
batch_size = check_train_batch_size(model, imgsz, amp)
|
||||
loggers.on_params_update({'batch_size': batch_size})
|
||||
batch_size = check_train_batch_size(model, imgsz)
|
||||
|
||||
# Optimizer
|
||||
nbs = 64 # nominal batch size
|
||||
accumulate = max(round(nbs / batch_size), 1) # accumulate loss before optimizing
|
||||
hyp['weight_decay'] *= batch_size * accumulate / nbs # scale weight_decay
|
||||
optimizer = smart_optimizer(model, opt.optimizer, hyp['lr0'], hyp['momentum'], hyp['weight_decay'])
|
||||
LOGGER.info(f"Scaled weight_decay = {hyp['weight_decay']}")
|
||||
|
||||
g0, g1, g2 = [], [], [] # optimizer parameter groups
|
||||
for v in model.modules():
|
||||
if hasattr(v, 'bias') and isinstance(v.bias, nn.Parameter): # bias
|
||||
g2.append(v.bias)
|
||||
if isinstance(v, nn.BatchNorm2d): # weight (no decay)
|
||||
g0.append(v.weight)
|
||||
elif hasattr(v, 'weight') and isinstance(v.weight, nn.Parameter): # weight (with decay)
|
||||
g1.append(v.weight)
|
||||
|
||||
if opt.adam:
|
||||
optimizer = Adam(g0, lr=hyp['lr0'], betas=(hyp['momentum'], 0.999)) # adjust beta1 to momentum
|
||||
else:
|
||||
optimizer = SGD(g0, lr=hyp['lr0'], momentum=hyp['momentum'], nesterov=True)
|
||||
|
||||
optimizer.add_param_group({'params': g1, 'weight_decay': hyp['weight_decay']}) # add g1 with weight_decay
|
||||
optimizer.add_param_group({'params': g2}) # add g2 (biases)
|
||||
LOGGER.info(f"{colorstr('optimizer:')} {type(optimizer).__name__} with parameter groups "
|
||||
f"{len(g0)} weight, {len(g1)} weight (no decay), {len(g2)} bias")
|
||||
del g0, g1, g2
|
||||
|
||||
# Scheduler
|
||||
if opt.cos_lr:
|
||||
lf = one_cycle(1, hyp['lrf'], epochs) # cosine 1->hyp['lrf']
|
||||
if opt.linear_lr:
|
||||
lf = lambda x: (1 - x / (epochs - 1)) * (1.0 - hyp['lrf']) + hyp['lrf'] # linear
|
||||
else:
|
||||
lf = lambda x: (1 - x / epochs) * (1.0 - hyp['lrf']) + hyp['lrf'] # linear
|
||||
lf = one_cycle(1, hyp['lrf'], epochs) # cosine 1->hyp['lrf']
|
||||
scheduler = lr_scheduler.LambdaLR(optimizer, lr_lambda=lf) # plot_lr_scheduler(optimizer, scheduler, epochs)
|
||||
|
||||
# EMA
|
||||
ema = ModelEMA(model) if RANK in {-1, 0} else None
|
||||
ema = ModelEMA(model) if RANK in [-1, 0] else None
|
||||
|
||||
# Resume
|
||||
best_fitness, start_epoch = 0.0, 0
|
||||
start_epoch, best_fitness = 0, 0.0
|
||||
if pretrained:
|
||||
# Optimizer
|
||||
if ckpt['optimizer'] is not None:
|
||||
optimizer.load_state_dict(ckpt['optimizer'])
|
||||
best_fitness = ckpt['best_fitness']
|
||||
|
||||
# EMA
|
||||
if ema and ckpt.get('ema'):
|
||||
ema.ema.load_state_dict(ckpt['ema'].float().state_dict())
|
||||
ema.updates = ckpt['updates']
|
||||
|
||||
# Epochs
|
||||
start_epoch = ckpt['epoch'] + 1
|
||||
if resume:
|
||||
best_fitness, start_epoch, epochs = smart_resume(ckpt, optimizer, ema, weights, epochs, resume)
|
||||
assert start_epoch > 0, f'{weights} training to {epochs} epochs is finished, nothing to resume.'
|
||||
if epochs < start_epoch:
|
||||
LOGGER.info(f"{weights} has been trained for {ckpt['epoch']} epochs. Fine-tuning for {epochs} more epochs.")
|
||||
epochs += ckpt['epoch'] # finetune additional epochs
|
||||
|
||||
del ckpt, csd
|
||||
|
||||
# DP mode
|
||||
if cuda and RANK == -1 and torch.cuda.device_count() > 1:
|
||||
LOGGER.warning('WARNING ⚠️ DP not recommended, use torch.distributed.run for best DDP Multi-GPU results.\n'
|
||||
LOGGER.warning('WARNING: DP not recommended, use torch.distributed.run for best DDP Multi-GPU results.\n'
|
||||
'See Multi-GPU Tutorial at https://github.com/ultralytics/yolov5/issues/475 to get started.')
|
||||
model = torch.nn.DataParallel(model)
|
||||
|
||||
@ -184,53 +209,41 @@ def train(hyp, opt, device, callbacks): # hyp is path/to/hyp.yaml or hyp dictio
|
||||
LOGGER.info('Using SyncBatchNorm()')
|
||||
|
||||
# Trainloader
|
||||
train_loader, dataset = create_dataloader(train_path,
|
||||
imgsz,
|
||||
batch_size // WORLD_SIZE,
|
||||
gs,
|
||||
single_cls,
|
||||
hyp=hyp,
|
||||
augment=True,
|
||||
cache=None if opt.cache == 'val' else opt.cache,
|
||||
rect=opt.rect,
|
||||
rank=LOCAL_RANK,
|
||||
workers=workers,
|
||||
image_weights=opt.image_weights,
|
||||
quad=opt.quad,
|
||||
prefix=colorstr('train: '),
|
||||
shuffle=True,
|
||||
seed=opt.seed)
|
||||
labels = np.concatenate(dataset.labels, 0)
|
||||
mlc = int(labels[:, 0].max()) # max label class
|
||||
train_loader, dataset = create_dataloader(train_path, imgsz, batch_size // WORLD_SIZE, gs, single_cls,
|
||||
hyp=hyp, augment=True, cache=opt.cache, rect=opt.rect, rank=LOCAL_RANK,
|
||||
workers=workers, image_weights=opt.image_weights, quad=opt.quad,
|
||||
prefix=colorstr('train: '), shuffle=True)
|
||||
mlc = int(np.concatenate(dataset.labels, 0)[:, 0].max()) # max label class
|
||||
nb = len(train_loader) # number of batches
|
||||
assert mlc < nc, f'Label class {mlc} exceeds nc={nc} in {data}. Possible class labels are 0-{nc - 1}'
|
||||
|
||||
# Process 0
|
||||
if RANK in {-1, 0}:
|
||||
val_loader = create_dataloader(val_path,
|
||||
imgsz,
|
||||
batch_size // WORLD_SIZE * 2,
|
||||
gs,
|
||||
single_cls,
|
||||
hyp=hyp,
|
||||
cache=None if noval else opt.cache,
|
||||
rect=True,
|
||||
rank=-1,
|
||||
workers=workers * 2,
|
||||
pad=0.5,
|
||||
if RANK in [-1, 0]:
|
||||
val_loader = create_dataloader(val_path, imgsz, batch_size // WORLD_SIZE * 2, gs, single_cls,
|
||||
hyp=hyp, cache=None if noval else opt.cache, rect=True, rank=-1,
|
||||
workers=workers, pad=0.5,
|
||||
prefix=colorstr('val: '))[0]
|
||||
|
||||
if not resume:
|
||||
labels = np.concatenate(dataset.labels, 0)
|
||||
# c = torch.tensor(labels[:, 0]) # classes
|
||||
# cf = torch.bincount(c.long(), minlength=nc) + 1. # frequency
|
||||
# model._initialize_biases(cf.to(device))
|
||||
if plots:
|
||||
plot_labels(labels, names, save_dir)
|
||||
|
||||
# Anchors
|
||||
if not opt.noautoanchor:
|
||||
check_anchors(dataset, model=model, thr=hyp['anchor_t'], imgsz=imgsz) # run AutoAnchor
|
||||
check_anchors(dataset, model=model, thr=hyp['anchor_t'], imgsz=imgsz)
|
||||
model.half().float() # pre-reduce anchor precision
|
||||
|
||||
callbacks.run('on_pretrain_routine_end', labels, names)
|
||||
callbacks.run('on_pretrain_routine_end')
|
||||
|
||||
# DDP mode
|
||||
if cuda and RANK != -1:
|
||||
model = smart_DDP(model)
|
||||
model = DDP(model, device_ids=[LOCAL_RANK], output_device=LOCAL_RANK)
|
||||
|
||||
# Model attributes
|
||||
# Model parameters
|
||||
nl = de_parallel(model).model[-1].nl # number of detection layers (to scale hyps)
|
||||
hyp['box'] *= 3 / nl # scale to layers
|
||||
hyp['cls'] *= nc / 80 * 3 / nl # scale to classes and layers
|
||||
@ -243,23 +256,20 @@ def train(hyp, opt, device, callbacks): # hyp is path/to/hyp.yaml or hyp dictio
|
||||
|
||||
# Start training
|
||||
t0 = time.time()
|
||||
nb = len(train_loader) # number of batches
|
||||
nw = max(round(hyp['warmup_epochs'] * nb), 100) # number of warmup iterations, max(3 epochs, 100 iterations)
|
||||
nw = max(round(hyp['warmup_epochs'] * nb), 1000) # number of warmup iterations, max(3 epochs, 1k iterations)
|
||||
# nw = min(nw, (epochs - start_epoch) / 2 * nb) # limit warmup to < 1/2 of training
|
||||
last_opt_step = -1
|
||||
maps = np.zeros(nc) # mAP per class
|
||||
results = (0, 0, 0, 0, 0, 0, 0) # P, R, mAP@.5, mAP@.5-.95, val_loss(box, obj, cls)
|
||||
scheduler.last_epoch = start_epoch - 1 # do not move
|
||||
scaler = torch.cuda.amp.GradScaler(enabled=amp)
|
||||
stopper, stop = EarlyStopping(patience=opt.patience), False
|
||||
scaler = amp.GradScaler(enabled=cuda)
|
||||
stopper = EarlyStopping(patience=opt.patience)
|
||||
compute_loss = ComputeLoss(model) # init loss class
|
||||
callbacks.run('on_train_start')
|
||||
LOGGER.info(f'Image sizes {imgsz} train, {imgsz} val\n'
|
||||
f'Using {train_loader.num_workers * WORLD_SIZE} dataloader workers\n'
|
||||
f"Logging results to {colorstr('bold', save_dir)}\n"
|
||||
f'Starting training for {epochs} epochs...')
|
||||
for epoch in range(start_epoch, epochs): # epoch ------------------------------------------------------------------
|
||||
callbacks.run('on_train_epoch_start')
|
||||
model.train()
|
||||
|
||||
# Update image weights (optional, single-GPU only)
|
||||
@ -276,12 +286,11 @@ def train(hyp, opt, device, callbacks): # hyp is path/to/hyp.yaml or hyp dictio
|
||||
if RANK != -1:
|
||||
train_loader.sampler.set_epoch(epoch)
|
||||
pbar = enumerate(train_loader)
|
||||
LOGGER.info(('\n' + '%11s' * 7) % ('Epoch', 'GPU_mem', 'box_loss', 'obj_loss', 'cls_loss', 'Instances', 'Size'))
|
||||
if RANK in {-1, 0}:
|
||||
pbar = tqdm(pbar, total=nb, bar_format=TQDM_BAR_FORMAT) # progress bar
|
||||
LOGGER.info(('\n' + '%10s' * 7) % ('Epoch', 'gpu_mem', 'box', 'obj', 'cls', 'labels', 'img_size'))
|
||||
if RANK in [-1, 0]:
|
||||
pbar = tqdm(pbar, total=nb, ncols=NCOLS, bar_format='{l_bar}{bar:10}{r_bar}{bar:-10b}') # progress bar
|
||||
optimizer.zero_grad()
|
||||
for i, (imgs, targets, paths, _) in pbar: # batch -------------------------------------------------------------
|
||||
callbacks.run('on_train_batch_start')
|
||||
ni = i + nb * epoch # number integrated batches (since train start)
|
||||
imgs = imgs.to(device, non_blocking=True).float() / 255 # uint8 to float32, 0-255 to 0.0-1.0
|
||||
|
||||
@ -292,7 +301,7 @@ def train(hyp, opt, device, callbacks): # hyp is path/to/hyp.yaml or hyp dictio
|
||||
accumulate = max(1, np.interp(ni, xi, [1, nbs / batch_size]).round())
|
||||
for j, x in enumerate(optimizer.param_groups):
|
||||
# bias lr falls from 0.1 to lr0, all other lrs rise from 0.0 to lr0
|
||||
x['lr'] = np.interp(ni, xi, [hyp['warmup_bias_lr'] if j == 0 else 0.0, x['initial_lr'] * lf(epoch)])
|
||||
x['lr'] = np.interp(ni, xi, [hyp['warmup_bias_lr'] if j == 2 else 0.0, x['initial_lr'] * lf(epoch)])
|
||||
if 'momentum' in x:
|
||||
x['momentum'] = np.interp(ni, xi, [hyp['warmup_momentum'], hyp['momentum']])
|
||||
|
||||
@ -305,7 +314,7 @@ def train(hyp, opt, device, callbacks): # hyp is path/to/hyp.yaml or hyp dictio
|
||||
imgs = nn.functional.interpolate(imgs, size=ns, mode='bilinear', align_corners=False)
|
||||
|
||||
# Forward
|
||||
with torch.cuda.amp.autocast(amp):
|
||||
with amp.autocast(enabled=cuda):
|
||||
pred = model(imgs) # forward
|
||||
loss, loss_items = compute_loss(pred, targets.to(device)) # loss scaled by batch_size
|
||||
if RANK != -1:
|
||||
@ -316,10 +325,8 @@ def train(hyp, opt, device, callbacks): # hyp is path/to/hyp.yaml or hyp dictio
|
||||
# Backward
|
||||
scaler.scale(loss).backward()
|
||||
|
||||
# Optimize - https://pytorch.org/docs/master/notes/amp_examples.html
|
||||
# Optimize
|
||||
if ni - last_opt_step >= accumulate:
|
||||
scaler.unscale_(optimizer) # unscale gradients
|
||||
torch.nn.utils.clip_grad_norm_(model.parameters(), max_norm=10.0) # clip gradients
|
||||
scaler.step(optimizer) # optimizer.step
|
||||
scaler.update()
|
||||
optimizer.zero_grad()
|
||||
@ -328,41 +335,37 @@ def train(hyp, opt, device, callbacks): # hyp is path/to/hyp.yaml or hyp dictio
|
||||
last_opt_step = ni
|
||||
|
||||
# Log
|
||||
if RANK in {-1, 0}:
|
||||
if RANK in [-1, 0]:
|
||||
mloss = (mloss * i + loss_items) / (i + 1) # update mean losses
|
||||
mem = f'{torch.cuda.memory_reserved() / 1E9 if torch.cuda.is_available() else 0:.3g}G' # (GB)
|
||||
pbar.set_description(('%11s' * 2 + '%11.4g' * 5) %
|
||||
(f'{epoch}/{epochs - 1}', mem, *mloss, targets.shape[0], imgs.shape[-1]))
|
||||
callbacks.run('on_train_batch_end', model, ni, imgs, targets, paths, list(mloss))
|
||||
if callbacks.stop_training:
|
||||
return
|
||||
pbar.set_description(('%10s' * 2 + '%10.4g' * 5) % (
|
||||
f'{epoch}/{epochs - 1}', mem, *mloss, targets.shape[0], imgs.shape[-1]))
|
||||
callbacks.run('on_train_batch_end', ni, model, imgs, targets, paths, plots, opt.sync_bn)
|
||||
# end batch ------------------------------------------------------------------------------------------------
|
||||
|
||||
# Scheduler
|
||||
lr = [x['lr'] for x in optimizer.param_groups] # for loggers
|
||||
scheduler.step()
|
||||
|
||||
if RANK in {-1, 0}:
|
||||
if RANK in [-1, 0]:
|
||||
# mAP
|
||||
callbacks.run('on_train_epoch_end', epoch=epoch)
|
||||
ema.update_attr(model, include=['yaml', 'nc', 'hyp', 'names', 'stride', 'class_weights'])
|
||||
final_epoch = (epoch + 1 == epochs) or stopper.possible_stop
|
||||
if not noval or final_epoch: # Calculate mAP
|
||||
results, maps, _ = validate.run(data_dict,
|
||||
batch_size=batch_size // WORLD_SIZE * 2,
|
||||
imgsz=imgsz,
|
||||
half=amp,
|
||||
model=ema.ema,
|
||||
single_cls=single_cls,
|
||||
dataloader=val_loader,
|
||||
save_dir=save_dir,
|
||||
plots=False,
|
||||
callbacks=callbacks,
|
||||
compute_loss=compute_loss)
|
||||
results, maps, _ = val.run(data_dict,
|
||||
batch_size=batch_size // WORLD_SIZE * 2,
|
||||
imgsz=imgsz,
|
||||
model=ema.ema,
|
||||
single_cls=single_cls,
|
||||
dataloader=val_loader,
|
||||
save_dir=save_dir,
|
||||
plots=False,
|
||||
callbacks=callbacks,
|
||||
compute_loss=compute_loss)
|
||||
|
||||
# Update best mAP
|
||||
fi = fitness(np.array(results).reshape(1, -1)) # weighted combination of [P, R, mAP@.5, mAP@.5-.95]
|
||||
stop = stopper(epoch=epoch, fitness=fi) # early stop check
|
||||
if fi > best_fitness:
|
||||
best_fitness = fi
|
||||
log_vals = list(mloss) + list(results) + lr
|
||||
@ -370,62 +373,65 @@ def train(hyp, opt, device, callbacks): # hyp is path/to/hyp.yaml or hyp dictio
|
||||
|
||||
# Save model
|
||||
if (not nosave) or (final_epoch and not evolve): # if save
|
||||
ckpt = {
|
||||
'epoch': epoch,
|
||||
'best_fitness': best_fitness,
|
||||
'model': deepcopy(de_parallel(model)).half(),
|
||||
'ema': deepcopy(ema.ema).half(),
|
||||
'updates': ema.updates,
|
||||
'optimizer': optimizer.state_dict(),
|
||||
'opt': vars(opt),
|
||||
'git': GIT_INFO, # {remote, branch, commit} if a git repo
|
||||
'date': datetime.now().isoformat()}
|
||||
ckpt = {'epoch': epoch,
|
||||
'best_fitness': best_fitness,
|
||||
'model': deepcopy(de_parallel(model)).half(),
|
||||
'ema': deepcopy(ema.ema).half(),
|
||||
'updates': ema.updates,
|
||||
'optimizer': optimizer.state_dict(),
|
||||
'wandb_id': loggers.wandb.wandb_run.id if loggers.wandb else None,
|
||||
'date': datetime.now().isoformat()}
|
||||
|
||||
# Save last, best and delete
|
||||
torch.save(ckpt, last)
|
||||
if best_fitness == fi:
|
||||
torch.save(ckpt, best)
|
||||
if opt.save_period > 0 and epoch % opt.save_period == 0:
|
||||
if (epoch > 0) and (opt.save_period > 0) and (epoch % opt.save_period == 0):
|
||||
torch.save(ckpt, w / f'epoch{epoch}.pt')
|
||||
del ckpt
|
||||
callbacks.run('on_model_save', last, epoch, final_epoch, best_fitness, fi)
|
||||
|
||||
# EarlyStopping
|
||||
if RANK != -1: # if DDP training
|
||||
broadcast_list = [stop if RANK == 0 else None]
|
||||
dist.broadcast_object_list(broadcast_list, 0) # broadcast 'stop' to all ranks
|
||||
if RANK != 0:
|
||||
stop = broadcast_list[0]
|
||||
if stop:
|
||||
break # must break all DDP ranks
|
||||
# Stop Single-GPU
|
||||
if RANK == -1 and stopper(epoch=epoch, fitness=fi):
|
||||
break
|
||||
|
||||
# Stop DDP TODO: known issues shttps://github.com/ultralytics/yolov5/pull/4576
|
||||
# stop = stopper(epoch=epoch, fitness=fi)
|
||||
# if RANK == 0:
|
||||
# dist.broadcast_object_list([stop], 0) # broadcast 'stop' to all ranks
|
||||
|
||||
# Stop DPP
|
||||
# with torch_distributed_zero_first(RANK):
|
||||
# if stop:
|
||||
# break # must break all DDP ranks
|
||||
|
||||
# end epoch ----------------------------------------------------------------------------------------------------
|
||||
# end training -----------------------------------------------------------------------------------------------------
|
||||
if RANK in {-1, 0}:
|
||||
if RANK in [-1, 0]:
|
||||
LOGGER.info(f'\n{epoch - start_epoch + 1} epochs completed in {(time.time() - t0) / 3600:.3f} hours.')
|
||||
for f in last, best:
|
||||
if f.exists():
|
||||
strip_optimizer(f) # strip optimizers
|
||||
if f is best:
|
||||
LOGGER.info(f'\nValidating {f}...')
|
||||
results, _, _ = validate.run(
|
||||
data_dict,
|
||||
batch_size=batch_size // WORLD_SIZE * 2,
|
||||
imgsz=imgsz,
|
||||
model=attempt_load(f, device).half(),
|
||||
iou_thres=0.65 if is_coco else 0.60, # best pycocotools at iou 0.65
|
||||
single_cls=single_cls,
|
||||
dataloader=val_loader,
|
||||
save_dir=save_dir,
|
||||
save_json=is_coco,
|
||||
verbose=True,
|
||||
plots=plots,
|
||||
callbacks=callbacks,
|
||||
compute_loss=compute_loss) # val best model with plots
|
||||
results, _, _ = val.run(data_dict,
|
||||
batch_size=batch_size // WORLD_SIZE * 2,
|
||||
imgsz=imgsz,
|
||||
model=attempt_load(f, device).half(),
|
||||
iou_thres=0.65 if is_coco else 0.60, # best pycocotools results at 0.65
|
||||
single_cls=single_cls,
|
||||
dataloader=val_loader,
|
||||
save_dir=save_dir,
|
||||
save_json=is_coco,
|
||||
verbose=True,
|
||||
plots=True,
|
||||
callbacks=callbacks,
|
||||
compute_loss=compute_loss) # val best model with plots
|
||||
if is_coco:
|
||||
callbacks.run('on_fit_epoch_end', list(mloss) + list(results) + lr, epoch, best_fitness, fi)
|
||||
|
||||
callbacks.run('on_train_end', last, best, epoch, results)
|
||||
callbacks.run('on_train_end', last, best, plots, epoch, results)
|
||||
LOGGER.info(f"Results saved to {colorstr('bold', save_dir)}")
|
||||
|
||||
torch.cuda.empty_cache()
|
||||
return results
|
||||
@ -433,146 +439,133 @@ def train(hyp, opt, device, callbacks): # hyp is path/to/hyp.yaml or hyp dictio
|
||||
|
||||
def parse_opt(known=False):
|
||||
parser = argparse.ArgumentParser()
|
||||
parser.add_argument('--weights', type=str, default=ROOT / 'yolov3-tiny.pt', help='initial weights path')
|
||||
parser.add_argument('--weights', type=str, default=ROOT / 'yolov3.pt', help='initial weights path')
|
||||
parser.add_argument('--cfg', type=str, default='', help='model.yaml path')
|
||||
parser.add_argument('--data', type=str, default=ROOT / 'data/coco128.yaml', help='dataset.yaml path')
|
||||
parser.add_argument('--hyp', type=str, default=ROOT / 'data/hyps/hyp.scratch-low.yaml', help='hyperparameters path')
|
||||
parser.add_argument('--epochs', type=int, default=100, help='total training epochs')
|
||||
parser.add_argument('--hyp', type=str, default=ROOT / 'data/hyps/hyp.scratch.yaml', help='hyperparameters path')
|
||||
parser.add_argument('--epochs', type=int, default=300)
|
||||
parser.add_argument('--batch-size', type=int, default=16, help='total batch size for all GPUs, -1 for autobatch')
|
||||
parser.add_argument('--imgsz', '--img', '--img-size', type=int, default=640, help='train, val image size (pixels)')
|
||||
parser.add_argument('--rect', action='store_true', help='rectangular training')
|
||||
parser.add_argument('--resume', nargs='?', const=True, default=False, help='resume most recent training')
|
||||
parser.add_argument('--nosave', action='store_true', help='only save final checkpoint')
|
||||
parser.add_argument('--noval', action='store_true', help='only validate final epoch')
|
||||
parser.add_argument('--noautoanchor', action='store_true', help='disable AutoAnchor')
|
||||
parser.add_argument('--noplots', action='store_true', help='save no plot files')
|
||||
parser.add_argument('--noautoanchor', action='store_true', help='disable autoanchor check')
|
||||
parser.add_argument('--evolve', type=int, nargs='?', const=300, help='evolve hyperparameters for x generations')
|
||||
parser.add_argument('--bucket', type=str, default='', help='gsutil bucket')
|
||||
parser.add_argument('--cache', type=str, nargs='?', const='ram', help='image --cache ram/disk')
|
||||
parser.add_argument('--cache', type=str, nargs='?', const='ram', help='--cache images in "ram" (default) or "disk"')
|
||||
parser.add_argument('--image-weights', action='store_true', help='use weighted image selection for training')
|
||||
parser.add_argument('--device', default='', help='cuda device, i.e. 0 or 0,1,2,3 or cpu')
|
||||
parser.add_argument('--multi-scale', action='store_true', help='vary img-size +/- 50%%')
|
||||
parser.add_argument('--single-cls', action='store_true', help='train multi-class data as single-class')
|
||||
parser.add_argument('--optimizer', type=str, choices=['SGD', 'Adam', 'AdamW'], default='SGD', help='optimizer')
|
||||
parser.add_argument('--adam', action='store_true', help='use torch.optim.Adam() optimizer')
|
||||
parser.add_argument('--sync-bn', action='store_true', help='use SyncBatchNorm, only available in DDP mode')
|
||||
parser.add_argument('--workers', type=int, default=8, help='max dataloader workers (per RANK in DDP mode)')
|
||||
parser.add_argument('--project', default=ROOT / 'runs/train', help='save to project/name')
|
||||
parser.add_argument('--name', default='exp', help='save to project/name')
|
||||
parser.add_argument('--exist-ok', action='store_true', help='existing project/name ok, do not increment')
|
||||
parser.add_argument('--quad', action='store_true', help='quad dataloader')
|
||||
parser.add_argument('--cos-lr', action='store_true', help='cosine LR scheduler')
|
||||
parser.add_argument('--linear-lr', action='store_true', help='linear LR')
|
||||
parser.add_argument('--label-smoothing', type=float, default=0.0, help='Label smoothing epsilon')
|
||||
parser.add_argument('--patience', type=int, default=100, help='EarlyStopping patience (epochs without improvement)')
|
||||
parser.add_argument('--freeze', nargs='+', type=int, default=[0], help='Freeze layers: backbone=10, first3=0 1 2')
|
||||
parser.add_argument('--freeze', type=int, default=0, help='Number of layers to freeze. backbone=10, all=24')
|
||||
parser.add_argument('--save-period', type=int, default=-1, help='Save checkpoint every x epochs (disabled if < 1)')
|
||||
parser.add_argument('--seed', type=int, default=0, help='Global training seed')
|
||||
parser.add_argument('--local_rank', type=int, default=-1, help='Automatic DDP Multi-GPU argument, do not modify')
|
||||
parser.add_argument('--local_rank', type=int, default=-1, help='DDP parameter, do not modify')
|
||||
|
||||
# Logger arguments
|
||||
parser.add_argument('--entity', default=None, help='Entity')
|
||||
parser.add_argument('--upload_dataset', nargs='?', const=True, default=False, help='Upload data, "val" option')
|
||||
parser.add_argument('--bbox_interval', type=int, default=-1, help='Set bounding-box image logging interval')
|
||||
parser.add_argument('--artifact_alias', type=str, default='latest', help='Version of dataset artifact to use')
|
||||
# Weights & Biases arguments
|
||||
parser.add_argument('--entity', default=None, help='W&B: Entity')
|
||||
parser.add_argument('--upload_dataset', action='store_true', help='W&B: Upload dataset as artifact table')
|
||||
parser.add_argument('--bbox_interval', type=int, default=-1, help='W&B: Set bounding-box image logging interval')
|
||||
parser.add_argument('--artifact_alias', type=str, default='latest', help='W&B: Version of dataset artifact to use')
|
||||
|
||||
return parser.parse_known_args()[0] if known else parser.parse_args()
|
||||
opt = parser.parse_known_args()[0] if known else parser.parse_args()
|
||||
return opt
|
||||
|
||||
|
||||
def main(opt, callbacks=Callbacks()):
|
||||
# Checks
|
||||
if RANK in {-1, 0}:
|
||||
print_args(vars(opt))
|
||||
if RANK in [-1, 0]:
|
||||
print_args(FILE.stem, opt)
|
||||
check_git_status()
|
||||
check_requirements()
|
||||
check_requirements(exclude=['thop'])
|
||||
|
||||
# Resume (from specified or most recent last.pt)
|
||||
if opt.resume and not check_comet_resume(opt) and not opt.evolve:
|
||||
last = Path(check_file(opt.resume) if isinstance(opt.resume, str) else get_latest_run())
|
||||
opt_yaml = last.parent.parent / 'opt.yaml' # train options yaml
|
||||
opt_data = opt.data # original dataset
|
||||
if opt_yaml.is_file():
|
||||
with open(opt_yaml, errors='ignore') as f:
|
||||
d = yaml.safe_load(f)
|
||||
else:
|
||||
d = torch.load(last, map_location='cpu')['opt']
|
||||
opt = argparse.Namespace(**d) # replace
|
||||
opt.cfg, opt.weights, opt.resume = '', str(last), True # reinstate
|
||||
if is_url(opt_data):
|
||||
opt.data = check_file(opt_data) # avoid HUB resume auth timeout
|
||||
# Resume
|
||||
if opt.resume and not check_wandb_resume(opt) and not opt.evolve: # resume an interrupted run
|
||||
ckpt = opt.resume if isinstance(opt.resume, str) else get_latest_run() # specified or most recent path
|
||||
assert os.path.isfile(ckpt), 'ERROR: --resume checkpoint does not exist'
|
||||
with open(Path(ckpt).parent.parent / 'opt.yaml', errors='ignore') as f:
|
||||
opt = argparse.Namespace(**yaml.safe_load(f)) # replace
|
||||
opt.cfg, opt.weights, opt.resume = '', ckpt, True # reinstate
|
||||
LOGGER.info(f'Resuming training from {ckpt}')
|
||||
else:
|
||||
opt.data, opt.cfg, opt.hyp, opt.weights, opt.project = \
|
||||
check_file(opt.data), check_yaml(opt.cfg), check_yaml(opt.hyp), str(opt.weights), str(opt.project) # checks
|
||||
assert len(opt.cfg) or len(opt.weights), 'either --cfg or --weights must be specified'
|
||||
if opt.evolve:
|
||||
if opt.project == str(ROOT / 'runs/train'): # if default project name, rename to runs/evolve
|
||||
opt.project = str(ROOT / 'runs/evolve')
|
||||
opt.project = str(ROOT / 'runs/evolve')
|
||||
opt.exist_ok, opt.resume = opt.resume, False # pass resume to exist_ok and disable resume
|
||||
if opt.name == 'cfg':
|
||||
opt.name = Path(opt.cfg).stem # use model.yaml as name
|
||||
opt.save_dir = str(increment_path(Path(opt.project) / opt.name, exist_ok=opt.exist_ok))
|
||||
|
||||
# DDP mode
|
||||
device = select_device(opt.device, batch_size=opt.batch_size)
|
||||
if LOCAL_RANK != -1:
|
||||
msg = 'is not compatible with YOLOv3 Multi-GPU DDP training'
|
||||
assert not opt.image_weights, f'--image-weights {msg}'
|
||||
assert not opt.evolve, f'--evolve {msg}'
|
||||
assert opt.batch_size != -1, f'AutoBatch with --batch-size -1 {msg}, please pass a valid --batch-size'
|
||||
assert opt.batch_size % WORLD_SIZE == 0, f'--batch-size {opt.batch_size} must be multiple of WORLD_SIZE'
|
||||
assert torch.cuda.device_count() > LOCAL_RANK, 'insufficient CUDA devices for DDP command'
|
||||
assert opt.batch_size % WORLD_SIZE == 0, '--batch-size must be multiple of CUDA device count'
|
||||
assert not opt.image_weights, '--image-weights argument is not compatible with DDP training'
|
||||
assert not opt.evolve, '--evolve argument is not compatible with DDP training'
|
||||
torch.cuda.set_device(LOCAL_RANK)
|
||||
device = torch.device('cuda', LOCAL_RANK)
|
||||
dist.init_process_group(backend='nccl' if dist.is_nccl_available() else 'gloo')
|
||||
dist.init_process_group(backend="nccl" if dist.is_nccl_available() else "gloo")
|
||||
|
||||
# Train
|
||||
if not opt.evolve:
|
||||
train(opt.hyp, opt, device, callbacks)
|
||||
if WORLD_SIZE > 1 and RANK == 0:
|
||||
LOGGER.info('Destroying process group... ')
|
||||
dist.destroy_process_group()
|
||||
|
||||
# Evolve hyperparameters (optional)
|
||||
else:
|
||||
# Hyperparameter evolution metadata (mutation scale 0-1, lower_limit, upper_limit)
|
||||
meta = {
|
||||
'lr0': (1, 1e-5, 1e-1), # initial learning rate (SGD=1E-2, Adam=1E-3)
|
||||
'lrf': (1, 0.01, 1.0), # final OneCycleLR learning rate (lr0 * lrf)
|
||||
'momentum': (0.3, 0.6, 0.98), # SGD momentum/Adam beta1
|
||||
'weight_decay': (1, 0.0, 0.001), # optimizer weight decay
|
||||
'warmup_epochs': (1, 0.0, 5.0), # warmup epochs (fractions ok)
|
||||
'warmup_momentum': (1, 0.0, 0.95), # warmup initial momentum
|
||||
'warmup_bias_lr': (1, 0.0, 0.2), # warmup initial bias lr
|
||||
'box': (1, 0.02, 0.2), # box loss gain
|
||||
'cls': (1, 0.2, 4.0), # cls loss gain
|
||||
'cls_pw': (1, 0.5, 2.0), # cls BCELoss positive_weight
|
||||
'obj': (1, 0.2, 4.0), # obj loss gain (scale with pixels)
|
||||
'obj_pw': (1, 0.5, 2.0), # obj BCELoss positive_weight
|
||||
'iou_t': (0, 0.1, 0.7), # IoU training threshold
|
||||
'anchor_t': (1, 2.0, 8.0), # anchor-multiple threshold
|
||||
'anchors': (2, 2.0, 10.0), # anchors per output grid (0 to ignore)
|
||||
'fl_gamma': (0, 0.0, 2.0), # focal loss gamma (efficientDet default gamma=1.5)
|
||||
'hsv_h': (1, 0.0, 0.1), # image HSV-Hue augmentation (fraction)
|
||||
'hsv_s': (1, 0.0, 0.9), # image HSV-Saturation augmentation (fraction)
|
||||
'hsv_v': (1, 0.0, 0.9), # image HSV-Value augmentation (fraction)
|
||||
'degrees': (1, 0.0, 45.0), # image rotation (+/- deg)
|
||||
'translate': (1, 0.0, 0.9), # image translation (+/- fraction)
|
||||
'scale': (1, 0.0, 0.9), # image scale (+/- gain)
|
||||
'shear': (1, 0.0, 10.0), # image shear (+/- deg)
|
||||
'perspective': (0, 0.0, 0.001), # image perspective (+/- fraction), range 0-0.001
|
||||
'flipud': (1, 0.0, 1.0), # image flip up-down (probability)
|
||||
'fliplr': (0, 0.0, 1.0), # image flip left-right (probability)
|
||||
'mosaic': (1, 0.0, 1.0), # image mixup (probability)
|
||||
'mixup': (1, 0.0, 1.0), # image mixup (probability)
|
||||
'copy_paste': (1, 0.0, 1.0)} # segment copy-paste (probability)
|
||||
meta = {'lr0': (1, 1e-5, 1e-1), # initial learning rate (SGD=1E-2, Adam=1E-3)
|
||||
'lrf': (1, 0.01, 1.0), # final OneCycleLR learning rate (lr0 * lrf)
|
||||
'momentum': (0.3, 0.6, 0.98), # SGD momentum/Adam beta1
|
||||
'weight_decay': (1, 0.0, 0.001), # optimizer weight decay
|
||||
'warmup_epochs': (1, 0.0, 5.0), # warmup epochs (fractions ok)
|
||||
'warmup_momentum': (1, 0.0, 0.95), # warmup initial momentum
|
||||
'warmup_bias_lr': (1, 0.0, 0.2), # warmup initial bias lr
|
||||
'box': (1, 0.02, 0.2), # box loss gain
|
||||
'cls': (1, 0.2, 4.0), # cls loss gain
|
||||
'cls_pw': (1, 0.5, 2.0), # cls BCELoss positive_weight
|
||||
'obj': (1, 0.2, 4.0), # obj loss gain (scale with pixels)
|
||||
'obj_pw': (1, 0.5, 2.0), # obj BCELoss positive_weight
|
||||
'iou_t': (0, 0.1, 0.7), # IoU training threshold
|
||||
'anchor_t': (1, 2.0, 8.0), # anchor-multiple threshold
|
||||
'anchors': (2, 2.0, 10.0), # anchors per output grid (0 to ignore)
|
||||
'fl_gamma': (0, 0.0, 2.0), # focal loss gamma (efficientDet default gamma=1.5)
|
||||
'hsv_h': (1, 0.0, 0.1), # image HSV-Hue augmentation (fraction)
|
||||
'hsv_s': (1, 0.0, 0.9), # image HSV-Saturation augmentation (fraction)
|
||||
'hsv_v': (1, 0.0, 0.9), # image HSV-Value augmentation (fraction)
|
||||
'degrees': (1, 0.0, 45.0), # image rotation (+/- deg)
|
||||
'translate': (1, 0.0, 0.9), # image translation (+/- fraction)
|
||||
'scale': (1, 0.0, 0.9), # image scale (+/- gain)
|
||||
'shear': (1, 0.0, 10.0), # image shear (+/- deg)
|
||||
'perspective': (0, 0.0, 0.001), # image perspective (+/- fraction), range 0-0.001
|
||||
'flipud': (1, 0.0, 1.0), # image flip up-down (probability)
|
||||
'fliplr': (0, 0.0, 1.0), # image flip left-right (probability)
|
||||
'mosaic': (1, 0.0, 1.0), # image mixup (probability)
|
||||
'mixup': (1, 0.0, 1.0), # image mixup (probability)
|
||||
'copy_paste': (1, 0.0, 1.0)} # segment copy-paste (probability)
|
||||
|
||||
with open(opt.hyp, errors='ignore') as f:
|
||||
hyp = yaml.safe_load(f) # load hyps dict
|
||||
if 'anchors' not in hyp: # anchors commented in hyp.yaml
|
||||
hyp['anchors'] = 3
|
||||
if opt.noautoanchor:
|
||||
del hyp['anchors'], meta['anchors']
|
||||
opt.noval, opt.nosave, save_dir = True, True, Path(opt.save_dir) # only val/save final epoch
|
||||
# ei = [isinstance(x, (int, float)) for x in hyp.values()] # evolvable indices
|
||||
evolve_yaml, evolve_csv = save_dir / 'hyp_evolve.yaml', save_dir / 'evolve.csv'
|
||||
if opt.bucket:
|
||||
subprocess.run(
|
||||
f'gsutil cp gs://{opt.bucket}/evolve.csv {evolve_csv}'.split()) # download evolve.csv if exists
|
||||
os.system(f'gsutil cp gs://{opt.bucket}/evolve.csv {save_dir}') # download evolve.csv if exists
|
||||
|
||||
for _ in range(opt.evolve): # generations to evolve
|
||||
if evolve_csv.exists(): # if evolve.csv exists: select best hyps and mutate
|
||||
@ -608,28 +601,25 @@ def main(opt, callbacks=Callbacks()):
|
||||
|
||||
# Train mutation
|
||||
results = train(hyp.copy(), opt, device, callbacks)
|
||||
callbacks = Callbacks()
|
||||
|
||||
# Write mutation results
|
||||
keys = ('metrics/precision', 'metrics/recall', 'metrics/mAP_0.5', 'metrics/mAP_0.5:0.95', 'val/box_loss',
|
||||
'val/obj_loss', 'val/cls_loss')
|
||||
print_mutation(keys, results, hyp.copy(), save_dir, opt.bucket)
|
||||
print_mutation(results, hyp.copy(), save_dir, opt.bucket)
|
||||
|
||||
# Plot results
|
||||
plot_evolve(evolve_csv)
|
||||
LOGGER.info(f'Hyperparameter evolution finished {opt.evolve} generations\n'
|
||||
LOGGER.info(f'Hyperparameter evolution finished\n'
|
||||
f"Results saved to {colorstr('bold', save_dir)}\n"
|
||||
f'Usage example: $ python train.py --hyp {evolve_yaml}')
|
||||
f'Use best hyperparameters example: $ python train.py --hyp {evolve_yaml}')
|
||||
|
||||
|
||||
def run(**kwargs):
|
||||
# Usage: import train; train.run(data='coco128.yaml', imgsz=320, weights='yolov5m.pt')
|
||||
# Usage: import train; train.run(data='coco128.yaml', imgsz=320, weights='yolov3.pt')
|
||||
opt = parse_opt(True)
|
||||
for k, v in kwargs.items():
|
||||
setattr(opt, k, v)
|
||||
main(opt)
|
||||
return opt
|
||||
|
||||
|
||||
if __name__ == '__main__':
|
||||
if __name__ == "__main__":
|
||||
opt = parse_opt()
|
||||
main(opt)
|
||||
|
||||
1247
yolov3/tutorial.ipynb
vendored
1247
yolov3/tutorial.ipynb
vendored
File diff suppressed because it is too large
Load Diff
@ -3,78 +3,16 @@
|
||||
utils/initialization
|
||||
"""
|
||||
|
||||
import contextlib
|
||||
import platform
|
||||
import threading
|
||||
|
||||
|
||||
def emojis(str=''):
|
||||
# Return platform-dependent emoji-safe version of string
|
||||
return str.encode().decode('ascii', 'ignore') if platform.system() == 'Windows' else str
|
||||
|
||||
|
||||
class TryExcept(contextlib.ContextDecorator):
|
||||
# YOLOv3 TryExcept class. Usage: @TryExcept() decorator or 'with TryExcept():' context manager
|
||||
def __init__(self, msg=''):
|
||||
self.msg = msg
|
||||
|
||||
def __enter__(self):
|
||||
pass
|
||||
|
||||
def __exit__(self, exc_type, value, traceback):
|
||||
if value:
|
||||
print(emojis(f"{self.msg}{': ' if self.msg else ''}{value}"))
|
||||
return True
|
||||
|
||||
|
||||
def threaded(func):
|
||||
# Multi-threads a target function and returns thread. Usage: @threaded decorator
|
||||
def wrapper(*args, **kwargs):
|
||||
thread = threading.Thread(target=func, args=args, kwargs=kwargs, daemon=True)
|
||||
thread.start()
|
||||
return thread
|
||||
|
||||
return wrapper
|
||||
|
||||
|
||||
def join_threads(verbose=False):
|
||||
# Join all daemon threads, i.e. atexit.register(lambda: join_threads())
|
||||
main_thread = threading.current_thread()
|
||||
for t in threading.enumerate():
|
||||
if t is not main_thread:
|
||||
if verbose:
|
||||
print(f'Joining thread {t.name}')
|
||||
t.join()
|
||||
|
||||
|
||||
def notebook_init(verbose=True):
|
||||
# Check system software and hardware
|
||||
def notebook_init():
|
||||
# For notebooks
|
||||
print('Checking setup...')
|
||||
|
||||
import os
|
||||
import shutil
|
||||
|
||||
from utils.general import check_font, check_requirements, is_colab
|
||||
from utils.torch_utils import select_device # imports
|
||||
|
||||
check_font()
|
||||
|
||||
import psutil
|
||||
from IPython import display # to display images and clear console output
|
||||
|
||||
if is_colab():
|
||||
shutil.rmtree('/content/sample_data', ignore_errors=True) # remove colab /sample_data directory
|
||||
|
||||
# System info
|
||||
if verbose:
|
||||
gb = 1 << 30 # bytes to GiB (1024 ** 3)
|
||||
ram = psutil.virtual_memory().total
|
||||
total, used, free = shutil.disk_usage('/')
|
||||
display.clear_output()
|
||||
s = f'({os.cpu_count()} CPUs, {ram / gb:.1f} GB RAM, {(total - free) / gb:.1f}/{total / gb:.1f} GB disk)'
|
||||
else:
|
||||
s = ''
|
||||
from utils.general import emojis
|
||||
from utils.torch_utils import select_device # imports
|
||||
|
||||
display.clear_output()
|
||||
select_device(newline=False)
|
||||
print(emojis(f'Setup complete ✅ {s}'))
|
||||
print(emojis('Setup complete ✅'))
|
||||
return display
|
||||
|
||||
@ -8,32 +8,29 @@ import torch.nn as nn
|
||||
import torch.nn.functional as F
|
||||
|
||||
|
||||
class SiLU(nn.Module):
|
||||
# SiLU activation https://arxiv.org/pdf/1606.08415.pdf
|
||||
# SiLU https://arxiv.org/pdf/1606.08415.pdf ----------------------------------------------------------------------------
|
||||
class SiLU(nn.Module): # export-friendly version of nn.SiLU()
|
||||
@staticmethod
|
||||
def forward(x):
|
||||
return x * torch.sigmoid(x)
|
||||
|
||||
|
||||
class Hardswish(nn.Module):
|
||||
# Hard-SiLU activation
|
||||
class Hardswish(nn.Module): # export-friendly version of nn.Hardswish()
|
||||
@staticmethod
|
||||
def forward(x):
|
||||
# return x * F.hardsigmoid(x) # for TorchScript and CoreML
|
||||
return x * F.hardtanh(x + 3, 0.0, 6.0) / 6.0 # for TorchScript, CoreML and ONNX
|
||||
# return x * F.hardsigmoid(x) # for torchscript and CoreML
|
||||
return x * F.hardtanh(x + 3, 0.0, 6.0) / 6.0 # for torchscript, CoreML and ONNX
|
||||
|
||||
|
||||
# Mish https://github.com/digantamisra98/Mish --------------------------------------------------------------------------
|
||||
class Mish(nn.Module):
|
||||
# Mish activation https://github.com/digantamisra98/Mish
|
||||
@staticmethod
|
||||
def forward(x):
|
||||
return x * F.softplus(x).tanh()
|
||||
|
||||
|
||||
class MemoryEfficientMish(nn.Module):
|
||||
# Mish activation memory-efficient
|
||||
class F(torch.autograd.Function):
|
||||
|
||||
@staticmethod
|
||||
def forward(ctx, x):
|
||||
ctx.save_for_backward(x)
|
||||
@ -50,8 +47,8 @@ class MemoryEfficientMish(nn.Module):
|
||||
return self.F.apply(x)
|
||||
|
||||
|
||||
# FReLU https://arxiv.org/abs/2007.11824 -------------------------------------------------------------------------------
|
||||
class FReLU(nn.Module):
|
||||
# FReLU activation https://arxiv.org/abs/2007.11824
|
||||
def __init__(self, c1, k=3): # ch_in, kernel
|
||||
super().__init__()
|
||||
self.conv = nn.Conv2d(c1, c1, k, 1, 1, groups=c1, bias=False)
|
||||
@ -61,8 +58,9 @@ class FReLU(nn.Module):
|
||||
return torch.max(x, self.bn(self.conv(x)))
|
||||
|
||||
|
||||
# ACON https://arxiv.org/pdf/2009.04759.pdf ----------------------------------------------------------------------------
|
||||
class AconC(nn.Module):
|
||||
r""" ACON activation (activate or not)
|
||||
r""" ACON activation (activate or not).
|
||||
AconC: (p1*x-p2*x) * sigmoid(beta*(p1*x-p2*x)) + p2*x, beta is a learnable parameter
|
||||
according to "Activate or Not: Learning Customized Activation" <https://arxiv.org/pdf/2009.04759.pdf>.
|
||||
"""
|
||||
@ -79,7 +77,7 @@ class AconC(nn.Module):
|
||||
|
||||
|
||||
class MetaAconC(nn.Module):
|
||||
r""" ACON activation (activate or not)
|
||||
r""" ACON activation (activate or not).
|
||||
MetaAconC: (p1*x-p2*x) * sigmoid(beta*(p1*x-p2*x)) + p2*x, beta is generated by a small network
|
||||
according to "Activate or Not: Learning Customized Activation" <https://arxiv.org/pdf/2009.04759.pdf>.
|
||||
"""
|
||||
|
||||
@ -8,42 +8,34 @@ import random
|
||||
|
||||
import cv2
|
||||
import numpy as np
|
||||
import torch
|
||||
import torchvision.transforms as T
|
||||
import torchvision.transforms.functional as TF
|
||||
|
||||
from utils.general import LOGGER, check_version, colorstr, resample_segments, segment2box, xywhn2xyxy
|
||||
from utils.general import LOGGER, check_version, colorstr, resample_segments, segment2box
|
||||
from utils.metrics import bbox_ioa
|
||||
|
||||
IMAGENET_MEAN = 0.485, 0.456, 0.406 # RGB mean
|
||||
IMAGENET_STD = 0.229, 0.224, 0.225 # RGB standard deviation
|
||||
|
||||
|
||||
class Albumentations:
|
||||
# YOLOv3 Albumentations class (optional, only used if package is installed)
|
||||
def __init__(self, size=640):
|
||||
# Albumentations class (optional, only used if package is installed)
|
||||
def __init__(self):
|
||||
self.transform = None
|
||||
prefix = colorstr('albumentations: ')
|
||||
try:
|
||||
import albumentations as A
|
||||
check_version(A.__version__, '1.0.3', hard=True) # version requirement
|
||||
|
||||
T = [
|
||||
A.RandomResizedCrop(height=size, width=size, scale=(0.8, 1.0), ratio=(0.9, 1.11), p=0.0),
|
||||
self.transform = A.Compose([
|
||||
A.Blur(p=0.01),
|
||||
A.MedianBlur(p=0.01),
|
||||
A.ToGray(p=0.01),
|
||||
A.CLAHE(p=0.01),
|
||||
A.RandomBrightnessContrast(p=0.0),
|
||||
A.RandomGamma(p=0.0),
|
||||
A.ImageCompression(quality_lower=75, p=0.0)] # transforms
|
||||
self.transform = A.Compose(T, bbox_params=A.BboxParams(format='yolo', label_fields=['class_labels']))
|
||||
A.ImageCompression(quality_lower=75, p=0.0)],
|
||||
bbox_params=A.BboxParams(format='yolo', label_fields=['class_labels']))
|
||||
|
||||
LOGGER.info(prefix + ', '.join(f'{x}'.replace('always_apply=False, ', '') for x in T if x.p))
|
||||
LOGGER.info(colorstr('albumentations: ') + ', '.join(f'{x}' for x in self.transform.transforms if x.p))
|
||||
except ImportError: # package not installed, skip
|
||||
pass
|
||||
except Exception as e:
|
||||
LOGGER.info(f'{prefix}{e}')
|
||||
LOGGER.info(colorstr('albumentations: ') + f'{e}')
|
||||
|
||||
def __call__(self, im, labels, p=1.0):
|
||||
if self.transform and random.random() < p:
|
||||
@ -52,18 +44,6 @@ class Albumentations:
|
||||
return im, labels
|
||||
|
||||
|
||||
def normalize(x, mean=IMAGENET_MEAN, std=IMAGENET_STD, inplace=False):
|
||||
# Denormalize RGB images x per ImageNet stats in BCHW format, i.e. = (x - mean) / std
|
||||
return TF.normalize(x, mean, std, inplace=inplace)
|
||||
|
||||
|
||||
def denormalize(x, mean=IMAGENET_MEAN, std=IMAGENET_STD):
|
||||
# Denormalize RGB images x per ImageNet stats in BCHW format, i.e. = x * std + mean
|
||||
for i in range(3):
|
||||
x[:, i] = x[:, i] * std[i] + mean[i]
|
||||
return x
|
||||
|
||||
|
||||
def augment_hsv(im, hgain=0.5, sgain=0.5, vgain=0.5):
|
||||
# HSV color-space augmentation
|
||||
if hgain or sgain or vgain:
|
||||
@ -141,14 +121,7 @@ def letterbox(im, new_shape=(640, 640), color=(114, 114, 114), auto=True, scaleF
|
||||
return im, ratio, (dw, dh)
|
||||
|
||||
|
||||
def random_perspective(im,
|
||||
targets=(),
|
||||
segments=(),
|
||||
degrees=10,
|
||||
translate=.1,
|
||||
scale=.1,
|
||||
shear=10,
|
||||
perspective=0.0,
|
||||
def random_perspective(im, targets=(), segments=(), degrees=10, translate=.1, scale=.1, shear=10, perspective=0.0,
|
||||
border=(0, 0)):
|
||||
# torchvision.transforms.RandomAffine(degrees=(-10, 10), translate=(0.1, 0.1), scale=(0.9, 1.1), shear=(-10, 10))
|
||||
# targets = [cls, xyxy]
|
||||
@ -201,7 +174,7 @@ def random_perspective(im,
|
||||
# Transform label coordinates
|
||||
n = len(targets)
|
||||
if n:
|
||||
use_segments = any(x.any() for x in segments) and len(segments) == n
|
||||
use_segments = any(x.any() for x in segments)
|
||||
new = np.zeros((n, 4))
|
||||
if use_segments: # warp segments
|
||||
segments = resample_segments(segments) # upsample
|
||||
@ -250,10 +223,12 @@ def copy_paste(im, labels, segments, p=0.5):
|
||||
if (ioa < 0.30).all(): # allow 30% obscuration of existing labels
|
||||
labels = np.concatenate((labels, [[l[0], *box]]), 0)
|
||||
segments.append(np.concatenate((w - s[:, 0:1], s[:, 1:2]), 1))
|
||||
cv2.drawContours(im_new, [segments[j].astype(np.int32)], -1, (1, 1, 1), cv2.FILLED)
|
||||
cv2.drawContours(im_new, [segments[j].astype(np.int32)], -1, (255, 255, 255), cv2.FILLED)
|
||||
|
||||
result = cv2.flip(im, 1) # augment segments (flip left-right)
|
||||
i = cv2.flip(im_new, 1).astype(bool)
|
||||
result = cv2.bitwise_and(src1=im, src2=im_new)
|
||||
result = cv2.flip(result, 1) # augment segments (flip left-right)
|
||||
i = result > 0 # pixels to replace
|
||||
# i[:, :] = result.max(2).reshape(h, w, 1) # act over ch
|
||||
im[i] = result[i] # cv2.imwrite('debug.jpg', im) # debug
|
||||
|
||||
return im, labels, segments
|
||||
@ -280,7 +255,7 @@ def cutout(im, labels, p=0.5):
|
||||
# return unobscured labels
|
||||
if len(labels) and s > 0.03:
|
||||
box = np.array([xmin, ymin, xmax, ymax], dtype=np.float32)
|
||||
ioa = bbox_ioa(box, xywhn2xyxy(labels[:, 1:5], w, h)) # intersection over area
|
||||
ioa = bbox_ioa(box, labels[:, 1:5]) # intersection over area
|
||||
labels = labels[ioa < 0.60] # remove >60% obscured labels
|
||||
|
||||
return labels
|
||||
@ -294,104 +269,9 @@ def mixup(im, labels, im2, labels2):
|
||||
return im, labels
|
||||
|
||||
|
||||
def box_candidates(box1, box2, wh_thr=2, ar_thr=100, area_thr=0.1, eps=1e-16): # box1(4,n), box2(4,n)
|
||||
def box_candidates(box1, box2, wh_thr=2, ar_thr=20, area_thr=0.1, eps=1e-16): # box1(4,n), box2(4,n)
|
||||
# Compute candidate boxes: box1 before augment, box2 after augment, wh_thr (pixels), aspect_ratio_thr, area_ratio
|
||||
w1, h1 = box1[2] - box1[0], box1[3] - box1[1]
|
||||
w2, h2 = box2[2] - box2[0], box2[3] - box2[1]
|
||||
ar = np.maximum(w2 / (h2 + eps), h2 / (w2 + eps)) # aspect ratio
|
||||
return (w2 > wh_thr) & (h2 > wh_thr) & (w2 * h2 / (w1 * h1 + eps) > area_thr) & (ar < ar_thr) # candidates
|
||||
|
||||
|
||||
def classify_albumentations(
|
||||
augment=True,
|
||||
size=224,
|
||||
scale=(0.08, 1.0),
|
||||
ratio=(0.75, 1.0 / 0.75), # 0.75, 1.33
|
||||
hflip=0.5,
|
||||
vflip=0.0,
|
||||
jitter=0.4,
|
||||
mean=IMAGENET_MEAN,
|
||||
std=IMAGENET_STD,
|
||||
auto_aug=False):
|
||||
# YOLOv3 classification Albumentations (optional, only used if package is installed)
|
||||
prefix = colorstr('albumentations: ')
|
||||
try:
|
||||
import albumentations as A
|
||||
from albumentations.pytorch import ToTensorV2
|
||||
check_version(A.__version__, '1.0.3', hard=True) # version requirement
|
||||
if augment: # Resize and crop
|
||||
T = [A.RandomResizedCrop(height=size, width=size, scale=scale, ratio=ratio)]
|
||||
if auto_aug:
|
||||
# TODO: implement AugMix, AutoAug & RandAug in albumentation
|
||||
LOGGER.info(f'{prefix}auto augmentations are currently not supported')
|
||||
else:
|
||||
if hflip > 0:
|
||||
T += [A.HorizontalFlip(p=hflip)]
|
||||
if vflip > 0:
|
||||
T += [A.VerticalFlip(p=vflip)]
|
||||
if jitter > 0:
|
||||
color_jitter = (float(jitter),) * 3 # repeat value for brightness, contrast, satuaration, 0 hue
|
||||
T += [A.ColorJitter(*color_jitter, 0)]
|
||||
else: # Use fixed crop for eval set (reproducibility)
|
||||
T = [A.SmallestMaxSize(max_size=size), A.CenterCrop(height=size, width=size)]
|
||||
T += [A.Normalize(mean=mean, std=std), ToTensorV2()] # Normalize and convert to Tensor
|
||||
LOGGER.info(prefix + ', '.join(f'{x}'.replace('always_apply=False, ', '') for x in T if x.p))
|
||||
return A.Compose(T)
|
||||
|
||||
except ImportError: # package not installed, skip
|
||||
LOGGER.warning(f'{prefix}⚠️ not found, install with `pip install albumentations` (recommended)')
|
||||
except Exception as e:
|
||||
LOGGER.info(f'{prefix}{e}')
|
||||
|
||||
|
||||
def classify_transforms(size=224):
|
||||
# Transforms to apply if albumentations not installed
|
||||
assert isinstance(size, int), f'ERROR: classify_transforms size {size} must be integer, not (list, tuple)'
|
||||
# T.Compose([T.ToTensor(), T.Resize(size), T.CenterCrop(size), T.Normalize(IMAGENET_MEAN, IMAGENET_STD)])
|
||||
return T.Compose([CenterCrop(size), ToTensor(), T.Normalize(IMAGENET_MEAN, IMAGENET_STD)])
|
||||
|
||||
|
||||
class LetterBox:
|
||||
# YOLOv3 LetterBox class for image preprocessing, i.e. T.Compose([LetterBox(size), ToTensor()])
|
||||
def __init__(self, size=(640, 640), auto=False, stride=32):
|
||||
super().__init__()
|
||||
self.h, self.w = (size, size) if isinstance(size, int) else size
|
||||
self.auto = auto # pass max size integer, automatically solve for short side using stride
|
||||
self.stride = stride # used with auto
|
||||
|
||||
def __call__(self, im): # im = np.array HWC
|
||||
imh, imw = im.shape[:2]
|
||||
r = min(self.h / imh, self.w / imw) # ratio of new/old
|
||||
h, w = round(imh * r), round(imw * r) # resized image
|
||||
hs, ws = (math.ceil(x / self.stride) * self.stride for x in (h, w)) if self.auto else self.h, self.w
|
||||
top, left = round((hs - h) / 2 - 0.1), round((ws - w) / 2 - 0.1)
|
||||
im_out = np.full((self.h, self.w, 3), 114, dtype=im.dtype)
|
||||
im_out[top:top + h, left:left + w] = cv2.resize(im, (w, h), interpolation=cv2.INTER_LINEAR)
|
||||
return im_out
|
||||
|
||||
|
||||
class CenterCrop:
|
||||
# YOLOv3 CenterCrop class for image preprocessing, i.e. T.Compose([CenterCrop(size), ToTensor()])
|
||||
def __init__(self, size=640):
|
||||
super().__init__()
|
||||
self.h, self.w = (size, size) if isinstance(size, int) else size
|
||||
|
||||
def __call__(self, im): # im = np.array HWC
|
||||
imh, imw = im.shape[:2]
|
||||
m = min(imh, imw) # min dimension
|
||||
top, left = (imh - m) // 2, (imw - m) // 2
|
||||
return cv2.resize(im[top:top + m, left:left + m], (self.w, self.h), interpolation=cv2.INTER_LINEAR)
|
||||
|
||||
|
||||
class ToTensor:
|
||||
# YOLOv3 ToTensor class for image preprocessing, i.e. T.Compose([LetterBox(size), ToTensor()])
|
||||
def __init__(self, half=False):
|
||||
super().__init__()
|
||||
self.half = half
|
||||
|
||||
def __call__(self, im): # im = np.array HWC in BGR order
|
||||
im = np.ascontiguousarray(im.transpose((2, 0, 1))[::-1]) # HWC to CHW -> BGR to RGB -> contiguous
|
||||
im = torch.from_numpy(im) # to torch
|
||||
im = im.half() if self.half else im.float() # uint8 to fp16/32
|
||||
im /= 255.0 # 0-255 to 0.0-1.0
|
||||
return im
|
||||
|
||||
@ -1,6 +1,6 @@
|
||||
# YOLOv3 🚀 by Ultralytics, GPL-3.0 license
|
||||
"""
|
||||
AutoAnchor utils
|
||||
Auto-anchor utils
|
||||
"""
|
||||
|
||||
import random
|
||||
@ -10,23 +10,21 @@ import torch
|
||||
import yaml
|
||||
from tqdm import tqdm
|
||||
|
||||
from utils import TryExcept
|
||||
from utils.general import LOGGER, TQDM_BAR_FORMAT, colorstr
|
||||
from utils.general import LOGGER, colorstr, emojis
|
||||
|
||||
PREFIX = colorstr('AutoAnchor: ')
|
||||
|
||||
|
||||
def check_anchor_order(m):
|
||||
# Check anchor order against stride order for YOLOv3 Detect() module m, and correct if necessary
|
||||
a = m.anchors.prod(-1).mean(-1).view(-1) # mean anchor area per output layer
|
||||
# Check anchor order against stride order for Detect() module m, and correct if necessary
|
||||
a = m.anchors.prod(-1).view(-1) # anchor area
|
||||
da = a[-1] - a[0] # delta a
|
||||
ds = m.stride[-1] - m.stride[0] # delta s
|
||||
if da and (da.sign() != ds.sign()): # same order
|
||||
if da.sign() != ds.sign(): # same order
|
||||
LOGGER.info(f'{PREFIX}Reversing anchor order')
|
||||
m.anchors[:] = m.anchors.flip(0)
|
||||
|
||||
|
||||
@TryExcept(f'{PREFIX}ERROR')
|
||||
def check_anchors(dataset, model, thr=4.0, imgsz=640):
|
||||
# Check anchor fit to data, recompute if necessary
|
||||
m = model.module.model[-1] if hasattr(model, 'module') else model.model[-1] # Detect()
|
||||
@ -42,26 +40,26 @@ def check_anchors(dataset, model, thr=4.0, imgsz=640):
|
||||
bpr = (best > 1 / thr).float().mean() # best possible recall
|
||||
return bpr, aat
|
||||
|
||||
stride = m.stride.to(m.anchors.device).view(-1, 1, 1) # model strides
|
||||
anchors = m.anchors.clone() * stride # current anchors
|
||||
anchors = m.anchors.clone() * m.stride.to(m.anchors.device).view(-1, 1, 1) # current anchors
|
||||
bpr, aat = metric(anchors.cpu().view(-1, 2))
|
||||
s = f'\n{PREFIX}{aat:.2f} anchors/target, {bpr:.3f} Best Possible Recall (BPR). '
|
||||
if bpr > 0.98: # threshold to recompute
|
||||
LOGGER.info(f'{s}Current anchors are a good fit to dataset ✅')
|
||||
LOGGER.info(emojis(f'{s}Current anchors are a good fit to dataset ✅'))
|
||||
else:
|
||||
LOGGER.info(f'{s}Anchors are a poor fit to dataset ⚠️, attempting to improve...')
|
||||
LOGGER.info(emojis(f'{s}Anchors are a poor fit to dataset ⚠️, attempting to improve...'))
|
||||
na = m.anchors.numel() // 2 # number of anchors
|
||||
anchors = kmean_anchors(dataset, n=na, img_size=imgsz, thr=thr, gen=1000, verbose=False)
|
||||
try:
|
||||
anchors = kmean_anchors(dataset, n=na, img_size=imgsz, thr=thr, gen=1000, verbose=False)
|
||||
except Exception as e:
|
||||
LOGGER.info(f'{PREFIX}ERROR: {e}')
|
||||
new_bpr = metric(anchors)[0]
|
||||
if new_bpr > bpr: # replace anchors
|
||||
anchors = torch.tensor(anchors, device=m.anchors.device).type_as(m.anchors)
|
||||
m.anchors[:] = anchors.clone().view_as(m.anchors)
|
||||
check_anchor_order(m) # must be in pixel-space (not grid-space)
|
||||
m.anchors /= stride
|
||||
s = f'{PREFIX}Done ✅ (optional: update model *.yaml to use these anchors in the future)'
|
||||
m.anchors[:] = anchors.clone().view_as(m.anchors) / m.stride.to(m.anchors.device).view(-1, 1, 1) # loss
|
||||
check_anchor_order(m)
|
||||
LOGGER.info(f'{PREFIX}New anchors saved to model. Update model *.yaml to use these anchors in the future.')
|
||||
else:
|
||||
s = f'{PREFIX}Done ⚠️ (original anchors better than new anchors, proceeding with original anchors)'
|
||||
LOGGER.info(s)
|
||||
LOGGER.info(f'{PREFIX}Original anchors better than new anchors. Proceeding with original anchors.')
|
||||
|
||||
|
||||
def kmean_anchors(dataset='./data/coco128.yaml', n=9, img_size=640, thr=4.0, gen=1000, verbose=True):
|
||||
@ -83,7 +81,6 @@ def kmean_anchors(dataset='./data/coco128.yaml', n=9, img_size=640, thr=4.0, gen
|
||||
"""
|
||||
from scipy.cluster.vq import kmeans
|
||||
|
||||
npr = np.random
|
||||
thr = 1 / thr
|
||||
|
||||
def metric(k, wh): # compute metrics
|
||||
@ -103,7 +100,7 @@ def kmean_anchors(dataset='./data/coco128.yaml', n=9, img_size=640, thr=4.0, gen
|
||||
s = f'{PREFIX}thr={thr:.2f}: {bpr:.4f} best possible recall, {aat:.2f} anchors past thr\n' \
|
||||
f'{PREFIX}n={n}, img_size={img_size}, metric_all={x.mean():.3f}/{best.mean():.3f}-mean/best, ' \
|
||||
f'past_thr={x[x > thr].mean():.3f}-mean: '
|
||||
for x in k:
|
||||
for i, x in enumerate(k):
|
||||
s += '%i,%i, ' % (round(x[0]), round(x[1]))
|
||||
if verbose:
|
||||
LOGGER.info(s[:-2])
|
||||
@ -112,7 +109,7 @@ def kmean_anchors(dataset='./data/coco128.yaml', n=9, img_size=640, thr=4.0, gen
|
||||
if isinstance(dataset, str): # *.yaml file
|
||||
with open(dataset, errors='ignore') as f:
|
||||
data_dict = yaml.safe_load(f) # model dict
|
||||
from utils.dataloaders import LoadImagesAndLabels
|
||||
from utils.datasets import LoadImagesAndLabels
|
||||
dataset = LoadImagesAndLabels(data_dict['train'], augment=True, rect=True)
|
||||
|
||||
# Get label wh
|
||||
@ -122,21 +119,18 @@ def kmean_anchors(dataset='./data/coco128.yaml', n=9, img_size=640, thr=4.0, gen
|
||||
# Filter
|
||||
i = (wh0 < 3.0).any(1).sum()
|
||||
if i:
|
||||
LOGGER.info(f'{PREFIX}WARNING ⚠️ Extremely small objects found: {i} of {len(wh0)} labels are <3 pixels in size')
|
||||
wh = wh0[(wh0 >= 2.0).any(1)].astype(np.float32) # filter > 2 pixels
|
||||
# wh = wh * (npr.rand(wh.shape[0], 1) * 0.9 + 0.1) # multiply by random scale 0-1
|
||||
LOGGER.info(f'{PREFIX}WARNING: Extremely small objects found. {i} of {len(wh0)} labels are < 3 pixels in size.')
|
||||
wh = wh0[(wh0 >= 2.0).any(1)] # filter > 2 pixels
|
||||
# wh = wh * (np.random.rand(wh.shape[0], 1) * 0.9 + 0.1) # multiply by random scale 0-1
|
||||
|
||||
# Kmeans init
|
||||
try:
|
||||
LOGGER.info(f'{PREFIX}Running kmeans for {n} anchors on {len(wh)} points...')
|
||||
assert n <= len(wh) # apply overdetermined constraint
|
||||
s = wh.std(0) # sigmas for whitening
|
||||
k = kmeans(wh / s, n, iter=30)[0] * s # points
|
||||
assert n == len(k) # kmeans may return fewer points than requested if wh is insufficient or too similar
|
||||
except Exception:
|
||||
LOGGER.warning(f'{PREFIX}WARNING ⚠️ switching strategies from kmeans to random init')
|
||||
k = np.sort(npr.rand(n * 2)).reshape(n, 2) * img_size # random init
|
||||
wh, wh0 = (torch.tensor(x, dtype=torch.float32) for x in (wh, wh0))
|
||||
# Kmeans calculation
|
||||
LOGGER.info(f'{PREFIX}Running kmeans for {n} anchors on {len(wh)} points...')
|
||||
s = wh.std(0) # sigmas for whitening
|
||||
k, dist = kmeans(wh / s, n, iter=30) # points, mean distance
|
||||
assert len(k) == n, f'{PREFIX}ERROR: scipy.cluster.vq.kmeans requested {n} points but returned only {len(k)}'
|
||||
k *= s
|
||||
wh = torch.tensor(wh, dtype=torch.float32) # filtered
|
||||
wh0 = torch.tensor(wh0, dtype=torch.float32) # unfiltered
|
||||
k = print_results(k, verbose=False)
|
||||
|
||||
# Plot
|
||||
@ -152,8 +146,9 @@ def kmean_anchors(dataset='./data/coco128.yaml', n=9, img_size=640, thr=4.0, gen
|
||||
# fig.savefig('wh.png', dpi=200)
|
||||
|
||||
# Evolve
|
||||
npr = np.random
|
||||
f, sh, mp, s = anchor_fitness(k), k.shape, 0.9, 0.1 # fitness, generations, mutation prob, sigma
|
||||
pbar = tqdm(range(gen), bar_format=TQDM_BAR_FORMAT) # progress bar
|
||||
pbar = tqdm(range(gen), desc=f'{PREFIX}Evolving anchors with Genetic Algorithm:') # progress bar
|
||||
for _ in pbar:
|
||||
v = np.ones(sh)
|
||||
while (v == 1).all(): # mutate until a change occurs (prevent duplicates)
|
||||
@ -166,4 +161,4 @@ def kmean_anchors(dataset='./data/coco128.yaml', n=9, img_size=640, thr=4.0, gen
|
||||
if verbose:
|
||||
print_results(k, verbose)
|
||||
|
||||
return print_results(k).astype(np.float32)
|
||||
return print_results(k)
|
||||
|
||||
@ -7,66 +7,51 @@ from copy import deepcopy
|
||||
|
||||
import numpy as np
|
||||
import torch
|
||||
from torch.cuda import amp
|
||||
|
||||
from utils.general import LOGGER, colorstr
|
||||
from utils.torch_utils import profile
|
||||
|
||||
|
||||
def check_train_batch_size(model, imgsz=640, amp=True):
|
||||
# Check YOLOv3 training batch size
|
||||
with torch.cuda.amp.autocast(amp):
|
||||
def check_train_batch_size(model, imgsz=640):
|
||||
# Check training batch size
|
||||
with amp.autocast():
|
||||
return autobatch(deepcopy(model).train(), imgsz) # compute optimal batch size
|
||||
|
||||
|
||||
def autobatch(model, imgsz=640, fraction=0.8, batch_size=16):
|
||||
# Automatically estimate best YOLOv3 batch size to use `fraction` of available CUDA memory
|
||||
def autobatch(model, imgsz=640, fraction=0.9, batch_size=16):
|
||||
# Automatically estimate best batch size to use `fraction` of available CUDA memory
|
||||
# Usage:
|
||||
# import torch
|
||||
# from utils.autobatch import autobatch
|
||||
# model = torch.hub.load('ultralytics/yolov5', 'yolov5s', autoshape=False)
|
||||
# model = torch.hub.load('ultralytics/yolov3', 'yolov3', autoshape=False)
|
||||
# print(autobatch(model))
|
||||
|
||||
# Check device
|
||||
prefix = colorstr('AutoBatch: ')
|
||||
LOGGER.info(f'{prefix}Computing optimal batch size for --imgsz {imgsz}')
|
||||
device = next(model.parameters()).device # get model device
|
||||
if device.type == 'cpu':
|
||||
LOGGER.info(f'{prefix}CUDA not detected, using default CPU batch-size {batch_size}')
|
||||
return batch_size
|
||||
if torch.backends.cudnn.benchmark:
|
||||
LOGGER.info(f'{prefix} ⚠️ Requires torch.backends.cudnn.benchmark=False, using default batch-size {batch_size}')
|
||||
return batch_size
|
||||
|
||||
# Inspect CUDA memory
|
||||
gb = 1 << 30 # bytes to GiB (1024 ** 3)
|
||||
d = str(device).upper() # 'CUDA:0'
|
||||
properties = torch.cuda.get_device_properties(device) # device properties
|
||||
t = properties.total_memory / gb # GiB total
|
||||
r = torch.cuda.memory_reserved(device) / gb # GiB reserved
|
||||
a = torch.cuda.memory_allocated(device) / gb # GiB allocated
|
||||
f = t - (r + a) # GiB free
|
||||
t = properties.total_memory / 1024 ** 3 # (GiB)
|
||||
r = torch.cuda.memory_reserved(device) / 1024 ** 3 # (GiB)
|
||||
a = torch.cuda.memory_allocated(device) / 1024 ** 3 # (GiB)
|
||||
f = t - (r + a) # free inside reserved
|
||||
LOGGER.info(f'{prefix}{d} ({properties.name}) {t:.2f}G total, {r:.2f}G reserved, {a:.2f}G allocated, {f:.2f}G free')
|
||||
|
||||
# Profile batch sizes
|
||||
batch_sizes = [1, 2, 4, 8, 16]
|
||||
try:
|
||||
img = [torch.empty(b, 3, imgsz, imgsz) for b in batch_sizes]
|
||||
results = profile(img, model, n=3, device=device)
|
||||
img = [torch.zeros(b, 3, imgsz, imgsz) for b in batch_sizes]
|
||||
y = profile(img, model, n=3, device=device)
|
||||
except Exception as e:
|
||||
LOGGER.warning(f'{prefix}{e}')
|
||||
|
||||
# Fit a solution
|
||||
y = [x[2] for x in results if x] # memory [2]
|
||||
p = np.polyfit(batch_sizes[:len(y)], y, deg=1) # first degree polynomial fit
|
||||
y = [x[2] for x in y if x] # memory [2]
|
||||
batch_sizes = batch_sizes[:len(y)]
|
||||
p = np.polyfit(batch_sizes, y, deg=1) # first degree polynomial fit
|
||||
b = int((f * fraction - p[1]) / p[0]) # y intercept (optimal batch size)
|
||||
if None in results: # some sizes failed
|
||||
i = results.index(None) # first fail index
|
||||
if b >= batch_sizes[i]: # y intercept above failure point
|
||||
b = batch_sizes[max(i - 1, 0)] # select prior safe point
|
||||
if b < 1 or b > 1024: # b outside of safe range
|
||||
b = batch_size
|
||||
LOGGER.warning(f'{prefix}WARNING ⚠️ CUDA anomaly detected, recommend restart environment and retry command.')
|
||||
|
||||
fraction = (np.polyval(p, b) + r + a) / t # actual fraction predicted
|
||||
LOGGER.info(f'{prefix}Using batch-size {b} for {d} {t * fraction:.2f}G/{t:.2f}G ({fraction * 100:.0f}%) ✅')
|
||||
LOGGER.info(f'{prefix}Using batch-size {b} for {d} {t * fraction:.2f}G/{t:.2f}G ({fraction * 100:.0f}%)')
|
||||
return b
|
||||
|
||||
@ -3,46 +3,46 @@
|
||||
Callback utils
|
||||
"""
|
||||
|
||||
import threading
|
||||
|
||||
|
||||
class Callbacks:
|
||||
""""
|
||||
Handles all registered callbacks for YOLOv3 Hooks
|
||||
Handles all registered callbacks for Hooks
|
||||
"""
|
||||
|
||||
def __init__(self):
|
||||
# Define the available callbacks
|
||||
self._callbacks = {
|
||||
'on_pretrain_routine_start': [],
|
||||
'on_pretrain_routine_end': [],
|
||||
'on_train_start': [],
|
||||
'on_train_epoch_start': [],
|
||||
'on_train_batch_start': [],
|
||||
'optimizer_step': [],
|
||||
'on_before_zero_grad': [],
|
||||
'on_train_batch_end': [],
|
||||
'on_train_epoch_end': [],
|
||||
'on_val_start': [],
|
||||
'on_val_batch_start': [],
|
||||
'on_val_image_end': [],
|
||||
'on_val_batch_end': [],
|
||||
'on_val_end': [],
|
||||
'on_fit_epoch_end': [], # fit = train + val
|
||||
'on_model_save': [],
|
||||
'on_train_end': [],
|
||||
'on_params_update': [],
|
||||
'teardown': [],}
|
||||
self.stop_training = False # set True to interrupt training
|
||||
# Define the available callbacks
|
||||
_callbacks = {
|
||||
'on_pretrain_routine_start': [],
|
||||
'on_pretrain_routine_end': [],
|
||||
|
||||
'on_train_start': [],
|
||||
'on_train_epoch_start': [],
|
||||
'on_train_batch_start': [],
|
||||
'optimizer_step': [],
|
||||
'on_before_zero_grad': [],
|
||||
'on_train_batch_end': [],
|
||||
'on_train_epoch_end': [],
|
||||
|
||||
'on_val_start': [],
|
||||
'on_val_batch_start': [],
|
||||
'on_val_image_end': [],
|
||||
'on_val_batch_end': [],
|
||||
'on_val_end': [],
|
||||
|
||||
'on_fit_epoch_end': [], # fit = train + val
|
||||
'on_model_save': [],
|
||||
'on_train_end': [],
|
||||
|
||||
'teardown': [],
|
||||
}
|
||||
|
||||
def register_action(self, hook, name='', callback=None):
|
||||
"""
|
||||
Register a new action to a callback hook
|
||||
|
||||
Args:
|
||||
hook: The callback hook name to register the action to
|
||||
name: The name of the action for later reference
|
||||
callback: The callback to fire
|
||||
hook The callback hook name to register the action to
|
||||
name The name of the action for later reference
|
||||
callback The callback to fire
|
||||
"""
|
||||
assert hook in self._callbacks, f"hook '{hook}' not found in callbacks {self._callbacks}"
|
||||
assert callable(callback), f"callback '{callback}' is not callable"
|
||||
@ -53,24 +53,24 @@ class Callbacks:
|
||||
Returns all the registered actions by callback hook
|
||||
|
||||
Args:
|
||||
hook: The name of the hook to check, defaults to all
|
||||
hook The name of the hook to check, defaults to all
|
||||
"""
|
||||
return self._callbacks[hook] if hook else self._callbacks
|
||||
if hook:
|
||||
return self._callbacks[hook]
|
||||
else:
|
||||
return self._callbacks
|
||||
|
||||
def run(self, hook, *args, thread=False, **kwargs):
|
||||
def run(self, hook, *args, **kwargs):
|
||||
"""
|
||||
Loop through the registered actions and fire all callbacks on main thread
|
||||
Loop through the registered actions and fire all callbacks
|
||||
|
||||
Args:
|
||||
hook: The name of the hook to check, defaults to all
|
||||
args: Arguments to receive from YOLOv3
|
||||
thread: (boolean) Run callbacks in daemon thread
|
||||
kwargs: Keyword Arguments to receive from YOLOv3
|
||||
hook The name of the hook to check, defaults to all
|
||||
args Arguments to receive from
|
||||
kwargs Keyword Arguments to receive from
|
||||
"""
|
||||
|
||||
assert hook in self._callbacks, f"hook '{hook}' not found in callbacks {self._callbacks}"
|
||||
|
||||
for logger in self._callbacks[hook]:
|
||||
if thread:
|
||||
threading.Thread(target=logger['callback'], args=args, kwargs=kwargs, daemon=True).start()
|
||||
else:
|
||||
logger['callback'](*args, **kwargs)
|
||||
logger['callback'](*args, **kwargs)
|
||||
|
||||
@ -3,104 +3,147 @@
|
||||
Download utils
|
||||
"""
|
||||
|
||||
import logging
|
||||
import os
|
||||
import platform
|
||||
import subprocess
|
||||
import time
|
||||
import urllib
|
||||
from pathlib import Path
|
||||
from zipfile import ZipFile
|
||||
|
||||
import requests
|
||||
import torch
|
||||
|
||||
|
||||
def is_url(url, check=True):
|
||||
# Check if string is URL and check if URL exists
|
||||
try:
|
||||
url = str(url)
|
||||
result = urllib.parse.urlparse(url)
|
||||
assert all([result.scheme, result.netloc]) # check if is url
|
||||
return (urllib.request.urlopen(url).getcode() == 200) if check else True # check if exists online
|
||||
except (AssertionError, urllib.request.HTTPError):
|
||||
return False
|
||||
|
||||
|
||||
def gsutil_getsize(url=''):
|
||||
# gs://bucket/file size https://cloud.google.com/storage/docs/gsutil/commands/du
|
||||
s = subprocess.check_output(f'gsutil du {url}', shell=True).decode('utf-8')
|
||||
return eval(s.split(' ')[0]) if len(s) else 0 # bytes
|
||||
|
||||
|
||||
def url_getsize(url='https://ultralytics.com/images/bus.jpg'):
|
||||
# Return downloadable file size in bytes
|
||||
response = requests.head(url, allow_redirects=True)
|
||||
return int(response.headers.get('content-length', -1))
|
||||
|
||||
|
||||
def safe_download(file, url, url2=None, min_bytes=1E0, error_msg=''):
|
||||
# Attempts to download file from url or url2, checks and removes incomplete downloads < min_bytes
|
||||
from utils.general import LOGGER
|
||||
|
||||
file = Path(file)
|
||||
assert_msg = f"Downloaded file '{file}' does not exist or size is < min_bytes={min_bytes}"
|
||||
try: # url1
|
||||
LOGGER.info(f'Downloading {url} to {file}...')
|
||||
torch.hub.download_url_to_file(url, str(file), progress=LOGGER.level <= logging.INFO)
|
||||
print(f'Downloading {url} to {file}...')
|
||||
torch.hub.download_url_to_file(url, str(file))
|
||||
assert file.exists() and file.stat().st_size > min_bytes, assert_msg # check
|
||||
except Exception as e: # url2
|
||||
if file.exists():
|
||||
file.unlink() # remove partial downloads
|
||||
LOGGER.info(f'ERROR: {e}\nRe-attempting {url2 or url} to {file}...')
|
||||
subprocess.run(
|
||||
f"curl -# -L '{url2 or url}' -o '{file}' --retry 3 -C -".split()) # curl download, retry and resume on fail
|
||||
file.unlink(missing_ok=True) # remove partial downloads
|
||||
print(f'ERROR: {e}\nRe-attempting {url2 or url} to {file}...')
|
||||
os.system(f"curl -L '{url2 or url}' -o '{file}' --retry 3 -C -") # curl download, retry and resume on fail
|
||||
finally:
|
||||
if not file.exists() or file.stat().st_size < min_bytes: # check
|
||||
if file.exists():
|
||||
file.unlink() # remove partial downloads
|
||||
LOGGER.info(f'ERROR: {assert_msg}\n{error_msg}')
|
||||
LOGGER.info('')
|
||||
file.unlink(missing_ok=True) # remove partial downloads
|
||||
print(f"ERROR: {assert_msg}\n{error_msg}")
|
||||
print('')
|
||||
|
||||
|
||||
def attempt_download(file, repo='ultralytics/yolov5', release='v7.0'):
|
||||
# Attempt file download from GitHub release assets if not found locally. release = 'latest', 'v7.0', etc.
|
||||
from utils.general import LOGGER
|
||||
|
||||
def github_assets(repository, version='latest'):
|
||||
# Return GitHub repo tag (i.e. 'v7.0') and assets (i.e. ['yolov5s.pt', 'yolov5m.pt', ...])
|
||||
if version != 'latest':
|
||||
version = f'tags/{version}' # i.e. tags/v7.0
|
||||
response = requests.get(f'https://api.github.com/repos/{repository}/releases/{version}').json() # github api
|
||||
return response['tag_name'], [x['name'] for x in response['assets']] # tag, assets
|
||||
|
||||
def attempt_download(file, repo='ultralytics/yolov3'): # from utils.downloads import *; attempt_download()
|
||||
# Attempt file download if does not exist
|
||||
file = Path(str(file).strip().replace("'", ''))
|
||||
|
||||
if not file.exists():
|
||||
# URL specified
|
||||
name = Path(urllib.parse.unquote(str(file))).name # decode '%2F' to '/' etc.
|
||||
if str(file).startswith(('http:/', 'https:/')): # download
|
||||
url = str(file).replace(':/', '://') # Pathlib turns :// -> :/
|
||||
file = name.split('?')[0] # parse authentication https://url.com/file.txt?auth...
|
||||
if Path(file).is_file():
|
||||
LOGGER.info(f'Found {url} locally at {file}') # file already exists
|
||||
else:
|
||||
safe_download(file=file, url=url, min_bytes=1E5)
|
||||
return file
|
||||
name = name.split('?')[0] # parse authentication https://url.com/file.txt?auth...
|
||||
safe_download(file=name, url=url, min_bytes=1E5)
|
||||
return name
|
||||
|
||||
# GitHub assets
|
||||
assets = [f'yolov5{size}{suffix}.pt' for size in 'nsmlx' for suffix in ('', '6', '-cls', '-seg')] # default
|
||||
try:
|
||||
tag, assets = github_assets(repo, release)
|
||||
except Exception:
|
||||
try:
|
||||
tag, assets = github_assets(repo) # latest release
|
||||
except Exception:
|
||||
try:
|
||||
tag = subprocess.check_output('git tag', shell=True, stderr=subprocess.STDOUT).decode().split()[-1]
|
||||
except Exception:
|
||||
tag = release
|
||||
|
||||
file.parent.mkdir(parents=True, exist_ok=True) # make parent dir (if required)
|
||||
try:
|
||||
response = requests.get(f'https://api.github.com/repos/{repo}/releases/latest').json() # github api
|
||||
assets = [x['name'] for x in response['assets']] # release assets, i.e. ['yolov3.pt'...]
|
||||
tag = response['tag_name'] # i.e. 'v1.0'
|
||||
except: # fallback plan
|
||||
assets = ['yolov3.pt', 'yolov3-spp.pt', 'yolov3-tiny.pt']
|
||||
try:
|
||||
tag = subprocess.check_output('git tag', shell=True, stderr=subprocess.STDOUT).decode().split()[-1]
|
||||
except:
|
||||
tag = 'v9.5.0' # current release
|
||||
|
||||
if name in assets:
|
||||
safe_download(file,
|
||||
url=f'https://github.com/{repo}/releases/download/{tag}/{name}',
|
||||
# url2=f'https://storage.googleapis.com/{repo}/ckpt/{name}', # backup url (optional)
|
||||
min_bytes=1E5,
|
||||
error_msg=f'{file} missing, try downloading from https://github.com/{repo}/releases/{tag}')
|
||||
error_msg=f'{file} missing, try downloading from https://github.com/{repo}/releases/')
|
||||
|
||||
return str(file)
|
||||
|
||||
|
||||
def gdrive_download(id='16TiPfZj7htmTyhntwcZyEEAejOUxuT6m', file='tmp.zip'):
|
||||
# Downloads a file from Google Drive. from yolov3.utils.downloads import *; gdrive_download()
|
||||
t = time.time()
|
||||
file = Path(file)
|
||||
cookie = Path('cookie') # gdrive cookie
|
||||
print(f'Downloading https://drive.google.com/uc?export=download&id={id} as {file}... ', end='')
|
||||
file.unlink(missing_ok=True) # remove existing file
|
||||
cookie.unlink(missing_ok=True) # remove existing cookie
|
||||
|
||||
# Attempt file download
|
||||
out = "NUL" if platform.system() == "Windows" else "/dev/null"
|
||||
os.system(f'curl -c ./cookie -s -L "drive.google.com/uc?export=download&id={id}" > {out}')
|
||||
if os.path.exists('cookie'): # large file
|
||||
s = f'curl -Lb ./cookie "drive.google.com/uc?export=download&confirm={get_token()}&id={id}" -o {file}'
|
||||
else: # small file
|
||||
s = f'curl -s -L -o {file} "drive.google.com/uc?export=download&id={id}"'
|
||||
r = os.system(s) # execute, capture return
|
||||
cookie.unlink(missing_ok=True) # remove existing cookie
|
||||
|
||||
# Error check
|
||||
if r != 0:
|
||||
file.unlink(missing_ok=True) # remove partial
|
||||
print('Download error ') # raise Exception('Download error')
|
||||
return r
|
||||
|
||||
# Unzip if archive
|
||||
if file.suffix == '.zip':
|
||||
print('unzipping... ', end='')
|
||||
ZipFile(file).extractall(path=file.parent) # unzip
|
||||
file.unlink() # remove zip
|
||||
|
||||
print(f'Done ({time.time() - t:.1f}s)')
|
||||
return r
|
||||
|
||||
|
||||
def get_token(cookie="./cookie"):
|
||||
with open(cookie) as f:
|
||||
for line in f:
|
||||
if "download" in line:
|
||||
return line.split()[-1]
|
||||
return ""
|
||||
|
||||
# Google utils: https://cloud.google.com/storage/docs/reference/libraries ----------------------------------------------
|
||||
#
|
||||
#
|
||||
# def upload_blob(bucket_name, source_file_name, destination_blob_name):
|
||||
# # Uploads a file to a bucket
|
||||
# # https://cloud.google.com/storage/docs/uploading-objects#storage-upload-object-python
|
||||
#
|
||||
# storage_client = storage.Client()
|
||||
# bucket = storage_client.get_bucket(bucket_name)
|
||||
# blob = bucket.blob(destination_blob_name)
|
||||
#
|
||||
# blob.upload_from_filename(source_file_name)
|
||||
#
|
||||
# print('File {} uploaded to {}.'.format(
|
||||
# source_file_name,
|
||||
# destination_blob_name))
|
||||
#
|
||||
#
|
||||
# def download_blob(bucket_name, source_blob_name, destination_file_name):
|
||||
# # Uploads a blob from a bucket
|
||||
# storage_client = storage.Client()
|
||||
# bucket = storage_client.get_bucket(bucket_name)
|
||||
# blob = bucket.blob(source_blob_name)
|
||||
#
|
||||
# blob.download_to_filename(destination_file_name)
|
||||
#
|
||||
# print('Blob {} downloaded to {}.'.format(
|
||||
# source_blob_name,
|
||||
# destination_file_name))
|
||||
|
||||
968
yolov3/utils/general.py
Normal file → Executable file
968
yolov3/utils/general.py
Normal file → Executable file
File diff suppressed because it is too large
Load Diff
@ -5,26 +5,25 @@ Logging utils
|
||||
|
||||
import os
|
||||
import warnings
|
||||
from pathlib import Path
|
||||
from threading import Thread
|
||||
|
||||
import pkg_resources as pkg
|
||||
import torch
|
||||
from torch.utils.tensorboard import SummaryWriter
|
||||
|
||||
from utils.general import LOGGER, colorstr, cv2
|
||||
from utils.loggers.clearml.clearml_utils import ClearmlLogger
|
||||
from utils.general import colorstr, emojis
|
||||
from utils.loggers.wandb.wandb_utils import WandbLogger
|
||||
from utils.plots import plot_images, plot_labels, plot_results
|
||||
from utils.plots import plot_images, plot_results
|
||||
from utils.torch_utils import de_parallel
|
||||
|
||||
LOGGERS = ('csv', 'tb', 'wandb', 'clearml', 'comet') # *.csv, TensorBoard, Weights & Biases, ClearML
|
||||
LOGGERS = ('csv', 'tb', 'wandb') # text-file, TensorBoard, Weights & Biases
|
||||
RANK = int(os.getenv('RANK', -1))
|
||||
|
||||
try:
|
||||
import wandb
|
||||
|
||||
assert hasattr(wandb, '__version__') # verify package import not local dir
|
||||
if pkg.parse_version(wandb.__version__) >= pkg.parse_version('0.12.2') and RANK in {0, -1}:
|
||||
if pkg.parse_version(wandb.__version__) >= pkg.parse_version('0.12.2') and RANK in [0, -1]:
|
||||
try:
|
||||
wandb_login_success = wandb.login(timeout=30)
|
||||
except wandb.errors.UsageError: # known non-TTY terminal issue
|
||||
@ -34,64 +33,30 @@ try:
|
||||
except (ImportError, AssertionError):
|
||||
wandb = None
|
||||
|
||||
try:
|
||||
import clearml
|
||||
|
||||
assert hasattr(clearml, '__version__') # verify package import not local dir
|
||||
except (ImportError, AssertionError):
|
||||
clearml = None
|
||||
|
||||
try:
|
||||
if RANK not in [0, -1]:
|
||||
comet_ml = None
|
||||
else:
|
||||
import comet_ml
|
||||
|
||||
assert hasattr(comet_ml, '__version__') # verify package import not local dir
|
||||
from utils.loggers.comet import CometLogger
|
||||
|
||||
except (ModuleNotFoundError, ImportError, AssertionError):
|
||||
comet_ml = None
|
||||
|
||||
|
||||
class Loggers():
|
||||
# YOLOv3 Loggers class
|
||||
# Loggers class
|
||||
def __init__(self, save_dir=None, weights=None, opt=None, hyp=None, logger=None, include=LOGGERS):
|
||||
self.save_dir = save_dir
|
||||
self.weights = weights
|
||||
self.opt = opt
|
||||
self.hyp = hyp
|
||||
self.plots = not opt.noplots # plot results
|
||||
self.logger = logger # for printing results to console
|
||||
self.include = include
|
||||
self.keys = [
|
||||
'train/box_loss',
|
||||
'train/obj_loss',
|
||||
'train/cls_loss', # train loss
|
||||
'metrics/precision',
|
||||
'metrics/recall',
|
||||
'metrics/mAP_0.5',
|
||||
'metrics/mAP_0.5:0.95', # metrics
|
||||
'val/box_loss',
|
||||
'val/obj_loss',
|
||||
'val/cls_loss', # val loss
|
||||
'x/lr0',
|
||||
'x/lr1',
|
||||
'x/lr2'] # params
|
||||
self.best_keys = ['best/epoch', 'best/precision', 'best/recall', 'best/mAP_0.5', 'best/mAP_0.5:0.95']
|
||||
self.keys = ['train/box_loss', 'train/obj_loss', 'train/cls_loss', # train loss
|
||||
'metrics/precision', 'metrics/recall', 'metrics/mAP_0.5', 'metrics/mAP_0.5:0.95', # metrics
|
||||
'val/box_loss', 'val/obj_loss', 'val/cls_loss', # val loss
|
||||
'x/lr0', 'x/lr1', 'x/lr2'] # params
|
||||
for k in LOGGERS:
|
||||
setattr(self, k, None) # init empty logger dictionary
|
||||
self.csv = True # always log to csv
|
||||
|
||||
# Messages
|
||||
if not clearml:
|
||||
prefix = colorstr('ClearML: ')
|
||||
s = f"{prefix}run 'pip install clearml' to automatically track, visualize and remotely train YOLOv3 🚀 in ClearML"
|
||||
self.logger.info(s)
|
||||
if not comet_ml:
|
||||
prefix = colorstr('Comet: ')
|
||||
s = f"{prefix}run 'pip install comet_ml' to automatically track and visualize YOLOv3 🚀 runs in Comet"
|
||||
self.logger.info(s)
|
||||
# Message
|
||||
if not wandb:
|
||||
prefix = colorstr('Weights & Biases: ')
|
||||
s = f"{prefix}run 'pip install wandb' to automatically track and visualize YOLOv3 🚀 runs (RECOMMENDED)"
|
||||
print(emojis(s))
|
||||
|
||||
# TensorBoard
|
||||
s = self.save_dir
|
||||
if 'tb' in self.include and not self.opt.evolve:
|
||||
@ -101,127 +66,53 @@ class Loggers():
|
||||
|
||||
# W&B
|
||||
if wandb and 'wandb' in self.include:
|
||||
wandb_artifact_resume = isinstance(self.opt.resume, str) and self.opt.resume.startswith('wandb-artifact://')
|
||||
run_id = torch.load(self.weights).get('wandb_id') if self.opt.resume and not wandb_artifact_resume else None
|
||||
self.opt.hyp = self.hyp # add hyperparameters
|
||||
self.wandb = WandbLogger(self.opt)
|
||||
self.wandb = WandbLogger(self.opt, run_id)
|
||||
else:
|
||||
self.wandb = None
|
||||
|
||||
# ClearML
|
||||
if clearml and 'clearml' in self.include:
|
||||
try:
|
||||
self.clearml = ClearmlLogger(self.opt, self.hyp)
|
||||
except Exception:
|
||||
self.clearml = None
|
||||
prefix = colorstr('ClearML: ')
|
||||
LOGGER.warning(f'{prefix}WARNING ⚠️ ClearML is installed but not configured, skipping ClearML logging.'
|
||||
f' See https://github.com/ultralytics/yolov5/tree/master/utils/loggers/clearml#readme')
|
||||
|
||||
else:
|
||||
self.clearml = None
|
||||
|
||||
# Comet
|
||||
if comet_ml and 'comet' in self.include:
|
||||
if isinstance(self.opt.resume, str) and self.opt.resume.startswith('comet://'):
|
||||
run_id = self.opt.resume.split('/')[-1]
|
||||
self.comet_logger = CometLogger(self.opt, self.hyp, run_id=run_id)
|
||||
|
||||
else:
|
||||
self.comet_logger = CometLogger(self.opt, self.hyp)
|
||||
|
||||
else:
|
||||
self.comet_logger = None
|
||||
|
||||
@property
|
||||
def remote_dataset(self):
|
||||
# Get data_dict if custom dataset artifact link is provided
|
||||
data_dict = None
|
||||
if self.clearml:
|
||||
data_dict = self.clearml.data_dict
|
||||
if self.wandb:
|
||||
data_dict = self.wandb.data_dict
|
||||
if self.comet_logger:
|
||||
data_dict = self.comet_logger.data_dict
|
||||
|
||||
return data_dict
|
||||
|
||||
def on_train_start(self):
|
||||
if self.comet_logger:
|
||||
self.comet_logger.on_train_start()
|
||||
|
||||
def on_pretrain_routine_start(self):
|
||||
if self.comet_logger:
|
||||
self.comet_logger.on_pretrain_routine_start()
|
||||
|
||||
def on_pretrain_routine_end(self, labels, names):
|
||||
def on_pretrain_routine_end(self):
|
||||
# Callback runs on pre-train routine end
|
||||
if self.plots:
|
||||
plot_labels(labels, names, self.save_dir)
|
||||
paths = self.save_dir.glob('*labels*.jpg') # training labels
|
||||
if self.wandb:
|
||||
self.wandb.log({'Labels': [wandb.Image(str(x), caption=x.name) for x in paths]})
|
||||
# if self.clearml:
|
||||
# pass # ClearML saves these images automatically using hooks
|
||||
if self.comet_logger:
|
||||
self.comet_logger.on_pretrain_routine_end(paths)
|
||||
paths = self.save_dir.glob('*labels*.jpg') # training labels
|
||||
if self.wandb:
|
||||
self.wandb.log({"Labels": [wandb.Image(str(x), caption=x.name) for x in paths]})
|
||||
|
||||
def on_train_batch_end(self, model, ni, imgs, targets, paths, vals):
|
||||
log_dict = dict(zip(self.keys[:3], vals))
|
||||
def on_train_batch_end(self, ni, model, imgs, targets, paths, plots, sync_bn):
|
||||
# Callback runs on train batch end
|
||||
# ni: number integrated batches (since train start)
|
||||
if self.plots:
|
||||
if plots:
|
||||
if ni == 0:
|
||||
if not sync_bn: # tb.add_graph() --sync known issue https://github.com/ultralytics/yolov5/issues/3754
|
||||
with warnings.catch_warnings():
|
||||
warnings.simplefilter('ignore') # suppress jit trace warning
|
||||
self.tb.add_graph(torch.jit.trace(de_parallel(model), imgs[0:1], strict=False), [])
|
||||
if ni < 3:
|
||||
f = self.save_dir / f'train_batch{ni}.jpg' # filename
|
||||
plot_images(imgs, targets, paths, f)
|
||||
if ni == 0 and self.tb and not self.opt.sync_bn:
|
||||
log_tensorboard_graph(self.tb, model, imgsz=(self.opt.imgsz, self.opt.imgsz))
|
||||
if ni == 10 and (self.wandb or self.clearml):
|
||||
Thread(target=plot_images, args=(imgs, targets, paths, f), daemon=True).start()
|
||||
if self.wandb and ni == 10:
|
||||
files = sorted(self.save_dir.glob('train*.jpg'))
|
||||
if self.wandb:
|
||||
self.wandb.log({'Mosaics': [wandb.Image(str(f), caption=f.name) for f in files if f.exists()]})
|
||||
if self.clearml:
|
||||
self.clearml.log_debug_samples(files, title='Mosaics')
|
||||
|
||||
if self.comet_logger:
|
||||
self.comet_logger.on_train_batch_end(log_dict, step=ni)
|
||||
self.wandb.log({'Mosaics': [wandb.Image(str(f), caption=f.name) for f in files if f.exists()]})
|
||||
|
||||
def on_train_epoch_end(self, epoch):
|
||||
# Callback runs on train epoch end
|
||||
if self.wandb:
|
||||
self.wandb.current_epoch = epoch + 1
|
||||
|
||||
if self.comet_logger:
|
||||
self.comet_logger.on_train_epoch_end(epoch)
|
||||
|
||||
def on_val_start(self):
|
||||
if self.comet_logger:
|
||||
self.comet_logger.on_val_start()
|
||||
|
||||
def on_val_image_end(self, pred, predn, path, names, im):
|
||||
# Callback runs on val image end
|
||||
if self.wandb:
|
||||
self.wandb.val_one_image(pred, predn, path, names, im)
|
||||
if self.clearml:
|
||||
self.clearml.log_image_with_boxes(path, pred, names, im)
|
||||
|
||||
def on_val_batch_end(self, batch_i, im, targets, paths, shapes, out):
|
||||
if self.comet_logger:
|
||||
self.comet_logger.on_val_batch_end(batch_i, im, targets, paths, shapes, out)
|
||||
|
||||
def on_val_end(self, nt, tp, fp, p, r, f1, ap, ap50, ap_class, confusion_matrix):
|
||||
def on_val_end(self):
|
||||
# Callback runs on val end
|
||||
if self.wandb or self.clearml:
|
||||
files = sorted(self.save_dir.glob('val*.jpg'))
|
||||
if self.wandb:
|
||||
self.wandb.log({'Validation': [wandb.Image(str(f), caption=f.name) for f in files]})
|
||||
if self.clearml:
|
||||
self.clearml.log_debug_samples(files, title='Validation')
|
||||
|
||||
if self.comet_logger:
|
||||
self.comet_logger.on_val_end(nt, tp, fp, p, r, f1, ap, ap50, ap_class, confusion_matrix)
|
||||
files = sorted(self.save_dir.glob('val*.jpg'))
|
||||
self.wandb.log({"Validation": [wandb.Image(str(f), caption=f.name) for f in files]})
|
||||
|
||||
def on_fit_epoch_end(self, vals, epoch, best_fitness, fi):
|
||||
# Callback runs at the end of each fit (train+val) epoch
|
||||
x = dict(zip(self.keys, vals))
|
||||
x = {k: v for k, v in zip(self.keys, vals)} # dict
|
||||
if self.csv:
|
||||
file = self.save_dir / 'results.csv'
|
||||
n = len(x) + 1 # number of cols
|
||||
@ -232,170 +123,37 @@ class Loggers():
|
||||
if self.tb:
|
||||
for k, v in x.items():
|
||||
self.tb.add_scalar(k, v, epoch)
|
||||
elif self.clearml: # log to ClearML if TensorBoard not used
|
||||
for k, v in x.items():
|
||||
title, series = k.split('/')
|
||||
self.clearml.task.get_logger().report_scalar(title, series, v, epoch)
|
||||
|
||||
if self.wandb:
|
||||
if best_fitness == fi:
|
||||
best_results = [epoch] + vals[3:7]
|
||||
for i, name in enumerate(self.best_keys):
|
||||
self.wandb.wandb_run.summary[name] = best_results[i] # log best results in the summary
|
||||
self.wandb.log(x)
|
||||
self.wandb.end_epoch()
|
||||
|
||||
if self.clearml:
|
||||
self.clearml.current_epoch_logged_images = set() # reset epoch image limit
|
||||
self.clearml.current_epoch += 1
|
||||
|
||||
if self.comet_logger:
|
||||
self.comet_logger.on_fit_epoch_end(x, epoch=epoch)
|
||||
self.wandb.end_epoch(best_result=best_fitness == fi)
|
||||
|
||||
def on_model_save(self, last, epoch, final_epoch, best_fitness, fi):
|
||||
# Callback runs on model save event
|
||||
if (epoch + 1) % self.opt.save_period == 0 and not final_epoch and self.opt.save_period != -1:
|
||||
if self.wandb:
|
||||
if self.wandb:
|
||||
if ((epoch + 1) % self.opt.save_period == 0 and not final_epoch) and self.opt.save_period != -1:
|
||||
self.wandb.log_model(last.parent, self.opt, epoch, fi, best_model=best_fitness == fi)
|
||||
if self.clearml:
|
||||
self.clearml.task.update_output_model(model_path=str(last),
|
||||
model_name='Latest Model',
|
||||
auto_delete_file=False)
|
||||
|
||||
if self.comet_logger:
|
||||
self.comet_logger.on_model_save(last, epoch, final_epoch, best_fitness, fi)
|
||||
|
||||
def on_train_end(self, last, best, epoch, results):
|
||||
# Callback runs on training end, i.e. saving best model
|
||||
if self.plots:
|
||||
def on_train_end(self, last, best, plots, epoch, results):
|
||||
# Callback runs on training end
|
||||
if plots:
|
||||
plot_results(file=self.save_dir / 'results.csv') # save results.png
|
||||
files = ['results.png', 'confusion_matrix.png', *(f'{x}_curve.png' for x in ('F1', 'PR', 'P', 'R'))]
|
||||
files = [(self.save_dir / f) for f in files if (self.save_dir / f).exists()] # filter
|
||||
self.logger.info(f"Results saved to {colorstr('bold', self.save_dir)}")
|
||||
|
||||
if self.tb and not self.clearml: # These images are already captured by ClearML by now, we don't want doubles
|
||||
if self.tb:
|
||||
import cv2
|
||||
for f in files:
|
||||
self.tb.add_image(f.stem, cv2.imread(str(f))[..., ::-1], epoch, dataformats='HWC')
|
||||
|
||||
if self.wandb:
|
||||
self.wandb.log(dict(zip(self.keys[3:10], results)))
|
||||
self.wandb.log({'Results': [wandb.Image(str(f), caption=f.name) for f in files]})
|
||||
self.wandb.log({"Results": [wandb.Image(str(f), caption=f.name) for f in files]})
|
||||
# Calling wandb.log. TODO: Refactor this into WandbLogger.log_model
|
||||
if not self.opt.evolve:
|
||||
wandb.log_artifact(str(best if best.exists() else last),
|
||||
type='model',
|
||||
name=f'run_{self.wandb.wandb_run.id}_model',
|
||||
wandb.log_artifact(str(best if best.exists() else last), type='model',
|
||||
name='run_' + self.wandb.wandb_run.id + '_model',
|
||||
aliases=['latest', 'best', 'stripped'])
|
||||
self.wandb.finish_run()
|
||||
|
||||
if self.clearml and not self.opt.evolve:
|
||||
self.clearml.task.update_output_model(model_path=str(best if best.exists() else last),
|
||||
name='Best Model',
|
||||
auto_delete_file=False)
|
||||
|
||||
if self.comet_logger:
|
||||
final_results = dict(zip(self.keys[3:10], results))
|
||||
self.comet_logger.on_train_end(files, self.save_dir, last, best, epoch, final_results)
|
||||
|
||||
def on_params_update(self, params: dict):
|
||||
# Update hyperparams or configs of the experiment
|
||||
if self.wandb:
|
||||
self.wandb.wandb_run.config.update(params, allow_val_change=True)
|
||||
if self.comet_logger:
|
||||
self.comet_logger.on_params_update(params)
|
||||
|
||||
|
||||
class GenericLogger:
|
||||
"""
|
||||
YOLOv5 General purpose logger for non-task specific logging
|
||||
Usage: from utils.loggers import GenericLogger; logger = GenericLogger(...)
|
||||
Arguments
|
||||
opt: Run arguments
|
||||
console_logger: Console logger
|
||||
include: loggers to include
|
||||
"""
|
||||
|
||||
def __init__(self, opt, console_logger, include=('tb', 'wandb')):
|
||||
# init default loggers
|
||||
self.save_dir = Path(opt.save_dir)
|
||||
self.include = include
|
||||
self.console_logger = console_logger
|
||||
self.csv = self.save_dir / 'results.csv' # CSV logger
|
||||
if 'tb' in self.include:
|
||||
prefix = colorstr('TensorBoard: ')
|
||||
self.console_logger.info(
|
||||
f"{prefix}Start with 'tensorboard --logdir {self.save_dir.parent}', view at http://localhost:6006/")
|
||||
self.tb = SummaryWriter(str(self.save_dir))
|
||||
|
||||
if wandb and 'wandb' in self.include:
|
||||
self.wandb = wandb.init(project=web_project_name(str(opt.project)),
|
||||
name=None if opt.name == 'exp' else opt.name,
|
||||
config=opt)
|
||||
else:
|
||||
self.wandb = None
|
||||
|
||||
def log_metrics(self, metrics, epoch):
|
||||
# Log metrics dictionary to all loggers
|
||||
if self.csv:
|
||||
keys, vals = list(metrics.keys()), list(metrics.values())
|
||||
n = len(metrics) + 1 # number of cols
|
||||
s = '' if self.csv.exists() else (('%23s,' * n % tuple(['epoch'] + keys)).rstrip(',') + '\n') # header
|
||||
with open(self.csv, 'a') as f:
|
||||
f.write(s + ('%23.5g,' * n % tuple([epoch] + vals)).rstrip(',') + '\n')
|
||||
|
||||
if self.tb:
|
||||
for k, v in metrics.items():
|
||||
self.tb.add_scalar(k, v, epoch)
|
||||
|
||||
if self.wandb:
|
||||
self.wandb.log(metrics, step=epoch)
|
||||
|
||||
def log_images(self, files, name='Images', epoch=0):
|
||||
# Log images to all loggers
|
||||
files = [Path(f) for f in (files if isinstance(files, (tuple, list)) else [files])] # to Path
|
||||
files = [f for f in files if f.exists()] # filter by exists
|
||||
|
||||
if self.tb:
|
||||
for f in files:
|
||||
self.tb.add_image(f.stem, cv2.imread(str(f))[..., ::-1], epoch, dataformats='HWC')
|
||||
|
||||
if self.wandb:
|
||||
self.wandb.log({name: [wandb.Image(str(f), caption=f.name) for f in files]}, step=epoch)
|
||||
|
||||
def log_graph(self, model, imgsz=(640, 640)):
|
||||
# Log model graph to all loggers
|
||||
if self.tb:
|
||||
log_tensorboard_graph(self.tb, model, imgsz)
|
||||
|
||||
def log_model(self, model_path, epoch=0, metadata={}):
|
||||
# Log model to all loggers
|
||||
if self.wandb:
|
||||
art = wandb.Artifact(name=f'run_{wandb.run.id}_model', type='model', metadata=metadata)
|
||||
art.add_file(str(model_path))
|
||||
wandb.log_artifact(art)
|
||||
|
||||
def update_params(self, params):
|
||||
# Update the parameters logged
|
||||
if self.wandb:
|
||||
wandb.run.config.update(params, allow_val_change=True)
|
||||
|
||||
|
||||
def log_tensorboard_graph(tb, model, imgsz=(640, 640)):
|
||||
# Log model graph to TensorBoard
|
||||
try:
|
||||
p = next(model.parameters()) # for device, type
|
||||
imgsz = (imgsz, imgsz) if isinstance(imgsz, int) else imgsz # expand
|
||||
im = torch.zeros((1, 3, *imgsz)).to(p.device).type_as(p) # input image (WARNING: must be zeros, not empty)
|
||||
with warnings.catch_warnings():
|
||||
warnings.simplefilter('ignore') # suppress jit trace warning
|
||||
tb.add_graph(torch.jit.trace(de_parallel(model), im, strict=False), [])
|
||||
except Exception as e:
|
||||
LOGGER.warning(f'WARNING ⚠️ TensorBoard graph visualization failure {e}')
|
||||
|
||||
|
||||
def web_project_name(project):
|
||||
# Convert local project name to web project name
|
||||
if not project.startswith('runs/train'):
|
||||
return project
|
||||
suffix = '-Classify' if project.endswith('-cls') else '-Segment' if project.endswith('-seg') else ''
|
||||
return f'YOLOv5{suffix}'
|
||||
self.wandb.finish_run()
|
||||
else:
|
||||
self.wandb.finish_run()
|
||||
self.wandb = WandbLogger(self.opt)
|
||||
|
||||
@ -1,32 +1,108 @@
|
||||
# YOLOv3 🚀 by Ultralytics, GPL-3.0 license
|
||||
|
||||
# WARNING ⚠️ wandb is deprecated and will be removed in future release.
|
||||
# See supported integrations at https://github.com/ultralytics/yolov5#integrations
|
||||
"""Utilities and tools for tracking runs with Weights & Biases."""
|
||||
|
||||
import logging
|
||||
import os
|
||||
import sys
|
||||
from contextlib import contextmanager
|
||||
from pathlib import Path
|
||||
from typing import Dict
|
||||
|
||||
from utils.general import LOGGER, colorstr
|
||||
import pkg_resources as pkg
|
||||
import yaml
|
||||
from tqdm import tqdm
|
||||
|
||||
FILE = Path(__file__).resolve()
|
||||
ROOT = FILE.parents[3] # YOLOv5 root directory
|
||||
ROOT = FILE.parents[3] # root directory
|
||||
if str(ROOT) not in sys.path:
|
||||
sys.path.append(str(ROOT)) # add ROOT to PATH
|
||||
RANK = int(os.getenv('RANK', -1))
|
||||
DEPRECATION_WARNING = f"{colorstr('wandb')}: WARNING ⚠️ wandb is deprecated and will be removed in a future release. " \
|
||||
f'See supported integrations at https://github.com/ultralytics/yolov5#integrations.'
|
||||
|
||||
from utils.datasets import LoadImagesAndLabels, img2label_paths
|
||||
from utils.general import LOGGER, check_dataset, check_file
|
||||
|
||||
try:
|
||||
import wandb
|
||||
|
||||
assert hasattr(wandb, '__version__') # verify package import not local dir
|
||||
LOGGER.warning(DEPRECATION_WARNING)
|
||||
except (ImportError, AssertionError):
|
||||
wandb = None
|
||||
|
||||
RANK = int(os.getenv('RANK', -1))
|
||||
WANDB_ARTIFACT_PREFIX = 'wandb-artifact://'
|
||||
|
||||
|
||||
def remove_prefix(from_string, prefix=WANDB_ARTIFACT_PREFIX):
|
||||
return from_string[len(prefix):]
|
||||
|
||||
|
||||
def check_wandb_config_file(data_config_file):
|
||||
wandb_config = '_wandb.'.join(data_config_file.rsplit('.', 1)) # updated data.yaml path
|
||||
if Path(wandb_config).is_file():
|
||||
return wandb_config
|
||||
return data_config_file
|
||||
|
||||
|
||||
def check_wandb_dataset(data_file):
|
||||
is_trainset_wandb_artifact = False
|
||||
is_valset_wandb_artifact = False
|
||||
if check_file(data_file) and data_file.endswith('.yaml'):
|
||||
with open(data_file, errors='ignore') as f:
|
||||
data_dict = yaml.safe_load(f)
|
||||
is_trainset_wandb_artifact = (isinstance(data_dict['train'], str) and
|
||||
data_dict['train'].startswith(WANDB_ARTIFACT_PREFIX))
|
||||
is_valset_wandb_artifact = (isinstance(data_dict['val'], str) and
|
||||
data_dict['val'].startswith(WANDB_ARTIFACT_PREFIX))
|
||||
if is_trainset_wandb_artifact or is_valset_wandb_artifact:
|
||||
return data_dict
|
||||
else:
|
||||
return check_dataset(data_file)
|
||||
|
||||
|
||||
def get_run_info(run_path):
|
||||
run_path = Path(remove_prefix(run_path, WANDB_ARTIFACT_PREFIX))
|
||||
run_id = run_path.stem
|
||||
project = run_path.parent.stem
|
||||
entity = run_path.parent.parent.stem
|
||||
model_artifact_name = 'run_' + run_id + '_model'
|
||||
return entity, project, run_id, model_artifact_name
|
||||
|
||||
|
||||
def check_wandb_resume(opt):
|
||||
process_wandb_config_ddp_mode(opt) if RANK not in [-1, 0] else None
|
||||
if isinstance(opt.resume, str):
|
||||
if opt.resume.startswith(WANDB_ARTIFACT_PREFIX):
|
||||
if RANK not in [-1, 0]: # For resuming DDP runs
|
||||
entity, project, run_id, model_artifact_name = get_run_info(opt.resume)
|
||||
api = wandb.Api()
|
||||
artifact = api.artifact(entity + '/' + project + '/' + model_artifact_name + ':latest')
|
||||
modeldir = artifact.download()
|
||||
opt.weights = str(Path(modeldir) / "last.pt")
|
||||
return True
|
||||
return None
|
||||
|
||||
|
||||
def process_wandb_config_ddp_mode(opt):
|
||||
with open(check_file(opt.data), errors='ignore') as f:
|
||||
data_dict = yaml.safe_load(f) # data dict
|
||||
train_dir, val_dir = None, None
|
||||
if isinstance(data_dict['train'], str) and data_dict['train'].startswith(WANDB_ARTIFACT_PREFIX):
|
||||
api = wandb.Api()
|
||||
train_artifact = api.artifact(remove_prefix(data_dict['train']) + ':' + opt.artifact_alias)
|
||||
train_dir = train_artifact.download()
|
||||
train_path = Path(train_dir) / 'data/images/'
|
||||
data_dict['train'] = str(train_path)
|
||||
|
||||
if isinstance(data_dict['val'], str) and data_dict['val'].startswith(WANDB_ARTIFACT_PREFIX):
|
||||
api = wandb.Api()
|
||||
val_artifact = api.artifact(remove_prefix(data_dict['val']) + ':' + opt.artifact_alias)
|
||||
val_dir = val_artifact.download()
|
||||
val_path = Path(val_dir) / 'data/images/'
|
||||
data_dict['val'] = str(val_path)
|
||||
if train_dir or val_dir:
|
||||
ddp_data_path = str(Path(val_dir) / 'wandb_local_data.yaml')
|
||||
with open(ddp_data_path, 'w') as f:
|
||||
yaml.safe_dump(data_dict, f)
|
||||
opt.data = ddp_data_path
|
||||
|
||||
|
||||
class WandbLogger():
|
||||
"""Log training runs, datasets, models, and predictions to Weights & Biases.
|
||||
@ -46,7 +122,7 @@ class WandbLogger():
|
||||
"""
|
||||
- Initialize WandbLogger instance
|
||||
- Upload dataset if opt.upload_dataset is True
|
||||
- Setup training processes if job_type is 'Training'
|
||||
- Setup trainig processes if job_type is 'Training'
|
||||
|
||||
arguments:
|
||||
opt (namespace) -- Commandline arguments for this run
|
||||
@ -56,31 +132,82 @@ class WandbLogger():
|
||||
"""
|
||||
# Pre-training routine --
|
||||
self.job_type = job_type
|
||||
self.wandb, self.wandb_run = wandb, wandb.run if wandb else None
|
||||
self.wandb, self.wandb_run = wandb, None if not wandb else wandb.run
|
||||
self.val_artifact, self.train_artifact = None, None
|
||||
self.train_artifact_path, self.val_artifact_path = None, None
|
||||
self.result_artifact = None
|
||||
self.val_table, self.result_table = None, None
|
||||
self.bbox_media_panel_images = []
|
||||
self.val_table_path_map = None
|
||||
self.max_imgs_to_log = 16
|
||||
self.wandb_artifact_data_dict = None
|
||||
self.data_dict = None
|
||||
if self.wandb:
|
||||
# It's more elegant to stick to 1 wandb.init call,
|
||||
# but useful config data is overwritten in the WandbLogger's wandb.init call
|
||||
if isinstance(opt.resume, str): # checks resume from artifact
|
||||
if opt.resume.startswith(WANDB_ARTIFACT_PREFIX):
|
||||
entity, project, run_id, model_artifact_name = get_run_info(opt.resume)
|
||||
model_artifact_name = WANDB_ARTIFACT_PREFIX + model_artifact_name
|
||||
assert wandb, 'install wandb to resume wandb runs'
|
||||
# Resume wandb-artifact:// runs here| workaround for not overwriting wandb.config
|
||||
self.wandb_run = wandb.init(id=run_id,
|
||||
project=project,
|
||||
entity=entity,
|
||||
resume='allow',
|
||||
allow_val_change=True)
|
||||
opt.resume = model_artifact_name
|
||||
elif self.wandb:
|
||||
self.wandb_run = wandb.init(config=opt,
|
||||
resume='allow',
|
||||
project='YOLOv5' if opt.project == 'runs/train' else Path(opt.project).stem,
|
||||
resume="allow",
|
||||
project='YOLOv3' if opt.project == 'runs/train' else Path(opt.project).stem,
|
||||
entity=opt.entity,
|
||||
name=opt.name if opt.name != 'exp' else None,
|
||||
job_type=job_type,
|
||||
id=run_id,
|
||||
allow_val_change=True) if not wandb.run else wandb.run
|
||||
|
||||
if self.wandb_run:
|
||||
if self.job_type == 'Training':
|
||||
if isinstance(opt.data, dict):
|
||||
# This means another dataset manager has already processed the dataset info (e.g. ClearML)
|
||||
# and they will have stored the already processed dict in opt.data
|
||||
self.data_dict = opt.data
|
||||
if opt.upload_dataset:
|
||||
if not opt.resume:
|
||||
self.wandb_artifact_data_dict = self.check_and_upload_dataset(opt)
|
||||
|
||||
if opt.resume:
|
||||
# resume from artifact
|
||||
if isinstance(opt.resume, str) and opt.resume.startswith(WANDB_ARTIFACT_PREFIX):
|
||||
self.data_dict = dict(self.wandb_run.config.data_dict)
|
||||
else: # local resume
|
||||
self.data_dict = check_wandb_dataset(opt.data)
|
||||
else:
|
||||
self.data_dict = check_wandb_dataset(opt.data)
|
||||
self.wandb_artifact_data_dict = self.wandb_artifact_data_dict or self.data_dict
|
||||
|
||||
# write data_dict to config. useful for resuming from artifacts. Do this only when not resuming.
|
||||
self.wandb_run.config.update({'data_dict': self.wandb_artifact_data_dict},
|
||||
allow_val_change=True)
|
||||
self.setup_training(opt)
|
||||
|
||||
if self.job_type == 'Dataset Creation':
|
||||
self.data_dict = self.check_and_upload_dataset(opt)
|
||||
|
||||
def check_and_upload_dataset(self, opt):
|
||||
"""
|
||||
Check if the dataset format is compatible and upload it as W&B artifact
|
||||
|
||||
arguments:
|
||||
opt (namespace)-- Commandline arguments for current run
|
||||
|
||||
returns:
|
||||
Updated dataset info dictionary where local dataset paths are replaced by WAND_ARFACT_PREFIX links.
|
||||
"""
|
||||
assert wandb, 'Install wandb to upload dataset'
|
||||
config_path = self.log_dataset_artifact(opt.data,
|
||||
opt.single_cls,
|
||||
'YOLOv3' if opt.project == 'runs/train' else Path(opt.project).stem)
|
||||
LOGGER.info(f"Created dataset config file {config_path}")
|
||||
with open(config_path, errors='ignore') as f:
|
||||
wandb_data_dict = yaml.safe_load(f)
|
||||
return wandb_data_dict
|
||||
|
||||
def setup_training(self, opt):
|
||||
"""
|
||||
Setup the necessary processes for training YOLO models:
|
||||
@ -95,18 +222,77 @@ class WandbLogger():
|
||||
self.log_dict, self.current_epoch = {}, 0
|
||||
self.bbox_interval = opt.bbox_interval
|
||||
if isinstance(opt.resume, str):
|
||||
model_dir, _ = self.download_model_artifact(opt)
|
||||
if model_dir:
|
||||
self.weights = Path(model_dir) / 'last.pt'
|
||||
modeldir, _ = self.download_model_artifact(opt)
|
||||
if modeldir:
|
||||
self.weights = Path(modeldir) / "last.pt"
|
||||
config = self.wandb_run.config
|
||||
opt.weights, opt.save_period, opt.batch_size, opt.bbox_interval, opt.epochs, opt.hyp, opt.imgsz = str(
|
||||
opt.weights, opt.save_period, opt.batch_size, opt.bbox_interval, opt.epochs, opt.hyp = str(
|
||||
self.weights), config.save_period, config.batch_size, config.bbox_interval, config.epochs, \
|
||||
config.hyp, config.imgsz
|
||||
config.hyp
|
||||
data_dict = self.data_dict
|
||||
if self.val_artifact is None: # If --upload_dataset is set, use the existing artifact, don't download
|
||||
self.train_artifact_path, self.train_artifact = self.download_dataset_artifact(data_dict.get('train'),
|
||||
opt.artifact_alias)
|
||||
self.val_artifact_path, self.val_artifact = self.download_dataset_artifact(data_dict.get('val'),
|
||||
opt.artifact_alias)
|
||||
|
||||
if self.train_artifact_path is not None:
|
||||
train_path = Path(self.train_artifact_path) / 'data/images/'
|
||||
data_dict['train'] = str(train_path)
|
||||
if self.val_artifact_path is not None:
|
||||
val_path = Path(self.val_artifact_path) / 'data/images/'
|
||||
data_dict['val'] = str(val_path)
|
||||
|
||||
if self.val_artifact is not None:
|
||||
self.result_artifact = wandb.Artifact("run_" + wandb.run.id + "_progress", "evaluation")
|
||||
self.result_table = wandb.Table(["epoch", "id", "ground truth", "prediction", "avg_confidence"])
|
||||
self.val_table = self.val_artifact.get("val")
|
||||
if self.val_table_path_map is None:
|
||||
self.map_val_table_path()
|
||||
if opt.bbox_interval == -1:
|
||||
self.bbox_interval = opt.bbox_interval = (opt.epochs // 10) if opt.epochs > 10 else 1
|
||||
if opt.evolve or opt.noplots:
|
||||
self.bbox_interval = opt.bbox_interval = opt.epochs + 1 # disable bbox_interval
|
||||
train_from_artifact = self.train_artifact_path is not None and self.val_artifact_path is not None
|
||||
# Update the the data_dict to point to local artifacts dir
|
||||
if train_from_artifact:
|
||||
self.data_dict = data_dict
|
||||
|
||||
def download_dataset_artifact(self, path, alias):
|
||||
"""
|
||||
download the model checkpoint artifact if the path starts with WANDB_ARTIFACT_PREFIX
|
||||
|
||||
arguments:
|
||||
path -- path of the dataset to be used for training
|
||||
alias (str)-- alias of the artifact to be download/used for training
|
||||
|
||||
returns:
|
||||
(str, wandb.Artifact) -- path of the downladed dataset and it's corresponding artifact object if dataset
|
||||
is found otherwise returns (None, None)
|
||||
"""
|
||||
if isinstance(path, str) and path.startswith(WANDB_ARTIFACT_PREFIX):
|
||||
artifact_path = Path(remove_prefix(path, WANDB_ARTIFACT_PREFIX) + ":" + alias)
|
||||
dataset_artifact = wandb.use_artifact(artifact_path.as_posix().replace("\\", "/"))
|
||||
assert dataset_artifact is not None, "'Error: W&B dataset artifact doesn\'t exist'"
|
||||
datadir = dataset_artifact.download()
|
||||
return datadir, dataset_artifact
|
||||
return None, None
|
||||
|
||||
def download_model_artifact(self, opt):
|
||||
"""
|
||||
download the model checkpoint artifact if the resume path starts with WANDB_ARTIFACT_PREFIX
|
||||
|
||||
arguments:
|
||||
opt (namespace) -- Commandline arguments for this run
|
||||
"""
|
||||
if opt.resume.startswith(WANDB_ARTIFACT_PREFIX):
|
||||
model_artifact = wandb.use_artifact(remove_prefix(opt.resume, WANDB_ARTIFACT_PREFIX) + ":latest")
|
||||
assert model_artifact is not None, 'Error: W&B model artifact doesn\'t exist'
|
||||
modeldir = model_artifact.download()
|
||||
epochs_trained = model_artifact.metadata.get('epochs_trained')
|
||||
total_epochs = model_artifact.metadata.get('total_epochs')
|
||||
is_finished = total_epochs is None
|
||||
assert not is_finished, 'training is finished, can only resume incomplete runs.'
|
||||
return modeldir, model_artifact
|
||||
return None, None
|
||||
|
||||
def log_model(self, path, opt, epoch, fitness_score, best_model=False):
|
||||
"""
|
||||
@ -119,22 +305,166 @@ class WandbLogger():
|
||||
fitness_score (float) -- fitness score for current epoch
|
||||
best_model (boolean) -- Boolean representing if the current checkpoint is the best yet.
|
||||
"""
|
||||
model_artifact = wandb.Artifact('run_' + wandb.run.id + '_model',
|
||||
type='model',
|
||||
metadata={
|
||||
'original_url': str(path),
|
||||
'epochs_trained': epoch + 1,
|
||||
'save period': opt.save_period,
|
||||
'project': opt.project,
|
||||
'total_epochs': opt.epochs,
|
||||
'fitness_score': fitness_score})
|
||||
model_artifact = wandb.Artifact('run_' + wandb.run.id + '_model', type='model', metadata={
|
||||
'original_url': str(path),
|
||||
'epochs_trained': epoch + 1,
|
||||
'save period': opt.save_period,
|
||||
'project': opt.project,
|
||||
'total_epochs': opt.epochs,
|
||||
'fitness_score': fitness_score
|
||||
})
|
||||
model_artifact.add_file(str(path / 'last.pt'), name='last.pt')
|
||||
wandb.log_artifact(model_artifact,
|
||||
aliases=['latest', 'last', 'epoch ' + str(self.current_epoch), 'best' if best_model else ''])
|
||||
LOGGER.info(f'Saving model artifact on epoch {epoch + 1}')
|
||||
LOGGER.info(f"Saving model artifact on epoch {epoch + 1}")
|
||||
|
||||
def log_dataset_artifact(self, data_file, single_cls, project, overwrite_config=False):
|
||||
"""
|
||||
Log the dataset as W&B artifact and return the new data file with W&B links
|
||||
|
||||
arguments:
|
||||
data_file (str) -- the .yaml file with information about the dataset like - path, classes etc.
|
||||
single_class (boolean) -- train multi-class data as single-class
|
||||
project (str) -- project name. Used to construct the artifact path
|
||||
overwrite_config (boolean) -- overwrites the data.yaml file if set to true otherwise creates a new
|
||||
file with _wandb postfix. Eg -> data_wandb.yaml
|
||||
|
||||
returns:
|
||||
the new .yaml file with artifact links. it can be used to start training directly from artifacts
|
||||
"""
|
||||
self.data_dict = check_dataset(data_file) # parse and check
|
||||
data = dict(self.data_dict)
|
||||
nc, names = (1, ['item']) if single_cls else (int(data['nc']), data['names'])
|
||||
names = {k: v for k, v in enumerate(names)} # to index dictionary
|
||||
self.train_artifact = self.create_dataset_table(LoadImagesAndLabels(
|
||||
data['train'], rect=True, batch_size=1), names, name='train') if data.get('train') else None
|
||||
self.val_artifact = self.create_dataset_table(LoadImagesAndLabels(
|
||||
data['val'], rect=True, batch_size=1), names, name='val') if data.get('val') else None
|
||||
if data.get('train'):
|
||||
data['train'] = WANDB_ARTIFACT_PREFIX + str(Path(project) / 'train')
|
||||
if data.get('val'):
|
||||
data['val'] = WANDB_ARTIFACT_PREFIX + str(Path(project) / 'val')
|
||||
path = Path(data_file).stem
|
||||
path = (path if overwrite_config else path + '_wandb') + '.yaml' # updated data.yaml path
|
||||
data.pop('download', None)
|
||||
data.pop('path', None)
|
||||
with open(path, 'w') as f:
|
||||
yaml.safe_dump(data, f)
|
||||
|
||||
if self.job_type == 'Training': # builds correct artifact pipeline graph
|
||||
self.wandb_run.use_artifact(self.val_artifact)
|
||||
self.wandb_run.use_artifact(self.train_artifact)
|
||||
self.val_artifact.wait()
|
||||
self.val_table = self.val_artifact.get('val')
|
||||
self.map_val_table_path()
|
||||
else:
|
||||
self.wandb_run.log_artifact(self.train_artifact)
|
||||
self.wandb_run.log_artifact(self.val_artifact)
|
||||
return path
|
||||
|
||||
def map_val_table_path(self):
|
||||
"""
|
||||
Map the validation dataset Table like name of file -> it's id in the W&B Table.
|
||||
Useful for - referencing artifacts for evaluation.
|
||||
"""
|
||||
self.val_table_path_map = {}
|
||||
LOGGER.info("Mapping dataset")
|
||||
for i, data in enumerate(tqdm(self.val_table.data)):
|
||||
self.val_table_path_map[data[3]] = data[0]
|
||||
|
||||
def create_dataset_table(self, dataset: LoadImagesAndLabels, class_to_id: Dict[int,str], name: str = 'dataset'):
|
||||
"""
|
||||
Create and return W&B artifact containing W&B Table of the dataset.
|
||||
|
||||
arguments:
|
||||
dataset -- instance of LoadImagesAndLabels class used to iterate over the data to build Table
|
||||
class_to_id -- hash map that maps class ids to labels
|
||||
name -- name of the artifact
|
||||
|
||||
returns:
|
||||
dataset artifact to be logged or used
|
||||
"""
|
||||
# TODO: Explore multiprocessing to slpit this loop parallely| This is essential for speeding up the the logging
|
||||
artifact = wandb.Artifact(name=name, type="dataset")
|
||||
img_files = tqdm([dataset.path]) if isinstance(dataset.path, str) and Path(dataset.path).is_dir() else None
|
||||
img_files = tqdm(dataset.img_files) if not img_files else img_files
|
||||
for img_file in img_files:
|
||||
if Path(img_file).is_dir():
|
||||
artifact.add_dir(img_file, name='data/images')
|
||||
labels_path = 'labels'.join(dataset.path.rsplit('images', 1))
|
||||
artifact.add_dir(labels_path, name='data/labels')
|
||||
else:
|
||||
artifact.add_file(img_file, name='data/images/' + Path(img_file).name)
|
||||
label_file = Path(img2label_paths([img_file])[0])
|
||||
artifact.add_file(str(label_file),
|
||||
name='data/labels/' + label_file.name) if label_file.exists() else None
|
||||
table = wandb.Table(columns=["id", "train_image", "Classes", "name"])
|
||||
class_set = wandb.Classes([{'id': id, 'name': name} for id, name in class_to_id.items()])
|
||||
for si, (img, labels, paths, shapes) in enumerate(tqdm(dataset)):
|
||||
box_data, img_classes = [], {}
|
||||
for cls, *xywh in labels[:, 1:].tolist():
|
||||
cls = int(cls)
|
||||
box_data.append({"position": {"middle": [xywh[0], xywh[1]], "width": xywh[2], "height": xywh[3]},
|
||||
"class_id": cls,
|
||||
"box_caption": "%s" % (class_to_id[cls])})
|
||||
img_classes[cls] = class_to_id[cls]
|
||||
boxes = {"ground_truth": {"box_data": box_data, "class_labels": class_to_id}} # inference-space
|
||||
table.add_data(si, wandb.Image(paths, classes=class_set, boxes=boxes), list(img_classes.values()),
|
||||
Path(paths).name)
|
||||
artifact.add(table, name)
|
||||
return artifact
|
||||
|
||||
def log_training_progress(self, predn, path, names):
|
||||
"""
|
||||
Build evaluation Table. Uses reference from validation dataset table.
|
||||
|
||||
arguments:
|
||||
predn (list): list of predictions in the native space in the format - [xmin, ymin, xmax, ymax, confidence, class]
|
||||
path (str): local path of the current evaluation image
|
||||
names (dict(int, str)): hash map that maps class ids to labels
|
||||
"""
|
||||
class_set = wandb.Classes([{'id': id, 'name': name} for id, name in names.items()])
|
||||
box_data = []
|
||||
total_conf = 0
|
||||
for *xyxy, conf, cls in predn.tolist():
|
||||
if conf >= 0.25:
|
||||
box_data.append(
|
||||
{"position": {"minX": xyxy[0], "minY": xyxy[1], "maxX": xyxy[2], "maxY": xyxy[3]},
|
||||
"class_id": int(cls),
|
||||
"box_caption": f"{names[cls]} {conf:.3f}",
|
||||
"scores": {"class_score": conf},
|
||||
"domain": "pixel"})
|
||||
total_conf += conf
|
||||
boxes = {"predictions": {"box_data": box_data, "class_labels": names}} # inference-space
|
||||
id = self.val_table_path_map[Path(path).name]
|
||||
self.result_table.add_data(self.current_epoch,
|
||||
id,
|
||||
self.val_table.data[id][1],
|
||||
wandb.Image(self.val_table.data[id][1], boxes=boxes, classes=class_set),
|
||||
total_conf / max(1, len(box_data))
|
||||
)
|
||||
|
||||
def val_one_image(self, pred, predn, path, names, im):
|
||||
pass
|
||||
"""
|
||||
Log validation data for one image. updates the result Table if validation dataset is uploaded and log bbox media panel
|
||||
|
||||
arguments:
|
||||
pred (list): list of scaled predictions in the format - [xmin, ymin, xmax, ymax, confidence, class]
|
||||
predn (list): list of predictions in the native space - [xmin, ymin, xmax, ymax, confidence, class]
|
||||
path (str): local path of the current evaluation image
|
||||
"""
|
||||
if self.val_table and self.result_table: # Log Table if Val dataset is uploaded as artifact
|
||||
self.log_training_progress(predn, path, names)
|
||||
|
||||
if len(self.bbox_media_panel_images) < self.max_imgs_to_log and self.current_epoch > 0:
|
||||
if self.current_epoch % self.bbox_interval == 0:
|
||||
box_data = [{"position": {"minX": xyxy[0], "minY": xyxy[1], "maxX": xyxy[2], "maxY": xyxy[3]},
|
||||
"class_id": int(cls),
|
||||
"box_caption": f"{names[cls]} {conf:.3f}",
|
||||
"scores": {"class_score": conf},
|
||||
"domain": "pixel"} for *xyxy, conf, cls in pred.tolist()]
|
||||
boxes = {"predictions": {"box_data": box_data, "class_labels": names}} # inference-space
|
||||
self.bbox_media_panel_images.append(wandb.Image(im, boxes=boxes, caption=path.name))
|
||||
|
||||
def log(self, log_dict):
|
||||
"""
|
||||
@ -147,7 +477,7 @@ class WandbLogger():
|
||||
for key, value in log_dict.items():
|
||||
self.log_dict[key] = value
|
||||
|
||||
def end_epoch(self):
|
||||
def end_epoch(self, best_result=False):
|
||||
"""
|
||||
commit the log_dict, model artifacts and Tables to W&B and flush the log_dict.
|
||||
|
||||
@ -156,15 +486,25 @@ class WandbLogger():
|
||||
"""
|
||||
if self.wandb_run:
|
||||
with all_logging_disabled():
|
||||
if self.bbox_media_panel_images:
|
||||
self.log_dict["BoundingBoxDebugger"] = self.bbox_media_panel_images
|
||||
try:
|
||||
wandb.log(self.log_dict)
|
||||
except BaseException as e:
|
||||
LOGGER.info(
|
||||
f'An error occurred in wandb logger. The training will proceed without interruption. More info\n{e}'
|
||||
)
|
||||
LOGGER.info(f"An error occurred in wandb logger. The training will proceed without interruption. More info\n{e}")
|
||||
self.wandb_run.finish()
|
||||
self.wandb_run = None
|
||||
|
||||
self.log_dict = {}
|
||||
self.bbox_media_panel_images = []
|
||||
if self.result_artifact:
|
||||
self.result_artifact.add(self.result_table, 'result')
|
||||
wandb.log_artifact(self.result_artifact, aliases=['latest', 'last', 'epoch ' + str(self.current_epoch),
|
||||
('best' if best_result else '')])
|
||||
|
||||
wandb.log({"evaluation": self.result_table})
|
||||
self.result_table = wandb.Table(["epoch", "id", "ground truth", "prediction", "avg_confidence"])
|
||||
self.result_artifact = wandb.Artifact("run_" + wandb.run.id + "_progress", "evaluation")
|
||||
|
||||
def finish_run(self):
|
||||
"""
|
||||
@ -175,7 +515,6 @@ class WandbLogger():
|
||||
with all_logging_disabled():
|
||||
wandb.log(self.log_dict)
|
||||
wandb.run.finish()
|
||||
LOGGER.warning(DEPRECATION_WARNING)
|
||||
|
||||
|
||||
@contextmanager
|
||||
|
||||
@ -11,8 +11,6 @@ import matplotlib.pyplot as plt
|
||||
import numpy as np
|
||||
import torch
|
||||
|
||||
from utils import TryExcept, threaded
|
||||
|
||||
|
||||
def fitness(x):
|
||||
# Model fitness as a weighted combination of metrics
|
||||
@ -20,15 +18,7 @@ def fitness(x):
|
||||
return (x[:, :4] * w).sum(1)
|
||||
|
||||
|
||||
def smooth(y, f=0.05):
|
||||
# Box filter of fraction f
|
||||
nf = round(len(y) * f * 2) // 2 + 1 # number of filter elements (must be odd)
|
||||
p = np.ones(nf // 2) # ones padding
|
||||
yp = np.concatenate((p * y[0], y, p * y[-1]), 0) # y padded
|
||||
return np.convolve(yp, np.ones(nf) / nf, mode='valid') # y-smoothed
|
||||
|
||||
|
||||
def ap_per_class(tp, conf, pred_cls, target_cls, plot=False, save_dir='.', names=(), eps=1e-16, prefix=''):
|
||||
def ap_per_class(tp, conf, pred_cls, target_cls, plot=False, save_dir='.', names=()):
|
||||
""" Compute the average precision, given the recall and precision curves.
|
||||
Source: https://github.com/rafaelpadilla/Object-Detection-Metrics.
|
||||
# Arguments
|
||||
@ -47,7 +37,7 @@ def ap_per_class(tp, conf, pred_cls, target_cls, plot=False, save_dir='.', names
|
||||
tp, conf, pred_cls = tp[i], conf[i], pred_cls[i]
|
||||
|
||||
# Find unique classes
|
||||
unique_classes, nt = np.unique(target_cls, return_counts=True)
|
||||
unique_classes = np.unique(target_cls)
|
||||
nc = unique_classes.shape[0] # number of classes, number of detections
|
||||
|
||||
# Create Precision-Recall curve and compute AP for each class
|
||||
@ -55,44 +45,42 @@ def ap_per_class(tp, conf, pred_cls, target_cls, plot=False, save_dir='.', names
|
||||
ap, p, r = np.zeros((nc, tp.shape[1])), np.zeros((nc, 1000)), np.zeros((nc, 1000))
|
||||
for ci, c in enumerate(unique_classes):
|
||||
i = pred_cls == c
|
||||
n_l = nt[ci] # number of labels
|
||||
n_l = (target_cls == c).sum() # number of labels
|
||||
n_p = i.sum() # number of predictions
|
||||
|
||||
if n_p == 0 or n_l == 0:
|
||||
continue
|
||||
else:
|
||||
# Accumulate FPs and TPs
|
||||
fpc = (1 - tp[i]).cumsum(0)
|
||||
tpc = tp[i].cumsum(0)
|
||||
|
||||
# Accumulate FPs and TPs
|
||||
fpc = (1 - tp[i]).cumsum(0)
|
||||
tpc = tp[i].cumsum(0)
|
||||
# Recall
|
||||
recall = tpc / (n_l + 1e-16) # recall curve
|
||||
r[ci] = np.interp(-px, -conf[i], recall[:, 0], left=0) # negative x, xp because xp decreases
|
||||
|
||||
# Recall
|
||||
recall = tpc / (n_l + eps) # recall curve
|
||||
r[ci] = np.interp(-px, -conf[i], recall[:, 0], left=0) # negative x, xp because xp decreases
|
||||
# Precision
|
||||
precision = tpc / (tpc + fpc) # precision curve
|
||||
p[ci] = np.interp(-px, -conf[i], precision[:, 0], left=1) # p at pr_score
|
||||
|
||||
# Precision
|
||||
precision = tpc / (tpc + fpc) # precision curve
|
||||
p[ci] = np.interp(-px, -conf[i], precision[:, 0], left=1) # p at pr_score
|
||||
|
||||
# AP from recall-precision curve
|
||||
for j in range(tp.shape[1]):
|
||||
ap[ci, j], mpre, mrec = compute_ap(recall[:, j], precision[:, j])
|
||||
if plot and j == 0:
|
||||
py.append(np.interp(px, mrec, mpre)) # precision at mAP@0.5
|
||||
# AP from recall-precision curve
|
||||
for j in range(tp.shape[1]):
|
||||
ap[ci, j], mpre, mrec = compute_ap(recall[:, j], precision[:, j])
|
||||
if plot and j == 0:
|
||||
py.append(np.interp(px, mrec, mpre)) # precision at mAP@0.5
|
||||
|
||||
# Compute F1 (harmonic mean of precision and recall)
|
||||
f1 = 2 * p * r / (p + r + eps)
|
||||
f1 = 2 * p * r / (p + r + 1e-16)
|
||||
names = [v for k, v in names.items() if k in unique_classes] # list: only classes that have data
|
||||
names = dict(enumerate(names)) # to dict
|
||||
names = {i: v for i, v in enumerate(names)} # to dict
|
||||
if plot:
|
||||
plot_pr_curve(px, py, ap, Path(save_dir) / f'{prefix}PR_curve.png', names)
|
||||
plot_mc_curve(px, f1, Path(save_dir) / f'{prefix}F1_curve.png', names, ylabel='F1')
|
||||
plot_mc_curve(px, p, Path(save_dir) / f'{prefix}P_curve.png', names, ylabel='Precision')
|
||||
plot_mc_curve(px, r, Path(save_dir) / f'{prefix}R_curve.png', names, ylabel='Recall')
|
||||
plot_pr_curve(px, py, ap, Path(save_dir) / 'PR_curve.png', names)
|
||||
plot_mc_curve(px, f1, Path(save_dir) / 'F1_curve.png', names, ylabel='F1')
|
||||
plot_mc_curve(px, p, Path(save_dir) / 'P_curve.png', names, ylabel='Precision')
|
||||
plot_mc_curve(px, r, Path(save_dir) / 'R_curve.png', names, ylabel='Recall')
|
||||
|
||||
i = smooth(f1.mean(0), 0.1).argmax() # max F1 index
|
||||
p, r, f1 = p[:, i], r[:, i], f1[:, i]
|
||||
tp = (r * nt).round() # true positives
|
||||
fp = (tp / (p + eps) - tp).round() # false positives
|
||||
return tp, fp, p, r, f1, ap, unique_classes.astype(int)
|
||||
i = f1.mean(0).argmax() # max F1 index
|
||||
return p[:, i], r[:, i], ap, f1[:, i], unique_classes.astype('int32')
|
||||
|
||||
|
||||
def compute_ap(recall, precision):
|
||||
@ -141,12 +129,6 @@ class ConfusionMatrix:
|
||||
Returns:
|
||||
None, updates confusion matrix accordingly
|
||||
"""
|
||||
if detections is None:
|
||||
gt_classes = labels.int()
|
||||
for gc in gt_classes:
|
||||
self.matrix[self.nc, gc] += 1 # background FN
|
||||
return
|
||||
|
||||
detections = detections[detections[:, 4] > self.conf]
|
||||
gt_classes = labels[:, 0].int()
|
||||
detection_classes = detections[:, 5].int()
|
||||
@ -164,55 +146,43 @@ class ConfusionMatrix:
|
||||
matches = np.zeros((0, 3))
|
||||
|
||||
n = matches.shape[0] > 0
|
||||
m0, m1, _ = matches.transpose().astype(int)
|
||||
m0, m1, _ = matches.transpose().astype(np.int16)
|
||||
for i, gc in enumerate(gt_classes):
|
||||
j = m0 == i
|
||||
if n and sum(j) == 1:
|
||||
self.matrix[detection_classes[m1[j]], gc] += 1 # correct
|
||||
else:
|
||||
self.matrix[self.nc, gc] += 1 # true background
|
||||
self.matrix[self.nc, gc] += 1 # background FP
|
||||
|
||||
if n:
|
||||
for i, dc in enumerate(detection_classes):
|
||||
if not any(m1 == i):
|
||||
self.matrix[dc, self.nc] += 1 # predicted background
|
||||
self.matrix[dc, self.nc] += 1 # background FN
|
||||
|
||||
def tp_fp(self):
|
||||
tp = self.matrix.diagonal() # true positives
|
||||
fp = self.matrix.sum(1) - tp # false positives
|
||||
# fn = self.matrix.sum(0) - tp # false negatives (missed detections)
|
||||
return tp[:-1], fp[:-1] # remove background class
|
||||
def matrix(self):
|
||||
return self.matrix
|
||||
|
||||
@TryExcept('WARNING ⚠️ ConfusionMatrix plot failure')
|
||||
def plot(self, normalize=True, save_dir='', names=()):
|
||||
import seaborn as sn
|
||||
try:
|
||||
import seaborn as sn
|
||||
|
||||
array = self.matrix / ((self.matrix.sum(0).reshape(1, -1) + 1E-9) if normalize else 1) # normalize columns
|
||||
array[array < 0.005] = np.nan # don't annotate (would appear as 0.00)
|
||||
array = self.matrix / ((self.matrix.sum(0).reshape(1, -1) + 1E-6) if normalize else 1) # normalize columns
|
||||
array[array < 0.005] = np.nan # don't annotate (would appear as 0.00)
|
||||
|
||||
fig, ax = plt.subplots(1, 1, figsize=(12, 9), tight_layout=True)
|
||||
nc, nn = self.nc, len(names) # number of classes, names
|
||||
sn.set(font_scale=1.0 if nc < 50 else 0.8) # for label size
|
||||
labels = (0 < nn < 99) and (nn == nc) # apply names to ticklabels
|
||||
ticklabels = (names + ['background']) if labels else 'auto'
|
||||
with warnings.catch_warnings():
|
||||
warnings.simplefilter('ignore') # suppress empty matrix RuntimeWarning: All-NaN slice encountered
|
||||
sn.heatmap(array,
|
||||
ax=ax,
|
||||
annot=nc < 30,
|
||||
annot_kws={
|
||||
'size': 8},
|
||||
cmap='Blues',
|
||||
fmt='.2f',
|
||||
square=True,
|
||||
vmin=0.0,
|
||||
xticklabels=ticklabels,
|
||||
yticklabels=ticklabels).set_facecolor((1, 1, 1))
|
||||
ax.set_xlabel('True')
|
||||
ax.set_ylabel('Predicted')
|
||||
ax.set_title('Confusion Matrix')
|
||||
fig.savefig(Path(save_dir) / 'confusion_matrix.png', dpi=250)
|
||||
plt.close(fig)
|
||||
fig = plt.figure(figsize=(12, 9), tight_layout=True)
|
||||
sn.set(font_scale=1.0 if self.nc < 50 else 0.8) # for label size
|
||||
labels = (0 < len(names) < 99) and len(names) == self.nc # apply names to ticklabels
|
||||
with warnings.catch_warnings():
|
||||
warnings.simplefilter('ignore') # suppress empty matrix RuntimeWarning: All-NaN slice encountered
|
||||
sn.heatmap(array, annot=self.nc < 30, annot_kws={"size": 8}, cmap='Blues', fmt='.2f', square=True,
|
||||
xticklabels=names + ['background FP'] if labels else "auto",
|
||||
yticklabels=names + ['background FN'] if labels else "auto").set_facecolor((1, 1, 1))
|
||||
fig.axes[0].set_xlabel('True')
|
||||
fig.axes[0].set_ylabel('Predicted')
|
||||
fig.savefig(Path(save_dir) / 'confusion_matrix.png', dpi=250)
|
||||
plt.close()
|
||||
except Exception as e:
|
||||
print(f'WARNING: ConfusionMatrix plot failure: {e}')
|
||||
|
||||
def print(self):
|
||||
for i in range(self.nc + 1):
|
||||
@ -224,19 +194,19 @@ def bbox_iou(box1, box2, xywh=True, GIoU=False, DIoU=False, CIoU=False, eps=1e-7
|
||||
|
||||
# Get the coordinates of bounding boxes
|
||||
if xywh: # transform from xywh to xyxy
|
||||
(x1, y1, w1, h1), (x2, y2, w2, h2) = box1.chunk(4, -1), box2.chunk(4, -1)
|
||||
(x1, y1, w1, h1), (x2, y2, w2, h2) = box1.chunk(4, 1), box2.chunk(4, 1)
|
||||
w1_, h1_, w2_, h2_ = w1 / 2, h1 / 2, w2 / 2, h2 / 2
|
||||
b1_x1, b1_x2, b1_y1, b1_y2 = x1 - w1_, x1 + w1_, y1 - h1_, y1 + h1_
|
||||
b2_x1, b2_x2, b2_y1, b2_y2 = x2 - w2_, x2 + w2_, y2 - h2_, y2 + h2_
|
||||
else: # x1, y1, x2, y2 = box1
|
||||
b1_x1, b1_y1, b1_x2, b1_y2 = box1.chunk(4, -1)
|
||||
b2_x1, b2_y1, b2_x2, b2_y2 = box2.chunk(4, -1)
|
||||
w1, h1 = b1_x2 - b1_x1, (b1_y2 - b1_y1).clamp(eps)
|
||||
w2, h2 = b2_x2 - b2_x1, (b2_y2 - b2_y1).clamp(eps)
|
||||
b1_x1, b1_y1, b1_x2, b1_y2 = box1.chunk(4, 1)
|
||||
b2_x1, b2_y1, b2_x2, b2_y2 = box2.chunk(4, 1)
|
||||
w1, h1 = b1_x2 - b1_x1, b1_y2 - b1_y1 + eps
|
||||
w2, h2 = b2_x2 - b2_x1, b2_y2 - b2_y1 + eps
|
||||
|
||||
# Intersection area
|
||||
inter = (b1_x2.minimum(b2_x2) - b1_x1.maximum(b2_x1)).clamp(0) * \
|
||||
(b1_y2.minimum(b2_y2) - b1_y1.maximum(b2_y1)).clamp(0)
|
||||
inter = (torch.min(b1_x2, b2_x2) - torch.max(b1_x1, b2_x1)).clamp(0) * \
|
||||
(torch.min(b1_y2, b2_y2) - torch.max(b1_y1, b2_y1)).clamp(0)
|
||||
|
||||
# Union Area
|
||||
union = w1 * h1 + w2 * h2 - inter + eps
|
||||
@ -244,13 +214,13 @@ def bbox_iou(box1, box2, xywh=True, GIoU=False, DIoU=False, CIoU=False, eps=1e-7
|
||||
# IoU
|
||||
iou = inter / union
|
||||
if CIoU or DIoU or GIoU:
|
||||
cw = b1_x2.maximum(b2_x2) - b1_x1.minimum(b2_x1) # convex (smallest enclosing box) width
|
||||
ch = b1_y2.maximum(b2_y2) - b1_y1.minimum(b2_y1) # convex height
|
||||
cw = torch.max(b1_x2, b2_x2) - torch.min(b1_x1, b2_x1) # convex (smallest enclosing box) width
|
||||
ch = torch.max(b1_y2, b2_y2) - torch.min(b1_y1, b2_y1) # convex height
|
||||
if CIoU or DIoU: # Distance or Complete IoU https://arxiv.org/abs/1911.08287v1
|
||||
c2 = cw ** 2 + ch ** 2 + eps # convex diagonal squared
|
||||
rho2 = ((b2_x1 + b2_x2 - b1_x1 - b1_x2) ** 2 + (b2_y1 + b2_y2 - b1_y1 - b1_y2) ** 2) / 4 # center dist ** 2
|
||||
if CIoU: # https://github.com/Zzh-tju/DIoU-SSD-pytorch/blob/master/utils/box/box_utils.py#L47
|
||||
v = (4 / math.pi ** 2) * (torch.atan(w2 / h2) - torch.atan(w1 / h1)).pow(2)
|
||||
v = (4 / math.pi ** 2) * torch.pow(torch.atan(w2 / h2) - torch.atan(w1 / h1), 2)
|
||||
with torch.no_grad():
|
||||
alpha = v / (v - iou + (1 + eps))
|
||||
return iou - (rho2 / c2 + v * alpha) # CIoU
|
||||
@ -260,7 +230,7 @@ def bbox_iou(box1, box2, xywh=True, GIoU=False, DIoU=False, CIoU=False, eps=1e-7
|
||||
return iou # IoU
|
||||
|
||||
|
||||
def box_iou(box1, box2, eps=1e-7):
|
||||
def box_iou(box1, box2):
|
||||
# https://github.com/pytorch/vision/blob/master/torchvision/ops/boxes.py
|
||||
"""
|
||||
Return intersection-over-union (Jaccard index) of boxes.
|
||||
@ -273,24 +243,30 @@ def box_iou(box1, box2, eps=1e-7):
|
||||
IoU values for every element in boxes1 and boxes2
|
||||
"""
|
||||
|
||||
def box_area(box):
|
||||
# box = 4xn
|
||||
return (box[2] - box[0]) * (box[3] - box[1])
|
||||
|
||||
area1 = box_area(box1.T)
|
||||
area2 = box_area(box2.T)
|
||||
|
||||
# inter(N,M) = (rb(N,M,2) - lt(N,M,2)).clamp(0).prod(2)
|
||||
(a1, a2), (b1, b2) = box1.unsqueeze(1).chunk(2, 2), box2.unsqueeze(0).chunk(2, 2)
|
||||
inter = (torch.min(a2, b2) - torch.max(a1, b1)).clamp(0).prod(2)
|
||||
|
||||
# IoU = inter / (area1 + area2 - inter)
|
||||
return inter / ((a2 - a1).prod(2) + (b2 - b1).prod(2) - inter + eps)
|
||||
inter = (torch.min(box1[:, None, 2:], box2[:, 2:]) - torch.max(box1[:, None, :2], box2[:, :2])).clamp(0).prod(2)
|
||||
return inter / (area1[:, None] + area2 - inter) # iou = inter / (area1 + area2 - inter)
|
||||
|
||||
|
||||
def bbox_ioa(box1, box2, eps=1e-7):
|
||||
def bbox_ioa(box1, box2, eps=1E-7):
|
||||
""" Returns the intersection over box2 area given box1, box2. Boxes are x1y1x2y2
|
||||
box1: np.array of shape(4)
|
||||
box2: np.array of shape(nx4)
|
||||
returns: np.array of shape(n)
|
||||
"""
|
||||
|
||||
box2 = box2.transpose()
|
||||
|
||||
# Get the coordinates of bounding boxes
|
||||
b1_x1, b1_y1, b1_x2, b1_y2 = box1
|
||||
b2_x1, b2_y1, b2_x2, b2_y2 = box2.T
|
||||
b1_x1, b1_y1, b1_x2, b1_y2 = box1[0], box1[1], box1[2], box1[3]
|
||||
b2_x1, b2_y1, b2_x2, b2_y2 = box2[0], box2[1], box2[2], box2[3]
|
||||
|
||||
# Intersection area
|
||||
inter_area = (np.minimum(b1_x2, b2_x2) - np.maximum(b1_x1, b2_x1)).clip(0) * \
|
||||
@ -303,19 +279,17 @@ def bbox_ioa(box1, box2, eps=1e-7):
|
||||
return inter_area / box2_area
|
||||
|
||||
|
||||
def wh_iou(wh1, wh2, eps=1e-7):
|
||||
def wh_iou(wh1, wh2):
|
||||
# Returns the nxm IoU matrix. wh1 is nx2, wh2 is mx2
|
||||
wh1 = wh1[:, None] # [N,1,2]
|
||||
wh2 = wh2[None] # [1,M,2]
|
||||
inter = torch.min(wh1, wh2).prod(2) # [N,M]
|
||||
return inter / (wh1.prod(2) + wh2.prod(2) - inter + eps) # iou = inter / (area1 + area2 - inter)
|
||||
return inter / (wh1.prod(2) + wh2.prod(2) - inter) # iou = inter / (area1 + area2 - inter)
|
||||
|
||||
|
||||
# Plots ----------------------------------------------------------------------------------------------------------------
|
||||
|
||||
|
||||
@threaded
|
||||
def plot_pr_curve(px, py, ap, save_dir=Path('pr_curve.png'), names=()):
|
||||
def plot_pr_curve(px, py, ap, save_dir='pr_curve.png', names=()):
|
||||
# Precision-recall curve
|
||||
fig, ax = plt.subplots(1, 1, figsize=(9, 6), tight_layout=True)
|
||||
py = np.stack(py, axis=1)
|
||||
@ -331,14 +305,12 @@ def plot_pr_curve(px, py, ap, save_dir=Path('pr_curve.png'), names=()):
|
||||
ax.set_ylabel('Precision')
|
||||
ax.set_xlim(0, 1)
|
||||
ax.set_ylim(0, 1)
|
||||
ax.legend(bbox_to_anchor=(1.04, 1), loc='upper left')
|
||||
ax.set_title('Precision-Recall Curve')
|
||||
fig.savefig(save_dir, dpi=250)
|
||||
plt.close(fig)
|
||||
plt.legend(bbox_to_anchor=(1.04, 1), loc="upper left")
|
||||
fig.savefig(Path(save_dir), dpi=250)
|
||||
plt.close()
|
||||
|
||||
|
||||
@threaded
|
||||
def plot_mc_curve(px, py, save_dir=Path('mc_curve.png'), names=(), xlabel='Confidence', ylabel='Metric'):
|
||||
def plot_mc_curve(px, py, save_dir='mc_curve.png', names=(), xlabel='Confidence', ylabel='Metric'):
|
||||
# Metric-confidence curve
|
||||
fig, ax = plt.subplots(1, 1, figsize=(9, 6), tight_layout=True)
|
||||
|
||||
@ -348,13 +320,12 @@ def plot_mc_curve(px, py, save_dir=Path('mc_curve.png'), names=(), xlabel='Confi
|
||||
else:
|
||||
ax.plot(px, py.T, linewidth=1, color='grey') # plot(confidence, metric)
|
||||
|
||||
y = smooth(py.mean(0), 0.05)
|
||||
y = py.mean(0)
|
||||
ax.plot(px, y, linewidth=3, color='blue', label=f'all classes {y.max():.2f} at {px[y.argmax()]:.3f}')
|
||||
ax.set_xlabel(xlabel)
|
||||
ax.set_ylabel(ylabel)
|
||||
ax.set_xlim(0, 1)
|
||||
ax.set_ylim(0, 1)
|
||||
ax.legend(bbox_to_anchor=(1.04, 1), loc='upper left')
|
||||
ax.set_title(f'{ylabel}-Confidence Curve')
|
||||
fig.savefig(save_dir, dpi=250)
|
||||
plt.close(fig)
|
||||
plt.legend(bbox_to_anchor=(1.04, 1), loc="upper left")
|
||||
fig.savefig(Path(save_dir), dpi=250)
|
||||
plt.close()
|
||||
|
||||
@ -3,12 +3,10 @@
|
||||
Plotting utils
|
||||
"""
|
||||
|
||||
import contextlib
|
||||
import math
|
||||
import os
|
||||
from copy import copy
|
||||
from pathlib import Path
|
||||
from urllib.error import URLError
|
||||
|
||||
import cv2
|
||||
import matplotlib
|
||||
@ -19,13 +17,12 @@ import seaborn as sn
|
||||
import torch
|
||||
from PIL import Image, ImageDraw, ImageFont
|
||||
|
||||
from utils import TryExcept, threaded
|
||||
from utils.general import (CONFIG_DIR, FONT, LOGGER, check_font, check_requirements, clip_boxes, increment_path,
|
||||
is_ascii, xywh2xyxy, xyxy2xywh)
|
||||
from utils.general import (LOGGER, Timeout, check_requirements, clip_coords, increment_path, is_ascii, is_chinese,
|
||||
try_except, user_config_dir, xywh2xyxy, xyxy2xywh)
|
||||
from utils.metrics import fitness
|
||||
from utils.segment.general import scale_image
|
||||
|
||||
# Settings
|
||||
CONFIG_DIR = user_config_dir() # Ultralytics settings dir
|
||||
RANK = int(os.getenv('RANK', -1))
|
||||
matplotlib.rc('font', **{'size': 11})
|
||||
matplotlib.use('Agg') # for writing to files only
|
||||
@ -35,9 +32,9 @@ class Colors:
|
||||
# Ultralytics color palette https://ultralytics.com/
|
||||
def __init__(self):
|
||||
# hex = matplotlib.colors.TABLEAU_COLORS.values()
|
||||
hexs = ('FF3838', 'FF9D97', 'FF701F', 'FFB21D', 'CFD231', '48F90A', '92CC17', '3DDB86', '1A9334', '00D4BB',
|
||||
'2C99A8', '00C2FF', '344593', '6473FF', '0018EC', '8438FF', '520085', 'CB38FF', 'FF95C8', 'FF37C7')
|
||||
self.palette = [self.hex2rgb(f'#{c}') for c in hexs]
|
||||
hex = ('FF3838', 'FF9D97', 'FF701F', 'FFB21D', 'CFD231', '48F90A', '92CC17', '3DDB86', '1A9334', '00D4BB',
|
||||
'2C99A8', '00C2FF', '344593', '6473FF', '0018EC', '8438FF', '520085', 'CB38FF', 'FF95C8', 'FF37C7')
|
||||
self.palette = [self.hex2rgb('#' + c) for c in hex]
|
||||
self.n = len(self.palette)
|
||||
|
||||
def __call__(self, i, bgr=False):
|
||||
@ -52,33 +49,35 @@ class Colors:
|
||||
colors = Colors() # create instance for 'from utils.plots import colors'
|
||||
|
||||
|
||||
def check_pil_font(font=FONT, size=10):
|
||||
def check_font(font='Arial.ttf', size=10):
|
||||
# Return a PIL TrueType Font, downloading to CONFIG_DIR if necessary
|
||||
font = Path(font)
|
||||
font = font if font.exists() else (CONFIG_DIR / font.name)
|
||||
try:
|
||||
return ImageFont.truetype(str(font) if font.exists() else font.name, size)
|
||||
except Exception: # download if missing
|
||||
except Exception as e: # download if missing
|
||||
url = "https://ultralytics.com/assets/" + font.name
|
||||
print(f'Downloading {url} to {font}...')
|
||||
torch.hub.download_url_to_file(url, str(font), progress=False)
|
||||
try:
|
||||
check_font(font)
|
||||
return ImageFont.truetype(str(font), size)
|
||||
except TypeError:
|
||||
check_requirements('Pillow>=8.4.0') # known issue https://github.com/ultralytics/yolov5/issues/5374
|
||||
except URLError: # not online
|
||||
return ImageFont.load_default()
|
||||
|
||||
|
||||
class Annotator:
|
||||
# YOLOv3 Annotator for train/val mosaics and jpgs and detect/hub inference annotations
|
||||
if RANK in (-1, 0):
|
||||
check_font() # download TTF if necessary
|
||||
|
||||
# Annotator for train/val mosaics and jpgs and detect/hub inference annotations
|
||||
def __init__(self, im, line_width=None, font_size=None, font='Arial.ttf', pil=False, example='abc'):
|
||||
assert im.data.contiguous, 'Image not contiguous. Apply np.ascontiguousarray(im) to Annotator() input images.'
|
||||
non_ascii = not is_ascii(example) # non-latin labels, i.e. asian, arabic, cyrillic
|
||||
self.pil = pil or non_ascii
|
||||
self.pil = pil or not is_ascii(example) or is_chinese(example)
|
||||
if self.pil: # use PIL
|
||||
self.im = im if isinstance(im, Image.Image) else Image.fromarray(im)
|
||||
self.draw = ImageDraw.Draw(self.im)
|
||||
self.font = check_pil_font(font='Arial.Unicode.ttf' if non_ascii else font,
|
||||
size=font_size or max(round(sum(self.im.size) / 2 * 0.035), 12))
|
||||
self.font = check_font(font='Arial.Unicode.ttf' if is_chinese(example) else font,
|
||||
size=font_size or max(round(sum(self.im.size) / 2 * 0.035), 12))
|
||||
else: # use cv2
|
||||
self.im = im
|
||||
self.lw = line_width or max(round(sum(im.shape) / 2 * 0.003), 2) # line width
|
||||
@ -88,14 +87,12 @@ class Annotator:
|
||||
if self.pil or not is_ascii(label):
|
||||
self.draw.rectangle(box, width=self.lw, outline=color) # box
|
||||
if label:
|
||||
w, h = self.font.getsize(label) # text width, height (WARNING: deprecated) in 9.2.0
|
||||
# _, _, w, h = self.font.getbbox(label) # text width, height (New)
|
||||
w, h = self.font.getsize(label) # text width, height
|
||||
outside = box[1] - h >= 0 # label fits outside box
|
||||
self.draw.rectangle(
|
||||
(box[0], box[1] - h if outside else box[1], box[0] + w + 1,
|
||||
box[1] + 1 if outside else box[1] + h + 1),
|
||||
fill=color,
|
||||
)
|
||||
self.draw.rectangle([box[0],
|
||||
box[1] - h if outside else box[1],
|
||||
box[0] + w + 1,
|
||||
box[1] + 1 if outside else box[1] + h + 1], fill=color)
|
||||
# self.draw.text((box[0], box[1]), label, fill=txt_color, font=self.font, anchor='ls') # for PIL>8.0
|
||||
self.draw.text((box[0], box[1] - h if outside else box[1]), label, fill=txt_color, font=self.font)
|
||||
else: # cv2
|
||||
@ -104,62 +101,20 @@ class Annotator:
|
||||
if label:
|
||||
tf = max(self.lw - 1, 1) # font thickness
|
||||
w, h = cv2.getTextSize(label, 0, fontScale=self.lw / 3, thickness=tf)[0] # text width, height
|
||||
outside = p1[1] - h >= 3
|
||||
outside = p1[1] - h - 3 >= 0 # label fits outside box
|
||||
p2 = p1[0] + w, p1[1] - h - 3 if outside else p1[1] + h + 3
|
||||
cv2.rectangle(self.im, p1, p2, color, -1, cv2.LINE_AA) # filled
|
||||
cv2.putText(self.im,
|
||||
label, (p1[0], p1[1] - 2 if outside else p1[1] + h + 2),
|
||||
0,
|
||||
self.lw / 3,
|
||||
txt_color,
|
||||
thickness=tf,
|
||||
lineType=cv2.LINE_AA)
|
||||
|
||||
def masks(self, masks, colors, im_gpu, alpha=0.5, retina_masks=False):
|
||||
"""Plot masks at once.
|
||||
Args:
|
||||
masks (tensor): predicted masks on cuda, shape: [n, h, w]
|
||||
colors (List[List[Int]]): colors for predicted masks, [[r, g, b] * n]
|
||||
im_gpu (tensor): img is in cuda, shape: [3, h, w], range: [0, 1]
|
||||
alpha (float): mask transparency: 0.0 fully transparent, 1.0 opaque
|
||||
"""
|
||||
if self.pil:
|
||||
# convert to numpy first
|
||||
self.im = np.asarray(self.im).copy()
|
||||
if len(masks) == 0:
|
||||
self.im[:] = im_gpu.permute(1, 2, 0).contiguous().cpu().numpy() * 255
|
||||
colors = torch.tensor(colors, device=im_gpu.device, dtype=torch.float32) / 255.0
|
||||
colors = colors[:, None, None] # shape(n,1,1,3)
|
||||
masks = masks.unsqueeze(3) # shape(n,h,w,1)
|
||||
masks_color = masks * (colors * alpha) # shape(n,h,w,3)
|
||||
|
||||
inv_alph_masks = (1 - masks * alpha).cumprod(0) # shape(n,h,w,1)
|
||||
mcs = (masks_color * inv_alph_masks).sum(0) * 2 # mask color summand shape(n,h,w,3)
|
||||
|
||||
im_gpu = im_gpu.flip(dims=[0]) # flip channel
|
||||
im_gpu = im_gpu.permute(1, 2, 0).contiguous() # shape(h,w,3)
|
||||
im_gpu = im_gpu * inv_alph_masks[-1] + mcs
|
||||
im_mask = (im_gpu * 255).byte().cpu().numpy()
|
||||
self.im[:] = im_mask if retina_masks else scale_image(im_gpu.shape, im_mask, self.im.shape)
|
||||
if self.pil:
|
||||
# convert im back to PIL and update draw
|
||||
self.fromarray(self.im)
|
||||
cv2.putText(self.im, label, (p1[0], p1[1] - 2 if outside else p1[1] + h + 2), 0, self.lw / 3, txt_color,
|
||||
thickness=tf, lineType=cv2.LINE_AA)
|
||||
|
||||
def rectangle(self, xy, fill=None, outline=None, width=1):
|
||||
# Add rectangle to image (PIL-only)
|
||||
self.draw.rectangle(xy, fill, outline, width)
|
||||
|
||||
def text(self, xy, text, txt_color=(255, 255, 255), anchor='top'):
|
||||
def text(self, xy, text, txt_color=(255, 255, 255)):
|
||||
# Add text to image (PIL-only)
|
||||
if anchor == 'bottom': # start y from font bottom
|
||||
w, h = self.font.getsize(text) # text width, height
|
||||
xy[1] += 1 - h
|
||||
self.draw.text(xy, text, fill=txt_color, font=self.font)
|
||||
|
||||
def fromarray(self, im):
|
||||
# Update self.im from a numpy array
|
||||
self.im = im if isinstance(im, Image.Image) else Image.fromarray(im)
|
||||
self.draw = ImageDraw.Draw(self.im)
|
||||
w, h = self.font.getsize(text) # text width, height
|
||||
self.draw.text((xy[0], xy[1] - h + 1), text, fill=txt_color, font=self.font)
|
||||
|
||||
def result(self):
|
||||
# Return annotated image as array
|
||||
@ -177,7 +132,7 @@ def feature_visualization(x, module_type, stage, n=32, save_dir=Path('runs/detec
|
||||
if 'Detect' not in module_type:
|
||||
batch, channels, height, width = x.shape # batch, channels, height, width
|
||||
if height > 1 and width > 1:
|
||||
f = save_dir / f"stage{stage}_{module_type.split('.')[-1]}_features.png" # filename
|
||||
f = f"stage{stage}_{module_type.split('.')[-1]}_features.png" # filename
|
||||
|
||||
blocks = torch.chunk(x[0].cpu(), channels, dim=0) # select batch index 0, block by channels
|
||||
n = min(n, channels) # number of plots
|
||||
@ -188,10 +143,9 @@ def feature_visualization(x, module_type, stage, n=32, save_dir=Path('runs/detec
|
||||
ax[i].imshow(blocks[i].squeeze()) # cmap='gray'
|
||||
ax[i].axis('off')
|
||||
|
||||
LOGGER.info(f'Saving {f}... ({n}/{channels})')
|
||||
plt.savefig(f, dpi=300, bbox_inches='tight')
|
||||
print(f'Saving {save_dir / f}... ({n}/{channels})')
|
||||
plt.savefig(save_dir / f, dpi=300, bbox_inches='tight')
|
||||
plt.close()
|
||||
np.save(str(f.with_suffix('.npy')), x[0].cpu().numpy()) # npy save
|
||||
|
||||
|
||||
def hist2d(x, y, n=100):
|
||||
@ -216,31 +170,26 @@ def butter_lowpass_filtfilt(data, cutoff=1500, fs=50000, order=5):
|
||||
return filtfilt(b, a, data) # forward-backward filter
|
||||
|
||||
|
||||
def output_to_target(output, max_det=300):
|
||||
# Convert model output to target format [batch_id, class_id, x, y, w, h, conf] for plotting
|
||||
def output_to_target(output):
|
||||
# Convert model output to target format [batch_id, class_id, x, y, w, h, conf]
|
||||
targets = []
|
||||
for i, o in enumerate(output):
|
||||
box, conf, cls = o[:max_det, :6].cpu().split((4, 1, 1), 1)
|
||||
j = torch.full((conf.shape[0], 1), i)
|
||||
targets.append(torch.cat((j, cls, xyxy2xywh(box), conf), 1))
|
||||
return torch.cat(targets, 0).numpy()
|
||||
for *box, conf, cls in o.cpu().numpy():
|
||||
targets.append([i, cls, *list(*xyxy2xywh(np.array(box)[None])), conf])
|
||||
return np.array(targets)
|
||||
|
||||
|
||||
@threaded
|
||||
def plot_images(images, targets, paths=None, fname='images.jpg', names=None):
|
||||
def plot_images(images, targets, paths=None, fname='images.jpg', names=None, max_size=1920, max_subplots=16):
|
||||
# Plot image grid with labels
|
||||
if isinstance(images, torch.Tensor):
|
||||
images = images.cpu().float().numpy()
|
||||
if isinstance(targets, torch.Tensor):
|
||||
targets = targets.cpu().numpy()
|
||||
|
||||
max_size = 1920 # max image size
|
||||
max_subplots = 16 # max image subplots, i.e. 4x4
|
||||
if np.max(images[0]) <= 1:
|
||||
images *= 255 # de-normalise (optional)
|
||||
bs, _, h, w = images.shape # batch size, _, height, width
|
||||
bs = min(bs, max_subplots) # limit plot images
|
||||
ns = np.ceil(bs ** 0.5) # number of subplots (square)
|
||||
if np.max(images[0]) <= 1:
|
||||
images *= 255 # de-normalise (optional)
|
||||
|
||||
# Build Image
|
||||
mosaic = np.full((int(ns * h), int(ns * w), 3), 255, dtype=np.uint8) # init
|
||||
@ -260,12 +209,12 @@ def plot_images(images, targets, paths=None, fname='images.jpg', names=None):
|
||||
|
||||
# Annotate
|
||||
fs = int((h + w) * ns * 0.01) # font size
|
||||
annotator = Annotator(mosaic, line_width=round(fs / 10), font_size=fs, pil=True, example=names)
|
||||
annotator = Annotator(mosaic, line_width=round(fs / 10), font_size=fs, pil=True)
|
||||
for i in range(i + 1):
|
||||
x, y = int(w * (i // ns)), int(h * (i % ns)) # block origin
|
||||
annotator.rectangle([x, y, x + w, y + h], None, (255, 255, 255), width=2) # borders
|
||||
if paths:
|
||||
annotator.text((x + 5, y + 5), text=Path(paths[i]).name[:40], txt_color=(220, 220, 220)) # filenames
|
||||
annotator.text((x + 5, y + 5 + h), text=Path(paths[i]).name[:40], txt_color=(220, 220, 220)) # filenames
|
||||
if len(targets) > 0:
|
||||
ti = targets[targets[:, 0] == i] # image targets
|
||||
boxes = xywh2xyxy(ti[:, 2:6]).T
|
||||
@ -346,7 +295,7 @@ def plot_val_study(file='', dir='', x=None): # from utils.plots import *; plot_
|
||||
ax = plt.subplots(2, 4, figsize=(10, 6), tight_layout=True)[1].ravel()
|
||||
|
||||
fig2, ax2 = plt.subplots(1, 1, figsize=(8, 4), tight_layout=True)
|
||||
# for f in [save_dir / f'study_coco_{x}.txt' for x in ['yolov5n6', 'yolov5s6', 'yolov5m6', 'yolov5l6', 'yolov5x6']]:
|
||||
# for f in [save_dir / f'study_coco_{x}.txt' for x in ['yolov3', 'yolov3-spp', 'yolov3-tiny']]:
|
||||
for f in sorted(save_dir.glob('study*.txt')):
|
||||
y = np.loadtxt(f, dtype=np.float32, usecols=[0, 1, 2, 3, 7, 8, 9], ndmin=2).T
|
||||
x = np.arange(y.shape[1]) if x is None else np.array(x)
|
||||
@ -357,19 +306,11 @@ def plot_val_study(file='', dir='', x=None): # from utils.plots import *; plot_
|
||||
ax[i].set_title(s[i])
|
||||
|
||||
j = y[3].argmax() + 1
|
||||
ax2.plot(y[5, 1:j],
|
||||
y[3, 1:j] * 1E2,
|
||||
'.-',
|
||||
linewidth=2,
|
||||
markersize=8,
|
||||
ax2.plot(y[5, 1:j], y[3, 1:j] * 1E2, '.-', linewidth=2, markersize=8,
|
||||
label=f.stem.replace('study_coco_', '').replace('yolo', 'YOLO'))
|
||||
|
||||
ax2.plot(1E3 / np.array([209, 140, 97, 58, 35, 18]), [34.6, 40.5, 43.0, 47.5, 49.7, 51.5],
|
||||
'k.-',
|
||||
linewidth=2,
|
||||
markersize=8,
|
||||
alpha=.25,
|
||||
label='EfficientDet')
|
||||
'k.-', linewidth=2, markersize=8, alpha=.25, label='EfficientDet')
|
||||
|
||||
ax2.grid(alpha=0.2)
|
||||
ax2.set_yticks(np.arange(20, 60, 5))
|
||||
@ -383,7 +324,8 @@ def plot_val_study(file='', dir='', x=None): # from utils.plots import *; plot_
|
||||
plt.savefig(f, dpi=300)
|
||||
|
||||
|
||||
@TryExcept() # known issue https://github.com/ultralytics/yolov5/issues/5395
|
||||
@try_except # known issue https://github.com/ultralytics/yolov5/issues/5395
|
||||
@Timeout(30) # known issue https://github.com/ultralytics/yolov5/issues/5611
|
||||
def plot_labels(labels, names=(), save_dir=Path('')):
|
||||
# plot dataset labels
|
||||
LOGGER.info(f"Plotting labels to {save_dir / 'labels.jpg'}... ")
|
||||
@ -400,12 +342,11 @@ def plot_labels(labels, names=(), save_dir=Path('')):
|
||||
matplotlib.use('svg') # faster
|
||||
ax = plt.subplots(2, 2, figsize=(8, 8), tight_layout=True)[1].ravel()
|
||||
y = ax[0].hist(c, bins=np.linspace(0, nc, nc + 1) - 0.5, rwidth=0.8)
|
||||
with contextlib.suppress(Exception): # color histogram bars by class
|
||||
[y[2].patches[i].set_color([x / 255 for x in colors(i)]) for i in range(nc)] # known issue #3195
|
||||
# [y[2].patches[i].set_color([x / 255 for x in colors(i)]) for i in range(nc)] # update colors bug #3195
|
||||
ax[0].set_ylabel('instances')
|
||||
if 0 < len(names) < 30:
|
||||
ax[0].set_xticks(range(len(names)))
|
||||
ax[0].set_xticklabels(list(names.values()), rotation=90, fontsize=10)
|
||||
ax[0].set_xticklabels(names, rotation=90, fontsize=10)
|
||||
else:
|
||||
ax[0].set_xlabel('classes')
|
||||
sn.histplot(x, x='x', y='y', ax=ax[2], bins=50, pmax=0.9)
|
||||
@ -429,35 +370,6 @@ def plot_labels(labels, names=(), save_dir=Path('')):
|
||||
plt.close()
|
||||
|
||||
|
||||
def imshow_cls(im, labels=None, pred=None, names=None, nmax=25, verbose=False, f=Path('images.jpg')):
|
||||
# Show classification image grid with labels (optional) and predictions (optional)
|
||||
from utils.augmentations import denormalize
|
||||
|
||||
names = names or [f'class{i}' for i in range(1000)]
|
||||
blocks = torch.chunk(denormalize(im.clone()).cpu().float(), len(im),
|
||||
dim=0) # select batch index 0, block by channels
|
||||
n = min(len(blocks), nmax) # number of plots
|
||||
m = min(8, round(n ** 0.5)) # 8 x 8 default
|
||||
fig, ax = plt.subplots(math.ceil(n / m), m) # 8 rows x n/8 cols
|
||||
ax = ax.ravel() if m > 1 else [ax]
|
||||
# plt.subplots_adjust(wspace=0.05, hspace=0.05)
|
||||
for i in range(n):
|
||||
ax[i].imshow(blocks[i].squeeze().permute((1, 2, 0)).numpy().clip(0.0, 1.0))
|
||||
ax[i].axis('off')
|
||||
if labels is not None:
|
||||
s = names[labels[i]] + (f'—{names[pred[i]]}' if pred is not None else '')
|
||||
ax[i].set_title(s, fontsize=8, verticalalignment='top')
|
||||
plt.savefig(f, dpi=300, bbox_inches='tight')
|
||||
plt.close()
|
||||
if verbose:
|
||||
LOGGER.info(f'Saving {f}')
|
||||
if labels is not None:
|
||||
LOGGER.info('True: ' + ' '.join(f'{names[i]:3s}' for i in labels[:nmax]))
|
||||
if pred is not None:
|
||||
LOGGER.info('Predicted:' + ' '.join(f'{names[i]:3s}' for i in pred[:nmax]))
|
||||
return f
|
||||
|
||||
|
||||
def plot_evolve(evolve_csv='path/to/evolve.csv'): # from utils.plots import *; plot_evolve()
|
||||
# Plot evolve.csv hyp evolution results
|
||||
evolve_csv = Path(evolve_csv)
|
||||
@ -468,7 +380,6 @@ def plot_evolve(evolve_csv='path/to/evolve.csv'): # from utils.plots import *;
|
||||
j = np.argmax(f) # max fitness index
|
||||
plt.figure(figsize=(10, 12), tight_layout=True)
|
||||
matplotlib.rc('font', **{'size': 8})
|
||||
print(f'Best results from row {j} of {evolve_csv}:')
|
||||
for i, k in enumerate(keys[7:]):
|
||||
v = x[:, 7 + i]
|
||||
mu = v[j] # best single result
|
||||
@ -492,20 +403,20 @@ def plot_results(file='path/to/results.csv', dir=''):
|
||||
ax = ax.ravel()
|
||||
files = list(save_dir.glob('results*.csv'))
|
||||
assert len(files), f'No results.csv files found in {save_dir.resolve()}, nothing to plot.'
|
||||
for f in files:
|
||||
for fi, f in enumerate(files):
|
||||
try:
|
||||
data = pd.read_csv(f)
|
||||
s = [x.strip() for x in data.columns]
|
||||
x = data.values[:, 0]
|
||||
for i, j in enumerate([1, 2, 3, 4, 5, 8, 9, 10, 6, 7]):
|
||||
y = data.values[:, j].astype('float')
|
||||
y = data.values[:, j]
|
||||
# y[y == 0] = np.nan # don't show zero values
|
||||
ax[i].plot(x, y, marker='.', label=f.stem, linewidth=2, markersize=8)
|
||||
ax[i].set_title(s[j], fontsize=12)
|
||||
# if j in [8, 9, 10]: # share train and val loss y axes
|
||||
# ax[i].get_shared_y_axes().join(ax[i], ax[i - 5])
|
||||
except Exception as e:
|
||||
LOGGER.info(f'Warning: Plotting error for {f}: {e}')
|
||||
print(f'Warning: Plotting error for {f}: {e}')
|
||||
ax[1].legend()
|
||||
fig.savefig(save_dir / 'results.png', dpi=200)
|
||||
plt.close()
|
||||
@ -542,7 +453,7 @@ def profile_idetection(start=0, stop=0, labels=(), save_dir=''):
|
||||
plt.savefig(Path(save_dir) / 'idetection_profile.png', dpi=200)
|
||||
|
||||
|
||||
def save_one_box(xyxy, im, file=Path('im.jpg'), gain=1.02, pad=10, square=False, BGR=False, save=True):
|
||||
def save_one_box(xyxy, im, file='image.jpg', gain=1.02, pad=10, square=False, BGR=False, save=True):
|
||||
# Save image crop as {file} with crop size multiple {gain} and {pad} pixels. Save and/or return crop
|
||||
xyxy = torch.tensor(xyxy).view(-1, 4)
|
||||
b = xyxy2xywh(xyxy) # boxes
|
||||
@ -550,11 +461,9 @@ def save_one_box(xyxy, im, file=Path('im.jpg'), gain=1.02, pad=10, square=False,
|
||||
b[:, 2:] = b[:, 2:].max(1)[0].unsqueeze(1) # attempt rectangle to square
|
||||
b[:, 2:] = b[:, 2:] * gain + pad # box wh * gain + pad
|
||||
xyxy = xywh2xyxy(b).long()
|
||||
clip_boxes(xyxy, im.shape)
|
||||
clip_coords(xyxy, im.shape)
|
||||
crop = im[int(xyxy[0, 1]):int(xyxy[0, 3]), int(xyxy[0, 0]):int(xyxy[0, 2]), ::(1 if BGR else -1)]
|
||||
if save:
|
||||
file.parent.mkdir(parents=True, exist_ok=True) # make directory
|
||||
f = str(increment_path(file).with_suffix('.jpg'))
|
||||
# cv2.imwrite(f, crop) # save BGR, https://github.com/ultralytics/yolov5/issues/7007 chroma subsampling issue
|
||||
Image.fromarray(crop[..., ::-1]).save(f, quality=95, subsampling=0) # save RGB
|
||||
cv2.imwrite(str(increment_path(file).with_suffix('.jpg')), crop)
|
||||
return crop
|
||||
|
||||
@ -3,12 +3,12 @@
|
||||
PyTorch utils
|
||||
"""
|
||||
|
||||
import datetime
|
||||
import math
|
||||
import os
|
||||
import platform
|
||||
import subprocess
|
||||
import time
|
||||
import warnings
|
||||
from contextlib import contextmanager
|
||||
from copy import deepcopy
|
||||
from pathlib import Path
|
||||
@ -17,77 +17,20 @@ import torch
|
||||
import torch.distributed as dist
|
||||
import torch.nn as nn
|
||||
import torch.nn.functional as F
|
||||
from torch.nn.parallel import DistributedDataParallel as DDP
|
||||
|
||||
from utils.general import LOGGER, check_version, colorstr, file_date, git_describe
|
||||
|
||||
LOCAL_RANK = int(os.getenv('LOCAL_RANK', -1)) # https://pytorch.org/docs/stable/elastic/run.html
|
||||
RANK = int(os.getenv('RANK', -1))
|
||||
WORLD_SIZE = int(os.getenv('WORLD_SIZE', 1))
|
||||
from utils.general import LOGGER
|
||||
|
||||
try:
|
||||
import thop # for FLOPs computation
|
||||
except ImportError:
|
||||
thop = None
|
||||
|
||||
# Suppress PyTorch warnings
|
||||
warnings.filterwarnings('ignore', message='User provided device_type of \'cuda\', but CUDA is not available. Disabling')
|
||||
warnings.filterwarnings('ignore', category=UserWarning)
|
||||
|
||||
|
||||
def smart_inference_mode(torch_1_9=check_version(torch.__version__, '1.9.0')):
|
||||
# Applies torch.inference_mode() decorator if torch>=1.9.0 else torch.no_grad() decorator
|
||||
def decorate(fn):
|
||||
return (torch.inference_mode if torch_1_9 else torch.no_grad)()(fn)
|
||||
|
||||
return decorate
|
||||
|
||||
|
||||
def smartCrossEntropyLoss(label_smoothing=0.0):
|
||||
# Returns nn.CrossEntropyLoss with label smoothing enabled for torch>=1.10.0
|
||||
if check_version(torch.__version__, '1.10.0'):
|
||||
return nn.CrossEntropyLoss(label_smoothing=label_smoothing)
|
||||
if label_smoothing > 0:
|
||||
LOGGER.warning(f'WARNING ⚠️ label smoothing {label_smoothing} requires torch>=1.10.0')
|
||||
return nn.CrossEntropyLoss()
|
||||
|
||||
|
||||
def smart_DDP(model):
|
||||
# Model DDP creation with checks
|
||||
assert not check_version(torch.__version__, '1.12.0', pinned=True), \
|
||||
'torch==1.12.0 torchvision==0.13.0 DDP training is not supported due to a known issue. ' \
|
||||
'Please upgrade or downgrade torch to use DDP. See https://github.com/ultralytics/yolov5/issues/8395'
|
||||
if check_version(torch.__version__, '1.11.0'):
|
||||
return DDP(model, device_ids=[LOCAL_RANK], output_device=LOCAL_RANK, static_graph=True)
|
||||
else:
|
||||
return DDP(model, device_ids=[LOCAL_RANK], output_device=LOCAL_RANK)
|
||||
|
||||
|
||||
def reshape_classifier_output(model, n=1000):
|
||||
# Update a TorchVision classification model to class count 'n' if required
|
||||
from models.common import Classify
|
||||
name, m = list((model.model if hasattr(model, 'model') else model).named_children())[-1] # last module
|
||||
if isinstance(m, Classify): # YOLOv3 Classify() head
|
||||
if m.linear.out_features != n:
|
||||
m.linear = nn.Linear(m.linear.in_features, n)
|
||||
elif isinstance(m, nn.Linear): # ResNet, EfficientNet
|
||||
if m.out_features != n:
|
||||
setattr(model, name, nn.Linear(m.in_features, n))
|
||||
elif isinstance(m, nn.Sequential):
|
||||
types = [type(x) for x in m]
|
||||
if nn.Linear in types:
|
||||
i = types.index(nn.Linear) # nn.Linear index
|
||||
if m[i].out_features != n:
|
||||
m[i] = nn.Linear(m[i].in_features, n)
|
||||
elif nn.Conv2d in types:
|
||||
i = types.index(nn.Conv2d) # nn.Conv2d index
|
||||
if m[i].out_channels != n:
|
||||
m[i] = nn.Conv2d(m[i].in_channels, n, m[i].kernel_size, m[i].stride, bias=m[i].bias is not None)
|
||||
|
||||
|
||||
@contextmanager
|
||||
def torch_distributed_zero_first(local_rank: int):
|
||||
# Decorator to make all processes in distributed training wait for each local_master to do something
|
||||
"""
|
||||
Decorator to make all processes in distributed training wait for each local_master to do something.
|
||||
"""
|
||||
if local_rank not in [-1, 0]:
|
||||
dist.barrier(device_ids=[local_rank])
|
||||
yield
|
||||
@ -95,70 +38,69 @@ def torch_distributed_zero_first(local_rank: int):
|
||||
dist.barrier(device_ids=[0])
|
||||
|
||||
|
||||
def device_count():
|
||||
# Returns number of CUDA devices available. Safe version of torch.cuda.device_count(). Supports Linux and Windows
|
||||
assert platform.system() in ('Linux', 'Windows'), 'device_count() only supported on Linux or Windows'
|
||||
def date_modified(path=__file__):
|
||||
# return human-readable file modification date, i.e. '2021-3-26'
|
||||
t = datetime.datetime.fromtimestamp(Path(path).stat().st_mtime)
|
||||
return f'{t.year}-{t.month}-{t.day}'
|
||||
|
||||
|
||||
def git_describe(path=Path(__file__).parent): # path must be a directory
|
||||
# return human-readable git description, i.e. v5.0-5-g3e25f1e https://git-scm.com/docs/git-describe
|
||||
s = f'git -C {path} describe --tags --long --always'
|
||||
try:
|
||||
cmd = 'nvidia-smi -L | wc -l' if platform.system() == 'Linux' else 'nvidia-smi -L | find /c /v ""' # Windows
|
||||
return int(subprocess.run(cmd, shell=True, capture_output=True, check=True).stdout.decode().split()[-1])
|
||||
except Exception:
|
||||
return 0
|
||||
return subprocess.check_output(s, shell=True, stderr=subprocess.STDOUT).decode()[:-1]
|
||||
except subprocess.CalledProcessError as e:
|
||||
return '' # not a git repository
|
||||
|
||||
|
||||
def select_device(device='', batch_size=0, newline=True):
|
||||
# device = None or 'cpu' or 0 or '0' or '0,1,2,3'
|
||||
s = f'YOLOv3 🚀 {git_describe() or file_date()} Python-{platform.python_version()} torch-{torch.__version__} '
|
||||
device = str(device).strip().lower().replace('cuda:', '').replace('none', '') # to string, 'cuda:0' to '0'
|
||||
def select_device(device='', batch_size=None, newline=True):
|
||||
# device = 'cpu' or '0' or '0,1,2,3'
|
||||
s = f'YOLOv3 🚀 {git_describe() or date_modified()} torch {torch.__version__} ' # string
|
||||
device = str(device).strip().lower().replace('cuda:', '') # to string, 'cuda:0' to '0'
|
||||
cpu = device == 'cpu'
|
||||
mps = device == 'mps' # Apple Metal Performance Shaders (MPS)
|
||||
if cpu or mps:
|
||||
if cpu:
|
||||
os.environ['CUDA_VISIBLE_DEVICES'] = '-1' # force torch.cuda.is_available() = False
|
||||
elif device: # non-cpu device requested
|
||||
os.environ['CUDA_VISIBLE_DEVICES'] = device # set environment variable - must be before assert is_available()
|
||||
assert torch.cuda.is_available() and torch.cuda.device_count() >= len(device.replace(',', '')), \
|
||||
f"Invalid CUDA '--device {device}' requested, use '--device cpu' or pass valid CUDA device(s)"
|
||||
os.environ['CUDA_VISIBLE_DEVICES'] = device # set environment variable
|
||||
assert torch.cuda.is_available(), f'CUDA unavailable, invalid device {device} requested' # check availability
|
||||
|
||||
if not cpu and not mps and torch.cuda.is_available(): # prefer GPU if available
|
||||
cuda = not cpu and torch.cuda.is_available()
|
||||
if cuda:
|
||||
devices = device.split(',') if device else '0' # range(torch.cuda.device_count()) # i.e. 0,1,6,7
|
||||
n = len(devices) # device count
|
||||
if n > 1 and batch_size > 0: # check batch_size is divisible by device_count
|
||||
if n > 1 and batch_size: # check batch_size is divisible by device_count
|
||||
assert batch_size % n == 0, f'batch-size {batch_size} not multiple of GPU count {n}'
|
||||
space = ' ' * (len(s) + 1)
|
||||
for i, d in enumerate(devices):
|
||||
p = torch.cuda.get_device_properties(i)
|
||||
s += f"{'' if i == 0 else space}CUDA:{d} ({p.name}, {p.total_memory / (1 << 20):.0f}MiB)\n" # bytes to MB
|
||||
arg = 'cuda:0'
|
||||
elif mps and getattr(torch, 'has_mps', False) and torch.backends.mps.is_available(): # prefer MPS if available
|
||||
s += 'MPS\n'
|
||||
arg = 'mps'
|
||||
else: # revert to CPU
|
||||
s += f"{'' if i == 0 else space}CUDA:{d} ({p.name}, {p.total_memory / 1024 ** 2:.0f}MiB)\n" # bytes to MB
|
||||
else:
|
||||
s += 'CPU\n'
|
||||
arg = 'cpu'
|
||||
|
||||
if not newline:
|
||||
s = s.rstrip()
|
||||
LOGGER.info(s)
|
||||
return torch.device(arg)
|
||||
LOGGER.info(s.encode().decode('ascii', 'ignore') if platform.system() == 'Windows' else s) # emoji-safe
|
||||
return torch.device('cuda:0' if cuda else 'cpu')
|
||||
|
||||
|
||||
def time_sync():
|
||||
# PyTorch-accurate time
|
||||
# pytorch-accurate time
|
||||
if torch.cuda.is_available():
|
||||
torch.cuda.synchronize()
|
||||
return time.time()
|
||||
|
||||
|
||||
def profile(input, ops, n=10, device=None):
|
||||
""" YOLOv3 speed/memory/FLOPs profiler
|
||||
Usage:
|
||||
input = torch.randn(16, 3, 640, 640)
|
||||
m1 = lambda x: x * torch.sigmoid(x)
|
||||
m2 = nn.SiLU()
|
||||
profile(input, [m1, m2], n=100) # profile over 100 iterations
|
||||
"""
|
||||
# speed/memory/FLOPs profiler
|
||||
#
|
||||
# Usage:
|
||||
# input = torch.randn(16, 3, 640, 640)
|
||||
# m1 = lambda x: x * torch.sigmoid(x)
|
||||
# m2 = nn.SiLU()
|
||||
# profile(input, [m1, m2], n=100) # profile over 100 iterations
|
||||
|
||||
results = []
|
||||
if not isinstance(device, torch.device):
|
||||
device = select_device(device)
|
||||
device = device or select_device()
|
||||
print(f"{'Params':>12s}{'GFLOPs':>12s}{'GPU_mem (GB)':>14s}{'forward (ms)':>14s}{'backward (ms)':>14s}"
|
||||
f"{'input':>24s}{'output':>24s}")
|
||||
|
||||
@ -171,7 +113,7 @@ def profile(input, ops, n=10, device=None):
|
||||
tf, tb, t = 0, 0, [0, 0, 0] # dt forward, backward
|
||||
try:
|
||||
flops = thop.profile(m, inputs=(x,), verbose=False)[0] / 1E9 * 2 # GFLOPs
|
||||
except Exception:
|
||||
except:
|
||||
flops = 0
|
||||
|
||||
try:
|
||||
@ -182,14 +124,15 @@ def profile(input, ops, n=10, device=None):
|
||||
try:
|
||||
_ = (sum(yi.sum() for yi in y) if isinstance(y, list) else y).sum().backward()
|
||||
t[2] = time_sync()
|
||||
except Exception: # no backward method
|
||||
except Exception as e: # no backward method
|
||||
# print(e) # for debug
|
||||
t[2] = float('nan')
|
||||
tf += (t[1] - t[0]) * 1000 / n # ms per op forward
|
||||
tb += (t[2] - t[1]) * 1000 / n # ms per op backward
|
||||
mem = torch.cuda.memory_reserved() / 1E9 if torch.cuda.is_available() else 0 # (GB)
|
||||
s_in, s_out = (tuple(x.shape) if isinstance(x, torch.Tensor) else 'list' for x in (x, y)) # shapes
|
||||
p = sum(x.numel() for x in m.parameters()) if isinstance(m, nn.Module) else 0 # parameters
|
||||
s_in = tuple(x.shape) if isinstance(x, torch.Tensor) else 'list'
|
||||
s_out = tuple(y.shape) if isinstance(y, torch.Tensor) else 'list'
|
||||
p = sum(list(x.numel() for x in m.parameters())) if isinstance(m, nn.Module) else 0 # parameters
|
||||
print(f'{p:12}{flops:12.4g}{mem:>14.3f}{tf:14.4g}{tb:14.4g}{str(s_in):>24s}{str(s_out):>24s}')
|
||||
results.append([p, flops, mem, tf, tb, s_in, s_out])
|
||||
except Exception as e:
|
||||
@ -238,30 +181,30 @@ def sparsity(model):
|
||||
def prune(model, amount=0.3):
|
||||
# Prune model to requested global sparsity
|
||||
import torch.nn.utils.prune as prune
|
||||
print('Pruning model... ', end='')
|
||||
for name, m in model.named_modules():
|
||||
if isinstance(m, nn.Conv2d):
|
||||
prune.l1_unstructured(m, name='weight', amount=amount) # prune
|
||||
prune.remove(m, 'weight') # make permanent
|
||||
LOGGER.info(f'Model pruned to {sparsity(model):.3g} global sparsity')
|
||||
print(' %.3g global sparsity' % sparsity(model))
|
||||
|
||||
|
||||
def fuse_conv_and_bn(conv, bn):
|
||||
# Fuse Conv2d() and BatchNorm2d() layers https://tehnokv.com/posts/fusing-batchnorm-and-conv/
|
||||
# Fuse convolution and batchnorm layers https://tehnokv.com/posts/fusing-batchnorm-and-conv/
|
||||
fusedconv = nn.Conv2d(conv.in_channels,
|
||||
conv.out_channels,
|
||||
kernel_size=conv.kernel_size,
|
||||
stride=conv.stride,
|
||||
padding=conv.padding,
|
||||
dilation=conv.dilation,
|
||||
groups=conv.groups,
|
||||
bias=True).requires_grad_(False).to(conv.weight.device)
|
||||
|
||||
# Prepare filters
|
||||
# prepare filters
|
||||
w_conv = conv.weight.clone().view(conv.out_channels, -1)
|
||||
w_bn = torch.diag(bn.weight.div(torch.sqrt(bn.eps + bn.running_var)))
|
||||
fusedconv.weight.copy_(torch.mm(w_bn, w_conv).view(fusedconv.weight.shape))
|
||||
|
||||
# Prepare spatial bias
|
||||
# prepare spatial bias
|
||||
b_conv = torch.zeros(conv.weight.size(0), device=conv.weight.device) if conv.bias is None else conv.bias
|
||||
b_bn = bn.bias - bn.weight.mul(bn.running_mean).div(torch.sqrt(bn.running_var + bn.eps))
|
||||
fusedconv.bias.copy_(torch.mm(w_bn, b_conv.reshape(-1, 1)).reshape(-1) + b_bn)
|
||||
@ -269,7 +212,7 @@ def fuse_conv_and_bn(conv, bn):
|
||||
return fusedconv
|
||||
|
||||
|
||||
def model_info(model, verbose=False, imgsz=640):
|
||||
def model_info(model, verbose=False, img_size=640):
|
||||
# Model information. img_size may be int or list, i.e. img_size=640 or img_size=[640, 320]
|
||||
n_p = sum(x.numel() for x in model.parameters()) # number parameters
|
||||
n_g = sum(x.numel() for x in model.parameters() if x.requires_grad) # number gradients
|
||||
@ -281,29 +224,29 @@ def model_info(model, verbose=False, imgsz=640):
|
||||
(i, name, p.requires_grad, p.numel(), list(p.shape), p.mean(), p.std()))
|
||||
|
||||
try: # FLOPs
|
||||
p = next(model.parameters())
|
||||
stride = max(int(model.stride.max()), 32) if hasattr(model, 'stride') else 32 # max stride
|
||||
im = torch.empty((1, p.shape[1], stride, stride), device=p.device) # input image in BCHW format
|
||||
flops = thop.profile(deepcopy(model), inputs=(im,), verbose=False)[0] / 1E9 * 2 # stride GFLOPs
|
||||
imgsz = imgsz if isinstance(imgsz, list) else [imgsz, imgsz] # expand if int/float
|
||||
fs = f', {flops * imgsz[0] / stride * imgsz[1] / stride:.1f} GFLOPs' # 640x640 GFLOPs
|
||||
except Exception:
|
||||
from thop import profile
|
||||
stride = max(int(model.stride.max()), 32) if hasattr(model, 'stride') else 32
|
||||
img = torch.zeros((1, model.yaml.get('ch', 3), stride, stride), device=next(model.parameters()).device) # input
|
||||
flops = profile(deepcopy(model), inputs=(img,), verbose=False)[0] / 1E9 * 2 # stride GFLOPs
|
||||
img_size = img_size if isinstance(img_size, list) else [img_size, img_size] # expand if int/float
|
||||
fs = ', %.1f GFLOPs' % (flops * img_size[0] / stride * img_size[1] / stride) # 640x640 GFLOPs
|
||||
except (ImportError, Exception):
|
||||
fs = ''
|
||||
|
||||
name = Path(model.yaml_file).stem.replace('yolov5', 'YOLOv3') if hasattr(model, 'yaml_file') else 'Model'
|
||||
LOGGER.info(f'{name} summary: {len(list(model.modules()))} layers, {n_p} parameters, {n_g} gradients{fs}')
|
||||
LOGGER.info(f"Model Summary: {len(list(model.modules()))} layers, {n_p} parameters, {n_g} gradients{fs}")
|
||||
|
||||
|
||||
def scale_img(img, ratio=1.0, same_shape=False, gs=32): # img(16,3,256,416)
|
||||
# Scales img(bs,3,y,x) by ratio constrained to gs-multiple
|
||||
# scales img(bs,3,y,x) by ratio constrained to gs-multiple
|
||||
if ratio == 1.0:
|
||||
return img
|
||||
h, w = img.shape[2:]
|
||||
s = (int(h * ratio), int(w * ratio)) # new size
|
||||
img = F.interpolate(img, size=s, mode='bilinear', align_corners=False) # resize
|
||||
if not same_shape: # pad/crop img
|
||||
h, w = (math.ceil(x * ratio / gs) * gs for x in (h, w))
|
||||
return F.pad(img, [0, w - s[1], 0, h - s[0]], value=0.447) # value = imagenet mean
|
||||
else:
|
||||
h, w = img.shape[2:]
|
||||
s = (int(h * ratio), int(w * ratio)) # new size
|
||||
img = F.interpolate(img, size=s, mode='bilinear', align_corners=False) # resize
|
||||
if not same_shape: # pad/crop img
|
||||
h, w = (math.ceil(x * ratio / gs) * gs for x in (h, w))
|
||||
return F.pad(img, [0, w - s[1], 0, h - s[0]], value=0.447) # value = imagenet mean
|
||||
|
||||
|
||||
def copy_attr(a, b, include=(), exclude=()):
|
||||
@ -315,71 +258,8 @@ def copy_attr(a, b, include=(), exclude=()):
|
||||
setattr(a, k, v)
|
||||
|
||||
|
||||
def smart_optimizer(model, name='Adam', lr=0.001, momentum=0.9, decay=1e-5):
|
||||
# YOLOv3 3-param group optimizer: 0) weights with decay, 1) weights no decay, 2) biases no decay
|
||||
g = [], [], [] # optimizer parameter groups
|
||||
bn = tuple(v for k, v in nn.__dict__.items() if 'Norm' in k) # normalization layers, i.e. BatchNorm2d()
|
||||
for v in model.modules():
|
||||
for p_name, p in v.named_parameters(recurse=0):
|
||||
if p_name == 'bias': # bias (no decay)
|
||||
g[2].append(p)
|
||||
elif p_name == 'weight' and isinstance(v, bn): # weight (no decay)
|
||||
g[1].append(p)
|
||||
else:
|
||||
g[0].append(p) # weight (with decay)
|
||||
|
||||
if name == 'Adam':
|
||||
optimizer = torch.optim.Adam(g[2], lr=lr, betas=(momentum, 0.999)) # adjust beta1 to momentum
|
||||
elif name == 'AdamW':
|
||||
optimizer = torch.optim.AdamW(g[2], lr=lr, betas=(momentum, 0.999), weight_decay=0.0)
|
||||
elif name == 'RMSProp':
|
||||
optimizer = torch.optim.RMSprop(g[2], lr=lr, momentum=momentum)
|
||||
elif name == 'SGD':
|
||||
optimizer = torch.optim.SGD(g[2], lr=lr, momentum=momentum, nesterov=True)
|
||||
else:
|
||||
raise NotImplementedError(f'Optimizer {name} not implemented.')
|
||||
|
||||
optimizer.add_param_group({'params': g[0], 'weight_decay': decay}) # add g0 with weight_decay
|
||||
optimizer.add_param_group({'params': g[1], 'weight_decay': 0.0}) # add g1 (BatchNorm2d weights)
|
||||
LOGGER.info(f"{colorstr('optimizer:')} {type(optimizer).__name__}(lr={lr}) with parameter groups "
|
||||
f'{len(g[1])} weight(decay=0.0), {len(g[0])} weight(decay={decay}), {len(g[2])} bias')
|
||||
return optimizer
|
||||
|
||||
|
||||
def smart_hub_load(repo='ultralytics/yolov5', model='yolov5s', **kwargs):
|
||||
# YOLOv3 torch.hub.load() wrapper with smart error/issue handling
|
||||
if check_version(torch.__version__, '1.9.1'):
|
||||
kwargs['skip_validation'] = True # validation causes GitHub API rate limit errors
|
||||
if check_version(torch.__version__, '1.12.0'):
|
||||
kwargs['trust_repo'] = True # argument required starting in torch 0.12
|
||||
try:
|
||||
return torch.hub.load(repo, model, **kwargs)
|
||||
except Exception:
|
||||
return torch.hub.load(repo, model, force_reload=True, **kwargs)
|
||||
|
||||
|
||||
def smart_resume(ckpt, optimizer, ema=None, weights='yolov5s.pt', epochs=300, resume=True):
|
||||
# Resume training from a partially trained checkpoint
|
||||
best_fitness = 0.0
|
||||
start_epoch = ckpt['epoch'] + 1
|
||||
if ckpt['optimizer'] is not None:
|
||||
optimizer.load_state_dict(ckpt['optimizer']) # optimizer
|
||||
best_fitness = ckpt['best_fitness']
|
||||
if ema and ckpt.get('ema'):
|
||||
ema.ema.load_state_dict(ckpt['ema'].float().state_dict()) # EMA
|
||||
ema.updates = ckpt['updates']
|
||||
if resume:
|
||||
assert start_epoch > 0, f'{weights} training to {epochs} epochs is finished, nothing to resume.\n' \
|
||||
f"Start a new training without --resume, i.e. 'python train.py --weights {weights}'"
|
||||
LOGGER.info(f'Resuming training from {weights} from epoch {start_epoch} to {epochs} total epochs')
|
||||
if epochs < start_epoch:
|
||||
LOGGER.info(f"{weights} has been trained for {ckpt['epoch']} epochs. Fine-tuning for {epochs} more epochs.")
|
||||
epochs += ckpt['epoch'] # finetune additional epochs
|
||||
return best_fitness, start_epoch, epochs
|
||||
|
||||
|
||||
class EarlyStopping:
|
||||
# YOLOv3 simple early stopper
|
||||
# simple early stopper
|
||||
def __init__(self, patience=30):
|
||||
self.best_fitness = 0.0 # i.e. mAP
|
||||
self.best_epoch = 0
|
||||
@ -402,30 +282,36 @@ class EarlyStopping:
|
||||
|
||||
|
||||
class ModelEMA:
|
||||
""" Updated Exponential Moving Average (EMA) from https://github.com/rwightman/pytorch-image-models
|
||||
Keeps a moving average of everything in the model state_dict (parameters and buffers)
|
||||
For EMA details see https://www.tensorflow.org/api_docs/python/tf/train/ExponentialMovingAverage
|
||||
""" Model Exponential Moving Average from https://github.com/rwightman/pytorch-image-models
|
||||
Keep a moving average of everything in the model state_dict (parameters and buffers).
|
||||
This is intended to allow functionality like
|
||||
https://www.tensorflow.org/api_docs/python/tf/train/ExponentialMovingAverage
|
||||
A smoothed version of the weights is necessary for some training schemes to perform well.
|
||||
This class is sensitive where it is initialized in the sequence of model init,
|
||||
GPU assignment and distributed training wrappers.
|
||||
"""
|
||||
|
||||
def __init__(self, model, decay=0.9999, tau=2000, updates=0):
|
||||
def __init__(self, model, decay=0.9999, updates=0):
|
||||
# Create EMA
|
||||
self.ema = deepcopy(de_parallel(model)).eval() # FP32 EMA
|
||||
self.ema = deepcopy(model.module if is_parallel(model) else model).eval() # FP32 EMA
|
||||
# if next(model.parameters()).device.type != 'cpu':
|
||||
# self.ema.half() # FP16 EMA
|
||||
self.updates = updates # number of EMA updates
|
||||
self.decay = lambda x: decay * (1 - math.exp(-x / tau)) # decay exponential ramp (to help early epochs)
|
||||
self.decay = lambda x: decay * (1 - math.exp(-x / 2000)) # decay exponential ramp (to help early epochs)
|
||||
for p in self.ema.parameters():
|
||||
p.requires_grad_(False)
|
||||
|
||||
def update(self, model):
|
||||
# Update EMA parameters
|
||||
self.updates += 1
|
||||
d = self.decay(self.updates)
|
||||
with torch.no_grad():
|
||||
self.updates += 1
|
||||
d = self.decay(self.updates)
|
||||
|
||||
msd = de_parallel(model).state_dict() # model state_dict
|
||||
for k, v in self.ema.state_dict().items():
|
||||
if v.dtype.is_floating_point: # true for FP16 and FP32
|
||||
v *= d
|
||||
v += (1 - d) * msd[k].detach()
|
||||
# assert v.dtype == msd[k].dtype == torch.float32, f'{k}: EMA {v.dtype} and model {msd[k].dtype} must be FP32'
|
||||
msd = model.module.state_dict() if is_parallel(model) else model.state_dict() # model state_dict
|
||||
for k, v in self.ema.state_dict().items():
|
||||
if v.dtype.is_floating_point:
|
||||
v *= d
|
||||
v += (1 - d) * msd[k].detach()
|
||||
|
||||
def update_attr(self, model, include=(), exclude=('process_group', 'reducer')):
|
||||
# Update EMA attributes
|
||||
|
||||
252
yolov3/val.py
252
yolov3/val.py
@ -1,30 +1,17 @@
|
||||
# YOLOv3 🚀 by Ultralytics, GPL-3.0 license
|
||||
"""
|
||||
Validate a trained YOLOv3 detection model on a detection dataset
|
||||
Validate a trained model accuracy on a custom dataset
|
||||
|
||||
Usage:
|
||||
$ python val.py --weights yolov5s.pt --data coco128.yaml --img 640
|
||||
|
||||
Usage - formats:
|
||||
$ python val.py --weights yolov5s.pt # PyTorch
|
||||
yolov5s.torchscript # TorchScript
|
||||
yolov5s.onnx # ONNX Runtime or OpenCV DNN with --dnn
|
||||
yolov5s_openvino_model # OpenVINO
|
||||
yolov5s.engine # TensorRT
|
||||
yolov5s.mlmodel # CoreML (macOS-only)
|
||||
yolov5s_saved_model # TensorFlow SavedModel
|
||||
yolov5s.pb # TensorFlow GraphDef
|
||||
yolov5s.tflite # TensorFlow Lite
|
||||
yolov5s_edgetpu.tflite # TensorFlow Edge TPU
|
||||
yolov5s_paddle_model # PaddlePaddle
|
||||
$ python path/to/val.py --data coco128.yaml --weights yolov3.pt --img 640
|
||||
"""
|
||||
|
||||
import argparse
|
||||
import json
|
||||
import os
|
||||
import subprocess
|
||||
import sys
|
||||
from pathlib import Path
|
||||
from threading import Thread
|
||||
|
||||
import numpy as np
|
||||
import torch
|
||||
@ -38,13 +25,13 @@ ROOT = Path(os.path.relpath(ROOT, Path.cwd())) # relative
|
||||
|
||||
from models.common import DetectMultiBackend
|
||||
from utils.callbacks import Callbacks
|
||||
from utils.dataloaders import create_dataloader
|
||||
from utils.general import (LOGGER, TQDM_BAR_FORMAT, Profile, check_dataset, check_img_size, check_requirements,
|
||||
check_yaml, coco80_to_coco91_class, colorstr, increment_path, non_max_suppression,
|
||||
print_args, scale_boxes, xywh2xyxy, xyxy2xywh)
|
||||
from utils.metrics import ConfusionMatrix, ap_per_class, box_iou
|
||||
from utils.datasets import create_dataloader
|
||||
from utils.general import (LOGGER, NCOLS, box_iou, check_dataset, check_img_size, check_requirements, check_yaml,
|
||||
coco80_to_coco91_class, colorstr, increment_path, non_max_suppression, print_args,
|
||||
scale_coords, xywh2xyxy, xyxy2xywh)
|
||||
from utils.metrics import ConfusionMatrix, ap_per_class
|
||||
from utils.plots import output_to_target, plot_images, plot_val_study
|
||||
from utils.torch_utils import select_device, smart_inference_mode
|
||||
from utils.torch_utils import select_device, time_sync
|
||||
|
||||
|
||||
def save_one_txt(predn, save_conf, shape, file):
|
||||
@ -63,50 +50,45 @@ def save_one_json(predn, jdict, path, class_map):
|
||||
box = xyxy2xywh(predn[:, :4]) # xywh
|
||||
box[:, :2] -= box[:, 2:] / 2 # xy center to top-left corner
|
||||
for p, b in zip(predn.tolist(), box.tolist()):
|
||||
jdict.append({
|
||||
'image_id': image_id,
|
||||
'category_id': class_map[int(p[5])],
|
||||
'bbox': [round(x, 3) for x in b],
|
||||
'score': round(p[4], 5)})
|
||||
jdict.append({'image_id': image_id,
|
||||
'category_id': class_map[int(p[5])],
|
||||
'bbox': [round(x, 3) for x in b],
|
||||
'score': round(p[4], 5)})
|
||||
|
||||
|
||||
def process_batch(detections, labels, iouv):
|
||||
"""
|
||||
Return correct prediction matrix
|
||||
Return correct predictions matrix. Both sets of boxes are in (x1, y1, x2, y2) format.
|
||||
Arguments:
|
||||
detections (array[N, 6]), x1, y1, x2, y2, conf, class
|
||||
labels (array[M, 5]), class, x1, y1, x2, y2
|
||||
detections (Array[N, 6]), x1, y1, x2, y2, conf, class
|
||||
labels (Array[M, 5]), class, x1, y1, x2, y2
|
||||
Returns:
|
||||
correct (array[N, 10]), for 10 IoU levels
|
||||
correct (Array[N, 10]), for 10 IoU levels
|
||||
"""
|
||||
correct = np.zeros((detections.shape[0], iouv.shape[0])).astype(bool)
|
||||
correct = torch.zeros(detections.shape[0], iouv.shape[0], dtype=torch.bool, device=iouv.device)
|
||||
iou = box_iou(labels[:, 1:], detections[:, :4])
|
||||
correct_class = labels[:, 0:1] == detections[:, 5]
|
||||
for i in range(len(iouv)):
|
||||
x = torch.where((iou >= iouv[i]) & correct_class) # IoU > threshold and classes match
|
||||
if x[0].shape[0]:
|
||||
matches = torch.cat((torch.stack(x, 1), iou[x[0], x[1]][:, None]), 1).cpu().numpy() # [label, detect, iou]
|
||||
if x[0].shape[0] > 1:
|
||||
matches = matches[matches[:, 2].argsort()[::-1]]
|
||||
matches = matches[np.unique(matches[:, 1], return_index=True)[1]]
|
||||
# matches = matches[matches[:, 2].argsort()[::-1]]
|
||||
matches = matches[np.unique(matches[:, 0], return_index=True)[1]]
|
||||
correct[matches[:, 1].astype(int), i] = True
|
||||
return torch.tensor(correct, dtype=torch.bool, device=iouv.device)
|
||||
x = torch.where((iou >= iouv[0]) & (labels[:, 0:1] == detections[:, 5])) # IoU above threshold and classes match
|
||||
if x[0].shape[0]:
|
||||
matches = torch.cat((torch.stack(x, 1), iou[x[0], x[1]][:, None]), 1).cpu().numpy() # [label, detection, iou]
|
||||
if x[0].shape[0] > 1:
|
||||
matches = matches[matches[:, 2].argsort()[::-1]]
|
||||
matches = matches[np.unique(matches[:, 1], return_index=True)[1]]
|
||||
# matches = matches[matches[:, 2].argsort()[::-1]]
|
||||
matches = matches[np.unique(matches[:, 0], return_index=True)[1]]
|
||||
matches = torch.Tensor(matches).to(iouv.device)
|
||||
correct[matches[:, 1].long()] = matches[:, 2:3] >= iouv
|
||||
return correct
|
||||
|
||||
|
||||
@smart_inference_mode()
|
||||
def run(
|
||||
data,
|
||||
@torch.no_grad()
|
||||
def run(data,
|
||||
weights=None, # model.pt path(s)
|
||||
batch_size=32, # batch size
|
||||
imgsz=640, # inference size (pixels)
|
||||
conf_thres=0.001, # confidence threshold
|
||||
iou_thres=0.6, # NMS IoU threshold
|
||||
max_det=300, # maximum detections per image
|
||||
task='val', # train, val, test, speed or study
|
||||
device='', # cuda device, i.e. 0 or 0,1,2,3 or cpu
|
||||
workers=8, # max dataloader workers (per RANK in DDP mode)
|
||||
single_cls=False, # treat as single-class dataset
|
||||
augment=False, # augmented inference
|
||||
verbose=False, # verbose output
|
||||
@ -125,11 +107,12 @@ def run(
|
||||
plots=True,
|
||||
callbacks=Callbacks(),
|
||||
compute_loss=None,
|
||||
):
|
||||
):
|
||||
# Initialize/load model and set device
|
||||
training = model is not None
|
||||
if training: # called by train.py
|
||||
device, pt, jit, engine = next(model.parameters()).device, True, False, False # get model device, PyTorch model
|
||||
device, pt = next(model.parameters()).device, True # get model device, PyTorch model
|
||||
|
||||
half &= device.type != 'cpu' # half precision only supported on CUDA
|
||||
model.half() if half else model.float()
|
||||
else: # called directly
|
||||
@ -140,149 +123,130 @@ def run(
|
||||
(save_dir / 'labels' if save_txt else save_dir).mkdir(parents=True, exist_ok=True) # make dir
|
||||
|
||||
# Load model
|
||||
model = DetectMultiBackend(weights, device=device, dnn=dnn, data=data, fp16=half)
|
||||
stride, pt, jit, engine = model.stride, model.pt, model.jit, model.engine
|
||||
model = DetectMultiBackend(weights, device=device, dnn=dnn)
|
||||
stride, pt = model.stride, model.pt
|
||||
imgsz = check_img_size(imgsz, s=stride) # check image size
|
||||
half = model.fp16 # FP16 supported on limited backends with CUDA
|
||||
if engine:
|
||||
batch_size = model.batch_size
|
||||
half &= pt and device.type != 'cpu' # half precision only supported by PyTorch on CUDA
|
||||
if pt:
|
||||
model.model.half() if half else model.model.float()
|
||||
else:
|
||||
device = model.device
|
||||
if not (pt or jit):
|
||||
batch_size = 1 # export.py models default to batch-size 1
|
||||
LOGGER.info(f'Forcing --batch-size 1 square inference (1,3,{imgsz},{imgsz}) for non-PyTorch models')
|
||||
half = False
|
||||
batch_size = 1 # export.py models default to batch-size 1
|
||||
device = torch.device('cpu')
|
||||
LOGGER.info(f'Forcing --batch-size 1 square inference shape(1,3,{imgsz},{imgsz}) for non-PyTorch backends')
|
||||
|
||||
# Data
|
||||
data = check_dataset(data) # check
|
||||
|
||||
# Configure
|
||||
model.eval()
|
||||
cuda = device.type != 'cpu'
|
||||
is_coco = isinstance(data.get('val'), str) and data['val'].endswith(f'coco{os.sep}val2017.txt') # COCO dataset
|
||||
is_coco = isinstance(data.get('val'), str) and data['val'].endswith('coco/val2017.txt') # COCO dataset
|
||||
nc = 1 if single_cls else int(data['nc']) # number of classes
|
||||
iouv = torch.linspace(0.5, 0.95, 10, device=device) # iou vector for mAP@0.5:0.95
|
||||
iouv = torch.linspace(0.5, 0.95, 10).to(device) # iou vector for mAP@0.5:0.95
|
||||
niou = iouv.numel()
|
||||
|
||||
# Dataloader
|
||||
if not training:
|
||||
if pt and not single_cls: # check --weights are trained on --data
|
||||
ncm = model.model.nc
|
||||
assert ncm == nc, f'{weights} ({ncm} classes) trained on different --data than what you passed ({nc} ' \
|
||||
f'classes). Pass correct combination of --weights and --data that are trained together.'
|
||||
model.warmup(imgsz=(1 if pt else batch_size, 3, imgsz, imgsz)) # warmup
|
||||
pad, rect = (0.0, False) if task == 'speed' else (0.5, pt) # square inference for benchmarks
|
||||
if pt and device.type != 'cpu':
|
||||
model(torch.zeros(1, 3, imgsz, imgsz).to(device).type_as(next(model.model.parameters()))) # warmup
|
||||
pad = 0.0 if task == 'speed' else 0.5
|
||||
task = task if task in ('train', 'val', 'test') else 'val' # path to train/val/test images
|
||||
dataloader = create_dataloader(data[task],
|
||||
imgsz,
|
||||
batch_size,
|
||||
stride,
|
||||
single_cls,
|
||||
pad=pad,
|
||||
rect=rect,
|
||||
workers=workers,
|
||||
dataloader = create_dataloader(data[task], imgsz, batch_size, stride, single_cls, pad=pad, rect=pt,
|
||||
prefix=colorstr(f'{task}: '))[0]
|
||||
|
||||
seen = 0
|
||||
confusion_matrix = ConfusionMatrix(nc=nc)
|
||||
names = model.names if hasattr(model, 'names') else model.module.names # get class names
|
||||
if isinstance(names, (list, tuple)): # old format
|
||||
names = dict(enumerate(names))
|
||||
names = {k: v for k, v in enumerate(model.names if hasattr(model, 'names') else model.module.names)}
|
||||
class_map = coco80_to_coco91_class() if is_coco else list(range(1000))
|
||||
s = ('%22s' + '%11s' * 6) % ('Class', 'Images', 'Instances', 'P', 'R', 'mAP50', 'mAP50-95')
|
||||
tp, fp, p, r, f1, mp, mr, map50, ap50, map = 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0
|
||||
dt = Profile(), Profile(), Profile() # profiling times
|
||||
s = ('%20s' + '%11s' * 6) % ('Class', 'Images', 'Labels', 'P', 'R', 'mAP@.5', 'mAP@.5:.95')
|
||||
dt, p, r, f1, mp, mr, map50, map = [0.0, 0.0, 0.0], 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0
|
||||
loss = torch.zeros(3, device=device)
|
||||
jdict, stats, ap, ap_class = [], [], [], []
|
||||
callbacks.run('on_val_start')
|
||||
pbar = tqdm(dataloader, desc=s, bar_format=TQDM_BAR_FORMAT) # progress bar
|
||||
pbar = tqdm(dataloader, desc=s, ncols=NCOLS, bar_format='{l_bar}{bar:10}{r_bar}{bar:-10b}') # progress bar
|
||||
for batch_i, (im, targets, paths, shapes) in enumerate(pbar):
|
||||
callbacks.run('on_val_batch_start')
|
||||
with dt[0]:
|
||||
if cuda:
|
||||
im = im.to(device, non_blocking=True)
|
||||
targets = targets.to(device)
|
||||
im = im.half() if half else im.float() # uint8 to fp16/32
|
||||
im /= 255 # 0 - 255 to 0.0 - 1.0
|
||||
nb, _, height, width = im.shape # batch size, channels, height, width
|
||||
t1 = time_sync()
|
||||
if pt:
|
||||
im = im.to(device, non_blocking=True)
|
||||
targets = targets.to(device)
|
||||
im = im.half() if half else im.float() # uint8 to fp16/32
|
||||
im /= 255 # 0 - 255 to 0.0 - 1.0
|
||||
nb, _, height, width = im.shape # batch size, channels, height, width
|
||||
t2 = time_sync()
|
||||
dt[0] += t2 - t1
|
||||
|
||||
# Inference
|
||||
with dt[1]:
|
||||
preds, train_out = model(im) if compute_loss else (model(im, augment=augment), None)
|
||||
out, train_out = model(im) if training else model(im, augment=augment, val=True) # inference, loss outputs
|
||||
dt[1] += time_sync() - t2
|
||||
|
||||
# Loss
|
||||
if compute_loss:
|
||||
loss += compute_loss(train_out, targets)[1] # box, obj, cls
|
||||
loss += compute_loss([x.float() for x in train_out], targets)[1] # box, obj, cls
|
||||
|
||||
# NMS
|
||||
targets[:, 2:] *= torch.tensor((width, height, width, height), device=device) # to pixels
|
||||
targets[:, 2:] *= torch.Tensor([width, height, width, height]).to(device) # to pixels
|
||||
lb = [targets[targets[:, 0] == i, 1:] for i in range(nb)] if save_hybrid else [] # for autolabelling
|
||||
with dt[2]:
|
||||
preds = non_max_suppression(preds,
|
||||
conf_thres,
|
||||
iou_thres,
|
||||
labels=lb,
|
||||
multi_label=True,
|
||||
agnostic=single_cls,
|
||||
max_det=max_det)
|
||||
t3 = time_sync()
|
||||
out = non_max_suppression(out, conf_thres, iou_thres, labels=lb, multi_label=True, agnostic=single_cls)
|
||||
dt[2] += time_sync() - t3
|
||||
|
||||
# Metrics
|
||||
for si, pred in enumerate(preds):
|
||||
for si, pred in enumerate(out):
|
||||
labels = targets[targets[:, 0] == si, 1:]
|
||||
nl, npr = labels.shape[0], pred.shape[0] # number of labels, predictions
|
||||
nl = len(labels)
|
||||
tcls = labels[:, 0].tolist() if nl else [] # target class
|
||||
path, shape = Path(paths[si]), shapes[si][0]
|
||||
correct = torch.zeros(npr, niou, dtype=torch.bool, device=device) # init
|
||||
seen += 1
|
||||
|
||||
if npr == 0:
|
||||
if len(pred) == 0:
|
||||
if nl:
|
||||
stats.append((correct, *torch.zeros((2, 0), device=device), labels[:, 0]))
|
||||
if plots:
|
||||
confusion_matrix.process_batch(detections=None, labels=labels[:, 0])
|
||||
stats.append((torch.zeros(0, niou, dtype=torch.bool), torch.Tensor(), torch.Tensor(), tcls))
|
||||
continue
|
||||
|
||||
# Predictions
|
||||
if single_cls:
|
||||
pred[:, 5] = 0
|
||||
predn = pred.clone()
|
||||
scale_boxes(im[si].shape[1:], predn[:, :4], shape, shapes[si][1]) # native-space pred
|
||||
scale_coords(im[si].shape[1:], predn[:, :4], shape, shapes[si][1]) # native-space pred
|
||||
|
||||
# Evaluate
|
||||
if nl:
|
||||
tbox = xywh2xyxy(labels[:, 1:5]) # target boxes
|
||||
scale_boxes(im[si].shape[1:], tbox, shape, shapes[si][1]) # native-space labels
|
||||
scale_coords(im[si].shape[1:], tbox, shape, shapes[si][1]) # native-space labels
|
||||
labelsn = torch.cat((labels[:, 0:1], tbox), 1) # native-space labels
|
||||
correct = process_batch(predn, labelsn, iouv)
|
||||
if plots:
|
||||
confusion_matrix.process_batch(predn, labelsn)
|
||||
stats.append((correct, pred[:, 4], pred[:, 5], labels[:, 0])) # (correct, conf, pcls, tcls)
|
||||
else:
|
||||
correct = torch.zeros(pred.shape[0], niou, dtype=torch.bool)
|
||||
stats.append((correct.cpu(), pred[:, 4].cpu(), pred[:, 5].cpu(), tcls)) # (correct, conf, pcls, tcls)
|
||||
|
||||
# Save/log
|
||||
if save_txt:
|
||||
save_one_txt(predn, save_conf, shape, file=save_dir / 'labels' / f'{path.stem}.txt')
|
||||
save_one_txt(predn, save_conf, shape, file=save_dir / 'labels' / (path.stem + '.txt'))
|
||||
if save_json:
|
||||
save_one_json(predn, jdict, path, class_map) # append to COCO-JSON dictionary
|
||||
callbacks.run('on_val_image_end', pred, predn, path, names, im[si])
|
||||
|
||||
# Plot images
|
||||
if plots and batch_i < 3:
|
||||
plot_images(im, targets, paths, save_dir / f'val_batch{batch_i}_labels.jpg', names) # labels
|
||||
plot_images(im, output_to_target(preds), paths, save_dir / f'val_batch{batch_i}_pred.jpg', names) # pred
|
||||
|
||||
callbacks.run('on_val_batch_end', batch_i, im, targets, paths, shapes, preds)
|
||||
f = save_dir / f'val_batch{batch_i}_labels.jpg' # labels
|
||||
Thread(target=plot_images, args=(im, targets, paths, f, names), daemon=True).start()
|
||||
f = save_dir / f'val_batch{batch_i}_pred.jpg' # predictions
|
||||
Thread(target=plot_images, args=(im, output_to_target(out), paths, f, names), daemon=True).start()
|
||||
|
||||
# Compute metrics
|
||||
stats = [torch.cat(x, 0).cpu().numpy() for x in zip(*stats)] # to numpy
|
||||
stats = [np.concatenate(x, 0) for x in zip(*stats)] # to numpy
|
||||
if len(stats) and stats[0].any():
|
||||
tp, fp, p, r, f1, ap, ap_class = ap_per_class(*stats, plot=plots, save_dir=save_dir, names=names)
|
||||
p, r, ap, f1, ap_class = ap_per_class(*stats, plot=plots, save_dir=save_dir, names=names)
|
||||
ap50, ap = ap[:, 0], ap.mean(1) # AP@0.5, AP@0.5:0.95
|
||||
mp, mr, map50, map = p.mean(), r.mean(), ap50.mean(), ap.mean()
|
||||
nt = np.bincount(stats[3].astype(int), minlength=nc) # number of targets per class
|
||||
nt = np.bincount(stats[3].astype(np.int64), minlength=nc) # number of targets per class
|
||||
else:
|
||||
nt = torch.zeros(1)
|
||||
|
||||
# Print results
|
||||
pf = '%22s' + '%11i' * 2 + '%11.3g' * 4 # print format
|
||||
pf = '%20s' + '%11i' * 2 + '%11.3g' * 4 # print format
|
||||
LOGGER.info(pf % ('all', seen, nt.sum(), mp, mr, map50, map))
|
||||
if nt.sum() == 0:
|
||||
LOGGER.warning(f'WARNING ⚠️ no labels found in {task} set, can not compute metrics without labels')
|
||||
|
||||
# Print results per class
|
||||
if (verbose or (nc < 50 and not training)) and nc > 1 and len(stats):
|
||||
@ -290,7 +254,7 @@ def run(
|
||||
LOGGER.info(pf % (names[c], seen, nt[c], p[i], r[i], ap50[i], ap[i]))
|
||||
|
||||
# Print speeds
|
||||
t = tuple(x.t / seen * 1E3 for x in dt) # speeds per image
|
||||
t = tuple(x / seen * 1E3 for x in dt) # speeds per image
|
||||
if not training:
|
||||
shape = (batch_size, 3, imgsz, imgsz)
|
||||
LOGGER.info(f'Speed: %.1fms pre-process, %.1fms inference, %.1fms NMS per image at shape {shape}' % t)
|
||||
@ -298,19 +262,19 @@ def run(
|
||||
# Plots
|
||||
if plots:
|
||||
confusion_matrix.plot(save_dir=save_dir, names=list(names.values()))
|
||||
callbacks.run('on_val_end', nt, tp, fp, p, r, f1, ap, ap50, ap_class, confusion_matrix)
|
||||
callbacks.run('on_val_end')
|
||||
|
||||
# Save JSON
|
||||
if save_json and len(jdict):
|
||||
w = Path(weights[0] if isinstance(weights, list) else weights).stem if weights is not None else '' # weights
|
||||
anno_json = str(Path('../datasets/coco/annotations/instances_val2017.json')) # annotations
|
||||
pred_json = str(save_dir / f'{w}_predictions.json') # predictions
|
||||
anno_json = str(Path(data.get('path', '../coco')) / 'annotations/instances_val2017.json') # annotations json
|
||||
pred_json = str(save_dir / f"{w}_predictions.json") # predictions json
|
||||
LOGGER.info(f'\nEvaluating pycocotools mAP... saving {pred_json}...')
|
||||
with open(pred_json, 'w') as f:
|
||||
json.dump(jdict, f)
|
||||
|
||||
try: # https://github.com/cocodataset/cocoapi/blob/master/PythonAPI/pycocoEvalDemo.ipynb
|
||||
check_requirements('pycocotools>=2.0.6')
|
||||
check_requirements(['pycocotools'])
|
||||
from pycocotools.coco import COCO
|
||||
from pycocotools.cocoeval import COCOeval
|
||||
|
||||
@ -318,7 +282,7 @@ def run(
|
||||
pred = anno.loadRes(pred_json) # init predictions api
|
||||
eval = COCOeval(anno, pred, 'bbox')
|
||||
if is_coco:
|
||||
eval.params.imgIds = [int(Path(x).stem) for x in dataloader.dataset.im_files] # image IDs to evaluate
|
||||
eval.params.imgIds = [int(Path(x).stem) for x in dataloader.dataset.img_files] # image IDs to evaluate
|
||||
eval.evaluate()
|
||||
eval.accumulate()
|
||||
eval.summarize()
|
||||
@ -340,15 +304,13 @@ def run(
|
||||
def parse_opt():
|
||||
parser = argparse.ArgumentParser()
|
||||
parser.add_argument('--data', type=str, default=ROOT / 'data/coco128.yaml', help='dataset.yaml path')
|
||||
parser.add_argument('--weights', nargs='+', type=str, default=ROOT / 'yolov3-tiny.pt', help='model path(s)')
|
||||
parser.add_argument('--weights', nargs='+', type=str, default=ROOT / 'yolov3.pt', help='model.pt path(s)')
|
||||
parser.add_argument('--batch-size', type=int, default=32, help='batch size')
|
||||
parser.add_argument('--imgsz', '--img', '--img-size', type=int, default=640, help='inference size (pixels)')
|
||||
parser.add_argument('--conf-thres', type=float, default=0.001, help='confidence threshold')
|
||||
parser.add_argument('--iou-thres', type=float, default=0.6, help='NMS IoU threshold')
|
||||
parser.add_argument('--max-det', type=int, default=300, help='maximum detections per image')
|
||||
parser.add_argument('--task', default='val', help='train, val, test, speed or study')
|
||||
parser.add_argument('--device', default='', help='cuda device, i.e. 0 or 0,1,2,3 or cpu')
|
||||
parser.add_argument('--workers', type=int, default=8, help='max dataloader workers (per RANK in DDP mode)')
|
||||
parser.add_argument('--single-cls', action='store_true', help='treat as single-class dataset')
|
||||
parser.add_argument('--augment', action='store_true', help='augmented inference')
|
||||
parser.add_argument('--verbose', action='store_true', help='report mAP by class')
|
||||
@ -365,31 +327,29 @@ def parse_opt():
|
||||
opt.data = check_yaml(opt.data) # check YAML
|
||||
opt.save_json |= opt.data.endswith('coco.yaml')
|
||||
opt.save_txt |= opt.save_hybrid
|
||||
print_args(vars(opt))
|
||||
print_args(FILE.stem, opt)
|
||||
return opt
|
||||
|
||||
|
||||
def main(opt):
|
||||
check_requirements(exclude=('tensorboard', 'thop'))
|
||||
check_requirements(requirements=ROOT / 'requirements.txt', exclude=('tensorboard', 'thop'))
|
||||
|
||||
if opt.task in ('train', 'val', 'test'): # run normally
|
||||
if opt.conf_thres > 0.001: # https://github.com/ultralytics/yolov5/issues/1466
|
||||
LOGGER.info(f'WARNING ⚠️ confidence threshold {opt.conf_thres} > 0.001 produces invalid results')
|
||||
if opt.save_hybrid:
|
||||
LOGGER.info('WARNING ⚠️ --save-hybrid will return high mAP from hybrid labels, not from predictions alone')
|
||||
LOGGER.info(f'WARNING: confidence threshold {opt.conf_thres} >> 0.001 will produce invalid mAP values.')
|
||||
run(**vars(opt))
|
||||
|
||||
else:
|
||||
weights = opt.weights if isinstance(opt.weights, list) else [opt.weights]
|
||||
opt.half = torch.cuda.is_available() and opt.device != 'cpu' # FP16 for fastest results
|
||||
opt.half = True # FP16 for fastest results
|
||||
if opt.task == 'speed': # speed benchmarks
|
||||
# python val.py --task speed --data coco.yaml --batch 1 --weights yolov5n.pt yolov5s.pt...
|
||||
# python val.py --task speed --data coco.yaml --batch 1 --weights yolov3.pt yolov3-spp.pt...
|
||||
opt.conf_thres, opt.iou_thres, opt.save_json = 0.25, 0.45, False
|
||||
for opt.weights in weights:
|
||||
run(**vars(opt), plots=False)
|
||||
|
||||
elif opt.task == 'study': # speed vs mAP benchmarks
|
||||
# python val.py --task study --data coco.yaml --iou 0.7 --weights yolov5n.pt yolov5s.pt...
|
||||
# python val.py --task study --data coco.yaml --iou 0.7 --weights yolov3.pt yolov3-spp.pt...
|
||||
for opt.weights in weights:
|
||||
f = f'study_{Path(opt.data).stem}_{Path(opt.weights).stem}.txt' # filename to save to
|
||||
x, y = list(range(256, 1536 + 128, 128)), [] # x axis (image sizes), y axis
|
||||
@ -398,12 +358,10 @@ def main(opt):
|
||||
r, _, t = run(**vars(opt), plots=False)
|
||||
y.append(r + t) # results and times
|
||||
np.savetxt(f, y, fmt='%10.4g') # save
|
||||
subprocess.run('zip -r study.zip study_*.txt'.split())
|
||||
os.system('zip -r study.zip study_*.txt')
|
||||
plot_val_study(x=x) # plot
|
||||
else:
|
||||
raise NotImplementedError(f'--task {opt.task} not in ("train", "val", "test", "speed", "study")')
|
||||
|
||||
|
||||
if __name__ == '__main__':
|
||||
if __name__ == "__main__":
|
||||
opt = parse_opt()
|
||||
main(opt)
|
||||
|
||||
Loading…
x
Reference in New Issue
Block a user