-
- 
- 
- 
-
-
- 
- 
-
-
-
-
-
- -
-
-
- -
- YOLOv5🚀是一个在COCO数据集上预训练的物体检测架构和模型系列，它代表了Ultralytics对未来视觉AI方法的公开研究，其中包含了在数千小时的研究和开发中所获得的经验和最佳实践。 -
- - -
-
-
-
-
-
- - YOLOv5🚀是一个在COCO数据集上预训练的物体检测架构和模型系列，它代表了Ultralytics对未来视觉AI方法的公开研究，其中包含了在数千小时的研究和开发中所获得的经验和最佳实践。 -
- - -
-
-
-
-##
+ - **Notebooks** with free GPU: 
- **Google Cloud** Deep Learning VM. See [GCP Quickstart Guide](https://github.com/ultralytics/yolov5/wiki/GCP-Quickstart)
- **Amazon** Deep Learning AMI. See [AWS Quickstart Guide](https://github.com/ultralytics/yolov5/wiki/AWS-Quickstart)
- **Docker Image**. See [Docker Quickstart Guide](https://github.com/ultralytics/yolov5/wiki/Docker-Quickstart)
-
## Status
-
+
+
+ If this badge is green, all [YOLOv5 GitHub Actions](https://github.com/ultralytics/yolov5/actions) Continuous Integration (CI) tests are currently passing. CI tests verify correct operation of YOLOv5 [training](https://github.com/ultralytics/yolov5/blob/master/train.py), [validation](https://github.com/ultralytics/yolov5/blob/master/val.py), [inference](https://github.com/ultralytics/yolov5/blob/master/detect.py), [export](https://github.com/ultralytics/yolov5/blob/master/export.py) and [benchmarks](https://github.com/ultralytics/yolov5/blob/master/benchmarks.py) on MacOS, Windows, and Ubuntu every 24 hours and on every commit.
+
+ ## Introducing YOLOv8 🚀
- If this badge is green, all [YOLOv5 GitHub Actions](https://github.com/ultralytics/yolov5/actions) Continuous Integration (CI) tests are currently passing. CI tests verify correct operation of YOLOv5 training ([train.py](https://github.com/ultralytics/yolov5/blob/master/train.py)), validation ([val.py](https://github.com/ultralytics/yolov5/blob/master/val.py)), inference ([detect.py](https://github.com/ultralytics/yolov5/blob/master/detect.py)) and export ([export.py](https://github.com/ultralytics/yolov5/blob/master/export.py)) on macOS, Windows, and Ubuntu every 24 hours and on every commit.
+ We're excited to announce the launch of our latest state-of-the-art (SOTA) object detection model for 2023 - [YOLOv8](https://github.com/ultralytics/ultralytics) 🚀!
+
+ Designed to be fast, accurate, and easy to use, YOLOv8 is an ideal choice for a wide range of object detection, image segmentation and image classification tasks. With YOLOv8, you'll be able to quickly and accurately detect objects in real-time, streamline your workflows, and achieve new levels of accuracy in your projects.
+
+ Check out our [YOLOv8 Docs](https://docs.ultralytics.com/) for details and get started with:
+ ```bash
+ pip install ultralytics
+ ```
diff --git a/.github/workflows/stale.yml b/.github/workflows/stale.yml
index 03d99790a4a7..b21e9c00e6c5 100644
--- a/.github/workflows/stale.yml
+++ b/.github/workflows/stale.yml
@@ -9,7 +9,7 @@ jobs:
stale:
runs-on: ubuntu-latest
steps:
- - uses: actions/stale@v5
+ - uses: actions/stale@v7
with:
repo-token: ${{ secrets.GITHUB_TOKEN }}
stale-issue-message: |
diff --git a/.github/workflows/translate-readme.yml b/.github/workflows/translate-readme.yml
new file mode 100644
index 000000000000..2bb351ec7e81
--- /dev/null
+++ b/.github/workflows/translate-readme.yml
@@ -0,0 +1,26 @@
+# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
+# README translation action to translate README.md to Chinese as README.zh-CN.md on any change to README.md
+
+name: Translate README
+
+on:
+ push:
+ branches:
+ - translate_readme # replace with 'master' to enable action
+ paths:
+ - README.md
+
+jobs:
+ Translate:
+ runs-on: ubuntu-latest
+ steps:
+ - uses: actions/checkout@v3
+ - name: Setup Node.js
+ uses: actions/setup-node@v3
+ with:
+ node-version: 16
+ # ISO Language Codes: https://cloud.google.com/translate/docs/languages
+ - name: Adding README - Chinese Simplified
+ uses: dephraiim/translate-readme@main
+ with:
+ LANG: zh-CN
diff --git a/.gitignore b/.gitignore
index 69a00843ea42..6bcedfac610d 100755
--- a/.gitignore
+++ b/.gitignore
@@ -60,6 +60,7 @@ VOC/
*_saved_model/
*_web_model/
*_openvino_model/
+*_paddle_model/
darknet53.conv.74
yolov3-tiny.conv.15
diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml
index ba8005535397..c5162378ab81 100644
--- a/.pre-commit-config.yaml
+++ b/.pre-commit-config.yaml
@@ -1,9 +1,7 @@
-# Define hooks for code formations
-# Will be applied on any updated commit files if a user has installed and linked commit hook
-
-default_language_version:
- python: python3.8
+# Ultralytics YOLO 🚀, GPL-3.0 license
+# Pre-commit hooks. For more information see https://github.com/pre-commit/pre-commit-hooks/blob/main/README.md
+exclude: 'docs/'
# Define bot property if installed via https://github.com/marketplace/pre-commit-ci
ci:
autofix_prs: true
@@ -13,30 +11,30 @@ ci:
repos:
- repo: https://github.com/pre-commit/pre-commit-hooks
- rev: v4.3.0
+ rev: v4.4.0
hooks:
- # - id: end-of-file-fixer
+ - id: end-of-file-fixer
- id: trailing-whitespace
- id: check-case-conflict
- id: check-yaml
- - id: check-toml
- - id: pretty-format-json
- id: check-docstring-first
+ - id: double-quote-string-fixer
+ - id: detect-private-key
- repo: https://github.com/asottile/pyupgrade
- rev: v2.37.3
+ rev: v3.3.1
hooks:
- id: pyupgrade
name: Upgrade code
- args: [ --py37-plus ]
+ args: [--py37-plus]
- repo: https://github.com/PyCQA/isort
- rev: 5.10.1
+ rev: 5.12.0
hooks:
- id: isort
name: Sort imports
- - repo: https://github.com/pre-commit/mirrors-yapf
+ - repo: https://github.com/google/yapf
rev: v0.32.0
hooks:
- id: yapf
@@ -50,15 +48,22 @@ repos:
additional_dependencies:
- mdformat-gfm
- mdformat-black
- exclude: "README.md|README_cn.md"
-
- - repo: https://github.com/asottile/yesqa
- rev: v1.4.0
- hooks:
- - id: yesqa
+ # exclude: "README.md|README.zh-CN.md|CONTRIBUTING.md"
- repo: https://github.com/PyCQA/flake8
- rev: 5.0.4
+ rev: 6.0.0
hooks:
- id: flake8
name: PEP8
+
+ - repo: https://github.com/codespell-project/codespell
+ rev: v2.2.2
+ hooks:
+ - id: codespell
+ args:
+ - --ignore-words-list=crate,nd,strack,dota
+
+ #- repo: https://github.com/asottile/yesqa
+ # rev: v1.4.0
+ # hooks:
+ # - id: yesqa
diff --git a/CITATION.cff b/CITATION.cff
new file mode 100644
index 000000000000..8e2cf1148b92
--- /dev/null
+++ b/CITATION.cff
@@ -0,0 +1,14 @@
+cff-version: 1.2.0
+preferred-citation:
+ type: software
+ message: If you use YOLOv5, please cite it as below.
+ authors:
+ - family-names: Jocher
+ given-names: Glenn
+ orcid: "https://orcid.org/0000-0001-5950-6979"
+ title: "YOLOv5 by Ultralytics"
+ version: 7.0
+ doi: 10.5281/zenodo.3908559
+ date-released: 2020-5-29
+ license: GPL-3.0
+ url: "https://github.com/ultralytics/yolov5"
diff --git a/CONTRIBUTING.md b/CONTRIBUTING.md
index 7498f8995d40..71857faddb89 100644
--- a/CONTRIBUTING.md
+++ b/CONTRIBUTING.md
@@ -23,13 +23,13 @@ Select `requirements.txt` to update by clicking on it in GitHub.
### 2. Click 'Edit this file'
-Button is in top-right corner.
+The button is in the top-right corner.
+[English](README.md) | [简体中文](README.zh-CN.md)
+
-
- YOLOv5 🚀 is a family of object detection architectures and models pretrained on the COCO dataset, and represents Ultralytics - open-source research into future vision AI methods, incorporating lessons learned and best practices evolved over thousands of hours of research and development. -
-
+
@@ -214,57 +228,65 @@ python train.py --data coco.yaml --cfg yolov5n.yaml --weights '' --batch-size 12
- [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
-- [Weights & Biases Logging](https://github.com/ultralytics/yolov5/issues/1289)
- [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
-- [Deci Platform](https://github.com/ultralytics/yolov5/wiki/Deci-Platform) 🌟 NEW
+- [YOLOv5 with Neural Magic's Deepsparse](https://bit.ly/yolov5-neuralmagic) 🌟 NEW
- [Comet Logging](https://github.com/ultralytics/yolov5/tree/master/utils/loggers/comet) 🌟 NEW
+
+
##
+
+
+
+
+
+##
+
+ 
+
+
+
+
+
+
+
+
+
+
+
+
+##
+
+
+## ![](\"https://raw.githubusercontent.com/ultralytics/assets/main/yolov5/v70/splash.png\")
\n",
+ "\n",
+ "\n",
+ "![\"Run](\"https://assets.paperspace.io/img/gradient-badge.svg\"){kind=icon}
\n",
+ " ![\"Open](\"https://colab.research.google.com/assets/colab-badge.svg\"){kind=icon}
\n",
+ " ![\"Open](\"https://kaggle.com/static/images/open-in-kaggle.svg\")
\n",
+ "![](\"https://user-images.githubusercontent.com/26833433/202808393-50deb439-ae1b-4246-a685-7560c9b37211.jpg\")
"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "0eq1SMWl6Sfn"
+ },
+ "source": [
+ "# 2. Validate\n",
+ "Validate a model's accuracy on the [Imagenet](https://image-net.org/) dataset's `val` or `test` splits. Models are downloaded automatically from the [latest YOLOv5 release](https://github.com/ultralytics/yolov5/releases). To show results by class use the `--verbose` flag."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "WQPtK1QYVaD_",
+ "outputId": "20fc0630-141e-4a90-ea06-342cbd7ce496"
+ },
+ "outputs": [
+ {
+ "output_type": "stream",
+ "name": "stdout",
+ "text": [
+ "--2022-11-22 19:53:40-- https://image-net.org/data/ILSVRC/2012/ILSVRC2012_img_val.tar\n",
+ "Resolving image-net.org (image-net.org)... 171.64.68.16\n",
+ "Connecting to image-net.org (image-net.org)|171.64.68.16|:443... connected.\n",
+ "HTTP request sent, awaiting response... 200 OK\n",
+ "Length: 6744924160 (6.3G) [application/x-tar]\n",
+ "Saving to: ‘ILSVRC2012_img_val.tar’\n",
+ "\n",
+ "ILSVRC2012_img_val. 100%[===================>] 6.28G 16.1MB/s in 10m 52s \n",
+ "\n",
+ "2022-11-22 20:04:32 (9.87 MB/s) - ‘ILSVRC2012_img_val.tar’ saved [6744924160/6744924160]\n",
+ "\n"
+ ]
+ }
+ ],
+ "source": [
+ "# Download Imagenet val (6.3G, 50000 images)\n",
+ "!bash data/scripts/get_imagenet.sh --val"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "X58w8JLpMnjH",
+ "outputId": "41843132-98e2-4c25-d474-4cd7b246fb8e"
+ },
+ "outputs": [
+ {
+ "output_type": "stream",
+ "name": "stdout",
+ "text": [
+ "\u001b[34m\u001b[1mclassify/val: \u001b[0mdata=../datasets/imagenet, weights=['yolov5s-cls.pt'], batch_size=128, imgsz=224, device=, workers=8, verbose=True, project=runs/val-cls, name=exp, exist_ok=False, half=True, dnn=False\n",
+ "YOLOv5 🚀 v7.0-3-g61ebf5e Python-3.7.15 torch-1.12.1+cu113 CUDA:0 (Tesla T4, 15110MiB)\n",
+ "\n",
+ "Fusing layers... \n",
+ "Model summary: 117 layers, 5447688 parameters, 0 gradients, 11.4 GFLOPs\n",
+ "validating: 100% 391/391 [04:57<00:00, 1.31it/s]\n",
+ " Class Images top1_acc top5_acc\n",
+ " all 50000 0.715 0.902\n",
+ " tench 50 0.94 0.98\n",
+ " goldfish 50 0.88 0.92\n",
+ " great white shark 50 0.78 0.96\n",
+ " tiger shark 50 0.68 0.96\n",
+ " hammerhead shark 50 0.82 0.92\n",
+ " electric ray 50 0.76 0.9\n",
+ " stingray 50 0.7 0.9\n",
+ " cock 50 0.78 0.92\n",
+ " hen 50 0.84 0.96\n",
+ " ostrich 50 0.98 1\n",
+ " brambling 50 0.9 0.96\n",
+ " goldfinch 50 0.92 0.98\n",
+ " house finch 50 0.88 0.96\n",
+ " junco 50 0.94 0.98\n",
+ " indigo bunting 50 0.86 0.88\n",
+ " American robin 50 0.9 0.96\n",
+ " bulbul 50 0.84 0.96\n",
+ " jay 50 0.9 0.96\n",
+ " magpie 50 0.84 0.96\n",
+ " chickadee 50 0.9 1\n",
+ " American dipper 50 0.82 0.92\n",
+ " kite 50 0.76 0.94\n",
+ " bald eagle 50 0.92 1\n",
+ " vulture 50 0.96 1\n",
+ " great grey owl 50 0.94 0.98\n",
+ " fire salamander 50 0.96 0.98\n",
+ " smooth newt 50 0.58 0.94\n",
+ " newt 50 0.74 0.9\n",
+ " spotted salamander 50 0.86 0.94\n",
+ " axolotl 50 0.86 0.96\n",
+ " American bullfrog 50 0.78 0.92\n",
+ " tree frog 50 0.84 0.96\n",
+ " tailed frog 50 0.48 0.8\n",
+ " loggerhead sea turtle 50 0.68 0.94\n",
+ " leatherback sea turtle 50 0.5 0.8\n",
+ " mud turtle 50 0.64 0.84\n",
+ " terrapin 50 0.52 0.98\n",
+ " box turtle 50 0.84 0.98\n",
+ " banded gecko 50 0.7 0.88\n",
+ " green iguana 50 0.76 0.94\n",
+ " Carolina anole 50 0.58 0.96\n",
+ "desert grassland whiptail lizard 50 0.82 0.94\n",
+ " agama 50 0.74 0.92\n",
+ " frilled-necked lizard 50 0.84 0.86\n",
+ " alligator lizard 50 0.58 0.78\n",
+ " Gila monster 50 0.72 0.8\n",
+ " European green lizard 50 0.42 0.9\n",
+ " chameleon 50 0.76 0.84\n",
+ " Komodo dragon 50 0.86 0.96\n",
+ " Nile crocodile 50 0.7 0.84\n",
+ " American alligator 50 0.76 0.96\n",
+ " triceratops 50 0.9 0.94\n",
+ " worm snake 50 0.76 0.88\n",
+ " ring-necked snake 50 0.8 0.92\n",
+ " eastern hog-nosed snake 50 0.58 0.88\n",
+ " smooth green snake 50 0.6 0.94\n",
+ " kingsnake 50 0.82 0.9\n",
+ " garter snake 50 0.88 0.94\n",
+ " water snake 50 0.7 0.94\n",
+ " vine snake 50 0.66 0.76\n",
+ " night snake 50 0.34 0.82\n",
+ " boa constrictor 50 0.8 0.96\n",
+ " African rock python 50 0.48 0.76\n",
+ " Indian cobra 50 0.82 0.94\n",
+ " green mamba 50 0.54 0.86\n",
+ " sea snake 50 0.62 0.9\n",
+ " Saharan horned viper 50 0.56 0.86\n",
+ "eastern diamondback rattlesnake 50 0.6 0.86\n",
+ " sidewinder 50 0.28 0.86\n",
+ " trilobite 50 0.98 0.98\n",
+ " harvestman 50 0.86 0.94\n",
+ " scorpion 50 0.86 0.94\n",
+ " yellow garden spider 50 0.92 0.96\n",
+ " barn spider 50 0.38 0.98\n",
+ " European garden spider 50 0.62 0.98\n",
+ " southern black widow 50 0.88 0.94\n",
+ " tarantula 50 0.94 1\n",
+ " wolf spider 50 0.82 0.92\n",
+ " tick 50 0.74 0.84\n",
+ " centipede 50 0.68 0.82\n",
+ " black grouse 50 0.88 0.98\n",
+ " ptarmigan 50 0.78 0.94\n",
+ " ruffed grouse 50 0.88 1\n",
+ " prairie grouse 50 0.92 1\n",
+ " peacock 50 0.88 0.9\n",
+ " quail 50 0.9 0.94\n",
+ " partridge 50 0.74 0.96\n",
+ " grey parrot 50 0.9 0.96\n",
+ " macaw 50 0.88 0.98\n",
+ "sulphur-crested cockatoo 50 0.86 0.92\n",
+ " lorikeet 50 0.96 1\n",
+ " coucal 50 0.82 0.88\n",
+ " bee eater 50 0.96 0.98\n",
+ " hornbill 50 0.9 0.96\n",
+ " hummingbird 50 0.88 0.96\n",
+ " jacamar 50 0.92 0.94\n",
+ " toucan 50 0.84 0.94\n",
+ " duck 50 0.76 0.94\n",
+ " red-breasted merganser 50 0.86 0.96\n",
+ " goose 50 0.74 0.96\n",
+ " black swan 50 0.94 0.98\n",
+ " tusker 50 0.54 0.92\n",
+ " echidna 50 0.98 1\n",
+ " platypus 50 0.72 0.84\n",
+ " wallaby 50 0.78 0.88\n",
+ " koala 50 0.84 0.92\n",
+ " wombat 50 0.78 0.84\n",
+ " jellyfish 50 0.88 0.96\n",
+ " sea anemone 50 0.72 0.9\n",
+ " brain coral 50 0.88 0.96\n",
+ " flatworm 50 0.8 0.98\n",
+ " nematode 50 0.86 0.9\n",
+ " conch 50 0.74 0.88\n",
+ " snail 50 0.78 0.88\n",
+ " slug 50 0.74 0.82\n",
+ " sea slug 50 0.88 0.98\n",
+ " chiton 50 0.88 0.98\n",
+ " chambered nautilus 50 0.88 0.92\n",
+ " Dungeness crab 50 0.78 0.94\n",
+ " rock crab 50 0.68 0.86\n",
+ " fiddler crab 50 0.64 0.86\n",
+ " red king crab 50 0.76 0.96\n",
+ " American lobster 50 0.78 0.96\n",
+ " spiny lobster 50 0.74 0.88\n",
+ " crayfish 50 0.56 0.86\n",
+ " hermit crab 50 0.78 0.96\n",
+ " isopod 50 0.66 0.78\n",
+ " white stork 50 0.88 0.96\n",
+ " black stork 50 0.84 0.98\n",
+ " spoonbill 50 0.96 1\n",
+ " flamingo 50 0.94 1\n",
+ " little blue heron 50 0.92 0.98\n",
+ " great egret 50 0.9 0.96\n",
+ " bittern 50 0.86 0.94\n",
+ " crane (bird) 50 0.62 0.9\n",
+ " limpkin 50 0.98 1\n",
+ " common gallinule 50 0.92 0.96\n",
+ " American coot 50 0.9 0.98\n",
+ " bustard 50 0.92 0.96\n",
+ " ruddy turnstone 50 0.94 1\n",
+ " dunlin 50 0.86 0.94\n",
+ " common redshank 50 0.9 0.96\n",
+ " dowitcher 50 0.84 0.96\n",
+ " oystercatcher 50 0.86 0.94\n",
+ " pelican 50 0.92 0.96\n",
+ " king penguin 50 0.88 0.96\n",
+ " albatross 50 0.9 1\n",
+ " grey whale 50 0.84 0.92\n",
+ " killer whale 50 0.92 1\n",
+ " dugong 50 0.84 0.96\n",
+ " sea lion 50 0.82 0.92\n",
+ " Chihuahua 50 0.66 0.84\n",
+ " Japanese Chin 50 0.72 0.98\n",
+ " Maltese 50 0.76 0.94\n",
+ " Pekingese 50 0.84 0.94\n",
+ " Shih Tzu 50 0.74 0.96\n",
+ " King Charles Spaniel 50 0.88 0.98\n",
+ " Papillon 50 0.86 0.94\n",
+ " toy terrier 50 0.48 0.94\n",
+ " Rhodesian Ridgeback 50 0.76 0.98\n",
+ " Afghan Hound 50 0.84 1\n",
+ " Basset Hound 50 0.8 0.92\n",
+ " Beagle 50 0.82 0.96\n",
+ " Bloodhound 50 0.48 0.72\n",
+ " Bluetick Coonhound 50 0.86 0.94\n",
+ " Black and Tan Coonhound 50 0.54 0.8\n",
+ "Treeing Walker Coonhound 50 0.66 0.98\n",
+ " English foxhound 50 0.32 0.84\n",
+ " Redbone Coonhound 50 0.62 0.94\n",
+ " borzoi 50 0.92 1\n",
+ " Irish Wolfhound 50 0.48 0.88\n",
+ " Italian Greyhound 50 0.76 0.98\n",
+ " Whippet 50 0.74 0.92\n",
+ " Ibizan Hound 50 0.6 0.86\n",
+ " Norwegian Elkhound 50 0.88 0.98\n",
+ " Otterhound 50 0.62 0.9\n",
+ " Saluki 50 0.72 0.92\n",
+ " Scottish Deerhound 50 0.86 0.98\n",
+ " Weimaraner 50 0.88 0.94\n",
+ "Staffordshire Bull Terrier 50 0.66 0.98\n",
+ "American Staffordshire Terrier 50 0.64 0.92\n",
+ " Bedlington Terrier 50 0.9 0.92\n",
+ " Border Terrier 50 0.86 0.92\n",
+ " Kerry Blue Terrier 50 0.78 0.98\n",
+ " Irish Terrier 50 0.7 0.96\n",
+ " Norfolk Terrier 50 0.68 0.9\n",
+ " Norwich Terrier 50 0.72 1\n",
+ " Yorkshire Terrier 50 0.66 0.9\n",
+ " Wire Fox Terrier 50 0.64 0.98\n",
+ " Lakeland Terrier 50 0.74 0.92\n",
+ " Sealyham Terrier 50 0.76 0.9\n",
+ " Airedale Terrier 50 0.82 0.92\n",
+ " Cairn Terrier 50 0.76 0.9\n",
+ " Australian Terrier 50 0.48 0.84\n",
+ " Dandie Dinmont Terrier 50 0.82 0.92\n",
+ " Boston Terrier 50 0.92 1\n",
+ " Miniature Schnauzer 50 0.68 0.9\n",
+ " Giant Schnauzer 50 0.72 0.98\n",
+ " Standard Schnauzer 50 0.74 1\n",
+ " Scottish Terrier 50 0.76 0.96\n",
+ " Tibetan Terrier 50 0.48 1\n",
+ "Australian Silky Terrier 50 0.66 0.96\n",
+ "Soft-coated Wheaten Terrier 50 0.74 0.96\n",
+ "West Highland White Terrier 50 0.88 0.96\n",
+ " Lhasa Apso 50 0.68 0.96\n",
+ " Flat-Coated Retriever 50 0.72 0.94\n",
+ " Curly-coated Retriever 50 0.82 0.94\n",
+ " Golden Retriever 50 0.86 0.94\n",
+ " Labrador Retriever 50 0.82 0.94\n",
+ "Chesapeake Bay Retriever 50 0.76 0.96\n",
+ "German Shorthaired Pointer 50 0.8 0.96\n",
+ " Vizsla 50 0.68 0.96\n",
+ " English Setter 50 0.7 1\n",
+ " Irish Setter 50 0.8 0.9\n",
+ " Gordon Setter 50 0.84 0.92\n",
+ " Brittany 50 0.84 0.96\n",
+ " Clumber Spaniel 50 0.92 0.96\n",
+ "English Springer Spaniel 50 0.88 1\n",
+ " Welsh Springer Spaniel 50 0.92 1\n",
+ " Cocker Spaniels 50 0.7 0.94\n",
+ " Sussex Spaniel 50 0.72 0.92\n",
+ " Irish Water Spaniel 50 0.88 0.98\n",
+ " Kuvasz 50 0.66 0.9\n",
+ " Schipperke 50 0.9 0.98\n",
+ " Groenendael 50 0.8 0.94\n",
+ " Malinois 50 0.86 0.98\n",
+ " Briard 50 0.52 0.8\n",
+ " Australian Kelpie 50 0.6 0.88\n",
+ " Komondor 50 0.88 0.94\n",
+ " Old English Sheepdog 50 0.94 0.98\n",
+ " Shetland Sheepdog 50 0.74 0.9\n",
+ " collie 50 0.6 0.96\n",
+ " Border Collie 50 0.74 0.96\n",
+ " Bouvier des Flandres 50 0.78 0.94\n",
+ " Rottweiler 50 0.88 0.96\n",
+ " German Shepherd Dog 50 0.8 0.98\n",
+ " Dobermann 50 0.68 0.96\n",
+ " Miniature Pinscher 50 0.76 0.88\n",
+ "Greater Swiss Mountain Dog 50 0.68 0.94\n",
+ " Bernese Mountain Dog 50 0.96 1\n",
+ " Appenzeller Sennenhund 50 0.22 1\n",
+ " Entlebucher Sennenhund 50 0.64 0.98\n",
+ " Boxer 50 0.7 0.92\n",
+ " Bullmastiff 50 0.78 0.98\n",
+ " Tibetan Mastiff 50 0.88 0.96\n",
+ " French Bulldog 50 0.84 0.94\n",
+ " Great Dane 50 0.54 0.9\n",
+ " St. Bernard 50 0.92 1\n",
+ " husky 50 0.46 0.98\n",
+ " Alaskan Malamute 50 0.76 0.96\n",
+ " Siberian Husky 50 0.46 0.98\n",
+ " Dalmatian 50 0.94 0.98\n",
+ " Affenpinscher 50 0.78 0.9\n",
+ " Basenji 50 0.92 0.94\n",
+ " pug 50 0.94 0.98\n",
+ " Leonberger 50 1 1\n",
+ " Newfoundland 50 0.78 0.96\n",
+ " Pyrenean Mountain Dog 50 0.78 0.96\n",
+ " Samoyed 50 0.96 1\n",
+ " Pomeranian 50 0.98 1\n",
+ " Chow Chow 50 0.9 0.96\n",
+ " Keeshond 50 0.88 0.94\n",
+ " Griffon Bruxellois 50 0.84 0.98\n",
+ " Pembroke Welsh Corgi 50 0.82 0.94\n",
+ " Cardigan Welsh Corgi 50 0.66 0.98\n",
+ " Toy Poodle 50 0.52 0.88\n",
+ " Miniature Poodle 50 0.52 0.92\n",
+ " Standard Poodle 50 0.8 1\n",
+ " Mexican hairless dog 50 0.88 0.98\n",
+ " grey wolf 50 0.82 0.92\n",
+ " Alaskan tundra wolf 50 0.78 0.98\n",
+ " red wolf 50 0.48 0.9\n",
+ " coyote 50 0.64 0.86\n",
+ " dingo 50 0.76 0.88\n",
+ " dhole 50 0.9 0.98\n",
+ " African wild dog 50 0.98 1\n",
+ " hyena 50 0.88 0.96\n",
+ " red fox 50 0.54 0.92\n",
+ " kit fox 50 0.72 0.98\n",
+ " Arctic fox 50 0.94 1\n",
+ " grey fox 50 0.7 0.94\n",
+ " tabby cat 50 0.54 0.92\n",
+ " tiger cat 50 0.22 0.94\n",
+ " Persian cat 50 0.9 0.98\n",
+ " Siamese cat 50 0.96 1\n",
+ " Egyptian Mau 50 0.54 0.8\n",
+ " cougar 50 0.9 1\n",
+ " lynx 50 0.72 0.88\n",
+ " leopard 50 0.78 0.98\n",
+ " snow leopard 50 0.9 0.98\n",
+ " jaguar 50 0.7 0.94\n",
+ " lion 50 0.9 0.98\n",
+ " tiger 50 0.92 0.98\n",
+ " cheetah 50 0.94 0.98\n",
+ " brown bear 50 0.94 0.98\n",
+ " American black bear 50 0.8 1\n",
+ " polar bear 50 0.84 0.96\n",
+ " sloth bear 50 0.72 0.92\n",
+ " mongoose 50 0.7 0.92\n",
+ " meerkat 50 0.82 0.92\n",
+ " tiger beetle 50 0.92 0.94\n",
+ " ladybug 50 0.86 0.94\n",
+ " ground beetle 50 0.64 0.94\n",
+ " longhorn beetle 50 0.62 0.88\n",
+ " leaf beetle 50 0.64 0.98\n",
+ " dung beetle 50 0.86 0.98\n",
+ " rhinoceros beetle 50 0.86 0.94\n",
+ " weevil 50 0.9 1\n",
+ " fly 50 0.78 0.94\n",
+ " bee 50 0.68 0.94\n",
+ " ant 50 0.68 0.78\n",
+ " grasshopper 50 0.5 0.92\n",
+ " cricket 50 0.64 0.92\n",
+ " stick insect 50 0.64 0.92\n",
+ " cockroach 50 0.72 0.8\n",
+ " mantis 50 0.64 0.86\n",
+ " cicada 50 0.9 0.96\n",
+ " leafhopper 50 0.88 0.94\n",
+ " lacewing 50 0.78 0.92\n",
+ " dragonfly 50 0.82 0.98\n",
+ " damselfly 50 0.82 1\n",
+ " red admiral 50 0.94 0.96\n",
+ " ringlet 50 0.86 0.98\n",
+ " monarch butterfly 50 0.9 0.92\n",
+ " small white 50 0.9 1\n",
+ " sulphur butterfly 50 0.92 1\n",
+ "gossamer-winged butterfly 50 0.88 1\n",
+ " starfish 50 0.88 0.92\n",
+ " sea urchin 50 0.84 0.94\n",
+ " sea cucumber 50 0.66 0.84\n",
+ " cottontail rabbit 50 0.72 0.94\n",
+ " hare 50 0.84 0.96\n",
+ " Angora rabbit 50 0.94 0.98\n",
+ " hamster 50 0.96 1\n",
+ " porcupine 50 0.88 0.98\n",
+ " fox squirrel 50 0.76 0.94\n",
+ " marmot 50 0.92 0.96\n",
+ " beaver 50 0.78 0.94\n",
+ " guinea pig 50 0.78 0.94\n",
+ " common sorrel 50 0.96 0.98\n",
+ " zebra 50 0.94 0.96\n",
+ " pig 50 0.5 0.76\n",
+ " wild boar 50 0.84 0.96\n",
+ " warthog 50 0.84 0.96\n",
+ " hippopotamus 50 0.88 0.96\n",
+ " ox 50 0.48 0.94\n",
+ " water buffalo 50 0.78 0.94\n",
+ " bison 50 0.88 0.96\n",
+ " ram 50 0.58 0.92\n",
+ " bighorn sheep 50 0.66 1\n",
+ " Alpine ibex 50 0.92 0.98\n",
+ " hartebeest 50 0.94 1\n",
+ " impala 50 0.82 0.96\n",
+ " gazelle 50 0.7 0.96\n",
+ " dromedary 50 0.9 1\n",
+ " llama 50 0.82 0.94\n",
+ " weasel 50 0.44 0.92\n",
+ " mink 50 0.78 0.96\n",
+ " European polecat 50 0.46 0.9\n",
+ " black-footed ferret 50 0.68 0.96\n",
+ " otter 50 0.66 0.88\n",
+ " skunk 50 0.96 0.96\n",
+ " badger 50 0.86 0.92\n",
+ " armadillo 50 0.88 0.9\n",
+ " three-toed sloth 50 0.96 1\n",
+ " orangutan 50 0.78 0.92\n",
+ " gorilla 50 0.82 0.94\n",
+ " chimpanzee 50 0.84 0.94\n",
+ " gibbon 50 0.76 0.86\n",
+ " siamang 50 0.68 0.94\n",
+ " guenon 50 0.8 0.94\n",
+ " patas monkey 50 0.62 0.82\n",
+ " baboon 50 0.9 0.98\n",
+ " macaque 50 0.8 0.86\n",
+ " langur 50 0.6 0.82\n",
+ " black-and-white colobus 50 0.86 0.9\n",
+ " proboscis monkey 50 1 1\n",
+ " marmoset 50 0.74 0.98\n",
+ " white-headed capuchin 50 0.72 0.9\n",
+ " howler monkey 50 0.86 0.94\n",
+ " titi 50 0.5 0.9\n",
+ "Geoffroy's spider monkey 50 0.42 0.8\n",
+ " common squirrel monkey 50 0.76 0.92\n",
+ " ring-tailed lemur 50 0.72 0.94\n",
+ " indri 50 0.9 0.96\n",
+ " Asian elephant 50 0.58 0.92\n",
+ " African bush elephant 50 0.7 0.98\n",
+ " red panda 50 0.94 0.94\n",
+ " giant panda 50 0.94 0.98\n",
+ " snoek 50 0.74 0.9\n",
+ " eel 50 0.6 0.84\n",
+ " coho salmon 50 0.84 0.96\n",
+ " rock beauty 50 0.88 0.98\n",
+ " clownfish 50 0.78 0.98\n",
+ " sturgeon 50 0.68 0.94\n",
+ " garfish 50 0.62 0.8\n",
+ " lionfish 50 0.96 0.96\n",
+ " pufferfish 50 0.88 0.96\n",
+ " abacus 50 0.74 0.88\n",
+ " abaya 50 0.84 0.92\n",
+ " academic gown 50 0.42 0.86\n",
+ " accordion 50 0.8 0.9\n",
+ " acoustic guitar 50 0.5 0.76\n",
+ " aircraft carrier 50 0.8 0.96\n",
+ " airliner 50 0.92 1\n",
+ " airship 50 0.76 0.82\n",
+ " altar 50 0.64 0.98\n",
+ " ambulance 50 0.88 0.98\n",
+ " amphibious vehicle 50 0.64 0.94\n",
+ " analog clock 50 0.52 0.92\n",
+ " apiary 50 0.82 0.96\n",
+ " apron 50 0.7 0.84\n",
+ " waste container 50 0.4 0.8\n",
+ " assault rifle 50 0.42 0.84\n",
+ " backpack 50 0.34 0.64\n",
+ " bakery 50 0.4 0.68\n",
+ " balance beam 50 0.8 0.98\n",
+ " balloon 50 0.86 0.96\n",
+ " ballpoint pen 50 0.52 0.96\n",
+ " Band-Aid 50 0.7 0.9\n",
+ " banjo 50 0.84 1\n",
+ " baluster 50 0.68 0.94\n",
+ " barbell 50 0.56 0.9\n",
+ " barber chair 50 0.7 0.92\n",
+ " barbershop 50 0.54 0.86\n",
+ " barn 50 0.96 0.96\n",
+ " barometer 50 0.84 0.98\n",
+ " barrel 50 0.56 0.88\n",
+ " wheelbarrow 50 0.66 0.88\n",
+ " baseball 50 0.74 0.98\n",
+ " basketball 50 0.88 0.98\n",
+ " bassinet 50 0.66 0.92\n",
+ " bassoon 50 0.74 0.98\n",
+ " swimming cap 50 0.62 0.88\n",
+ " bath towel 50 0.54 0.78\n",
+ " bathtub 50 0.4 0.88\n",
+ " station wagon 50 0.66 0.84\n",
+ " lighthouse 50 0.78 0.94\n",
+ " beaker 50 0.52 0.68\n",
+ " military cap 50 0.84 0.96\n",
+ " beer bottle 50 0.66 0.88\n",
+ " beer glass 50 0.6 0.84\n",
+ " bell-cot 50 0.56 0.96\n",
+ " bib 50 0.58 0.82\n",
+ " tandem bicycle 50 0.86 0.96\n",
+ " bikini 50 0.56 0.88\n",
+ " ring binder 50 0.64 0.84\n",
+ " binoculars 50 0.54 0.78\n",
+ " birdhouse 50 0.86 0.94\n",
+ " boathouse 50 0.74 0.92\n",
+ " bobsleigh 50 0.92 0.96\n",
+ " bolo tie 50 0.8 0.94\n",
+ " poke bonnet 50 0.64 0.86\n",
+ " bookcase 50 0.66 0.92\n",
+ " bookstore 50 0.62 0.88\n",
+ " bottle cap 50 0.58 0.7\n",
+ " bow 50 0.72 0.86\n",
+ " bow tie 50 0.7 0.9\n",
+ " brass 50 0.92 0.96\n",
+ " bra 50 0.5 0.7\n",
+ " breakwater 50 0.62 0.86\n",
+ " breastplate 50 0.4 0.9\n",
+ " broom 50 0.6 0.86\n",
+ " bucket 50 0.66 0.8\n",
+ " buckle 50 0.5 0.68\n",
+ " bulletproof vest 50 0.5 0.78\n",
+ " high-speed train 50 0.94 0.96\n",
+ " butcher shop 50 0.74 0.94\n",
+ " taxicab 50 0.64 0.86\n",
+ " cauldron 50 0.44 0.66\n",
+ " candle 50 0.48 0.74\n",
+ " cannon 50 0.88 0.94\n",
+ " canoe 50 0.94 1\n",
+ " can opener 50 0.66 0.86\n",
+ " cardigan 50 0.68 0.8\n",
+ " car mirror 50 0.94 0.96\n",
+ " carousel 50 0.94 0.98\n",
+ " tool kit 50 0.56 0.78\n",
+ " carton 50 0.42 0.7\n",
+ " car wheel 50 0.38 0.74\n",
+ "automated teller machine 50 0.76 0.94\n",
+ " cassette 50 0.52 0.8\n",
+ " cassette player 50 0.28 0.9\n",
+ " castle 50 0.78 0.88\n",
+ " catamaran 50 0.78 1\n",
+ " CD player 50 0.52 0.82\n",
+ " cello 50 0.82 1\n",
+ " mobile phone 50 0.68 0.86\n",
+ " chain 50 0.38 0.66\n",
+ " chain-link fence 50 0.7 0.84\n",
+ " chain mail 50 0.64 0.9\n",
+ " chainsaw 50 0.84 0.92\n",
+ " chest 50 0.68 0.92\n",
+ " chiffonier 50 0.26 0.64\n",
+ " chime 50 0.62 0.84\n",
+ " china cabinet 50 0.82 0.96\n",
+ " Christmas stocking 50 0.92 0.94\n",
+ " church 50 0.62 0.9\n",
+ " movie theater 50 0.58 0.88\n",
+ " cleaver 50 0.32 0.62\n",
+ " cliff dwelling 50 0.88 1\n",
+ " cloak 50 0.32 0.64\n",
+ " clogs 50 0.58 0.88\n",
+ " cocktail shaker 50 0.62 0.7\n",
+ " coffee mug 50 0.44 0.72\n",
+ " coffeemaker 50 0.64 0.92\n",
+ " coil 50 0.66 0.84\n",
+ " combination lock 50 0.64 0.84\n",
+ " computer keyboard 50 0.7 0.82\n",
+ " confectionery store 50 0.54 0.86\n",
+ " container ship 50 0.82 0.98\n",
+ " convertible 50 0.78 0.98\n",
+ " corkscrew 50 0.82 0.92\n",
+ " cornet 50 0.46 0.88\n",
+ " cowboy boot 50 0.64 0.8\n",
+ " cowboy hat 50 0.64 0.82\n",
+ " cradle 50 0.38 0.8\n",
+ " crane (machine) 50 0.78 0.94\n",
+ " crash helmet 50 0.92 0.96\n",
+ " crate 50 0.52 0.82\n",
+ " infant bed 50 0.74 1\n",
+ " Crock Pot 50 0.78 0.9\n",
+ " croquet ball 50 0.9 0.96\n",
+ " crutch 50 0.46 0.7\n",
+ " cuirass 50 0.54 0.86\n",
+ " dam 50 0.74 0.92\n",
+ " desk 50 0.6 0.86\n",
+ " desktop computer 50 0.54 0.94\n",
+ " rotary dial telephone 50 0.88 0.94\n",
+ " diaper 50 0.68 0.84\n",
+ " digital clock 50 0.54 0.76\n",
+ " digital watch 50 0.58 0.86\n",
+ " dining table 50 0.76 0.9\n",
+ " dishcloth 50 0.94 1\n",
+ " dishwasher 50 0.44 0.78\n",
+ " disc brake 50 0.98 1\n",
+ " dock 50 0.54 0.94\n",
+ " dog sled 50 0.84 1\n",
+ " dome 50 0.72 0.92\n",
+ " doormat 50 0.56 0.82\n",
+ " drilling rig 50 0.84 0.96\n",
+ " drum 50 0.38 0.68\n",
+ " drumstick 50 0.56 0.72\n",
+ " dumbbell 50 0.62 0.9\n",
+ " Dutch oven 50 0.7 0.84\n",
+ " electric fan 50 0.82 0.86\n",
+ " electric guitar 50 0.62 0.84\n",
+ " electric locomotive 50 0.92 0.98\n",
+ " entertainment center 50 0.9 0.98\n",
+ " envelope 50 0.44 0.86\n",
+ " espresso machine 50 0.72 0.94\n",
+ " face powder 50 0.7 0.92\n",
+ " feather boa 50 0.7 0.84\n",
+ " filing cabinet 50 0.88 0.98\n",
+ " fireboat 50 0.94 0.98\n",
+ " fire engine 50 0.84 0.9\n",
+ " fire screen sheet 50 0.62 0.76\n",
+ " flagpole 50 0.74 0.88\n",
+ " flute 50 0.36 0.72\n",
+ " folding chair 50 0.62 0.84\n",
+ " football helmet 50 0.86 0.94\n",
+ " forklift 50 0.8 0.92\n",
+ " fountain 50 0.84 0.94\n",
+ " fountain pen 50 0.76 0.92\n",
+ " four-poster bed 50 0.78 0.94\n",
+ " freight car 50 0.96 1\n",
+ " French horn 50 0.76 0.92\n",
+ " frying pan 50 0.36 0.78\n",
+ " fur coat 50 0.84 0.96\n",
+ " garbage truck 50 0.9 0.98\n",
+ " gas mask 50 0.84 0.92\n",
+ " gas pump 50 0.9 0.98\n",
+ " goblet 50 0.68 0.82\n",
+ " go-kart 50 0.9 1\n",
+ " golf ball 50 0.84 0.9\n",
+ " golf cart 50 0.78 0.86\n",
+ " gondola 50 0.98 0.98\n",
+ " gong 50 0.74 0.92\n",
+ " gown 50 0.62 0.96\n",
+ " grand piano 50 0.7 0.96\n",
+ " greenhouse 50 0.8 0.98\n",
+ " grille 50 0.72 0.9\n",
+ " grocery store 50 0.66 0.94\n",
+ " guillotine 50 0.86 0.92\n",
+ " barrette 50 0.52 0.66\n",
+ " hair spray 50 0.5 0.74\n",
+ " half-track 50 0.78 0.9\n",
+ " hammer 50 0.56 0.76\n",
+ " hamper 50 0.64 0.84\n",
+ " hair dryer 50 0.56 0.74\n",
+ " hand-held computer 50 0.42 0.86\n",
+ " handkerchief 50 0.78 0.94\n",
+ " hard disk drive 50 0.76 0.84\n",
+ " harmonica 50 0.7 0.88\n",
+ " harp 50 0.88 0.96\n",
+ " harvester 50 0.78 1\n",
+ " hatchet 50 0.54 0.74\n",
+ " holster 50 0.66 0.84\n",
+ " home theater 50 0.64 0.94\n",
+ " honeycomb 50 0.56 0.88\n",
+ " hook 50 0.3 0.6\n",
+ " hoop skirt 50 0.64 0.86\n",
+ " horizontal bar 50 0.68 0.98\n",
+ " horse-drawn vehicle 50 0.88 0.94\n",
+ " hourglass 50 0.88 0.96\n",
+ " iPod 50 0.76 0.94\n",
+ " clothes iron 50 0.82 0.88\n",
+ " jack-o'-lantern 50 0.98 0.98\n",
+ " jeans 50 0.68 0.84\n",
+ " jeep 50 0.72 0.9\n",
+ " T-shirt 50 0.72 0.96\n",
+ " jigsaw puzzle 50 0.84 0.94\n",
+ " pulled rickshaw 50 0.86 0.94\n",
+ " joystick 50 0.8 0.9\n",
+ " kimono 50 0.84 0.96\n",
+ " knee pad 50 0.62 0.88\n",
+ " knot 50 0.66 0.8\n",
+ " lab coat 50 0.8 0.96\n",
+ " ladle 50 0.36 0.64\n",
+ " lampshade 50 0.48 0.84\n",
+ " laptop computer 50 0.26 0.88\n",
+ " lawn mower 50 0.78 0.96\n",
+ " lens cap 50 0.46 0.72\n",
+ " paper knife 50 0.26 0.5\n",
+ " library 50 0.54 0.9\n",
+ " lifeboat 50 0.92 0.98\n",
+ " lighter 50 0.56 0.78\n",
+ " limousine 50 0.76 0.92\n",
+ " ocean liner 50 0.88 0.94\n",
+ " lipstick 50 0.74 0.9\n",
+ " slip-on shoe 50 0.74 0.92\n",
+ " lotion 50 0.5 0.86\n",
+ " speaker 50 0.52 0.68\n",
+ " loupe 50 0.32 0.52\n",
+ " sawmill 50 0.72 0.9\n",
+ " magnetic compass 50 0.52 0.82\n",
+ " mail bag 50 0.68 0.92\n",
+ " mailbox 50 0.82 0.92\n",
+ " tights 50 0.22 0.94\n",
+ " tank suit 50 0.24 0.9\n",
+ " manhole cover 50 0.96 0.98\n",
+ " maraca 50 0.74 0.9\n",
+ " marimba 50 0.84 0.94\n",
+ " mask 50 0.44 0.82\n",
+ " match 50 0.66 0.9\n",
+ " maypole 50 0.96 1\n",
+ " maze 50 0.8 0.96\n",
+ " measuring cup 50 0.54 0.76\n",
+ " medicine chest 50 0.6 0.84\n",
+ " megalith 50 0.8 0.92\n",
+ " microphone 50 0.52 0.7\n",
+ " microwave oven 50 0.48 0.72\n",
+ " military uniform 50 0.62 0.84\n",
+ " milk can 50 0.68 0.82\n",
+ " minibus 50 0.7 1\n",
+ " miniskirt 50 0.46 0.76\n",
+ " minivan 50 0.38 0.8\n",
+ " missile 50 0.4 0.84\n",
+ " mitten 50 0.76 0.88\n",
+ " mixing bowl 50 0.8 0.92\n",
+ " mobile home 50 0.54 0.78\n",
+ " Model T 50 0.92 0.96\n",
+ " modem 50 0.58 0.86\n",
+ " monastery 50 0.44 0.9\n",
+ " monitor 50 0.4 0.86\n",
+ " moped 50 0.56 0.94\n",
+ " mortar 50 0.68 0.94\n",
+ " square academic cap 50 0.5 0.84\n",
+ " mosque 50 0.9 1\n",
+ " mosquito net 50 0.9 0.98\n",
+ " scooter 50 0.9 0.98\n",
+ " mountain bike 50 0.78 0.96\n",
+ " tent 50 0.88 0.96\n",
+ " computer mouse 50 0.42 0.82\n",
+ " mousetrap 50 0.76 0.88\n",
+ " moving van 50 0.4 0.72\n",
+ " muzzle 50 0.5 0.72\n",
+ " nail 50 0.68 0.74\n",
+ " neck brace 50 0.56 0.68\n",
+ " necklace 50 0.86 1\n",
+ " nipple 50 0.7 0.88\n",
+ " notebook computer 50 0.34 0.84\n",
+ " obelisk 50 0.8 0.92\n",
+ " oboe 50 0.6 0.84\n",
+ " ocarina 50 0.8 0.86\n",
+ " odometer 50 0.96 1\n",
+ " oil filter 50 0.58 0.82\n",
+ " organ 50 0.82 0.9\n",
+ " oscilloscope 50 0.9 0.96\n",
+ " overskirt 50 0.2 0.7\n",
+ " bullock cart 50 0.7 0.94\n",
+ " oxygen mask 50 0.46 0.84\n",
+ " packet 50 0.5 0.78\n",
+ " paddle 50 0.56 0.94\n",
+ " paddle wheel 50 0.86 0.96\n",
+ " padlock 50 0.74 0.78\n",
+ " paintbrush 50 0.62 0.8\n",
+ " pajamas 50 0.56 0.92\n",
+ " palace 50 0.64 0.96\n",
+ " pan flute 50 0.84 0.86\n",
+ " paper towel 50 0.66 0.84\n",
+ " parachute 50 0.92 0.94\n",
+ " parallel bars 50 0.62 0.96\n",
+ " park bench 50 0.74 0.9\n",
+ " parking meter 50 0.84 0.92\n",
+ " passenger car 50 0.5 0.82\n",
+ " patio 50 0.58 0.84\n",
+ " payphone 50 0.74 0.92\n",
+ " pedestal 50 0.52 0.9\n",
+ " pencil case 50 0.64 0.92\n",
+ " pencil sharpener 50 0.52 0.78\n",
+ " perfume 50 0.7 0.9\n",
+ " Petri dish 50 0.6 0.8\n",
+ " photocopier 50 0.88 0.98\n",
+ " plectrum 50 0.7 0.84\n",
+ " Pickelhaube 50 0.72 0.86\n",
+ " picket fence 50 0.84 0.94\n",
+ " pickup truck 50 0.64 0.92\n",
+ " pier 50 0.52 0.82\n",
+ " piggy bank 50 0.82 0.94\n",
+ " pill bottle 50 0.76 0.86\n",
+ " pillow 50 0.76 0.9\n",
+ " ping-pong ball 50 0.84 0.88\n",
+ " pinwheel 50 0.76 0.88\n",
+ " pirate ship 50 0.76 0.94\n",
+ " pitcher 50 0.46 0.84\n",
+ " hand plane 50 0.84 0.94\n",
+ " planetarium 50 0.88 0.98\n",
+ " plastic bag 50 0.36 0.62\n",
+ " plate rack 50 0.52 0.78\n",
+ " plow 50 0.78 0.88\n",
+ " plunger 50 0.42 0.7\n",
+ " Polaroid camera 50 0.84 0.92\n",
+ " pole 50 0.38 0.74\n",
+ " police van 50 0.76 0.94\n",
+ " poncho 50 0.58 0.86\n",
+ " billiard table 50 0.8 0.88\n",
+ " soda bottle 50 0.56 0.94\n",
+ " pot 50 0.78 0.92\n",
+ " potter's wheel 50 0.9 0.94\n",
+ " power drill 50 0.42 0.72\n",
+ " prayer rug 50 0.7 0.86\n",
+ " printer 50 0.54 0.86\n",
+ " prison 50 0.7 0.9\n",
+ " projectile 50 0.28 0.9\n",
+ " projector 50 0.62 0.84\n",
+ " hockey puck 50 0.92 0.96\n",
+ " punching bag 50 0.6 0.68\n",
+ " purse 50 0.42 0.78\n",
+ " quill 50 0.68 0.84\n",
+ " quilt 50 0.64 0.9\n",
+ " race car 50 0.72 0.92\n",
+ " racket 50 0.72 0.9\n",
+ " radiator 50 0.66 0.76\n",
+ " radio 50 0.64 0.92\n",
+ " radio telescope 50 0.9 0.96\n",
+ " rain barrel 50 0.8 0.98\n",
+ " recreational vehicle 50 0.84 0.94\n",
+ " reel 50 0.72 0.82\n",
+ " reflex camera 50 0.72 0.92\n",
+ " refrigerator 50 0.7 0.9\n",
+ " remote control 50 0.7 0.88\n",
+ " restaurant 50 0.5 0.66\n",
+ " revolver 50 0.82 1\n",
+ " rifle 50 0.38 0.7\n",
+ " rocking chair 50 0.62 0.84\n",
+ " rotisserie 50 0.88 0.92\n",
+ " eraser 50 0.54 0.76\n",
+ " rugby ball 50 0.86 0.94\n",
+ " ruler 50 0.68 0.86\n",
+ " running shoe 50 0.78 0.94\n",
+ " safe 50 0.82 0.92\n",
+ " safety pin 50 0.4 0.62\n",
+ " salt shaker 50 0.66 0.9\n",
+ " sandal 50 0.66 0.86\n",
+ " sarong 50 0.64 0.86\n",
+ " saxophone 50 0.66 0.88\n",
+ " scabbard 50 0.76 0.92\n",
+ " weighing scale 50 0.58 0.78\n",
+ " school bus 50 0.92 1\n",
+ " schooner 50 0.84 1\n",
+ " scoreboard 50 0.9 0.96\n",
+ " CRT screen 50 0.14 0.7\n",
+ " screw 50 0.9 0.98\n",
+ " screwdriver 50 0.3 0.58\n",
+ " seat belt 50 0.88 0.94\n",
+ " sewing machine 50 0.76 0.9\n",
+ " shield 50 0.56 0.82\n",
+ " shoe store 50 0.78 0.96\n",
+ " shoji 50 0.8 0.92\n",
+ " shopping basket 50 0.52 0.88\n",
+ " shopping cart 50 0.76 0.92\n",
+ " shovel 50 0.62 0.84\n",
+ " shower cap 50 0.7 0.84\n",
+ " shower curtain 50 0.64 0.82\n",
+ " ski 50 0.74 0.92\n",
+ " ski mask 50 0.72 0.88\n",
+ " sleeping bag 50 0.68 0.8\n",
+ " slide rule 50 0.72 0.88\n",
+ " sliding door 50 0.44 0.78\n",
+ " slot machine 50 0.94 0.98\n",
+ " snorkel 50 0.86 0.98\n",
+ " snowmobile 50 0.88 1\n",
+ " snowplow 50 0.84 0.98\n",
+ " soap dispenser 50 0.56 0.86\n",
+ " soccer ball 50 0.86 0.96\n",
+ " sock 50 0.62 0.76\n",
+ " solar thermal collector 50 0.72 0.96\n",
+ " sombrero 50 0.6 0.84\n",
+ " soup bowl 50 0.56 0.94\n",
+ " space bar 50 0.34 0.88\n",
+ " space heater 50 0.52 0.74\n",
+ " space shuttle 50 0.82 0.96\n",
+ " spatula 50 0.3 0.6\n",
+ " motorboat 50 0.86 1\n",
+ " spider web 50 0.7 0.9\n",
+ " spindle 50 0.86 0.98\n",
+ " sports car 50 0.6 0.94\n",
+ " spotlight 50 0.26 0.6\n",
+ " stage 50 0.68 0.86\n",
+ " steam locomotive 50 0.94 1\n",
+ " through arch bridge 50 0.84 0.96\n",
+ " steel drum 50 0.82 0.9\n",
+ " stethoscope 50 0.6 0.82\n",
+ " scarf 50 0.5 0.92\n",
+ " stone wall 50 0.76 0.9\n",
+ " stopwatch 50 0.58 0.9\n",
+ " stove 50 0.46 0.74\n",
+ " strainer 50 0.64 0.84\n",
+ " tram 50 0.88 0.96\n",
+ " stretcher 50 0.6 0.8\n",
+ " couch 50 0.8 0.96\n",
+ " stupa 50 0.88 0.88\n",
+ " submarine 50 0.72 0.92\n",
+ " suit 50 0.4 0.78\n",
+ " sundial 50 0.58 0.74\n",
+ " sunglass 50 0.14 0.58\n",
+ " sunglasses 50 0.28 0.58\n",
+ " sunscreen 50 0.32 0.7\n",
+ " suspension bridge 50 0.6 0.94\n",
+ " mop 50 0.74 0.92\n",
+ " sweatshirt 50 0.28 0.66\n",
+ " swimsuit 50 0.52 0.82\n",
+ " swing 50 0.76 0.84\n",
+ " switch 50 0.56 0.76\n",
+ " syringe 50 0.62 0.82\n",
+ " table lamp 50 0.6 0.88\n",
+ " tank 50 0.8 0.96\n",
+ " tape player 50 0.46 0.76\n",
+ " teapot 50 0.84 1\n",
+ " teddy bear 50 0.82 0.94\n",
+ " television 50 0.6 0.9\n",
+ " tennis ball 50 0.7 0.94\n",
+ " thatched roof 50 0.88 0.9\n",
+ " front curtain 50 0.8 0.92\n",
+ " thimble 50 0.6 0.8\n",
+ " threshing machine 50 0.56 0.88\n",
+ " throne 50 0.72 0.82\n",
+ " tile roof 50 0.72 0.94\n",
+ " toaster 50 0.66 0.84\n",
+ " tobacco shop 50 0.42 0.7\n",
+ " toilet seat 50 0.62 0.88\n",
+ " torch 50 0.64 0.84\n",
+ " totem pole 50 0.92 0.98\n",
+ " tow truck 50 0.62 0.88\n",
+ " toy store 50 0.6 0.94\n",
+ " tractor 50 0.76 0.98\n",
+ " semi-trailer truck 50 0.78 0.92\n",
+ " tray 50 0.46 0.64\n",
+ " trench coat 50 0.54 0.72\n",
+ " tricycle 50 0.72 0.94\n",
+ " trimaran 50 0.7 0.98\n",
+ " tripod 50 0.58 0.86\n",
+ " triumphal arch 50 0.92 0.98\n",
+ " trolleybus 50 0.9 1\n",
+ " trombone 50 0.54 0.88\n",
+ " tub 50 0.24 0.82\n",
+ " turnstile 50 0.84 0.94\n",
+ " typewriter keyboard 50 0.68 0.98\n",
+ " umbrella 50 0.52 0.7\n",
+ " unicycle 50 0.74 0.96\n",
+ " upright piano 50 0.76 0.9\n",
+ " vacuum cleaner 50 0.62 0.9\n",
+ " vase 50 0.5 0.78\n",
+ " vault 50 0.76 0.92\n",
+ " velvet 50 0.2 0.42\n",
+ " vending machine 50 0.9 1\n",
+ " vestment 50 0.54 0.82\n",
+ " viaduct 50 0.78 0.86\n",
+ " violin 50 0.68 0.78\n",
+ " volleyball 50 0.86 1\n",
+ " waffle iron 50 0.72 0.88\n",
+ " wall clock 50 0.54 0.88\n",
+ " wallet 50 0.52 0.9\n",
+ " wardrobe 50 0.68 0.88\n",
+ " military aircraft 50 0.9 0.98\n",
+ " sink 50 0.72 0.96\n",
+ " washing machine 50 0.78 0.94\n",
+ " water bottle 50 0.54 0.74\n",
+ " water jug 50 0.22 0.74\n",
+ " water tower 50 0.9 0.96\n",
+ " whiskey jug 50 0.64 0.74\n",
+ " whistle 50 0.72 0.84\n",
+ " wig 50 0.84 0.9\n",
+ " window screen 50 0.68 0.8\n",
+ " window shade 50 0.52 0.76\n",
+ " Windsor tie 50 0.22 0.66\n",
+ " wine bottle 50 0.42 0.82\n",
+ " wing 50 0.54 0.96\n",
+ " wok 50 0.46 0.82\n",
+ " wooden spoon 50 0.58 0.8\n",
+ " wool 50 0.32 0.82\n",
+ " split-rail fence 50 0.74 0.9\n",
+ " shipwreck 50 0.84 0.96\n",
+ " yawl 50 0.78 0.96\n",
+ " yurt 50 0.84 1\n",
+ " website 50 0.98 1\n",
+ " comic book 50 0.62 0.9\n",
+ " crossword 50 0.84 0.88\n",
+ " traffic sign 50 0.78 0.9\n",
+ " traffic light 50 0.8 0.94\n",
+ " dust jacket 50 0.72 0.94\n",
+ " menu 50 0.82 0.96\n",
+ " plate 50 0.44 0.88\n",
+ " guacamole 50 0.8 0.92\n",
+ " consomme 50 0.54 0.88\n",
+ " hot pot 50 0.86 0.98\n",
+ " trifle 50 0.92 0.98\n",
+ " ice cream 50 0.68 0.94\n",
+ " ice pop 50 0.62 0.84\n",
+ " baguette 50 0.62 0.88\n",
+ " bagel 50 0.64 0.92\n",
+ " pretzel 50 0.72 0.88\n",
+ " cheeseburger 50 0.9 1\n",
+ " hot dog 50 0.74 0.94\n",
+ " mashed potato 50 0.74 0.9\n",
+ " cabbage 50 0.84 0.96\n",
+ " broccoli 50 0.9 0.96\n",
+ " cauliflower 50 0.82 1\n",
+ " zucchini 50 0.74 0.9\n",
+ " spaghetti squash 50 0.8 0.96\n",
+ " acorn squash 50 0.82 0.96\n",
+ " butternut squash 50 0.7 0.94\n",
+ " cucumber 50 0.6 0.96\n",
+ " artichoke 50 0.84 0.94\n",
+ " bell pepper 50 0.84 0.98\n",
+ " cardoon 50 0.88 0.94\n",
+ " mushroom 50 0.38 0.92\n",
+ " Granny Smith 50 0.9 0.96\n",
+ " strawberry 50 0.6 0.88\n",
+ " orange 50 0.7 0.92\n",
+ " lemon 50 0.78 0.98\n",
+ " fig 50 0.82 0.96\n",
+ " pineapple 50 0.86 0.96\n",
+ " banana 50 0.84 0.96\n",
+ " jackfruit 50 0.9 0.98\n",
+ " custard apple 50 0.86 0.96\n",
+ " pomegranate 50 0.82 0.98\n",
+ " hay 50 0.8 0.92\n",
+ " carbonara 50 0.88 0.94\n",
+ " chocolate syrup 50 0.46 0.84\n",
+ " dough 50 0.4 0.6\n",
+ " meatloaf 50 0.58 0.84\n",
+ " pizza 50 0.84 0.96\n",
+ " pot pie 50 0.68 0.9\n",
+ " burrito 50 0.8 0.98\n",
+ " red wine 50 0.54 0.82\n",
+ " espresso 50 0.64 0.88\n",
+ " cup 50 0.38 0.7\n",
+ " eggnog 50 0.38 0.7\n",
+ " alp 50 0.54 0.88\n",
+ " bubble 50 0.8 0.96\n",
+ " cliff 50 0.64 1\n",
+ " coral reef 50 0.72 0.96\n",
+ " geyser 50 0.94 1\n",
+ " lakeshore 50 0.54 0.88\n",
+ " promontory 50 0.58 0.94\n",
+ " shoal 50 0.6 0.96\n",
+ " seashore 50 0.44 0.78\n",
+ " valley 50 0.72 0.94\n",
+ " volcano 50 0.78 0.96\n",
+ " baseball player 50 0.72 0.94\n",
+ " bridegroom 50 0.72 0.88\n",
+ " scuba diver 50 0.8 1\n",
+ " rapeseed 50 0.94 0.98\n",
+ " daisy 50 0.96 0.98\n",
+ " yellow lady's slipper 50 1 1\n",
+ " corn 50 0.4 0.88\n",
+ " acorn 50 0.92 0.98\n",
+ " rose hip 50 0.92 0.98\n",
+ " horse chestnut seed 50 0.94 0.98\n",
+ " coral fungus 50 0.96 0.96\n",
+ " agaric 50 0.82 0.94\n",
+ " gyromitra 50 0.98 1\n",
+ " stinkhorn mushroom 50 0.8 0.94\n",
+ " earth star 50 0.98 1\n",
+ " hen-of-the-woods 50 0.8 0.96\n",
+ " bolete 50 0.74 0.94\n",
+ " ear 50 0.48 0.94\n",
+ " toilet paper 50 0.36 0.68\n",
+ "Speed: 0.1ms pre-process, 0.3ms inference, 0.0ms post-process per image at shape (1, 3, 224, 224)\n",
+ "Results saved to \u001b[1mruns/val-cls/exp\u001b[0m\n"
+ ]
+ }
+ ],
+ "source": [
+ "# Validate YOLOv5s on Imagenet val\n",
+ "!python classify/val.py --weights yolov5s-cls.pt --data ../datasets/imagenet --img 224 --half"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "ZY2VXXXu74w5"
+ },
+ "source": [
+ "# 3. Train\n",
+ "\n",
+ "![](\"https://github.com/ultralytics/assets/raw/main/im/integrations-loop.png\"/)
![](\"https://user-images.githubusercontent.com/26833433/202802162-92e60571-ab58-4409-948d-b31fddcd3c6f.png\"/)
![\"Comet](\"https://user-images.githubusercontent.com/26833433/202851203-164e94e1-2238-46dd-91f8-de020e9d6b41.png\")
"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "Lay2WsTjNJzP"
+ },
+ "source": [
+ "## ClearML Logging and Automation 🌟 NEW\n",
+ "\n",
+ "[ClearML](https://cutt.ly/yolov5-notebook-clearml) is completely integrated into YOLOv5 to track your experimentation, manage dataset versions and even remotely execute training runs. To enable ClearML (check cells above):\n",
+ "\n",
+ "- `pip install clearml`\n",
+ "- run `clearml-init` to connect to a ClearML server (**deploy your own [open-source server](https://github.com/allegroai/clearml-server)**, or use our [free hosted server](https://cutt.ly/yolov5-notebook-clearml))\n",
+ "\n",
+ "You'll get all the great expected features from an experiment manager: live updates, model upload, experiment comparison etc. but ClearML also tracks uncommitted changes and installed packages for example. Thanks to that ClearML Tasks (which is what we call experiments) are also reproducible on different machines! With only 1 extra line, we can schedule a YOLOv5 training task on a queue to be executed by any number of ClearML Agents (workers).\n",
+ "\n",
+ "You can use ClearML Data to version your dataset and then pass it to YOLOv5 simply using its unique ID. This will help you keep track of your data without adding extra hassle. Explore the [ClearML Tutorial](https://github.com/ultralytics/yolov5/tree/master/utils/loggers/clearml) for details!\n",
+ "\n",
+ "\n",
+ "![\"ClearML](\"https://github.com/thepycoder/clearml_screenshots/raw/main/scalars.jpg\")
"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "-WPvRbS5Swl6"
+ },
+ "source": [
+ "## Local Logging\n",
+ "\n",
+ "Training results are automatically logged with [Tensorboard](https://www.tensorflow.org/tensorboard) and [CSV](https://github.com/ultralytics/yolov5/pull/4148) loggers to `runs/train`, with a new experiment directory created for each new training as `runs/train/exp2`, `runs/train/exp3`, etc.\n",
+ "\n",
+ "This directory contains train and val statistics, mosaics, labels, predictions and augmentated mosaics, as well as metrics and charts including precision-recall (PR) curves and confusion matrices. \n",
+ "\n",
+ "![\"Local](\"https://user-images.githubusercontent.com/26833433/183222430-e1abd1b7-782c-4cde-b04d-ad52926bf818.jpg\")
\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "Zelyeqbyt3GD"
+ },
+ "source": [
+ "# Environments\n",
+ "\n",
+ "YOLOv5 may be run in any of the following up-to-date verified environments (with all dependencies including [CUDA](https://developer.nvidia.com/cuda)/[CUDNN](https://developer.nvidia.com/cudnn), [Python](https://www.python.org/) and [PyTorch](https://pytorch.org/) preinstalled):\n",
+ "\n",
+ "- **Notebooks** with free GPU: \n",
+ "- **Google Cloud** Deep Learning VM. See [GCP Quickstart Guide](https://github.com/ultralytics/yolov5/wiki/GCP-Quickstart)\n",
+ "- **Amazon** Deep Learning AMI. See [AWS Quickstart Guide](https://github.com/ultralytics/yolov5/wiki/AWS-Quickstart)\n",
+ "- **Docker Image**. See [Docker Quickstart Guide](https://github.com/ultralytics/yolov5/wiki/Docker-Quickstart) ![\"Docker](\"https://img.shields.io/docker/pulls/ultralytics/yolov5?logo=docker\")
\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "6Qu7Iesl0p54"
+ },
+ "source": [
+ "# Status\n",
+ "\n",
+ "\n",
+ "\n",
+ "If this badge is green, all [YOLOv5 GitHub Actions](https://github.com/ultralytics/yolov5/actions) Continuous Integration (CI) tests are currently passing. CI tests verify correct operation of YOLOv5 training ([train.py](https://github.com/ultralytics/yolov5/blob/master/train.py)), testing ([val.py](https://github.com/ultralytics/yolov5/blob/master/val.py)), inference ([detect.py](https://github.com/ultralytics/yolov5/blob/master/detect.py)) and export ([export.py](https://github.com/ultralytics/yolov5/blob/master/export.py)) on macOS, Windows, and Ubuntu every 24 hours and on every commit.\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "IEijrePND_2I"
+ },
+ "source": [
+ "# Appendix\n",
+ "\n",
+ "Additional content below."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "id": "GMusP4OAxFu6"
+ },
+ "outputs": [],
+ "source": [
+ "# YOLOv5 PyTorch HUB Inference (DetectionModels only)\n",
+ "import torch\n",
+ "\n",
+ "model = torch.hub.load('ultralytics/yolov5', 'yolov5s') # yolov5n - yolov5x6 or custom\n",
+ "im = 'https://ultralytics.com/images/zidane.jpg' # file, Path, PIL.Image, OpenCV, nparray, list\n",
+ "results = model(im) # inference\n",
+ "results.print() # or .show(), .save(), .crop(), .pandas(), etc."
+ ]
+ }
+ ],
+ "metadata": {
+ "accelerator": "GPU",
+ "colab": {
+ "name": "YOLOv5 Classification Tutorial",
+ "provenance": []
+ },
+ "kernelspec": {
+ "display_name": "Python 3 (ipykernel)",
+ "language": "python",
+ "name": "python3"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.7.12"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
+}
diff --git a/classify/val.py b/classify/val.py
index 3c16ec8092d8..4edd5a1f5e9e 100644
--- a/classify/val.py
+++ b/classify/val.py
@@ -10,7 +10,7 @@
$ python classify/val.py --weights yolov5s-cls.pt # PyTorch
yolov5s-cls.torchscript # TorchScript
yolov5s-cls.onnx # ONNX Runtime or OpenCV DNN with --dnn
- yolov5s-cls.xml # OpenVINO
+ yolov5s-cls_openvino_model # OpenVINO
yolov5s-cls.engine # TensorRT
yolov5s-cls.mlmodel # CoreML (macOS-only)
yolov5s-cls_saved_model # TensorFlow SavedModel
@@ -36,7 +36,8 @@
from models.common import DetectMultiBackend
from utils.dataloaders import create_classification_dataloader
-from utils.general import LOGGER, Profile, check_img_size, check_requirements, colorstr, increment_path, print_args
+from utils.general import (LOGGER, TQDM_BAR_FORMAT, Profile, check_img_size, check_requirements, colorstr,
+ increment_path, print_args)
from utils.torch_utils import select_device, smart_inference_mode
@@ -99,8 +100,8 @@ def run(
pred, targets, loss, dt = [], [], 0, (Profile(), Profile(), Profile())
n = len(dataloader) # number of batches
action = 'validating' if dataloader.dataset.root.stem == 'val' else 'testing'
- desc = f"{pbar.desc[:-36]}{action:>36}" if pbar else f"{action}"
- bar = tqdm(dataloader, desc, n, not training, bar_format='{l_bar}{bar:10}{r_bar}{bar:-10b}', position=0)
+ desc = f'{pbar.desc[:-36]}{action:>36}' if pbar else f'{action}'
+ bar = tqdm(dataloader, desc, n, not training, bar_format=TQDM_BAR_FORMAT, position=0)
with torch.cuda.amp.autocast(enabled=device.type != 'cpu'):
for images, labels in bar:
with dt[0]:
@@ -122,14 +123,14 @@ def run(
top1, top5 = acc.mean(0).tolist()
if pbar:
- pbar.desc = f"{pbar.desc[:-36]}{loss:>12.3g}{top1:>12.3g}{top5:>12.3g}"
+ pbar.desc = f'{pbar.desc[:-36]}{loss:>12.3g}{top1:>12.3g}{top5:>12.3g}'
if verbose: # all classes
LOGGER.info(f"{'Class':>24}{'Images':>12}{'top1_acc':>12}{'top5_acc':>12}")
LOGGER.info(f"{'all':>24}{targets.shape[0]:>12}{top1:>12.3g}{top5:>12.3g}")
for i, c in model.names.items():
- aci = acc[targets == i]
- top1i, top5i = aci.mean(0).tolist()
- LOGGER.info(f"{c:>24}{aci.shape[0]:>12}{top1i:>12.3g}{top5i:>12.3g}")
+ acc_i = acc[targets == i]
+ top1i, top5i = acc_i.mean(0).tolist()
+ LOGGER.info(f'{c:>24}{acc_i.shape[0]:>12}{top1i:>12.3g}{top5i:>12.3g}')
# Print results
t = tuple(x.t / len(dataloader.dataset.samples) * 1E3 for x in dt) # speeds per image
@@ -164,6 +165,6 @@ def main(opt):
run(**vars(opt))
-if __name__ == "__main__":
+if __name__ == '__main__':
opt = parse_opt()
main(opt)
diff --git a/data/Argoverse.yaml b/data/Argoverse.yaml
index e3e9ba161ed0..558151dc849e 100644
--- a/data/Argoverse.yaml
+++ b/data/Argoverse.yaml
@@ -63,7 +63,7 @@ download: |
# Download
- dir = Path('../datasets/Argoverse') # dataset root dir
+ dir = Path(yaml['path']) # dataset root dir
urls = ['https://argoverse-hd.s3.us-east-2.amazonaws.com/Argoverse-HD-Full.zip']
download(urls, dir=dir, delete=False)
diff --git a/data/coco128-seg.yaml b/data/coco128-seg.yaml
new file mode 100644
index 000000000000..5e81910cc456
--- /dev/null
+++ b/data/coco128-seg.yaml
@@ -0,0 +1,101 @@
+# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
+# COCO128-seg dataset https://www.kaggle.com/ultralytics/coco128 (first 128 images from COCO train2017) by Ultralytics
+# Example usage: python train.py --data coco128.yaml
+# parent
+# ├── yolov5
+# └── datasets
+# └── coco128-seg ← downloads here (7 MB)
+
+
+# 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/coco128-seg # dataset root dir
+train: images/train2017 # train images (relative to 'path') 128 images
+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
+
+
+# Download script/URL (optional)
+download: https://ultralytics.com/assets/coco128-seg.zip
diff --git a/data/hyps/hyp.no-augmentation.yaml b/data/hyps/hyp.no-augmentation.yaml
new file mode 100644
index 000000000000..8fbd5b262afa
--- /dev/null
+++ b/data/hyps/hyp.no-augmentation.yaml
@@ -0,0 +1,35 @@
+# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
+# Hyperparameters when using Albumentations frameworks
+# python train.py --hyp hyp.no-augmentation.yaml
+# See https://github.com/ultralytics/yolov5/pull/3882 for YOLOv5 + Albumentations Usage examples
+
+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: 0.3 # cls loss gain
+cls_pw: 1.0 # cls BCELoss positive_weight
+obj: 0.7 # 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)
+# this parameters are all zero since we want to use albumentation framework
+fl_gamma: 0.0 # focal loss gamma (efficientDet default gamma=1.5)
+hsv_h: 0 # image HSV-Hue augmentation (fraction)
+hsv_s: 00 # image HSV-Saturation augmentation (fraction)
+hsv_v: 0 # image HSV-Value augmentation (fraction)
+degrees: 0.0 # image rotation (+/- deg)
+translate: 0 # image translation (+/- fraction)
+scale: 0 # image scale (+/- gain)
+shear: 0 # image shear (+/- deg)
+perspective: 0.0 # image perspective (+/- fraction), range 0-0.001
+flipud: 0.0 # image flip up-down (probability)
+fliplr: 0.0 # image flip left-right (probability)
+mosaic: 0.0 # image mosaic (probability)
+mixup: 0.0 # image mixup (probability)
+copy_paste: 0.0 # segment copy-paste (probability)
diff --git a/data/scripts/download_weights.sh b/data/scripts/download_weights.sh
index a4f3becfdbeb..31e0a15569f2 100755
--- a/data/scripts/download_weights.sh
+++ b/data/scripts/download_weights.sh
@@ -11,11 +11,12 @@
python - <\n",
+ "\n",
+ "\n",
+ "\n",
+ " \n",
+ " \n",
+ "![](\"https://user-images.githubusercontent.com/26833433/199030123-08c72f8d-6871-4116-8ed3-c373642cf28e.jpg\")
"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "0eq1SMWl6Sfn"
+ },
+ "source": [
+ "# 2. Validate\n",
+ "Validate a model's accuracy on the [COCO](https://cocodataset.org/#home) dataset's `val` or `test` splits. Models are downloaded automatically from the [latest YOLOv5 release](https://github.com/ultralytics/yolov5/releases). To show results by class use the `--verbose` flag."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "WQPtK1QYVaD_",
+ "outputId": "9d751d8c-bee8-4339-cf30-9854ca530449"
+ },
+ "outputs": [
+ {
+ "output_type": "stream",
+ "name": "stdout",
+ "text": [
+ "Downloading https://github.com/ultralytics/yolov5/releases/download/v1.0/coco2017labels-segments.zip ...\n",
+ "Downloading http://images.cocodataset.org/zips/val2017.zip ...\n",
+ "######################################################################## 100.0%\n",
+ "######################################################################## 100.0%\n"
+ ]
+ }
+ ],
+ "source": [
+ "# Download COCO val\n",
+ "!bash data/scripts/get_coco.sh --val --segments # download (780M - 5000 images)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "X58w8JLpMnjH",
+ "outputId": "a140d67a-02da-479e-9ddb-7d54bf9e407a"
+ },
+ "outputs": [
+ {
+ "output_type": "stream",
+ "name": "stdout",
+ "text": [
+ "\u001b[34m\u001b[1msegment/val: \u001b[0mdata=/content/yolov5/data/coco.yaml, weights=['yolov5s-seg.pt'], batch_size=32, imgsz=640, conf_thres=0.001, iou_thres=0.6, max_det=300, task=val, device=, workers=8, single_cls=False, augment=False, verbose=False, save_txt=False, save_hybrid=False, save_conf=False, save_json=False, project=runs/val-seg, name=exp, exist_ok=False, half=True, dnn=False\n",
+ "YOLOv5 🚀 v7.0-2-gc9d47ae Python-3.7.15 torch-1.12.1+cu113 CUDA:0 (Tesla T4, 15110MiB)\n",
+ "\n",
+ "Fusing layers... \n",
+ "YOLOv5s-seg summary: 224 layers, 7611485 parameters, 0 gradients, 26.4 GFLOPs\n",
+ "\u001b[34m\u001b[1mval: \u001b[0mScanning /content/datasets/coco/val2017... 4952 images, 48 backgrounds, 0 corrupt: 100% 5000/5000 [00:03<00:00, 1361.31it/s]\n",
+ "\u001b[34m\u001b[1mval: \u001b[0mNew cache created: /content/datasets/coco/val2017.cache\n",
+ " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100% 157/157 [01:54<00:00, 1.37it/s]\n",
+ " all 5000 36335 0.673 0.517 0.566 0.373 0.672 0.49 0.532 0.319\n",
+ "Speed: 0.6ms pre-process, 4.4ms inference, 2.9ms NMS per image at shape (32, 3, 640, 640)\n",
+ "Results saved to \u001b[1mruns/val-seg/exp\u001b[0m\n"
+ ]
+ }
+ ],
+ "source": [
+ "# Validate YOLOv5s-seg on COCO val\n",
+ "!python segment/val.py --weights yolov5s-seg.pt --data coco.yaml --img 640 --half"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "ZY2VXXXu74w5"
+ },
+ "source": [
+ "# 3. Train\n",
+ "\n",
+ "
![](\"https://robflow-public-assets.s3.amazonaws.com/how-to-train-yolov5-segmentation-annotation.gif\"/)
"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "Lay2WsTjNJzP"
+ },
+ "source": [
+ "## ClearML Logging and Automation 🌟 NEW\n",
+ "\n",
+ "[ClearML](https://cutt.ly/yolov5-notebook-clearml) is completely integrated into YOLOv5 to track your experimentation, manage dataset versions and even remotely execute training runs. To enable ClearML (check cells above):\n",
+ "\n",
+ "- `pip install clearml`\n",
+ "- run `clearml-init` to connect to a ClearML server (**deploy your own [open-source server](https://github.com/allegroai/clearml-server)**, or use our [free hosted server](https://cutt.ly/yolov5-notebook-clearml))\n",
+ "\n",
+ "You'll get all the great expected features from an experiment manager: live updates, model upload, experiment comparison etc. but ClearML also tracks uncommitted changes and installed packages for example. Thanks to that ClearML Tasks (which is what we call experiments) are also reproducible on different machines! With only 1 extra line, we can schedule a YOLOv5 training task on a queue to be executed by any number of ClearML Agents (workers).\n",
+ "\n",
+ "You can use ClearML Data to version your dataset and then pass it to YOLOv5 simply using its unique ID. This will help you keep track of your data without adding extra hassle. Explore the [ClearML Tutorial](https://github.com/ultralytics/yolov5/tree/master/utils/loggers/clearml) for details!\n",
+ "\n",
+ "\n",
+ ""
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "-WPvRbS5Swl6"
+ },
+ "source": [
+ "## Local Logging\n",
+ "\n",
+ "Training results are automatically logged with [Tensorboard](https://www.tensorflow.org/tensorboard) and [CSV](https://github.com/ultralytics/yolov5/pull/4148) loggers to `runs/train`, with a new experiment directory created for each new training as `runs/train/exp2`, `runs/train/exp3`, etc.\n",
+ "\n",
+ "This directory contains train and val statistics, mosaics, labels, predictions and augmentated mosaics, as well as metrics and charts including precision-recall (PR) curves and confusion matrices. \n",
+ "\n",
+ "\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "Zelyeqbyt3GD"
+ },
+ "source": [
+ "# Environments\n",
+ "\n",
+ "YOLOv5 may be run in any of the following up-to-date verified environments (with all dependencies including [CUDA](https://developer.nvidia.com/cuda)/[CUDNN](https://developer.nvidia.com/cudnn), [Python](https://www.python.org/) and [PyTorch](https://pytorch.org/) preinstalled):\n",
+ "\n",
+ "- **Notebooks** with free GPU: \n",
+ "- **Google Cloud** Deep Learning VM. See [GCP Quickstart Guide](https://github.com/ultralytics/yolov5/wiki/GCP-Quickstart)\n",
+ "- **Amazon** Deep Learning AMI. See [AWS Quickstart Guide](https://github.com/ultralytics/yolov5/wiki/AWS-Quickstart)\n",
+ "- **Docker Image**. See [Docker Quickstart Guide](https://github.com/ultralytics/yolov5/wiki/Docker-Quickstart) \n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "6Qu7Iesl0p54"
+ },
+ "source": [
+ "# Status\n",
+ "\n",
+ "\n",
+ "\n",
+ "If this badge is green, all [YOLOv5 GitHub Actions](https://github.com/ultralytics/yolov5/actions) Continuous Integration (CI) tests are currently passing. CI tests verify correct operation of YOLOv5 training ([train.py](https://github.com/ultralytics/yolov5/blob/master/train.py)), testing ([val.py](https://github.com/ultralytics/yolov5/blob/master/val.py)), inference ([detect.py](https://github.com/ultralytics/yolov5/blob/master/detect.py)) and export ([export.py](https://github.com/ultralytics/yolov5/blob/master/export.py)) on macOS, Windows, and Ubuntu every 24 hours and on every commit.\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "IEijrePND_2I"
+ },
+ "source": [
+ "# Appendix\n",
+ "\n",
+ "Additional content below."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "id": "GMusP4OAxFu6"
+ },
+ "outputs": [],
+ "source": [
+ "# YOLOv5 PyTorch HUB Inference (DetectionModels only)\n",
+ "import torch\n",
+ "\n",
+ "model = torch.hub.load('ultralytics/yolov5', 'yolov5s-seg') # yolov5n - yolov5x6 or custom\n",
+ "im = 'https://ultralytics.com/images/zidane.jpg' # file, Path, PIL.Image, OpenCV, nparray, list\n",
+ "results = model(im) # inference\n",
+ "results.print() # or .show(), .save(), .crop(), .pandas(), etc."
+ ]
+ }
+ ],
+ "metadata": {
+ "accelerator": "GPU",
+ "colab": {
+ "name": "YOLOv5 Segmentation Tutorial",
+ "provenance": [],
+ "toc_visible": true
+ },
+ "kernelspec": {
+ "display_name": "Python 3 (ipykernel)",
+ "language": "python",
+ "name": "python3"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.7.12"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
+}
diff --git a/segment/val.py b/segment/val.py
new file mode 100644
index 000000000000..a7f95fe9b6fc
--- /dev/null
+++ b/segment/val.py
@@ -0,0 +1,473 @@
+# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
+"""
+Validate a trained YOLOv5 segment model on a segment dataset
+
+Usage:
+ $ bash data/scripts/get_coco.sh --val --segments # download COCO-segments val split (1G, 5000 images)
+ $ python segment/val.py --weights yolov5s-seg.pt --data coco.yaml --img 640 # validate COCO-segments
+
+Usage - formats:
+ $ python segment/val.py --weights yolov5s-seg.pt # PyTorch
+ yolov5s-seg.torchscript # TorchScript
+ yolov5s-seg.onnx # ONNX Runtime or OpenCV DNN with --dnn
+ yolov5s-seg_openvino_label # OpenVINO
+ yolov5s-seg.engine # TensorRT
+ yolov5s-seg.mlmodel # CoreML (macOS-only)
+ yolov5s-seg_saved_model # TensorFlow SavedModel
+ yolov5s-seg.pb # TensorFlow GraphDef
+ yolov5s-seg.tflite # TensorFlow Lite
+ yolov5s-seg_edgetpu.tflite # TensorFlow Edge TPU
+ yolov5s-seg_paddle_model # PaddlePaddle
+"""
+
+import argparse
+import json
+import os
+import subprocess
+import sys
+from multiprocessing.pool import ThreadPool
+from pathlib import Path
+
+import numpy as np
+import torch
+from tqdm import tqdm
+
+FILE = Path(__file__).resolve()
+ROOT = FILE.parents[1] # YOLOv5 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 torch.nn.functional as F
+
+from models.common import DetectMultiBackend
+from models.yolo import SegmentationModel
+from utils.callbacks import Callbacks
+from utils.general import (LOGGER, NUM_THREADS, 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, box_iou
+from utils.plots import output_to_target, plot_val_study
+from utils.segment.dataloaders import create_dataloader
+from utils.segment.general import mask_iou, process_mask, process_mask_native, scale_image
+from utils.segment.metrics import Metrics, ap_per_class_box_and_mask
+from utils.segment.plots import plot_images_and_masks
+from utils.torch_utils import de_parallel, select_device, smart_inference_mode
+
+
+def save_one_txt(predn, save_conf, shape, file):
+ # Save one txt result
+ gn = torch.tensor(shape)[[1, 0, 1, 0]] # normalization gain whwh
+ for *xyxy, conf, cls in predn.tolist():
+ 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(file, 'a') as f:
+ f.write(('%g ' * len(line)).rstrip() % line + '\n')
+
+
+def save_one_json(predn, jdict, path, class_map, pred_masks):
+ # Save one JSON result {"image_id": 42, "category_id": 18, "bbox": [258.15, 41.29, 348.26, 243.78], "score": 0.236}
+ from pycocotools.mask import encode
+
+ def single_encode(x):
+ rle = encode(np.asarray(x[:, :, None], order='F', dtype='uint8'))[0]
+ rle['counts'] = rle['counts'].decode('utf-8')
+ return rle
+
+ image_id = int(path.stem) if path.stem.isnumeric() else path.stem
+ box = xyxy2xywh(predn[:, :4]) # xywh
+ box[:, :2] -= box[:, 2:] / 2 # xy center to top-left corner
+ pred_masks = np.transpose(pred_masks, (2, 0, 1))
+ with ThreadPool(NUM_THREADS) as pool:
+ rles = pool.map(single_encode, pred_masks)
+ for i, (p, b) in enumerate(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),
+ 'segmentation': rles[i]})
+
+
+def process_batch(detections, labels, iouv, pred_masks=None, gt_masks=None, overlap=False, masks=False):
+ """
+ Return correct prediction matrix
+ Arguments:
+ 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
+ """
+ if masks:
+ if overlap:
+ nl = len(labels)
+ index = torch.arange(nl, device=gt_masks.device).view(nl, 1, 1) + 1
+ gt_masks = gt_masks.repeat(nl, 1, 1) # shape(1,640,640) -> (n,640,640)
+ gt_masks = torch.where(gt_masks == index, 1.0, 0.0)
+ if gt_masks.shape[1:] != pred_masks.shape[1:]:
+ gt_masks = F.interpolate(gt_masks[None], pred_masks.shape[1:], mode='bilinear', align_corners=False)[0]
+ gt_masks = gt_masks.gt_(0.5)
+ iou = mask_iou(gt_masks.view(gt_masks.shape[0], -1), pred_masks.view(pred_masks.shape[0], -1))
+ else: # boxes
+ iou = box_iou(labels[:, 1:], detections[:, :4])
+
+ correct = np.zeros((detections.shape[0], iouv.shape[0])).astype(bool)
+ 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)
+
+
+@smart_inference_mode()
+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
+ save_txt=False, # save results to *.txt
+ save_hybrid=False, # save label+prediction hybrid results to *.txt
+ save_conf=False, # save confidences in --save-txt labels
+ save_json=False, # save a COCO-JSON results file
+ project=ROOT / 'runs/val-seg', # save to project/name
+ name='exp', # save to project/name
+ exist_ok=False, # existing project/name ok, do not increment
+ half=True, # use FP16 half-precision inference
+ dnn=False, # use OpenCV DNN for ONNX inference
+ model=None,
+ dataloader=None,
+ save_dir=Path(''),
+ plots=True,
+ overlap=False,
+ mask_downsample_ratio=1,
+ compute_loss=None,
+ callbacks=Callbacks(),
+):
+ if save_json:
+ check_requirements('pycocotools>=2.0.6')
+ process = process_mask_native # more accurate
+ else:
+ process = process_mask # faster
+
+ # 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
+ half &= device.type != 'cpu' # half precision only supported on CUDA
+ model.half() if half else model.float()
+ nm = de_parallel(model).model[-1].nm # number of masks
+ else: # called directly
+ device = select_device(device, batch_size=batch_size)
+
+ # Directories
+ save_dir = increment_path(Path(project) / name, exist_ok=exist_ok) # increment 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
+ imgsz = check_img_size(imgsz, s=stride) # check image size
+ half = model.fp16 # FP16 supported on limited backends with CUDA
+ nm = de_parallel(model).model.model[-1].nm if isinstance(model, SegmentationModel) else 32 # number of masks
+ if engine:
+ batch_size = model.batch_size
+ 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')
+
+ # 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
+ 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
+ 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
+ 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,
+ prefix=colorstr(f'{task}: '),
+ overlap_mask=overlap,
+ mask_downsample_ratio=mask_downsample_ratio)[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))
+ class_map = coco80_to_coco91_class() if is_coco else list(range(1000))
+ s = ('%22s' + '%11s' * 10) % ('Class', 'Images', 'Instances', 'Box(P', 'R', 'mAP50', 'mAP50-95)', 'Mask(P', 'R',
+ 'mAP50', 'mAP50-95)')
+ dt = Profile(), Profile(), Profile()
+ metrics = Metrics()
+ loss = torch.zeros(4, device=device)
+ jdict, stats = [], []
+ # callbacks.run('on_val_start')
+ pbar = tqdm(dataloader, desc=s, bar_format=TQDM_BAR_FORMAT) # progress bar
+ for batch_i, (im, targets, paths, shapes, masks) 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)
+ masks = masks.to(device)
+ masks = masks.float()
+ 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
+
+ # Inference
+ with dt[1]:
+ preds, protos, train_out = model(im) if compute_loss else (*model(im, augment=augment)[:2], None)
+
+ # Loss
+ if compute_loss:
+ loss += compute_loss((train_out, protos), targets, masks)[1] # box, obj, cls
+
+ # NMS
+ targets[:, 2:] *= torch.tensor((width, height, width, height), device=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,
+ nm=nm)
+
+ # Metrics
+ plot_masks = [] # masks for plotting
+ for si, (pred, proto) in enumerate(zip(preds, protos)):
+ labels = targets[targets[:, 0] == si, 1:]
+ nl, npr = labels.shape[0], pred.shape[0] # number of labels, predictions
+ path, shape = Path(paths[si]), shapes[si][0]
+ correct_masks = torch.zeros(npr, niou, dtype=torch.bool, device=device) # init
+ correct_bboxes = torch.zeros(npr, niou, dtype=torch.bool, device=device) # init
+ seen += 1
+
+ if npr == 0:
+ if nl:
+ stats.append((correct_masks, correct_bboxes, *torch.zeros((2, 0), device=device), labels[:, 0]))
+ if plots:
+ confusion_matrix.process_batch(detections=None, labels=labels[:, 0])
+ continue
+
+ # Masks
+ midx = [si] if overlap else targets[:, 0] == si
+ gt_masks = masks[midx]
+ pred_masks = process(proto, pred[:, 6:], pred[:, :4], shape=im[si].shape[1:])
+
+ # 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
+
+ # Evaluate
+ if nl:
+ tbox = xywh2xyxy(labels[:, 1:5]) # target boxes
+ scale_boxes(im[si].shape[1:], tbox, shape, shapes[si][1]) # native-space labels
+ labelsn = torch.cat((labels[:, 0:1], tbox), 1) # native-space labels
+ correct_bboxes = process_batch(predn, labelsn, iouv)
+ correct_masks = process_batch(predn, labelsn, iouv, pred_masks, gt_masks, overlap=overlap, masks=True)
+ if plots:
+ confusion_matrix.process_batch(predn, labelsn)
+ stats.append((correct_masks, correct_bboxes, pred[:, 4], pred[:, 5], labels[:, 0])) # (conf, pcls, tcls)
+
+ pred_masks = torch.as_tensor(pred_masks, dtype=torch.uint8)
+ if plots and batch_i < 3:
+ plot_masks.append(pred_masks[:15]) # filter top 15 to plot
+
+ # Save/log
+ if save_txt:
+ save_one_txt(predn, save_conf, shape, file=save_dir / 'labels' / f'{path.stem}.txt')
+ if save_json:
+ pred_masks = scale_image(im[si].shape[1:],
+ pred_masks.permute(1, 2, 0).contiguous().cpu().numpy(), shape, shapes[si][1])
+ save_one_json(predn, jdict, path, class_map, pred_masks) # 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:
+ if len(plot_masks):
+ plot_masks = torch.cat(plot_masks, dim=0)
+ plot_images_and_masks(im, targets, masks, paths, save_dir / f'val_batch{batch_i}_labels.jpg', names)
+ plot_images_and_masks(im, output_to_target(preds, max_det=15), plot_masks, paths,
+ save_dir / f'val_batch{batch_i}_pred.jpg', names) # pred
+
+ # callbacks.run('on_val_batch_end')
+
+ # Compute metrics
+ stats = [torch.cat(x, 0).cpu().numpy() for x in zip(*stats)] # to numpy
+ if len(stats) and stats[0].any():
+ results = ap_per_class_box_and_mask(*stats, plot=plots, save_dir=save_dir, names=names)
+ metrics.update(results)
+ nt = np.bincount(stats[4].astype(int), minlength=nc) # number of targets per class
+
+ # Print results
+ pf = '%22s' + '%11i' * 2 + '%11.3g' * 8 # print format
+ LOGGER.info(pf % ('all', seen, nt.sum(), *metrics.mean_results()))
+ 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):
+ for i, c in enumerate(metrics.ap_class_index):
+ LOGGER.info(pf % (names[c], seen, nt[c], *metrics.class_result(i)))
+
+ # Print speeds
+ t = tuple(x.t / 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)
+
+ # Plots
+ if plots:
+ confusion_matrix.plot(save_dir=save_dir, names=list(names.values()))
+ # callbacks.run('on_val_end')
+
+ mp_bbox, mr_bbox, map50_bbox, map_bbox, mp_mask, mr_mask, map50_mask, map_mask = metrics.mean_results()
+
+ # 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
+ 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
+ from pycocotools.coco import COCO
+ from pycocotools.cocoeval import COCOeval
+
+ anno = COCO(anno_json) # init annotations api
+ pred = anno.loadRes(pred_json) # init predictions api
+ results = []
+ for eval in COCOeval(anno, pred, 'bbox'), COCOeval(anno, pred, 'segm'):
+ if is_coco:
+ eval.params.imgIds = [int(Path(x).stem) for x in dataloader.dataset.im_files] # img ID to evaluate
+ eval.evaluate()
+ eval.accumulate()
+ eval.summarize()
+ results.extend(eval.stats[:2]) # update results (mAP@0.5:0.95, mAP@0.5)
+ map_bbox, map50_bbox, map_mask, map50_mask = results
+ except Exception as e:
+ LOGGER.info(f'pycocotools unable to run: {e}')
+
+ # Return results
+ model.float() # for training
+ if not training:
+ 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}")
+ final_metric = mp_bbox, mr_bbox, map50_bbox, map_bbox, mp_mask, mr_mask, map50_mask, map_mask
+ return (*final_metric, *(loss.cpu() / len(dataloader)).tolist()), metrics.get_maps(nc), t
+
+
+def parse_opt():
+ parser = argparse.ArgumentParser()
+ parser.add_argument('--data', type=str, default=ROOT / 'data/coco128-seg.yaml', help='dataset.yaml path')
+ parser.add_argument('--weights', nargs='+', type=str, default=ROOT / 'yolov5s-seg.pt', help='model 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')
+ parser.add_argument('--save-txt', action='store_true', help='save results to *.txt')
+ parser.add_argument('--save-hybrid', action='store_true', help='save label+prediction hybrid results to *.txt')
+ parser.add_argument('--save-conf', action='store_true', help='save confidences in --save-txt labels')
+ parser.add_argument('--save-json', action='store_true', help='save a COCO-JSON results file')
+ parser.add_argument('--project', default=ROOT / 'runs/val-seg', help='save results 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('--half', action='store_true', help='use FP16 half-precision inference')
+ parser.add_argument('--dnn', action='store_true', help='use OpenCV DNN for ONNX inference')
+ opt = parser.parse_args()
+ 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))
+ return opt
+
+
+def main(opt):
+ 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.warning(f'WARNING ⚠️ confidence threshold {opt.conf_thres} > 0.001 produces invalid results')
+ if opt.save_hybrid:
+ LOGGER.warning('WARNING ⚠️ --save-hybrid returns high mAP from hybrid labels, not from predictions alone')
+ 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
+ if opt.task == 'speed': # speed benchmarks
+ # python val.py --task speed --data coco.yaml --batch 1 --weights yolov5n.pt yolov5s.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...
+ 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
+ for opt.imgsz in x: # img-size
+ LOGGER.info(f'\nRunning {f} --imgsz {opt.imgsz}...')
+ 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'])
+ plot_val_study(x=x) # plot
+ else:
+ raise NotImplementedError(f'--task {opt.task} not in ("train", "val", "test", "speed", "study")')
+
+
+if __name__ == '__main__':
+ opt = parse_opt()
+ main(opt)
diff --git a/setup.cfg b/setup.cfg
index f12995da3e8e..d7c4cb3e1a4d 100644
--- a/setup.cfg
+++ b/setup.cfg
@@ -6,7 +6,6 @@
license_file = LICENSE
description_file = README.md
-
[tool:pytest]
norecursedirs =
.git
@@ -17,7 +16,6 @@ addopts =
--durations=25
--color=yes
-
[flake8]
max-line-length = 120
exclude = .tox,*.egg,build,temp
@@ -27,17 +25,16 @@ verbose = 2
# https://pep8.readthedocs.io/en/latest/intro.html#error-codes
format = pylint
# see: https://www.flake8rules.com/
-ignore =
- 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,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
[isort]
# https://pycqa.github.io/isort/docs/configuration/options.html
@@ -45,7 +42,6 @@ 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
diff --git a/train.py b/train.py
index 4eff6e5d645a..c4e3aac3561a 100644
--- a/train.py
+++ b/train.py
@@ -19,6 +19,7 @@
import math
import os
import random
+import subprocess
import sys
import time
from copy import deepcopy
@@ -47,13 +48,13 @@
from utils.callbacks import Callbacks
from utils.dataloaders import create_dataloader
from utils.downloads import attempt_download, is_url
-from utils.general import (LOGGER, check_amp, 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, yaml_save)
+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.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
@@ -63,6 +64,7 @@
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
@@ -146,7 +148,7 @@ 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})
+ loggers.on_params_update({'batch_size': batch_size})
# Optimizer
nbs = 64 # nominal batch size
@@ -173,7 +175,7 @@ def train(hyp, opt, device, callbacks): # hyp is path/to/hyp.yaml or hyp dictio
# 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)
@@ -197,7 +199,8 @@ def train(hyp, opt, device, callbacks): # hyp is path/to/hyp.yaml or hyp dictio
image_weights=opt.image_weights,
quad=opt.quad,
prefix=colorstr('train: '),
- shuffle=True)
+ shuffle=True,
+ seed=opt.seed)
labels = np.concatenate(dataset.labels, 0)
mlc = int(labels[:, 0].max()) # max label class
assert mlc < nc, f'Label class {mlc} exceeds nc={nc} in {data}. Possible class labels are 0-{nc - 1}'
@@ -276,7 +279,7 @@ def train(hyp, opt, device, callbacks): # hyp is path/to/hyp.yaml or hyp dictio
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='{l_bar}{bar:10}{r_bar}{bar:-10b}') # progress bar
+ pbar = tqdm(pbar, total=nb, bar_format=TQDM_BAR_FORMAT) # progress bar
optimizer.zero_grad()
for i, (imgs, targets, paths, _) in pbar: # batch -------------------------------------------------------------
callbacks.run('on_train_batch_start')
@@ -375,8 +378,8 @@ def train(hyp, opt, device, callbacks): # hyp is path/to/hyp.yaml or hyp dictio
'ema': deepcopy(ema.ema).half(),
'updates': ema.updates,
'optimizer': optimizer.state_dict(),
- 'wandb_id': loggers.wandb.wandb_run.id if loggers.wandb else None,
'opt': vars(opt),
+ 'git': GIT_INFO, # {remote, branch, commit} if a git repo
'date': datetime.now().isoformat()}
# Save last, best and delete
@@ -435,7 +438,7 @@ def parse_opt(known=False):
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=300, help='total training epochs')
+ parser.add_argument('--epochs', type=int, default=100, help='total training epochs')
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')
@@ -446,7 +449,7 @@ def parse_opt(known=False):
parser.add_argument('--noplots', action='store_true', help='save no plot files')
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='--cache images in "ram" (default) or "disk"')
+ parser.add_argument('--cache', type=str, nargs='?', const='ram', help='image --cache ram/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%%')
@@ -483,7 +486,7 @@ def main(opt, callbacks=Callbacks()):
check_requirements()
# Resume (from specified or most recent last.pt)
- if opt.resume and not check_wandb_resume(opt) and not check_comet_resume(opt) and not opt.evolve:
+ 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
@@ -519,7 +522,7 @@ def main(opt, callbacks=Callbacks()):
assert torch.cuda.device_count() > LOCAL_RANK, 'insufficient CUDA devices for DDP command'
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:
@@ -569,7 +572,12 @@ def main(opt, callbacks=Callbacks()):
# 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:
- os.system(f'gsutil cp gs://{opt.bucket}/evolve.csv {evolve_csv}') # download evolve.csv if exists
+ # download evolve.csv if exists
+ subprocess.run([
+ 'gsutil',
+ 'cp',
+ f'gs://{opt.bucket}/evolve.csv',
+ str(evolve_csv),])
for _ in range(opt.evolve): # generations to evolve
if evolve_csv.exists(): # if evolve.csv exists: select best hyps and mutate
@@ -607,7 +615,9 @@ def main(opt, callbacks=Callbacks()):
results = train(hyp.copy(), opt, device, callbacks)
callbacks = Callbacks()
# Write mutation results
- print_mutation(results, hyp.copy(), save_dir, opt.bucket)
+ 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)
# Plot results
plot_evolve(evolve_csv)
@@ -625,6 +635,6 @@ def run(**kwargs):
return opt
-if __name__ == "__main__":
+if __name__ == '__main__':
opt = parse_opt()
main(opt)
diff --git a/tutorial.ipynb b/tutorial.ipynb
index 957437b2be6d..32af68b57945 100644
--- a/tutorial.ipynb
+++ b/tutorial.ipynb
@@ -5,8 +5,6 @@
"colab": {
"name": "YOLOv5 Tutorial",
"provenance": [],
- "collapsed_sections": [],
- "machine_shape": "hm",
"toc_visible": true
},
"kernelspec": {
@@ -16,7 +14,7 @@
"accelerator": "GPU",
"widgets": {
"application/vnd.jupyter.widget-state+json": {
- "9b8caa3522fc4cbab31e13b5dfc7808d": {
+ "1f7df330663048998adcf8a45bc8f69b": {
"model_module": "@jupyter-widgets/controls",
"model_name": "HBoxModel",
"model_module_version": "1.5.0",
@@ -31,14 +29,14 @@
"_view_name": "HBoxView",
"box_style": "",
"children": [
- "IPY_MODEL_574140e4c4bc48c9a171541a02cd0211",
- "IPY_MODEL_35e03ce5090346c9ae602891470fc555",
- "IPY_MODEL_c942c208e72d46568b476bb0f2d75496"
+ "IPY_MODEL_e896e6096dd244c59d7955e2035cd729",
+ "IPY_MODEL_a6ff238c29984b24bf6d0bd175c19430",
+ "IPY_MODEL_3c085ba3f3fd4c3c8a6bb41b41ce1479"
],
- "layout": "IPY_MODEL_65881db1db8a4e9c930fab9172d45143"
+ "layout": "IPY_MODEL_16b0c8aa6e0f427e8a54d3791abb7504"
}
},
- "574140e4c4bc48c9a171541a02cd0211": {
+ "e896e6096dd244c59d7955e2035cd729": {
"model_module": "@jupyter-widgets/controls",
"model_name": "HTMLModel",
"model_module_version": "1.5.0",
@@ -53,13 +51,13 @@
"_view_name": "HTMLView",
"description": "",
"description_tooltip": null,
- "layout": "IPY_MODEL_60b913d755b34d638478e30705a2dde1",
+ "layout": "IPY_MODEL_c7b2dd0f78384cad8e400b282996cdf5",
"placeholder": "",
- "style": "IPY_MODEL_0856bea36ec148b68522ff9c9eb258d8",
+ "style": "IPY_MODEL_6a27e43b0e434edd82ee63f0a91036ca",
"value": "100%"
}
},
- "35e03ce5090346c9ae602891470fc555": {
+ "a6ff238c29984b24bf6d0bd175c19430": {
"model_module": "@jupyter-widgets/controls",
"model_name": "FloatProgressModel",
"model_module_version": "1.5.0",
@@ -75,15 +73,15 @@
"bar_style": "success",
"description": "",
"description_tooltip": null,
- "layout": "IPY_MODEL_76879f6f2aa54637a7a07faeea2bd684",
+ "layout": "IPY_MODEL_cce0e6c0c4ec442cb47e65c674e02e92",
"max": 818322941,
"min": 0,
"orientation": "horizontal",
- "style": "IPY_MODEL_0ace3934ec6f4d36a1b3a9e086390926",
+ "style": "IPY_MODEL_c5b9f38e2f0d4f9aa97fe87265263743",
"value": 818322941
}
},
- "c942c208e72d46568b476bb0f2d75496": {
+ "3c085ba3f3fd4c3c8a6bb41b41ce1479": {
"model_module": "@jupyter-widgets/controls",
"model_name": "HTMLModel",
"model_module_version": "1.5.0",
@@ -98,13 +96,13 @@
"_view_name": "HTMLView",
"description": "",
"description_tooltip": null,
- "layout": "IPY_MODEL_d6b7a2243e0c4beca714d99dceec23d6",
+ "layout": "IPY_MODEL_df554fb955c7454696beac5a82889386",
"placeholder": "",
- "style": "IPY_MODEL_5966ba6e6f114d8c9d8d1d6b1bd4f4c7",
- "value": " 780M/780M [02:19<00:00, 6.24MB/s]"
+ "style": "IPY_MODEL_74e9112a87a242f4831b7d68c7da6333",
+ "value": " 780M/780M [00:05<00:00, 126MB/s]"
}
},
- "65881db1db8a4e9c930fab9172d45143": {
+ "16b0c8aa6e0f427e8a54d3791abb7504": {
"model_module": "@jupyter-widgets/base",
"model_name": "LayoutModel",
"model_module_version": "1.2.0",
@@ -156,7 +154,7 @@
"width": null
}
},
- "60b913d755b34d638478e30705a2dde1": {
+ "c7b2dd0f78384cad8e400b282996cdf5": {
"model_module": "@jupyter-widgets/base",
"model_name": "LayoutModel",
"model_module_version": "1.2.0",
@@ -208,7 +206,7 @@
"width": null
}
},
- "0856bea36ec148b68522ff9c9eb258d8": {
+ "6a27e43b0e434edd82ee63f0a91036ca": {
"model_module": "@jupyter-widgets/controls",
"model_name": "DescriptionStyleModel",
"model_module_version": "1.5.0",
@@ -223,7 +221,7 @@
"description_width": ""
}
},
- "76879f6f2aa54637a7a07faeea2bd684": {
+ "cce0e6c0c4ec442cb47e65c674e02e92": {
"model_module": "@jupyter-widgets/base",
"model_name": "LayoutModel",
"model_module_version": "1.2.0",
@@ -275,7 +273,7 @@
"width": null
}
},
- "0ace3934ec6f4d36a1b3a9e086390926": {
+ "c5b9f38e2f0d4f9aa97fe87265263743": {
"model_module": "@jupyter-widgets/controls",
"model_name": "ProgressStyleModel",
"model_module_version": "1.5.0",
@@ -291,7 +289,7 @@
"description_width": ""
}
},
- "d6b7a2243e0c4beca714d99dceec23d6": {
+ "df554fb955c7454696beac5a82889386": {
"model_module": "@jupyter-widgets/base",
"model_name": "LayoutModel",
"model_module_version": "1.2.0",
@@ -343,7 +341,7 @@
"width": null
}
},
- "5966ba6e6f114d8c9d8d1d6b1bd4f4c7": {
+ "74e9112a87a242f4831b7d68c7da6333": {
"model_module": "@jupyter-widgets/controls",
"model_name": "DescriptionStyleModel",
"model_module_version": "1.5.0",
@@ -371,10 +369,11 @@
"![](\"https://github.com/ultralytics/assets/raw/master/yolov5/v62/splash_notebook.png\")
\n",
+ " \n",
"\n",
"\n",
"\n",
" \n",
" \n",
"![](\"https://uploads-ssl.webflow.com/5f6bc60e665f54545a1e52a5/615627e5824c9c6195abfda9_computer-vision-cycle.png\"/)
![\"yolo-ui\"](\"https://user-images.githubusercontent.com/7529846/187608607-ff89c3d5-1b8b-4743-a974-9275301b0524.png\")
"
+ "\n",
+ ""
],
"metadata": {
"id": "nWOsI5wJR1o3"
@@ -903,22 +900,6 @@
"id": "Lay2WsTjNJzP"
}
},
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "DLI1JmHU7B0l"
- },
- "source": [
- "## Weights & Biases Logging\n",
- "\n",
- "[Weights & Biases](https://wandb.ai/site?utm_campaign=repo_yolo_notebook) (W&B) is integrated with YOLOv5 for real-time visualization and cloud logging of training runs. This allows for better run comparison and introspection, as well improved visibility and collaboration for teams. To enable W&B `pip install wandb`, and then train normally (you will be guided through setup on first use). \n",
- "\n",
- "During training you will see live updates at [https://wandb.ai/home](https://wandb.ai/home?utm_campaign=repo_yolo_notebook), and you can create and share detailed [Reports](https://wandb.ai/glenn-jocher/yolov5_tutorial/reports/YOLOv5-COCO128-Tutorial-Results--VmlldzozMDI5OTY) of your results. For more information see the [YOLOv5 Weights & Biases Tutorial](https://github.com/ultralytics/yolov5/issues/1289). \n",
- "\n",
- "\n",
- "![\"Weights](\"https://user-images.githubusercontent.com/26833433/182482859-288a9622-4661-48db-99de-650d1dead5c6.jpg\")
"
- ]
- },
{
"cell_type": "markdown",
"metadata": {
@@ -944,7 +925,7 @@
"\n",
"YOLOv5 may be run in any of the following up-to-date verified environments (with all dependencies including [CUDA](https://developer.nvidia.com/cuda)/[CUDNN](https://developer.nvidia.com/cudnn), [Python](https://www.python.org/) and [PyTorch](https://pytorch.org/) preinstalled):\n",
"\n",
- "- **Google Colab and Kaggle** notebooks with free GPU: \n",
+ "- **Notebooks** with free GPU: \n",
"- **Google Cloud** Deep Learning VM. See [GCP Quickstart Guide](https://github.com/ultralytics/yolov5/wiki/GCP-Quickstart)\n",
"- **Amazon** Deep Learning AMI. See [AWS Quickstart Guide](https://github.com/ultralytics/yolov5/wiki/AWS-Quickstart)\n",
"- **Docker Image**. See [Docker Quickstart Guide](https://github.com/ultralytics/yolov5/wiki/Docker-Quickstart) \n"
@@ -958,7 +939,7 @@
"source": [
"# Status\n",
"\n",
- "\n",
+ "\n",
"\n",
"If this badge is green, all [YOLOv5 GitHub Actions](https://github.com/ultralytics/yolov5/actions) Continuous Integration (CI) tests are currently passing. CI tests verify correct operation of YOLOv5 training ([train.py](https://github.com/ultralytics/yolov5/blob/master/train.py)), testing ([val.py](https://github.com/ultralytics/yolov5/blob/master/val.py)), inference ([detect.py](https://github.com/ultralytics/yolov5/blob/master/detect.py)) and export ([export.py](https://github.com/ultralytics/yolov5/blob/master/export.py)) on macOS, Windows, and Ubuntu every 24 hours and on every commit.\n"
]
@@ -971,7 +952,7 @@
"source": [
"# Appendix\n",
"\n",
- "Additional content below for PyTorch Hub, CI, reproducing results, profiling speeds, VOC training, classification training and TensorRT example."
+ "Additional content below."
]
},
{
@@ -980,145 +961,16 @@
"id": "GMusP4OAxFu6"
},
"source": [
+ "# YOLOv5 PyTorch HUB Inference (DetectionModels only)\n",
"import torch\n",
"\n",
- "# PyTorch Hub Model\n",
- "model = torch.hub.load('ultralytics/yolov5', 'yolov5s') # or yolov5n - yolov5x6, custom\n",
- "\n",
- "# Images\n",
- "img = 'https://ultralytics.com/images/zidane.jpg' # or file, Path, PIL, OpenCV, numpy, list\n",
- "\n",
- "# Inference\n",
- "results = model(img)\n",
- "\n",
- "# Results\n",
+ "model = torch.hub.load('ultralytics/yolov5', 'yolov5s', force_reload=True) # yolov5n - yolov5x6 or custom\n",
+ "im = 'https://ultralytics.com/images/zidane.jpg' # file, Path, PIL.Image, OpenCV, nparray, list\n",
+ "results = model(im) # inference\n",
"results.print() # or .show(), .save(), .crop(), .pandas(), etc."
],
"execution_count": null,
"outputs": []
- },
- {
- "cell_type": "code",
- "metadata": {
- "id": "FGH0ZjkGjejy"
- },
- "source": [
- "# YOLOv5 CI\n",
- "%%shell\n",
- "rm -rf runs # remove runs/\n",
- "m=yolov5n # official weights\n",
- "b=runs/train/exp/weights/best # best.pt checkpoint\n",
- "python train.py --imgsz 64 --batch 32 --weights $m.pt --cfg $m.yaml --epochs 1 --device 0 # train\n",
- "for d in 0 cpu; do # devices\n",
- " for w in $m $b; do # weights\n",
- " python val.py --imgsz 64 --batch 32 --weights $w.pt --device $d # val\n",
- " python detect.py --imgsz 64 --weights $w.pt --device $d # detect\n",
- " done\n",
- "done\n",
- "python hubconf.py --model $m # hub\n",
- "python models/tf.py --weights $m.pt # build TF model\n",
- "python models/yolo.py --cfg $m.yaml # build PyTorch model\n",
- "python export.py --weights $m.pt --img 64 --include torchscript # export"
- ],
- "execution_count": null,
- "outputs": []
- },
- {
- "cell_type": "code",
- "metadata": {
- "id": "mcKoSIK2WSzj"
- },
- "source": [
- "# Reproduce\n",
- "for x in (f'yolov5{x}' for x in 'nsmlx'):\n",
- " !python val.py --weights {x}.pt --data coco.yaml --img 640 --task speed # speed\n",
- " !python val.py --weights {x}.pt --data coco.yaml --img 640 --conf 0.001 --iou 0.65 # mAP"
- ],
- "execution_count": null,
- "outputs": []
- },
- {
- "cell_type": "code",
- "metadata": {
- "id": "gogI-kwi3Tye"
- },
- "source": [
- "# Profile\n",
- "from utils.torch_utils import profile\n",
- "\n",
- "m1 = lambda x: x * torch.sigmoid(x)\n",
- "m2 = torch.nn.SiLU()\n",
- "results = profile(input=torch.randn(16, 3, 640, 640), ops=[m1, m2], n=100)"
- ],
- "execution_count": null,
- "outputs": []
- },
- {
- "cell_type": "code",
- "metadata": {
- "id": "BSgFCAcMbk1R"
- },
- "source": [
- "# VOC\n",
- "for b, m in zip([64, 64, 64, 32, 16], [f'yolov5{x}' for x in 'nsmlx']): # batch, model\n",
- " !python train.py --batch {b} --weights {m}.pt --data VOC.yaml --epochs 50 --img 512 --hyp hyp.VOC.yaml --project VOC --name {m} --cache"
- ],
- "execution_count": null,
- "outputs": []
- },
- {
- "cell_type": "code",
- "source": [
- "# Classification train\n",
- "for m in [*(f'yolov5{x}-cls.pt' for x in 'nsmlx'), 'resnet50.pt', 'resnet101.pt', 'efficientnet_b0.pt', 'efficientnet_b1.pt']:\n",
- " for d in 'mnist', 'fashion-mnist', 'cifar10', 'cifar100', 'imagenette160', 'imagenette320', 'imagenette', 'imagewoof160', 'imagewoof320', 'imagewoof':\n",
- " !python classify/train.py --model {m} --data {d} --epochs 10 --project YOLOv5-cls --name {m}-{d}"
- ],
- "metadata": {
- "id": "UWGH7H6yakVl"
- },
- "execution_count": null,
- "outputs": []
- },
- {
- "cell_type": "code",
- "source": [
- "# Classification val\n",
- "!bash data/scripts/get_imagenet.sh --val # download ImageNet val split (6.3G - 50000 images)\n",
- "!python classify/val.py --weights yolov5m-cls.pt --data ../datasets/imagenet --img 224 # validate"
- ],
- "metadata": {
- "id": "yYgOiFNHZx-1"
- },
- "execution_count": null,
- "outputs": []
- },
- {
- "cell_type": "code",
- "source": [
- "# Validate on COCO test. Zip results.json and submit to eval server at https://competitions.codalab.org/competitions/20794\n",
- "!bash data/scripts/get_coco.sh --test # download COCO test-dev2017 (7G - 40000 images, test 20000)\n",
- "!python val.py --weights yolov5x.pt --data coco.yaml --img 640 --iou 0.65 --half --task test"
- ],
- "metadata": {
- "id": "aq4DPWGu0Bl1"
- },
- "execution_count": null,
- "outputs": []
- },
- {
- "cell_type": "code",
- "metadata": {
- "id": "VTRwsvA9u7ln"
- },
- "source": [
- "# TensorRT \n",
- "!pip install -U nvidia-tensorrt --index-url https://pypi.ngc.nvidia.com # install\n",
- "!python export.py --weights yolov5s.pt --include engine --imgsz 640 --device 0 # export\n",
- "!python detect.py --weights yolov5s.engine --imgsz 640 --device 0 # inference"
- ],
- "execution_count": null,
- "outputs": []
}
]
}
diff --git a/utils/__init__.py b/utils/__init__.py
index 46225c2208ce..5b9fcd517e03 100644
--- a/utils/__init__.py
+++ b/utils/__init__.py
@@ -4,9 +4,15 @@
"""
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):
# YOLOv5 TryExcept class. Usage: @TryExcept() decorator or 'with TryExcept():' context manager
def __init__(self, msg=''):
@@ -17,7 +23,7 @@ def __enter__(self):
def __exit__(self, exc_type, value, traceback):
if value:
- print(f'{self.msg}{value}')
+ print(emojis(f"{self.msg}{': ' if self.msg else ''}{value}"))
return True
@@ -31,6 +37,16 @@ def wrapper(*args, **kwargs):
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
print('Checking setup...')
@@ -38,24 +54,25 @@ def notebook_init(verbose=True):
import os
import shutil
- from utils.general import check_font, check_requirements, emojis, is_colab
+ from utils.general import check_font, check_requirements, is_colab
from utils.torch_utils import select_device # imports
- check_requirements(('psutil', 'IPython'))
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
+ display = None
if verbose:
gb = 1 << 30 # bytes to GiB (1024 ** 3)
ram = psutil.virtual_memory().total
- total, used, free = shutil.disk_usage("/")
- display.clear_output()
+ total, used, free = shutil.disk_usage('/')
+ with contextlib.suppress(Exception): # clear display if ipython is installed
+ from IPython import display
+ 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 = ''
diff --git a/utils/augmentations.py b/utils/augmentations.py
index a5587351f75b..7ab75f17fb18 100644
--- a/utils/augmentations.py
+++ b/utils/augmentations.py
@@ -12,7 +12,7 @@
import torchvision.transforms as T
import torchvision.transforms.functional as TF
-from utils.general import LOGGER, check_version, colorstr, resample_segments, segment2box
+from utils.general import LOGGER, check_version, colorstr, resample_segments, segment2box, xywhn2xyxy
from utils.metrics import bbox_ioa
IMAGENET_MEAN = 0.485, 0.456, 0.406 # RGB mean
@@ -21,7 +21,7 @@
class Albumentations:
# YOLOv5 Albumentations class (optional, only used if package is installed)
- def __init__(self):
+ def __init__(self, size=640):
self.transform = None
prefix = colorstr('albumentations: ')
try:
@@ -29,6 +29,7 @@ def __init__(self):
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),
A.Blur(p=0.01),
A.MedianBlur(p=0.01),
A.ToGray(p=0.01),
@@ -200,7 +201,7 @@ def random_perspective(im,
# Transform label coordinates
n = len(targets)
if n:
- use_segments = any(x.any() for x in segments)
+ use_segments = any(x.any() for x in segments) and len(segments) == n
new = np.zeros((n, 4))
if use_segments: # warp segments
segments = resample_segments(segments) # upsample
@@ -249,12 +250,10 @@ 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, (255, 255, 255), cv2.FILLED)
+ cv2.drawContours(im_new, [segments[j].astype(np.int32)], -1, (1, 1, 1), cv2.FILLED)
- 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
+ result = cv2.flip(im, 1) # augment segments (flip left-right)
+ i = cv2.flip(im_new, 1).astype(bool)
im[i] = result[i] # cv2.imwrite('debug.jpg', im) # debug
return im, labels, segments
@@ -281,7 +280,7 @@ 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, labels[:, 1:5]) # intersection over area
+ ioa = bbox_ioa(box, xywhn2xyxy(labels[:, 1:5], w, h)) # intersection over area
labels = labels[ioa < 0.60] # remove >60% obscured labels
return labels
@@ -303,15 +302,17 @@ def box_candidates(box1, box2, wh_thr=2, ar_thr=100, area_thr=0.1, eps=1e-16):
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),
- hflip=0.5,
- vflip=0.0,
- jitter=0.4,
- mean=IMAGENET_MEAN,
- std=IMAGENET_STD,
- auto_aug=False):
+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):
# YOLOv5 classification Albumentations (optional, only used if package is installed)
prefix = colorstr('albumentations: ')
try:
@@ -319,7 +320,7 @@ def classify_albumentations(augment=True,
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)]
+ 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')
@@ -338,7 +339,7 @@ def classify_albumentations(augment=True,
return A.Compose(T)
except ImportError: # package not installed, skip
- pass
+ LOGGER.warning(f'{prefix}⚠️ not found, install with `pip install albumentations` (recommended)')
except Exception as e:
LOGGER.info(f'{prefix}{e}')
diff --git a/utils/autoanchor.py b/utils/autoanchor.py
index 0b49ab3319c0..bb5cf6e6965e 100644
--- a/utils/autoanchor.py
+++ b/utils/autoanchor.py
@@ -11,7 +11,7 @@
from tqdm import tqdm
from utils import TryExcept
-from utils.general import LOGGER, colorstr
+from utils.general import LOGGER, TQDM_BAR_FORMAT, colorstr
PREFIX = colorstr('AutoAnchor: ')
@@ -26,7 +26,7 @@ def check_anchor_order(m):
m.anchors[:] = m.anchors.flip(0)
-@TryExcept(f'{PREFIX}ERROR: ')
+@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()
@@ -122,7 +122,7 @@ def print_results(k, verbose=True):
# 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')
+ 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
@@ -134,7 +134,7 @@ def print_results(k, verbose=True):
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')
+ 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))
k = print_results(k, verbose=False)
@@ -153,7 +153,7 @@ def print_results(k, verbose=True):
# Evolve
f, sh, mp, s = anchor_fitness(k), k.shape, 0.9, 0.1 # fitness, generations, mutation prob, sigma
- pbar = tqdm(range(gen), bar_format='{l_bar}{bar:10}{r_bar}{bar:-10b}') # progress bar
+ pbar = tqdm(range(gen), bar_format=TQDM_BAR_FORMAT) # progress bar
for _ in pbar:
v = np.ones(sh)
while (v == 1).all(): # mutate until a change occurs (prevent duplicates)
diff --git a/utils/autobatch.py b/utils/autobatch.py
index 641b055b9fe3..bdeb91c3d2bd 100644
--- a/utils/autobatch.py
+++ b/utils/autobatch.py
@@ -19,7 +19,7 @@ def check_train_batch_size(model, imgsz=640, amp=True):
def autobatch(model, imgsz=640, fraction=0.8, batch_size=16):
- # Automatically estimate best batch size to use `fraction` of available CUDA memory
+ # Automatically estimate best YOLOv5 batch size to use `fraction` of available CUDA memory
# Usage:
# import torch
# from utils.autobatch import autobatch
@@ -33,6 +33,9 @@ def autobatch(model, imgsz=640, fraction=0.8, batch_size=16):
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)
@@ -62,8 +65,8 @@ def autobatch(model, imgsz=640, fraction=0.8, batch_size=16):
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.')
+ LOGGER.warning(f'{prefix}WARNING ⚠️ CUDA anomaly detected, recommend restart environment and retry command.')
- fraction = np.polyval(p, b) / t # actual fraction predicted
+ 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}%) ✅')
return b
diff --git a/utils/dataloaders.py b/utils/dataloaders.py
old mode 100755
new mode 100644
index d8ef11fd94b4..7687a2ba2665
--- a/utils/dataloaders.py
+++ b/utils/dataloaders.py
@@ -17,9 +17,9 @@
from pathlib import Path
from threading import Thread
from urllib.parse import urlparse
-from zipfile import ZipFile
import numpy as np
+import psutil
import torch
import torch.nn.functional as F
import torchvision
@@ -30,16 +30,17 @@
from utils.augmentations import (Albumentations, augment_hsv, classify_albumentations, classify_transforms, copy_paste,
letterbox, mixup, random_perspective)
-from utils.general import (DATASETS_DIR, LOGGER, NUM_THREADS, check_dataset, check_requirements, check_yaml, clean_str,
- cv2, is_colab, is_kaggle, segments2boxes, xyn2xy, xywh2xyxy, xywhn2xyxy, xyxy2xywhn)
+from utils.general import (DATASETS_DIR, LOGGER, NUM_THREADS, TQDM_BAR_FORMAT, check_dataset, check_requirements,
+ check_yaml, clean_str, cv2, is_colab, is_kaggle, segments2boxes, unzip_file, xyn2xy,
+ xywh2xyxy, xywhn2xyxy, xyxy2xywhn)
from utils.torch_utils import torch_distributed_zero_first
# Parameters
HELP_URL = 'See https://github.com/ultralytics/yolov5/wiki/Train-Custom-Data'
IMG_FORMATS = 'bmp', 'dng', 'jpeg', 'jpg', 'mpo', 'png', 'tif', 'tiff', 'webp', 'pfm' # include image suffixes
VID_FORMATS = 'asf', 'avi', 'gif', 'm4v', 'mkv', 'mov', 'mp4', 'mpeg', 'mpg', 'ts', 'wmv' # include video suffixes
-BAR_FORMAT = '{l_bar}{bar:10}{r_bar}{bar:-10b}' # tqdm bar format
LOCAL_RANK = int(os.getenv('LOCAL_RANK', -1)) # https://pytorch.org/docs/stable/elastic/run.html
+RANK = int(os.getenv('RANK', -1))
PIN_MEMORY = str(os.getenv('PIN_MEMORY', True)).lower() == 'true' # global pin_memory for dataloaders
# Get orientation exif tag
@@ -51,7 +52,7 @@
def get_hash(paths):
# Returns a single hash value of a list of paths (files or dirs)
size = sum(os.path.getsize(p) for p in paths if os.path.exists(p)) # sizes
- h = hashlib.md5(str(size).encode()) # hash sizes
+ h = hashlib.sha256(str(size).encode()) # hash sizes
h.update(''.join(paths).encode()) # hash paths
return h.hexdigest() # return hash
@@ -88,7 +89,7 @@ def exif_transpose(image):
if method is not None:
image = image.transpose(method)
del exif[0x0112]
- image.info["exif"] = exif.tobytes()
+ image.info['exif'] = exif.tobytes()
return image
@@ -114,9 +115,10 @@ def create_dataloader(path,
image_weights=False,
quad=False,
prefix='',
- shuffle=False):
+ shuffle=False,
+ seed=0):
if rect and shuffle:
- LOGGER.warning('WARNING: --rect is incompatible with DataLoader shuffle, setting shuffle=False')
+ LOGGER.warning('WARNING ⚠️ --rect is incompatible with DataLoader shuffle, setting shuffle=False')
shuffle = False
with torch_distributed_zero_first(rank): # init dataset *.cache only once if DDP
dataset = LoadImagesAndLabels(
@@ -139,7 +141,7 @@ def create_dataloader(path,
sampler = None if rank == -1 else distributed.DistributedSampler(dataset, shuffle=shuffle)
loader = DataLoader if image_weights else InfiniteDataLoader # only DataLoader allows for attribute updates
generator = torch.Generator()
- generator.manual_seed(0)
+ generator.manual_seed(6148914691236517205 + seed + RANK)
return loader(dataset,
batch_size=batch_size,
shuffle=shuffle and sampler is None,
@@ -185,9 +187,60 @@ def __iter__(self):
yield from iter(self.sampler)
+class LoadScreenshots:
+ # YOLOv5 screenshot dataloader, i.e. `python detect.py --source "screen 0 100 100 512 256"`
+ def __init__(self, source, img_size=640, stride=32, auto=True, transforms=None):
+ # source = [screen_number left top width height] (pixels)
+ check_requirements('mss')
+ import mss
+
+ source, *params = source.split()
+ self.screen, left, top, width, height = 0, None, None, None, None # default to full screen 0
+ if len(params) == 1:
+ self.screen = int(params[0])
+ elif len(params) == 4:
+ left, top, width, height = (int(x) for x in params)
+ elif len(params) == 5:
+ self.screen, left, top, width, height = (int(x) for x in params)
+ self.img_size = img_size
+ self.stride = stride
+ self.transforms = transforms
+ self.auto = auto
+ self.mode = 'stream'
+ self.frame = 0
+ self.sct = mss.mss()
+
+ # Parse monitor shape
+ monitor = self.sct.monitors[self.screen]
+ self.top = monitor['top'] if top is None else (monitor['top'] + top)
+ self.left = monitor['left'] if left is None else (monitor['left'] + left)
+ self.width = width or monitor['width']
+ self.height = height or monitor['height']
+ self.monitor = {'left': self.left, 'top': self.top, 'width': self.width, 'height': self.height}
+
+ def __iter__(self):
+ return self
+
+ def __next__(self):
+ # mss screen capture: get raw pixels from the screen as np array
+ im0 = np.array(self.sct.grab(self.monitor))[:, :, :3] # [:, :, :3] BGRA to BGR
+ s = f'screen {self.screen} (LTWH): {self.left},{self.top},{self.width},{self.height}: '
+
+ if self.transforms:
+ im = self.transforms(im0) # transforms
+ else:
+ im = letterbox(im0, self.img_size, stride=self.stride, auto=self.auto)[0] # padded resize
+ im = im.transpose((2, 0, 1))[::-1] # HWC to CHW, BGR to RGB
+ im = np.ascontiguousarray(im) # contiguous
+ self.frame += 1
+ return str(self.screen), im, im0, None, s # screen, img, original img, im0s, s
+
+
class LoadImages:
# YOLOv5 image/video dataloader, i.e. `python detect.py --source image.jpg/vid.mp4`
def __init__(self, path, img_size=640, stride=32, auto=True, transforms=None, vid_stride=1):
+ if isinstance(path, str) and Path(path).suffix == '.txt': # *.txt file with img/vid/dir on each line
+ path = Path(path).read_text().rsplit()
files = []
for p in sorted(path) if isinstance(path, (list, tuple)) else [path]:
p = str(Path(p).resolve())
@@ -232,8 +285,9 @@ def __next__(self):
if self.video_flag[self.count]:
# Read video
self.mode = 'video'
- ret_val, im0 = self.cap.read()
- self.cap.set(cv2.CAP_PROP_POS_FRAMES, self.vid_stride * (self.frame + 1)) # read at vid_stride
+ for _ in range(self.vid_stride):
+ self.cap.grab()
+ ret_val, im0 = self.cap.retrieve()
while not ret_val:
self.count += 1
self.cap.release()
@@ -287,13 +341,13 @@ def __len__(self):
class LoadStreams:
# YOLOv5 streamloader, i.e. `python detect.py --source 'rtsp://example.com/media.mp4' # RTSP, RTMP, HTTP streams`
- def __init__(self, sources='streams.txt', img_size=640, stride=32, auto=True, transforms=None, vid_stride=1):
+ def __init__(self, sources='file.streams', img_size=640, stride=32, auto=True, transforms=None, vid_stride=1):
torch.backends.cudnn.benchmark = True # faster for fixed-size inference
self.mode = 'stream'
self.img_size = img_size
self.stride = stride
self.vid_stride = vid_stride # video frame-rate stride
- sources = Path(sources).read_text().rsplit() if Path(sources).is_file() else [sources]
+ sources = Path(sources).read_text().rsplit() if os.path.isfile(sources) else [sources]
n = len(sources)
self.sources = [clean_str(x) for x in sources] # clean source names for later
self.imgs, self.fps, self.frames, self.threads = [None] * n, [0] * n, [0] * n, [None] * n
@@ -301,9 +355,10 @@ def __init__(self, sources='streams.txt', img_size=640, stride=32, auto=True, tr
# Start thread to read frames from video stream
st = f'{i + 1}/{n}: {s}... '
if urlparse(s).hostname in ('www.youtube.com', 'youtube.com', 'youtu.be'): # if source is YouTube video
+ # YouTube format i.e. 'https://www.youtube.com/watch?v=Zgi9g1ksQHc' or 'https://youtu.be/Zgi9g1ksQHc'
check_requirements(('pafy', 'youtube_dl==2020.12.2'))
import pafy
- s = pafy.new(s).getbest(preftype="mp4").url # YouTube URL
+ s = pafy.new(s).getbest(preftype='mp4').url # YouTube URL
s = eval(s) if s.isnumeric() else s # i.e. s = '0' local webcam
if s == 0:
assert not is_colab(), '--source 0 webcam unsupported on Colab. Rerun command in a local environment.'
@@ -318,7 +373,7 @@ def __init__(self, sources='streams.txt', img_size=640, stride=32, auto=True, tr
_, self.imgs[i] = cap.read() # guarantee first frame
self.threads[i] = Thread(target=self.update, args=([i, cap, s]), daemon=True)
- LOGGER.info(f"{st} Success ({self.frames[i]} frames {w}x{h} at {self.fps[i]:.2f} FPS)")
+ LOGGER.info(f'{st} Success ({self.frames[i]} frames {w}x{h} at {self.fps[i]:.2f} FPS)')
self.threads[i].start()
LOGGER.info('') # newline
@@ -328,7 +383,7 @@ def __init__(self, sources='streams.txt', img_size=640, stride=32, auto=True, tr
self.auto = auto and self.rect
self.transforms = transforms # optional
if not self.rect:
- LOGGER.warning('WARNING: Stream shapes differ. For optimal performance supply similarly-shaped streams.')
+ LOGGER.warning('WARNING ⚠️ Stream shapes differ. For optimal performance supply similarly-shaped streams.')
def update(self, i, cap, stream):
# Read stream `i` frames in daemon thread
@@ -341,7 +396,7 @@ def update(self, i, cap, stream):
if success:
self.imgs[i] = im
else:
- LOGGER.warning('WARNING: Video stream unresponsive, please check your IP camera connection.')
+ LOGGER.warning('WARNING ⚠️ Video stream unresponsive, please check your IP camera connection.')
self.imgs[i] = np.zeros_like(self.imgs[i])
cap.open(stream) # re-open stream if signal was lost
time.sleep(0.0) # wait time
@@ -393,6 +448,7 @@ def __init__(self,
single_cls=False,
stride=32,
pad=0.0,
+ min_items=0,
prefix=''):
self.img_size = img_size
self.augment = augment
@@ -403,7 +459,7 @@ def __init__(self,
self.mosaic_border = [-img_size // 2, -img_size // 2]
self.stride = stride
self.path = path
- self.albumentations = Albumentations() if augment else None
+ self.albumentations = Albumentations(size=img_size) if augment else None
try:
f = [] # image files
@@ -416,15 +472,15 @@ def __init__(self,
with open(p) as t:
t = t.read().strip().splitlines()
parent = str(p.parent) + os.sep
- f += [x.replace('./', parent) if x.startswith('./') else x for x in t] # local to global path
- # f += [p.parent / x.lstrip(os.sep) for x in t] # local to global path (pathlib)
+ f += [x.replace('./', parent, 1) if x.startswith('./') else x for x in t] # to global path
+ # f += [p.parent / x.lstrip(os.sep) for x in t] # to global path (pathlib)
else:
raise FileNotFoundError(f'{prefix}{p} does not exist')
self.im_files = sorted(x.replace('/', os.sep) for x in f if x.split('.')[-1].lower() in IMG_FORMATS)
# self.img_files = sorted([x for x in f if x.suffix[1:].lower() in IMG_FORMATS]) # pathlib
assert self.im_files, f'{prefix}No images found'
except Exception as e:
- raise Exception(f'{prefix}Error loading data from {path}: {e}\n{HELP_URL}')
+ raise Exception(f'{prefix}Error loading data from {path}: {e}\n{HELP_URL}') from e
# Check cache
self.label_files = img2label_paths(self.im_files) # labels
@@ -439,8 +495,8 @@ def __init__(self,
# Display cache
nf, nm, ne, nc, n = cache.pop('results') # found, missing, empty, corrupt, total
if exists and LOCAL_RANK in {-1, 0}:
- d = f"Scanning '{cache_path}' images and labels... {nf} found, {nm} missing, {ne} empty, {nc} corrupt"
- tqdm(None, desc=prefix + d, total=n, initial=n, bar_format=BAR_FORMAT) # display cache results
+ d = f'Scanning {cache_path}... {nf} images, {nm + ne} backgrounds, {nc} corrupt'
+ tqdm(None, desc=prefix + d, total=n, initial=n, bar_format=TQDM_BAR_FORMAT) # display cache results
if cache['msgs']:
LOGGER.info('\n'.join(cache['msgs'])) # display warnings
assert nf > 0 or not augment, f'{prefix}No labels found in {cache_path}, can not start training. {HELP_URL}'
@@ -454,7 +510,19 @@ def __init__(self,
self.shapes = np.array(shapes)
self.im_files = list(cache.keys()) # update
self.label_files = img2label_paths(cache.keys()) # update
- n = len(shapes) # number of images
+
+ # Filter images
+ if min_items:
+ include = np.array([len(x) >= min_items for x in self.labels]).nonzero()[0].astype(int)
+ LOGGER.info(f'{prefix}{n - len(include)}/{n} images filtered from dataset')
+ self.im_files = [self.im_files[i] for i in include]
+ self.label_files = [self.label_files[i] for i in include]
+ self.labels = [self.labels[i] for i in include]
+ self.segments = [self.segments[i] for i in include]
+ self.shapes = self.shapes[include] # wh
+
+ # Create indices
+ n = len(self.shapes) # number of images
bi = np.floor(np.arange(n) / batch_size).astype(int) # batch index
nb = bi[-1] + 1 # number of batches
self.batch = bi # batch index of image
@@ -472,8 +540,6 @@ def __init__(self,
self.segments[i] = segment[j]
if single_cls: # single-class training, merge all classes into 0
self.labels[i][:, 0] = 0
- if segment:
- self.segments[i][:, 0] = 0
# Rectangular Training
if self.rect:
@@ -484,6 +550,7 @@ def __init__(self,
self.im_files = [self.im_files[i] for i in irect]
self.label_files = [self.label_files[i] for i in irect]
self.labels = [self.labels[i] for i in irect]
+ self.segments = [self.segments[i] for i in irect]
self.shapes = s[irect] # wh
ar = ar[irect]
@@ -499,34 +566,53 @@ def __init__(self,
self.batch_shapes = np.ceil(np.array(shapes) * img_size / stride + pad).astype(int) * stride
- # Cache images into RAM/disk for faster training (WARNING: large datasets may exceed system resources)
+ # Cache images into RAM/disk for faster training
+ if cache_images == 'ram' and not self.check_cache_ram(prefix=prefix):
+ cache_images = False
self.ims = [None] * n
self.npy_files = [Path(f).with_suffix('.npy') for f in self.im_files]
if cache_images:
- gb = 0 # Gigabytes of cached images
+ b, gb = 0, 1 << 30 # bytes of cached images, bytes per gigabytes
self.im_hw0, self.im_hw = [None] * n, [None] * n
fcn = self.cache_images_to_disk if cache_images == 'disk' else self.load_image
results = ThreadPool(NUM_THREADS).imap(fcn, range(n))
- pbar = tqdm(enumerate(results), total=n, bar_format=BAR_FORMAT, disable=LOCAL_RANK > 0)
+ pbar = tqdm(enumerate(results), total=n, bar_format=TQDM_BAR_FORMAT, disable=LOCAL_RANK > 0)
for i, x in pbar:
if cache_images == 'disk':
- gb += self.npy_files[i].stat().st_size
+ b += self.npy_files[i].stat().st_size
else: # 'ram'
self.ims[i], self.im_hw0[i], self.im_hw[i] = x # im, hw_orig, hw_resized = load_image(self, i)
- gb += self.ims[i].nbytes
- pbar.desc = f'{prefix}Caching images ({gb / 1E9:.1f}GB {cache_images})'
+ b += self.ims[i].nbytes
+ pbar.desc = f'{prefix}Caching images ({b / gb:.1f}GB {cache_images})'
pbar.close()
+ def check_cache_ram(self, safety_margin=0.1, prefix=''):
+ # Check image caching requirements vs available memory
+ b, gb = 0, 1 << 30 # bytes of cached images, bytes per gigabytes
+ n = min(self.n, 30) # extrapolate from 30 random images
+ for _ in range(n):
+ im = cv2.imread(random.choice(self.im_files)) # sample image
+ ratio = self.img_size / max(im.shape[0], im.shape[1]) # max(h, w) # ratio
+ b += im.nbytes * ratio ** 2
+ mem_required = b * self.n / n # GB required to cache dataset into RAM
+ mem = psutil.virtual_memory()
+ cache = mem_required * (1 + safety_margin) < mem.available # to cache or not to cache, that is the question
+ if not cache:
+ LOGGER.info(f'{prefix}{mem_required / gb:.1f}GB RAM required, '
+ f'{mem.available / gb:.1f}/{mem.total / gb:.1f}GB available, '
+ f"{'caching images ✅' if cache else 'not caching images ⚠️'}")
+ return cache
+
def cache_labels(self, path=Path('./labels.cache'), prefix=''):
# Cache dataset labels, check images and read shapes
x = {} # dict
nm, nf, ne, nc, msgs = 0, 0, 0, 0, [] # number missing, found, empty, corrupt, messages
- desc = f"{prefix}Scanning '{path.parent / path.stem}' images and labels..."
+ desc = f'{prefix}Scanning {path.parent / path.stem}...'
with Pool(NUM_THREADS) as pool:
pbar = tqdm(pool.imap(verify_image_label, zip(self.im_files, self.label_files, repeat(prefix))),
desc=desc,
total=len(self.im_files),
- bar_format=BAR_FORMAT)
+ bar_format=TQDM_BAR_FORMAT)
for im_file, lb, shape, segments, nm_f, nf_f, ne_f, nc_f, msg in pbar:
nm += nm_f
nf += nf_f
@@ -536,13 +622,13 @@ def cache_labels(self, path=Path('./labels.cache'), prefix=''):
x[im_file] = [lb, shape, segments]
if msg:
msgs.append(msg)
- pbar.desc = f"{desc}{nf} found, {nm} missing, {ne} empty, {nc} corrupt"
+ pbar.desc = f'{desc} {nf} images, {nm + ne} backgrounds, {nc} corrupt'
pbar.close()
if msgs:
LOGGER.info('\n'.join(msgs))
if nf == 0:
- LOGGER.warning(f'{prefix}WARNING: No labels found in {path}. {HELP_URL}')
+ LOGGER.warning(f'{prefix}WARNING ⚠️ No labels found in {path}. {HELP_URL}')
x['hash'] = get_hash(self.label_files + self.im_files)
x['results'] = nf, nm, ne, nc, len(self.im_files)
x['msgs'] = msgs # warnings
@@ -552,7 +638,7 @@ def cache_labels(self, path=Path('./labels.cache'), prefix=''):
path.with_suffix('.cache.npy').rename(path) # remove .npy suffix
LOGGER.info(f'{prefix}New cache created: {path}')
except Exception as e:
- LOGGER.warning(f'{prefix}WARNING: Cache directory {path.parent} is not writeable: {e}') # not writeable
+ LOGGER.warning(f'{prefix}WARNING ⚠️ Cache directory {path.parent} is not writeable: {e}') # not writeable
return x
def __len__(self):
@@ -651,7 +737,7 @@ def load_image(self, i):
r = self.img_size / max(h0, w0) # ratio
if r != 1: # if sizes are not equal
interp = cv2.INTER_LINEAR if (self.augment or r > 1) else cv2.INTER_AREA
- im = cv2.resize(im, (int(w0 * r), int(h0 * r)), interpolation=interp)
+ im = cv2.resize(im, (math.ceil(w0 * r), math.ceil(h0 * r)), interpolation=interp)
return im, (h0, w0), im.shape[:2] # im, hw_original, hw_resized
return self.ims[i], self.im_hw0[i], self.im_hw[i] # im, hw_original, hw_resized
@@ -783,6 +869,7 @@ def load_mosaic9(self, index):
# img9, labels9 = replicate(img9, labels9) # replicate
# Augment
+ img9, labels9, segments9 = copy_paste(img9, labels9, segments9, p=self.hyp['copy_paste'])
img9, labels9 = random_perspective(img9,
labels9,
segments9,
@@ -916,7 +1003,7 @@ def verify_image_label(args):
f.seek(-2, 2)
if f.read() != b'\xff\xd9': # corrupt JPEG
ImageOps.exif_transpose(Image.open(im_file)).save(im_file, 'JPEG', subsampling=0, quality=100)
- msg = f'{prefix}WARNING: {im_file}: corrupt JPEG restored and saved'
+ msg = f'{prefix}WARNING ⚠️ {im_file}: corrupt JPEG restored and saved'
# verify labels
if os.path.isfile(lb_file):
@@ -938,7 +1025,7 @@ def verify_image_label(args):
lb = lb[i] # remove duplicates
if segments:
segments = [segments[x] for x in i]
- msg = f'{prefix}WARNING: {im_file}: {nl - len(i)} duplicate labels removed'
+ msg = f'{prefix}WARNING ⚠️ {im_file}: {nl - len(i)} duplicate labels removed'
else:
ne = 1 # label empty
lb = np.zeros((0, 5), dtype=np.float32)
@@ -948,18 +1035,23 @@ def verify_image_label(args):
return im_file, lb, shape, segments, nm, nf, ne, nc, msg
except Exception as e:
nc = 1
- msg = f'{prefix}WARNING: {im_file}: ignoring corrupt image/label: {e}'
+ msg = f'{prefix}WARNING ⚠️ {im_file}: ignoring corrupt image/label: {e}'
return [None, None, None, None, nm, nf, ne, nc, msg]
class HUBDatasetStats():
- """ Return dataset statistics dictionary with images and instances counts per split per class
- To run in parent directory: export PYTHONPATH="$PWD/yolov5"
- Usage1: from utils.dataloaders import *; HUBDatasetStats('coco128.yaml', autodownload=True)
- Usage2: from utils.dataloaders import *; HUBDatasetStats('path/to/coco128_with_yaml.zip')
+ """ Class for generating HUB dataset JSON and `-hub` dataset directory
+
Arguments
path: Path to data.yaml or data.zip (with data.yaml inside data.zip)
autodownload: Attempt to download dataset if not found locally
+
+ Usage
+ from utils.dataloaders import HUBDatasetStats
+ stats = HUBDatasetStats('coco128.yaml', autodownload=True) # usage 1
+ stats = HUBDatasetStats('path/to/coco128.zip') # usage 2
+ stats.get_json(save=False)
+ stats.process_images()
"""
def __init__(self, path='coco128.yaml', autodownload=False):
@@ -971,7 +1063,7 @@ def __init__(self, path='coco128.yaml', autodownload=False):
if zipped:
data['path'] = data_dir
except Exception as e:
- raise Exception("error/HUB/dataset_stats/yaml_load") from e
+ raise Exception('error/HUB/dataset_stats/yaml_load') from e
check_dataset(data, autodownload) # download dataset if missing
self.hub_dir = Path(data['path'] + '-hub')
@@ -996,7 +1088,7 @@ def _unzip(self, path):
if not str(path).endswith('.zip'): # path is data.yaml
return False, None, path
assert Path(path).is_file(), f'Error unzipping {path}, file not found'
- ZipFile(path).extractall(path=path.parent) # unzip
+ unzip_file(path, path=path.parent)
dir = path.with_suffix('') # dataset directory == zip name
assert dir.is_dir(), f'Error unzipping {path}, {dir} not found. path/to/abc.zip MUST unzip to path/to/abc/'
return True, str(dir), self._find_yaml(dir) # zipped, data_dir, yaml_path
@@ -1011,7 +1103,7 @@ def _hub_ops(self, f, max_dim=1920):
im = im.resize((int(im.width * r), int(im.height * r)))
im.save(f_new, 'JPEG', quality=50, optimize=True) # save
except Exception as e: # use OpenCV
- print(f'WARNING: HUB ops PIL failure {f}: {e}')
+ LOGGER.info(f'WARNING ⚠️ HUB ops PIL failure {f}: {e}')
im = cv2.imread(f)
im_height, im_width = im.shape[:2]
r = max_dim / max(im_height, im_width) # ratio
@@ -1096,7 +1188,7 @@ def __getitem__(self, i):
else: # read image
im = cv2.imread(f) # BGR
if self.album_transforms:
- sample = self.album_transforms(image=cv2.cvtColor(im, cv2.COLOR_BGR2RGB))["image"]
+ sample = self.album_transforms(image=cv2.cvtColor(im, cv2.COLOR_BGR2RGB))['image']
else:
sample = self.torch_transforms(im)
return sample, j
@@ -1118,7 +1210,7 @@ def create_classification_dataloader(path,
nw = min([os.cpu_count() // max(nd, 1), batch_size if batch_size > 1 else 0, workers])
sampler = None if rank == -1 else distributed.DistributedSampler(dataset, shuffle=shuffle)
generator = torch.Generator()
- generator.manual_seed(0)
+ generator.manual_seed(6148914691236517205 + RANK)
return InfiniteDataLoader(dataset,
batch_size=batch_size,
shuffle=shuffle and sampler is None,
diff --git a/utils/docker/Dockerfile b/utils/docker/Dockerfile
index 4b9367cc27db..b5d2af9fb08e 100644
--- a/utils/docker/Dockerfile
+++ b/utils/docker/Dockerfile
@@ -3,33 +3,43 @@
# Image is CUDA-optimized for YOLOv5 single/multi-GPU training and inference
# Start FROM NVIDIA PyTorch image https://ngc.nvidia.com/catalog/containers/nvidia:pytorch
-FROM nvcr.io/nvidia/pytorch:22.07-py3
-RUN rm -rf /opt/pytorch # remove 1.2GB dir
+# FROM docker.io/pytorch/pytorch:latest
+FROM pytorch/pytorch:latest
# Downloads to user config dir
ADD https://ultralytics.com/assets/Arial.ttf https://ultralytics.com/assets/Arial.Unicode.ttf /root/.config/Ultralytics/
# Install linux packages
-RUN apt update && apt install --no-install-recommends -y zip htop screen libgl1-mesa-glx
+ENV DEBIAN_FRONTEND noninteractive
+RUN apt update
+RUN TZ=Etc/UTC apt install -y tzdata
+RUN apt install --no-install-recommends -y gcc git zip curl htop libgl1-mesa-glx libglib2.0-0 libpython3-dev gnupg
+# RUN alias python=python3
-# Install pip packages
-COPY requirements.txt .
-RUN python -m pip install --upgrade pip wheel
-RUN pip uninstall -y Pillow torchtext torch torchvision
-RUN pip install --no-cache -r requirements.txt albumentations wandb gsutil notebook Pillow>=9.1.0 \
- 'opencv-python<4.6.0.66' \
- --extra-index-url https://download.pytorch.org/whl/cu113
+# Security updates
+# https://security.snyk.io/vuln/SNYK-UBUNTU1804-OPENSSL-3314796
+RUN apt upgrade --no-install-recommends -y openssl
# Create working directory
-RUN mkdir -p /usr/src/app
+RUN rm -rf /usr/src/app && mkdir -p /usr/src/app
WORKDIR /usr/src/app
# Copy contents
# COPY . /usr/src/app (issues as not a .git directory)
RUN git clone https://github.com/ultralytics/yolov5 /usr/src/app
+# Install pip packages
+COPY requirements.txt .
+RUN python3 -m pip install --upgrade pip wheel
+RUN pip install --no-cache -r requirements.txt albumentations comet gsutil notebook \
+ coremltools onnx onnx-simplifier onnxruntime 'openvino-dev>=2022.3'
+ # tensorflow tensorflowjs \
+
# Set environment variables
-ENV OMP_NUM_THREADS=8
+ENV OMP_NUM_THREADS=1
+
+# Cleanup
+ENV DEBIAN_FRONTEND teletype
# Usage Examples -------------------------------------------------------------------------------------------------------
@@ -53,7 +63,7 @@ ENV OMP_NUM_THREADS=8
# t=ultralytics/yolov5:latest tnew=ultralytics/yolov5:v6.2 && sudo docker pull $t && sudo docker tag $t $tnew && sudo docker push $tnew
# Clean up
-# docker system prune -a --volumes
+# sudo docker system prune -a --volumes
# Update Ubuntu drivers
# https://www.maketecheasier.com/install-nvidia-drivers-ubuntu/
diff --git a/utils/docker/Dockerfile-arm64 b/utils/docker/Dockerfile-arm64
index 6e8ff77545c5..7023c6a4bb1f 100644
--- a/utils/docker/Dockerfile-arm64
+++ b/utils/docker/Dockerfile-arm64
@@ -3,25 +3,24 @@
# Image is aarch64-compatible for Apple M1 and other ARM architectures i.e. Jetson Nano and Raspberry Pi
# Start FROM Ubuntu image https://hub.docker.com/_/ubuntu
-FROM arm64v8/ubuntu:20.04
+FROM arm64v8/ubuntu:rolling
# Downloads to user config dir
ADD https://ultralytics.com/assets/Arial.ttf https://ultralytics.com/assets/Arial.Unicode.ttf /root/.config/Ultralytics/
# Install linux packages
+ENV DEBIAN_FRONTEND noninteractive
RUN apt update
-RUN DEBIAN_FRONTEND=noninteractive TZ=Etc/UTC apt install -y tzdata
+RUN TZ=Etc/UTC apt install -y tzdata
RUN apt install --no-install-recommends -y python3-pip git zip curl htop gcc libgl1-mesa-glx libglib2.0-0 libpython3-dev
# RUN alias python=python3
# Install pip packages
COPY requirements.txt .
RUN python3 -m pip install --upgrade pip wheel
-RUN pip install --no-cache -r requirements.txt gsutil notebook \
- tensorflow-aarch64
- # tensorflowjs \
- # onnx onnx-simplifier onnxruntime \
- # coremltools openvino-dev \
+RUN pip install --no-cache -r requirements.txt albumentations gsutil notebook \
+ coremltools onnx onnxruntime
+ # tensorflow-aarch64 tensorflowjs \
# Create working directory
RUN mkdir -p /usr/src/app
@@ -30,12 +29,13 @@ WORKDIR /usr/src/app
# Copy contents
# COPY . /usr/src/app (issues as not a .git directory)
RUN git clone https://github.com/ultralytics/yolov5 /usr/src/app
+ENV DEBIAN_FRONTEND teletype
# Usage Examples -------------------------------------------------------------------------------------------------------
# Build and Push
-# t=ultralytics/yolov5:latest-M1 && sudo docker build --platform linux/arm64 -f utils/docker/Dockerfile-arm64 -t $t . && sudo docker push $t
+# t=ultralytics/yolov5:latest-arm64 && sudo docker build --platform linux/arm64 -f utils/docker/Dockerfile-arm64 -t $t . && sudo docker push $t
# Pull and Run
-# t=ultralytics/yolov5:latest-M1 && sudo docker pull $t && sudo docker run -it --ipc=host -v "$(pwd)"/datasets:/usr/src/datasets $t
+# t=ultralytics/yolov5:latest-arm64 && sudo docker pull $t && sudo docker run -it --ipc=host -v "$(pwd)"/datasets:/usr/src/datasets $t
diff --git a/utils/docker/Dockerfile-cpu b/utils/docker/Dockerfile-cpu
index d6fac645dba1..06bad9a3790d 100644
--- a/utils/docker/Dockerfile-cpu
+++ b/utils/docker/Dockerfile-cpu
@@ -3,23 +3,24 @@
# Image is CPU-optimized for ONNX, OpenVINO and PyTorch YOLOv5 deployments
# Start FROM Ubuntu image https://hub.docker.com/_/ubuntu
-FROM ubuntu:20.04
+FROM ubuntu:rolling
# Downloads to user config dir
ADD https://ultralytics.com/assets/Arial.ttf https://ultralytics.com/assets/Arial.Unicode.ttf /root/.config/Ultralytics/
# Install linux packages
+ENV DEBIAN_FRONTEND noninteractive
RUN apt update
-RUN DEBIAN_FRONTEND=noninteractive TZ=Etc/UTC apt install -y tzdata
-RUN apt install --no-install-recommends -y python3-pip git zip curl htop libgl1-mesa-glx libglib2.0-0 libpython3-dev
+RUN TZ=Etc/UTC apt install -y tzdata
+RUN apt install --no-install-recommends -y python3-pip git zip curl htop libgl1-mesa-glx libglib2.0-0 libpython3-dev gnupg
# RUN alias python=python3
# Install pip packages
COPY requirements.txt .
RUN python3 -m pip install --upgrade pip wheel
RUN pip install --no-cache -r requirements.txt albumentations gsutil notebook \
- coremltools onnx onnx-simplifier onnxruntime tensorflow-cpu tensorflowjs \
- # openvino-dev \
+ coremltools onnx onnx-simplifier onnxruntime 'openvino-dev>=2022.3' \
+ # tensorflow tensorflowjs \
--extra-index-url https://download.pytorch.org/whl/cpu
# Create working directory
@@ -29,6 +30,7 @@ WORKDIR /usr/src/app
# Copy contents
# COPY . /usr/src/app (issues as not a .git directory)
RUN git clone https://github.com/ultralytics/yolov5 /usr/src/app
+ENV DEBIAN_FRONTEND teletype
# Usage Examples -------------------------------------------------------------------------------------------------------
diff --git a/utils/downloads.py b/utils/downloads.py
index dd2698f995a4..643b529fba3b 100644
--- a/utils/downloads.py
+++ b/utils/downloads.py
@@ -5,32 +5,31 @@
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_online=True):
- # Check if online file exists
+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, result.path]) # check if is url
- return (urllib.request.urlopen(url).getcode() == 200) if check_online else True # check if exists online
+ 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
+ output = subprocess.check_output(['gsutil', 'du', url], shell=True, encoding='utf-8')
+ if output:
+ return int(output.split()[0])
+ return 0
def url_getsize(url='https://ultralytics.com/images/bus.jpg'):
@@ -39,6 +38,25 @@ def url_getsize(url='https://ultralytics.com/images/bus.jpg'):
return int(response.headers.get('content-length', -1))
+def curl_download(url, filename, *, silent: bool = False) -> bool:
+ """
+ Download a file from a url to a filename using curl.
+ """
+ silent_option = 'sS' if silent else '' # silent
+ proc = subprocess.run([
+ 'curl',
+ '-#',
+ f'-{silent_option}L',
+ url,
+ '--output',
+ filename,
+ '--retry',
+ '9',
+ '-C',
+ '-',])
+ return proc.returncode == 0
+
+
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
@@ -53,23 +71,24 @@ def safe_download(file, url, url2=None, min_bytes=1E0, error_msg=''):
if file.exists():
file.unlink() # remove partial downloads
LOGGER.info(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
+ # curl download, retry and resume on fail
+ curl_download(url2 or url, file)
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(f'ERROR: {assert_msg}\n{error_msg}')
LOGGER.info('')
-def attempt_download(file, repo='ultralytics/yolov5', release='v6.2'):
- # Attempt file download from GitHub release assets if not found locally. release = 'latest', 'v6.2', etc.
+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. 'v6.2') and assets (i.e. ['yolov5s.pt', 'yolov5m.pt', ...])
+ # 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/v6.2
+ 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
@@ -87,9 +106,7 @@ def github_assets(repository, version='latest'):
return file
# GitHub assets
- assets = [
- 'yolov5n.pt', 'yolov5s.pt', 'yolov5m.pt', 'yolov5l.pt', 'yolov5x.pt', 'yolov5n6.pt', 'yolov5s6.pt',
- 'yolov5m6.pt', 'yolov5l6.pt', 'yolov5x6.pt']
+ 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:
@@ -103,90 +120,9 @@ def github_assets(repository, version='latest'):
file.parent.mkdir(parents=True, exist_ok=True) # make parent dir (if required)
if name in assets:
- url3 = 'https://drive.google.com/drive/folders/1EFQTEUeXWSFww0luse2jB9M1QNZQGwNl' # backup gdrive mirror
- safe_download(
- file,
- url=f'https://github.com/{repo}/releases/download/{tag}/{name}',
- url2=f'https://storage.googleapis.com/{repo}/{tag}/{name}', # backup url (optional)
- min_bytes=1E5,
- error_msg=f'{file} missing, try downloading from https://github.com/{repo}/releases/{tag} or {url3}')
+ safe_download(file,
+ url=f'https://github.com/{repo}/releases/download/{tag}/{name}',
+ min_bytes=1E5,
+ error_msg=f'{file} missing, try downloading from https://github.com/{repo}/releases/{tag}')
return str(file)
-
-
-def gdrive_download(id='16TiPfZj7htmTyhntwcZyEEAejOUxuT6m', file='tmp.zip'):
- # Downloads a file from Google Drive. from yolov5.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='')
- if file.exists():
- file.unlink() # remove existing file
- if cookie.exists():
- cookie.unlink() # 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
- if cookie.exists():
- cookie.unlink() # remove existing cookie
-
- # Error check
- if r != 0:
- if file.exists():
- file.unlink() # 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))
diff --git a/utils/flask_rest_api/example_request.py b/utils/flask_rest_api/example_request.py
index 773ad8932967..952e5dcb90fa 100644
--- a/utils/flask_rest_api/example_request.py
+++ b/utils/flask_rest_api/example_request.py
@@ -7,13 +7,13 @@
import requests
-DETECTION_URL = "http://localhost:5000/v1/object-detection/yolov5s"
-IMAGE = "zidane.jpg"
+DETECTION_URL = 'http://localhost:5000/v1/object-detection/yolov5s'
+IMAGE = 'zidane.jpg'
# Read image
-with open(IMAGE, "rb") as f:
+with open(IMAGE, 'rb') as f:
image_data = f.read()
-response = requests.post(DETECTION_URL, files={"image": image_data}).json()
+response = requests.post(DETECTION_URL, files={'image': image_data}).json()
pprint.pprint(response)
diff --git a/utils/flask_rest_api/restapi.py b/utils/flask_rest_api/restapi.py
index 8482435c861e..9258b1a68860 100644
--- a/utils/flask_rest_api/restapi.py
+++ b/utils/flask_rest_api/restapi.py
@@ -13,36 +13,36 @@
app = Flask(__name__)
models = {}
-DETECTION_URL = "/v1/object-detection/
+
# Try out an Example!
-Check out an example of a [completed run here](https://www.comet.com/examples/comet-example-yolov5/a0e29e0e9b984e4a822db2a62d0cb357?experiment-tab=chart&showOutliers=true&smoothing=0&transformY=smoothing&xAxis=step&ref=yolov5&utm_source=yolov5&utm_medium=affilliate&utm_campaign=yolov5_comet_integration)
+
+Check out an example of a [completed run here](https://www.comet.com/examples/comet-example-yolov5/a0e29e0e9b984e4a822db2a62d0cb357?experiment-tab=chart&showOutliers=true&smoothing=0&transformY=smoothing&xAxis=step&utm_source=yolov5&utm_medium=partner&utm_campaign=partner_yolov5_2022&utm_content=github)
Or better yet, try it out yourself in this Colab Notebook
@@ -65,6 +66,7 @@ Or better yet, try it out yourself in this Colab Notebook
By default, Comet will log the following items
## Metrics
+
- Box Loss, Object Loss, Classification Loss for the training and validation data
- mAP_0.5, mAP_0.5:0.95 metrics for the validation data.
- Precision and Recall for the validation data
@@ -119,8 +121,7 @@ You can control the frequency of logged predictions and the associated images by
**Note:** The YOLOv5 validation dataloader will default to a batch size of 32, so you will have to set the logging frequency accordingly.
-Here is an [example project using the Panel](https://www.comet.com/examples/comet-example-yolov5?shareable=YcwMiJaZSXfcEXpGOHDD12vA1&ref=yolov5&utm_source=yolov5&utm_medium=affilliate&utm_campaign=yolov5_comet_integration)
-
+Here is an [example project using the Panel](https://www.comet.com/examples/comet-example-yolov5?shareable=YcwMiJaZSXfcEXpGOHDD12vA1&utm_source=yolov5&utm_medium=partner&utm_campaign=partner_yolov5_2022&utm_content=github)
```shell
python train.py \
@@ -161,7 +162,7 @@ env COMET_LOG_PER_CLASS_METRICS=true python train.py \
## Uploading a Dataset to Comet Artifacts
-If you would like to store your data using [Comet Artifacts](https://www.comet.com/docs/v2/guides/data-management/using-artifacts/#learn-more?ref=yolov5&utm_source=yolov5&utm_medium=affilliate&utm_campaign=yolov5_comet_integration), you can do so using the `upload_dataset` flag.
+If you would like to store your data using [Comet Artifacts](https://www.comet.com/docs/v2/guides/data-management/using-artifacts/#learn-more?utm_source=yolov5&utm_medium=partner&utm_campaign=partner_yolov5_2022&utm_content=github), you can do so using the `upload_dataset` flag.
The dataset be organized in the way described in the [YOLOv5 documentation](https://docs.ultralytics.com/tutorials/train-custom-datasets/#3-organize-directories). The dataset config `yaml` file must follow the same format as that of the `coco128.yaml` file.
@@ -192,6 +193,7 @@ If you would like to use a dataset from Comet Artifacts, set the `path` variable
# contents of artifact.yaml file
path: "comet://
\ No newline at end of file
+
diff --git a/utils/loggers/comet/__init__.py b/utils/loggers/comet/__init__.py
index 4ee86dd70d6e..d4599841c9fc 100644
--- a/utils/loggers/comet/__init__.py
+++ b/utils/loggers/comet/__init__.py
@@ -17,45 +17,46 @@
# Project Configuration
config = comet_ml.config.get_config()
- COMET_PROJECT_NAME = config.get_string(os.getenv("COMET_PROJECT_NAME"), "comet.project_name", default="yolov5")
+ COMET_PROJECT_NAME = config.get_string(os.getenv('COMET_PROJECT_NAME'), 'comet.project_name', default='yolov5')
except (ModuleNotFoundError, ImportError):
comet_ml = None
COMET_PROJECT_NAME = None
+import PIL
import torch
import torchvision.transforms as T
import yaml
from utils.dataloaders import img2label_paths
-from utils.general import check_dataset, scale_coords, xywh2xyxy
+from utils.general import check_dataset, scale_boxes, xywh2xyxy
from utils.metrics import box_iou
-COMET_PREFIX = "comet://"
+COMET_PREFIX = 'comet://'
-COMET_MODE = os.getenv("COMET_MODE", "online")
+COMET_MODE = os.getenv('COMET_MODE', 'online')
# Model Saving Settings
-COMET_MODEL_NAME = os.getenv("COMET_MODEL_NAME", "yolov5")
+COMET_MODEL_NAME = os.getenv('COMET_MODEL_NAME', 'yolov5')
# Dataset Artifact Settings
-COMET_UPLOAD_DATASET = os.getenv("COMET_UPLOAD_DATASET", "false").lower() == "true"
+COMET_UPLOAD_DATASET = os.getenv('COMET_UPLOAD_DATASET', 'false').lower() == 'true'
# Evaluation Settings
-COMET_LOG_CONFUSION_MATRIX = os.getenv("COMET_LOG_CONFUSION_MATRIX", "true").lower() == "true"
-COMET_LOG_PREDICTIONS = os.getenv("COMET_LOG_PREDICTIONS", "true").lower() == "true"
-COMET_MAX_IMAGE_UPLOADS = int(os.getenv("COMET_MAX_IMAGE_UPLOADS", 100))
+COMET_LOG_CONFUSION_MATRIX = os.getenv('COMET_LOG_CONFUSION_MATRIX', 'true').lower() == 'true'
+COMET_LOG_PREDICTIONS = os.getenv('COMET_LOG_PREDICTIONS', 'true').lower() == 'true'
+COMET_MAX_IMAGE_UPLOADS = int(os.getenv('COMET_MAX_IMAGE_UPLOADS', 100))
# Confusion Matrix Settings
-CONF_THRES = float(os.getenv("CONF_THRES", 0.001))
-IOU_THRES = float(os.getenv("IOU_THRES", 0.6))
+CONF_THRES = float(os.getenv('CONF_THRES', 0.001))
+IOU_THRES = float(os.getenv('IOU_THRES', 0.6))
# Batch Logging Settings
-COMET_LOG_BATCH_METRICS = os.getenv("COMET_LOG_BATCH_METRICS", "false").lower() == "true"
-COMET_BATCH_LOGGING_INTERVAL = os.getenv("COMET_BATCH_LOGGING_INTERVAL", 1)
-COMET_PREDICTION_LOGGING_INTERVAL = os.getenv("COMET_PREDICTION_LOGGING_INTERVAL", 1)
-COMET_LOG_PER_CLASS_METRICS = os.getenv("COMET_LOG_PER_CLASS_METRICS", "false").lower() == "true"
+COMET_LOG_BATCH_METRICS = os.getenv('COMET_LOG_BATCH_METRICS', 'false').lower() == 'true'
+COMET_BATCH_LOGGING_INTERVAL = os.getenv('COMET_BATCH_LOGGING_INTERVAL', 1)
+COMET_PREDICTION_LOGGING_INTERVAL = os.getenv('COMET_PREDICTION_LOGGING_INTERVAL', 1)
+COMET_LOG_PER_CLASS_METRICS = os.getenv('COMET_LOG_PER_CLASS_METRICS', 'false').lower() == 'true'
-RANK = int(os.getenv("RANK", -1))
+RANK = int(os.getenv('RANK', -1))
to_pil = T.ToPILImage()
@@ -65,7 +66,7 @@ class CometLogger:
with Comet
"""
- def __init__(self, opt, hyp, run_id=None, job_type="Training", **experiment_kwargs) -> None:
+ def __init__(self, opt, hyp, run_id=None, job_type='Training', **experiment_kwargs) -> None:
self.job_type = job_type
self.opt = opt
self.hyp = hyp
@@ -86,51 +87,53 @@ def __init__(self, opt, hyp, run_id=None, job_type="Training", **experiment_kwar
# Default parameters to pass to Experiment objects
self.default_experiment_kwargs = {
- "log_code": False,
- "log_env_gpu": True,
- "log_env_cpu": True,
- "project_name": COMET_PROJECT_NAME,}
+ 'log_code': False,
+ 'log_env_gpu': True,
+ 'log_env_cpu': True,
+ 'project_name': COMET_PROJECT_NAME,}
self.default_experiment_kwargs.update(experiment_kwargs)
self.experiment = self._get_experiment(self.comet_mode, run_id)
self.data_dict = self.check_dataset(self.opt.data)
- self.class_names = self.data_dict["names"]
- self.num_classes = self.data_dict["nc"]
+ self.class_names = self.data_dict['names']
+ self.num_classes = self.data_dict['nc']
self.logged_images_count = 0
self.max_images = COMET_MAX_IMAGE_UPLOADS
if run_id is None:
- self.experiment.log_other("Created from", "YOLOv5")
+ self.experiment.log_other('Created from', 'YOLOv5')
if not isinstance(self.experiment, comet_ml.OfflineExperiment):
- workspace, project_name, experiment_id = self.experiment.url.split("/")[-3:]
+ workspace, project_name, experiment_id = self.experiment.url.split('/')[-3:]
self.experiment.log_other(
- "Run Path",
- f"{workspace}/{project_name}/{experiment_id}",
+ 'Run Path',
+ f'{workspace}/{project_name}/{experiment_id}',
)
self.log_parameters(vars(opt))
self.log_parameters(self.opt.hyp)
self.log_asset_data(
self.opt.hyp,
- name="hyperparameters.json",
- metadata={"type": "hyp-config-file"},
+ name='hyperparameters.json',
+ metadata={'type': 'hyp-config-file'},
)
self.log_asset(
- f"{self.opt.save_dir}/opt.yaml",
- metadata={"type": "opt-config-file"},
+ f'{self.opt.save_dir}/opt.yaml',
+ metadata={'type': 'opt-config-file'},
)
self.comet_log_confusion_matrix = COMET_LOG_CONFUSION_MATRIX
- if hasattr(self.opt, "conf_thres"):
+ if hasattr(self.opt, 'conf_thres'):
self.conf_thres = self.opt.conf_thres
else:
self.conf_thres = CONF_THRES
- if hasattr(self.opt, "iou_thres"):
+ if hasattr(self.opt, 'iou_thres'):
self.iou_thres = self.opt.iou_thres
else:
self.iou_thres = IOU_THRES
+ self.log_parameters({'val_iou_threshold': self.iou_thres, 'val_conf_threshold': self.conf_thres})
+
self.comet_log_predictions = COMET_LOG_PREDICTIONS
if self.opt.bbox_interval == -1:
self.comet_log_prediction_interval = 1 if self.opt.epochs < 10 else self.opt.epochs // 10
@@ -139,26 +142,27 @@ def __init__(self, opt, hyp, run_id=None, job_type="Training", **experiment_kwar
if self.comet_log_predictions:
self.metadata_dict = {}
+ self.logged_image_names = []
self.comet_log_per_class_metrics = COMET_LOG_PER_CLASS_METRICS
self.experiment.log_others({
- "comet_mode": COMET_MODE,
- "comet_max_image_uploads": COMET_MAX_IMAGE_UPLOADS,
- "comet_log_per_class_metrics": COMET_LOG_PER_CLASS_METRICS,
- "comet_log_batch_metrics": COMET_LOG_BATCH_METRICS,
- "comet_log_confusion_matrix": COMET_LOG_CONFUSION_MATRIX,
- "comet_model_name": COMET_MODEL_NAME,})
+ 'comet_mode': COMET_MODE,
+ 'comet_max_image_uploads': COMET_MAX_IMAGE_UPLOADS,
+ 'comet_log_per_class_metrics': COMET_LOG_PER_CLASS_METRICS,
+ 'comet_log_batch_metrics': COMET_LOG_BATCH_METRICS,
+ 'comet_log_confusion_matrix': COMET_LOG_CONFUSION_MATRIX,
+ 'comet_model_name': COMET_MODEL_NAME,})
# Check if running the Experiment with the Comet Optimizer
- if hasattr(self.opt, "comet_optimizer_id"):
- self.experiment.log_other("optimizer_id", self.opt.comet_optimizer_id)
- self.experiment.log_other("optimizer_objective", self.opt.comet_optimizer_objective)
- self.experiment.log_other("optimizer_metric", self.opt.comet_optimizer_metric)
- self.experiment.log_other("optimizer_parameters", json.dumps(self.hyp))
+ if hasattr(self.opt, 'comet_optimizer_id'):
+ self.experiment.log_other('optimizer_id', self.opt.comet_optimizer_id)
+ self.experiment.log_other('optimizer_objective', self.opt.comet_optimizer_objective)
+ self.experiment.log_other('optimizer_metric', self.opt.comet_optimizer_metric)
+ self.experiment.log_other('optimizer_parameters', json.dumps(self.hyp))
def _get_experiment(self, mode, experiment_id=None):
- if mode == "offline":
+ if mode == 'offline':
if experiment_id is not None:
return comet_ml.ExistingOfflineExperiment(
previous_experiment=experiment_id,
@@ -178,11 +182,11 @@ def _get_experiment(self, mode, experiment_id=None):
return comet_ml.Experiment(**self.default_experiment_kwargs)
except ValueError:
- logger.warning("COMET WARNING: "
- "Comet credentials have not been set. "
- "Comet will default to offline logging. "
- "Please set your credentials to enable online logging.")
- return self._get_experiment("offline", experiment_id)
+ logger.warning('COMET WARNING: '
+ 'Comet credentials have not been set. '
+ 'Comet will default to offline logging. '
+ 'Please set your credentials to enable online logging.')
+ return self._get_experiment('offline', experiment_id)
return
@@ -206,12 +210,12 @@ def log_model(self, path, opt, epoch, fitness_score, best_model=False):
return
model_metadata = {
- "fitness_score": fitness_score[-1],
- "epochs_trained": epoch + 1,
- "save_period": opt.save_period,
- "total_epochs": opt.epochs,}
+ 'fitness_score': fitness_score[-1],
+ 'epochs_trained': epoch + 1,
+ 'save_period': opt.save_period,
+ 'total_epochs': opt.epochs,}
- model_files = glob.glob(f"{path}/*.pt")
+ model_files = glob.glob(f'{path}/*.pt')
for model_path in model_files:
name = Path(model_path).name
@@ -228,12 +232,12 @@ def check_dataset(self, data_file):
data_config = yaml.safe_load(f)
if data_config['path'].startswith(COMET_PREFIX):
- path = data_config['path'].replace(COMET_PREFIX, "")
+ path = data_config['path'].replace(COMET_PREFIX, '')
data_dict = self.download_dataset_artifact(path)
return data_dict
- self.log_asset(self.opt.data, metadata={"type": "data-config-file"})
+ self.log_asset(self.opt.data, metadata={'type': 'data-config-file'})
return check_dataset(data_file)
@@ -249,31 +253,32 @@ def log_predictions(self, image, labelsn, path, shape, predn):
filtered_detections = detections[mask]
filtered_labels = labelsn[mask]
- processed_image = (image * 255).to(torch.uint8)
-
- image_id = path.split("/")[-1].split(".")[0]
- image_name = f"{image_id}_curr_epoch_{self.experiment.curr_epoch}"
- self.log_image(to_pil(processed_image), name=image_name)
+ image_id = path.split('/')[-1].split('.')[0]
+ image_name = f'{image_id}_curr_epoch_{self.experiment.curr_epoch}'
+ if image_name not in self.logged_image_names:
+ native_scale_image = PIL.Image.open(path)
+ self.log_image(native_scale_image, name=image_name)
+ self.logged_image_names.append(image_name)
metadata = []
for cls, *xyxy in filtered_labels.tolist():
metadata.append({
- "label": f"{self.class_names[int(cls)]}-gt",
- "score": 100,
- "box": {
- "x": xyxy[0],
- "y": xyxy[1],
- "x2": xyxy[2],
- "y2": xyxy[3]},})
+ 'label': f'{self.class_names[int(cls)]}-gt',
+ 'score': 100,
+ 'box': {
+ 'x': xyxy[0],
+ 'y': xyxy[1],
+ 'x2': xyxy[2],
+ 'y2': xyxy[3]},})
for *xyxy, conf, cls in filtered_detections.tolist():
metadata.append({
- "label": f"{self.class_names[int(cls)]}",
- "score": conf * 100,
- "box": {
- "x": xyxy[0],
- "y": xyxy[1],
- "x2": xyxy[2],
- "y2": xyxy[3]},})
+ 'label': f'{self.class_names[int(cls)]}',
+ 'score': conf * 100,
+ 'box': {
+ 'x': xyxy[0],
+ 'y': xyxy[1],
+ 'x2': xyxy[2],
+ 'y2': xyxy[3]},})
self.metadata_dict[image_name] = metadata
self.logged_images_count += 1
@@ -288,47 +293,47 @@ def preprocess_prediction(self, image, labels, shape, pred):
pred[:, 5] = 0
predn = pred.clone()
- scale_coords(image.shape[1:], predn[:, :4], shape[0], shape[1])
+ scale_boxes(image.shape[1:], predn[:, :4], shape[0], shape[1])
labelsn = None
if nl:
tbox = xywh2xyxy(labels[:, 1:5]) # target boxes
- scale_coords(image.shape[1:], tbox, shape[0], shape[1]) # native-space labels
+ scale_boxes(image.shape[1:], tbox, shape[0], shape[1]) # native-space labels
labelsn = torch.cat((labels[:, 0:1], tbox), 1) # native-space labels
- scale_coords(image.shape[1:], predn[:, :4], shape[0], shape[1]) # native-space pred
+ scale_boxes(image.shape[1:], predn[:, :4], shape[0], shape[1]) # native-space pred
return predn, labelsn
def add_assets_to_artifact(self, artifact, path, asset_path, split):
- img_paths = sorted(glob.glob(f"{asset_path}/*"))
+ img_paths = sorted(glob.glob(f'{asset_path}/*'))
label_paths = img2label_paths(img_paths)
for image_file, label_file in zip(img_paths, label_paths):
image_logical_path, label_logical_path = map(lambda x: os.path.relpath(x, path), [image_file, label_file])
try:
- artifact.add(image_file, logical_path=image_logical_path, metadata={"split": split})
- artifact.add(label_file, logical_path=label_logical_path, metadata={"split": split})
+ artifact.add(image_file, logical_path=image_logical_path, metadata={'split': split})
+ artifact.add(label_file, logical_path=label_logical_path, metadata={'split': split})
except ValueError as e:
logger.error('COMET ERROR: Error adding file to Artifact. Skipping file.')
- logger.error(f"COMET ERROR: {e}")
+ logger.error(f'COMET ERROR: {e}')
continue
return artifact
def upload_dataset_artifact(self):
- dataset_name = self.data_dict.get("dataset_name", "yolov5-dataset")
- path = str((ROOT / Path(self.data_dict["path"])).resolve())
+ dataset_name = self.data_dict.get('dataset_name', 'yolov5-dataset')
+ path = str((ROOT / Path(self.data_dict['path'])).resolve())
metadata = self.data_dict.copy()
- for key in ["train", "val", "test"]:
+ for key in ['train', 'val', 'test']:
split_path = metadata.get(key)
if split_path is not None:
- metadata[key] = split_path.replace(path, "")
+ metadata[key] = split_path.replace(path, '')
- artifact = comet_ml.Artifact(name=dataset_name, artifact_type="dataset", metadata=metadata)
+ artifact = comet_ml.Artifact(name=dataset_name, artifact_type='dataset', metadata=metadata)
for key in metadata.keys():
- if key in ["train", "val", "test"]:
+ if key in ['train', 'val', 'test']:
if isinstance(self.upload_dataset, str) and (key != self.upload_dataset):
continue
@@ -347,20 +352,27 @@ def download_dataset_artifact(self, artifact_path):
metadata = logged_artifact.metadata
data_dict = metadata.copy()
- data_dict["path"] = artifact_save_dir
- data_dict["names"] = {int(k): v for k, v in metadata.get("names").items()}
+ data_dict['path'] = artifact_save_dir
+
+ metadata_names = metadata.get('names')
+ if type(metadata_names) == dict:
+ data_dict['names'] = {int(k): v for k, v in metadata.get('names').items()}
+ elif type(metadata_names) == list:
+ data_dict['names'] = {int(k): v for k, v in zip(range(len(metadata_names)), metadata_names)}
+ else:
+ raise "Invalid 'names' field in dataset yaml file. Please use a list or dictionary"
data_dict = self.update_data_paths(data_dict)
return data_dict
def update_data_paths(self, data_dict):
- path = data_dict.get("path", "")
+ path = data_dict.get('path', '')
- for split in ["train", "val", "test"]:
+ for split in ['train', 'val', 'test']:
if data_dict.get(split):
split_path = data_dict.get(split)
- data_dict[split] = (f"{path}/{split_path}" if isinstance(split, str) else [
- f"{path}/{x}" for x in split_path])
+ data_dict[split] = (f'{path}/{split_path}' if isinstance(split, str) else [
+ f'{path}/{x}' for x in split_path])
return data_dict
@@ -401,11 +413,11 @@ def on_train_batch_end(self, log_dict, step):
def on_train_end(self, files, save_dir, last, best, epoch, results):
if self.comet_log_predictions:
curr_epoch = self.experiment.curr_epoch
- self.experiment.log_asset_data(self.metadata_dict, "image-metadata.json", epoch=curr_epoch)
+ self.experiment.log_asset_data(self.metadata_dict, 'image-metadata.json', epoch=curr_epoch)
for f in files:
- self.log_asset(f, metadata={"epoch": epoch})
- self.log_asset(f"{save_dir}/results.csv", metadata={"epoch": epoch})
+ self.log_asset(f, metadata={'epoch': epoch})
+ self.log_asset(f'{save_dir}/results.csv', metadata={'epoch': epoch})
if not self.opt.evolve:
model_path = str(best if best.exists() else last)
@@ -469,7 +481,7 @@ def on_val_end(self, nt, tp, fp, p, r, f1, ap, ap50, ap_class, confusion_matrix)
if self.comet_log_confusion_matrix:
epoch = self.experiment.curr_epoch
class_names = list(self.class_names.values())
- class_names.append("background")
+ class_names.append('background')
num_classes = len(class_names)
self.experiment.log_confusion_matrix(
@@ -479,7 +491,7 @@ def on_val_end(self, nt, tp, fp, p, r, f1, ap, ap50, ap_class, confusion_matrix)
epoch=epoch,
column_label='Actual Category',
row_label='Predicted Category',
- file_name=f"confusion-matrix-epoch-{epoch}.json",
+ file_name=f'confusion-matrix-epoch-{epoch}.json',
)
def on_fit_epoch_end(self, result, epoch):
diff --git a/utils/loggers/comet/comet_utils.py b/utils/loggers/comet/comet_utils.py
index 3cbd45156b57..27600761ad28 100644
--- a/utils/loggers/comet/comet_utils.py
+++ b/utils/loggers/comet/comet_utils.py
@@ -11,28 +11,28 @@
logger = logging.getLogger(__name__)
-COMET_PREFIX = "comet://"
-COMET_MODEL_NAME = os.getenv("COMET_MODEL_NAME", "yolov5")
-COMET_DEFAULT_CHECKPOINT_FILENAME = os.getenv("COMET_DEFAULT_CHECKPOINT_FILENAME", "last.pt")
+COMET_PREFIX = 'comet://'
+COMET_MODEL_NAME = os.getenv('COMET_MODEL_NAME', 'yolov5')
+COMET_DEFAULT_CHECKPOINT_FILENAME = os.getenv('COMET_DEFAULT_CHECKPOINT_FILENAME', 'last.pt')
def download_model_checkpoint(opt, experiment):
- model_dir = f"{opt.project}/{experiment.name}"
+ model_dir = f'{opt.project}/{experiment.name}'
os.makedirs(model_dir, exist_ok=True)
model_name = COMET_MODEL_NAME
model_asset_list = experiment.get_model_asset_list(model_name)
if len(model_asset_list) == 0:
- logger.error(f"COMET ERROR: No checkpoints found for model name : {model_name}")
+ logger.error(f'COMET ERROR: No checkpoints found for model name : {model_name}')
return
model_asset_list = sorted(
model_asset_list,
- key=lambda x: x["step"],
+ key=lambda x: x['step'],
reverse=True,
)
- logged_checkpoint_map = {asset["fileName"]: asset["assetId"] for asset in model_asset_list}
+ logged_checkpoint_map = {asset['fileName']: asset['assetId'] for asset in model_asset_list}
resource_url = urlparse(opt.weights)
checkpoint_filename = resource_url.query
@@ -44,22 +44,22 @@ def download_model_checkpoint(opt, experiment):
checkpoint_filename = COMET_DEFAULT_CHECKPOINT_FILENAME
if asset_id is None:
- logger.error(f"COMET ERROR: Checkpoint {checkpoint_filename} not found in the given Experiment")
+ logger.error(f'COMET ERROR: Checkpoint {checkpoint_filename} not found in the given Experiment')
return
try:
- logger.info(f"COMET INFO: Downloading checkpoint {checkpoint_filename}")
+ logger.info(f'COMET INFO: Downloading checkpoint {checkpoint_filename}')
asset_filename = checkpoint_filename
- model_binary = experiment.get_asset(asset_id, return_type="binary", stream=False)
- model_download_path = f"{model_dir}/{asset_filename}"
- with open(model_download_path, "wb") as f:
+ model_binary = experiment.get_asset(asset_id, return_type='binary', stream=False)
+ model_download_path = f'{model_dir}/{asset_filename}'
+ with open(model_download_path, 'wb') as f:
f.write(model_binary)
opt.weights = model_download_path
except Exception as e:
- logger.warning("COMET WARNING: Unable to download checkpoint from Comet")
+ logger.warning('COMET WARNING: Unable to download checkpoint from Comet')
logger.exception(e)
@@ -75,9 +75,9 @@ def set_opt_parameters(opt, experiment):
resume_string = opt.resume
for asset in asset_list:
- if asset["fileName"] == "opt.yaml":
- asset_id = asset["assetId"]
- asset_binary = experiment.get_asset(asset_id, return_type="binary", stream=False)
+ if asset['fileName'] == 'opt.yaml':
+ asset_id = asset['assetId']
+ asset_binary = experiment.get_asset(asset_id, return_type='binary', stream=False)
opt_dict = yaml.safe_load(asset_binary)
for key, value in opt_dict.items():
setattr(opt, key, value)
@@ -85,11 +85,11 @@ def set_opt_parameters(opt, experiment):
# Save hyperparameters to YAML file
# Necessary to pass checks in training script
- save_dir = f"{opt.project}/{experiment.name}"
+ save_dir = f'{opt.project}/{experiment.name}'
os.makedirs(save_dir, exist_ok=True)
- hyp_yaml_path = f"{save_dir}/hyp.yaml"
- with open(hyp_yaml_path, "w") as f:
+ hyp_yaml_path = f'{save_dir}/hyp.yaml'
+ with open(hyp_yaml_path, 'w') as f:
yaml.dump(opt.hyp, f)
opt.hyp = hyp_yaml_path
@@ -113,7 +113,7 @@ def check_comet_weights(opt):
if opt.weights.startswith(COMET_PREFIX):
api = comet_ml.API()
resource = urlparse(opt.weights)
- experiment_path = f"{resource.netloc}{resource.path}"
+ experiment_path = f'{resource.netloc}{resource.path}'
experiment = api.get(experiment_path)
download_model_checkpoint(opt, experiment)
return True
@@ -140,7 +140,7 @@ def check_comet_resume(opt):
if opt.resume.startswith(COMET_PREFIX):
api = comet_ml.API()
resource = urlparse(opt.resume)
- experiment_path = f"{resource.netloc}{resource.path}"
+ experiment_path = f'{resource.netloc}{resource.path}'
experiment = api.get(experiment_path)
set_opt_parameters(opt, experiment)
download_model_checkpoint(opt, experiment)
diff --git a/utils/loggers/comet/hpo.py b/utils/loggers/comet/hpo.py
index eab4df9978cf..fc49115c1358 100644
--- a/utils/loggers/comet/hpo.py
+++ b/utils/loggers/comet/hpo.py
@@ -14,14 +14,14 @@
if str(ROOT) not in sys.path:
sys.path.append(str(ROOT)) # add ROOT to PATH
-from train import parse_opt, train
+from train import train
from utils.callbacks import Callbacks
from utils.general import increment_path
from utils.torch_utils import select_device
# Project Configuration
config = comet_ml.config.get_config()
-COMET_PROJECT_NAME = config.get_string(os.getenv("COMET_PROJECT_NAME"), "comet.project_name", default="yolov5")
+COMET_PROJECT_NAME = config.get_string(os.getenv('COMET_PROJECT_NAME'), 'comet.project_name', default='yolov5')
def get_args(known=False):
@@ -68,30 +68,30 @@ def get_args(known=False):
parser.add_argument('--artifact_alias', type=str, default='latest', help='W&B: Version of dataset artifact to use')
# Comet Arguments
- parser.add_argument("--comet_optimizer_config", type=str, help="Comet: Path to a Comet Optimizer Config File.")
- parser.add_argument("--comet_optimizer_id", type=str, help="Comet: ID of the Comet Optimizer sweep.")
- parser.add_argument("--comet_optimizer_objective", type=str, help="Comet: Set to 'minimize' or 'maximize'.")
- parser.add_argument("--comet_optimizer_metric", type=str, help="Comet: Metric to Optimize.")
- parser.add_argument("--comet_optimizer_workers",
+ parser.add_argument('--comet_optimizer_config', type=str, help='Comet: Path to a Comet Optimizer Config File.')
+ parser.add_argument('--comet_optimizer_id', type=str, help='Comet: ID of the Comet Optimizer sweep.')
+ parser.add_argument('--comet_optimizer_objective', type=str, help="Comet: Set to 'minimize' or 'maximize'.")
+ parser.add_argument('--comet_optimizer_metric', type=str, help='Comet: Metric to Optimize.')
+ parser.add_argument('--comet_optimizer_workers',
type=int,
default=1,
- help="Comet: Number of Parallel Workers to use with the Comet Optimizer.")
+ help='Comet: Number of Parallel Workers to use with the Comet Optimizer.')
return parser.parse_known_args()[0] if known else parser.parse_args()
def run(parameters, opt):
- hyp_dict = {k: v for k, v in parameters.items() if k not in ["epochs", "batch_size"]}
+ hyp_dict = {k: v for k, v in parameters.items() if k not in ['epochs', 'batch_size']}
opt.save_dir = str(increment_path(Path(opt.project) / opt.name, exist_ok=opt.exist_ok or opt.evolve))
- opt.batch_size = parameters.get("batch_size")
- opt.epochs = parameters.get("epochs")
+ opt.batch_size = parameters.get('batch_size')
+ opt.epochs = parameters.get('epochs')
device = select_device(opt.device, batch_size=opt.batch_size)
train(hyp_dict, opt, device, callbacks=Callbacks())
-if __name__ == "__main__":
+if __name__ == '__main__':
opt = get_args(known=True)
opt.weights = str(opt.weights)
@@ -99,7 +99,7 @@ def run(parameters, opt):
opt.data = str(opt.data)
opt.project = str(opt.project)
- optimizer_id = os.getenv("COMET_OPTIMIZER_ID")
+ optimizer_id = os.getenv('COMET_OPTIMIZER_ID')
if optimizer_id is None:
with open(opt.comet_optimizer_config) as f:
optimizer_config = json.load(f)
@@ -110,9 +110,9 @@ def run(parameters, opt):
opt.comet_optimizer_id = optimizer.id
status = optimizer.status()
- opt.comet_optimizer_objective = status["spec"]["objective"]
- opt.comet_optimizer_metric = status["spec"]["metric"]
+ opt.comet_optimizer_objective = status['spec']['objective']
+ opt.comet_optimizer_metric = status['spec']['metric']
- logger.info("COMET INFO: Starting Hyperparameter Sweep")
+ logger.info('COMET INFO: Starting Hyperparameter Sweep')
for parameter in optimizer.get_parameters():
- run(parameter["parameters"], opt)
+ run(parameter['parameters'], opt)
diff --git a/utils/loggers/wandb/README.md b/utils/loggers/wandb/README.md
deleted file mode 100644
index d78324b4c8e9..000000000000
--- a/utils/loggers/wandb/README.md
+++ /dev/null
@@ -1,162 +0,0 @@
-📚 This guide explains how to use **Weights & Biases** (W&B) with YOLOv5 🚀. UPDATED 29 September 2021.
-
-- [About Weights & Biases](#about-weights-&-biases)
-- [First-Time Setup](#first-time-setup)
-- [Viewing runs](#viewing-runs)
-- [Disabling wandb](#disabling-wandb)
-- [Advanced Usage: Dataset Versioning and Evaluation](#advanced-usage)
-- [Reports: Share your work with the world!](#reports)
-
-## About Weights & Biases
-
-Think of [W&B](https://wandb.ai/site?utm_campaign=repo_yolo_wandbtutorial) like GitHub for machine learning models. With a few lines of code, save everything you need to debug, compare and reproduce your models — architecture, hyperparameters, git commits, model weights, GPU usage, and even datasets and predictions.
-
-Used by top researchers including teams at OpenAI, Lyft, Github, and MILA, W&B is part of the new standard of best practices for machine learning. How W&B can help you optimize your machine learning workflows:
-
-- [Debug](https://wandb.ai/wandb/getting-started/reports/Visualize-Debug-Machine-Learning-Models--VmlldzoyNzY5MDk#Free-2) model performance in real time
-- [GPU usage](https://wandb.ai/wandb/getting-started/reports/Visualize-Debug-Machine-Learning-Models--VmlldzoyNzY5MDk#System-4) visualized automatically
-- [Custom charts](https://wandb.ai/wandb/customizable-charts/reports/Powerful-Custom-Charts-To-Debug-Model-Peformance--VmlldzoyNzY4ODI) for powerful, extensible visualization
-- [Share insights](https://wandb.ai/wandb/getting-started/reports/Visualize-Debug-Machine-Learning-Models--VmlldzoyNzY5MDk#Share-8) interactively with collaborators
-- [Optimize hyperparameters](https://docs.wandb.com/sweeps) efficiently
-- [Track](https://docs.wandb.com/artifacts) datasets, pipelines, and production models
-
-## First-Time Setup
-
-
-
-
-
- 
- 
- 
- 
- 
- 
+ 
+ 
+ 
+ 
+ 
+ 
+ 
$ python train.py --upload_data val
-
-
-
-{dataset}_wandb.yaml file which can be used to train from dataset artifact.
- $ python utils/logger/wandb/log_dataset.py --project ... --name ... --data ..
-
-
-
- $ python train.py --data {data}_wandb.yaml
-
-
-
- $ python train.py --save_period 1
-
-
-
---resume argument starts with wandb-artifact:// prefix followed by the run path, i.e, wandb-artifact://username/project/runid . This doesn't require the model checkpoint to be present on the local system.
-
- $ python train.py --resume wandb-artifact://{run_path}
-
-
-
---upload_dataset or
- train from _wandb.yaml file and set --save_period
-
- $ python train.py --resume wandb-artifact://{run_path}
-
-
-
-
-
-## Environments
-
-YOLOv5 may be run in any of the following up-to-date verified environments (with all dependencies including [CUDA](https://developer.nvidia.com/cuda)/[CUDNN](https://developer.nvidia.com/cudnn), [Python](https://www.python.org/) and [PyTorch](https://pytorch.org/) preinstalled):
-
-- **Google Colab and Kaggle** notebooks with free GPU:
-- **Google Cloud** Deep Learning VM. See [GCP Quickstart Guide](https://github.com/ultralytics/yolov5/wiki/GCP-Quickstart)
-- **Amazon** Deep Learning AMI. See [AWS Quickstart Guide](https://github.com/ultralytics/yolov5/wiki/AWS-Quickstart)
-- **Docker Image**. See [Docker Quickstart Guide](https://github.com/ultralytics/yolov5/wiki/Docker-Quickstart)
-
-## Status
-
-
-
-If this badge is green, all [YOLOv5 GitHub Actions](https://github.com/ultralytics/yolov5/actions) Continuous Integration (CI) tests are currently passing. CI tests verify correct operation of YOLOv5 training ([train.py](https://github.com/ultralytics/yolov5/blob/master/train.py)), validation ([val.py](https://github.com/ultralytics/yolov5/blob/master/val.py)), inference ([detect.py](https://github.com/ultralytics/yolov5/blob/master/detect.py)) and export ([export.py](https://github.com/ultralytics/yolov5/blob/master/export.py)) on macOS, Windows, and Ubuntu every 24 hours and on every commit.
diff --git a/utils/loggers/wandb/log_dataset.py b/utils/loggers/wandb/log_dataset.py
deleted file mode 100644
index 06e81fb69307..000000000000
--- a/utils/loggers/wandb/log_dataset.py
+++ /dev/null
@@ -1,27 +0,0 @@
-import argparse
-
-from wandb_utils import WandbLogger
-
-from utils.general import LOGGER
-
-WANDB_ARTIFACT_PREFIX = 'wandb-artifact://'
-
-
-def create_dataset_artifact(opt):
- logger = WandbLogger(opt, None, job_type='Dataset Creation') # TODO: return value unused
- if not logger.wandb:
- LOGGER.info("install wandb using `pip install wandb` to log the dataset")
-
-
-if __name__ == '__main__':
- parser = argparse.ArgumentParser()
- parser.add_argument('--data', type=str, default='data/coco128.yaml', help='data.yaml path')
- parser.add_argument('--single-cls', action='store_true', help='train as single-class dataset')
- parser.add_argument('--project', type=str, default='YOLOv5', help='name of W&B Project')
- parser.add_argument('--entity', default=None, help='W&B entity')
- parser.add_argument('--name', type=str, default='log dataset', help='name of W&B run')
-
- opt = parser.parse_args()
- opt.resume = False # Explicitly disallow resume check for dataset upload job
-
- create_dataset_artifact(opt)
diff --git a/utils/loggers/wandb/sweep.py b/utils/loggers/wandb/sweep.py
deleted file mode 100644
index d49ea6f2778b..000000000000
--- a/utils/loggers/wandb/sweep.py
+++ /dev/null
@@ -1,41 +0,0 @@
-import sys
-from pathlib import Path
-
-import wandb
-
-FILE = Path(__file__).resolve()
-ROOT = FILE.parents[3] # YOLOv5 root directory
-if str(ROOT) not in sys.path:
- sys.path.append(str(ROOT)) # add ROOT to PATH
-
-from train import parse_opt, train
-from utils.callbacks import Callbacks
-from utils.general import increment_path
-from utils.torch_utils import select_device
-
-
-def sweep():
- wandb.init()
- # Get hyp dict from sweep agent. Copy because train() modifies parameters which confused wandb.
- hyp_dict = vars(wandb.config).get("_items").copy()
-
- # Workaround: get necessary opt args
- opt = parse_opt(known=True)
- opt.batch_size = hyp_dict.get("batch_size")
- opt.save_dir = str(increment_path(Path(opt.project) / opt.name, exist_ok=opt.exist_ok or opt.evolve))
- opt.epochs = hyp_dict.get("epochs")
- opt.nosave = True
- opt.data = hyp_dict.get("data")
- opt.weights = str(opt.weights)
- opt.cfg = str(opt.cfg)
- opt.data = str(opt.data)
- opt.hyp = str(opt.hyp)
- opt.project = str(opt.project)
- device = select_device(opt.device, batch_size=opt.batch_size)
-
- # train
- train(hyp_dict, opt, device, callbacks=Callbacks())
-
-
-if __name__ == "__main__":
- sweep()
diff --git a/utils/loggers/wandb/sweep.yaml b/utils/loggers/wandb/sweep.yaml
deleted file mode 100644
index 688b1ea0285f..000000000000
--- a/utils/loggers/wandb/sweep.yaml
+++ /dev/null
@@ -1,143 +0,0 @@
-# Hyperparameters for training
-# To set range-
-# Provide min and max values as:
-# parameter:
-#
-# min: scalar
-# max: scalar
-# OR
-#
-# Set a specific list of search space-
-# parameter:
-# values: [scalar1, scalar2, scalar3...]
-#
-# You can use grid, bayesian and hyperopt search strategy
-# For more info on configuring sweeps visit - https://docs.wandb.ai/guides/sweeps/configuration
-
-program: utils/loggers/wandb/sweep.py
-method: random
-metric:
- name: metrics/mAP_0.5
- goal: maximize
-
-parameters:
- # hyperparameters: set either min, max range or values list
- data:
- value: "data/coco128.yaml"
- batch_size:
- values: [64]
- epochs:
- values: [10]
-
- lr0:
- distribution: uniform
- min: 1e-5
- max: 1e-1
- lrf:
- distribution: uniform
- min: 0.01
- max: 1.0
- momentum:
- distribution: uniform
- min: 0.6
- max: 0.98
- weight_decay:
- distribution: uniform
- min: 0.0
- max: 0.001
- warmup_epochs:
- distribution: uniform
- min: 0.0
- max: 5.0
- warmup_momentum:
- distribution: uniform
- min: 0.0
- max: 0.95
- warmup_bias_lr:
- distribution: uniform
- min: 0.0
- max: 0.2
- box:
- distribution: uniform
- min: 0.02
- max: 0.2
- cls:
- distribution: uniform
- min: 0.2
- max: 4.0
- cls_pw:
- distribution: uniform
- min: 0.5
- max: 2.0
- obj:
- distribution: uniform
- min: 0.2
- max: 4.0
- obj_pw:
- distribution: uniform
- min: 0.5
- max: 2.0
- iou_t:
- distribution: uniform
- min: 0.1
- max: 0.7
- anchor_t:
- distribution: uniform
- min: 2.0
- max: 8.0
- fl_gamma:
- distribution: uniform
- min: 0.0
- max: 4.0
- hsv_h:
- distribution: uniform
- min: 0.0
- max: 0.1
- hsv_s:
- distribution: uniform
- min: 0.0
- max: 0.9
- hsv_v:
- distribution: uniform
- min: 0.0
- max: 0.9
- degrees:
- distribution: uniform
- min: 0.0
- max: 45.0
- translate:
- distribution: uniform
- min: 0.0
- max: 0.9
- scale:
- distribution: uniform
- min: 0.0
- max: 0.9
- shear:
- distribution: uniform
- min: 0.0
- max: 10.0
- perspective:
- distribution: uniform
- min: 0.0
- max: 0.001
- flipud:
- distribution: uniform
- min: 0.0
- max: 1.0
- fliplr:
- distribution: uniform
- min: 0.0
- max: 1.0
- mosaic:
- distribution: uniform
- min: 0.0
- max: 1.0
- mixup:
- distribution: uniform
- min: 0.0
- max: 1.0
- copy_paste:
- distribution: uniform
- min: 0.0
- max: 1.0
diff --git a/utils/loggers/wandb/wandb_utils.py b/utils/loggers/wandb/wandb_utils.py
index e850d2ac8a7c..c8ab38197381 100644
--- a/utils/loggers/wandb/wandb_utils.py
+++ b/utils/loggers/wandb/wandb_utils.py
@@ -1,110 +1,32 @@
-"""Utilities and tools for tracking runs with Weights & Biases."""
+# YOLOv5 🚀 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
import logging
import os
import sys
from contextlib import contextmanager
from pathlib import Path
-from typing import Dict
-import yaml
-from tqdm import tqdm
+from utils.general import LOGGER, colorstr
FILE = Path(__file__).resolve()
ROOT = FILE.parents[3] # YOLOv5 root directory
if str(ROOT) not in sys.path:
sys.path.append(str(ROOT)) # add ROOT to PATH
-
-from utils.dataloaders import LoadImagesAndLabels, img2label_paths
-from utils.general import LOGGER, check_dataset, check_file
+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.'
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 isinstance(data_file, dict):
- # In that case another dataset manager has already processed it and we don't have to
- return data_file
- 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.
@@ -134,84 +56,31 @@ def __init__(self, opt, run_id=None, job_type='Training'):
"""
# Pre-training routine --
self.job_type = job_type
- self.wandb, self.wandb_run = wandb, None if not wandb else wandb.run
+ self.wandb, self.wandb_run = wandb, wandb.run if wandb else None
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
- # 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:
+ if self.wandb:
self.wandb_run = wandb.init(config=opt,
- resume="allow",
+ resume='allow',
project='YOLOv5' 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 opt.upload_dataset:
- if not opt.resume:
- self.wandb_artifact_data_dict = self.check_and_upload_dataset(opt)
-
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
- elif 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.wandb_run.config.update({"upload_dataset": True})
- 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,
- 'YOLOv5' if opt.project == 'runs/train' else Path(opt.project).stem)
- 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:
@@ -226,81 +95,18 @@ def setup_training(self, opt):
self.log_dict, self.current_epoch = {}, 0
self.bbox_interval = opt.bbox_interval
if isinstance(opt.resume, str):
- modeldir, _ = self.download_model_artifact(opt)
- if modeldir:
- self.weights = Path(modeldir) / "last.pt"
+ model_dir, _ = self.download_model_artifact(opt)
+ if model_dir:
+ self.weights = Path(model_dir) / '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(
- self.weights), config.save_period, config.batch_size, config.bbox_interval, config.epochs,\
+ self.weights), config.save_period, config.batch_size, config.bbox_interval, config.epochs, \
config.hyp, config.imgsz
- 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")
- columns = ["epoch", "id", "ground truth", "prediction"]
- columns.extend(self.data_dict['names'])
- self.result_table = wandb.Table(columns)
- 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):
"""
@@ -325,192 +131,10 @@ def log_model(self, path, opt, epoch, fitness_score, best_model=False):
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}")
-
- 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
- """
- upload_dataset = self.wandb_run.config.upload_dataset
- log_val_only = isinstance(upload_dataset, str) and upload_dataset == 'val'
- 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
-
- # log train set
- if not log_val_only:
- self.train_artifact = self.create_dataset_table(LoadImagesAndLabels(data['train'], rect=True, batch_size=1),
- names,
- name='train') if data.get('train') else None
- if data.get('train'):
- data['train'] = WANDB_ARTIFACT_PREFIX + str(Path(project) / 'train')
-
- 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('val'):
- data['val'] = WANDB_ARTIFACT_PREFIX + str(Path(project) / 'val')
-
- path = Path(data_file)
- # create a _wandb.yaml file with artifacts links if both train and test set are logged
- if not log_val_only:
- path = (path.stem if overwrite_config else path.stem + '_wandb') + '.yaml' # updated data.yaml path
- path = ROOT / 'data' / path
- data.pop('download', None)
- data.pop('path', None)
- with open(path, 'w') as f:
- yaml.safe_dump(data, f)
- LOGGER.info(f"Created dataset config file {path}")
-
- if self.job_type == 'Training': # builds correct artifact pipeline graph
- if not log_val_only:
- self.wandb_run.log_artifact(
- self.train_artifact) # calling use_artifact downloads the dataset. NOT NEEDED!
- self.wandb_run.use_artifact(self.val_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.im_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 = []
- avg_conf_per_class = [0] * len(self.data_dict['names'])
- pred_class_count = {}
- for *xyxy, conf, cls in predn.tolist():
- if conf >= 0.25:
- cls = int(cls)
- box_data.append({
- "position": {
- "minX": xyxy[0],
- "minY": xyxy[1],
- "maxX": xyxy[2],
- "maxY": xyxy[3]},
- "class_id": cls,
- "box_caption": f"{names[cls]} {conf:.3f}",
- "scores": {
- "class_score": conf},
- "domain": "pixel"})
- avg_conf_per_class[cls] += conf
-
- if cls in pred_class_count:
- pred_class_count[cls] += 1
- else:
- pred_class_count[cls] = 1
-
- for pred_class in pred_class_count.keys():
- avg_conf_per_class[pred_class] = avg_conf_per_class[pred_class] / pred_class_count[pred_class]
-
- 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),
- *avg_conf_per_class)
+ LOGGER.info(f'Saving model artifact on epoch {epoch + 1}')
def val_one_image(self, pred, predn, path, names, im):
- """
- 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[int(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))
+ pass
def log(self, log_dict):
"""
@@ -523,7 +147,7 @@ def log(self, log_dict):
for key, value in log_dict.items():
self.log_dict[key] = value
- def end_epoch(self, best_result=False):
+ def end_epoch(self):
"""
commit the log_dict, model artifacts and Tables to W&B and flush the log_dict.
@@ -532,31 +156,15 @@ def end_epoch(self, best_result=False):
"""
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}"
+ 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})
- columns = ["epoch", "id", "ground truth", "prediction"]
- columns.extend(self.data_dict['names'])
- self.result_table = wandb.Table(columns)
- self.result_artifact = wandb.Artifact("run_" + wandb.run.id + "_progress", "evaluation")
def finish_run(self):
"""
@@ -567,6 +175,7 @@ def finish_run(self):
with all_logging_disabled():
wandb.log(self.log_dict)
wandb.run.finish()
+ LOGGER.warning(DEPRECATION_WARNING)
@contextmanager
diff --git a/utils/metrics.py b/utils/metrics.py
index ee7d33982cfc..95f364c23f34 100644
--- a/utils/metrics.py
+++ b/utils/metrics.py
@@ -28,7 +28,7 @@ def smooth(y, f=0.05):
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):
+def ap_per_class(tp, conf, pred_cls, target_cls, plot=False, save_dir='.', names=(), eps=1e-16, prefix=''):
""" Compute the average precision, given the recall and precision curves.
Source: https://github.com/rafaelpadilla/Object-Detection-Metrics.
# Arguments
@@ -83,10 +83,10 @@ def ap_per_class(tp, conf, pred_cls, target_cls, plot=False, save_dir='.', names
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
if plot:
- 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')
+ 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')
i = smooth(f1.mean(0), 0.1).argmax() # max F1 index
p, r, f1 = p[:, i], r[:, i], f1[:, i]
@@ -170,15 +170,12 @@ def process_batch(self, detections, labels):
if n and sum(j) == 1:
self.matrix[detection_classes[m1[j]], gc] += 1 # correct
else:
- self.matrix[self.nc, gc] += 1 # background FP
+ self.matrix[self.nc, gc] += 1 # true background
if n:
for i, dc in enumerate(detection_classes):
if not any(m1 == i):
- self.matrix[dc, self.nc] += 1 # background FN
-
- def matrix(self):
- return self.matrix
+ self.matrix[dc, self.nc] += 1 # predicted background
def tp_fp(self):
tp = self.matrix.diagonal() # true positives
@@ -186,7 +183,7 @@ def tp_fp(self):
# fn = self.matrix.sum(0) - tp # false negatives (missed detections)
return tp[:-1], fp[:-1] # remove background class
- @TryExcept('WARNING: ConfusionMatrix plot failure: ')
+ @TryExcept('WARNING ⚠️ ConfusionMatrix plot failure')
def plot(self, normalize=True, save_dir='', names=()):
import seaborn as sn
@@ -197,20 +194,21 @@ def plot(self, normalize=True, save_dir='', names=()):
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},
+ 'size': 8},
cmap='Blues',
fmt='.2f',
square=True,
vmin=0.0,
- xticklabels=names + ['background FP'] if labels else "auto",
- yticklabels=names + ['background FN'] if labels else "auto").set_facecolor((1, 1, 1))
- ax.set_ylabel('True')
+ 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)
@@ -226,19 +224,19 @@ 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
- w2, h2 = b2_x2 - b2_x1, b2_y2 - b2_y1
+ 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)
# Intersection area
- 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)
+ 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)
# Union Area
union = w1 * h1 + w2 * h2 - inter + eps
@@ -246,13 +244,13 @@ 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 = 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
+ 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
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.pow(torch.atan(w2 / (h2 + eps)) - torch.atan(w1 / (h1 + eps)), 2)
+ v = (4 / math.pi ** 2) * (torch.atan(w2 / h2) - torch.atan(w1 / h1)).pow(2)
with torch.no_grad():
alpha = v / (v - iou + (1 + eps))
return iou - (rho2 / c2 + v * alpha) # CIoU
@@ -262,11 +260,6 @@ def bbox_iou(box1, box2, xywh=True, GIoU=False, DIoU=False, CIoU=False, eps=1e-7
return iou # IoU
-def box_area(box):
- # box = xyxy(4,n)
- return (box[2] - box[0]) * (box[3] - box[1])
-
-
def box_iou(box1, box2, eps=1e-7):
# https://github.com/pytorch/vision/blob/master/torchvision/ops/boxes.py
"""
@@ -281,11 +274,11 @@ def box_iou(box1, box2, eps=1e-7):
"""
# inter(N,M) = (rb(N,M,2) - lt(N,M,2)).clamp(0).prod(2)
- (a1, a2), (b1, b2) = box1[:, None].chunk(2, 2), box2.chunk(2, 1)
+ (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 / (box_area(box1.T)[:, None] + box_area(box2.T) - inter + eps)
+ return inter / ((a2 - a1).prod(2) + (b2 - b1).prod(2) - inter + eps)
def bbox_ioa(box1, box2, eps=1e-7):
@@ -338,7 +331,7 @@ 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.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)
@@ -361,7 +354,7 @@ def plot_mc_curve(px, py, save_dir=Path('mc_curve.png'), names=(), xlabel='Confi
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.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)
diff --git a/utils/plots.py b/utils/plots.py
index 0530d0abdf48..24c618c80b59 100644
--- a/utils/plots.py
+++ b/utils/plots.py
@@ -20,9 +20,10 @@
from PIL import Image, ImageDraw, ImageFont
from utils import TryExcept, threaded
-from utils.general import (CONFIG_DIR, FONT, LOGGER, check_font, check_requirements, clip_coords, increment_path,
+from utils.general import (CONFIG_DIR, FONT, LOGGER, check_font, check_requirements, clip_boxes, increment_path,
is_ascii, xywh2xyxy, xyxy2xywh)
from utils.metrics import fitness
+from utils.segment.general import scale_image
# Settings
RANK = int(os.getenv('RANK', -1))
@@ -87,7 +88,8 @@ def box_label(self, box, label='', color=(128, 128, 128), txt_color=(255, 255, 2
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
+ 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)
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,
@@ -113,6 +115,36 @@ def box_label(self, box, label='', color=(128, 128, 128), txt_color=(255, 255, 2
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)
+
def rectangle(self, xy, fill=None, outline=None, width=1):
# Add rectangle to image (PIL-only)
self.draw.rectangle(xy, fill, outline, width)
@@ -124,6 +156,11 @@ def text(self, xy, text, txt_color=(255, 255, 255), anchor='top'):
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)
+
def result(self):
# Return annotated image as array
return np.asarray(self.im)
@@ -152,7 +189,6 @@ def feature_visualization(x, module_type, stage, n=32, save_dir=Path('runs/detec
ax[i].axis('off')
LOGGER.info(f'Saving {f}... ({n}/{channels})')
- plt.title('Features')
plt.savefig(f, dpi=300, bbox_inches='tight')
plt.close()
np.save(str(f.with_suffix('.npy')), x[0].cpu().numpy()) # npy save
@@ -180,26 +216,31 @@ def butter_lowpass(cutoff, fs, order):
return filtfilt(b, a, data) # forward-backward filter
-def output_to_target(output):
- # Convert model output to target format [batch_id, class_id, x, y, w, h, conf]
+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
targets = []
for i, o in enumerate(output):
- targets.extend([i, cls, *list(*xyxy2xywh(np.array(box)[None])), conf] for *box, conf, cls in o.cpu().numpy())
- return np.array(targets)
+ 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()
@threaded
-def plot_images(images, targets, paths=None, fname='images.jpg', names=None, max_size=1920, max_subplots=16):
+def plot_images(images, targets, paths=None, fname='images.jpg', names=None):
# Plot image grid with labels
if isinstance(images, torch.Tensor):
images = images.cpu().float().numpy()
if isinstance(targets, torch.Tensor):
targets = targets.cpu().numpy()
- if np.max(images[0]) <= 1:
- images *= 255 # de-normalise (optional)
+
+ max_size = 1920 # max image size
+ max_subplots = 16 # max image subplots, i.e. 4x4
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
@@ -409,7 +450,7 @@ def imshow_cls(im, labels=None, pred=None, names=None, nmax=25, verbose=False, f
plt.savefig(f, dpi=300, bbox_inches='tight')
plt.close()
if verbose:
- LOGGER.info(f"Saving {f}")
+ 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:
@@ -509,7 +550,7 @@ 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_coords(xyxy, im.shape)
+ clip_boxes(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
diff --git a/utils/segment/__init__.py b/utils/segment/__init__.py
new file mode 100644
index 000000000000..e69de29bb2d1
diff --git a/utils/segment/augmentations.py b/utils/segment/augmentations.py
new file mode 100644
index 000000000000..169addedf0f5
--- /dev/null
+++ b/utils/segment/augmentations.py
@@ -0,0 +1,104 @@
+# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
+"""
+Image augmentation functions
+"""
+
+import math
+import random
+
+import cv2
+import numpy as np
+
+from ..augmentations import box_candidates
+from ..general import resample_segments, segment2box
+
+
+def mixup(im, labels, segments, im2, labels2, segments2):
+ # Applies MixUp augmentation https://arxiv.org/pdf/1710.09412.pdf
+ r = np.random.beta(32.0, 32.0) # mixup ratio, alpha=beta=32.0
+ im = (im * r + im2 * (1 - r)).astype(np.uint8)
+ labels = np.concatenate((labels, labels2), 0)
+ segments = np.concatenate((segments, segments2), 0)
+ return im, labels, segments
+
+
+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=(.1, .1), scale=(.9, 1.1), shear=(-10, 10))
+ # targets = [cls, xyxy]
+
+ height = im.shape[0] + border[0] * 2 # shape(h,w,c)
+ width = im.shape[1] + border[1] * 2
+
+ # Center
+ C = np.eye(3)
+ C[0, 2] = -im.shape[1] / 2 # x translation (pixels)
+ C[1, 2] = -im.shape[0] / 2 # y translation (pixels)
+
+ # Perspective
+ P = np.eye(3)
+ P[2, 0] = random.uniform(-perspective, perspective) # x perspective (about y)
+ P[2, 1] = random.uniform(-perspective, perspective) # y perspective (about x)
+
+ # Rotation and Scale
+ R = np.eye(3)
+ a = random.uniform(-degrees, degrees)
+ # a += random.choice([-180, -90, 0, 90]) # add 90deg rotations to small rotations
+ s = random.uniform(1 - scale, 1 + scale)
+ # s = 2 ** random.uniform(-scale, scale)
+ R[:2] = cv2.getRotationMatrix2D(angle=a, center=(0, 0), scale=s)
+
+ # Shear
+ S = np.eye(3)
+ S[0, 1] = math.tan(random.uniform(-shear, shear) * math.pi / 180) # x shear (deg)
+ S[1, 0] = math.tan(random.uniform(-shear, shear) * math.pi / 180) # y shear (deg)
+
+ # Translation
+ T = np.eye(3)
+ T[0, 2] = (random.uniform(0.5 - translate, 0.5 + translate) * width) # x translation (pixels)
+ T[1, 2] = (random.uniform(0.5 - translate, 0.5 + translate) * height) # y translation (pixels)
+
+ # Combined rotation matrix
+ M = T @ S @ R @ P @ C # order of operations (right to left) is IMPORTANT
+ if (border[0] != 0) or (border[1] != 0) or (M != np.eye(3)).any(): # image changed
+ if perspective:
+ im = cv2.warpPerspective(im, M, dsize=(width, height), borderValue=(114, 114, 114))
+ else: # affine
+ im = cv2.warpAffine(im, M[:2], dsize=(width, height), borderValue=(114, 114, 114))
+
+ # Visualize
+ # import matplotlib.pyplot as plt
+ # ax = plt.subplots(1, 2, figsize=(12, 6))[1].ravel()
+ # ax[0].imshow(im[:, :, ::-1]) # base
+ # ax[1].imshow(im2[:, :, ::-1]) # warped
+
+ # Transform label coordinates
+ n = len(targets)
+ new_segments = []
+ if n:
+ new = np.zeros((n, 4))
+ segments = resample_segments(segments) # upsample
+ for i, segment in enumerate(segments):
+ xy = np.ones((len(segment), 3))
+ xy[:, :2] = segment
+ xy = xy @ M.T # transform
+ xy = (xy[:, :2] / xy[:, 2:3] if perspective else xy[:, :2]) # perspective rescale or affine
+
+ # clip
+ new[i] = segment2box(xy, width, height)
+ new_segments.append(xy)
+
+ # filter candidates
+ i = box_candidates(box1=targets[:, 1:5].T * s, box2=new.T, area_thr=0.01)
+ targets = targets[i]
+ targets[:, 1:5] = new[i]
+ new_segments = np.array(new_segments)[i]
+
+ return im, targets, new_segments
diff --git a/utils/segment/dataloaders.py b/utils/segment/dataloaders.py
new file mode 100644
index 000000000000..097a5d5cb058
--- /dev/null
+++ b/utils/segment/dataloaders.py
@@ -0,0 +1,332 @@
+# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
+"""
+Dataloaders
+"""
+
+import os
+import random
+
+import cv2
+import numpy as np
+import torch
+from torch.utils.data import DataLoader, distributed
+
+from ..augmentations import augment_hsv, copy_paste, letterbox
+from ..dataloaders import InfiniteDataLoader, LoadImagesAndLabels, seed_worker
+from ..general import LOGGER, xyn2xy, xywhn2xyxy, xyxy2xywhn
+from ..torch_utils import torch_distributed_zero_first
+from .augmentations import mixup, random_perspective
+
+RANK = int(os.getenv('RANK', -1))
+
+
+def create_dataloader(path,
+ imgsz,
+ batch_size,
+ stride,
+ single_cls=False,
+ hyp=None,
+ augment=False,
+ cache=False,
+ pad=0.0,
+ rect=False,
+ rank=-1,
+ workers=8,
+ image_weights=False,
+ quad=False,
+ prefix='',
+ shuffle=False,
+ mask_downsample_ratio=1,
+ overlap_mask=False,
+ seed=0):
+ if rect and shuffle:
+ LOGGER.warning('WARNING ⚠️ --rect is incompatible with DataLoader shuffle, setting shuffle=False')
+ shuffle = False
+ with torch_distributed_zero_first(rank): # init dataset *.cache only once if DDP
+ dataset = LoadImagesAndLabelsAndMasks(
+ path,
+ imgsz,
+ batch_size,
+ augment=augment, # augmentation
+ hyp=hyp, # hyperparameters
+ rect=rect, # rectangular batches
+ cache_images=cache,
+ single_cls=single_cls,
+ stride=int(stride),
+ pad=pad,
+ image_weights=image_weights,
+ prefix=prefix,
+ downsample_ratio=mask_downsample_ratio,
+ overlap=overlap_mask)
+
+ batch_size = min(batch_size, len(dataset))
+ nd = torch.cuda.device_count() # number of CUDA devices
+ nw = min([os.cpu_count() // max(nd, 1), batch_size if batch_size > 1 else 0, workers]) # number of workers
+ sampler = None if rank == -1 else distributed.DistributedSampler(dataset, shuffle=shuffle)
+ loader = DataLoader if image_weights else InfiniteDataLoader # only DataLoader allows for attribute updates
+ generator = torch.Generator()
+ generator.manual_seed(6148914691236517205 + seed + RANK)
+ return loader(
+ dataset,
+ batch_size=batch_size,
+ shuffle=shuffle and sampler is None,
+ num_workers=nw,
+ sampler=sampler,
+ pin_memory=True,
+ collate_fn=LoadImagesAndLabelsAndMasks.collate_fn4 if quad else LoadImagesAndLabelsAndMasks.collate_fn,
+ worker_init_fn=seed_worker,
+ generator=generator,
+ ), dataset
+
+
+class LoadImagesAndLabelsAndMasks(LoadImagesAndLabels): # for training/testing
+
+ def __init__(
+ self,
+ path,
+ img_size=640,
+ batch_size=16,
+ augment=False,
+ hyp=None,
+ rect=False,
+ image_weights=False,
+ cache_images=False,
+ single_cls=False,
+ stride=32,
+ pad=0,
+ min_items=0,
+ prefix='',
+ downsample_ratio=1,
+ overlap=False,
+ ):
+ super().__init__(path, img_size, batch_size, augment, hyp, rect, image_weights, cache_images, single_cls,
+ stride, pad, min_items, prefix)
+ self.downsample_ratio = downsample_ratio
+ self.overlap = overlap
+
+ def __getitem__(self, index):
+ index = self.indices[index] # linear, shuffled, or image_weights
+
+ hyp = self.hyp
+ mosaic = self.mosaic and random.random() < hyp['mosaic']
+ masks = []
+ if mosaic:
+ # Load mosaic
+ img, labels, segments = self.load_mosaic(index)
+ shapes = None
+
+ # MixUp augmentation
+ if random.random() < hyp['mixup']:
+ img, labels, segments = mixup(img, labels, segments, *self.load_mosaic(random.randint(0, self.n - 1)))
+
+ else:
+ # Load image
+ img, (h0, w0), (h, w) = self.load_image(index)
+
+ # Letterbox
+ shape = self.batch_shapes[self.batch[index]] if self.rect else self.img_size # final letterboxed shape
+ img, ratio, pad = letterbox(img, shape, auto=False, scaleup=self.augment)
+ shapes = (h0, w0), ((h / h0, w / w0), pad) # for COCO mAP rescaling
+
+ labels = self.labels[index].copy()
+ # [array, array, ....], array.shape=(num_points, 2), xyxyxyxy
+ segments = self.segments[index].copy()
+ if len(segments):
+ for i_s in range(len(segments)):
+ segments[i_s] = xyn2xy(
+ segments[i_s],
+ ratio[0] * w,
+ ratio[1] * h,
+ padw=pad[0],
+ padh=pad[1],
+ )
+ if labels.size: # normalized xywh to pixel xyxy format
+ labels[:, 1:] = xywhn2xyxy(labels[:, 1:], ratio[0] * w, ratio[1] * h, padw=pad[0], padh=pad[1])
+
+ if self.augment:
+ img, labels, segments = random_perspective(img,
+ labels,
+ segments=segments,
+ degrees=hyp['degrees'],
+ translate=hyp['translate'],
+ scale=hyp['scale'],
+ shear=hyp['shear'],
+ perspective=hyp['perspective'])
+
+ nl = len(labels) # number of labels
+ if nl:
+ labels[:, 1:5] = xyxy2xywhn(labels[:, 1:5], w=img.shape[1], h=img.shape[0], clip=True, eps=1e-3)
+ if self.overlap:
+ masks, sorted_idx = polygons2masks_overlap(img.shape[:2],
+ segments,
+ downsample_ratio=self.downsample_ratio)
+ masks = masks[None] # (640, 640) -> (1, 640, 640)
+ labels = labels[sorted_idx]
+ else:
+ masks = polygons2masks(img.shape[:2], segments, color=1, downsample_ratio=self.downsample_ratio)
+
+ masks = (torch.from_numpy(masks) if len(masks) else torch.zeros(1 if self.overlap else nl, img.shape[0] //
+ self.downsample_ratio, img.shape[1] //
+ self.downsample_ratio))
+ # TODO: albumentations support
+ if self.augment:
+ # Albumentations
+ # there are some augmentation that won't change boxes and masks,
+ # so just be it for now.
+ img, labels = self.albumentations(img, labels)
+ nl = len(labels) # update after albumentations
+
+ # HSV color-space
+ augment_hsv(img, hgain=hyp['hsv_h'], sgain=hyp['hsv_s'], vgain=hyp['hsv_v'])
+
+ # Flip up-down
+ if random.random() < hyp['flipud']:
+ img = np.flipud(img)
+ if nl:
+ labels[:, 2] = 1 - labels[:, 2]
+ masks = torch.flip(masks, dims=[1])
+
+ # Flip left-right
+ if random.random() < hyp['fliplr']:
+ img = np.fliplr(img)
+ if nl:
+ labels[:, 1] = 1 - labels[:, 1]
+ masks = torch.flip(masks, dims=[2])
+
+ # Cutouts # labels = cutout(img, labels, p=0.5)
+
+ labels_out = torch.zeros((nl, 6))
+ if nl:
+ labels_out[:, 1:] = torch.from_numpy(labels)
+
+ # Convert
+ img = img.transpose((2, 0, 1))[::-1] # HWC to CHW, BGR to RGB
+ img = np.ascontiguousarray(img)
+
+ return (torch.from_numpy(img), labels_out, self.im_files[index], shapes, masks)
+
+ def load_mosaic(self, index):
+ # YOLOv5 4-mosaic loader. Loads 1 image + 3 random images into a 4-image mosaic
+ labels4, segments4 = [], []
+ s = self.img_size
+ yc, xc = (int(random.uniform(-x, 2 * s + x)) for x in self.mosaic_border) # mosaic center x, y
+
+ # 3 additional image indices
+ indices = [index] + random.choices(self.indices, k=3) # 3 additional image indices
+ for i, index in enumerate(indices):
+ # Load image
+ img, _, (h, w) = self.load_image(index)
+
+ # place img in img4
+ if i == 0: # top left
+ img4 = np.full((s * 2, s * 2, img.shape[2]), 114, dtype=np.uint8) # base image with 4 tiles
+ x1a, y1a, x2a, y2a = max(xc - w, 0), max(yc - h, 0), xc, yc # xmin, ymin, xmax, ymax (large image)
+ x1b, y1b, x2b, y2b = w - (x2a - x1a), h - (y2a - y1a), w, h # xmin, ymin, xmax, ymax (small image)
+ elif i == 1: # top right
+ x1a, y1a, x2a, y2a = xc, max(yc - h, 0), min(xc + w, s * 2), yc
+ x1b, y1b, x2b, y2b = 0, h - (y2a - y1a), min(w, x2a - x1a), h
+ elif i == 2: # bottom left
+ x1a, y1a, x2a, y2a = max(xc - w, 0), yc, xc, min(s * 2, yc + h)
+ x1b, y1b, x2b, y2b = w - (x2a - x1a), 0, w, min(y2a - y1a, h)
+ elif i == 3: # bottom right
+ x1a, y1a, x2a, y2a = xc, yc, min(xc + w, s * 2), min(s * 2, yc + h)
+ x1b, y1b, x2b, y2b = 0, 0, min(w, x2a - x1a), min(y2a - y1a, h)
+
+ img4[y1a:y2a, x1a:x2a] = img[y1b:y2b, x1b:x2b] # img4[ymin:ymax, xmin:xmax]
+ padw = x1a - x1b
+ padh = y1a - y1b
+
+ labels, segments = self.labels[index].copy(), self.segments[index].copy()
+
+ if labels.size:
+ labels[:, 1:] = xywhn2xyxy(labels[:, 1:], w, h, padw, padh) # normalized xywh to pixel xyxy format
+ segments = [xyn2xy(x, w, h, padw, padh) for x in segments]
+ labels4.append(labels)
+ segments4.extend(segments)
+
+ # Concat/clip labels
+ labels4 = np.concatenate(labels4, 0)
+ for x in (labels4[:, 1:], *segments4):
+ np.clip(x, 0, 2 * s, out=x) # clip when using random_perspective()
+ # img4, labels4 = replicate(img4, labels4) # replicate
+
+ # Augment
+ img4, labels4, segments4 = copy_paste(img4, labels4, segments4, p=self.hyp['copy_paste'])
+ img4, labels4, segments4 = random_perspective(img4,
+ labels4,
+ segments4,
+ degrees=self.hyp['degrees'],
+ translate=self.hyp['translate'],
+ scale=self.hyp['scale'],
+ shear=self.hyp['shear'],
+ perspective=self.hyp['perspective'],
+ border=self.mosaic_border) # border to remove
+ return img4, labels4, segments4
+
+ @staticmethod
+ def collate_fn(batch):
+ img, label, path, shapes, masks = zip(*batch) # transposed
+ batched_masks = torch.cat(masks, 0)
+ for i, l in enumerate(label):
+ l[:, 0] = i # add target image index for build_targets()
+ return torch.stack(img, 0), torch.cat(label, 0), path, shapes, batched_masks
+
+
+def polygon2mask(img_size, polygons, color=1, downsample_ratio=1):
+ """
+ Args:
+ img_size (tuple): The image size.
+ polygons (np.ndarray): [N, M], N is the number of polygons,
+ M is the number of points(Be divided by 2).
+ """
+ mask = np.zeros(img_size, dtype=np.uint8)
+ polygons = np.asarray(polygons)
+ polygons = polygons.astype(np.int32)
+ shape = polygons.shape
+ polygons = polygons.reshape(shape[0], -1, 2)
+ cv2.fillPoly(mask, polygons, color=color)
+ nh, nw = (img_size[0] // downsample_ratio, img_size[1] // downsample_ratio)
+ # NOTE: fillPoly firstly then resize is trying the keep the same way
+ # of loss calculation when mask-ratio=1.
+ mask = cv2.resize(mask, (nw, nh))
+ return mask
+
+
+def polygons2masks(img_size, polygons, color, downsample_ratio=1):
+ """
+ Args:
+ img_size (tuple): The image size.
+ polygons (list[np.ndarray]): each polygon is [N, M],
+ N is the number of polygons,
+ M is the number of points(Be divided by 2).
+ """
+ masks = []
+ for si in range(len(polygons)):
+ mask = polygon2mask(img_size, [polygons[si].reshape(-1)], color, downsample_ratio)
+ masks.append(mask)
+ return np.array(masks)
+
+
+def polygons2masks_overlap(img_size, segments, downsample_ratio=1):
+ """Return a (640, 640) overlap mask."""
+ masks = np.zeros((img_size[0] // downsample_ratio, img_size[1] // downsample_ratio),
+ dtype=np.int32 if len(segments) > 255 else np.uint8)
+ areas = []
+ ms = []
+ for si in range(len(segments)):
+ mask = polygon2mask(
+ img_size,
+ [segments[si].reshape(-1)],
+ downsample_ratio=downsample_ratio,
+ color=1,
+ )
+ ms.append(mask)
+ areas.append(mask.sum())
+ areas = np.asarray(areas)
+ index = np.argsort(-areas)
+ ms = np.array(ms)[index]
+ for i in range(len(segments)):
+ mask = ms[i] * (i + 1)
+ masks = masks + mask
+ masks = np.clip(masks, a_min=0, a_max=i + 1)
+ return masks, index
diff --git a/utils/segment/general.py b/utils/segment/general.py
new file mode 100644
index 000000000000..9da894538665
--- /dev/null
+++ b/utils/segment/general.py
@@ -0,0 +1,160 @@
+import cv2
+import numpy as np
+import torch
+import torch.nn.functional as F
+
+
+def crop_mask(masks, boxes):
+ """
+ "Crop" predicted masks by zeroing out everything not in the predicted bbox.
+ Vectorized by Chong (thanks Chong).
+
+ Args:
+ - masks should be a size [h, w, n] tensor of masks
+ - boxes should be a size [n, 4] tensor of bbox coords in relative point form
+ """
+
+ n, h, w = masks.shape
+ x1, y1, x2, y2 = torch.chunk(boxes[:, :, None], 4, 1) # x1 shape(1,1,n)
+ r = torch.arange(w, device=masks.device, dtype=x1.dtype)[None, None, :] # rows shape(1,w,1)
+ c = torch.arange(h, device=masks.device, dtype=x1.dtype)[None, :, None] # cols shape(h,1,1)
+
+ return masks * ((r >= x1) * (r < x2) * (c >= y1) * (c < y2))
+
+
+def process_mask_upsample(protos, masks_in, bboxes, shape):
+ """
+ Crop after upsample.
+ protos: [mask_dim, mask_h, mask_w]
+ masks_in: [n, mask_dim], n is number of masks after nms
+ bboxes: [n, 4], n is number of masks after nms
+ shape: input_image_size, (h, w)
+
+ return: h, w, n
+ """
+
+ c, mh, mw = protos.shape # CHW
+ masks = (masks_in @ protos.float().view(c, -1)).sigmoid().view(-1, mh, mw)
+ masks = F.interpolate(masks[None], shape, mode='bilinear', align_corners=False)[0] # CHW
+ masks = crop_mask(masks, bboxes) # CHW
+ return masks.gt_(0.5)
+
+
+def process_mask(protos, masks_in, bboxes, shape, upsample=False):
+ """
+ Crop before upsample.
+ proto_out: [mask_dim, mask_h, mask_w]
+ out_masks: [n, mask_dim], n is number of masks after nms
+ bboxes: [n, 4], n is number of masks after nms
+ shape:input_image_size, (h, w)
+
+ return: h, w, n
+ """
+
+ c, mh, mw = protos.shape # CHW
+ ih, iw = shape
+ masks = (masks_in @ protos.float().view(c, -1)).sigmoid().view(-1, mh, mw) # CHW
+
+ downsampled_bboxes = bboxes.clone()
+ downsampled_bboxes[:, 0] *= mw / iw
+ downsampled_bboxes[:, 2] *= mw / iw
+ downsampled_bboxes[:, 3] *= mh / ih
+ downsampled_bboxes[:, 1] *= mh / ih
+
+ masks = crop_mask(masks, downsampled_bboxes) # CHW
+ if upsample:
+ masks = F.interpolate(masks[None], shape, mode='bilinear', align_corners=False)[0] # CHW
+ return masks.gt_(0.5)
+
+
+def process_mask_native(protos, masks_in, bboxes, shape):
+ """
+ Crop after upsample.
+ protos: [mask_dim, mask_h, mask_w]
+ masks_in: [n, mask_dim], n is number of masks after nms
+ bboxes: [n, 4], n is number of masks after nms
+ shape: input_image_size, (h, w)
+
+ return: h, w, n
+ """
+ c, mh, mw = protos.shape # CHW
+ masks = (masks_in @ protos.float().view(c, -1)).sigmoid().view(-1, mh, mw)
+ gain = min(mh / shape[0], mw / shape[1]) # gain = old / new
+ pad = (mw - shape[1] * gain) / 2, (mh - shape[0] * gain) / 2 # wh padding
+ top, left = int(pad[1]), int(pad[0]) # y, x
+ bottom, right = int(mh - pad[1]), int(mw - pad[0])
+ masks = masks[:, top:bottom, left:right]
+
+ masks = F.interpolate(masks[None], shape, mode='bilinear', align_corners=False)[0] # CHW
+ masks = crop_mask(masks, bboxes) # CHW
+ return masks.gt_(0.5)
+
+
+def scale_image(im1_shape, masks, im0_shape, ratio_pad=None):
+ """
+ img1_shape: model input shape, [h, w]
+ img0_shape: origin pic shape, [h, w, 3]
+ masks: [h, w, num]
+ """
+ # Rescale coordinates (xyxy) from im1_shape to im0_shape
+ if ratio_pad is None: # calculate from im0_shape
+ gain = min(im1_shape[0] / im0_shape[0], im1_shape[1] / im0_shape[1]) # gain = old / new
+ pad = (im1_shape[1] - im0_shape[1] * gain) / 2, (im1_shape[0] - im0_shape[0] * gain) / 2 # wh padding
+ else:
+ pad = ratio_pad[1]
+ top, left = int(pad[1]), int(pad[0]) # y, x
+ bottom, right = int(im1_shape[0] - pad[1]), int(im1_shape[1] - pad[0])
+
+ if len(masks.shape) < 2:
+ raise ValueError(f'"len of masks shape" should be 2 or 3, but got {len(masks.shape)}')
+ masks = masks[top:bottom, left:right]
+ # masks = masks.permute(2, 0, 1).contiguous()
+ # masks = F.interpolate(masks[None], im0_shape[:2], mode='bilinear', align_corners=False)[0]
+ # masks = masks.permute(1, 2, 0).contiguous()
+ masks = cv2.resize(masks, (im0_shape[1], im0_shape[0]))
+
+ if len(masks.shape) == 2:
+ masks = masks[:, :, None]
+ return masks
+
+
+def mask_iou(mask1, mask2, eps=1e-7):
+ """
+ mask1: [N, n] m1 means number of predicted objects
+ mask2: [M, n] m2 means number of gt objects
+ Note: n means image_w x image_h
+
+ return: masks iou, [N, M]
+ """
+ intersection = torch.matmul(mask1, mask2.t()).clamp(0)
+ union = (mask1.sum(1)[:, None] + mask2.sum(1)[None]) - intersection # (area1 + area2) - intersection
+ return intersection / (union + eps)
+
+
+def masks_iou(mask1, mask2, eps=1e-7):
+ """
+ mask1: [N, n] m1 means number of predicted objects
+ mask2: [N, n] m2 means number of gt objects
+ Note: n means image_w x image_h
+
+ return: masks iou, (N, )
+ """
+ intersection = (mask1 * mask2).sum(1).clamp(0) # (N, )
+ union = (mask1.sum(1) + mask2.sum(1))[None] - intersection # (area1 + area2) - intersection
+ return intersection / (union + eps)
+
+
+def masks2segments(masks, strategy='largest'):
+ # Convert masks(n,160,160) into segments(n,xy)
+ segments = []
+ for x in masks.int().cpu().numpy().astype('uint8'):
+ c = cv2.findContours(x, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)[0]
+ if c:
+ if strategy == 'concat': # concatenate all segments
+ c = np.concatenate([x.reshape(-1, 2) for x in c])
+ elif strategy == 'largest': # select largest segment
+ c = np.array(c[np.array([len(x) for x in c]).argmax()]).reshape(-1, 2)
+ else:
+ c = np.zeros((0, 2)) # no segments found
+ segments.append(c.astype('float32'))
+ return segments
diff --git a/utils/segment/loss.py b/utils/segment/loss.py
new file mode 100644
index 000000000000..2a8a4c680f6f
--- /dev/null
+++ b/utils/segment/loss.py
@@ -0,0 +1,186 @@
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+
+from ..general import xywh2xyxy
+from ..loss import FocalLoss, smooth_BCE
+from ..metrics import bbox_iou
+from ..torch_utils import de_parallel
+from .general import crop_mask
+
+
+class ComputeLoss:
+ # Compute losses
+ def __init__(self, model, autobalance=False, overlap=False):
+ self.sort_obj_iou = False
+ self.overlap = overlap
+ device = next(model.parameters()).device # get model device
+ h = model.hyp # hyperparameters
+ self.device = device
+
+ # Define criteria
+ BCEcls = nn.BCEWithLogitsLoss(pos_weight=torch.tensor([h['cls_pw']], device=device))
+ BCEobj = nn.BCEWithLogitsLoss(pos_weight=torch.tensor([h['obj_pw']], device=device))
+
+ # Class label smoothing https://arxiv.org/pdf/1902.04103.pdf eqn 3
+ self.cp, self.cn = smooth_BCE(eps=h.get('label_smoothing', 0.0)) # positive, negative BCE targets
+
+ # Focal loss
+ g = h['fl_gamma'] # focal loss gamma
+ if g > 0:
+ BCEcls, BCEobj = FocalLoss(BCEcls, g), FocalLoss(BCEobj, g)
+
+ m = de_parallel(model).model[-1] # Detect() module
+ self.balance = {3: [4.0, 1.0, 0.4]}.get(m.nl, [4.0, 1.0, 0.25, 0.06, 0.02]) # P3-P7
+ self.ssi = list(m.stride).index(16) if autobalance else 0 # stride 16 index
+ self.BCEcls, self.BCEobj, self.gr, self.hyp, self.autobalance = BCEcls, BCEobj, 1.0, h, autobalance
+ self.na = m.na # number of anchors
+ self.nc = m.nc # number of classes
+ self.nl = m.nl # number of layers
+ self.nm = m.nm # number of masks
+ self.anchors = m.anchors
+ self.device = device
+
+ def __call__(self, preds, targets, masks): # predictions, targets, model
+ p, proto = preds
+ bs, nm, mask_h, mask_w = proto.shape # batch size, number of masks, mask height, mask width
+ lcls = torch.zeros(1, device=self.device)
+ lbox = torch.zeros(1, device=self.device)
+ lobj = torch.zeros(1, device=self.device)
+ lseg = torch.zeros(1, device=self.device)
+ tcls, tbox, indices, anchors, tidxs, xywhn = self.build_targets(p, targets) # targets
+
+ # Losses
+ for i, pi in enumerate(p): # layer index, layer predictions
+ b, a, gj, gi = indices[i] # image, anchor, gridy, gridx
+ tobj = torch.zeros(pi.shape[:4], dtype=pi.dtype, device=self.device) # target obj
+
+ n = b.shape[0] # number of targets
+ if n:
+ pxy, pwh, _, pcls, pmask = pi[b, a, gj, gi].split((2, 2, 1, self.nc, nm), 1) # subset of predictions
+
+ # Box regression
+ pxy = pxy.sigmoid() * 2 - 0.5
+ pwh = (pwh.sigmoid() * 2) ** 2 * anchors[i]
+ pbox = torch.cat((pxy, pwh), 1) # predicted box
+ iou = bbox_iou(pbox, tbox[i], CIoU=True).squeeze() # iou(prediction, target)
+ lbox += (1.0 - iou).mean() # iou loss
+
+ # Objectness
+ iou = iou.detach().clamp(0).type(tobj.dtype)
+ if self.sort_obj_iou:
+ j = iou.argsort()
+ b, a, gj, gi, iou = b[j], a[j], gj[j], gi[j], iou[j]
+ if self.gr < 1:
+ iou = (1.0 - self.gr) + self.gr * iou
+ tobj[b, a, gj, gi] = iou # iou ratio
+
+ # Classification
+ if self.nc > 1: # cls loss (only if multiple classes)
+ t = torch.full_like(pcls, self.cn, device=self.device) # targets
+ t[range(n), tcls[i]] = self.cp
+ lcls += self.BCEcls(pcls, t) # BCE
+
+ # Mask regression
+ if tuple(masks.shape[-2:]) != (mask_h, mask_w): # downsample
+ masks = F.interpolate(masks[None], (mask_h, mask_w), mode='nearest')[0]
+ marea = xywhn[i][:, 2:].prod(1) # mask width, height normalized
+ mxyxy = xywh2xyxy(xywhn[i] * torch.tensor([mask_w, mask_h, mask_w, mask_h], device=self.device))
+ for bi in b.unique():
+ j = b == bi # matching index
+ if self.overlap:
+ mask_gti = torch.where(masks[bi][None] == tidxs[i][j].view(-1, 1, 1), 1.0, 0.0)
+ else:
+ mask_gti = masks[tidxs[i]][j]
+ lseg += self.single_mask_loss(mask_gti, pmask[j], proto[bi], mxyxy[j], marea[j])
+
+ obji = self.BCEobj(pi[..., 4], tobj)
+ lobj += obji * self.balance[i] # obj loss
+ if self.autobalance:
+ self.balance[i] = self.balance[i] * 0.9999 + 0.0001 / obji.detach().item()
+
+ if self.autobalance:
+ self.balance = [x / self.balance[self.ssi] for x in self.balance]
+ lbox *= self.hyp['box']
+ lobj *= self.hyp['obj']
+ lcls *= self.hyp['cls']
+ lseg *= self.hyp['box'] / bs
+
+ loss = lbox + lobj + lcls + lseg
+ return loss * bs, torch.cat((lbox, lseg, lobj, lcls)).detach()
+
+ def single_mask_loss(self, gt_mask, pred, proto, xyxy, area):
+ # Mask loss for one image
+ pred_mask = (pred @ proto.view(self.nm, -1)).view(-1, *proto.shape[1:]) # (n,32) @ (32,80,80) -> (n,80,80)
+ loss = F.binary_cross_entropy_with_logits(pred_mask, gt_mask, reduction='none')
+ return (crop_mask(loss, xyxy).mean(dim=(1, 2)) / area).mean()
+
+ def build_targets(self, p, targets):
+ # Build targets for compute_loss(), input targets(image,class,x,y,w,h)
+ na, nt = self.na, targets.shape[0] # number of anchors, targets
+ tcls, tbox, indices, anch, tidxs, xywhn = [], [], [], [], [], []
+ gain = torch.ones(8, device=self.device) # normalized to gridspace gain
+ ai = torch.arange(na, device=self.device).float().view(na, 1).repeat(1, nt) # same as .repeat_interleave(nt)
+ if self.overlap:
+ batch = p[0].shape[0]
+ ti = []
+ for i in range(batch):
+ num = (targets[:, 0] == i).sum() # find number of targets of each image
+ ti.append(torch.arange(num, device=self.device).float().view(1, num).repeat(na, 1) + 1) # (na, num)
+ ti = torch.cat(ti, 1) # (na, nt)
+ else:
+ ti = torch.arange(nt, device=self.device).float().view(1, nt).repeat(na, 1)
+ targets = torch.cat((targets.repeat(na, 1, 1), ai[..., None], ti[..., None]), 2) # append anchor indices
+
+ g = 0.5 # bias
+ off = torch.tensor(
+ [
+ [0, 0],
+ [1, 0],
+ [0, 1],
+ [-1, 0],
+ [0, -1], # j,k,l,m
+ # [1, 1], [1, -1], [-1, 1], [-1, -1], # jk,jm,lk,lm
+ ],
+ device=self.device).float() * g # offsets
+
+ for i in range(self.nl):
+ anchors, shape = self.anchors[i], p[i].shape
+ gain[2:6] = torch.tensor(shape)[[3, 2, 3, 2]] # xyxy gain
+
+ # Match targets to anchors
+ t = targets * gain # shape(3,n,7)
+ if nt:
+ # Matches
+ r = t[..., 4:6] / anchors[:, None] # wh ratio
+ j = torch.max(r, 1 / r).max(2)[0] < self.hyp['anchor_t'] # compare
+ # j = wh_iou(anchors, t[:, 4:6]) > model.hyp['iou_t'] # iou(3,n)=wh_iou(anchors(3,2), gwh(n,2))
+ t = t[j] # filter
+
+ # Offsets
+ gxy = t[:, 2:4] # grid xy
+ gxi = gain[[2, 3]] - gxy # inverse
+ j, k = ((gxy % 1 < g) & (gxy > 1)).T
+ l, m = ((gxi % 1 < g) & (gxi > 1)).T
+ j = torch.stack((torch.ones_like(j), j, k, l, m))
+ t = t.repeat((5, 1, 1))[j]
+ offsets = (torch.zeros_like(gxy)[None] + off[:, None])[j]
+ else:
+ t = targets[0]
+ offsets = 0
+
+ # Define
+ bc, gxy, gwh, at = t.chunk(4, 1) # (image, class), grid xy, grid wh, anchors
+ (a, tidx), (b, c) = at.long().T, bc.long().T # anchors, image, class
+ gij = (gxy - offsets).long()
+ gi, gj = gij.T # grid indices
+
+ # Append
+ indices.append((b, a, gj.clamp_(0, shape[2] - 1), gi.clamp_(0, shape[3] - 1))) # image, anchor, grid
+ tbox.append(torch.cat((gxy - gij, gwh), 1)) # box
+ anch.append(anchors[a]) # anchors
+ tcls.append(c) # class
+ tidxs.append(tidx)
+ xywhn.append(torch.cat((gxy, gwh), 1) / gain[2:6]) # xywh normalized
+
+ return tcls, tbox, indices, anch, tidxs, xywhn
diff --git a/utils/segment/metrics.py b/utils/segment/metrics.py
new file mode 100644
index 000000000000..c9f137e38ead
--- /dev/null
+++ b/utils/segment/metrics.py
@@ -0,0 +1,210 @@
+# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
+"""
+Model validation metrics
+"""
+
+import numpy as np
+
+from ..metrics import ap_per_class
+
+
+def fitness(x):
+ # Model fitness as a weighted combination of metrics
+ w = [0.0, 0.0, 0.1, 0.9, 0.0, 0.0, 0.1, 0.9]
+ return (x[:, :8] * w).sum(1)
+
+
+def ap_per_class_box_and_mask(
+ tp_m,
+ tp_b,
+ conf,
+ pred_cls,
+ target_cls,
+ plot=False,
+ save_dir='.',
+ names=(),
+):
+ """
+ Args:
+ tp_b: tp of boxes.
+ tp_m: tp of masks.
+ other arguments see `func: ap_per_class`.
+ """
+ results_boxes = ap_per_class(tp_b,
+ conf,
+ pred_cls,
+ target_cls,
+ plot=plot,
+ save_dir=save_dir,
+ names=names,
+ prefix='Box')[2:]
+ results_masks = ap_per_class(tp_m,
+ conf,
+ pred_cls,
+ target_cls,
+ plot=plot,
+ save_dir=save_dir,
+ names=names,
+ prefix='Mask')[2:]
+
+ results = {
+ 'boxes': {
+ 'p': results_boxes[0],
+ 'r': results_boxes[1],
+ 'ap': results_boxes[3],
+ 'f1': results_boxes[2],
+ 'ap_class': results_boxes[4]},
+ 'masks': {
+ 'p': results_masks[0],
+ 'r': results_masks[1],
+ 'ap': results_masks[3],
+ 'f1': results_masks[2],
+ 'ap_class': results_masks[4]}}
+ return results
+
+
+class Metric:
+
+ def __init__(self) -> None:
+ self.p = [] # (nc, )
+ self.r = [] # (nc, )
+ self.f1 = [] # (nc, )
+ self.all_ap = [] # (nc, 10)
+ self.ap_class_index = [] # (nc, )
+
+ @property
+ def ap50(self):
+ """AP@0.5 of all classes.
+ Return:
+ (nc, ) or [].
+ """
+ return self.all_ap[:, 0] if len(self.all_ap) else []
+
+ @property
+ def ap(self):
+ """AP@0.5:0.95
+ Return:
+ (nc, ) or [].
+ """
+ return self.all_ap.mean(1) if len(self.all_ap) else []
+
+ @property
+ def mp(self):
+ """mean precision of all classes.
+ Return:
+ float.
+ """
+ return self.p.mean() if len(self.p) else 0.0
+
+ @property
+ def mr(self):
+ """mean recall of all classes.
+ Return:
+ float.
+ """
+ return self.r.mean() if len(self.r) else 0.0
+
+ @property
+ def map50(self):
+ """Mean AP@0.5 of all classes.
+ Return:
+ float.
+ """
+ return self.all_ap[:, 0].mean() if len(self.all_ap) else 0.0
+
+ @property
+ def map(self):
+ """Mean AP@0.5:0.95 of all classes.
+ Return:
+ float.
+ """
+ return self.all_ap.mean() if len(self.all_ap) else 0.0
+
+ def mean_results(self):
+ """Mean of results, return mp, mr, map50, map"""
+ return (self.mp, self.mr, self.map50, self.map)
+
+ def class_result(self, i):
+ """class-aware result, return p[i], r[i], ap50[i], ap[i]"""
+ return (self.p[i], self.r[i], self.ap50[i], self.ap[i])
+
+ def get_maps(self, nc):
+ maps = np.zeros(nc) + self.map
+ for i, c in enumerate(self.ap_class_index):
+ maps[c] = self.ap[i]
+ return maps
+
+ def update(self, results):
+ """
+ Args:
+ results: tuple(p, r, ap, f1, ap_class)
+ """
+ p, r, all_ap, f1, ap_class_index = results
+ self.p = p
+ self.r = r
+ self.all_ap = all_ap
+ self.f1 = f1
+ self.ap_class_index = ap_class_index
+
+
+class Metrics:
+ """Metric for boxes and masks."""
+
+ def __init__(self) -> None:
+ self.metric_box = Metric()
+ self.metric_mask = Metric()
+
+ def update(self, results):
+ """
+ Args:
+ results: Dict{'boxes': Dict{}, 'masks': Dict{}}
+ """
+ self.metric_box.update(list(results['boxes'].values()))
+ self.metric_mask.update(list(results['masks'].values()))
+
+ def mean_results(self):
+ return self.metric_box.mean_results() + self.metric_mask.mean_results()
+
+ def class_result(self, i):
+ return self.metric_box.class_result(i) + self.metric_mask.class_result(i)
+
+ def get_maps(self, nc):
+ return self.metric_box.get_maps(nc) + self.metric_mask.get_maps(nc)
+
+ @property
+ def ap_class_index(self):
+ # boxes and masks have the same ap_class_index
+ return self.metric_box.ap_class_index
+
+
+KEYS = [
+ 'train/box_loss',
+ 'train/seg_loss', # train loss
+ 'train/obj_loss',
+ 'train/cls_loss',
+ 'metrics/precision(B)',
+ 'metrics/recall(B)',
+ 'metrics/mAP_0.5(B)',
+ 'metrics/mAP_0.5:0.95(B)', # metrics
+ 'metrics/precision(M)',
+ 'metrics/recall(M)',
+ 'metrics/mAP_0.5(M)',
+ 'metrics/mAP_0.5:0.95(M)', # metrics
+ 'val/box_loss',
+ 'val/seg_loss', # val loss
+ 'val/obj_loss',
+ 'val/cls_loss',
+ 'x/lr0',
+ 'x/lr1',
+ 'x/lr2',]
+
+BEST_KEYS = [
+ 'best/epoch',
+ 'best/precision(B)',
+ 'best/recall(B)',
+ 'best/mAP_0.5(B)',
+ 'best/mAP_0.5:0.95(B)',
+ 'best/precision(M)',
+ 'best/recall(M)',
+ 'best/mAP_0.5(M)',
+ 'best/mAP_0.5:0.95(M)',]
diff --git a/utils/segment/plots.py b/utils/segment/plots.py
new file mode 100644
index 000000000000..1b22ec838ac9
--- /dev/null
+++ b/utils/segment/plots.py
@@ -0,0 +1,143 @@
+import contextlib
+import math
+from pathlib import Path
+
+import cv2
+import matplotlib.pyplot as plt
+import numpy as np
+import pandas as pd
+import torch
+
+from .. import threaded
+from ..general import xywh2xyxy
+from ..plots import Annotator, colors
+
+
+@threaded
+def plot_images_and_masks(images, targets, masks, paths=None, fname='images.jpg', names=None):
+ # Plot image grid with labels
+ if isinstance(images, torch.Tensor):
+ images = images.cpu().float().numpy()
+ if isinstance(targets, torch.Tensor):
+ targets = targets.cpu().numpy()
+ if isinstance(masks, torch.Tensor):
+ masks = masks.cpu().numpy().astype(int)
+
+ max_size = 1920 # max image size
+ max_subplots = 16 # max image subplots, i.e. 4x4
+ 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
+ for i, im in enumerate(images):
+ if i == max_subplots: # if last batch has fewer images than we expect
+ break
+ x, y = int(w * (i // ns)), int(h * (i % ns)) # block origin
+ im = im.transpose(1, 2, 0)
+ mosaic[y:y + h, x:x + w, :] = im
+
+ # Resize (optional)
+ scale = max_size / ns / max(h, w)
+ if scale < 1:
+ h = math.ceil(scale * h)
+ w = math.ceil(scale * w)
+ mosaic = cv2.resize(mosaic, tuple(int(x * ns) for x in (w, h)))
+
+ # 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)
+ 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
+ if len(targets) > 0:
+ idx = targets[:, 0] == i
+ ti = targets[idx] # image targets
+
+ boxes = xywh2xyxy(ti[:, 2:6]).T
+ classes = ti[:, 1].astype('int')
+ labels = ti.shape[1] == 6 # labels if no conf column
+ conf = None if labels else ti[:, 6] # check for confidence presence (label vs pred)
+
+ if boxes.shape[1]:
+ if boxes.max() <= 1.01: # if normalized with tolerance 0.01
+ boxes[[0, 2]] *= w # scale to pixels
+ boxes[[1, 3]] *= h
+ elif scale < 1: # absolute coords need scale if image scales
+ boxes *= scale
+ boxes[[0, 2]] += x
+ boxes[[1, 3]] += y
+ for j, box in enumerate(boxes.T.tolist()):
+ cls = classes[j]
+ color = colors(cls)
+ cls = names[cls] if names else cls
+ if labels or conf[j] > 0.25: # 0.25 conf thresh
+ label = f'{cls}' if labels else f'{cls} {conf[j]:.1f}'
+ annotator.box_label(box, label, color=color)
+
+ # Plot masks
+ if len(masks):
+ if masks.max() > 1.0: # mean that masks are overlap
+ image_masks = masks[[i]] # (1, 640, 640)
+ nl = len(ti)
+ index = np.arange(nl).reshape(nl, 1, 1) + 1
+ image_masks = np.repeat(image_masks, nl, axis=0)
+ image_masks = np.where(image_masks == index, 1.0, 0.0)
+ else:
+ image_masks = masks[idx]
+
+ im = np.asarray(annotator.im).copy()
+ for j, box in enumerate(boxes.T.tolist()):
+ if labels or conf[j] > 0.25: # 0.25 conf thresh
+ color = colors(classes[j])
+ mh, mw = image_masks[j].shape
+ if mh != h or mw != w:
+ mask = image_masks[j].astype(np.uint8)
+ mask = cv2.resize(mask, (w, h))
+ mask = mask.astype(bool)
+ else:
+ mask = image_masks[j].astype(bool)
+ with contextlib.suppress(Exception):
+ im[y:y + h, x:x + w, :][mask] = im[y:y + h, x:x + w, :][mask] * 0.4 + np.array(color) * 0.6
+ annotator.fromarray(im)
+ annotator.im.save(fname) # save
+
+
+def plot_results_with_masks(file='path/to/results.csv', dir='', best=True):
+ # Plot training results.csv. Usage: from utils.plots import *; plot_results('path/to/results.csv')
+ save_dir = Path(file).parent if file else Path(dir)
+ fig, ax = plt.subplots(2, 8, figsize=(18, 6), tight_layout=True)
+ 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:
+ try:
+ data = pd.read_csv(f)
+ index = np.argmax(0.9 * data.values[:, 8] + 0.1 * data.values[:, 7] + 0.9 * data.values[:, 12] +
+ 0.1 * data.values[:, 11])
+ s = [x.strip() for x in data.columns]
+ x = data.values[:, 0]
+ for i, j in enumerate([1, 2, 3, 4, 5, 6, 9, 10, 13, 14, 15, 16, 7, 8, 11, 12]):
+ 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=2)
+ if best:
+ # best
+ ax[i].scatter(index, y[index], color='r', label=f'best:{index}', marker='*', linewidth=3)
+ ax[i].set_title(s[j] + f'\n{round(y[index], 5)}')
+ else:
+ # last
+ ax[i].scatter(x[-1], y[-1], color='r', label='last', marker='*', linewidth=3)
+ ax[i].set_title(s[j] + f'\n{round(y[-1], 5)}')
+ # 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:
+ print(f'Warning: Plotting error for {f}: {e}')
+ ax[1].legend()
+ fig.savefig(save_dir / 'results.png', dpi=200)
+ plt.close()
diff --git a/utils/torch_utils.py b/utils/torch_utils.py
index 8a3366ca3e27..5b67b3fa7a06 100644
--- a/utils/torch_utils.py
+++ b/utils/torch_utils.py
@@ -32,6 +32,7 @@
# 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')):
@@ -47,7 +48,7 @@ def smartCrossEntropyLoss(label_smoothing=0.0):
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')
+ LOGGER.warning(f'WARNING ⚠️ label smoothing {label_smoothing} requires torch>=1.10.0')
return nn.CrossEntropyLoss()
@@ -81,7 +82,7 @@ def reshape_classifier_output(model, n=1000):
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)
+ m[i] = nn.Conv2d(m[i].in_channels, n, m[i].kernel_size, m[i].stride, bias=m[i].bias is not None)
@contextmanager
@@ -290,7 +291,7 @@ def model_info(model, verbose=False, imgsz=640):
fs = ''
name = Path(model.yaml_file).stem.replace('yolov5', 'YOLOv5') 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'{name} 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)
@@ -319,12 +320,13 @@ def smart_optimizer(model, name='Adam', lr=0.001, momentum=0.9, decay=1e-5):
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():
- if hasattr(v, 'bias') and isinstance(v.bias, nn.Parameter): # bias (no decay)
- g[2].append(v.bias)
- if isinstance(v, bn): # weight (no decay)
- g[1].append(v.weight)
- elif hasattr(v, 'weight') and isinstance(v.weight, nn.Parameter): # weight (with decay)
- g[0].append(v.weight)
+ 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
@@ -340,7 +342,7 @@ def smart_optimizer(model, name='Adam', lr=0.001, momentum=0.9, decay=1e-5):
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")
+ f'{len(g[1])} weight(decay=0.0), {len(g[0])} weight(decay={decay}), {len(g[2])} bias')
return optimizer
diff --git a/utils/triton.py b/utils/triton.py
new file mode 100644
index 000000000000..25928021477e
--- /dev/null
+++ b/utils/triton.py
@@ -0,0 +1,85 @@
+# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
+""" Utils to interact with the Triton Inference Server
+"""
+
+import typing
+from urllib.parse import urlparse
+
+import torch
+
+
+class TritonRemoteModel:
+ """ A wrapper over a model served by the Triton Inference Server. It can
+ be configured to communicate over GRPC or HTTP. It accepts Torch Tensors
+ as input and returns them as outputs.
+ """
+
+ def __init__(self, url: str):
+ """
+ Keyword arguments:
+ url: Fully qualified address of the Triton server - for e.g. grpc://localhost:8000
+ """
+
+ parsed_url = urlparse(url)
+ if parsed_url.scheme == 'grpc':
+ from tritonclient.grpc import InferenceServerClient, InferInput
+
+ self.client = InferenceServerClient(parsed_url.netloc) # Triton GRPC client
+ model_repository = self.client.get_model_repository_index()
+ self.model_name = model_repository.models[0].name
+ self.metadata = self.client.get_model_metadata(self.model_name, as_json=True)
+
+ def create_input_placeholders() -> typing.List[InferInput]:
+ return [
+ InferInput(i['name'], [int(s) for s in i['shape']], i['datatype']) for i in self.metadata['inputs']]
+
+ else:
+ from tritonclient.http import InferenceServerClient, InferInput
+
+ self.client = InferenceServerClient(parsed_url.netloc) # Triton HTTP client
+ model_repository = self.client.get_model_repository_index()
+ self.model_name = model_repository[0]['name']
+ self.metadata = self.client.get_model_metadata(self.model_name)
+
+ def create_input_placeholders() -> typing.List[InferInput]:
+ return [
+ InferInput(i['name'], [int(s) for s in i['shape']], i['datatype']) for i in self.metadata['inputs']]
+
+ self._create_input_placeholders_fn = create_input_placeholders
+
+ @property
+ def runtime(self):
+ """Returns the model runtime"""
+ return self.metadata.get('backend', self.metadata.get('platform'))
+
+ def __call__(self, *args, **kwargs) -> typing.Union[torch.Tensor, typing.Tuple[torch.Tensor, ...]]:
+ """ Invokes the model. Parameters can be provided via args or kwargs.
+ args, if provided, are assumed to match the order of inputs of the model.
+ kwargs are matched with the model input names.
+ """
+ inputs = self._create_inputs(*args, **kwargs)
+ response = self.client.infer(model_name=self.model_name, inputs=inputs)
+ result = []
+ for output in self.metadata['outputs']:
+ tensor = torch.as_tensor(response.as_numpy(output['name']))
+ result.append(tensor)
+ return result[0] if len(result) == 1 else result
+
+ def _create_inputs(self, *args, **kwargs):
+ args_len, kwargs_len = len(args), len(kwargs)
+ if not args_len and not kwargs_len:
+ raise RuntimeError('No inputs provided.')
+ if args_len and kwargs_len:
+ raise RuntimeError('Cannot specify args and kwargs at the same time')
+
+ placeholders = self._create_input_placeholders_fn()
+ if args_len:
+ if args_len != len(placeholders):
+ raise RuntimeError(f'Expected {len(placeholders)} inputs, got {args_len}.')
+ for input, value in zip(placeholders, args):
+ input.set_data_from_numpy(value.cpu().numpy())
+ else:
+ for input in placeholders:
+ value = kwargs[input.name]
+ input.set_data_from_numpy(value.cpu().numpy())
+ return placeholders
diff --git a/val.py b/val.py
index 4b0bdddae3b1..d4073b42fe78 100644
--- a/val.py
+++ b/val.py
@@ -9,7 +9,7 @@
$ python val.py --weights yolov5s.pt # PyTorch
yolov5s.torchscript # TorchScript
yolov5s.onnx # ONNX Runtime or OpenCV DNN with --dnn
- yolov5s.xml # OpenVINO
+ yolov5s_openvino_model # OpenVINO
yolov5s.engine # TensorRT
yolov5s.mlmodel # CoreML (macOS-only)
yolov5s_saved_model # TensorFlow SavedModel
@@ -22,6 +22,7 @@
import argparse
import json
import os
+import subprocess
import sys
from pathlib import Path
@@ -38,9 +39,9 @@
from models.common import DetectMultiBackend
from utils.callbacks import Callbacks
from utils.dataloaders import create_dataloader
-from utils.general import (LOGGER, Profile, 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.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.plots import output_to_target, plot_images, plot_val_study
from utils.torch_utils import select_device, smart_inference_mode
@@ -71,12 +72,12 @@ def save_one_json(predn, jdict, path, class_map):
def process_batch(detections, labels, iouv):
"""
- Return correct predictions matrix. Both sets of boxes are in (x1, y1, x2, y2) format.
+ Return correct prediction matrix
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)
iou = box_iou(labels[:, 1:], detections[:, :4])
@@ -102,6 +103,7 @@ def run(
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)
@@ -168,8 +170,7 @@ def run(
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 = 0.0 if task in ('speed', 'benchmark') else 0.5
- rect = False if task == 'benchmark' else pt # square inference for benchmarks
+ pad, rect = (0.0, False) if task == 'speed' else (0.5, pt) # square inference for benchmarks
task = task if task in ('train', 'val', 'test') else 'val' # path to train/val/test images
dataloader = create_dataloader(data[task],
imgsz,
@@ -187,12 +188,13 @@ def run(
if isinstance(names, (list, tuple)): # old format
names = dict(enumerate(names))
class_map = coco80_to_coco91_class() if is_coco else list(range(1000))
- s = ('%22s' + '%11s' * 6) % ('Class', 'Images', 'Instances', 'P', 'R', 'mAP@.5', 'mAP@.5:.95')
- dt, p, r, f1, mp, mr, map50, map = (Profile(), Profile(), Profile()), 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0
+ 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
loss = torch.zeros(3, device=device)
jdict, stats, ap, ap_class = [], [], [], []
callbacks.run('on_val_start')
- pbar = tqdm(dataloader, desc=s, bar_format='{l_bar}{bar:10}{r_bar}{bar:-10b}') # progress bar
+ pbar = tqdm(dataloader, desc=s, bar_format=TQDM_BAR_FORMAT) # progress bar
for batch_i, (im, targets, paths, shapes) in enumerate(pbar):
callbacks.run('on_val_batch_start')
with dt[0]:
@@ -205,7 +207,7 @@ def run(
# Inference
with dt[1]:
- out, train_out = model(im) if compute_loss else (model(im, augment=augment), None)
+ preds, train_out = model(im) if compute_loss else (model(im, augment=augment), None)
# Loss
if compute_loss:
@@ -215,10 +217,16 @@ def run(
targets[:, 2:] *= torch.tensor((width, height, width, height), device=device) # to pixels
lb = [targets[targets[:, 0] == i, 1:] for i in range(nb)] if save_hybrid else [] # for autolabelling
with dt[2]:
- out = non_max_suppression(out, conf_thres, iou_thres, labels=lb, multi_label=True, agnostic=single_cls)
+ preds = non_max_suppression(preds,
+ conf_thres,
+ iou_thres,
+ labels=lb,
+ multi_label=True,
+ agnostic=single_cls,
+ max_det=max_det)
# Metrics
- for si, pred in enumerate(out):
+ for si, pred in enumerate(preds):
labels = targets[targets[:, 0] == si, 1:]
nl, npr = labels.shape[0], pred.shape[0] # number of labels, predictions
path, shape = Path(paths[si]), shapes[si][0]
@@ -236,12 +244,12 @@ def run(
if single_cls:
pred[:, 5] = 0
predn = pred.clone()
- scale_coords(im[si].shape[1:], predn[:, :4], shape, shapes[si][1]) # native-space pred
+ scale_boxes(im[si].shape[1:], predn[:, :4], shape, shapes[si][1]) # native-space pred
# Evaluate
if nl:
tbox = xywh2xyxy(labels[:, 1:5]) # target boxes
- scale_coords(im[si].shape[1:], tbox, shape, shapes[si][1]) # native-space labels
+ scale_boxes(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:
@@ -258,9 +266,9 @@ def run(
# 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(out), paths, save_dir / f'val_batch{batch_i}_pred.jpg', names) # pred
+ 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, out)
+ callbacks.run('on_val_batch_end', batch_i, im, targets, paths, shapes, preds)
# Compute metrics
stats = [torch.cat(x, 0).cpu().numpy() for x in zip(*stats)] # to numpy
@@ -274,7 +282,7 @@ def run(
pf = '%22s' + '%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 ⚠️')
+ 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):
@@ -295,14 +303,14 @@ def run(
# 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(data.get('path', '../coco')) / 'annotations/instances_val2017.json') # annotations json
- pred_json = str(save_dir / f"{w}_predictions.json") # predictions json
+ anno_json = str(Path('../datasets/coco/annotations/instances_val2017.json')) # annotations
+ pred_json = str(save_dir / f'{w}_predictions.json') # predictions
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')
+ check_requirements('pycocotools>=2.0.6')
from pycocotools.coco import COCO
from pycocotools.cocoeval import COCOeval
@@ -332,11 +340,12 @@ 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 / 'yolov5s.pt', help='model.pt path(s)')
+ parser.add_argument('--weights', nargs='+', type=str, default=ROOT / 'yolov5s.pt', help='model 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)')
@@ -365,14 +374,14 @@ def main(opt):
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 ⚠️')
+ 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('WARNING ⚠️ --save-hybrid will return high mAP from hybrid labels, not from predictions alone')
run(**vars(opt))
else:
weights = opt.weights if isinstance(opt.weights, list) else [opt.weights]
- opt.half = True # FP16 for fastest results
+ opt.half = torch.cuda.is_available() and opt.device != 'cpu' # 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...
opt.conf_thres, opt.iou_thres, opt.save_json = 0.25, 0.45, False
@@ -389,10 +398,12 @@ 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
- os.system('zip -r study.zip study_*.txt')
+ subprocess.run(['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)