diff --git a/.gitignore b/.gitignore new file mode 100644 index 0000000..ad68280 --- /dev/null +++ b/.gitignore @@ -0,0 +1 @@ +files/output/* diff --git a/files/input/contr_short.mp4 b/files/input/contr_short.mp4 new file mode 100644 index 0000000..c6d713e Binary files /dev/null and b/files/input/contr_short.mp4 differ diff --git a/files/input/corner2.mp4 b/files/input/corner2.mp4 new file mode 100644 index 0000000..eff7b52 Binary files /dev/null and b/files/input/corner2.mp4 differ diff --git a/files/input/frame.PNG b/files/input/frame.PNG new file mode 100644 index 0000000..a1b9e96 Binary files /dev/null and b/files/input/frame.PNG differ diff --git a/files/output/exp/contr_short.mp4 b/files/output/exp/contr_short.mp4 new file mode 100644 index 0000000..21dda41 Binary files /dev/null and b/files/output/exp/contr_short.mp4 differ diff --git a/files/output/frame.png b/files/output/frame.png new file mode 100644 index 0000000..6b7882e Binary files /dev/null and b/files/output/frame.png differ diff --git a/files/output/frame_deep.png b/files/output/frame_deep.png new file mode 100644 index 0000000..420f2bf Binary files /dev/null and b/files/output/frame_deep.png differ diff --git a/yolov5/.gitignore b/yolov5/.gitignore new file mode 100644 index 0000000..91ce33f --- /dev/null +++ b/yolov5/.gitignore @@ -0,0 +1,252 @@ +# Repo-specific GitIgnore ---------------------------------------------------------------------------------------------- +*.jpg +*.jpeg +*.png +*.bmp +*.tif +*.tiff +*.heic +*.JPG +*.JPEG +*.PNG +*.BMP +*.TIF +*.TIFF +*.HEIC +*.mp4 +*.mov +*.MOV +*.avi +*.data +*.json + +*.cfg +!cfg/yolov3*.cfg + +storage.googleapis.com +runs/* +data/* +!data/images/zidane.jpg +!data/images/bus.jpg +!data/coco.names +!data/coco_paper.names +!data/coco.data +!data/coco_*.data +!data/coco_*.txt +!data/trainvalno5k.shapes +!data/*.sh + +pycocotools/* +results*.txt +gcp_test*.sh + +# Datasets ------------------------------------------------------------------------------------------------------------- +coco/ +coco128/ +VOC/ + +# MATLAB GitIgnore ----------------------------------------------------------------------------------------------------- +*.m~ +*.mat +!targets*.mat + +# Neural Network weights ----------------------------------------------------------------------------------------------- +*.weights +*.pt +*.onnx +*.mlmodel +*.torchscript +darknet53.conv.74 +yolov3-tiny.conv.15 + +# GitHub Python GitIgnore ---------------------------------------------------------------------------------------------- +# Byte-compiled / optimized / DLL files +__pycache__/ +*.py[cod] +*$py.class + +# C extensions +*.so + +# Distribution / packaging +.Python +env/ +build/ +develop-eggs/ +dist/ +downloads/ +eggs/ +.eggs/ +lib/ +lib64/ +parts/ +sdist/ +var/ +wheels/ +*.egg-info/ +wandb/ +.installed.cfg +*.egg + + +# PyInstaller +# Usually these files are written by a python script from a template +# before PyInstaller builds the exe, so as to inject date/other infos into it. +*.manifest +*.spec + +# Installer logs +pip-log.txt +pip-delete-this-directory.txt + +# Unit test / coverage reports +htmlcov/ +.tox/ +.coverage +.coverage.* +.cache +nosetests.xml +coverage.xml +*.cover +.hypothesis/ + +# Translations +*.mo +*.pot + +# Django stuff: +*.log +local_settings.py + +# Flask stuff: +instance/ +.webassets-cache + +# Scrapy stuff: +.scrapy + +# Sphinx documentation +docs/_build/ + +# PyBuilder +target/ + +# Jupyter Notebook +.ipynb_checkpoints + +# pyenv +.python-version + +# celery beat schedule file +celerybeat-schedule + +# SageMath parsed files +*.sage.py + +# dotenv +.env + +# virtualenv +.venv* +venv*/ +ENV*/ + +# Spyder project settings +.spyderproject +.spyproject + +# Rope project settings +.ropeproject + +# mkdocs documentation +/site + +# mypy +.mypy_cache/ + + +# https://github.com/github/gitignore/blob/master/Global/macOS.gitignore ----------------------------------------------- + +# General +.DS_Store +.AppleDouble +.LSOverride + +# Icon must end with two \r +Icon +Icon? + +# Thumbnails +._* + +# Files that might appear in the root of a volume +.DocumentRevisions-V100 +.fseventsd +.Spotlight-V100 +.TemporaryItems +.Trashes +.VolumeIcon.icns +.com.apple.timemachine.donotpresent + +# Directories potentially created on remote AFP share +.AppleDB +.AppleDesktop +Network Trash Folder +Temporary Items +.apdisk + + +# https://github.com/github/gitignore/blob/master/Global/JetBrains.gitignore +# Covers JetBrains IDEs: IntelliJ, RubyMine, PhpStorm, AppCode, PyCharm, CLion, Android Studio and WebStorm +# Reference: https://intellij-support.jetbrains.com/hc/en-us/articles/206544839 + +# User-specific stuff: +.idea/* +.idea/**/workspace.xml +.idea/**/tasks.xml +.idea/dictionaries +.html # Bokeh Plots +.pg # TensorFlow Frozen Graphs +.avi # videos + +# Sensitive or high-churn files: +.idea/**/dataSources/ +.idea/**/dataSources.ids +.idea/**/dataSources.local.xml +.idea/**/sqlDataSources.xml +.idea/**/dynamic.xml +.idea/**/uiDesigner.xml + +# Gradle: +.idea/**/gradle.xml +.idea/**/libraries + +# CMake +cmake-build-debug/ +cmake-build-release/ + +# Mongo Explorer plugin: +.idea/**/mongoSettings.xml + +## File-based project format: +*.iws + +## Plugin-specific files: + +# IntelliJ +out/ + +# mpeltonen/sbt-idea plugin +.idea_modules/ + +# JIRA plugin +atlassian-ide-plugin.xml + +# Cursive Clojure plugin +.idea/replstate.xml + +# Crashlytics plugin (for Android Studio and IntelliJ) +com_crashlytics_export_strings.xml +crashlytics.properties +crashlytics-build.properties +fabric.properties diff --git a/yolov5/Dockerfile b/yolov5/Dockerfile new file mode 100644 index 0000000..24529d2 --- /dev/null +++ b/yolov5/Dockerfile @@ -0,0 +1,55 @@ +# Start FROM Nvidia PyTorch image https://ngc.nvidia.com/catalog/containers/nvidia:pytorch +FROM nvcr.io/nvidia/pytorch:20.12-py3 + +# Install linux packages +RUN apt update && apt install -y screen libgl1-mesa-glx + +# Install python dependencies +RUN pip install --upgrade pip +COPY requirements.txt . +RUN pip install -r requirements.txt +RUN pip install gsutil + +# Create working directory +RUN mkdir -p /usr/src/app +WORKDIR /usr/src/app + +# Copy contents +COPY . /usr/src/app + +# Copy weights +#RUN python3 -c "from models import *; \ +#attempt_download('weights/yolov5s.pt'); \ +#attempt_download('weights/yolov5m.pt'); \ +#attempt_download('weights/yolov5l.pt')" + + +# --------------------------------------------------- Extras Below --------------------------------------------------- + +# Build and Push +# t=ultralytics/yolov5:latest && sudo docker build -t $t . && sudo docker push $t +# for v in {300..303}; do t=ultralytics/coco:v$v && sudo docker build -t $t . && sudo docker push $t; done + +# Pull and Run +# t=ultralytics/yolov5:latest && sudo docker pull $t && sudo docker run -it --ipc=host --gpus all $t + +# Pull and Run with local directory access +# t=ultralytics/yolov5:latest && sudo docker pull $t && sudo docker run -it --ipc=host --gpus all -v "$(pwd)"/coco:/usr/src/coco $t + +# Kill all +# sudo docker kill $(sudo docker ps -q) + +# Kill all image-based +# sudo docker kill $(sudo docker ps -a -q --filter ancestor=ultralytics/yolov5:latest) + +# Bash into running container +# sudo docker container exec -it ba65811811ab bash + +# Bash into stopped container +# sudo docker commit 092b16b25c5b usr/resume && sudo docker run -it --gpus all --ipc=host -v "$(pwd)"/coco:/usr/src/coco --entrypoint=sh usr/resume + +# Send weights to GCP +# python -c "from utils.general import *; strip_optimizer('runs/train/exp0_*/weights/best.pt', 'tmp.pt')" && gsutil cp tmp.pt gs://*.pt + +# Clean up +# docker system prune -a --volumes diff --git a/yolov5/deep_sort_pytorch/.gitignore b/yolov5/deep_sort_pytorch/.gitignore new file mode 100644 index 0000000..37ed2f4 --- /dev/null +++ b/yolov5/deep_sort_pytorch/.gitignore @@ -0,0 +1,13 @@ +# Folders +__pycache__/ +build/ +*.egg-info + + +# Files +*.weights +*.t7 +*.mp4 +*.avi +*.so +*.txt diff --git a/yolov5/deep_sort_pytorch/LICENSE b/yolov5/deep_sort_pytorch/LICENSE new file mode 100644 index 0000000..92a1ed5 --- /dev/null +++ b/yolov5/deep_sort_pytorch/LICENSE @@ -0,0 +1,21 @@ +MIT License + +Copyright (c) 2020 Ziqiang + +Permission is hereby granted, free of charge, to any person obtaining a copy +of this software and associated documentation files (the "Software"), to deal +in the Software without restriction, including without limitation the rights +to use, copy, modify, merge, publish, distribute, sublicense, and/or sell +copies of the Software, and to permit persons to whom the Software is +furnished to do so, subject to the following conditions: + +The above copyright notice and this permission notice shall be included in all +copies or substantial portions of the Software. + +THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE +SOFTWARE. \ No newline at end of file diff --git a/yolov5/deep_sort_pytorch/README.md b/yolov5/deep_sort_pytorch/README.md new file mode 100644 index 0000000..6073f80 --- /dev/null +++ b/yolov5/deep_sort_pytorch/README.md @@ -0,0 +1,137 @@ +# Deep Sort with PyTorch + +![](demo/demo.gif) + +## Update(1-1-2020) +Changes +- fix bugs +- refactor code +- accerate detection by adding nms on gpu + +## Latest Update(07-22) +Changes +- bug fix (Thanks @JieChen91 and @yingsen1 for bug reporting). +- using batch for feature extracting for each frame, which lead to a small speed up. +- code improvement. + +Futher improvement direction +- Train detector on specific dataset rather than the official one. +- Retrain REID model on pedestrain dataset for better performance. +- Replace YOLOv3 detector with advanced ones. + +**Any contributions to this repository is welcome!** + + +## Introduction +This is an implement of MOT tracking algorithm deep sort. Deep sort is basicly the same with sort but added a CNN model to extract features in image of human part bounded by a detector. This CNN model is indeed a RE-ID model and the detector used in [PAPER](https://arxiv.org/abs/1703.07402) is FasterRCNN , and the original source code is [HERE](https://github.com/nwojke/deep_sort). +However in original code, the CNN model is implemented with tensorflow, which I'm not familier with. SO I re-implemented the CNN feature extraction model with PyTorch, and changed the CNN model a little bit. Also, I use **YOLOv3** to generate bboxes instead of FasterRCNN. + +## Dependencies +- python 3 (python2 not sure) +- numpy +- scipy +- opencv-python +- sklearn +- torch >= 0.4 +- torchvision >= 0.1 +- pillow +- vizer +- edict + +## Quick Start +0. Check all dependencies installed +```bash +pip install -r requirements.txt +``` +for user in china, you can specify pypi source to accelerate install like: +```bash +pip install -r requirements.txt -i https://pypi.tuna.tsinghua.edu.cn/simple +``` + +1. Clone this repository +``` +git clone git@github.com:ZQPei/deep_sort_pytorch.git +``` + +2. Download YOLOv3 parameters +``` +cd detector/YOLOv3/weight/ +wget https://pjreddie.com/media/files/yolov3.weights +wget https://pjreddie.com/media/files/yolov3-tiny.weights +cd ../../../ +``` + +3. Download deepsort parameters ckpt.t7 +``` +cd deep_sort/deep/checkpoint +# download ckpt.t7 from +https://drive.google.com/drive/folders/1xhG0kRH1EX5B9_Iz8gQJb7UNnn_riXi6 to this folder +cd ../../../ +``` + +4. Compile nms module +```bash +cd detector/YOLOv3/nms +sh build.sh +cd ../../.. +``` + +Notice: +If compiling failed, the simplist way is to **Upgrade your pytorch >= 1.1 and torchvision >= 0.3" and you can avoid the troublesome compiling problems which are most likely caused by either `gcc version too low` or `libraries missing`. + +5. Run demo +``` +usage: python yolov3_deepsort.py VIDEO_PATH + [--help] + [--frame_interval FRAME_INTERVAL] + [--config_detection CONFIG_DETECTION] + [--config_deepsort CONFIG_DEEPSORT] + [--display] + [--display_width DISPLAY_WIDTH] + [--display_height DISPLAY_HEIGHT] + [--save_path SAVE_PATH] + [--cpu] + +# yolov3 + deepsort +python yolov3_deepsort.py [VIDEO_PATH] + +# yolov3_tiny + deepsort +python yolov3_deepsort.py [VIDEO_PATH] --config_detection ./configs/yolov3_tiny.yaml + +# yolov3 + deepsort on webcam +python3 yolov3_deepsort.py /dev/video0 --camera 0 + +# yolov3_tiny + deepsort on webcam +python3 yolov3_deepsort.py /dev/video0 --config_detection ./configs/yolov3_tiny.yaml --camera 0 +``` +Use `--display` to enable display. +Results will be saved to `./output/results.avi` and `./output/results.txt`. + +All files above can also be accessed from BaiduDisk! +linker：[BaiduDisk](https://pan.baidu.com/s/1YJ1iPpdFTlUyLFoonYvozg) +passwd：fbuw + +## Training the RE-ID model +The original model used in paper is in original_model.py, and its parameter here [original_ckpt.t7](https://drive.google.com/drive/folders/1xhG0kRH1EX5B9_Iz8gQJb7UNnn_riXi6). + +To train the model, first you need download [Market1501](http://www.liangzheng.com.cn/Project/project_reid.html) dataset or [Mars](http://www.liangzheng.com.cn/Project/project_mars.html) dataset. + +Then you can try [train.py](deep_sort/deep/train.py) to train your own parameter and evaluate it using [test.py](deep_sort/deep/test.py) and [evaluate.py](deep_sort/deep/evalute.py). +![train.jpg](deep_sort/deep/train.jpg) + +## Demo videos and images +[demo.avi](https://drive.google.com/drive/folders/1xhG0kRH1EX5B9_Iz8gQJb7UNnn_riXi6) +[demo2.avi](https://drive.google.com/drive/folders/1xhG0kRH1EX5B9_Iz8gQJb7UNnn_riXi6) + +![1.jpg](demo/1.jpg) +![2.jpg](demo/2.jpg) + + +## References +- paper: [Simple Online and Realtime Tracking with a Deep Association Metric](https://arxiv.org/abs/1703.07402) + +- code: [nwojke/deep_sort](https://github.com/nwojke/deep_sort) + +- paper: [YOLOv3](https://pjreddie.com/media/files/papers/YOLOv3.pdf) + +- code: [Joseph Redmon/yolov3](https://pjreddie.com/darknet/yolo/) diff --git a/yolov5/deep_sort_pytorch/configs/deep_sort.yaml b/yolov5/deep_sort_pytorch/configs/deep_sort.yaml new file mode 100644 index 0000000..28c6bf5 --- /dev/null +++ b/yolov5/deep_sort_pytorch/configs/deep_sort.yaml @@ -0,0 +1,10 @@ +DEEPSORT: + REID_CKPT: "deep_sort_pytorch/deep_sort/deep/checkpoint/ckpt.t7" + MAX_DIST: 0.3 + MIN_CONFIDENCE: 0.3 + NMS_MAX_OVERLAP: 0.5 + MAX_IOU_DISTANCE: 0.7 + MAX_AGE: 70 + N_INIT: 3 + NN_BUDGET: 100 + diff --git a/yolov5/deep_sort_pytorch/deep_sort/README.md b/yolov5/deep_sort_pytorch/deep_sort/README.md new file mode 100644 index 0000000..e89c9b3 --- /dev/null +++ b/yolov5/deep_sort_pytorch/deep_sort/README.md @@ -0,0 +1,3 @@ +# Deep Sort + +This is the implemention of deep sort with pytorch. \ No newline at end of file diff --git a/yolov5/deep_sort_pytorch/deep_sort/__init__.py b/yolov5/deep_sort_pytorch/deep_sort/__init__.py new file mode 100644 index 0000000..5fe5d0f --- /dev/null +++ b/yolov5/deep_sort_pytorch/deep_sort/__init__.py @@ -0,0 +1,21 @@ +from .deep_sort import DeepSort + + +__all__ = ['DeepSort', 'build_tracker'] + + +def build_tracker(cfg, use_cuda): + return DeepSort(cfg.DEEPSORT.REID_CKPT, + max_dist=cfg.DEEPSORT.MAX_DIST, min_confidence=cfg.DEEPSORT.MIN_CONFIDENCE, + nms_max_overlap=cfg.DEEPSORT.NMS_MAX_OVERLAP, max_iou_distance=cfg.DEEPSORT.MAX_IOU_DISTANCE, + max_age=cfg.DEEPSORT.MAX_AGE, n_init=cfg.DEEPSORT.N_INIT, nn_budget=cfg.DEEPSORT.NN_BUDGET, use_cuda=use_cuda) + + + + + + + + + + diff --git a/yolov5/deep_sort_pytorch/deep_sort/deep/__init__.py b/yolov5/deep_sort_pytorch/deep_sort/deep/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/yolov5/deep_sort_pytorch/deep_sort/deep/checkpoint/.gitkeep b/yolov5/deep_sort_pytorch/deep_sort/deep/checkpoint/.gitkeep new file mode 100644 index 0000000..e69de29 diff --git a/yolov5/deep_sort_pytorch/deep_sort/deep/evaluate.py b/yolov5/deep_sort_pytorch/deep_sort/deep/evaluate.py new file mode 100644 index 0000000..a0458ac --- /dev/null +++ b/yolov5/deep_sort_pytorch/deep_sort/deep/evaluate.py @@ -0,0 +1,13 @@ +import torch + +features = torch.load("features.pth") +qf = features["qf"] +ql = features["ql"] +gf = features["gf"] +gl = features["gl"] + +scores = qf.mm(gf.t()) +res = scores.topk(5, dim=1)[1][:, 0] +top1correct = gl[res].eq(ql).sum().item() + +print("Acc top1:{:.3f}".format(top1correct / ql.size(0))) diff --git a/yolov5/deep_sort_pytorch/deep_sort/deep/feature_extractor.py b/yolov5/deep_sort_pytorch/deep_sort/deep/feature_extractor.py new file mode 100644 index 0000000..d869f2c --- /dev/null +++ b/yolov5/deep_sort_pytorch/deep_sort/deep/feature_extractor.py @@ -0,0 +1,54 @@ +import torch +import torchvision.transforms as transforms +import numpy as np +import cv2 +import logging + +from .model import Net + + +class Extractor(object): + def __init__(self, model_path, use_cuda=True): + self.net = Net(reid=True) + self.device = "cuda" if torch.cuda.is_available() and use_cuda else "cpu" + state_dict = torch.load(model_path, map_location=lambda storage, loc: storage)[ + 'net_dict'] + self.net.load_state_dict(state_dict) + logger = logging.getLogger("root.tracker") + logger.info("Loading weights from {}... Done!".format(model_path)) + self.net.to(self.device) + self.size = (64, 128) + self.norm = transforms.Compose([ + transforms.ToTensor(), + transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225]), + ]) + + def _preprocess(self, im_crops): + """ + TODO: + 1. to float with scale from 0 to 1 + 2. resize to (64, 128) as Market1501 dataset did + 3. concatenate to a numpy array + 3. to torch Tensor + 4. normalize + """ + def _resize(im, size): + return cv2.resize(im.astype(np.float32)/255., size) + + im_batch = torch.cat([self.norm(_resize(im, self.size)).unsqueeze( + 0) for im in im_crops], dim=0).float() + return im_batch + + def __call__(self, im_crops): + im_batch = self._preprocess(im_crops) + with torch.no_grad(): + im_batch = im_batch.to(self.device) + features = self.net(im_batch) + return features.cpu().numpy() + + +if __name__ == '__main__': + img = cv2.imread("demo.jpg")[:, :, (2, 1, 0)] + extr = Extractor("checkpoint/ckpt.t7") + feature = extr(img) + print(feature.shape) diff --git a/yolov5/deep_sort_pytorch/deep_sort/deep/model.py b/yolov5/deep_sort_pytorch/deep_sort/deep/model.py new file mode 100644 index 0000000..b992474 --- /dev/null +++ b/yolov5/deep_sort_pytorch/deep_sort/deep/model.py @@ -0,0 +1,109 @@ +import torch +import torch.nn as nn +import torch.nn.functional as F + + +class BasicBlock(nn.Module): + def __init__(self, c_in, c_out, is_downsample=False): + super(BasicBlock, self).__init__() + self.is_downsample = is_downsample + if is_downsample: + self.conv1 = nn.Conv2d( + c_in, c_out, 3, stride=2, padding=1, bias=False) + else: + self.conv1 = nn.Conv2d( + c_in, c_out, 3, stride=1, padding=1, bias=False) + self.bn1 = nn.BatchNorm2d(c_out) + self.relu = nn.ReLU(True) + self.conv2 = nn.Conv2d(c_out, c_out, 3, stride=1, + padding=1, bias=False) + self.bn2 = nn.BatchNorm2d(c_out) + if is_downsample: + self.downsample = nn.Sequential( + nn.Conv2d(c_in, c_out, 1, stride=2, bias=False), + nn.BatchNorm2d(c_out) + ) + elif c_in != c_out: + self.downsample = nn.Sequential( + nn.Conv2d(c_in, c_out, 1, stride=1, bias=False), + nn.BatchNorm2d(c_out) + ) + self.is_downsample = True + + def forward(self, x): + y = self.conv1(x) + y = self.bn1(y) + y = self.relu(y) + y = self.conv2(y) + y = self.bn2(y) + if self.is_downsample: + x = self.downsample(x) + return F.relu(x.add(y), True) + + +def make_layers(c_in, c_out, repeat_times, is_downsample=False): + blocks = [] + for i in range(repeat_times): + if i == 0: + blocks += [BasicBlock(c_in, c_out, is_downsample=is_downsample), ] + else: + blocks += [BasicBlock(c_out, c_out), ] + return nn.Sequential(*blocks) + + +class Net(nn.Module): + def __init__(self, num_classes=751, reid=False): + super(Net, self).__init__() + # 3 128 64 + self.conv = nn.Sequential( + nn.Conv2d(3, 64, 3, stride=1, padding=1), + nn.BatchNorm2d(64), + nn.ReLU(inplace=True), + # nn.Conv2d(32,32,3,stride=1,padding=1), + # nn.BatchNorm2d(32), + # nn.ReLU(inplace=True), + nn.MaxPool2d(3, 2, padding=1), + ) + # 32 64 32 + self.layer1 = make_layers(64, 64, 2, False) + # 32 64 32 + self.layer2 = make_layers(64, 128, 2, True) + # 64 32 16 + self.layer3 = make_layers(128, 256, 2, True) + # 128 16 8 + self.layer4 = make_layers(256, 512, 2, True) + # 256 8 4 + self.avgpool = nn.AvgPool2d((8, 4), 1) + # 256 1 1 + self.reid = reid + self.classifier = nn.Sequential( + nn.Linear(512, 256), + nn.BatchNorm1d(256), + nn.ReLU(inplace=True), + nn.Dropout(), + nn.Linear(256, num_classes), + ) + + def forward(self, x): + x = self.conv(x) + x = self.layer1(x) + x = self.layer2(x) + x = self.layer3(x) + x = self.layer4(x) + x = self.avgpool(x) + x = x.view(x.size(0), -1) + # B x 128 + if self.reid: + x = x.div(x.norm(p=2, dim=1, keepdim=True)) + return x + # classifier + x = self.classifier(x) + return x + + +if __name__ == '__main__': + net = Net() + x = torch.randn(4, 3, 128, 64) + y = net(x) + import ipdb + ipdb.set_trace() diff --git a/yolov5/deep_sort_pytorch/deep_sort/deep/original_model.py b/yolov5/deep_sort_pytorch/deep_sort/deep/original_model.py new file mode 100644 index 0000000..27734ad --- /dev/null +++ b/yolov5/deep_sort_pytorch/deep_sort/deep/original_model.py @@ -0,0 +1,111 @@ +import torch +import torch.nn as nn +import torch.nn.functional as F + + +class BasicBlock(nn.Module): + def __init__(self, c_in, c_out, is_downsample=False): + super(BasicBlock, self).__init__() + self.is_downsample = is_downsample + if is_downsample: + self.conv1 = nn.Conv2d( + c_in, c_out, 3, stride=2, padding=1, bias=False) + else: + self.conv1 = nn.Conv2d( + c_in, c_out, 3, stride=1, padding=1, bias=False) + self.bn1 = nn.BatchNorm2d(c_out) + self.relu = nn.ReLU(True) + self.conv2 = nn.Conv2d(c_out, c_out, 3, stride=1, + padding=1, bias=False) + self.bn2 = nn.BatchNorm2d(c_out) + if is_downsample: + self.downsample = nn.Sequential( + nn.Conv2d(c_in, c_out, 1, stride=2, bias=False), + nn.BatchNorm2d(c_out) + ) + elif c_in != c_out: + self.downsample = nn.Sequential( + nn.Conv2d(c_in, c_out, 1, stride=1, bias=False), + nn.BatchNorm2d(c_out) + ) + self.is_downsample = True + + def forward(self, x): + y = self.conv1(x) + y = self.bn1(y) + y = self.relu(y) + y = self.conv2(y) + y = self.bn2(y) + if self.is_downsample: + x = self.downsample(x) + return F.relu(x.add(y), True) + + +def make_layers(c_in, c_out, repeat_times, is_downsample=False): + blocks = [] + for i in range(repeat_times): + if i == 0: + blocks += [BasicBlock(c_in, c_out, is_downsample=is_downsample), ] + else: + blocks += [BasicBlock(c_out, c_out), ] + return nn.Sequential(*blocks) + + +class Net(nn.Module): + def __init__(self, num_classes=625, reid=False): + super(Net, self).__init__() + # 3 128 64 + self.conv = nn.Sequential( + nn.Conv2d(3, 32, 3, stride=1, padding=1), + nn.BatchNorm2d(32), + nn.ELU(inplace=True), + nn.Conv2d(32, 32, 3, stride=1, padding=1), + nn.BatchNorm2d(32), + nn.ELU(inplace=True), + nn.MaxPool2d(3, 2, padding=1), + ) + # 32 64 32 + self.layer1 = make_layers(32, 32, 2, False) + # 32 64 32 + self.layer2 = make_layers(32, 64, 2, True) + # 64 32 16 + self.layer3 = make_layers(64, 128, 2, True) + # 128 16 8 + self.dense = nn.Sequential( + nn.Dropout(p=0.6), + nn.Linear(128*16*8, 128), + nn.BatchNorm1d(128), + nn.ELU(inplace=True) + ) + # 256 1 1 + self.reid = reid + self.batch_norm = nn.BatchNorm1d(128) + self.classifier = nn.Sequential( + nn.Linear(128, num_classes), + ) + + def forward(self, x): + x = self.conv(x) + x = self.layer1(x) + x = self.layer2(x) + x = self.layer3(x) + + x = x.view(x.size(0), -1) + if self.reid: + x = self.dense[0](x) + x = self.dense[1](x) + x = x.div(x.norm(p=2, dim=1, keepdim=True)) + return x + x = self.dense(x) + # B x 128 + # classifier + x = self.classifier(x) + return x + + +if __name__ == '__main__': + net = Net(reid=True) + x = torch.randn(4, 3, 128, 64) + y = net(x) + import ipdb + ipdb.set_trace() diff --git a/yolov5/deep_sort_pytorch/deep_sort/deep/test.py b/yolov5/deep_sort_pytorch/deep_sort/deep/test.py new file mode 100644 index 0000000..0ba3050 --- /dev/null +++ b/yolov5/deep_sort_pytorch/deep_sort/deep/test.py @@ -0,0 +1,80 @@ +import torch +import torch.backends.cudnn as cudnn +import torchvision + +import argparse +import os + +from model import Net + +parser = argparse.ArgumentParser(description="Train on market1501") +parser.add_argument("--data-dir", default='data', type=str) +parser.add_argument("--no-cuda", action="store_true") +parser.add_argument("--gpu-id", default=0, type=int) +args = parser.parse_args() + +# device +device = "cuda:{}".format( + args.gpu_id) if torch.cuda.is_available() and not args.no_cuda else "cpu" +if torch.cuda.is_available() and not args.no_cuda: + cudnn.benchmark = True + +# data loader +root = args.data_dir +query_dir = os.path.join(root, "query") +gallery_dir = os.path.join(root, "gallery") +transform = torchvision.transforms.Compose([ + torchvision.transforms.Resize((128, 64)), + torchvision.transforms.ToTensor(), + torchvision.transforms.Normalize( + [0.485, 0.456, 0.406], [0.229, 0.224, 0.225]) +]) +queryloader = torch.utils.data.DataLoader( + torchvision.datasets.ImageFolder(query_dir, transform=transform), + batch_size=64, shuffle=False +) +galleryloader = torch.utils.data.DataLoader( + torchvision.datasets.ImageFolder(gallery_dir, transform=transform), + batch_size=64, shuffle=False +) + +# net definition +net = Net(reid=True) +assert os.path.isfile( + "./checkpoint/ckpt.t7"), "Error: no checkpoint file found!" +print('Loading from checkpoint/ckpt.t7') +checkpoint = torch.load("./checkpoint/ckpt.t7") +net_dict = checkpoint['net_dict'] +net.load_state_dict(net_dict, strict=False) +net.eval() +net.to(device) + +# compute features +query_features = torch.tensor([]).float() +query_labels = torch.tensor([]).long() +gallery_features = torch.tensor([]).float() +gallery_labels = torch.tensor([]).long() + +with torch.no_grad(): + for idx, (inputs, labels) in enumerate(queryloader): + inputs = inputs.to(device) + features = net(inputs).cpu() + query_features = torch.cat((query_features, features), dim=0) + query_labels = torch.cat((query_labels, labels)) + + for idx, (inputs, labels) in enumerate(galleryloader): + inputs = inputs.to(device) + features = net(inputs).cpu() + gallery_features = torch.cat((gallery_features, features), dim=0) + gallery_labels = torch.cat((gallery_labels, labels)) + +gallery_labels -= 2 + +# save features +features = { + "qf": query_features, + "ql": query_labels, + "gf": gallery_features, + "gl": gallery_labels +} +torch.save(features, "features.pth") diff --git a/yolov5/deep_sort_pytorch/deep_sort/deep/train.py b/yolov5/deep_sort_pytorch/deep_sort/deep/train.py new file mode 100644 index 0000000..67f4756 --- /dev/null +++ b/yolov5/deep_sort_pytorch/deep_sort/deep/train.py @@ -0,0 +1,206 @@ +import argparse +import os +import time + +import numpy as np +import matplotlib.pyplot as plt +import torch +import torch.backends.cudnn as cudnn +import torchvision + +from model import Net + +parser = argparse.ArgumentParser(description="Train on market1501") +parser.add_argument("--data-dir", default='data', type=str) +parser.add_argument("--no-cuda", action="store_true") +parser.add_argument("--gpu-id", default=0, type=int) +parser.add_argument("--lr", default=0.1, type=float) +parser.add_argument("--interval", '-i', default=20, type=int) +parser.add_argument('--resume', '-r', action='store_true') +args = parser.parse_args() + +# device +device = "cuda:{}".format( + args.gpu_id) if torch.cuda.is_available() and not args.no_cuda else "cpu" +if torch.cuda.is_available() and not args.no_cuda: + cudnn.benchmark = True + +# data loading +root = args.data_dir +train_dir = os.path.join(root, "train") +test_dir = os.path.join(root, "test") +transform_train = torchvision.transforms.Compose([ + torchvision.transforms.RandomCrop((128, 64), padding=4), + torchvision.transforms.RandomHorizontalFlip(), + torchvision.transforms.ToTensor(), + torchvision.transforms.Normalize( + [0.485, 0.456, 0.406], [0.229, 0.224, 0.225]) +]) +transform_test = torchvision.transforms.Compose([ + torchvision.transforms.Resize((128, 64)), + torchvision.transforms.ToTensor(), + torchvision.transforms.Normalize( + [0.485, 0.456, 0.406], [0.229, 0.224, 0.225]) +]) +trainloader = torch.utils.data.DataLoader( + torchvision.datasets.ImageFolder(train_dir, transform=transform_train), + batch_size=64, shuffle=True +) +testloader = torch.utils.data.DataLoader( + torchvision.datasets.ImageFolder(test_dir, transform=transform_test), + batch_size=64, shuffle=True +) +num_classes = max(len(trainloader.dataset.classes), + len(testloader.dataset.classes)) + +# net definition +start_epoch = 0 +net = Net(num_classes=num_classes) +if args.resume: + assert os.path.isfile( + "./checkpoint/ckpt.t7"), "Error: no checkpoint file found!" + print('Loading from checkpoint/ckpt.t7') + checkpoint = torch.load("./checkpoint/ckpt.t7") + # import ipdb; ipdb.set_trace() + net_dict = checkpoint['net_dict'] + net.load_state_dict(net_dict) + best_acc = checkpoint['acc'] + start_epoch = checkpoint['epoch'] +net.to(device) + +# loss and optimizer +criterion = torch.nn.CrossEntropyLoss() +optimizer = torch.optim.SGD( + net.parameters(), args.lr, momentum=0.9, weight_decay=5e-4) +best_acc = 0. + +# train function for each epoch + + +def train(epoch): + print("\nEpoch : %d" % (epoch+1)) + net.train() + training_loss = 0. + train_loss = 0. + correct = 0 + total = 0 + interval = args.interval + start = time.time() + for idx, (inputs, labels) in enumerate(trainloader): + # forward + inputs, labels = inputs.to(device), labels.to(device) + outputs = net(inputs) + loss = criterion(outputs, labels) + + # backward + optimizer.zero_grad() + loss.backward() + optimizer.step() + + # accumurating + training_loss += loss.item() + train_loss += loss.item() + correct += outputs.max(dim=1)[1].eq(labels).sum().item() + total += labels.size(0) + + # print + if (idx+1) % interval == 0: + end = time.time() + print("[progress:{:.1f}%]time:{:.2f}s Loss:{:.5f} Correct:{}/{} Acc:{:.3f}%".format( + 100.*(idx+1)/len(trainloader), end-start, training_loss / + interval, correct, total, 100.*correct/total + )) + training_loss = 0. + start = time.time() + + return train_loss/len(trainloader), 1. - correct/total + + +def test(epoch): + global best_acc + net.eval() + test_loss = 0. + correct = 0 + total = 0 + start = time.time() + with torch.no_grad(): + for idx, (inputs, labels) in enumerate(testloader): + inputs, labels = inputs.to(device), labels.to(device) + outputs = net(inputs) + loss = criterion(outputs, labels) + + test_loss += loss.item() + correct += outputs.max(dim=1)[1].eq(labels).sum().item() + total += labels.size(0) + + print("Testing ...") + end = time.time() + print("[progress:{:.1f}%]time:{:.2f}s Loss:{:.5f} Correct:{}/{} Acc:{:.3f}%".format( + 100.*(idx+1)/len(testloader), end-start, test_loss / + len(testloader), correct, total, 100.*correct/total + )) + + # saving checkpoint + acc = 100.*correct/total + if acc > best_acc: + best_acc = acc + print("Saving parameters to checkpoint/ckpt.t7") + checkpoint = { + 'net_dict': net.state_dict(), + 'acc': acc, + 'epoch': epoch, + } + if not os.path.isdir('checkpoint'): + os.mkdir('checkpoint') + torch.save(checkpoint, './checkpoint/ckpt.t7') + + return test_loss/len(testloader), 1. - correct/total + + +# plot figure +x_epoch = [] +record = {'train_loss': [], 'train_err': [], 'test_loss': [], 'test_err': []} +fig = plt.figure() +ax0 = fig.add_subplot(121, title="loss") +ax1 = fig.add_subplot(122, title="top1err") + + +def draw_curve(epoch, train_loss, train_err, test_loss, test_err): + global record + record['train_loss'].append(train_loss) + record['train_err'].append(train_err) + record['test_loss'].append(test_loss) + record['test_err'].append(test_err) + + x_epoch.append(epoch) + ax0.plot(x_epoch, record['train_loss'], 'bo-', label='train') + ax0.plot(x_epoch, record['test_loss'], 'ro-', label='val') + ax1.plot(x_epoch, record['train_err'], 'bo-', label='train') + ax1.plot(x_epoch, record['test_err'], 'ro-', label='val') + if epoch == 0: + ax0.legend() + ax1.legend() + fig.savefig("train.jpg") + +# lr decay + + +def lr_decay(): + global optimizer + for params in optimizer.param_groups: + params['lr'] *= 0.1 + lr = params['lr'] + print("Learning rate adjusted to {}".format(lr)) + + +def main(): + for epoch in range(start_epoch, start_epoch+40): + train_loss, train_err = train(epoch) + test_loss, test_err = test(epoch) + draw_curve(epoch, train_loss, train_err, test_loss, test_err) + if (epoch+1) % 20 == 0: + lr_decay() + + +if __name__ == '__main__': + main() diff --git a/yolov5/deep_sort_pytorch/deep_sort/deep_sort.py b/yolov5/deep_sort_pytorch/deep_sort/deep_sort.py new file mode 100644 index 0000000..3f88cb8 --- /dev/null +++ b/yolov5/deep_sort_pytorch/deep_sort/deep_sort.py @@ -0,0 +1,117 @@ +import numpy as np +import torch + +from .deep.feature_extractor import Extractor +from .sort.nn_matching import NearestNeighborDistanceMetric +from .sort.preprocessing import non_max_suppression +from .sort.detection import Detection +from .sort.tracker import Tracker + + +__all__ = ['DeepSort'] + + +class DeepSort(object): + def __init__(self, model_path, max_dist=0.2, min_confidence=0.3, nms_max_overlap=1.0, max_iou_distance=0.7, max_age=70, n_init=3, nn_budget=100, use_cuda=True): + self.min_confidence = min_confidence + self.nms_max_overlap = nms_max_overlap + + self.extractor = Extractor(model_path, use_cuda=use_cuda) + + max_cosine_distance = max_dist + nn_budget = 100 + metric = NearestNeighborDistanceMetric( + "cosine", max_cosine_distance, nn_budget) + self.tracker = Tracker( + metric, max_iou_distance=max_iou_distance, max_age=max_age, n_init=n_init) + + def update(self, bbox_xywh, confidences, ori_img): + self.height, self.width = ori_img.shape[:2] + # generate detections + features = self._get_features(bbox_xywh, ori_img) + bbox_tlwh = self._xywh_to_tlwh(bbox_xywh) + detections = [Detection(bbox_tlwh[i], conf, features[i]) for i, conf in enumerate( + confidences) if conf > self.min_confidence] + + # run on non-maximum supression + boxes = np.array([d.tlwh for d in detections]) + scores = np.array([d.confidence for d in detections]) + indices = non_max_suppression(boxes, self.nms_max_overlap, scores) + detections = [detections[i] for i in indices] + + # update tracker + self.tracker.predict() + self.tracker.update(detections) + + # output bbox identities + outputs = [] + for track in self.tracker.tracks: + if not track.is_confirmed() or track.time_since_update > 1: + continue + box = track.to_tlwh() + x1, y1, x2, y2 = self._tlwh_to_xyxy(box) + track_id = track.track_id + outputs.append(np.array([x1, y1, x2, y2, track_id], dtype=np.int)) + if len(outputs) > 0: + outputs = np.stack(outputs, axis=0) + return outputs + + """ + TODO: + Convert bbox from xc_yc_w_h to xtl_ytl_w_h + Thanks JieChen91@github.com for reporting this bug! + """ + @staticmethod + def _xywh_to_tlwh(bbox_xywh): + if isinstance(bbox_xywh, np.ndarray): + bbox_tlwh = bbox_xywh.copy() + elif isinstance(bbox_xywh, torch.Tensor): + bbox_tlwh = bbox_xywh.clone() + bbox_tlwh[:, 0] = bbox_xywh[:, 0] - bbox_xywh[:, 2] / 2. + bbox_tlwh[:, 1] = bbox_xywh[:, 1] - bbox_xywh[:, 3] / 2. + return bbox_tlwh + + def _xywh_to_xyxy(self, bbox_xywh): + x, y, w, h = bbox_xywh + x1 = max(int(x - w / 2), 0) + x2 = min(int(x + w / 2), self.width - 1) + y1 = max(int(y - h / 2), 0) + y2 = min(int(y + h / 2), self.height - 1) + return x1, y1, x2, y2 + + def _tlwh_to_xyxy(self, bbox_tlwh): + """ + TODO: + Convert bbox from xtl_ytl_w_h to xc_yc_w_h + Thanks JieChen91@github.com for reporting this bug! + """ + x, y, w, h = bbox_tlwh + x1 = max(int(x), 0) + x2 = min(int(x+w), self.width - 1) + y1 = max(int(y), 0) + y2 = min(int(y+h), self.height - 1) + return x1, y1, x2, y2 + + def increment_ages(self): + self.tracker.increment_ages() + + def _xyxy_to_tlwh(self, bbox_xyxy): + x1, y1, x2, y2 = bbox_xyxy + + t = x1 + l = y1 + w = int(x2 - x1) + h = int(y2 - y1) + return t, l, w, h + + def _get_features(self, bbox_xywh, ori_img): + im_crops = [] + for box in bbox_xywh: + x1, y1, x2, y2 = self._xywh_to_xyxy(box) + im = ori_img[y1:y2, x1:x2] + im_crops.append(im) + if im_crops: + features = self.extractor(im_crops) + else: + features = np.array([]) + return features diff --git a/yolov5/deep_sort_pytorch/deep_sort/sort/__init__.py b/yolov5/deep_sort_pytorch/deep_sort/sort/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/yolov5/deep_sort_pytorch/deep_sort/sort/detection.py b/yolov5/deep_sort_pytorch/deep_sort/sort/detection.py new file mode 100644 index 0000000..5c884bb --- /dev/null +++ b/yolov5/deep_sort_pytorch/deep_sort/sort/detection.py @@ -0,0 +1,49 @@ +# vim: expandtab:ts=4:sw=4 +import numpy as np + + +class Detection(object): + """ + This class represents a bounding box detection in a single image. + + Parameters + ---------- + tlwh : array_like + Bounding box in format `(x, y, w, h)`. + confidence : float + Detector confidence score. + feature : array_like + A feature vector that describes the object contained in this image. + + Attributes + ---------- + tlwh : ndarray + Bounding box in format `(top left x, top left y, width, height)`. + confidence : ndarray + Detector confidence score. + feature : ndarray | NoneType + A feature vector that describes the object contained in this image. + + """ + + def __init__(self, tlwh, confidence, feature): + self.tlwh = np.asarray(tlwh, dtype=np.float) + self.confidence = float(confidence) + self.feature = np.asarray(feature, dtype=np.float32) + + def to_tlbr(self): + """Convert bounding box to format `(min x, min y, max x, max y)`, i.e., + `(top left, bottom right)`. + """ + ret = self.tlwh.copy() + ret[2:] += ret[:2] + return ret + + def to_xyah(self): + """Convert bounding box to format `(center x, center y, aspect ratio, + height)`, where the aspect ratio is `width / height`. + """ + ret = self.tlwh.copy() + ret[:2] += ret[2:] / 2 + ret[2] /= ret[3] + return ret diff --git a/yolov5/deep_sort_pytorch/deep_sort/sort/iou_matching.py b/yolov5/deep_sort_pytorch/deep_sort/sort/iou_matching.py new file mode 100644 index 0000000..62d5a3f --- /dev/null +++ b/yolov5/deep_sort_pytorch/deep_sort/sort/iou_matching.py @@ -0,0 +1,82 @@ +# vim: expandtab:ts=4:sw=4 +from __future__ import absolute_import +import numpy as np +from . import linear_assignment + + +def iou(bbox, candidates): + """Computer intersection over union. + + Parameters + ---------- + bbox : ndarray + A bounding box in format `(top left x, top left y, width, height)`. + candidates : ndarray + A matrix of candidate bounding boxes (one per row) in the same format + as `bbox`. + + Returns + ------- + ndarray + The intersection over union in [0, 1] between the `bbox` and each + candidate. A higher score means a larger fraction of the `bbox` is + occluded by the candidate. + + """ + bbox_tl, bbox_br = bbox[:2], bbox[:2] + bbox[2:] + candidates_tl = candidates[:, :2] + candidates_br = candidates[:, :2] + candidates[:, 2:] + + tl = np.c_[np.maximum(bbox_tl[0], candidates_tl[:, 0])[:, np.newaxis], + np.maximum(bbox_tl[1], candidates_tl[:, 1])[:, np.newaxis]] + br = np.c_[np.minimum(bbox_br[0], candidates_br[:, 0])[:, np.newaxis], + np.minimum(bbox_br[1], candidates_br[:, 1])[:, np.newaxis]] + wh = np.maximum(0., br - tl) + + area_intersection = wh.prod(axis=1) + area_bbox = bbox[2:].prod() + area_candidates = candidates[:, 2:].prod(axis=1) + return area_intersection / (area_bbox + area_candidates - area_intersection) + + +def iou_cost(tracks, detections, track_indices=None, + detection_indices=None): + """An intersection over union distance metric. + + Parameters + ---------- + tracks : List[deep_sort.track.Track] + A list of tracks. + detections : List[deep_sort.detection.Detection] + A list of detections. + track_indices : Optional[List[int]] + A list of indices to tracks that should be matched. Defaults to + all `tracks`. + detection_indices : Optional[List[int]] + A list of indices to detections that should be matched. Defaults + to all `detections`. + + Returns + ------- + ndarray + Returns a cost matrix of shape + len(track_indices), len(detection_indices) where entry (i, j) is + `1 - iou(tracks[track_indices[i]], detections[detection_indices[j]])`. + + """ + if track_indices is None: + track_indices = np.arange(len(tracks)) + if detection_indices is None: + detection_indices = np.arange(len(detections)) + + cost_matrix = np.zeros((len(track_indices), len(detection_indices))) + for row, track_idx in enumerate(track_indices): + if tracks[track_idx].time_since_update > 1: + cost_matrix[row, :] = linear_assignment.INFTY_COST + continue + + bbox = tracks[track_idx].to_tlwh() + candidates = np.asarray( + [detections[i].tlwh for i in detection_indices]) + cost_matrix[row, :] = 1. - iou(bbox, candidates) + return cost_matrix diff --git a/yolov5/deep_sort_pytorch/deep_sort/sort/kalman_filter.py b/yolov5/deep_sort_pytorch/deep_sort/sort/kalman_filter.py new file mode 100644 index 0000000..787a76e --- /dev/null +++ b/yolov5/deep_sort_pytorch/deep_sort/sort/kalman_filter.py @@ -0,0 +1,229 @@ +# vim: expandtab:ts=4:sw=4 +import numpy as np +import scipy.linalg + + +""" +Table for the 0.95 quantile of the chi-square distribution with N degrees of +freedom (contains values for N=1, ..., 9). Taken from MATLAB/Octave's chi2inv +function and used as Mahalanobis gating threshold. +""" +chi2inv95 = { + 1: 3.8415, + 2: 5.9915, + 3: 7.8147, + 4: 9.4877, + 5: 11.070, + 6: 12.592, + 7: 14.067, + 8: 15.507, + 9: 16.919} + + +class KalmanFilter(object): + """ + A simple Kalman filter for tracking bounding boxes in image space. + + The 8-dimensional state space + + x, y, a, h, vx, vy, va, vh + + contains the bounding box center position (x, y), aspect ratio a, height h, + and their respective velocities. + + Object motion follows a constant velocity model. The bounding box location + (x, y, a, h) is taken as direct observation of the state space (linear + observation model). + + """ + + def __init__(self): + ndim, dt = 4, 1. + + # Create Kalman filter model matrices. + self._motion_mat = np.eye(2 * ndim, 2 * ndim) + for i in range(ndim): + self._motion_mat[i, ndim + i] = dt + self._update_mat = np.eye(ndim, 2 * ndim) + + # Motion and observation uncertainty are chosen relative to the current + # state estimate. These weights control the amount of uncertainty in + # the model. This is a bit hacky. + self._std_weight_position = 1. / 20 + self._std_weight_velocity = 1. / 160 + + def initiate(self, measurement): + """Create track from unassociated measurement. + + Parameters + ---------- + measurement : ndarray + Bounding box coordinates (x, y, a, h) with center position (x, y), + aspect ratio a, and height h. + + Returns + ------- + (ndarray, ndarray) + Returns the mean vector (8 dimensional) and covariance matrix (8x8 + dimensional) of the new track. Unobserved velocities are initialized + to 0 mean. + + """ + mean_pos = measurement + mean_vel = np.zeros_like(mean_pos) + mean = np.r_[mean_pos, mean_vel] + + std = [ + 2 * self._std_weight_position * measurement[3], + 2 * self._std_weight_position * measurement[3], + 1e-2, + 2 * self._std_weight_position * measurement[3], + 10 * self._std_weight_velocity * measurement[3], + 10 * self._std_weight_velocity * measurement[3], + 1e-5, + 10 * self._std_weight_velocity * measurement[3]] + covariance = np.diag(np.square(std)) + return mean, covariance + + def predict(self, mean, covariance): + """Run Kalman filter prediction step. + + Parameters + ---------- + mean : ndarray + The 8 dimensional mean vector of the object state at the previous + time step. + covariance : ndarray + The 8x8 dimensional covariance matrix of the object state at the + previous time step. + + Returns + ------- + (ndarray, ndarray) + Returns the mean vector and covariance matrix of the predicted + state. Unobserved velocities are initialized to 0 mean. + + """ + std_pos = [ + self._std_weight_position * mean[3], + self._std_weight_position * mean[3], + 1e-2, + self._std_weight_position * mean[3]] + std_vel = [ + self._std_weight_velocity * mean[3], + self._std_weight_velocity * mean[3], + 1e-5, + self._std_weight_velocity * mean[3]] + motion_cov = np.diag(np.square(np.r_[std_pos, std_vel])) + + mean = np.dot(self._motion_mat, mean) + covariance = np.linalg.multi_dot(( + self._motion_mat, covariance, self._motion_mat.T)) + motion_cov + + return mean, covariance + + def project(self, mean, covariance): + """Project state distribution to measurement space. + + Parameters + ---------- + mean : ndarray + The state's mean vector (8 dimensional array). + covariance : ndarray + The state's covariance matrix (8x8 dimensional). + + Returns + ------- + (ndarray, ndarray) + Returns the projected mean and covariance matrix of the given state + estimate. + + """ + std = [ + self._std_weight_position * mean[3], + self._std_weight_position * mean[3], + 1e-1, + self._std_weight_position * mean[3]] + innovation_cov = np.diag(np.square(std)) + + mean = np.dot(self._update_mat, mean) + covariance = np.linalg.multi_dot(( + self._update_mat, covariance, self._update_mat.T)) + return mean, covariance + innovation_cov + + def update(self, mean, covariance, measurement): + """Run Kalman filter correction step. + + Parameters + ---------- + mean : ndarray + The predicted state's mean vector (8 dimensional). + covariance : ndarray + The state's covariance matrix (8x8 dimensional). + measurement : ndarray + The 4 dimensional measurement vector (x, y, a, h), where (x, y) + is the center position, a the aspect ratio, and h the height of the + bounding box. + + Returns + ------- + (ndarray, ndarray) + Returns the measurement-corrected state distribution. + + """ + projected_mean, projected_cov = self.project(mean, covariance) + + chol_factor, lower = scipy.linalg.cho_factor( + projected_cov, lower=True, check_finite=False) + kalman_gain = scipy.linalg.cho_solve( + (chol_factor, lower), np.dot(covariance, self._update_mat.T).T, + check_finite=False).T + innovation = measurement - projected_mean + + new_mean = mean + np.dot(innovation, kalman_gain.T) + new_covariance = covariance - np.linalg.multi_dot(( + kalman_gain, projected_cov, kalman_gain.T)) + return new_mean, new_covariance + + def gating_distance(self, mean, covariance, measurements, + only_position=False): + """Compute gating distance between state distribution and measurements. + + A suitable distance threshold can be obtained from `chi2inv95`. If + `only_position` is False, the chi-square distribution has 4 degrees of + freedom, otherwise 2. + + Parameters + ---------- + mean : ndarray + Mean vector over the state distribution (8 dimensional). + covariance : ndarray + Covariance of the state distribution (8x8 dimensional). + measurements : ndarray + An Nx4 dimensional matrix of N measurements, each in + format (x, y, a, h) where (x, y) is the bounding box center + position, a the aspect ratio, and h the height. + only_position : Optional[bool] + If True, distance computation is done with respect to the bounding + box center position only. + + Returns + ------- + ndarray + Returns an array of length N, where the i-th element contains the + squared Mahalanobis distance between (mean, covariance) and + `measurements[i]`. + + """ + mean, covariance = self.project(mean, covariance) + if only_position: + mean, covariance = mean[:2], covariance[:2, :2] + measurements = measurements[:, :2] + + cholesky_factor = np.linalg.cholesky(covariance) + d = measurements - mean + z = scipy.linalg.solve_triangular( + cholesky_factor, d.T, lower=True, check_finite=False, + overwrite_b=True) + squared_maha = np.sum(z * z, axis=0) + return squared_maha diff --git a/yolov5/deep_sort_pytorch/deep_sort/sort/linear_assignment.py b/yolov5/deep_sort_pytorch/deep_sort/sort/linear_assignment.py new file mode 100644 index 0000000..858b71a --- /dev/null +++ b/yolov5/deep_sort_pytorch/deep_sort/sort/linear_assignment.py @@ -0,0 +1,192 @@ +# vim: expandtab:ts=4:sw=4 +from __future__ import absolute_import +import numpy as np +# from sklearn.utils.linear_assignment_ import linear_assignment +from scipy.optimize import linear_sum_assignment as linear_assignment +from . import kalman_filter + + +INFTY_COST = 1e+5 + + +def min_cost_matching( + distance_metric, max_distance, tracks, detections, track_indices=None, + detection_indices=None): + """Solve linear assignment problem. + + Parameters + ---------- + distance_metric : Callable[List[Track], List[Detection], List[int], List[int]) -> ndarray + The distance metric is given a list of tracks and detections as well as + a list of N track indices and M detection indices. The metric should + return the NxM dimensional cost matrix, where element (i, j) is the + association cost between the i-th track in the given track indices and + the j-th detection in the given detection_indices. + max_distance : float + Gating threshold. Associations with cost larger than this value are + disregarded. + tracks : List[track.Track] + A list of predicted tracks at the current time step. + detections : List[detection.Detection] + A list of detections at the current time step. + track_indices : List[int] + List of track indices that maps rows in `cost_matrix` to tracks in + `tracks` (see description above). + detection_indices : List[int] + List of detection indices that maps columns in `cost_matrix` to + detections in `detections` (see description above). + + Returns + ------- + (List[(int, int)], List[int], List[int]) + Returns a tuple with the following three entries: + * A list of matched track and detection indices. + * A list of unmatched track indices. + * A list of unmatched detection indices. + + """ + if track_indices is None: + track_indices = np.arange(len(tracks)) + if detection_indices is None: + detection_indices = np.arange(len(detections)) + + if len(detection_indices) == 0 or len(track_indices) == 0: + return [], track_indices, detection_indices # Nothing to match. + + cost_matrix = distance_metric( + tracks, detections, track_indices, detection_indices) + cost_matrix[cost_matrix > max_distance] = max_distance + 1e-5 + + row_indices, col_indices = linear_assignment(cost_matrix) + + matches, unmatched_tracks, unmatched_detections = [], [], [] + for col, detection_idx in enumerate(detection_indices): + if col not in col_indices: + unmatched_detections.append(detection_idx) + for row, track_idx in enumerate(track_indices): + if row not in row_indices: + unmatched_tracks.append(track_idx) + for row, col in zip(row_indices, col_indices): + track_idx = track_indices[row] + detection_idx = detection_indices[col] + if cost_matrix[row, col] > max_distance: + unmatched_tracks.append(track_idx) + unmatched_detections.append(detection_idx) + else: + matches.append((track_idx, detection_idx)) + return matches, unmatched_tracks, unmatched_detections + + +def matching_cascade( + distance_metric, max_distance, cascade_depth, tracks, detections, + track_indices=None, detection_indices=None): + """Run matching cascade. + + Parameters + ---------- + distance_metric : Callable[List[Track], List[Detection], List[int], List[int]) -> ndarray + The distance metric is given a list of tracks and detections as well as + a list of N track indices and M detection indices. The metric should + return the NxM dimensional cost matrix, where element (i, j) is the + association cost between the i-th track in the given track indices and + the j-th detection in the given detection indices. + max_distance : float + Gating threshold. Associations with cost larger than this value are + disregarded. + cascade_depth: int + The cascade depth, should be se to the maximum track age. + tracks : List[track.Track] + A list of predicted tracks at the current time step. + detections : List[detection.Detection] + A list of detections at the current time step. + track_indices : Optional[List[int]] + List of track indices that maps rows in `cost_matrix` to tracks in + `tracks` (see description above). Defaults to all tracks. + detection_indices : Optional[List[int]] + List of detection indices that maps columns in `cost_matrix` to + detections in `detections` (see description above). Defaults to all + detections. + + Returns + ------- + (List[(int, int)], List[int], List[int]) + Returns a tuple with the following three entries: + * A list of matched track and detection indices. + * A list of unmatched track indices. + * A list of unmatched detection indices. + + """ + if track_indices is None: + track_indices = list(range(len(tracks))) + if detection_indices is None: + detection_indices = list(range(len(detections))) + + unmatched_detections = detection_indices + matches = [] + for level in range(cascade_depth): + if len(unmatched_detections) == 0: # No detections left + break + + track_indices_l = [ + k for k in track_indices + if tracks[k].time_since_update == 1 + level + ] + if len(track_indices_l) == 0: # Nothing to match at this level + continue + + matches_l, _, unmatched_detections = \ + min_cost_matching( + distance_metric, max_distance, tracks, detections, + track_indices_l, unmatched_detections) + matches += matches_l + unmatched_tracks = list(set(track_indices) - set(k for k, _ in matches)) + return matches, unmatched_tracks, unmatched_detections + + +def gate_cost_matrix( + kf, cost_matrix, tracks, detections, track_indices, detection_indices, + gated_cost=INFTY_COST, only_position=False): + """Invalidate infeasible entries in cost matrix based on the state + distributions obtained by Kalman filtering. + + Parameters + ---------- + kf : The Kalman filter. + cost_matrix : ndarray + The NxM dimensional cost matrix, where N is the number of track indices + and M is the number of detection indices, such that entry (i, j) is the + association cost between `tracks[track_indices[i]]` and + `detections[detection_indices[j]]`. + tracks : List[track.Track] + A list of predicted tracks at the current time step. + detections : List[detection.Detection] + A list of detections at the current time step. + track_indices : List[int] + List of track indices that maps rows in `cost_matrix` to tracks in + `tracks` (see description above). + detection_indices : List[int] + List of detection indices that maps columns in `cost_matrix` to + detections in `detections` (see description above). + gated_cost : Optional[float] + Entries in the cost matrix corresponding to infeasible associations are + set this value. Defaults to a very large value. + only_position : Optional[bool] + If True, only the x, y position of the state distribution is considered + during gating. Defaults to False. + + Returns + ------- + ndarray + Returns the modified cost matrix. + + """ + gating_dim = 2 if only_position else 4 + gating_threshold = kalman_filter.chi2inv95[gating_dim] + measurements = np.asarray( + [detections[i].to_xyah() for i in detection_indices]) + for row, track_idx in enumerate(track_indices): + track = tracks[track_idx] + gating_distance = kf.gating_distance( + track.mean, track.covariance, measurements, only_position) + cost_matrix[row, gating_distance > gating_threshold] = gated_cost + return cost_matrix diff --git a/yolov5/deep_sort_pytorch/deep_sort/sort/nn_matching.py b/yolov5/deep_sort_pytorch/deep_sort/sort/nn_matching.py new file mode 100644 index 0000000..21e5b4f --- /dev/null +++ b/yolov5/deep_sort_pytorch/deep_sort/sort/nn_matching.py @@ -0,0 +1,176 @@ +# vim: expandtab:ts=4:sw=4 +import numpy as np + + +def _pdist(a, b): + """Compute pair-wise squared distance between points in `a` and `b`. + + Parameters + ---------- + a : array_like + An NxM matrix of N samples of dimensionality M. + b : array_like + An LxM matrix of L samples of dimensionality M. + + Returns + ------- + ndarray + Returns a matrix of size len(a), len(b) such that eleement (i, j) + contains the squared distance between `a[i]` and `b[j]`. + + """ + a, b = np.asarray(a), np.asarray(b) + if len(a) == 0 or len(b) == 0: + return np.zeros((len(a), len(b))) + a2, b2 = np.square(a).sum(axis=1), np.square(b).sum(axis=1) + r2 = -2. * np.dot(a, b.T) + a2[:, None] + b2[None, :] + r2 = np.clip(r2, 0., float(np.inf)) + return r2 + + +def _cosine_distance(a, b, data_is_normalized=False): + """Compute pair-wise cosine distance between points in `a` and `b`. + + Parameters + ---------- + a : array_like + An NxM matrix of N samples of dimensionality M. + b : array_like + An LxM matrix of L samples of dimensionality M. + data_is_normalized : Optional[bool] + If True, assumes rows in a and b are unit length vectors. + Otherwise, a and b are explicitly normalized to lenght 1. + + Returns + ------- + ndarray + Returns a matrix of size len(a), len(b) such that eleement (i, j) + contains the squared distance between `a[i]` and `b[j]`. + + """ + if not data_is_normalized: + a = np.asarray(a) / np.linalg.norm(a, axis=1, keepdims=True) + b = np.asarray(b) / np.linalg.norm(b, axis=1, keepdims=True) + return 1. - np.dot(a, b.T) + + +def _nn_euclidean_distance(x, y): + """ Helper function for nearest neighbor distance metric (Euclidean). + + Parameters + ---------- + x : ndarray + A matrix of N row-vectors (sample points). + y : ndarray + A matrix of M row-vectors (query points). + + Returns + ------- + ndarray + A vector of length M that contains for each entry in `y` the + smallest Euclidean distance to a sample in `x`. + + """ + distances = _pdist(x, y) + return np.maximum(0.0, distances.min(axis=0)) + + +def _nn_cosine_distance(x, y): + """ Helper function for nearest neighbor distance metric (cosine). + + Parameters + ---------- + x : ndarray + A matrix of N row-vectors (sample points). + y : ndarray + A matrix of M row-vectors (query points). + + Returns + ------- + ndarray + A vector of length M that contains for each entry in `y` the + smallest cosine distance to a sample in `x`. + + """ + distances = _cosine_distance(x, y) + return distances.min(axis=0) + + +class NearestNeighborDistanceMetric(object): + """ + A nearest neighbor distance metric that, for each target, returns + the closest distance to any sample that has been observed so far. + + Parameters + ---------- + metric : str + Either "euclidean" or "cosine". + matching_threshold: float + The matching threshold. Samples with larger distance are considered an + invalid match. + budget : Optional[int] + If not None, fix samples per class to at most this number. Removes + the oldest samples when the budget is reached. + + Attributes + ---------- + samples : Dict[int -> List[ndarray]] + A dictionary that maps from target identities to the list of samples + that have been observed so far. + + """ + + def __init__(self, metric, matching_threshold, budget=None): + + if metric == "euclidean": + self._metric = _nn_euclidean_distance + elif metric == "cosine": + self._metric = _nn_cosine_distance + else: + raise ValueError( + "Invalid metric; must be either 'euclidean' or 'cosine'") + self.matching_threshold = matching_threshold + self.budget = budget + self.samples = {} + + def partial_fit(self, features, targets, active_targets): + """Update the distance metric with new data. + + Parameters + ---------- + features : ndarray + An NxM matrix of N features of dimensionality M. + targets : ndarray + An integer array of associated target identities. + active_targets : List[int] + A list of targets that are currently present in the scene. + + """ + for feature, target in zip(features, targets): + self.samples.setdefault(target, []).append(feature) + if self.budget is not None: + self.samples[target] = self.samples[target][-self.budget:] + self.samples = {k: self.samples[k] for k in active_targets} + + def distance(self, features, targets): + """Compute distance between features and targets. + + Parameters + ---------- + features : ndarray + An NxM matrix of N features of dimensionality M. + targets : List[int] + A list of targets to match the given `features` against. + + Returns + ------- + ndarray + Returns a cost matrix of shape len(targets), len(features), where + element (i, j) contains the closest squared distance between + `targets[i]` and `features[j]`. + + """ + cost_matrix = np.zeros((len(targets), len(features))) + for i, target in enumerate(targets): + cost_matrix[i, :] = self._metric(self.samples[target], features) + return cost_matrix diff --git a/yolov5/deep_sort_pytorch/deep_sort/sort/preprocessing.py b/yolov5/deep_sort_pytorch/deep_sort/sort/preprocessing.py new file mode 100644 index 0000000..5493b12 --- /dev/null +++ b/yolov5/deep_sort_pytorch/deep_sort/sort/preprocessing.py @@ -0,0 +1,73 @@ +# vim: expandtab:ts=4:sw=4 +import numpy as np +import cv2 + + +def non_max_suppression(boxes, max_bbox_overlap, scores=None): + """Suppress overlapping detections. + + Original code from [1]_ has been adapted to include confidence score. + + .. [1] http://www.pyimagesearch.com/2015/02/16/ + faster-non-maximum-suppression-python/ + + Examples + -------- + + >>> boxes = [d.roi for d in detections] + >>> scores = [d.confidence for d in detections] + >>> indices = non_max_suppression(boxes, max_bbox_overlap, scores) + >>> detections = [detections[i] for i in indices] + + Parameters + ---------- + boxes : ndarray + Array of ROIs (x, y, width, height). + max_bbox_overlap : float + ROIs that overlap more than this values are suppressed. + scores : Optional[array_like] + Detector confidence score. + + Returns + ------- + List[int] + Returns indices of detections that have survived non-maxima suppression. + + """ + if len(boxes) == 0: + return [] + + boxes = boxes.astype(np.float) + pick = [] + + x1 = boxes[:, 0] + y1 = boxes[:, 1] + x2 = boxes[:, 2] + boxes[:, 0] + y2 = boxes[:, 3] + boxes[:, 1] + + area = (x2 - x1 + 1) * (y2 - y1 + 1) + if scores is not None: + idxs = np.argsort(scores) + else: + idxs = np.argsort(y2) + + while len(idxs) > 0: + last = len(idxs) - 1 + i = idxs[last] + pick.append(i) + + xx1 = np.maximum(x1[i], x1[idxs[:last]]) + yy1 = np.maximum(y1[i], y1[idxs[:last]]) + xx2 = np.minimum(x2[i], x2[idxs[:last]]) + yy2 = np.minimum(y2[i], y2[idxs[:last]]) + + w = np.maximum(0, xx2 - xx1 + 1) + h = np.maximum(0, yy2 - yy1 + 1) + + overlap = (w * h) / area[idxs[:last]] + + idxs = np.delete( + idxs, np.concatenate( + ([last], np.where(overlap > max_bbox_overlap)[0]))) + + return pick diff --git a/yolov5/deep_sort_pytorch/deep_sort/sort/track.py b/yolov5/deep_sort_pytorch/deep_sort/sort/track.py new file mode 100644 index 0000000..1848e55 --- /dev/null +++ b/yolov5/deep_sort_pytorch/deep_sort/sort/track.py @@ -0,0 +1,169 @@ +# vim: expandtab:ts=4:sw=4 + + +class TrackState: + """ + Enumeration type for the single target track state. Newly created tracks are + classified as `tentative` until enough evidence has been collected. Then, + the track state is changed to `confirmed`. Tracks that are no longer alive + are classified as `deleted` to mark them for removal from the set of active + tracks. + + """ + + Tentative = 1 + Confirmed = 2 + Deleted = 3 + + +class Track: + """ + A single target track with state space `(x, y, a, h)` and associated + velocities, where `(x, y)` is the center of the bounding box, `a` is the + aspect ratio and `h` is the height. + + Parameters + ---------- + mean : ndarray + Mean vector of the initial state distribution. + covariance : ndarray + Covariance matrix of the initial state distribution. + track_id : int + A unique track identifier. + n_init : int + Number of consecutive detections before the track is confirmed. The + track state is set to `Deleted` if a miss occurs within the first + `n_init` frames. + max_age : int + The maximum number of consecutive misses before the track state is + set to `Deleted`. + feature : Optional[ndarray] + Feature vector of the detection this track originates from. If not None, + this feature is added to the `features` cache. + + Attributes + ---------- + mean : ndarray + Mean vector of the initial state distribution. + covariance : ndarray + Covariance matrix of the initial state distribution. + track_id : int + A unique track identifier. + hits : int + Total number of measurement updates. + age : int + Total number of frames since first occurance. + time_since_update : int + Total number of frames since last measurement update. + state : TrackState + The current track state. + features : List[ndarray] + A cache of features. On each measurement update, the associated feature + vector is added to this list. + + """ + + def __init__(self, mean, covariance, track_id, n_init, max_age, + feature=None): + self.mean = mean + self.covariance = covariance + self.track_id = track_id + self.hits = 1 + self.age = 1 + self.time_since_update = 0 + + self.state = TrackState.Tentative + self.features = [] + if feature is not None: + self.features.append(feature) + + self._n_init = n_init + self._max_age = max_age + + def to_tlwh(self): + """Get current position in bounding box format `(top left x, top left y, + width, height)`. + + Returns + ------- + ndarray + The bounding box. + + """ + ret = self.mean[:4].copy() + ret[2] *= ret[3] + ret[:2] -= ret[2:] / 2 + return ret + + def to_tlbr(self): + """Get current position in bounding box format `(min x, miny, max x, + max y)`. + + Returns + ------- + ndarray + The bounding box. + + """ + ret = self.to_tlwh() + ret[2:] = ret[:2] + ret[2:] + return ret + + def increment_age(self): + self.age += 1 + self.time_since_update += 1 + + def predict(self, kf): + """Propagate the state distribution to the current time step using a + Kalman filter prediction step. + + Parameters + ---------- + kf : kalman_filter.KalmanFilter + The Kalman filter. + + """ + self.mean, self.covariance = kf.predict(self.mean, self.covariance) + self.increment_age() + + def update(self, kf, detection): + """Perform Kalman filter measurement update step and update the feature + cache. + + Parameters + ---------- + kf : kalman_filter.KalmanFilter + The Kalman filter. + detection : Detection + The associated detection. + + """ + self.mean, self.covariance = kf.update( + self.mean, self.covariance, detection.to_xyah()) + self.features.append(detection.feature) + + self.hits += 1 + self.time_since_update = 0 + if self.state == TrackState.Tentative and self.hits >= self._n_init: + self.state = TrackState.Confirmed + + def mark_missed(self): + """Mark this track as missed (no association at the current time step). + """ + if self.state == TrackState.Tentative: + self.state = TrackState.Deleted + elif self.time_since_update > self._max_age: + self.state = TrackState.Deleted + + def is_tentative(self): + """Returns True if this track is tentative (unconfirmed). + """ + return self.state == TrackState.Tentative + + def is_confirmed(self): + """Returns True if this track is confirmed.""" + return self.state == TrackState.Confirmed + + def is_deleted(self): + """Returns True if this track is dead and should be deleted.""" + return self.state == TrackState.Deleted diff --git a/yolov5/deep_sort_pytorch/deep_sort/sort/tracker.py b/yolov5/deep_sort_pytorch/deep_sort/sort/tracker.py new file mode 100644 index 0000000..d422e1b --- /dev/null +++ b/yolov5/deep_sort_pytorch/deep_sort/sort/tracker.py @@ -0,0 +1,143 @@ +# vim: expandtab:ts=4:sw=4 +from __future__ import absolute_import +import numpy as np +from . import kalman_filter +from . import linear_assignment +from . import iou_matching +from .track import Track + + +class Tracker: + """ + This is the multi-target tracker. + + Parameters + ---------- + metric : nn_matching.NearestNeighborDistanceMetric + A distance metric for measurement-to-track association. + max_age : int + Maximum number of missed misses before a track is deleted. + n_init : int + Number of consecutive detections before the track is confirmed. The + track state is set to `Deleted` if a miss occurs within the first + `n_init` frames. + + Attributes + ---------- + metric : nn_matching.NearestNeighborDistanceMetric + The distance metric used for measurement to track association. + max_age : int + Maximum number of missed misses before a track is deleted. + n_init : int + Number of frames that a track remains in initialization phase. + kf : kalman_filter.KalmanFilter + A Kalman filter to filter target trajectories in image space. + tracks : List[Track] + The list of active tracks at the current time step. + + """ + + def __init__(self, metric, max_iou_distance=0.7, max_age=70, n_init=3): + self.metric = metric + self.max_iou_distance = max_iou_distance + self.max_age = max_age + self.n_init = n_init + + self.kf = kalman_filter.KalmanFilter() + self.tracks = [] + self._next_id = 1 + + def predict(self): + """Propagate track state distributions one time step forward. + + This function should be called once every time step, before `update`. + """ + for track in self.tracks: + track.predict(self.kf) + + def increment_ages(self): + for track in self.tracks: + track.increment_age() + track.mark_missed() + + def update(self, detections): + """Perform measurement update and track management. + + Parameters + ---------- + detections : List[deep_sort.detection.Detection] + A list of detections at the current time step. + + """ + # Run matching cascade. + matches, unmatched_tracks, unmatched_detections = \ + self._match(detections) + + # Update track set. + for track_idx, detection_idx in matches: + self.tracks[track_idx].update( + self.kf, detections[detection_idx]) + for track_idx in unmatched_tracks: + self.tracks[track_idx].mark_missed() + for detection_idx in unmatched_detections: + self._initiate_track(detections[detection_idx]) + self.tracks = [t for t in self.tracks if not t.is_deleted()] + + # Update distance metric. + active_targets = [t.track_id for t in self.tracks if t.is_confirmed()] + features, targets = [], [] + for track in self.tracks: + if not track.is_confirmed(): + continue + features += track.features + targets += [track.track_id for _ in track.features] + track.features = [] + self.metric.partial_fit( + np.asarray(features), np.asarray(targets), active_targets) + + def _match(self, detections): + + def gated_metric(tracks, dets, track_indices, detection_indices): + features = np.array([dets[i].feature for i in detection_indices]) + targets = np.array([tracks[i].track_id for i in track_indices]) + cost_matrix = self.metric.distance(features, targets) + cost_matrix = linear_assignment.gate_cost_matrix( + self.kf, cost_matrix, tracks, dets, track_indices, + detection_indices) + + return cost_matrix + + # Split track set into confirmed and unconfirmed tracks. + confirmed_tracks = [ + i for i, t in enumerate(self.tracks) if t.is_confirmed()] + unconfirmed_tracks = [ + i for i, t in enumerate(self.tracks) if not t.is_confirmed()] + + # Associate confirmed tracks using appearance features. + matches_a, unmatched_tracks_a, unmatched_detections = \ + linear_assignment.matching_cascade( + gated_metric, self.metric.matching_threshold, self.max_age, + self.tracks, detections, confirmed_tracks) + + # Associate remaining tracks together with unconfirmed tracks using IOU. + iou_track_candidates = unconfirmed_tracks + [ + k for k in unmatched_tracks_a if + self.tracks[k].time_since_update == 1] + unmatched_tracks_a = [ + k for k in unmatched_tracks_a if + self.tracks[k].time_since_update != 1] + matches_b, unmatched_tracks_b, unmatched_detections = \ + linear_assignment.min_cost_matching( + iou_matching.iou_cost, self.max_iou_distance, self.tracks, + detections, iou_track_candidates, unmatched_detections) + + matches = matches_a + matches_b + unmatched_tracks = list(set(unmatched_tracks_a + unmatched_tracks_b)) + return matches, unmatched_tracks, unmatched_detections + + def _initiate_track(self, detection): + mean, covariance = self.kf.initiate(detection.to_xyah()) + self.tracks.append(Track( + mean, covariance, self._next_id, self.n_init, self.max_age, + detection.feature)) + self._next_id += 1 diff --git a/yolov5/deep_sort_pytorch/utils/__init__.py b/yolov5/deep_sort_pytorch/utils/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/yolov5/deep_sort_pytorch/utils/asserts.py b/yolov5/deep_sort_pytorch/utils/asserts.py new file mode 100644 index 0000000..59a73cc --- /dev/null +++ b/yolov5/deep_sort_pytorch/utils/asserts.py @@ -0,0 +1,13 @@ +from os import environ + + +def assert_in(file, files_to_check): + if file not in files_to_check: + raise AssertionError("{} does not exist in the list".format(str(file))) + return True + + +def assert_in_env(check_list: list): + for item in check_list: + assert_in(item, environ.keys()) + return True diff --git a/yolov5/deep_sort_pytorch/utils/draw.py b/yolov5/deep_sort_pytorch/utils/draw.py new file mode 100644 index 0000000..bc7cb53 --- /dev/null +++ b/yolov5/deep_sort_pytorch/utils/draw.py @@ -0,0 +1,36 @@ +import numpy as np +import cv2 + +palette = (2 ** 11 - 1, 2 ** 15 - 1, 2 ** 20 - 1) + + +def compute_color_for_labels(label): + """ + Simple function that adds fixed color depending on the class + """ + color = [int((p * (label ** 2 - label + 1)) % 255) for p in palette] + return tuple(color) + + +def draw_boxes(img, bbox, identities=None, offset=(0,0)): + for i,box in enumerate(bbox): + x1,y1,x2,y2 = [int(i) for i in box] + x1 += offset[0] + x2 += offset[0] + y1 += offset[1] + y2 += offset[1] + # box text and bar + id = int(identities[i]) if identities is not None else 0 + color = compute_color_for_labels(id) + label = '{}{:d}'.format("", id) + t_size = cv2.getTextSize(label, cv2.FONT_HERSHEY_PLAIN, 2 , 2)[0] + cv2.rectangle(img,(x1, y1),(x2,y2),color,3) + cv2.rectangle(img,(x1, y1),(x1+t_size[0]+3,y1+t_size[1]+4), color,-1) + cv2.putText(img,label,(x1,y1+t_size[1]+4), cv2.FONT_HERSHEY_PLAIN, 2, [255,255,255], 2) + return img + + + +if __name__ == '__main__': + for i in range(82): + print(compute_color_for_labels(i)) diff --git a/yolov5/deep_sort_pytorch/utils/evaluation.py b/yolov5/deep_sort_pytorch/utils/evaluation.py new file mode 100644 index 0000000..1001794 --- /dev/null +++ b/yolov5/deep_sort_pytorch/utils/evaluation.py @@ -0,0 +1,103 @@ +import os +import numpy as np +import copy +import motmetrics as mm +mm.lap.default_solver = 'lap' +from utils.io import read_results, unzip_objs + + +class Evaluator(object): + + def __init__(self, data_root, seq_name, data_type): + self.data_root = data_root + self.seq_name = seq_name + self.data_type = data_type + + self.load_annotations() + self.reset_accumulator() + + def load_annotations(self): + assert self.data_type == 'mot' + + gt_filename = os.path.join(self.data_root, self.seq_name, 'gt', 'gt.txt') + self.gt_frame_dict = read_results(gt_filename, self.data_type, is_gt=True) + self.gt_ignore_frame_dict = read_results(gt_filename, self.data_type, is_ignore=True) + + def reset_accumulator(self): + self.acc = mm.MOTAccumulator(auto_id=True) + + def eval_frame(self, frame_id, trk_tlwhs, trk_ids, rtn_events=False): + # results + trk_tlwhs = np.copy(trk_tlwhs) + trk_ids = np.copy(trk_ids) + + # gts + gt_objs = self.gt_frame_dict.get(frame_id, []) + gt_tlwhs, gt_ids = unzip_objs(gt_objs)[:2] + + # ignore boxes + ignore_objs = self.gt_ignore_frame_dict.get(frame_id, []) + ignore_tlwhs = unzip_objs(ignore_objs)[0] + + + # remove ignored results + keep = np.ones(len(trk_tlwhs), dtype=bool) + iou_distance = mm.distances.iou_matrix(ignore_tlwhs, trk_tlwhs, max_iou=0.5) + if len(iou_distance) > 0: + match_is, match_js = mm.lap.linear_sum_assignment(iou_distance) + match_is, match_js = map(lambda a: np.asarray(a, dtype=int), [match_is, match_js]) + match_ious = iou_distance[match_is, match_js] + + match_js = np.asarray(match_js, dtype=int) + match_js = match_js[np.logical_not(np.isnan(match_ious))] + keep[match_js] = False + trk_tlwhs = trk_tlwhs[keep] + trk_ids = trk_ids[keep] + + # get distance matrix + iou_distance = mm.distances.iou_matrix(gt_tlwhs, trk_tlwhs, max_iou=0.5) + + # acc + self.acc.update(gt_ids, trk_ids, iou_distance) + + if rtn_events and iou_distance.size > 0 and hasattr(self.acc, 'last_mot_events'): + events = self.acc.last_mot_events # only supported by https://github.com/longcw/py-motmetrics + else: + events = None + return events + + def eval_file(self, filename): + self.reset_accumulator() + + result_frame_dict = read_results(filename, self.data_type, is_gt=False) + frames = sorted(list(set(self.gt_frame_dict.keys()) | set(result_frame_dict.keys()))) + for frame_id in frames: + trk_objs = result_frame_dict.get(frame_id, []) + trk_tlwhs, trk_ids = unzip_objs(trk_objs)[:2] + self.eval_frame(frame_id, trk_tlwhs, trk_ids, rtn_events=False) + + return self.acc + + @staticmethod + def get_summary(accs, names, metrics=('mota', 'num_switches', 'idp', 'idr', 'idf1', 'precision', 'recall')): + names = copy.deepcopy(names) + if metrics is None: + metrics = mm.metrics.motchallenge_metrics + metrics = copy.deepcopy(metrics) + + mh = mm.metrics.create() + summary = mh.compute_many( + accs, + metrics=metrics, + names=names, + generate_overall=True + ) + + return summary + + @staticmethod + def save_summary(summary, filename): + import pandas as pd + writer = pd.ExcelWriter(filename) + summary.to_excel(writer) + writer.save() diff --git a/yolov5/deep_sort_pytorch/utils/io.py b/yolov5/deep_sort_pytorch/utils/io.py new file mode 100644 index 0000000..2dc9afd --- /dev/null +++ b/yolov5/deep_sort_pytorch/utils/io.py @@ -0,0 +1,133 @@ +import os +from typing import Dict +import numpy as np + +# from utils.log import get_logger + + +def write_results(filename, results, data_type): + if data_type == 'mot': + save_format = '{frame},{id},{x1},{y1},{w},{h},-1,-1,-1,-1\n' + elif data_type == 'kitti': + save_format = '{frame} {id} pedestrian 0 0 -10 {x1} {y1} {x2} {y2} -10 -10 -10 -1000 -1000 -1000 -10\n' + else: + raise ValueError(data_type) + + with open(filename, 'w') as f: + for frame_id, tlwhs, track_ids in results: + if data_type == 'kitti': + frame_id -= 1 + for tlwh, track_id in zip(tlwhs, track_ids): + if track_id < 0: + continue + x1, y1, w, h = tlwh + x2, y2 = x1 + w, y1 + h + line = save_format.format(frame=frame_id, id=track_id, x1=x1, y1=y1, x2=x2, y2=y2, w=w, h=h) + f.write(line) + + +# def write_results(filename, results_dict: Dict, data_type: str): +# if not filename: +# return +# path = os.path.dirname(filename) +# if not os.path.exists(path): +# os.makedirs(path) + +# if data_type in ('mot', 'mcmot', 'lab'): +# save_format = '{frame},{id},{x1},{y1},{w},{h},1,-1,-1,-1\n' +# elif data_type == 'kitti': +# save_format = '{frame} {id} pedestrian -1 -1 -10 {x1} {y1} {x2} {y2} -1 -1 -1 -1000 -1000 -1000 -10 {score}\n' +# else: +# raise ValueError(data_type) + +# with open(filename, 'w') as f: +# for frame_id, frame_data in results_dict.items(): +# if data_type == 'kitti': +# frame_id -= 1 +# for tlwh, track_id in frame_data: +# if track_id < 0: +# continue +# x1, y1, w, h = tlwh +# x2, y2 = x1 + w, y1 + h +# line = save_format.format(frame=frame_id, id=track_id, x1=x1, y1=y1, x2=x2, y2=y2, w=w, h=h, score=1.0) +# f.write(line) +# logger.info('Save results to {}'.format(filename)) + + +def read_results(filename, data_type: str, is_gt=False, is_ignore=False): + if data_type in ('mot', 'lab'): + read_fun = read_mot_results + else: + raise ValueError('Unknown data type: {}'.format(data_type)) + + return read_fun(filename, is_gt, is_ignore) + + +""" +labels={'ped', ... % 1 +'person_on_vhcl', ... % 2 +'car', ... % 3 +'bicycle', ... % 4 +'mbike', ... % 5 +'non_mot_vhcl', ... % 6 +'static_person', ... % 7 +'distractor', ... % 8 +'occluder', ... % 9 +'occluder_on_grnd', ... %10 +'occluder_full', ... % 11 +'reflection', ... % 12 +'crowd' ... % 13 +}; +""" + + +def read_mot_results(filename, is_gt, is_ignore): + valid_labels = {1} + ignore_labels = {2, 7, 8, 12} + results_dict = dict() + if os.path.isfile(filename): + with open(filename, 'r') as f: + for line in f.readlines(): + linelist = line.split(',') + if len(linelist) < 7: + continue + fid = int(linelist[0]) + if fid < 1: + continue + results_dict.setdefault(fid, list()) + + if is_gt: + if 'MOT16-' in filename or 'MOT17-' in filename: + label = int(float(linelist[7])) + mark = int(float(linelist[6])) + if mark == 0 or label not in valid_labels: + continue + score = 1 + elif is_ignore: + if 'MOT16-' in filename or 'MOT17-' in filename: + label = int(float(linelist[7])) + vis_ratio = float(linelist[8]) + if label not in ignore_labels and vis_ratio >= 0: + continue + else: + continue + score = 1 + else: + score = float(linelist[6]) + + tlwh = tuple(map(float, linelist[2:6])) + target_id = int(linelist[1]) + + results_dict[fid].append((tlwh, target_id, score)) + + return results_dict + + +def unzip_objs(objs): + if len(objs) > 0: + tlwhs, ids, scores = zip(*objs) + else: + tlwhs, ids, scores = [], [], [] + tlwhs = np.asarray(tlwhs, dtype=float).reshape(-1, 4) + + return tlwhs, ids, scores \ No newline at end of file diff --git a/yolov5/deep_sort_pytorch/utils/json_logger.py b/yolov5/deep_sort_pytorch/utils/json_logger.py new file mode 100644 index 0000000..0afd0b4 --- /dev/null +++ b/yolov5/deep_sort_pytorch/utils/json_logger.py @@ -0,0 +1,383 @@ +""" +References: + https://medium.com/analytics-vidhya/creating-a-custom-logging-mechanism-for-real-time-object-detection-using-tdd-4ca2cfcd0a2f +""" +import json +from os import makedirs +from os.path import exists, join +from datetime import datetime + + +class JsonMeta(object): + HOURS = 3 + MINUTES = 59 + SECONDS = 59 + PATH_TO_SAVE = 'LOGS' + DEFAULT_FILE_NAME = 'remaining' + + +class BaseJsonLogger(object): + """ + This is the base class that returns __dict__ of its own + it also returns the dicts of objects in the attributes that are list instances + + """ + + def dic(self): + # returns dicts of objects + out = {} + for k, v in self.__dict__.items(): + if hasattr(v, 'dic'): + out[k] = v.dic() + elif isinstance(v, list): + out[k] = self.list(v) + else: + out[k] = v + return out + + @staticmethod + def list(values): + # applies the dic method on items in the list + return [v.dic() if hasattr(v, 'dic') else v for v in values] + + +class Label(BaseJsonLogger): + """ + For each bounding box there are various categories with confidences. Label class keeps track of that information. + """ + + def __init__(self, category: str, confidence: float): + self.category = category + self.confidence = confidence + + +class Bbox(BaseJsonLogger): + """ + This module stores the information for each frame and use them in JsonParser + Attributes: + labels (list): List of label module. + top (int): + left (int): + width (int): + height (int): + + Args: + bbox_id (float): + top (int): + left (int): + width (int): + height (int): + + References: + Check Label module for better understanding. + + + """ + + def __init__(self, bbox_id, top, left, width, height): + self.labels = [] + self.bbox_id = bbox_id + self.top = top + self.left = left + self.width = width + self.height = height + + def add_label(self, category, confidence): + # adds category and confidence only if top_k is not exceeded. + self.labels.append(Label(category, confidence)) + + def labels_full(self, value): + return len(self.labels) == value + + +class Frame(BaseJsonLogger): + """ + This module stores the information for each frame and use them in JsonParser + Attributes: + timestamp (float): The elapsed time of captured frame + frame_id (int): The frame number of the captured video + bboxes (list of Bbox objects): Stores the list of bbox objects. + + References: + Check Bbox class for better information + + Args: + timestamp (float): + frame_id (int): + + """ + + def __init__(self, frame_id: int, timestamp: float = None): + self.frame_id = frame_id + self.timestamp = timestamp + self.bboxes = [] + + def add_bbox(self, bbox_id: int, top: int, left: int, width: int, height: int): + bboxes_ids = [bbox.bbox_id for bbox in self.bboxes] + if bbox_id not in bboxes_ids: + self.bboxes.append(Bbox(bbox_id, top, left, width, height)) + else: + raise ValueError("Frame with id: {} already has a Bbox with id: {}".format(self.frame_id, bbox_id)) + + def add_label_to_bbox(self, bbox_id: int, category: str, confidence: float): + bboxes = {bbox.id: bbox for bbox in self.bboxes} + if bbox_id in bboxes.keys(): + res = bboxes.get(bbox_id) + res.add_label(category, confidence) + else: + raise ValueError('the bbox with id: {} does not exists!'.format(bbox_id)) + + +class BboxToJsonLogger(BaseJsonLogger): + """ + ُ This module is designed to automate the task of logging jsons. An example json is used + to show the contents of json file shortly + Example: + { + "video_details": { + "frame_width": 1920, + "frame_height": 1080, + "frame_rate": 20, + "video_name": "/home/gpu/codes/MSD/pedestrian_2/project/public/camera1.avi" + }, + "frames": [ + { + "frame_id": 329, + "timestamp": 3365.1254 + "bboxes": [ + { + "labels": [ + { + "category": "pedestrian", + "confidence": 0.9 + } + ], + "bbox_id": 0, + "top": 1257, + "left": 138, + "width": 68, + "height": 109 + } + ] + }], + + Attributes: + frames (dict): It's a dictionary that maps each frame_id to json attributes. + video_details (dict): information about video file. + top_k_labels (int): shows the allowed number of labels + start_time (datetime object): we use it to automate the json output by time. + + Args: + top_k_labels (int): shows the allowed number of labels + + """ + + def __init__(self, top_k_labels: int = 1): + self.frames = {} + self.video_details = self.video_details = dict(frame_width=None, frame_height=None, frame_rate=None, + video_name=None) + self.top_k_labels = top_k_labels + self.start_time = datetime.now() + + def set_top_k(self, value): + self.top_k_labels = value + + def frame_exists(self, frame_id: int) -> bool: + """ + Args: + frame_id (int): + + Returns: + bool: true if frame_id is recognized + """ + return frame_id in self.frames.keys() + + def add_frame(self, frame_id: int, timestamp: float = None) -> None: + """ + Args: + frame_id (int): + timestamp (float): opencv captured frame time property + + Raises: + ValueError: if frame_id would not exist in class frames attribute + + Returns: + None + + """ + if not self.frame_exists(frame_id): + self.frames[frame_id] = Frame(frame_id, timestamp) + else: + raise ValueError("Frame id: {} already exists".format(frame_id)) + + def bbox_exists(self, frame_id: int, bbox_id: int) -> bool: + """ + Args: + frame_id: + bbox_id: + + Returns: + bool: if bbox exists in frame bboxes list + """ + bboxes = [] + if self.frame_exists(frame_id=frame_id): + bboxes = [bbox.bbox_id for bbox in self.frames[frame_id].bboxes] + return bbox_id in bboxes + + def find_bbox(self, frame_id: int, bbox_id: int): + """ + + Args: + frame_id: + bbox_id: + + Returns: + bbox_id (int): + + Raises: + ValueError: if bbox_id does not exist in the bbox list of specific frame. + """ + if not self.bbox_exists(frame_id, bbox_id): + raise ValueError("frame with id: {} does not contain bbox with id: {}".format(frame_id, bbox_id)) + bboxes = {bbox.bbox_id: bbox for bbox in self.frames[frame_id].bboxes} + return bboxes.get(bbox_id) + + def add_bbox_to_frame(self, frame_id: int, bbox_id: int, top: int, left: int, width: int, height: int) -> None: + """ + + Args: + frame_id (int): + bbox_id (int): + top (int): + left (int): + width (int): + height (int): + + Returns: + None + + Raises: + ValueError: if bbox_id already exist in frame information with frame_id + ValueError: if frame_id does not exist in frames attribute + """ + if self.frame_exists(frame_id): + frame = self.frames[frame_id] + if not self.bbox_exists(frame_id, bbox_id): + frame.add_bbox(bbox_id, top, left, width, height) + else: + raise ValueError( + "frame with frame_id: {} already contains the bbox with id: {} ".format(frame_id, bbox_id)) + else: + raise ValueError("frame with frame_id: {} does not exist".format(frame_id)) + + def add_label_to_bbox(self, frame_id: int, bbox_id: int, category: str, confidence: float): + """ + Args: + frame_id: + bbox_id: + category: + confidence: the confidence value returned from yolo detection + + Returns: + None + + Raises: + ValueError: if labels quota (top_k_labels) exceeds. + """ + bbox = self.find_bbox(frame_id, bbox_id) + if not bbox.labels_full(self.top_k_labels): + bbox.add_label(category, confidence) + else: + raise ValueError("labels in frame_id: {}, bbox_id: {} is fulled".format(frame_id, bbox_id)) + + def add_video_details(self, frame_width: int = None, frame_height: int = None, frame_rate: int = None, + video_name: str = None): + self.video_details['frame_width'] = frame_width + self.video_details['frame_height'] = frame_height + self.video_details['frame_rate'] = frame_rate + self.video_details['video_name'] = video_name + + def output(self): + output = {'video_details': self.video_details} + result = list(self.frames.values()) + output['frames'] = [item.dic() for item in result] + return output + + def json_output(self, output_name): + """ + Args: + output_name: + + Returns: + None + + Notes: + It creates the json output with `output_name` name. + """ + if not output_name.endswith('.json'): + output_name += '.json' + with open(output_name, 'w') as file: + json.dump(self.output(), file) + file.close() + + def set_start(self): + self.start_time = datetime.now() + + def schedule_output_by_time(self, output_dir=JsonMeta.PATH_TO_SAVE, hours: int = 0, minutes: int = 0, + seconds: int = 60) -> None: + """ + Notes: + Creates folder and then periodically stores the jsons on that address. + + Args: + output_dir (str): the directory where output files will be stored + hours (int): + minutes (int): + seconds (int): + + Returns: + None + + """ + end = datetime.now() + interval = 0 + interval += abs(min([hours, JsonMeta.HOURS]) * 3600) + interval += abs(min([minutes, JsonMeta.MINUTES]) * 60) + interval += abs(min([seconds, JsonMeta.SECONDS])) + diff = (end - self.start_time).seconds + + if diff > interval: + output_name = self.start_time.strftime('%Y-%m-%d %H-%M-%S') + '.json' + if not exists(output_dir): + makedirs(output_dir) + output = join(output_dir, output_name) + self.json_output(output_name=output) + self.frames = {} + self.start_time = datetime.now() + + def schedule_output_by_frames(self, frames_quota, frame_counter, output_dir=JsonMeta.PATH_TO_SAVE): + """ + saves as the number of frames quota increases higher. + :param frames_quota: + :param frame_counter: + :param output_dir: + :return: + """ + pass + + def flush(self, output_dir): + """ + Notes: + We use this function to output jsons whenever possible. + like the time that we exit the while loop of opencv. + + Args: + output_dir: + + Returns: + None + + """ + filename = self.start_time.strftime('%Y-%m-%d %H-%M-%S') + '-remaining.json' + output = join(output_dir, filename) + self.json_output(output_name=output) diff --git a/yolov5/deep_sort_pytorch/utils/log.py b/yolov5/deep_sort_pytorch/utils/log.py new file mode 100644 index 0000000..0d48757 --- /dev/null +++ b/yolov5/deep_sort_pytorch/utils/log.py @@ -0,0 +1,17 @@ +import logging + + +def get_logger(name='root'): + formatter = logging.Formatter( + # fmt='%(asctime)s [%(levelname)s]: %(filename)s(%(funcName)s:%(lineno)s) >> %(message)s') + fmt='%(asctime)s [%(levelname)s]: %(message)s', datefmt='%Y-%m-%d %H:%M:%S') + + handler = logging.StreamHandler() + handler.setFormatter(formatter) + + logger = logging.getLogger(name) + logger.setLevel(logging.INFO) + logger.addHandler(handler) + return logger + + diff --git a/yolov5/deep_sort_pytorch/utils/parser.py b/yolov5/deep_sort_pytorch/utils/parser.py new file mode 100644 index 0000000..5801a2a --- /dev/null +++ b/yolov5/deep_sort_pytorch/utils/parser.py @@ -0,0 +1,39 @@ +import os +import yaml +from easydict import EasyDict as edict + + +class YamlParser(edict): + """ + This is yaml parser based on EasyDict. + """ + + def __init__(self, cfg_dict=None, config_file=None): + if cfg_dict is None: + cfg_dict = {} + + if config_file is not None: + assert(os.path.isfile(config_file)) + with open(config_file, 'r') as fo: + cfg_dict.update(yaml.load(fo.read())) + + super(YamlParser, self).__init__(cfg_dict) + + def merge_from_file(self, config_file): + with open(config_file, 'r') as fo: + self.update(yaml.load(fo.read())) + + def merge_from_dict(self, config_dict): + self.update(config_dict) + + +def get_config(config_file=None): + return YamlParser(config_file=config_file) + + +if __name__ == "__main__": + cfg = YamlParser(config_file="../configs/yolov3.yaml") + cfg.merge_from_file("../configs/deep_sort.yaml") + + import ipdb + ipdb.set_trace() diff --git a/yolov5/deep_sort_pytorch/utils/tools.py b/yolov5/deep_sort_pytorch/utils/tools.py new file mode 100644 index 0000000..965fb69 --- /dev/null +++ b/yolov5/deep_sort_pytorch/utils/tools.py @@ -0,0 +1,39 @@ +from functools import wraps +from time import time + + +def is_video(ext: str): + """ + Returns true if ext exists in + allowed_exts for video files. + + Args: + ext: + + Returns: + + """ + + allowed_exts = ('.mp4', '.webm', '.ogg', '.avi', '.wmv', '.mkv', '.3gp') + return any((ext.endswith(x) for x in allowed_exts)) + + +def tik_tok(func): + """ + keep track of time for each process. + Args: + func: + + Returns: + + """ + @wraps(func) + def _time_it(*args, **kwargs): + start = time() + try: + return func(*args, **kwargs) + finally: + end_ = time() + print("time: {:.03f}s, fps: {:.03f}".format(end_ - start, 1 / (end_ - start))) + + return _time_it diff --git a/yolov5/detect.py b/yolov5/detect.py new file mode 100644 index 0000000..99eb95c --- /dev/null +++ b/yolov5/detect.py @@ -0,0 +1,276 @@ +import argparse +import time +import os +from pathlib import Path + +import cv2 +import sklearn +import torch +import torch.backends.cudnn as cudnn +import numpy as np +from numpy import random + +from models.experimental import attempt_load +from utils.datasets import LoadStreams, LoadImages +from utils.general import check_img_size, non_max_suppression, apply_classifier, scale_coords, xyxy2xywh, \ + strip_optimizer, set_logging, increment_path +from utils.plots import plot_one_box +from utils.torch_utils import select_device, load_classifier, time_synchronized +from deep_sort_pytorch.utils.parser import get_config +from deep_sort_pytorch.deep_sort import DeepSort + +import player + +def bbox_rel(*xyxy): + """" Calculates the relative bounding box from absolute pixel values. """ + bbox_left = min([xyxy[0].item(), xyxy[2].item()]) + bbox_top = min([xyxy[1].item(), xyxy[3].item()]) + bbox_w = abs(xyxy[0].item() - xyxy[2].item()) + bbox_h = abs(xyxy[1].item() - xyxy[3].item()) + x_c = (bbox_left + bbox_w / 2) + y_c = (bbox_top + bbox_h / 2) + w = bbox_w + h = bbox_h + return x_c, y_c, w, h + + +players = {} + + +def draw_boxes(img, bbox, identities=None, offset=(0, 0)): + for i, box in enumerate(bbox): + x1, y1, x2, y2 = [int(i) for i in box] + x1 += offset[0] + x2 += offset[0] + y1 += offset[1] + y2 += offset[1] + # box text and bar + id = int(identities[i]) if identities is not None else 0 + if id in players.keys(): + current_player = players.get(id) + # only if checking colors automatically: + current_player.assignTeam(players) + label = current_player.team + else: + # check color manually + # team, color = player.check_color_manual2(left_clicks,img,x1,x2,y1,y2) + + # check color automatically + color = player.detectPlayerColor(img,x1,x2,y1,y2) + + current_player = player.Player(id,color=color,x=x2-(x2-x1),y=y2) + label = "?" + players[id] = current_player + + # label = current_player.team + plot_one_box(box, img, label=label, color=(int(current_player.color[0]), int(current_player.color[1]), int(current_player.color[2])), line_thickness=1) + #plot_one_box(box, img, label=label, color=current_player.color, line_thickness=1) + + return img + + +def detect(save_img=False): + source, weights, view_img, save_txt, imgsz = opt.source, opt.weights, opt.view_img, opt.save_txt, opt.img_size + webcam = source.isnumeric() or source.endswith('.txt') or source.lower().startswith( + ('rtsp://', 'rtmp://', 'http://')) + + # Directories + save_dir = Path(increment_path(Path(opt.project) / opt.name, exist_ok=opt.exist_ok)) # increment run + (save_dir / 'labels' if save_txt else save_dir).mkdir(parents=True, exist_ok=True) # make dir + + # DeepSort Initialize + cfg = get_config() + cfg.merge_from_file(opt.config_deepsort) + deepsort = DeepSort(cfg.DEEPSORT.REID_CKPT, + max_dist=cfg.DEEPSORT.MAX_DIST, min_confidence=cfg.DEEPSORT.MIN_CONFIDENCE, + nms_max_overlap=cfg.DEEPSORT.NMS_MAX_OVERLAP, max_iou_distance=cfg.DEEPSORT.MAX_IOU_DISTANCE, + max_age=cfg.DEEPSORT.MAX_AGE, n_init=cfg.DEEPSORT.N_INIT, nn_budget=cfg.DEEPSORT.NN_BUDGET, + use_cuda=True) + + # Initialize + set_logging() + device = select_device(opt.device) + half = device.type != 'cpu' # half precision only supported on CUDA + + # Load model + model = attempt_load(weights, map_location=device) # load FP32 model + imgsz = check_img_size(imgsz, s=model.stride.max()) # check img_size + if half: + model.half() # to FP16 + + # Second-stage classifier + classify = False + if classify: + modelc = load_classifier(name='resnet101', n=2) # initialize + modelc.load_state_dict(torch.load('weights/resnet101.pt', map_location=device)['model']).to(device).eval() + + # Set Dataloader + vid_path, vid_writer = None, None + if webcam: + view_img = True + cudnn.benchmark = True # set True to speed up constant image size inference + dataset = LoadStreams(source, img_size=imgsz) + else: + save_img = True + dataset = LoadImages(source, img_size=imgsz) + + # Get names + names = model.module.names if hasattr(model, 'module') else model.names + # Run inference + t0 = time.time() + img = torch.zeros((1, 3, imgsz, imgsz), device=device) # init img + _ = model(img.half() if half else img) if device.type != 'cpu' else None # run once + + + for path, img, im0s, vid_cap in dataset: + img = torch.from_numpy(img).to(device) + img = img.half() if half else img.float() # uint8 to fp16/32 + img /= 255.0 # 0 - 255 to 0.0 - 1.0 + if img.ndimension() == 3: + img = img.unsqueeze(0) + + # Inference + t1 = time_synchronized() + pred = model(img, augment=opt.augment)[0] + + # Apply NMS + pred = non_max_suppression(pred, opt.conf_thres, opt.iou_thres, classes=opt.classes, agnostic=opt.agnostic_nms) + t2 = time_synchronized() + + # Apply Classifier + if classify: + pred = apply_classifier(pred, modelc, img, im0s) + # Process detections + for i, det in enumerate(pred): # detections per image + if webcam: # batch_size >= 1 + p, s, im0, frame = Path(path[i]), '%g: ' % i, im0s[i].copy(), dataset.count + else: + p, s, im0, frame = Path(path), '', im0s, getattr(dataset, 'frame', 0) + + save_path = str(save_dir / p.name) + txt_path = str(save_dir / 'labels' / p.stem) + ('' if dataset.mode == 'image' else f'_{frame}') + s += '%gx%g ' % img.shape[2:] # print string + gn = torch.tensor(im0.shape)[[1, 0, 1, 0]] # normalization gain whwh + if len(det): + # Rescale boxes from img_size to im0 size + det[:, :4] = scale_coords(img.shape[2:], det[:, :4], im0.shape).round() + + # Print results + for c in det[:, -1].unique(): + n = (det[:, -1] == c).sum() # detections per class + s += f'{n} {names[int(c)]}s, ' # add to string + + bbox_xywh = [] + confs = [] + + # Adapt detections to deep sort input format + for *xyxy, conf, cls in det: + if cls == 0: + x_c, y_c, bbox_w, bbox_h = bbox_rel(*xyxy) + obj = [x_c, y_c, bbox_w, bbox_h] + bbox_xywh.append(obj) + confs.append([conf.item()]) + + xywhs = torch.Tensor(bbox_xywh) + confss = torch.Tensor(confs) + + # Pass detections to deepsort + outputs = deepsort.update(xywhs, confss, im0) + + # draw boxes for visualization + if len(outputs) > 0: + bbox_xyxy = outputs[:, :4] + identities = outputs[:, -1] + draw_boxes(im0, bbox_xyxy, identities) + + + for *xyxy, conf, cls in reversed(det): + if save_txt: # Write to file + xywh = (xyxy2xywh(torch.tensor(xyxy).view(1, 4)) / gn).view(-1).tolist() # normalized xywh + line = (cls, *xywh, conf) if opt.save_conf else (cls, *xywh) # label format + with open(txt_path + '.txt', 'a') as f: + f.write(('%g ' * len(line)).rstrip() % line + '\n') + + if cls == 32 and (save_img or view_img): # Add bbox to ball + #label = f'{names[int(cls)]}' + label = 'ball' + plot_one_box(xyxy, im0, label=label, color=[0,0,0], line_thickness=2) + + + # Write MOT compliant results to file + """ if save_txt and len(outputs) != 0: + for j, output in enumerate(outputs): + bbox_left = output[0] + bbox_top = output[1] + bbox_w = output[2] + bbox_h = output[3] + identity = output[-1] + with open(txt_path + '.txt', 'a') as f: + f.write(('%g ' * 10 + '\n') % (j, identity, bbox_left, + bbox_top, bbox_w, bbox_h, -1, -1, -1, -1)) # label format """ + + else: + deepsort.increment_ages() + + # Print time (inference + NMS) + print(f'{s}Done. ({t2 - t1:.3f}s)') + + # Stream results + if view_img: + cv2.imshow(str(p), im0) + if cv2.waitKey(1) == ord('q'): # q to quit + raise StopIteration + + # Save results (image with detections) + if save_img: + if dataset.mode == 'image': + cv2.imwrite(save_path, im0) + else: # 'video' + if vid_path != save_path: # new video + vid_path = save_path + if isinstance(vid_writer, cv2.VideoWriter): + vid_writer.release() # release previous video writer + + fourcc = 'mp4v' # output video codec + fps = vid_cap.get(cv2.CAP_PROP_FPS) + w = int(vid_cap.get(cv2.CAP_PROP_FRAME_WIDTH)) + h = int(vid_cap.get(cv2.CAP_PROP_FRAME_HEIGHT)) + vid_writer = cv2.VideoWriter(save_path, cv2.VideoWriter_fourcc(*fourcc), fps, (w, h)) + vid_writer.write(im0) + + if save_txt or save_img: + s = f"\n{len(list(save_dir.glob('labels/*.txt')))} labels saved to {save_dir / 'labels'}" if save_txt else '' + print(f"Results saved to {save_dir}{s}") + + print(f'Done. ({time.time() - t0:.3f}s)') + + +if __name__ == '__main__': + parser = argparse.ArgumentParser() + parser.add_argument('--weights', nargs='+', type=str, default='yolov5l.pt', help='model.pt path(s)') + parser.add_argument('--source', type=str, default='data/images', help='source') # file/folder, 0 for webcam + parser.add_argument('--img-size', type=int, default=640, help='inference size (pixels)') + parser.add_argument('--conf-thres', type=float, default=0.25, help='object confidence threshold') + parser.add_argument('--iou-thres', type=float, default=0.45, help='IOU threshold for NMS') + parser.add_argument('--device', default='', help='cuda device, i.e. 0 or 0,1,2,3 or cpu') + parser.add_argument('--view-img', action='store_true', help='display results') + parser.add_argument('--save-txt', action='store_true', help='save results to *.txt') + parser.add_argument('--save-conf', action='store_true', help='save confidences in --save-txt labels') + parser.add_argument('--classes', nargs='+', type=int, default=[0, 32], help='filter by class: --class 0, or --class 0 2 3') + parser.add_argument('--agnostic-nms', action='store_true', help='class-agnostic NMS') + parser.add_argument('--augment', action='store_true', help='augmented inference') + parser.add_argument('--update', action='store_true', help='update all models') + parser.add_argument('--project', default='../files/output', help='save results to project/name') + parser.add_argument('--name', default='exp', help='save results to project/name') + parser.add_argument('--exist-ok', action='store_true', help='existing project/name ok, do not increment') + parser.add_argument("--config_deepsort", type=str, default="deep_sort_pytorch/configs/deep_sort.yaml") + opt = parser.parse_args() + print(opt) + + with torch.no_grad(): + if opt.update: # update all models (to fix SourceChangeWarning) + for opt.weights in ['yolov5s.pt', 'yolov5m.pt', 'yolov5l.pt', 'yolov5x.pt']: + detect() + strip_optimizer(opt.weights) + else: + detect() \ No newline at end of file diff --git a/yolov5/hubconf.py b/yolov5/hubconf.py new file mode 100644 index 0000000..c4485a4 --- /dev/null +++ b/yolov5/hubconf.py @@ -0,0 +1,141 @@ +"""File for accessing YOLOv5 via PyTorch Hub https://pytorch.org/hub/ + +Usage: + import torch + model = torch.hub.load('ultralytics/yolov5', 'yolov5s', pretrained=True, channels=3, classes=80) +""" + +from pathlib import Path + +import torch + +from models.yolo import Model +from utils.general import set_logging +from utils.google_utils import attempt_download + +dependencies = ['torch', 'yaml'] +set_logging() + + +def create(name, pretrained, channels, classes, autoshape): + """Creates a specified YOLOv5 model + + Arguments: + name (str): name of model, i.e. 'yolov5s' + pretrained (bool): load pretrained weights into the model + channels (int): number of input channels + classes (int): number of model classes + + Returns: + pytorch model + """ + config = Path(__file__).parent / 'models' / f'{name}.yaml' # model.yaml path + try: + model = Model(config, channels, classes) + if pretrained: + fname = f'{name}.pt' # checkpoint filename + attempt_download(fname) # download if not found locally + ckpt = torch.load(fname, map_location=torch.device('cpu')) # load + state_dict = ckpt['model'].float().state_dict() # to FP32 + state_dict = {k: v for k, v in state_dict.items() if model.state_dict()[k].shape == v.shape} # filter + model.load_state_dict(state_dict, strict=False) # load + if len(ckpt['model'].names) == classes: + model.names = ckpt['model'].names # set class names attribute + if autoshape: + model = model.autoshape() # for file/URI/PIL/cv2/np inputs and NMS + return model + + except Exception as e: + help_url = 'https://github.com/ultralytics/yolov5/issues/36' + s = 'Cache maybe be out of date, try force_reload=True. See %s for help.' % help_url + raise Exception(s) from e + + +def yolov5s(pretrained=False, channels=3, classes=80, autoshape=True): + """YOLOv5-small model from https://github.com/ultralytics/yolov5 + + Arguments: + pretrained (bool): load pretrained weights into the model, default=False + channels (int): number of input channels, default=3 + classes (int): number of model classes, default=80 + + Returns: + pytorch model + """ + return create('yolov5s', pretrained, channels, classes, autoshape) + + +def yolov5m(pretrained=False, channels=3, classes=80, autoshape=True): + """YOLOv5-medium model from https://github.com/ultralytics/yolov5 + + Arguments: + pretrained (bool): load pretrained weights into the model, default=False + channels (int): number of input channels, default=3 + classes (int): number of model classes, default=80 + + Returns: + pytorch model + """ + return create('yolov5m', pretrained, channels, classes, autoshape) + + +def yolov5l(pretrained=False, channels=3, classes=80, autoshape=True): + """YOLOv5-large model from https://github.com/ultralytics/yolov5 + + Arguments: + pretrained (bool): load pretrained weights into the model, default=False + channels (int): number of input channels, default=3 + classes (int): number of model classes, default=80 + + Returns: + pytorch model + """ + return create('yolov5l', pretrained, channels, classes, autoshape) + + +def yolov5x(pretrained=False, channels=3, classes=80, autoshape=True): + """YOLOv5-xlarge model from https://github.com/ultralytics/yolov5 + + Arguments: + pretrained (bool): load pretrained weights into the model, default=False + channels (int): number of input channels, default=3 + classes (int): number of model classes, default=80 + + Returns: + pytorch model + """ + return create('yolov5x', pretrained, channels, classes, autoshape) + + +def custom(path_or_model='path/to/model.pt', autoshape=True): + """YOLOv5-custom model from https://github.com/ultralytics/yolov5 + + Arguments (3 options): + path_or_model (str): 'path/to/model.pt' + path_or_model (dict): torch.load('path/to/model.pt') + path_or_model (nn.Module): torch.load('path/to/model.pt')['model'] + + Returns: + pytorch model + """ + model = torch.load(path_or_model) if isinstance(path_or_model, str) else path_or_model # load checkpoint + if isinstance(model, dict): + model = model['model'] # load model + + hub_model = Model(model.yaml).to(next(model.parameters()).device) # create + hub_model.load_state_dict(model.float().state_dict()) # load state_dict + hub_model.names = model.names # class names + return hub_model.autoshape() if autoshape else hub_model + + +if __name__ == '__main__': + model = create(name='yolov5s', pretrained=True, channels=3, classes=80, autoshape=True) # pretrained example + # model = custom(path_or_model='path/to/model.pt') # custom example + + # Verify inference + from PIL import Image + + imgs = [Image.open(x) for x in Path('data/images').glob('*.jpg')] + results = model(imgs) + results.show() + results.print() diff --git a/yolov5/models/__init__.py b/yolov5/models/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/yolov5/models/common.py b/yolov5/models/common.py new file mode 100644 index 0000000..17b9f01 --- /dev/null +++ b/yolov5/models/common.py @@ -0,0 +1,273 @@ +# This file contains modules common to various models + +import math +import numpy as np +import requests +import torch +import torch.nn as nn +from PIL import Image, ImageDraw + +from utils.datasets import letterbox +from utils.general import non_max_suppression, make_divisible, scale_coords, xyxy2xywh +from utils.plots import color_list + + +def autopad(k, p=None): # kernel, padding + # Pad to 'same' + if p is None: + p = k // 2 if isinstance(k, int) else [x // 2 for x in k] # auto-pad + return p + + +def DWConv(c1, c2, k=1, s=1, act=True): + # Depthwise convolution + return Conv(c1, c2, k, s, g=math.gcd(c1, c2), act=act) + + +class Conv(nn.Module): + # Standard convolution + def __init__(self, c1, c2, k=1, s=1, p=None, g=1, act=True): # ch_in, ch_out, kernel, stride, padding, groups + super(Conv, self).__init__() + self.conv = nn.Conv2d(c1, c2, k, s, autopad(k, p), groups=g, bias=False) + self.bn = nn.BatchNorm2d(c2) + self.act = nn.Hardswish() if act is True else (act if isinstance(act, nn.Module) else nn.Identity()) + + def forward(self, x): + return self.act(self.bn(self.conv(x))) + + def fuseforward(self, x): + return self.act(self.conv(x)) + + +class Bottleneck(nn.Module): + # Standard bottleneck + def __init__(self, c1, c2, shortcut=True, g=1, e=0.5): # ch_in, ch_out, shortcut, groups, expansion + super(Bottleneck, self).__init__() + c_ = int(c2 * e) # hidden channels + self.cv1 = Conv(c1, c_, 1, 1) + self.cv2 = Conv(c_, c2, 3, 1, g=g) + self.add = shortcut and c1 == c2 + + def forward(self, x): + return x + self.cv2(self.cv1(x)) if self.add else self.cv2(self.cv1(x)) + + +class BottleneckCSP(nn.Module): + # CSP Bottleneck https://github.com/WongKinYiu/CrossStagePartialNetworks + def __init__(self, c1, c2, n=1, shortcut=True, g=1, e=0.5): # ch_in, ch_out, number, shortcut, groups, expansion + super(BottleneckCSP, self).__init__() + c_ = int(c2 * e) # hidden channels + self.cv1 = Conv(c1, c_, 1, 1) + self.cv2 = nn.Conv2d(c1, c_, 1, 1, bias=False) + self.cv3 = nn.Conv2d(c_, c_, 1, 1, bias=False) + self.cv4 = Conv(2 * c_, c2, 1, 1) + self.bn = nn.BatchNorm2d(2 * c_) # applied to cat(cv2, cv3) + self.act = nn.LeakyReLU(0.1, inplace=True) + self.m = nn.Sequential(*[Bottleneck(c_, c_, shortcut, g, e=1.0) for _ in range(n)]) + + def forward(self, x): + y1 = self.cv3(self.m(self.cv1(x))) + y2 = self.cv2(x) + return self.cv4(self.act(self.bn(torch.cat((y1, y2), dim=1)))) + + +class C3(nn.Module): + # CSP Bottleneck with 3 convolutions + def __init__(self, c1, c2, n=1, shortcut=True, g=1, e=0.5): # ch_in, ch_out, number, shortcut, groups, expansion + super(C3, self).__init__() + c_ = int(c2 * e) # hidden channels + self.cv1 = Conv(c1, c_, 1, 1) + self.cv2 = Conv(c1, c_, 1, 1) + self.cv3 = Conv(2 * c_, c2, 1) # act=FReLU(c2) + self.m = nn.Sequential(*[Bottleneck(c_, c_, shortcut, g, e=1.0) for _ in range(n)]) + # self.m = nn.Sequential(*[CrossConv(c_, c_, 3, 1, g, 1.0, shortcut) for _ in range(n)]) + + def forward(self, x): + return self.cv3(torch.cat((self.m(self.cv1(x)), self.cv2(x)), dim=1)) + + +class SPP(nn.Module): + # Spatial pyramid pooling layer used in YOLOv3-SPP + def __init__(self, c1, c2, k=(5, 9, 13)): + super(SPP, self).__init__() + c_ = c1 // 2 # hidden channels + self.cv1 = Conv(c1, c_, 1, 1) + self.cv2 = Conv(c_ * (len(k) + 1), c2, 1, 1) + self.m = nn.ModuleList([nn.MaxPool2d(kernel_size=x, stride=1, padding=x // 2) for x in k]) + + def forward(self, x): + x = self.cv1(x) + return self.cv2(torch.cat([x] + [m(x) for m in self.m], 1)) + + +class Focus(nn.Module): + # Focus wh information into c-space + def __init__(self, c1, c2, k=1, s=1, p=None, g=1, act=True): # ch_in, ch_out, kernel, stride, padding, groups + super(Focus, self).__init__() + self.conv = Conv(c1 * 4, c2, k, s, p, g, act) + + def forward(self, x): # x(b,c,w,h) -> y(b,4c,w/2,h/2) + return self.conv(torch.cat([x[..., ::2, ::2], x[..., 1::2, ::2], x[..., ::2, 1::2], x[..., 1::2, 1::2]], 1)) + + +class Concat(nn.Module): + # Concatenate a list of tensors along dimension + def __init__(self, dimension=1): + super(Concat, self).__init__() + self.d = dimension + + def forward(self, x): + return torch.cat(x, self.d) + + +class NMS(nn.Module): + # Non-Maximum Suppression (NMS) module + conf = 0.25 # confidence threshold + iou = 0.45 # IoU threshold + classes = None # (optional list) filter by class + + def __init__(self): + super(NMS, self).__init__() + + def forward(self, x): + return non_max_suppression(x[0], conf_thres=self.conf, iou_thres=self.iou, classes=self.classes) + + +class autoShape(nn.Module): + # input-robust model wrapper for passing cv2/np/PIL/torch inputs. Includes preprocessing, inference and NMS + img_size = 640 # inference size (pixels) + conf = 0.25 # NMS confidence threshold + iou = 0.45 # NMS IoU threshold + classes = None # (optional list) filter by class + + def __init__(self, model): + super(autoShape, self).__init__() + self.model = model.eval() + + def autoshape(self): + print('autoShape already enabled, skipping... ') # model already converted to model.autoshape() + return self + + def forward(self, imgs, size=640, augment=False, profile=False): + # Inference from various sources. For height=720, width=1280, RGB images example inputs are: + # filename: imgs = 'data/samples/zidane.jpg' + # URI: = 'https://github.com/ultralytics/yolov5/releases/download/v1.0/zidane.jpg' + # OpenCV: = cv2.imread('image.jpg')[:,:,::-1] # HWC BGR to RGB x(720,1280,3) + # PIL: = Image.open('image.jpg') # HWC x(720,1280,3) + # numpy: = np.zeros((720,1280,3)) # HWC + # torch: = torch.zeros(16,3,720,1280) # BCHW + # multiple: = [Image.open('image1.jpg'), Image.open('image2.jpg'), ...] # list of images + + p = next(self.model.parameters()) # for device and type + if isinstance(imgs, torch.Tensor): # torch + return self.model(imgs.to(p.device).type_as(p), augment, profile) # inference + + # Pre-process + n, imgs = (len(imgs), imgs) if isinstance(imgs, list) else (1, [imgs]) # number of images, list of images + shape0, shape1 = [], [] # image and inference shapes + for i, im in enumerate(imgs): + if isinstance(im, str): # filename or uri + im = Image.open(requests.get(im, stream=True).raw if im.startswith('http') else im) # open + im = np.array(im) # to numpy + if im.shape[0] < 5: # image in CHW + im = im.transpose((1, 2, 0)) # reverse dataloader .transpose(2, 0, 1) + im = im[:, :, :3] if im.ndim == 3 else np.tile(im[:, :, None], 3) # enforce 3ch input + s = im.shape[:2] # HWC + shape0.append(s) # image shape + g = (size / max(s)) # gain + shape1.append([y * g for y in s]) + imgs[i] = im # update + shape1 = [make_divisible(x, int(self.stride.max())) for x in np.stack(shape1, 0).max(0)] # inference shape + x = [letterbox(im, new_shape=shape1, auto=False)[0] for im in imgs] # pad + x = np.stack(x, 0) if n > 1 else x[0][None] # stack + x = np.ascontiguousarray(x.transpose((0, 3, 1, 2))) # BHWC to BCHW + x = torch.from_numpy(x).to(p.device).type_as(p) / 255. # uint8 to fp16/32 + + # Inference + with torch.no_grad(): + y = self.model(x, augment, profile)[0] # forward + y = non_max_suppression(y, conf_thres=self.conf, iou_thres=self.iou, classes=self.classes) # NMS + + # Post-process + for i in range(n): + scale_coords(shape1, y[i][:, :4], shape0[i]) + + return Detections(imgs, y, self.names) + + +class Detections: + # detections class for YOLOv5 inference results + def __init__(self, imgs, pred, names=None): + super(Detections, self).__init__() + d = pred[0].device # device + gn = [torch.tensor([*[im.shape[i] for i in [1, 0, 1, 0]], 1., 1.], device=d) for im in imgs] # normalizations + self.imgs = imgs # list of images as numpy arrays + self.pred = pred # list of tensors pred[0] = (xyxy, conf, cls) + self.names = names # class names + self.xyxy = pred # xyxy pixels + self.xywh = [xyxy2xywh(x) for x in pred] # xywh pixels + self.xyxyn = [x / g for x, g in zip(self.xyxy, gn)] # xyxy normalized + self.xywhn = [x / g for x, g in zip(self.xywh, gn)] # xywh normalized + self.n = len(self.pred) + + def display(self, pprint=False, show=False, save=False): + colors = color_list() + for i, (img, pred) in enumerate(zip(self.imgs, self.pred)): + str = f'Image {i + 1}/{len(self.pred)}: {img.shape[0]}x{img.shape[1]} ' + if pred is not None: + for c in pred[:, -1].unique(): + n = (pred[:, -1] == c).sum() # detections per class + str += f'{n} {self.names[int(c)]}s, ' # add to string + if show or save: + img = Image.fromarray(img.astype(np.uint8)) if isinstance(img, np.ndarray) else img # from np + for *box, conf, cls in pred: # xyxy, confidence, class + # str += '%s %.2f, ' % (names[int(cls)], conf) # label + ImageDraw.Draw(img).rectangle(box, width=4, outline=colors[int(cls) % 10]) # plot + if save: + f = f'results{i}.jpg' + str += f"saved to '{f}'" + img.save(f) # save + if show: + img.show(f'Image {i}') # show + if pprint: + print(str) + + def print(self): + self.display(pprint=True) # print results + + def show(self): + self.display(show=True) # show results + + def save(self): + self.display(save=True) # save results + + def __len__(self): + return self.n + + def tolist(self): + # return a list of Detections objects, i.e. 'for result in results.tolist():' + x = [Detections([self.imgs[i]], [self.pred[i]], self.names) for i in range(self.n)] + for d in x: + for k in ['imgs', 'pred', 'xyxy', 'xyxyn', 'xywh', 'xywhn']: + setattr(d, k, getattr(d, k)[0]) # pop out of list + return x + + +class Flatten(nn.Module): + # Use after nn.AdaptiveAvgPool2d(1) to remove last 2 dimensions + @staticmethod + def forward(x): + return x.view(x.size(0), -1) + + +class Classify(nn.Module): + # Classification head, i.e. x(b,c1,20,20) to x(b,c2) + def __init__(self, c1, c2, k=1, s=1, p=None, g=1): # ch_in, ch_out, kernel, stride, padding, groups + super(Classify, self).__init__() + self.aap = nn.AdaptiveAvgPool2d(1) # to x(b,c1,1,1) + self.conv = nn.Conv2d(c1, c2, k, s, autopad(k, p), groups=g) # to x(b,c2,1,1) + self.flat = Flatten() + + def forward(self, x): + z = torch.cat([self.aap(y) for y in (x if isinstance(x, list) else [x])], 1) # cat if list + return self.flat(self.conv(z)) # flatten to x(b,c2) diff --git a/yolov5/models/experimental.py b/yolov5/models/experimental.py new file mode 100644 index 0000000..136e86d --- /dev/null +++ b/yolov5/models/experimental.py @@ -0,0 +1,133 @@ +# This file contains experimental modules + +import numpy as np +import torch +import torch.nn as nn + +from models.common import Conv, DWConv +from utils.google_utils import attempt_download + + +class CrossConv(nn.Module): + # Cross Convolution Downsample + def __init__(self, c1, c2, k=3, s=1, g=1, e=1.0, shortcut=False): + # ch_in, ch_out, kernel, stride, groups, expansion, shortcut + super(CrossConv, self).__init__() + c_ = int(c2 * e) # hidden channels + self.cv1 = Conv(c1, c_, (1, k), (1, s)) + self.cv2 = Conv(c_, c2, (k, 1), (s, 1), g=g) + self.add = shortcut and c1 == c2 + + def forward(self, x): + return x + self.cv2(self.cv1(x)) if self.add else self.cv2(self.cv1(x)) + + +class Sum(nn.Module): + # Weighted sum of 2 or more layers https://arxiv.org/abs/1911.09070 + def __init__(self, n, weight=False): # n: number of inputs + super(Sum, self).__init__() + self.weight = weight # apply weights boolean + self.iter = range(n - 1) # iter object + if weight: + self.w = nn.Parameter(-torch.arange(1., n) / 2, requires_grad=True) # layer weights + + def forward(self, x): + y = x[0] # no weight + if self.weight: + w = torch.sigmoid(self.w) * 2 + for i in self.iter: + y = y + x[i + 1] * w[i] + else: + for i in self.iter: + y = y + x[i + 1] + return y + + +class GhostConv(nn.Module): + # Ghost Convolution https://github.com/huawei-noah/ghostnet + def __init__(self, c1, c2, k=1, s=1, g=1, act=True): # ch_in, ch_out, kernel, stride, groups + super(GhostConv, self).__init__() + c_ = c2 // 2 # hidden channels + self.cv1 = Conv(c1, c_, k, s, None, g, act) + self.cv2 = Conv(c_, c_, 5, 1, None, c_, act) + + def forward(self, x): + y = self.cv1(x) + return torch.cat([y, self.cv2(y)], 1) + + +class GhostBottleneck(nn.Module): + # Ghost Bottleneck https://github.com/huawei-noah/ghostnet + def __init__(self, c1, c2, k, s): + super(GhostBottleneck, self).__init__() + c_ = c2 // 2 + self.conv = nn.Sequential(GhostConv(c1, c_, 1, 1), # pw + DWConv(c_, c_, k, s, act=False) if s == 2 else nn.Identity(), # dw + GhostConv(c_, c2, 1, 1, act=False)) # pw-linear + self.shortcut = nn.Sequential(DWConv(c1, c1, k, s, act=False), + Conv(c1, c2, 1, 1, act=False)) if s == 2 else nn.Identity() + + def forward(self, x): + return self.conv(x) + self.shortcut(x) + + +class MixConv2d(nn.Module): + # Mixed Depthwise Conv https://arxiv.org/abs/1907.09595 + def __init__(self, c1, c2, k=(1, 3), s=1, equal_ch=True): + super(MixConv2d, self).__init__() + groups = len(k) + if equal_ch: # equal c_ per group + i = torch.linspace(0, groups - 1E-6, c2).floor() # c2 indices + c_ = [(i == g).sum() for g in range(groups)] # intermediate channels + else: # equal weight.numel() per group + b = [c2] + [0] * groups + a = np.eye(groups + 1, groups, k=-1) + a -= np.roll(a, 1, axis=1) + a *= np.array(k) ** 2 + a[0] = 1 + c_ = np.linalg.lstsq(a, b, rcond=None)[0].round() # solve for equal weight indices, ax = b + + self.m = nn.ModuleList([nn.Conv2d(c1, int(c_[g]), k[g], s, k[g] // 2, bias=False) for g in range(groups)]) + self.bn = nn.BatchNorm2d(c2) + self.act = nn.LeakyReLU(0.1, inplace=True) + + def forward(self, x): + return x + self.act(self.bn(torch.cat([m(x) for m in self.m], 1))) + + +class Ensemble(nn.ModuleList): + # Ensemble of models + def __init__(self): + super(Ensemble, self).__init__() + + def forward(self, x, augment=False): + y = [] + for module in self: + y.append(module(x, augment)[0]) + # y = torch.stack(y).max(0)[0] # max ensemble + # y = torch.cat(y, 1) # nms ensemble + y = torch.stack(y).mean(0) # mean ensemble + return y, None # inference, train output + + +def attempt_load(weights, map_location=None): + # Loads an ensemble of models weights=[a,b,c] or a single model weights=[a] or weights=a + model = Ensemble() + for w in weights if isinstance(weights, list) else [weights]: + attempt_download(w) + model.append(torch.load(w, map_location=map_location)['model'].float().fuse().eval()) # load FP32 model + + # Compatibility updates + for m in model.modules(): + if type(m) in [nn.Hardswish, nn.LeakyReLU, nn.ReLU, nn.ReLU6]: + m.inplace = True # pytorch 1.7.0 compatibility + elif type(m) is Conv: + m._non_persistent_buffers_set = set() # pytorch 1.6.0 compatibility + + if len(model) == 1: + return model[-1] # return model + else: + print('Ensemble created with %s\n' % weights) + for k in ['names', 'stride']: + setattr(model, k, getattr(model[-1], k)) + return model # return ensemble diff --git a/yolov5/models/export.py b/yolov5/models/export.py new file mode 100644 index 0000000..057658a --- /dev/null +++ b/yolov5/models/export.py @@ -0,0 +1,97 @@ +"""Exports a YOLOv5 *.pt model to ONNX and TorchScript formats + +Usage: + $ export PYTHONPATH="$PWD" && python models/export.py --weights ./weights/yolov5s.pt --img 640 --batch 1 +""" + +import argparse +import sys +import time + +sys.path.append('./') # to run '$ python *.py' files in subdirectories + +import torch +import torch.nn as nn + +import models +from models.experimental import attempt_load +from utils.activations import Hardswish, SiLU +from utils.general import set_logging, check_img_size + +if __name__ == '__main__': + parser = argparse.ArgumentParser() + parser.add_argument('--weights', type=str, default='./yolov5s.pt', help='weights path') # from yolov5/models/ + parser.add_argument('--img-size', nargs='+', type=int, default=[640, 640], help='image size') # height, width + parser.add_argument('--batch-size', type=int, default=1, help='batch size') + opt = parser.parse_args() + opt.img_size *= 2 if len(opt.img_size) == 1 else 1 # expand + print(opt) + set_logging() + t = time.time() + + # Load PyTorch model + model = attempt_load(opt.weights, map_location=torch.device('cpu')) # load FP32 model + labels = model.names + + # Checks + gs = int(max(model.stride)) # grid size (max stride) + opt.img_size = [check_img_size(x, gs) for x in opt.img_size] # verify img_size are gs-multiples + + # Input + img = torch.zeros(opt.batch_size, 3, *opt.img_size) # image size(1,3,320,192) iDetection + + # Update model + for k, m in model.named_modules(): + m._non_persistent_buffers_set = set() # pytorch 1.6.0 compatibility + if isinstance(m, models.common.Conv): # assign export-friendly activations + if isinstance(m.act, nn.Hardswish): + m.act = Hardswish() + elif isinstance(m.act, nn.SiLU): + m.act = SiLU() + # elif isinstance(m, models.yolo.Detect): + # m.forward = m.forward_export # assign forward (optional) + model.model[-1].export = True # set Detect() layer export=True + y = model(img) # dry run + + # TorchScript export + try: + print('\nStarting TorchScript export with torch %s...' % torch.__version__) + f = opt.weights.replace('.pt', '.torchscript.pt') # filename + ts = torch.jit.trace(model, img) + ts.save(f) + print('TorchScript export success, saved as %s' % f) + except Exception as e: + print('TorchScript export failure: %s' % e) + + # ONNX export + try: + import onnx + + print('\nStarting ONNX export with onnx %s...' % onnx.__version__) + f = opt.weights.replace('.pt', '.onnx') # filename + torch.onnx.export(model, img, f, verbose=False, opset_version=12, input_names=['images'], + output_names=['classes', 'boxes'] if y is None else ['output']) + + # Checks + onnx_model = onnx.load(f) # load onnx model + onnx.checker.check_model(onnx_model) # check onnx model + # print(onnx.helper.printable_graph(onnx_model.graph)) # print a human readable model + print('ONNX export success, saved as %s' % f) + except Exception as e: + print('ONNX export failure: %s' % e) + + # CoreML export + try: + import coremltools as ct + + print('\nStarting CoreML export with coremltools %s...' % ct.__version__) + # convert model from torchscript and apply pixel scaling as per detect.py + model = ct.convert(ts, inputs=[ct.ImageType(name='image', shape=img.shape, scale=1 / 255.0, bias=[0, 0, 0])]) + f = opt.weights.replace('.pt', '.mlmodel') # filename + model.save(f) + print('CoreML export success, saved as %s' % f) + except Exception as e: + print('CoreML export failure: %s' % e) + + # Finish + print('\nExport complete (%.2fs). Visualize with https://github.com/lutzroeder/netron.' % (time.time() - t)) diff --git a/yolov5/models/hub/yolov3-spp.yaml b/yolov5/models/hub/yolov3-spp.yaml new file mode 100644 index 0000000..38dcc44 --- /dev/null +++ b/yolov5/models/hub/yolov3-spp.yaml @@ -0,0 +1,51 @@ +# parameters +nc: 80 # number of classes +depth_multiple: 1.0 # model depth multiple +width_multiple: 1.0 # layer channel multiple + +# anchors +anchors: + - [10,13, 16,30, 33,23] # P3/8 + - [30,61, 62,45, 59,119] # P4/16 + - [116,90, 156,198, 373,326] # P5/32 + +# darknet53 backbone +backbone: + # [from, number, module, args] + [[-1, 1, Conv, [32, 3, 1]], # 0 + [-1, 1, Conv, [64, 3, 2]], # 1-P1/2 + [-1, 1, Bottleneck, [64]], + [-1, 1, Conv, [128, 3, 2]], # 3-P2/4 + [-1, 2, Bottleneck, [128]], + [-1, 1, Conv, [256, 3, 2]], # 5-P3/8 + [-1, 8, Bottleneck, [256]], + [-1, 1, Conv, [512, 3, 2]], # 7-P4/16 + [-1, 8, Bottleneck, [512]], + [-1, 1, Conv, [1024, 3, 2]], # 9-P5/32 + [-1, 4, Bottleneck, [1024]], # 10 + ] + +# YOLOv3-SPP head +head: + [[-1, 1, Bottleneck, [1024, False]], + [-1, 1, SPP, [512, [5, 9, 13]]], + [-1, 1, Conv, [1024, 3, 1]], + [-1, 1, Conv, [512, 1, 1]], + [-1, 1, Conv, [1024, 3, 1]], # 15 (P5/32-large) + + [-2, 1, Conv, [256, 1, 1]], + [-1, 1, nn.Upsample, [None, 2, 'nearest']], + [[-1, 8], 1, Concat, [1]], # cat backbone P4 + [-1, 1, Bottleneck, [512, False]], + [-1, 1, Bottleneck, [512, False]], + [-1, 1, Conv, [256, 1, 1]], + [-1, 1, Conv, [512, 3, 1]], # 22 (P4/16-medium) + + [-2, 1, Conv, [128, 1, 1]], + [-1, 1, nn.Upsample, [None, 2, 'nearest']], + [[-1, 6], 1, Concat, [1]], # cat backbone P3 + [-1, 1, Bottleneck, [256, False]], + [-1, 2, Bottleneck, [256, False]], # 27 (P3/8-small) + + [[27, 22, 15], 1, Detect, [nc, anchors]], # Detect(P3, P4, P5) + ] diff --git a/yolov5/models/hub/yolov3-tiny.yaml b/yolov5/models/hub/yolov3-tiny.yaml new file mode 100644 index 0000000..ff7638c --- /dev/null +++ b/yolov5/models/hub/yolov3-tiny.yaml @@ -0,0 +1,41 @@ +# parameters +nc: 80 # number of classes +depth_multiple: 1.0 # model depth multiple +width_multiple: 1.0 # layer channel multiple + +# anchors +anchors: + - [10,14, 23,27, 37,58] # P4/16 + - [81,82, 135,169, 344,319] # P5/32 + +# YOLOv3-tiny backbone +backbone: + # [from, number, module, args] + [[-1, 1, Conv, [16, 3, 1]], # 0 + [-1, 1, nn.MaxPool2d, [2, 2, 0]], # 1-P1/2 + [-1, 1, Conv, [32, 3, 1]], + [-1, 1, nn.MaxPool2d, [2, 2, 0]], # 3-P2/4 + [-1, 1, Conv, [64, 3, 1]], + [-1, 1, nn.MaxPool2d, [2, 2, 0]], # 5-P3/8 + [-1, 1, Conv, [128, 3, 1]], + [-1, 1, nn.MaxPool2d, [2, 2, 0]], # 7-P4/16 + [-1, 1, Conv, [256, 3, 1]], + [-1, 1, nn.MaxPool2d, [2, 2, 0]], # 9-P5/32 + [-1, 1, Conv, [512, 3, 1]], + [-1, 1, nn.ZeroPad2d, [[0, 1, 0, 1]]], # 11 + [-1, 1, nn.MaxPool2d, [2, 1, 0]], # 12 + ] + +# YOLOv3-tiny head +head: + [[-1, 1, Conv, [1024, 3, 1]], + [-1, 1, Conv, [256, 1, 1]], + [-1, 1, Conv, [512, 3, 1]], # 15 (P5/32-large) + + [-2, 1, Conv, [128, 1, 1]], + [-1, 1, nn.Upsample, [None, 2, 'nearest']], + [[-1, 8], 1, Concat, [1]], # cat backbone P4 + [-1, 1, Conv, [256, 3, 1]], # 19 (P4/16-medium) + + [[19, 15], 1, Detect, [nc, anchors]], # Detect(P4, P5) + ] diff --git a/yolov5/models/hub/yolov3.yaml b/yolov5/models/hub/yolov3.yaml new file mode 100644 index 0000000..f2e7613 --- /dev/null +++ b/yolov5/models/hub/yolov3.yaml @@ -0,0 +1,51 @@ +# parameters +nc: 80 # number of classes +depth_multiple: 1.0 # model depth multiple +width_multiple: 1.0 # layer channel multiple + +# anchors +anchors: + - [10,13, 16,30, 33,23] # P3/8 + - [30,61, 62,45, 59,119] # P4/16 + - [116,90, 156,198, 373,326] # P5/32 + +# darknet53 backbone +backbone: + # [from, number, module, args] + [[-1, 1, Conv, [32, 3, 1]], # 0 + [-1, 1, Conv, [64, 3, 2]], # 1-P1/2 + [-1, 1, Bottleneck, [64]], + [-1, 1, Conv, [128, 3, 2]], # 3-P2/4 + [-1, 2, Bottleneck, [128]], + [-1, 1, Conv, [256, 3, 2]], # 5-P3/8 + [-1, 8, Bottleneck, [256]], + [-1, 1, Conv, [512, 3, 2]], # 7-P4/16 + [-1, 8, Bottleneck, [512]], + [-1, 1, Conv, [1024, 3, 2]], # 9-P5/32 + [-1, 4, Bottleneck, [1024]], # 10 + ] + +# YOLOv3 head +head: + [[-1, 1, Bottleneck, [1024, False]], + [-1, 1, Conv, [512, [1, 1]]], + [-1, 1, Conv, [1024, 3, 1]], + [-1, 1, Conv, [512, 1, 1]], + [-1, 1, Conv, [1024, 3, 1]], # 15 (P5/32-large) + + [-2, 1, Conv, [256, 1, 1]], + [-1, 1, nn.Upsample, [None, 2, 'nearest']], + [[-1, 8], 1, Concat, [1]], # cat backbone P4 + [-1, 1, Bottleneck, [512, False]], + [-1, 1, Bottleneck, [512, False]], + [-1, 1, Conv, [256, 1, 1]], + [-1, 1, Conv, [512, 3, 1]], # 22 (P4/16-medium) + + [-2, 1, Conv, [128, 1, 1]], + [-1, 1, nn.Upsample, [None, 2, 'nearest']], + [[-1, 6], 1, Concat, [1]], # cat backbone P3 + [-1, 1, Bottleneck, [256, False]], + [-1, 2, Bottleneck, [256, False]], # 27 (P3/8-small) + + [[27, 22, 15], 1, Detect, [nc, anchors]], # Detect(P3, P4, P5) + ] diff --git a/yolov5/models/hub/yolov5-fpn.yaml b/yolov5/models/hub/yolov5-fpn.yaml new file mode 100644 index 0000000..e772bff --- /dev/null +++ b/yolov5/models/hub/yolov5-fpn.yaml @@ -0,0 +1,42 @@ +# parameters +nc: 80 # number of classes +depth_multiple: 1.0 # model depth multiple +width_multiple: 1.0 # layer channel multiple + +# anchors +anchors: + - [10,13, 16,30, 33,23] # P3/8 + - [30,61, 62,45, 59,119] # P4/16 + - [116,90, 156,198, 373,326] # P5/32 + +# YOLOv5 backbone +backbone: + # [from, number, module, args] + [[-1, 1, Focus, [64, 3]], # 0-P1/2 + [-1, 1, Conv, [128, 3, 2]], # 1-P2/4 + [-1, 3, Bottleneck, [128]], + [-1, 1, Conv, [256, 3, 2]], # 3-P3/8 + [-1, 9, BottleneckCSP, [256]], + [-1, 1, Conv, [512, 3, 2]], # 5-P4/16 + [-1, 9, BottleneckCSP, [512]], + [-1, 1, Conv, [1024, 3, 2]], # 7-P5/32 + [-1, 1, SPP, [1024, [5, 9, 13]]], + [-1, 6, BottleneckCSP, [1024]], # 9 + ] + +# YOLOv5 FPN head +head: + [[-1, 3, BottleneckCSP, [1024, False]], # 10 (P5/32-large) + + [-1, 1, nn.Upsample, [None, 2, 'nearest']], + [[-1, 6], 1, Concat, [1]], # cat backbone P4 + [-1, 1, Conv, [512, 1, 1]], + [-1, 3, BottleneckCSP, [512, False]], # 14 (P4/16-medium) + + [-1, 1, nn.Upsample, [None, 2, 'nearest']], + [[-1, 4], 1, Concat, [1]], # cat backbone P3 + [-1, 1, Conv, [256, 1, 1]], + [-1, 3, BottleneckCSP, [256, False]], # 18 (P3/8-small) + + [[18, 14, 10], 1, Detect, [nc, anchors]], # Detect(P3, P4, P5) + ] diff --git a/yolov5/models/hub/yolov5-panet.yaml b/yolov5/models/hub/yolov5-panet.yaml new file mode 100644 index 0000000..340f95a --- /dev/null +++ b/yolov5/models/hub/yolov5-panet.yaml @@ -0,0 +1,48 @@ +# parameters +nc: 80 # number of classes +depth_multiple: 1.0 # model depth multiple +width_multiple: 1.0 # layer channel multiple + +# anchors +anchors: + - [10,13, 16,30, 33,23] # P3/8 + - [30,61, 62,45, 59,119] # P4/16 + - [116,90, 156,198, 373,326] # P5/32 + +# YOLOv5 backbone +backbone: + # [from, number, module, args] + [[-1, 1, Focus, [64, 3]], # 0-P1/2 + [-1, 1, Conv, [128, 3, 2]], # 1-P2/4 + [-1, 3, BottleneckCSP, [128]], + [-1, 1, Conv, [256, 3, 2]], # 3-P3/8 + [-1, 9, BottleneckCSP, [256]], + [-1, 1, Conv, [512, 3, 2]], # 5-P4/16 + [-1, 9, BottleneckCSP, [512]], + [-1, 1, Conv, [1024, 3, 2]], # 7-P5/32 + [-1, 1, SPP, [1024, [5, 9, 13]]], + [-1, 3, BottleneckCSP, [1024, False]], # 9 + ] + +# YOLOv5 PANet head +head: + [[-1, 1, Conv, [512, 1, 1]], + [-1, 1, nn.Upsample, [None, 2, 'nearest']], + [[-1, 6], 1, Concat, [1]], # cat backbone P4 + [-1, 3, BottleneckCSP, [512, False]], # 13 + + [-1, 1, Conv, [256, 1, 1]], + [-1, 1, nn.Upsample, [None, 2, 'nearest']], + [[-1, 4], 1, Concat, [1]], # cat backbone P3 + [-1, 3, BottleneckCSP, [256, False]], # 17 (P3/8-small) + + [-1, 1, Conv, [256, 3, 2]], + [[-1, 14], 1, Concat, [1]], # cat head P4 + [-1, 3, BottleneckCSP, [512, False]], # 20 (P4/16-medium) + + [-1, 1, Conv, [512, 3, 2]], + [[-1, 10], 1, Concat, [1]], # cat head P5 + [-1, 3, BottleneckCSP, [1024, False]], # 23 (P5/32-large) + + [[17, 20, 23], 1, Detect, [nc, anchors]], # Detect(P3, P4, P5) + ] diff --git a/yolov5/models/yolo.py b/yolov5/models/yolo.py new file mode 100644 index 0000000..b75e939 --- /dev/null +++ b/yolov5/models/yolo.py @@ -0,0 +1,286 @@ +import argparse +import logging +import math +import sys +from copy import deepcopy +from pathlib import Path + +import torch +import torch.nn as nn + +sys.path.append('./') # to run '$ python *.py' files in subdirectories +logger = logging.getLogger(__name__) + +from models.common import Conv, Bottleneck, SPP, DWConv, Focus, BottleneckCSP, C3, Concat, NMS, autoShape +from models.experimental import MixConv2d, CrossConv +from utils.autoanchor import check_anchor_order +from utils.general import make_divisible, check_file, set_logging +from utils.torch_utils import time_synchronized, fuse_conv_and_bn, model_info, scale_img, initialize_weights, \ + select_device, copy_attr + +try: + import thop # for FLOPS computation +except ImportError: + thop = None + + +class Detect(nn.Module): + stride = None # strides computed during build + export = False # onnx export + + def __init__(self, nc=80, anchors=(), ch=()): # detection layer + super(Detect, self).__init__() + self.nc = nc # number of classes + self.no = nc + 5 # number of outputs per anchor + self.nl = len(anchors) # number of detection layers + self.na = len(anchors[0]) // 2 # number of anchors + self.grid = [torch.zeros(1)] * self.nl # init grid + a = torch.tensor(anchors).float().view(self.nl, -1, 2) + self.register_buffer('anchors', a) # shape(nl,na,2) + self.register_buffer('anchor_grid', a.clone().view(self.nl, 1, -1, 1, 1, 2)) # shape(nl,1,na,1,1,2) + self.m = nn.ModuleList(nn.Conv2d(x, self.no * self.na, 1) for x in ch) # output conv + + def forward(self, x): + # x = x.copy() # for profiling + z = [] # inference output + self.training |= self.export + for i in range(self.nl): + x[i] = self.m[i](x[i]) # conv + bs, _, ny, nx = x[i].shape # x(bs,255,20,20) to x(bs,3,20,20,85) + x[i] = x[i].view(bs, self.na, self.no, ny, nx).permute(0, 1, 3, 4, 2).contiguous() + + if not self.training: # inference + if self.grid[i].shape[2:4] != x[i].shape[2:4]: + self.grid[i] = self._make_grid(nx, ny).to(x[i].device) + + y = x[i].sigmoid() + y[..., 0:2] = (y[..., 0:2] * 2. - 0.5 + self.grid[i].to(x[i].device)) * self.stride[i] # xy + y[..., 2:4] = (y[..., 2:4] * 2) ** 2 * self.anchor_grid[i] # wh + z.append(y.view(bs, -1, self.no)) + + return x if self.training else (torch.cat(z, 1), x) + + @staticmethod + def _make_grid(nx=20, ny=20): + yv, xv = torch.meshgrid([torch.arange(ny), torch.arange(nx)]) + return torch.stack((xv, yv), 2).view((1, 1, ny, nx, 2)).float() + + +class Model(nn.Module): + def __init__(self, cfg='yolov5s.yaml', ch=3, nc=None): # model, input channels, number of classes + super(Model, self).__init__() + if isinstance(cfg, dict): + self.yaml = cfg # model dict + else: # is *.yaml + import yaml # for torch hub + self.yaml_file = Path(cfg).name + with open(cfg) as f: + self.yaml = yaml.load(f, Loader=yaml.FullLoader) # model dict + + # Define model + ch = self.yaml['ch'] = self.yaml.get('ch', ch) # input channels + if nc and nc != self.yaml['nc']: + logger.info('Overriding model.yaml nc=%g with nc=%g' % (self.yaml['nc'], nc)) + self.yaml['nc'] = nc # override yaml value + self.model, self.save = parse_model(deepcopy(self.yaml), ch=[ch]) # model, savelist + self.names = [str(i) for i in range(self.yaml['nc'])] # default names + # print([x.shape for x in self.forward(torch.zeros(1, ch, 64, 64))]) + + # Build strides, anchors + m = self.model[-1] # Detect() + if isinstance(m, Detect): + s = 128 # 2x min stride + m.stride = torch.tensor([s / x.shape[-2] for x in self.forward(torch.zeros(1, ch, s, s))]) # forward + m.anchors /= m.stride.view(-1, 1, 1) + check_anchor_order(m) + self.stride = m.stride + self._initialize_biases() # only run once + # print('Strides: %s' % m.stride.tolist()) + + # Init weights, biases + initialize_weights(self) + self.info() + logger.info('') + + def forward(self, x, augment=False, profile=False): + if augment: + img_size = x.shape[-2:] # height, width + s = [1, 0.83, 0.67] # scales + f = [None, 3, None] # flips (2-ud, 3-lr) + y = [] # outputs + for si, fi in zip(s, f): + xi = scale_img(x.flip(fi) if fi else x, si) + yi = self.forward_once(xi)[0] # forward + # cv2.imwrite('img%g.jpg' % s, 255 * xi[0].numpy().transpose((1, 2, 0))[:, :, ::-1]) # save + yi[..., :4] /= si # de-scale + if fi == 2: + yi[..., 1] = img_size[0] - yi[..., 1] # de-flip ud + elif fi == 3: + yi[..., 0] = img_size[1] - yi[..., 0] # de-flip lr + y.append(yi) + return torch.cat(y, 1), None # augmented inference, train + else: + return self.forward_once(x, profile) # single-scale inference, train + + def forward_once(self, x, profile=False): + y, dt = [], [] # outputs + for m in self.model: + if m.f != -1: # if not from previous layer + x = y[m.f] if isinstance(m.f, int) else [x if j == -1 else y[j] for j in m.f] # from earlier layers + + if profile: + o = thop.profile(m, inputs=(x,), verbose=False)[0] / 1E9 * 2 if thop else 0 # FLOPS + t = time_synchronized() + for _ in range(10): + _ = m(x) + dt.append((time_synchronized() - t) * 100) + print('%10.1f%10.0f%10.1fms %-40s' % (o, m.np, dt[-1], m.type)) + + x = m(x) # run + y.append(x if m.i in self.save else None) # save output + + if profile: + print('%.1fms total' % sum(dt)) + return x + + def _initialize_biases(self, cf=None): # initialize biases into Detect(), cf is class frequency + # https://arxiv.org/abs/1708.02002 section 3.3 + # cf = torch.bincount(torch.tensor(np.concatenate(dataset.labels, 0)[:, 0]).long(), minlength=nc) + 1. + m = self.model[-1] # Detect() module + for mi, s in zip(m.m, m.stride): # from + b = mi.bias.view(m.na, -1) # conv.bias(255) to (3,85) + b.data[:, 4] += math.log(8 / (640 / s) ** 2) # obj (8 objects per 640 image) + b.data[:, 5:] += math.log(0.6 / (m.nc - 0.99)) if cf is None else torch.log(cf / cf.sum()) # cls + mi.bias = torch.nn.Parameter(b.view(-1), requires_grad=True) + + def _print_biases(self): + m = self.model[-1] # Detect() module + for mi in m.m: # from + b = mi.bias.detach().view(m.na, -1).T # conv.bias(255) to (3,85) + print(('%6g Conv2d.bias:' + '%10.3g' * 6) % (mi.weight.shape[1], *b[:5].mean(1).tolist(), b[5:].mean())) + + # def _print_weights(self): + # for m in self.model.modules(): + # if type(m) is Bottleneck: + # print('%10.3g' % (m.w.detach().sigmoid() * 2)) # shortcut weights + + def fuse(self): # fuse model Conv2d() + BatchNorm2d() layers + print('Fusing layers... ') + for m in self.model.modules(): + if type(m) is Conv and hasattr(m, 'bn'): + m.conv = fuse_conv_and_bn(m.conv, m.bn) # update conv + delattr(m, 'bn') # remove batchnorm + m.forward = m.fuseforward # update forward + self.info() + return self + + def nms(self, mode=True): # add or remove NMS module + present = type(self.model[-1]) is NMS # last layer is NMS + if mode and not present: + print('Adding NMS... ') + m = NMS() # module + m.f = -1 # from + m.i = self.model[-1].i + 1 # index + self.model.add_module(name='%s' % m.i, module=m) # add + self.eval() + elif not mode and present: + print('Removing NMS... ') + self.model = self.model[:-1] # remove + return self + + def autoshape(self): # add autoShape module + print('Adding autoShape... ') + m = autoShape(self) # wrap model + copy_attr(m, self, include=('yaml', 'nc', 'hyp', 'names', 'stride'), exclude=()) # copy attributes + return m + + def info(self, verbose=False, img_size=640): # print model information + model_info(self, verbose, img_size) + + +def parse_model(d, ch): # model_dict, input_channels(3) + logger.info('\n%3s%18s%3s%10s %-40s%-30s' % ('', 'from', 'n', 'params', 'module', 'arguments')) + anchors, nc, gd, gw = d['anchors'], d['nc'], d['depth_multiple'], d['width_multiple'] + na = (len(anchors[0]) // 2) if isinstance(anchors, list) else anchors # number of anchors + no = na * (nc + 5) # number of outputs = anchors * (classes + 5) + + layers, save, c2 = [], [], ch[-1] # layers, savelist, ch out + for i, (f, n, m, args) in enumerate(d['backbone'] + d['head']): # from, number, module, args + m = eval(m) if isinstance(m, str) else m # eval strings + for j, a in enumerate(args): + try: + args[j] = eval(a) if isinstance(a, str) else a # eval strings + except: + pass + + n = max(round(n * gd), 1) if n > 1 else n # depth gain + if m in [Conv, Bottleneck, SPP, DWConv, MixConv2d, Focus, CrossConv, BottleneckCSP, C3]: + c1, c2 = ch[f], args[0] + + # Normal + # if i > 0 and args[0] != no: # channel expansion factor + # ex = 1.75 # exponential (default 2.0) + # e = math.log(c2 / ch[1]) / math.log(2) + # c2 = int(ch[1] * ex ** e) + # if m != Focus: + + c2 = make_divisible(c2 * gw, 8) if c2 != no else c2 + + # Experimental + # if i > 0 and args[0] != no: # channel expansion factor + # ex = 1 + gw # exponential (default 2.0) + # ch1 = 32 # ch[1] + # e = math.log(c2 / ch1) / math.log(2) # level 1-n + # c2 = int(ch1 * ex ** e) + # if m != Focus: + # c2 = make_divisible(c2, 8) if c2 != no else c2 + + args = [c1, c2, *args[1:]] + if m in [BottleneckCSP, C3]: + args.insert(2, n) + n = 1 + elif m is nn.BatchNorm2d: + args = [ch[f]] + elif m is Concat: + c2 = sum([ch[-1 if x == -1 else x + 1] for x in f]) + elif m is Detect: + args.append([ch[x + 1] for x in f]) + if isinstance(args[1], int): # number of anchors + args[1] = [list(range(args[1] * 2))] * len(f) + else: + c2 = ch[f] + + m_ = nn.Sequential(*[m(*args) for _ in range(n)]) if n > 1 else m(*args) # module + t = str(m)[8:-2].replace('__main__.', '') # module type + np = sum([x.numel() for x in m_.parameters()]) # number params + m_.i, m_.f, m_.type, m_.np = i, f, t, np # attach index, 'from' index, type, number params + logger.info('%3s%18s%3s%10.0f %-40s%-30s' % (i, f, n, np, t, args)) # print + save.extend(x % i for x in ([f] if isinstance(f, int) else f) if x != -1) # append to savelist + layers.append(m_) + ch.append(c2) + return nn.Sequential(*layers), sorted(save) + + +if __name__ == '__main__': + parser = argparse.ArgumentParser() + parser.add_argument('--cfg', type=str, default='yolov5s.yaml', help='model.yaml') + parser.add_argument('--device', default='', help='cuda device, i.e. 0 or 0,1,2,3 or cpu') + opt = parser.parse_args() + opt.cfg = check_file(opt.cfg) # check file + set_logging() + device = select_device(opt.device) + + # Create model + model = Model(opt.cfg).to(device) + model.train() + + # Profile + # img = torch.rand(8 if torch.cuda.is_available() else 1, 3, 640, 640).to(device) + # y = model(img, profile=True) + + # Tensorboard + # from torch.utils.tensorboard import SummaryWriter + # tb_writer = SummaryWriter() + # print("Run 'tensorboard --logdir=models/runs' to view tensorboard at http://localhost:6006/") + # tb_writer.add_graph(model.model, img) # add model to tensorboard + # tb_writer.add_image('test', img[0], dataformats='CWH') # add model to tensorboard diff --git a/yolov5/models/yolov5l.yaml b/yolov5/models/yolov5l.yaml new file mode 100644 index 0000000..1309554 --- /dev/null +++ b/yolov5/models/yolov5l.yaml @@ -0,0 +1,48 @@ +# parameters +nc: 80 # number of classes +depth_multiple: 1.0 # model depth multiple +width_multiple: 1.0 # layer channel multiple + +# anchors +anchors: + - [10,13, 16,30, 33,23] # P3/8 + - [30,61, 62,45, 59,119] # P4/16 + - [116,90, 156,198, 373,326] # P5/32 + +# YOLOv5 backbone +backbone: + # [from, number, module, args] + [[-1, 1, Focus, [64, 3]], # 0-P1/2 + [-1, 1, Conv, [128, 3, 2]], # 1-P2/4 + [-1, 3, BottleneckCSP, [128]], + [-1, 1, Conv, [256, 3, 2]], # 3-P3/8 + [-1, 9, BottleneckCSP, [256]], + [-1, 1, Conv, [512, 3, 2]], # 5-P4/16 + [-1, 9, BottleneckCSP, [512]], + [-1, 1, Conv, [1024, 3, 2]], # 7-P5/32 + [-1, 1, SPP, [1024, [5, 9, 13]]], + [-1, 3, BottleneckCSP, [1024, False]], # 9 + ] + +# YOLOv5 head +head: + [[-1, 1, Conv, [512, 1, 1]], + [-1, 1, nn.Upsample, [None, 2, 'nearest']], + [[-1, 6], 1, Concat, [1]], # cat backbone P4 + [-1, 3, BottleneckCSP, [512, False]], # 13 + + [-1, 1, Conv, [256, 1, 1]], + [-1, 1, nn.Upsample, [None, 2, 'nearest']], + [[-1, 4], 1, Concat, [1]], # cat backbone P3 + [-1, 3, BottleneckCSP, [256, False]], # 17 (P3/8-small) + + [-1, 1, Conv, [256, 3, 2]], + [[-1, 14], 1, Concat, [1]], # cat head P4 + [-1, 3, BottleneckCSP, [512, False]], # 20 (P4/16-medium) + + [-1, 1, Conv, [512, 3, 2]], + [[-1, 10], 1, Concat, [1]], # cat head P5 + [-1, 3, BottleneckCSP, [1024, False]], # 23 (P5/32-large) + + [[17, 20, 23], 1, Detect, [nc, anchors]], # Detect(P3, P4, P5) + ] diff --git a/yolov5/models/yolov5m.yaml b/yolov5/models/yolov5m.yaml new file mode 100644 index 0000000..eb50a71 --- /dev/null +++ b/yolov5/models/yolov5m.yaml @@ -0,0 +1,48 @@ +# parameters +nc: 80 # number of classes +depth_multiple: 0.67 # model depth multiple +width_multiple: 0.75 # layer channel multiple + +# anchors +anchors: + - [10,13, 16,30, 33,23] # P3/8 + - [30,61, 62,45, 59,119] # P4/16 + - [116,90, 156,198, 373,326] # P5/32 + +# YOLOv5 backbone +backbone: + # [from, number, module, args] + [[-1, 1, Focus, [64, 3]], # 0-P1/2 + [-1, 1, Conv, [128, 3, 2]], # 1-P2/4 + [-1, 3, BottleneckCSP, [128]], + [-1, 1, Conv, [256, 3, 2]], # 3-P3/8 + [-1, 9, BottleneckCSP, [256]], + [-1, 1, Conv, [512, 3, 2]], # 5-P4/16 + [-1, 9, BottleneckCSP, [512]], + [-1, 1, Conv, [1024, 3, 2]], # 7-P5/32 + [-1, 1, SPP, [1024, [5, 9, 13]]], + [-1, 3, BottleneckCSP, [1024, False]], # 9 + ] + +# YOLOv5 head +head: + [[-1, 1, Conv, [512, 1, 1]], + [-1, 1, nn.Upsample, [None, 2, 'nearest']], + [[-1, 6], 1, Concat, [1]], # cat backbone P4 + [-1, 3, BottleneckCSP, [512, False]], # 13 + + [-1, 1, Conv, [256, 1, 1]], + [-1, 1, nn.Upsample, [None, 2, 'nearest']], + [[-1, 4], 1, Concat, [1]], # cat backbone P3 + [-1, 3, BottleneckCSP, [256, False]], # 17 (P3/8-small) + + [-1, 1, Conv, [256, 3, 2]], + [[-1, 14], 1, Concat, [1]], # cat head P4 + [-1, 3, BottleneckCSP, [512, False]], # 20 (P4/16-medium) + + [-1, 1, Conv, [512, 3, 2]], + [[-1, 10], 1, Concat, [1]], # cat head P5 + [-1, 3, BottleneckCSP, [1024, False]], # 23 (P5/32-large) + + [[17, 20, 23], 1, Detect, [nc, anchors]], # Detect(P3, P4, P5) + ] diff --git a/yolov5/models/yolov5s.yaml b/yolov5/models/yolov5s.yaml new file mode 100644 index 0000000..2bec452 --- /dev/null +++ b/yolov5/models/yolov5s.yaml @@ -0,0 +1,48 @@ +# parameters +nc: 80 # number of classes +depth_multiple: 0.33 # model depth multiple +width_multiple: 0.50 # layer channel multiple + +# anchors +anchors: + - [10,13, 16,30, 33,23] # P3/8 + - [30,61, 62,45, 59,119] # P4/16 + - [116,90, 156,198, 373,326] # P5/32 + +# YOLOv5 backbone +backbone: + # [from, number, module, args] + [[-1, 1, Focus, [64, 3]], # 0-P1/2 + [-1, 1, Conv, [128, 3, 2]], # 1-P2/4 + [-1, 3, BottleneckCSP, [128]], + [-1, 1, Conv, [256, 3, 2]], # 3-P3/8 + [-1, 9, BottleneckCSP, [256]], + [-1, 1, Conv, [512, 3, 2]], # 5-P4/16 + [-1, 9, BottleneckCSP, [512]], + [-1, 1, Conv, [1024, 3, 2]], # 7-P5/32 + [-1, 1, SPP, [1024, [5, 9, 13]]], + [-1, 3, BottleneckCSP, [1024, False]], # 9 + ] + +# YOLOv5 head +head: + [[-1, 1, Conv, [512, 1, 1]], + [-1, 1, nn.Upsample, [None, 2, 'nearest']], + [[-1, 6], 1, Concat, [1]], # cat backbone P4 + [-1, 3, BottleneckCSP, [512, False]], # 13 + + [-1, 1, Conv, [256, 1, 1]], + [-1, 1, nn.Upsample, [None, 2, 'nearest']], + [[-1, 4], 1, Concat, [1]], # cat backbone P3 + [-1, 3, BottleneckCSP, [256, False]], # 17 (P3/8-small) + + [-1, 1, Conv, [256, 3, 2]], + [[-1, 14], 1, Concat, [1]], # cat head P4 + [-1, 3, BottleneckCSP, [512, False]], # 20 (P4/16-medium) + + [-1, 1, Conv, [512, 3, 2]], + [[-1, 10], 1, Concat, [1]], # cat head P5 + [-1, 3, BottleneckCSP, [1024, False]], # 23 (P5/32-large) + + [[17, 20, 23], 1, Detect, [nc, anchors]], # Detect(P3, P4, P5) + ] diff --git a/yolov5/models/yolov5x.yaml b/yolov5/models/yolov5x.yaml new file mode 100644 index 0000000..9676402 --- /dev/null +++ b/yolov5/models/yolov5x.yaml @@ -0,0 +1,48 @@ +# parameters +nc: 80 # number of classes +depth_multiple: 1.33 # model depth multiple +width_multiple: 1.25 # layer channel multiple + +# anchors +anchors: + - [10,13, 16,30, 33,23] # P3/8 + - [30,61, 62,45, 59,119] # P4/16 + - [116,90, 156,198, 373,326] # P5/32 + +# YOLOv5 backbone +backbone: + # [from, number, module, args] + [[-1, 1, Focus, [64, 3]], # 0-P1/2 + [-1, 1, Conv, [128, 3, 2]], # 1-P2/4 + [-1, 3, BottleneckCSP, [128]], + [-1, 1, Conv, [256, 3, 2]], # 3-P3/8 + [-1, 9, BottleneckCSP, [256]], + [-1, 1, Conv, [512, 3, 2]], # 5-P4/16 + [-1, 9, BottleneckCSP, [512]], + [-1, 1, Conv, [1024, 3, 2]], # 7-P5/32 + [-1, 1, SPP, [1024, [5, 9, 13]]], + [-1, 3, BottleneckCSP, [1024, False]], # 9 + ] + +# YOLOv5 head +head: + [[-1, 1, Conv, [512, 1, 1]], + [-1, 1, nn.Upsample, [None, 2, 'nearest']], + [[-1, 6], 1, Concat, [1]], # cat backbone P4 + [-1, 3, BottleneckCSP, [512, False]], # 13 + + [-1, 1, Conv, [256, 1, 1]], + [-1, 1, nn.Upsample, [None, 2, 'nearest']], + [[-1, 4], 1, Concat, [1]], # cat backbone P3 + [-1, 3, BottleneckCSP, [256, False]], # 17 (P3/8-small) + + [-1, 1, Conv, [256, 3, 2]], + [[-1, 14], 1, Concat, [1]], # cat head P4 + [-1, 3, BottleneckCSP, [512, False]], # 20 (P4/16-medium) + + [-1, 1, Conv, [512, 3, 2]], + [[-1, 10], 1, Concat, [1]], # cat head P5 + [-1, 3, BottleneckCSP, [1024, False]], # 23 (P5/32-large) + + [[17, 20, 23], 1, Detect, [nc, anchors]], # Detect(P3, P4, P5) + ] diff --git a/yolov5/player.py b/yolov5/player.py new file mode 100644 index 0000000..db8d942 --- /dev/null +++ b/yolov5/player.py @@ -0,0 +1,227 @@ +import numpy as np +import cv2 +import torch +import colorsys +from sklearn.cluster import KMeans +from collections import Counter + +class Player: + + def __init__(self,id,color=None,team=None,x=None,y=None): + self.id = id + self.x = x + self.y = y + self.team = team + self.color = color + + def updatePosition(self, x, y): + self.x = x + self.y = y + + def assignTeam(self, players): + if self.team is None: + temp_list = [] + for key in players: + temp_list.append(players[key].color) + color_matrix = np.vstack((temp_list)) + clt = KMeans(n_clusters=3) + clt.fit(color_matrix) + n_pixels = len(clt.labels_) + counter = Counter(clt.labels_) + perc = {} + for i in counter: + perc[i] = np.round(counter[i]/n_pixels, 2) + perc = dict(sorted(perc.items())) + + main_colors = clt.cluster_centers_ + + max_value = max(perc, key=perc.get) + med_temp = list(sorted(perc.values()))[-2] + med_value = list(perc.keys())[list(perc.values()).index(med_temp)] + min_value = min(perc, key=perc.get) + + # hsv_player = cv2.cvtColor(np.uint8([[self.color]]), cv2.COLOR_BGR2HSV) + + # bgr_max = np.uint8([[main_colors[max_value]]]) + # hsv_max = cv2.cvtColor(bgr_max, cv2.COLOR_BGR2HSV) + + # bgr_med = np.uint8([[main_colors[med_value]]]) + # hsv_med = cv2.cvtColor(bgr_med, cv2.COLOR_BGR2HSV) + + # bgr_min = np.uint8([[main_colors[min_value]]]) + # hsv_min = cv2.cvtColor(bgr_min, cv2.COLOR_BGR2HSV) + + # adjust_array = np.array([10, 10, 40]) + + # lower_team1 = np.subtract(hsv_max, adjust_array) + # upper_team1 = np.add(hsv_max, adjust_array) + + # lower_team2 = np.subtract(hsv_med, adjust_array) + # upper_team2 = np.add(hsv_med, adjust_array) + + # lower_team3 = np.subtract(hsv_min, adjust_array) + # upper_team3 = np.add(hsv_min, adjust_array) + + # mask_team1 = cv2.inRange(hsv_player, lower_team1, upper_team1) + # mask_team2 = cv2.inRange(hsv_player, lower_team2, upper_team2) + # mask_team3 = cv2.inRange(hsv_player, lower_team3, upper_team3) + + # nonZero1 = cv2.countNonZero(mask_team1) + # nonZero2 = cv2.countNonZero(mask_team2) + # nonZero3 = cv2.countNonZero(mask_team3) + + # maxNonZero = max(nonZero1, nonZero2, nonZero3) + + # if maxNonZero == nonZero1: + # self.team = 1 + # self.color = main_colors[max_value] + # elif maxNonZero == nonZero2: + # self.team = 2 + # self.color = main_colors[med_value] + # else: + # self.team = 3 + # self.color = main_colors[min_value] + + + distances = np.sqrt(np.sum((main_colors-self.color)**2,axis=1)) + + index_of_smallest = np.where(distances==np.amin(distances)) + smallest_distance = main_colors[index_of_smallest] + if np.all(smallest_distance == main_colors[max_value]): + self.color = smallest_distance.flatten() + self.team = "Team_1" + # print(self.color, self.team) + elif np.all(smallest_distance == main_colors[med_value]): + self.color = smallest_distance.flatten() + self.team = "Team_2" + # print(self.color, self.team) + else: + self.color = self.color + self.team = "Other" + # print(self.color, self.team) + + # print(smallest_distance) + # print (main_colors[max_value], main_colors[med_value], main_colors[min_value]) + +def k_means(img): + clt = KMeans(n_clusters=4) + clt = clt.fit(img.reshape(-1, 3)) + n_pixels = len(clt.labels_) + counter = Counter(clt.labels_) + perc = {} + for i in counter: + perc[i] = np.round(counter[i]/n_pixels, 2) + perc = dict(sorted(perc.items())) + + return perc, clt.cluster_centers_ + + +def detectPlayerColor(img,x1,x2,y1,y2): + crop = img[y1:y2, x1:x2] + height, width, channels = crop.shape + qrt = crop[int(height/4):int(height/2), int(width/5):int(width/1.25)] + + perc, colors = k_means(qrt) + max_value = max(perc, key=perc.get) + return colors[max_value] + +def check_color_manual(img,x1,x2,y1,y2): + hsv = cv2.cvtColor(img, cv2.COLOR_BGR2HSV) + crop = hsv[y1:y2, x1:x2] + + height, width, channels = crop.shape + qrt = crop[int(height/4):int(height/2), int(width/5):int(width/1.25)] + + # rgb + team1 = np.array([37,69,234]) + team2 = np.array([255,217,215]) + team3 = np.array([0,0,0]) + + # hsv + lower_team1 = np.array([-5, 225, 215]) + upper_team1 = np.array([15, 245, 295]) + + lower_team2 = np.array([108, 33, 215]) + upper_team2 = np.array([128, 53, 295]) + + lower_team3 = np.array([144, 25, 11]) + upper_team3 = np.array([164, 45, 91]) + + mask_team1 = cv2.inRange(qrt, lower_team1, upper_team1) + mask_team2 = cv2.inRange(qrt, lower_team2, upper_team2) + mask_team3 = cv2.inRange(qrt, lower_team3, upper_team3) + + # out1 = cv2.bitwise_and(crop, crop, mask=mask_team1) + # out2 = cv2.bitwise_and(crop, crop, mask=mask_team2) + + nonZero1 = cv2.countNonZero(mask_team1) + nonZero2 = cv2.countNonZero(mask_team2) + nonZero3 = cv2.countNonZero(mask_team3) + + maxNonZero = max(nonZero1, nonZero2, nonZero3) + + if maxNonZero == nonZero1: + team = 1 + color = team1 + elif maxNonZero == nonZero2: + team = 2 + color = team2 + else: + team = 3 + color = team3 + + return (team, color) + +def check_color_manual2(clicks,img,x1,x2,y1,y2): + hsv = cv2.cvtColor(img, cv2.COLOR_BGR2HSV) + crop = hsv[y1:y2, x1:x2] + + height, width, channels = crop.shape + qrt = crop[int(height/4):int(height/2), int(width/5):int(width/1.25)] + + team1 = np.array(clicks[0]) + team2 = np.array(clicks[1]) + team3 = np.array(clicks[2]) + + bgr_A = np.uint8([[clicks[0]]]) + hsv_A = cv2.cvtColor(bgr_A, cv2.COLOR_BGR2HSV) + + bgr_B = np.uint8([[clicks[1]]]) + hsv_B = cv2.cvtColor(bgr_B, cv2.COLOR_BGR2HSV) + + bgr_C = np.uint8([[clicks[2]]]) + hsv_C = cv2.cvtColor(bgr_C, cv2.COLOR_BGR2HSV) + + adjust_array = np.array([10, 10, 40]) + + lower_team1 = np.subtract(hsv_A, adjust_array) + upper_team1 = np.add(hsv_A, adjust_array) + + lower_team2 = np.subtract(hsv_B, adjust_array) + upper_team2 = np.add(hsv_B, adjust_array) + + lower_team3 = np.subtract(hsv_C, adjust_array) + upper_team3 = np.add(hsv_C, adjust_array) + + mask_team1 = cv2.inRange(qrt, lower_team1, upper_team1) + mask_team2 = cv2.inRange(qrt, lower_team2, upper_team2) + mask_team3 = cv2.inRange(qrt, lower_team3, upper_team3) + + nonZero1 = cv2.countNonZero(mask_team1) + nonZero2 = cv2.countNonZero(mask_team2) + nonZero3 = cv2.countNonZero(mask_team3) + + maxNonZero = max(nonZero1, nonZero2, nonZero3) + + if maxNonZero == nonZero1: + team = 1 + color = team1 + elif maxNonZero == nonZero2: + team = 2 + color = team2 + else: + team = 3 + color = team3 + # print (color) + return (team, color) + diff --git a/yolov5/requirements.txt b/yolov5/requirements.txt new file mode 100644 index 0000000..3c23f2b --- /dev/null +++ b/yolov5/requirements.txt @@ -0,0 +1,30 @@ +# pip install -r requirements.txt + +# base ---------------------------------------- +Cython +matplotlib>=3.2.2 +numpy>=1.18.5 +opencv-python>=4.1.2 +Pillow +PyYAML>=5.3 +scipy>=1.4.1 +tensorboard>=2.2 +torch>=1.7.0 +torchvision>=0.8.1 +tqdm>=4.41.0 + +# logging ------------------------------------- +# wandb + +# plotting ------------------------------------ +seaborn>=0.11.0 +pandas + +# export -------------------------------------- +# coremltools==4.0 +# onnx>=1.8.0 +# scikit-learn==0.19.2 # for coreml quantization + +# extras -------------------------------------- +thop # FLOPS computation +pycocotools>=2.0 # COCO mAP diff --git a/yolov5/test.py b/yolov5/test.py new file mode 100644 index 0000000..b520eae --- /dev/null +++ b/yolov5/test.py @@ -0,0 +1,334 @@ +import argparse +import json +import os +from pathlib import Path +from threading import Thread + +import numpy as np +import torch +import yaml +from tqdm import tqdm + +from models.experimental import attempt_load +from utils.datasets import create_dataloader +from utils.general import coco80_to_coco91_class, check_dataset, check_file, check_img_size, box_iou, \ + non_max_suppression, scale_coords, xyxy2xywh, xywh2xyxy, set_logging, increment_path +from utils.loss import compute_loss +from utils.metrics import ap_per_class, ConfusionMatrix +from utils.plots import plot_images, output_to_target, plot_study_txt +from utils.torch_utils import select_device, time_synchronized + + +def test(data, + weights=None, + batch_size=32, + imgsz=640, + conf_thres=0.001, + iou_thres=0.6, # for NMS + save_json=False, + single_cls=False, + augment=False, + verbose=False, + model=None, + dataloader=None, + save_dir=Path(''), # for saving images + save_txt=False, # for auto-labelling + save_hybrid=False, # for hybrid auto-labelling + save_conf=False, # save auto-label confidences + plots=True, + log_imgs=0): # number of logged images + + # Initialize/load model and set device + training = model is not None + if training: # called by train.py + device = next(model.parameters()).device # get model device + + else: # called directly + set_logging() + device = select_device(opt.device, batch_size=batch_size) + + # Directories + save_dir = Path(increment_path(Path(opt.project) / opt.name, exist_ok=opt.exist_ok)) # increment run + (save_dir / 'labels' if save_txt else save_dir).mkdir(parents=True, exist_ok=True) # make dir + + # Load model + model = attempt_load(weights, map_location=device) # load FP32 model + imgsz = check_img_size(imgsz, s=model.stride.max()) # check img_size + + # Multi-GPU disabled, incompatible with .half() https://github.com/ultralytics/yolov5/issues/99 + # if device.type != 'cpu' and torch.cuda.device_count() > 1: + # model = nn.DataParallel(model) + + # Half + half = device.type != 'cpu' # half precision only supported on CUDA + if half: + model.half() + + # Configure + model.eval() + is_coco = data.endswith('coco.yaml') # is COCO dataset + with open(data) as f: + data = yaml.load(f, Loader=yaml.FullLoader) # model dict + check_dataset(data) # check + nc = 1 if single_cls else int(data['nc']) # number of classes + iouv = torch.linspace(0.5, 0.95, 10).to(device) # iou vector for mAP@0.5:0.95 + niou = iouv.numel() + + # Logging + log_imgs, wandb = min(log_imgs, 100), None # ceil + try: + import wandb # Weights & Biases + except ImportError: + log_imgs = 0 + + # Dataloader + if not training: + img = torch.zeros((1, 3, imgsz, imgsz), device=device) # init img + _ = model(img.half() if half else img) if device.type != 'cpu' else None # run once + path = data['test'] if opt.task == 'test' else data['val'] # path to val/test images + dataloader = create_dataloader(path, imgsz, batch_size, model.stride.max(), opt, pad=0.5, rect=True)[0] + + seen = 0 + confusion_matrix = ConfusionMatrix(nc=nc) + names = {k: v for k, v in enumerate(model.names if hasattr(model, 'names') else model.module.names)} + coco91class = coco80_to_coco91_class() + s = ('%20s' + '%12s' * 6) % ('Class', 'Images', 'Targets', 'P', 'R', 'mAP@.5', 'mAP@.5:.95') + p, r, f1, mp, mr, map50, map, t0, t1 = 0., 0., 0., 0., 0., 0., 0., 0., 0. + loss = torch.zeros(3, device=device) + jdict, stats, ap, ap_class, wandb_images = [], [], [], [], [] + for batch_i, (img, targets, paths, shapes) in enumerate(tqdm(dataloader, desc=s)): + img = img.to(device, non_blocking=True) + img = img.half() if half else img.float() # uint8 to fp16/32 + img /= 255.0 # 0 - 255 to 0.0 - 1.0 + targets = targets.to(device) + nb, _, height, width = img.shape # batch size, channels, height, width + + with torch.no_grad(): + # Run model + t = time_synchronized() + inf_out, train_out = model(img, augment=augment) # inference and training outputs + t0 += time_synchronized() - t + + # Compute loss + if training: + loss += compute_loss([x.float() for x in train_out], targets, model)[1][:3] # box, obj, cls + + # Run NMS + targets[:, 2:] *= torch.Tensor([width, height, width, height]).to(device) # to pixels + lb = [targets[targets[:, 0] == i, 1:] for i in range(nb)] if save_hybrid else [] # for autolabelling + t = time_synchronized() + output = non_max_suppression(inf_out, conf_thres=conf_thres, iou_thres=iou_thres, labels=lb) + t1 += time_synchronized() - t + + # Statistics per image + for si, pred in enumerate(output): + labels = targets[targets[:, 0] == si, 1:] + nl = len(labels) + tcls = labels[:, 0].tolist() if nl else [] # target class + path = Path(paths[si]) + seen += 1 + + if len(pred) == 0: + if nl: + stats.append((torch.zeros(0, niou, dtype=torch.bool), torch.Tensor(), torch.Tensor(), tcls)) + continue + + # Predictions + predn = pred.clone() + scale_coords(img[si].shape[1:], predn[:, :4], shapes[si][0], shapes[si][1]) # native-space pred + + # Append to text file + if save_txt: + gn = torch.tensor(shapes[si][0])[[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(save_dir / 'labels' / (path.stem + '.txt'), 'a') as f: + f.write(('%g ' * len(line)).rstrip() % line + '\n') + + # W&B logging + if plots and len(wandb_images) < log_imgs: + box_data = [{"position": {"minX": xyxy[0], "minY": xyxy[1], "maxX": xyxy[2], "maxY": xyxy[3]}, + "class_id": int(cls), + "box_caption": "%s %.3f" % (names[cls], conf), + "scores": {"class_score": conf}, + "domain": "pixel"} for *xyxy, conf, cls in pred.tolist()] + boxes = {"predictions": {"box_data": box_data, "class_labels": names}} # inference-space + wandb_images.append(wandb.Image(img[si], boxes=boxes, caption=path.name)) + + # Append to pycocotools JSON dictionary + if save_json: + # [{"image_id": 42, "category_id": 18, "bbox": [258.15, 41.29, 348.26, 243.78], "score": 0.236}, ... + 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 + for p, b in zip(pred.tolist(), box.tolist()): + jdict.append({'image_id': image_id, + 'category_id': coco91class[int(p[5])] if is_coco else int(p[5]), + 'bbox': [round(x, 3) for x in b], + 'score': round(p[4], 5)}) + + # Assign all predictions as incorrect + correct = torch.zeros(pred.shape[0], niou, dtype=torch.bool, device=device) + if nl: + detected = [] # target indices + tcls_tensor = labels[:, 0] + + # target boxes + tbox = xywh2xyxy(labels[:, 1:5]) + scale_coords(img[si].shape[1:], tbox, shapes[si][0], shapes[si][1]) # native-space labels + if plots: + confusion_matrix.process_batch(pred, torch.cat((labels[:, 0:1], tbox), 1)) + + # Per target class + for cls in torch.unique(tcls_tensor): + ti = (cls == tcls_tensor).nonzero(as_tuple=False).view(-1) # prediction indices + pi = (cls == pred[:, 5]).nonzero(as_tuple=False).view(-1) # target indices + + # Search for detections + if pi.shape[0]: + # Prediction to target ious + ious, i = box_iou(predn[pi, :4], tbox[ti]).max(1) # best ious, indices + + # Append detections + detected_set = set() + for j in (ious > iouv[0]).nonzero(as_tuple=False): + d = ti[i[j]] # detected target + if d.item() not in detected_set: + detected_set.add(d.item()) + detected.append(d) + correct[pi[j]] = ious[j] > iouv # iou_thres is 1xn + if len(detected) == nl: # all targets already located in image + break + + # Append statistics (correct, conf, pcls, tcls) + stats.append((correct.cpu(), pred[:, 4].cpu(), pred[:, 5].cpu(), tcls)) + + # Plot images + if plots and batch_i < 3: + f = save_dir / f'test_batch{batch_i}_labels.jpg' # labels + Thread(target=plot_images, args=(img, targets, paths, f, names), daemon=True).start() + f = save_dir / f'test_batch{batch_i}_pred.jpg' # predictions + Thread(target=plot_images, args=(img, output_to_target(output), paths, f, names), daemon=True).start() + + # Compute statistics + stats = [np.concatenate(x, 0) for x in zip(*stats)] # to numpy + if len(stats) and stats[0].any(): + p, r, ap, f1, ap_class = ap_per_class(*stats, plot=plots, save_dir=save_dir, names=names) + p, r, ap50, ap = p[:, 0], r[:, 0], ap[:, 0], ap.mean(1) # [P, R, AP@0.5, AP@0.5:0.95] + mp, mr, map50, map = p.mean(), r.mean(), ap50.mean(), ap.mean() + nt = np.bincount(stats[3].astype(np.int64), minlength=nc) # number of targets per class + else: + nt = torch.zeros(1) + + # Print results + pf = '%20s' + '%12.3g' * 6 # print format + print(pf % ('all', seen, nt.sum(), mp, mr, map50, map)) + + # Print results per class + if verbose and nc > 1 and len(stats): + for i, c in enumerate(ap_class): + print(pf % (names[c], seen, nt[c], p[i], r[i], ap50[i], ap[i])) + + # Print speeds + t = tuple(x / seen * 1E3 for x in (t0, t1, t0 + t1)) + (imgsz, imgsz, batch_size) # tuple + if not training: + print('Speed: %.1f/%.1f/%.1f ms inference/NMS/total per %gx%g image at batch-size %g' % t) + + # Plots + if plots: + confusion_matrix.plot(save_dir=save_dir, names=list(names.values())) + if wandb and wandb.run: + wandb.log({"Images": wandb_images}) + wandb.log({"Validation": [wandb.Image(str(f), caption=f.name) for f in sorted(save_dir.glob('test*.jpg'))]}) + + # 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 = '../coco/annotations/instances_val2017.json' # annotations json + pred_json = str(save_dir / f"{w}_predictions.json") # predictions json + print('\nEvaluating pycocotools mAP... saving %s...' % 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 + eval = COCOeval(anno, pred, 'bbox') + if is_coco: + eval.params.imgIds = [int(Path(x).stem) for x in dataloader.dataset.img_files] # image IDs to evaluate + eval.evaluate() + eval.accumulate() + eval.summarize() + map, map50 = eval.stats[:2] # update results (mAP@0.5:0.95, mAP@0.5) + except Exception as e: + print(f'pycocotools unable to run: {e}') + + # Return results + if not training: + s = f"\n{len(list(save_dir.glob('labels/*.txt')))} labels saved to {save_dir / 'labels'}" if save_txt else '' + print(f"Results saved to {save_dir}{s}") + model.float() # for training + maps = np.zeros(nc) + map + for i, c in enumerate(ap_class): + maps[c] = ap[i] + return (mp, mr, map50, map, *(loss.cpu() / len(dataloader)).tolist()), maps, t + + +if __name__ == '__main__': + parser = argparse.ArgumentParser(prog='test.py') + parser.add_argument('--weights', nargs='+', type=str, default='yolov5s.pt', help='model.pt path(s)') + parser.add_argument('--data', type=str, default='data/coco128.yaml', help='*.data path') + parser.add_argument('--batch-size', type=int, default=32, help='size of each image batch') + parser.add_argument('--img-size', type=int, default=640, help='inference size (pixels)') + parser.add_argument('--conf-thres', type=float, default=0.001, help='object confidence threshold') + parser.add_argument('--iou-thres', type=float, default=0.6, help='IOU threshold for NMS') + parser.add_argument('--task', default='val', help="'val', 'test', 'study'") + parser.add_argument('--device', default='', help='cuda device, i.e. 0 or 0,1,2,3 or cpu') + 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 cocoapi-compatible JSON results file') + parser.add_argument('--project', default='runs/test', help='save to project/name') + parser.add_argument('--name', default='exp', help='save to project/name') + parser.add_argument('--exist-ok', action='store_true', help='existing project/name ok, do not increment') + opt = parser.parse_args() + opt.save_json |= opt.data.endswith('coco.yaml') + opt.data = check_file(opt.data) # check file + print(opt) + + if opt.task in ['val', 'test']: # run normally + test(opt.data, + opt.weights, + opt.batch_size, + opt.img_size, + opt.conf_thres, + opt.iou_thres, + opt.save_json, + opt.single_cls, + opt.augment, + opt.verbose, + save_txt=opt.save_txt | opt.save_hybrid, + save_hybrid=opt.save_hybrid, + save_conf=opt.save_conf, + ) + + elif opt.task == 'study': # run over a range of settings and save/plot + for weights in ['yolov5s.pt', 'yolov5m.pt', 'yolov5l.pt', 'yolov5x.pt']: + f = 'study_%s_%s.txt' % (Path(opt.data).stem, Path(weights).stem) # filename to save to + x = list(range(320, 800, 64)) # x axis + y = [] # y axis + for i in x: # img-size + print('\nRunning %s point %s...' % (f, i)) + r, _, t = test(opt.data, weights, opt.batch_size, i, opt.conf_thres, opt.iou_thres, opt.save_json, + 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') + plot_study_txt(f, x) # plot diff --git a/yolov5/train.py b/yolov5/train.py new file mode 100644 index 0000000..14d7ac8 --- /dev/null +++ b/yolov5/train.py @@ -0,0 +1,595 @@ +import argparse +import logging +import math +import os +import random +import time +from pathlib import Path +from threading import Thread +from warnings import warn + +import numpy as np +import torch.distributed as dist +import torch.nn as nn +import torch.nn.functional as F +import torch.optim as optim +import torch.optim.lr_scheduler as lr_scheduler +import torch.utils.data +import yaml +from torch.cuda import amp +from torch.nn.parallel import DistributedDataParallel as DDP +from torch.utils.tensorboard import SummaryWriter +from tqdm import tqdm + +import test # import test.py to get mAP after each epoch +from models.experimental import attempt_load +from models.yolo import Model +from utils.autoanchor import check_anchors +from utils.datasets import create_dataloader +from utils.general import labels_to_class_weights, increment_path, labels_to_image_weights, init_seeds, \ + fitness, strip_optimizer, get_latest_run, check_dataset, check_file, check_git_status, check_img_size, \ + print_mutation, set_logging +from utils.google_utils import attempt_download +from utils.loss import compute_loss +from utils.plots import plot_images, plot_labels, plot_results, plot_evolution +from utils.torch_utils import ModelEMA, select_device, intersect_dicts, torch_distributed_zero_first + +logger = logging.getLogger(__name__) + +try: + import wandb +except ImportError: + wandb = None + logger.info("Install Weights & Biases for experiment logging via 'pip install wandb' (recommended)") + + +def train(hyp, opt, device, tb_writer=None, wandb=None): + logger.info(f'Hyperparameters {hyp}') + save_dir, epochs, batch_size, total_batch_size, weights, rank = \ + Path(opt.save_dir), opt.epochs, opt.batch_size, opt.total_batch_size, opt.weights, opt.global_rank + + # Directories + wdir = save_dir / 'weights' + wdir.mkdir(parents=True, exist_ok=True) # make dir + last = wdir / 'last.pt' + best = wdir / 'best.pt' + results_file = save_dir / 'results.txt' + + # Save run settings + with open(save_dir / 'hyp.yaml', 'w') as f: + yaml.dump(hyp, f, sort_keys=False) + with open(save_dir / 'opt.yaml', 'w') as f: + yaml.dump(vars(opt), f, sort_keys=False) + + # Configure + plots = not opt.evolve # create plots + cuda = device.type != 'cpu' + init_seeds(2 + rank) + with open(opt.data) as f: + data_dict = yaml.load(f, Loader=yaml.FullLoader) # data dict + with torch_distributed_zero_first(rank): + check_dataset(data_dict) # check + train_path = data_dict['train'] + test_path = data_dict['val'] + nc = 1 if opt.single_cls else int(data_dict['nc']) # number of classes + names = ['item'] if opt.single_cls and len(data_dict['names']) != 1 else data_dict['names'] # class names + assert len(names) == nc, '%g names found for nc=%g dataset in %s' % (len(names), nc, opt.data) # check + + # Model + pretrained = weights.endswith('.pt') + if pretrained: + with torch_distributed_zero_first(rank): + attempt_download(weights) # download if not found locally + ckpt = torch.load(weights, map_location=device) # load checkpoint + if hyp.get('anchors'): + ckpt['model'].yaml['anchors'] = round(hyp['anchors']) # force autoanchor + model = Model(opt.cfg or ckpt['model'].yaml, ch=3, nc=nc).to(device) # create + exclude = ['anchor'] if opt.cfg or hyp.get('anchors') else [] # exclude keys + state_dict = ckpt['model'].float().state_dict() # to FP32 + state_dict = intersect_dicts(state_dict, model.state_dict(), exclude=exclude) # intersect + model.load_state_dict(state_dict, strict=False) # load + logger.info('Transferred %g/%g items from %s' % (len(state_dict), len(model.state_dict()), weights)) # report + else: + model = Model(opt.cfg, ch=3, nc=nc).to(device) # create + + # Freeze + freeze = [] # parameter names to freeze (full or partial) + for k, v in model.named_parameters(): + v.requires_grad = True # train all layers + if any(x in k for x in freeze): + print('freezing %s' % k) + v.requires_grad = False + + # Optimizer + nbs = 64 # nominal batch size + accumulate = max(round(nbs / total_batch_size), 1) # accumulate loss before optimizing + hyp['weight_decay'] *= total_batch_size * accumulate / nbs # scale weight_decay + + pg0, pg1, pg2 = [], [], [] # optimizer parameter groups + for k, v in model.named_modules(): + if hasattr(v, 'bias') and isinstance(v.bias, nn.Parameter): + pg2.append(v.bias) # biases + if isinstance(v, nn.BatchNorm2d): + pg0.append(v.weight) # no decay + elif hasattr(v, 'weight') and isinstance(v.weight, nn.Parameter): + pg1.append(v.weight) # apply decay + + if opt.adam: + optimizer = optim.Adam(pg0, lr=hyp['lr0'], betas=(hyp['momentum'], 0.999)) # adjust beta1 to momentum + else: + optimizer = optim.SGD(pg0, lr=hyp['lr0'], momentum=hyp['momentum'], nesterov=True) + + optimizer.add_param_group({'params': pg1, 'weight_decay': hyp['weight_decay']}) # add pg1 with weight_decay + optimizer.add_param_group({'params': pg2}) # add pg2 (biases) + logger.info('Optimizer groups: %g .bias, %g conv.weight, %g other' % (len(pg2), len(pg1), len(pg0))) + del pg0, pg1, pg2 + + # Scheduler https://arxiv.org/pdf/1812.01187.pdf + # https://pytorch.org/docs/stable/_modules/torch/optim/lr_scheduler.html#OneCycleLR + lf = lambda x: ((1 + math.cos(x * math.pi / epochs)) / 2) * (1 - hyp['lrf']) + hyp['lrf'] # cosine + scheduler = lr_scheduler.LambdaLR(optimizer, lr_lambda=lf) + # plot_lr_scheduler(optimizer, scheduler, epochs) + + # Logging + if wandb and wandb.run is None: + opt.hyp = hyp # add hyperparameters + wandb_run = wandb.init(config=opt, resume="allow", + project='YOLOv5' if opt.project == 'runs/train' else Path(opt.project).stem, + name=save_dir.stem, + id=ckpt.get('wandb_id') if 'ckpt' in locals() else None) + loggers = {'wandb': wandb} # loggers dict + + # Resume + start_epoch, best_fitness = 0, 0.0 + if pretrained: + # Optimizer + if ckpt['optimizer'] is not None: + optimizer.load_state_dict(ckpt['optimizer']) + best_fitness = ckpt['best_fitness'] + + # Results + if ckpt.get('training_results') is not None: + with open(results_file, 'w') as file: + file.write(ckpt['training_results']) # write results.txt + + # Epochs + start_epoch = ckpt['epoch'] + 1 + if opt.resume: + assert start_epoch > 0, '%s training to %g epochs is finished, nothing to resume.' % (weights, epochs) + if epochs < start_epoch: + logger.info('%s has been trained for %g epochs. Fine-tuning for %g additional epochs.' % + (weights, ckpt['epoch'], epochs)) + epochs += ckpt['epoch'] # finetune additional epochs + + del ckpt, state_dict + + # Image sizes + gs = int(max(model.stride)) # grid size (max stride) + imgsz, imgsz_test = [check_img_size(x, gs) for x in opt.img_size] # verify imgsz are gs-multiples + + # DP mode + if cuda and rank == -1 and torch.cuda.device_count() > 1: + model = torch.nn.DataParallel(model) + + # SyncBatchNorm + if opt.sync_bn and cuda and rank != -1: + model = torch.nn.SyncBatchNorm.convert_sync_batchnorm(model).to(device) + logger.info('Using SyncBatchNorm()') + + # EMA + ema = ModelEMA(model) if rank in [-1, 0] else None + + # DDP mode + if cuda and rank != -1: + model = DDP(model, device_ids=[opt.local_rank], output_device=opt.local_rank) + + # Trainloader + dataloader, dataset = create_dataloader(train_path, imgsz, batch_size, gs, opt, + hyp=hyp, augment=True, cache=opt.cache_images, rect=opt.rect, rank=rank, + world_size=opt.world_size, workers=opt.workers, + image_weights=opt.image_weights) + mlc = np.concatenate(dataset.labels, 0)[:, 0].max() # max label class + nb = len(dataloader) # number of batches + assert mlc < nc, 'Label class %g exceeds nc=%g in %s. Possible class labels are 0-%g' % (mlc, nc, opt.data, nc - 1) + + # Process 0 + if rank in [-1, 0]: + ema.updates = start_epoch * nb // accumulate # set EMA updates + testloader = create_dataloader(test_path, imgsz_test, total_batch_size, gs, opt, # testloader + hyp=hyp, cache=opt.cache_images and not opt.notest, rect=True, + rank=-1, world_size=opt.world_size, workers=opt.workers, pad=0.5)[0] + + if not opt.resume: + labels = np.concatenate(dataset.labels, 0) + c = torch.tensor(labels[:, 0]) # classes + # cf = torch.bincount(c.long(), minlength=nc) + 1. # frequency + # model._initialize_biases(cf.to(device)) + if plots: + plot_labels(labels, save_dir, loggers) + if tb_writer: + tb_writer.add_histogram('classes', c, 0) + + # Anchors + if not opt.noautoanchor: + check_anchors(dataset, model=model, thr=hyp['anchor_t'], imgsz=imgsz) + + # Model parameters + hyp['cls'] *= nc / 80. # scale coco-tuned hyp['cls'] to current dataset + model.nc = nc # attach number of classes to model + model.hyp = hyp # attach hyperparameters to model + model.gr = 1.0 # iou loss ratio (obj_loss = 1.0 or iou) + model.class_weights = labels_to_class_weights(dataset.labels, nc).to(device) * nc # attach class weights + model.names = names + + # Start training + t0 = time.time() + nw = max(round(hyp['warmup_epochs'] * nb), 1000) # number of warmup iterations, max(3 epochs, 1k iterations) + # nw = min(nw, (epochs - start_epoch) / 2 * nb) # limit warmup to < 1/2 of training + maps = np.zeros(nc) # mAP per class + results = (0, 0, 0, 0, 0, 0, 0) # P, R, mAP@.5, mAP@.5-.95, val_loss(box, obj, cls) + scheduler.last_epoch = start_epoch - 1 # do not move + scaler = amp.GradScaler(enabled=cuda) + logger.info('Image sizes %g train, %g test\n' + 'Using %g dataloader workers\nLogging results to %s\n' + 'Starting training for %g epochs...' % (imgsz, imgsz_test, dataloader.num_workers, save_dir, epochs)) + for epoch in range(start_epoch, epochs): # epoch ------------------------------------------------------------------ + model.train() + + # Update image weights (optional) + if opt.image_weights: + # Generate indices + if rank in [-1, 0]: + cw = model.class_weights.cpu().numpy() * (1 - maps) ** 2 / nc # class weights + iw = labels_to_image_weights(dataset.labels, nc=nc, class_weights=cw) # image weights + dataset.indices = random.choices(range(dataset.n), weights=iw, k=dataset.n) # rand weighted idx + # Broadcast if DDP + if rank != -1: + indices = (torch.tensor(dataset.indices) if rank == 0 else torch.zeros(dataset.n)).int() + dist.broadcast(indices, 0) + if rank != 0: + dataset.indices = indices.cpu().numpy() + + # Update mosaic border + # b = int(random.uniform(0.25 * imgsz, 0.75 * imgsz + gs) // gs * gs) + # dataset.mosaic_border = [b - imgsz, -b] # height, width borders + + mloss = torch.zeros(4, device=device) # mean losses + if rank != -1: + dataloader.sampler.set_epoch(epoch) + pbar = enumerate(dataloader) + logger.info(('\n' + '%10s' * 8) % ('Epoch', 'gpu_mem', 'box', 'obj', 'cls', 'total', 'targets', 'img_size')) + if rank in [-1, 0]: + pbar = tqdm(pbar, total=nb) # progress bar + optimizer.zero_grad() + for i, (imgs, targets, paths, _) in pbar: # batch ------------------------------------------------------------- + ni = i + nb * epoch # number integrated batches (since train start) + imgs = imgs.to(device, non_blocking=True).float() / 255.0 # uint8 to float32, 0-255 to 0.0-1.0 + + # Warmup + if ni <= nw: + xi = [0, nw] # x interp + # model.gr = np.interp(ni, xi, [0.0, 1.0]) # iou loss ratio (obj_loss = 1.0 or iou) + accumulate = max(1, np.interp(ni, xi, [1, nbs / total_batch_size]).round()) + for j, x in enumerate(optimizer.param_groups): + # bias lr falls from 0.1 to lr0, all other lrs rise from 0.0 to lr0 + x['lr'] = np.interp(ni, xi, [hyp['warmup_bias_lr'] if j == 2 else 0.0, x['initial_lr'] * lf(epoch)]) + if 'momentum' in x: + x['momentum'] = np.interp(ni, xi, [hyp['warmup_momentum'], hyp['momentum']]) + + # Multi-scale + if opt.multi_scale: + sz = random.randrange(imgsz * 0.5, imgsz * 1.5 + gs) // gs * gs # size + sf = sz / max(imgs.shape[2:]) # scale factor + if sf != 1: + ns = [math.ceil(x * sf / gs) * gs for x in imgs.shape[2:]] # new shape (stretched to gs-multiple) + imgs = F.interpolate(imgs, size=ns, mode='bilinear', align_corners=False) + + # Forward + with amp.autocast(enabled=cuda): + pred = model(imgs) # forward + loss, loss_items = compute_loss(pred, targets.to(device), model) # loss scaled by batch_size + if rank != -1: + loss *= opt.world_size # gradient averaged between devices in DDP mode + + # Backward + scaler.scale(loss).backward() + + # Optimize + if ni % accumulate == 0: + scaler.step(optimizer) # optimizer.step + scaler.update() + optimizer.zero_grad() + if ema: + ema.update(model) + + # Print + if rank in [-1, 0]: + mloss = (mloss * i + loss_items) / (i + 1) # update mean losses + mem = '%.3gG' % (torch.cuda.memory_reserved() / 1E9 if torch.cuda.is_available() else 0) # (GB) + s = ('%10s' * 2 + '%10.4g' * 6) % ( + '%g/%g' % (epoch, epochs - 1), mem, *mloss, targets.shape[0], imgs.shape[-1]) + pbar.set_description(s) + + # Plot + if plots and ni < 3: + f = save_dir / f'train_batch{ni}.jpg' # filename + Thread(target=plot_images, args=(imgs, targets, paths, f), daemon=True).start() + # if tb_writer: + # tb_writer.add_image(f, result, dataformats='HWC', global_step=epoch) + # tb_writer.add_graph(model, imgs) # add model to tensorboard + elif plots and ni == 3 and wandb: + wandb.log({"Mosaics": [wandb.Image(str(x), caption=x.name) for x in save_dir.glob('train*.jpg')]}) + + # end batch ------------------------------------------------------------------------------------------------ + # end epoch ---------------------------------------------------------------------------------------------------- + + # Scheduler + lr = [x['lr'] for x in optimizer.param_groups] # for tensorboard + scheduler.step() + + # DDP process 0 or single-GPU + if rank in [-1, 0]: + # mAP + if ema: + ema.update_attr(model, include=['yaml', 'nc', 'hyp', 'gr', 'names', 'stride', 'class_weights']) + final_epoch = epoch + 1 == epochs + if not opt.notest or final_epoch: # Calculate mAP + results, maps, times = test.test(opt.data, + batch_size=total_batch_size, + imgsz=imgsz_test, + model=ema.ema, + single_cls=opt.single_cls, + dataloader=testloader, + save_dir=save_dir, + plots=plots and final_epoch, + log_imgs=opt.log_imgs if wandb else 0) + + # Write + with open(results_file, 'a') as f: + f.write(s + '%10.4g' * 7 % results + '\n') # P, R, mAP@.5, mAP@.5-.95, val_loss(box, obj, cls) + if len(opt.name) and opt.bucket: + os.system('gsutil cp %s gs://%s/results/results%s.txt' % (results_file, opt.bucket, opt.name)) + + # Log + tags = ['train/box_loss', 'train/obj_loss', 'train/cls_loss', # train loss + 'metrics/precision', 'metrics/recall', 'metrics/mAP_0.5', 'metrics/mAP_0.5:0.95', + 'val/box_loss', 'val/obj_loss', 'val/cls_loss', # val loss + 'x/lr0', 'x/lr1', 'x/lr2'] # params + for x, tag in zip(list(mloss[:-1]) + list(results) + lr, tags): + if tb_writer: + tb_writer.add_scalar(tag, x, epoch) # tensorboard + if wandb: + wandb.log({tag: x}) # W&B + + # Update best mAP + fi = fitness(np.array(results).reshape(1, -1)) # weighted combination of [P, R, mAP@.5, mAP@.5-.95] + if fi > best_fitness: + best_fitness = fi + + # Save model + save = (not opt.nosave) or (final_epoch and not opt.evolve) + if save: + with open(results_file, 'r') as f: # create checkpoint + ckpt = {'epoch': epoch, + 'best_fitness': best_fitness, + 'training_results': f.read(), + 'model': ema.ema, + 'optimizer': None if final_epoch else optimizer.state_dict(), + 'wandb_id': wandb_run.id if wandb else None} + + # Save last, best and delete + torch.save(ckpt, last) + if best_fitness == fi: + torch.save(ckpt, best) + del ckpt + # end epoch ---------------------------------------------------------------------------------------------------- + # end training + + if rank in [-1, 0]: + # Strip optimizers + final = best if best.exists() else last # final model + for f in [last, best]: + if f.exists(): + strip_optimizer(f) # strip optimizers + if opt.bucket: + os.system(f'gsutil cp {final} gs://{opt.bucket}/weights') # upload + + # Plots + if plots: + plot_results(save_dir=save_dir) # save as results.png + if wandb: + files = ['results.png', 'precision_recall_curve.png', 'confusion_matrix.png'] + wandb.log({"Results": [wandb.Image(str(save_dir / f), caption=f) for f in files + if (save_dir / f).exists()]}) + if opt.log_artifacts: + wandb.log_artifact(artifact_or_path=str(final), type='model', name=save_dir.stem) + + # Test best.pt + logger.info('%g epochs completed in %.3f hours.\n' % (epoch - start_epoch + 1, (time.time() - t0) / 3600)) + if opt.data.endswith('coco.yaml') and nc == 80: # if COCO + for conf, iou, save_json in ([0.25, 0.45, False], [0.001, 0.65, True]): # speed, mAP tests + results, _, _ = test.test(opt.data, + batch_size=total_batch_size, + imgsz=imgsz_test, + conf_thres=conf, + iou_thres=iou, + model=attempt_load(final, device).half(), + single_cls=opt.single_cls, + dataloader=testloader, + save_dir=save_dir, + save_json=save_json, + plots=False) + + else: + dist.destroy_process_group() + + wandb.run.finish() if wandb and wandb.run else None + torch.cuda.empty_cache() + return results + + +if __name__ == '__main__': + parser = argparse.ArgumentParser() + parser.add_argument('--weights', type=str, default='yolov5s.pt', help='initial weights path') + parser.add_argument('--cfg', type=str, default='', help='model.yaml path') + parser.add_argument('--data', type=str, default='data/coco128.yaml', help='data.yaml path') + parser.add_argument('--hyp', type=str, default='data/hyp.scratch.yaml', help='hyperparameters path') + parser.add_argument('--epochs', type=int, default=300) + parser.add_argument('--batch-size', type=int, default=16, help='total batch size for all GPUs') + parser.add_argument('--img-size', nargs='+', type=int, default=[640, 640], help='[train, test] image sizes') + parser.add_argument('--rect', action='store_true', help='rectangular training') + parser.add_argument('--resume', nargs='?', const=True, default=False, help='resume most recent training') + parser.add_argument('--nosave', action='store_true', help='only save final checkpoint') + parser.add_argument('--notest', action='store_true', help='only test final epoch') + parser.add_argument('--noautoanchor', action='store_true', help='disable autoanchor check') + parser.add_argument('--evolve', action='store_true', help='evolve hyperparameters') + parser.add_argument('--bucket', type=str, default='', help='gsutil bucket') + parser.add_argument('--cache-images', action='store_true', help='cache images for faster training') + parser.add_argument('--image-weights', action='store_true', help='use weighted image selection for training') + parser.add_argument('--device', default='', help='cuda device, i.e. 0 or 0,1,2,3 or cpu') + parser.add_argument('--multi-scale', action='store_true', help='vary img-size +/- 50%%') + parser.add_argument('--single-cls', action='store_true', help='train multi-class data as single-class') + parser.add_argument('--adam', action='store_true', help='use torch.optim.Adam() optimizer') + parser.add_argument('--sync-bn', action='store_true', help='use SyncBatchNorm, only available in DDP mode') + parser.add_argument('--local_rank', type=int, default=-1, help='DDP parameter, do not modify') + parser.add_argument('--log-imgs', type=int, default=16, help='number of images for W&B logging, max 100') + parser.add_argument('--log-artifacts', action='store_true', help='log artifacts, i.e. final trained model') + parser.add_argument('--workers', type=int, default=8, help='maximum number of dataloader workers') + parser.add_argument('--project', default='runs/train', help='save to project/name') + parser.add_argument('--name', default='exp', help='save to project/name') + parser.add_argument('--exist-ok', action='store_true', help='existing project/name ok, do not increment') + opt = parser.parse_args() + + # Set DDP variables + opt.total_batch_size = opt.batch_size + opt.world_size = int(os.environ['WORLD_SIZE']) if 'WORLD_SIZE' in os.environ else 1 + opt.global_rank = int(os.environ['RANK']) if 'RANK' in os.environ else -1 + set_logging(opt.global_rank) + if opt.global_rank in [-1, 0]: + check_git_status() + + # Resume + if opt.resume: # resume an interrupted run + ckpt = opt.resume if isinstance(opt.resume, str) else get_latest_run() # specified or most recent path + assert os.path.isfile(ckpt), 'ERROR: --resume checkpoint does not exist' + apriori = opt.global_rank, opt.local_rank + with open(Path(ckpt).parent.parent / 'opt.yaml') as f: + opt = argparse.Namespace(**yaml.load(f, Loader=yaml.FullLoader)) # replace + opt.cfg, opt.weights, opt.resume, opt.global_rank, opt.local_rank = '', ckpt, True, *apriori # reinstate + logger.info('Resuming training from %s' % ckpt) + else: + # opt.hyp = opt.hyp or ('hyp.finetune.yaml' if opt.weights else 'hyp.scratch.yaml') + opt.data, opt.cfg, opt.hyp = check_file(opt.data), check_file(opt.cfg), check_file(opt.hyp) # check files + assert len(opt.cfg) or len(opt.weights), 'either --cfg or --weights must be specified' + opt.img_size.extend([opt.img_size[-1]] * (2 - len(opt.img_size))) # extend to 2 sizes (train, test) + opt.name = 'evolve' if opt.evolve else opt.name + opt.save_dir = increment_path(Path(opt.project) / opt.name, exist_ok=opt.exist_ok | opt.evolve) # increment run + + # DDP mode + device = select_device(opt.device, batch_size=opt.batch_size) + if opt.local_rank != -1: + assert torch.cuda.device_count() > opt.local_rank + torch.cuda.set_device(opt.local_rank) + device = torch.device('cuda', opt.local_rank) + dist.init_process_group(backend='nccl', init_method='env://') # distributed backend + assert opt.batch_size % opt.world_size == 0, '--batch-size must be multiple of CUDA device count' + opt.batch_size = opt.total_batch_size // opt.world_size + + # Hyperparameters + with open(opt.hyp) as f: + hyp = yaml.load(f, Loader=yaml.FullLoader) # load hyps + if 'box' not in hyp: + warn('Compatibility: %s missing "box" which was renamed from "giou" in %s' % + (opt.hyp, 'https://github.com/ultralytics/yolov5/pull/1120')) + hyp['box'] = hyp.pop('giou') + + # Train + logger.info(opt) + if not opt.evolve: + tb_writer = None # init loggers + if opt.global_rank in [-1, 0]: + logger.info(f'Start Tensorboard with "tensorboard --logdir {opt.project}", view at http://localhost:6006/') + tb_writer = SummaryWriter(opt.save_dir) # Tensorboard + train(hyp, opt, device, tb_writer, wandb) + + # Evolve hyperparameters (optional) + else: + # Hyperparameter evolution metadata (mutation scale 0-1, lower_limit, upper_limit) + meta = {'lr0': (1, 1e-5, 1e-1), # initial learning rate (SGD=1E-2, Adam=1E-3) + 'lrf': (1, 0.01, 1.0), # final OneCycleLR learning rate (lr0 * lrf) + 'momentum': (0.3, 0.6, 0.98), # SGD momentum/Adam beta1 + 'weight_decay': (1, 0.0, 0.001), # optimizer weight decay + 'warmup_epochs': (1, 0.0, 5.0), # warmup epochs (fractions ok) + 'warmup_momentum': (1, 0.0, 0.95), # warmup initial momentum + 'warmup_bias_lr': (1, 0.0, 0.2), # warmup initial bias lr + 'box': (1, 0.02, 0.2), # box loss gain + 'cls': (1, 0.2, 4.0), # cls loss gain + 'cls_pw': (1, 0.5, 2.0), # cls BCELoss positive_weight + 'obj': (1, 0.2, 4.0), # obj loss gain (scale with pixels) + 'obj_pw': (1, 0.5, 2.0), # obj BCELoss positive_weight + 'iou_t': (0, 0.1, 0.7), # IoU training threshold + 'anchor_t': (1, 2.0, 8.0), # anchor-multiple threshold + 'anchors': (2, 2.0, 10.0), # anchors per output grid (0 to ignore) + 'fl_gamma': (0, 0.0, 2.0), # focal loss gamma (efficientDet default gamma=1.5) + 'hsv_h': (1, 0.0, 0.1), # image HSV-Hue augmentation (fraction) + 'hsv_s': (1, 0.0, 0.9), # image HSV-Saturation augmentation (fraction) + 'hsv_v': (1, 0.0, 0.9), # image HSV-Value augmentation (fraction) + 'degrees': (1, 0.0, 45.0), # image rotation (+/- deg) + 'translate': (1, 0.0, 0.9), # image translation (+/- fraction) + 'scale': (1, 0.0, 0.9), # image scale (+/- gain) + 'shear': (1, 0.0, 10.0), # image shear (+/- deg) + 'perspective': (0, 0.0, 0.001), # image perspective (+/- fraction), range 0-0.001 + 'flipud': (1, 0.0, 1.0), # image flip up-down (probability) + 'fliplr': (0, 0.0, 1.0), # image flip left-right (probability) + 'mosaic': (1, 0.0, 1.0), # image mixup (probability) + 'mixup': (1, 0.0, 1.0)} # image mixup (probability) + + assert opt.local_rank == -1, 'DDP mode not implemented for --evolve' + opt.notest, opt.nosave = True, True # only test/save final epoch + # ei = [isinstance(x, (int, float)) for x in hyp.values()] # evolvable indices + yaml_file = Path(opt.save_dir) / 'hyp_evolved.yaml' # save best result here + if opt.bucket: + os.system('gsutil cp gs://%s/evolve.txt .' % opt.bucket) # download evolve.txt if exists + + for _ in range(300): # generations to evolve + if Path('evolve.txt').exists(): # if evolve.txt exists: select best hyps and mutate + # Select parent(s) + parent = 'single' # parent selection method: 'single' or 'weighted' + x = np.loadtxt('evolve.txt', ndmin=2) + n = min(5, len(x)) # number of previous results to consider + x = x[np.argsort(-fitness(x))][:n] # top n mutations + w = fitness(x) - fitness(x).min() # weights + if parent == 'single' or len(x) == 1: + # x = x[random.randint(0, n - 1)] # random selection + x = x[random.choices(range(n), weights=w)[0]] # weighted selection + elif parent == 'weighted': + x = (x * w.reshape(n, 1)).sum(0) / w.sum() # weighted combination + + # Mutate + mp, s = 0.8, 0.2 # mutation probability, sigma + npr = np.random + npr.seed(int(time.time())) + g = np.array([x[0] for x in meta.values()]) # gains 0-1 + ng = len(meta) + v = np.ones(ng) + while all(v == 1): # mutate until a change occurs (prevent duplicates) + v = (g * (npr.random(ng) < mp) * npr.randn(ng) * npr.random() * s + 1).clip(0.3, 3.0) + for i, k in enumerate(hyp.keys()): # plt.hist(v.ravel(), 300) + hyp[k] = float(x[i + 7] * v[i]) # mutate + + # Constrain to limits + for k, v in meta.items(): + hyp[k] = max(hyp[k], v[1]) # lower limit + hyp[k] = min(hyp[k], v[2]) # upper limit + hyp[k] = round(hyp[k], 5) # significant digits + + # Train mutation + results = train(hyp.copy(), opt, device, wandb=wandb) + + # Write mutation results + print_mutation(hyp.copy(), results, yaml_file, opt.bucket) + + # Plot results + plot_evolution(yaml_file) + print(f'Hyperparameter evolution complete. 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null, + "overflow_y": null, + "overflow_x": null, + "grid_auto_flow": null, + "grid_area": null, + "grid_template_columns": null, + "flex": null, + "_model_name": "LayoutModel", + "justify_items": null, + "grid_row": null, + "max_height": null, + "align_content": null, + "visibility": null, + "align_self": null, + "height": null, + "min_height": null, + "padding": null, + "grid_auto_rows": null, + "grid_gap": null, + "max_width": null, + "order": null, + "_view_module_version": "1.2.0", + "grid_template_areas": null, + "object_position": null, + "object_fit": null, + "grid_auto_columns": null, + "margin": null, + "display": null, + "left": null + } + } + } + } + }, + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "id": "view-in-github", + "colab_type": "text" + }, + "source": [ + "\"Open" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "HvhYZrIZCEyo" + }, + "source": [ + "\n", + "\n", + "This notebook was written by Ultralytics LLC, and is freely available for redistribution under the [GPL-3.0 license](https://choosealicense.com/licenses/gpl-3.0/). \n", + "For more information please visit https://github.com/ultralytics/yolov5 and https://www.ultralytics.com." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "7mGmQbAO5pQb" + }, + "source": [ + "# Setup\n", + "\n", + "Clone repo, install dependencies and check PyTorch and GPU." + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "wbvMlHd_QwMG", + "colab": { + "base_uri": "https://localhost:8080/" + }, + "outputId": "888d5c41-00e9-47d8-d230-dded99325bea" + }, + "source": [ + "!git clone https://github.com/ultralytics/yolov5 # clone repo\n", + "%cd yolov5\n", + "%pip install -qr requirements.txt # install dependencies\n", + "\n", + "import torch\n", + "from IPython.display import Image, clear_output # to display images\n", + "\n", + "clear_output()\n", + "print('Setup complete. Using torch %s %s' % (torch.__version__, torch.cuda.get_device_properties(0) if torch.cuda.is_available() else 'CPU'))" + ], + "execution_count": null, + "outputs": [ + { + "output_type": "stream", + "text": [ + "Setup complete. Using torch 1.7.0+cu101 _CudaDeviceProperties(name='Tesla V100-SXM2-16GB', major=7, minor=0, total_memory=16130MB, multi_processor_count=80)\n" + ], + "name": "stdout" + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "4JnkELT0cIJg" + }, + "source": [ + "# 1. Inference\n", + "\n", + "`detect.py` runs inference on a variety of sources, downloading models automatically from the [latest YOLOv5 release](https://github.com/ultralytics/yolov5/releases)." + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "zR9ZbuQCH7FX", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 534 + }, + "outputId": "c9a308f7-2216-4805-8003-eca8dd0dc30d" + }, + "source": [ + "!python detect.py --weights yolov5s.pt --img 640 --conf 0.25 --source data/images/\n", + "Image(filename='runs/detect/exp/zidane.jpg', width=600)" + ], + "execution_count": null, + "outputs": [ + { + "output_type": "stream", + "text": [ + "Namespace(agnostic_nms=False, augment=False, classes=None, conf_thres=0.25, device='', img_size=640, iou_thres=0.45, save_conf=False, save_dir='runs/detect', save_txt=False, source='data/images/', update=False, view_img=False, weights=['yolov5s.pt'])\n", + "Using torch 1.7.0+cu101 CUDA:0 (Tesla V100-SXM2-16GB, 16130MB)\n", + "\n", + "Fusing layers... \n", + "Model Summary: 232 layers, 7459581 parameters, 0 gradients\n", + "image 1/2 /content/yolov5/data/images/bus.jpg: 640x480 4 persons, 1 buss, 1 skateboards, Done. (0.012s)\n", + "image 2/2 /content/yolov5/data/images/zidane.jpg: 384x640 2 persons, 2 ties, Done. (0.012s)\n", + "Results saved to runs/detect/exp\n", + "Done. (0.113s)\n" + ], + "name": "stdout" + }, + { + "output_type": "execute_result", + "data": { + "image/jpeg": 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\n", + "text/plain": [ + "" + ] + }, + "metadata": { + "tags": [], + "image/jpeg": { + "width": 600 + } + }, + "execution_count": 38 + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "4qbaa3iEcrcE" + }, + "source": [ + "Results are saved to `runs/detect`. A full list of available inference sources:\n", + " " + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "0eq1SMWl6Sfn" + }, + "source": [ + "# 2. Test\n", + "Test a model on [COCO](https://cocodataset.org/#home) val or test-dev dataset to evaluate trained accuracy. Models are downloaded automatically from the [latest YOLOv5 release](https://github.com/ultralytics/yolov5/releases). To show results by class use the `--verbose` flag. Note that `pycocotools` metrics may be 1-2% better than the equivalent repo metrics, as is visible below, due to slight differences in mAP computation." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "eyTZYGgRjnMc" + }, + "source": [ + "## COCO val2017\n", + "Download [COCO val 2017](https://github.com/ultralytics/yolov5/blob/74b34872fdf41941cddcf243951cdb090fbac17b/data/coco.yaml#L14) dataset (1GB - 5000 images), and test model accuracy." + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "WQPtK1QYVaD_", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 66, + "referenced_widgets": [ + "02ac0588602847eea00a0205f87bcce2", + "c472ea49806447a68b5a9221a4ddae85", + "091fdf499bd44a80af7281d16da4aa93", + "c79f69c959de4427ba102a87a9f46d80", + "c42ae5af74a0491187827d0a1fc259bb", + "5a90f72d3a2d46cb9ad915daa3ead8b4", + "2a7ed6611da34662b10e37fd4f4e4438", + "fead0160658445bf9e966daa4481cad0" + ] + }, + "outputId": "780d8f5f-766e-4b99-e370-11f9b884c27a" + }, + "source": [ + "# Download COCO val2017\n", + "torch.hub.download_url_to_file('https://github.com/ultralytics/yolov5/releases/download/v1.0/coco2017val.zip', 'tmp.zip')\n", + "!unzip -q tmp.zip -d ../ && rm tmp.zip" + ], + "execution_count": null, + "outputs": [ + { + "output_type": "display_data", + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "02ac0588602847eea00a0205f87bcce2", + "version_minor": 0, + "version_major": 2 + }, + "text/plain": [ + "HBox(children=(FloatProgress(value=0.0, max=819257867.0), HTML(value='')))" + ] + }, + "metadata": { + "tags": [] + } + }, + { + "output_type": "stream", + "text": [ + "\n" + ], + "name": "stdout" + } + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "X58w8JLpMnjH", + "colab": { + "base_uri": "https://localhost:8080/" + }, + "outputId": "013935a5-ba81-4810-b723-0cb01cf7bc79" + }, + "source": [ + "# Run YOLOv5x on COCO val2017\n", + "!python test.py --weights yolov5x.pt --data coco.yaml --img 640 --iou 0.65" + ], + "execution_count": null, + "outputs": [ + { + "output_type": "stream", + "text": [ + "Namespace(augment=False, batch_size=32, conf_thres=0.001, data='./data/coco.yaml', device='', exist_ok=False, img_size=640, iou_thres=0.65, name='exp', project='runs/test', save_conf=False, save_json=True, save_txt=False, single_cls=False, task='val', verbose=False, weights=['yolov5x.pt'])\n", + "Using torch 1.7.0+cu101 CUDA:0 (Tesla V100-SXM2-16GB, 16130MB)\n", + "\n", + "Downloading https://github.com/ultralytics/yolov5/releases/download/v3.1/yolov5x.pt to yolov5x.pt...\n", + "100% 170M/170M [00:05<00:00, 32.6MB/s]\n", + "\n", + "Fusing layers... \n", + "Model Summary: 484 layers, 88922205 parameters, 0 gradients\n", + "Scanning labels ../coco/labels/val2017.cache (4952 found, 0 missing, 48 empty, 0 duplicate, for 5000 images): 5000it [00:00, 14785.71it/s]\n", + " Class Images Targets P R mAP@.5 mAP@.5:.95: 100% 157/157 [01:30<00:00, 1.74it/s]\n", + " all 5e+03 3.63e+04 0.409 0.754 0.672 0.484\n", + "Speed: 5.9/2.1/7.9 ms inference/NMS/total per 640x640 image at batch-size 32\n", + "\n", + "Evaluating pycocotools mAP... saving runs/test/exp/yolov5x_predictions.json...\n", + "loading annotations into memory...\n", + "Done (t=0.43s)\n", + "creating index...\n", + "index created!\n", + "Loading and preparing results...\n", + "DONE (t=4.67s)\n", + "creating index...\n", + "index created!\n", + "Running per image evaluation...\n", + "Evaluate annotation type *bbox*\n", + "DONE (t=92.11s).\n", + "Accumulating evaluation results...\n", + "DONE (t=13.24s).\n", + " Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.492\n", + " Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.676\n", + " Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.534\n", + " Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.318\n", + " Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.541\n", + " Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.633\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.376\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.617\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.670\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.493\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.723\n", + " Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.812\n", + "Results saved to runs/test/exp\n" + ], + "name": "stdout" + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "rc_KbFk0juX2" + }, + "source": [ + "## COCO test-dev2017\n", + "Download [COCO test2017](https://github.com/ultralytics/yolov5/blob/74b34872fdf41941cddcf243951cdb090fbac17b/data/coco.yaml#L15) dataset (7GB - 40,000 images), to test model accuracy on test-dev set (20,000 images). Results are saved to a `*.json` file which can be submitted to the evaluation server at https://competitions.codalab.org/competitions/20794." + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "V0AJnSeCIHyJ" + }, + "source": [ + "# Download COCO test-dev2017\n", + "torch.hub.download_url_to_file('https://github.com/ultralytics/yolov5/releases/download/v1.0/coco2017labels.zip', 'tmp.zip')\n", + "!unzip -q tmp.zip -d ../ && rm tmp.zip # unzip labels\n", + "!f=\"test2017.zip\" && curl http://images.cocodataset.org/zips/$f -o $f && unzip -q $f && rm $f # 7GB, 41k images\n", + "%mv ./test2017 ./coco/images && mv ./coco ../ # move images to /coco and move /coco next to /yolov5" + ], + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "metadata": { + "id": "29GJXAP_lPrt" + }, + "source": [ + "# Run YOLOv5s on COCO test-dev2017 using --task test\n", + "!python test.py --weights yolov5s.pt --data coco.yaml --task test" + ], + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "VUOiNLtMP5aG" + }, + "source": [ + "# 3. Train\n", + "\n", + "Download [COCO128](https://www.kaggle.com/ultralytics/coco128), a small 128-image tutorial dataset, start tensorboard and train YOLOv5s from a pretrained checkpoint for 3 epochs (note actual training is typically much longer, around **300-1000 epochs**, depending on your dataset)." + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "Knxi2ncxWffW", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 66, + "referenced_widgets": [ + "cf1ab9fde7444d3e874fcd407ba8f0f8", + "9ee03f9c85f34155b2645e89c9211547", + "933ebc451c09490aadf71afbbb3dff2a", + "8e7c55cbca624432a84fa7ad8f3a4016", + "dd62d83b35d04a178840772e82bd2f2e", + "d5c4f3d1c8b046e3a163faaa6b3a51ab", + "78d1da8efb504b03878ca9ce5b404006", + "d28208ba1213436a93926a01d99d97ae" + ] + }, + "outputId": "59f9a94b-21e1-4626-f36a-a8e1b1e5c8f6" + }, + "source": [ + "# Download COCO128\n", + "torch.hub.download_url_to_file('https://github.com/ultralytics/yolov5/releases/download/v1.0/coco128.zip', 'tmp.zip')\n", + "!unzip -q tmp.zip -d ../ && rm tmp.zip" + ], + "execution_count": null, + "outputs": [ + { + "output_type": "display_data", + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "cf1ab9fde7444d3e874fcd407ba8f0f8", + "version_minor": 0, + "version_major": 2 + }, + "text/plain": [ + "HBox(children=(FloatProgress(value=0.0, max=22090455.0), HTML(value='')))" + ] + }, + "metadata": { + "tags": [] + } + }, + { + "output_type": "stream", + "text": [ + "\n" + ], + "name": "stdout" + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "_pOkGLv1dMqh" + }, + "source": [ + "Train a YOLOv5s model on [COCO128](https://www.kaggle.com/ultralytics/coco128) with `--data coco128.yaml`, starting from pretrained `--weights yolov5s.pt`, or from randomly initialized `--weights '' --cfg yolov5s.yaml`. Models are downloaded automatically from the [latest YOLOv5 release](https://github.com/ultralytics/yolov5/releases), and **COCO, COCO128, and VOC datasets are downloaded automatically** on first use.\n", + "\n", + "All training results are saved to `runs/train/` with incrementing run directories, i.e. `runs/train/exp2`, `runs/train/exp3` etc.\n" + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "bOy5KI2ncnWd" + }, + "source": [ + "# Tensorboard (optional)\n", + "%load_ext tensorboard\n", + "%tensorboard --logdir runs/train" + ], + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "metadata": { + "id": "2fLAV42oNb7M" + }, + "source": [ + "# Weights & Biases (optional)\n", + "%pip install -q wandb \n", + "!wandb login # use 'wandb disabled' or 'wandb enabled' to disable or enable" + ], + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "metadata": { + "id": "1NcFxRcFdJ_O", + "colab": { + "base_uri": "https://localhost:8080/" + }, + "outputId": "138f2d1d-364c-405a-cf13-ea91a2aff915" + }, + "source": [ + "# Train YOLOv5s on COCO128 for 3 epochs\n", + "!python train.py --img 640 --batch 16 --epochs 3 --data coco128.yaml --weights yolov5s.pt --nosave --cache" + ], + "execution_count": null, + "outputs": [ + { + "output_type": "stream", + "text": [ + "Using torch 1.7.0+cu101 CUDA:0 (Tesla V100-SXM2-16GB, 16130MB)\n", + "\n", + "Namespace(adam=False, batch_size=16, bucket='', cache_images=True, cfg='', data='./data/coco128.yaml', device='', epochs=3, evolve=False, exist_ok=False, global_rank=-1, hyp='data/hyp.scratch.yaml', image_weights=False, img_size=[640, 640], local_rank=-1, log_imgs=16, multi_scale=False, name='exp', noautoanchor=False, nosave=True, notest=False, project='runs/train', rect=False, resume=False, save_dir='runs/train/exp', single_cls=False, sync_bn=False, total_batch_size=16, weights='yolov5s.pt', workers=8, world_size=1)\n", + "Start Tensorboard with \"tensorboard --logdir runs/train\", view at http://localhost:6006/\n", + "2020-11-20 11:45:17.042357: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library libcudart.so.10.1\n", + "Hyperparameters {'lr0': 0.01, 'lrf': 0.2, 'momentum': 0.937, 'weight_decay': 0.0005, 'warmup_epochs': 3.0, 'warmup_momentum': 0.8, 'warmup_bias_lr': 0.1, 'box': 0.05, 'cls': 0.5, 'cls_pw': 1.0, 'obj': 1.0, 'obj_pw': 1.0, 'iou_t': 0.2, 'anchor_t': 4.0, 'fl_gamma': 0.0, 'hsv_h': 0.015, 'hsv_s': 0.7, 'hsv_v': 0.4, 'degrees': 0.0, 'translate': 0.1, 'scale': 0.5, 'shear': 0.0, 'perspective': 0.0, 'flipud': 0.0, 'fliplr': 0.5, 'mosaic': 1.0, 'mixup': 0.0}\n", + "Downloading https://github.com/ultralytics/yolov5/releases/download/v3.1/yolov5s.pt to yolov5s.pt...\n", + "100% 14.5M/14.5M [00:01<00:00, 14.8MB/s]\n", + "\n", + "\n", + " from n params module arguments \n", + " 0 -1 1 3520 models.common.Focus [3, 32, 3] \n", + " 1 -1 1 18560 models.common.Conv [32, 64, 3, 2] \n", + " 2 -1 1 19904 models.common.BottleneckCSP [64, 64, 1] \n", + " 3 -1 1 73984 models.common.Conv [64, 128, 3, 2] \n", + " 4 -1 1 161152 models.common.BottleneckCSP [128, 128, 3] \n", + " 5 -1 1 295424 models.common.Conv [128, 256, 3, 2] \n", + " 6 -1 1 641792 models.common.BottleneckCSP [256, 256, 3] \n", + " 7 -1 1 1180672 models.common.Conv [256, 512, 3, 2] \n", + " 8 -1 1 656896 models.common.SPP [512, 512, [5, 9, 13]] \n", + " 9 -1 1 1248768 models.common.BottleneckCSP [512, 512, 1, False] \n", + " 10 -1 1 131584 models.common.Conv [512, 256, 1, 1] \n", + " 11 -1 1 0 torch.nn.modules.upsampling.Upsample [None, 2, 'nearest'] \n", + " 12 [-1, 6] 1 0 models.common.Concat [1] \n", + " 13 -1 1 378624 models.common.BottleneckCSP [512, 256, 1, False] \n", + " 14 -1 1 33024 models.common.Conv [256, 128, 1, 1] \n", + " 15 -1 1 0 torch.nn.modules.upsampling.Upsample [None, 2, 'nearest'] \n", + " 16 [-1, 4] 1 0 models.common.Concat [1] \n", + " 17 -1 1 95104 models.common.BottleneckCSP [256, 128, 1, False] \n", + " 18 -1 1 147712 models.common.Conv [128, 128, 3, 2] \n", + " 19 [-1, 14] 1 0 models.common.Concat [1] \n", + " 20 -1 1 313088 models.common.BottleneckCSP [256, 256, 1, False] \n", + " 21 -1 1 590336 models.common.Conv [256, 256, 3, 2] \n", + " 22 [-1, 10] 1 0 models.common.Concat [1] \n", + " 23 -1 1 1248768 models.common.BottleneckCSP [512, 512, 1, False] \n", + " 24 [17, 20, 23] 1 229245 models.yolo.Detect [80, [[10, 13, 16, 30, 33, 23], [30, 61, 62, 45, 59, 119], [116, 90, 156, 198, 373, 326]], [128, 256, 512]]\n", + "Model Summary: 283 layers, 7468157 parameters, 7468157 gradients\n", + "\n", + "Transferred 370/370 items from yolov5s.pt\n", + "Optimizer groups: 62 .bias, 70 conv.weight, 59 other\n", + "Scanning images: 100% 128/128 [00:00<00:00, 5395.63it/s]\n", + "Scanning labels ../coco128/labels/train2017.cache (126 found, 0 missing, 2 empty, 0 duplicate, for 128 images): 128it [00:00, 13972.28it/s]\n", + "Caching images (0.1GB): 100% 128/128 [00:00<00:00, 173.55it/s]\n", + "Scanning labels ../coco128/labels/train2017.cache (126 found, 0 missing, 2 empty, 0 duplicate, for 128 images): 128it [00:00, 8693.98it/s]\n", + "Caching images (0.1GB): 100% 128/128 [00:00<00:00, 133.30it/s]\n", + "NumExpr defaulting to 2 threads.\n", + "\n", + "Analyzing anchors... anchors/target = 4.26, Best Possible Recall (BPR) = 0.9946\n", + "Image sizes 640 train, 640 test\n", + "Using 2 dataloader workers\n", + "Logging results to runs/train/exp\n", + "Starting training for 3 epochs...\n", + "\n", + " Epoch gpu_mem box obj cls total targets img_size\n", + " 0/2 5.24G 0.04202 0.06745 0.01503 0.1245 194 640: 100% 8/8 [00:03<00:00, 2.01it/s]\n", + " Class Images Targets P R mAP@.5 mAP@.5:.95: 100% 8/8 [00:03<00:00, 2.40it/s]\n", + " all 128 929 0.404 0.758 0.701 0.45\n", + "\n", + " Epoch gpu_mem box obj cls total targets img_size\n", + " 1/2 5.12G 0.04461 0.05874 0.0169 0.1202 142 640: 100% 8/8 [00:01<00:00, 4.14it/s]\n", + " Class Images Targets P R mAP@.5 mAP@.5:.95: 100% 8/8 [00:01<00:00, 5.75it/s]\n", + " all 128 929 0.403 0.772 0.703 0.453\n", + "\n", + " Epoch gpu_mem box obj cls total targets img_size\n", + " 2/2 5.12G 0.04445 0.06545 0.01667 0.1266 149 640: 100% 8/8 [00:01<00:00, 4.15it/s]\n", + " Class Images Targets P R mAP@.5 mAP@.5:.95: 100% 8/8 [00:06<00:00, 1.18it/s]\n", + " all 128 929 0.395 0.767 0.702 0.452\n", + "Optimizer stripped from runs/train/exp/weights/last.pt, 15.2MB\n", + "3 epochs completed in 0.006 hours.\n", + "\n" + ], + "name": "stdout" + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "15glLzbQx5u0" + }, + "source": [ + "# 4. Visualize" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "DLI1JmHU7B0l" + }, + "source": [ + "## Weights & Biases Logging 🌟 NEW\n", + "\n", + "[Weights & Biases](https://www.wandb.com/) (W&B) is now 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), 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", + "" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "-WPvRbS5Swl6" + }, + "source": [ + "## Local Logging\n", + "\n", + "All results are logged by default to `runs/train`, with a new experiment directory created for each new training as `runs/train/exp2`, `runs/train/exp3`, etc. View train and test jpgs to see mosaics, labels, predictions and augmentation effects. Note a **Mosaic Dataloader** is used for training (shown below), a new concept developed by Ultralytics and first featured in [YOLOv4](https://arxiv.org/abs/2004.10934)." + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "riPdhraOTCO0" + }, + "source": [ + "Image(filename='runs/train/exp/train_batch0.jpg', width=800) # train batch 0 mosaics and labels\n", + "Image(filename='runs/train/exp/test_batch0_labels.jpg', width=800) # test batch 0 labels\n", + "Image(filename='runs/train/exp/test_batch0_pred.jpg', width=800) # test batch 0 predictions" + ], + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "OYG4WFEnTVrI" + }, + "source": [ + "> \n", + "`train_batch0.jpg` shows train batch 0 mosaics and labels\n", + "\n", + "> \n", + "`test_batch0_labels.jpg` shows test batch 0 labels\n", + "\n", + "> \n", + "`test_batch0_pred.jpg` shows test batch 0 _predictions_\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "7KN5ghjE6ZWh" + }, + "source": [ + "Training losses and performance metrics are also logged to [Tensorboard](https://www.tensorflow.org/tensorboard) and a custom `results.txt` logfile which is plotted as `results.png` (below) after training completes. Here we show YOLOv5s trained on COCO128 to 300 epochs, starting from scratch (blue), and from pretrained `--weights yolov5s.pt` (orange)." + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "MDznIqPF7nk3" + }, + "source": [ + "from utils.plots import plot_results \n", + "plot_results(save_dir='runs/train/exp') # plot all results*.txt as results.png\n", + "Image(filename='runs/train/exp/results.png', width=800)" + ], + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "lfrEegCSW3fK" + }, + "source": [ + "\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", + "- **Google Colab Notebook** with free GPU: \"Open\n", + "- **Kaggle Notebook** with free GPU: [https://www.kaggle.com/ultralytics/yolov5](https://www.kaggle.com/ultralytics/yolov5)\n", + "- **Google Cloud** Deep Learning VM. See [GCP Quickstart Guide](https://github.com/ultralytics/yolov5/wiki/GCP-Quickstart) \n", + "- **Docker Image** https://hub.docker.com/r/ultralytics/yolov5. See [Docker Quickstart Guide](https://github.com/ultralytics/yolov5/wiki/Docker-Quickstart) ![Docker Pulls](https://img.shields.io/docker/pulls/ultralytics/yolov5?logo=docker)\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "IEijrePND_2I" + }, + "source": [ + "# Appendix\n", + "\n", + "Optional extras below. Unit tests validate repo functionality and should be run on any PRs submitted.\n" + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "gI6NoBev8Ib1" + }, + "source": [ + "# Re-clone repo\n", + "%cd ..\n", + "%rm -rf yolov5 && git clone https://github.com/ultralytics/yolov5\n", + "%cd yolov5" + ], + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "metadata": { + "id": "mcKoSIK2WSzj" + }, + "source": [ + "# Reproduce\n", + "%%shell\n", + "for x in yolov5s yolov5m yolov5l yolov5x; do\n", + " python test.py --weights $x.pt --data coco.yaml --img 640 --conf 0.25 --iou 0.45 # speed\n", + " python test.py --weights $x.pt --data coco.yaml --img 640 --conf 0.001 --iou 0.65 # mAP\n", + "done" + ], + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "metadata": { + "id": "FGH0ZjkGjejy" + }, + "source": [ + "# Unit tests\n", + "%%shell\n", + "export PYTHONPATH=\"$PWD\" # to run *.py. files in subdirectories\n", + "\n", + "rm -rf runs # remove runs/\n", + "for m in yolov5s; do # models\n", + " python train.py --weights $m.pt --epochs 3 --img 320 --device 0 # train pretrained\n", + " python train.py --weights '' --cfg $m.yaml --epochs 3 --img 320 --device 0 # train scratch\n", + " for d in 0 cpu; do # devices\n", + " python detect.py --weights $m.pt --device $d # detect official\n", + " python detect.py --weights runs/train/exp/weights/best.pt --device $d # detect custom\n", + " python test.py --weights $m.pt --device $d # test official\n", + " python test.py --weights runs/train/exp/weights/best.pt --device $d # test custom\n", + " done\n", + " python hubconf.py # hub\n", + " python models/yolo.py --cfg $m.yaml # inspect\n", + " python models/export.py --weights $m.pt --img 640 --batch 1 # export\n", + "done" + ], + "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", + "profile(x=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, 48, 32, 16], ['yolov5s', 'yolov5m', 'yolov5l', 'yolov5x']): # zip(batch_size, model)\n", + " !python train.py --batch {b} --weights {m}.pt --data voc.yaml --epochs 50 --cache --img 512 --nosave --hyp hyp.finetune.yaml --project VOC --name {m}" + ], + "execution_count": null, + "outputs": [] + } + ] +} \ No newline at end of file diff --git a/yolov5/utils/__init__.py b/yolov5/utils/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/yolov5/utils/activations.py b/yolov5/utils/activations.py new file mode 100644 index 0000000..954d2e1 --- /dev/null +++ b/yolov5/utils/activations.py @@ -0,0 +1,72 @@ +# Activation functions + +import torch +import torch.nn as nn +import torch.nn.functional as F + + +# SiLU https://arxiv.org/pdf/1905.02244.pdf ---------------------------------------------------------------------------- +class SiLU(nn.Module): # export-friendly version of nn.SiLU() + @staticmethod + def forward(x): + return x * torch.sigmoid(x) + + +class Hardswish(nn.Module): # export-friendly version of nn.Hardswish() + @staticmethod + def forward(x): + # return x * F.hardsigmoid(x) # for torchscript and CoreML + return x * F.hardtanh(x + 3, 0., 6.) / 6. # for torchscript, CoreML and ONNX + + +class MemoryEfficientSwish(nn.Module): + class F(torch.autograd.Function): + @staticmethod + def forward(ctx, x): + ctx.save_for_backward(x) + return x * torch.sigmoid(x) + + @staticmethod + def backward(ctx, grad_output): + x = ctx.saved_tensors[0] + sx = torch.sigmoid(x) + return grad_output * (sx * (1 + x * (1 - sx))) + + def forward(self, x): + return self.F.apply(x) + + +# Mish https://github.com/digantamisra98/Mish -------------------------------------------------------------------------- +class Mish(nn.Module): + @staticmethod + def forward(x): + return x * F.softplus(x).tanh() + + +class MemoryEfficientMish(nn.Module): + class F(torch.autograd.Function): + @staticmethod + def forward(ctx, x): + ctx.save_for_backward(x) + return x.mul(torch.tanh(F.softplus(x))) # x * tanh(ln(1 + exp(x))) + + @staticmethod + def backward(ctx, grad_output): + x = ctx.saved_tensors[0] + sx = torch.sigmoid(x) + fx = F.softplus(x).tanh() + return grad_output * (fx + x * sx * (1 - fx * fx)) + + def forward(self, x): + return self.F.apply(x) + + +# FReLU https://arxiv.org/abs/2007.11824 ------------------------------------------------------------------------------- +class FReLU(nn.Module): + def __init__(self, c1, k=3): # ch_in, kernel + super().__init__() + self.conv = nn.Conv2d(c1, c1, k, 1, 1, groups=c1, bias=False) + self.bn = nn.BatchNorm2d(c1) + + def forward(self, x): + return torch.max(x, self.bn(self.conv(x))) diff --git a/yolov5/utils/autoanchor.py b/yolov5/utils/autoanchor.py new file mode 100644 index 0000000..0c33dcb --- /dev/null +++ b/yolov5/utils/autoanchor.py @@ -0,0 +1,151 @@ +# Auto-anchor utils + +import numpy as np +import torch +import yaml +from scipy.cluster.vq import kmeans +from tqdm import tqdm + + +def check_anchor_order(m): + # Check anchor order against stride order for YOLOv5 Detect() module m, and correct if necessary + a = m.anchor_grid.prod(-1).view(-1) # anchor area + da = a[-1] - a[0] # delta a + ds = m.stride[-1] - m.stride[0] # delta s + if da.sign() != ds.sign(): # same order + print('Reversing anchor order') + m.anchors[:] = m.anchors.flip(0) + m.anchor_grid[:] = m.anchor_grid.flip(0) + + +def check_anchors(dataset, model, thr=4.0, imgsz=640): + # Check anchor fit to data, recompute if necessary + print('\nAnalyzing anchors... ', end='') + m = model.module.model[-1] if hasattr(model, 'module') else model.model[-1] # Detect() + shapes = imgsz * dataset.shapes / dataset.shapes.max(1, keepdims=True) + scale = np.random.uniform(0.9, 1.1, size=(shapes.shape[0], 1)) # augment scale + wh = torch.tensor(np.concatenate([l[:, 3:5] * s for s, l in zip(shapes * scale, dataset.labels)])).float() # wh + + def metric(k): # compute metric + r = wh[:, None] / k[None] + x = torch.min(r, 1. / r).min(2)[0] # ratio metric + best = x.max(1)[0] # best_x + aat = (x > 1. / thr).float().sum(1).mean() # anchors above threshold + bpr = (best > 1. / thr).float().mean() # best possible recall + return bpr, aat + + bpr, aat = metric(m.anchor_grid.clone().cpu().view(-1, 2)) + print('anchors/target = %.2f, Best Possible Recall (BPR) = %.4f' % (aat, bpr), end='') + if bpr < 0.98: # threshold to recompute + print('. Attempting to improve anchors, please wait...') + na = m.anchor_grid.numel() // 2 # number of anchors + new_anchors = kmean_anchors(dataset, n=na, img_size=imgsz, thr=thr, gen=1000, verbose=False) + new_bpr = metric(new_anchors.reshape(-1, 2))[0] + if new_bpr > bpr: # replace anchors + new_anchors = torch.tensor(new_anchors, device=m.anchors.device).type_as(m.anchors) + m.anchor_grid[:] = new_anchors.clone().view_as(m.anchor_grid) # for inference + m.anchors[:] = new_anchors.clone().view_as(m.anchors) / m.stride.to(m.anchors.device).view(-1, 1, 1) # loss + check_anchor_order(m) + print('New anchors saved to model. Update model *.yaml to use these anchors in the future.') + else: + print('Original anchors better than new anchors. Proceeding with original anchors.') + print('') # newline + + +def kmean_anchors(path='./data/coco128.yaml', n=9, img_size=640, thr=4.0, gen=1000, verbose=True): + """ Creates kmeans-evolved anchors from training dataset + + Arguments: + path: path to dataset *.yaml, or a loaded dataset + n: number of anchors + img_size: image size used for training + thr: anchor-label wh ratio threshold hyperparameter hyp['anchor_t'] used for training, default=4.0 + gen: generations to evolve anchors using genetic algorithm + verbose: print all results + + Return: + k: kmeans evolved anchors + + Usage: + from utils.autoanchor import *; _ = kmean_anchors() + """ + thr = 1. / thr + + def metric(k, wh): # compute metrics + r = wh[:, None] / k[None] + x = torch.min(r, 1. / r).min(2)[0] # ratio metric + # x = wh_iou(wh, torch.tensor(k)) # iou metric + return x, x.max(1)[0] # x, best_x + + def anchor_fitness(k): # mutation fitness + _, best = metric(torch.tensor(k, dtype=torch.float32), wh) + return (best * (best > thr).float()).mean() # fitness + + def print_results(k): + k = k[np.argsort(k.prod(1))] # sort small to large + x, best = metric(k, wh0) + bpr, aat = (best > thr).float().mean(), (x > thr).float().mean() * n # best possible recall, anch > thr + print('thr=%.2f: %.4f best possible recall, %.2f anchors past thr' % (thr, bpr, aat)) + print('n=%g, img_size=%s, metric_all=%.3f/%.3f-mean/best, past_thr=%.3f-mean: ' % + (n, img_size, x.mean(), best.mean(), x[x > thr].mean()), end='') + for i, x in enumerate(k): + print('%i,%i' % (round(x[0]), round(x[1])), end=', ' if i < len(k) - 1 else '\n') # use in *.cfg + return k + + if isinstance(path, str): # *.yaml file + with open(path) as f: + data_dict = yaml.load(f, Loader=yaml.FullLoader) # model dict + from utils.datasets import LoadImagesAndLabels + dataset = LoadImagesAndLabels(data_dict['train'], augment=True, rect=True) + else: + dataset = path # dataset + + # Get label wh + shapes = img_size * dataset.shapes / dataset.shapes.max(1, keepdims=True) + wh0 = np.concatenate([l[:, 3:5] * s for s, l in zip(shapes, dataset.labels)]) # wh + + # Filter + i = (wh0 < 3.0).any(1).sum() + if i: + print('WARNING: Extremely small objects found. ' + '%g of %g labels are < 3 pixels in width or height.' % (i, len(wh0))) + wh = wh0[(wh0 >= 2.0).any(1)] # filter > 2 pixels + + # Kmeans calculation + print('Running kmeans for %g anchors on %g points...' % (n, len(wh))) + s = wh.std(0) # sigmas for whitening + k, dist = kmeans(wh / s, n, iter=30) # points, mean distance + k *= s + wh = torch.tensor(wh, dtype=torch.float32) # filtered + wh0 = torch.tensor(wh0, dtype=torch.float32) # unfiltered + k = print_results(k) + + # Plot + # k, d = [None] * 20, [None] * 20 + # for i in tqdm(range(1, 21)): + # k[i-1], d[i-1] = kmeans(wh / s, i) # points, mean distance + # fig, ax = plt.subplots(1, 2, figsize=(14, 7), tight_layout=True) + # ax = ax.ravel() + # ax[0].plot(np.arange(1, 21), np.array(d) ** 2, marker='.') + # fig, ax = plt.subplots(1, 2, figsize=(14, 7)) # plot wh + # ax[0].hist(wh[wh[:, 0]<100, 0],400) + # ax[1].hist(wh[wh[:, 1]<100, 1],400) + # fig.savefig('wh.png', dpi=200) + + # Evolve + npr = np.random + f, sh, mp, s = anchor_fitness(k), k.shape, 0.9, 0.1 # fitness, generations, mutation prob, sigma + pbar = tqdm(range(gen), desc='Evolving anchors with Genetic Algorithm') # progress bar + for _ in pbar: + v = np.ones(sh) + while (v == 1).all(): # mutate until a change occurs (prevent duplicates) + v = ((npr.random(sh) < mp) * npr.random() * npr.randn(*sh) * s + 1).clip(0.3, 3.0) + kg = (k.copy() * v).clip(min=2.0) + fg = anchor_fitness(kg) + if fg > f: + f, k = fg, kg.copy() + pbar.desc = 'Evolving anchors with Genetic Algorithm: fitness = %.4f' % f + if verbose: + print_results(k) + + return print_results(k) diff --git a/yolov5/utils/datasets.py b/yolov5/utils/datasets.py new file mode 100644 index 0000000..e2b139f --- /dev/null +++ b/yolov5/utils/datasets.py @@ -0,0 +1,933 @@ +# Dataset utils and dataloaders + +import glob +import logging +import math +import os +import random +import shutil +import time +from itertools import repeat +from multiprocessing.pool import ThreadPool +from pathlib import Path +from threading import Thread + +import cv2 +import numpy as np +import torch +from PIL import Image, ExifTags +from torch.utils.data import Dataset +from tqdm import tqdm + +from utils.general import xyxy2xywh, xywh2xyxy, clean_str +from utils.torch_utils import torch_distributed_zero_first + +# Parameters +help_url = 'https://github.com/ultralytics/yolov5/wiki/Train-Custom-Data' +img_formats = ['bmp', 'jpg', 'jpeg', 'png', 'tif', 'tiff', 'dng'] # acceptable image suffixes +vid_formats = ['mov', 'avi', 'mp4', 'mpg', 'mpeg', 'm4v', 'wmv', 'mkv'] # acceptable video suffixes +logger = logging.getLogger(__name__) + +# Get orientation exif tag +for orientation in ExifTags.TAGS.keys(): + if ExifTags.TAGS[orientation] == 'Orientation': + break + + +def get_hash(files): + # Returns a single hash value of a list of files + return sum(os.path.getsize(f) for f in files if os.path.isfile(f)) + + +def exif_size(img): + # Returns exif-corrected PIL size + s = img.size # (width, height) + try: + rotation = dict(img._getexif().items())[orientation] + if rotation == 6: # rotation 270 + s = (s[1], s[0]) + elif rotation == 8: # rotation 90 + s = (s[1], s[0]) + except: + pass + + return s + + +def create_dataloader(path, imgsz, batch_size, stride, opt, hyp=None, augment=False, cache=False, pad=0.0, rect=False, + rank=-1, world_size=1, workers=8, image_weights=False): + # Make sure only the first process in DDP process the dataset first, and the following others can use the cache + with torch_distributed_zero_first(rank): + dataset = LoadImagesAndLabels(path, imgsz, batch_size, + augment=augment, # augment images + hyp=hyp, # augmentation hyperparameters + rect=rect, # rectangular training + cache_images=cache, + single_cls=opt.single_cls, + stride=int(stride), + pad=pad, + rank=rank, + image_weights=image_weights) + + batch_size = min(batch_size, len(dataset)) + nw = min([os.cpu_count() // world_size, batch_size if batch_size > 1 else 0, workers]) # number of workers + sampler = torch.utils.data.distributed.DistributedSampler(dataset) if rank != -1 else None + loader = torch.utils.data.DataLoader if image_weights else InfiniteDataLoader + # Use torch.utils.data.DataLoader() if dataset.properties will update during training else InfiniteDataLoader() + dataloader = loader(dataset, + batch_size=batch_size, + num_workers=nw, + sampler=sampler, + pin_memory=True, + collate_fn=LoadImagesAndLabels.collate_fn) + return dataloader, dataset + + +class InfiniteDataLoader(torch.utils.data.dataloader.DataLoader): + """ Dataloader that reuses workers + + Uses same syntax as vanilla DataLoader + """ + + def __init__(self, *args, **kwargs): + super().__init__(*args, **kwargs) + object.__setattr__(self, 'batch_sampler', _RepeatSampler(self.batch_sampler)) + self.iterator = super().__iter__() + + def __len__(self): + return len(self.batch_sampler.sampler) + + def __iter__(self): + for i in range(len(self)): + yield next(self.iterator) + + +class _RepeatSampler(object): + """ Sampler that repeats forever + + Args: + sampler (Sampler) + """ + + def __init__(self, sampler): + self.sampler = sampler + + def __iter__(self): + while True: + yield from iter(self.sampler) + + +class LoadImages: # for inference + def __init__(self, path, img_size=640): + p = str(Path(path)) # os-agnostic + p = os.path.abspath(p) # absolute path + if '*' in p: + files = sorted(glob.glob(p, recursive=True)) # glob + elif os.path.isdir(p): + files = sorted(glob.glob(os.path.join(p, '*.*'))) # dir + elif os.path.isfile(p): + files = [p] # files + else: + raise Exception('ERROR: %s does not exist' % p) + + images = [x for x in files if x.split('.')[-1].lower() in img_formats] + videos = [x for x in files if x.split('.')[-1].lower() in vid_formats] + ni, nv = len(images), len(videos) + + self.img_size = img_size + self.files = images + videos + self.nf = ni + nv # number of files + self.video_flag = [False] * ni + [True] * nv + self.mode = 'image' + if any(videos): + self.new_video(videos[0]) # new video + else: + self.cap = None + assert self.nf > 0, 'No images or videos found in %s. Supported formats are:\nimages: %s\nvideos: %s' % \ + (p, img_formats, vid_formats) + + def __iter__(self): + self.count = 0 + return self + + def __next__(self): + if self.count == self.nf: + raise StopIteration + path = self.files[self.count] + + if self.video_flag[self.count]: + # Read video + self.mode = 'video' + ret_val, img0 = self.cap.read() + if not ret_val: + self.count += 1 + self.cap.release() + if self.count == self.nf: # last video + raise StopIteration + else: + path = self.files[self.count] + self.new_video(path) + ret_val, img0 = self.cap.read() + + self.frame += 1 + print('video %g/%g (%g/%g) %s: ' % (self.count + 1, self.nf, self.frame, self.nframes, path), end='') + + else: + # Read image + self.count += 1 + img0 = cv2.imread(path) # BGR + assert img0 is not None, 'Image Not Found ' + path + print('image %g/%g %s: ' % (self.count, self.nf, path), end='') + + # Padded resize + img = letterbox(img0, new_shape=self.img_size)[0] + + # Convert + img = img[:, :, ::-1].transpose(2, 0, 1) # BGR to RGB, to 3x416x416 + img = np.ascontiguousarray(img) + + return path, img, img0, self.cap + + def new_video(self, path): + self.frame = 0 + self.cap = cv2.VideoCapture(path) + self.nframes = int(self.cap.get(cv2.CAP_PROP_FRAME_COUNT)) + + def __len__(self): + return self.nf # number of files + + +class LoadWebcam: # for inference + def __init__(self, pipe='0', img_size=640): + self.img_size = img_size + + if pipe.isnumeric(): + pipe = eval(pipe) # local camera + # pipe = 'rtsp://192.168.1.64/1' # IP camera + # pipe = 'rtsp://username:password@192.168.1.64/1' # IP camera with login + # pipe = 'http://wmccpinetop.axiscam.net/mjpg/video.mjpg' # IP golf camera + + self.pipe = pipe + self.cap = cv2.VideoCapture(pipe) # video capture object + self.cap.set(cv2.CAP_PROP_BUFFERSIZE, 3) # set buffer size + + def __iter__(self): + self.count = -1 + return self + + def __next__(self): + self.count += 1 + if cv2.waitKey(1) == ord('q'): # q to quit + self.cap.release() + cv2.destroyAllWindows() + raise StopIteration + + # Read frame + if self.pipe == 0: # local camera + ret_val, img0 = self.cap.read() + img0 = cv2.flip(img0, 1) # flip left-right + else: # IP camera + n = 0 + while True: + n += 1 + self.cap.grab() + if n % 30 == 0: # skip frames + ret_val, img0 = self.cap.retrieve() + if ret_val: + break + + # Print + assert ret_val, 'Camera Error %s' % self.pipe + img_path = 'webcam.jpg' + print('webcam %g: ' % self.count, end='') + + # Padded resize + img = letterbox(img0, new_shape=self.img_size)[0] + + # Convert + img = img[:, :, ::-1].transpose(2, 0, 1) # BGR to RGB, to 3x416x416 + img = np.ascontiguousarray(img) + + return img_path, img, img0, None + + def __len__(self): + return 0 + + +class LoadStreams: # multiple IP or RTSP cameras + def __init__(self, sources='streams.txt', img_size=640): + self.mode = 'stream' + self.img_size = img_size + + if os.path.isfile(sources): + with open(sources, 'r') as f: + sources = [x.strip() for x in f.read().strip().splitlines() if len(x.strip())] + else: + sources = [sources] + + n = len(sources) + self.imgs = [None] * n + self.sources = [clean_str(x) for x in sources] # clean source names for later + for i, s in enumerate(sources): + # Start the thread to read frames from the video stream + print('%g/%g: %s... ' % (i + 1, n, s), end='') + cap = cv2.VideoCapture(eval(s) if s.isnumeric() else s) + assert cap.isOpened(), 'Failed to open %s' % s + w = int(cap.get(cv2.CAP_PROP_FRAME_WIDTH)) + h = int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT)) + fps = cap.get(cv2.CAP_PROP_FPS) % 100 + _, self.imgs[i] = cap.read() # guarantee first frame + thread = Thread(target=self.update, args=([i, cap]), daemon=True) + print(' success (%gx%g at %.2f FPS).' % (w, h, fps)) + thread.start() + print('') # newline + + # check for common shapes + s = np.stack([letterbox(x, new_shape=self.img_size)[0].shape for x in self.imgs], 0) # inference shapes + self.rect = np.unique(s, axis=0).shape[0] == 1 # rect inference if all shapes equal + if not self.rect: + print('WARNING: Different stream shapes detected. For optimal performance supply similarly-shaped streams.') + + def update(self, index, cap): + # Read next stream frame in a daemon thread + n = 0 + while cap.isOpened(): + n += 1 + # _, self.imgs[index] = cap.read() + cap.grab() + if n == 4: # read every 4th frame + _, self.imgs[index] = cap.retrieve() + n = 0 + time.sleep(0.01) # wait time + + def __iter__(self): + self.count = -1 + return self + + def __next__(self): + self.count += 1 + img0 = self.imgs.copy() + if cv2.waitKey(1) == ord('q'): # q to quit + cv2.destroyAllWindows() + raise StopIteration + + # Letterbox + img = [letterbox(x, new_shape=self.img_size, auto=self.rect)[0] for x in img0] + + # Stack + img = np.stack(img, 0) + + # Convert + img = img[:, :, :, ::-1].transpose(0, 3, 1, 2) # BGR to RGB, to bsx3x416x416 + img = np.ascontiguousarray(img) + + return self.sources, img, img0, None + + def __len__(self): + return 0 # 1E12 frames = 32 streams at 30 FPS for 30 years + + +def img2label_paths(img_paths): + # Define label paths as a function of image paths + sa, sb = os.sep + 'images' + os.sep, os.sep + 'labels' + os.sep # /images/, /labels/ substrings + return [x.replace(sa, sb, 1).replace('.' + x.split('.')[-1], '.txt') for x in img_paths] + + +class LoadImagesAndLabels(Dataset): # 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=0.0, rank=-1): + self.img_size = img_size + self.augment = augment + self.hyp = hyp + self.image_weights = image_weights + self.rect = False if image_weights else rect + self.mosaic = self.augment and not self.rect # load 4 images at a time into a mosaic (only during training) + self.mosaic_border = [-img_size // 2, -img_size // 2] + self.stride = stride + + try: + f = [] # image files + for p in path if isinstance(path, list) else [path]: + p = Path(p) # os-agnostic + if p.is_dir(): # dir + f += glob.glob(str(p / '**' / '*.*'), recursive=True) + elif p.is_file(): # file + with open(p, 'r') 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 + else: + raise Exception('%s does not exist' % p) + self.img_files = sorted([x.replace('/', os.sep) for x in f if x.split('.')[-1].lower() in img_formats]) + assert self.img_files, 'No images found' + except Exception as e: + raise Exception('Error loading data from %s: %s\nSee %s' % (path, e, help_url)) + + # Check cache + self.label_files = img2label_paths(self.img_files) # labels + cache_path = Path(self.label_files[0]).parent.with_suffix('.cache') # cached labels + if cache_path.is_file(): + cache = torch.load(cache_path) # load + if cache['hash'] != get_hash(self.label_files + self.img_files) or 'results' not in cache: # changed + cache = self.cache_labels(cache_path) # re-cache + else: + cache = self.cache_labels(cache_path) # cache + + # Display cache + [nf, nm, ne, nc, n] = cache.pop('results') # found, missing, empty, corrupted, total + desc = f"Scanning '{cache_path}' for images and labels... {nf} found, {nm} missing, {ne} empty, {nc} corrupted" + tqdm(None, desc=desc, total=n, initial=n) + assert nf > 0 or not augment, f'No labels found in {cache_path}. Can not train without labels. See {help_url}' + + # Read cache + cache.pop('hash') # remove hash + labels, shapes = zip(*cache.values()) + self.labels = list(labels) + self.shapes = np.array(shapes, dtype=np.float64) + self.img_files = list(cache.keys()) # update + self.label_files = img2label_paths(cache.keys()) # update + if single_cls: + for x in self.labels: + x[:, 0] = 0 + + n = len(shapes) # number of images + bi = np.floor(np.arange(n) / batch_size).astype(np.int) # batch index + nb = bi[-1] + 1 # number of batches + self.batch = bi # batch index of image + self.n = n + self.indices = range(n) + + # Rectangular Training + if self.rect: + # Sort by aspect ratio + s = self.shapes # wh + ar = s[:, 1] / s[:, 0] # aspect ratio + irect = ar.argsort() + self.img_files = [self.img_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.shapes = s[irect] # wh + ar = ar[irect] + + # Set training image shapes + shapes = [[1, 1]] * nb + for i in range(nb): + ari = ar[bi == i] + mini, maxi = ari.min(), ari.max() + if maxi < 1: + shapes[i] = [maxi, 1] + elif mini > 1: + shapes[i] = [1, 1 / mini] + + self.batch_shapes = np.ceil(np.array(shapes) * img_size / stride + pad).astype(np.int) * stride + + # Cache images into memory for faster training (WARNING: large datasets may exceed system RAM) + self.imgs = [None] * n + if cache_images: + gb = 0 # Gigabytes of cached images + self.img_hw0, self.img_hw = [None] * n, [None] * n + results = ThreadPool(8).imap(lambda x: load_image(*x), zip(repeat(self), range(n))) # 8 threads + pbar = tqdm(enumerate(results), total=n) + for i, x in pbar: + self.imgs[i], self.img_hw0[i], self.img_hw[i] = x # img, hw_original, hw_resized = load_image(self, i) + gb += self.imgs[i].nbytes + pbar.desc = 'Caching images (%.1fGB)' % (gb / 1E9) + + def cache_labels(self, path=Path('./labels.cache')): + # Cache dataset labels, check images and read shapes + x = {} # dict + nm, nf, ne, nc = 0, 0, 0, 0 # number missing, found, empty, duplicate + pbar = tqdm(zip(self.img_files, self.label_files), desc='Scanning images', total=len(self.img_files)) + for i, (im_file, lb_file) in enumerate(pbar): + try: + # verify images + im = Image.open(im_file) + im.verify() # PIL verify + shape = exif_size(im) # image size + assert (shape[0] > 9) & (shape[1] > 9), 'image size <10 pixels' + + # verify labels + if os.path.isfile(lb_file): + nf += 1 # label found + with open(lb_file, 'r') as f: + l = np.array([x.split() for x in f.read().strip().splitlines()], dtype=np.float32) # labels + if len(l): + assert l.shape[1] == 5, 'labels require 5 columns each' + assert (l >= 0).all(), 'negative labels' + assert (l[:, 1:] <= 1).all(), 'non-normalized or out of bounds coordinate labels' + assert np.unique(l, axis=0).shape[0] == l.shape[0], 'duplicate labels' + else: + ne += 1 # label empty + l = np.zeros((0, 5), dtype=np.float32) + else: + nm += 1 # label missing + l = np.zeros((0, 5), dtype=np.float32) + x[im_file] = [l, shape] + except Exception as e: + nc += 1 + print('WARNING: Ignoring corrupted image and/or label %s: %s' % (im_file, e)) + + pbar.desc = f"Scanning '{path.parent / path.stem}' for images and labels... " \ + f"{nf} found, {nm} missing, {ne} empty, {nc} corrupted" + + if nf == 0: + print(f'WARNING: No labels found in {path}. See {help_url}') + + x['hash'] = get_hash(self.label_files + self.img_files) + x['results'] = [nf, nm, ne, nc, i + 1] + torch.save(x, path) # save for next time + logging.info(f"New cache created: {path}") + return x + + def __len__(self): + return len(self.img_files) + + # def __iter__(self): + # self.count = -1 + # print('ran dataset iter') + # #self.shuffled_vector = np.random.permutation(self.nF) if self.augment else np.arange(self.nF) + # return self + + def __getitem__(self, index): + index = self.indices[index] # linear, shuffled, or image_weights + + hyp = self.hyp + mosaic = self.mosaic and random.random() < hyp['mosaic'] + if mosaic: + # Load mosaic + img, labels = load_mosaic(self, index) + shapes = None + + # MixUp https://arxiv.org/pdf/1710.09412.pdf + if random.random() < hyp['mixup']: + img2, labels2 = load_mosaic(self, random.randint(0, self.n - 1)) + r = np.random.beta(8.0, 8.0) # mixup ratio, alpha=beta=8.0 + img = (img * r + img2 * (1 - r)).astype(np.uint8) + labels = np.concatenate((labels, labels2), 0) + + else: + # Load image + img, (h0, w0), (h, w) = load_image(self, 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 + + # Load labels + labels = [] + x = self.labels[index] + if x.size > 0: + # Normalized xywh to pixel xyxy format + labels = x.copy() + labels[:, 1] = ratio[0] * w * (x[:, 1] - x[:, 3] / 2) + pad[0] # pad width + labels[:, 2] = ratio[1] * h * (x[:, 2] - x[:, 4] / 2) + pad[1] # pad height + labels[:, 3] = ratio[0] * w * (x[:, 1] + x[:, 3] / 2) + pad[0] + labels[:, 4] = ratio[1] * h * (x[:, 2] + x[:, 4] / 2) + pad[1] + + if self.augment: + # Augment imagespace + if not mosaic: + img, labels = random_perspective(img, labels, + degrees=hyp['degrees'], + translate=hyp['translate'], + scale=hyp['scale'], + shear=hyp['shear'], + perspective=hyp['perspective']) + + # Augment colorspace + augment_hsv(img, hgain=hyp['hsv_h'], sgain=hyp['hsv_s'], vgain=hyp['hsv_v']) + + # Apply cutouts + # if random.random() < 0.9: + # labels = cutout(img, labels) + + nL = len(labels) # number of labels + if nL: + labels[:, 1:5] = xyxy2xywh(labels[:, 1:5]) # convert xyxy to xywh + labels[:, [2, 4]] /= img.shape[0] # normalized height 0-1 + labels[:, [1, 3]] /= img.shape[1] # normalized width 0-1 + + if self.augment: + # flip up-down + if random.random() < hyp['flipud']: + img = np.flipud(img) + if nL: + labels[:, 2] = 1 - labels[:, 2] + + # flip left-right + if random.random() < hyp['fliplr']: + img = np.fliplr(img) + if nL: + labels[:, 1] = 1 - labels[:, 1] + + labels_out = torch.zeros((nL, 6)) + if nL: + labels_out[:, 1:] = torch.from_numpy(labels) + + # Convert + img = img[:, :, ::-1].transpose(2, 0, 1) # BGR to RGB, to 3x416x416 + img = np.ascontiguousarray(img) + + return torch.from_numpy(img), labels_out, self.img_files[index], shapes + + @staticmethod + def collate_fn(batch): + img, label, path, shapes = zip(*batch) # transposed + 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 + + +# Ancillary functions -------------------------------------------------------------------------------------------------- +def load_image(self, index): + # loads 1 image from dataset, returns img, original hw, resized hw + img = self.imgs[index] + if img is None: # not cached + path = self.img_files[index] + img = cv2.imread(path) # BGR + assert img is not None, 'Image Not Found ' + path + h0, w0 = img.shape[:2] # orig hw + r = self.img_size / max(h0, w0) # resize image to img_size + if r != 1: # always resize down, only resize up if training with augmentation + interp = cv2.INTER_AREA if r < 1 and not self.augment else cv2.INTER_LINEAR + img = cv2.resize(img, (int(w0 * r), int(h0 * r)), interpolation=interp) + return img, (h0, w0), img.shape[:2] # img, hw_original, hw_resized + else: + return self.imgs[index], self.img_hw0[index], self.img_hw[index] # img, hw_original, hw_resized + + +def augment_hsv(img, hgain=0.5, sgain=0.5, vgain=0.5): + r = np.random.uniform(-1, 1, 3) * [hgain, sgain, vgain] + 1 # random gains + hue, sat, val = cv2.split(cv2.cvtColor(img, cv2.COLOR_BGR2HSV)) + dtype = img.dtype # uint8 + + x = np.arange(0, 256, dtype=np.int16) + lut_hue = ((x * r[0]) % 180).astype(dtype) + lut_sat = np.clip(x * r[1], 0, 255).astype(dtype) + lut_val = np.clip(x * r[2], 0, 255).astype(dtype) + + img_hsv = cv2.merge((cv2.LUT(hue, lut_hue), cv2.LUT(sat, lut_sat), cv2.LUT(val, lut_val))).astype(dtype) + cv2.cvtColor(img_hsv, cv2.COLOR_HSV2BGR, dst=img) # no return needed + + # Histogram equalization + # if random.random() < 0.2: + # for i in range(3): + # img[:, :, i] = cv2.equalizeHist(img[:, :, i]) + + +def load_mosaic(self, index): + # loads images in a mosaic + + labels4 = [] + s = self.img_size + yc, xc = [int(random.uniform(-x, 2 * s + x)) for x in self.mosaic_border] # mosaic center x, y + indices = [index] + [self.indices[random.randint(0, self.n - 1)] for _ in range(3)] # 3 additional image indices + for i, index in enumerate(indices): + # Load image + img, _, (h, w) = load_image(self, 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 + x = self.labels[index] + labels = x.copy() + if x.size > 0: # Normalized xywh to pixel xyxy format + labels[:, 1] = w * (x[:, 1] - x[:, 3] / 2) + padw + labels[:, 2] = h * (x[:, 2] - x[:, 4] / 2) + padh + labels[:, 3] = w * (x[:, 1] + x[:, 3] / 2) + padw + labels[:, 4] = h * (x[:, 2] + x[:, 4] / 2) + padh + labels4.append(labels) + + # Concat/clip labels + if len(labels4): + labels4 = np.concatenate(labels4, 0) + np.clip(labels4[:, 1:], 0, 2 * s, out=labels4[:, 1:]) # use with random_perspective + # img4, labels4 = replicate(img4, labels4) # replicate + + # Augment + img4, labels4 = random_perspective(img4, labels4, + 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 + + +def replicate(img, labels): + # Replicate labels + h, w = img.shape[:2] + boxes = labels[:, 1:].astype(int) + x1, y1, x2, y2 = boxes.T + s = ((x2 - x1) + (y2 - y1)) / 2 # side length (pixels) + for i in s.argsort()[:round(s.size * 0.5)]: # smallest indices + x1b, y1b, x2b, y2b = boxes[i] + bh, bw = y2b - y1b, x2b - x1b + yc, xc = int(random.uniform(0, h - bh)), int(random.uniform(0, w - bw)) # offset x, y + x1a, y1a, x2a, y2a = [xc, yc, xc + bw, yc + bh] + img[y1a:y2a, x1a:x2a] = img[y1b:y2b, x1b:x2b] # img4[ymin:ymax, xmin:xmax] + labels = np.append(labels, [[labels[i, 0], x1a, y1a, x2a, y2a]], axis=0) + + return img, labels + + +def letterbox(img, new_shape=(640, 640), color=(114, 114, 114), auto=True, scaleFill=False, scaleup=True): + # Resize image to a 32-pixel-multiple rectangle https://github.com/ultralytics/yolov3/issues/232 + shape = img.shape[:2] # current shape [height, width] + if isinstance(new_shape, int): + new_shape = (new_shape, new_shape) + + # Scale ratio (new / old) + r = min(new_shape[0] / shape[0], new_shape[1] / shape[1]) + if not scaleup: # only scale down, do not scale up (for better test mAP) + r = min(r, 1.0) + + # Compute padding + ratio = r, r # width, height ratios + new_unpad = int(round(shape[1] * r)), int(round(shape[0] * r)) + dw, dh = new_shape[1] - new_unpad[0], new_shape[0] - new_unpad[1] # wh padding + if auto: # minimum rectangle + dw, dh = np.mod(dw, 32), np.mod(dh, 32) # wh padding + elif scaleFill: # stretch + dw, dh = 0.0, 0.0 + new_unpad = (new_shape[1], new_shape[0]) + ratio = new_shape[1] / shape[1], new_shape[0] / shape[0] # width, height ratios + + dw /= 2 # divide padding into 2 sides + dh /= 2 + + if shape[::-1] != new_unpad: # resize + img = cv2.resize(img, new_unpad, interpolation=cv2.INTER_LINEAR) + top, bottom = int(round(dh - 0.1)), int(round(dh + 0.1)) + left, right = int(round(dw - 0.1)), int(round(dw + 0.1)) + img = cv2.copyMakeBorder(img, top, bottom, left, right, cv2.BORDER_CONSTANT, value=color) # add border + return img, ratio, (dw, dh) + + +def random_perspective(img, targets=(), 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 = img.shape[0] + border[0] * 2 # shape(h,w,c) + width = img.shape[1] + border[1] * 2 + + # Center + C = np.eye(3) + C[0, 2] = -img.shape[1] / 2 # x translation (pixels) + C[1, 2] = -img.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: + img = cv2.warpPerspective(img, M, dsize=(width, height), borderValue=(114, 114, 114)) + else: # affine + img = cv2.warpAffine(img, 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(img[:, :, ::-1]) # base + # ax[1].imshow(img2[:, :, ::-1]) # warped + + # Transform label coordinates + n = len(targets) + if n: + # warp points + xy = np.ones((n * 4, 3)) + xy[:, :2] = targets[:, [1, 2, 3, 4, 1, 4, 3, 2]].reshape(n * 4, 2) # x1y1, x2y2, x1y2, x2y1 + xy = xy @ M.T # transform + if perspective: + xy = (xy[:, :2] / xy[:, 2:3]).reshape(n, 8) # rescale + else: # affine + xy = xy[:, :2].reshape(n, 8) + + # create new boxes + x = xy[:, [0, 2, 4, 6]] + y = xy[:, [1, 3, 5, 7]] + xy = np.concatenate((x.min(1), y.min(1), x.max(1), y.max(1))).reshape(4, n).T + + # # apply angle-based reduction of bounding boxes + # radians = a * math.pi / 180 + # reduction = max(abs(math.sin(radians)), abs(math.cos(radians))) ** 0.5 + # x = (xy[:, 2] + xy[:, 0]) / 2 + # y = (xy[:, 3] + xy[:, 1]) / 2 + # w = (xy[:, 2] - xy[:, 0]) * reduction + # h = (xy[:, 3] - xy[:, 1]) * reduction + # xy = np.concatenate((x - w / 2, y - h / 2, x + w / 2, y + h / 2)).reshape(4, n).T + + # clip boxes + xy[:, [0, 2]] = xy[:, [0, 2]].clip(0, width) + xy[:, [1, 3]] = xy[:, [1, 3]].clip(0, height) + + # filter candidates + i = box_candidates(box1=targets[:, 1:5].T * s, box2=xy.T) + targets = targets[i] + targets[:, 1:5] = xy[i] + + return img, targets + + +def box_candidates(box1, box2, wh_thr=2, ar_thr=20, area_thr=0.1): # box1(4,n), box2(4,n) + # Compute candidate boxes: box1 before augment, box2 after augment, wh_thr (pixels), aspect_ratio_thr, area_ratio + w1, h1 = box1[2] - box1[0], box1[3] - box1[1] + w2, h2 = box2[2] - box2[0], box2[3] - box2[1] + ar = np.maximum(w2 / (h2 + 1e-16), h2 / (w2 + 1e-16)) # aspect ratio + return (w2 > wh_thr) & (h2 > wh_thr) & (w2 * h2 / (w1 * h1 + 1e-16) > area_thr) & (ar < ar_thr) # candidates + + +def cutout(image, labels): + # Applies image cutout augmentation https://arxiv.org/abs/1708.04552 + h, w = image.shape[:2] + + def bbox_ioa(box1, box2): + # Returns the intersection over box2 area given box1, box2. box1 is 4, box2 is nx4. boxes are x1y1x2y2 + box2 = box2.transpose() + + # Get the coordinates of bounding boxes + b1_x1, b1_y1, b1_x2, b1_y2 = box1[0], box1[1], box1[2], box1[3] + b2_x1, b2_y1, b2_x2, b2_y2 = box2[0], box2[1], box2[2], box2[3] + + # Intersection area + inter_area = (np.minimum(b1_x2, b2_x2) - np.maximum(b1_x1, b2_x1)).clip(0) * \ + (np.minimum(b1_y2, b2_y2) - np.maximum(b1_y1, b2_y1)).clip(0) + + # box2 area + box2_area = (b2_x2 - b2_x1) * (b2_y2 - b2_y1) + 1e-16 + + # Intersection over box2 area + return inter_area / box2_area + + # create random masks + scales = [0.5] * 1 + [0.25] * 2 + [0.125] * 4 + [0.0625] * 8 + [0.03125] * 16 # image size fraction + for s in scales: + mask_h = random.randint(1, int(h * s)) + mask_w = random.randint(1, int(w * s)) + + # box + xmin = max(0, random.randint(0, w) - mask_w // 2) + ymin = max(0, random.randint(0, h) - mask_h // 2) + xmax = min(w, xmin + mask_w) + ymax = min(h, ymin + mask_h) + + # apply random color mask + image[ymin:ymax, xmin:xmax] = [random.randint(64, 191) for _ in range(3)] + + # 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 + labels = labels[ioa < 0.60] # remove >60% obscured labels + + return labels + + +def create_folder(path='./new'): + # Create folder + if os.path.exists(path): + shutil.rmtree(path) # delete output folder + os.makedirs(path) # make new output folder + + +def flatten_recursive(path='../coco128'): + # Flatten a recursive directory by bringing all files to top level + new_path = Path(path + '_flat') + create_folder(new_path) + for file in tqdm(glob.glob(str(Path(path)) + '/**/*.*', recursive=True)): + shutil.copyfile(file, new_path / Path(file).name) + + +def extract_boxes(path='../coco128/'): # from utils.datasets import *; extract_boxes('../coco128') + # Convert detection dataset into classification dataset, with one directory per class + + path = Path(path) # images dir + shutil.rmtree(path / 'classifier') if (path / 'classifier').is_dir() else None # remove existing + files = list(path.rglob('*.*')) + n = len(files) # number of files + for im_file in tqdm(files, total=n): + if im_file.suffix[1:] in img_formats: + # image + im = cv2.imread(str(im_file))[..., ::-1] # BGR to RGB + h, w = im.shape[:2] + + # labels + lb_file = Path(img2label_paths([str(im_file)])[0]) + if Path(lb_file).exists(): + with open(lb_file, 'r') as f: + lb = np.array([x.split() for x in f.read().strip().splitlines()], dtype=np.float32) # labels + + for j, x in enumerate(lb): + c = int(x[0]) # class + f = (path / 'classifier') / f'{c}' / f'{path.stem}_{im_file.stem}_{j}.jpg' # new filename + if not f.parent.is_dir(): + f.parent.mkdir(parents=True) + + b = x[1:] * [w, h, w, h] # box + # b[2:] = b[2:].max() # rectangle to square + b[2:] = b[2:] * 1.2 + 3 # pad + b = xywh2xyxy(b.reshape(-1, 4)).ravel().astype(np.int) + + b[[0, 2]] = np.clip(b[[0, 2]], 0, w) # clip boxes outside of image + b[[1, 3]] = np.clip(b[[1, 3]], 0, h) + assert cv2.imwrite(str(f), im[b[1]:b[3], b[0]:b[2]]), f'box failure in {f}' + + +def autosplit(path='../coco128', weights=(0.9, 0.1, 0.0)): # from utils.datasets import *; autosplit('../coco128') + """ Autosplit a dataset into train/val/test splits and save path/autosplit_*.txt files + # Arguments + path: Path to images directory + weights: Train, val, test weights (list) + """ + path = Path(path) # images dir + files = list(path.rglob('*.*')) + n = len(files) # number of files + indices = random.choices([0, 1, 2], weights=weights, k=n) # assign each image to a split + txt = ['autosplit_train.txt', 'autosplit_val.txt', 'autosplit_test.txt'] # 3 txt files + [(path / x).unlink() for x in txt if (path / x).exists()] # remove existing + for i, img in tqdm(zip(indices, files), total=n): + if img.suffix[1:] in img_formats: + with open(path / txt[i], 'a') as f: + f.write(str(img) + '\n') # add image to txt file diff --git a/yolov5/utils/general.py b/yolov5/utils/general.py new file mode 100644 index 0000000..1224905 --- /dev/null +++ b/yolov5/utils/general.py @@ -0,0 +1,445 @@ +# General utils + +import glob +import logging +import math +import os +import platform +import random +import re +import subprocess +import time +from pathlib import Path + +import cv2 +import numpy as np +import torch +import torchvision +import yaml + +from utils.google_utils import gsutil_getsize +from utils.metrics import fitness +from utils.torch_utils import init_torch_seeds + +# Settings +torch.set_printoptions(linewidth=320, precision=5, profile='long') +np.set_printoptions(linewidth=320, formatter={'float_kind': '{:11.5g}'.format}) # format short g, %precision=5 +cv2.setNumThreads(0) # prevent OpenCV from multithreading (incompatible with PyTorch DataLoader) + + +def set_logging(rank=-1): + logging.basicConfig( + format="%(message)s", + level=logging.INFO if rank in [-1, 0] else logging.WARN) + + +def init_seeds(seed=0): + random.seed(seed) + np.random.seed(seed) + init_torch_seeds(seed) + + +def get_latest_run(search_dir='.'): + # Return path to most recent 'last.pt' in /runs (i.e. to --resume from) + last_list = glob.glob(f'{search_dir}/**/last*.pt', recursive=True) + return max(last_list, key=os.path.getctime) if last_list else '' + + +def check_git_status(): + # Suggest 'git pull' if repo is out of date + if platform.system() in ['Linux', 'Darwin'] and not os.path.isfile('/.dockerenv'): + s = subprocess.check_output('if [ -d .git ]; then git fetch && git status -uno; fi', shell=True).decode('utf-8') + if 'Your branch is behind' in s: + print(s[s.find('Your branch is behind'):s.find('\n\n')] + '\n') + + +def check_img_size(img_size, s=32): + # Verify img_size is a multiple of stride s + new_size = make_divisible(img_size, int(s)) # ceil gs-multiple + if new_size != img_size: + print('WARNING: --img-size %g must be multiple of max stride %g, updating to %g' % (img_size, s, new_size)) + return new_size + + +def check_file(file): + # Search for file if not found + if os.path.isfile(file) or file == '': + return file + else: + files = glob.glob('./**/' + file, recursive=True) # find file + assert len(files), 'File Not Found: %s' % file # assert file was found + assert len(files) == 1, "Multiple files match '%s', specify exact path: %s" % (file, files) # assert unique + return files[0] # return file + + +def check_dataset(dict): + # Download dataset if not found locally + val, s = dict.get('val'), dict.get('download') + if val and len(val): + val = [Path(x).resolve() for x in (val if isinstance(val, list) else [val])] # val path + if not all(x.exists() for x in val): + print('\nWARNING: Dataset not found, nonexistent paths: %s' % [str(x) for x in val if not x.exists()]) + if s and len(s): # download script + print('Downloading %s ...' % s) + if s.startswith('http') and s.endswith('.zip'): # URL + f = Path(s).name # filename + torch.hub.download_url_to_file(s, f) + r = os.system('unzip -q %s -d ../ && rm %s' % (f, f)) # unzip + else: # bash script + r = os.system(s) + print('Dataset autodownload %s\n' % ('success' if r == 0 else 'failure')) # analyze return value + else: + raise Exception('Dataset not found.') + + +def make_divisible(x, divisor): + # Returns x evenly divisible by divisor + return math.ceil(x / divisor) * divisor + + +def clean_str(s): + # Cleans a string by replacing special characters with underscore _ + return re.sub(pattern="[|@#!¡·$€%&()=?¿^*;:,¨´><+]", repl="_", string=s) + + +def labels_to_class_weights(labels, nc=80): + # Get class weights (inverse frequency) from training labels + if labels[0] is None: # no labels loaded + return torch.Tensor() + + labels = np.concatenate(labels, 0) # labels.shape = (866643, 5) for COCO + classes = labels[:, 0].astype(np.int) # labels = [class xywh] + weights = np.bincount(classes, minlength=nc) # occurrences per class + + # Prepend gridpoint count (for uCE training) + # gpi = ((320 / 32 * np.array([1, 2, 4])) ** 2 * 3).sum() # gridpoints per image + # weights = np.hstack([gpi * len(labels) - weights.sum() * 9, weights * 9]) ** 0.5 # prepend gridpoints to start + + weights[weights == 0] = 1 # replace empty bins with 1 + weights = 1 / weights # number of targets per class + weights /= weights.sum() # normalize + return torch.from_numpy(weights) + + +def labels_to_image_weights(labels, nc=80, class_weights=np.ones(80)): + # Produces image weights based on class_weights and image contents + class_counts = np.array([np.bincount(x[:, 0].astype(np.int), minlength=nc) for x in labels]) + image_weights = (class_weights.reshape(1, nc) * class_counts).sum(1) + # index = random.choices(range(n), weights=image_weights, k=1) # weight image sample + return image_weights + + +def coco80_to_coco91_class(): # converts 80-index (val2014) to 91-index (paper) + # https://tech.amikelive.com/node-718/what-object-categories-labels-are-in-coco-dataset/ + # a = np.loadtxt('data/coco.names', dtype='str', delimiter='\n') + # b = np.loadtxt('data/coco_paper.names', dtype='str', delimiter='\n') + # x1 = [list(a[i] == b).index(True) + 1 for i in range(80)] # darknet to coco + # x2 = [list(b[i] == a).index(True) if any(b[i] == a) else None for i in range(91)] # coco to darknet + x = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 27, 28, 31, 32, 33, 34, + 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, + 64, 65, 67, 70, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 84, 85, 86, 87, 88, 89, 90] + return x + + +def xyxy2xywh(x): + # Convert nx4 boxes from [x1, y1, x2, y2] to [x, y, w, h] where xy1=top-left, xy2=bottom-right + y = x.clone() if isinstance(x, torch.Tensor) else np.copy(x) + y[:, 0] = (x[:, 0] + x[:, 2]) / 2 # x center + y[:, 1] = (x[:, 1] + x[:, 3]) / 2 # y center + y[:, 2] = x[:, 2] - x[:, 0] # width + y[:, 3] = x[:, 3] - x[:, 1] # height + return y + + +def xywh2xyxy(x): + # Convert nx4 boxes from [x, y, w, h] to [x1, y1, x2, y2] where xy1=top-left, xy2=bottom-right + y = x.clone() if isinstance(x, torch.Tensor) else np.copy(x) + y[:, 0] = x[:, 0] - x[:, 2] / 2 # top left x + y[:, 1] = x[:, 1] - x[:, 3] / 2 # top left y + y[:, 2] = x[:, 0] + x[:, 2] / 2 # bottom right x + y[:, 3] = x[:, 1] + x[:, 3] / 2 # bottom right y + return y + + +def scale_coords(img1_shape, coords, img0_shape, ratio_pad=None): + # Rescale coords (xyxy) from img1_shape to img0_shape + if ratio_pad is None: # calculate from img0_shape + gain = min(img1_shape[0] / img0_shape[0], img1_shape[1] / img0_shape[1]) # gain = old / new + pad = (img1_shape[1] - img0_shape[1] * gain) / 2, (img1_shape[0] - img0_shape[0] * gain) / 2 # wh padding + else: + gain = ratio_pad[0][0] + pad = ratio_pad[1] + + coords[:, [0, 2]] -= pad[0] # x padding + coords[:, [1, 3]] -= pad[1] # y padding + coords[:, :4] /= gain + clip_coords(coords, img0_shape) + return coords + + +def clip_coords(boxes, img_shape): + # Clip bounding xyxy bounding boxes to image shape (height, width) + boxes[:, 0].clamp_(0, img_shape[1]) # x1 + boxes[:, 1].clamp_(0, img_shape[0]) # y1 + boxes[:, 2].clamp_(0, img_shape[1]) # x2 + boxes[:, 3].clamp_(0, img_shape[0]) # y2 + + +def bbox_iou(box1, box2, x1y1x2y2=True, GIoU=False, DIoU=False, CIoU=False, eps=1e-9): + # Returns the IoU of box1 to box2. box1 is 4, box2 is nx4 + box2 = box2.T + + # Get the coordinates of bounding boxes + if x1y1x2y2: # x1, y1, x2, y2 = box1 + b1_x1, b1_y1, b1_x2, b1_y2 = box1[0], box1[1], box1[2], box1[3] + b2_x1, b2_y1, b2_x2, b2_y2 = box2[0], box2[1], box2[2], box2[3] + else: # transform from xywh to xyxy + b1_x1, b1_x2 = box1[0] - box1[2] / 2, box1[0] + box1[2] / 2 + b1_y1, b1_y2 = box1[1] - box1[3] / 2, box1[1] + box1[3] / 2 + b2_x1, b2_x2 = box2[0] - box2[2] / 2, box2[0] + box2[2] / 2 + b2_y1, b2_y2 = box2[1] - box2[3] / 2, box2[1] + box2[3] / 2 + + # 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) + + # Union Area + w1, h1 = b1_x2 - b1_x1, b1_y2 - b1_y1 + eps + w2, h2 = b2_x2 - b2_x1, b2_y2 - b2_y1 + eps + union = w1 * h1 + w2 * h2 - inter + eps + + iou = inter / union + if GIoU or DIoU or CIoU: + cw = torch.max(b1_x2, b2_x2) - torch.min(b1_x1, b2_x1) # convex (smallest enclosing box) width + ch = torch.max(b1_y2, b2_y2) - torch.min(b1_y1, b2_y1) # convex height + if CIoU or DIoU: # Distance or Complete IoU https://arxiv.org/abs/1911.08287v1 + c2 = cw ** 2 + ch ** 2 + eps # convex diagonal squared + rho2 = ((b2_x1 + b2_x2 - b1_x1 - b1_x2) ** 2 + + (b2_y1 + b2_y2 - b1_y1 - b1_y2) ** 2) / 4 # center distance squared + if DIoU: + return iou - rho2 / c2 # DIoU + elif 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) - torch.atan(w1 / h1), 2) + with torch.no_grad(): + alpha = v / ((1 + eps) - iou + v) + return iou - (rho2 / c2 + v * alpha) # CIoU + else: # GIoU https://arxiv.org/pdf/1902.09630.pdf + c_area = cw * ch + eps # convex area + return iou - (c_area - union) / c_area # GIoU + else: + return iou # IoU + + +def box_iou(box1, box2): + # https://github.com/pytorch/vision/blob/master/torchvision/ops/boxes.py + """ + Return intersection-over-union (Jaccard index) of boxes. + Both sets of boxes are expected to be in (x1, y1, x2, y2) format. + Arguments: + box1 (Tensor[N, 4]) + box2 (Tensor[M, 4]) + Returns: + iou (Tensor[N, M]): the NxM matrix containing the pairwise + IoU values for every element in boxes1 and boxes2 + """ + + def box_area(box): + # box = 4xn + return (box[2] - box[0]) * (box[3] - box[1]) + + area1 = box_area(box1.T) + area2 = box_area(box2.T) + + # inter(N,M) = (rb(N,M,2) - lt(N,M,2)).clamp(0).prod(2) + inter = (torch.min(box1[:, None, 2:], box2[:, 2:]) - torch.max(box1[:, None, :2], box2[:, :2])).clamp(0).prod(2) + return inter / (area1[:, None] + area2 - inter) # iou = inter / (area1 + area2 - inter) + + +def wh_iou(wh1, wh2): + # Returns the nxm IoU matrix. wh1 is nx2, wh2 is mx2 + wh1 = wh1[:, None] # [N,1,2] + wh2 = wh2[None] # [1,M,2] + inter = torch.min(wh1, wh2).prod(2) # [N,M] + return inter / (wh1.prod(2) + wh2.prod(2) - inter) # iou = inter / (area1 + area2 - inter) + + +def non_max_suppression(prediction, conf_thres=0.25, iou_thres=0.45, classes=None, agnostic=False, labels=()): + """Performs Non-Maximum Suppression (NMS) on inference results + + Returns: + detections with shape: nx6 (x1, y1, x2, y2, conf, cls) + """ + + nc = prediction.shape[2] - 5 # number of classes + xc = prediction[..., 4] > conf_thres # candidates + + # Settings + min_wh, max_wh = 2, 4096 # (pixels) minimum and maximum box width and height + max_det = 300 # maximum number of detections per image + time_limit = 10.0 # seconds to quit after + redundant = True # require redundant detections + multi_label = nc > 1 # multiple labels per box (adds 0.5ms/img) + merge = False # use merge-NMS + + t = time.time() + output = [torch.zeros((0, 6), device=prediction.device)] * prediction.shape[0] + for xi, x in enumerate(prediction): # image index, image inference + # Apply constraints + # x[((x[..., 2:4] < min_wh) | (x[..., 2:4] > max_wh)).any(1), 4] = 0 # width-height + x = x[xc[xi]] # confidence + + # Cat apriori labels if autolabelling + if labels and len(labels[xi]): + l = labels[xi] + v = torch.zeros((len(l), nc + 5), device=x.device) + v[:, :4] = l[:, 1:5] # box + v[:, 4] = 1.0 # conf + v[range(len(l)), l[:, 0].long() + 5] = 1.0 # cls + x = torch.cat((x, v), 0) + + # If none remain process next image + if not x.shape[0]: + continue + + # Compute conf + x[:, 5:] *= x[:, 4:5] # conf = obj_conf * cls_conf + + # Box (center x, center y, width, height) to (x1, y1, x2, y2) + box = xywh2xyxy(x[:, :4]) + + # Detections matrix nx6 (xyxy, conf, cls) + if multi_label: + i, j = (x[:, 5:] > conf_thres).nonzero(as_tuple=False).T + x = torch.cat((box[i], x[i, j + 5, None], j[:, None].float()), 1) + else: # best class only + conf, j = x[:, 5:].max(1, keepdim=True) + x = torch.cat((box, conf, j.float()), 1)[conf.view(-1) > conf_thres] + + # Filter by class + if classes is not None: + x = x[(x[:, 5:6] == torch.tensor(classes, device=x.device)).any(1)] + + # Apply finite constraint + # if not torch.isfinite(x).all(): + # x = x[torch.isfinite(x).all(1)] + + # If none remain process next image + n = x.shape[0] # number of boxes + if not n: + continue + + # Sort by confidence + # x = x[x[:, 4].argsort(descending=True)] + + # Batched NMS + c = x[:, 5:6] * (0 if agnostic else max_wh) # classes + boxes, scores = x[:, :4] + c, x[:, 4] # boxes (offset by class), scores + i = torchvision.ops.nms(boxes, scores, iou_thres) # NMS + if i.shape[0] > max_det: # limit detections + i = i[:max_det] + if merge and (1 < n < 3E3): # Merge NMS (boxes merged using weighted mean) + # update boxes as boxes(i,4) = weights(i,n) * boxes(n,4) + iou = box_iou(boxes[i], boxes) > iou_thres # iou matrix + weights = iou * scores[None] # box weights + x[i, :4] = torch.mm(weights, x[:, :4]).float() / weights.sum(1, keepdim=True) # merged boxes + if redundant: + i = i[iou.sum(1) > 1] # require redundancy + + output[xi] = x[i] + if (time.time() - t) > time_limit: + break # time limit exceeded + + return output + + +def strip_optimizer(f='weights/best.pt', s=''): # from utils.general import *; strip_optimizer() + # Strip optimizer from 'f' to finalize training, optionally save as 's' + x = torch.load(f, map_location=torch.device('cpu')) + x['optimizer'] = None + x['training_results'] = None + x['epoch'] = -1 + x['model'].half() # to FP16 + for p in x['model'].parameters(): + p.requires_grad = False + torch.save(x, s or f) + mb = os.path.getsize(s or f) / 1E6 # filesize + print('Optimizer stripped from %s,%s %.1fMB' % (f, (' saved as %s,' % s) if s else '', mb)) + + +def print_mutation(hyp, results, yaml_file='hyp_evolved.yaml', bucket=''): + # Print mutation results to evolve.txt (for use with train.py --evolve) + a = '%10s' * len(hyp) % tuple(hyp.keys()) # hyperparam keys + b = '%10.3g' * len(hyp) % tuple(hyp.values()) # hyperparam values + c = '%10.4g' * len(results) % results # results (P, R, mAP@0.5, mAP@0.5:0.95, val_losses x 3) + print('\n%s\n%s\nEvolved fitness: %s\n' % (a, b, c)) + + if bucket: + url = 'gs://%s/evolve.txt' % bucket + if gsutil_getsize(url) > (os.path.getsize('evolve.txt') if os.path.exists('evolve.txt') else 0): + os.system('gsutil cp %s .' % url) # download evolve.txt if larger than local + + with open('evolve.txt', 'a') as f: # append result + f.write(c + b + '\n') + x = np.unique(np.loadtxt('evolve.txt', ndmin=2), axis=0) # load unique rows + x = x[np.argsort(-fitness(x))] # sort + np.savetxt('evolve.txt', x, '%10.3g') # save sort by fitness + + # Save yaml + for i, k in enumerate(hyp.keys()): + hyp[k] = float(x[0, i + 7]) + with open(yaml_file, 'w') as f: + results = tuple(x[0, :7]) + c = '%10.4g' * len(results) % results # results (P, R, mAP@0.5, mAP@0.5:0.95, val_losses x 3) + f.write('# Hyperparameter Evolution Results\n# Generations: %g\n# Metrics: ' % len(x) + c + '\n\n') + yaml.dump(hyp, f, sort_keys=False) + + if bucket: + os.system('gsutil cp evolve.txt %s gs://%s' % (yaml_file, bucket)) # upload + + +def apply_classifier(x, model, img, im0): + # applies a second stage classifier to yolo outputs + im0 = [im0] if isinstance(im0, np.ndarray) else im0 + for i, d in enumerate(x): # per image + if d is not None and len(d): + d = d.clone() + + # Reshape and pad cutouts + b = xyxy2xywh(d[:, :4]) # boxes + b[:, 2:] = b[:, 2:].max(1)[0].unsqueeze(1) # rectangle to square + b[:, 2:] = b[:, 2:] * 1.3 + 30 # pad + d[:, :4] = xywh2xyxy(b).long() + + # Rescale boxes from img_size to im0 size + scale_coords(img.shape[2:], d[:, :4], im0[i].shape) + + # Classes + pred_cls1 = d[:, 5].long() + ims = [] + for j, a in enumerate(d): # per item + cutout = im0[i][int(a[1]):int(a[3]), int(a[0]):int(a[2])] + im = cv2.resize(cutout, (224, 224)) # BGR + # cv2.imwrite('test%i.jpg' % j, cutout) + + im = im[:, :, ::-1].transpose(2, 0, 1) # BGR to RGB, to 3x416x416 + im = np.ascontiguousarray(im, dtype=np.float32) # uint8 to float32 + im /= 255.0 # 0 - 255 to 0.0 - 1.0 + ims.append(im) + + pred_cls2 = model(torch.Tensor(ims).to(d.device)).argmax(1) # classifier prediction + x[i] = x[i][pred_cls1 == pred_cls2] # retain matching class detections + + return x + + +def increment_path(path, exist_ok=True, sep=''): + # Increment path, i.e. runs/exp --> runs/exp{sep}0, runs/exp{sep}1 etc. + path = Path(path) # os-agnostic + if (path.exists() and exist_ok) or (not path.exists()): + return str(path) + else: + dirs = glob.glob(f"{path}{sep}*") # similar paths + matches = [re.search(rf"%s{sep}(\d+)" % path.stem, d) for d in dirs] + i = [int(m.groups()[0]) for m in matches if m] # indices + n = max(i) + 1 if i else 2 # increment number + return f"{path}{sep}{n}" # update path diff --git a/yolov5/utils/google_app_engine/Dockerfile b/yolov5/utils/google_app_engine/Dockerfile new file mode 100644 index 0000000..0155618 --- /dev/null +++ b/yolov5/utils/google_app_engine/Dockerfile @@ -0,0 +1,25 @@ +FROM gcr.io/google-appengine/python + +# Create a virtualenv for dependencies. This isolates these packages from +# system-level packages. +# Use -p python3 or -p python3.7 to select python version. Default is version 2. +RUN virtualenv /env -p python3 + +# Setting these environment variables are the same as running +# source /env/bin/activate. +ENV VIRTUAL_ENV /env +ENV PATH /env/bin:$PATH + +RUN apt-get update && apt-get install -y python-opencv + +# Copy the application's requirements.txt and run pip to install all +# dependencies into the virtualenv. +ADD requirements.txt /app/requirements.txt +RUN pip install -r /app/requirements.txt + +# Add the application source code. +ADD . /app + +# Run a WSGI server to serve the application. gunicorn must be declared as +# a dependency in requirements.txt. +CMD gunicorn -b :$PORT main:app diff --git a/yolov5/utils/google_app_engine/additional_requirements.txt b/yolov5/utils/google_app_engine/additional_requirements.txt new file mode 100644 index 0000000..5fcc305 --- /dev/null +++ b/yolov5/utils/google_app_engine/additional_requirements.txt @@ -0,0 +1,4 @@ +# add these requirements in your app on top of the existing ones +pip==18.1 +Flask==1.0.2 +gunicorn==19.9.0 diff --git a/yolov5/utils/google_app_engine/app.yaml b/yolov5/utils/google_app_engine/app.yaml new file mode 100644 index 0000000..ac29d10 --- /dev/null +++ b/yolov5/utils/google_app_engine/app.yaml @@ -0,0 +1,14 @@ +runtime: custom +env: flex + +service: yolov5app + +liveness_check: + initial_delay_sec: 600 + +manual_scaling: + instances: 1 +resources: + cpu: 1 + memory_gb: 4 + disk_size_gb: 20 \ No newline at end of file diff --git a/yolov5/utils/google_utils.py b/yolov5/utils/google_utils.py new file mode 100644 index 0000000..f97349d --- /dev/null +++ b/yolov5/utils/google_utils.py @@ -0,0 +1,122 @@ +# Google utils: https://cloud.google.com/storage/docs/reference/libraries + +import os +import platform +import subprocess +import time +from pathlib import Path + +import torch + + +def gsutil_getsize(url=''): + # gs://bucket/file size https://cloud.google.com/storage/docs/gsutil/commands/du + s = subprocess.check_output('gsutil du %s' % url, shell=True).decode('utf-8') + return eval(s.split(' ')[0]) if len(s) else 0 # bytes + + +def attempt_download(weights): + # Attempt to download pretrained weights if not found locally + weights = str(weights).strip().replace("'", '') + file = Path(weights).name.lower() + + msg = weights + ' missing, try downloading from https://github.com/ultralytics/yolov5/releases/' + models = ['yolov5s.pt', 'yolov5m.pt', 'yolov5l.pt', 'yolov5x.pt'] # available models + redundant = False # offer second download option + + if file in models and not os.path.isfile(weights): + # Google Drive + # d = {'yolov5s.pt': '1R5T6rIyy3lLwgFXNms8whc-387H0tMQO', + # 'yolov5m.pt': '1vobuEExpWQVpXExsJ2w-Mbf3HJjWkQJr', + # 'yolov5l.pt': '1hrlqD1Wdei7UT4OgT785BEk1JwnSvNEV', + # 'yolov5x.pt': '1mM8aZJlWTxOg7BZJvNUMrTnA2AbeCVzS'} + # r = gdrive_download(id=d[file], name=weights) if file in d else 1 + # if r == 0 and os.path.exists(weights) and os.path.getsize(weights) > 1E6: # check + # return + + try: # GitHub + url = 'https://github.com/ultralytics/yolov5/releases/download/v3.1/' + file + print('Downloading %s to %s...' % (url, weights)) + torch.hub.download_url_to_file(url, weights) + assert os.path.exists(weights) and os.path.getsize(weights) > 1E6 # check + except Exception as e: # GCP + print('Download error: %s' % e) + assert redundant, 'No secondary mirror' + url = 'https://storage.googleapis.com/ultralytics/yolov5/ckpt/' + file + print('Downloading %s to %s...' % (url, weights)) + r = os.system('curl -L %s -o %s' % (url, weights)) # torch.hub.download_url_to_file(url, weights) + finally: + if not (os.path.exists(weights) and os.path.getsize(weights) > 1E6): # check + os.remove(weights) if os.path.exists(weights) else None # remove partial downloads + print('ERROR: Download failure: %s' % msg) + print('') + return + + +def gdrive_download(id='1uH2BylpFxHKEGXKL6wJJlsgMU2YEjxuc', name='tmp.zip'): + # Downloads a file from Google Drive. from utils.google_utils import *; gdrive_download() + t = time.time() + + print('Downloading https://drive.google.com/uc?export=download&id=%s as %s... ' % (id, name), end='') + os.remove(name) if os.path.exists(name) else None # remove existing + os.remove('cookie') if os.path.exists('cookie') else None + + # Attempt file download + out = "NUL" if platform.system() == "Windows" else "/dev/null" + os.system('curl -c ./cookie -s -L "drive.google.com/uc?export=download&id=%s" > %s ' % (id, out)) + if os.path.exists('cookie'): # large file + s = 'curl -Lb ./cookie "drive.google.com/uc?export=download&confirm=%s&id=%s" -o %s' % (get_token(), id, name) + else: # small file + s = 'curl -s -L -o %s "drive.google.com/uc?export=download&id=%s"' % (name, id) + r = os.system(s) # execute, capture return + os.remove('cookie') if os.path.exists('cookie') else None + + # Error check + if r != 0: + os.remove(name) if os.path.exists(name) else None # remove partial + print('Download error ') # raise Exception('Download error') + return r + + # Unzip if archive + if name.endswith('.zip'): + print('unzipping... ', end='') + os.system('unzip -q %s' % name) # unzip + os.remove(name) # remove zip to free space + + print('Done (%.1fs)' % (time.time() - t)) + 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 "" + +# 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/yolov5/utils/loss.py b/yolov5/utils/loss.py new file mode 100644 index 0000000..2049516 --- /dev/null +++ b/yolov5/utils/loss.py @@ -0,0 +1,205 @@ +# Loss functions + +import torch +import torch.nn as nn + +from utils.general import bbox_iou +from utils.torch_utils import is_parallel + + +def smooth_BCE(eps=0.1): # https://github.com/ultralytics/yolov3/issues/238#issuecomment-598028441 + # return positive, negative label smoothing BCE targets + return 1.0 - 0.5 * eps, 0.5 * eps + + +class BCEBlurWithLogitsLoss(nn.Module): + # BCEwithLogitLoss() with reduced missing label effects. + def __init__(self, alpha=0.05): + super(BCEBlurWithLogitsLoss, self).__init__() + self.loss_fcn = nn.BCEWithLogitsLoss(reduction='none') # must be nn.BCEWithLogitsLoss() + self.alpha = alpha + + def forward(self, pred, true): + loss = self.loss_fcn(pred, true) + pred = torch.sigmoid(pred) # prob from logits + dx = pred - true # reduce only missing label effects + # dx = (pred - true).abs() # reduce missing label and false label effects + alpha_factor = 1 - torch.exp((dx - 1) / (self.alpha + 1e-4)) + loss *= alpha_factor + return loss.mean() + + +class FocalLoss(nn.Module): + # Wraps focal loss around existing loss_fcn(), i.e. criteria = FocalLoss(nn.BCEWithLogitsLoss(), gamma=1.5) + def __init__(self, loss_fcn, gamma=1.5, alpha=0.25): + super(FocalLoss, self).__init__() + self.loss_fcn = loss_fcn # must be nn.BCEWithLogitsLoss() + self.gamma = gamma + self.alpha = alpha + self.reduction = loss_fcn.reduction + self.loss_fcn.reduction = 'none' # required to apply FL to each element + + def forward(self, pred, true): + loss = self.loss_fcn(pred, true) + # p_t = torch.exp(-loss) + # loss *= self.alpha * (1.000001 - p_t) ** self.gamma # non-zero power for gradient stability + + # TF implementation https://github.com/tensorflow/addons/blob/v0.7.1/tensorflow_addons/losses/focal_loss.py + pred_prob = torch.sigmoid(pred) # prob from logits + p_t = true * pred_prob + (1 - true) * (1 - pred_prob) + alpha_factor = true * self.alpha + (1 - true) * (1 - self.alpha) + modulating_factor = (1.0 - p_t) ** self.gamma + loss *= alpha_factor * modulating_factor + + if self.reduction == 'mean': + return loss.mean() + elif self.reduction == 'sum': + return loss.sum() + else: # 'none' + return loss + + +class QFocalLoss(nn.Module): + # Wraps Quality focal loss around existing loss_fcn(), i.e. criteria = FocalLoss(nn.BCEWithLogitsLoss(), gamma=1.5) + def __init__(self, loss_fcn, gamma=1.5, alpha=0.25): + super(QFocalLoss, self).__init__() + self.loss_fcn = loss_fcn # must be nn.BCEWithLogitsLoss() + self.gamma = gamma + self.alpha = alpha + self.reduction = loss_fcn.reduction + self.loss_fcn.reduction = 'none' # required to apply FL to each element + + def forward(self, pred, true): + loss = self.loss_fcn(pred, true) + + pred_prob = torch.sigmoid(pred) # prob from logits + alpha_factor = true * self.alpha + (1 - true) * (1 - self.alpha) + modulating_factor = torch.abs(true - pred_prob) ** self.gamma + loss *= alpha_factor * modulating_factor + + if self.reduction == 'mean': + return loss.mean() + elif self.reduction == 'sum': + return loss.sum() + else: # 'none' + return loss + + +def compute_loss(p, targets, model): # predictions, targets, model + device = targets.device + lcls, lbox, lobj = torch.zeros(1, device=device), torch.zeros(1, device=device), torch.zeros(1, device=device) + tcls, tbox, indices, anchors = build_targets(p, targets, model) # targets + h = model.hyp # hyperparameters + + # Define criteria + BCEcls = nn.BCEWithLogitsLoss(pos_weight=torch.tensor([h['cls_pw']], device=device)) # weight=model.class_weights) + BCEobj = nn.BCEWithLogitsLoss(pos_weight=torch.tensor([h['obj_pw']], device=device)) + + # Class label smoothing https://arxiv.org/pdf/1902.04103.pdf eqn 3 + cp, cn = smooth_BCE(eps=0.0) + + # Focal loss + g = h['fl_gamma'] # focal loss gamma + if g > 0: + BCEcls, BCEobj = FocalLoss(BCEcls, g), FocalLoss(BCEobj, g) + + # Losses + nt = 0 # number of targets + no = len(p) # number of outputs + balance = [4.0, 1.0, 0.4] if no == 3 else [4.0, 1.0, 0.4, 0.1] # P3-5 or P3-6 + for i, pi in enumerate(p): # layer index, layer predictions + b, a, gj, gi = indices[i] # image, anchor, gridy, gridx + tobj = torch.zeros_like(pi[..., 0], device=device) # target obj + + n = b.shape[0] # number of targets + if n: + nt += n # cumulative targets + ps = pi[b, a, gj, gi] # prediction subset corresponding to targets + + # Regression + pxy = ps[:, :2].sigmoid() * 2. - 0.5 + pwh = (ps[:, 2:4].sigmoid() * 2) ** 2 * anchors[i] + pbox = torch.cat((pxy, pwh), 1) # predicted box + iou = bbox_iou(pbox.T, tbox[i], x1y1x2y2=False, CIoU=True) # iou(prediction, target) + lbox += (1.0 - iou).mean() # iou loss + + # Objectness + tobj[b, a, gj, gi] = (1.0 - model.gr) + model.gr * iou.detach().clamp(0).type(tobj.dtype) # iou ratio + + # Classification + if model.nc > 1: # cls loss (only if multiple classes) + t = torch.full_like(ps[:, 5:], cn, device=device) # targets + t[range(n), tcls[i]] = cp + lcls += BCEcls(ps[:, 5:], t) # BCE + + # Append targets to text file + # with open('targets.txt', 'a') as file: + # [file.write('%11.5g ' * 4 % tuple(x) + '\n') for x in torch.cat((txy[i], twh[i]), 1)] + + lobj += BCEobj(pi[..., 4], tobj) * balance[i] # obj loss + + s = 3 / no # output count scaling + lbox *= h['box'] * s + lobj *= h['obj'] * s * (1.4 if no == 4 else 1.) + lcls *= h['cls'] * s + bs = tobj.shape[0] # batch size + + loss = lbox + lobj + lcls + return loss * bs, torch.cat((lbox, lobj, lcls, loss)).detach() + + +def build_targets(p, targets, model): + # Build targets for compute_loss(), input targets(image,class,x,y,w,h) + det = model.module.model[-1] if is_parallel(model) else model.model[-1] # Detect() module + na, nt = det.na, targets.shape[0] # number of anchors, targets + tcls, tbox, indices, anch = [], [], [], [] + gain = torch.ones(7, device=targets.device) # normalized to gridspace gain + ai = torch.arange(na, device=targets.device).float().view(na, 1).repeat(1, nt) # same as .repeat_interleave(nt) + targets = torch.cat((targets.repeat(na, 1, 1), ai[:, :, 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=targets.device).float() * g # offsets + + for i in range(det.nl): + anchors = det.anchors[i] + gain[2:6] = torch.tensor(p[i].shape)[[3, 2, 3, 2]] # xyxy gain + + # Match targets to anchors + t = targets * gain + if nt: + # Matches + r = t[:, :, 4:6] / anchors[:, None] # wh ratio + j = torch.max(r, 1. / r).max(2)[0] < model.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 + b, c = t[:, :2].long().T # image, class + gxy = t[:, 2:4] # grid xy + gwh = t[:, 4:6] # grid wh + gij = (gxy - offsets).long() + gi, gj = gij.T # grid xy indices + + # Append + a = t[:, 6].long() # anchor indices + indices.append((b, a, gj.clamp_(0, gain[3] - 1), gi.clamp_(0, gain[2] - 1))) # image, anchor, grid indices + tbox.append(torch.cat((gxy - gij, gwh), 1)) # box + anch.append(anchors[a]) # anchors + tcls.append(c) # class + + return tcls, tbox, indices, anch diff --git a/yolov5/utils/metrics.py b/yolov5/utils/metrics.py new file mode 100644 index 0000000..99d5bcf --- /dev/null +++ b/yolov5/utils/metrics.py @@ -0,0 +1,200 @@ +# Model validation metrics + +from pathlib import Path + +import matplotlib.pyplot as plt +import numpy as np +import torch + +from . import general + + +def fitness(x): + # Model fitness as a weighted combination of metrics + w = [0.0, 0.0, 0.1, 0.9] # weights for [P, R, mAP@0.5, mAP@0.5:0.95] + return (x[:, :4] * w).sum(1) + + +def ap_per_class(tp, conf, pred_cls, target_cls, plot=False, save_dir='precision-recall_curve.png', names=[]): + """ Compute the average precision, given the recall and precision curves. + Source: https://github.com/rafaelpadilla/Object-Detection-Metrics. + # Arguments + tp: True positives (nparray, nx1 or nx10). + conf: Objectness value from 0-1 (nparray). + pred_cls: Predicted object classes (nparray). + target_cls: True object classes (nparray). + plot: Plot precision-recall curve at mAP@0.5 + save_dir: Plot save directory + # Returns + The average precision as computed in py-faster-rcnn. + """ + + # Sort by objectness + i = np.argsort(-conf) + tp, conf, pred_cls = tp[i], conf[i], pred_cls[i] + + # Find unique classes + unique_classes = np.unique(target_cls) + + # Create Precision-Recall curve and compute AP for each class + px, py = np.linspace(0, 1, 1000), [] # for plotting + pr_score = 0.1 # score to evaluate P and R https://github.com/ultralytics/yolov3/issues/898 + s = [unique_classes.shape[0], tp.shape[1]] # number class, number iou thresholds (i.e. 10 for mAP0.5...0.95) + ap, p, r = np.zeros(s), np.zeros(s), np.zeros(s) + for ci, c in enumerate(unique_classes): + i = pred_cls == c + n_l = (target_cls == c).sum() # number of labels + n_p = i.sum() # number of predictions + + if n_p == 0 or n_l == 0: + continue + else: + # Accumulate FPs and TPs + fpc = (1 - tp[i]).cumsum(0) + tpc = tp[i].cumsum(0) + + # Recall + recall = tpc / (n_l + 1e-16) # recall curve + r[ci] = np.interp(-pr_score, -conf[i], recall[:, 0]) # r at pr_score, negative x, xp because xp decreases + + # Precision + precision = tpc / (tpc + fpc) # precision curve + p[ci] = np.interp(-pr_score, -conf[i], precision[:, 0]) # p at pr_score + + # AP from recall-precision curve + for j in range(tp.shape[1]): + ap[ci, j], mpre, mrec = compute_ap(recall[:, j], precision[:, j]) + if plot and (j == 0): + py.append(np.interp(px, mrec, mpre)) # precision at mAP@0.5 + + # Compute F1 score (harmonic mean of precision and recall) + f1 = 2 * p * r / (p + r + 1e-16) + + if plot: + plot_pr_curve(px, py, ap, save_dir, names) + + return p, r, ap, f1, unique_classes.astype('int32') + + +def compute_ap(recall, precision): + """ Compute the average precision, given the recall and precision curves + # Arguments + recall: The recall curve (list) + precision: The precision curve (list) + # Returns + Average precision, precision curve, recall curve + """ + + # Append sentinel values to beginning and end + mrec = np.concatenate(([0.], recall, [recall[-1] + 0.01])) + mpre = np.concatenate(([1.], precision, [0.])) + + # Compute the precision envelope + mpre = np.flip(np.maximum.accumulate(np.flip(mpre))) + + # Integrate area under curve + method = 'interp' # methods: 'continuous', 'interp' + if method == 'interp': + x = np.linspace(0, 1, 101) # 101-point interp (COCO) + ap = np.trapz(np.interp(x, mrec, mpre), x) # integrate + else: # 'continuous' + i = np.where(mrec[1:] != mrec[:-1])[0] # points where x axis (recall) changes + ap = np.sum((mrec[i + 1] - mrec[i]) * mpre[i + 1]) # area under curve + + return ap, mpre, mrec + + +class ConfusionMatrix: + # Updated version of https://github.com/kaanakan/object_detection_confusion_matrix + def __init__(self, nc, conf=0.25, iou_thres=0.45): + self.matrix = np.zeros((nc + 1, nc + 1)) + self.nc = nc # number of classes + self.conf = conf + self.iou_thres = iou_thres + + def process_batch(self, detections, labels): + """ + Return intersection-over-union (Jaccard index) of boxes. + Both sets of boxes are expected to be in (x1, y1, x2, y2) format. + Arguments: + detections (Array[N, 6]), x1, y1, x2, y2, conf, class + labels (Array[M, 5]), class, x1, y1, x2, y2 + Returns: + None, updates confusion matrix accordingly + """ + detections = detections[detections[:, 4] > self.conf] + gt_classes = labels[:, 0].int() + detection_classes = detections[:, 5].int() + iou = general.box_iou(labels[:, 1:], detections[:, :4]) + + x = torch.where(iou > self.iou_thres) + if x[0].shape[0]: + matches = torch.cat((torch.stack(x, 1), iou[x[0], x[1]][:, None]), 1).cpu().numpy() + 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]] + else: + matches = np.zeros((0, 3)) + + n = matches.shape[0] > 0 + m0, m1, _ = matches.transpose().astype(np.int16) + for i, gc in enumerate(gt_classes): + j = m0 == i + if n and sum(j) == 1: + self.matrix[gc, detection_classes[m1[j]]] += 1 # correct + else: + self.matrix[gc, self.nc] += 1 # background FP + + if n: + for i, dc in enumerate(detection_classes): + if not any(m1 == i): + self.matrix[self.nc, dc] += 1 # background FN + + def matrix(self): + return self.matrix + + def plot(self, save_dir='', names=()): + try: + import seaborn as sn + + array = self.matrix / (self.matrix.sum(0).reshape(1, self.nc + 1) + 1E-6) # normalize + array[array < 0.005] = np.nan # don't annotate (would appear as 0.00) + + fig = plt.figure(figsize=(12, 9), tight_layout=True) + sn.set(font_scale=1.0 if self.nc < 50 else 0.8) # for label size + labels = (0 < len(names) < 99) and len(names) == self.nc # apply names to ticklabels + sn.heatmap(array, annot=self.nc < 30, annot_kws={"size": 8}, cmap='Blues', fmt='.2f', square=True, + xticklabels=names + ['background FN'] if labels else "auto", + yticklabels=names + ['background FP'] if labels else "auto").set_facecolor((1, 1, 1)) + fig.axes[0].set_xlabel('True') + fig.axes[0].set_ylabel('Predicted') + fig.savefig(Path(save_dir) / 'confusion_matrix.png', dpi=250) + except Exception as e: + pass + + def print(self): + for i in range(self.nc + 1): + print(' '.join(map(str, self.matrix[i]))) + + +# Plots ---------------------------------------------------------------------------------------------------------------- + +def plot_pr_curve(px, py, ap, save_dir='.', names=()): + fig, ax = plt.subplots(1, 1, figsize=(9, 6), tight_layout=True) + py = np.stack(py, axis=1) + + if 0 < len(names) < 21: # show mAP in legend if < 10 classes + for i, y in enumerate(py.T): + ax.plot(px, y, linewidth=1, label=f'{names[i]} %.3f' % ap[i, 0]) # plot(recall, precision) + else: + ax.plot(px, py, linewidth=1, color='grey') # plot(recall, precision) + + ax.plot(px, py.mean(1), linewidth=3, color='blue', label='all classes %.3f mAP@0.5' % ap[:, 0].mean()) + ax.set_xlabel('Recall') + ax.set_ylabel('Precision') + ax.set_xlim(0, 1) + ax.set_ylim(0, 1) + plt.legend(bbox_to_anchor=(1.04, 1), loc="upper left") + fig.savefig(Path(save_dir) / 'precision_recall_curve.png', dpi=250) diff --git a/yolov5/utils/plots.py b/yolov5/utils/plots.py new file mode 100644 index 0000000..3a4dccd --- /dev/null +++ b/yolov5/utils/plots.py @@ -0,0 +1,412 @@ +# Plotting utils + +import glob +import math +import os +import random +from copy import copy +from pathlib import Path + +import cv2 +import matplotlib +import matplotlib.pyplot as plt +import numpy as np +import pandas as pd +import seaborn as sns +import torch +import yaml +from PIL import Image, ImageDraw +from scipy.signal import butter, filtfilt + +from utils.general import xywh2xyxy, xyxy2xywh +from utils.metrics import fitness + +# Settings +matplotlib.rc('font', **{'size': 11}) +matplotlib.use('Agg') # for writing to files only + + +def color_list(): + # Return first 10 plt colors as (r,g,b) https://stackoverflow.com/questions/51350872/python-from-color-name-to-rgb + def hex2rgb(h): + return tuple(int(h[1 + i:1 + i + 2], 16) for i in (0, 2, 4)) + + return [hex2rgb(h) for h in plt.rcParams['axes.prop_cycle'].by_key()['color']] + + +def hist2d(x, y, n=100): + # 2d histogram used in labels.png and evolve.png + xedges, yedges = np.linspace(x.min(), x.max(), n), np.linspace(y.min(), y.max(), n) + hist, xedges, yedges = np.histogram2d(x, y, (xedges, yedges)) + xidx = np.clip(np.digitize(x, xedges) - 1, 0, hist.shape[0] - 1) + yidx = np.clip(np.digitize(y, yedges) - 1, 0, hist.shape[1] - 1) + return np.log(hist[xidx, yidx]) + + +def butter_lowpass_filtfilt(data, cutoff=1500, fs=50000, order=5): + # https://stackoverflow.com/questions/28536191/how-to-filter-smooth-with-scipy-numpy + def butter_lowpass(cutoff, fs, order): + nyq = 0.5 * fs + normal_cutoff = cutoff / nyq + return butter(order, normal_cutoff, btype='low', analog=False) + + b, a = butter_lowpass(cutoff, fs, order=order) + return filtfilt(b, a, data) # forward-backward filter + + +def plot_one_box(x, img, color=None, label=None, line_thickness=None): + # Plots one bounding box on image img + tl = line_thickness or round(0.002 * (img.shape[0] + img.shape[1]) / 2) + 1 # line/font thickness + color = color or [random.randint(0, 255) for _ in range(3)] + c1, c2 = (int(x[0]), int(x[1])), (int(x[2]), int(x[3])) + cv2.rectangle(img, c1, c2, color, thickness=tl, lineType=cv2.LINE_AA) + if label: + tf = max(tl - 1, 1) # font thickness + t_size = cv2.getTextSize(label, 0, fontScale=tl / 3, thickness=tf)[0] + c2 = c1[0] + t_size[0], c1[1] - t_size[1] - 3 + cv2.rectangle(img, c1, c2, color, -1, cv2.LINE_AA) # filled + cv2.putText(img, label, (c1[0], c1[1] - 2), 0, tl / 3, [225, 255, 255], thickness=tf, lineType=cv2.LINE_AA) + + +def plot_wh_methods(): # from utils.plots import *; plot_wh_methods() + # Compares the two methods for width-height anchor multiplication + # https://github.com/ultralytics/yolov3/issues/168 + x = np.arange(-4.0, 4.0, .1) + ya = np.exp(x) + yb = torch.sigmoid(torch.from_numpy(x)).numpy() * 2 + + fig = plt.figure(figsize=(6, 3), tight_layout=True) + plt.plot(x, ya, '.-', label='YOLOv3') + plt.plot(x, yb ** 2, '.-', label='YOLOv5 ^2') + plt.plot(x, yb ** 1.6, '.-', label='YOLOv5 ^1.6') + plt.xlim(left=-4, right=4) + plt.ylim(bottom=0, top=6) + plt.xlabel('input') + plt.ylabel('output') + plt.grid() + plt.legend() + fig.savefig('comparison.png', dpi=200) + + +def output_to_target(output): + # Convert model output to target format [batch_id, class_id, x, y, w, h, conf] + targets = [] + for i, o in enumerate(output): + for *box, conf, cls in o.cpu().numpy(): + targets.append([i, cls, *list(*xyxy2xywh(np.array(box)[None])), conf]) + return np.array(targets) + + +def plot_images(images, targets, paths=None, fname='images.jpg', names=None, max_size=640, max_subplots=16): + # Plot image grid with labels + + if isinstance(images, torch.Tensor): + images = images.cpu().float().numpy() + if isinstance(targets, torch.Tensor): + targets = targets.cpu().numpy() + + # un-normalise + if np.max(images[0]) <= 1: + images *= 255 + + tl = 3 # line thickness + tf = max(tl - 1, 1) # font thickness + 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) + + # Check if we should resize + scale_factor = max_size / max(h, w) + if scale_factor < 1: + h = math.ceil(scale_factor * h) + w = math.ceil(scale_factor * w) + + colors = color_list() # list of colors + mosaic = np.full((int(ns * h), int(ns * w), 3), 255, dtype=np.uint8) # init + for i, img in enumerate(images): + if i == max_subplots: # if last batch has fewer images than we expect + break + + block_x = int(w * (i // ns)) + block_y = int(h * (i % ns)) + + img = img.transpose(1, 2, 0) + if scale_factor < 1: + img = cv2.resize(img, (w, h)) + + mosaic[block_y:block_y + h, block_x:block_x + w, :] = img + if len(targets) > 0: + image_targets = targets[targets[:, 0] == i] + boxes = xywh2xyxy(image_targets[:, 2:6]).T + classes = image_targets[:, 1].astype('int') + labels = image_targets.shape[1] == 6 # labels if no conf column + conf = None if labels else image_targets[:, 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_factor < 1: # absolute coords need scale if image scales + boxes *= scale_factor + boxes[[0, 2]] += block_x + boxes[[1, 3]] += block_y + for j, box in enumerate(boxes.T): + cls = int(classes[j]) + color = colors[cls % len(colors)] + cls = names[cls] if names else cls + if labels or conf[j] > 0.25: # 0.25 conf thresh + label = '%s' % cls if labels else '%s %.1f' % (cls, conf[j]) + plot_one_box(box, mosaic, label=label, color=color, line_thickness=tl) + + # Draw image filename labels + if paths: + label = Path(paths[i]).name[:40] # trim to 40 char + t_size = cv2.getTextSize(label, 0, fontScale=tl / 3, thickness=tf)[0] + cv2.putText(mosaic, label, (block_x + 5, block_y + t_size[1] + 5), 0, tl / 3, [220, 220, 220], thickness=tf, + lineType=cv2.LINE_AA) + + # Image border + cv2.rectangle(mosaic, (block_x, block_y), (block_x + w, block_y + h), (255, 255, 255), thickness=3) + + if fname: + r = min(1280. / max(h, w) / ns, 1.0) # ratio to limit image size + mosaic = cv2.resize(mosaic, (int(ns * w * r), int(ns * h * r)), interpolation=cv2.INTER_AREA) + # cv2.imwrite(fname, cv2.cvtColor(mosaic, cv2.COLOR_BGR2RGB)) # cv2 save + Image.fromarray(mosaic).save(fname) # PIL save + return mosaic + + +def plot_lr_scheduler(optimizer, scheduler, epochs=300, save_dir=''): + # Plot LR simulating training for full epochs + optimizer, scheduler = copy(optimizer), copy(scheduler) # do not modify originals + y = [] + for _ in range(epochs): + scheduler.step() + y.append(optimizer.param_groups[0]['lr']) + plt.plot(y, '.-', label='LR') + plt.xlabel('epoch') + plt.ylabel('LR') + plt.grid() + plt.xlim(0, epochs) + plt.ylim(0) + plt.savefig(Path(save_dir) / 'LR.png', dpi=200) + + +def plot_test_txt(): # from utils.plots import *; plot_test() + # Plot test.txt histograms + x = np.loadtxt('test.txt', dtype=np.float32) + box = xyxy2xywh(x[:, :4]) + cx, cy = box[:, 0], box[:, 1] + + fig, ax = plt.subplots(1, 1, figsize=(6, 6), tight_layout=True) + ax.hist2d(cx, cy, bins=600, cmax=10, cmin=0) + ax.set_aspect('equal') + plt.savefig('hist2d.png', dpi=300) + + fig, ax = plt.subplots(1, 2, figsize=(12, 6), tight_layout=True) + ax[0].hist(cx, bins=600) + ax[1].hist(cy, bins=600) + plt.savefig('hist1d.png', dpi=200) + + +def plot_targets_txt(): # from utils.plots import *; plot_targets_txt() + # Plot targets.txt histograms + x = np.loadtxt('targets.txt', dtype=np.float32).T + s = ['x targets', 'y targets', 'width targets', 'height targets'] + fig, ax = plt.subplots(2, 2, figsize=(8, 8), tight_layout=True) + ax = ax.ravel() + for i in range(4): + ax[i].hist(x[i], bins=100, label='%.3g +/- %.3g' % (x[i].mean(), x[i].std())) + ax[i].legend() + ax[i].set_title(s[i]) + plt.savefig('targets.jpg', dpi=200) + + +def plot_study_txt(path='', x=None): # from utils.plots import *; plot_study_txt() + # Plot study.txt generated by test.py + fig, ax = plt.subplots(2, 4, figsize=(10, 6), tight_layout=True) + ax = ax.ravel() + + fig2, ax2 = plt.subplots(1, 1, figsize=(8, 4), tight_layout=True) + for f in [Path(path) / f'study_coco_{x}.txt' for x in ['yolov5s', 'yolov5m', 'yolov5l', 'yolov5x']]: + y = np.loadtxt(f, dtype=np.float32, usecols=[0, 1, 2, 3, 7, 8, 9], ndmin=2).T + x = np.arange(y.shape[1]) if x is None else np.array(x) + s = ['P', 'R', 'mAP@.5', 'mAP@.5:.95', 't_inference (ms/img)', 't_NMS (ms/img)', 't_total (ms/img)'] + for i in range(7): + ax[i].plot(x, y[i], '.-', linewidth=2, markersize=8) + ax[i].set_title(s[i]) + + j = y[3].argmax() + 1 + ax2.plot(y[6, :j], y[3, :j] * 1E2, '.-', linewidth=2, markersize=8, + label=f.stem.replace('study_coco_', '').replace('yolo', 'YOLO')) + + ax2.plot(1E3 / np.array([209, 140, 97, 58, 35, 18]), [34.6, 40.5, 43.0, 47.5, 49.7, 51.5], + 'k.-', linewidth=2, markersize=8, alpha=.25, label='EfficientDet') + + ax2.grid() + ax2.set_xlim(0, 30) + ax2.set_ylim(28, 50) + ax2.set_yticks(np.arange(30, 55, 5)) + ax2.set_xlabel('GPU Speed (ms/img)') + ax2.set_ylabel('COCO AP val') + ax2.legend(loc='lower right') + plt.savefig('test_study.png', dpi=300) + + +def plot_labels(labels, save_dir=Path(''), loggers=None): + # plot dataset labels + print('Plotting labels... ') + c, b = labels[:, 0], labels[:, 1:].transpose() # classes, boxes + nc = int(c.max() + 1) # number of classes + colors = color_list() + x = pd.DataFrame(b.transpose(), columns=['x', 'y', 'width', 'height']) + + # seaborn correlogram + sns.pairplot(x, corner=True, diag_kind='auto', kind='hist', diag_kws=dict(bins=50), plot_kws=dict(pmax=0.9)) + plt.savefig(save_dir / 'labels_correlogram.jpg', dpi=200) + plt.close() + + # matplotlib labels + matplotlib.use('svg') # faster + ax = plt.subplots(2, 2, figsize=(8, 8), tight_layout=True)[1].ravel() + ax[0].hist(c, bins=np.linspace(0, nc, nc + 1) - 0.5, rwidth=0.8) + ax[0].set_xlabel('classes') + sns.histplot(x, x='x', y='y', ax=ax[2], bins=50, pmax=0.9) + sns.histplot(x, x='width', y='height', ax=ax[3], bins=50, pmax=0.9) + + # rectangles + labels[:, 1:3] = 0.5 # center + labels[:, 1:] = xywh2xyxy(labels[:, 1:]) * 2000 + img = Image.fromarray(np.ones((2000, 2000, 3), dtype=np.uint8) * 255) + for cls, *box in labels[:1000]: + ImageDraw.Draw(img).rectangle(box, width=1, outline=colors[int(cls) % 10]) # plot + ax[1].imshow(img) + ax[1].axis('off') + + for a in [0, 1, 2, 3]: + for s in ['top', 'right', 'left', 'bottom']: + ax[a].spines[s].set_visible(False) + + plt.savefig(save_dir / 'labels.jpg', dpi=200) + matplotlib.use('Agg') + plt.close() + + # loggers + for k, v in loggers.items() or {}: + if k == 'wandb' and v: + v.log({"Labels": [v.Image(str(x), caption=x.name) for x in save_dir.glob('*labels*.jpg')]}) + + +def plot_evolution(yaml_file='data/hyp.finetune.yaml'): # from utils.plots import *; plot_evolution() + # Plot hyperparameter evolution results in evolve.txt + with open(yaml_file) as f: + hyp = yaml.load(f, Loader=yaml.FullLoader) + x = np.loadtxt('evolve.txt', ndmin=2) + f = fitness(x) + # weights = (f - f.min()) ** 2 # for weighted results + plt.figure(figsize=(10, 12), tight_layout=True) + matplotlib.rc('font', **{'size': 8}) + for i, (k, v) in enumerate(hyp.items()): + y = x[:, i + 7] + # mu = (y * weights).sum() / weights.sum() # best weighted result + mu = y[f.argmax()] # best single result + plt.subplot(6, 5, i + 1) + plt.scatter(y, f, c=hist2d(y, f, 20), cmap='viridis', alpha=.8, edgecolors='none') + plt.plot(mu, f.max(), 'k+', markersize=15) + plt.title('%s = %.3g' % (k, mu), fontdict={'size': 9}) # limit to 40 characters + if i % 5 != 0: + plt.yticks([]) + print('%15s: %.3g' % (k, mu)) + plt.savefig('evolve.png', dpi=200) + print('\nPlot saved as evolve.png') + + +def profile_idetection(start=0, stop=0, labels=(), save_dir=''): + # Plot iDetection '*.txt' per-image logs. from utils.plots import *; profile_idetection() + ax = plt.subplots(2, 4, figsize=(12, 6), tight_layout=True)[1].ravel() + s = ['Images', 'Free Storage (GB)', 'RAM Usage (GB)', 'Battery', 'dt_raw (ms)', 'dt_smooth (ms)', 'real-world FPS'] + files = list(Path(save_dir).glob('frames*.txt')) + for fi, f in enumerate(files): + try: + results = np.loadtxt(f, ndmin=2).T[:, 90:-30] # clip first and last rows + n = results.shape[1] # number of rows + x = np.arange(start, min(stop, n) if stop else n) + results = results[:, x] + t = (results[0] - results[0].min()) # set t0=0s + results[0] = x + for i, a in enumerate(ax): + if i < len(results): + label = labels[fi] if len(labels) else f.stem.replace('frames_', '') + a.plot(t, results[i], marker='.', label=label, linewidth=1, markersize=5) + a.set_title(s[i]) + a.set_xlabel('time (s)') + # if fi == len(files) - 1: + # a.set_ylim(bottom=0) + for side in ['top', 'right']: + a.spines[side].set_visible(False) + else: + a.remove() + except Exception as e: + print('Warning: Plotting error for %s; %s' % (f, e)) + + ax[1].legend() + plt.savefig(Path(save_dir) / 'idetection_profile.png', dpi=200) + + +def plot_results_overlay(start=0, stop=0): # from utils.plots import *; plot_results_overlay() + # Plot training 'results*.txt', overlaying train and val losses + s = ['train', 'train', 'train', 'Precision', 'mAP@0.5', 'val', 'val', 'val', 'Recall', 'mAP@0.5:0.95'] # legends + t = ['Box', 'Objectness', 'Classification', 'P-R', 'mAP-F1'] # titles + for f in sorted(glob.glob('results*.txt') + glob.glob('../../Downloads/results*.txt')): + results = np.loadtxt(f, usecols=[2, 3, 4, 8, 9, 12, 13, 14, 10, 11], ndmin=2).T + n = results.shape[1] # number of rows + x = range(start, min(stop, n) if stop else n) + fig, ax = plt.subplots(1, 5, figsize=(14, 3.5), tight_layout=True) + ax = ax.ravel() + for i in range(5): + for j in [i, i + 5]: + y = results[j, x] + ax[i].plot(x, y, marker='.', label=s[j]) + # y_smooth = butter_lowpass_filtfilt(y) + # ax[i].plot(x, np.gradient(y_smooth), marker='.', label=s[j]) + + ax[i].set_title(t[i]) + ax[i].legend() + ax[i].set_ylabel(f) if i == 0 else None # add filename + fig.savefig(f.replace('.txt', '.png'), dpi=200) + + +def plot_results(start=0, stop=0, bucket='', id=(), labels=(), save_dir=''): + # Plot training 'results*.txt'. from utils.plots import *; plot_results(save_dir='runs/train/exp') + fig, ax = plt.subplots(2, 5, figsize=(12, 6), tight_layout=True) + ax = ax.ravel() + s = ['Box', 'Objectness', 'Classification', 'Precision', 'Recall', + 'val Box', 'val Objectness', 'val Classification', 'mAP@0.5', 'mAP@0.5:0.95'] + if bucket: + # files = ['https://storage.googleapis.com/%s/results%g.txt' % (bucket, x) for x in id] + files = ['results%g.txt' % x for x in id] + c = ('gsutil cp ' + '%s ' * len(files) + '.') % tuple('gs://%s/results%g.txt' % (bucket, x) for x in id) + os.system(c) + else: + files = list(Path(save_dir).glob('results*.txt')) + assert len(files), 'No results.txt files found in %s, nothing to plot.' % os.path.abspath(save_dir) + for fi, f in enumerate(files): + try: + results = np.loadtxt(f, usecols=[2, 3, 4, 8, 9, 12, 13, 14, 10, 11], ndmin=2).T + n = results.shape[1] # number of rows + x = range(start, min(stop, n) if stop else n) + for i in range(10): + y = results[i, x] + if i in [0, 1, 2, 5, 6, 7]: + y[y == 0] = np.nan # don't show zero loss values + # y /= y[0] # normalize + label = labels[fi] if len(labels) else f.stem + ax[i].plot(x, y, marker='.', label=label, linewidth=2, markersize=8) + ax[i].set_title(s[i]) + # if i in [5, 6, 7]: # share train and val loss y axes + # ax[i].get_shared_y_axes().join(ax[i], ax[i - 5]) + except Exception as e: + print('Warning: Plotting error for %s; %s' % (f, e)) + + ax[1].legend() + fig.savefig(Path(save_dir) / 'results.png', dpi=200) diff --git a/yolov5/utils/torch_utils.py b/yolov5/utils/torch_utils.py new file mode 100644 index 0000000..69a3121 --- /dev/null +++ b/yolov5/utils/torch_utils.py @@ -0,0 +1,285 @@ +# PyTorch utils + +import logging +import math +import os +import time +from contextlib import contextmanager +from copy import deepcopy + +import torch +import torch.backends.cudnn as cudnn +import torch.nn as nn +import torch.nn.functional as F +import torchvision + +try: + import thop # for FLOPS computation +except ImportError: + thop = None +logger = logging.getLogger(__name__) + + +@contextmanager +def torch_distributed_zero_first(local_rank: int): + """ + Decorator to make all processes in distributed training wait for each local_master to do something. + """ + if local_rank not in [-1, 0]: + torch.distributed.barrier() + yield + if local_rank == 0: + torch.distributed.barrier() + + +def init_torch_seeds(seed=0): + # Speed-reproducibility tradeoff https://pytorch.org/docs/stable/notes/randomness.html + torch.manual_seed(seed) + if seed == 0: # slower, more reproducible + cudnn.benchmark, cudnn.deterministic = False, True + else: # faster, less reproducible + cudnn.benchmark, cudnn.deterministic = True, False + + +def select_device(device='', batch_size=None): + # device = 'cpu' or '0' or '0,1,2,3' + s = f'Using torch {torch.__version__} ' # string + cpu = device.lower() == 'cpu' + if cpu: + os.environ['CUDA_VISIBLE_DEVICES'] = '-1' # force torch.cuda.is_available() = False + elif device: # non-cpu device requested + os.environ['CUDA_VISIBLE_DEVICES'] = device # set environment variable + assert torch.cuda.is_available(), f'CUDA unavailable, invalid device {device} requested' # check availability + + cuda = torch.cuda.is_available() and not cpu + if cuda: + n = torch.cuda.device_count() + if n > 1 and batch_size: # check that batch_size is compatible with device_count + assert batch_size % n == 0, f'batch-size {batch_size} not multiple of GPU count {n}' + space = ' ' * len(s) + for i, d in enumerate(device.split(',') if device else range(n)): + p = torch.cuda.get_device_properties(i) + s += f"{'' if i == 0 else space}CUDA:{d} ({p.name}, {p.total_memory / 1024 ** 2}MB)\n" # bytes to MB + else: + s += 'CPU' + + logger.info(f'{s}\n') # skip a line + return torch.device('cuda:0' if cuda else 'cpu') + + +def time_synchronized(): + # pytorch-accurate time + if torch.cuda.is_available(): + torch.cuda.synchronize() + return time.time() + + +def profile(x, ops, n=100, device=None): + # profile a pytorch module or list of modules. Example usage: + # x = torch.randn(16, 3, 640, 640) # input + # m1 = lambda x: x * torch.sigmoid(x) + # m2 = nn.SiLU() + # profile(x, [m1, m2], n=100) # profile speed over 100 iterations + + device = device or torch.device('cuda:0' if torch.cuda.is_available() else 'cpu') + x = x.to(device) + x.requires_grad = True + print(torch.__version__, device.type, torch.cuda.get_device_properties(0) if device.type == 'cuda' else '') + print(f"\n{'Params':>12s}{'GFLOPS':>12s}{'forward (ms)':>16s}{'backward (ms)':>16s}{'input':>24s}{'output':>24s}") + for m in ops if isinstance(ops, list) else [ops]: + m = m.to(device) if hasattr(m, 'to') else m # device + m = m.half() if hasattr(m, 'half') and isinstance(x, torch.Tensor) and x.dtype is torch.float16 else m # type + dtf, dtb, t = 0., 0., [0., 0., 0.] # dt forward, backward + try: + flops = thop.profile(m, inputs=(x,), verbose=False)[0] / 1E9 * 2 # GFLOPS + except: + flops = 0 + + for _ in range(n): + t[0] = time_synchronized() + y = m(x) + t[1] = time_synchronized() + try: + _ = y.sum().backward() + t[2] = time_synchronized() + except: # no backward method + t[2] = float('nan') + dtf += (t[1] - t[0]) * 1000 / n # ms per op forward + dtb += (t[2] - t[1]) * 1000 / n # ms per op backward + + s_in = tuple(x.shape) if isinstance(x, torch.Tensor) else 'list' + s_out = tuple(y.shape) if isinstance(y, torch.Tensor) else 'list' + p = sum(list(x.numel() for x in m.parameters())) if isinstance(m, nn.Module) else 0 # parameters + print(f'{p:12.4g}{flops:12.4g}{dtf:16.4g}{dtb:16.4g}{str(s_in):>24s}{str(s_out):>24s}') + + +def is_parallel(model): + return type(model) in (nn.parallel.DataParallel, nn.parallel.DistributedDataParallel) + + +def intersect_dicts(da, db, exclude=()): + # Dictionary intersection of matching keys and shapes, omitting 'exclude' keys, using da values + return {k: v for k, v in da.items() if k in db and not any(x in k for x in exclude) and v.shape == db[k].shape} + + +def initialize_weights(model): + for m in model.modules(): + t = type(m) + if t is nn.Conv2d: + pass # nn.init.kaiming_normal_(m.weight, mode='fan_out', nonlinearity='relu') + elif t is nn.BatchNorm2d: + m.eps = 1e-3 + m.momentum = 0.03 + elif t in [nn.Hardswish, nn.LeakyReLU, nn.ReLU, nn.ReLU6]: + m.inplace = True + + +def find_modules(model, mclass=nn.Conv2d): + # Finds layer indices matching module class 'mclass' + return [i for i, m in enumerate(model.module_list) if isinstance(m, mclass)] + + +def sparsity(model): + # Return global model sparsity + a, b = 0., 0. + for p in model.parameters(): + a += p.numel() + b += (p == 0).sum() + return b / a + + +def prune(model, amount=0.3): + # Prune model to requested global sparsity + import torch.nn.utils.prune as prune + print('Pruning model... ', end='') + for name, m in model.named_modules(): + if isinstance(m, nn.Conv2d): + prune.l1_unstructured(m, name='weight', amount=amount) # prune + prune.remove(m, 'weight') # make permanent + print(' %.3g global sparsity' % sparsity(model)) + + +def fuse_conv_and_bn(conv, bn): + # Fuse convolution and batchnorm layers https://tehnokv.com/posts/fusing-batchnorm-and-conv/ + fusedconv = nn.Conv2d(conv.in_channels, + conv.out_channels, + kernel_size=conv.kernel_size, + stride=conv.stride, + padding=conv.padding, + groups=conv.groups, + bias=True).requires_grad_(False).to(conv.weight.device) + + # prepare filters + w_conv = conv.weight.clone().view(conv.out_channels, -1) + w_bn = torch.diag(bn.weight.div(torch.sqrt(bn.eps + bn.running_var))) + fusedconv.weight.copy_(torch.mm(w_bn, w_conv).view(fusedconv.weight.size())) + + # prepare spatial bias + b_conv = torch.zeros(conv.weight.size(0), device=conv.weight.device) if conv.bias is None else conv.bias + b_bn = bn.bias - bn.weight.mul(bn.running_mean).div(torch.sqrt(bn.running_var + bn.eps)) + fusedconv.bias.copy_(torch.mm(w_bn, b_conv.reshape(-1, 1)).reshape(-1) + b_bn) + + return fusedconv + + +def model_info(model, verbose=False, img_size=640): + # Model information. img_size may be int or list, i.e. img_size=640 or img_size=[640, 320] + n_p = sum(x.numel() for x in model.parameters()) # number parameters + n_g = sum(x.numel() for x in model.parameters() if x.requires_grad) # number gradients + if verbose: + print('%5s %40s %9s %12s %20s %10s %10s' % ('layer', 'name', 'gradient', 'parameters', 'shape', 'mu', 'sigma')) + for i, (name, p) in enumerate(model.named_parameters()): + name = name.replace('module_list.', '') + print('%5g %40s %9s %12g %20s %10.3g %10.3g' % + (i, name, p.requires_grad, p.numel(), list(p.shape), p.mean(), p.std())) + + try: # FLOPS + from thop import profile + stride = int(model.stride.max()) if hasattr(model, 'stride') else 32 + img = torch.zeros((1, model.yaml.get('ch', 3), stride, stride), device=next(model.parameters()).device) # input + flops = profile(deepcopy(model), inputs=(img,), verbose=False)[0] / 1E9 * 2 # stride GFLOPS + img_size = img_size if isinstance(img_size, list) else [img_size, img_size] # expand if int/float + fs = ', %.1f GFLOPS' % (flops * img_size[0] / stride * img_size[1] / stride) # 640x640 GFLOPS + except (ImportError, Exception): + fs = '' + + logger.info(f"Model Summary: {len(list(model.modules()))} layers, {n_p} parameters, {n_g} gradients{fs}") + + +def load_classifier(name='resnet101', n=2): + # Loads a pretrained model reshaped to n-class output + model = torchvision.models.__dict__[name](pretrained=True) + + # ResNet model properties + # input_size = [3, 224, 224] + # input_space = 'RGB' + # input_range = [0, 1] + # mean = [0.485, 0.456, 0.406] + # std = [0.229, 0.224, 0.225] + + # Reshape output to n classes + filters = model.fc.weight.shape[1] + model.fc.bias = nn.Parameter(torch.zeros(n), requires_grad=True) + model.fc.weight = nn.Parameter(torch.zeros(n, filters), requires_grad=True) + model.fc.out_features = n + return model + + +def scale_img(img, ratio=1.0, same_shape=False): # img(16,3,256,416), r=ratio + # scales img(bs,3,y,x) by ratio + if ratio == 1.0: + return img + else: + h, w = img.shape[2:] + s = (int(h * ratio), int(w * ratio)) # new size + img = F.interpolate(img, size=s, mode='bilinear', align_corners=False) # resize + if not same_shape: # pad/crop img + gs = 32 # (pixels) grid size + h, w = [math.ceil(x * ratio / gs) * gs for x in (h, w)] + return F.pad(img, [0, w - s[1], 0, h - s[0]], value=0.447) # value = imagenet mean + + +def copy_attr(a, b, include=(), exclude=()): + # Copy attributes from b to a, options to only include [...] and to exclude [...] + for k, v in b.__dict__.items(): + if (len(include) and k not in include) or k.startswith('_') or k in exclude: + continue + else: + setattr(a, k, v) + + +class ModelEMA: + """ Model Exponential Moving Average from https://github.com/rwightman/pytorch-image-models + Keep a moving average of everything in the model state_dict (parameters and buffers). + This is intended to allow functionality like + https://www.tensorflow.org/api_docs/python/tf/train/ExponentialMovingAverage + A smoothed version of the weights is necessary for some training schemes to perform well. + This class is sensitive where it is initialized in the sequence of model init, + GPU assignment and distributed training wrappers. + """ + + def __init__(self, model, decay=0.9999, updates=0): + # Create EMA + self.ema = deepcopy(model.module if is_parallel(model) else model).eval() # FP32 EMA + # if next(model.parameters()).device.type != 'cpu': + # self.ema.half() # FP16 EMA + self.updates = updates # number of EMA updates + self.decay = lambda x: decay * (1 - math.exp(-x / 2000)) # decay exponential ramp (to help early epochs) + for p in self.ema.parameters(): + p.requires_grad_(False) + + def update(self, model): + # Update EMA parameters + with torch.no_grad(): + self.updates += 1 + d = self.decay(self.updates) + + msd = model.module.state_dict() if is_parallel(model) else model.state_dict() # model state_dict + for k, v in self.ema.state_dict().items(): + if v.dtype.is_floating_point: + v *= d + v += (1. - d) * msd[k].detach() + + def update_attr(self, model, include=(), exclude=('process_group', 'reducer')): + # Update EMA attributes + copy_attr(self.ema, model, include, exclude) diff --git a/yolov5/weights/download_weights.sh b/yolov5/weights/download_weights.sh new file mode 100644 index 0000000..43c8e31 --- /dev/null +++ b/yolov5/weights/download_weights.sh @@ -0,0 +1,12 @@ +#!/bin/bash +# Download latest models from https://github.com/ultralytics/yolov5/releases +# Usage: +# $ bash weights/download_weights.sh + +python - <