diff --git a/README.md b/README.md deleted file mode 100644 index 34ce7e5..0000000 --- a/README.md +++ /dev/null @@ -1,2 +0,0 @@ -# services - diff --git a/YOLO/Dockerfiles/app/Dockerfile b/YOLO/Dockerfiles/app/Dockerfile deleted file mode 100755 index 1140cc5..0000000 --- a/YOLO/Dockerfiles/app/Dockerfile +++ /dev/null @@ -1,21 +0,0 @@ -FROM python:3.7 - -RUN mkdir /code -WORKDIR /code - -ADD entrypoint.sh /code/entrypoint.sh -ADD requirements.txt /code/requirements.txt -ADD app/ /code - -RUN ls -#RUN pip install -r requirements.txt --no-cache-dir -#RUN apt-get install apt-transport-https -RUN apt update -y -RUN apt install python3-pip -y - -#RUN pip3 install --default-timeout=100 -r requirements.txt - -EXPOSE 50002 -ENV FLASK_APP "/code/app.py" -CMD ["/code/entrypoint.sh"] - diff --git a/YOLO/Dockerfiles/app/app/CONTRIBUTING.md b/YOLO/Dockerfiles/app/app/CONTRIBUTING.md deleted file mode 100644 index fcceba2..0000000 --- a/YOLO/Dockerfiles/app/app/CONTRIBUTING.md +++ /dev/null @@ -1,94 +0,0 @@ -## Contributing to YOLOv5 🚀 - -We love your input! We want to make contributing to YOLOv5 as easy and transparent as possible, whether it's: - -- Reporting a bug -- Discussing the current state of the code -- Submitting a fix -- Proposing a new feature -- Becoming a maintainer - -YOLOv5 works so well due to our combined community effort, and for every small improvement you contribute you will be -helping push the frontiers of what's possible in AI 😃! - -## Submitting a Pull Request (PR) 🛠️ - -Submitting a PR is easy! This example shows how to submit a PR for updating `requirements.txt` in 4 steps: - -### 1. Select File to Update - -Select `requirements.txt` to update by clicking on it in GitHub. -

PR_step1

- -### 2. Click 'Edit this file' - -Button is in top-right corner. -

PR_step2

- -### 3. Make Changes - -Change `matplotlib` version from `3.2.2` to `3.3`. -

PR_step3

- -### 4. Preview Changes and Submit PR - -Click on the **Preview changes** tab to verify your updates. At the bottom of the screen select 'Create a **new branch** -for this commit', assign your branch a descriptive name such as `fix/matplotlib_version` and click the green **Propose -changes** button. All done, your PR is now submitted to YOLOv5 for review and approval 😃! -

PR_step4

- -### PR recommendations - -To allow your work to be integrated as seamlessly as possible, we advise you to: - -- ✅ Verify your PR is **up-to-date with upstream/master.** If your PR is behind upstream/master an - automatic [GitHub actions](https://github.com/ultralytics/yolov5/blob/master/.github/workflows/rebase.yml) rebase may - be attempted by including the /rebase command in a comment body, or by running the following code, replacing 'feature' - with the name of your local branch: - - ```bash - git remote add upstream https://github.com/ultralytics/yolov5.git - git fetch upstream - git checkout feature # <----- replace 'feature' with local branch name - git merge upstream/master - git push -u origin -f - ``` - -- ✅ Verify all Continuous Integration (CI) **checks are passing**. -- ✅ Reduce changes to the absolute **minimum** required for your bug fix or feature addition. _"It is not daily increase - but daily decrease, hack away the unessential. The closer to the source, the less wastage there is."_ — Bruce Lee - -## Submitting a Bug Report 🐛 - -If you spot a problem with YOLOv5 please submit a Bug Report! - -For us to start investigating a possible problem we need to be able to reproduce it ourselves first. We've created a few -short guidelines below to help users provide what we need in order to get started. - -When asking a question, people will be better able to provide help if you provide **code** that they can easily -understand and use to **reproduce** the problem. This is referred to by community members as creating -a [minimum reproducible example](https://stackoverflow.com/help/minimal-reproducible-example). Your code that reproduces -the problem should be: - -* ✅ **Minimal** – Use as little code as possible that still produces the same problem -* ✅ **Complete** – Provide **all** parts someone else needs to reproduce your problem in the question itself -* ✅ **Reproducible** – Test the code you're about to provide to make sure it reproduces the problem - -In addition to the above requirements, for [Ultralytics](https://ultralytics.com/) to provide assistance your code -should be: - -* ✅ **Current** – Verify that your code is up-to-date with current - GitHub [master](https://github.com/ultralytics/yolov5/tree/master), and if necessary `git pull` or `git clone` a new - copy to ensure your problem has not already been resolved by previous commits. -* ✅ **Unmodified** – Your problem must be reproducible without any modifications to the codebase in this - repository. [Ultralytics](https://ultralytics.com/) does not provide support for custom code ⚠️. - -If you believe your problem meets all of the above criteria, please close this issue and raise a new one using the 🐛 ** -Bug Report** [template](https://github.com/ultralytics/yolov5/issues/new/choose) and providing -a [minimum reproducible example](https://stackoverflow.com/help/minimal-reproducible-example) to help us better -understand and diagnose your problem. - -## License - -By contributing, you agree that your contributions will be licensed under -the [GPL-3.0 license](https://choosealicense.com/licenses/gpl-3.0/) diff --git a/YOLO/Dockerfiles/app/app/Dockerfile b/YOLO/Dockerfiles/app/app/Dockerfile deleted file mode 100644 index 9a55005..0000000 --- a/YOLO/Dockerfiles/app/app/Dockerfile +++ /dev/null @@ -1,64 +0,0 @@ -# YOLOv5 🚀 by Ultralytics, GPL-3.0 license - -# Start FROM Nvidia PyTorch image https://ngc.nvidia.com/catalog/containers/nvidia:pytorch -FROM nvcr.io/nvidia/pytorch:21.10-py3 - -# Install linux packages -RUN apt update && apt install -y zip htop screen libgl1-mesa-glx - -# Install python dependencies -COPY requirements.txt . -RUN python -m pip install --upgrade pip -RUN pip uninstall -y nvidia-tensorboard nvidia-tensorboard-plugin-dlprof -RUN pip install --no-cache -r requirements.txt coremltools onnx gsutil notebook wandb>=0.12.2 -RUN pip install --no-cache -U torch torchvision numpy Pillow -# RUN pip install --no-cache torch==1.10.0+cu113 torchvision==0.11.1+cu113 -f https://download.pytorch.org/whl/cu113/torch_stable.html - -# Create working directory -RUN mkdir -p /usr/src/app -WORKDIR /usr/src/app - -# Copy contents -COPY . /usr/src/app - -# Downloads to user config dir -ADD https://ultralytics.com/assets/Arial.ttf /root/.config/Ultralytics/ - -# Set environment variables -# ENV HOME=/usr/src/app - - -# Usage Examples ------------------------------------------------------------------------------------------------------- - -# Build and Push -# t=ultralytics/yolov5:latest && sudo docker build -t $t . && sudo docker push $t - -# 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)"/datasets:/usr/src/datasets $t - -# Kill all -# sudo docker kill $(sudo docker ps -q) - -# Kill all image-based -# sudo docker kill $(sudo docker ps -qa --filter ancestor=ultralytics/yolov5:latest) - -# Bash into running container -# sudo docker exec -it 5a9b5863d93d bash - -# Bash into stopped container -# id=$(sudo docker ps -qa) && sudo docker start $id && sudo docker exec -it $id bash - -# Clean up -# docker system prune -a --volumes - -# Update Ubuntu drivers -# https://www.maketecheasier.com/install-nvidia-drivers-ubuntu/ - -# DDP test -# python -m torch.distributed.run --nproc_per_node 2 --master_port 1 train.py --epochs 3 - -# GCP VM from Image -# docker.io/ultralytics/yolov5:latest diff --git a/YOLO/Dockerfiles/app/app/LICENSE b/YOLO/Dockerfiles/app/app/LICENSE deleted file mode 100644 index 92b370f..0000000 --- a/YOLO/Dockerfiles/app/app/LICENSE +++ /dev/null @@ -1,674 +0,0 @@ -GNU GENERAL PUBLIC LICENSE - Version 3, 29 June 2007 - - Copyright (C) 2007 Free Software Foundation, Inc. - Everyone is permitted to copy and distribute verbatim copies - of this license document, but changing it is not allowed. - - Preamble - - The GNU General Public License is a free, copyleft license for -software and other kinds of works. - - The licenses for most software and other practical works are designed -to take away your freedom to share and change the works. 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If your program is a subroutine library, you -may consider it more useful to permit linking proprietary applications with -the library. If this is what you want to do, use the GNU Lesser General -Public License instead of this License. But first, please read -. diff --git a/YOLO/Dockerfiles/app/app/README.md b/YOLO/Dockerfiles/app/app/README.md deleted file mode 100644 index 5f45eb4..0000000 --- a/YOLO/Dockerfiles/app/app/README.md +++ /dev/null @@ -1,296 +0,0 @@ -
-

- - -

-
-
- CI CPU testing - YOLOv5 Citation - Docker Pulls -
- Open In Colab - Open In Kaggle - Join Forum -
-
- - -
-

-YOLOv5 🚀 is a family of object detection architectures and models pretrained on the COCO dataset, and represents Ultralytics - open-source research into future vision AI methods, incorporating lessons learned and best practices evolved over thousands of hours of research and development. -

- - - -
- -##
Documentation
- -See the [YOLOv5 Docs](https://docs.ultralytics.com) for full documentation on training, testing and deployment. - -##
Quick Start Examples
- -
-Install - -Clone repo and install [requirements.txt](https://github.com/ultralytics/yolov5/blob/master/requirements.txt) in a -[**Python>=3.6.0**](https://www.python.org/) environment, including -[**PyTorch>=1.7**](https://pytorch.org/get-started/locally/). - -```bash -git clone https://github.com/ultralytics/yolov5 # clone -cd yolov5 -pip install -r requirements.txt # install -``` - -
- -
-Inference - -Inference with YOLOv5 and [PyTorch Hub](https://github.com/ultralytics/yolov5/issues/36) -. [Models](https://github.com/ultralytics/yolov5/tree/master/models) download automatically from the latest -YOLOv5 [release](https://github.com/ultralytics/yolov5/releases). - -```python -import torch - -# Model -model = torch.hub.load('ultralytics/yolov5', 'yolov5s') # or yolov5m, yolov5l, yolov5x, custom - -# Images -img = 'https://ultralytics.com/images/zidane.jpg' # or file, Path, PIL, OpenCV, numpy, list - -# Inference -results = model(img) - -# Results -results.print() # or .show(), .save(), .crop(), .pandas(), etc. -``` - -
- - - -
-Inference with detect.py - -`detect.py` runs inference on a variety of sources, downloading [models](https://github.com/ultralytics/yolov5/tree/master/models) automatically from -the latest YOLOv5 [release](https://github.com/ultralytics/yolov5/releases) and saving results to `runs/detect`. - -```bash -python detect.py --source 0 # webcam - img.jpg # image - vid.mp4 # video - path/ # directory - path/*.jpg # glob - 'https://youtu.be/Zgi9g1ksQHc' # YouTube - 'rtsp://example.com/media.mp4' # RTSP, RTMP, HTTP stream -``` - -
- -
-Training - -The commands below reproduce YOLOv5 [COCO](https://github.com/ultralytics/yolov5/blob/master/data/scripts/get_coco.sh) -results. [Models](https://github.com/ultralytics/yolov5/tree/master/models) -and [datasets](https://github.com/ultralytics/yolov5/tree/master/data) download automatically from the latest -YOLOv5 [release](https://github.com/ultralytics/yolov5/releases). Training times for YOLOv5n/s/m/l/x are -1/2/4/6/8 days on a V100 GPU ([Multi-GPU](https://github.com/ultralytics/yolov5/issues/475) times faster). Use the -largest `--batch-size` possible, or pass `--batch-size -1` for -YOLOv5 [AutoBatch](https://github.com/ultralytics/yolov5/pull/5092). Batch sizes shown for V100-16GB. - -```bash -python train.py --data coco.yaml --cfg yolov5n.yaml --weights '' --batch-size 128 - yolov5s 64 - yolov5m 40 - yolov5l 24 - yolov5x 16 -``` - - - -
- -
-Tutorials - -* [Train Custom Data](https://github.com/ultralytics/yolov5/wiki/Train-Custom-Data)  🚀 RECOMMENDED -* [Tips for Best Training Results](https://github.com/ultralytics/yolov5/wiki/Tips-for-Best-Training-Results)  ☘️ - RECOMMENDED -* [Weights & Biases Logging](https://github.com/ultralytics/yolov5/issues/1289)  🌟 NEW -* [Roboflow for Datasets, Labeling, and Active Learning](https://github.com/ultralytics/yolov5/issues/4975)  🌟 NEW -* [Multi-GPU Training](https://github.com/ultralytics/yolov5/issues/475) -* [PyTorch Hub](https://github.com/ultralytics/yolov5/issues/36)  ⭐ NEW -* [TFLite, ONNX, CoreML, TensorRT Export](https://github.com/ultralytics/yolov5/issues/251) 🚀 -* [Test-Time Augmentation (TTA)](https://github.com/ultralytics/yolov5/issues/303) -* [Model Ensembling](https://github.com/ultralytics/yolov5/issues/318) -* [Model Pruning/Sparsity](https://github.com/ultralytics/yolov5/issues/304) -* [Hyperparameter Evolution](https://github.com/ultralytics/yolov5/issues/607) -* [Transfer Learning with Frozen Layers](https://github.com/ultralytics/yolov5/issues/1314)  ⭐ NEW -* [TensorRT Deployment](https://github.com/wang-xinyu/tensorrtx) - -
- -##
Environments
- -Get started in seconds with our verified environments. Click each icon below for details. - - - -##
Integrations
- - - -|Weights and Biases|Roboflow ⭐ NEW| -|:-:|:-:| -|Automatically track and visualize all your YOLOv5 training runs in the cloud with [Weights & Biases](https://wandb.ai/site?utm_campaign=repo_yolo_readme)|Label and export your custom datasets directly to YOLOv5 for training with [Roboflow](https://roboflow.com/?ref=ultralytics) | - - - - -##
Why YOLOv5
- -

-
- YOLOv5-P5 640 Figure (click to expand) - -

-
-
- Figure Notes (click to expand) - -* **COCO AP val** denotes mAP@0.5:0.95 metric measured on the 5000-image [COCO val2017](http://cocodataset.org) dataset over various inference sizes from 256 to 1536. -* **GPU Speed** measures average inference time per image on [COCO val2017](http://cocodataset.org) dataset using a [AWS p3.2xlarge](https://aws.amazon.com/ec2/instance-types/p3/) V100 instance at batch-size 32. -* **EfficientDet** data from [google/automl](https://github.com/google/automl) at batch size 8. -* **Reproduce** by `python val.py --task study --data coco.yaml --iou 0.7 --weights yolov5n6.pt yolov5s6.pt yolov5m6.pt yolov5l6.pt yolov5x6.pt` -
- -### Pretrained Checkpoints - -[assets]: https://github.com/ultralytics/yolov5/releases - -[TTA]: https://github.com/ultralytics/yolov5/issues/303 - -|Model |size
(pixels) |mAPval
0.5:0.95 |mAPval
0.5 |Speed
CPU b1
(ms) |Speed
V100 b1
(ms) |Speed
V100 b32
(ms) |params
(M) |FLOPs
@640 (B) -|--- |--- |--- |--- |--- |--- |--- |--- |--- -|[YOLOv5n][assets] |640 |28.4 |46.0 |**45** |**6.3**|**0.6**|**1.9**|**4.5** -|[YOLOv5s][assets] |640 |37.2 |56.0 |98 |6.4 |0.9 |7.2 |16.5 -|[YOLOv5m][assets] |640 |45.2 |63.9 |224 |8.2 |1.7 |21.2 |49.0 -|[YOLOv5l][assets] |640 |48.8 |67.2 |430 |10.1 |2.7 |46.5 |109.1 -|[YOLOv5x][assets] |640 |50.7 |68.9 |766 |12.1 |4.8 |86.7 |205.7 -| | | | | | | | | -|[YOLOv5n6][assets] |1280 |34.0 |50.7 |153 |8.1 |2.1 |3.2 |4.6 -|[YOLOv5s6][assets] |1280 |44.5 |63.0 |385 |8.2 |3.6 |12.6 |16.8 -|[YOLOv5m6][assets] |1280 |51.0 |69.0 |887 |11.1 |6.8 |35.7 |50.0 -|[YOLOv5l6][assets] |1280 |53.6 |71.6 |1784 |15.8 |10.5 |76.7 |111.4 -|[YOLOv5x6][assets]
+ [TTA][TTA]|1280
1536 |54.7
**55.4** |**72.4**
72.3 |3136
- |26.2
- |19.4
- |140.7
- |209.8
- - -
- Table Notes (click to expand) - -* All checkpoints are trained to 300 epochs with default settings and hyperparameters. -* **mAPval** values are for single-model single-scale on [COCO val2017](http://cocodataset.org) dataset.
Reproduce by `python val.py --data coco.yaml --img 640 --conf 0.001 --iou 0.65` -* **Speed** averaged over COCO val images using a [AWS p3.2xlarge](https://aws.amazon.com/ec2/instance-types/p3/) instance. NMS times (~1 ms/img) not included.
Reproduce by `python val.py --data coco.yaml --img 640 --task speed --batch 1` -* **TTA** [Test Time Augmentation](https://github.com/ultralytics/yolov5/issues/303) includes reflection and scale augmentations.
Reproduce by `python val.py --data coco.yaml --img 1536 --iou 0.7 --augment` - -
- -##
Contribute
- -We love your input! We want to make contributing to YOLOv5 as easy and transparent as possible. Please see our [Contributing Guide](CONTRIBUTING.md) to get started, and fill out the [YOLOv5 Survey](https://ultralytics.com/survey?utm_source=github&utm_medium=social&utm_campaign=Survey) to send us feedback on your experiences. Thank you to all our contributors! - - - -##
Contact
- -For YOLOv5 bugs and feature requests please visit [GitHub Issues](https://github.com/ultralytics/yolov5/issues). For business inquiries or -professional support requests please visit [https://ultralytics.com/contact](https://ultralytics.com/contact). - -
- - diff --git a/YOLO/Dockerfiles/app/app/app.py b/YOLO/Dockerfiles/app/app/app.py deleted file mode 100644 index 77209a0..0000000 --- a/YOLO/Dockerfiles/app/app/app.py +++ /dev/null @@ -1,33 +0,0 @@ -# app/__init__.py - -from flask import Flask -import requests -import json -import sys -import shutil - - -def create_app(): - app = Flask(__name__) - @app.route('/create//') - def create(id): - import detect - import create_csv - attributes = ['Smiling', 'Bald', 'Bangs', 'Black_Hair', 'Blond_Hair', 'Brown_Hair', 'Chubby', - 'Eyeglasses', 'Heavy_Makeup', 'Male', 'Mustache', 'No_Beard', 'Pale_Skin', - 'Straight_Hair', 'Wavy_Hair', 'Wearing_Earrings', 'Wearing_Hat', 'Wearing_Necklace', - 'Wearing_Lipstick', 'Young'] - filename = id+'.png' - in_path = "/Datasets/grids/"+filename #PATH TO VOLUME - out_path = './images/'+filename - weight_path="runs/train/exp4/weights/last.pt" - #r = requests.get("http://127.0.0.1:50011/update/"+id+"/"+attr+"/"+str(value)+"/") - #print("Message",file=sys.stdout) - shutil.copy(in_path,out_path) - detect.run(source=out_path,weights=weight_path,name=id,save_txt=True) - file_yolo="./runs/detect/"+id+"/labels/"+id+".txt" - file_csv="/Datasets/database/"+id+".csv" - create_csv.create_csv(file_yolo,file_csv,attributes) - return str(5) - - return app diff --git a/YOLO/Dockerfiles/app/app/create_csv.py b/YOLO/Dockerfiles/app/app/create_csv.py deleted file mode 100644 index d63df0e..0000000 --- a/YOLO/Dockerfiles/app/app/create_csv.py +++ /dev/null @@ -1,73 +0,0 @@ -# -*- coding: utf-8 -*- -"""Create csv - -Automatically generated by Colaboratory. - -Original file is located at - https://colab.research.google.com/drive/1sCluAC_Woog1maeWw4n-gS3G2rk_0EK2 -""" - -import cv2 -import os -import random -import numpy as np -import csv - -import pandas as pd - -# Création d'un fichier csv à partir de la sortie de yolo -# file_yolo : path du fichier de sortie concat.txt yolo -# file_csv : path du fichier à créer csv -# attributes : liste des caractéristiques utilisées dans l'éxecution de yolo - -def create_csv(file_yolo, file_csv, attributes): - #on récupère la sortie du yolo - yolo = open(file_yolo, 'r') - lines = yolo.readlines() - yolo.close() - - # définition des noms de colonnes - fields = ['Excluded', 'id', 'x', 'y', 'Smiling', 'Bald', 'Bangs', 'Black_Hair', 'Blond_Hair', 'Brown_Hair', 'Chubby', - 'Eyeglasses', 'Heavy_Makeup', 'Male', 'Mustache', 'No_Beard', 'Pale_Skin', - 'Straight_Hair', 'Wavy_Hair', 'Wearing_Earrings', 'Wearing_Hat', 'Wearing_Necklace', - 'Wearing_Lipstick', 'Young'] - - i = 0 - coords = [] # liste des coordonnées des classes détectés (emplacement des images) - rows = [] # liste des lignes to add dans le fichier csv - - for line in lines: - args = line.split() - classe = attributes[int(args[0])] - x = round(float(args[1]), 1) - y = round(float(args[2]), 1) - if (x,y) in coords: - # si on a déjà une ligne correspondant à cette image - rows[coords.index((x,y))][classe] = 1 - else: - # on a pas encore rencontré cette image - coords.append((x,y)) - rows.append({'Excluded' : 0, 'id' : i, 'x' : x, 'y' : y, classe : 1}) - i += 1 - - # Création du csv - with open(file_csv, 'w') as csvfile: - # creating a csv dict writer object - writer = csv.DictWriter(csvfile, fieldnames = fields, restval = 0) - # writing headers (field names) - writer.writeheader() - # writing data rows - writer.writerows(rows) - df=pd.read_csv(file_csv,delimiter=',') - df.rename(columns={'No_Beard':'Beard','Black_Hair':'Black','Blond_Hair':'Blond','Brown_Hair':'Brown','Wavy_Hair':'Wavy','Straight_Hair':'Straight','Wearing_Earrings':'Earrings','Wearing_Hat':'Hat','Wearing_Necklace':'Necklace','Wearing_Lipstick':'Lipstick'},inplace=True) - df.loc[df.Beard == 1,"Beard"] = 2 - df.loc[df.Beard == 0,"Beard"] = 1 - df.loc[df.Beard == 2,"Beard"] = 0 - df.to_csv(file_csv,index=False) - -attributes = ['Smiling', 'Bald', 'Bangs', 'Black_Hair', 'Blond_Hair', 'Brown_Hair', 'Chubby', - 'Eyeglasses', 'Heavy_Makeup', 'Male', 'Mustache', 'No_Beard', 'Pale_Skin', - 'Straight_Hair', 'Wavy_Hair', 'Wearing_Earrings', 'Wearing_Hat', 'Wearing_Necklace', - 'Wearing_Lipstick', 'Young'] - - diff --git a/YOLO/Dockerfiles/app/app/data/Argoverse.yaml b/YOLO/Dockerfiles/app/app/data/Argoverse.yaml deleted file mode 100644 index 312791b..0000000 --- a/YOLO/Dockerfiles/app/app/data/Argoverse.yaml +++ /dev/null @@ -1,67 +0,0 @@ -# YOLOv5 🚀 by Ultralytics, GPL-3.0 license -# Argoverse-HD dataset (ring-front-center camera) http://www.cs.cmu.edu/~mengtial/proj/streaming/ by Argo AI -# Example usage: python train.py --data Argoverse.yaml -# parent -# ├── yolov5 -# └── datasets -# └── Argoverse ← downloads here - - -# Train/val/test sets as 1) dir: path/to/imgs, 2) file: path/to/imgs.txt, or 3) list: [path/to/imgs1, path/to/imgs2, ..] -path: ../datasets/Argoverse # dataset root dir -train: Argoverse-1.1/images/train/ # train images (relative to 'path') 39384 images -val: Argoverse-1.1/images/val/ # val images (relative to 'path') 15062 images -test: Argoverse-1.1/images/test/ # test images (optional) https://eval.ai/web/challenges/challenge-page/800/overview - -# Classes -nc: 8 # number of classes -names: ['person', 'bicycle', 'car', 'motorcycle', 'bus', 'truck', 'traffic_light', 'stop_sign'] # class names - - -# Download script/URL (optional) --------------------------------------------------------------------------------------- -download: | - import json - - from tqdm import tqdm - from utils.general import download, Path - - - def argoverse2yolo(set): - labels = {} - a = json.load(open(set, "rb")) - for annot in tqdm(a['annotations'], desc=f"Converting {set} to YOLOv5 format..."): - img_id = annot['image_id'] - img_name = a['images'][img_id]['name'] - img_label_name = img_name[:-3] + "txt" - - cls = annot['category_id'] # instance class id - x_center, y_center, width, height = annot['bbox'] - x_center = (x_center + width / 2) / 1920.0 # offset and scale - y_center = (y_center + height / 2) / 1200.0 # offset and scale - width /= 1920.0 # scale - height /= 1200.0 # scale - - img_dir = set.parents[2] / 'Argoverse-1.1' / 'labels' / a['seq_dirs'][a['images'][annot['image_id']]['sid']] - if not img_dir.exists(): - img_dir.mkdir(parents=True, exist_ok=True) - - k = str(img_dir / img_label_name) - if k not in labels: - labels[k] = [] - labels[k].append(f"{cls} {x_center} {y_center} {width} {height}\n") - - for k in labels: - with open(k, "w") as f: - f.writelines(labels[k]) - - - # Download - dir = Path('../datasets/Argoverse') # dataset root dir - urls = ['https://argoverse-hd.s3.us-east-2.amazonaws.com/Argoverse-HD-Full.zip'] - download(urls, dir=dir, delete=False) - - # Convert - annotations_dir = 'Argoverse-HD/annotations/' - (dir / 'Argoverse-1.1' / 'tracking').rename(dir / 'Argoverse-1.1' / 'images') # rename 'tracking' to 'images' - for d in "train.json", "val.json": - argoverse2yolo(dir / annotations_dir / d) # convert VisDrone annotations to YOLO labels diff --git a/YOLO/Dockerfiles/app/app/data/GlobalWheat2020.yaml b/YOLO/Dockerfiles/app/app/data/GlobalWheat2020.yaml deleted file mode 100644 index 869dace..0000000 --- a/YOLO/Dockerfiles/app/app/data/GlobalWheat2020.yaml +++ /dev/null @@ -1,53 +0,0 @@ -# YOLOv5 🚀 by Ultralytics, GPL-3.0 license -# Global Wheat 2020 dataset http://www.global-wheat.com/ by University of Saskatchewan -# Example usage: python train.py --data GlobalWheat2020.yaml -# parent -# ├── yolov5 -# └── datasets -# └── GlobalWheat2020 ← downloads here - - -# Train/val/test sets as 1) dir: path/to/imgs, 2) file: path/to/imgs.txt, or 3) list: [path/to/imgs1, path/to/imgs2, ..] -path: ../datasets/GlobalWheat2020 # dataset root dir -train: # train images (relative to 'path') 3422 images - - images/arvalis_1 - - images/arvalis_2 - - images/arvalis_3 - - images/ethz_1 - - images/rres_1 - - images/inrae_1 - - images/usask_1 -val: # val images (relative to 'path') 748 images (WARNING: train set contains ethz_1) - - images/ethz_1 -test: # test images (optional) 1276 images - - images/utokyo_1 - - images/utokyo_2 - - images/nau_1 - - images/uq_1 - -# Classes -nc: 1 # number of classes -names: ['wheat_head'] # class names - - -# Download script/URL (optional) --------------------------------------------------------------------------------------- -download: | - from utils.general import download, Path - - # Download - dir = Path(yaml['path']) # dataset root dir - urls = ['https://zenodo.org/record/4298502/files/global-wheat-codalab-official.zip', - 'https://github.com/ultralytics/yolov5/releases/download/v1.0/GlobalWheat2020_labels.zip'] - download(urls, dir=dir) - - # Make Directories - for p in 'annotations', 'images', 'labels': - (dir / p).mkdir(parents=True, exist_ok=True) - - # Move - for p in 'arvalis_1', 'arvalis_2', 'arvalis_3', 'ethz_1', 'rres_1', 'inrae_1', 'usask_1', \ - 'utokyo_1', 'utokyo_2', 'nau_1', 'uq_1': - (dir / p).rename(dir / 'images' / p) # move to /images - f = (dir / p).with_suffix('.json') # json file - if f.exists(): - f.rename((dir / 'annotations' / p).with_suffix('.json')) # move to /annotations diff --git a/YOLO/Dockerfiles/app/app/data/Objects365.yaml b/YOLO/Dockerfiles/app/app/data/Objects365.yaml deleted file mode 100644 index 4c7cf3f..0000000 --- a/YOLO/Dockerfiles/app/app/data/Objects365.yaml +++ /dev/null @@ -1,112 +0,0 @@ -# YOLOv5 🚀 by Ultralytics, GPL-3.0 license -# Objects365 dataset https://www.objects365.org/ by Megvii -# Example usage: python train.py --data Objects365.yaml -# parent -# ├── yolov5 -# └── datasets -# └── Objects365 ← downloads here - - -# Train/val/test sets as 1) dir: path/to/imgs, 2) file: path/to/imgs.txt, or 3) list: [path/to/imgs1, path/to/imgs2, ..] -path: ../datasets/Objects365 # dataset root dir -train: images/train # train images (relative to 'path') 1742289 images -val: images/val # val images (relative to 'path') 80000 images -test: # test images (optional) - -# Classes -nc: 365 # number of classes -names: ['Person', 'Sneakers', 'Chair', 'Other Shoes', 'Hat', 'Car', 'Lamp', 'Glasses', 'Bottle', 'Desk', 'Cup', - 'Street Lights', 'Cabinet/shelf', 'Handbag/Satchel', 'Bracelet', 'Plate', 'Picture/Frame', 'Helmet', 'Book', - 'Gloves', 'Storage box', 'Boat', 'Leather Shoes', 'Flower', 'Bench', 'Potted Plant', 'Bowl/Basin', 'Flag', - 'Pillow', 'Boots', 'Vase', 'Microphone', 'Necklace', 'Ring', 'SUV', 'Wine Glass', 'Belt', 'Monitor/TV', - 'Backpack', 'Umbrella', 'Traffic Light', 'Speaker', 'Watch', 'Tie', 'Trash bin Can', 'Slippers', 'Bicycle', - 'Stool', 'Barrel/bucket', 'Van', 'Couch', 'Sandals', 'Basket', 'Drum', 'Pen/Pencil', 'Bus', 'Wild Bird', - 'High Heels', 'Motorcycle', 'Guitar', 'Carpet', 'Cell Phone', 'Bread', 'Camera', 'Canned', 'Truck', - 'Traffic cone', 'Cymbal', 'Lifesaver', 'Towel', 'Stuffed Toy', 'Candle', 'Sailboat', 'Laptop', 'Awning', - 'Bed', 'Faucet', 'Tent', 'Horse', 'Mirror', 'Power outlet', 'Sink', 'Apple', 'Air Conditioner', 'Knife', - 'Hockey Stick', 'Paddle', 'Pickup Truck', 'Fork', 'Traffic Sign', 'Balloon', 'Tripod', 'Dog', 'Spoon', 'Clock', - 'Pot', 'Cow', 'Cake', 'Dinning Table', 'Sheep', 'Hanger', 'Blackboard/Whiteboard', 'Napkin', 'Other Fish', - 'Orange/Tangerine', 'Toiletry', 'Keyboard', 'Tomato', 'Lantern', 'Machinery Vehicle', 'Fan', - 'Green Vegetables', 'Banana', 'Baseball Glove', 'Airplane', 'Mouse', 'Train', 'Pumpkin', 'Soccer', 'Skiboard', - 'Luggage', 'Nightstand', 'Tea pot', 'Telephone', 'Trolley', 'Head Phone', 'Sports Car', 'Stop Sign', - 'Dessert', 'Scooter', 'Stroller', 'Crane', 'Remote', 'Refrigerator', 'Oven', 'Lemon', 'Duck', 'Baseball Bat', - 'Surveillance Camera', 'Cat', 'Jug', 'Broccoli', 'Piano', 'Pizza', 'Elephant', 'Skateboard', 'Surfboard', - 'Gun', 'Skating and Skiing shoes', 'Gas stove', 'Donut', 'Bow Tie', 'Carrot', 'Toilet', 'Kite', 'Strawberry', - 'Other Balls', 'Shovel', 'Pepper', 'Computer Box', 'Toilet Paper', 'Cleaning Products', 'Chopsticks', - 'Microwave', 'Pigeon', 'Baseball', 'Cutting/chopping Board', 'Coffee Table', 'Side Table', 'Scissors', - 'Marker', 'Pie', 'Ladder', 'Snowboard', 'Cookies', 'Radiator', 'Fire Hydrant', 'Basketball', 'Zebra', 'Grape', - 'Giraffe', 'Potato', 'Sausage', 'Tricycle', 'Violin', 'Egg', 'Fire Extinguisher', 'Candy', 'Fire Truck', - 'Billiards', 'Converter', 'Bathtub', 'Wheelchair', 'Golf Club', 'Briefcase', 'Cucumber', 'Cigar/Cigarette', - 'Paint Brush', 'Pear', 'Heavy Truck', 'Hamburger', 'Extractor', 'Extension Cord', 'Tong', 'Tennis Racket', - 'Folder', 'American Football', 'earphone', 'Mask', 'Kettle', 'Tennis', 'Ship', 'Swing', 'Coffee Machine', - 'Slide', 'Carriage', 'Onion', 'Green beans', 'Projector', 'Frisbee', 'Washing Machine/Drying Machine', - 'Chicken', 'Printer', 'Watermelon', 'Saxophone', 'Tissue', 'Toothbrush', 'Ice cream', 'Hot-air balloon', - 'Cello', 'French Fries', 'Scale', 'Trophy', 'Cabbage', 'Hot dog', 'Blender', 'Peach', 'Rice', 'Wallet/Purse', - 'Volleyball', 'Deer', 'Goose', 'Tape', 'Tablet', 'Cosmetics', 'Trumpet', 'Pineapple', 'Golf Ball', - 'Ambulance', 'Parking meter', 'Mango', 'Key', 'Hurdle', 'Fishing Rod', 'Medal', 'Flute', 'Brush', 'Penguin', - 'Megaphone', 'Corn', 'Lettuce', 'Garlic', 'Swan', 'Helicopter', 'Green Onion', 'Sandwich', 'Nuts', - 'Speed Limit Sign', 'Induction Cooker', 'Broom', 'Trombone', 'Plum', 'Rickshaw', 'Goldfish', 'Kiwi fruit', - 'Router/modem', 'Poker Card', 'Toaster', 'Shrimp', 'Sushi', 'Cheese', 'Notepaper', 'Cherry', 'Pliers', 'CD', - 'Pasta', 'Hammer', 'Cue', 'Avocado', 'Hamimelon', 'Flask', 'Mushroom', 'Screwdriver', 'Soap', 'Recorder', - 'Bear', 'Eggplant', 'Board Eraser', 'Coconut', 'Tape Measure/Ruler', 'Pig', 'Showerhead', 'Globe', 'Chips', - 'Steak', 'Crosswalk Sign', 'Stapler', 'Camel', 'Formula 1', 'Pomegranate', 'Dishwasher', 'Crab', - 'Hoverboard', 'Meat ball', 'Rice Cooker', 'Tuba', 'Calculator', 'Papaya', 'Antelope', 'Parrot', 'Seal', - 'Butterfly', 'Dumbbell', 'Donkey', 'Lion', 'Urinal', 'Dolphin', 'Electric Drill', 'Hair Dryer', 'Egg tart', - 'Jellyfish', 'Treadmill', 'Lighter', 'Grapefruit', 'Game board', 'Mop', 'Radish', 'Baozi', 'Target', 'French', - 'Spring Rolls', 'Monkey', 'Rabbit', 'Pencil Case', 'Yak', 'Red Cabbage', 'Binoculars', 'Asparagus', 'Barbell', - 'Scallop', 'Noddles', 'Comb', 'Dumpling', 'Oyster', 'Table Tennis paddle', 'Cosmetics Brush/Eyeliner Pencil', - 'Chainsaw', 'Eraser', 'Lobster', 'Durian', 'Okra', 'Lipstick', 'Cosmetics Mirror', 'Curling', 'Table Tennis'] - - -# Download script/URL (optional) --------------------------------------------------------------------------------------- -download: | - from pycocotools.coco import COCO - from tqdm import tqdm - - from utils.general import Path, download, np, xyxy2xywhn - - # Make Directories - dir = Path(yaml['path']) # dataset root dir - for p in 'images', 'labels': - (dir / p).mkdir(parents=True, exist_ok=True) - for q in 'train', 'val': - (dir / p / q).mkdir(parents=True, exist_ok=True) - - # Train, Val Splits - for split, patches in [('train', 50 + 1), ('val', 43 + 1)]: - print(f"Processing {split} in {patches} patches ...") - images, labels = dir / 'images' / split, dir / 'labels' / split - - # Download - url = f"https://dorc.ks3-cn-beijing.ksyun.com/data-set/2020Objects365%E6%95%B0%E6%8D%AE%E9%9B%86/{split}/" - if split == 'train': - download([f'{url}zhiyuan_objv2_{split}.tar.gz'], dir=dir, delete=False) # annotations json - download([f'{url}patch{i}.tar.gz' for i in range(patches)], dir=images, curl=True, delete=False, threads=8) - elif split == 'val': - download([f'{url}zhiyuan_objv2_{split}.json'], dir=dir, delete=False) # annotations json - download([f'{url}images/v1/patch{i}.tar.gz' for i in range(15 + 1)], dir=images, curl=True, delete=False, threads=8) - download([f'{url}images/v2/patch{i}.tar.gz' for i in range(16, patches)], dir=images, curl=True, delete=False, threads=8) - - # Move - for f in tqdm(images.rglob('*.jpg'), desc=f'Moving {split} images'): - f.rename(images / f.name) # move to /images/{split} - - # Labels - coco = COCO(dir / f'zhiyuan_objv2_{split}.json') - names = [x["name"] for x in coco.loadCats(coco.getCatIds())] - for cid, cat in enumerate(names): - catIds = coco.getCatIds(catNms=[cat]) - imgIds = coco.getImgIds(catIds=catIds) - for im in tqdm(coco.loadImgs(imgIds), desc=f'Class {cid + 1}/{len(names)} {cat}'): - width, height = im["width"], im["height"] - path = Path(im["file_name"]) # image filename - try: - with open(labels / path.with_suffix('.txt').name, 'a') as file: - annIds = coco.getAnnIds(imgIds=im["id"], catIds=catIds, iscrowd=None) - for a in coco.loadAnns(annIds): - x, y, w, h = a['bbox'] # bounding box in xywh (xy top-left corner) - xyxy = np.array([x, y, x + w, y + h])[None] # pixels(1,4) - x, y, w, h = xyxy2xywhn(xyxy, w=width, h=height, clip=True)[0] # normalized and clipped - file.write(f"{cid} {x:.5f} {y:.5f} {w:.5f} {h:.5f}\n") - except Exception as e: - print(e) diff --git a/YOLO/Dockerfiles/app/app/data/SKU-110K.yaml b/YOLO/Dockerfiles/app/app/data/SKU-110K.yaml deleted file mode 100644 index 9481b7a..0000000 --- a/YOLO/Dockerfiles/app/app/data/SKU-110K.yaml +++ /dev/null @@ -1,52 +0,0 @@ -# YOLOv5 🚀 by Ultralytics, GPL-3.0 license -# SKU-110K retail items dataset https://github.com/eg4000/SKU110K_CVPR19 by Trax Retail -# Example usage: python train.py --data SKU-110K.yaml -# parent -# ├── yolov5 -# └── datasets -# └── SKU-110K ← downloads here - - -# Train/val/test sets as 1) dir: path/to/imgs, 2) file: path/to/imgs.txt, or 3) list: [path/to/imgs1, path/to/imgs2, ..] -path: ../datasets/SKU-110K # dataset root dir -train: train.txt # train images (relative to 'path') 8219 images -val: val.txt # val images (relative to 'path') 588 images -test: test.txt # test images (optional) 2936 images - -# Classes -nc: 1 # number of classes -names: ['object'] # class names - - -# Download script/URL (optional) --------------------------------------------------------------------------------------- -download: | - import shutil - from tqdm import tqdm - from utils.general import np, pd, Path, download, xyxy2xywh - - # Download - dir = Path(yaml['path']) # dataset root dir - parent = Path(dir.parent) # download dir - urls = ['http://trax-geometry.s3.amazonaws.com/cvpr_challenge/SKU110K_fixed.tar.gz'] - download(urls, dir=parent, delete=False) - - # Rename directories - if dir.exists(): - shutil.rmtree(dir) - (parent / 'SKU110K_fixed').rename(dir) # rename dir - (dir / 'labels').mkdir(parents=True, exist_ok=True) # create labels dir - - # Convert labels - names = 'image', 'x1', 'y1', 'x2', 'y2', 'class', 'image_width', 'image_height' # column names - for d in 'annotations_train.csv', 'annotations_val.csv', 'annotations_test.csv': - x = pd.read_csv(dir / 'annotations' / d, names=names).values # annotations - images, unique_images = x[:, 0], np.unique(x[:, 0]) - with open((dir / d).with_suffix('.txt').__str__().replace('annotations_', ''), 'w') as f: - f.writelines(f'./images/{s}\n' for s in unique_images) - for im in tqdm(unique_images, desc=f'Converting {dir / d}'): - cls = 0 # single-class dataset - with open((dir / 'labels' / im).with_suffix('.txt'), 'a') as f: - for r in x[images == im]: - w, h = r[6], r[7] # image width, height - xywh = xyxy2xywh(np.array([[r[1] / w, r[2] / h, r[3] / w, r[4] / h]]))[0] # instance - f.write(f"{cls} {xywh[0]:.5f} {xywh[1]:.5f} {xywh[2]:.5f} {xywh[3]:.5f}\n") # write label diff --git a/YOLO/Dockerfiles/app/app/data/VOC.yaml b/YOLO/Dockerfiles/app/app/data/VOC.yaml deleted file mode 100644 index 975d564..0000000 --- a/YOLO/Dockerfiles/app/app/data/VOC.yaml +++ /dev/null @@ -1,80 +0,0 @@ -# YOLOv5 🚀 by Ultralytics, GPL-3.0 license -# PASCAL VOC dataset http://host.robots.ox.ac.uk/pascal/VOC by University of Oxford -# Example usage: python train.py --data VOC.yaml -# parent -# ├── yolov5 -# └── datasets -# └── VOC ← downloads here - - -# Train/val/test sets as 1) dir: path/to/imgs, 2) file: path/to/imgs.txt, or 3) list: [path/to/imgs1, path/to/imgs2, ..] -path: ../datasets/VOC -train: # train images (relative to 'path') 16551 images - - images/train2012 - - images/train2007 - - images/val2012 - - images/val2007 -val: # val images (relative to 'path') 4952 images - - images/test2007 -test: # test images (optional) - - images/test2007 - -# Classes -nc: 20 # number of classes -names: ['aeroplane', 'bicycle', 'bird', 'boat', 'bottle', 'bus', 'car', 'cat', 'chair', 'cow', 'diningtable', 'dog', - 'horse', 'motorbike', 'person', 'pottedplant', 'sheep', 'sofa', 'train', 'tvmonitor'] # class names - - -# Download script/URL (optional) --------------------------------------------------------------------------------------- -download: | - import xml.etree.ElementTree as ET - - from tqdm import tqdm - from utils.general import download, Path - - - def convert_label(path, lb_path, year, image_id): - def convert_box(size, box): - dw, dh = 1. / size[0], 1. / size[1] - x, y, w, h = (box[0] + box[1]) / 2.0 - 1, (box[2] + box[3]) / 2.0 - 1, box[1] - box[0], box[3] - box[2] - return x * dw, y * dh, w * dw, h * dh - - in_file = open(path / f'VOC{year}/Annotations/{image_id}.xml') - out_file = open(lb_path, 'w') - tree = ET.parse(in_file) - root = tree.getroot() - size = root.find('size') - w = int(size.find('width').text) - h = int(size.find('height').text) - - for obj in root.iter('object'): - cls = obj.find('name').text - if cls in yaml['names'] and not int(obj.find('difficult').text) == 1: - xmlbox = obj.find('bndbox') - bb = convert_box((w, h), [float(xmlbox.find(x).text) for x in ('xmin', 'xmax', 'ymin', 'ymax')]) - cls_id = yaml['names'].index(cls) # class id - out_file.write(" ".join([str(a) for a in (cls_id, *bb)]) + '\n') - - - # Download - dir = Path(yaml['path']) # dataset root dir - url = 'https://github.com/ultralytics/yolov5/releases/download/v1.0/' - urls = [url + 'VOCtrainval_06-Nov-2007.zip', # 446MB, 5012 images - url + 'VOCtest_06-Nov-2007.zip', # 438MB, 4953 images - url + 'VOCtrainval_11-May-2012.zip'] # 1.95GB, 17126 images - download(urls, dir=dir / 'images', delete=False) - - # Convert - path = dir / f'images/VOCdevkit' - for year, image_set in ('2012', 'train'), ('2012', 'val'), ('2007', 'train'), ('2007', 'val'), ('2007', 'test'): - imgs_path = dir / 'images' / f'{image_set}{year}' - lbs_path = dir / 'labels' / f'{image_set}{year}' - imgs_path.mkdir(exist_ok=True, parents=True) - lbs_path.mkdir(exist_ok=True, parents=True) - - image_ids = open(path / f'VOC{year}/ImageSets/Main/{image_set}.txt').read().strip().split() - for id in tqdm(image_ids, desc=f'{image_set}{year}'): - f = path / f'VOC{year}/JPEGImages/{id}.jpg' # old img path - lb_path = (lbs_path / f.name).with_suffix('.txt') # new label path - f.rename(imgs_path / f.name) # move image - convert_label(path, lb_path, year, id) # convert labels to YOLO format diff --git a/YOLO/Dockerfiles/app/app/data/VisDrone.yaml b/YOLO/Dockerfiles/app/app/data/VisDrone.yaml deleted file mode 100644 index 83a5c7d..0000000 --- a/YOLO/Dockerfiles/app/app/data/VisDrone.yaml +++ /dev/null @@ -1,61 +0,0 @@ -# YOLOv5 🚀 by Ultralytics, GPL-3.0 license -# VisDrone2019-DET dataset https://github.com/VisDrone/VisDrone-Dataset by Tianjin University -# Example usage: python train.py --data VisDrone.yaml -# parent -# ├── yolov5 -# └── datasets -# └── VisDrone ← downloads here - - -# Train/val/test sets as 1) dir: path/to/imgs, 2) file: path/to/imgs.txt, or 3) list: [path/to/imgs1, path/to/imgs2, ..] -path: ../datasets/VisDrone # dataset root dir -train: VisDrone2019-DET-train/images # train images (relative to 'path') 6471 images -val: VisDrone2019-DET-val/images # val images (relative to 'path') 548 images -test: VisDrone2019-DET-test-dev/images # test images (optional) 1610 images - -# Classes -nc: 10 # number of classes -names: ['pedestrian', 'people', 'bicycle', 'car', 'van', 'truck', 'tricycle', 'awning-tricycle', 'bus', 'motor'] - - -# Download script/URL (optional) --------------------------------------------------------------------------------------- -download: | - from utils.general import download, os, Path - - def visdrone2yolo(dir): - from PIL import Image - from tqdm import tqdm - - def convert_box(size, box): - # Convert VisDrone box to YOLO xywh box - dw = 1. / size[0] - dh = 1. / size[1] - return (box[0] + box[2] / 2) * dw, (box[1] + box[3] / 2) * dh, box[2] * dw, box[3] * dh - - (dir / 'labels').mkdir(parents=True, exist_ok=True) # make labels directory - pbar = tqdm((dir / 'annotations').glob('*.txt'), desc=f'Converting {dir}') - for f in pbar: - img_size = Image.open((dir / 'images' / f.name).with_suffix('.jpg')).size - lines = [] - with open(f, 'r') as file: # read annotation.txt - for row in [x.split(',') for x in file.read().strip().splitlines()]: - if row[4] == '0': # VisDrone 'ignored regions' class 0 - continue - cls = int(row[5]) - 1 - box = convert_box(img_size, tuple(map(int, row[:4]))) - lines.append(f"{cls} {' '.join(f'{x:.6f}' for x in box)}\n") - with open(str(f).replace(os.sep + 'annotations' + os.sep, os.sep + 'labels' + os.sep), 'w') as fl: - fl.writelines(lines) # write label.txt - - - # Download - dir = Path(yaml['path']) # dataset root dir - urls = ['https://github.com/ultralytics/yolov5/releases/download/v1.0/VisDrone2019-DET-train.zip', - 'https://github.com/ultralytics/yolov5/releases/download/v1.0/VisDrone2019-DET-val.zip', - 'https://github.com/ultralytics/yolov5/releases/download/v1.0/VisDrone2019-DET-test-dev.zip', - 'https://github.com/ultralytics/yolov5/releases/download/v1.0/VisDrone2019-DET-test-challenge.zip'] - download(urls, dir=dir) - - # Convert - for d in 'VisDrone2019-DET-train', 'VisDrone2019-DET-val', 'VisDrone2019-DET-test-dev': - visdrone2yolo(dir / d) # convert VisDrone annotations to YOLO labels diff --git a/YOLO/Dockerfiles/app/app/data/coco.yaml b/YOLO/Dockerfiles/app/app/data/coco.yaml deleted file mode 100644 index 3ed7e48..0000000 --- a/YOLO/Dockerfiles/app/app/data/coco.yaml +++ /dev/null @@ -1,44 +0,0 @@ -# YOLOv5 🚀 by Ultralytics, GPL-3.0 license -# COCO 2017 dataset http://cocodataset.org by Microsoft -# Example usage: python train.py --data coco.yaml -# parent -# ├── yolov5 -# └── datasets -# └── coco ← downloads here - - -# Train/val/test sets as 1) dir: path/to/imgs, 2) file: path/to/imgs.txt, or 3) list: [path/to/imgs1, path/to/imgs2, ..] -path: ../datasets/coco # dataset root dir -train: train2017.txt # train images (relative to 'path') 118287 images -val: val2017.txt # val images (relative to 'path') 5000 images -test: test-dev2017.txt # 20288 of 40670 images, submit to https://competitions.codalab.org/competitions/20794 - -# Classes -nc: 80 # number of classes -names: ['person', 'bicycle', 'car', 'motorcycle', 'airplane', 'bus', 'train', 'truck', 'boat', 'traffic light', - 'fire hydrant', 'stop sign', 'parking meter', 'bench', 'bird', 'cat', 'dog', 'horse', 'sheep', 'cow', - 'elephant', 'bear', 'zebra', 'giraffe', 'backpack', 'umbrella', 'handbag', 'tie', 'suitcase', 'frisbee', - 'skis', 'snowboard', 'sports ball', 'kite', 'baseball bat', 'baseball glove', 'skateboard', 'surfboard', - 'tennis racket', 'bottle', 'wine glass', 'cup', 'fork', 'knife', 'spoon', 'bowl', 'banana', 'apple', - 'sandwich', 'orange', 'broccoli', 'carrot', 'hot dog', 'pizza', 'donut', 'cake', 'chair', 'couch', - 'potted plant', 'bed', 'dining table', 'toilet', 'tv', 'laptop', 'mouse', 'remote', 'keyboard', 'cell phone', - 'microwave', 'oven', 'toaster', 'sink', 'refrigerator', 'book', 'clock', 'vase', 'scissors', 'teddy bear', - 'hair drier', 'toothbrush'] # class names - - -# Download script/URL (optional) -download: | - from utils.general import download, Path - - # Download labels - segments = False # segment or box labels - dir = Path(yaml['path']) # dataset root dir - url = 'https://github.com/ultralytics/yolov5/releases/download/v1.0/' - urls = [url + ('coco2017labels-segments.zip' if segments else 'coco2017labels.zip')] # labels - download(urls, dir=dir.parent) - - # Download data - urls = ['http://images.cocodataset.org/zips/train2017.zip', # 19G, 118k images - 'http://images.cocodataset.org/zips/val2017.zip', # 1G, 5k images - 'http://images.cocodataset.org/zips/test2017.zip'] # 7G, 41k images (optional) - download(urls, dir=dir / 'images', threads=3) diff --git a/YOLO/Dockerfiles/app/app/data/coco128.yaml b/YOLO/Dockerfiles/app/app/data/coco128.yaml deleted file mode 100644 index d07c704..0000000 --- a/YOLO/Dockerfiles/app/app/data/coco128.yaml +++ /dev/null @@ -1,30 +0,0 @@ -# YOLOv5 🚀 by Ultralytics, GPL-3.0 license -# COCO128 dataset https://www.kaggle.com/ultralytics/coco128 (first 128 images from COCO train2017) by Ultralytics -# Example usage: python train.py --data coco128.yaml -# parent -# ├── yolov5 -# └── datasets -# └── coco128 ← downloads here - - -# Train/val/test sets as 1) dir: path/to/imgs, 2) file: path/to/imgs.txt, or 3) list: [path/to/imgs1, path/to/imgs2, ..] -path: ../datasets/coco128 # dataset root dir -train: images/train2017 # train images (relative to 'path') 128 images -val: images/train2017 # val images (relative to 'path') 128 images -test: # test images (optional) - -# Classes -nc: 80 # number of classes -names: ['person', 'bicycle', 'car', 'motorcycle', 'airplane', 'bus', 'train', 'truck', 'boat', 'traffic light', - 'fire hydrant', 'stop sign', 'parking meter', 'bench', 'bird', 'cat', 'dog', 'horse', 'sheep', 'cow', - 'elephant', 'bear', 'zebra', 'giraffe', 'backpack', 'umbrella', 'handbag', 'tie', 'suitcase', 'frisbee', - 'skis', 'snowboard', 'sports ball', 'kite', 'baseball bat', 'baseball glove', 'skateboard', 'surfboard', - 'tennis racket', 'bottle', 'wine glass', 'cup', 'fork', 'knife', 'spoon', 'bowl', 'banana', 'apple', - 'sandwich', 'orange', 'broccoli', 'carrot', 'hot dog', 'pizza', 'donut', 'cake', 'chair', 'couch', - 'potted plant', 'bed', 'dining table', 'toilet', 'tv', 'laptop', 'mouse', 'remote', 'keyboard', 'cell phone', - 'microwave', 'oven', 'toaster', 'sink', 'refrigerator', 'book', 'clock', 'vase', 'scissors', 'teddy bear', - 'hair drier', 'toothbrush'] # class names - - -# Download script/URL (optional) -download: https://ultralytics.com/assets/coco128.zip diff --git a/YOLO/Dockerfiles/app/app/data/hyps/hyp.finetune.yaml b/YOLO/Dockerfiles/app/app/data/hyps/hyp.finetune.yaml deleted file mode 100644 index b89d66f..0000000 --- a/YOLO/Dockerfiles/app/app/data/hyps/hyp.finetune.yaml +++ /dev/null @@ -1,39 +0,0 @@ -# YOLOv5 🚀 by Ultralytics, GPL-3.0 license -# Hyperparameters for VOC finetuning -# python train.py --batch 64 --weights yolov5m.pt --data VOC.yaml --img 512 --epochs 50 -# See tutorials for hyperparameter evolution https://github.com/ultralytics/yolov5#tutorials - -# Hyperparameter Evolution Results -# Generations: 306 -# P R mAP.5 mAP.5:.95 box obj cls -# Metrics: 0.6 0.936 0.896 0.684 0.0115 0.00805 0.00146 - -lr0: 0.0032 -lrf: 0.12 -momentum: 0.843 -weight_decay: 0.00036 -warmup_epochs: 2.0 -warmup_momentum: 0.5 -warmup_bias_lr: 0.05 -box: 0.0296 -cls: 0.243 -cls_pw: 0.631 -obj: 0.301 -obj_pw: 0.911 -iou_t: 0.2 -anchor_t: 2.91 -# anchors: 3.63 -fl_gamma: 0.0 -hsv_h: 0.0138 -hsv_s: 0.664 -hsv_v: 0.464 -degrees: 0.373 -translate: 0.245 -scale: 0.898 -shear: 0.602 -perspective: 0.0 -flipud: 0.00856 -fliplr: 0.5 -mosaic: 1.0 -mixup: 0.243 -copy_paste: 0.0 diff --git a/YOLO/Dockerfiles/app/app/data/hyps/hyp.finetune_objects365.yaml b/YOLO/Dockerfiles/app/app/data/hyps/hyp.finetune_objects365.yaml deleted file mode 100644 index 073720a..0000000 --- a/YOLO/Dockerfiles/app/app/data/hyps/hyp.finetune_objects365.yaml +++ /dev/null @@ -1,31 +0,0 @@ -# YOLOv5 🚀 by Ultralytics, GPL-3.0 license - -lr0: 0.00258 -lrf: 0.17 -momentum: 0.779 -weight_decay: 0.00058 -warmup_epochs: 1.33 -warmup_momentum: 0.86 -warmup_bias_lr: 0.0711 -box: 0.0539 -cls: 0.299 -cls_pw: 0.825 -obj: 0.632 -obj_pw: 1.0 -iou_t: 0.2 -anchor_t: 3.44 -anchors: 3.2 -fl_gamma: 0.0 -hsv_h: 0.0188 -hsv_s: 0.704 -hsv_v: 0.36 -degrees: 0.0 -translate: 0.0902 -scale: 0.491 -shear: 0.0 -perspective: 0.0 -flipud: 0.0 -fliplr: 0.5 -mosaic: 1.0 -mixup: 0.0 -copy_paste: 0.0 diff --git a/YOLO/Dockerfiles/app/app/data/hyps/hyp.scratch-high.yaml b/YOLO/Dockerfiles/app/app/data/hyps/hyp.scratch-high.yaml deleted file mode 100644 index 5a586cc..0000000 --- a/YOLO/Dockerfiles/app/app/data/hyps/hyp.scratch-high.yaml +++ /dev/null @@ -1,34 +0,0 @@ -# YOLOv5 🚀 by Ultralytics, GPL-3.0 license -# Hyperparameters for high-augmentation COCO training from scratch -# python train.py --batch 32 --cfg yolov5m6.yaml --weights '' --data coco.yaml --img 1280 --epochs 300 -# See tutorials for hyperparameter evolution https://github.com/ultralytics/yolov5#tutorials - -lr0: 0.01 # initial learning rate (SGD=1E-2, Adam=1E-3) -lrf: 0.2 # final OneCycleLR learning rate (lr0 * lrf) -momentum: 0.937 # SGD momentum/Adam beta1 -weight_decay: 0.0005 # optimizer weight decay 5e-4 -warmup_epochs: 3.0 # warmup epochs (fractions ok) -warmup_momentum: 0.8 # warmup initial momentum -warmup_bias_lr: 0.1 # warmup initial bias lr -box: 0.05 # box loss gain -cls: 0.3 # cls loss gain -cls_pw: 1.0 # cls BCELoss positive_weight -obj: 0.7 # obj loss gain (scale with pixels) -obj_pw: 1.0 # obj BCELoss positive_weight -iou_t: 0.20 # IoU training threshold -anchor_t: 4.0 # anchor-multiple threshold -# anchors: 3 # anchors per output layer (0 to ignore) -fl_gamma: 0.0 # focal loss gamma (efficientDet default gamma=1.5) -hsv_h: 0.015 # image HSV-Hue augmentation (fraction) -hsv_s: 0.7 # image HSV-Saturation augmentation (fraction) -hsv_v: 0.4 # image HSV-Value augmentation (fraction) -degrees: 0.0 # image rotation (+/- deg) -translate: 0.1 # image translation (+/- fraction) -scale: 0.9 # image scale (+/- gain) -shear: 0.0 # image shear (+/- deg) -perspective: 0.0 # image perspective (+/- fraction), range 0-0.001 -flipud: 0.0 # image flip up-down (probability) -fliplr: 0.5 # image flip left-right (probability) -mosaic: 1.0 # image mosaic (probability) -mixup: 0.1 # image mixup (probability) -copy_paste: 0.1 # segment copy-paste (probability) diff --git a/YOLO/Dockerfiles/app/app/data/hyps/hyp.scratch-low.yaml b/YOLO/Dockerfiles/app/app/data/hyps/hyp.scratch-low.yaml deleted file mode 100644 index b9ef1d5..0000000 --- a/YOLO/Dockerfiles/app/app/data/hyps/hyp.scratch-low.yaml +++ /dev/null @@ -1,34 +0,0 @@ -# YOLOv5 🚀 by Ultralytics, GPL-3.0 license -# Hyperparameters for low-augmentation COCO training from scratch -# python train.py --batch 64 --cfg yolov5n6.yaml --weights '' --data coco.yaml --img 640 --epochs 300 --linear -# See tutorials for hyperparameter evolution https://github.com/ultralytics/yolov5#tutorials - -lr0: 0.01 # initial learning rate (SGD=1E-2, Adam=1E-3) -lrf: 0.01 # final OneCycleLR learning rate (lr0 * lrf) -momentum: 0.937 # SGD momentum/Adam beta1 -weight_decay: 0.0005 # optimizer weight decay 5e-4 -warmup_epochs: 3.0 # warmup epochs (fractions ok) -warmup_momentum: 0.8 # warmup initial momentum -warmup_bias_lr: 0.1 # warmup initial bias lr -box: 0.05 # box loss gain -cls: 0.5 # cls loss gain -cls_pw: 1.0 # cls BCELoss positive_weight -obj: 1.0 # obj loss gain (scale with pixels) -obj_pw: 1.0 # obj BCELoss positive_weight -iou_t: 0.20 # IoU training threshold -anchor_t: 4.0 # anchor-multiple threshold -# anchors: 3 # anchors per output layer (0 to ignore) -fl_gamma: 0.0 # focal loss gamma (efficientDet default gamma=1.5) -hsv_h: 0.015 # image HSV-Hue augmentation (fraction) -hsv_s: 0.7 # image HSV-Saturation augmentation (fraction) -hsv_v: 0.4 # image HSV-Value augmentation (fraction) -degrees: 0.0 # image rotation (+/- deg) -translate: 0.1 # image translation (+/- fraction) -scale: 0.5 # image scale (+/- gain) -shear: 0.0 # image shear (+/- deg) -perspective: 0.0 # image perspective (+/- fraction), range 0-0.001 -flipud: 0.0 # image flip up-down (probability) -fliplr: 0.5 # image flip left-right (probability) -mosaic: 1.0 # image mosaic (probability) -mixup: 0.0 # image mixup (probability) -copy_paste: 0.0 # segment copy-paste (probability) diff --git a/YOLO/Dockerfiles/app/app/data/hyps/hyp.scratch-med.yaml b/YOLO/Dockerfiles/app/app/data/hyps/hyp.scratch-med.yaml deleted file mode 100644 index d6867d7..0000000 --- a/YOLO/Dockerfiles/app/app/data/hyps/hyp.scratch-med.yaml +++ /dev/null @@ -1,34 +0,0 @@ -# YOLOv5 🚀 by Ultralytics, GPL-3.0 license -# Hyperparameters for medium-augmentation COCO training from scratch -# python train.py --batch 32 --cfg yolov5m6.yaml --weights '' --data coco.yaml --img 1280 --epochs 300 -# See tutorials for hyperparameter evolution https://github.com/ultralytics/yolov5#tutorials - -lr0: 0.01 # initial learning rate (SGD=1E-2, Adam=1E-3) -lrf: 0.1 # final OneCycleLR learning rate (lr0 * lrf) -momentum: 0.937 # SGD momentum/Adam beta1 -weight_decay: 0.0005 # optimizer weight decay 5e-4 -warmup_epochs: 3.0 # warmup epochs (fractions ok) -warmup_momentum: 0.8 # warmup initial momentum -warmup_bias_lr: 0.1 # warmup initial bias lr -box: 0.05 # box loss gain -cls: 0.3 # cls loss gain -cls_pw: 1.0 # cls BCELoss positive_weight -obj: 0.7 # obj loss gain (scale with pixels) -obj_pw: 1.0 # obj BCELoss positive_weight -iou_t: 0.20 # IoU training threshold -anchor_t: 4.0 # anchor-multiple threshold -# anchors: 3 # anchors per output layer (0 to ignore) -fl_gamma: 0.0 # focal loss gamma (efficientDet default gamma=1.5) -hsv_h: 0.015 # image HSV-Hue augmentation (fraction) -hsv_s: 0.7 # image HSV-Saturation augmentation (fraction) -hsv_v: 0.4 # image HSV-Value augmentation (fraction) -degrees: 0.0 # image rotation (+/- deg) -translate: 0.1 # image translation (+/- fraction) -scale: 0.9 # image scale (+/- gain) -shear: 0.0 # image shear (+/- deg) -perspective: 0.0 # image perspective (+/- fraction), range 0-0.001 -flipud: 0.0 # image flip up-down (probability) -fliplr: 0.5 # image flip left-right (probability) -mosaic: 1.0 # image mosaic (probability) -mixup: 0.1 # image mixup (probability) -copy_paste: 0.0 # segment copy-paste (probability) diff --git a/YOLO/Dockerfiles/app/app/data/hyps/hyp.scratch.yaml b/YOLO/Dockerfiles/app/app/data/hyps/hyp.scratch.yaml deleted file mode 100644 index 31f6d14..0000000 --- a/YOLO/Dockerfiles/app/app/data/hyps/hyp.scratch.yaml +++ /dev/null @@ -1,34 +0,0 @@ -# YOLOv5 🚀 by Ultralytics, GPL-3.0 license -# Hyperparameters for COCO training from scratch -# python train.py --batch 40 --cfg yolov5m.yaml --weights '' --data coco.yaml --img 640 --epochs 300 -# See tutorials for hyperparameter evolution https://github.com/ultralytics/yolov5#tutorials - -lr0: 0.01 # initial learning rate (SGD=1E-2, Adam=1E-3) -lrf: 0.1 # final OneCycleLR learning rate (lr0 * lrf) -momentum: 0.937 # SGD momentum/Adam beta1 -weight_decay: 0.0005 # optimizer weight decay 5e-4 -warmup_epochs: 3.0 # warmup epochs (fractions ok) -warmup_momentum: 0.8 # warmup initial momentum -warmup_bias_lr: 0.1 # warmup initial bias lr -box: 0.05 # box loss gain -cls: 0.5 # cls loss gain -cls_pw: 1.0 # cls BCELoss positive_weight -obj: 1.0 # obj loss gain (scale with pixels) -obj_pw: 1.0 # obj BCELoss positive_weight -iou_t: 0.20 # IoU training threshold -anchor_t: 4.0 # anchor-multiple threshold -# anchors: 3 # anchors per output layer (0 to ignore) -fl_gamma: 0.0 # focal loss gamma (efficientDet default gamma=1.5) -hsv_h: 0.015 # image HSV-Hue augmentation (fraction) -hsv_s: 0.7 # image HSV-Saturation augmentation (fraction) -hsv_v: 0.4 # image HSV-Value augmentation (fraction) -degrees: 0.0 # image rotation (+/- deg) -translate: 0.1 # image translation (+/- fraction) -scale: 0.5 # image scale (+/- gain) -shear: 0.0 # image shear (+/- deg) -perspective: 0.0 # image perspective (+/- fraction), range 0-0.001 -flipud: 0.0 # image flip up-down (probability) -fliplr: 0.5 # image flip left-right (probability) -mosaic: 1.0 # image mosaic (probability) -mixup: 0.0 # image mixup (probability) -copy_paste: 0.0 # segment copy-paste (probability) diff --git a/YOLO/Dockerfiles/app/app/data/images/bus.jpg b/YOLO/Dockerfiles/app/app/data/images/bus.jpg deleted file mode 100644 index b43e311..0000000 Binary files a/YOLO/Dockerfiles/app/app/data/images/bus.jpg and /dev/null differ diff --git a/YOLO/Dockerfiles/app/app/data/images/zidane.jpg b/YOLO/Dockerfiles/app/app/data/images/zidane.jpg deleted file mode 100644 index 92d72ea..0000000 Binary files a/YOLO/Dockerfiles/app/app/data/images/zidane.jpg and /dev/null differ diff --git a/YOLO/Dockerfiles/app/app/data/scripts/download_weights.sh b/YOLO/Dockerfiles/app/app/data/scripts/download_weights.sh deleted file mode 100755 index e9fa653..0000000 --- a/YOLO/Dockerfiles/app/app/data/scripts/download_weights.sh +++ /dev/null @@ -1,20 +0,0 @@ -#!/bin/bash -# YOLOv5 🚀 by Ultralytics, GPL-3.0 license -# Download latest models from https://github.com/ultralytics/yolov5/releases -# Example usage: bash path/to/download_weights.sh -# parent -# └── yolov5 -# ├── yolov5s.pt ← downloads here -# ├── yolov5m.pt -# └── ... - -python - <= cls >= 0, f'incorrect class index {cls}' - - # Write YOLO label - if id not in shapes: - shapes[id] = Image.open(file).size - box = xyxy2xywhn(box[None].astype(np.float), w=shapes[id][0], h=shapes[id][1], clip=True) - with open((labels / id).with_suffix('.txt'), 'a') as f: - f.write(f"{cls} {' '.join(f'{x:.6f}' for x in box[0])}\n") # write label.txt - except Exception as e: - print(f'WARNING: skipping one label for {file}: {e}') - - - # Download manually from https://challenge.xviewdataset.org - dir = Path(yaml['path']) # dataset root dir - # urls = ['https://d307kc0mrhucc3.cloudfront.net/train_labels.zip', # train labels - # 'https://d307kc0mrhucc3.cloudfront.net/train_images.zip', # 15G, 847 train images - # 'https://d307kc0mrhucc3.cloudfront.net/val_images.zip'] # 5G, 282 val images (no labels) - # download(urls, dir=dir, delete=False) - - # Convert labels - convert_labels(dir / 'xView_train.geojson') - - # Move images - images = Path(dir / 'images') - images.mkdir(parents=True, exist_ok=True) - Path(dir / 'train_images').rename(dir / 'images' / 'train') - Path(dir / 'val_images').rename(dir / 'images' / 'val') - - # Split - autosplit(dir / 'images' / 'train') diff --git a/YOLO/Dockerfiles/app/app/detect.py b/YOLO/Dockerfiles/app/app/detect.py deleted file mode 100644 index 2d1963a..0000000 --- a/YOLO/Dockerfiles/app/app/detect.py +++ /dev/null @@ -1,257 +0,0 @@ -# YOLOv5 🚀 by Ultralytics, GPL-3.0 license -""" -Run inference on images, videos, directories, streams, etc. - -Usage - sources: - $ python path/to/detect.py --weights yolov5s.pt --source 0 # webcam - img.jpg # image - vid.mp4 # video - path/ # directory - path/*.jpg # glob - 'https://youtu.be/Zgi9g1ksQHc' # YouTube - 'rtsp://example.com/media.mp4' # RTSP, RTMP, HTTP stream - -Usage - formats: - $ python path/to/detect.py --weights yolov5s.pt # PyTorch - yolov5s.torchscript # TorchScript - yolov5s.onnx # ONNX Runtime or OpenCV DNN with --dnn - yolov5s.xml # OpenVINO - yolov5s.engine # TensorRT - yolov5s.mlmodel # CoreML (MacOS-only) - yolov5s_saved_model # TensorFlow SavedModel - yolov5s.pb # TensorFlow GraphDef - yolov5s.tflite # TensorFlow Lite - yolov5s_edgetpu.tflite # TensorFlow Edge TPU -""" - -import argparse -import os -import sys -from pathlib import Path - -import cv2 -import torch -import torch.backends.cudnn as cudnn - -FILE = Path(__file__).resolve() -ROOT = FILE.parents[0] # YOLOv5 root directory -if str(ROOT) not in sys.path: - sys.path.append(str(ROOT)) # add ROOT to PATH -ROOT = Path(os.path.relpath(ROOT, Path.cwd())) # relative - -from models.common import DetectMultiBackend -from utils.datasets import IMG_FORMATS, VID_FORMATS, LoadImages, LoadStreams -from utils.general import (LOGGER, check_file, check_img_size, check_imshow, check_requirements, colorstr, - increment_path, non_max_suppression, print_args, scale_coords, strip_optimizer, xyxy2xywh) -from utils.plots import Annotator, colors, save_one_box -from utils.torch_utils import select_device, time_sync - - -@torch.no_grad() -def run(weights=ROOT / 'yolov5s.pt', # model.pt path(s) - source=ROOT / 'data/images', # file/dir/URL/glob, 0 for webcam - data=ROOT / 'data/coco128.yaml', # dataset.yaml path - imgsz=(640, 640), # inference size (height, width) - conf_thres=0.25, # confidence threshold - iou_thres=0.45, # NMS IOU threshold - max_det=1000, # maximum detections per image - device='', # cuda device, i.e. 0 or 0,1,2,3 or cpu - view_img=False, # show results - save_txt=False, # save results to *.txt - save_conf=False, # save confidences in --save-txt labels - save_crop=False, # save cropped prediction boxes - nosave=False, # do not save images/videos - classes=None, # filter by class: --class 0, or --class 0 2 3 - agnostic_nms=False, # class-agnostic NMS - augment=False, # augmented inference - visualize=False, # visualize features - update=False, # update all models - project=ROOT / 'runs/detect', # save results to project/name - name='exp', # save results to project/name - exist_ok=False, # existing project/name ok, do not increment - line_thickness=3, # bounding box thickness (pixels) - hide_labels=False, # hide labels - hide_conf=False, # hide confidences - half=False, # use FP16 half-precision inference - dnn=False, # use OpenCV DNN for ONNX inference - ): - source = str(source) - save_img = not nosave and not source.endswith('.txt') # save inference images - is_file = Path(source).suffix[1:] in (IMG_FORMATS + VID_FORMATS) - is_url = source.lower().startswith(('rtsp://', 'rtmp://', 'http://', 'https://')) - webcam = source.isnumeric() or source.endswith('.txt') or (is_url and not is_file) - if is_url and is_file: - source = check_file(source) # download - - # Directories - save_dir = increment_path(Path(project) / name, exist_ok=exist_ok) # increment run - (save_dir / 'labels' if save_txt else save_dir).mkdir(parents=True, exist_ok=True) # make dir - - # Load model - device = select_device(device) - model = DetectMultiBackend(weights, device=device, dnn=dnn, data=data) - stride, names, pt, jit, onnx, engine = model.stride, model.names, model.pt, model.jit, model.onnx, model.engine - imgsz = check_img_size(imgsz, s=stride) # check image size - - # Half - half &= (pt or jit or engine) and device.type != 'cpu' # half precision only supported by PyTorch on CUDA - if pt or jit: - model.model.half() if half else model.model.float() - - # Dataloader - if webcam: - view_img = check_imshow() - cudnn.benchmark = True # set True to speed up constant image size inference - dataset = LoadStreams(source, img_size=imgsz, stride=stride, auto=pt) - bs = len(dataset) # batch_size - else: - dataset = LoadImages(source, img_size=imgsz, stride=stride, auto=pt) - bs = 1 # batch_size - vid_path, vid_writer = [None] * bs, [None] * bs - - # Run inference - model.warmup(imgsz=(1, 3, *imgsz), half=half) # warmup - dt, seen = [0.0, 0.0, 0.0], 0 - for path, im, im0s, vid_cap, s in dataset: - t1 = time_sync() - im = torch.from_numpy(im).to(device) - im = im.half() if half else im.float() # uint8 to fp16/32 - im /= 255 # 0 - 255 to 0.0 - 1.0 - if len(im.shape) == 3: - im = im[None] # expand for batch dim - t2 = time_sync() - dt[0] += t2 - t1 - - # Inference - visualize = increment_path(save_dir / Path(path).stem, mkdir=True) if visualize else False - pred = model(im, augment=augment, visualize=visualize) - t3 = time_sync() - dt[1] += t3 - t2 - - # NMS - pred = non_max_suppression(pred, conf_thres, iou_thres, classes, agnostic_nms, max_det=max_det) - dt[2] += time_sync() - t3 - - # Second-stage classifier (optional) - # pred = utils.general.apply_classifier(pred, classifier_model, im, im0s) - - # Process predictions - for i, det in enumerate(pred): # per image - seen += 1 - if webcam: # batch_size >= 1 - p, im0, frame = path[i], im0s[i].copy(), dataset.count - s += f'{i}: ' - else: - p, im0, frame = path, im0s.copy(), getattr(dataset, 'frame', 0) - - p = Path(p) # to Path - save_path = str(save_dir / p.name) # im.jpg - txt_path = str(save_dir / 'labels' / p.stem) + ('' if dataset.mode == 'image' else f'_{frame}') # im.txt - s += '%gx%g ' % im.shape[2:] # print string - gn = torch.tensor(im0.shape)[[1, 0, 1, 0]] # normalization gain whwh - imc = im0.copy() if save_crop else im0 # for save_crop - annotator = Annotator(im0, line_width=line_thickness, example=str(names)) - if len(det): - # Rescale boxes from img_size to im0 size - det[:, :4] = scale_coords(im.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' * (n > 1)}, " # add to string - - # Write results - 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 save_conf else (cls, *xywh) # label format - with open(txt_path + '.txt', 'a') as f: - f.write(('%g ' * len(line)).rstrip() % line + '\n') - - if save_img or save_crop or view_img: # Add bbox to image - c = int(cls) # integer class - label = None if hide_labels else (names[c] if hide_conf else f'{names[c]} {conf:.2f}') - annotator.box_label(xyxy, label, color=colors(c, True)) - if save_crop: - save_one_box(xyxy, imc, file=save_dir / 'crops' / names[c] / f'{p.stem}.jpg', BGR=True) - - # Print time (inference-only) - LOGGER.info(f'{s}Done. ({t3 - t2:.3f}s)') - - # Stream results - im0 = annotator.result() - if view_img: - cv2.imshow(str(p), im0) - cv2.waitKey(1) # 1 millisecond - - # Save results (image with detections) - if save_img: - if dataset.mode == 'image': - cv2.imwrite(save_path, im0) - else: # 'video' or 'stream' - if vid_path[i] != save_path: # new video - vid_path[i] = save_path - if isinstance(vid_writer[i], cv2.VideoWriter): - vid_writer[i].release() # release previous video writer - if vid_cap: # video - 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)) - else: # stream - fps, w, h = 30, im0.shape[1], im0.shape[0] - save_path += '.mp4' - vid_writer[i] = cv2.VideoWriter(save_path, cv2.VideoWriter_fourcc(*'mp4v'), fps, (w, h)) - vid_writer[i].write(im0) - - # Print results - t = tuple(x / seen * 1E3 for x in dt) # speeds per image - LOGGER.info(f'Speed: %.1fms pre-process, %.1fms inference, %.1fms NMS per image at shape {(1, 3, *imgsz)}' % t) - if save_txt or save_img: - s = f"\n{len(list(save_dir.glob('labels/*.txt')))} labels saved to {save_dir / 'labels'}" if save_txt else '' - LOGGER.info(f"Results saved to {colorstr('bold', save_dir)}{s}") - if update: - strip_optimizer(weights) # update model (to fix SourceChangeWarning) - - -def parse_opt(): - parser = argparse.ArgumentParser() - parser.add_argument('--weights', nargs='+', type=str, default=ROOT / 'yolov5s.pt', help='model path(s)') - parser.add_argument('--source', type=str, default=ROOT / 'data/images', help='file/dir/URL/glob, 0 for webcam') - parser.add_argument('--data', type=str, default=ROOT / 'data/coco128.yaml', help='(optional) dataset.yaml path') - parser.add_argument('--imgsz', '--img', '--img-size', nargs='+', type=int, default=[640], help='inference size h,w') - parser.add_argument('--conf-thres', type=float, default=0.25, help='confidence threshold') - parser.add_argument('--iou-thres', type=float, default=0.45, help='NMS IoU threshold') - parser.add_argument('--max-det', type=int, default=1000, help='maximum detections per image') - 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='show 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('--save-crop', action='store_true', help='save cropped prediction boxes') - parser.add_argument('--nosave', action='store_true', help='do not save images/videos') - parser.add_argument('--classes', nargs='+', type=int, help='filter by class: --classes 0, or --classes 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('--visualize', action='store_true', help='visualize features') - parser.add_argument('--update', action='store_true', help='update all models') - parser.add_argument('--project', default=ROOT / 'runs/detect', 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('--line-thickness', default=3, type=int, help='bounding box thickness (pixels)') - parser.add_argument('--hide-labels', default=False, action='store_true', help='hide labels') - parser.add_argument('--hide-conf', default=False, action='store_true', help='hide confidences') - parser.add_argument('--half', action='store_true', help='use FP16 half-precision inference') - parser.add_argument('--dnn', action='store_true', help='use OpenCV DNN for ONNX inference') - opt = parser.parse_args() - opt.imgsz *= 2 if len(opt.imgsz) == 1 else 1 # expand - print_args(FILE.stem, opt) - return opt - - -def main(opt): - check_requirements(exclude=('tensorboard', 'thop')) - run(**vars(opt)) - - -if __name__ == "__main__": - opt = parse_opt() - main(opt) diff --git a/YOLO/Dockerfiles/app/app/export.py b/YOLO/Dockerfiles/app/app/export.py deleted file mode 100644 index 0236872..0000000 --- a/YOLO/Dockerfiles/app/app/export.py +++ /dev/null @@ -1,506 +0,0 @@ -# YOLOv5 🚀 by Ultralytics, GPL-3.0 license -""" -Export a YOLOv5 PyTorch model to other formats. TensorFlow exports authored by https://github.com/zldrobit - -Format | `export.py --include` | Model ---- | --- | --- -PyTorch | - | yolov5s.pt -TorchScript | `torchscript` | yolov5s.torchscript -ONNX | `onnx` | yolov5s.onnx -OpenVINO | `openvino` | yolov5s_openvino_model/ -TensorRT | `engine` | yolov5s.engine -CoreML | `coreml` | yolov5s.mlmodel -TensorFlow SavedModel | `saved_model` | yolov5s_saved_model/ -TensorFlow GraphDef | `pb` | yolov5s.pb -TensorFlow Lite | `tflite` | yolov5s.tflite -TensorFlow Edge TPU | `edgetpu` | yolov5s_edgetpu.tflite -TensorFlow.js | `tfjs` | yolov5s_web_model/ - -Usage: - $ python path/to/export.py --weights yolov5s.pt --include torchscript onnx openvino engine coreml tflite ... - -Inference: - $ python path/to/detect.py --weights yolov5s.pt # PyTorch - yolov5s.torchscript # TorchScript - yolov5s.onnx # ONNX Runtime or OpenCV DNN with --dnn - yolov5s.xml # OpenVINO - yolov5s.engine # TensorRT - yolov5s.mlmodel # CoreML (MacOS-only) - yolov5s_saved_model # TensorFlow SavedModel - yolov5s.pb # TensorFlow GraphDef - yolov5s.tflite # TensorFlow Lite - yolov5s_edgetpu.tflite # TensorFlow Edge TPU - -TensorFlow.js: - $ cd .. && git clone https://github.com/zldrobit/tfjs-yolov5-example.git && cd tfjs-yolov5-example - $ npm install - $ ln -s ../../yolov5/yolov5s_web_model public/yolov5s_web_model - $ npm start -""" - -import argparse -import json -import os -import platform -import subprocess -import sys -import time -from pathlib import Path - -import torch -import torch.nn as nn -from torch.utils.mobile_optimizer import optimize_for_mobile - -FILE = Path(__file__).resolve() -ROOT = FILE.parents[0] # YOLOv5 root directory -if str(ROOT) not in sys.path: - sys.path.append(str(ROOT)) # add ROOT to PATH -ROOT = Path(os.path.relpath(ROOT, Path.cwd())) # relative - -from models.common import Conv -from models.experimental import attempt_load -from models.yolo import Detect -from utils.activations import SiLU -from utils.datasets import LoadImages -from utils.general import (LOGGER, check_dataset, check_img_size, check_requirements, check_version, colorstr, - file_size, print_args, url2file) -from utils.torch_utils import select_device - - -def export_torchscript(model, im, file, optimize, prefix=colorstr('TorchScript:')): - # YOLOv5 TorchScript model export - try: - LOGGER.info(f'\n{prefix} starting export with torch {torch.__version__}...') - f = file.with_suffix('.torchscript') - - ts = torch.jit.trace(model, im, strict=False) - d = {"shape": im.shape, "stride": int(max(model.stride)), "names": model.names} - extra_files = {'config.txt': json.dumps(d)} # torch._C.ExtraFilesMap() - if optimize: # https://pytorch.org/tutorials/recipes/mobile_interpreter.html - optimize_for_mobile(ts)._save_for_lite_interpreter(str(f), _extra_files=extra_files) - else: - ts.save(str(f), _extra_files=extra_files) - - LOGGER.info(f'{prefix} export success, saved as {f} ({file_size(f):.1f} MB)') - except Exception as e: - LOGGER.info(f'{prefix} export failure: {e}') - - -def export_onnx(model, im, file, opset, train, dynamic, simplify, prefix=colorstr('ONNX:')): - # YOLOv5 ONNX export - try: - check_requirements(('onnx',)) - import onnx - - LOGGER.info(f'\n{prefix} starting export with onnx {onnx.__version__}...') - f = file.with_suffix('.onnx') - - torch.onnx.export(model, im, f, verbose=False, opset_version=opset, - training=torch.onnx.TrainingMode.TRAINING if train else torch.onnx.TrainingMode.EVAL, - do_constant_folding=not train, - input_names=['images'], - output_names=['output'], - dynamic_axes={'images': {0: 'batch', 2: 'height', 3: 'width'}, # shape(1,3,640,640) - 'output': {0: 'batch', 1: 'anchors'} # shape(1,25200,85) - } if dynamic else None) - - # Checks - model_onnx = onnx.load(f) # load onnx model - onnx.checker.check_model(model_onnx) # check onnx model - # LOGGER.info(onnx.helper.printable_graph(model_onnx.graph)) # print - - # Simplify - if simplify: - try: - check_requirements(('onnx-simplifier',)) - import onnxsim - - LOGGER.info(f'{prefix} simplifying with onnx-simplifier {onnxsim.__version__}...') - model_onnx, check = onnxsim.simplify( - model_onnx, - dynamic_input_shape=dynamic, - input_shapes={'images': list(im.shape)} if dynamic else None) - assert check, 'assert check failed' - onnx.save(model_onnx, f) - except Exception as e: - LOGGER.info(f'{prefix} simplifier failure: {e}') - LOGGER.info(f'{prefix} export success, saved as {f} ({file_size(f):.1f} MB)') - LOGGER.info(f"{prefix} run --dynamic ONNX model inference with: 'python detect.py --weights {f}'") - except Exception as e: - LOGGER.info(f'{prefix} export failure: {e}') - - -def export_openvino(model, im, file, prefix=colorstr('OpenVINO:')): - # YOLOv5 OpenVINO export - try: - check_requirements(('openvino-dev',)) # requires openvino-dev: https://pypi.org/project/openvino-dev/ - import openvino.inference_engine as ie - - LOGGER.info(f'\n{prefix} starting export with openvino {ie.__version__}...') - f = str(file).replace('.pt', '_openvino_model' + os.sep) - - cmd = f"mo --input_model {file.with_suffix('.onnx')} --output_dir {f}" - subprocess.check_output(cmd, shell=True) - - LOGGER.info(f'{prefix} export success, saved as {f} ({file_size(f):.1f} MB)') - except Exception as e: - LOGGER.info(f'\n{prefix} export failure: {e}') - - -def export_coreml(model, im, file, prefix=colorstr('CoreML:')): - # YOLOv5 CoreML export - ct_model = None - try: - check_requirements(('coremltools',)) - import coremltools as ct - - LOGGER.info(f'\n{prefix} starting export with coremltools {ct.__version__}...') - f = file.with_suffix('.mlmodel') - - ts = torch.jit.trace(model, im, strict=False) # TorchScript model - ct_model = ct.convert(ts, inputs=[ct.ImageType('image', shape=im.shape, scale=1 / 255, bias=[0, 0, 0])]) - ct_model.save(f) - - LOGGER.info(f'{prefix} export success, saved as {f} ({file_size(f):.1f} MB)') - except Exception as e: - LOGGER.info(f'\n{prefix} export failure: {e}') - - return ct_model - - -def export_engine(model, im, file, train, half, simplify, workspace=4, verbose=False, prefix=colorstr('TensorRT:')): - # YOLOv5 TensorRT export https://developer.nvidia.com/tensorrt - try: - check_requirements(('tensorrt',)) - import tensorrt as trt - - if trt.__version__[0] == '7': # TensorRT 7 handling https://github.com/ultralytics/yolov5/issues/6012 - grid = model.model[-1].anchor_grid - model.model[-1].anchor_grid = [a[..., :1, :1, :] for a in grid] - export_onnx(model, im, file, 12, train, False, simplify) # opset 12 - model.model[-1].anchor_grid = grid - else: # TensorRT >= 8 - check_version(trt.__version__, '8.0.0', hard=True) # require tensorrt>=8.0.0 - export_onnx(model, im, file, 13, train, False, simplify) # opset 13 - onnx = file.with_suffix('.onnx') - assert onnx.exists(), f'failed to export ONNX file: {onnx}' - - LOGGER.info(f'\n{prefix} starting export with TensorRT {trt.__version__}...') - f = file.with_suffix('.engine') # TensorRT engine file - logger = trt.Logger(trt.Logger.INFO) - if verbose: - logger.min_severity = trt.Logger.Severity.VERBOSE - - builder = trt.Builder(logger) - config = builder.create_builder_config() - config.max_workspace_size = workspace * 1 << 30 - - flag = (1 << int(trt.NetworkDefinitionCreationFlag.EXPLICIT_BATCH)) - network = builder.create_network(flag) - parser = trt.OnnxParser(network, logger) - if not parser.parse_from_file(str(onnx)): - raise RuntimeError(f'failed to load ONNX file: {onnx}') - - inputs = [network.get_input(i) for i in range(network.num_inputs)] - outputs = [network.get_output(i) for i in range(network.num_outputs)] - LOGGER.info(f'{prefix} Network Description:') - for inp in inputs: - LOGGER.info(f'{prefix}\tinput "{inp.name}" with shape {inp.shape} and dtype {inp.dtype}') - for out in outputs: - LOGGER.info(f'{prefix}\toutput "{out.name}" with shape {out.shape} and dtype {out.dtype}') - - half &= builder.platform_has_fast_fp16 - LOGGER.info(f'{prefix} building FP{16 if half else 32} engine in {f}') - if half: - config.set_flag(trt.BuilderFlag.FP16) - with builder.build_engine(network, config) as engine, open(f, 'wb') as t: - t.write(engine.serialize()) - LOGGER.info(f'{prefix} export success, saved as {f} ({file_size(f):.1f} MB)') - - except Exception as e: - LOGGER.info(f'\n{prefix} export failure: {e}') - - -def export_saved_model(model, im, file, dynamic, - tf_nms=False, agnostic_nms=False, topk_per_class=100, topk_all=100, iou_thres=0.45, - conf_thres=0.25, prefix=colorstr('TensorFlow SavedModel:')): - # YOLOv5 TensorFlow SavedModel export - keras_model = None - try: - import tensorflow as tf - from tensorflow import keras - - from models.tf import TFDetect, TFModel - - LOGGER.info(f'\n{prefix} starting export with tensorflow {tf.__version__}...') - f = str(file).replace('.pt', '_saved_model') - batch_size, ch, *imgsz = list(im.shape) # BCHW - - tf_model = TFModel(cfg=model.yaml, model=model, nc=model.nc, imgsz=imgsz) - im = tf.zeros((batch_size, *imgsz, 3)) # BHWC order for TensorFlow - y = tf_model.predict(im, tf_nms, agnostic_nms, topk_per_class, topk_all, iou_thres, conf_thres) - inputs = keras.Input(shape=(*imgsz, 3), batch_size=None if dynamic else batch_size) - outputs = tf_model.predict(inputs, tf_nms, agnostic_nms, topk_per_class, topk_all, iou_thres, conf_thres) - keras_model = keras.Model(inputs=inputs, outputs=outputs) - keras_model.trainable = False - keras_model.summary() - keras_model.save(f, save_format='tf') - - LOGGER.info(f'{prefix} export success, saved as {f} ({file_size(f):.1f} MB)') - except Exception as e: - LOGGER.info(f'\n{prefix} export failure: {e}') - - return keras_model - - -def export_pb(keras_model, im, file, prefix=colorstr('TensorFlow GraphDef:')): - # YOLOv5 TensorFlow GraphDef *.pb export https://github.com/leimao/Frozen_Graph_TensorFlow - try: - import tensorflow as tf - from tensorflow.python.framework.convert_to_constants import convert_variables_to_constants_v2 - - LOGGER.info(f'\n{prefix} starting export with tensorflow {tf.__version__}...') - f = file.with_suffix('.pb') - - m = tf.function(lambda x: keras_model(x)) # full model - m = m.get_concrete_function(tf.TensorSpec(keras_model.inputs[0].shape, keras_model.inputs[0].dtype)) - frozen_func = convert_variables_to_constants_v2(m) - frozen_func.graph.as_graph_def() - tf.io.write_graph(graph_or_graph_def=frozen_func.graph, logdir=str(f.parent), name=f.name, as_text=False) - - LOGGER.info(f'{prefix} export success, saved as {f} ({file_size(f):.1f} MB)') - except Exception as e: - LOGGER.info(f'\n{prefix} export failure: {e}') - - -def export_tflite(keras_model, im, file, int8, data, ncalib, prefix=colorstr('TensorFlow Lite:')): - # YOLOv5 TensorFlow Lite export - try: - import tensorflow as tf - - LOGGER.info(f'\n{prefix} starting export with tensorflow {tf.__version__}...') - batch_size, ch, *imgsz = list(im.shape) # BCHW - f = str(file).replace('.pt', '-fp16.tflite') - - converter = tf.lite.TFLiteConverter.from_keras_model(keras_model) - converter.target_spec.supported_ops = [tf.lite.OpsSet.TFLITE_BUILTINS] - converter.target_spec.supported_types = [tf.float16] - converter.optimizations = [tf.lite.Optimize.DEFAULT] - if int8: - from models.tf import representative_dataset_gen - dataset = LoadImages(check_dataset(data)['train'], img_size=imgsz, auto=False) # representative data - converter.representative_dataset = lambda: representative_dataset_gen(dataset, ncalib) - converter.target_spec.supported_ops = [tf.lite.OpsSet.TFLITE_BUILTINS_INT8] - converter.target_spec.supported_types = [] - converter.inference_input_type = tf.uint8 # or tf.int8 - converter.inference_output_type = tf.uint8 # or tf.int8 - converter.experimental_new_quantizer = False - f = str(file).replace('.pt', '-int8.tflite') - - tflite_model = converter.convert() - open(f, "wb").write(tflite_model) - LOGGER.info(f'{prefix} export success, saved as {f} ({file_size(f):.1f} MB)') - - except Exception as e: - LOGGER.info(f'\n{prefix} export failure: {e}') - - -def export_edgetpu(keras_model, im, file, prefix=colorstr('Edge TPU:')): - # YOLOv5 Edge TPU export https://coral.ai/docs/edgetpu/models-intro/ - try: - cmd = 'edgetpu_compiler --version' - help_url = 'https://coral.ai/docs/edgetpu/compiler/' - assert platform.system() == 'Linux', f'export only supported on Linux. See {help_url}' - if subprocess.run(cmd, shell=True).returncode != 0: - LOGGER.info(f'\n{prefix} export requires Edge TPU compiler. Attempting install from {help_url}') - for c in ['curl https://packages.cloud.google.com/apt/doc/apt-key.gpg | sudo apt-key add -', - 'echo "deb https://packages.cloud.google.com/apt coral-edgetpu-stable main" | sudo tee /etc/apt/sources.list.d/coral-edgetpu.list', - 'sudo apt-get update', - 'sudo apt-get install edgetpu-compiler']: - subprocess.run(c, shell=True, check=True) - ver = subprocess.run(cmd, shell=True, capture_output=True, check=True).stdout.decode().split()[-1] - - LOGGER.info(f'\n{prefix} starting export with Edge TPU compiler {ver}...') - f = str(file).replace('.pt', '-int8_edgetpu.tflite') # Edge TPU model - f_tfl = str(file).replace('.pt', '-int8.tflite') # TFLite model - - cmd = f"edgetpu_compiler -s {f_tfl}" - subprocess.run(cmd, shell=True, check=True) - - LOGGER.info(f'{prefix} export success, saved as {f} ({file_size(f):.1f} MB)') - except Exception as e: - LOGGER.info(f'\n{prefix} export failure: {e}') - - -def export_tfjs(keras_model, im, file, prefix=colorstr('TensorFlow.js:')): - # YOLOv5 TensorFlow.js export - try: - check_requirements(('tensorflowjs',)) - import re - - import tensorflowjs as tfjs - - LOGGER.info(f'\n{prefix} starting export with tensorflowjs {tfjs.__version__}...') - f = str(file).replace('.pt', '_web_model') # js dir - f_pb = file.with_suffix('.pb') # *.pb path - f_json = f + '/model.json' # *.json path - - cmd = f'tensorflowjs_converter --input_format=tf_frozen_model ' \ - f'--output_node_names="Identity,Identity_1,Identity_2,Identity_3" {f_pb} {f}' - subprocess.run(cmd, shell=True) - - json = open(f_json).read() - with open(f_json, 'w') as j: # sort JSON Identity_* in ascending order - subst = re.sub( - r'{"outputs": {"Identity.?.?": {"name": "Identity.?.?"}, ' - r'"Identity.?.?": {"name": "Identity.?.?"}, ' - r'"Identity.?.?": {"name": "Identity.?.?"}, ' - r'"Identity.?.?": {"name": "Identity.?.?"}}}', - r'{"outputs": {"Identity": {"name": "Identity"}, ' - r'"Identity_1": {"name": "Identity_1"}, ' - r'"Identity_2": {"name": "Identity_2"}, ' - r'"Identity_3": {"name": "Identity_3"}}}', - json) - j.write(subst) - - LOGGER.info(f'{prefix} export success, saved as {f} ({file_size(f):.1f} MB)') - except Exception as e: - LOGGER.info(f'\n{prefix} export failure: {e}') - - -@torch.no_grad() -def run(data=ROOT / 'data/coco128.yaml', # 'dataset.yaml path' - weights=ROOT / 'yolov5s.pt', # weights path - imgsz=(640, 640), # image (height, width) - batch_size=1, # batch size - device='cpu', # cuda device, i.e. 0 or 0,1,2,3 or cpu - include=('torchscript', 'onnx'), # include formats - half=False, # FP16 half-precision export - inplace=False, # set YOLOv5 Detect() inplace=True - train=False, # model.train() mode - optimize=False, # TorchScript: optimize for mobile - int8=False, # CoreML/TF INT8 quantization - dynamic=False, # ONNX/TF: dynamic axes - simplify=False, # ONNX: simplify model - opset=12, # ONNX: opset version - verbose=False, # TensorRT: verbose log - workspace=4, # TensorRT: workspace size (GB) - nms=False, # TF: add NMS to model - agnostic_nms=False, # TF: add agnostic NMS to model - topk_per_class=100, # TF.js NMS: topk per class to keep - topk_all=100, # TF.js NMS: topk for all classes to keep - iou_thres=0.45, # TF.js NMS: IoU threshold - conf_thres=0.25 # TF.js NMS: confidence threshold - ): - t = time.time() - include = [x.lower() for x in include] - tf_exports = list(x in include for x in ('saved_model', 'pb', 'tflite', 'edgetpu', 'tfjs')) # TensorFlow exports - file = Path(url2file(weights) if str(weights).startswith(('http:/', 'https:/')) else weights) - - # Checks - imgsz *= 2 if len(imgsz) == 1 else 1 # expand - opset = 12 if ('openvino' in include) else opset # OpenVINO requires opset <= 12 - - # Load PyTorch model - device = select_device(device) - assert not (device.type == 'cpu' and half), '--half only compatible with GPU export, i.e. use --device 0' - model = attempt_load(weights, map_location=device, inplace=True, fuse=True) # load FP32 model - nc, names = model.nc, model.names # number of classes, class names - - # Input - gs = int(max(model.stride)) # grid size (max stride) - imgsz = [check_img_size(x, gs) for x in imgsz] # verify img_size are gs-multiples - im = torch.zeros(batch_size, 3, *imgsz).to(device) # image size(1,3,320,192) BCHW iDetection - - # Update model - if half: - im, model = im.half(), model.half() # to FP16 - model.train() if train else model.eval() # training mode = no Detect() layer grid construction - for k, m in model.named_modules(): - if isinstance(m, Conv): # assign export-friendly activations - if isinstance(m.act, nn.SiLU): - m.act = SiLU() - elif isinstance(m, Detect): - m.inplace = inplace - m.onnx_dynamic = dynamic - # m.forward = m.forward_export # assign forward (optional) - - for _ in range(2): - y = model(im) # dry runs - LOGGER.info(f"\n{colorstr('PyTorch:')} starting from {file} ({file_size(file):.1f} MB)") - - # Exports - if 'torchscript' in include: - export_torchscript(model, im, file, optimize) - if 'engine' in include: # TensorRT required before ONNX - export_engine(model, im, file, train, half, simplify, workspace, verbose) - if ('onnx' in include) or ('openvino' in include): # OpenVINO requires ONNX - export_onnx(model, im, file, opset, train, dynamic, simplify) - if 'openvino' in include: - export_openvino(model, im, file) - if 'coreml' in include: - export_coreml(model, im, file) - - # TensorFlow Exports - if any(tf_exports): - pb, tflite, edgetpu, tfjs = tf_exports[1:] - if int8 or edgetpu: # TFLite --int8 bug https://github.com/ultralytics/yolov5/issues/5707 - check_requirements(('flatbuffers==1.12',)) # required before `import tensorflow` - assert not (tflite and tfjs), 'TFLite and TF.js models must be exported separately, please pass only one type.' - model = export_saved_model(model, im, file, dynamic, tf_nms=nms or agnostic_nms or tfjs, - agnostic_nms=agnostic_nms or tfjs, topk_per_class=topk_per_class, topk_all=topk_all, - conf_thres=conf_thres, iou_thres=iou_thres) # keras model - if pb or tfjs: # pb prerequisite to tfjs - export_pb(model, im, file) - if tflite or edgetpu: - export_tflite(model, im, file, int8=int8 or edgetpu, data=data, ncalib=100) - if edgetpu: - export_edgetpu(model, im, file) - if tfjs: - export_tfjs(model, im, file) - - # Finish - LOGGER.info(f'\nExport complete ({time.time() - t:.2f}s)' - f"\nResults saved to {colorstr('bold', file.parent.resolve())}" - f'\nVisualize with https://netron.app') - - -def parse_opt(): - parser = argparse.ArgumentParser() - parser.add_argument('--data', type=str, default=ROOT / 'data/coco128.yaml', help='dataset.yaml path') - parser.add_argument('--weights', nargs='+', type=str, default=ROOT / 'yolov5s.pt', help='model.pt path(s)') - parser.add_argument('--imgsz', '--img', '--img-size', nargs='+', type=int, default=[640, 640], help='image (h, w)') - parser.add_argument('--batch-size', type=int, default=1, help='batch size') - parser.add_argument('--device', default='cpu', help='cuda device, i.e. 0 or 0,1,2,3 or cpu') - parser.add_argument('--half', action='store_true', help='FP16 half-precision export') - parser.add_argument('--inplace', action='store_true', help='set YOLOv5 Detect() inplace=True') - parser.add_argument('--train', action='store_true', help='model.train() mode') - parser.add_argument('--optimize', action='store_true', help='TorchScript: optimize for mobile') - parser.add_argument('--int8', action='store_true', help='CoreML/TF INT8 quantization') - parser.add_argument('--dynamic', action='store_true', help='ONNX/TF: dynamic axes') - parser.add_argument('--simplify', action='store_true', help='ONNX: simplify model') - parser.add_argument('--opset', type=int, default=12, help='ONNX: opset version') - parser.add_argument('--verbose', action='store_true', help='TensorRT: verbose log') - parser.add_argument('--workspace', type=int, default=4, help='TensorRT: workspace size (GB)') - parser.add_argument('--nms', action='store_true', help='TF: add NMS to model') - parser.add_argument('--agnostic-nms', action='store_true', help='TF: add agnostic NMS to model') - parser.add_argument('--topk-per-class', type=int, default=100, help='TF.js NMS: topk per class to keep') - parser.add_argument('--topk-all', type=int, default=100, help='TF.js NMS: topk for all classes to keep') - parser.add_argument('--iou-thres', type=float, default=0.45, help='TF.js NMS: IoU threshold') - parser.add_argument('--conf-thres', type=float, default=0.25, help='TF.js NMS: confidence threshold') - parser.add_argument('--include', nargs='+', - default=['torchscript', 'onnx'], - help='available formats are (torchscript, onnx, engine, coreml, saved_model, pb, tflite, tfjs)') - opt = parser.parse_args() - print_args(FILE.stem, opt) - return opt - - -def main(opt): - for opt.weights in (opt.weights if isinstance(opt.weights, list) else [opt.weights]): - run(**vars(opt)) - - -if __name__ == "__main__": - opt = parse_opt() - main(opt) diff --git a/YOLO/Dockerfiles/app/app/hubconf.py b/YOLO/Dockerfiles/app/app/hubconf.py deleted file mode 100644 index 6bf4b0b..0000000 --- a/YOLO/Dockerfiles/app/app/hubconf.py +++ /dev/null @@ -1,143 +0,0 @@ -# YOLOv5 🚀 by Ultralytics, GPL-3.0 license -""" -PyTorch Hub models https://pytorch.org/hub/ultralytics_yolov5/ - -Usage: - import torch - model = torch.hub.load('ultralytics/yolov5', 'yolov5s') - model = torch.hub.load('ultralytics/yolov5:master', 'custom', 'path/to/yolov5s.onnx') # file from branch -""" - -import torch - - -def _create(name, pretrained=True, channels=3, classes=80, autoshape=True, verbose=True, device=None): - """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 - autoshape (bool): apply YOLOv5 .autoshape() wrapper to model - verbose (bool): print all information to screen - device (str, torch.device, None): device to use for model parameters - - Returns: - YOLOv5 pytorch model - """ - from pathlib import Path - - from models.common import AutoShape, DetectMultiBackend - from models.yolo import Model - from utils.downloads import attempt_download - from utils.general import check_requirements, intersect_dicts, set_logging - from utils.torch_utils import select_device - - check_requirements(exclude=('tensorboard', 'thop', 'opencv-python')) - set_logging(verbose=verbose) - - name = Path(name) - path = name.with_suffix('.pt') if name.suffix == '' else name # checkpoint path - try: - device = select_device(('0' if torch.cuda.is_available() else 'cpu') if device is None else device) - - if pretrained and channels == 3 and classes == 80: - model = DetectMultiBackend(path, device=device) # download/load FP32 model - # model = models.experimental.attempt_load(path, map_location=device) # download/load FP32 model - else: - cfg = list((Path(__file__).parent / 'models').rglob(f'{path.stem}.yaml'))[0] # model.yaml path - model = Model(cfg, channels, classes) # create model - if pretrained: - ckpt = torch.load(attempt_download(path), map_location=device) # load - csd = ckpt['model'].float().state_dict() # checkpoint state_dict as FP32 - csd = intersect_dicts(csd, model.state_dict(), exclude=['anchors']) # intersect - model.load_state_dict(csd, strict=False) # load - if len(ckpt['model'].names) == classes: - model.names = ckpt['model'].names # set class names attribute - if autoshape: - model = AutoShape(model) # for file/URI/PIL/cv2/np inputs and NMS - return model.to(device) - - except Exception as e: - help_url = 'https://github.com/ultralytics/yolov5/issues/36' - s = 'Cache may be out of date, try `force_reload=True`. See %s for help.' % help_url - raise Exception(s) from e - - -def custom(path='path/to/model.pt', autoshape=True, verbose=True, device=None): - # YOLOv5 custom or local model - return _create(path, autoshape=autoshape, verbose=verbose, device=device) - - -def yolov5n(pretrained=True, channels=3, classes=80, autoshape=True, verbose=True, device=None): - # YOLOv5-nano model https://github.com/ultralytics/yolov5 - return _create('yolov5n', pretrained, channels, classes, autoshape, verbose, device) - - -def yolov5s(pretrained=True, channels=3, classes=80, autoshape=True, verbose=True, device=None): - # YOLOv5-small model https://github.com/ultralytics/yolov5 - return _create('yolov5s', pretrained, channels, classes, autoshape, verbose, device) - - -def yolov5m(pretrained=True, channels=3, classes=80, autoshape=True, verbose=True, device=None): - # YOLOv5-medium model https://github.com/ultralytics/yolov5 - return _create('yolov5m', pretrained, channels, classes, autoshape, verbose, device) - - -def yolov5l(pretrained=True, channels=3, classes=80, autoshape=True, verbose=True, device=None): - # YOLOv5-large model https://github.com/ultralytics/yolov5 - return _create('yolov5l', pretrained, channels, classes, autoshape, verbose, device) - - -def yolov5x(pretrained=True, channels=3, classes=80, autoshape=True, verbose=True, device=None): - # YOLOv5-xlarge model https://github.com/ultralytics/yolov5 - return _create('yolov5x', pretrained, channels, classes, autoshape, verbose, device) - - -def yolov5n6(pretrained=True, channels=3, classes=80, autoshape=True, verbose=True, device=None): - # YOLOv5-nano-P6 model https://github.com/ultralytics/yolov5 - return _create('yolov5n6', pretrained, channels, classes, autoshape, verbose, device) - - -def yolov5s6(pretrained=True, channels=3, classes=80, autoshape=True, verbose=True, device=None): - # YOLOv5-small-P6 model https://github.com/ultralytics/yolov5 - return _create('yolov5s6', pretrained, channels, classes, autoshape, verbose, device) - - -def yolov5m6(pretrained=True, channels=3, classes=80, autoshape=True, verbose=True, device=None): - # YOLOv5-medium-P6 model https://github.com/ultralytics/yolov5 - return _create('yolov5m6', pretrained, channels, classes, autoshape, verbose, device) - - -def yolov5l6(pretrained=True, channels=3, classes=80, autoshape=True, verbose=True, device=None): - # YOLOv5-large-P6 model https://github.com/ultralytics/yolov5 - return _create('yolov5l6', pretrained, channels, classes, autoshape, verbose, device) - - -def yolov5x6(pretrained=True, channels=3, classes=80, autoshape=True, verbose=True, device=None): - # YOLOv5-xlarge-P6 model https://github.com/ultralytics/yolov5 - return _create('yolov5x6', pretrained, channels, classes, autoshape, verbose, device) - - -if __name__ == '__main__': - model = _create(name='yolov5s', pretrained=True, channels=3, classes=80, autoshape=True, verbose=True) # pretrained - # model = custom(path='path/to/model.pt') # custom - - # Verify inference - from pathlib import Path - - import cv2 - import numpy as np - from PIL import Image - - imgs = ['data/images/zidane.jpg', # filename - Path('data/images/zidane.jpg'), # Path - 'https://ultralytics.com/images/zidane.jpg', # URI - cv2.imread('data/images/bus.jpg')[:, :, ::-1], # OpenCV - Image.open('data/images/bus.jpg'), # PIL - np.zeros((320, 640, 3))] # numpy - - results = model(imgs, size=320) # batched inference - results.print() - results.save() diff --git a/YOLO/Dockerfiles/app/app/images/1YBGKB4W.png b/YOLO/Dockerfiles/app/app/images/1YBGKB4W.png deleted file mode 100644 index c5f8a6b..0000000 Binary files a/YOLO/Dockerfiles/app/app/images/1YBGKB4W.png and /dev/null differ diff --git a/YOLO/Dockerfiles/app/app/images/2RSXZ2IT.png b/YOLO/Dockerfiles/app/app/images/2RSXZ2IT.png deleted file mode 100644 index 6577361..0000000 Binary files a/YOLO/Dockerfiles/app/app/images/2RSXZ2IT.png and /dev/null differ diff --git a/YOLO/Dockerfiles/app/app/images/3EKW3K1A.png 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a/YOLO/Dockerfiles/app/app/images/Z8EWCF6S.png b/YOLO/Dockerfiles/app/app/images/Z8EWCF6S.png deleted file mode 100644 index bcfccfb..0000000 Binary files a/YOLO/Dockerfiles/app/app/images/Z8EWCF6S.png and /dev/null differ diff --git a/YOLO/Dockerfiles/app/app/models/__init__.py b/YOLO/Dockerfiles/app/app/models/__init__.py deleted file mode 100644 index e69de29..0000000 diff --git a/YOLO/Dockerfiles/app/app/models/common.py b/YOLO/Dockerfiles/app/app/models/common.py deleted file mode 100644 index e375507..0000000 --- a/YOLO/Dockerfiles/app/app/models/common.py +++ /dev/null @@ -1,662 +0,0 @@ -# YOLOv5 🚀 by Ultralytics, GPL-3.0 license -""" -Common modules -""" - -import json -import math -import platform -import warnings -from collections import OrderedDict, namedtuple -from copy import copy -from pathlib import Path - -import cv2 -import numpy as np -import pandas as pd -import requests -import torch -import torch.nn as nn -import yaml -from PIL import Image -from torch.cuda import amp - -from utils.datasets import exif_transpose, letterbox -from utils.general import (LOGGER, check_requirements, check_suffix, check_version, colorstr, increment_path, - make_divisible, non_max_suppression, scale_coords, xywh2xyxy, xyxy2xywh) -from utils.plots import Annotator, colors, save_one_box -from utils.torch_utils import copy_attr, time_sync - - -def autopad(k, p=None): # kernel, padding - # Pad to 'same' - if p is None: - p = k // 2 if isinstance(k, int) else [x // 2 for x in k] # auto-pad - return p - - -class Conv(nn.Module): - # Standard convolution - def __init__(self, c1, c2, k=1, s=1, p=None, g=1, act=True): # ch_in, ch_out, kernel, stride, padding, groups - super().__init__() - self.conv = nn.Conv2d(c1, c2, k, s, autopad(k, p), groups=g, bias=False) - self.bn = nn.BatchNorm2d(c2) - self.act = nn.SiLU() if act is True else (act if isinstance(act, nn.Module) else nn.Identity()) - - def forward(self, x): - return self.act(self.bn(self.conv(x))) - - def forward_fuse(self, x): - return self.act(self.conv(x)) - - -class DWConv(Conv): - # Depth-wise convolution class - def __init__(self, c1, c2, k=1, s=1, act=True): # ch_in, ch_out, kernel, stride, padding, groups - super().__init__(c1, c2, k, s, g=math.gcd(c1, c2), act=act) - - -class TransformerLayer(nn.Module): - # Transformer layer https://arxiv.org/abs/2010.11929 (LayerNorm layers removed for better performance) - def __init__(self, c, num_heads): - super().__init__() - self.q = nn.Linear(c, c, bias=False) - self.k = nn.Linear(c, c, bias=False) - self.v = nn.Linear(c, c, bias=False) - self.ma = nn.MultiheadAttention(embed_dim=c, num_heads=num_heads) - self.fc1 = nn.Linear(c, c, bias=False) - self.fc2 = nn.Linear(c, c, bias=False) - - def forward(self, x): - x = self.ma(self.q(x), self.k(x), self.v(x))[0] + x - x = self.fc2(self.fc1(x)) + x - return x - - -class TransformerBlock(nn.Module): - # Vision Transformer https://arxiv.org/abs/2010.11929 - def __init__(self, c1, c2, num_heads, num_layers): - super().__init__() - self.conv = None - if c1 != c2: - self.conv = Conv(c1, c2) - self.linear = nn.Linear(c2, c2) # learnable position embedding - self.tr = nn.Sequential(*(TransformerLayer(c2, num_heads) for _ in range(num_layers))) - self.c2 = c2 - - def forward(self, x): - if self.conv is not None: - x = self.conv(x) - b, _, w, h = x.shape - p = x.flatten(2).permute(2, 0, 1) - return self.tr(p + self.linear(p)).permute(1, 2, 0).reshape(b, self.c2, w, h) - - -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().__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().__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.SiLU() - 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().__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 C3TR(C3): - # C3 module with TransformerBlock() - def __init__(self, c1, c2, n=1, shortcut=True, g=1, e=0.5): - super().__init__(c1, c2, n, shortcut, g, e) - c_ = int(c2 * e) - self.m = TransformerBlock(c_, c_, 4, n) - - -class C3SPP(C3): - # C3 module with SPP() - def __init__(self, c1, c2, k=(5, 9, 13), n=1, shortcut=True, g=1, e=0.5): - super().__init__(c1, c2, n, shortcut, g, e) - c_ = int(c2 * e) - self.m = SPP(c_, c_, k) - - -class C3Ghost(C3): - # C3 module with GhostBottleneck() - def __init__(self, c1, c2, n=1, shortcut=True, g=1, e=0.5): - super().__init__(c1, c2, n, shortcut, g, e) - c_ = int(c2 * e) # hidden channels - self.m = nn.Sequential(*(GhostBottleneck(c_, c_) for _ in range(n))) - - -class SPP(nn.Module): - # Spatial Pyramid Pooling (SPP) layer https://arxiv.org/abs/1406.4729 - def __init__(self, c1, c2, k=(5, 9, 13)): - super().__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) - with warnings.catch_warnings(): - warnings.simplefilter('ignore') # suppress torch 1.9.0 max_pool2d() warning - return self.cv2(torch.cat([x] + [m(x) for m in self.m], 1)) - - -class SPPF(nn.Module): - # Spatial Pyramid Pooling - Fast (SPPF) layer for YOLOv5 by Glenn Jocher - def __init__(self, c1, c2, k=5): # equivalent to SPP(k=(5, 9, 13)) - super().__init__() - c_ = c1 // 2 # hidden channels - self.cv1 = Conv(c1, c_, 1, 1) - self.cv2 = Conv(c_ * 4, c2, 1, 1) - self.m = nn.MaxPool2d(kernel_size=k, stride=1, padding=k // 2) - - def forward(self, x): - x = self.cv1(x) - with warnings.catch_warnings(): - warnings.simplefilter('ignore') # suppress torch 1.9.0 max_pool2d() warning - y1 = self.m(x) - y2 = self.m(y1) - return self.cv2(torch.cat([x, y1, y2, self.m(y2)], 1)) - - -class Focus(nn.Module): - # Focus wh information into c-space - def __init__(self, c1, c2, k=1, s=1, p=None, g=1, act=True): # ch_in, ch_out, kernel, stride, padding, groups - super().__init__() - self.conv = Conv(c1 * 4, c2, k, s, p, g, act) - # self.contract = Contract(gain=2) - - def forward(self, x): # x(b,c,w,h) -> y(b,4c,w/2,h/2) - return self.conv(torch.cat([x[..., ::2, ::2], x[..., 1::2, ::2], x[..., ::2, 1::2], x[..., 1::2, 1::2]], 1)) - # return self.conv(self.contract(x)) - - -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().__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=3, s=1): # ch_in, ch_out, kernel, stride - super().__init__() - c_ = c2 // 2 - self.conv = nn.Sequential(GhostConv(c1, c_, 1, 1), # pw - DWConv(c_, c_, k, s, act=False) if s == 2 else nn.Identity(), # dw - GhostConv(c_, c2, 1, 1, act=False)) # pw-linear - self.shortcut = nn.Sequential(DWConv(c1, c1, k, s, act=False), - Conv(c1, c2, 1, 1, act=False)) if s == 2 else nn.Identity() - - def forward(self, x): - return self.conv(x) + self.shortcut(x) - - -class Contract(nn.Module): - # Contract width-height into channels, i.e. x(1,64,80,80) to x(1,256,40,40) - def __init__(self, gain=2): - super().__init__() - self.gain = gain - - def forward(self, x): - b, c, h, w = x.size() # assert (h / s == 0) and (W / s == 0), 'Indivisible gain' - s = self.gain - x = x.view(b, c, h // s, s, w // s, s) # x(1,64,40,2,40,2) - x = x.permute(0, 3, 5, 1, 2, 4).contiguous() # x(1,2,2,64,40,40) - return x.view(b, c * s * s, h // s, w // s) # x(1,256,40,40) - - -class Expand(nn.Module): - # Expand channels into width-height, i.e. x(1,64,80,80) to x(1,16,160,160) - def __init__(self, gain=2): - super().__init__() - self.gain = gain - - def forward(self, x): - b, c, h, w = x.size() # assert C / s ** 2 == 0, 'Indivisible gain' - s = self.gain - x = x.view(b, s, s, c // s ** 2, h, w) # x(1,2,2,16,80,80) - x = x.permute(0, 3, 4, 1, 5, 2).contiguous() # x(1,16,80,2,80,2) - return x.view(b, c // s ** 2, h * s, w * s) # x(1,16,160,160) - - -class Concat(nn.Module): - # Concatenate a list of tensors along dimension - def __init__(self, dimension=1): - super().__init__() - self.d = dimension - - def forward(self, x): - return torch.cat(x, self.d) - - -class DetectMultiBackend(nn.Module): - # YOLOv5 MultiBackend class for python inference on various backends - def __init__(self, weights='yolov5s.pt', device=None, dnn=False, data=None): - # Usage: - # PyTorch: weights = *.pt - # TorchScript: *.torchscript - # CoreML: *.mlmodel - # OpenVINO: *.xml - # TensorFlow: *_saved_model - # TensorFlow: *.pb - # TensorFlow Lite: *.tflite - # TensorFlow Edge TPU: *_edgetpu.tflite - # ONNX Runtime: *.onnx - # OpenCV DNN: *.onnx with dnn=True - # TensorRT: *.engine - from models.experimental import attempt_download, attempt_load # scoped to avoid circular import - - super().__init__() - w = str(weights[0] if isinstance(weights, list) else weights) - suffix = Path(w).suffix.lower() - suffixes = ['.pt', '.torchscript', '.onnx', '.engine', '.tflite', '.pb', '', '.mlmodel', '.xml'] - check_suffix(w, suffixes) # check weights have acceptable suffix - pt, jit, onnx, engine, tflite, pb, saved_model, coreml, xml = (suffix == x for x in suffixes) # backends - stride, names = 64, [f'class{i}' for i in range(1000)] # assign defaults - w = attempt_download(w) # download if not local - if data: # data.yaml path (optional) - with open(data, errors='ignore') as f: - names = yaml.safe_load(f)['names'] # class names - - if pt: # PyTorch - model = attempt_load(weights if isinstance(weights, list) else w, map_location=device) - stride = max(int(model.stride.max()), 32) # model stride - names = model.module.names if hasattr(model, 'module') else model.names # get class names - self.model = model # explicitly assign for to(), cpu(), cuda(), half() - elif jit: # TorchScript - LOGGER.info(f'Loading {w} for TorchScript inference...') - extra_files = {'config.txt': ''} # model metadata - model = torch.jit.load(w, _extra_files=extra_files) - if extra_files['config.txt']: - d = json.loads(extra_files['config.txt']) # extra_files dict - stride, names = int(d['stride']), d['names'] - elif dnn: # ONNX OpenCV DNN - LOGGER.info(f'Loading {w} for ONNX OpenCV DNN inference...') - check_requirements(('opencv-python>=4.5.4',)) - net = cv2.dnn.readNetFromONNX(w) - elif onnx: # ONNX Runtime - LOGGER.info(f'Loading {w} for ONNX Runtime inference...') - cuda = torch.cuda.is_available() - check_requirements(('onnx', 'onnxruntime-gpu' if cuda else 'onnxruntime')) - import onnxruntime - providers = ['CUDAExecutionProvider', 'CPUExecutionProvider'] if cuda else ['CPUExecutionProvider'] - session = onnxruntime.InferenceSession(w, providers=providers) - elif xml: # OpenVINO - LOGGER.info(f'Loading {w} for OpenVINO inference...') - check_requirements(('openvino-dev',)) # requires openvino-dev: https://pypi.org/project/openvino-dev/ - import openvino.inference_engine as ie - core = ie.IECore() - network = core.read_network(model=w, weights=Path(w).with_suffix('.bin')) # *.xml, *.bin paths - executable_network = core.load_network(network, device_name='CPU', num_requests=1) - elif engine: # TensorRT - LOGGER.info(f'Loading {w} for TensorRT inference...') - import tensorrt as trt # https://developer.nvidia.com/nvidia-tensorrt-download - check_version(trt.__version__, '7.0.0', hard=True) # require tensorrt>=7.0.0 - Binding = namedtuple('Binding', ('name', 'dtype', 'shape', 'data', 'ptr')) - logger = trt.Logger(trt.Logger.INFO) - with open(w, 'rb') as f, trt.Runtime(logger) as runtime: - model = runtime.deserialize_cuda_engine(f.read()) - bindings = OrderedDict() - for index in range(model.num_bindings): - name = model.get_binding_name(index) - dtype = trt.nptype(model.get_binding_dtype(index)) - shape = tuple(model.get_binding_shape(index)) - data = torch.from_numpy(np.empty(shape, dtype=np.dtype(dtype))).to(device) - bindings[name] = Binding(name, dtype, shape, data, int(data.data_ptr())) - binding_addrs = OrderedDict((n, d.ptr) for n, d in bindings.items()) - context = model.create_execution_context() - batch_size = bindings['images'].shape[0] - elif coreml: # CoreML - LOGGER.info(f'Loading {w} for CoreML inference...') - import coremltools as ct - model = ct.models.MLModel(w) - else: # TensorFlow (SavedModel, GraphDef, Lite, Edge TPU) - if saved_model: # SavedModel - LOGGER.info(f'Loading {w} for TensorFlow SavedModel inference...') - import tensorflow as tf - model = tf.keras.models.load_model(w) - elif pb: # GraphDef https://www.tensorflow.org/guide/migrate#a_graphpb_or_graphpbtxt - LOGGER.info(f'Loading {w} for TensorFlow GraphDef inference...') - import tensorflow as tf - - def wrap_frozen_graph(gd, inputs, outputs): - x = tf.compat.v1.wrap_function(lambda: tf.compat.v1.import_graph_def(gd, name=""), []) # wrapped - return x.prune(tf.nest.map_structure(x.graph.as_graph_element, inputs), - tf.nest.map_structure(x.graph.as_graph_element, outputs)) - - graph_def = tf.Graph().as_graph_def() - graph_def.ParseFromString(open(w, 'rb').read()) - frozen_func = wrap_frozen_graph(gd=graph_def, inputs="x:0", outputs="Identity:0") - elif tflite: # https://www.tensorflow.org/lite/guide/python#install_tensorflow_lite_for_python - if 'edgetpu' in w.lower(): # Edge TPU - LOGGER.info(f'Loading {w} for TensorFlow Lite Edge TPU inference...') - import tflite_runtime.interpreter as tfli # install https://coral.ai/software/#edgetpu-runtime - delegate = {'Linux': 'libedgetpu.so.1', - 'Darwin': 'libedgetpu.1.dylib', - 'Windows': 'edgetpu.dll'}[platform.system()] - interpreter = tfli.Interpreter(model_path=w, experimental_delegates=[tfli.load_delegate(delegate)]) - else: # Lite - LOGGER.info(f'Loading {w} for TensorFlow Lite inference...') - import tensorflow as tf - interpreter = tf.lite.Interpreter(model_path=w) # load TFLite model - interpreter.allocate_tensors() # allocate - input_details = interpreter.get_input_details() # inputs - output_details = interpreter.get_output_details() # outputs - self.__dict__.update(locals()) # assign all variables to self - - def forward(self, im, augment=False, visualize=False, val=False): - # YOLOv5 MultiBackend inference - b, ch, h, w = im.shape # batch, channel, height, width - if self.pt or self.jit: # PyTorch - y = self.model(im) if self.jit else self.model(im, augment=augment, visualize=visualize) - return y if val else y[0] - elif self.dnn: # ONNX OpenCV DNN - im = im.cpu().numpy() # torch to numpy - self.net.setInput(im) - y = self.net.forward() - elif self.onnx: # ONNX Runtime - im = im.cpu().numpy() # torch to numpy - y = self.session.run([self.session.get_outputs()[0].name], {self.session.get_inputs()[0].name: im})[0] - elif self.xml: # OpenVINO - im = im.cpu().numpy() # FP32 - desc = self.ie.TensorDesc(precision='FP32', dims=im.shape, layout='NCHW') # Tensor Description - request = self.executable_network.requests[0] # inference request - request.set_blob(blob_name='images', blob=self.ie.Blob(desc, im)) # name=next(iter(request.input_blobs)) - request.infer() - y = request.output_blobs['output'].buffer # name=next(iter(request.output_blobs)) - elif self.engine: # TensorRT - assert im.shape == self.bindings['images'].shape, (im.shape, self.bindings['images'].shape) - self.binding_addrs['images'] = int(im.data_ptr()) - self.context.execute_v2(list(self.binding_addrs.values())) - y = self.bindings['output'].data - elif self.coreml: # CoreML - im = im.permute(0, 2, 3, 1).cpu().numpy() # torch BCHW to numpy BHWC shape(1,320,192,3) - im = Image.fromarray((im[0] * 255).astype('uint8')) - # im = im.resize((192, 320), Image.ANTIALIAS) - y = self.model.predict({'image': im}) # coordinates are xywh normalized - if 'confidence' in y: - box = xywh2xyxy(y['coordinates'] * [[w, h, w, h]]) # xyxy pixels - conf, cls = y['confidence'].max(1), y['confidence'].argmax(1).astype(np.float) - y = np.concatenate((box, conf.reshape(-1, 1), cls.reshape(-1, 1)), 1) - else: - y = y[list(y)[-1]] # last output - else: # TensorFlow (SavedModel, GraphDef, Lite, Edge TPU) - im = im.permute(0, 2, 3, 1).cpu().numpy() # torch BCHW to numpy BHWC shape(1,320,192,3) - if self.saved_model: # SavedModel - y = self.model(im, training=False).numpy() - elif self.pb: # GraphDef - y = self.frozen_func(x=self.tf.constant(im)).numpy() - elif self.tflite: # Lite - input, output = self.input_details[0], self.output_details[0] - int8 = input['dtype'] == np.uint8 # is TFLite quantized uint8 model - if int8: - scale, zero_point = input['quantization'] - im = (im / scale + zero_point).astype(np.uint8) # de-scale - self.interpreter.set_tensor(input['index'], im) - self.interpreter.invoke() - y = self.interpreter.get_tensor(output['index']) - if int8: - scale, zero_point = output['quantization'] - y = (y.astype(np.float32) - zero_point) * scale # re-scale - y[..., 0] *= w # x - y[..., 1] *= h # y - y[..., 2] *= w # w - y[..., 3] *= h # h - - y = torch.tensor(y) if isinstance(y, np.ndarray) else y - return (y, []) if val else y - - def warmup(self, imgsz=(1, 3, 640, 640), half=False): - # Warmup model by running inference once - if self.pt or self.jit or self.onnx or self.engine: # warmup types - if isinstance(self.device, torch.device) and self.device.type != 'cpu': # only warmup GPU models - im = torch.zeros(*imgsz).to(self.device).type(torch.half if half else torch.float) # input image - self.forward(im) # warmup - - -class AutoShape(nn.Module): - # YOLOv5 input-robust model wrapper for passing cv2/np/PIL/torch inputs. Includes preprocessing, inference and NMS - conf = 0.25 # NMS confidence threshold - iou = 0.45 # NMS IoU threshold - agnostic = False # NMS class-agnostic - multi_label = False # NMS multiple labels per box - classes = None # (optional list) filter by class, i.e. = [0, 15, 16] for COCO persons, cats and dogs - max_det = 1000 # maximum number of detections per image - amp = False # Automatic Mixed Precision (AMP) inference - - def __init__(self, model): - super().__init__() - LOGGER.info('Adding AutoShape... ') - copy_attr(self, model, include=('yaml', 'nc', 'hyp', 'names', 'stride', 'abc'), exclude=()) # copy attributes - self.dmb = isinstance(model, DetectMultiBackend) # DetectMultiBackend() instance - self.pt = not self.dmb or model.pt # PyTorch model - self.model = model.eval() - - def _apply(self, fn): - # Apply to(), cpu(), cuda(), half() to model tensors that are not parameters or registered buffers - self = super()._apply(fn) - if self.pt: - m = self.model.model.model[-1] if self.dmb else self.model.model[-1] # Detect() - m.stride = fn(m.stride) - m.grid = list(map(fn, m.grid)) - if isinstance(m.anchor_grid, list): - m.anchor_grid = list(map(fn, m.anchor_grid)) - return self - - @torch.no_grad() - def forward(self, imgs, size=640, augment=False, profile=False): - # Inference from various sources. For height=640, width=1280, RGB images example inputs are: - # file: imgs = 'data/images/zidane.jpg' # str or PosixPath - # URI: = 'https://ultralytics.com/images/zidane.jpg' - # OpenCV: = cv2.imread('image.jpg')[:,:,::-1] # HWC BGR to RGB x(640,1280,3) - # PIL: = Image.open('image.jpg') or ImageGrab.grab() # HWC x(640,1280,3) - # numpy: = np.zeros((640,1280,3)) # HWC - # torch: = torch.zeros(16,3,320,640) # BCHW (scaled to size=640, 0-1 values) - # multiple: = [Image.open('image1.jpg'), Image.open('image2.jpg'), ...] # list of images - - t = [time_sync()] - p = next(self.model.parameters()) if self.pt else torch.zeros(1) # for device and type - autocast = self.amp and (p.device.type != 'cpu') # Automatic Mixed Precision (AMP) inference - if isinstance(imgs, torch.Tensor): # torch - with amp.autocast(enabled=autocast): - 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, files = [], [], [] # image and inference shapes, filenames - for i, im in enumerate(imgs): - f = f'image{i}' # filename - if isinstance(im, (str, Path)): # filename or uri - im, f = Image.open(requests.get(im, stream=True).raw if str(im).startswith('http') else im), im - im = np.asarray(exif_transpose(im)) - elif isinstance(im, Image.Image): # PIL Image - im, f = np.asarray(exif_transpose(im)), getattr(im, 'filename', f) or f - files.append(Path(f).with_suffix('.jpg').name) - if im.shape[0] < 5: # image in CHW - im = im.transpose((1, 2, 0)) # reverse dataloader .transpose(2, 0, 1) - im = im[..., :3] if im.ndim == 3 else np.tile(im[..., None], 3) # enforce 3ch input - s = im.shape[:2] # HWC - shape0.append(s) # image shape - g = (size / max(s)) # gain - shape1.append([y * g for y in s]) - imgs[i] = im if im.data.contiguous else np.ascontiguousarray(im) # update - shape1 = [make_divisible(x, self.stride) for x in np.stack(shape1, 0).max(0)] # inference shape - x = [letterbox(im, new_shape=shape1 if self.pt else size, auto=False)[0] for im in imgs] # pad - x = np.stack(x, 0) if n > 1 else x[0][None] # stack - x = np.ascontiguousarray(x.transpose((0, 3, 1, 2))) # BHWC to BCHW - x = torch.from_numpy(x).to(p.device).type_as(p) / 255 # uint8 to fp16/32 - t.append(time_sync()) - - with amp.autocast(enabled=autocast): - # Inference - y = self.model(x, augment, profile) # forward - t.append(time_sync()) - - # Post-process - y = non_max_suppression(y if self.dmb else y[0], self.conf, iou_thres=self.iou, classes=self.classes, - agnostic=self.agnostic, multi_label=self.multi_label, max_det=self.max_det) # NMS - for i in range(n): - scale_coords(shape1, y[i][:, :4], shape0[i]) - - t.append(time_sync()) - return Detections(imgs, y, files, t, self.names, x.shape) - - -class Detections: - # YOLOv5 detections class for inference results - def __init__(self, imgs, pred, files, times=(0, 0, 0, 0), names=None, shape=None): - super().__init__() - d = pred[0].device # device - gn = [torch.tensor([*(im.shape[i] for i in [1, 0, 1, 0]), 1, 1], device=d) for im in imgs] # normalizations - self.imgs = imgs # list of images as numpy arrays - self.pred = pred # list of tensors pred[0] = (xyxy, conf, cls) - self.names = names # class names - self.files = files # image filenames - self.times = times # profiling times - self.xyxy = pred # xyxy pixels - self.xywh = [xyxy2xywh(x) for x in pred] # xywh pixels - self.xyxyn = [x / g for x, g in zip(self.xyxy, gn)] # xyxy normalized - self.xywhn = [x / g for x, g in zip(self.xywh, gn)] # xywh normalized - self.n = len(self.pred) # number of images (batch size) - self.t = tuple((times[i + 1] - times[i]) * 1000 / self.n for i in range(3)) # timestamps (ms) - self.s = shape # inference BCHW shape - - def display(self, pprint=False, show=False, save=False, crop=False, render=False, save_dir=Path('')): - crops = [] - for i, (im, pred) in enumerate(zip(self.imgs, self.pred)): - s = f'image {i + 1}/{len(self.pred)}: {im.shape[0]}x{im.shape[1]} ' # string - if pred.shape[0]: - for c in pred[:, -1].unique(): - n = (pred[:, -1] == c).sum() # detections per class - s += f"{n} {self.names[int(c)]}{'s' * (n > 1)}, " # add to string - if show or save or render or crop: - annotator = Annotator(im, example=str(self.names)) - for *box, conf, cls in reversed(pred): # xyxy, confidence, class - label = f'{self.names[int(cls)]} {conf:.2f}' - if crop: - file = save_dir / 'crops' / self.names[int(cls)] / self.files[i] if save else None - crops.append({'box': box, 'conf': conf, 'cls': cls, 'label': label, - 'im': save_one_box(box, im, file=file, save=save)}) - else: # all others - annotator.box_label(box, label, color=colors(cls)) - im = annotator.im - else: - s += '(no detections)' - - im = Image.fromarray(im.astype(np.uint8)) if isinstance(im, np.ndarray) else im # from np - if pprint: - LOGGER.info(s.rstrip(', ')) - if show: - im.show(self.files[i]) # show - if save: - f = self.files[i] - im.save(save_dir / f) # save - if i == self.n - 1: - LOGGER.info(f"Saved {self.n} image{'s' * (self.n > 1)} to {colorstr('bold', save_dir)}") - if render: - self.imgs[i] = np.asarray(im) - if crop: - if save: - LOGGER.info(f'Saved results to {save_dir}\n') - return crops - - def print(self): - self.display(pprint=True) # print results - LOGGER.info(f'Speed: %.1fms pre-process, %.1fms inference, %.1fms NMS per image at shape {tuple(self.s)}' % - self.t) - - def show(self): - self.display(show=True) # show results - - def save(self, save_dir='runs/detect/exp'): - save_dir = increment_path(save_dir, exist_ok=save_dir != 'runs/detect/exp', mkdir=True) # increment save_dir - self.display(save=True, save_dir=save_dir) # save results - - def crop(self, save=True, save_dir='runs/detect/exp'): - save_dir = increment_path(save_dir, exist_ok=save_dir != 'runs/detect/exp', mkdir=True) if save else None - return self.display(crop=True, save=save, save_dir=save_dir) # crop results - - def render(self): - self.display(render=True) # render results - return self.imgs - - def pandas(self): - # return detections as pandas DataFrames, i.e. print(results.pandas().xyxy[0]) - new = copy(self) # return copy - ca = 'xmin', 'ymin', 'xmax', 'ymax', 'confidence', 'class', 'name' # xyxy columns - cb = 'xcenter', 'ycenter', 'width', 'height', 'confidence', 'class', 'name' # xywh columns - for k, c in zip(['xyxy', 'xyxyn', 'xywh', 'xywhn'], [ca, ca, cb, cb]): - a = [[x[:5] + [int(x[5]), self.names[int(x[5])]] for x in x.tolist()] for x in getattr(self, k)] # update - setattr(new, k, [pd.DataFrame(x, columns=c) for x in a]) - return new - - def tolist(self): - # return a list of Detections objects, i.e. 'for result in results.tolist():' - r = range(self.n) # iterable - x = [Detections([self.imgs[i]], [self.pred[i]], [self.files[i]], self.times, self.names, self.s) for i in r] - # for d in x: - # for k in ['imgs', 'pred', 'xyxy', 'xyxyn', 'xywh', 'xywhn']: - # setattr(d, k, getattr(d, k)[0]) # pop out of list - return x - - def __len__(self): - return self.n - - -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().__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 = nn.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/YOLO/Dockerfiles/app/app/models/experimental.py b/YOLO/Dockerfiles/app/app/models/experimental.py deleted file mode 100644 index 463e551..0000000 --- a/YOLO/Dockerfiles/app/app/models/experimental.py +++ /dev/null @@ -1,120 +0,0 @@ -# YOLOv5 🚀 by Ultralytics, GPL-3.0 license -""" -Experimental modules -""" -import math - -import numpy as np -import torch -import torch.nn as nn - -from models.common import Conv -from utils.downloads import attempt_download - - -class CrossConv(nn.Module): - # Cross Convolution Downsample - def __init__(self, c1, c2, k=3, s=1, g=1, e=1.0, shortcut=False): - # ch_in, ch_out, kernel, stride, groups, expansion, shortcut - super().__init__() - c_ = int(c2 * e) # hidden channels - self.cv1 = Conv(c1, c_, (1, k), (1, s)) - self.cv2 = Conv(c_, c2, (k, 1), (s, 1), g=g) - self.add = shortcut and c1 == c2 - - def forward(self, x): - return x + self.cv2(self.cv1(x)) if self.add else self.cv2(self.cv1(x)) - - -class Sum(nn.Module): - # Weighted sum of 2 or more layers https://arxiv.org/abs/1911.09070 - def __init__(self, n, weight=False): # n: number of inputs - super().__init__() - self.weight = weight # apply weights boolean - self.iter = range(n - 1) # iter object - if weight: - self.w = nn.Parameter(-torch.arange(1.0, 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 MixConv2d(nn.Module): - # Mixed Depth-wise Conv https://arxiv.org/abs/1907.09595 - def __init__(self, c1, c2, k=(1, 3), s=1, equal_ch=True): # ch_in, ch_out, kernel, stride, ch_strategy - super().__init__() - n = len(k) # number of convolutions - if equal_ch: # equal c_ per group - i = torch.linspace(0, n - 1E-6, c2).floor() # c2 indices - c_ = [(i == g).sum() for g in range(n)] # intermediate channels - else: # equal weight.numel() per group - b = [c2] + [0] * n - a = np.eye(n + 1, n, 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_), k, s, k // 2, groups=math.gcd(c1, int(c_)), bias=False) for k, c_ in zip(k, c_)]) - self.bn = nn.BatchNorm2d(c2) - self.act = nn.SiLU() - - def forward(self, x): - return 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().__init__() - - def forward(self, x, augment=False, profile=False, visualize=False): - y = [] - for module in self: - y.append(module(x, augment, profile, visualize)[0]) - # y = torch.stack(y).max(0)[0] # max ensemble - # y = torch.stack(y).mean(0) # mean ensemble - y = torch.cat(y, 1) # nms ensemble - return y, None # inference, train output - - -def attempt_load(weights, map_location=None, inplace=True, fuse=True): - from models.yolo import Detect, Model - - # Loads an ensemble of models weights=[a,b,c] or a single model weights=[a] or weights=a - model = Ensemble() - for w in weights if isinstance(weights, list) else [weights]: - ckpt = torch.load(attempt_download(w), map_location=map_location) # load - if fuse: - model.append(ckpt['ema' if ckpt.get('ema') else 'model'].float().fuse().eval()) # FP32 model - else: - model.append(ckpt['ema' if ckpt.get('ema') else 'model'].float().eval()) # without layer fuse - - # Compatibility updates - for m in model.modules(): - if type(m) in [nn.Hardswish, nn.LeakyReLU, nn.ReLU, nn.ReLU6, nn.SiLU, Detect, Model]: - m.inplace = inplace # pytorch 1.7.0 compatibility - if type(m) is Detect: - if not isinstance(m.anchor_grid, list): # new Detect Layer compatibility - delattr(m, 'anchor_grid') - setattr(m, 'anchor_grid', [torch.zeros(1)] * m.nl) - elif 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(f'Ensemble created with {weights}\n') - for k in ['names']: - setattr(model, k, getattr(model[-1], k)) - model.stride = model[torch.argmax(torch.tensor([m.stride.max() for m in model])).int()].stride # max stride - return model # return ensemble diff --git a/YOLO/Dockerfiles/app/app/models/hub/anchors.yaml b/YOLO/Dockerfiles/app/app/models/hub/anchors.yaml deleted file mode 100644 index e4d7beb..0000000 --- a/YOLO/Dockerfiles/app/app/models/hub/anchors.yaml +++ /dev/null @@ -1,59 +0,0 @@ -# YOLOv5 🚀 by Ultralytics, GPL-3.0 license -# Default anchors for COCO data - - -# P5 ------------------------------------------------------------------------------------------------------------------- -# P5-640: -anchors_p5_640: - - [10,13, 16,30, 33,23] # P3/8 - - [30,61, 62,45, 59,119] # P4/16 - - [116,90, 156,198, 373,326] # P5/32 - - -# P6 ------------------------------------------------------------------------------------------------------------------- -# P6-640: thr=0.25: 0.9964 BPR, 5.54 anchors past thr, n=12, img_size=640, metric_all=0.281/0.716-mean/best, past_thr=0.469-mean: 9,11, 21,19, 17,41, 43,32, 39,70, 86,64, 65,131, 134,130, 120,265, 282,180, 247,354, 512,387 -anchors_p6_640: - - [9,11, 21,19, 17,41] # P3/8 - - [43,32, 39,70, 86,64] # P4/16 - - [65,131, 134,130, 120,265] # P5/32 - - [282,180, 247,354, 512,387] # P6/64 - -# P6-1280: thr=0.25: 0.9950 BPR, 5.55 anchors past thr, n=12, img_size=1280, metric_all=0.281/0.714-mean/best, past_thr=0.468-mean: 19,27, 44,40, 38,94, 96,68, 86,152, 180,137, 140,301, 303,264, 238,542, 436,615, 739,380, 925,792 -anchors_p6_1280: - - [19,27, 44,40, 38,94] # P3/8 - - [96,68, 86,152, 180,137] # P4/16 - - [140,301, 303,264, 238,542] # P5/32 - - [436,615, 739,380, 925,792] # P6/64 - -# P6-1920: thr=0.25: 0.9950 BPR, 5.55 anchors past thr, n=12, img_size=1920, metric_all=0.281/0.714-mean/best, past_thr=0.468-mean: 28,41, 67,59, 57,141, 144,103, 129,227, 270,205, 209,452, 455,396, 358,812, 653,922, 1109,570, 1387,1187 -anchors_p6_1920: - - [28,41, 67,59, 57,141] # P3/8 - - [144,103, 129,227, 270,205] # P4/16 - - [209,452, 455,396, 358,812] # P5/32 - - [653,922, 1109,570, 1387,1187] # P6/64 - - -# P7 ------------------------------------------------------------------------------------------------------------------- -# P7-640: thr=0.25: 0.9962 BPR, 6.76 anchors past thr, n=15, img_size=640, metric_all=0.275/0.733-mean/best, past_thr=0.466-mean: 11,11, 13,30, 29,20, 30,46, 61,38, 39,92, 78,80, 146,66, 79,163, 149,150, 321,143, 157,303, 257,402, 359,290, 524,372 -anchors_p7_640: - - [11,11, 13,30, 29,20] # P3/8 - - [30,46, 61,38, 39,92] # P4/16 - - [78,80, 146,66, 79,163] # P5/32 - - [149,150, 321,143, 157,303] # P6/64 - - [257,402, 359,290, 524,372] # P7/128 - -# P7-1280: thr=0.25: 0.9968 BPR, 6.71 anchors past thr, n=15, img_size=1280, metric_all=0.273/0.732-mean/best, past_thr=0.463-mean: 19,22, 54,36, 32,77, 70,83, 138,71, 75,173, 165,159, 148,334, 375,151, 334,317, 251,626, 499,474, 750,326, 534,814, 1079,818 -anchors_p7_1280: - - [19,22, 54,36, 32,77] # P3/8 - - [70,83, 138,71, 75,173] # P4/16 - - [165,159, 148,334, 375,151] # P5/32 - - [334,317, 251,626, 499,474] # P6/64 - - [750,326, 534,814, 1079,818] # P7/128 - -# P7-1920: thr=0.25: 0.9968 BPR, 6.71 anchors past thr, n=15, img_size=1920, metric_all=0.273/0.732-mean/best, past_thr=0.463-mean: 29,34, 81,55, 47,115, 105,124, 207,107, 113,259, 247,238, 222,500, 563,227, 501,476, 376,939, 749,711, 1126,489, 801,1222, 1618,1227 -anchors_p7_1920: - - [29,34, 81,55, 47,115] # P3/8 - - [105,124, 207,107, 113,259] # P4/16 - - [247,238, 222,500, 563,227] # P5/32 - - [501,476, 376,939, 749,711] # P6/64 - - [1126,489, 801,1222, 1618,1227] # P7/128 diff --git a/YOLO/Dockerfiles/app/app/models/hub/yolov3-spp.yaml b/YOLO/Dockerfiles/app/app/models/hub/yolov3-spp.yaml deleted file mode 100644 index c669821..0000000 --- a/YOLO/Dockerfiles/app/app/models/hub/yolov3-spp.yaml +++ /dev/null @@ -1,51 +0,0 @@ -# YOLOv5 🚀 by Ultralytics, GPL-3.0 license - -# Parameters -nc: 80 # number of classes -depth_multiple: 1.0 # model depth multiple -width_multiple: 1.0 # layer channel multiple -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/YOLO/Dockerfiles/app/app/models/hub/yolov3-tiny.yaml b/YOLO/Dockerfiles/app/app/models/hub/yolov3-tiny.yaml deleted file mode 100644 index b28b443..0000000 --- a/YOLO/Dockerfiles/app/app/models/hub/yolov3-tiny.yaml +++ /dev/null @@ -1,41 +0,0 @@ -# YOLOv5 🚀 by Ultralytics, GPL-3.0 license - -# Parameters -nc: 80 # number of classes -depth_multiple: 1.0 # model depth multiple -width_multiple: 1.0 # layer channel multiple -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/YOLO/Dockerfiles/app/app/models/hub/yolov3.yaml b/YOLO/Dockerfiles/app/app/models/hub/yolov3.yaml deleted file mode 100644 index 4f4b240..0000000 --- a/YOLO/Dockerfiles/app/app/models/hub/yolov3.yaml +++ /dev/null @@ -1,51 +0,0 @@ -# YOLOv5 🚀 by Ultralytics, GPL-3.0 license - -# Parameters -nc: 80 # number of classes -depth_multiple: 1.0 # model depth multiple -width_multiple: 1.0 # layer channel multiple -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/YOLO/Dockerfiles/app/app/models/hub/yolov5-bifpn.yaml b/YOLO/Dockerfiles/app/app/models/hub/yolov5-bifpn.yaml deleted file mode 100644 index 504815f..0000000 --- a/YOLO/Dockerfiles/app/app/models/hub/yolov5-bifpn.yaml +++ /dev/null @@ -1,48 +0,0 @@ -# YOLOv5 🚀 by Ultralytics, GPL-3.0 license - -# Parameters -nc: 80 # number of classes -depth_multiple: 1.0 # model depth multiple -width_multiple: 1.0 # layer channel multiple -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 v6.0 backbone -backbone: - # [from, number, module, args] - [[-1, 1, Conv, [64, 6, 2, 2]], # 0-P1/2 - [-1, 1, Conv, [128, 3, 2]], # 1-P2/4 - [-1, 3, C3, [128]], - [-1, 1, Conv, [256, 3, 2]], # 3-P3/8 - [-1, 6, C3, [256]], - [-1, 1, Conv, [512, 3, 2]], # 5-P4/16 - [-1, 9, C3, [512]], - [-1, 1, Conv, [1024, 3, 2]], # 7-P5/32 - [-1, 3, C3, [1024]], - [-1, 1, SPPF, [1024, 5]], # 9 - ] - -# YOLOv5 v6.0 BiFPN 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, C3, [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, C3, [256, False]], # 17 (P3/8-small) - - [-1, 1, Conv, [256, 3, 2]], - [[-1, 14, 6], 1, Concat, [1]], # cat P4 <--- BiFPN change - [-1, 3, C3, [512, False]], # 20 (P4/16-medium) - - [-1, 1, Conv, [512, 3, 2]], - [[-1, 10], 1, Concat, [1]], # cat head P5 - [-1, 3, C3, [1024, False]], # 23 (P5/32-large) - - [[17, 20, 23], 1, Detect, [nc, anchors]], # Detect(P3, P4, P5) - ] diff --git a/YOLO/Dockerfiles/app/app/models/hub/yolov5-fpn.yaml b/YOLO/Dockerfiles/app/app/models/hub/yolov5-fpn.yaml deleted file mode 100644 index a23e9c6..0000000 --- a/YOLO/Dockerfiles/app/app/models/hub/yolov5-fpn.yaml +++ /dev/null @@ -1,42 +0,0 @@ -# YOLOv5 🚀 by Ultralytics, GPL-3.0 license - -# Parameters -nc: 80 # number of classes -depth_multiple: 1.0 # model depth multiple -width_multiple: 1.0 # layer channel multiple -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 v6.0 backbone -backbone: - # [from, number, module, args] - [[-1, 1, Conv, [64, 6, 2, 2]], # 0-P1/2 - [-1, 1, Conv, [128, 3, 2]], # 1-P2/4 - [-1, 3, C3, [128]], - [-1, 1, Conv, [256, 3, 2]], # 3-P3/8 - [-1, 6, C3, [256]], - [-1, 1, Conv, [512, 3, 2]], # 5-P4/16 - [-1, 9, C3, [512]], - [-1, 1, Conv, [1024, 3, 2]], # 7-P5/32 - [-1, 3, C3, [1024]], - [-1, 1, SPPF, [1024, 5]], # 9 - ] - -# YOLOv5 v6.0 FPN head -head: - [[-1, 3, C3, [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, C3, [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, C3, [256, False]], # 18 (P3/8-small) - - [[18, 14, 10], 1, Detect, [nc, anchors]], # Detect(P3, P4, P5) - ] diff --git a/YOLO/Dockerfiles/app/app/models/hub/yolov5-p2.yaml b/YOLO/Dockerfiles/app/app/models/hub/yolov5-p2.yaml deleted file mode 100644 index 554117d..0000000 --- a/YOLO/Dockerfiles/app/app/models/hub/yolov5-p2.yaml +++ /dev/null @@ -1,54 +0,0 @@ -# YOLOv5 🚀 by Ultralytics, GPL-3.0 license - -# Parameters -nc: 80 # number of classes -depth_multiple: 1.0 # model depth multiple -width_multiple: 1.0 # layer channel multiple -anchors: 3 # AutoAnchor evolves 3 anchors per P output layer - -# YOLOv5 v6.0 backbone -backbone: - # [from, number, module, args] - [[-1, 1, Conv, [64, 6, 2, 2]], # 0-P1/2 - [-1, 1, Conv, [128, 3, 2]], # 1-P2/4 - [-1, 3, C3, [128]], - [-1, 1, Conv, [256, 3, 2]], # 3-P3/8 - [-1, 6, C3, [256]], - [-1, 1, Conv, [512, 3, 2]], # 5-P4/16 - [-1, 9, C3, [512]], - [-1, 1, Conv, [1024, 3, 2]], # 7-P5/32 - [-1, 3, C3, [1024]], - [-1, 1, SPPF, [1024, 5]], # 9 - ] - -# YOLOv5 v6.0 head with (P2, P3, P4, P5) outputs -head: - [[-1, 1, Conv, [512, 1, 1]], - [-1, 1, nn.Upsample, [None, 2, 'nearest']], - [[-1, 6], 1, Concat, [1]], # cat backbone P4 - [-1, 3, C3, [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, C3, [256, False]], # 17 (P3/8-small) - - [-1, 1, Conv, [128, 1, 1]], - [-1, 1, nn.Upsample, [None, 2, 'nearest']], - [[-1, 2], 1, Concat, [1]], # cat backbone P2 - [-1, 1, C3, [128, False]], # 21 (P2/4-xsmall) - - [-1, 1, Conv, [128, 3, 2]], - [[-1, 18], 1, Concat, [1]], # cat head P3 - [-1, 3, C3, [256, False]], # 24 (P3/8-small) - - [-1, 1, Conv, [256, 3, 2]], - [[-1, 14], 1, Concat, [1]], # cat head P4 - [-1, 3, C3, [512, False]], # 27 (P4/16-medium) - - [-1, 1, Conv, [512, 3, 2]], - [[-1, 10], 1, Concat, [1]], # cat head P5 - [-1, 3, C3, [1024, False]], # 30 (P5/32-large) - - [[21, 24, 27, 30], 1, Detect, [nc, anchors]], # Detect(P2, P3, P4, P5) - ] diff --git a/YOLO/Dockerfiles/app/app/models/hub/yolov5-p34.yaml b/YOLO/Dockerfiles/app/app/models/hub/yolov5-p34.yaml deleted file mode 100644 index dbf0f85..0000000 --- a/YOLO/Dockerfiles/app/app/models/hub/yolov5-p34.yaml +++ /dev/null @@ -1,41 +0,0 @@ -# YOLOv5 🚀 by Ultralytics, GPL-3.0 license - -# Parameters -nc: 80 # number of classes -depth_multiple: 0.33 # model depth multiple -width_multiple: 0.50 # layer channel multiple -anchors: 3 # AutoAnchor evolves 3 anchors per P output layer - -# YOLOv5 v6.0 backbone -backbone: - # [from, number, module, args] - [ [ -1, 1, Conv, [ 64, 6, 2, 2 ] ], # 0-P1/2 - [ -1, 1, Conv, [ 128, 3, 2 ] ], # 1-P2/4 - [ -1, 3, C3, [ 128 ] ], - [ -1, 1, Conv, [ 256, 3, 2 ] ], # 3-P3/8 - [ -1, 6, C3, [ 256 ] ], - [ -1, 1, Conv, [ 512, 3, 2 ] ], # 5-P4/16 - [ -1, 9, C3, [ 512 ] ], - [ -1, 1, Conv, [ 1024, 3, 2 ] ], # 7-P5/32 - [ -1, 3, C3, [ 1024 ] ], - [ -1, 1, SPPF, [ 1024, 5 ] ], # 9 - ] - -# YOLOv5 v6.0 head with (P3, P4) outputs -head: - [ [ -1, 1, Conv, [ 512, 1, 1 ] ], - [ -1, 1, nn.Upsample, [ None, 2, 'nearest' ] ], - [ [ -1, 6 ], 1, Concat, [ 1 ] ], # cat backbone P4 - [ -1, 3, C3, [ 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, C3, [ 256, False ] ], # 17 (P3/8-small) - - [ -1, 1, Conv, [ 256, 3, 2 ] ], - [ [ -1, 14 ], 1, Concat, [ 1 ] ], # cat head P4 - [ -1, 3, C3, [ 512, False ] ], # 20 (P4/16-medium) - - [ [ 17, 20 ], 1, Detect, [ nc, anchors ] ], # Detect(P3, P4) - ] diff --git a/YOLO/Dockerfiles/app/app/models/hub/yolov5-p6.yaml b/YOLO/Dockerfiles/app/app/models/hub/yolov5-p6.yaml deleted file mode 100644 index a17202f..0000000 --- a/YOLO/Dockerfiles/app/app/models/hub/yolov5-p6.yaml +++ /dev/null @@ -1,56 +0,0 @@ -# YOLOv5 🚀 by Ultralytics, GPL-3.0 license - -# Parameters -nc: 80 # number of classes -depth_multiple: 1.0 # model depth multiple -width_multiple: 1.0 # layer channel multiple -anchors: 3 # AutoAnchor evolves 3 anchors per P output layer - -# YOLOv5 v6.0 backbone -backbone: - # [from, number, module, args] - [[-1, 1, Conv, [64, 6, 2, 2]], # 0-P1/2 - [-1, 1, Conv, [128, 3, 2]], # 1-P2/4 - [-1, 3, C3, [128]], - [-1, 1, Conv, [256, 3, 2]], # 3-P3/8 - [-1, 6, C3, [256]], - [-1, 1, Conv, [512, 3, 2]], # 5-P4/16 - [-1, 9, C3, [512]], - [-1, 1, Conv, [768, 3, 2]], # 7-P5/32 - [-1, 3, C3, [768]], - [-1, 1, Conv, [1024, 3, 2]], # 9-P6/64 - [-1, 3, C3, [1024]], - [-1, 1, SPPF, [1024, 5]], # 11 - ] - -# YOLOv5 v6.0 head with (P3, P4, P5, P6) outputs -head: - [[-1, 1, Conv, [768, 1, 1]], - [-1, 1, nn.Upsample, [None, 2, 'nearest']], - [[-1, 8], 1, Concat, [1]], # cat backbone P5 - [-1, 3, C3, [768, False]], # 15 - - [-1, 1, Conv, [512, 1, 1]], - [-1, 1, nn.Upsample, [None, 2, 'nearest']], - [[-1, 6], 1, Concat, [1]], # cat backbone P4 - [-1, 3, C3, [512, False]], # 19 - - [-1, 1, Conv, [256, 1, 1]], - [-1, 1, nn.Upsample, [None, 2, 'nearest']], - [[-1, 4], 1, Concat, [1]], # cat backbone P3 - [-1, 3, C3, [256, False]], # 23 (P3/8-small) - - [-1, 1, Conv, [256, 3, 2]], - [[-1, 20], 1, Concat, [1]], # cat head P4 - [-1, 3, C3, [512, False]], # 26 (P4/16-medium) - - [-1, 1, Conv, [512, 3, 2]], - [[-1, 16], 1, Concat, [1]], # cat head P5 - [-1, 3, C3, [768, False]], # 29 (P5/32-large) - - [-1, 1, Conv, [768, 3, 2]], - [[-1, 12], 1, Concat, [1]], # cat head P6 - [-1, 3, C3, [1024, False]], # 32 (P6/64-xlarge) - - [[23, 26, 29, 32], 1, Detect, [nc, anchors]], # Detect(P3, P4, P5, P6) - ] diff --git a/YOLO/Dockerfiles/app/app/models/hub/yolov5-p7.yaml b/YOLO/Dockerfiles/app/app/models/hub/yolov5-p7.yaml deleted file mode 100644 index edd7d13..0000000 --- a/YOLO/Dockerfiles/app/app/models/hub/yolov5-p7.yaml +++ /dev/null @@ -1,67 +0,0 @@ -# YOLOv5 🚀 by Ultralytics, GPL-3.0 license - -# Parameters -nc: 80 # number of classes -depth_multiple: 1.0 # model depth multiple -width_multiple: 1.0 # layer channel multiple -anchors: 3 # AutoAnchor evolves 3 anchors per P output layer - -# YOLOv5 v6.0 backbone -backbone: - # [from, number, module, args] - [[-1, 1, Conv, [64, 6, 2, 2]], # 0-P1/2 - [-1, 1, Conv, [128, 3, 2]], # 1-P2/4 - [-1, 3, C3, [128]], - [-1, 1, Conv, [256, 3, 2]], # 3-P3/8 - [-1, 6, C3, [256]], - [-1, 1, Conv, [512, 3, 2]], # 5-P4/16 - [-1, 9, C3, [512]], - [-1, 1, Conv, [768, 3, 2]], # 7-P5/32 - [-1, 3, C3, [768]], - [-1, 1, Conv, [1024, 3, 2]], # 9-P6/64 - [-1, 3, C3, [1024]], - [-1, 1, Conv, [1280, 3, 2]], # 11-P7/128 - [-1, 3, C3, [1280]], - [-1, 1, SPPF, [1280, 5]], # 13 - ] - -# YOLOv5 v6.0 head with (P3, P4, P5, P6, P7) outputs -head: - [[-1, 1, Conv, [1024, 1, 1]], - [-1, 1, nn.Upsample, [None, 2, 'nearest']], - [[-1, 10], 1, Concat, [1]], # cat backbone P6 - [-1, 3, C3, [1024, False]], # 17 - - [-1, 1, Conv, [768, 1, 1]], - [-1, 1, nn.Upsample, [None, 2, 'nearest']], - [[-1, 8], 1, Concat, [1]], # cat backbone P5 - [-1, 3, C3, [768, False]], # 21 - - [-1, 1, Conv, [512, 1, 1]], - [-1, 1, nn.Upsample, [None, 2, 'nearest']], - [[-1, 6], 1, Concat, [1]], # cat backbone P4 - [-1, 3, C3, [512, False]], # 25 - - [-1, 1, Conv, [256, 1, 1]], - [-1, 1, nn.Upsample, [None, 2, 'nearest']], - [[-1, 4], 1, Concat, [1]], # cat backbone P3 - [-1, 3, C3, [256, False]], # 29 (P3/8-small) - - [-1, 1, Conv, [256, 3, 2]], - [[-1, 26], 1, Concat, [1]], # cat head P4 - [-1, 3, C3, [512, False]], # 32 (P4/16-medium) - - [-1, 1, Conv, [512, 3, 2]], - [[-1, 22], 1, Concat, [1]], # cat head P5 - [-1, 3, C3, [768, False]], # 35 (P5/32-large) - - [-1, 1, Conv, [768, 3, 2]], - [[-1, 18], 1, Concat, [1]], # cat head P6 - [-1, 3, C3, [1024, False]], # 38 (P6/64-xlarge) - - [-1, 1, Conv, [1024, 3, 2]], - [[-1, 14], 1, Concat, [1]], # cat head P7 - [-1, 3, C3, [1280, False]], # 41 (P7/128-xxlarge) - - [[29, 32, 35, 38, 41], 1, Detect, [nc, anchors]], # Detect(P3, P4, P5, P6, P7) - ] diff --git a/YOLO/Dockerfiles/app/app/models/hub/yolov5-panet.yaml b/YOLO/Dockerfiles/app/app/models/hub/yolov5-panet.yaml deleted file mode 100644 index ccfbf90..0000000 --- a/YOLO/Dockerfiles/app/app/models/hub/yolov5-panet.yaml +++ /dev/null @@ -1,48 +0,0 @@ -# YOLOv5 🚀 by Ultralytics, GPL-3.0 license - -# Parameters -nc: 80 # number of classes -depth_multiple: 1.0 # model depth multiple -width_multiple: 1.0 # layer channel multiple -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 v6.0 backbone -backbone: - # [from, number, module, args] - [[-1, 1, Conv, [64, 6, 2, 2]], # 0-P1/2 - [-1, 1, Conv, [128, 3, 2]], # 1-P2/4 - [-1, 3, C3, [128]], - [-1, 1, Conv, [256, 3, 2]], # 3-P3/8 - [-1, 6, C3, [256]], - [-1, 1, Conv, [512, 3, 2]], # 5-P4/16 - [-1, 9, C3, [512]], - [-1, 1, Conv, [1024, 3, 2]], # 7-P5/32 - [-1, 3, C3, [1024]], - [-1, 1, SPPF, [1024, 5]], # 9 - ] - -# YOLOv5 v6.0 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, C3, [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, C3, [256, False]], # 17 (P3/8-small) - - [-1, 1, Conv, [256, 3, 2]], - [[-1, 14], 1, Concat, [1]], # cat head P4 - [-1, 3, C3, [512, False]], # 20 (P4/16-medium) - - [-1, 1, Conv, [512, 3, 2]], - [[-1, 10], 1, Concat, [1]], # cat head P5 - [-1, 3, C3, [1024, False]], # 23 (P5/32-large) - - [[17, 20, 23], 1, Detect, [nc, anchors]], # Detect(P3, P4, P5) - ] diff --git a/YOLO/Dockerfiles/app/app/models/hub/yolov5l6.yaml b/YOLO/Dockerfiles/app/app/models/hub/yolov5l6.yaml deleted file mode 100644 index 632c2cb..0000000 --- a/YOLO/Dockerfiles/app/app/models/hub/yolov5l6.yaml +++ /dev/null @@ -1,60 +0,0 @@ -# YOLOv5 🚀 by Ultralytics, GPL-3.0 license - -# Parameters -nc: 80 # number of classes -depth_multiple: 1.0 # model depth multiple -width_multiple: 1.0 # layer channel multiple -anchors: - - [19,27, 44,40, 38,94] # P3/8 - - [96,68, 86,152, 180,137] # P4/16 - - [140,301, 303,264, 238,542] # P5/32 - - [436,615, 739,380, 925,792] # P6/64 - -# YOLOv5 v6.0 backbone -backbone: - # [from, number, module, args] - [[-1, 1, Conv, [64, 6, 2, 2]], # 0-P1/2 - [-1, 1, Conv, [128, 3, 2]], # 1-P2/4 - [-1, 3, C3, [128]], - [-1, 1, Conv, [256, 3, 2]], # 3-P3/8 - [-1, 6, C3, [256]], - [-1, 1, Conv, [512, 3, 2]], # 5-P4/16 - [-1, 9, C3, [512]], - [-1, 1, Conv, [768, 3, 2]], # 7-P5/32 - [-1, 3, C3, [768]], - [-1, 1, Conv, [1024, 3, 2]], # 9-P6/64 - [-1, 3, C3, [1024]], - [-1, 1, SPPF, [1024, 5]], # 11 - ] - -# YOLOv5 v6.0 head -head: - [[-1, 1, Conv, [768, 1, 1]], - [-1, 1, nn.Upsample, [None, 2, 'nearest']], - [[-1, 8], 1, Concat, [1]], # cat backbone P5 - [-1, 3, C3, [768, False]], # 15 - - [-1, 1, Conv, [512, 1, 1]], - [-1, 1, nn.Upsample, [None, 2, 'nearest']], - [[-1, 6], 1, Concat, [1]], # cat backbone P4 - [-1, 3, C3, [512, False]], # 19 - - [-1, 1, Conv, [256, 1, 1]], - [-1, 1, nn.Upsample, [None, 2, 'nearest']], - [[-1, 4], 1, Concat, [1]], # cat backbone P3 - [-1, 3, C3, [256, False]], # 23 (P3/8-small) - - [-1, 1, Conv, [256, 3, 2]], - [[-1, 20], 1, Concat, [1]], # cat head P4 - [-1, 3, C3, [512, False]], # 26 (P4/16-medium) - - [-1, 1, Conv, [512, 3, 2]], - [[-1, 16], 1, Concat, [1]], # cat head P5 - [-1, 3, C3, [768, False]], # 29 (P5/32-large) - - [-1, 1, Conv, [768, 3, 2]], - [[-1, 12], 1, Concat, [1]], # cat head P6 - [-1, 3, C3, [1024, False]], # 32 (P6/64-xlarge) - - [[23, 26, 29, 32], 1, Detect, [nc, anchors]], # Detect(P3, P4, P5, P6) - ] diff --git a/YOLO/Dockerfiles/app/app/models/hub/yolov5m6.yaml b/YOLO/Dockerfiles/app/app/models/hub/yolov5m6.yaml deleted file mode 100644 index ecc53fd..0000000 --- a/YOLO/Dockerfiles/app/app/models/hub/yolov5m6.yaml +++ /dev/null @@ -1,60 +0,0 @@ -# YOLOv5 🚀 by Ultralytics, GPL-3.0 license - -# Parameters -nc: 80 # number of classes -depth_multiple: 0.67 # model depth multiple -width_multiple: 0.75 # layer channel multiple -anchors: - - [19,27, 44,40, 38,94] # P3/8 - - [96,68, 86,152, 180,137] # P4/16 - - [140,301, 303,264, 238,542] # P5/32 - - [436,615, 739,380, 925,792] # P6/64 - -# YOLOv5 v6.0 backbone -backbone: - # [from, number, module, args] - [[-1, 1, Conv, [64, 6, 2, 2]], # 0-P1/2 - [-1, 1, Conv, [128, 3, 2]], # 1-P2/4 - [-1, 3, C3, [128]], - [-1, 1, Conv, [256, 3, 2]], # 3-P3/8 - [-1, 6, C3, [256]], - [-1, 1, Conv, [512, 3, 2]], # 5-P4/16 - [-1, 9, C3, [512]], - [-1, 1, Conv, [768, 3, 2]], # 7-P5/32 - [-1, 3, C3, [768]], - [-1, 1, Conv, [1024, 3, 2]], # 9-P6/64 - [-1, 3, C3, [1024]], - [-1, 1, SPPF, [1024, 5]], # 11 - ] - -# YOLOv5 v6.0 head -head: - [[-1, 1, Conv, [768, 1, 1]], - [-1, 1, nn.Upsample, [None, 2, 'nearest']], - [[-1, 8], 1, Concat, [1]], # cat backbone P5 - [-1, 3, C3, [768, False]], # 15 - - [-1, 1, Conv, [512, 1, 1]], - [-1, 1, nn.Upsample, [None, 2, 'nearest']], - [[-1, 6], 1, Concat, [1]], # cat backbone P4 - [-1, 3, C3, [512, False]], # 19 - - [-1, 1, Conv, [256, 1, 1]], - [-1, 1, nn.Upsample, [None, 2, 'nearest']], - [[-1, 4], 1, Concat, [1]], # cat backbone P3 - [-1, 3, C3, [256, False]], # 23 (P3/8-small) - - [-1, 1, Conv, [256, 3, 2]], - [[-1, 20], 1, Concat, [1]], # cat head P4 - [-1, 3, C3, [512, False]], # 26 (P4/16-medium) - - [-1, 1, Conv, [512, 3, 2]], - [[-1, 16], 1, Concat, [1]], # cat head P5 - [-1, 3, C3, [768, False]], # 29 (P5/32-large) - - [-1, 1, Conv, [768, 3, 2]], - [[-1, 12], 1, Concat, [1]], # cat head P6 - [-1, 3, C3, [1024, False]], # 32 (P6/64-xlarge) - - [[23, 26, 29, 32], 1, Detect, [nc, anchors]], # Detect(P3, P4, P5, P6) - ] diff --git a/YOLO/Dockerfiles/app/app/models/hub/yolov5n6.yaml b/YOLO/Dockerfiles/app/app/models/hub/yolov5n6.yaml deleted file mode 100644 index 0c0c71d..0000000 --- a/YOLO/Dockerfiles/app/app/models/hub/yolov5n6.yaml +++ /dev/null @@ -1,60 +0,0 @@ -# YOLOv5 🚀 by Ultralytics, GPL-3.0 license - -# Parameters -nc: 80 # number of classes -depth_multiple: 0.33 # model depth multiple -width_multiple: 0.25 # layer channel multiple -anchors: - - [19,27, 44,40, 38,94] # P3/8 - - [96,68, 86,152, 180,137] # P4/16 - - [140,301, 303,264, 238,542] # P5/32 - - [436,615, 739,380, 925,792] # P6/64 - -# YOLOv5 v6.0 backbone -backbone: - # [from, number, module, args] - [[-1, 1, Conv, [64, 6, 2, 2]], # 0-P1/2 - [-1, 1, Conv, [128, 3, 2]], # 1-P2/4 - [-1, 3, C3, [128]], - [-1, 1, Conv, [256, 3, 2]], # 3-P3/8 - [-1, 6, C3, [256]], - [-1, 1, Conv, [512, 3, 2]], # 5-P4/16 - [-1, 9, C3, [512]], - [-1, 1, Conv, [768, 3, 2]], # 7-P5/32 - [-1, 3, C3, [768]], - [-1, 1, Conv, [1024, 3, 2]], # 9-P6/64 - [-1, 3, C3, [1024]], - [-1, 1, SPPF, [1024, 5]], # 11 - ] - -# YOLOv5 v6.0 head -head: - [[-1, 1, Conv, [768, 1, 1]], - [-1, 1, nn.Upsample, [None, 2, 'nearest']], - [[-1, 8], 1, Concat, [1]], # cat backbone P5 - [-1, 3, C3, [768, False]], # 15 - - [-1, 1, Conv, [512, 1, 1]], - [-1, 1, nn.Upsample, [None, 2, 'nearest']], - [[-1, 6], 1, Concat, [1]], # cat backbone P4 - [-1, 3, C3, [512, False]], # 19 - - [-1, 1, Conv, [256, 1, 1]], - [-1, 1, nn.Upsample, [None, 2, 'nearest']], - [[-1, 4], 1, Concat, [1]], # cat backbone P3 - [-1, 3, C3, [256, False]], # 23 (P3/8-small) - - [-1, 1, Conv, [256, 3, 2]], - [[-1, 20], 1, Concat, [1]], # cat head P4 - [-1, 3, C3, [512, False]], # 26 (P4/16-medium) - - [-1, 1, Conv, [512, 3, 2]], - [[-1, 16], 1, Concat, [1]], # cat head P5 - [-1, 3, C3, [768, False]], # 29 (P5/32-large) - - [-1, 1, Conv, [768, 3, 2]], - [[-1, 12], 1, Concat, [1]], # cat head P6 - [-1, 3, C3, [1024, False]], # 32 (P6/64-xlarge) - - [[23, 26, 29, 32], 1, Detect, [nc, anchors]], # Detect(P3, P4, P5, P6) - ] diff --git a/YOLO/Dockerfiles/app/app/models/hub/yolov5s-ghost.yaml b/YOLO/Dockerfiles/app/app/models/hub/yolov5s-ghost.yaml deleted file mode 100644 index ff9519c..0000000 --- a/YOLO/Dockerfiles/app/app/models/hub/yolov5s-ghost.yaml +++ /dev/null @@ -1,48 +0,0 @@ -# YOLOv5 🚀 by Ultralytics, GPL-3.0 license - -# Parameters -nc: 80 # number of classes -depth_multiple: 0.33 # model depth multiple -width_multiple: 0.50 # layer channel multiple -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 v6.0 backbone -backbone: - # [from, number, module, args] - [[-1, 1, Conv, [64, 6, 2, 2]], # 0-P1/2 - [-1, 1, GhostConv, [128, 3, 2]], # 1-P2/4 - [-1, 3, C3Ghost, [128]], - [-1, 1, GhostConv, [256, 3, 2]], # 3-P3/8 - [-1, 6, C3Ghost, [256]], - [-1, 1, GhostConv, [512, 3, 2]], # 5-P4/16 - [-1, 9, C3Ghost, [512]], - [-1, 1, GhostConv, [1024, 3, 2]], # 7-P5/32 - [-1, 3, C3Ghost, [1024]], - [-1, 1, SPPF, [1024, 5]], # 9 - ] - -# YOLOv5 v6.0 head -head: - [[-1, 1, GhostConv, [512, 1, 1]], - [-1, 1, nn.Upsample, [None, 2, 'nearest']], - [[-1, 6], 1, Concat, [1]], # cat backbone P4 - [-1, 3, C3Ghost, [512, False]], # 13 - - [-1, 1, GhostConv, [256, 1, 1]], - [-1, 1, nn.Upsample, [None, 2, 'nearest']], - [[-1, 4], 1, Concat, [1]], # cat backbone P3 - [-1, 3, C3Ghost, [256, False]], # 17 (P3/8-small) - - [-1, 1, GhostConv, [256, 3, 2]], - [[-1, 14], 1, Concat, [1]], # cat head P4 - [-1, 3, C3Ghost, [512, False]], # 20 (P4/16-medium) - - [-1, 1, GhostConv, [512, 3, 2]], - [[-1, 10], 1, Concat, [1]], # cat head P5 - [-1, 3, C3Ghost, [1024, False]], # 23 (P5/32-large) - - [[17, 20, 23], 1, Detect, [nc, anchors]], # Detect(P3, P4, P5) - ] diff --git a/YOLO/Dockerfiles/app/app/models/hub/yolov5s-transformer.yaml b/YOLO/Dockerfiles/app/app/models/hub/yolov5s-transformer.yaml deleted file mode 100644 index 100d7c4..0000000 --- a/YOLO/Dockerfiles/app/app/models/hub/yolov5s-transformer.yaml +++ /dev/null @@ -1,48 +0,0 @@ -# YOLOv5 🚀 by Ultralytics, GPL-3.0 license - -# Parameters -nc: 80 # number of classes -depth_multiple: 0.33 # model depth multiple -width_multiple: 0.50 # layer channel multiple -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 v6.0 backbone -backbone: - # [from, number, module, args] - [[-1, 1, Conv, [64, 6, 2, 2]], # 0-P1/2 - [-1, 1, Conv, [128, 3, 2]], # 1-P2/4 - [-1, 3, C3, [128]], - [-1, 1, Conv, [256, 3, 2]], # 3-P3/8 - [-1, 6, C3, [256]], - [-1, 1, Conv, [512, 3, 2]], # 5-P4/16 - [-1, 9, C3, [512]], - [-1, 1, Conv, [1024, 3, 2]], # 7-P5/32 - [-1, 3, C3TR, [1024]], # 9 <--- C3TR() Transformer module - [-1, 1, SPPF, [1024, 5]], # 9 - ] - -# YOLOv5 v6.0 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, C3, [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, C3, [256, False]], # 17 (P3/8-small) - - [-1, 1, Conv, [256, 3, 2]], - [[-1, 14], 1, Concat, [1]], # cat head P4 - [-1, 3, C3, [512, False]], # 20 (P4/16-medium) - - [-1, 1, Conv, [512, 3, 2]], - [[-1, 10], 1, Concat, [1]], # cat head P5 - [-1, 3, C3, [1024, False]], # 23 (P5/32-large) - - [[17, 20, 23], 1, Detect, [nc, anchors]], # Detect(P3, P4, P5) - ] diff --git a/YOLO/Dockerfiles/app/app/models/hub/yolov5s6.yaml b/YOLO/Dockerfiles/app/app/models/hub/yolov5s6.yaml deleted file mode 100644 index a28fb55..0000000 --- a/YOLO/Dockerfiles/app/app/models/hub/yolov5s6.yaml +++ /dev/null @@ -1,60 +0,0 @@ -# YOLOv5 🚀 by Ultralytics, GPL-3.0 license - -# Parameters -nc: 80 # number of classes -depth_multiple: 0.33 # model depth multiple -width_multiple: 0.50 # layer channel multiple -anchors: - - [19,27, 44,40, 38,94] # P3/8 - - [96,68, 86,152, 180,137] # P4/16 - - [140,301, 303,264, 238,542] # P5/32 - - [436,615, 739,380, 925,792] # P6/64 - -# YOLOv5 v6.0 backbone -backbone: - # [from, number, module, args] - [[-1, 1, Conv, [64, 6, 2, 2]], # 0-P1/2 - [-1, 1, Conv, [128, 3, 2]], # 1-P2/4 - [-1, 3, C3, [128]], - [-1, 1, Conv, [256, 3, 2]], # 3-P3/8 - [-1, 6, C3, [256]], - [-1, 1, Conv, [512, 3, 2]], # 5-P4/16 - [-1, 9, C3, [512]], - [-1, 1, Conv, [768, 3, 2]], # 7-P5/32 - [-1, 3, C3, [768]], - [-1, 1, Conv, [1024, 3, 2]], # 9-P6/64 - [-1, 3, C3, [1024]], - [-1, 1, SPPF, [1024, 5]], # 11 - ] - -# YOLOv5 v6.0 head -head: - [[-1, 1, Conv, [768, 1, 1]], - [-1, 1, nn.Upsample, [None, 2, 'nearest']], - [[-1, 8], 1, Concat, [1]], # cat backbone P5 - [-1, 3, C3, [768, False]], # 15 - - [-1, 1, Conv, [512, 1, 1]], - [-1, 1, nn.Upsample, [None, 2, 'nearest']], - [[-1, 6], 1, Concat, [1]], # cat backbone P4 - [-1, 3, C3, [512, False]], # 19 - - [-1, 1, Conv, [256, 1, 1]], - [-1, 1, nn.Upsample, [None, 2, 'nearest']], - [[-1, 4], 1, Concat, [1]], # cat backbone P3 - [-1, 3, C3, [256, False]], # 23 (P3/8-small) - - [-1, 1, Conv, [256, 3, 2]], - [[-1, 20], 1, Concat, [1]], # cat head P4 - [-1, 3, C3, [512, False]], # 26 (P4/16-medium) - - [-1, 1, Conv, [512, 3, 2]], - [[-1, 16], 1, Concat, [1]], # cat head P5 - [-1, 3, C3, [768, False]], # 29 (P5/32-large) - - [-1, 1, Conv, [768, 3, 2]], - [[-1, 12], 1, Concat, [1]], # cat head P6 - [-1, 3, C3, [1024, False]], # 32 (P6/64-xlarge) - - [[23, 26, 29, 32], 1, Detect, [nc, anchors]], # Detect(P3, P4, P5, P6) - ] diff --git a/YOLO/Dockerfiles/app/app/models/hub/yolov5x6.yaml b/YOLO/Dockerfiles/app/app/models/hub/yolov5x6.yaml deleted file mode 100644 index ba795c4..0000000 --- a/YOLO/Dockerfiles/app/app/models/hub/yolov5x6.yaml +++ /dev/null @@ -1,60 +0,0 @@ -# YOLOv5 🚀 by Ultralytics, GPL-3.0 license - -# Parameters -nc: 80 # number of classes -depth_multiple: 1.33 # model depth multiple -width_multiple: 1.25 # layer channel multiple -anchors: - - [19,27, 44,40, 38,94] # P3/8 - - [96,68, 86,152, 180,137] # P4/16 - - [140,301, 303,264, 238,542] # P5/32 - - [436,615, 739,380, 925,792] # P6/64 - -# YOLOv5 v6.0 backbone -backbone: - # [from, number, module, args] - [[-1, 1, Conv, [64, 6, 2, 2]], # 0-P1/2 - [-1, 1, Conv, [128, 3, 2]], # 1-P2/4 - [-1, 3, C3, [128]], - [-1, 1, Conv, [256, 3, 2]], # 3-P3/8 - [-1, 6, C3, [256]], - [-1, 1, Conv, [512, 3, 2]], # 5-P4/16 - [-1, 9, C3, [512]], - [-1, 1, Conv, [768, 3, 2]], # 7-P5/32 - [-1, 3, C3, [768]], - [-1, 1, Conv, [1024, 3, 2]], # 9-P6/64 - [-1, 3, C3, [1024]], - [-1, 1, SPPF, [1024, 5]], # 11 - ] - -# YOLOv5 v6.0 head -head: - [[-1, 1, Conv, [768, 1, 1]], - [-1, 1, nn.Upsample, [None, 2, 'nearest']], - [[-1, 8], 1, Concat, [1]], # cat backbone P5 - [-1, 3, C3, [768, False]], # 15 - - [-1, 1, Conv, [512, 1, 1]], - [-1, 1, nn.Upsample, [None, 2, 'nearest']], - [[-1, 6], 1, Concat, [1]], # cat backbone P4 - [-1, 3, C3, [512, False]], # 19 - - [-1, 1, Conv, [256, 1, 1]], - [-1, 1, nn.Upsample, [None, 2, 'nearest']], - [[-1, 4], 1, Concat, [1]], # cat backbone P3 - [-1, 3, C3, [256, False]], # 23 (P3/8-small) - - [-1, 1, Conv, [256, 3, 2]], - [[-1, 20], 1, Concat, [1]], # cat head P4 - [-1, 3, C3, [512, False]], # 26 (P4/16-medium) - - [-1, 1, Conv, [512, 3, 2]], - [[-1, 16], 1, Concat, [1]], # cat head P5 - [-1, 3, C3, [768, False]], # 29 (P5/32-large) - - [-1, 1, Conv, [768, 3, 2]], - [[-1, 12], 1, Concat, [1]], # cat head P6 - [-1, 3, C3, [1024, False]], # 32 (P6/64-xlarge) - - [[23, 26, 29, 32], 1, Detect, [nc, anchors]], # Detect(P3, P4, P5, P6) - ] diff --git a/YOLO/Dockerfiles/app/app/models/tf.py b/YOLO/Dockerfiles/app/app/models/tf.py deleted file mode 100644 index 84359c4..0000000 --- a/YOLO/Dockerfiles/app/app/models/tf.py +++ /dev/null @@ -1,464 +0,0 @@ -# YOLOv5 🚀 by Ultralytics, GPL-3.0 license -""" -TensorFlow, Keras and TFLite versions of YOLOv5 -Authored by https://github.com/zldrobit in PR https://github.com/ultralytics/yolov5/pull/1127 - -Usage: - $ python models/tf.py --weights yolov5s.pt - -Export: - $ python path/to/export.py --weights yolov5s.pt --include saved_model pb tflite tfjs -""" - -import argparse -import sys -from copy import deepcopy -from pathlib import Path - -FILE = Path(__file__).resolve() -ROOT = FILE.parents[1] # YOLOv5 root directory -if str(ROOT) not in sys.path: - sys.path.append(str(ROOT)) # add ROOT to PATH -# ROOT = ROOT.relative_to(Path.cwd()) # relative - -import numpy as np -import tensorflow as tf -import torch -import torch.nn as nn -from tensorflow import keras - -from models.common import C3, SPP, SPPF, Bottleneck, BottleneckCSP, Concat, Conv, DWConv, Focus, autopad -from models.experimental import CrossConv, MixConv2d, attempt_load -from models.yolo import Detect -from utils.activations import SiLU -from utils.general import LOGGER, make_divisible, print_args - - -class TFBN(keras.layers.Layer): - # TensorFlow BatchNormalization wrapper - def __init__(self, w=None): - super().__init__() - self.bn = keras.layers.BatchNormalization( - beta_initializer=keras.initializers.Constant(w.bias.numpy()), - gamma_initializer=keras.initializers.Constant(w.weight.numpy()), - moving_mean_initializer=keras.initializers.Constant(w.running_mean.numpy()), - moving_variance_initializer=keras.initializers.Constant(w.running_var.numpy()), - epsilon=w.eps) - - def call(self, inputs): - return self.bn(inputs) - - -class TFPad(keras.layers.Layer): - def __init__(self, pad): - super().__init__() - self.pad = tf.constant([[0, 0], [pad, pad], [pad, pad], [0, 0]]) - - def call(self, inputs): - return tf.pad(inputs, self.pad, mode='constant', constant_values=0) - - -class TFConv(keras.layers.Layer): - # Standard convolution - def __init__(self, c1, c2, k=1, s=1, p=None, g=1, act=True, w=None): - # ch_in, ch_out, weights, kernel, stride, padding, groups - super().__init__() - assert g == 1, "TF v2.2 Conv2D does not support 'groups' argument" - assert isinstance(k, int), "Convolution with multiple kernels are not allowed." - # TensorFlow convolution padding is inconsistent with PyTorch (e.g. k=3 s=2 'SAME' padding) - # see https://stackoverflow.com/questions/52975843/comparing-conv2d-with-padding-between-tensorflow-and-pytorch - - conv = keras.layers.Conv2D( - c2, k, s, 'SAME' if s == 1 else 'VALID', use_bias=False if hasattr(w, 'bn') else True, - kernel_initializer=keras.initializers.Constant(w.conv.weight.permute(2, 3, 1, 0).numpy()), - bias_initializer='zeros' if hasattr(w, 'bn') else keras.initializers.Constant(w.conv.bias.numpy())) - self.conv = conv if s == 1 else keras.Sequential([TFPad(autopad(k, p)), conv]) - self.bn = TFBN(w.bn) if hasattr(w, 'bn') else tf.identity - - # YOLOv5 activations - if isinstance(w.act, nn.LeakyReLU): - self.act = (lambda x: keras.activations.relu(x, alpha=0.1)) if act else tf.identity - elif isinstance(w.act, nn.Hardswish): - self.act = (lambda x: x * tf.nn.relu6(x + 3) * 0.166666667) if act else tf.identity - elif isinstance(w.act, (nn.SiLU, SiLU)): - self.act = (lambda x: keras.activations.swish(x)) if act else tf.identity - else: - raise Exception(f'no matching TensorFlow activation found for {w.act}') - - def call(self, inputs): - return self.act(self.bn(self.conv(inputs))) - - -class TFFocus(keras.layers.Layer): - # Focus wh information into c-space - def __init__(self, c1, c2, k=1, s=1, p=None, g=1, act=True, w=None): - # ch_in, ch_out, kernel, stride, padding, groups - super().__init__() - self.conv = TFConv(c1 * 4, c2, k, s, p, g, act, w.conv) - - def call(self, inputs): # x(b,w,h,c) -> y(b,w/2,h/2,4c) - # inputs = inputs / 255 # normalize 0-255 to 0-1 - return self.conv(tf.concat([inputs[:, ::2, ::2, :], - inputs[:, 1::2, ::2, :], - inputs[:, ::2, 1::2, :], - inputs[:, 1::2, 1::2, :]], 3)) - - -class TFBottleneck(keras.layers.Layer): - # Standard bottleneck - def __init__(self, c1, c2, shortcut=True, g=1, e=0.5, w=None): # ch_in, ch_out, shortcut, groups, expansion - super().__init__() - c_ = int(c2 * e) # hidden channels - self.cv1 = TFConv(c1, c_, 1, 1, w=w.cv1) - self.cv2 = TFConv(c_, c2, 3, 1, g=g, w=w.cv2) - self.add = shortcut and c1 == c2 - - def call(self, inputs): - return inputs + self.cv2(self.cv1(inputs)) if self.add else self.cv2(self.cv1(inputs)) - - -class TFConv2d(keras.layers.Layer): - # Substitution for PyTorch nn.Conv2D - def __init__(self, c1, c2, k, s=1, g=1, bias=True, w=None): - super().__init__() - assert g == 1, "TF v2.2 Conv2D does not support 'groups' argument" - self.conv = keras.layers.Conv2D( - c2, k, s, 'VALID', use_bias=bias, - kernel_initializer=keras.initializers.Constant(w.weight.permute(2, 3, 1, 0).numpy()), - bias_initializer=keras.initializers.Constant(w.bias.numpy()) if bias else None, ) - - def call(self, inputs): - return self.conv(inputs) - - -class TFBottleneckCSP(keras.layers.Layer): - # CSP Bottleneck https://github.com/WongKinYiu/CrossStagePartialNetworks - def __init__(self, c1, c2, n=1, shortcut=True, g=1, e=0.5, w=None): - # ch_in, ch_out, number, shortcut, groups, expansion - super().__init__() - c_ = int(c2 * e) # hidden channels - self.cv1 = TFConv(c1, c_, 1, 1, w=w.cv1) - self.cv2 = TFConv2d(c1, c_, 1, 1, bias=False, w=w.cv2) - self.cv3 = TFConv2d(c_, c_, 1, 1, bias=False, w=w.cv3) - self.cv4 = TFConv(2 * c_, c2, 1, 1, w=w.cv4) - self.bn = TFBN(w.bn) - self.act = lambda x: keras.activations.relu(x, alpha=0.1) - self.m = keras.Sequential([TFBottleneck(c_, c_, shortcut, g, e=1.0, w=w.m[j]) for j in range(n)]) - - def call(self, inputs): - y1 = self.cv3(self.m(self.cv1(inputs))) - y2 = self.cv2(inputs) - return self.cv4(self.act(self.bn(tf.concat((y1, y2), axis=3)))) - - -class TFC3(keras.layers.Layer): - # CSP Bottleneck with 3 convolutions - def __init__(self, c1, c2, n=1, shortcut=True, g=1, e=0.5, w=None): - # ch_in, ch_out, number, shortcut, groups, expansion - super().__init__() - c_ = int(c2 * e) # hidden channels - self.cv1 = TFConv(c1, c_, 1, 1, w=w.cv1) - self.cv2 = TFConv(c1, c_, 1, 1, w=w.cv2) - self.cv3 = TFConv(2 * c_, c2, 1, 1, w=w.cv3) - self.m = keras.Sequential([TFBottleneck(c_, c_, shortcut, g, e=1.0, w=w.m[j]) for j in range(n)]) - - def call(self, inputs): - return self.cv3(tf.concat((self.m(self.cv1(inputs)), self.cv2(inputs)), axis=3)) - - -class TFSPP(keras.layers.Layer): - # Spatial pyramid pooling layer used in YOLOv3-SPP - def __init__(self, c1, c2, k=(5, 9, 13), w=None): - super().__init__() - c_ = c1 // 2 # hidden channels - self.cv1 = TFConv(c1, c_, 1, 1, w=w.cv1) - self.cv2 = TFConv(c_ * (len(k) + 1), c2, 1, 1, w=w.cv2) - self.m = [keras.layers.MaxPool2D(pool_size=x, strides=1, padding='SAME') for x in k] - - def call(self, inputs): - x = self.cv1(inputs) - return self.cv2(tf.concat([x] + [m(x) for m in self.m], 3)) - - -class TFSPPF(keras.layers.Layer): - # Spatial pyramid pooling-Fast layer - def __init__(self, c1, c2, k=5, w=None): - super().__init__() - c_ = c1 // 2 # hidden channels - self.cv1 = TFConv(c1, c_, 1, 1, w=w.cv1) - self.cv2 = TFConv(c_ * 4, c2, 1, 1, w=w.cv2) - self.m = keras.layers.MaxPool2D(pool_size=k, strides=1, padding='SAME') - - def call(self, inputs): - x = self.cv1(inputs) - y1 = self.m(x) - y2 = self.m(y1) - return self.cv2(tf.concat([x, y1, y2, self.m(y2)], 3)) - - -class TFDetect(keras.layers.Layer): - def __init__(self, nc=80, anchors=(), ch=(), imgsz=(640, 640), w=None): # detection layer - super().__init__() - self.stride = tf.convert_to_tensor(w.stride.numpy(), dtype=tf.float32) - 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 = [tf.zeros(1)] * self.nl # init grid - self.anchors = tf.convert_to_tensor(w.anchors.numpy(), dtype=tf.float32) - self.anchor_grid = tf.reshape(self.anchors * tf.reshape(self.stride, [self.nl, 1, 1]), - [self.nl, 1, -1, 1, 2]) - self.m = [TFConv2d(x, self.no * self.na, 1, w=w.m[i]) for i, x in enumerate(ch)] - self.training = False # set to False after building model - self.imgsz = imgsz - for i in range(self.nl): - ny, nx = self.imgsz[0] // self.stride[i], self.imgsz[1] // self.stride[i] - self.grid[i] = self._make_grid(nx, ny) - - def call(self, inputs): - z = [] # inference output - x = [] - for i in range(self.nl): - x.append(self.m[i](inputs[i])) - # x(bs,20,20,255) to x(bs,3,20,20,85) - ny, nx = self.imgsz[0] // self.stride[i], self.imgsz[1] // self.stride[i] - x[i] = tf.transpose(tf.reshape(x[i], [-1, ny * nx, self.na, self.no]), [0, 2, 1, 3]) - - if not self.training: # inference - y = tf.sigmoid(x[i]) - xy = (y[..., 0:2] * 2 - 0.5 + self.grid[i]) * self.stride[i] # xy - wh = (y[..., 2:4] * 2) ** 2 * self.anchor_grid[i] - # Normalize xywh to 0-1 to reduce calibration error - xy /= tf.constant([[self.imgsz[1], self.imgsz[0]]], dtype=tf.float32) - wh /= tf.constant([[self.imgsz[1], self.imgsz[0]]], dtype=tf.float32) - y = tf.concat([xy, wh, y[..., 4:]], -1) - z.append(tf.reshape(y, [-1, self.na * ny * nx, self.no])) - - return x if self.training else (tf.concat(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() - xv, yv = tf.meshgrid(tf.range(nx), tf.range(ny)) - return tf.cast(tf.reshape(tf.stack([xv, yv], 2), [1, 1, ny * nx, 2]), dtype=tf.float32) - - -class TFUpsample(keras.layers.Layer): - def __init__(self, size, scale_factor, mode, w=None): # warning: all arguments needed including 'w' - super().__init__() - assert scale_factor == 2, "scale_factor must be 2" - self.upsample = lambda x: tf.image.resize(x, (x.shape[1] * 2, x.shape[2] * 2), method=mode) - # self.upsample = keras.layers.UpSampling2D(size=scale_factor, interpolation=mode) - # with default arguments: align_corners=False, half_pixel_centers=False - # self.upsample = lambda x: tf.raw_ops.ResizeNearestNeighbor(images=x, - # size=(x.shape[1] * 2, x.shape[2] * 2)) - - def call(self, inputs): - return self.upsample(inputs) - - -class TFConcat(keras.layers.Layer): - def __init__(self, dimension=1, w=None): - super().__init__() - assert dimension == 1, "convert only NCHW to NHWC concat" - self.d = 3 - - def call(self, inputs): - return tf.concat(inputs, self.d) - - -def parse_model(d, ch, model, imgsz): # model_dict, input_channels(3) - LOGGER.info(f"\n{'':>3}{'from':>18}{'n':>3}{'params':>10} {'module':<40}{'arguments':<30}") - 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_str = m - 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 NameError: - pass - - n = max(round(n * gd), 1) if n > 1 else n # depth gain - if m in [nn.Conv2d, Conv, Bottleneck, SPP, SPPF, DWConv, MixConv2d, Focus, CrossConv, BottleneckCSP, C3]: - c1, c2 = ch[f], args[0] - c2 = make_divisible(c2 * gw, 8) if c2 != no else c2 - - args = [c1, c2, *args[1:]] - if m in [BottleneckCSP, C3]: - 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) - args.append(imgsz) - else: - c2 = ch[f] - - tf_m = eval('TF' + m_str.replace('nn.', '')) - m_ = keras.Sequential([tf_m(*args, w=model.model[i][j]) for j in range(n)]) if n > 1 \ - else tf_m(*args, w=model.model[i]) # module - - torch_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 torch_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(f'{i:>3}{str(f):>18}{str(n):>3}{np:>10} {t:<40}{str(args):<30}') # 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 keras.Sequential(layers), sorted(save) - - -class TFModel: - def __init__(self, cfg='yolov5s.yaml', ch=3, nc=None, model=None, imgsz=(640, 640)): # model, channels, classes - super().__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 - if nc and nc != self.yaml['nc']: - LOGGER.info(f"Overriding {cfg} nc={self.yaml['nc']} with nc={nc}") - self.yaml['nc'] = nc # override yaml value - self.model, self.savelist = parse_model(deepcopy(self.yaml), ch=[ch], model=model, imgsz=imgsz) - - def predict(self, inputs, tf_nms=False, agnostic_nms=False, topk_per_class=100, topk_all=100, iou_thres=0.45, - conf_thres=0.25): - y = [] # outputs - x = inputs - for i, m in enumerate(self.model.layers): - if m.f != -1: # if not from previous layer - x = y[m.f] if isinstance(m.f, int) else [x if j == -1 else y[j] for j in m.f] # from earlier layers - - x = m(x) # run - y.append(x if m.i in self.savelist else None) # save output - - # Add TensorFlow NMS - if tf_nms: - boxes = self._xywh2xyxy(x[0][..., :4]) - probs = x[0][:, :, 4:5] - classes = x[0][:, :, 5:] - scores = probs * classes - if agnostic_nms: - nms = AgnosticNMS()((boxes, classes, scores), topk_all, iou_thres, conf_thres) - return nms, x[1] - else: - boxes = tf.expand_dims(boxes, 2) - nms = tf.image.combined_non_max_suppression( - boxes, scores, topk_per_class, topk_all, iou_thres, conf_thres, clip_boxes=False) - return nms, x[1] - - return x[0] # output only first tensor [1,6300,85] = [xywh, conf, class0, class1, ...] - # x = x[0][0] # [x(1,6300,85), ...] to x(6300,85) - # xywh = x[..., :4] # x(6300,4) boxes - # conf = x[..., 4:5] # x(6300,1) confidences - # cls = tf.reshape(tf.cast(tf.argmax(x[..., 5:], axis=1), tf.float32), (-1, 1)) # x(6300,1) classes - # return tf.concat([conf, cls, xywh], 1) - - @staticmethod - def _xywh2xyxy(xywh): - # Convert nx4 boxes from [x, y, w, h] to [x1, y1, x2, y2] where xy1=top-left, xy2=bottom-right - x, y, w, h = tf.split(xywh, num_or_size_splits=4, axis=-1) - return tf.concat([x - w / 2, y - h / 2, x + w / 2, y + h / 2], axis=-1) - - -class AgnosticNMS(keras.layers.Layer): - # TF Agnostic NMS - def call(self, input, topk_all, iou_thres, conf_thres): - # wrap map_fn to avoid TypeSpec related error https://stackoverflow.com/a/65809989/3036450 - return tf.map_fn(lambda x: self._nms(x, topk_all, iou_thres, conf_thres), input, - fn_output_signature=(tf.float32, tf.float32, tf.float32, tf.int32), - name='agnostic_nms') - - @staticmethod - def _nms(x, topk_all=100, iou_thres=0.45, conf_thres=0.25): # agnostic NMS - boxes, classes, scores = x - class_inds = tf.cast(tf.argmax(classes, axis=-1), tf.float32) - scores_inp = tf.reduce_max(scores, -1) - selected_inds = tf.image.non_max_suppression( - boxes, scores_inp, max_output_size=topk_all, iou_threshold=iou_thres, score_threshold=conf_thres) - selected_boxes = tf.gather(boxes, selected_inds) - padded_boxes = tf.pad(selected_boxes, - paddings=[[0, topk_all - tf.shape(selected_boxes)[0]], [0, 0]], - mode="CONSTANT", constant_values=0.0) - selected_scores = tf.gather(scores_inp, selected_inds) - padded_scores = tf.pad(selected_scores, - paddings=[[0, topk_all - tf.shape(selected_boxes)[0]]], - mode="CONSTANT", constant_values=-1.0) - selected_classes = tf.gather(class_inds, selected_inds) - padded_classes = tf.pad(selected_classes, - paddings=[[0, topk_all - tf.shape(selected_boxes)[0]]], - mode="CONSTANT", constant_values=-1.0) - valid_detections = tf.shape(selected_inds)[0] - return padded_boxes, padded_scores, padded_classes, valid_detections - - -def representative_dataset_gen(dataset, ncalib=100): - # Representative dataset generator for use with converter.representative_dataset, returns a generator of np arrays - for n, (path, img, im0s, vid_cap, string) in enumerate(dataset): - input = np.transpose(img, [1, 2, 0]) - input = np.expand_dims(input, axis=0).astype(np.float32) - input /= 255 - yield [input] - if n >= ncalib: - break - - -def run(weights=ROOT / 'yolov5s.pt', # weights path - imgsz=(640, 640), # inference size h,w - batch_size=1, # batch size - dynamic=False, # dynamic batch size - ): - # PyTorch model - im = torch.zeros((batch_size, 3, *imgsz)) # BCHW image - model = attempt_load(weights, map_location=torch.device('cpu'), inplace=True, fuse=False) - y = model(im) # inference - model.info() - - # TensorFlow model - im = tf.zeros((batch_size, *imgsz, 3)) # BHWC image - tf_model = TFModel(cfg=model.yaml, model=model, nc=model.nc, imgsz=imgsz) - y = tf_model.predict(im) # inference - - # Keras model - im = keras.Input(shape=(*imgsz, 3), batch_size=None if dynamic else batch_size) - keras_model = keras.Model(inputs=im, outputs=tf_model.predict(im)) - keras_model.summary() - - LOGGER.info('PyTorch, TensorFlow and Keras models successfully verified.\nUse export.py for TF model export.') - - -def parse_opt(): - parser = argparse.ArgumentParser() - parser.add_argument('--weights', type=str, default=ROOT / 'yolov5s.pt', help='weights path') - parser.add_argument('--imgsz', '--img', '--img-size', nargs='+', type=int, default=[640], help='inference size h,w') - parser.add_argument('--batch-size', type=int, default=1, help='batch size') - parser.add_argument('--dynamic', action='store_true', help='dynamic batch size') - opt = parser.parse_args() - opt.imgsz *= 2 if len(opt.imgsz) == 1 else 1 # expand - print_args(FILE.stem, opt) - return opt - - -def main(opt): - run(**vars(opt)) - - -if __name__ == "__main__": - opt = parse_opt() - main(opt) diff --git a/YOLO/Dockerfiles/app/app/models/yolo.py b/YOLO/Dockerfiles/app/app/models/yolo.py deleted file mode 100644 index f659a04..0000000 --- a/YOLO/Dockerfiles/app/app/models/yolo.py +++ /dev/null @@ -1,329 +0,0 @@ -# YOLOv5 🚀 by Ultralytics, GPL-3.0 license -""" -YOLO-specific modules - -Usage: - $ python path/to/models/yolo.py --cfg yolov5s.yaml -""" - -import argparse -import sys -from copy import deepcopy -from pathlib import Path - -FILE = Path(__file__).resolve() -ROOT = FILE.parents[1] # YOLOv5 root directory -if str(ROOT) not in sys.path: - sys.path.append(str(ROOT)) # add ROOT to PATH -# ROOT = ROOT.relative_to(Path.cwd()) # relative - -from models.common import * -from models.experimental import * -from utils.autoanchor import check_anchor_order -from utils.general import LOGGER, check_version, check_yaml, make_divisible, print_args -from utils.plots import feature_visualization -from utils.torch_utils import fuse_conv_and_bn, initialize_weights, model_info, scale_img, select_device, time_sync - -try: - import thop # for FLOPs computation -except ImportError: - thop = None - - -class Detect(nn.Module): - stride = None # strides computed during build - onnx_dynamic = False # ONNX export parameter - - def __init__(self, nc=80, anchors=(), ch=(), inplace=True): # detection layer - super().__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 - self.anchor_grid = [torch.zeros(1)] * self.nl # init anchor grid - self.register_buffer('anchors', torch.tensor(anchors).float().view(self.nl, -1, 2)) # shape(nl,na,2) - self.m = nn.ModuleList(nn.Conv2d(x, self.no * self.na, 1) for x in ch) # output conv - self.inplace = inplace # use in-place ops (e.g. slice assignment) - - def forward(self, x): - z = [] # inference output - 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.onnx_dynamic or self.grid[i].shape[2:4] != x[i].shape[2:4]: - self.grid[i], self.anchor_grid[i] = self._make_grid(nx, ny, i) - - y = x[i].sigmoid() - if self.inplace: - y[..., 0:2] = (y[..., 0:2] * 2 - 0.5 + self.grid[i]) * self.stride[i] # xy - y[..., 2:4] = (y[..., 2:4] * 2) ** 2 * self.anchor_grid[i] # wh - else: # for YOLOv5 on AWS Inferentia https://github.com/ultralytics/yolov5/pull/2953 - xy = (y[..., 0:2] * 2 - 0.5 + self.grid[i]) * self.stride[i] # xy - wh = (y[..., 2:4] * 2) ** 2 * self.anchor_grid[i] # wh - y = torch.cat((xy, wh, y[..., 4:]), -1) - z.append(y.view(bs, -1, self.no)) - - return x if self.training else (torch.cat(z, 1), x) - - def _make_grid(self, nx=20, ny=20, i=0): - d = self.anchors[i].device - if check_version(torch.__version__, '1.10.0'): # torch>=1.10.0 meshgrid workaround for torch>=0.7 compatibility - yv, xv = torch.meshgrid([torch.arange(ny, device=d), torch.arange(nx, device=d)], indexing='ij') - else: - yv, xv = torch.meshgrid([torch.arange(ny, device=d), torch.arange(nx, device=d)]) - grid = torch.stack((xv, yv), 2).expand((1, self.na, ny, nx, 2)).float() - anchor_grid = (self.anchors[i].clone() * self.stride[i]) \ - .view((1, self.na, 1, 1, 2)).expand((1, self.na, ny, nx, 2)).float() - return grid, anchor_grid - - -class Model(nn.Module): - def __init__(self, cfg='yolov5s.yaml', ch=3, nc=None, anchors=None): # model, input channels, number of classes - super().__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, encoding='ascii', errors='ignore') as f: - self.yaml = yaml.safe_load(f) # model dict - - # Define model - ch = self.yaml['ch'] = self.yaml.get('ch', ch) # input channels - if nc and nc != self.yaml['nc']: - LOGGER.info(f"Overriding model.yaml nc={self.yaml['nc']} with nc={nc}") - self.yaml['nc'] = nc # override yaml value - if anchors: - LOGGER.info(f'Overriding model.yaml anchors with anchors={anchors}') - self.yaml['anchors'] = round(anchors) # 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 - self.inplace = self.yaml.get('inplace', True) - - # Build strides, anchors - m = self.model[-1] # Detect() - if isinstance(m, Detect): - s = 256 # 2x min stride - m.inplace = self.inplace - 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 - - # Init weights, biases - initialize_weights(self) - self.info() - LOGGER.info('') - - def forward(self, x, augment=False, profile=False, visualize=False): - if augment: - return self._forward_augment(x) # augmented inference, None - return self._forward_once(x, profile, visualize) # single-scale inference, train - - def _forward_augment(self, x): - 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, gs=int(self.stride.max())) - yi = self._forward_once(xi)[0] # forward - # cv2.imwrite(f'img_{si}.jpg', 255 * xi[0].cpu().numpy().transpose((1, 2, 0))[:, :, ::-1]) # save - yi = self._descale_pred(yi, fi, si, img_size) - y.append(yi) - y = self._clip_augmented(y) # clip augmented tails - return torch.cat(y, 1), None # augmented inference, train - - def _forward_once(self, x, profile=False, visualize=False): - y, dt = [], [] # outputs - for m in self.model: - if m.f != -1: # if not from previous layer - x = y[m.f] if isinstance(m.f, int) else [x if j == -1 else y[j] for j in m.f] # from earlier layers - if profile: - self._profile_one_layer(m, x, dt) - x = m(x) # run - y.append(x if m.i in self.save else None) # save output - if visualize: - feature_visualization(x, m.type, m.i, save_dir=visualize) - return x - - def _descale_pred(self, p, flips, scale, img_size): - # de-scale predictions following augmented inference (inverse operation) - if self.inplace: - p[..., :4] /= scale # de-scale - if flips == 2: - p[..., 1] = img_size[0] - p[..., 1] # de-flip ud - elif flips == 3: - p[..., 0] = img_size[1] - p[..., 0] # de-flip lr - else: - x, y, wh = p[..., 0:1] / scale, p[..., 1:2] / scale, p[..., 2:4] / scale # de-scale - if flips == 2: - y = img_size[0] - y # de-flip ud - elif flips == 3: - x = img_size[1] - x # de-flip lr - p = torch.cat((x, y, wh, p[..., 4:]), -1) - return p - - def _clip_augmented(self, y): - # Clip YOLOv5 augmented inference tails - nl = self.model[-1].nl # number of detection layers (P3-P5) - g = sum(4 ** x for x in range(nl)) # grid points - e = 1 # exclude layer count - i = (y[0].shape[1] // g) * sum(4 ** x for x in range(e)) # indices - y[0] = y[0][:, :-i] # large - i = (y[-1].shape[1] // g) * sum(4 ** (nl - 1 - x) for x in range(e)) # indices - y[-1] = y[-1][:, i:] # small - return y - - def _profile_one_layer(self, m, x, dt): - c = isinstance(m, Detect) # is final layer, copy input as inplace fix - o = thop.profile(m, inputs=(x.copy() if c else x,), verbose=False)[0] / 1E9 * 2 if thop else 0 # FLOPs - t = time_sync() - for _ in range(10): - m(x.copy() if c else x) - dt.append((time_sync() - t) * 100) - if m == self.model[0]: - LOGGER.info(f"{'time (ms)':>10s} {'GFLOPs':>10s} {'params':>10s} {'module'}") - LOGGER.info(f'{dt[-1]:10.2f} {o:10.2f} {m.np:10.0f} {m.type}') - if c: - LOGGER.info(f"{sum(dt):10.2f} {'-':>10s} {'-':>10s} Total") - - def _initialize_biases(self, cf=None): # initialize biases into Detect(), cf is class frequency - # https://arxiv.org/abs/1708.02002 section 3.3 - # cf = torch.bincount(torch.tensor(np.concatenate(dataset.labels, 0)[:, 0]).long(), minlength=nc) + 1. - 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 - 0.999999)) if cf is None else torch.log(cf / cf.sum()) # cls - mi.bias = torch.nn.Parameter(b.view(-1), requires_grad=True) - - def _print_biases(self): - m = self.model[-1] # Detect() module - for mi in m.m: # from - b = mi.bias.detach().view(m.na, -1).T # conv.bias(255) to (3,85) - LOGGER.info( - ('%6g Conv2d.bias:' + '%10.3g' * 6) % (mi.weight.shape[1], *b[:5].mean(1).tolist(), b[5:].mean())) - - # def _print_weights(self): - # for m in self.model.modules(): - # if type(m) is Bottleneck: - # LOGGER.info('%10.3g' % (m.w.detach().sigmoid() * 2)) # shortcut weights - - def fuse(self): # fuse model Conv2d() + BatchNorm2d() layers - LOGGER.info('Fusing layers... ') - for m in self.model.modules(): - if isinstance(m, (Conv, DWConv)) and hasattr(m, 'bn'): - m.conv = fuse_conv_and_bn(m.conv, m.bn) # update conv - delattr(m, 'bn') # remove batchnorm - m.forward = m.forward_fuse # update forward - self.info() - return self - - def info(self, verbose=False, img_size=640): # print model information - model_info(self, verbose, img_size) - - def _apply(self, fn): - # Apply to(), cpu(), cuda(), half() to model tensors that are not parameters or registered buffers - self = super()._apply(fn) - m = self.model[-1] # Detect() - if isinstance(m, Detect): - m.stride = fn(m.stride) - m.grid = list(map(fn, m.grid)) - if isinstance(m.anchor_grid, list): - m.anchor_grid = list(map(fn, m.anchor_grid)) - return self - - -def parse_model(d, ch): # model_dict, input_channels(3) - LOGGER.info(f"\n{'':>3}{'from':>18}{'n':>3}{'params':>10} {'module':<40}{'arguments':<30}") - 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 NameError: - pass - - n = n_ = max(round(n * gd), 1) if n > 1 else n # depth gain - if m in [Conv, GhostConv, Bottleneck, GhostBottleneck, SPP, SPPF, DWConv, MixConv2d, Focus, CrossConv, - BottleneckCSP, C3, C3TR, C3SPP, C3Ghost]: - c1, c2 = ch[f], args[0] - if c2 != no: # if not output - c2 = make_divisible(c2 * gw, 8) - - args = [c1, c2, *args[1:]] - if m in [BottleneckCSP, C3, C3TR, C3Ghost]: - args.insert(2, n) # number of repeats - n = 1 - elif m is nn.BatchNorm2d: - args = [ch[f]] - elif m is Concat: - c2 = sum(ch[x] for x in f) - elif m is Detect: - args.append([ch[x] for x in f]) - if isinstance(args[1], int): # number of anchors - args[1] = [list(range(args[1] * 2))] * len(f) - elif m is Contract: - c2 = ch[f] * args[0] ** 2 - elif m is Expand: - c2 = ch[f] // args[0] ** 2 - 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(f'{i:>3}{str(f):>18}{n_:>3}{np:10.0f} {t:<40}{str(args):<30}') # print - save.extend(x % i for x in ([f] if isinstance(f, int) else f) if x != -1) # append to savelist - layers.append(m_) - if i == 0: - ch = [] - 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') - parser.add_argument('--profile', action='store_true', help='profile model speed') - parser.add_argument('--test', action='store_true', help='test all yolo*.yaml') - opt = parser.parse_args() - opt.cfg = check_yaml(opt.cfg) # check YAML - print_args(FILE.stem, opt) - device = select_device(opt.device) - - # Create model - model = Model(opt.cfg).to(device) - model.train() - - # Profile - if opt.profile: - img = torch.rand(8 if torch.cuda.is_available() else 1, 3, 640, 640).to(device) - y = model(img, profile=True) - - # Test all models - if opt.test: - for cfg in Path(ROOT / 'models').rglob('yolo*.yaml'): - try: - _ = Model(cfg) - except Exception as e: - print(f'Error in {cfg}: {e}') - - # Tensorboard (not working https://github.com/ultralytics/yolov5/issues/2898) - # from torch.utils.tensorboard import SummaryWriter - # tb_writer = SummaryWriter('.') - # LOGGER.info("Run 'tensorboard --logdir=models' to view tensorboard at http://localhost:6006/") - # tb_writer.add_graph(torch.jit.trace(model, img, strict=False), []) # add model graph diff --git a/YOLO/Dockerfiles/app/app/models/yolov5l.yaml b/YOLO/Dockerfiles/app/app/models/yolov5l.yaml deleted file mode 100644 index ce8a5de..0000000 --- a/YOLO/Dockerfiles/app/app/models/yolov5l.yaml +++ /dev/null @@ -1,48 +0,0 @@ -# YOLOv5 🚀 by Ultralytics, GPL-3.0 license - -# Parameters -nc: 80 # number of classes -depth_multiple: 1.0 # model depth multiple -width_multiple: 1.0 # layer channel multiple -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 v6.0 backbone -backbone: - # [from, number, module, args] - [[-1, 1, Conv, [64, 6, 2, 2]], # 0-P1/2 - [-1, 1, Conv, [128, 3, 2]], # 1-P2/4 - [-1, 3, C3, [128]], - [-1, 1, Conv, [256, 3, 2]], # 3-P3/8 - [-1, 6, C3, [256]], - [-1, 1, Conv, [512, 3, 2]], # 5-P4/16 - [-1, 9, C3, [512]], - [-1, 1, Conv, [1024, 3, 2]], # 7-P5/32 - [-1, 3, C3, [1024]], - [-1, 1, SPPF, [1024, 5]], # 9 - ] - -# YOLOv5 v6.0 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, C3, [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, C3, [256, False]], # 17 (P3/8-small) - - [-1, 1, Conv, [256, 3, 2]], - [[-1, 14], 1, Concat, [1]], # cat head P4 - [-1, 3, C3, [512, False]], # 20 (P4/16-medium) - - [-1, 1, Conv, [512, 3, 2]], - [[-1, 10], 1, Concat, [1]], # cat head P5 - [-1, 3, C3, [1024, False]], # 23 (P5/32-large) - - [[17, 20, 23], 1, Detect, [nc, anchors]], # Detect(P3, P4, P5) - ] diff --git a/YOLO/Dockerfiles/app/app/models/yolov5m.yaml b/YOLO/Dockerfiles/app/app/models/yolov5m.yaml deleted file mode 100644 index ad13ab3..0000000 --- a/YOLO/Dockerfiles/app/app/models/yolov5m.yaml +++ /dev/null @@ -1,48 +0,0 @@ -# YOLOv5 🚀 by Ultralytics, GPL-3.0 license - -# Parameters -nc: 80 # number of classes -depth_multiple: 0.67 # model depth multiple -width_multiple: 0.75 # layer channel multiple -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 v6.0 backbone -backbone: - # [from, number, module, args] - [[-1, 1, Conv, [64, 6, 2, 2]], # 0-P1/2 - [-1, 1, Conv, [128, 3, 2]], # 1-P2/4 - [-1, 3, C3, [128]], - [-1, 1, Conv, [256, 3, 2]], # 3-P3/8 - [-1, 6, C3, [256]], - [-1, 1, Conv, [512, 3, 2]], # 5-P4/16 - [-1, 9, C3, [512]], - [-1, 1, Conv, [1024, 3, 2]], # 7-P5/32 - [-1, 3, C3, [1024]], - [-1, 1, SPPF, [1024, 5]], # 9 - ] - -# YOLOv5 v6.0 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, C3, [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, C3, [256, False]], # 17 (P3/8-small) - - [-1, 1, Conv, [256, 3, 2]], - [[-1, 14], 1, Concat, [1]], # cat head P4 - [-1, 3, C3, [512, False]], # 20 (P4/16-medium) - - [-1, 1, Conv, [512, 3, 2]], - [[-1, 10], 1, Concat, [1]], # cat head P5 - [-1, 3, C3, [1024, False]], # 23 (P5/32-large) - - [[17, 20, 23], 1, Detect, [nc, anchors]], # Detect(P3, P4, P5) - ] diff --git a/YOLO/Dockerfiles/app/app/models/yolov5n.yaml b/YOLO/Dockerfiles/app/app/models/yolov5n.yaml deleted file mode 100644 index 8a28a40..0000000 --- a/YOLO/Dockerfiles/app/app/models/yolov5n.yaml +++ /dev/null @@ -1,48 +0,0 @@ -# YOLOv5 🚀 by Ultralytics, GPL-3.0 license - -# Parameters -nc: 80 # number of classes -depth_multiple: 0.33 # model depth multiple -width_multiple: 0.25 # layer channel multiple -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 v6.0 backbone -backbone: - # [from, number, module, args] - [[-1, 1, Conv, [64, 6, 2, 2]], # 0-P1/2 - [-1, 1, Conv, [128, 3, 2]], # 1-P2/4 - [-1, 3, C3, [128]], - [-1, 1, Conv, [256, 3, 2]], # 3-P3/8 - [-1, 6, C3, [256]], - [-1, 1, Conv, [512, 3, 2]], # 5-P4/16 - [-1, 9, C3, [512]], - [-1, 1, Conv, [1024, 3, 2]], # 7-P5/32 - [-1, 3, C3, [1024]], - [-1, 1, SPPF, [1024, 5]], # 9 - ] - -# YOLOv5 v6.0 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, C3, [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, C3, [256, False]], # 17 (P3/8-small) - - [-1, 1, Conv, [256, 3, 2]], - [[-1, 14], 1, Concat, [1]], # cat head P4 - [-1, 3, C3, [512, False]], # 20 (P4/16-medium) - - [-1, 1, Conv, [512, 3, 2]], - [[-1, 10], 1, Concat, [1]], # cat head P5 - [-1, 3, C3, [1024, False]], # 23 (P5/32-large) - - [[17, 20, 23], 1, Detect, [nc, anchors]], # Detect(P3, P4, P5) - ] diff --git a/YOLO/Dockerfiles/app/app/models/yolov5s.yaml b/YOLO/Dockerfiles/app/app/models/yolov5s.yaml deleted file mode 100644 index f35beab..0000000 --- a/YOLO/Dockerfiles/app/app/models/yolov5s.yaml +++ /dev/null @@ -1,48 +0,0 @@ -# YOLOv5 🚀 by Ultralytics, GPL-3.0 license - -# Parameters -nc: 80 # number of classes -depth_multiple: 0.33 # model depth multiple -width_multiple: 0.50 # layer channel multiple -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 v6.0 backbone -backbone: - # [from, number, module, args] - [[-1, 1, Conv, [64, 6, 2, 2]], # 0-P1/2 - [-1, 1, Conv, [128, 3, 2]], # 1-P2/4 - [-1, 3, C3, [128]], - [-1, 1, Conv, [256, 3, 2]], # 3-P3/8 - [-1, 6, C3, [256]], - [-1, 1, Conv, [512, 3, 2]], # 5-P4/16 - [-1, 9, C3, [512]], - [-1, 1, Conv, [1024, 3, 2]], # 7-P5/32 - [-1, 3, C3, [1024]], - [-1, 1, SPPF, [1024, 5]], # 9 - ] - -# YOLOv5 v6.0 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, C3, [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, C3, [256, False]], # 17 (P3/8-small) - - [-1, 1, Conv, [256, 3, 2]], - [[-1, 14], 1, Concat, [1]], # cat head P4 - [-1, 3, C3, [512, False]], # 20 (P4/16-medium) - - [-1, 1, Conv, [512, 3, 2]], - [[-1, 10], 1, Concat, [1]], # cat head P5 - [-1, 3, C3, [1024, False]], # 23 (P5/32-large) - - [[17, 20, 23], 1, Detect, [nc, anchors]], # Detect(P3, P4, P5) - ] diff --git a/YOLO/Dockerfiles/app/app/models/yolov5x.yaml b/YOLO/Dockerfiles/app/app/models/yolov5x.yaml deleted file mode 100644 index f617a02..0000000 --- a/YOLO/Dockerfiles/app/app/models/yolov5x.yaml +++ /dev/null @@ -1,48 +0,0 @@ -# YOLOv5 🚀 by Ultralytics, GPL-3.0 license - -# Parameters -nc: 80 # number of classes -depth_multiple: 1.33 # model depth multiple -width_multiple: 1.25 # layer channel multiple -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 v6.0 backbone -backbone: - # [from, number, module, args] - [[-1, 1, Conv, [64, 6, 2, 2]], # 0-P1/2 - [-1, 1, Conv, [128, 3, 2]], # 1-P2/4 - [-1, 3, C3, [128]], - [-1, 1, Conv, [256, 3, 2]], # 3-P3/8 - [-1, 6, C3, [256]], - [-1, 1, Conv, [512, 3, 2]], # 5-P4/16 - [-1, 9, C3, [512]], - [-1, 1, Conv, [1024, 3, 2]], # 7-P5/32 - [-1, 3, C3, [1024]], - [-1, 1, SPPF, [1024, 5]], # 9 - ] - -# YOLOv5 v6.0 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, C3, [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, C3, [256, False]], # 17 (P3/8-small) - - [-1, 1, Conv, [256, 3, 2]], - [[-1, 14], 1, Concat, [1]], # cat head P4 - [-1, 3, C3, [512, False]], # 20 (P4/16-medium) - - [-1, 1, Conv, [512, 3, 2]], - [[-1, 10], 1, Concat, [1]], # cat head P5 - [-1, 3, C3, [1024, False]], # 23 (P5/32-large) - - [[17, 20, 23], 1, Detect, [nc, anchors]], # Detect(P3, P4, P5) - ] diff --git a/YOLO/Dockerfiles/app/app/requirements.txt b/YOLO/Dockerfiles/app/app/requirements.txt deleted file mode 100755 index 96fc9d1..0000000 --- a/YOLO/Dockerfiles/app/app/requirements.txt +++ /dev/null @@ -1,37 +0,0 @@ -# pip install -r requirements.txt - -# Base ---------------------------------------- -matplotlib>=3.2.2 -numpy>=1.18.5 -opencv-python>=4.1.2 -Pillow>=7.1.2 -PyYAML>=5.3.1 -requests>=2.23.0 -scipy>=1.4.1 -torch>=1.7.0 -torchvision>=0.8.1 -tqdm>=4.41.0 - -# Logging ------------------------------------- -tensorboard>=2.4.1 -# wandb - -# Plotting ------------------------------------ -pandas>=1.1.4 -seaborn>=0.11.0 - -# Export -------------------------------------- -# coremltools>=4.1 # CoreML export -# onnx>=1.9.0 # ONNX export -# onnx-simplifier>=0.3.6 # ONNX simplifier -# scikit-learn==0.19.2 # CoreML quantization -# tensorflow>=2.4.1 # TFLite export -# tensorflowjs>=3.9.0 # TF.js export -# openvino-dev # OpenVINO export - -# Extras -------------------------------------- -# albumentations>=1.0.3 -# Cython # for pycocotools https://github.com/cocodataset/cocoapi/issues/172 -# pycocotools>=2.0 # COCO mAP -# roboflow -thop # FLOPs computation diff --git a/YOLO/Dockerfiles/app/app/runs/detect/1YBGKB4W/1YBGKB4W.png b/YOLO/Dockerfiles/app/app/runs/detect/1YBGKB4W/1YBGKB4W.png deleted file mode 100644 index 4148f98..0000000 Binary files a/YOLO/Dockerfiles/app/app/runs/detect/1YBGKB4W/1YBGKB4W.png and /dev/null differ diff --git a/YOLO/Dockerfiles/app/app/runs/detect/1YBGKB4W/labels/1YBGKB4W.txt b/YOLO/Dockerfiles/app/app/runs/detect/1YBGKB4W/labels/1YBGKB4W.txt deleted file mode 100644 index 0de1a89..0000000 --- a/YOLO/Dockerfiles/app/app/runs/detect/1YBGKB4W/labels/1YBGKB4W.txt +++ /dev/null @@ -1,70 +0,0 @@ -9 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0.5 0.2 0.2 -7 0.0994382 0.300917 0.198876 0.2 -11 0.0994382 0.900459 0.198876 0.197248 -11 0.9 0.700917 0.2 0.2 -18 0.698315 0.300917 0.201124 0.198165 -19 0.0994382 0.700459 0.198876 0.199083 -15 0.7 0.1 0.202247 0.198165 -11 0.298315 0.500917 0.198876 0.2 -11 0.7 0.101376 0.202247 0.197248 -11 0.0994382 0.100917 0.198876 0.198165 -11 0.9 0.500459 0.2 0.200917 -11 0.898876 0.900459 0.2 0.199083 -6 0.298315 0.700459 0.201124 0.199083 diff --git a/YOLO/Dockerfiles/app/app/runs/detect/1YBGKB4W2/1YBGKB4W.png b/YOLO/Dockerfiles/app/app/runs/detect/1YBGKB4W2/1YBGKB4W.png deleted file mode 100644 index 4148f98..0000000 Binary files a/YOLO/Dockerfiles/app/app/runs/detect/1YBGKB4W2/1YBGKB4W.png and /dev/null differ diff --git a/YOLO/Dockerfiles/app/app/runs/detect/1YBGKB4W2/labels/1YBGKB4W.txt b/YOLO/Dockerfiles/app/app/runs/detect/1YBGKB4W2/labels/1YBGKB4W.txt deleted file mode 100644 index 0de1a89..0000000 --- a/YOLO/Dockerfiles/app/app/runs/detect/1YBGKB4W2/labels/1YBGKB4W.txt +++ 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0.197248 -9 0.7 0.9 0.2 0.198165 -19 0.899438 0.700917 0.198876 0.2 -19 0.1 0.101835 0.2 0.2 -19 0.7 0.300459 0.202247 0.199083 -6 0.698876 0.1 0.202247 0.2 -11 0.900562 0.9 0.198876 0.2 diff --git a/YOLO/Dockerfiles/app/app/runs/detect/2RSXZ2IT2/2RSXZ2IT.png b/YOLO/Dockerfiles/app/app/runs/detect/2RSXZ2IT2/2RSXZ2IT.png deleted file mode 100644 index 8f0c2de..0000000 Binary files a/YOLO/Dockerfiles/app/app/runs/detect/2RSXZ2IT2/2RSXZ2IT.png and /dev/null differ diff --git a/YOLO/Dockerfiles/app/app/runs/detect/2RSXZ2IT2/labels/2RSXZ2IT.txt b/YOLO/Dockerfiles/app/app/runs/detect/2RSXZ2IT2/labels/2RSXZ2IT.txt deleted file mode 100644 index de44954..0000000 --- a/YOLO/Dockerfiles/app/app/runs/detect/2RSXZ2IT2/labels/2RSXZ2IT.txt +++ /dev/null @@ -1,63 +0,0 @@ -5 0.7 0.499541 0.2 0.200917 -9 0.498315 0.701376 0.203371 0.200917 -13 0.0994382 0.101835 0.198876 0.2 -14 0.7 0.9 0.2 0.2 -17 0.1 0.501376 0.2 0.197248 -11 0.899438 0.500459 0.201124 0.200917 -9 0.898876 0.3 0.197753 0.198165 -19 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b/YOLO/Dockerfiles/app/app/runs/train/exp4/weights/last.pt deleted file mode 100644 index 82079f5..0000000 Binary files a/YOLO/Dockerfiles/app/app/runs/train/exp4/weights/last.pt and /dev/null differ diff --git a/YOLO/Dockerfiles/app/app/setup.cfg b/YOLO/Dockerfiles/app/app/setup.cfg deleted file mode 100644 index 4ca0f0d..0000000 --- a/YOLO/Dockerfiles/app/app/setup.cfg +++ /dev/null @@ -1,51 +0,0 @@ -# Project-wide configuration file, can be used for package metadata and other toll configurations -# Example usage: global configuration for PEP8 (via flake8) setting or default pytest arguments - -[metadata] -license_file = LICENSE -description-file = README.md - - -[tool:pytest] -norecursedirs = - .git - dist - build -addopts = - --doctest-modules - --durations=25 - --color=yes - - -[flake8] -max-line-length = 120 -exclude = .tox,*.egg,build,temp -select = E,W,F -doctests = True -verbose = 2 -# https://pep8.readthedocs.io/en/latest/intro.html#error-codes -format = pylint -# see: https://www.flake8rules.com/ -ignore = - E731 # Do not assign a lambda expression, use a def - F405 - E402 - F841 - E741 - F821 - E722 - F401 - W504 - E127 - W504 - E231 - E501 - F403 - E302 - F541 - - -[isort] -# https://pycqa.github.io/isort/docs/configuration/options.html -line_length = 120 -multi_line_output = 0 diff --git a/YOLO/Dockerfiles/app/app/train.py b/YOLO/Dockerfiles/app/app/train.py deleted file mode 100644 index 410f16f..0000000 --- a/YOLO/Dockerfiles/app/app/train.py +++ /dev/null @@ -1,636 +0,0 @@ -# YOLOv5 🚀 by Ultralytics, GPL-3.0 license -""" -Train a YOLOv5 model on a custom dataset. - -Models and datasets download automatically from the latest YOLOv5 release. -Models: https://github.com/ultralytics/yolov5/tree/master/models -Datasets: https://github.com/ultralytics/yolov5/tree/master/data -Tutorial: https://github.com/ultralytics/yolov5/wiki/Train-Custom-Data - -Usage: - $ python path/to/train.py --data coco128.yaml --weights yolov5s.pt --img 640 # from pretrained (RECOMMENDED) - $ python path/to/train.py --data coco128.yaml --weights '' --cfg yolov5s.yaml --img 640 # from scratch -""" - -import argparse -import math -import os -import random -import sys -import time -from copy import deepcopy -from datetime import datetime -from pathlib import Path - -import numpy as np -import torch -import torch.distributed as dist -import torch.nn as nn -import yaml -from torch.cuda import amp -from torch.nn.parallel import DistributedDataParallel as DDP -from torch.optim import SGD, Adam, AdamW, lr_scheduler -from tqdm import tqdm - -FILE = Path(__file__).resolve() -ROOT = FILE.parents[0] # YOLOv5 root directory -if str(ROOT) not in sys.path: - sys.path.append(str(ROOT)) # add ROOT to PATH -ROOT = Path(os.path.relpath(ROOT, Path.cwd())) # relative - -import val # for end-of-epoch mAP -from models.experimental import attempt_load -from models.yolo import Model -from utils.autoanchor import check_anchors -from utils.autobatch import check_train_batch_size -from utils.callbacks import Callbacks -from utils.datasets import create_dataloader -from utils.downloads import attempt_download -from utils.general import (LOGGER, check_dataset, check_file, check_git_status, check_img_size, check_requirements, - check_suffix, check_yaml, colorstr, get_latest_run, increment_path, init_seeds, - intersect_dicts, labels_to_class_weights, labels_to_image_weights, methods, one_cycle, - print_args, print_mutation, strip_optimizer) -from utils.loggers import Loggers -from utils.loggers.wandb.wandb_utils import check_wandb_resume -from utils.loss import ComputeLoss -from utils.metrics import fitness -from utils.plots import plot_evolve, plot_labels -from utils.torch_utils import EarlyStopping, ModelEMA, de_parallel, select_device, torch_distributed_zero_first - -LOCAL_RANK = int(os.getenv('LOCAL_RANK', -1)) # https://pytorch.org/docs/stable/elastic/run.html -RANK = int(os.getenv('RANK', -1)) -WORLD_SIZE = int(os.getenv('WORLD_SIZE', 1)) - - -def train(hyp, # path/to/hyp.yaml or hyp dictionary - opt, - device, - callbacks - ): - save_dir, epochs, batch_size, weights, single_cls, evolve, data, cfg, resume, noval, nosave, workers, freeze = \ - Path(opt.save_dir), opt.epochs, opt.batch_size, opt.weights, opt.single_cls, opt.evolve, opt.data, opt.cfg, \ - opt.resume, opt.noval, opt.nosave, opt.workers, opt.freeze - - # Directories - w = save_dir / 'weights' # weights dir - (w.parent if evolve else w).mkdir(parents=True, exist_ok=True) # make dir - last, best = w / 'last.pt', w / 'best.pt' - - # Hyperparameters - if isinstance(hyp, str): - with open(hyp, errors='ignore') as f: - hyp = yaml.safe_load(f) # load hyps dict - LOGGER.info(colorstr('hyperparameters: ') + ', '.join(f'{k}={v}' for k, v in hyp.items())) - - # Save run settings - if not evolve: - with open(save_dir / 'hyp.yaml', 'w') as f: - yaml.safe_dump(hyp, f, sort_keys=False) - with open(save_dir / 'opt.yaml', 'w') as f: - yaml.safe_dump(vars(opt), f, sort_keys=False) - - # Loggers - data_dict = None - if RANK in [-1, 0]: - loggers = Loggers(save_dir, weights, opt, hyp, LOGGER) # loggers instance - if loggers.wandb: - data_dict = loggers.wandb.data_dict - if resume: - weights, epochs, hyp = opt.weights, opt.epochs, opt.hyp - - # Register actions - for k in methods(loggers): - callbacks.register_action(k, callback=getattr(loggers, k)) - - # Config - plots = not evolve # create plots - cuda = device.type != 'cpu' - init_seeds(1 + RANK) - with torch_distributed_zero_first(LOCAL_RANK): - data_dict = data_dict or check_dataset(data) # check if None - train_path, val_path = data_dict['train'], data_dict['val'] - nc = 1 if single_cls else int(data_dict['nc']) # number of classes - names = ['item'] if single_cls and len(data_dict['names']) != 1 else data_dict['names'] # class names - assert len(names) == nc, f'{len(names)} names found for nc={nc} dataset in {data}' # check - is_coco = isinstance(val_path, str) and val_path.endswith('coco/val2017.txt') # COCO dataset - - # Model - check_suffix(weights, '.pt') # check weights - pretrained = weights.endswith('.pt') - if pretrained: - with torch_distributed_zero_first(LOCAL_RANK): - weights = attempt_download(weights) # download if not found locally - ckpt = torch.load(weights, map_location=device) # load checkpoint - model = Model(cfg or ckpt['model'].yaml, ch=3, nc=nc, anchors=hyp.get('anchors')).to(device) # create - exclude = ['anchor'] if (cfg or hyp.get('anchors')) and not resume else [] # exclude keys - csd = ckpt['model'].float().state_dict() # checkpoint state_dict as FP32 - csd = intersect_dicts(csd, model.state_dict(), exclude=exclude) # intersect - model.load_state_dict(csd, strict=False) # load - LOGGER.info(f'Transferred {len(csd)}/{len(model.state_dict())} items from {weights}') # report - else: - model = Model(cfg, ch=3, nc=nc, anchors=hyp.get('anchors')).to(device) # create - - # Freeze - freeze = [f'model.{x}.' for x in (freeze if len(freeze) > 1 else range(freeze[0]))] # layers to freeze - for k, v in model.named_parameters(): - v.requires_grad = True # train all layers - if any(x in k for x in freeze): - LOGGER.info(f'freezing {k}') - v.requires_grad = False - - # Image size - gs = max(int(model.stride.max()), 32) # grid size (max stride) - imgsz = check_img_size(opt.imgsz, gs, floor=gs * 2) # verify imgsz is gs-multiple - - # Batch size - if RANK == -1 and batch_size == -1: # single-GPU only, estimate best batch size - batch_size = check_train_batch_size(model, imgsz) - loggers.on_params_update({"batch_size": batch_size}) - - # Optimizer - nbs = 64 # nominal batch size - accumulate = max(round(nbs / batch_size), 1) # accumulate loss before optimizing - hyp['weight_decay'] *= batch_size * accumulate / nbs # scale weight_decay - LOGGER.info(f"Scaled weight_decay = {hyp['weight_decay']}") - - g0, g1, g2 = [], [], [] # optimizer parameter groups - for v in model.modules(): - if hasattr(v, 'bias') and isinstance(v.bias, nn.Parameter): # bias - g2.append(v.bias) - if isinstance(v, nn.BatchNorm2d): # weight (no decay) - g0.append(v.weight) - elif hasattr(v, 'weight') and isinstance(v.weight, nn.Parameter): # weight (with decay) - g1.append(v.weight) - - if opt.optimizer == 'Adam': - optimizer = Adam(g0, lr=hyp['lr0'], betas=(hyp['momentum'], 0.999)) # adjust beta1 to momentum - elif opt.optimizer == 'AdamW': - optimizer = AdamW(g0, lr=hyp['lr0'], betas=(hyp['momentum'], 0.999)) # adjust beta1 to momentum - else: - optimizer = SGD(g0, lr=hyp['lr0'], momentum=hyp['momentum'], nesterov=True) - - optimizer.add_param_group({'params': g1, 'weight_decay': hyp['weight_decay']}) # add g1 with weight_decay - optimizer.add_param_group({'params': g2}) # add g2 (biases) - LOGGER.info(f"{colorstr('optimizer:')} {type(optimizer).__name__} with parameter groups " - f"{len(g0)} weight, {len(g1)} weight (no decay), {len(g2)} bias") - del g0, g1, g2 - - # Scheduler - if opt.linear_lr: - lf = lambda x: (1 - x / (epochs - 1)) * (1.0 - hyp['lrf']) + hyp['lrf'] # linear - else: - lf = one_cycle(1, hyp['lrf'], epochs) # cosine 1->hyp['lrf'] - scheduler = lr_scheduler.LambdaLR(optimizer, lr_lambda=lf) # plot_lr_scheduler(optimizer, scheduler, epochs) - - # EMA - ema = ModelEMA(model) if RANK in [-1, 0] else None - - # 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'] - - # EMA - if ema and ckpt.get('ema'): - ema.ema.load_state_dict(ckpt['ema'].float().state_dict()) - ema.updates = ckpt['updates'] - - # Epochs - start_epoch = ckpt['epoch'] + 1 - if resume: - assert start_epoch > 0, f'{weights} training to {epochs} epochs is finished, nothing to resume.' - if epochs < start_epoch: - LOGGER.info(f"{weights} has been trained for {ckpt['epoch']} epochs. Fine-tuning for {epochs} more epochs.") - epochs += ckpt['epoch'] # finetune additional epochs - - del ckpt, csd - - # DP mode - if cuda and RANK == -1 and torch.cuda.device_count() > 1: - LOGGER.warning('WARNING: DP not recommended, use torch.distributed.run for best DDP Multi-GPU results.\n' - 'See Multi-GPU Tutorial at https://github.com/ultralytics/yolov5/issues/475 to get started.') - 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()') - - # Trainloader - train_loader, dataset = create_dataloader(train_path, imgsz, batch_size // WORLD_SIZE, gs, single_cls, - hyp=hyp, augment=True, cache=opt.cache, rect=opt.rect, rank=LOCAL_RANK, - workers=workers, image_weights=opt.image_weights, quad=opt.quad, - prefix=colorstr('train: '), shuffle=True) - mlc = int(np.concatenate(dataset.labels, 0)[:, 0].max()) # max label class - nb = len(train_loader) # number of batches - assert mlc < nc, f'Label class {mlc} exceeds nc={nc} in {data}. Possible class labels are 0-{nc - 1}' - - # Process 0 - if RANK in [-1, 0]: - val_loader = create_dataloader(val_path, imgsz, batch_size // WORLD_SIZE * 2, gs, single_cls, - hyp=hyp, cache=None if noval else opt.cache, rect=True, rank=-1, - workers=workers, pad=0.5, - prefix=colorstr('val: '))[0] - - if not resume: - labels = np.concatenate(dataset.labels, 0) - # c = torch.tensor(labels[:, 0]) # classes - # cf = torch.bincount(c.long(), minlength=nc) + 1. # frequency - # model._initialize_biases(cf.to(device)) - if plots: - plot_labels(labels, names, save_dir) - - # Anchors - if not opt.noautoanchor: - check_anchors(dataset, model=model, thr=hyp['anchor_t'], imgsz=imgsz) - model.half().float() # pre-reduce anchor precision - - callbacks.run('on_pretrain_routine_end') - - # DDP mode - if cuda and RANK != -1: - model = DDP(model, device_ids=[LOCAL_RANK], output_device=LOCAL_RANK) - - # Model attributes - nl = de_parallel(model).model[-1].nl # number of detection layers (to scale hyps) - hyp['box'] *= 3 / nl # scale to layers - hyp['cls'] *= nc / 80 * 3 / nl # scale to classes and layers - hyp['obj'] *= (imgsz / 640) ** 2 * 3 / nl # scale to image size and layers - hyp['label_smoothing'] = opt.label_smoothing - model.nc = nc # attach number of classes to model - model.hyp = hyp # attach hyperparameters to model - 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 - last_opt_step = -1 - maps = np.zeros(nc) # mAP per class - results = (0, 0, 0, 0, 0, 0, 0) # P, R, mAP@.5, mAP@.5-.95, val_loss(box, obj, cls) - scheduler.last_epoch = start_epoch - 1 # do not move - scaler = amp.GradScaler(enabled=cuda) - stopper = EarlyStopping(patience=opt.patience) - compute_loss = ComputeLoss(model) # init loss class - LOGGER.info(f'Image sizes {imgsz} train, {imgsz} val\n' - f'Using {train_loader.num_workers * WORLD_SIZE} dataloader workers\n' - f"Logging results to {colorstr('bold', save_dir)}\n" - f'Starting training for {epochs} epochs...') - for epoch in range(start_epoch, epochs): # epoch ------------------------------------------------------------------ - model.train() - - # Update image weights (optional, single-GPU only) - if opt.image_weights: - 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 - - # Update mosaic border (optional) - # 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(3, device=device) # mean losses - if RANK != -1: - train_loader.sampler.set_epoch(epoch) - pbar = enumerate(train_loader) - LOGGER.info(('\n' + '%10s' * 7) % ('Epoch', 'gpu_mem', 'box', 'obj', 'cls', 'labels', 'img_size')) - if RANK in [-1, 0]: - pbar = tqdm(pbar, total=nb, bar_format='{l_bar}{bar:10}{r_bar}{bar:-10b}') # 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 # uint8 to float32, 0-255 to 0.0-1.0 - - # Warmup - if ni <= nw: - xi = [0, nw] # x interp - # compute_loss.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 / 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 = nn.functional.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)) # loss scaled by batch_size - if RANK != -1: - loss *= WORLD_SIZE # gradient averaged between devices in DDP mode - if opt.quad: - loss *= 4. - - # Backward - scaler.scale(loss).backward() - - # Optimize - if ni - last_opt_step >= accumulate: - scaler.step(optimizer) # optimizer.step - scaler.update() - optimizer.zero_grad() - if ema: - ema.update(model) - last_opt_step = ni - - # Log - if RANK in [-1, 0]: - mloss = (mloss * i + loss_items) / (i + 1) # update mean losses - mem = f'{torch.cuda.memory_reserved() / 1E9 if torch.cuda.is_available() else 0:.3g}G' # (GB) - pbar.set_description(('%10s' * 2 + '%10.4g' * 5) % ( - f'{epoch}/{epochs - 1}', mem, *mloss, targets.shape[0], imgs.shape[-1])) - callbacks.run('on_train_batch_end', ni, model, imgs, targets, paths, plots, opt.sync_bn) - # end batch ------------------------------------------------------------------------------------------------ - - # Scheduler - lr = [x['lr'] for x in optimizer.param_groups] # for loggers - scheduler.step() - - if RANK in [-1, 0]: - # mAP - callbacks.run('on_train_epoch_end', epoch=epoch) - ema.update_attr(model, include=['yaml', 'nc', 'hyp', 'names', 'stride', 'class_weights']) - final_epoch = (epoch + 1 == epochs) or stopper.possible_stop - if not noval or final_epoch: # Calculate mAP - results, maps, _ = val.run(data_dict, - batch_size=batch_size // WORLD_SIZE * 2, - imgsz=imgsz, - model=ema.ema, - single_cls=single_cls, - dataloader=val_loader, - save_dir=save_dir, - plots=False, - callbacks=callbacks, - compute_loss=compute_loss) - - # Update best mAP - fi = fitness(np.array(results).reshape(1, -1)) # weighted combination of [P, R, mAP@.5, mAP@.5-.95] - if fi > best_fitness: - best_fitness = fi - log_vals = list(mloss) + list(results) + lr - callbacks.run('on_fit_epoch_end', log_vals, epoch, best_fitness, fi) - - # Save model - if (not nosave) or (final_epoch and not evolve): # if save - ckpt = {'epoch': epoch, - 'best_fitness': best_fitness, - 'model': deepcopy(de_parallel(model)).half(), - 'ema': deepcopy(ema.ema).half(), - 'updates': ema.updates, - 'optimizer': optimizer.state_dict(), - 'wandb_id': loggers.wandb.wandb_run.id if loggers.wandb else None, - 'date': datetime.now().isoformat()} - - # Save last, best and delete - torch.save(ckpt, last) - if best_fitness == fi: - torch.save(ckpt, best) - if (epoch > 0) and (opt.save_period > 0) and (epoch % opt.save_period == 0): - torch.save(ckpt, w / f'epoch{epoch}.pt') - del ckpt - callbacks.run('on_model_save', last, epoch, final_epoch, best_fitness, fi) - - # Stop Single-GPU - if RANK == -1 and stopper(epoch=epoch, fitness=fi): - break - - # Stop DDP TODO: known issues shttps://github.com/ultralytics/yolov5/pull/4576 - # stop = stopper(epoch=epoch, fitness=fi) - # if RANK == 0: - # dist.broadcast_object_list([stop], 0) # broadcast 'stop' to all ranks - - # Stop DPP - # with torch_distributed_zero_first(RANK): - # if stop: - # break # must break all DDP ranks - - # end epoch ---------------------------------------------------------------------------------------------------- - # end training ----------------------------------------------------------------------------------------------------- - if RANK in [-1, 0]: - LOGGER.info(f'\n{epoch - start_epoch + 1} epochs completed in {(time.time() - t0) / 3600:.3f} hours.') - for f in last, best: - if f.exists(): - strip_optimizer(f) # strip optimizers - if f is best: - LOGGER.info(f'\nValidating {f}...') - results, _, _ = val.run(data_dict, - batch_size=batch_size // WORLD_SIZE * 2, - imgsz=imgsz, - model=attempt_load(f, device).half(), - iou_thres=0.65 if is_coco else 0.60, # best pycocotools results at 0.65 - single_cls=single_cls, - dataloader=val_loader, - save_dir=save_dir, - save_json=is_coco, - verbose=True, - plots=True, - callbacks=callbacks, - compute_loss=compute_loss) # val best model with plots - if is_coco: - callbacks.run('on_fit_epoch_end', list(mloss) + list(results) + lr, epoch, best_fitness, fi) - - callbacks.run('on_train_end', last, best, plots, epoch, results) - LOGGER.info(f"Results saved to {colorstr('bold', save_dir)}") - - torch.cuda.empty_cache() - return results - - -def parse_opt(known=False): - parser = argparse.ArgumentParser() - parser.add_argument('--weights', type=str, default=ROOT / 'yolov5s.pt', help='initial weights path') - parser.add_argument('--cfg', type=str, default='', help='model.yaml path') - parser.add_argument('--data', type=str, default=ROOT / 'data/coco128.yaml', help='dataset.yaml path') - parser.add_argument('--hyp', type=str, default=ROOT / 'data/hyps/hyp.scratch.yaml', help='hyperparameters path') - parser.add_argument('--epochs', type=int, default=300) - parser.add_argument('--batch-size', type=int, default=16, help='total batch size for all GPUs, -1 for autobatch') - parser.add_argument('--imgsz', '--img', '--img-size', type=int, default=640, help='train, val image size (pixels)') - parser.add_argument('--rect', action='store_true', help='rectangular training') - parser.add_argument('--resume', nargs='?', const=True, default=False, help='resume most recent training') - parser.add_argument('--nosave', action='store_true', help='only save final checkpoint') - parser.add_argument('--noval', action='store_true', help='only validate final epoch') - parser.add_argument('--noautoanchor', action='store_true', help='disable AutoAnchor') - parser.add_argument('--evolve', type=int, nargs='?', const=300, help='evolve hyperparameters for x generations') - parser.add_argument('--bucket', type=str, default='', help='gsutil bucket') - parser.add_argument('--cache', type=str, nargs='?', const='ram', help='--cache images in "ram" (default) or "disk"') - parser.add_argument('--image-weights', action='store_true', help='use weighted image selection for training') - parser.add_argument('--device', default='', help='cuda device, i.e. 0 or 0,1,2,3 or cpu') - parser.add_argument('--multi-scale', action='store_true', help='vary img-size +/- 50%%') - parser.add_argument('--single-cls', action='store_true', help='train multi-class data as single-class') - parser.add_argument('--optimizer', type=str, choices=['SGD', 'Adam', 'AdamW'], default='SGD', help='optimizer') - parser.add_argument('--sync-bn', action='store_true', help='use SyncBatchNorm, only available in DDP mode') - parser.add_argument('--workers', type=int, default=8, help='max dataloader workers (per RANK in DDP mode)') - parser.add_argument('--project', default=ROOT / 'runs/train', help='save to project/name') - parser.add_argument('--name', default='exp', help='save to project/name') - parser.add_argument('--exist-ok', action='store_true', help='existing project/name ok, do not increment') - parser.add_argument('--quad', action='store_true', help='quad dataloader') - parser.add_argument('--linear-lr', action='store_true', help='linear LR') - parser.add_argument('--label-smoothing', type=float, default=0.0, help='Label smoothing epsilon') - parser.add_argument('--patience', type=int, default=100, help='EarlyStopping patience (epochs without improvement)') - parser.add_argument('--freeze', nargs='+', type=int, default=[0], help='Freeze layers: backbone=10, first3=0 1 2') - parser.add_argument('--save-period', type=int, default=-1, help='Save checkpoint every x epochs (disabled if < 1)') - parser.add_argument('--local_rank', type=int, default=-1, help='DDP parameter, do not modify') - - # Weights & Biases arguments - parser.add_argument('--entity', default=None, help='W&B: Entity') - parser.add_argument('--upload_dataset', nargs='?', const=True, default=False, help='W&B: Upload data, "val" option') - parser.add_argument('--bbox_interval', type=int, default=-1, help='W&B: Set bounding-box image logging interval') - parser.add_argument('--artifact_alias', type=str, default='latest', help='W&B: Version of dataset artifact to use') - - opt = parser.parse_known_args()[0] if known else parser.parse_args() - return opt - - -def main(opt, callbacks=Callbacks()): - # Checks - if RANK in [-1, 0]: - print_args(FILE.stem, opt) - check_git_status() - check_requirements(exclude=['thop']) - - # Resume - if opt.resume and not check_wandb_resume(opt) and not opt.evolve: # resume an interrupted run - ckpt = opt.resume if isinstance(opt.resume, str) else get_latest_run() # specified or most recent path - assert os.path.isfile(ckpt), 'ERROR: --resume checkpoint does not exist' - with open(Path(ckpt).parent.parent / 'opt.yaml', errors='ignore') as f: - opt = argparse.Namespace(**yaml.safe_load(f)) # replace - opt.cfg, opt.weights, opt.resume = '', ckpt, True # reinstate - LOGGER.info(f'Resuming training from {ckpt}') - else: - opt.data, opt.cfg, opt.hyp, opt.weights, opt.project = \ - check_file(opt.data), check_yaml(opt.cfg), check_yaml(opt.hyp), str(opt.weights), str(opt.project) # checks - assert len(opt.cfg) or len(opt.weights), 'either --cfg or --weights must be specified' - if opt.evolve: - opt.project = str(ROOT / 'runs/evolve') - opt.exist_ok, opt.resume = opt.resume, False # pass resume to exist_ok and disable resume - opt.save_dir = str(increment_path(Path(opt.project) / opt.name, exist_ok=opt.exist_ok)) - - # DDP mode - device = select_device(opt.device, batch_size=opt.batch_size) - if LOCAL_RANK != -1: - assert torch.cuda.device_count() > LOCAL_RANK, 'insufficient CUDA devices for DDP command' - assert opt.batch_size % WORLD_SIZE == 0, '--batch-size must be multiple of CUDA device count' - assert not opt.image_weights, '--image-weights argument is not compatible with DDP training' - assert not opt.evolve, '--evolve argument is not compatible with DDP training' - torch.cuda.set_device(LOCAL_RANK) - device = torch.device('cuda', LOCAL_RANK) - dist.init_process_group(backend="nccl" if dist.is_nccl_available() else "gloo") - - # Train - if not opt.evolve: - train(opt.hyp, opt, device, callbacks) - if WORLD_SIZE > 1 and RANK == 0: - LOGGER.info('Destroying process group... ') - dist.destroy_process_group() - - # Evolve hyperparameters (optional) - else: - # Hyperparameter evolution metadata (mutation scale 0-1, lower_limit, upper_limit) - meta = {'lr0': (1, 1e-5, 1e-1), # initial learning rate (SGD=1E-2, Adam=1E-3) - 'lrf': (1, 0.01, 1.0), # final OneCycleLR learning rate (lr0 * lrf) - 'momentum': (0.3, 0.6, 0.98), # SGD momentum/Adam beta1 - 'weight_decay': (1, 0.0, 0.001), # optimizer weight decay - 'warmup_epochs': (1, 0.0, 5.0), # warmup epochs (fractions ok) - 'warmup_momentum': (1, 0.0, 0.95), # warmup initial momentum - 'warmup_bias_lr': (1, 0.0, 0.2), # warmup initial bias lr - 'box': (1, 0.02, 0.2), # box loss gain - 'cls': (1, 0.2, 4.0), # cls loss gain - 'cls_pw': (1, 0.5, 2.0), # cls BCELoss positive_weight - 'obj': (1, 0.2, 4.0), # obj loss gain (scale with pixels) - 'obj_pw': (1, 0.5, 2.0), # obj BCELoss positive_weight - 'iou_t': (0, 0.1, 0.7), # IoU training threshold - 'anchor_t': (1, 2.0, 8.0), # anchor-multiple threshold - 'anchors': (2, 2.0, 10.0), # anchors per output grid (0 to ignore) - 'fl_gamma': (0, 0.0, 2.0), # focal loss gamma (efficientDet default gamma=1.5) - 'hsv_h': (1, 0.0, 0.1), # image HSV-Hue augmentation (fraction) - 'hsv_s': (1, 0.0, 0.9), # image HSV-Saturation augmentation (fraction) - 'hsv_v': (1, 0.0, 0.9), # image HSV-Value augmentation (fraction) - 'degrees': (1, 0.0, 45.0), # image rotation (+/- deg) - 'translate': (1, 0.0, 0.9), # image translation (+/- fraction) - 'scale': (1, 0.0, 0.9), # image scale (+/- gain) - 'shear': (1, 0.0, 10.0), # image shear (+/- deg) - 'perspective': (0, 0.0, 0.001), # image perspective (+/- fraction), range 0-0.001 - 'flipud': (1, 0.0, 1.0), # image flip up-down (probability) - 'fliplr': (0, 0.0, 1.0), # image flip left-right (probability) - 'mosaic': (1, 0.0, 1.0), # image mixup (probability) - 'mixup': (1, 0.0, 1.0), # image mixup (probability) - 'copy_paste': (1, 0.0, 1.0)} # segment copy-paste (probability) - - with open(opt.hyp, errors='ignore') as f: - hyp = yaml.safe_load(f) # load hyps dict - if 'anchors' not in hyp: # anchors commented in hyp.yaml - hyp['anchors'] = 3 - opt.noval, opt.nosave, save_dir = True, True, Path(opt.save_dir) # only val/save final epoch - # ei = [isinstance(x, (int, float)) for x in hyp.values()] # evolvable indices - evolve_yaml, evolve_csv = save_dir / 'hyp_evolve.yaml', save_dir / 'evolve.csv' - if opt.bucket: - os.system(f'gsutil cp gs://{opt.bucket}/evolve.csv {save_dir}') # download evolve.csv if exists - - for _ in range(opt.evolve): # generations to evolve - if evolve_csv.exists(): # if evolve.csv exists: select best hyps and mutate - # Select parent(s) - parent = 'single' # parent selection method: 'single' or 'weighted' - x = np.loadtxt(evolve_csv, ndmin=2, delimiter=',', skiprows=1) - 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() + 1E-6 # weights (sum > 0) - 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([meta[k][0] for k in hyp.keys()]) # 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, callbacks) - - # Write mutation results - print_mutation(results, hyp.copy(), save_dir, opt.bucket) - - # Plot results - plot_evolve(evolve_csv) - LOGGER.info(f'Hyperparameter evolution finished\n' - 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"overflow": null, - "_model_module_version": "1.2.0", - "_view_count": null, - "flex_flow": null, - "width": null, - "min_width": null, - "border": null, - "align_items": null, - "bottom": null, - "_model_module": "@jupyter-widgets/base", - "top": null, - "grid_column": 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": "t6MPjfT5NrKQ" - }, - "source": [ - "\n", - "\n", - "\n", - "This is the **official YOLOv5 🚀 notebook** by **Ultralytics**, 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://ultralytics.com. Thank you!" - ] - }, - { - "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": "3809e5a9-dd41-4577-fe62-5531abf7cca2" - }, - "source": [ - "!git clone https://github.com/ultralytics/yolov5 # clone\n", - "%cd yolov5\n", - "%pip install -qr requirements.txt # install\n", - "\n", - "import torch\n", - "from yolov5 import utils\n", - "display = utils.notebook_init() # checks" - ], - "execution_count": null, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "YOLOv5 🚀 v6.0-48-g84a8099 torch 1.10.0+cu102 CUDA:0 (Tesla V100-SXM2-16GB, 16160MiB)\n", - "Setup complete ✅\n" - ] - } - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "4JnkELT0cIJg" - }, - "source": [ - "# 1. Inference\n", - "\n", - "`detect.py` runs YOLOv5 inference on a variety of sources, downloading models automatically from the [latest YOLOv5 release](https://github.com/ultralytics/yolov5/releases), and saving results to `runs/detect`. Example inference sources are:\n", - "\n", - "```shell\n", - "python detect.py --source 0 # webcam\n", - " img.jpg # image \n", - " vid.mp4 # video\n", - " path/ # directory\n", - " path/*.jpg # glob\n", - " 'https://youtu.be/Zgi9g1ksQHc' # YouTube\n", - " 'rtsp://example.com/media.mp4' # RTSP, RTMP, HTTP stream\n", - "```" - ] - }, - { - "cell_type": "code", - "metadata": { - "id": "zR9ZbuQCH7FX", - "colab": { - "base_uri": "https://localhost:8080/" - }, - "outputId": "8f7e6588-215d-4ebd-93af-88b871e770a7" - }, - "source": [ - "!python detect.py --weights yolov5s.pt --img 640 --conf 0.25 --source data/images\n", - "display.Image(filename='runs/detect/exp/zidane.jpg', width=600)" - ], - "execution_count": null, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "\u001b[34m\u001b[1mdetect: \u001b[0mweights=['yolov5s.pt'], source=data/images, imgsz=[640, 640], conf_thres=0.25, iou_thres=0.45, max_det=1000, device=, view_img=False, save_txt=False, save_conf=False, save_crop=False, nosave=False, classes=None, agnostic_nms=False, augment=False, visualize=False, update=False, project=runs/detect, name=exp, exist_ok=False, line_thickness=3, hide_labels=False, hide_conf=False, half=False, dnn=False\n", - "YOLOv5 🚀 v6.0-48-g84a8099 torch 1.10.0+cu102 CUDA:0 (Tesla V100-SXM2-16GB, 16160MiB)\n", - "\n", - "Fusing layers... \n", - "Model Summary: 213 layers, 7225885 parameters, 0 gradients\n", - "image 1/2 /content/yolov5/data/images/bus.jpg: 640x480 4 persons, 1 bus, Done. (0.007s)\n", - "image 2/2 /content/yolov5/data/images/zidane.jpg: 384x640 2 persons, 1 tie, Done. (0.007s)\n", - "Speed: 0.5ms pre-process, 6.9ms inference, 1.3ms NMS per image at shape (1, 3, 640, 640)\n", - "Results saved to \u001b[1mruns/detect/exp\u001b[0m\n" - ] - } - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "hkAzDWJ7cWTr" - }, - "source": [ - "        \n", - "" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "0eq1SMWl6Sfn" - }, - "source": [ - "# 2. Validate\n", - "Validate a model's accuracy on [COCO](https://cocodataset.org/#home) val or test-dev datasets. 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% 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 val\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": 48, - "referenced_widgets": [ - "eb95db7cae194218b3fcefb439b6352f", - "769ecde6f2e64bacb596ce972f8d3d2d", - "384a001876054c93b0af45cd1e960bfe", - "dded0aeae74440f7ba2ffa0beb8dd612", - "5296d28be75740b2892ae421bbec3657", - "9f09facb2a6c4a7096810d327c8b551c", - "25621cff5d16448cb7260e839fd0f543", - "0ce7164fc0c74bb9a2b5c7037375a727", - "c4c4593c10904cb5b8a5724d60c7e181", - "473371611126476c88d5d42ec7031ed6", - "65efdfd0d26c46e79c8c5ff3b77126cc" - ] - }, - "outputId": "bcf9a448-1f9b-4a41-ad49-12f181faf05a" - }, - "source": [ - "# Download COCO val\n", - "torch.hub.download_url_to_file('https://ultralytics.com/assets/coco2017val.zip', 'tmp.zip')\n", - "!unzip -q tmp.zip -d ../datasets && rm tmp.zip" - ], - "execution_count": null, - "outputs": [ - { - "output_type": "display_data", - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "eb95db7cae194218b3fcefb439b6352f", - "version_minor": 0, - "version_major": 2 - }, - "text/plain": [ - " 0%| | 0.00/780M [00:00

\n", - "Close the active learning loop by sampling images from your inference conditions with the `roboflow` pip package\n", - "

\n", - "\n", - "Train a YOLOv5s model on the [COCO128](https://www.kaggle.com/ultralytics/coco128) dataset with `--data coco128.yaml`, starting from pretrained `--weights yolov5s.pt`, or from randomly initialized `--weights '' --cfg yolov5s.yaml`.\n", - "\n", - "- **Pretrained [Models](https://github.com/ultralytics/yolov5/tree/master/models)** are downloaded\n", - "automatically from the [latest YOLOv5 release](https://github.com/ultralytics/yolov5/releases)\n", - "- **[Datasets](https://github.com/ultralytics/yolov5/tree/master/data)** available for autodownload include: [COCO](https://github.com/ultralytics/yolov5/blob/master/data/coco.yaml), [COCO128](https://github.com/ultralytics/yolov5/blob/master/data/coco128.yaml), [VOC](https://github.com/ultralytics/yolov5/blob/master/data/VOC.yaml), [Argoverse](https://github.com/ultralytics/yolov5/blob/master/data/Argoverse.yaml), [VisDrone](https://github.com/ultralytics/yolov5/blob/master/data/VisDrone.yaml), [GlobalWheat](https://github.com/ultralytics/yolov5/blob/master/data/GlobalWheat2020.yaml), [xView](https://github.com/ultralytics/yolov5/blob/master/data/xView.yaml), [Objects365](https://github.com/ultralytics/yolov5/blob/master/data/Objects365.yaml), [SKU-110K](https://github.com/ultralytics/yolov5/blob/master/data/SKU-110K.yaml).\n", - "- **Training Results** are saved to `runs/train/` with incrementing run directories, i.e. `runs/train/exp2`, `runs/train/exp3` etc.\n", - "

\n", - "\n", - "## Train on Custom Data with Roboflow 🌟 NEW\n", - "\n", - "[Roboflow](https://roboflow.com/?ref=ultralytics) enables you to easily **organize, label, and prepare** a high quality dataset with your own custom data. Roboflow also makes it easy to establish an active learning pipeline, collaborate with your team on dataset improvement, and integrate directly into your model building workflow with the `roboflow` pip package.\n", - "\n", - "- Custom Training Example: [https://blog.roboflow.com/how-to-train-yolov5-on-a-custom-dataset/](https://blog.roboflow.com/how-to-train-yolov5-on-a-custom-dataset/?ref=ultralytics)\n", - "- Custom Training Notebook: [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/roboflow-ai/yolov5-custom-training-tutorial/blob/main/yolov5-custom-training.ipynb)\n", - "
\n", - "\n", - "

Label images lightning fast (including with model-assisted labeling)" - ] - }, - { - "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", - "import wandb\n", - "wandb.login()" - ], - "execution_count": null, - "outputs": [] - }, - { - "cell_type": "code", - "metadata": { - "id": "1NcFxRcFdJ_O", - "colab": { - "base_uri": "https://localhost:8080/" - }, - "outputId": "8724d13d-6711-4a12-d96a-1c655e5c3549" - }, - "source": [ - "# Train YOLOv5s on COCO128 for 3 epochs\n", - "!python train.py --img 640 --batch 16 --epochs 3 --data coco128.yaml --weights yolov5s.pt --cache" - ], - "execution_count": null, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "\u001b[34m\u001b[1mtrain: \u001b[0mweights=yolov5s.pt, cfg=, data=coco128.yaml, hyp=data/hyps/hyp.scratch.yaml, epochs=3, batch_size=16, imgsz=640, rect=False, resume=False, nosave=False, noval=False, noautoanchor=False, evolve=None, bucket=, cache=ram, image_weights=False, device=, multi_scale=False, single_cls=False, adam=False, sync_bn=False, workers=8, project=runs/train, name=exp, exist_ok=False, quad=False, linear_lr=False, label_smoothing=0.0, patience=100, freeze=0, save_period=-1, local_rank=-1, entity=None, upload_dataset=False, bbox_interval=-1, artifact_alias=latest\n", - "\u001b[34m\u001b[1mgithub: \u001b[0mup to date with https://github.com/ultralytics/yolov5 ✅\n", - "YOLOv5 🚀 v6.0-48-g84a8099 torch 1.10.0+cu102 CUDA:0 (Tesla V100-SXM2-16GB, 16160MiB)\n", - "\n", - "\u001b[34m\u001b[1mhyperparameters: \u001b[0mlr0=0.01, lrf=0.1, 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, copy_paste=0.0\n", - "\u001b[34m\u001b[1mWeights & Biases: \u001b[0mrun 'pip install wandb' to automatically track and visualize YOLOv5 🚀 runs (RECOMMENDED)\n", - "\u001b[34m\u001b[1mTensorBoard: \u001b[0mStart with 'tensorboard --logdir runs/train', view at http://localhost:6006/\n", - "\n", - " from n params module arguments \n", - " 0 -1 1 3520 models.common.Conv [3, 32, 6, 2, 2] \n", - " 1 -1 1 18560 models.common.Conv [32, 64, 3, 2] \n", - " 2 -1 1 18816 models.common.C3 [64, 64, 1] \n", - " 3 -1 1 73984 models.common.Conv [64, 128, 3, 2] \n", - " 4 -1 2 115712 models.common.C3 [128, 128, 2] \n", - " 5 -1 1 295424 models.common.Conv [128, 256, 3, 2] \n", - " 6 -1 3 625152 models.common.C3 [256, 256, 3] \n", - " 7 -1 1 1180672 models.common.Conv [256, 512, 3, 2] \n", - " 8 -1 1 1182720 models.common.C3 [512, 512, 1] \n", - " 9 -1 1 656896 models.common.SPPF [512, 512, 5] \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 361984 models.common.C3 [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 90880 models.common.C3 [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 296448 models.common.C3 [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 1182720 models.common.C3 [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: 270 layers, 7235389 parameters, 7235389 gradients, 16.5 GFLOPs\n", - "\n", - "Transferred 349/349 items from yolov5s.pt\n", - "Scaled weight_decay = 0.0005\n", - "\u001b[34m\u001b[1moptimizer:\u001b[0m SGD with parameter groups 57 weight, 60 weight (no decay), 60 bias\n", - "\u001b[34m\u001b[1malbumentations: \u001b[0mversion 1.0.3 required by YOLOv5, but version 0.1.12 is currently installed\n", - "\u001b[34m\u001b[1mtrain: \u001b[0mScanning '../datasets/coco128/labels/train2017.cache' images and labels... 128 found, 0 missing, 2 empty, 0 corrupted: 100% 128/128 [00:00\"Weights

" - ] - }, - { - "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 val jpgs to see mosaics, labels, predictions and augmentation effects. Note an Ultralytics **Mosaic Dataloader** is used for training (shown below), which combines 4 images into 1 mosaic during training.\n", - "\n", - "> \n", - "`train_batch0.jpg` shows train batch 0 mosaics and labels\n", - "\n", - "> \n", - "`test_batch0_labels.jpg` shows val batch 0 labels\n", - "\n", - "> \n", - "`test_batch0_pred.jpg` shows val batch 0 _predictions_\n", - "\n", - "Training results are automatically logged to [Tensorboard](https://www.tensorflow.org/tensorboard) and [CSV](https://github.com/ultralytics/yolov5/pull/4148) as `results.csv`, which is plotted as `results.png` (below) after training completes. You can also plot any `results.csv` file manually:\n", - "\n", - "```python\n", - "from utils.plots import plot_results \n", - "plot_results('path/to/results.csv') # plot 'results.csv' as 'results.png'\n", - "```\n", - "\n", - "\"COCO128" - ] - }, - { - "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 and Kaggle** notebooks with free GPU: \"Open \"Open\n", - "- **Google Cloud** Deep Learning VM. See [GCP Quickstart Guide](https://github.com/ultralytics/yolov5/wiki/GCP-Quickstart)\n", - "- **Amazon** Deep Learning AMI. See [AWS Quickstart Guide](https://github.com/ultralytics/yolov5/wiki/AWS-Quickstart)\n", - "- **Docker Image**. See [Docker Quickstart Guide](https://github.com/ultralytics/yolov5/wiki/Docker-Quickstart) \"Docker\n" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "6Qu7Iesl0p54" - }, - "source": [ - "# Status\n", - "\n", - "![CI CPU testing](https://github.com/ultralytics/yolov5/workflows/CI%20CPU%20testing/badge.svg)\n", - "\n", - "If this badge is green, all [YOLOv5 GitHub Actions](https://github.com/ultralytics/yolov5/actions) Continuous Integration (CI) tests are currently passing. CI tests verify correct operation of YOLOv5 training ([train.py](https://github.com/ultralytics/yolov5/blob/master/train.py)), testing ([val.py](https://github.com/ultralytics/yolov5/blob/master/val.py)), inference ([detect.py](https://github.com/ultralytics/yolov5/blob/master/detect.py)) and export ([export.py](https://github.com/ultralytics/yolov5/blob/master/export.py)) on MacOS, Windows, and Ubuntu every 24 hours and on every commit.\n" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "IEijrePND_2I" - }, - "source": [ - "# Appendix\n", - "\n", - "Optional extras below. Unit tests validate repo functionality and should be run on any PRs submitted.\n" - ] - }, - { - "cell_type": "code", - "metadata": { - "id": "mcKoSIK2WSzj" - }, - "source": [ - "# Reproduce\n", - "for x in 'yolov5s', 'yolov5m', 'yolov5l', 'yolov5x':\n", - " !python val.py --weights {x}.pt --data coco.yaml --img 640 --task speed # speed\n", - " !python val.py --weights {x}.pt --data coco.yaml --img 640 --conf 0.001 --iou 0.65 # mAP" - ], - "execution_count": null, - "outputs": [] - }, - { - "cell_type": "code", - "metadata": { - "id": "GMusP4OAxFu6" - }, - "source": [ - "# PyTorch Hub\n", - "import torch\n", - "\n", - "# Model\n", - "model = torch.hub.load('ultralytics/yolov5', 'yolov5s')\n", - "\n", - "# Images\n", - "dir = 'https://ultralytics.com/images/'\n", - "imgs = [dir + f for f in ('zidane.jpg', 'bus.jpg')] # batch of images\n", - "\n", - "# Inference\n", - "results = model(imgs)\n", - "results.print() # or .show(), .save()" - ], - "execution_count": null, - "outputs": [] - }, - { - "cell_type": "code", - "metadata": { - "id": "FGH0ZjkGjejy" - }, - "source": [ - "# CI Checks\n", - "%%shell\n", - "export PYTHONPATH=\"$PWD\" # to run *.py. files in subdirectories\n", - "rm -rf runs # remove runs/\n", - "for m in yolov5n; do # models\n", - " python train.py --img 64 --batch 32 --weights $m.pt --epochs 1 --device 0 # train pretrained\n", - " python train.py --img 64 --batch 32 --weights '' --cfg $m.yaml --epochs 1 --device 0 # train scratch\n", - " for d in 0 cpu; do # devices\n", - " python val.py --weights $m.pt --device $d # val official\n", - " python val.py --weights runs/train/exp/weights/best.pt --device $d # val custom\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", - " done\n", - " python hubconf.py # hub\n", - " python models/yolo.py --cfg $m.yaml # build PyTorch model\n", - " python models/tf.py --weights $m.pt # build TensorFlow model\n", - " python export.py --img 64 --batch 1 --weights $m.pt --include torchscript onnx # 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", - "results = profile(input=torch.randn(16, 3, 640, 640), ops=[m1, m2], n=100)" - ], - "execution_count": null, - "outputs": [] - }, - { - "cell_type": "code", - "metadata": { - "id": "RVRSOhEvUdb5" - }, - "source": [ - "# Evolve\n", - "!python train.py --img 640 --batch 64 --epochs 100 --data coco128.yaml --weights yolov5s.pt --cache --noautoanchor --evolve\n", - "!d=runs/train/evolve && cp evolve.* $d && zip -r evolve.zip $d && gsutil mv evolve.zip gs://bucket # upload results (optional)" - ], - "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": [] - }, - { - "cell_type": "code", - "metadata": { - "id": "VTRwsvA9u7ln" - }, - "source": [ - "# TensorRT \n", - "# https://developer.nvidia.com/nvidia-tensorrt-download\n", - "!lsb_release -a # check system\n", - "%ls /usr/local | grep cuda # check CUDA\n", - "!wget https://ultralytics.com/assets/TensorRT-8.2.0.6.Linux.x86_64-gnu.cuda-11.4.cudnn8.2.tar.gz # download\n", - "![ -d /content/TensorRT-8.2.0.6/ ] || tar -C /content/ -zxf ./TensorRT-8.2.0.6.Linux.x86_64-gnu.cuda-11.4.cudnn8.2.tar.gz # unzip\n", - "%pip list | grep tensorrt || pip install /content/TensorRT-8.2.0.6/python/tensorrt-8.2.0.6-cp37-none-linux_x86_64.whl # install\n", - "%env LD_LIBRARY_PATH=/usr/local/cuda-11.1/lib64:/content/cuda-11.1/lib64:/content/TensorRT-8.2.0.6/lib:/usr/local/nvidia/lib:/usr/local/nvidia/lib64 # add to path\n", - "\n", - "!python export.py --weights yolov5s.pt --include engine --imgsz 640 640 --device 0\n", - "!python detect.py --weights yolov5s.engine --imgsz 640 640 --device 0" - ], - "execution_count": null, - "outputs": [] - } - ] -} diff --git a/YOLO/Dockerfiles/app/app/utils/__init__.py b/YOLO/Dockerfiles/app/app/utils/__init__.py deleted file mode 100644 index 4658ed6..0000000 --- a/YOLO/Dockerfiles/app/app/utils/__init__.py +++ /dev/null @@ -1,37 +0,0 @@ -# YOLOv5 🚀 by Ultralytics, GPL-3.0 license -""" -utils/initialization -""" - - -def notebook_init(verbose=True): - # Check system software and hardware - print('Checking setup...') - - import os - import shutil - - from utils.general import check_requirements, emojis, is_colab - from utils.torch_utils import select_device # imports - - check_requirements(('psutil', 'IPython')) - import psutil - from IPython import display # to display images and clear console output - - if is_colab(): - shutil.rmtree('/content/sample_data', ignore_errors=True) # remove colab /sample_data directory - - if verbose: - # System info - # gb = 1 / 1000 ** 3 # bytes to GB - gib = 1 / 1024 ** 3 # bytes to GiB - ram = psutil.virtual_memory().total - total, used, free = shutil.disk_usage("/") - display.clear_output() - s = f'({os.cpu_count()} CPUs, {ram * gib:.1f} GB RAM, {(total - free) * gib:.1f}/{total * gib:.1f} GB disk)' - else: - s = '' - - select_device(newline=False) - print(emojis(f'Setup complete ✅ {s}')) - return display diff --git a/YOLO/Dockerfiles/app/app/utils/activations.py b/YOLO/Dockerfiles/app/app/utils/activations.py deleted file mode 100644 index a4ff789..0000000 --- a/YOLO/Dockerfiles/app/app/utils/activations.py +++ /dev/null @@ -1,101 +0,0 @@ -# YOLOv5 🚀 by Ultralytics, GPL-3.0 license -""" -Activation functions -""" - -import torch -import torch.nn as nn -import torch.nn.functional as F - - -# SiLU https://arxiv.org/pdf/1606.08415.pdf ---------------------------------------------------------------------------- -class SiLU(nn.Module): # export-friendly version of nn.SiLU() - @staticmethod - def forward(x): - return x * torch.sigmoid(x) - - -class Hardswish(nn.Module): # export-friendly version of nn.Hardswish() - @staticmethod - def forward(x): - # return x * F.hardsigmoid(x) # for TorchScript and CoreML - return x * F.hardtanh(x + 3, 0.0, 6.0) / 6.0 # for TorchScript, CoreML and ONNX - - -# 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))) - - -# ACON https://arxiv.org/pdf/2009.04759.pdf ---------------------------------------------------------------------------- -class AconC(nn.Module): - r""" ACON activation (activate or not). - AconC: (p1*x-p2*x) * sigmoid(beta*(p1*x-p2*x)) + p2*x, beta is a learnable parameter - according to "Activate or Not: Learning Customized Activation" . - """ - - def __init__(self, c1): - super().__init__() - self.p1 = nn.Parameter(torch.randn(1, c1, 1, 1)) - self.p2 = nn.Parameter(torch.randn(1, c1, 1, 1)) - self.beta = nn.Parameter(torch.ones(1, c1, 1, 1)) - - def forward(self, x): - dpx = (self.p1 - self.p2) * x - return dpx * torch.sigmoid(self.beta * dpx) + self.p2 * x - - -class MetaAconC(nn.Module): - r""" ACON activation (activate or not). - MetaAconC: (p1*x-p2*x) * sigmoid(beta*(p1*x-p2*x)) + p2*x, beta is generated by a small network - according to "Activate or Not: Learning Customized Activation" . - """ - - def __init__(self, c1, k=1, s=1, r=16): # ch_in, kernel, stride, r - super().__init__() - c2 = max(r, c1 // r) - self.p1 = nn.Parameter(torch.randn(1, c1, 1, 1)) - self.p2 = nn.Parameter(torch.randn(1, c1, 1, 1)) - self.fc1 = nn.Conv2d(c1, c2, k, s, bias=True) - self.fc2 = nn.Conv2d(c2, c1, k, s, bias=True) - # self.bn1 = nn.BatchNorm2d(c2) - # self.bn2 = nn.BatchNorm2d(c1) - - def forward(self, x): - y = x.mean(dim=2, keepdims=True).mean(dim=3, keepdims=True) - # batch-size 1 bug/instabilities https://github.com/ultralytics/yolov5/issues/2891 - # beta = torch.sigmoid(self.bn2(self.fc2(self.bn1(self.fc1(y))))) # bug/unstable - beta = torch.sigmoid(self.fc2(self.fc1(y))) # bug patch BN layers removed - dpx = (self.p1 - self.p2) * x - return dpx * torch.sigmoid(beta * dpx) + self.p2 * x diff --git a/YOLO/Dockerfiles/app/app/utils/augmentations.py b/YOLO/Dockerfiles/app/app/utils/augmentations.py deleted file mode 100644 index 0311b97..0000000 --- a/YOLO/Dockerfiles/app/app/utils/augmentations.py +++ /dev/null @@ -1,277 +0,0 @@ -# YOLOv5 🚀 by Ultralytics, GPL-3.0 license -""" -Image augmentation functions -""" - -import math -import random - -import cv2 -import numpy as np - -from utils.general import LOGGER, check_version, colorstr, resample_segments, segment2box -from utils.metrics import bbox_ioa - - -class Albumentations: - # YOLOv5 Albumentations class (optional, only used if package is installed) - def __init__(self): - self.transform = None - try: - import albumentations as A - check_version(A.__version__, '1.0.3', hard=True) # version requirement - - self.transform = A.Compose([ - A.Blur(p=0.01), - A.MedianBlur(p=0.01), - A.ToGray(p=0.01), - A.CLAHE(p=0.01), - A.RandomBrightnessContrast(p=0.0), - A.RandomGamma(p=0.0), - A.ImageCompression(quality_lower=75, p=0.0)], - bbox_params=A.BboxParams(format='yolo', label_fields=['class_labels'])) - - LOGGER.info(colorstr('albumentations: ') + ', '.join(f'{x}' for x in self.transform.transforms if x.p)) - except ImportError: # package not installed, skip - pass - except Exception as e: - LOGGER.info(colorstr('albumentations: ') + f'{e}') - - def __call__(self, im, labels, p=1.0): - if self.transform and random.random() < p: - new = self.transform(image=im, bboxes=labels[:, 1:], class_labels=labels[:, 0]) # transformed - im, labels = new['image'], np.array([[c, *b] for c, b in zip(new['class_labels'], new['bboxes'])]) - return im, labels - - -def augment_hsv(im, hgain=0.5, sgain=0.5, vgain=0.5): - # HSV color-space augmentation - if hgain or sgain or vgain: - r = np.random.uniform(-1, 1, 3) * [hgain, sgain, vgain] + 1 # random gains - hue, sat, val = cv2.split(cv2.cvtColor(im, cv2.COLOR_BGR2HSV)) - dtype = im.dtype # uint8 - - x = np.arange(0, 256, dtype=r.dtype) - 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) - - im_hsv = cv2.merge((cv2.LUT(hue, lut_hue), cv2.LUT(sat, lut_sat), cv2.LUT(val, lut_val))) - cv2.cvtColor(im_hsv, cv2.COLOR_HSV2BGR, dst=im) # no return needed - - -def hist_equalize(im, clahe=True, bgr=False): - # Equalize histogram on BGR image 'im' with im.shape(n,m,3) and range 0-255 - yuv = cv2.cvtColor(im, cv2.COLOR_BGR2YUV if bgr else cv2.COLOR_RGB2YUV) - if clahe: - c = cv2.createCLAHE(clipLimit=2.0, tileGridSize=(8, 8)) - yuv[:, :, 0] = c.apply(yuv[:, :, 0]) - else: - yuv[:, :, 0] = cv2.equalizeHist(yuv[:, :, 0]) # equalize Y channel histogram - return cv2.cvtColor(yuv, cv2.COLOR_YUV2BGR if bgr else cv2.COLOR_YUV2RGB) # convert YUV image to RGB - - -def replicate(im, labels): - # Replicate labels - h, w = im.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] - im[y1a:y2a, x1a:x2a] = im[y1b:y2b, x1b:x2b] # im4[ymin:ymax, xmin:xmax] - labels = np.append(labels, [[labels[i, 0], x1a, y1a, x2a, y2a]], axis=0) - - return im, labels - - -def letterbox(im, new_shape=(640, 640), color=(114, 114, 114), auto=True, scaleFill=False, scaleup=True, stride=32): - # Resize and pad image while meeting stride-multiple constraints - shape = im.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 val 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, stride), np.mod(dh, stride) # 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 - im = cv2.resize(im, 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)) - im = cv2.copyMakeBorder(im, top, bottom, left, right, cv2.BORDER_CONSTANT, value=color) # add border - return im, ratio, (dw, dh) - - -def random_perspective(im, targets=(), segments=(), degrees=10, translate=.1, scale=.1, shear=10, perspective=0.0, - border=(0, 0)): - # torchvision.transforms.RandomAffine(degrees=(-10, 10), translate=(0.1, 0.1), scale=(0.9, 1.1), shear=(-10, 10)) - # targets = [cls, xyxy] - - height = im.shape[0] + border[0] * 2 # shape(h,w,c) - width = im.shape[1] + border[1] * 2 - - # Center - C = np.eye(3) - C[0, 2] = -im.shape[1] / 2 # x translation (pixels) - C[1, 2] = -im.shape[0] / 2 # y translation (pixels) - - # Perspective - P = np.eye(3) - P[2, 0] = random.uniform(-perspective, perspective) # x perspective (about y) - P[2, 1] = random.uniform(-perspective, perspective) # y perspective (about x) - - # Rotation and Scale - R = np.eye(3) - a = random.uniform(-degrees, degrees) - # a += random.choice([-180, -90, 0, 90]) # add 90deg rotations to small rotations - s = random.uniform(1 - scale, 1 + scale) - # s = 2 ** random.uniform(-scale, scale) - R[:2] = cv2.getRotationMatrix2D(angle=a, center=(0, 0), scale=s) - - # Shear - S = np.eye(3) - S[0, 1] = math.tan(random.uniform(-shear, shear) * math.pi / 180) # x shear (deg) - S[1, 0] = math.tan(random.uniform(-shear, shear) * math.pi / 180) # y shear (deg) - - # Translation - T = np.eye(3) - T[0, 2] = random.uniform(0.5 - translate, 0.5 + translate) * width # x translation (pixels) - T[1, 2] = random.uniform(0.5 - translate, 0.5 + translate) * height # y translation (pixels) - - # Combined rotation matrix - M = T @ S @ R @ P @ C # order of operations (right to left) is IMPORTANT - if (border[0] != 0) or (border[1] != 0) or (M != np.eye(3)).any(): # image changed - if perspective: - im = cv2.warpPerspective(im, M, dsize=(width, height), borderValue=(114, 114, 114)) - else: # affine - im = cv2.warpAffine(im, M[:2], dsize=(width, height), borderValue=(114, 114, 114)) - - # Visualize - # import matplotlib.pyplot as plt - # ax = plt.subplots(1, 2, figsize=(12, 6))[1].ravel() - # ax[0].imshow(im[:, :, ::-1]) # base - # ax[1].imshow(im2[:, :, ::-1]) # warped - - # Transform label coordinates - n = len(targets) - if n: - use_segments = any(x.any() for x in segments) - new = np.zeros((n, 4)) - if use_segments: # warp segments - segments = resample_segments(segments) # upsample - for i, segment in enumerate(segments): - xy = np.ones((len(segment), 3)) - xy[:, :2] = segment - xy = xy @ M.T # transform - xy = xy[:, :2] / xy[:, 2:3] if perspective else xy[:, :2] # perspective rescale or affine - - # clip - new[i] = segment2box(xy, width, height) - - else: # warp boxes - 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 - xy = (xy[:, :2] / xy[:, 2:3] if perspective else xy[:, :2]).reshape(n, 8) # perspective rescale or affine - - # create new boxes - x = xy[:, [0, 2, 4, 6]] - y = xy[:, [1, 3, 5, 7]] - new = np.concatenate((x.min(1), y.min(1), x.max(1), y.max(1))).reshape(4, n).T - - # clip - new[:, [0, 2]] = new[:, [0, 2]].clip(0, width) - new[:, [1, 3]] = new[:, [1, 3]].clip(0, height) - - # filter candidates - i = box_candidates(box1=targets[:, 1:5].T * s, box2=new.T, area_thr=0.01 if use_segments else 0.10) - targets = targets[i] - targets[:, 1:5] = new[i] - - return im, targets - - -def copy_paste(im, labels, segments, p=0.5): - # Implement Copy-Paste augmentation https://arxiv.org/abs/2012.07177, labels as nx5 np.array(cls, xyxy) - n = len(segments) - if p and n: - h, w, c = im.shape # height, width, channels - im_new = np.zeros(im.shape, np.uint8) - for j in random.sample(range(n), k=round(p * n)): - l, s = labels[j], segments[j] - box = w - l[3], l[2], w - l[1], l[4] - ioa = bbox_ioa(box, labels[:, 1:5]) # intersection over area - if (ioa < 0.30).all(): # allow 30% obscuration of existing labels - labels = np.concatenate((labels, [[l[0], *box]]), 0) - segments.append(np.concatenate((w - s[:, 0:1], s[:, 1:2]), 1)) - cv2.drawContours(im_new, [segments[j].astype(np.int32)], -1, (255, 255, 255), cv2.FILLED) - - result = cv2.bitwise_and(src1=im, src2=im_new) - result = cv2.flip(result, 1) # augment segments (flip left-right) - i = result > 0 # pixels to replace - # i[:, :] = result.max(2).reshape(h, w, 1) # act over ch - im[i] = result[i] # cv2.imwrite('debug.jpg', im) # debug - - return im, labels, segments - - -def cutout(im, labels, p=0.5): - # Applies image cutout augmentation https://arxiv.org/abs/1708.04552 - if random.random() < p: - h, w = im.shape[:2] - 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)) # create random masks - 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 - im[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 mixup(im, labels, im2, labels2): - # Applies MixUp augmentation https://arxiv.org/pdf/1710.09412.pdf - r = np.random.beta(32.0, 32.0) # mixup ratio, alpha=beta=32.0 - im = (im * r + im2 * (1 - r)).astype(np.uint8) - labels = np.concatenate((labels, labels2), 0) - return im, labels - - -def box_candidates(box1, box2, wh_thr=2, ar_thr=100, area_thr=0.1, eps=1e-16): # box1(4,n), box2(4,n) - # Compute candidate boxes: box1 before augment, box2 after augment, wh_thr (pixels), aspect_ratio_thr, area_ratio - w1, h1 = box1[2] - box1[0], box1[3] - box1[1] - w2, h2 = box2[2] - box2[0], box2[3] - box2[1] - ar = np.maximum(w2 / (h2 + eps), h2 / (w2 + eps)) # aspect ratio - return (w2 > wh_thr) & (h2 > wh_thr) & (w2 * h2 / (w1 * h1 + eps) > area_thr) & (ar < ar_thr) # candidates diff --git a/YOLO/Dockerfiles/app/app/utils/autoanchor.py b/YOLO/Dockerfiles/app/app/utils/autoanchor.py deleted file mode 100644 index eef8f64..0000000 --- a/YOLO/Dockerfiles/app/app/utils/autoanchor.py +++ /dev/null @@ -1,164 +0,0 @@ -# YOLOv5 🚀 by Ultralytics, GPL-3.0 license -""" -Auto-anchor utils -""" - -import random - -import numpy as np -import torch -import yaml -from tqdm import tqdm - -from utils.general import LOGGER, colorstr, emojis - -PREFIX = colorstr('AutoAnchor: ') - - -def check_anchor_order(m): - # Check anchor order against stride order for YOLOv5 Detect() module m, and correct if necessary - a = m.anchors.prod(-1).view(-1) # anchor area - da = a[-1] - a[0] # delta a - ds = m.stride[-1] - m.stride[0] # delta s - if da.sign() != ds.sign(): # same order - LOGGER.info(f'{PREFIX}Reversing anchor order') - m.anchors[:] = m.anchors.flip(0) - - -def check_anchors(dataset, model, thr=4.0, imgsz=640): - # Check anchor fit to data, recompute if necessary - m = model.module.model[-1] if hasattr(model, 'module') else model.model[-1] # Detect() - 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 - - anchors = m.anchors.clone() * m.stride.to(m.anchors.device).view(-1, 1, 1) # current anchors - bpr, aat = metric(anchors.cpu().view(-1, 2)) - s = f'\n{PREFIX}{aat:.2f} anchors/target, {bpr:.3f} Best Possible Recall (BPR). ' - if bpr > 0.98: # threshold to recompute - LOGGER.info(emojis(f'{s}Current anchors are a good fit to dataset ✅')) - else: - LOGGER.info(emojis(f'{s}Anchors are a poor fit to dataset ⚠️, attempting to improve...')) - na = m.anchors.numel() // 2 # number of anchors - try: - anchors = kmean_anchors(dataset, n=na, img_size=imgsz, thr=thr, gen=1000, verbose=False) - except Exception as e: - LOGGER.info(f'{PREFIX}ERROR: {e}') - new_bpr = metric(anchors)[0] - if new_bpr > bpr: # replace anchors - anchors = torch.tensor(anchors, device=m.anchors.device).type_as(m.anchors) - m.anchors[:] = anchors.clone().view_as(m.anchors) / m.stride.to(m.anchors.device).view(-1, 1, 1) # loss - check_anchor_order(m) - LOGGER.info(f'{PREFIX}New anchors saved to model. Update model *.yaml to use these anchors in the future.') - else: - LOGGER.info(f'{PREFIX}Original anchors better than new anchors. Proceeding with original anchors.') - - -def kmean_anchors(dataset='./data/coco128.yaml', n=9, img_size=640, thr=4.0, gen=1000, verbose=True): - """ Creates kmeans-evolved anchors from training dataset - - Arguments: - dataset: path to data.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() - """ - from scipy.cluster.vq import kmeans - - 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, verbose=True): - 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 - s = f'{PREFIX}thr={thr:.2f}: {bpr:.4f} best possible recall, {aat:.2f} anchors past thr\n' \ - f'{PREFIX}n={n}, img_size={img_size}, metric_all={x.mean():.3f}/{best.mean():.3f}-mean/best, ' \ - f'past_thr={x[x > thr].mean():.3f}-mean: ' - for i, x in enumerate(k): - s += '%i,%i, ' % (round(x[0]), round(x[1])) - if verbose: - LOGGER.info(s[:-2]) - return k - - if isinstance(dataset, str): # *.yaml file - with open(dataset, errors='ignore') as f: - data_dict = yaml.safe_load(f) # model dict - from utils.datasets import LoadImagesAndLabels - dataset = LoadImagesAndLabels(data_dict['train'], augment=True, rect=True) - - # 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: - LOGGER.info(f'{PREFIX}WARNING: Extremely small objects found. {i} of {len(wh0)} labels are < 3 pixels in size.') - wh = wh0[(wh0 >= 2.0).any(1)] # filter > 2 pixels - # wh = wh * (np.random.rand(wh.shape[0], 1) * 0.9 + 0.1) # multiply by random scale 0-1 - - # Kmeans calculation - LOGGER.info(f'{PREFIX}Running kmeans for {n} anchors on {len(wh)} points...') - s = wh.std(0) # sigmas for whitening - k, dist = kmeans(wh / s, n, iter=30) # points, mean distance - assert len(k) == n, f'{PREFIX}ERROR: scipy.cluster.vq.kmeans requested {n} points but returned only {len(k)}' - k *= s - wh = torch.tensor(wh, dtype=torch.float32) # filtered - wh0 = torch.tensor(wh0, dtype=torch.float32) # unfiltered - k = print_results(k, verbose=False) - - # Plot - # 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=f'{PREFIX}Evolving anchors with Genetic Algorithm:') # progress bar - for _ in pbar: - v = np.ones(sh) - while (v == 1).all(): # mutate until a change occurs (prevent duplicates) - v = ((npr.random(sh) < mp) * random.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 = f'{PREFIX}Evolving anchors with Genetic Algorithm: fitness = {f:.4f}' - if verbose: - print_results(k, verbose) - - return print_results(k) diff --git a/YOLO/Dockerfiles/app/app/utils/autobatch.py b/YOLO/Dockerfiles/app/app/utils/autobatch.py deleted file mode 100644 index cb94f04..0000000 --- a/YOLO/Dockerfiles/app/app/utils/autobatch.py +++ /dev/null @@ -1,57 +0,0 @@ -# YOLOv5 🚀 by Ultralytics, GPL-3.0 license -""" -Auto-batch utils -""" - -from copy import deepcopy - -import numpy as np -import torch -from torch.cuda import amp - -from utils.general import LOGGER, colorstr -from utils.torch_utils import profile - - -def check_train_batch_size(model, imgsz=640): - # Check YOLOv5 training batch size - with amp.autocast(): - return autobatch(deepcopy(model).train(), imgsz) # compute optimal batch size - - -def autobatch(model, imgsz=640, fraction=0.9, batch_size=16): - # Automatically estimate best batch size to use `fraction` of available CUDA memory - # Usage: - # import torch - # from utils.autobatch import autobatch - # model = torch.hub.load('ultralytics/yolov5', 'yolov5s', autoshape=False) - # print(autobatch(model)) - - prefix = colorstr('AutoBatch: ') - LOGGER.info(f'{prefix}Computing optimal batch size for --imgsz {imgsz}') - device = next(model.parameters()).device # get model device - if device.type == 'cpu': - LOGGER.info(f'{prefix}CUDA not detected, using default CPU batch-size {batch_size}') - return batch_size - - d = str(device).upper() # 'CUDA:0' - properties = torch.cuda.get_device_properties(device) # device properties - t = properties.total_memory / 1024 ** 3 # (GiB) - r = torch.cuda.memory_reserved(device) / 1024 ** 3 # (GiB) - a = torch.cuda.memory_allocated(device) / 1024 ** 3 # (GiB) - f = t - (r + a) # free inside reserved - LOGGER.info(f'{prefix}{d} ({properties.name}) {t:.2f}G total, {r:.2f}G reserved, {a:.2f}G allocated, {f:.2f}G free') - - batch_sizes = [1, 2, 4, 8, 16] - try: - img = [torch.zeros(b, 3, imgsz, imgsz) for b in batch_sizes] - y = profile(img, model, n=3, device=device) - except Exception as e: - LOGGER.warning(f'{prefix}{e}') - - y = [x[2] for x in y if x] # memory [2] - batch_sizes = batch_sizes[:len(y)] - p = np.polyfit(batch_sizes, y, deg=1) # first degree polynomial fit - b = int((f * fraction - p[1]) / p[0]) # y intercept (optimal batch size) - LOGGER.info(f'{prefix}Using batch-size {b} for {d} {t * fraction:.2f}G/{t:.2f}G ({fraction * 100:.0f}%)') - return b diff --git a/YOLO/Dockerfiles/app/app/utils/aws/__init__.py b/YOLO/Dockerfiles/app/app/utils/aws/__init__.py deleted file mode 100644 index e69de29..0000000 diff --git a/YOLO/Dockerfiles/app/app/utils/aws/mime.sh b/YOLO/Dockerfiles/app/app/utils/aws/mime.sh deleted file mode 100644 index c319a83..0000000 --- a/YOLO/Dockerfiles/app/app/utils/aws/mime.sh +++ /dev/null @@ -1,26 +0,0 @@ -# AWS EC2 instance startup 'MIME' script https://aws.amazon.com/premiumsupport/knowledge-center/execute-user-data-ec2/ -# This script will run on every instance restart, not only on first start -# --- DO NOT COPY ABOVE COMMENTS WHEN PASTING INTO USERDATA --- - -Content-Type: multipart/mixed; boundary="//" -MIME-Version: 1.0 - ---// -Content-Type: text/cloud-config; charset="us-ascii" -MIME-Version: 1.0 -Content-Transfer-Encoding: 7bit -Content-Disposition: attachment; filename="cloud-config.txt" - -#cloud-config -cloud_final_modules: -- [scripts-user, always] - ---// -Content-Type: text/x-shellscript; charset="us-ascii" -MIME-Version: 1.0 -Content-Transfer-Encoding: 7bit -Content-Disposition: attachment; filename="userdata.txt" - -#!/bin/bash -# --- paste contents of userdata.sh here --- ---// diff --git a/YOLO/Dockerfiles/app/app/utils/aws/resume.py b/YOLO/Dockerfiles/app/app/utils/aws/resume.py deleted file mode 100644 index b21731c..0000000 --- a/YOLO/Dockerfiles/app/app/utils/aws/resume.py +++ /dev/null @@ -1,40 +0,0 @@ -# Resume all interrupted trainings in yolov5/ dir including DDP trainings -# Usage: $ python utils/aws/resume.py - -import os -import sys -from pathlib import Path - -import torch -import yaml - -FILE = Path(__file__).resolve() -ROOT = FILE.parents[2] # YOLOv5 root directory -if str(ROOT) not in sys.path: - sys.path.append(str(ROOT)) # add ROOT to PATH - -port = 0 # --master_port -path = Path('').resolve() -for last in path.rglob('*/**/last.pt'): - ckpt = torch.load(last) - if ckpt['optimizer'] is None: - continue - - # Load opt.yaml - with open(last.parent.parent / 'opt.yaml', errors='ignore') as f: - opt = yaml.safe_load(f) - - # Get device count - d = opt['device'].split(',') # devices - nd = len(d) # number of devices - ddp = nd > 1 or (nd == 0 and torch.cuda.device_count() > 1) # distributed data parallel - - if ddp: # multi-GPU - port += 1 - cmd = f'python -m torch.distributed.run --nproc_per_node {nd} --master_port {port} train.py --resume {last}' - else: # single-GPU - cmd = f'python train.py --resume {last}' - - cmd += ' > /dev/null 2>&1 &' # redirect output to dev/null and run in daemon thread - print(cmd) - os.system(cmd) diff --git a/YOLO/Dockerfiles/app/app/utils/aws/userdata.sh b/YOLO/Dockerfiles/app/app/utils/aws/userdata.sh deleted file mode 100644 index 5fc1332..0000000 --- a/YOLO/Dockerfiles/app/app/utils/aws/userdata.sh +++ /dev/null @@ -1,27 +0,0 @@ -#!/bin/bash -# AWS EC2 instance startup script https://docs.aws.amazon.com/AWSEC2/latest/UserGuide/user-data.html -# This script will run only once on first instance start (for a re-start script see mime.sh) -# /home/ubuntu (ubuntu) or /home/ec2-user (amazon-linux) is working dir -# Use >300 GB SSD - -cd home/ubuntu -if [ ! -d yolov5 ]; then - echo "Running first-time script." # install dependencies, download COCO, pull Docker - git clone https://github.com/ultralytics/yolov5 -b master && sudo chmod -R 777 yolov5 - cd yolov5 - bash data/scripts/get_coco.sh && echo "COCO done." & - sudo docker pull ultralytics/yolov5:latest && echo "Docker done." & - python -m pip install --upgrade pip && pip install -r requirements.txt && python detect.py && echo "Requirements done." & - wait && echo "All tasks done." # finish background tasks -else - echo "Running re-start script." # resume interrupted runs - i=0 - list=$(sudo docker ps -qa) # container list i.e. $'one\ntwo\nthree\nfour' - while IFS= read -r id; do - ((i++)) - echo "restarting container $i: $id" - sudo docker start $id - # sudo docker exec -it $id python train.py --resume # single-GPU - sudo docker exec -d $id python utils/aws/resume.py # multi-scenario - done <<<"$list" -fi diff --git a/YOLO/Dockerfiles/app/app/utils/callbacks.py b/YOLO/Dockerfiles/app/app/utils/callbacks.py deleted file mode 100644 index 13d82eb..0000000 --- a/YOLO/Dockerfiles/app/app/utils/callbacks.py +++ /dev/null @@ -1,77 +0,0 @@ -# YOLOv5 🚀 by Ultralytics, GPL-3.0 license -""" -Callback utils -""" - - -class Callbacks: - """" - Handles all registered callbacks for YOLOv5 Hooks - """ - - def __init__(self): - # Define the available callbacks - self._callbacks = { - 'on_pretrain_routine_start': [], - 'on_pretrain_routine_end': [], - - 'on_train_start': [], - 'on_train_epoch_start': [], - 'on_train_batch_start': [], - 'optimizer_step': [], - 'on_before_zero_grad': [], - 'on_train_batch_end': [], - 'on_train_epoch_end': [], - - 'on_val_start': [], - 'on_val_batch_start': [], - 'on_val_image_end': [], - 'on_val_batch_end': [], - 'on_val_end': [], - - 'on_fit_epoch_end': [], # fit = train + val - 'on_model_save': [], - 'on_train_end': [], - 'on_params_update': [], - 'teardown': [], - } - - def register_action(self, hook, name='', callback=None): - """ - Register a new action to a callback hook - - Args: - hook The callback hook name to register the action to - name The name of the action for later reference - callback The callback to fire - """ - assert hook in self._callbacks, f"hook '{hook}' not found in callbacks {self._callbacks}" - assert callable(callback), f"callback '{callback}' is not callable" - self._callbacks[hook].append({'name': name, 'callback': callback}) - - def get_registered_actions(self, hook=None): - """" - Returns all the registered actions by callback hook - - Args: - hook The name of the hook to check, defaults to all - """ - if hook: - return self._callbacks[hook] - else: - return self._callbacks - - def run(self, hook, *args, **kwargs): - """ - Loop through the registered actions and fire all callbacks - - Args: - hook The name of the hook to check, defaults to all - args Arguments to receive from YOLOv5 - kwargs Keyword Arguments to receive from YOLOv5 - """ - - assert hook in self._callbacks, f"hook '{hook}' not found in callbacks {self._callbacks}" - - for logger in self._callbacks[hook]: - logger['callback'](*args, **kwargs) diff --git a/YOLO/Dockerfiles/app/app/utils/datasets.py b/YOLO/Dockerfiles/app/app/utils/datasets.py deleted file mode 100755 index 6584342..0000000 --- a/YOLO/Dockerfiles/app/app/utils/datasets.py +++ /dev/null @@ -1,1038 +0,0 @@ -# YOLOv5 🚀 by Ultralytics, GPL-3.0 license -""" -Dataloaders and dataset utils -""" - -import glob -import hashlib -import json -import math -import os -import random -import shutil -import time -from itertools import repeat -from multiprocessing.pool import Pool, ThreadPool -from pathlib import Path -from threading import Thread -from zipfile import ZipFile - -import cv2 -import numpy as np -import torch -import torch.nn.functional as F -import yaml -from PIL import ExifTags, Image, ImageOps -from torch.utils.data import DataLoader, Dataset, dataloader, distributed -from tqdm import tqdm - -from utils.augmentations import Albumentations, augment_hsv, copy_paste, letterbox, mixup, random_perspective -from utils.general import (LOGGER, NUM_THREADS, check_dataset, check_requirements, check_yaml, clean_str, - segments2boxes, xyn2xy, xywh2xyxy, xywhn2xyxy, xyxy2xywhn) -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', 'webp', 'mpo'] # acceptable image suffixes -VID_FORMATS = ['mov', 'avi', 'mp4', 'mpg', 'mpeg', 'm4v', 'wmv', 'mkv'] # acceptable video suffixes -WORLD_SIZE = int(os.getenv('WORLD_SIZE', 1)) # DPP - -# Get orientation exif tag -for orientation in ExifTags.TAGS.keys(): - if ExifTags.TAGS[orientation] == 'Orientation': - break - - -def get_hash(paths): - # Returns a single hash value of a list of paths (files or dirs) - size = sum(os.path.getsize(p) for p in paths if os.path.exists(p)) # sizes - h = hashlib.md5(str(size).encode()) # hash sizes - h.update(''.join(paths).encode()) # hash paths - return h.hexdigest() # return hash - - -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 exif_transpose(image): - """ - Transpose a PIL image accordingly if it has an EXIF Orientation tag. - Inplace version of https://github.com/python-pillow/Pillow/blob/master/src/PIL/ImageOps.py exif_transpose() - - :param image: The image to transpose. - :return: An image. - """ - exif = image.getexif() - orientation = exif.get(0x0112, 1) # default 1 - if orientation > 1: - method = {2: Image.FLIP_LEFT_RIGHT, - 3: Image.ROTATE_180, - 4: Image.FLIP_TOP_BOTTOM, - 5: Image.TRANSPOSE, - 6: Image.ROTATE_270, - 7: Image.TRANSVERSE, - 8: Image.ROTATE_90, - }.get(orientation) - if method is not None: - image = image.transpose(method) - del exif[0x0112] - image.info["exif"] = exif.tobytes() - return image - - -def create_dataloader(path, imgsz, batch_size, stride, single_cls=False, hyp=None, augment=False, cache=False, pad=0.0, - rect=False, rank=-1, workers=8, image_weights=False, quad=False, prefix='', shuffle=False): - if rect and shuffle: - LOGGER.warning('WARNING: --rect is incompatible with DataLoader shuffle, setting shuffle=False') - shuffle = False - with torch_distributed_zero_first(rank): # init dataset *.cache only once if DDP - dataset = LoadImagesAndLabels(path, imgsz, batch_size, - augment=augment, # augmentation - hyp=hyp, # hyperparameters - rect=rect, # rectangular batches - cache_images=cache, - single_cls=single_cls, - stride=int(stride), - pad=pad, - image_weights=image_weights, - prefix=prefix) - - 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 = None if rank == -1 else distributed.DistributedSampler(dataset, shuffle=shuffle) - loader = DataLoader if image_weights else InfiniteDataLoader # only DataLoader allows for attribute updates - return loader(dataset, - batch_size=batch_size, - shuffle=shuffle and sampler is None, - num_workers=nw, - sampler=sampler, - pin_memory=True, - collate_fn=LoadImagesAndLabels.collate_fn4 if quad else LoadImagesAndLabels.collate_fn), dataset - - -class InfiniteDataLoader(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: - """ 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: - # YOLOv5 image/video dataloader, i.e. `python detect.py --source image.jpg/vid.mp4` - def __init__(self, path, img_size=640, stride=32, auto=True): - p = str(Path(path).resolve()) # os-agnostic 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(f'ERROR: {p} does not exist') - - 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.stride = stride - self.files = images + videos - self.nf = ni + nv # number of files - self.video_flag = [False] * ni + [True] * nv - self.mode = 'image' - self.auto = auto - if any(videos): - self.new_video(videos[0]) # new video - else: - self.cap = None - assert self.nf > 0, f'No images or videos found in {p}. ' \ - f'Supported formats are:\nimages: {IMG_FORMATS}\nvideos: {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() - while 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 - s = f'video {self.count + 1}/{self.nf} ({self.frame}/{self.frames}) {path}: ' - - else: - # Read image - self.count += 1 - img0 = cv2.imread(path) # BGR - assert img0 is not None, f'Image Not Found {path}' - s = f'image {self.count}/{self.nf} {path}: ' - - # Padded resize - img = letterbox(img0, self.img_size, stride=self.stride, auto=self.auto)[0] - - # Convert - img = img.transpose((2, 0, 1))[::-1] # HWC to CHW, BGR to RGB - img = np.ascontiguousarray(img) - - return path, img, img0, self.cap, s - - def new_video(self, path): - self.frame = 0 - self.cap = cv2.VideoCapture(path) - self.frames = int(self.cap.get(cv2.CAP_PROP_FRAME_COUNT)) - - def __len__(self): - return self.nf # number of files - - -class LoadWebcam: # for inference - # YOLOv5 local webcam dataloader, i.e. `python detect.py --source 0` - def __init__(self, pipe='0', img_size=640, stride=32): - self.img_size = img_size - self.stride = stride - self.pipe = eval(pipe) if pipe.isnumeric() else pipe - self.cap = cv2.VideoCapture(self.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 - ret_val, img0 = self.cap.read() - img0 = cv2.flip(img0, 1) # flip left-right - - # Print - assert ret_val, f'Camera Error {self.pipe}' - img_path = 'webcam.jpg' - s = f'webcam {self.count}: ' - - # Padded resize - img = letterbox(img0, self.img_size, stride=self.stride)[0] - - # Convert - img = img.transpose((2, 0, 1))[::-1] # HWC to CHW, BGR to RGB - img = np.ascontiguousarray(img) - - return img_path, img, img0, None, s - - def __len__(self): - return 0 - - -class LoadStreams: - # YOLOv5 streamloader, i.e. `python detect.py --source 'rtsp://example.com/media.mp4' # RTSP, RTMP, HTTP streams` - def __init__(self, sources='streams.txt', img_size=640, stride=32, auto=True): - self.mode = 'stream' - self.img_size = img_size - self.stride = stride - - if os.path.isfile(sources): - with open(sources) as f: - sources = [x.strip() for x in f.read().strip().splitlines() if len(x.strip())] - else: - sources = [sources] - - n = len(sources) - self.imgs, self.fps, self.frames, self.threads = [None] * n, [0] * n, [0] * n, [None] * n - self.sources = [clean_str(x) for x in sources] # clean source names for later - self.auto = auto - for i, s in enumerate(sources): # index, source - # Start thread to read frames from video stream - st = f'{i + 1}/{n}: {s}... ' - if 'youtube.com/' in s or 'youtu.be/' in s: # if source is YouTube video - check_requirements(('pafy', 'youtube_dl')) - import pafy - s = pafy.new(s).getbest(preftype="mp4").url # YouTube URL - s = eval(s) if s.isnumeric() else s # i.e. s = '0' local webcam - cap = cv2.VideoCapture(s) - assert cap.isOpened(), f'{st}Failed to open {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) # warning: may return 0 or nan - self.frames[i] = max(int(cap.get(cv2.CAP_PROP_FRAME_COUNT)), 0) or float('inf') # infinite stream fallback - self.fps[i] = max((fps if math.isfinite(fps) else 0) % 100, 0) or 30 # 30 FPS fallback - - _, self.imgs[i] = cap.read() # guarantee first frame - self.threads[i] = Thread(target=self.update, args=([i, cap, s]), daemon=True) - LOGGER.info(f"{st} Success ({self.frames[i]} frames {w}x{h} at {self.fps[i]:.2f} FPS)") - self.threads[i].start() - LOGGER.info('') # newline - - # check for common shapes - s = np.stack([letterbox(x, self.img_size, stride=self.stride, auto=self.auto)[0].shape for x in self.imgs]) - self.rect = np.unique(s, axis=0).shape[0] == 1 # rect inference if all shapes equal - if not self.rect: - LOGGER.warning('WARNING: Stream shapes differ. For optimal performance supply similarly-shaped streams.') - - def update(self, i, cap, stream): - # Read stream `i` frames in daemon thread - n, f, read = 0, self.frames[i], 1 # frame number, frame array, inference every 'read' frame - while cap.isOpened() and n < f: - n += 1 - # _, self.imgs[index] = cap.read() - cap.grab() - if n % read == 0: - success, im = cap.retrieve() - if success: - self.imgs[i] = im - else: - LOGGER.warning('WARNING: Video stream unresponsive, please check your IP camera connection.') - self.imgs[i] = np.zeros_like(self.imgs[i]) - cap.open(stream) # re-open stream if signal was lost - time.sleep(1 / self.fps[i]) # wait time - - def __iter__(self): - self.count = -1 - return self - - def __next__(self): - self.count += 1 - if not all(x.is_alive() for x in self.threads) or cv2.waitKey(1) == ord('q'): # q to quit - cv2.destroyAllWindows() - raise StopIteration - - # Letterbox - img0 = self.imgs.copy() - img = [letterbox(x, self.img_size, stride=self.stride, auto=self.rect and self.auto)[0] for x in img0] - - # Stack - img = np.stack(img, 0) - - # Convert - img = img[..., ::-1].transpose((0, 3, 1, 2)) # BGR to RGB, BHWC to BCHW - img = np.ascontiguousarray(img) - - return self.sources, img, img0, None, '' - - def __len__(self): - return len(self.sources) # 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 [sb.join(x.rsplit(sa, 1)).rsplit('.', 1)[0] + '.txt' for x in img_paths] - - -class LoadImagesAndLabels(Dataset): - # YOLOv5 train_loader/val_loader, loads images and labels for training and validation - cache_version = 0.6 # dataset labels *.cache version - - 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, prefix=''): - 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 - self.path = path - self.albumentations = Albumentations() if augment else None - - 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) - # f = list(p.rglob('*.*')) # pathlib - elif p.is_file(): # file - with open(p) as t: - t = t.read().strip().splitlines() - parent = str(p.parent) + os.sep - f += [x.replace('./', parent) if x.startswith('./') else x for x in t] # local to global path - # f += [p.parent / x.lstrip(os.sep) for x in t] # local to global path (pathlib) - else: - raise Exception(f'{prefix}{p} does not exist') - self.img_files = sorted(x.replace('/', os.sep) for x in f if x.split('.')[-1].lower() in IMG_FORMATS) - # self.img_files = sorted([x for x in f if x.suffix[1:].lower() in IMG_FORMATS]) # pathlib - assert self.img_files, f'{prefix}No images found' - except Exception as e: - raise Exception(f'{prefix}Error loading data from {path}: {e}\nSee {HELP_URL}') - - # Check cache - self.label_files = img2label_paths(self.img_files) # labels - cache_path = (p if p.is_file() else Path(self.label_files[0]).parent).with_suffix('.cache') - try: - cache, exists = np.load(cache_path, allow_pickle=True).item(), True # load dict - assert cache['version'] == self.cache_version # same version - assert cache['hash'] == get_hash(self.label_files + self.img_files) # same hash - except: - cache, exists = self.cache_labels(cache_path, prefix), False # cache - - # Display cache - nf, nm, ne, nc, n = cache.pop('results') # found, missing, empty, corrupted, total - if exists: - d = f"Scanning '{cache_path}' images and labels... {nf} found, {nm} missing, {ne} empty, {nc} corrupted" - tqdm(None, desc=prefix + d, total=n, initial=n) # display cache results - if cache['msgs']: - LOGGER.info('\n'.join(cache['msgs'])) # display warnings - assert nf > 0 or not augment, f'{prefix}No labels in {cache_path}. Can not train without labels. See {HELP_URL}' - - # Read cache - [cache.pop(k) for k in ('hash', 'version', 'msgs')] # remove items - labels, shapes, self.segments = 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 - 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) - - # Update labels - include_class = [] # filter labels to include only these classes (optional) - include_class_array = np.array(include_class).reshape(1, -1) - for i, (label, segment) in enumerate(zip(self.labels, self.segments)): - if include_class: - j = (label[:, 0:1] == include_class_array).any(1) - self.labels[i] = label[j] - if segment: - self.segments[i] = segment[j] - if single_cls: # single-class training, merge all classes into 0 - self.labels[i][:, 0] = 0 - if segment: - self.segments[i][:, 0] = 0 - - # Rectangular Training - if self.rect: - # 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, self.img_npy = [None] * n, [None] * n - if cache_images: - if cache_images == 'disk': - self.im_cache_dir = Path(Path(self.img_files[0]).parent.as_posix() + '_npy') - self.img_npy = [self.im_cache_dir / Path(f).with_suffix('.npy').name for f in self.img_files] - self.im_cache_dir.mkdir(parents=True, exist_ok=True) - gb = 0 # Gigabytes of cached images - self.img_hw0, self.img_hw = [None] * n, [None] * n - results = ThreadPool(NUM_THREADS).imap(lambda x: load_image(*x), zip(repeat(self), range(n))) - pbar = tqdm(enumerate(results), total=n) - for i, x in pbar: - if cache_images == 'disk': - if not self.img_npy[i].exists(): - np.save(self.img_npy[i].as_posix(), x[0]) - gb += self.img_npy[i].stat().st_size - else: - self.imgs[i], self.img_hw0[i], self.img_hw[i] = x # im, hw_orig, hw_resized = load_image(self, i) - gb += self.imgs[i].nbytes - pbar.desc = f'{prefix}Caching images ({gb / 1E9:.1f}GB {cache_images})' - pbar.close() - - def cache_labels(self, path=Path('./labels.cache'), prefix=''): - # Cache dataset labels, check images and read shapes - x = {} # dict - nm, nf, ne, nc, msgs = 0, 0, 0, 0, [] # number missing, found, empty, corrupt, messages - desc = f"{prefix}Scanning '{path.parent / path.stem}' images and labels..." - with Pool(NUM_THREADS) as pool: - pbar = tqdm(pool.imap(verify_image_label, zip(self.img_files, self.label_files, repeat(prefix))), - desc=desc, total=len(self.img_files)) - for im_file, l, shape, segments, nm_f, nf_f, ne_f, nc_f, msg in pbar: - nm += nm_f - nf += nf_f - ne += ne_f - nc += nc_f - if im_file: - x[im_file] = [l, shape, segments] - if msg: - msgs.append(msg) - pbar.desc = f"{desc}{nf} found, {nm} missing, {ne} empty, {nc} corrupted" - - pbar.close() - if msgs: - LOGGER.info('\n'.join(msgs)) - if nf == 0: - LOGGER.warning(f'{prefix}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, len(self.img_files) - x['msgs'] = msgs # warnings - x['version'] = self.cache_version # cache version - try: - np.save(path, x) # save cache for next time - path.with_suffix('.cache.npy').rename(path) # remove .npy suffix - LOGGER.info(f'{prefix}New cache created: {path}') - except Exception as e: - LOGGER.warning(f'{prefix}WARNING: Cache directory {path.parent} is not writeable: {e}') # not writeable - 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 augmentation - if random.random() < hyp['mixup']: - img, labels = mixup(img, labels, *load_mosaic(self, random.randint(0, self.n - 1))) - - 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 - - labels = self.labels[index].copy() - if labels.size: # normalized xywh to pixel xyxy format - labels[:, 1:] = xywhn2xyxy(labels[:, 1:], ratio[0] * w, ratio[1] * h, padw=pad[0], padh=pad[1]) - - if self.augment: - img, labels = random_perspective(img, labels, - degrees=hyp['degrees'], - translate=hyp['translate'], - scale=hyp['scale'], - shear=hyp['shear'], - perspective=hyp['perspective']) - - nl = len(labels) # number of labels - if nl: - labels[:, 1:5] = xyxy2xywhn(labels[:, 1:5], w=img.shape[1], h=img.shape[0], clip=True, eps=1E-3) - - if self.augment: - # Albumentations - img, labels = self.albumentations(img, labels) - nl = len(labels) # update after albumentations - - # HSV color-space - augment_hsv(img, hgain=hyp['hsv_h'], sgain=hyp['hsv_s'], vgain=hyp['hsv_v']) - - # Flip up-down - if random.random() < hyp['flipud']: - img = np.flipud(img) - if nl: - labels[:, 2] = 1 - labels[:, 2] - - # Flip left-right - if random.random() < hyp['fliplr']: - img = np.fliplr(img) - if nl: - labels[:, 1] = 1 - labels[:, 1] - - # Cutouts - # labels = cutout(img, labels, p=0.5) - # nl = len(labels) # update after cutout - - labels_out = torch.zeros((nl, 6)) - if nl: - labels_out[:, 1:] = torch.from_numpy(labels) - - # Convert - img = img.transpose((2, 0, 1))[::-1] # HWC to CHW, BGR to RGB - img = np.ascontiguousarray(img) - - return torch.from_numpy(img), labels_out, self.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 - - @staticmethod - def collate_fn4(batch): - img, label, path, shapes = zip(*batch) # transposed - n = len(shapes) // 4 - img4, label4, path4, shapes4 = [], [], path[:n], shapes[:n] - - ho = torch.tensor([[0.0, 0, 0, 1, 0, 0]]) - wo = torch.tensor([[0.0, 0, 1, 0, 0, 0]]) - s = torch.tensor([[1, 1, 0.5, 0.5, 0.5, 0.5]]) # scale - for i in range(n): # zidane torch.zeros(16,3,720,1280) # BCHW - i *= 4 - if random.random() < 0.5: - im = F.interpolate(img[i].unsqueeze(0).float(), scale_factor=2.0, mode='bilinear', align_corners=False)[ - 0].type(img[i].type()) - l = label[i] - else: - im = torch.cat((torch.cat((img[i], img[i + 1]), 1), torch.cat((img[i + 2], img[i + 3]), 1)), 2) - l = torch.cat((label[i], label[i + 1] + ho, label[i + 2] + wo, label[i + 3] + ho + wo), 0) * s - img4.append(im) - label4.append(l) - - for i, l in enumerate(label4): - l[:, 0] = i # add target image index for build_targets() - - return torch.stack(img4, 0), torch.cat(label4, 0), path4, shapes4 - - -# Ancillary functions -------------------------------------------------------------------------------------------------- -def load_image(self, i): - # loads 1 image from dataset index 'i', returns im, original hw, resized hw - im = self.imgs[i] - if im is None: # not cached in ram - npy = self.img_npy[i] - if npy and npy.exists(): # load npy - im = np.load(npy) - else: # read image - path = self.img_files[i] - im = cv2.imread(path) # BGR - assert im is not None, f'Image Not Found {path}' - h0, w0 = im.shape[:2] # orig hw - r = self.img_size / max(h0, w0) # ratio - if r != 1: # if sizes are not equal - im = cv2.resize(im, (int(w0 * r), int(h0 * r)), - interpolation=cv2.INTER_AREA if r < 1 and not self.augment else cv2.INTER_LINEAR) - return im, (h0, w0), im.shape[:2] # im, hw_original, hw_resized - else: - return self.imgs[i], self.img_hw0[i], self.img_hw[i] # im, hw_original, hw_resized - - -def load_mosaic(self, index): - # YOLOv5 4-mosaic loader. Loads 1 image + 3 random images into a 4-image mosaic - labels4, segments4 = [], [] - s = self.img_size - yc, xc = (int(random.uniform(-x, 2 * s + x)) for x in self.mosaic_border) # mosaic center x, y - indices = [index] + random.choices(self.indices, k=3) # 3 additional image indices - random.shuffle(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 - labels, segments = self.labels[index].copy(), self.segments[index].copy() - if labels.size: - labels[:, 1:] = xywhn2xyxy(labels[:, 1:], w, h, padw, padh) # normalized xywh to pixel xyxy format - segments = [xyn2xy(x, w, h, padw, padh) for x in segments] - labels4.append(labels) - segments4.extend(segments) - - # Concat/clip labels - labels4 = np.concatenate(labels4, 0) - for x in (labels4[:, 1:], *segments4): - np.clip(x, 0, 2 * s, out=x) # clip when using random_perspective() - # img4, labels4 = replicate(img4, labels4) # replicate - - # Augment - img4, labels4, segments4 = copy_paste(img4, labels4, segments4, p=self.hyp['copy_paste']) - img4, labels4 = random_perspective(img4, labels4, segments4, - degrees=self.hyp['degrees'], - translate=self.hyp['translate'], - scale=self.hyp['scale'], - shear=self.hyp['shear'], - perspective=self.hyp['perspective'], - border=self.mosaic_border) # border to remove - - return img4, labels4 - - -def load_mosaic9(self, index): - # YOLOv5 9-mosaic loader. Loads 1 image + 8 random images into a 9-image mosaic - labels9, segments9 = [], [] - s = self.img_size - indices = [index] + random.choices(self.indices, k=8) # 8 additional image indices - random.shuffle(indices) - for i, index in enumerate(indices): - # Load image - img, _, (h, w) = load_image(self, index) - - # place img in img9 - if i == 0: # center - img9 = np.full((s * 3, s * 3, img.shape[2]), 114, dtype=np.uint8) # base image with 4 tiles - h0, w0 = h, w - c = s, s, s + w, s + h # xmin, ymin, xmax, ymax (base) coordinates - elif i == 1: # top - c = s, s - h, s + w, s - elif i == 2: # top right - c = s + wp, s - h, s + wp + w, s - elif i == 3: # right - c = s + w0, s, s + w0 + w, s + h - elif i == 4: # bottom right - c = s + w0, s + hp, s + w0 + w, s + hp + h - elif i == 5: # bottom - c = s + w0 - w, s + h0, s + w0, s + h0 + h - elif i == 6: # bottom left - c = s + w0 - wp - w, s + h0, s + w0 - wp, s + h0 + h - elif i == 7: # left - c = s - w, s + h0 - h, s, s + h0 - elif i == 8: # top left - c = s - w, s + h0 - hp - h, s, s + h0 - hp - - padx, pady = c[:2] - x1, y1, x2, y2 = (max(x, 0) for x in c) # allocate coords - - # Labels - labels, segments = self.labels[index].copy(), self.segments[index].copy() - if labels.size: - labels[:, 1:] = xywhn2xyxy(labels[:, 1:], w, h, padx, pady) # normalized xywh to pixel xyxy format - segments = [xyn2xy(x, w, h, padx, pady) for x in segments] - labels9.append(labels) - segments9.extend(segments) - - # Image - img9[y1:y2, x1:x2] = img[y1 - pady:, x1 - padx:] # img9[ymin:ymax, xmin:xmax] - hp, wp = h, w # height, width previous - - # Offset - yc, xc = (int(random.uniform(0, s)) for _ in self.mosaic_border) # mosaic center x, y - img9 = img9[yc:yc + 2 * s, xc:xc + 2 * s] - - # Concat/clip labels - labels9 = np.concatenate(labels9, 0) - labels9[:, [1, 3]] -= xc - labels9[:, [2, 4]] -= yc - c = np.array([xc, yc]) # centers - segments9 = [x - c for x in segments9] - - for x in (labels9[:, 1:], *segments9): - np.clip(x, 0, 2 * s, out=x) # clip when using random_perspective() - # img9, labels9 = replicate(img9, labels9) # replicate - - # Augment - img9, labels9 = random_perspective(img9, labels9, segments9, - 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 img9, labels9 - - -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='../datasets/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='../datasets/coco128'): # from utils.datasets import *; extract_boxes() - # 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) 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='../datasets/coco128/images', weights=(0.9, 0.1, 0.0), annotated_only=False): - """ Autosplit a dataset into train/val/test splits and save path/autosplit_*.txt files - Usage: from utils.datasets import *; autosplit() - Arguments - path: Path to images directory - weights: Train, val, test weights (list, tuple) - annotated_only: Only use images with an annotated txt file - """ - path = Path(path) # images dir - files = sorted(x for x in path.rglob('*.*') if x.suffix[1:].lower() in IMG_FORMATS) # image files only - n = len(files) # number of files - random.seed(0) # for reproducibility - 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.parent / x).unlink(missing_ok=True) for x in txt] # remove existing - - print(f'Autosplitting images from {path}' + ', using *.txt labeled images only' * annotated_only) - for i, img in tqdm(zip(indices, files), total=n): - if not annotated_only or Path(img2label_paths([str(img)])[0]).exists(): # check label - with open(path.parent / txt[i], 'a') as f: - f.write('./' + img.relative_to(path.parent).as_posix() + '\n') # add image to txt file - - -def verify_image_label(args): - # Verify one image-label pair - im_file, lb_file, prefix = args - nm, nf, ne, nc, msg, segments = 0, 0, 0, 0, '', [] # number (missing, found, empty, corrupt), message, segments - 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), f'image size {shape} <10 pixels' - assert im.format.lower() in IMG_FORMATS, f'invalid image format {im.format}' - if im.format.lower() in ('jpg', 'jpeg'): - with open(im_file, 'rb') as f: - f.seek(-2, 2) - if f.read() != b'\xff\xd9': # corrupt JPEG - ImageOps.exif_transpose(Image.open(im_file)).save(im_file, 'JPEG', subsampling=0, quality=100) - msg = f'{prefix}WARNING: {im_file}: corrupt JPEG restored and saved' - - # verify labels - if os.path.isfile(lb_file): - nf = 1 # label found - with open(lb_file) as f: - l = [x.split() for x in f.read().strip().splitlines() if len(x)] - if any([len(x) > 8 for x in l]): # is segment - classes = np.array([x[0] for x in l], dtype=np.float32) - segments = [np.array(x[1:], dtype=np.float32).reshape(-1, 2) for x in l] # (cls, xy1...) - l = np.concatenate((classes.reshape(-1, 1), segments2boxes(segments)), 1) # (cls, xywh) - l = np.array(l, dtype=np.float32) - nl = len(l) - if nl: - assert l.shape[1] == 5, f'labels require 5 columns, {l.shape[1]} columns detected' - assert (l >= 0).all(), f'negative label values {l[l < 0]}' - assert (l[:, 1:] <= 1).all(), f'non-normalized or out of bounds coordinates {l[:, 1:][l[:, 1:] > 1]}' - _, i = np.unique(l, axis=0, return_index=True) - if len(i) < nl: # duplicate row check - l = l[i] # remove duplicates - if segments: - segments = segments[i] - msg = f'{prefix}WARNING: {im_file}: {nl - len(i)} duplicate labels removed' - 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) - return im_file, l, shape, segments, nm, nf, ne, nc, msg - except Exception as e: - nc = 1 - msg = f'{prefix}WARNING: {im_file}: ignoring corrupt image/label: {e}' - return [None, None, None, None, nm, nf, ne, nc, msg] - - -def dataset_stats(path='coco128.yaml', autodownload=False, verbose=False, profile=False, hub=False): - """ Return dataset statistics dictionary with images and instances counts per split per class - To run in parent directory: export PYTHONPATH="$PWD/yolov5" - Usage1: from utils.datasets import *; dataset_stats('coco128.yaml', autodownload=True) - Usage2: from utils.datasets import *; dataset_stats('../datasets/coco128_with_yaml.zip') - Arguments - path: Path to data.yaml or data.zip (with data.yaml inside data.zip) - autodownload: Attempt to download dataset if not found locally - verbose: Print stats dictionary - """ - - def round_labels(labels): - # Update labels to integer class and 6 decimal place floats - return [[int(c), *(round(x, 4) for x in points)] for c, *points in labels] - - def unzip(path): - # Unzip data.zip TODO: CONSTRAINT: path/to/abc.zip MUST unzip to 'path/to/abc/' - if str(path).endswith('.zip'): # path is data.zip - assert Path(path).is_file(), f'Error unzipping {path}, file not found' - ZipFile(path).extractall(path=path.parent) # unzip - dir = path.with_suffix('') # dataset directory == zip name - return True, str(dir), next(dir.rglob('*.yaml')) # zipped, data_dir, yaml_path - else: # path is data.yaml - return False, None, path - - def hub_ops(f, max_dim=1920): - # HUB ops for 1 image 'f': resize and save at reduced quality in /dataset-hub for web/app viewing - f_new = im_dir / Path(f).name # dataset-hub image filename - try: # use PIL - im = Image.open(f) - r = max_dim / max(im.height, im.width) # ratio - if r < 1.0: # image too large - im = im.resize((int(im.width * r), int(im.height * r))) - im.save(f_new, 'JPEG', quality=75, optimize=True) # save - except Exception as e: # use OpenCV - print(f'WARNING: HUB ops PIL failure {f}: {e}') - im = cv2.imread(f) - im_height, im_width = im.shape[:2] - r = max_dim / max(im_height, im_width) # ratio - if r < 1.0: # image too large - im = cv2.resize(im, (int(im_width * r), int(im_height * r)), interpolation=cv2.INTER_AREA) - cv2.imwrite(str(f_new), im) - - zipped, data_dir, yaml_path = unzip(Path(path)) - with open(check_yaml(yaml_path), errors='ignore') as f: - data = yaml.safe_load(f) # data dict - if zipped: - data['path'] = data_dir # TODO: should this be dir.resolve()? - check_dataset(data, autodownload) # download dataset if missing - hub_dir = Path(data['path'] + ('-hub' if hub else '')) - stats = {'nc': data['nc'], 'names': data['names']} # statistics dictionary - for split in 'train', 'val', 'test': - if data.get(split) is None: - stats[split] = None # i.e. no test set - continue - x = [] - dataset = LoadImagesAndLabels(data[split]) # load dataset - for label in tqdm(dataset.labels, total=dataset.n, desc='Statistics'): - x.append(np.bincount(label[:, 0].astype(int), minlength=data['nc'])) - x = np.array(x) # shape(128x80) - stats[split] = {'instance_stats': {'total': int(x.sum()), 'per_class': x.sum(0).tolist()}, - 'image_stats': {'total': dataset.n, 'unlabelled': int(np.all(x == 0, 1).sum()), - 'per_class': (x > 0).sum(0).tolist()}, - 'labels': [{str(Path(k).name): round_labels(v.tolist())} for k, v in - zip(dataset.img_files, dataset.labels)]} - - if hub: - im_dir = hub_dir / 'images' - im_dir.mkdir(parents=True, exist_ok=True) - for _ in tqdm(ThreadPool(NUM_THREADS).imap(hub_ops, dataset.img_files), total=dataset.n, desc='HUB Ops'): - pass - - # Profile - stats_path = hub_dir / 'stats.json' - if profile: - for _ in range(1): - file = stats_path.with_suffix('.npy') - t1 = time.time() - np.save(file, stats) - t2 = time.time() - x = np.load(file, allow_pickle=True) - print(f'stats.npy times: {time.time() - t2:.3f}s read, {t2 - t1:.3f}s write') - - file = stats_path.with_suffix('.json') - t1 = time.time() - with open(file, 'w') as f: - json.dump(stats, f) # save stats *.json - t2 = time.time() - with open(file) as f: - x = json.load(f) # load hyps dict - print(f'stats.json times: {time.time() - t2:.3f}s read, {t2 - t1:.3f}s write') - - # Save, print and return - if hub: - print(f'Saving {stats_path.resolve()}...') - with open(stats_path, 'w') as f: - json.dump(stats, f) # save stats.json - if verbose: - print(json.dumps(stats, indent=2, sort_keys=False)) - return stats diff --git a/YOLO/Dockerfiles/app/app/utils/downloads.py b/YOLO/Dockerfiles/app/app/utils/downloads.py deleted file mode 100644 index a8bacae..0000000 --- a/YOLO/Dockerfiles/app/app/utils/downloads.py +++ /dev/null @@ -1,153 +0,0 @@ -# YOLOv5 🚀 by Ultralytics, GPL-3.0 license -""" -Download utils -""" - -import os -import platform -import subprocess -import time -import urllib -from pathlib import Path -from zipfile import ZipFile - -import requests -import torch - - -def gsutil_getsize(url=''): - # gs://bucket/file size https://cloud.google.com/storage/docs/gsutil/commands/du - s = subprocess.check_output(f'gsutil du {url}', shell=True).decode('utf-8') - return eval(s.split(' ')[0]) if len(s) else 0 # bytes - - -def safe_download(file, url, url2=None, min_bytes=1E0, error_msg=''): - # Attempts to download file from url or url2, checks and removes incomplete downloads < min_bytes - file = Path(file) - assert_msg = f"Downloaded file '{file}' does not exist or size is < min_bytes={min_bytes}" - try: # url1 - print(f'Downloading {url} to {file}...') - torch.hub.download_url_to_file(url, str(file)) - assert file.exists() and file.stat().st_size > min_bytes, assert_msg # check - except Exception as e: # url2 - file.unlink(missing_ok=True) # remove partial downloads - print(f'ERROR: {e}\nRe-attempting {url2 or url} to {file}...') - os.system(f"curl -L '{url2 or url}' -o '{file}' --retry 3 -C -") # curl download, retry and resume on fail - finally: - if not file.exists() or file.stat().st_size < min_bytes: # check - file.unlink(missing_ok=True) # remove partial downloads - print(f"ERROR: {assert_msg}\n{error_msg}") - print('') - - -def attempt_download(file, repo='ultralytics/yolov5'): # from utils.downloads import *; attempt_download() - # Attempt file download if does not exist - file = Path(str(file).strip().replace("'", '')) - - if not file.exists(): - # URL specified - name = Path(urllib.parse.unquote(str(file))).name # decode '%2F' to '/' etc. - if str(file).startswith(('http:/', 'https:/')): # download - url = str(file).replace(':/', '://') # Pathlib turns :// -> :/ - file = name.split('?')[0] # parse authentication https://url.com/file.txt?auth... - if Path(file).is_file(): - print(f'Found {url} locally at {file}') # file already exists - else: - safe_download(file=file, url=url, min_bytes=1E5) - return file - - # GitHub assets - file.parent.mkdir(parents=True, exist_ok=True) # make parent dir (if required) - try: - response = requests.get(f'https://api.github.com/repos/{repo}/releases/latest').json() # github api - assets = [x['name'] for x in response['assets']] # release assets, i.e. ['yolov5s.pt', 'yolov5m.pt', ...] - tag = response['tag_name'] # i.e. 'v1.0' - except: # fallback plan - assets = ['yolov5n.pt', 'yolov5s.pt', 'yolov5m.pt', 'yolov5l.pt', 'yolov5x.pt', - 'yolov5n6.pt', 'yolov5s6.pt', 'yolov5m6.pt', 'yolov5l6.pt', 'yolov5x6.pt'] - try: - tag = subprocess.check_output('git tag', shell=True, stderr=subprocess.STDOUT).decode().split()[-1] - except: - tag = 'v6.0' # current release - - if name in assets: - safe_download(file, - url=f'https://github.com/{repo}/releases/download/{tag}/{name}', - # url2=f'https://storage.googleapis.com/{repo}/ckpt/{name}', # backup url (optional) - min_bytes=1E5, - error_msg=f'{file} missing, try downloading from https://github.com/{repo}/releases/') - - return str(file) - - -def gdrive_download(id='16TiPfZj7htmTyhntwcZyEEAejOUxuT6m', file='tmp.zip'): - # Downloads a file from Google Drive. from yolov5.utils.downloads import *; gdrive_download() - t = time.time() - file = Path(file) - cookie = Path('cookie') # gdrive cookie - print(f'Downloading https://drive.google.com/uc?export=download&id={id} as {file}... ', end='') - file.unlink(missing_ok=True) # remove existing file - cookie.unlink(missing_ok=True) # remove existing cookie - - # Attempt file download - out = "NUL" if platform.system() == "Windows" else "/dev/null" - os.system(f'curl -c ./cookie -s -L "drive.google.com/uc?export=download&id={id}" > {out}') - if os.path.exists('cookie'): # large file - s = f'curl -Lb ./cookie "drive.google.com/uc?export=download&confirm={get_token()}&id={id}" -o {file}' - else: # small file - s = f'curl -s -L -o {file} "drive.google.com/uc?export=download&id={id}"' - r = os.system(s) # execute, capture return - cookie.unlink(missing_ok=True) # remove existing cookie - - # Error check - if r != 0: - file.unlink(missing_ok=True) # remove partial - print('Download error ') # raise Exception('Download error') - return r - - # Unzip if archive - if file.suffix == '.zip': - print('unzipping... ', end='') - ZipFile(file).extractall(path=file.parent) # unzip - file.unlink() # remove zip - - print(f'Done ({time.time() - t:.1f}s)') - return r - - -def get_token(cookie="./cookie"): - with open(cookie) as f: - for line in f: - if "download" in line: - return line.split()[-1] - return "" - -# Google utils: https://cloud.google.com/storage/docs/reference/libraries ---------------------------------------------- -# -# -# def upload_blob(bucket_name, source_file_name, destination_blob_name): -# # Uploads a file to a bucket -# # https://cloud.google.com/storage/docs/uploading-objects#storage-upload-object-python -# -# storage_client = storage.Client() -# bucket = storage_client.get_bucket(bucket_name) -# blob = bucket.blob(destination_blob_name) -# -# blob.upload_from_filename(source_file_name) -# -# print('File {} uploaded to {}.'.format( -# source_file_name, -# destination_blob_name)) -# -# -# def download_blob(bucket_name, source_blob_name, destination_file_name): -# # Uploads a blob from a bucket -# storage_client = storage.Client() -# bucket = storage_client.get_bucket(bucket_name) -# blob = bucket.blob(source_blob_name) -# -# blob.download_to_filename(destination_file_name) -# -# print('Blob {} downloaded to {}.'.format( -# source_blob_name, -# destination_file_name)) diff --git a/YOLO/Dockerfiles/app/app/utils/flask_rest_api/README.md b/YOLO/Dockerfiles/app/app/utils/flask_rest_api/README.md deleted file mode 100644 index a726acb..0000000 --- a/YOLO/Dockerfiles/app/app/utils/flask_rest_api/README.md +++ /dev/null @@ -1,73 +0,0 @@ -# Flask REST API - -[REST](https://en.wikipedia.org/wiki/Representational_state_transfer) [API](https://en.wikipedia.org/wiki/API)s are -commonly used to expose Machine Learning (ML) models to other services. This folder contains an example REST API -created using Flask to expose the YOLOv5s model from [PyTorch Hub](https://pytorch.org/hub/ultralytics_yolov5/). - -## Requirements - -[Flask](https://palletsprojects.com/p/flask/) is required. Install with: - -```shell -$ pip install Flask -``` - -## Run - -After Flask installation run: - -```shell -$ python3 restapi.py --port 5000 -``` - -Then use [curl](https://curl.se/) to perform a request: - -```shell -$ curl -X POST -F image=@zidane.jpg 'http://localhost:5000/v1/object-detection/yolov5s' -``` - -The model inference results are returned as a JSON response: - -```json -[ - { - "class": 0, - "confidence": 0.8900438547, - "height": 0.9318675399, - "name": "person", - "width": 0.3264600933, - "xcenter": 0.7438579798, - "ycenter": 0.5207948685 - }, - { - "class": 0, - "confidence": 0.8440024257, - "height": 0.7155083418, - "name": "person", - "width": 0.6546785235, - "xcenter": 0.427829951, - "ycenter": 0.6334488392 - }, - { - "class": 27, - "confidence": 0.3771208823, - "height": 0.3902671337, - "name": "tie", - "width": 0.0696444362, - "xcenter": 0.3675483763, - "ycenter": 0.7991207838 - }, - { - "class": 27, - "confidence": 0.3527112305, - "height": 0.1540903747, - "name": "tie", - "width": 0.0336618312, - "xcenter": 0.7814827561, - "ycenter": 0.5065554976 - } -] -``` - -An example python script to perform inference using [requests](https://docs.python-requests.org/en/master/) is given -in `example_request.py` diff --git a/YOLO/Dockerfiles/app/app/utils/flask_rest_api/example_request.py b/YOLO/Dockerfiles/app/app/utils/flask_rest_api/example_request.py deleted file mode 100644 index ff21f30..0000000 --- a/YOLO/Dockerfiles/app/app/utils/flask_rest_api/example_request.py +++ /dev/null @@ -1,13 +0,0 @@ -"""Perform test request""" -import pprint - -import requests - -DETECTION_URL = "http://localhost:5000/v1/object-detection/yolov5s" -TEST_IMAGE = "zidane.jpg" - -image_data = open(TEST_IMAGE, "rb").read() - -response = requests.post(DETECTION_URL, files={"image": image_data}).json() - -pprint.pprint(response) diff --git a/YOLO/Dockerfiles/app/app/utils/flask_rest_api/restapi.py b/YOLO/Dockerfiles/app/app/utils/flask_rest_api/restapi.py deleted file mode 100644 index b93ad16..0000000 --- a/YOLO/Dockerfiles/app/app/utils/flask_rest_api/restapi.py +++ /dev/null @@ -1,37 +0,0 @@ -""" -Run a rest API exposing the yolov5s object detection model -""" -import argparse -import io - -import torch -from flask import Flask, request -from PIL import Image - -app = Flask(__name__) - -DETECTION_URL = "/v1/object-detection/yolov5s" - - -@app.route(DETECTION_URL, methods=["POST"]) -def predict(): - if not request.method == "POST": - return - - if request.files.get("image"): - image_file = request.files["image"] - image_bytes = image_file.read() - - img = Image.open(io.BytesIO(image_bytes)) - - results = model(img, size=640) # reduce size=320 for faster inference - return results.pandas().xyxy[0].to_json(orient="records") - - -if __name__ == "__main__": - parser = argparse.ArgumentParser(description="Flask API exposing YOLOv5 model") - parser.add_argument("--port", default=5000, type=int, help="port number") - args = parser.parse_args() - - model = torch.hub.load("ultralytics/yolov5", "yolov5s", force_reload=True) # force_reload to recache - app.run(host="0.0.0.0", port=args.port) # debug=True causes Restarting with stat diff --git a/YOLO/Dockerfiles/app/app/utils/general.py b/YOLO/Dockerfiles/app/app/utils/general.py deleted file mode 100755 index 470e6d8..0000000 --- a/YOLO/Dockerfiles/app/app/utils/general.py +++ /dev/null @@ -1,848 +0,0 @@ -# YOLOv5 🚀 by Ultralytics, GPL-3.0 license -""" -General utils -""" - -import contextlib -import glob -import logging -import math -import os -import platform -import random -import re -import shutil -import signal -import time -import urllib -from itertools import repeat -from multiprocessing.pool import ThreadPool -from pathlib import Path -from subprocess import check_output -from zipfile import ZipFile - -import cv2 -import numpy as np -import pandas as pd -import pkg_resources as pkg -import torch -import torchvision -import yaml - -from utils.downloads import gsutil_getsize -from utils.metrics import box_iou, fitness - -# Settings -FILE = Path(__file__).resolve() -ROOT = FILE.parents[1] # YOLOv5 root directory -NUM_THREADS = min(8, max(1, os.cpu_count() - 1)) # number of YOLOv5 multiprocessing threads - -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 -pd.options.display.max_columns = 10 -cv2.setNumThreads(0) # prevent OpenCV from multithreading (incompatible with PyTorch DataLoader) -os.environ['NUMEXPR_MAX_THREADS'] = str(NUM_THREADS) # NumExpr max threads - - -def set_logging(name=None, verbose=True): - # Sets level and returns logger - for h in logging.root.handlers: - logging.root.removeHandler(h) # remove all handlers associated with the root logger object - rank = int(os.getenv('RANK', -1)) # rank in world for Multi-GPU trainings - logging.basicConfig(format="%(message)s", level=logging.INFO if (verbose and rank in (-1, 0)) else logging.WARNING) - return logging.getLogger(name) - - -LOGGER = set_logging(__name__) # define globally (used in train.py, val.py, detect.py, etc.) - - -class Profile(contextlib.ContextDecorator): - # Usage: @Profile() decorator or 'with Profile():' context manager - def __enter__(self): - self.start = time.time() - - def __exit__(self, type, value, traceback): - print(f'Profile results: {time.time() - self.start:.5f}s') - - -class Timeout(contextlib.ContextDecorator): - # Usage: @Timeout(seconds) decorator or 'with Timeout(seconds):' context manager - def __init__(self, seconds, *, timeout_msg='', suppress_timeout_errors=True): - self.seconds = int(seconds) - self.timeout_message = timeout_msg - self.suppress = bool(suppress_timeout_errors) - - def _timeout_handler(self, signum, frame): - raise TimeoutError(self.timeout_message) - - def __enter__(self): - signal.signal(signal.SIGALRM, self._timeout_handler) # Set handler for SIGALRM - signal.alarm(self.seconds) # start countdown for SIGALRM to be raised - - def __exit__(self, exc_type, exc_val, exc_tb): - signal.alarm(0) # Cancel SIGALRM if it's scheduled - if self.suppress and exc_type is TimeoutError: # Suppress TimeoutError - return True - - -class WorkingDirectory(contextlib.ContextDecorator): - # Usage: @WorkingDirectory(dir) decorator or 'with WorkingDirectory(dir):' context manager - def __init__(self, new_dir): - self.dir = new_dir # new dir - self.cwd = Path.cwd().resolve() # current dir - - def __enter__(self): - os.chdir(self.dir) - - def __exit__(self, exc_type, exc_val, exc_tb): - os.chdir(self.cwd) - - -def try_except(func): - # try-except function. Usage: @try_except decorator - def handler(*args, **kwargs): - try: - func(*args, **kwargs) - except Exception as e: - print(e) - - return handler - - -def methods(instance): - # Get class/instance methods - return [f for f in dir(instance) if callable(getattr(instance, f)) and not f.startswith("__")] - - -def print_args(name, opt): - # Print argparser arguments - LOGGER.info(colorstr(f'{name}: ') + ', '.join(f'{k}={v}' for k, v in vars(opt).items())) - - -def init_seeds(seed=0): - # Initialize random number generator (RNG) seeds https://pytorch.org/docs/stable/notes/randomness.html - # cudnn seed 0 settings are slower and more reproducible, else faster and less reproducible - import torch.backends.cudnn as cudnn - random.seed(seed) - np.random.seed(seed) - torch.manual_seed(seed) - cudnn.benchmark, cudnn.deterministic = (False, True) if seed == 0 else (True, False) - - -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 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 user_config_dir(dir='Ultralytics', env_var='YOLOV5_CONFIG_DIR'): - # Return path of user configuration directory. Prefer environment variable if exists. Make dir if required. - env = os.getenv(env_var) - if env: - path = Path(env) # use environment variable - else: - cfg = {'Windows': 'AppData/Roaming', 'Linux': '.config', 'Darwin': 'Library/Application Support'} # 3 OS dirs - path = Path.home() / cfg.get(platform.system(), '') # OS-specific config dir - path = (path if is_writeable(path) else Path('/tmp')) / dir # GCP and AWS lambda fix, only /tmp is writeable - path.mkdir(exist_ok=True) # make if required - return path - - -def is_writeable(dir, test=False): - # Return True if directory has write permissions, test opening a file with write permissions if test=True - if test: # method 1 - file = Path(dir) / 'tmp.txt' - try: - with open(file, 'w'): # open file with write permissions - pass - file.unlink() # remove file - return True - except OSError: - return False - else: # method 2 - return os.access(dir, os.R_OK) # possible issues on Windows - - -def is_docker(): - # Is environment a Docker container? - return Path('/workspace').exists() # or Path('/.dockerenv').exists() - - -def is_colab(): - # Is environment a Google Colab instance? - try: - import google.colab - return True - except ImportError: - return False - - -def is_pip(): - # Is file in a pip package? - return 'site-packages' in Path(__file__).resolve().parts - - -def is_ascii(s=''): - # Is string composed of all ASCII (no UTF) characters? (note str().isascii() introduced in python 3.7) - s = str(s) # convert list, tuple, None, etc. to str - return len(s.encode().decode('ascii', 'ignore')) == len(s) - - -def is_chinese(s='人工智能'): - # Is string composed of any Chinese characters? - return re.search('[\u4e00-\u9fff]', s) - - -def emojis(str=''): - # Return platform-dependent emoji-safe version of string - return str.encode().decode('ascii', 'ignore') if platform.system() == 'Windows' else str - - -def file_size(path): - # Return file/dir size (MB) - path = Path(path) - if path.is_file(): - return path.stat().st_size / 1E6 - elif path.is_dir(): - return sum(f.stat().st_size for f in path.glob('**/*') if f.is_file()) / 1E6 - else: - return 0.0 - - -def check_online(): - # Check internet connectivity - import socket - try: - socket.create_connection(("1.1.1.1", 443), 5) # check host accessibility - return True - except OSError: - return False - - -@try_except -@WorkingDirectory(ROOT) -def check_git_status(): - # Recommend 'git pull' if code is out of date - msg = ', for updates see https://github.com/ultralytics/yolov5' - print(colorstr('github: '), end='') - assert Path('.git').exists(), 'skipping check (not a git repository)' + msg - assert not is_docker(), 'skipping check (Docker image)' + msg - assert check_online(), 'skipping check (offline)' + msg - - cmd = 'git fetch && git config --get remote.origin.url' - url = check_output(cmd, shell=True, timeout=5).decode().strip().rstrip('.git') # git fetch - branch = check_output('git rev-parse --abbrev-ref HEAD', shell=True).decode().strip() # checked out - n = int(check_output(f'git rev-list {branch}..origin/master --count', shell=True)) # commits behind - if n > 0: - s = f"⚠️ YOLOv5 is out of date by {n} commit{'s' * (n > 1)}. Use `git pull` or `git clone {url}` to update." - else: - s = f'up to date with {url} ✅' - print(emojis(s)) # emoji-safe - - -def check_python(minimum='3.6.2'): - # Check current python version vs. required python version - check_version(platform.python_version(), minimum, name='Python ', hard=True) - - -def check_version(current='0.0.0', minimum='0.0.0', name='version ', pinned=False, hard=False, verbose=False): - # Check version vs. required version - current, minimum = (pkg.parse_version(x) for x in (current, minimum)) - result = (current == minimum) if pinned else (current >= minimum) # bool - s = f'{name}{minimum} required by YOLOv5, but {name}{current} is currently installed' # string - if hard: - assert result, s # assert min requirements met - if verbose and not result: - LOGGER.warning(s) - return result - - -@try_except -def check_requirements(requirements=ROOT / 'requirements.txt', exclude=(), install=True): - # Check installed dependencies meet requirements (pass *.txt file or list of packages) - prefix = colorstr('red', 'bold', 'requirements:') - check_python() # check python version - if isinstance(requirements, (str, Path)): # requirements.txt file - file = Path(requirements) - assert file.exists(), f"{prefix} {file.resolve()} not found, check failed." - with file.open() as f: - requirements = [f'{x.name}{x.specifier}' for x in pkg.parse_requirements(f) if x.name not in exclude] - else: # list or tuple of packages - requirements = [x for x in requirements if x not in exclude] - - n = 0 # number of packages updates - for r in requirements: - try: - pkg.require(r) - except Exception as e: # DistributionNotFound or VersionConflict if requirements not met - s = f"{prefix} {r} not found and is required by YOLOv5" - if install: - print(f"{s}, attempting auto-update...") - try: - assert check_online(), f"'pip install {r}' skipped (offline)" - print(check_output(f"pip install '{r}'", shell=True).decode()) - n += 1 - except Exception as e: - print(f'{prefix} {e}') - else: - print(f'{s}. Please install and rerun your command.') - - if n: # if packages updated - source = file.resolve() if 'file' in locals() else requirements - s = f"{prefix} {n} package{'s' * (n > 1)} updated per {source}\n" \ - f"{prefix} ⚠️ {colorstr('bold', 'Restart runtime or rerun command for updates to take effect')}\n" - print(emojis(s)) - - -def check_img_size(imgsz, s=32, floor=0): - # Verify image size is a multiple of stride s in each dimension - if isinstance(imgsz, int): # integer i.e. img_size=640 - new_size = max(make_divisible(imgsz, int(s)), floor) - else: # list i.e. img_size=[640, 480] - new_size = [max(make_divisible(x, int(s)), floor) for x in imgsz] - if new_size != imgsz: - print(f'WARNING: --img-size {imgsz} must be multiple of max stride {s}, updating to {new_size}') - return new_size - - -def check_imshow(): - # Check if environment supports image displays - try: - assert not is_docker(), 'cv2.imshow() is disabled in Docker environments' - assert not is_colab(), 'cv2.imshow() is disabled in Google Colab environments' - cv2.imshow('test', np.zeros((1, 1, 3))) - cv2.waitKey(1) - cv2.destroyAllWindows() - cv2.waitKey(1) - return True - except Exception as e: - print(f'WARNING: Environment does not support cv2.imshow() or PIL Image.show() image displays\n{e}') - return False - - -def check_suffix(file='yolov5s.pt', suffix=('.pt',), msg=''): - # Check file(s) for acceptable suffix - if file and suffix: - if isinstance(suffix, str): - suffix = [suffix] - for f in file if isinstance(file, (list, tuple)) else [file]: - s = Path(f).suffix.lower() # file suffix - if len(s): - assert s in suffix, f"{msg}{f} acceptable suffix is {suffix}" - - -def check_yaml(file, suffix=('.yaml', '.yml')): - # Search/download YAML file (if necessary) and return path, checking suffix - return check_file(file, suffix) - - -def check_file(file, suffix=''): - # Search/download file (if necessary) and return path - check_suffix(file, suffix) # optional - file = str(file) # convert to str() - if Path(file).is_file() or file == '': # exists - return file - elif file.startswith(('http:/', 'https:/')): # download - url = str(Path(file)).replace(':/', '://') # Pathlib turns :// -> :/ - file = Path(urllib.parse.unquote(file).split('?')[0]).name # '%2F' to '/', split https://url.com/file.txt?auth - if Path(file).is_file(): - print(f'Found {url} locally at {file}') # file already exists - else: - print(f'Downloading {url} to {file}...') - torch.hub.download_url_to_file(url, file) - assert Path(file).exists() and Path(file).stat().st_size > 0, f'File download failed: {url}' # check - return file - else: # search - files = [] - for d in 'data', 'models', 'utils': # search directories - files.extend(glob.glob(str(ROOT / d / '**' / file), recursive=True)) # find file - assert len(files), f'File not found: {file}' # assert file was found - assert len(files) == 1, f"Multiple files match '{file}', specify exact path: {files}" # assert unique - return files[0] # return file - - -def check_dataset(data, autodownload=True): - # Download and/or unzip dataset if not found locally - # Usage: https://github.com/ultralytics/yolov5/releases/download/v1.0/coco128_with_yaml.zip - - # Download (optional) - extract_dir = '' - if isinstance(data, (str, Path)) and str(data).endswith('.zip'): # i.e. gs://bucket/dir/coco128.zip - download(data, dir='../datasets', unzip=True, delete=False, curl=False, threads=1) - data = next((Path('../datasets') / Path(data).stem).rglob('*.yaml')) - extract_dir, autodownload = data.parent, False - - # Read yaml (optional) - if isinstance(data, (str, Path)): - with open(data, errors='ignore') as f: - data = yaml.safe_load(f) # dictionary - - # Parse yaml - path = extract_dir or Path(data.get('path') or '') # optional 'path' default to '.' - for k in 'train', 'val', 'test': - if data.get(k): # prepend path - data[k] = str(path / data[k]) if isinstance(data[k], str) else [str(path / x) for x in data[k]] - - assert 'nc' in data, "Dataset 'nc' key missing." - if 'names' not in data: - data['names'] = [f'class{i}' for i in range(data['nc'])] # assign class names if missing - train, val, test, s = (data.get(x) for x in ('train', 'val', 'test', 'download')) - if 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 autodownload: # download script - root = path.parent if 'path' in data else '..' # unzip directory i.e. '../' - if s.startswith('http') and s.endswith('.zip'): # URL - f = Path(s).name # filename - print(f'Downloading {s} to {f}...') - torch.hub.download_url_to_file(s, f) - Path(root).mkdir(parents=True, exist_ok=True) # create root - ZipFile(f).extractall(path=root) # unzip - Path(f).unlink() # remove zip - r = None # success - elif s.startswith('bash '): # bash script - print(f'Running {s} ...') - r = os.system(s) - else: # python script - r = exec(s, {'yaml': data}) # return None - print(f"Dataset autodownload {f'success, saved to {root}' if r in (0, None) else 'failure'}\n") - else: - raise Exception('Dataset not found.') - - return data # dictionary - - -def url2file(url): - # Convert URL to filename, i.e. https://url.com/file.txt?auth -> file.txt - url = str(Path(url)).replace(':/', '://') # Pathlib turns :// -> :/ - file = Path(urllib.parse.unquote(url)).name.split('?')[0] # '%2F' to '/', split https://url.com/file.txt?auth - return file - - -def download(url, dir='.', unzip=True, delete=True, curl=False, threads=1): - # Multi-threaded file download and unzip function, used in data.yaml for autodownload - def download_one(url, dir): - # Download 1 file - f = dir / Path(url).name # filename - if Path(url).is_file(): # exists in current path - Path(url).rename(f) # move to dir - elif not f.exists(): - print(f'Downloading {url} to {f}...') - if curl: - os.system(f"curl -L '{url}' -o '{f}' --retry 9 -C -") # curl download, retry and resume on fail - else: - torch.hub.download_url_to_file(url, f, progress=True) # torch download - if unzip and f.suffix in ('.zip', '.gz'): - print(f'Unzipping {f}...') - if f.suffix == '.zip': - ZipFile(f).extractall(path=dir) # unzip - elif f.suffix == '.gz': - os.system(f'tar xfz {f} --directory {f.parent}') # unzip - if delete: - f.unlink() # remove zip - - dir = Path(dir) - dir.mkdir(parents=True, exist_ok=True) # make directory - if threads > 1: - pool = ThreadPool(threads) - pool.imap(lambda x: download_one(*x), zip(url, repeat(dir))) # multi-threaded - pool.close() - pool.join() - else: - for u in [url] if isinstance(url, (str, Path)) else url: - download_one(u, dir) - - -def make_divisible(x, divisor): - # Returns nearest x divisible by divisor - if isinstance(divisor, torch.Tensor): - divisor = int(divisor.max()) # to int - 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 one_cycle(y1=0.0, y2=1.0, steps=100): - # lambda function for sinusoidal ramp from y1 to y2 https://arxiv.org/pdf/1812.01187.pdf - return lambda x: ((1 - math.cos(x * math.pi / steps)) / 2) * (y2 - y1) + y1 - - -def colorstr(*input): - # Colors a string https://en.wikipedia.org/wiki/ANSI_escape_code, i.e. colorstr('blue', 'hello world') - *args, string = input if len(input) > 1 else ('blue', 'bold', input[0]) # color arguments, string - colors = {'black': '\033[30m', # basic colors - 'red': '\033[31m', - 'green': '\033[32m', - 'yellow': '\033[33m', - 'blue': '\033[34m', - 'magenta': '\033[35m', - 'cyan': '\033[36m', - 'white': '\033[37m', - 'bright_black': '\033[90m', # bright colors - 'bright_red': '\033[91m', - 'bright_green': '\033[92m', - 'bright_yellow': '\033[93m', - 'bright_blue': '\033[94m', - 'bright_magenta': '\033[95m', - 'bright_cyan': '\033[96m', - 'bright_white': '\033[97m', - 'end': '\033[0m', # misc - 'bold': '\033[1m', - 'underline': '\033[4m'} - return ''.join(colors[x] for x in args) + f'{string}' + colors['end'] - - -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 xywhn2xyxy(x, w=640, h=640, padw=0, padh=0): - # Convert nx4 boxes from [x, y, w, h] normalized 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] = w * (x[:, 0] - x[:, 2] / 2) + padw # top left x - y[:, 1] = h * (x[:, 1] - x[:, 3] / 2) + padh # top left y - y[:, 2] = w * (x[:, 0] + x[:, 2] / 2) + padw # bottom right x - y[:, 3] = h * (x[:, 1] + x[:, 3] / 2) + padh # bottom right y - return y - - -def xyxy2xywhn(x, w=640, h=640, clip=False, eps=0.0): - # Convert nx4 boxes from [x1, y1, x2, y2] to [x, y, w, h] normalized where xy1=top-left, xy2=bottom-right - if clip: - clip_coords(x, (h - eps, w - eps)) # warning: inplace clip - y = x.clone() if isinstance(x, torch.Tensor) else np.copy(x) - y[:, 0] = ((x[:, 0] + x[:, 2]) / 2) / w # x center - y[:, 1] = ((x[:, 1] + x[:, 3]) / 2) / h # y center - y[:, 2] = (x[:, 2] - x[:, 0]) / w # width - y[:, 3] = (x[:, 3] - x[:, 1]) / h # height - return y - - -def xyn2xy(x, w=640, h=640, padw=0, padh=0): - # Convert normalized segments into pixel segments, shape (n,2) - y = x.clone() if isinstance(x, torch.Tensor) else np.copy(x) - y[:, 0] = w * x[:, 0] + padw # top left x - y[:, 1] = h * x[:, 1] + padh # top left y - return y - - -def segment2box(segment, width=640, height=640): - # Convert 1 segment label to 1 box label, applying inside-image constraint, i.e. (xy1, xy2, ...) to (xyxy) - x, y = segment.T # segment xy - inside = (x >= 0) & (y >= 0) & (x <= width) & (y <= height) - x, y, = x[inside], y[inside] - return np.array([x.min(), y.min(), x.max(), y.max()]) if any(x) else np.zeros((1, 4)) # xyxy - - -def segments2boxes(segments): - # Convert segment labels to box labels, i.e. (cls, xy1, xy2, ...) to (cls, xywh) - boxes = [] - for s in segments: - x, y = s.T # segment xy - boxes.append([x.min(), y.min(), x.max(), y.max()]) # cls, xyxy - return xyxy2xywh(np.array(boxes)) # cls, xywh - - -def resample_segments(segments, n=1000): - # Up-sample an (n,2) segment - for i, s in enumerate(segments): - x = np.linspace(0, len(s) - 1, n) - xp = np.arange(len(s)) - segments[i] = np.concatenate([np.interp(x, xp, s[:, i]) for i in range(2)]).reshape(2, -1).T # segment xy - return segments - - -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, shape): - # Clip bounding xyxy bounding boxes to image shape (height, width) - if isinstance(boxes, torch.Tensor): # faster individually - boxes[:, 0].clamp_(0, shape[1]) # x1 - boxes[:, 1].clamp_(0, shape[0]) # y1 - boxes[:, 2].clamp_(0, shape[1]) # x2 - boxes[:, 3].clamp_(0, shape[0]) # y2 - else: # np.array (faster grouped) - boxes[:, [0, 2]] = boxes[:, [0, 2]].clip(0, shape[1]) # x1, x2 - boxes[:, [1, 3]] = boxes[:, [1, 3]].clip(0, shape[0]) # y1, y2 - - -def non_max_suppression(prediction, conf_thres=0.25, iou_thres=0.45, classes=None, agnostic=False, multi_label=False, - labels=(), max_det=300): - """Runs Non-Maximum Suppression (NMS) on inference results - - Returns: - list of detections, on (n,6) tensor per image [xyxy, conf, cls] - """ - - nc = prediction.shape[2] - 5 # number of classes - xc = prediction[..., 4] > conf_thres # candidates - - # Checks - assert 0 <= conf_thres <= 1, f'Invalid Confidence threshold {conf_thres}, valid values are between 0.0 and 1.0' - assert 0 <= iou_thres <= 1, f'Invalid IoU {iou_thres}, valid values are between 0.0 and 1.0' - - # Settings - min_wh, max_wh = 2, 7680 # (pixels) minimum and maximum box width and height - max_nms = 30000 # maximum number of boxes into torchvision.ops.nms() - 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)] - - # Check shape - n = x.shape[0] # number of boxes - if not n: # no boxes - continue - elif n > max_nms: # excess boxes - x = x[x[:, 4].argsort(descending=True)[:max_nms]] # sort by confidence - - # 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: - print(f'WARNING: NMS time limit {time_limit}s exceeded') - break # time limit exceeded - - return output - - -def strip_optimizer(f='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')) - if x.get('ema'): - x['model'] = x['ema'] # replace model with ema - for k in 'optimizer', 'best_fitness', 'wandb_id', 'ema', 'updates': # keys - x[k] = 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(f"Optimizer stripped from {f},{(' saved as %s,' % s) if s else ''} {mb:.1f}MB") - - -def print_mutation(results, hyp, save_dir, bucket): - evolve_csv, results_csv, evolve_yaml = save_dir / 'evolve.csv', save_dir / 'results.csv', save_dir / 'hyp_evolve.yaml' - keys = ('metrics/precision', 'metrics/recall', 'metrics/mAP_0.5', 'metrics/mAP_0.5:0.95', - 'val/box_loss', 'val/obj_loss', 'val/cls_loss') + tuple(hyp.keys()) # [results + hyps] - keys = tuple(x.strip() for x in keys) - vals = results + tuple(hyp.values()) - n = len(keys) - - # Download (optional) - if bucket: - url = f'gs://{bucket}/evolve.csv' - if gsutil_getsize(url) > (os.path.getsize(evolve_csv) if os.path.exists(evolve_csv) else 0): - os.system(f'gsutil cp {url} {save_dir}') # download evolve.csv if larger than local - - # Log to evolve.csv - s = '' if evolve_csv.exists() else (('%20s,' * n % keys).rstrip(',') + '\n') # add header - with open(evolve_csv, 'a') as f: - f.write(s + ('%20.5g,' * n % vals).rstrip(',') + '\n') - - # Print to screen - print(colorstr('evolve: ') + ', '.join(f'{x.strip():>20s}' for x in keys)) - print(colorstr('evolve: ') + ', '.join(f'{x:20.5g}' for x in vals), end='\n\n\n') - - # Save yaml - with open(evolve_yaml, 'w') as f: - data = pd.read_csv(evolve_csv) - data = data.rename(columns=lambda x: x.strip()) # strip keys - i = np.argmax(fitness(data.values[:, :7])) # - f.write('# YOLOv5 Hyperparameter Evolution Results\n' + - f'# Best generation: {i}\n' + - f'# Last generation: {len(data) - 1}\n' + - '# ' + ', '.join(f'{x.strip():>20s}' for x in keys[:7]) + '\n' + - '# ' + ', '.join(f'{x:>20.5g}' for x in data.values[i, :7]) + '\n\n') - yaml.safe_dump(hyp, f, sort_keys=False) - - if bucket: - os.system(f'gsutil cp {evolve_csv} {evolve_yaml} gs://{bucket}') # upload - - -def apply_classifier(x, model, img, im0): - # Apply a second stage classifier to YOLO outputs - # Example model = torchvision.models.__dict__['efficientnet_b0'](pretrained=True).to(device).eval() - 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('example%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 - 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=False, sep='', mkdir=False): - # Increment file or directory path, i.e. runs/exp --> runs/exp{sep}2, runs/exp{sep}3, ... etc. - path = Path(path) # os-agnostic - if path.exists() and not exist_ok: - path, suffix = (path.with_suffix(''), path.suffix) if path.is_file() else (path, '') - 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 - path = Path(f"{path}{sep}{n}{suffix}") # increment path - if mkdir: - path.mkdir(parents=True, exist_ok=True) # make directory - return path - - -# Variables -NCOLS = 0 if is_docker() else shutil.get_terminal_size().columns # terminal window size for tqdm diff --git a/YOLO/Dockerfiles/app/app/utils/google_app_engine/Dockerfile b/YOLO/Dockerfiles/app/app/utils/google_app_engine/Dockerfile deleted file mode 100644 index 0155618..0000000 --- a/YOLO/Dockerfiles/app/app/utils/google_app_engine/Dockerfile +++ /dev/null @@ -1,25 +0,0 @@ -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/YOLO/Dockerfiles/app/app/utils/google_app_engine/additional_requirements.txt b/YOLO/Dockerfiles/app/app/utils/google_app_engine/additional_requirements.txt deleted file mode 100644 index 42d7ffc..0000000 --- a/YOLO/Dockerfiles/app/app/utils/google_app_engine/additional_requirements.txt +++ /dev/null @@ -1,4 +0,0 @@ -# add these requirements in your app on top of the existing ones -pip==21.1 -Flask==1.0.2 -gunicorn==19.9.0 diff --git a/YOLO/Dockerfiles/app/app/utils/google_app_engine/app.yaml b/YOLO/Dockerfiles/app/app/utils/google_app_engine/app.yaml deleted file mode 100644 index 5056b7c..0000000 --- a/YOLO/Dockerfiles/app/app/utils/google_app_engine/app.yaml +++ /dev/null @@ -1,14 +0,0 @@ -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 diff --git a/YOLO/Dockerfiles/app/app/utils/loggers/__init__.py b/YOLO/Dockerfiles/app/app/utils/loggers/__init__.py deleted file mode 100644 index 7679ee7..0000000 --- a/YOLO/Dockerfiles/app/app/utils/loggers/__init__.py +++ /dev/null @@ -1,171 +0,0 @@ -# YOLOv5 🚀 by Ultralytics, GPL-3.0 license -""" -Logging utils -""" - -import os -import warnings -from threading import Thread - -import pkg_resources as pkg -import torch -from torch.utils.tensorboard import SummaryWriter - -from utils.general import colorstr, emojis -from utils.loggers.wandb.wandb_utils import WandbLogger -from utils.plots import plot_images, plot_results -from utils.torch_utils import de_parallel - -LOGGERS = ('csv', 'tb', 'wandb') # text-file, TensorBoard, Weights & Biases -RANK = int(os.getenv('RANK', -1)) - -try: - import wandb - - assert hasattr(wandb, '__version__') # verify package import not local dir - if pkg.parse_version(wandb.__version__) >= pkg.parse_version('0.12.2') and RANK in [0, -1]: - try: - wandb_login_success = wandb.login(timeout=30) - except wandb.errors.UsageError: # known non-TTY terminal issue - wandb_login_success = False - if not wandb_login_success: - wandb = None -except (ImportError, AssertionError): - wandb = None - - -class Loggers(): - # YOLOv5 Loggers class - def __init__(self, save_dir=None, weights=None, opt=None, hyp=None, logger=None, include=LOGGERS): - self.save_dir = save_dir - self.weights = weights - self.opt = opt - self.hyp = hyp - self.logger = logger # for printing results to console - self.include = include - self.keys = ['train/box_loss', 'train/obj_loss', 'train/cls_loss', # train loss - 'metrics/precision', 'metrics/recall', 'metrics/mAP_0.5', 'metrics/mAP_0.5:0.95', # metrics - 'val/box_loss', 'val/obj_loss', 'val/cls_loss', # val loss - 'x/lr0', 'x/lr1', 'x/lr2'] # params - self.best_keys = ['best/epoch', 'best/precision', 'best/recall', 'best/mAP_0.5', 'best/mAP_0.5:0.95',] - for k in LOGGERS: - setattr(self, k, None) # init empty logger dictionary - self.csv = True # always log to csv - - # Message - if not wandb: - prefix = colorstr('Weights & Biases: ') - s = f"{prefix}run 'pip install wandb' to automatically track and visualize YOLOv5 🚀 runs (RECOMMENDED)" - print(emojis(s)) - - # TensorBoard - s = self.save_dir - if 'tb' in self.include and not self.opt.evolve: - prefix = colorstr('TensorBoard: ') - self.logger.info(f"{prefix}Start with 'tensorboard --logdir {s.parent}', view at http://localhost:6006/") - self.tb = SummaryWriter(str(s)) - - # W&B - if wandb and 'wandb' in self.include: - wandb_artifact_resume = isinstance(self.opt.resume, str) and self.opt.resume.startswith('wandb-artifact://') - run_id = torch.load(self.weights).get('wandb_id') if self.opt.resume and not wandb_artifact_resume else None - self.opt.hyp = self.hyp # add hyperparameters - self.wandb = WandbLogger(self.opt, run_id) - else: - self.wandb = None - - def on_pretrain_routine_end(self): - # Callback runs on pre-train routine end - paths = self.save_dir.glob('*labels*.jpg') # training labels - if self.wandb: - self.wandb.log({"Labels": [wandb.Image(str(x), caption=x.name) for x in paths]}) - - def on_train_batch_end(self, ni, model, imgs, targets, paths, plots, sync_bn): - # Callback runs on train batch end - if plots: - if ni == 0: - if not sync_bn: # tb.add_graph() --sync known issue https://github.com/ultralytics/yolov5/issues/3754 - with warnings.catch_warnings(): - warnings.simplefilter('ignore') # suppress jit trace warning - self.tb.add_graph(torch.jit.trace(de_parallel(model), imgs[0:1], strict=False), []) - if ni < 3: - f = self.save_dir / f'train_batch{ni}.jpg' # filename - Thread(target=plot_images, args=(imgs, targets, paths, f), daemon=True).start() - if self.wandb and ni == 10: - files = sorted(self.save_dir.glob('train*.jpg')) - self.wandb.log({'Mosaics': [wandb.Image(str(f), caption=f.name) for f in files if f.exists()]}) - - def on_train_epoch_end(self, epoch): - # Callback runs on train epoch end - if self.wandb: - self.wandb.current_epoch = epoch + 1 - - def on_val_image_end(self, pred, predn, path, names, im): - # Callback runs on val image end - if self.wandb: - self.wandb.val_one_image(pred, predn, path, names, im) - - def on_val_end(self): - # Callback runs on val end - if self.wandb: - files = sorted(self.save_dir.glob('val*.jpg')) - self.wandb.log({"Validation": [wandb.Image(str(f), caption=f.name) for f in files]}) - - def on_fit_epoch_end(self, vals, epoch, best_fitness, fi): - # Callback runs at the end of each fit (train+val) epoch - x = {k: v for k, v in zip(self.keys, vals)} # dict - if self.csv: - file = self.save_dir / 'results.csv' - n = len(x) + 1 # number of cols - s = '' if file.exists() else (('%20s,' * n % tuple(['epoch'] + self.keys)).rstrip(',') + '\n') # add header - with open(file, 'a') as f: - f.write(s + ('%20.5g,' * n % tuple([epoch] + vals)).rstrip(',') + '\n') - - if self.tb: - for k, v in x.items(): - self.tb.add_scalar(k, v, epoch) - - if self.wandb: - if best_fitness == fi: - best_results = [epoch] + vals[3:7] - for i, name in enumerate(self.best_keys): - self.wandb.wandb_run.summary[name] = best_results[i] # log best results in the summary - self.wandb.log(x) - self.wandb.end_epoch(best_result=best_fitness == fi) - - def on_model_save(self, last, epoch, final_epoch, best_fitness, fi): - # Callback runs on model save event - if self.wandb: - if ((epoch + 1) % self.opt.save_period == 0 and not final_epoch) and self.opt.save_period != -1: - self.wandb.log_model(last.parent, self.opt, epoch, fi, best_model=best_fitness == fi) - - def on_train_end(self, last, best, plots, epoch, results): - # Callback runs on training end - if plots: - plot_results(file=self.save_dir / 'results.csv') # save results.png - files = ['results.png', 'confusion_matrix.png', *(f'{x}_curve.png' for x in ('F1', 'PR', 'P', 'R'))] - files = [(self.save_dir / f) for f in files if (self.save_dir / f).exists()] # filter - - if self.tb: - import cv2 - for f in files: - self.tb.add_image(f.stem, cv2.imread(str(f))[..., ::-1], epoch, dataformats='HWC') - - if self.wandb: - self.wandb.log({k: v for k, v in zip(self.keys[3:10], results)}) # log best.pt val results - self.wandb.log({"Results": [wandb.Image(str(f), caption=f.name) for f in files]}) - # Calling wandb.log. TODO: Refactor this into WandbLogger.log_model - if not self.opt.evolve: - wandb.log_artifact(str(best if best.exists() else last), type='model', - name='run_' + self.wandb.wandb_run.id + '_model', - aliases=['latest', 'best', 'stripped']) - self.wandb.finish_run() - else: - self.wandb.finish_run() - self.wandb = WandbLogger(self.opt) - - def on_params_update(self, params): - # Update hyperparams or configs of the experiment - # params: A dict containing {param: value} pairs - if self.wandb: - self.wandb.wandb_run.config.update(params, allow_val_change=True) diff --git a/YOLO/Dockerfiles/app/app/utils/loggers/wandb/README.md b/YOLO/Dockerfiles/app/app/utils/loggers/wandb/README.md deleted file mode 100644 index 63d9998..0000000 --- a/YOLO/Dockerfiles/app/app/utils/loggers/wandb/README.md +++ /dev/null @@ -1,152 +0,0 @@ -📚 This guide explains how to use **Weights & Biases** (W&B) with YOLOv5 🚀. UPDATED 29 September 2021. -* [About Weights & Biases](#about-weights-&-biases) -* [First-Time Setup](#first-time-setup) -* [Viewing runs](#viewing-runs) -* [Disabling wandb](#disabling-wandb) -* [Advanced Usage: Dataset Versioning and Evaluation](#advanced-usage) -* [Reports: Share your work with the world!](#reports) - -## About Weights & Biases -Think of [W&B](https://wandb.ai/site?utm_campaign=repo_yolo_wandbtutorial) like GitHub for machine learning models. With a few lines of code, save everything you need to debug, compare and reproduce your models — architecture, hyperparameters, git commits, model weights, GPU usage, and even datasets and predictions. - -Used by top researchers including teams at OpenAI, Lyft, Github, and MILA, W&B is part of the new standard of best practices for machine learning. How W&B can help you optimize your machine learning workflows: - - * [Debug](https://wandb.ai/wandb/getting-started/reports/Visualize-Debug-Machine-Learning-Models--VmlldzoyNzY5MDk#Free-2) model performance in real time - * [GPU usage](https://wandb.ai/wandb/getting-started/reports/Visualize-Debug-Machine-Learning-Models--VmlldzoyNzY5MDk#System-4) visualized automatically - * [Custom charts](https://wandb.ai/wandb/customizable-charts/reports/Powerful-Custom-Charts-To-Debug-Model-Peformance--VmlldzoyNzY4ODI) for powerful, extensible visualization - * [Share insights](https://wandb.ai/wandb/getting-started/reports/Visualize-Debug-Machine-Learning-Models--VmlldzoyNzY5MDk#Share-8) interactively with collaborators - * [Optimize hyperparameters](https://docs.wandb.com/sweeps) efficiently - * [Track](https://docs.wandb.com/artifacts) datasets, pipelines, and production models - -## First-Time Setup -
- Toggle Details -When you first train, W&B will prompt you to create a new account and will generate an **API key** for you. If you are an existing user you can retrieve your key from https://wandb.ai/authorize. This key is used to tell W&B where to log your data. You only need to supply your key once, and then it is remembered on the same device. - -W&B will create a cloud **project** (default is 'YOLOv5') for your training runs, and each new training run will be provided a unique run **name** within that project as project/name. You can also manually set your project and run name as: - - ```shell - $ python train.py --project ... --name ... - ``` - -YOLOv5 notebook example: Open In Colab Open In Kaggle -Screen Shot 2021-09-29 at 10 23 13 PM - - -
- -## Viewing Runs -
- Toggle Details -Run information streams from your environment to the W&B cloud console as you train. This allows you to monitor and even cancel runs in realtime . All important information is logged: - - * Training & Validation losses - * Metrics: Precision, Recall, mAP@0.5, mAP@0.5:0.95 - * Learning Rate over time - * A bounding box debugging panel, showing the training progress over time - * GPU: Type, **GPU Utilization**, power, temperature, **CUDA memory usage** - * System: Disk I/0, CPU utilization, RAM memory usage - * Your trained model as W&B Artifact - * Environment: OS and Python types, Git repository and state, **training command** - -

Weights & Biases dashboard

-
- - ## Disabling wandb -* training after running `wandb disabled` inside that directory creates no wandb run -![Screenshot (84)](https://user-images.githubusercontent.com/15766192/143441777-c780bdd7-7cb4-4404-9559-b4316030a985.png) - -* To enable wandb again, run `wandb online` -![Screenshot (85)](https://user-images.githubusercontent.com/15766192/143441866-7191b2cb-22f0-4e0f-ae64-2dc47dc13078.png) - -## Advanced Usage -You can leverage W&B artifacts and Tables integration to easily visualize and manage your datasets, models and training evaluations. Here are some quick examples to get you started. -
-

1: Train and Log Evaluation simultaneousy

- This is an extension of the previous section, but it'll also training after uploading the dataset. This also evaluation Table - Evaluation table compares your predictions and ground truths across the validation set for each epoch. It uses the references to the already uploaded datasets, - so no images will be uploaded from your system more than once. -
- Usage - Code $ python train.py --upload_data val - -![Screenshot from 2021-11-21 17-40-06](https://user-images.githubusercontent.com/15766192/142761183-c1696d8c-3f38-45ab-991a-bb0dfd98ae7d.png) -
- -

2. Visualize and Version Datasets

- Log, visualize, dynamically query, and understand your data with W&B Tables. You can use the following command to log your dataset as a W&B Table. This will generate a {dataset}_wandb.yaml file which can be used to train from dataset artifact. -
- Usage - Code $ python utils/logger/wandb/log_dataset.py --project ... --name ... --data .. - - ![Screenshot (64)](https://user-images.githubusercontent.com/15766192/128486078-d8433890-98a3-4d12-8986-b6c0e3fc64b9.png) -
- -

3: Train using dataset artifact

- When you upload a dataset as described in the first section, you get a new config file with an added `_wandb` to its name. This file contains the information that - can be used to train a model directly from the dataset artifact. This also logs evaluation -
- Usage - Code $ python train.py --data {data}_wandb.yaml - -![Screenshot (72)](https://user-images.githubusercontent.com/15766192/128979739-4cf63aeb-a76f-483f-8861-1c0100b938a5.png) -
- -

4: Save model checkpoints as artifacts

- To enable saving and versioning checkpoints of your experiment, pass `--save_period n` with the base cammand, where `n` represents checkpoint interval. - You can also log both the dataset and model checkpoints simultaneously. If not passed, only the final model will be logged - -
- Usage - Code $ python train.py --save_period 1 - -![Screenshot (68)](https://user-images.githubusercontent.com/15766192/128726138-ec6c1f60-639d-437d-b4ee-3acd9de47ef3.png) -
- -
- -

5: Resume runs from checkpoint artifacts.

-Any run can be resumed using artifacts if the --resume argument starts with wandb-artifact:// prefix followed by the run path, i.e, wandb-artifact://username/project/runid . This doesn't require the model checkpoint to be present on the local system. - -
- Usage - Code $ python train.py --resume wandb-artifact://{run_path} - -![Screenshot (70)](https://user-images.githubusercontent.com/15766192/128728988-4e84b355-6c87-41ae-a591-14aecf45343e.png) -
- -

6: Resume runs from dataset artifact & checkpoint artifacts.

- Local dataset or model checkpoints are not required. This can be used to resume runs directly on a different device - The syntax is same as the previous section, but you'll need to lof both the dataset and model checkpoints as artifacts, i.e, set bot --upload_dataset or - train from _wandb.yaml file and set --save_period - -
- Usage - Code $ python train.py --resume wandb-artifact://{run_path} - -![Screenshot (70)](https://user-images.githubusercontent.com/15766192/128728988-4e84b355-6c87-41ae-a591-14aecf45343e.png) -
- - - -

Reports

-W&B Reports can be created from your saved runs for sharing online. Once a report is created you will receive a link you can use to publically share your results. Here is an example report created from the COCO128 tutorial trainings of all four YOLOv5 models ([link](https://wandb.ai/glenn-jocher/yolov5_tutorial/reports/YOLOv5-COCO128-Tutorial-Results--VmlldzozMDI5OTY)). - -Weights & Biases Reports - - -## Environments - -YOLOv5 may be run in any of the following up-to-date verified environments (with all dependencies including [CUDA](https://developer.nvidia.com/cuda)/[CUDNN](https://developer.nvidia.com/cudnn), [Python](https://www.python.org/) and [PyTorch](https://pytorch.org/) preinstalled): - -- **Google Colab and Kaggle** notebooks with free GPU: Open In Colab Open In Kaggle -- **Google Cloud** Deep Learning VM. See [GCP Quickstart Guide](https://github.com/ultralytics/yolov5/wiki/GCP-Quickstart) -- **Amazon** Deep Learning AMI. See [AWS Quickstart Guide](https://github.com/ultralytics/yolov5/wiki/AWS-Quickstart) -- **Docker Image**. See [Docker Quickstart Guide](https://github.com/ultralytics/yolov5/wiki/Docker-Quickstart) Docker Pulls - - -## Status - -![CI CPU testing](https://github.com/ultralytics/yolov5/workflows/CI%20CPU%20testing/badge.svg) - -If this badge is green, all [YOLOv5 GitHub Actions](https://github.com/ultralytics/yolov5/actions) Continuous Integration (CI) tests are currently passing. CI tests verify correct operation of YOLOv5 training ([train.py](https://github.com/ultralytics/yolov5/blob/master/train.py)), validation ([val.py](https://github.com/ultralytics/yolov5/blob/master/val.py)), inference ([detect.py](https://github.com/ultralytics/yolov5/blob/master/detect.py)) and export ([export.py](https://github.com/ultralytics/yolov5/blob/master/export.py)) on MacOS, Windows, and Ubuntu every 24 hours and on every commit. diff --git a/YOLO/Dockerfiles/app/app/utils/loggers/wandb/__init__.py b/YOLO/Dockerfiles/app/app/utils/loggers/wandb/__init__.py deleted file mode 100644 index e69de29..0000000 diff --git a/YOLO/Dockerfiles/app/app/utils/loggers/wandb/log_dataset.py b/YOLO/Dockerfiles/app/app/utils/loggers/wandb/log_dataset.py deleted file mode 100644 index 06e81fb..0000000 --- a/YOLO/Dockerfiles/app/app/utils/loggers/wandb/log_dataset.py +++ /dev/null @@ -1,27 +0,0 @@ -import argparse - -from wandb_utils import WandbLogger - -from utils.general import LOGGER - -WANDB_ARTIFACT_PREFIX = 'wandb-artifact://' - - -def create_dataset_artifact(opt): - logger = WandbLogger(opt, None, job_type='Dataset Creation') # TODO: return value unused - if not logger.wandb: - LOGGER.info("install wandb using `pip install wandb` to log the dataset") - - -if __name__ == '__main__': - parser = argparse.ArgumentParser() - parser.add_argument('--data', type=str, default='data/coco128.yaml', help='data.yaml path') - parser.add_argument('--single-cls', action='store_true', help='train as single-class dataset') - parser.add_argument('--project', type=str, default='YOLOv5', help='name of W&B Project') - parser.add_argument('--entity', default=None, help='W&B entity') - parser.add_argument('--name', type=str, default='log dataset', help='name of W&B run') - - opt = parser.parse_args() - opt.resume = False # Explicitly disallow resume check for dataset upload job - - create_dataset_artifact(opt) diff --git a/YOLO/Dockerfiles/app/app/utils/loggers/wandb/sweep.py b/YOLO/Dockerfiles/app/app/utils/loggers/wandb/sweep.py deleted file mode 100644 index 206059b..0000000 --- a/YOLO/Dockerfiles/app/app/utils/loggers/wandb/sweep.py +++ /dev/null @@ -1,41 +0,0 @@ -import sys -from pathlib import Path - -import wandb - -FILE = Path(__file__).resolve() -ROOT = FILE.parents[3] # YOLOv5 root directory -if str(ROOT) not in sys.path: - sys.path.append(str(ROOT)) # add ROOT to PATH - -from train import parse_opt, train -from utils.callbacks import Callbacks -from utils.general import increment_path -from utils.torch_utils import select_device - - -def sweep(): - wandb.init() - # Get hyp dict from sweep agent - hyp_dict = vars(wandb.config).get("_items") - - # Workaround: get necessary opt args - opt = parse_opt(known=True) - opt.batch_size = hyp_dict.get("batch_size") - opt.save_dir = str(increment_path(Path(opt.project) / opt.name, exist_ok=opt.exist_ok or opt.evolve)) - opt.epochs = hyp_dict.get("epochs") - opt.nosave = True - opt.data = hyp_dict.get("data") - opt.weights = str(opt.weights) - opt.cfg = str(opt.cfg) - opt.data = str(opt.data) - opt.hyp = str(opt.hyp) - opt.project = str(opt.project) - device = select_device(opt.device, batch_size=opt.batch_size) - - # train - train(hyp_dict, opt, device, callbacks=Callbacks()) - - -if __name__ == "__main__": - sweep() diff --git a/YOLO/Dockerfiles/app/app/utils/loggers/wandb/sweep.yaml b/YOLO/Dockerfiles/app/app/utils/loggers/wandb/sweep.yaml deleted file mode 100644 index c7790d7..0000000 --- a/YOLO/Dockerfiles/app/app/utils/loggers/wandb/sweep.yaml +++ /dev/null @@ -1,143 +0,0 @@ -# Hyperparameters for training -# To set range- -# Provide min and max values as: -# parameter: -# -# min: scalar -# max: scalar -# OR -# -# Set a specific list of search space- -# parameter: -# values: [scalar1, scalar2, scalar3...] -# -# You can use grid, bayesian and hyperopt search strategy -# For more info on configuring sweeps visit - https://docs.wandb.ai/guides/sweeps/configuration - -program: utils/loggers/wandb/sweep.py -method: random -metric: - name: metrics/mAP_0.5 - goal: maximize - -parameters: - # hyperparameters: set either min, max range or values list - data: - value: "data/coco128.yaml" - batch_size: - values: [64] - epochs: - values: [10] - - lr0: - distribution: uniform - min: 1e-5 - max: 1e-1 - lrf: - distribution: uniform - min: 0.01 - max: 1.0 - momentum: - distribution: uniform - min: 0.6 - max: 0.98 - weight_decay: - distribution: uniform - min: 0.0 - max: 0.001 - warmup_epochs: - distribution: uniform - min: 0.0 - max: 5.0 - warmup_momentum: - distribution: uniform - min: 0.0 - max: 0.95 - warmup_bias_lr: - distribution: uniform - min: 0.0 - max: 0.2 - box: - distribution: uniform - min: 0.02 - max: 0.2 - cls: - distribution: uniform - min: 0.2 - max: 4.0 - cls_pw: - distribution: uniform - min: 0.5 - max: 2.0 - obj: - distribution: uniform - min: 0.2 - max: 4.0 - obj_pw: - distribution: uniform - min: 0.5 - max: 2.0 - iou_t: - distribution: uniform - min: 0.1 - max: 0.7 - anchor_t: - distribution: uniform - min: 2.0 - max: 8.0 - fl_gamma: - distribution: uniform - min: 0.0 - max: 0.1 - hsv_h: - distribution: uniform - min: 0.0 - max: 0.1 - hsv_s: - distribution: uniform - min: 0.0 - max: 0.9 - hsv_v: - distribution: uniform - min: 0.0 - max: 0.9 - degrees: - distribution: uniform - min: 0.0 - max: 45.0 - translate: - distribution: uniform - min: 0.0 - max: 0.9 - scale: - distribution: uniform - min: 0.0 - max: 0.9 - shear: - distribution: uniform - min: 0.0 - max: 10.0 - perspective: - distribution: uniform - min: 0.0 - max: 0.001 - flipud: - distribution: uniform - min: 0.0 - max: 1.0 - fliplr: - distribution: uniform - min: 0.0 - max: 1.0 - mosaic: - distribution: uniform - min: 0.0 - max: 1.0 - mixup: - distribution: uniform - min: 0.0 - max: 1.0 - copy_paste: - distribution: uniform - min: 0.0 - max: 1.0 diff --git a/YOLO/Dockerfiles/app/app/utils/loggers/wandb/wandb_utils.py b/YOLO/Dockerfiles/app/app/utils/loggers/wandb/wandb_utils.py deleted file mode 100644 index 221d3c8..0000000 --- a/YOLO/Dockerfiles/app/app/utils/loggers/wandb/wandb_utils.py +++ /dev/null @@ -1,560 +0,0 @@ -"""Utilities and tools for tracking runs with Weights & Biases.""" - -import logging -import os -import sys -from contextlib import contextmanager -from pathlib import Path -from typing import Dict - -import yaml -from tqdm import tqdm - -FILE = Path(__file__).resolve() -ROOT = FILE.parents[3] # YOLOv5 root directory -if str(ROOT) not in sys.path: - sys.path.append(str(ROOT)) # add ROOT to PATH - -from utils.datasets import LoadImagesAndLabels, img2label_paths -from utils.general import LOGGER, check_dataset, check_file - -try: - import wandb - - assert hasattr(wandb, '__version__') # verify package import not local dir -except (ImportError, AssertionError): - wandb = None - -RANK = int(os.getenv('RANK', -1)) -WANDB_ARTIFACT_PREFIX = 'wandb-artifact://' - - -def remove_prefix(from_string, prefix=WANDB_ARTIFACT_PREFIX): - return from_string[len(prefix):] - - -def check_wandb_config_file(data_config_file): - wandb_config = '_wandb.'.join(data_config_file.rsplit('.', 1)) # updated data.yaml path - if Path(wandb_config).is_file(): - return wandb_config - return data_config_file - - -def check_wandb_dataset(data_file): - is_trainset_wandb_artifact = False - is_valset_wandb_artifact = False - if check_file(data_file) and data_file.endswith('.yaml'): - with open(data_file, errors='ignore') as f: - data_dict = yaml.safe_load(f) - is_trainset_wandb_artifact = (isinstance(data_dict['train'], str) and - data_dict['train'].startswith(WANDB_ARTIFACT_PREFIX)) - is_valset_wandb_artifact = (isinstance(data_dict['val'], str) and - data_dict['val'].startswith(WANDB_ARTIFACT_PREFIX)) - if is_trainset_wandb_artifact or is_valset_wandb_artifact: - return data_dict - else: - return check_dataset(data_file) - - -def get_run_info(run_path): - run_path = Path(remove_prefix(run_path, WANDB_ARTIFACT_PREFIX)) - run_id = run_path.stem - project = run_path.parent.stem - entity = run_path.parent.parent.stem - model_artifact_name = 'run_' + run_id + '_model' - return entity, project, run_id, model_artifact_name - - -def check_wandb_resume(opt): - process_wandb_config_ddp_mode(opt) if RANK not in [-1, 0] else None - if isinstance(opt.resume, str): - if opt.resume.startswith(WANDB_ARTIFACT_PREFIX): - if RANK not in [-1, 0]: # For resuming DDP runs - entity, project, run_id, model_artifact_name = get_run_info(opt.resume) - api = wandb.Api() - artifact = api.artifact(entity + '/' + project + '/' + model_artifact_name + ':latest') - modeldir = artifact.download() - opt.weights = str(Path(modeldir) / "last.pt") - return True - return None - - -def process_wandb_config_ddp_mode(opt): - with open(check_file(opt.data), errors='ignore') as f: - data_dict = yaml.safe_load(f) # data dict - train_dir, val_dir = None, None - if isinstance(data_dict['train'], str) and data_dict['train'].startswith(WANDB_ARTIFACT_PREFIX): - api = wandb.Api() - train_artifact = api.artifact(remove_prefix(data_dict['train']) + ':' + opt.artifact_alias) - train_dir = train_artifact.download() - train_path = Path(train_dir) / 'data/images/' - data_dict['train'] = str(train_path) - - if isinstance(data_dict['val'], str) and data_dict['val'].startswith(WANDB_ARTIFACT_PREFIX): - api = wandb.Api() - val_artifact = api.artifact(remove_prefix(data_dict['val']) + ':' + opt.artifact_alias) - val_dir = val_artifact.download() - val_path = Path(val_dir) / 'data/images/' - data_dict['val'] = str(val_path) - if train_dir or val_dir: - ddp_data_path = str(Path(val_dir) / 'wandb_local_data.yaml') - with open(ddp_data_path, 'w') as f: - yaml.safe_dump(data_dict, f) - opt.data = ddp_data_path - - -class WandbLogger(): - """Log training runs, datasets, models, and predictions to Weights & Biases. - - This logger sends information to W&B at wandb.ai. By default, this information - includes hyperparameters, system configuration and metrics, model metrics, - and basic data metrics and analyses. - - By providing additional command line arguments to train.py, datasets, - models and predictions can also be logged. - - For more on how this logger is used, see the Weights & Biases documentation: - https://docs.wandb.com/guides/integrations/yolov5 - """ - - def __init__(self, opt, run_id=None, job_type='Training'): - """ - - Initialize WandbLogger instance - - Upload dataset if opt.upload_dataset is True - - Setup trainig processes if job_type is 'Training' - - arguments: - opt (namespace) -- Commandline arguments for this run - run_id (str) -- Run ID of W&B run to be resumed - job_type (str) -- To set the job_type for this run - - """ - # Pre-training routine -- - self.job_type = job_type - self.wandb, self.wandb_run = wandb, None if not wandb else wandb.run - self.val_artifact, self.train_artifact = None, None - self.train_artifact_path, self.val_artifact_path = None, None - self.result_artifact = None - self.val_table, self.result_table = None, None - self.bbox_media_panel_images = [] - self.val_table_path_map = None - self.max_imgs_to_log = 16 - self.wandb_artifact_data_dict = None - self.data_dict = None - # It's more elegant to stick to 1 wandb.init call, - # but useful config data is overwritten in the WandbLogger's wandb.init call - if isinstance(opt.resume, str): # checks resume from artifact - if opt.resume.startswith(WANDB_ARTIFACT_PREFIX): - entity, project, run_id, model_artifact_name = get_run_info(opt.resume) - model_artifact_name = WANDB_ARTIFACT_PREFIX + model_artifact_name - assert wandb, 'install wandb to resume wandb runs' - # Resume wandb-artifact:// runs here| workaround for not overwriting wandb.config - self.wandb_run = wandb.init(id=run_id, - project=project, - entity=entity, - resume='allow', - allow_val_change=True) - opt.resume = model_artifact_name - elif self.wandb: - self.wandb_run = wandb.init(config=opt, - resume="allow", - project='YOLOv5' if opt.project == 'runs/train' else Path(opt.project).stem, - entity=opt.entity, - name=opt.name if opt.name != 'exp' else None, - job_type=job_type, - id=run_id, - allow_val_change=True) if not wandb.run else wandb.run - if self.wandb_run: - if self.job_type == 'Training': - if opt.upload_dataset: - if not opt.resume: - self.wandb_artifact_data_dict = self.check_and_upload_dataset(opt) - - if opt.resume: - # resume from artifact - if isinstance(opt.resume, str) and opt.resume.startswith(WANDB_ARTIFACT_PREFIX): - self.data_dict = dict(self.wandb_run.config.data_dict) - else: # local resume - self.data_dict = check_wandb_dataset(opt.data) - else: - self.data_dict = check_wandb_dataset(opt.data) - self.wandb_artifact_data_dict = self.wandb_artifact_data_dict or self.data_dict - - # write data_dict to config. useful for resuming from artifacts. Do this only when not resuming. - self.wandb_run.config.update({'data_dict': self.wandb_artifact_data_dict}, - allow_val_change=True) - self.setup_training(opt) - - if self.job_type == 'Dataset Creation': - self.wandb_run.config.update({"upload_dataset": True}) - self.data_dict = self.check_and_upload_dataset(opt) - - def check_and_upload_dataset(self, opt): - """ - Check if the dataset format is compatible and upload it as W&B artifact - - arguments: - opt (namespace)-- Commandline arguments for current run - - returns: - Updated dataset info dictionary where local dataset paths are replaced by WAND_ARFACT_PREFIX links. - """ - assert wandb, 'Install wandb to upload dataset' - config_path = self.log_dataset_artifact(opt.data, - opt.single_cls, - 'YOLOv5' if opt.project == 'runs/train' else Path(opt.project).stem) - with open(config_path, errors='ignore') as f: - wandb_data_dict = yaml.safe_load(f) - return wandb_data_dict - - def setup_training(self, opt): - """ - Setup the necessary processes for training YOLO models: - - Attempt to download model checkpoint and dataset artifacts if opt.resume stats with WANDB_ARTIFACT_PREFIX - - Update data_dict, to contain info of previous run if resumed and the paths of dataset artifact if downloaded - - Setup log_dict, initialize bbox_interval - - arguments: - opt (namespace) -- commandline arguments for this run - - """ - self.log_dict, self.current_epoch = {}, 0 - self.bbox_interval = opt.bbox_interval - if isinstance(opt.resume, str): - modeldir, _ = self.download_model_artifact(opt) - if modeldir: - self.weights = Path(modeldir) / "last.pt" - config = self.wandb_run.config - opt.weights, opt.save_period, opt.batch_size, opt.bbox_interval, opt.epochs, opt.hyp = str( - self.weights), config.save_period, config.batch_size, config.bbox_interval, config.epochs, \ - config.hyp - data_dict = self.data_dict - if self.val_artifact is None: # If --upload_dataset is set, use the existing artifact, don't download - self.train_artifact_path, self.train_artifact = self.download_dataset_artifact(data_dict.get('train'), - opt.artifact_alias) - self.val_artifact_path, self.val_artifact = self.download_dataset_artifact(data_dict.get('val'), - opt.artifact_alias) - - if self.train_artifact_path is not None: - train_path = Path(self.train_artifact_path) / 'data/images/' - data_dict['train'] = str(train_path) - if self.val_artifact_path is not None: - val_path = Path(self.val_artifact_path) / 'data/images/' - data_dict['val'] = str(val_path) - - if self.val_artifact is not None: - self.result_artifact = wandb.Artifact("run_" + wandb.run.id + "_progress", "evaluation") - columns = ["epoch", "id", "ground truth", "prediction"] - columns.extend(self.data_dict['names']) - self.result_table = wandb.Table(columns) - self.val_table = self.val_artifact.get("val") - if self.val_table_path_map is None: - self.map_val_table_path() - if opt.bbox_interval == -1: - self.bbox_interval = opt.bbox_interval = (opt.epochs // 10) if opt.epochs > 10 else 1 - train_from_artifact = self.train_artifact_path is not None and self.val_artifact_path is not None - # Update the the data_dict to point to local artifacts dir - if train_from_artifact: - self.data_dict = data_dict - - def download_dataset_artifact(self, path, alias): - """ - download the model checkpoint artifact if the path starts with WANDB_ARTIFACT_PREFIX - - arguments: - path -- path of the dataset to be used for training - alias (str)-- alias of the artifact to be download/used for training - - returns: - (str, wandb.Artifact) -- path of the downladed dataset and it's corresponding artifact object if dataset - is found otherwise returns (None, None) - """ - if isinstance(path, str) and path.startswith(WANDB_ARTIFACT_PREFIX): - artifact_path = Path(remove_prefix(path, WANDB_ARTIFACT_PREFIX) + ":" + alias) - dataset_artifact = wandb.use_artifact(artifact_path.as_posix().replace("\\", "/")) - assert dataset_artifact is not None, "'Error: W&B dataset artifact doesn\'t exist'" - datadir = dataset_artifact.download() - return datadir, dataset_artifact - return None, None - - def download_model_artifact(self, opt): - """ - download the model checkpoint artifact if the resume path starts with WANDB_ARTIFACT_PREFIX - - arguments: - opt (namespace) -- Commandline arguments for this run - """ - if opt.resume.startswith(WANDB_ARTIFACT_PREFIX): - model_artifact = wandb.use_artifact(remove_prefix(opt.resume, WANDB_ARTIFACT_PREFIX) + ":latest") - assert model_artifact is not None, 'Error: W&B model artifact doesn\'t exist' - modeldir = model_artifact.download() - epochs_trained = model_artifact.metadata.get('epochs_trained') - total_epochs = model_artifact.metadata.get('total_epochs') - is_finished = total_epochs is None - assert not is_finished, 'training is finished, can only resume incomplete runs.' - return modeldir, model_artifact - return None, None - - def log_model(self, path, opt, epoch, fitness_score, best_model=False): - """ - Log the model checkpoint as W&B artifact - - arguments: - path (Path) -- Path of directory containing the checkpoints - opt (namespace) -- Command line arguments for this run - epoch (int) -- Current epoch number - fitness_score (float) -- fitness score for current epoch - best_model (boolean) -- Boolean representing if the current checkpoint is the best yet. - """ - model_artifact = wandb.Artifact('run_' + wandb.run.id + '_model', type='model', metadata={ - 'original_url': str(path), - 'epochs_trained': epoch + 1, - 'save period': opt.save_period, - 'project': opt.project, - 'total_epochs': opt.epochs, - 'fitness_score': fitness_score - }) - model_artifact.add_file(str(path / 'last.pt'), name='last.pt') - wandb.log_artifact(model_artifact, - aliases=['latest', 'last', 'epoch ' + str(self.current_epoch), 'best' if best_model else '']) - LOGGER.info(f"Saving model artifact on epoch {epoch + 1}") - - def log_dataset_artifact(self, data_file, single_cls, project, overwrite_config=False): - """ - Log the dataset as W&B artifact and return the new data file with W&B links - - arguments: - data_file (str) -- the .yaml file with information about the dataset like - path, classes etc. - single_class (boolean) -- train multi-class data as single-class - project (str) -- project name. Used to construct the artifact path - overwrite_config (boolean) -- overwrites the data.yaml file if set to true otherwise creates a new - file with _wandb postfix. Eg -> data_wandb.yaml - - returns: - the new .yaml file with artifact links. it can be used to start training directly from artifacts - """ - upload_dataset = self.wandb_run.config.upload_dataset - log_val_only = isinstance(upload_dataset, str) and upload_dataset == 'val' - self.data_dict = check_dataset(data_file) # parse and check - data = dict(self.data_dict) - nc, names = (1, ['item']) if single_cls else (int(data['nc']), data['names']) - names = {k: v for k, v in enumerate(names)} # to index dictionary - - # log train set - if not log_val_only: - self.train_artifact = self.create_dataset_table(LoadImagesAndLabels( - data['train'], rect=True, batch_size=1), names, name='train') if data.get('train') else None - if data.get('train'): - data['train'] = WANDB_ARTIFACT_PREFIX + str(Path(project) / 'train') - - self.val_artifact = self.create_dataset_table(LoadImagesAndLabels( - data['val'], rect=True, batch_size=1), names, name='val') if data.get('val') else None - if data.get('val'): - data['val'] = WANDB_ARTIFACT_PREFIX + str(Path(project) / 'val') - - path = Path(data_file) - # create a _wandb.yaml file with artifacts links if both train and test set are logged - if not log_val_only: - path = (path.stem if overwrite_config else path.stem + '_wandb') + '.yaml' # updated data.yaml path - path = Path('data') / path - data.pop('download', None) - data.pop('path', None) - with open(path, 'w') as f: - yaml.safe_dump(data, f) - LOGGER.info(f"Created dataset config file {path}") - - if self.job_type == 'Training': # builds correct artifact pipeline graph - if not log_val_only: - self.wandb_run.log_artifact( - self.train_artifact) # calling use_artifact downloads the dataset. NOT NEEDED! - self.wandb_run.use_artifact(self.val_artifact) - self.val_artifact.wait() - self.val_table = self.val_artifact.get('val') - self.map_val_table_path() - else: - self.wandb_run.log_artifact(self.train_artifact) - self.wandb_run.log_artifact(self.val_artifact) - return path - - def map_val_table_path(self): - """ - Map the validation dataset Table like name of file -> it's id in the W&B Table. - Useful for - referencing artifacts for evaluation. - """ - self.val_table_path_map = {} - LOGGER.info("Mapping dataset") - for i, data in enumerate(tqdm(self.val_table.data)): - self.val_table_path_map[data[3]] = data[0] - - def create_dataset_table(self, dataset: LoadImagesAndLabels, class_to_id: Dict[int, str], name: str = 'dataset'): - """ - Create and return W&B artifact containing W&B Table of the dataset. - - arguments: - dataset -- instance of LoadImagesAndLabels class used to iterate over the data to build Table - class_to_id -- hash map that maps class ids to labels - name -- name of the artifact - - returns: - dataset artifact to be logged or used - """ - # TODO: Explore multiprocessing to slpit this loop parallely| This is essential for speeding up the the logging - artifact = wandb.Artifact(name=name, type="dataset") - img_files = tqdm([dataset.path]) if isinstance(dataset.path, str) and Path(dataset.path).is_dir() else None - img_files = tqdm(dataset.img_files) if not img_files else img_files - for img_file in img_files: - if Path(img_file).is_dir(): - artifact.add_dir(img_file, name='data/images') - labels_path = 'labels'.join(dataset.path.rsplit('images', 1)) - artifact.add_dir(labels_path, name='data/labels') - else: - artifact.add_file(img_file, name='data/images/' + Path(img_file).name) - label_file = Path(img2label_paths([img_file])[0]) - artifact.add_file(str(label_file), - name='data/labels/' + label_file.name) if label_file.exists() else None - table = wandb.Table(columns=["id", "train_image", "Classes", "name"]) - class_set = wandb.Classes([{'id': id, 'name': name} for id, name in class_to_id.items()]) - for si, (img, labels, paths, shapes) in enumerate(tqdm(dataset)): - box_data, img_classes = [], {} - for cls, *xywh in labels[:, 1:].tolist(): - cls = int(cls) - box_data.append({"position": {"middle": [xywh[0], xywh[1]], "width": xywh[2], "height": xywh[3]}, - "class_id": cls, - "box_caption": "%s" % (class_to_id[cls])}) - img_classes[cls] = class_to_id[cls] - boxes = {"ground_truth": {"box_data": box_data, "class_labels": class_to_id}} # inference-space - table.add_data(si, wandb.Image(paths, classes=class_set, boxes=boxes), list(img_classes.values()), - Path(paths).name) - artifact.add(table, name) - return artifact - - def log_training_progress(self, predn, path, names): - """ - Build evaluation Table. Uses reference from validation dataset table. - - arguments: - predn (list): list of predictions in the native space in the format - [xmin, ymin, xmax, ymax, confidence, class] - path (str): local path of the current evaluation image - names (dict(int, str)): hash map that maps class ids to labels - """ - class_set = wandb.Classes([{'id': id, 'name': name} for id, name in names.items()]) - box_data = [] - avg_conf_per_class = [0] * len(self.data_dict['names']) - pred_class_count = {} - for *xyxy, conf, cls in predn.tolist(): - if conf >= 0.25: - cls = int(cls) - box_data.append( - {"position": {"minX": xyxy[0], "minY": xyxy[1], "maxX": xyxy[2], "maxY": xyxy[3]}, - "class_id": cls, - "box_caption": f"{names[cls]} {conf:.3f}", - "scores": {"class_score": conf}, - "domain": "pixel"}) - avg_conf_per_class[cls] += conf - - if cls in pred_class_count: - pred_class_count[cls] += 1 - else: - pred_class_count[cls] = 1 - - for pred_class in pred_class_count.keys(): - avg_conf_per_class[pred_class] = avg_conf_per_class[pred_class] / pred_class_count[pred_class] - - boxes = {"predictions": {"box_data": box_data, "class_labels": names}} # inference-space - id = self.val_table_path_map[Path(path).name] - self.result_table.add_data(self.current_epoch, - id, - self.val_table.data[id][1], - wandb.Image(self.val_table.data[id][1], boxes=boxes, classes=class_set), - *avg_conf_per_class - ) - - def val_one_image(self, pred, predn, path, names, im): - """ - Log validation data for one image. updates the result Table if validation dataset is uploaded and log bbox media panel - - arguments: - pred (list): list of scaled predictions in the format - [xmin, ymin, xmax, ymax, confidence, class] - predn (list): list of predictions in the native space - [xmin, ymin, xmax, ymax, confidence, class] - path (str): local path of the current evaluation image - """ - if self.val_table and self.result_table: # Log Table if Val dataset is uploaded as artifact - self.log_training_progress(predn, path, names) - - if len(self.bbox_media_panel_images) < self.max_imgs_to_log and self.current_epoch > 0: - if self.current_epoch % self.bbox_interval == 0: - box_data = [{"position": {"minX": xyxy[0], "minY": xyxy[1], "maxX": xyxy[2], "maxY": xyxy[3]}, - "class_id": int(cls), - "box_caption": f"{names[cls]} {conf:.3f}", - "scores": {"class_score": conf}, - "domain": "pixel"} for *xyxy, conf, cls in pred.tolist()] - boxes = {"predictions": {"box_data": box_data, "class_labels": names}} # inference-space - self.bbox_media_panel_images.append(wandb.Image(im, boxes=boxes, caption=path.name)) - - def log(self, log_dict): - """ - save the metrics to the logging dictionary - - arguments: - log_dict (Dict) -- metrics/media to be logged in current step - """ - if self.wandb_run: - for key, value in log_dict.items(): - self.log_dict[key] = value - - def end_epoch(self, best_result=False): - """ - commit the log_dict, model artifacts and Tables to W&B and flush the log_dict. - - arguments: - best_result (boolean): Boolean representing if the result of this evaluation is best or not - """ - if self.wandb_run: - with all_logging_disabled(): - if self.bbox_media_panel_images: - self.log_dict["BoundingBoxDebugger"] = self.bbox_media_panel_images - try: - wandb.log(self.log_dict) - except BaseException as e: - LOGGER.info( - f"An error occurred in wandb logger. The training will proceed without interruption. More info\n{e}") - self.wandb_run.finish() - self.wandb_run = None - - self.log_dict = {} - self.bbox_media_panel_images = [] - if self.result_artifact: - self.result_artifact.add(self.result_table, 'result') - wandb.log_artifact(self.result_artifact, aliases=['latest', 'last', 'epoch ' + str(self.current_epoch), - ('best' if best_result else '')]) - - wandb.log({"evaluation": self.result_table}) - columns = ["epoch", "id", "ground truth", "prediction"] - columns.extend(self.data_dict['names']) - self.result_table = wandb.Table(columns) - self.result_artifact = wandb.Artifact("run_" + wandb.run.id + "_progress", "evaluation") - - def finish_run(self): - """ - Log metrics if any and finish the current W&B run - """ - if self.wandb_run: - if self.log_dict: - with all_logging_disabled(): - wandb.log(self.log_dict) - wandb.run.finish() - - -@contextmanager -def all_logging_disabled(highest_level=logging.CRITICAL): - """ source - https://gist.github.com/simon-weber/7853144 - A context manager that will prevent any logging messages triggered during the body from being processed. - :param highest_level: the maximum logging level in use. - This would only need to be changed if a custom level greater than CRITICAL is defined. - """ - previous_level = logging.root.manager.disable - logging.disable(highest_level) - try: - yield - finally: - logging.disable(previous_level) diff --git a/YOLO/Dockerfiles/app/app/utils/loss.py b/YOLO/Dockerfiles/app/app/utils/loss.py deleted file mode 100644 index 194c8e5..0000000 --- a/YOLO/Dockerfiles/app/app/utils/loss.py +++ /dev/null @@ -1,222 +0,0 @@ -# YOLOv5 🚀 by Ultralytics, GPL-3.0 license -""" -Loss functions -""" - -import torch -import torch.nn as nn - -from utils.metrics 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().__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().__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().__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 - - -class ComputeLoss: - # Compute losses - def __init__(self, model, autobalance=False): - self.sort_obj_iou = False - device = next(model.parameters()).device # get model device - h = model.hyp # hyperparameters - - # Define criteria - BCEcls = nn.BCEWithLogitsLoss(pos_weight=torch.tensor([h['cls_pw']], device=device)) - BCEobj = nn.BCEWithLogitsLoss(pos_weight=torch.tensor([h['obj_pw']], device=device)) - - # Class label smoothing https://arxiv.org/pdf/1902.04103.pdf eqn 3 - self.cp, self.cn = smooth_BCE(eps=h.get('label_smoothing', 0.0)) # positive, negative BCE targets - - # Focal loss - g = h['fl_gamma'] # focal loss gamma - if g > 0: - BCEcls, BCEobj = FocalLoss(BCEcls, g), FocalLoss(BCEobj, g) - - det = model.module.model[-1] if is_parallel(model) else model.model[-1] # Detect() module - self.balance = {3: [4.0, 1.0, 0.4]}.get(det.nl, [4.0, 1.0, 0.25, 0.06, 0.02]) # P3-P7 - self.ssi = list(det.stride).index(16) if autobalance else 0 # stride 16 index - self.BCEcls, self.BCEobj, self.gr, self.hyp, self.autobalance = BCEcls, BCEobj, 1.0, h, autobalance - for k in 'na', 'nc', 'nl', 'anchors': - setattr(self, k, getattr(det, k)) - - def __call__(self, p, targets): # 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 = self.build_targets(p, targets) # targets - - # Losses - for i, pi in enumerate(p): # layer index, layer predictions - b, a, gj, gi = indices[i] # image, anchor, gridy, gridx - tobj = torch.zeros_like(pi[..., 0], device=device) # target obj - - n = b.shape[0] # number of targets - if n: - 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 - score_iou = iou.detach().clamp(0).type(tobj.dtype) - if self.sort_obj_iou: - sort_id = torch.argsort(score_iou) - b, a, gj, gi, score_iou = b[sort_id], a[sort_id], gj[sort_id], gi[sort_id], score_iou[sort_id] - tobj[b, a, gj, gi] = (1.0 - self.gr) + self.gr * score_iou # iou ratio - - # Classification - if self.nc > 1: # cls loss (only if multiple classes) - t = torch.full_like(ps[:, 5:], self.cn, device=device) # targets - t[range(n), tcls[i]] = self.cp - lcls += self.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)] - - obji = self.BCEobj(pi[..., 4], tobj) - lobj += obji * self.balance[i] # obj loss - if self.autobalance: - self.balance[i] = self.balance[i] * 0.9999 + 0.0001 / obji.detach().item() - - if self.autobalance: - self.balance = [x / self.balance[self.ssi] for x in self.balance] - lbox *= self.hyp['box'] - lobj *= self.hyp['obj'] - lcls *= self.hyp['cls'] - bs = tobj.shape[0] # batch size - - return (lbox + lobj + lcls) * bs, torch.cat((lbox, lobj, lcls)).detach() - - def build_targets(self, p, targets): - # Build targets for compute_loss(), input targets(image,class,x,y,w,h) - na, nt = self.na, targets.shape[0] # number of anchors, targets - tcls, tbox, indices, anch = [], [], [], [] - 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(self.nl): - anchors = self.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] < self.hyp['anchor_t'] # compare - # j = wh_iou(anchors, t[:, 4:6]) > model.hyp['iou_t'] # iou(3,n)=wh_iou(anchors(3,2), gwh(n,2)) - t = t[j] # filter - - # Offsets - gxy = t[:, 2:4] # grid xy - gxi = gain[[2, 3]] - gxy # inverse - j, k = ((gxy % 1 < g) & (gxy > 1)).T - l, m = ((gxi % 1 < g) & (gxi > 1)).T - j = torch.stack((torch.ones_like(j), j, k, l, m)) - t = t.repeat((5, 1, 1))[j] - offsets = (torch.zeros_like(gxy)[None] + off[:, None])[j] - else: - t = targets[0] - offsets = 0 - - # Define - 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/YOLO/Dockerfiles/app/app/utils/metrics.py b/YOLO/Dockerfiles/app/app/utils/metrics.py deleted file mode 100644 index 83defa7..0000000 --- a/YOLO/Dockerfiles/app/app/utils/metrics.py +++ /dev/null @@ -1,340 +0,0 @@ -# YOLOv5 🚀 by Ultralytics, GPL-3.0 license -""" -Model validation metrics -""" - -import math -import warnings -from pathlib import Path - -import matplotlib.pyplot as plt -import numpy as np -import torch - - -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='.', names=(), eps=1e-16): - """ 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, nt = np.unique(target_cls, return_counts=True) - nc = unique_classes.shape[0] # number of classes, number of detections - - # Create Precision-Recall curve and compute AP for each class - px, py = np.linspace(0, 1, 1000), [] # for plotting - ap, p, r = np.zeros((nc, tp.shape[1])), np.zeros((nc, 1000)), np.zeros((nc, 1000)) - for ci, c in enumerate(unique_classes): - i = pred_cls == c - n_l = nt[ci] # number of labels - n_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 + eps) # recall curve - r[ci] = np.interp(-px, -conf[i], recall[:, 0], left=0) # negative x, xp because xp decreases - - # Precision - precision = tpc / (tpc + fpc) # precision curve - p[ci] = np.interp(-px, -conf[i], precision[:, 0], left=1) # p at pr_score - - # AP from recall-precision curve - for j in range(tp.shape[1]): - ap[ci, j], mpre, mrec = compute_ap(recall[:, j], precision[:, j]) - if plot and j == 0: - py.append(np.interp(px, mrec, mpre)) # precision at mAP@0.5 - - # Compute F1 (harmonic mean of precision and recall) - f1 = 2 * p * r / (p + r + eps) - names = [v for k, v in names.items() if k in unique_classes] # list: only classes that have data - names = {i: v for i, v in enumerate(names)} # to dict - if plot: - plot_pr_curve(px, py, ap, Path(save_dir) / 'PR_curve.png', names) - plot_mc_curve(px, f1, Path(save_dir) / 'F1_curve.png', names, ylabel='F1') - plot_mc_curve(px, p, Path(save_dir) / 'P_curve.png', names, ylabel='Precision') - plot_mc_curve(px, r, Path(save_dir) / 'R_curve.png', names, ylabel='Recall') - - i = f1.mean(0).argmax() # max F1 index - p, r, f1 = p[:, i], r[:, i], f1[:, i] - tp = (r * nt).round() # true positives - fp = (tp / (p + eps) - tp).round() # false positives - return tp, fp, p, r, f1, ap, unique_classes.astype('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(([0.0], recall, [1.0])) - mpre = np.concatenate(([1.0], precision, [0.0])) - - # 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 = 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[detection_classes[m1[j]], gc] += 1 # correct - else: - self.matrix[self.nc, gc] += 1 # background FP - - if n: - for i, dc in enumerate(detection_classes): - if not any(m1 == i): - self.matrix[dc, self.nc] += 1 # background FN - - def matrix(self): - return self.matrix - - def tp_fp(self): - tp = self.matrix.diagonal() # true positives - fp = self.matrix.sum(1) - tp # false positives - # fn = self.matrix.sum(0) - tp # false negatives (missed detections) - return tp[:-1], fp[:-1] # remove background class - - def plot(self, normalize=True, save_dir='', names=()): - try: - import seaborn as sn - - array = self.matrix / ((self.matrix.sum(0).reshape(1, -1) + 1E-6) if normalize else 1) # normalize columns - array[array < 0.005] = np.nan # don't annotate (would appear as 0.00) - - fig = plt.figure(figsize=(12, 9), tight_layout=True) - sn.set(font_scale=1.0 if self.nc < 50 else 0.8) # for label size - labels = (0 < len(names) < 99) and len(names) == self.nc # apply names to ticklabels - with warnings.catch_warnings(): - warnings.simplefilter('ignore') # suppress empty matrix RuntimeWarning: All-NaN slice encountered - sn.heatmap(array, annot=self.nc < 30, annot_kws={"size": 8}, cmap='Blues', fmt='.2f', square=True, - xticklabels=names + ['background FP'] if labels else "auto", - yticklabels=names + ['background FN'] if labels else "auto").set_facecolor((1, 1, 1)) - fig.axes[0].set_xlabel('True') - fig.axes[0].set_ylabel('Predicted') - fig.savefig(Path(save_dir) / 'confusion_matrix.png', dpi=250) - plt.close() - except Exception as e: - print(f'WARNING: ConfusionMatrix plot failure: {e}') - - def print(self): - for i in range(self.nc + 1): - print(' '.join(map(str, self.matrix[i]))) - - -def bbox_iou(box1, box2, x1y1x2y2=True, GIoU=False, DIoU=False, CIoU=False, eps=1e-7): - # 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 CIoU or DIoU or GIoU: - cw = torch.max(b1_x2, b2_x2) - torch.min(b1_x1, b2_x1) # convex (smallest enclosing box) width - ch = torch.max(b1_y2, b2_y2) - torch.min(b1_y1, b2_y1) # convex height - 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 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 / (v - iou + (1 + eps)) - return iou - (rho2 / c2 + v * alpha) # CIoU - return iou - rho2 / c2 # DIoU - c_area = cw * ch + eps # convex area - return iou - (c_area - union) / c_area # GIoU https://arxiv.org/pdf/1902.09630.pdf - 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 bbox_ioa(box1, box2, eps=1E-7): - """ Returns the intersection over box2 area given box1, box2. Boxes are x1y1x2y2 - box1: np.array of shape(4) - box2: np.array of shape(nx4) - returns: np.array of shape(n) - """ - - box2 = box2.transpose() - - # Get the coordinates of bounding boxes - b1_x1, b1_y1, b1_x2, b1_y2 = box1[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) + eps - - # Intersection over box2 area - return inter_area / box2_area - - -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) - - -# Plots ---------------------------------------------------------------------------------------------------------------- - -def plot_pr_curve(px, py, ap, save_dir='pr_curve.png', names=()): - # Precision-recall curve - fig, ax = plt.subplots(1, 1, figsize=(9, 6), tight_layout=True) - py = np.stack(py, axis=1) - - if 0 < len(names) < 21: # display per-class legend if < 21 classes - for i, y in enumerate(py.T): - ax.plot(px, y, linewidth=1, label=f'{names[i]} {ap[i, 0]:.3f}') # 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), dpi=250) - plt.close() - - -def plot_mc_curve(px, py, save_dir='mc_curve.png', names=(), xlabel='Confidence', ylabel='Metric'): - # Metric-confidence curve - fig, ax = plt.subplots(1, 1, figsize=(9, 6), tight_layout=True) - - if 0 < len(names) < 21: # display per-class legend if < 21 classes - for i, y in enumerate(py): - ax.plot(px, y, linewidth=1, label=f'{names[i]}') # plot(confidence, metric) - else: - ax.plot(px, py.T, linewidth=1, color='grey') # plot(confidence, metric) - - y = py.mean(0) - ax.plot(px, y, linewidth=3, color='blue', label=f'all classes {y.max():.2f} at {px[y.argmax()]:.3f}') - ax.set_xlabel(xlabel) - ax.set_ylabel(ylabel) - ax.set_xlim(0, 1) - ax.set_ylim(0, 1) - plt.legend(bbox_to_anchor=(1.04, 1), loc="upper left") - fig.savefig(Path(save_dir), dpi=250) - plt.close() diff --git a/YOLO/Dockerfiles/app/app/utils/plots.py b/YOLO/Dockerfiles/app/app/utils/plots.py deleted file mode 100644 index 69037ee..0000000 --- a/YOLO/Dockerfiles/app/app/utils/plots.py +++ /dev/null @@ -1,470 +0,0 @@ -# YOLOv5 🚀 by Ultralytics, GPL-3.0 license -""" -Plotting utils -""" - -import math -import os -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 sn -import torch -from PIL import Image, ImageDraw, ImageFont - -from utils.general import (LOGGER, Timeout, check_requirements, clip_coords, increment_path, is_ascii, is_chinese, - try_except, user_config_dir, xywh2xyxy, xyxy2xywh) -from utils.metrics import fitness - -# Settings -CONFIG_DIR = user_config_dir() # Ultralytics settings dir -RANK = int(os.getenv('RANK', -1)) -matplotlib.rc('font', **{'size': 11}) -matplotlib.use('Agg') # for writing to files only - - -class Colors: - # Ultralytics color palette https://ultralytics.com/ - def __init__(self): - # hex = matplotlib.colors.TABLEAU_COLORS.values() - hex = ('FF3838', 'FF9D97', 'FF701F', 'FFB21D', 'CFD231', '48F90A', '92CC17', '3DDB86', '1A9334', '00D4BB', - '2C99A8', '00C2FF', '344593', '6473FF', '0018EC', '8438FF', '520085', 'CB38FF', 'FF95C8', 'FF37C7') - self.palette = [self.hex2rgb('#' + c) for c in hex] - self.n = len(self.palette) - - def __call__(self, i, bgr=False): - c = self.palette[int(i) % self.n] - return (c[2], c[1], c[0]) if bgr else c - - @staticmethod - def hex2rgb(h): # rgb order (PIL) - return tuple(int(h[1 + i:1 + i + 2], 16) for i in (0, 2, 4)) - - -colors = Colors() # create instance for 'from utils.plots import colors' - - -def check_font(font='Arial.ttf', size=10): - # Return a PIL TrueType Font, downloading to CONFIG_DIR if necessary - font = Path(font) - font = font if font.exists() else (CONFIG_DIR / font.name) - try: - return ImageFont.truetype(str(font) if font.exists() else font.name, size) - except Exception as e: # download if missing - url = "https://ultralytics.com/assets/" + font.name - print(f'Downloading {url} to {font}...') - torch.hub.download_url_to_file(url, str(font), progress=False) - try: - return ImageFont.truetype(str(font), size) - except TypeError: - check_requirements('Pillow>=8.4.0') # known issue https://github.com/ultralytics/yolov5/issues/5374 - - -class Annotator: - if RANK in (-1, 0): - check_font() # download TTF if necessary - - # YOLOv5 Annotator for train/val mosaics and jpgs and detect/hub inference annotations - def __init__(self, im, line_width=None, font_size=None, font='Arial.ttf', pil=False, example='abc'): - assert im.data.contiguous, 'Image not contiguous. Apply np.ascontiguousarray(im) to Annotator() input images.' - self.pil = pil or not is_ascii(example) or is_chinese(example) - if self.pil: # use PIL - self.im = im if isinstance(im, Image.Image) else Image.fromarray(im) - self.draw = ImageDraw.Draw(self.im) - self.font = check_font(font='Arial.Unicode.ttf' if is_chinese(example) else font, - size=font_size or max(round(sum(self.im.size) / 2 * 0.035), 12)) - else: # use cv2 - self.im = im - self.lw = line_width or max(round(sum(im.shape) / 2 * 0.003), 2) # line width - - def box_label(self, box, label='', color=(128, 128, 128), txt_color=(255, 255, 255)): - # Add one xyxy box to image with label - if self.pil or not is_ascii(label): - self.draw.rectangle(box, width=self.lw, outline=color) # box - if label: - w, h = self.font.getsize(label) # text width, height - outside = box[1] - h >= 0 # label fits outside box - self.draw.rectangle([box[0], - box[1] - h if outside else box[1], - box[0] + w + 1, - box[1] + 1 if outside else box[1] + h + 1], fill=color) - # self.draw.text((box[0], box[1]), label, fill=txt_color, font=self.font, anchor='ls') # for PIL>8.0 - self.draw.text((box[0], box[1] - h if outside else box[1]), label, fill=txt_color, font=self.font) - else: # cv2 - p1, p2 = (int(box[0]), int(box[1])), (int(box[2]), int(box[3])) - cv2.rectangle(self.im, p1, p2, color, thickness=self.lw, lineType=cv2.LINE_AA) - if label: - tf = max(self.lw - 1, 1) # font thickness - w, h = cv2.getTextSize(label, 0, fontScale=self.lw / 3, thickness=tf)[0] # text width, height - outside = p1[1] - h - 3 >= 0 # label fits outside box - p2 = p1[0] + w, p1[1] - h - 3 if outside else p1[1] + h + 3 - cv2.rectangle(self.im, p1, p2, color, -1, cv2.LINE_AA) # filled - cv2.putText(self.im, label, (p1[0], p1[1] - 2 if outside else p1[1] + h + 2), 0, self.lw / 3, txt_color, - thickness=tf, lineType=cv2.LINE_AA) - - def rectangle(self, xy, fill=None, outline=None, width=1): - # Add rectangle to image (PIL-only) - self.draw.rectangle(xy, fill, outline, width) - - def text(self, xy, text, txt_color=(255, 255, 255)): - # Add text to image (PIL-only) - w, h = self.font.getsize(text) # text width, height - self.draw.text((xy[0], xy[1] - h + 1), text, fill=txt_color, font=self.font) - - def result(self): - # Return annotated image as array - return np.asarray(self.im) - - -def feature_visualization(x, module_type, stage, n=32, save_dir=Path('runs/detect/exp')): - """ - x: Features to be visualized - module_type: Module type - stage: Module stage within model - n: Maximum number of feature maps to plot - save_dir: Directory to save results - """ - if 'Detect' not in module_type: - batch, channels, height, width = x.shape # batch, channels, height, width - if height > 1 and width > 1: - f = save_dir / f"stage{stage}_{module_type.split('.')[-1]}_features.png" # filename - - blocks = torch.chunk(x[0].cpu(), channels, dim=0) # select batch index 0, block by channels - n = min(n, channels) # number of plots - fig, ax = plt.subplots(math.ceil(n / 8), 8, tight_layout=True) # 8 rows x n/8 cols - ax = ax.ravel() - plt.subplots_adjust(wspace=0.05, hspace=0.05) - for i in range(n): - ax[i].imshow(blocks[i].squeeze()) # cmap='gray' - ax[i].axis('off') - - print(f'Saving {f}... ({n}/{channels})') - plt.savefig(f, dpi=300, bbox_inches='tight') - plt.close() - np.save(str(f.with_suffix('.npy')), x[0].cpu().numpy()) # npy save - - -def hist2d(x, y, n=100): - # 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): - from scipy.signal import butter, filtfilt - - # 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 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=1920, max_subplots=16): - # Plot image grid with labels - if isinstance(images, torch.Tensor): - images = images.cpu().float().numpy() - if isinstance(targets, torch.Tensor): - targets = targets.cpu().numpy() - if np.max(images[0]) <= 1: - images *= 255 # de-normalise (optional) - bs, _, h, w = images.shape # batch size, _, height, width - bs = min(bs, max_subplots) # limit plot images - ns = np.ceil(bs ** 0.5) # number of subplots (square) - - # Build Image - mosaic = np.full((int(ns * h), int(ns * w), 3), 255, dtype=np.uint8) # init - for i, im in enumerate(images): - if i == max_subplots: # if last batch has fewer images than we expect - break - x, y = int(w * (i // ns)), int(h * (i % ns)) # block origin - im = im.transpose(1, 2, 0) - mosaic[y:y + h, x:x + w, :] = im - - # Resize (optional) - scale = max_size / ns / max(h, w) - if scale < 1: - h = math.ceil(scale * h) - w = math.ceil(scale * w) - mosaic = cv2.resize(mosaic, tuple(int(x * ns) for x in (w, h))) - - # Annotate - fs = int((h + w) * ns * 0.01) # font size - annotator = Annotator(mosaic, line_width=round(fs / 10), font_size=fs, pil=True) - for i in range(i + 1): - x, y = int(w * (i // ns)), int(h * (i % ns)) # block origin - annotator.rectangle([x, y, x + w, y + h], None, (255, 255, 255), width=2) # borders - if paths: - annotator.text((x + 5, y + 5 + h), text=Path(paths[i]).name[:40], txt_color=(220, 220, 220)) # filenames - if len(targets) > 0: - ti = targets[targets[:, 0] == i] # image targets - boxes = xywh2xyxy(ti[:, 2:6]).T - classes = ti[:, 1].astype('int') - labels = ti.shape[1] == 6 # labels if no conf column - conf = None if labels else ti[:, 6] # check for confidence presence (label vs pred) - - if boxes.shape[1]: - if boxes.max() <= 1.01: # if normalized with tolerance 0.01 - boxes[[0, 2]] *= w # scale to pixels - boxes[[1, 3]] *= h - elif scale < 1: # absolute coords need scale if image scales - boxes *= scale - boxes[[0, 2]] += x - boxes[[1, 3]] += y - for j, box in enumerate(boxes.T.tolist()): - cls = classes[j] - color = colors(cls) - cls = names[cls] if names else cls - if labels or conf[j] > 0.25: # 0.25 conf thresh - label = f'{cls}' if labels else f'{cls} {conf[j]:.1f}' - annotator.box_label(box, label, color=color) - annotator.im.save(fname) # save - - -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) - plt.close() - - -def plot_val_txt(): # from utils.plots import *; plot_val() - # Plot val.txt histograms - x = np.loadtxt('val.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=f'{x[i].mean():.3g} +/- {x[i].std():.3g}') - ax[i].legend() - ax[i].set_title(s[i]) - plt.savefig('targets.jpg', dpi=200) - - -def plot_val_study(file='', dir='', x=None): # from utils.plots import *; plot_val_study() - # Plot file=study.txt generated by val.py (or plot all study*.txt in dir) - save_dir = Path(file).parent if file else Path(dir) - plot2 = False # plot additional results - if plot2: - ax = plt.subplots(2, 4, figsize=(10, 6), tight_layout=True)[1].ravel() - - fig2, ax2 = plt.subplots(1, 1, figsize=(8, 4), tight_layout=True) - # for f in [save_dir / f'study_coco_{x}.txt' for x in ['yolov5n6', 'yolov5s6', 'yolov5m6', 'yolov5l6', 'yolov5x6']]: - for f in sorted(save_dir.glob('study*.txt')): - y = np.loadtxt(f, dtype=np.float32, usecols=[0, 1, 2, 3, 7, 8, 9], ndmin=2).T - x = np.arange(y.shape[1]) if x is None else np.array(x) - if plot2: - s = ['P', 'R', 'mAP@.5', 'mAP@.5:.95', 't_preprocess (ms/img)', 't_inference (ms/img)', 't_NMS (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[5, 1:j], y[3, 1:j] * 1E2, '.-', linewidth=2, markersize=8, - label=f.stem.replace('study_coco_', '').replace('yolo', 'YOLO')) - - ax2.plot(1E3 / np.array([209, 140, 97, 58, 35, 18]), [34.6, 40.5, 43.0, 47.5, 49.7, 51.5], - 'k.-', linewidth=2, markersize=8, alpha=.25, label='EfficientDet') - - ax2.grid(alpha=0.2) - ax2.set_yticks(np.arange(20, 60, 5)) - ax2.set_xlim(0, 57) - ax2.set_ylim(25, 55) - ax2.set_xlabel('GPU Speed (ms/img)') - ax2.set_ylabel('COCO AP val') - ax2.legend(loc='lower right') - f = save_dir / 'study.png' - print(f'Saving {f}...') - plt.savefig(f, dpi=300) - - -@try_except # known issue https://github.com/ultralytics/yolov5/issues/5395 -@Timeout(30) # known issue https://github.com/ultralytics/yolov5/issues/5611 -def plot_labels(labels, names=(), save_dir=Path('')): - # plot dataset labels - LOGGER.info(f"Plotting labels to {save_dir / 'labels.jpg'}... ") - c, b = labels[:, 0], labels[:, 1:].transpose() # classes, boxes - nc = int(c.max() + 1) # number of classes - x = pd.DataFrame(b.transpose(), columns=['x', 'y', 'width', 'height']) - - # seaborn correlogram - sn.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() - y = ax[0].hist(c, bins=np.linspace(0, nc, nc + 1) - 0.5, rwidth=0.8) - # [y[2].patches[i].set_color([x / 255 for x in colors(i)]) for i in range(nc)] # update colors bug #3195 - ax[0].set_ylabel('instances') - if 0 < len(names) < 30: - ax[0].set_xticks(range(len(names))) - ax[0].set_xticklabels(names, rotation=90, fontsize=10) - else: - ax[0].set_xlabel('classes') - sn.histplot(x, x='x', y='y', ax=ax[2], bins=50, pmax=0.9) - sn.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(cls)) # 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() - - -def plot_evolve(evolve_csv='path/to/evolve.csv'): # from utils.plots import *; plot_evolve() - # Plot evolve.csv hyp evolution results - evolve_csv = Path(evolve_csv) - data = pd.read_csv(evolve_csv) - keys = [x.strip() for x in data.columns] - x = data.values - f = fitness(x) - j = np.argmax(f) # max fitness index - plt.figure(figsize=(10, 12), tight_layout=True) - matplotlib.rc('font', **{'size': 8}) - for i, k in enumerate(keys[7:]): - v = x[:, 7 + i] - mu = v[j] # best single result - plt.subplot(6, 5, i + 1) - plt.scatter(v, f, c=hist2d(v, f, 20), cmap='viridis', alpha=.8, edgecolors='none') - plt.plot(mu, f.max(), 'k+', markersize=15) - plt.title(f'{k} = {mu:.3g}', fontdict={'size': 9}) # limit to 40 characters - if i % 5 != 0: - plt.yticks([]) - print(f'{k:>15}: {mu:.3g}') - f = evolve_csv.with_suffix('.png') # filename - plt.savefig(f, dpi=200) - plt.close() - print(f'Saved {f}') - - -def plot_results(file='path/to/results.csv', dir=''): - # Plot training results.csv. Usage: from utils.plots import *; plot_results('path/to/results.csv') - save_dir = Path(file).parent if file else Path(dir) - fig, ax = plt.subplots(2, 5, figsize=(12, 6), tight_layout=True) - ax = ax.ravel() - files = list(save_dir.glob('results*.csv')) - assert len(files), f'No results.csv files found in {save_dir.resolve()}, nothing to plot.' - for fi, f in enumerate(files): - try: - data = pd.read_csv(f) - s = [x.strip() for x in data.columns] - x = data.values[:, 0] - for i, j in enumerate([1, 2, 3, 4, 5, 8, 9, 10, 6, 7]): - y = data.values[:, j] - # y[y == 0] = np.nan # don't show zero values - ax[i].plot(x, y, marker='.', label=f.stem, linewidth=2, markersize=8) - ax[i].set_title(s[j], fontsize=12) - # if j in [8, 9, 10]: # share train and val loss y axes - # ax[i].get_shared_y_axes().join(ax[i], ax[i - 5]) - except Exception as e: - print(f'Warning: Plotting error for {f}: {e}') - ax[1].legend() - fig.savefig(save_dir / 'results.png', dpi=200) - plt.close() - - -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(f'Warning: Plotting error for {f}; {e}') - ax[1].legend() - plt.savefig(Path(save_dir) / 'idetection_profile.png', dpi=200) - - -def save_one_box(xyxy, im, file='image.jpg', gain=1.02, pad=10, square=False, BGR=False, save=True): - # Save image crop as {file} with crop size multiple {gain} and {pad} pixels. Save and/or return crop - xyxy = torch.tensor(xyxy).view(-1, 4) - b = xyxy2xywh(xyxy) # boxes - if square: - b[:, 2:] = b[:, 2:].max(1)[0].unsqueeze(1) # attempt rectangle to square - b[:, 2:] = b[:, 2:] * gain + pad # box wh * gain + pad - xyxy = xywh2xyxy(b).long() - clip_coords(xyxy, im.shape) - crop = im[int(xyxy[0, 1]):int(xyxy[0, 3]), int(xyxy[0, 0]):int(xyxy[0, 2]), ::(1 if BGR else -1)] - if save: - file.parent.mkdir(parents=True, exist_ok=True) # make directory - cv2.imwrite(str(increment_path(file).with_suffix('.jpg')), crop) - return crop diff --git a/YOLO/Dockerfiles/app/app/utils/torch_utils.py b/YOLO/Dockerfiles/app/app/utils/torch_utils.py deleted file mode 100644 index cddb173..0000000 --- a/YOLO/Dockerfiles/app/app/utils/torch_utils.py +++ /dev/null @@ -1,318 +0,0 @@ -# YOLOv5 🚀 by Ultralytics, GPL-3.0 license -""" -PyTorch utils -""" - -import datetime -import math -import os -import platform -import subprocess -import time -from contextlib import contextmanager -from copy import deepcopy -from pathlib import Path - -import torch -import torch.distributed as dist -import torch.nn as nn -import torch.nn.functional as F - -from utils.general import LOGGER - -try: - import thop # for FLOPs computation -except ImportError: - thop = None - - -@contextmanager -def torch_distributed_zero_first(local_rank: int): - """ - Decorator to make all processes in distributed training wait for each local_master to do something. - """ - if local_rank not in [-1, 0]: - dist.barrier(device_ids=[local_rank]) - yield - if local_rank == 0: - dist.barrier(device_ids=[0]) - - -def date_modified(path=__file__): - # return human-readable file modification date, i.e. '2021-3-26' - t = datetime.datetime.fromtimestamp(Path(path).stat().st_mtime) - return f'{t.year}-{t.month}-{t.day}' - - -def git_describe(path=Path(__file__).parent): # path must be a directory - # return human-readable git description, i.e. v5.0-5-g3e25f1e https://git-scm.com/docs/git-describe - s = f'git -C {path} describe --tags --long --always' - try: - return subprocess.check_output(s, shell=True, stderr=subprocess.STDOUT).decode()[:-1] - except subprocess.CalledProcessError as e: - return '' # not a git repository - - -def select_device(device='', batch_size=0, newline=True): - # device = 'cpu' or '0' or '0,1,2,3' - s = f'YOLOv5 🚀 {git_describe() or date_modified()} torch {torch.__version__} ' # string - device = str(device).strip().lower().replace('cuda:', '') # to string, 'cuda:0' to '0' - cpu = device == 'cpu' - 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 = not cpu and torch.cuda.is_available() - if cuda: - devices = device.split(',') if device else '0' # range(torch.cuda.device_count()) # i.e. 0,1,6,7 - n = len(devices) # device count - if n > 1 and batch_size > 0: # check batch_size is divisible by device_count - assert batch_size % n == 0, f'batch-size {batch_size} not multiple of GPU count {n}' - space = ' ' * (len(s) + 1) - for i, d in enumerate(devices): - p = torch.cuda.get_device_properties(i) - s += f"{'' if i == 0 else space}CUDA:{d} ({p.name}, {p.total_memory / 1024 ** 2:.0f}MiB)\n" # bytes to MB - else: - s += 'CPU\n' - - if not newline: - s = s.rstrip() - LOGGER.info(s.encode().decode('ascii', 'ignore') if platform.system() == 'Windows' else s) # emoji-safe - return torch.device('cuda:0' if cuda else 'cpu') - - -def time_sync(): - # pytorch-accurate time - if torch.cuda.is_available(): - torch.cuda.synchronize() - return time.time() - - -def profile(input, ops, n=10, device=None): - # YOLOv5 speed/memory/FLOPs profiler - # - # Usage: - # input = torch.randn(16, 3, 640, 640) - # m1 = lambda x: x * torch.sigmoid(x) - # m2 = nn.SiLU() - # profile(input, [m1, m2], n=100) # profile over 100 iterations - - results = [] - device = device or select_device() - print(f"{'Params':>12s}{'GFLOPs':>12s}{'GPU_mem (GB)':>14s}{'forward (ms)':>14s}{'backward (ms)':>14s}" - f"{'input':>24s}{'output':>24s}") - - for x in input if isinstance(input, list) else [input]: - x = x.to(device) - x.requires_grad = True - 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 - tf, tb, t = 0, 0, [0, 0, 0] # dt forward, backward - try: - flops = thop.profile(m, inputs=(x,), verbose=False)[0] / 1E9 * 2 # GFLOPs - except: - flops = 0 - - try: - for _ in range(n): - t[0] = time_sync() - y = m(x) - t[1] = time_sync() - try: - _ = (sum(yi.sum() for yi in y) if isinstance(y, list) else y).sum().backward() - t[2] = time_sync() - except Exception as e: # no backward method - # print(e) # for debug - t[2] = float('nan') - tf += (t[1] - t[0]) * 1000 / n # ms per op forward - tb += (t[2] - t[1]) * 1000 / n # ms per op backward - mem = torch.cuda.memory_reserved() / 1E9 if torch.cuda.is_available() else 0 # (GB) - s_in = tuple(x.shape) if isinstance(x, torch.Tensor) else 'list' - s_out = tuple(y.shape) if isinstance(y, torch.Tensor) else 'list' - p = sum(list(x.numel() for x in m.parameters())) if isinstance(m, nn.Module) else 0 # parameters - print(f'{p:12}{flops:12.4g}{mem:>14.3f}{tf:14.4g}{tb:14.4g}{str(s_in):>24s}{str(s_out):>24s}') - results.append([p, flops, mem, tf, tb, s_in, s_out]) - except Exception as e: - print(e) - results.append(None) - torch.cuda.empty_cache() - return results - - -def is_parallel(model): - # Returns True if model is of type DP or DDP - return type(model) in (nn.parallel.DataParallel, nn.parallel.DistributedDataParallel) - - -def de_parallel(model): - # De-parallelize a model: returns single-GPU model if model is of type DP or DDP - return model.module if is_parallel(model) else model - - -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, nn.SiLU]: - 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.shape)) - - # 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(f"{'layer':>5} {'name':>40} {'gradient':>9} {'parameters':>12} {'shape':>20} {'mu':>10} {'sigma':>10}") - 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 = max(int(model.stride.max()), 32) if hasattr(model, 'stride') else 32 - img = torch.zeros((1, model.yaml.get('ch', 3), stride, stride), device=next(model.parameters()).device) # input - flops = profile(deepcopy(model), inputs=(img,), verbose=False)[0] / 1E9 * 2 # stride GFLOPs - img_size = img_size if isinstance(img_size, list) else [img_size, img_size] # expand if int/float - fs = ', %.1f GFLOPs' % (flops * img_size[0] / stride * img_size[1] / stride) # 640x640 GFLOPs - except (ImportError, Exception): - fs = '' - - LOGGER.info(f"Model Summary: {len(list(model.modules()))} layers, {n_p} parameters, {n_g} gradients{fs}") - - -def scale_img(img, ratio=1.0, same_shape=False, gs=32): # img(16,3,256,416) - # scales img(bs,3,y,x) by ratio constrained to gs-multiple - 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 - 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 EarlyStopping: - # YOLOv5 simple early stopper - def __init__(self, patience=30): - self.best_fitness = 0.0 # i.e. mAP - self.best_epoch = 0 - self.patience = patience or float('inf') # epochs to wait after fitness stops improving to stop - self.possible_stop = False # possible stop may occur next epoch - - def __call__(self, epoch, fitness): - if fitness >= self.best_fitness: # >= 0 to allow for early zero-fitness stage of training - self.best_epoch = epoch - self.best_fitness = fitness - delta = epoch - self.best_epoch # epochs without improvement - self.possible_stop = delta >= (self.patience - 1) # possible stop may occur next epoch - stop = delta >= self.patience # stop training if patience exceeded - if stop: - LOGGER.info(f'Stopping training early as no improvement observed in last {self.patience} epochs. ' - f'Best results observed at epoch {self.best_epoch}, best model saved as best.pt.\n' - f'To update EarlyStopping(patience={self.patience}) pass a new patience value, ' - f'i.e. `python train.py --patience 300` or use `--patience 0` to disable EarlyStopping.') - return stop - - -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/YOLO/Dockerfiles/app/app/val.py b/YOLO/Dockerfiles/app/app/val.py deleted file mode 100644 index 4709f67..0000000 --- a/YOLO/Dockerfiles/app/app/val.py +++ /dev/null @@ -1,382 +0,0 @@ -# YOLOv5 🚀 by Ultralytics, GPL-3.0 license -""" -Validate a trained YOLOv5 model accuracy on a custom dataset - -Usage: - $ python path/to/val.py --weights yolov5s.pt --data coco128.yaml --img 640 - -Usage - formats: - $ python path/to/val.py --weights yolov5s.pt # PyTorch - yolov5s.torchscript # TorchScript - yolov5s.onnx # ONNX Runtime or OpenCV DNN with --dnn - yolov5s.xml # OpenVINO - yolov5s.engine # TensorRT - yolov5s.mlmodel # CoreML (MacOS-only) - yolov5s_saved_model # TensorFlow SavedModel - yolov5s.pb # TensorFlow GraphDef - yolov5s.tflite # TensorFlow Lite - yolov5s_edgetpu.tflite # TensorFlow Edge TPU -""" - -import argparse -import json -import os -import sys -from pathlib import Path -from threading import Thread - -import numpy as np -import torch -from tqdm import tqdm - -FILE = Path(__file__).resolve() -ROOT = FILE.parents[0] # YOLOv5 root directory -if str(ROOT) not in sys.path: - sys.path.append(str(ROOT)) # add ROOT to PATH -ROOT = Path(os.path.relpath(ROOT, Path.cwd())) # relative - -from models.common import DetectMultiBackend -from utils.callbacks import Callbacks -from utils.datasets import create_dataloader -from utils.general import (LOGGER, box_iou, check_dataset, check_img_size, check_requirements, check_yaml, - coco80_to_coco91_class, colorstr, increment_path, non_max_suppression, print_args, - scale_coords, xywh2xyxy, xyxy2xywh) -from utils.metrics import ConfusionMatrix, ap_per_class -from utils.plots import output_to_target, plot_images, plot_val_study -from utils.torch_utils import select_device, time_sync - - -def save_one_txt(predn, save_conf, shape, file): - # Save one txt result - gn = torch.tensor(shape)[[1, 0, 1, 0]] # normalization gain whwh - for *xyxy, conf, cls in predn.tolist(): - xywh = (xyxy2xywh(torch.tensor(xyxy).view(1, 4)) / gn).view(-1).tolist() # normalized xywh - line = (cls, *xywh, conf) if save_conf else (cls, *xywh) # label format - with open(file, 'a') as f: - f.write(('%g ' * len(line)).rstrip() % line + '\n') - - -def save_one_json(predn, jdict, path, class_map): - # Save one JSON result {"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(predn.tolist(), box.tolist()): - jdict.append({'image_id': image_id, - 'category_id': class_map[int(p[5])], - 'bbox': [round(x, 3) for x in b], - 'score': round(p[4], 5)}) - - -def process_batch(detections, labels, iouv): - """ - Return correct predictions matrix. Both sets of boxes are in (x1, y1, x2, y2) format. - Arguments: - detections (Array[N, 6]), x1, y1, x2, y2, conf, class - labels (Array[M, 5]), class, x1, y1, x2, y2 - Returns: - correct (Array[N, 10]), for 10 IoU levels - """ - correct = torch.zeros(detections.shape[0], iouv.shape[0], dtype=torch.bool, device=iouv.device) - iou = box_iou(labels[:, 1:], detections[:, :4]) - x = torch.where((iou >= iouv[0]) & (labels[:, 0:1] == detections[:, 5])) # IoU above threshold and classes match - if x[0].shape[0]: - matches = torch.cat((torch.stack(x, 1), iou[x[0], x[1]][:, None]), 1).cpu().numpy() # [label, detection, iou] - if x[0].shape[0] > 1: - matches = matches[matches[:, 2].argsort()[::-1]] - matches = matches[np.unique(matches[:, 1], return_index=True)[1]] - # matches = matches[matches[:, 2].argsort()[::-1]] - matches = matches[np.unique(matches[:, 0], return_index=True)[1]] - matches = torch.Tensor(matches).to(iouv.device) - correct[matches[:, 1].long()] = matches[:, 2:3] >= iouv - return correct - - -@torch.no_grad() -def run(data, - weights=None, # model.pt path(s) - batch_size=32, # batch size - imgsz=640, # inference size (pixels) - conf_thres=0.001, # confidence threshold - iou_thres=0.6, # NMS IoU threshold - task='val', # train, val, test, speed or study - device='', # cuda device, i.e. 0 or 0,1,2,3 or cpu - workers=8, # max dataloader workers (per RANK in DDP mode) - single_cls=False, # treat as single-class dataset - augment=False, # augmented inference - verbose=False, # verbose output - save_txt=False, # save results to *.txt - save_hybrid=False, # save label+prediction hybrid results to *.txt - save_conf=False, # save confidences in --save-txt labels - save_json=False, # save a COCO-JSON results file - project=ROOT / 'runs/val', # save to project/name - name='exp', # save to project/name - exist_ok=False, # existing project/name ok, do not increment - half=True, # use FP16 half-precision inference - dnn=False, # use OpenCV DNN for ONNX inference - model=None, - dataloader=None, - save_dir=Path(''), - plots=True, - callbacks=Callbacks(), - compute_loss=None, - ): - # Initialize/load model and set device - training = model is not None - if training: # called by train.py - device, pt, jit, engine = next(model.parameters()).device, True, False, False # get model device, PyTorch model - - half &= device.type != 'cpu' # half precision only supported on CUDA - model.half() if half else model.float() - else: # called directly - device = select_device(device, batch_size=batch_size) - - # Directories - save_dir = increment_path(Path(project) / name, exist_ok=exist_ok) # increment run - (save_dir / 'labels' if save_txt else save_dir).mkdir(parents=True, exist_ok=True) # make dir - - # Load model - model = DetectMultiBackend(weights, device=device, dnn=dnn, data=data) - stride, pt, jit, engine = model.stride, model.pt, model.jit, model.engine - imgsz = check_img_size(imgsz, s=stride) # check image size - half &= (pt or jit or engine) and device.type != 'cpu' # half precision only supported by PyTorch on CUDA - if pt or jit: - model.model.half() if half else model.model.float() - elif engine: - batch_size = model.batch_size - else: - half = False - batch_size = 1 # export.py models default to batch-size 1 - device = torch.device('cpu') - LOGGER.info(f'Forcing --batch-size 1 square inference shape(1,3,{imgsz},{imgsz}) for non-PyTorch backends') - - # Data - data = check_dataset(data) # check - - # Configure - model.eval() - is_coco = isinstance(data.get('val'), str) and data['val'].endswith('coco/val2017.txt') # COCO dataset - nc = 1 if single_cls else int(data['nc']) # number of classes - iouv = torch.linspace(0.5, 0.95, 10).to(device) # iou vector for mAP@0.5:0.95 - niou = iouv.numel() - - # Dataloader - if not training: - model.warmup(imgsz=(1, 3, imgsz, imgsz), half=half) # warmup - pad = 0.0 if task == 'speed' else 0.5 - task = task if task in ('train', 'val', 'test') else 'val' # path to train/val/test images - dataloader = create_dataloader(data[task], imgsz, batch_size, stride, single_cls, pad=pad, rect=pt, - workers=workers, prefix=colorstr(f'{task}: '))[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)} - class_map = coco80_to_coco91_class() if is_coco else list(range(1000)) - s = ('%20s' + '%11s' * 6) % ('Class', 'Images', 'Labels', 'P', 'R', 'mAP@.5', 'mAP@.5:.95') - dt, p, r, f1, mp, mr, map50, map = [0.0, 0.0, 0.0], 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0 - loss = torch.zeros(3, device=device) - jdict, stats, ap, ap_class = [], [], [], [] - pbar = tqdm(dataloader, desc=s, bar_format='{l_bar}{bar:10}{r_bar}{bar:-10b}') # progress bar - for batch_i, (im, targets, paths, shapes) in enumerate(pbar): - t1 = time_sync() - if pt or jit or engine: - im = im.to(device, non_blocking=True) - targets = targets.to(device) - im = im.half() if half else im.float() # uint8 to fp16/32 - im /= 255 # 0 - 255 to 0.0 - 1.0 - nb, _, height, width = im.shape # batch size, channels, height, width - t2 = time_sync() - dt[0] += t2 - t1 - - # Inference - out, train_out = model(im) if training else model(im, augment=augment, val=True) # inference, loss outputs - dt[1] += time_sync() - t2 - - # Loss - if compute_loss: - loss += compute_loss([x.float() for x in train_out], targets)[1] # box, obj, cls - - # 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 - t3 = time_sync() - out = non_max_suppression(out, conf_thres, iou_thres, labels=lb, multi_label=True, agnostic=single_cls) - dt[2] += time_sync() - t3 - - # Metrics - for si, pred in enumerate(out): - labels = targets[targets[:, 0] == si, 1:] - nl = len(labels) - tcls = labels[:, 0].tolist() if nl else [] # target class - path, shape = Path(paths[si]), shapes[si][0] - seen += 1 - - if len(pred) == 0: - if nl: - stats.append((torch.zeros(0, niou, dtype=torch.bool), torch.Tensor(), torch.Tensor(), tcls)) - continue - - # Predictions - if single_cls: - pred[:, 5] = 0 - predn = pred.clone() - scale_coords(im[si].shape[1:], predn[:, :4], shape, shapes[si][1]) # native-space pred - - # Evaluate - if nl: - tbox = xywh2xyxy(labels[:, 1:5]) # target boxes - scale_coords(im[si].shape[1:], tbox, shape, shapes[si][1]) # native-space labels - labelsn = torch.cat((labels[:, 0:1], tbox), 1) # native-space labels - correct = process_batch(predn, labelsn, iouv) - if plots: - confusion_matrix.process_batch(predn, labelsn) - else: - correct = torch.zeros(pred.shape[0], niou, dtype=torch.bool) - stats.append((correct.cpu(), pred[:, 4].cpu(), pred[:, 5].cpu(), tcls)) # (correct, conf, pcls, tcls) - - # Save/log - if save_txt: - save_one_txt(predn, save_conf, shape, file=save_dir / 'labels' / (path.stem + '.txt')) - if save_json: - save_one_json(predn, jdict, path, class_map) # append to COCO-JSON dictionary - callbacks.run('on_val_image_end', pred, predn, path, names, im[si]) - - # Plot images - if plots and batch_i < 3: - f = save_dir / f'val_batch{batch_i}_labels.jpg' # labels - Thread(target=plot_images, args=(im, targets, paths, f, names), daemon=True).start() - f = save_dir / f'val_batch{batch_i}_pred.jpg' # predictions - Thread(target=plot_images, args=(im, output_to_target(out), paths, f, names), daemon=True).start() - - # Compute metrics - stats = [np.concatenate(x, 0) for x in zip(*stats)] # to numpy - if len(stats) and stats[0].any(): - tp, fp, p, r, f1, ap, ap_class = ap_per_class(*stats, plot=plots, save_dir=save_dir, names=names) - ap50, ap = ap[:, 0], ap.mean(1) # AP@0.5, AP@0.5:0.95 - mp, mr, map50, map = p.mean(), r.mean(), ap50.mean(), ap.mean() - nt = np.bincount(stats[3].astype(np.int64), minlength=nc) # number of targets per class - else: - nt = torch.zeros(1) - - # Print results - pf = '%20s' + '%11i' * 2 + '%11.3g' * 4 # print format - LOGGER.info(pf % ('all', seen, nt.sum(), mp, mr, map50, map)) - - # Print results per class - if (verbose or (nc < 50 and not training)) and nc > 1 and len(stats): - for i, c in enumerate(ap_class): - LOGGER.info(pf % (names[c], seen, nt[c], p[i], r[i], ap50[i], ap[i])) - - # Print speeds - t = tuple(x / seen * 1E3 for x in dt) # speeds per image - if not training: - shape = (batch_size, 3, imgsz, imgsz) - LOGGER.info(f'Speed: %.1fms pre-process, %.1fms inference, %.1fms NMS per image at shape {shape}' % t) - - # Plots - if plots: - confusion_matrix.plot(save_dir=save_dir, names=list(names.values())) - callbacks.run('on_val_end') - - # Save JSON - if save_json and len(jdict): - w = Path(weights[0] if isinstance(weights, list) else weights).stem if weights is not None else '' # weights - anno_json = str(Path(data.get('path', '../coco')) / 'annotations/instances_val2017.json') # annotations json - pred_json = str(save_dir / f"{w}_predictions.json") # predictions json - LOGGER.info(f'\nEvaluating pycocotools mAP... saving {pred_json}...') - with open(pred_json, 'w') as f: - json.dump(jdict, f) - - try: # https://github.com/cocodataset/cocoapi/blob/master/PythonAPI/pycocoEvalDemo.ipynb - check_requirements(['pycocotools']) - 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: - LOGGER.info(f'pycocotools unable to run: {e}') - - # Return results - model.float() # for training - if not training: - s = f"\n{len(list(save_dir.glob('labels/*.txt')))} labels saved to {save_dir / 'labels'}" if save_txt else '' - LOGGER.info(f"Results saved to {colorstr('bold', save_dir)}{s}") - 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 - - -def parse_opt(): - parser = argparse.ArgumentParser() - parser.add_argument('--data', type=str, default=ROOT / 'data/coco128.yaml', help='dataset.yaml path') - parser.add_argument('--weights', nargs='+', type=str, default=ROOT / 'yolov5s.pt', help='model.pt path(s)') - parser.add_argument('--batch-size', type=int, default=32, help='batch size') - parser.add_argument('--imgsz', '--img', '--img-size', type=int, default=640, help='inference size (pixels)') - parser.add_argument('--conf-thres', type=float, default=0.001, help='confidence threshold') - parser.add_argument('--iou-thres', type=float, default=0.6, help='NMS IoU threshold') - parser.add_argument('--task', default='val', help='train, val, test, speed or study') - parser.add_argument('--device', default='', help='cuda device, i.e. 0 or 0,1,2,3 or cpu') - parser.add_argument('--workers', type=int, default=8, help='max dataloader workers (per RANK in DDP mode)') - parser.add_argument('--single-cls', action='store_true', help='treat as single-class dataset') - parser.add_argument('--augment', action='store_true', help='augmented inference') - parser.add_argument('--verbose', action='store_true', help='report mAP by class') - parser.add_argument('--save-txt', action='store_true', help='save results to *.txt') - parser.add_argument('--save-hybrid', action='store_true', help='save label+prediction hybrid results to *.txt') - parser.add_argument('--save-conf', action='store_true', help='save confidences in --save-txt labels') - parser.add_argument('--save-json', action='store_true', help='save a COCO-JSON results file') - parser.add_argument('--project', default=ROOT / 'runs/val', help='save to project/name') - parser.add_argument('--name', default='exp', help='save to project/name') - parser.add_argument('--exist-ok', action='store_true', help='existing project/name ok, do not increment') - parser.add_argument('--half', action='store_true', help='use FP16 half-precision inference') - parser.add_argument('--dnn', action='store_true', help='use OpenCV DNN for ONNX inference') - opt = parser.parse_args() - opt.data = check_yaml(opt.data) # check YAML - opt.save_json |= opt.data.endswith('coco.yaml') - opt.save_txt |= opt.save_hybrid - print_args(FILE.stem, opt) - return opt - - -def main(opt): - check_requirements(requirements=ROOT / 'requirements.txt', exclude=('tensorboard', 'thop')) - - if opt.task in ('train', 'val', 'test'): # run normally - if opt.conf_thres > 0.001: # https://github.com/ultralytics/yolov5/issues/1466 - LOGGER.info(f'WARNING: confidence threshold {opt.conf_thres} >> 0.001 will produce invalid mAP values.') - run(**vars(opt)) - - else: - weights = opt.weights if isinstance(opt.weights, list) else [opt.weights] - opt.half = True # FP16 for fastest results - if opt.task == 'speed': # speed benchmarks - # python val.py --task speed --data coco.yaml --batch 1 --weights yolov5n.pt yolov5s.pt... - opt.conf_thres, opt.iou_thres, opt.save_json = 0.25, 0.45, False - for opt.weights in weights: - run(**vars(opt), plots=False) - - elif opt.task == 'study': # speed vs mAP benchmarks - # python val.py --task study --data coco.yaml --iou 0.7 --weights yolov5n.pt yolov5s.pt... - for opt.weights in weights: - f = f'study_{Path(opt.data).stem}_{Path(opt.weights).stem}.txt' # filename to save to - x, y = list(range(256, 1536 + 128, 128)), [] # x axis (image sizes), y axis - for opt.imgsz in x: # img-size - LOGGER.info(f'\nRunning {f} --imgsz {opt.imgsz}...') - r, _, t = run(**vars(opt), plots=False) - y.append(r + t) # results and times - np.savetxt(f, y, fmt='%10.4g') # save - os.system('zip -r study.zip study_*.txt') - plot_val_study(x=x) # plot - - -if __name__ == "__main__": - opt = parse_opt() - main(opt) diff --git a/YOLO/Dockerfiles/app/app/yolov5s.pt b/YOLO/Dockerfiles/app/app/yolov5s.pt deleted file mode 100644 index cab379e..0000000 Binary files a/YOLO/Dockerfiles/app/app/yolov5s.pt and /dev/null differ diff --git a/YOLO/Dockerfiles/app/entrypoint.sh b/YOLO/Dockerfiles/app/entrypoint.sh deleted file mode 100755 index 5b9e6f1..0000000 --- a/YOLO/Dockerfiles/app/entrypoint.sh +++ /dev/null @@ -1,13 +0,0 @@ -#!/bin/sh - -sleep 5 - -pip3 install -U Flask -pip3 install Flask-RESTful -pip3 install opencv-python -pip3 install opencv-contrib-python -pip3 install opencv-python-headless -pip3 install opencv-contrib-python-headless - -flask run --host=0.0.0.0 --port=50008 - diff --git a/YOLO/Dockerfiles/app/requirements.txt b/YOLO/Dockerfiles/app/requirements.txt deleted file mode 100644 index 32e8968..0000000 --- a/YOLO/Dockerfiles/app/requirements.txt +++ /dev/null @@ -1 +0,0 @@ -Flask==1.1.1 diff --git a/YOLO/Dockerfiles/docker_run.sh b/YOLO/Dockerfiles/docker_run.sh deleted file mode 100755 index 37c0315..0000000 --- a/YOLO/Dockerfiles/docker_run.sh +++ /dev/null @@ -1,4 +0,0 @@ -#!/bin/sh - -docker build -t image_YOLO Dockerfiles/app -docker run -it --network="host" -v /home/user/Bureau/Datasets:/Datasets image_YOLO:latest diff --git a/YOLO/command_line.sh b/YOLO/command_line.sh deleted file mode 100755 index 8fd47fd..0000000 --- a/YOLO/command_line.sh +++ /dev/null @@ -1,2 +0,0 @@ -Dockerfiles/docker_run.sh - diff --git a/bdd/Dockerfiles/app/Dockerfile b/bdd/Dockerfiles/app/Dockerfile deleted file mode 100755 index 84a6c19..0000000 --- a/bdd/Dockerfiles/app/Dockerfile +++ /dev/null @@ -1,21 +0,0 @@ -FROM python:3.7 - -RUN mkdir /code -WORKDIR /code - -ADD entrypoint.sh /code/entrypoint.sh -ADD requirements.txt /code/requirements.txt -ADD app/ /code - -RUN ls -#RUN pip install -r requirements.txt --no-cache-dir -#RUN apt-get install apt-transport-https -RUN apt update -y -RUN apt install python3-pip -y - -#RUN pip3 install --default-timeout=100 -r requirements.txt - -EXPOSE 50000 -ENV FLASK_APP "/code/app.py" -CMD ["/code/entrypoint.sh"] - diff --git a/bdd/Dockerfiles/app/app/app.py b/bdd/Dockerfiles/app/app/app.py deleted file mode 100755 index ed166c0..0000000 --- a/bdd/Dockerfiles/app/app/app.py +++ /dev/null @@ -1,47 +0,0 @@ -# app/__init__.py - -from flask import Flask -import pandas as pd -import sqlite3 as sql -import json - - -def create_app(): - app = Flask(__name__) - with sql.connect("/Datasets/database/database.db") as con: - data=pd.read_csv("/Datasets/database/questions.csv",delimiter=",") - data.to_sql('questions',con,if_exists='replace',index=False) - - @app.route('/question//',methods=['GET']) - def get_question(id=1): - with sql.connect("/Datasets/database/database.db") as con: - cur = con.cursor() - row = cur.execute("select * from questions where id=\'"+str(id)+"\';").fetchone() - dico=dict() - dico["attribut"]=row[1] - dico["intitule"]=row[2] - dico["response"]=row[3] - return(json.dumps(dico)) - - @app.route('/create//') - def create(token='TEST'): - with sql.connect("/Datasets/database/database.db") as con: - data=pd.read_csv("/Datasets/database/"+token+".csv",delimiter=",") - data.to_sql(token,con,if_exists='replace',index=False) - return '1' - - @app.route('/update////') - def update(token,attr,value): - with sql.connect("/Datasets/database/database.db") as con: - cur = con.cursor() - sql_update= "update " + token + " set Excluded = 1 where "+attr+"="+str(value)+";" - cur.execute(sql_update) - cur.execute("select x,y from "+token+" where "+attr+"=\'"+str(value)+"\';") - rows = cur.fetchall() - dico = dict() - dico["img"]=rows - cur = con.cursor() - cur.execute("SELECT COUNT(*) FROM "+token+" where Excluded = 0") - dico["Nb"]=cur.fetchone()[0] - return(json.dumps(dico)) - return app diff --git a/bdd/Dockerfiles/app/entrypoint.sh b/bdd/Dockerfiles/app/entrypoint.sh deleted file mode 100755 index 0c168b0..0000000 --- a/bdd/Dockerfiles/app/entrypoint.sh +++ /dev/null @@ -1,11 +0,0 @@ -#!/bin/sh - -sleep 5 - -pip3 install -U Flask -pip3 install Flask-RESTful -pip3 install pysqlite3 -pip3 install pandas - -flask run --host=0.0.0.0 --port=50011 - diff --git a/bdd/Dockerfiles/app/requirements.txt b/bdd/Dockerfiles/app/requirements.txt deleted file mode 100755 index 32e8968..0000000 --- a/bdd/Dockerfiles/app/requirements.txt +++ /dev/null @@ -1 +0,0 @@ -Flask==1.1.1 diff --git a/bdd/Dockerfiles/docker_run.sh b/bdd/Dockerfiles/docker_run.sh deleted file mode 100755 index a14e17c..0000000 --- a/bdd/Dockerfiles/docker_run.sh +++ /dev/null @@ -1,4 +0,0 @@ -#!/bin/sh - -docker build -t db_image Dockerfiles/app -docker run -it --network="host" -v /home/user/Bureau/Datasets:/Datasets db_image:latest diff --git a/bdd/command_line.sh b/bdd/command_line.sh deleted file mode 100755 index 8fd47fd..0000000 --- a/bdd/command_line.sh +++ /dev/null @@ -1,2 +0,0 @@ -Dockerfiles/docker_run.sh - diff --git a/grid-generation/Dockerfiles/app/Dockerfile b/grid-generation/Dockerfiles/app/Dockerfile deleted file mode 100755 index 84a6c19..0000000 --- a/grid-generation/Dockerfiles/app/Dockerfile +++ /dev/null @@ -1,21 +0,0 @@ -FROM python:3.7 - -RUN mkdir /code -WORKDIR /code - -ADD entrypoint.sh /code/entrypoint.sh -ADD requirements.txt /code/requirements.txt -ADD app/ /code - -RUN ls -#RUN pip install -r requirements.txt --no-cache-dir -#RUN apt-get install apt-transport-https -RUN apt update -y -RUN apt install python3-pip -y - -#RUN pip3 install --default-timeout=100 -r requirements.txt - -EXPOSE 50000 -ENV FLASK_APP "/code/app.py" -CMD ["/code/entrypoint.sh"] - diff --git a/grid-generation/Dockerfiles/app/app/app.py b/grid-generation/Dockerfiles/app/app/app.py deleted file mode 100755 index 37fd2a4..0000000 --- a/grid-generation/Dockerfiles/app/app/app.py +++ /dev/null @@ -1,13 +0,0 @@ -# app/__init__.py - -from flask import Flask - -def create_app(): - app = Flask(__name__) - - @app.route('/') - def create(): - import grid_generation; - return grid_generation.create_grid() - - return app diff --git a/grid-generation/Dockerfiles/app/app/grid_generation.py b/grid-generation/Dockerfiles/app/app/grid_generation.py deleted file mode 100755 index d7630d3..0000000 --- a/grid-generation/Dockerfiles/app/app/grid_generation.py +++ /dev/null @@ -1,34 +0,0 @@ -"""GRID GENERATION""" -import string -import random -import os -import cv2 - -# Get the images in the directory -# Donner en nb_img un carre parfait -def read_image(dirPath, nb_img, nb_col): - list = [] - list_id = [] - list_path = random.sample(os.listdir(dirPath), nb_img) - for path in list_path: - img = cv2.imread(dirPath + path) - list.append(img) - list_id.append(path) - return to_matrix(list, nb_col), list_id - -# Transform a list into a matrix with n columns -def to_matrix(l, n): - return [l[i:i+n] for i in iter(range(0, len(l), n))] - -# Gather all images -def concat_vh(list_2d): - # return final image - return cv2.vconcat([cv2.hconcat(list_h) for list_h in list_2d]) - -def create_grid(): - path_image = "/Datasets/img_align_celeba/img_align_celeba/" # Path to volume - filename = ''.join(random.choice(string.ascii_uppercase) for _ in range(1)).join(random.choice(string.ascii_uppercase) for _ in range(7)) - path_grid = '/Datasets/grids/'+ filename + ".png" - list_img, list_id = read_image(path_image, 25, 5) - img = cv2.imwrite(path_grid, concat_vh(list_img)) - return filename; diff --git a/grid-generation/Dockerfiles/app/entrypoint.sh b/grid-generation/Dockerfiles/app/entrypoint.sh deleted file mode 100755 index 383b267..0000000 --- a/grid-generation/Dockerfiles/app/entrypoint.sh +++ /dev/null @@ -1,13 +0,0 @@ -#!/bin/sh - -sleep 5 - -pip3 install -U Flask -pip3 install Flask-RESTful -pip3 install opencv-python -pip3 install opencv-contrib-python -pip3 install opencv-python-headless -pip3 install opencv-contrib-python-headless - -flask run --host=0.0.0.0 --port=50001 - diff --git a/grid-generation/Dockerfiles/app/requirements.txt b/grid-generation/Dockerfiles/app/requirements.txt deleted file mode 100755 index 32e8968..0000000 --- a/grid-generation/Dockerfiles/app/requirements.txt +++ /dev/null @@ -1 +0,0 @@ -Flask==1.1.1 diff --git a/grid-generation/Dockerfiles/docker_run.sh b/grid-generation/Dockerfiles/docker_run.sh deleted file mode 100755 index c7c3db4..0000000 --- a/grid-generation/Dockerfiles/docker_run.sh +++ /dev/null @@ -1,4 +0,0 @@ -#!/bin/sh - -docker build -t grid_image Dockerfiles/app -docker run -it --network="host" -v /home/user/Bureau/Datasets:/Datasets grid_image:latest diff --git a/grid-generation/command_line.sh b/grid-generation/command_line.sh deleted file mode 100755 index 8fd47fd..0000000 --- a/grid-generation/command_line.sh +++ /dev/null @@ -1,2 +0,0 @@ -Dockerfiles/docker_run.sh - diff --git a/main/Dockerfiles/app/App/app/.idea/.gitignore b/main/Dockerfiles/app/App/app/.idea/.gitignore deleted file mode 100644 index b58b603..0000000 --- a/main/Dockerfiles/app/App/app/.idea/.gitignore +++ /dev/null @@ -1,5 +0,0 @@ -# Default ignored files -/shelf/ -/workspace.xml -# Editor-based HTTP Client requests -/httpRequests/ diff --git a/main/Dockerfiles/app/App/app/.idea/app.iml b/main/Dockerfiles/app/App/app/.idea/app.iml deleted file mode 100644 index 0c8867d..0000000 --- a/main/Dockerfiles/app/App/app/.idea/app.iml +++ /dev/null @@ -1,12 +0,0 @@ - - - - - - - - - - - - \ No newline at end of file diff --git a/main/Dockerfiles/app/App/app/.idea/modules.xml b/main/Dockerfiles/app/App/app/.idea/modules.xml deleted file mode 100644 index 8c4259d..0000000 --- a/main/Dockerfiles/app/App/app/.idea/modules.xml +++ /dev/null @@ -1,8 +0,0 @@ - - - - - - - - \ No newline at end of file diff --git a/main/Dockerfiles/app/App/app/app.py b/main/Dockerfiles/app/App/app/app.py deleted file mode 100644 index d040504..0000000 --- a/main/Dockerfiles/app/App/app/app.py +++ /dev/null @@ -1,107 +0,0 @@ -# app/__init__.py - -from flask import Flask, render_template , request, redirect, url_for, abort -from flask_bootstrap import Bootstrap -import requests -import shutil -import os -def create_app(): - app = Flask(__name__) - Bootstrap(app) - - - NB_QUESTIONS = 8 # Number of questions (i.e attributes handle by yolo) - - - - @app.route('/', methods=['POST', 'GET']) - def homepage(): - if request.method == "POST": - # TO DO - # GENERATE GRID - r = requests.get('http://127.0.0.1:50001/') - filename = r.text - shutil.copy('/Datasets/grids/'+filename+'.png', './static/images/'+filename+'.png') - #r = requests.get('http://127.0.0.1:50011/create/TEST/') - #r = requests.get('http://127.0.0.1:50011/create/'+filename+'/') - return redirect(url_for('play',n=1,id=filename)) - return render_template('homepage.html') - - - - - - @app.route('/play///', methods=['POST', 'GET']) - def play(id="grille",n=1): - if (n==1): - # Call YOLO - r = requests.get('http://127.0.0.1:50015/create/'+id+'/') - r = requests.get('http://127.0.0.1:50011/create/'+id+'/') - if (n > NB_QUESTIONS or n < 1) : - abort(404) - - # TO DO - # Get question n from database - r = requests.get("http://127.0.0.1:50011/question/"+str(n)).json() - q = r["intitule"] - attribute = r["attribut"] - answers=r["response"].split(";") - if request.method == "POST": - response="" - if 'YES' in request.form: # ON CLICK YES - response = "0" - elif 'NO' in request.form: # ON CLICK NO - response = "1" - elif 'STOP' in request.form: # ON CLICK STOP - # TO DO - # DELETE IMG - # parameters : id - return redirect(url_for('homepage')) - else: - for a in answers: - if a.upper() in request.form : - attribute=a - response="0" - # TO DO - # UPDATE GRID - # parameters : attribute , response, id - r = requests.get("http://127.0.0.1:50009/update/"+id+"/"+attribute+"/"+response) - nb_characters = int(r.text) - os.remove('./static/images/'+id+'.png') - shutil.copy('/Datasets/grids/'+id+'.png', './static/images/'+id+'.png') - if n == NB_QUESTIONS : - # EVERY QUESTIONS HAVE BEEN DONE - # Prendre un perso au pif ? - return redirect(url_for('end',id=id)) - elif nb_characters == 1 : - # OR THE COMPUTER FOUND THE CHARACTER - return redirect(url_for('end',id=id)) - elif nb_characters == 0 : - return render_template('lost.html') - else: - return redirect(url_for('play',n=n+1, id=id)) - return render_template('play.html', numero = n, question = q, img = id,answers=answers) - - - - - - @app.route('/end/', methods=['POST', 'GET']) - def end(id = "grille") : - if request.method == "POST": - if 'YES' in request.form: - # TO DO - # GENERATE GRID - filename = "grille" - return redirect(url_for('play',n=1,id=filename)) - elif 'NO' in request.form: - # TO DO - # DELETE IMG - # parameters : id - return redirect(url_for('homepage')) - - return render_template('end.html',img=id) - - - - return app diff --git a/main/Dockerfiles/app/App/app/app.py.save b/main/Dockerfiles/app/App/app/app.py.save deleted file mode 100644 index 2e985e1..0000000 --- a/main/Dockerfiles/app/App/app/app.py.save +++ /dev/null @@ -1,81 +0,0 @@ -# app/__init__.py - -from flask import Flask, render_template , request, redirect, url_for, abort -from flask_bootstrap import Bootstrap - -def create_app(): - app = Flask(__name__) - Bootstrap(app) - - - NB_QUESTIONS = 5 # Number of questions (i.e attributes handle by yolo) - - - - @app.route('/', methods=['POST', 'GET']) - def homepage(): - if request.method == "POST": - # TO DO - # GENERATE GRID - r = requests.get('127.0.0.1:50001/')filename = r - return redirect(url_for('play',n=1,id=filename)) - return render_template('homepage.html') - - - - - - @app.route('/play///', methods=['POST', 'GET']) - def play(id="grille",n=1): - if (n > NB_QUESTIONS or n < 1) : - abort(404) - # TO DO - # Get question n from database - q = "Does your character wear a hat ?" - attribute = "Hat" - if request.method == "POST": - if 'YES' in request.form: # ON CLICK YES - response = 1 - elif 'NO' in request.form: # ON CLICK NO - response = 0 - elif 'STOP' in request.form: # ON CLICK STOP - # TO DO - # DELETE IMG - # parameters : id - return redirect(url_for('homepage')) - # TO DO - # UPDATE GRID - # parameters : attribute , response, id - nb_characters = 2 # TO DO - if n == NB_QUESTIONS : - # EVERY QUESTIONS HAVE BEEN DONE - # Prendre un perso au pif ? - return redirect(url_for('end',id=id)) - elif nb_characters == 1 : - # OR THE COMPUTER FOUND THE CHARACTER - return redirect(url_for('end',id=id)) - else: - return redirect(url_for('play',n=n+1, id=id)) - return render_template('play.html', numero = n, question = q, img = id) - - - - - - @app.route('/end/', methods=['POST', 'GET']) - def end(id = "grille") : - if request.method == "POST": - if 'YES' in request.form: - # TO DO - # GENERATE GRID - filename = "grille" - return redirect(url_for('play',n=1,id=filename)) - elif 'NO' in request.form: - # TO DO - # DELETE IMG - # parameters : id - return redirect(url_for('homepage')) - - return render_template('end.html',img=id) - - return app diff --git a/main/Dockerfiles/app/App/app/static/css/homepage.css b/main/Dockerfiles/app/App/app/static/css/homepage.css deleted file mode 100644 index b78a5d3..0000000 --- a/main/Dockerfiles/app/App/app/static/css/homepage.css +++ /dev/null @@ -1,165 +0,0 @@ -/* BOUTON */ -/* ----------------------------------------------------------------- */ -@import url('https://fonts.googleapis.com/css?family=Poppins:900i'); -* { - box-sizing: border-box; -} - -.wrapper { - display: flex; - justify-content: center; - margin-bottom : 50px; -} - -.cta { - display: flex; - padding: 10px 45px; - text-decoration: none; - font-family: 'Poppins', sans-serif; - font-size: 40px; - color: white; - background: #6225E6; - transition: 1s; - box-shadow: 6px 6px 0 black; - transform: skewX(-15deg); -} - -.cta:focus { - outline: none; -} - -.cta:hover { - transition: 0.5s; - box-shadow: 10px 10px 0 #FBC638; -} - -.cta span:nth-child(2) { - transition: 0.5s; - margin-right: 0px; -} - -.cta:hover span:nth-child(2) { - transition: 0.5s; - margin-right: 45px; -} - -span { - transform: skewX(15deg) -} - -span:nth-child(2) { - width: 20px; - margin-left: 30px; - position: relative; - top: 12%; -} - -/**************SVG****************/ - -path.one { - transition: 0.4s; - transform: translateX(-60%); -} - -path.two { - transition: 0.5s; - transform: translateX(-30%); -} - -.cta:hover path.three { - animation: color_anim 1s infinite 0.2s; -} - -.cta:hover path.one { - transform: translateX(0%); - animation: color_anim 1s infinite 0.6s; -} - -.cta:hover path.two { - transform: translateX(0%); - animation: color_anim 1s infinite 0.4s; -} - -/* SVG animations */ - -@keyframes color_anim { - 0% { - fill: white; - } - 50% { - fill: #FBC638; - } - 100% { - fill: white; - } -} -/* ----------------------------------------------------------------- */ -body,h1,h2,h3,h4,h5,h6 {font-family: "Lato", sans-serif;} -body, html { - height: 100%; - color: black; - line-height: 1.8; -} - -/* Create a Parallax Effect */ -.bgimg-1, .bgimg-2, .bgimg-3 { - background-attachment: fixed; - background-position: center; - background-repeat: no-repeat; - background-size: cover; -} - -/* First image (Logo. Full height) */ -.bgimg-1 { - background-image: url("/static/images/paparazzi.png"); - min-height: 100%; -} - -.w3-wide {letter-spacing: 10px;} -.w3-hover-opacity {cursor: pointer;} - -/* Turn off parallax scrolling for tablets and phones */ -@media only screen and (max-device-width: 1600px) { - .bgimg-1, .bgimg-2, .bgimg-3 { - background-attachment: scroll; - min-height: 400px; - } -} - -body,h1,h2,h3,h4,h5,h6 {font-family: "Lato", sans-serif;} -body, html { - height: 100%; - color: black; - line-height: 1.8; -} - -/* Create a Parallax Effect */ -.bgimg-1, .bgimg-2, .bgimg-3 { - background-attachment: fixed; - background-position: center; - background-repeat: no-repeat; - background-size: cover; -} - -/* First image (Logo. Full height) */ -.bgimg-1 { - background-image: url("/static/images/paparazzi.png"); - min-height: 100%; - background-size:300px; -} - -.w3-wide {letter-spacing: 10px;} -.w3-hover-opacity {cursor: pointer;} - -/* Turn off parallax scrolling for tablets and phones */ -@media only screen and (max-device-width: 1600px) { - .bgimg-1, .bgimg-2, .bgimg-3 { - background-attachment: scroll; - min-height: 400px; - margin-top : 50px; - } -} - -.coloriage{ - background-color: khaki; -} diff --git a/main/Dockerfiles/app/App/app/static/css/play.css b/main/Dockerfiles/app/App/app/static/css/play.css deleted file mode 100644 index 4368149..0000000 --- a/main/Dockerfiles/app/App/app/static/css/play.css +++ /dev/null @@ -1,66 +0,0 @@ -@import url('https://fonts.googleapis.com/css?family=Patrick+Hand+SC'); -*{ - box-sizing:border-box; -} - -body{ - font-family: 'Patrick Hand SC', cursive; - text-align:center; -} - -.title{ - text-align:center; - color:#41403E; - font-size:5rem; - font-family: 'Patrick Hand SC', cursive; - border-radius: 255px 15px 225px 15px/15px 225px 15px 255px; - box-shadow:2px 8px 4px -6px hsla(0,0%,0%,.3); - border:solid 2px #41403E; - margin: 20px !important; - background-color: khaki; - } - -.grid{ - height: 400px; - padding-left: 20px; - margin-bottom: 50px; - margin-top:50px; - max-width: 100%; - max-height: 100%; -} - -.question{ - font-family: 'Patrick Hand SC', cursive; - text-align:center; -} - -.yes_no{ - border:dashed 2px #41403E; - align-self:center; - background:transparent; - padding:1rem 1rem; - margin:0 1rem; - transition:all .5s ease; - color:#41403E; - font-size:2rem; - letter-spacing:1px; - outline:none; - box-shadow: 20px 38px 34px -26px hsla(0,0%,0%,.2); - border-radius: 255px 15px 225px 15px/15px 225px 15px 255px; -} - -.buttons{ - display: flex; - justify-content: center; - align-items: center; -} - -.paparazzi{ - height: 200px; - margin-bottom: 20px; -} -.center{ - display: block; - margin-left: auto; - margin-right: auto; -} diff --git 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/dev/null @@ -1,77 +0,0 @@ - -{% extends 'base.html' %} -{% block content %} - - - -
Guess Who
- -
-
-

End ...

-

Is this your character ?

-
- Guess who -
-
- - - -
-
-
-
- grid -
-
- - - - - - - - - - -{% endblock %} diff --git a/main/Dockerfiles/app/App/app/templates/homepage.html b/main/Dockerfiles/app/App/app/templates/homepage.html deleted file mode 100644 index bb6d3b9..0000000 --- a/main/Dockerfiles/app/App/app/templates/homepage.html +++ /dev/null @@ -1,125 +0,0 @@ - -{% extends 'base.html' %} -{% block content %} - - - - - - - -
- - - - -
- - -
-
-
-
- -
-
- -
-
- - -
-
-

ABOUT US

-

Team Guess Who

-

- Lorem Ipsum is simply dummy text of the printing and typesetting industry. Lorem Ipsum has been the industry's - standard dummy text ever since the 1500s, when an unknown printer took a galley of type and scrambled it to make - a type specimen book. It has survived not only five centuries, but also the leap into electronic typesetting, - remaining essentially unchanged. It was popularised in the 1960s with the release of Letraset sheets containing - Lorem Ipsum passages, and more recently with desktop publishing software like Aldus PageMaker including versions - of Lorem Ipsum -

-
-
- -
-
- 270 000
- Images -
-
- 6
- Classmates -
-
- 3
- Tutors -
-
- 15
- Meetings -
-
- - - - - - - - - -{% endblock %} diff --git a/main/Dockerfiles/app/App/app/templates/lost.html b/main/Dockerfiles/app/App/app/templates/lost.html deleted file mode 100644 index 95e5f34..0000000 --- a/main/Dockerfiles/app/App/app/templates/lost.html +++ /dev/null @@ -1,37 +0,0 @@ - -{% extends 'base.html' %} - -{% block content %} - - - - - - - - -{% endblock %} diff --git a/main/Dockerfiles/app/App/app/templates/play.html b/main/Dockerfiles/app/App/app/templates/play.html deleted file mode 100644 index a57151a..0000000 --- a/main/Dockerfiles/app/App/app/templates/play.html +++ /dev/null @@ -1,37 +0,0 @@ - -{% extends 'base.html' %} -{% block content %}
Guess Who
- - -
-
- grid -
-
-

Question {{ numero }} :

-

{{ question }}

-
- Guess who -
-
- {% for item in answers %} - - {% endfor %} -
-
-
-
-
-
-

Bored and you want to stop ?

-
- -
-
-
- - - -{% endblock %} diff --git a/main/Dockerfiles/app/Dockerfile b/main/Dockerfiles/app/Dockerfile deleted file mode 100755 index 268f6f6..0000000 --- a/main/Dockerfiles/app/Dockerfile +++ /dev/null @@ -1,21 +0,0 @@ -FROM python:3.7 - -RUN mkdir /code -WORKDIR /code - -ADD entrypoint.sh /code/entrypoint.sh -ADD requirements.txt /code/requirements.txt -ADD App/app/ /code - -RUN ls -#RUN pip install -r requirements.txt --no-cache-dir -#RUN apt-get install apt-transport-https -RUN apt update -y -RUN apt install python3-pip -y - -#RUN pip3 install --default-timeout=100 -r requirements.txt - -EXPOSE 50000 -ENV FLASK_APP "/code/app.py" -CMD ["/code/entrypoint.sh"] - diff --git a/main/Dockerfiles/app/entrypoint.sh b/main/Dockerfiles/app/entrypoint.sh deleted file mode 100755 index b2f0f22..0000000 --- a/main/Dockerfiles/app/entrypoint.sh +++ /dev/null @@ -1,16 +0,0 @@ -#!/bin/sh - -sleep 5 - -pip3 install -U Flask -pip3 install -U Flask-Bootstrap -pip3 install Flask-RESTful -pip3 install opencv-python -pip3 install opencv-contrib-python -pip3 install opencv-python-headless -pip3 install opencv-contrib-python-headless -pip3 install requests -pip3 install shutil - -flask run --host=0.0.0.0 --port=50000 - diff --git a/main/Dockerfiles/app/requirements.txt b/main/Dockerfiles/app/requirements.txt deleted file mode 100644 index 32e8968..0000000 --- a/main/Dockerfiles/app/requirements.txt +++ /dev/null @@ -1 +0,0 @@ -Flask==1.1.1 diff --git a/main/Dockerfiles/docker_run.sh b/main/Dockerfiles/docker_run.sh deleted file mode 100755 index 66f4399..0000000 --- a/main/Dockerfiles/docker_run.sh +++ /dev/null @@ -1,4 +0,0 @@ -#!/bin/sh - -docker build -t main_image Dockerfiles/app -docker run -it --network="host" -v /home/user/Bureau/Datasets:/Datasets main_image:latest diff --git a/main/command_line.sh b/main/command_line.sh deleted file mode 100755 index 8fd47fd..0000000 --- a/main/command_line.sh +++ /dev/null @@ -1,2 +0,0 @@ -Dockerfiles/docker_run.sh - diff --git a/update/Dockerfiles/app/Dockerfile b/update/Dockerfiles/app/Dockerfile deleted file mode 100755 index 1140cc5..0000000 --- a/update/Dockerfiles/app/Dockerfile +++ /dev/null @@ -1,21 +0,0 @@ -FROM python:3.7 - -RUN mkdir /code -WORKDIR /code - -ADD entrypoint.sh /code/entrypoint.sh -ADD requirements.txt /code/requirements.txt -ADD app/ /code - -RUN ls -#RUN pip install -r requirements.txt --no-cache-dir -#RUN apt-get install apt-transport-https -RUN apt update -y -RUN apt install python3-pip -y - -#RUN pip3 install --default-timeout=100 -r requirements.txt - -EXPOSE 50002 -ENV FLASK_APP "/code/app.py" -CMD ["/code/entrypoint.sh"] - diff --git a/update/Dockerfiles/app/app/app.py b/update/Dockerfiles/app/app/app.py deleted file mode 100644 index 7acb976..0000000 --- a/update/Dockerfiles/app/app/app.py +++ /dev/null @@ -1,25 +0,0 @@ -# app/__init__.py - -from flask import Flask -import requests -import json -import sys -def create_app(): - app = Flask(__name__) - - @app.route('/update////') - def update(id,attr,value): - import update_grid; - path = "/Datasets/grids/"# PATH TO VOLUME - r = requests.get("http://127.0.0.1:50011/update/"+id+"/"+attr+"/"+str(value)+"/") - print("Message",file=sys.stdout) - print(r,file=sys.stdout) - dict = r.json() - # TO DO - # GET COORDINATES x and y - nb_img = dict["Nb"] - for x,y in dict["img"]: - update_grid.hide_image(path+id+".png", path+id+".png", float(x),float(y)) - return str(nb_img) - - return app diff --git a/update/Dockerfiles/app/app/update_grid.py b/update/Dockerfiles/app/app/update_grid.py deleted file mode 100644 index a0d2b38..0000000 --- a/update/Dockerfiles/app/app/update_grid.py +++ /dev/null @@ -1,16 +0,0 @@ -""" UPDATE GRID """ -import cv2 -import os - -# Path image : chemin vers la grille d'images de célébrités -# Output image : chemin vers la grille avec l'image cachée -# x,y : coordonées (x,y) de la sortie yolo (qui correspond au centre de l'image) - -def hide_image(path_img, output_img, x, y): - img = cv2.imread(path_img) - x1 = int((x-0.1)*890) - y1 = int((y-0.1)*1090) - x2 = int((x+0.1)*890) - y2 = int((y+0.1)*1090) - cv2.rectangle(img, ( x1 , y1), ( x2, y2), (255, 255, 255), -1) - cv2.imwrite(output_img, img) diff --git a/update/Dockerfiles/app/entrypoint.sh b/update/Dockerfiles/app/entrypoint.sh deleted file mode 100755 index abefdcb..0000000 --- a/update/Dockerfiles/app/entrypoint.sh +++ /dev/null @@ -1,13 +0,0 @@ -#!/bin/sh - -sleep 5 - -pip3 install -U Flask -pip3 install Flask-RESTful -pip3 install opencv-python -pip3 install opencv-contrib-python -pip3 install opencv-python-headless -pip3 install opencv-contrib-python-headless - -flask run --host=0.0.0.0 --port=50002 - diff --git a/update/Dockerfiles/app/requirements.txt b/update/Dockerfiles/app/requirements.txt deleted file mode 100644 index 32e8968..0000000 --- a/update/Dockerfiles/app/requirements.txt +++ /dev/null @@ -1 +0,0 @@ -Flask==1.1.1 diff --git a/update/Dockerfiles/docker_run.sh b/update/Dockerfiles/docker_run.sh deleted file mode 100755 index a0dad08..0000000 --- a/update/Dockerfiles/docker_run.sh +++ /dev/null @@ -1,4 +0,0 @@ -#!/bin/sh - -docker build -t image_test Dockerfiles/app -docker run -it --network="host" -v /home/user/Bureau/Datasets:/Datasets image_test:latest diff --git a/update/command_line.sh b/update/command_line.sh deleted file mode 100755 index 8fd47fd..0000000 --- a/update/command_line.sh +++ /dev/null @@ -1,2 +0,0 @@ -Dockerfiles/docker_run.sh -