├── models ├── __init__.py ├── yolov3-tiny.yaml ├── yolov3-tiny-SPPD.yaml ├── yolov3-tiny-spp.yaml ├── yolov3.yaml ├── yolov3-spp.yaml ├── LF-YOLO-0.5.yaml ├── LF-YOLO-0.75.yaml ├── LF-YOLO.yaml ├── LF-YOLO-1.25.yaml ├── export.py ├── experimental.py ├── yolo.py └── common.py ├── utils ├── __init__.py ├── aws │ ├── __init__.py │ ├── mime.sh │ ├── resume.py │ └── userdata.sh ├── wandb_logging │ ├── __init__.py │ ├── log_dataset.py │ └── wandb_utils.py ├── google_app_engine │ ├── additional_requirements.txt │ ├── app.yaml │ └── Dockerfile ├── activations.py ├── google_utils.py ├── autoanchor.py ├── metrics.py ├── loss.py ├── torch_utils.py └── plots.py ├── .idea ├── .gitignore ├── vcs.xml ├── inspectionProfiles │ └── profiles_settings.xml ├── modules.xml ├── misc.xml └── LF-YOLO.iml ├── data ├── images │ ├── bus.jpg │ └── zidane.jpg ├── voc.yaml ├── argoverse_hd.yaml ├── hyp.finetune.yaml ├── scripts │ ├── get_coco.sh │ ├── get_argoverse_hd.sh │ └── get_voc.sh ├── coco128.yaml ├── coco.yaml └── hyp.scratch.yaml ├── .gitattributes ├── weights └── download_weights.sh ├── requirements.txt ├── Dockerfile ├── .dockerignore ├── hubconf.py ├── README.md ├── detect.py └── test.py /models/__init__.py: -------------------------------------------------------------------------------- 1 | -------------------------------------------------------------------------------- /utils/__init__.py: -------------------------------------------------------------------------------- 1 | -------------------------------------------------------------------------------- /utils/aws/__init__.py: -------------------------------------------------------------------------------- 1 | -------------------------------------------------------------------------------- /utils/wandb_logging/__init__.py: -------------------------------------------------------------------------------- 1 | -------------------------------------------------------------------------------- /.idea/.gitignore: -------------------------------------------------------------------------------- 1 | # Default ignored files 2 | /shelf/ 3 | /workspace.xml 4 | -------------------------------------------------------------------------------- /data/images/bus.jpg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/lmomoy/LF-YOLO/HEAD/data/images/bus.jpg -------------------------------------------------------------------------------- /.gitattributes: -------------------------------------------------------------------------------- 1 | # this drop notebooks from GitHub language stats 2 | *.ipynb linguist-vendored 3 | -------------------------------------------------------------------------------- /data/images/zidane.jpg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/lmomoy/LF-YOLO/HEAD/data/images/zidane.jpg -------------------------------------------------------------------------------- /utils/google_app_engine/additional_requirements.txt: -------------------------------------------------------------------------------- 1 | # add these requirements in your app on top of the existing ones 2 | pip==18.1 3 | Flask==1.0.2 4 | gunicorn==19.9.0 5 | -------------------------------------------------------------------------------- /.idea/vcs.xml: -------------------------------------------------------------------------------- 1 | 2 | 3 | 4 | 5 | 6 | -------------------------------------------------------------------------------- /.idea/inspectionProfiles/profiles_settings.xml: -------------------------------------------------------------------------------- 1 | 2 | 3 | 6 | -------------------------------------------------------------------------------- /utils/google_app_engine/app.yaml: -------------------------------------------------------------------------------- 1 | runtime: custom 2 | env: flex 3 | 4 | service: yolov3app 5 | 6 | liveness_check: 7 | initial_delay_sec: 600 8 | 9 | manual_scaling: 10 | instances: 1 11 | resources: 12 | cpu: 1 13 | memory_gb: 4 14 | disk_size_gb: 20 -------------------------------------------------------------------------------- /.idea/modules.xml: -------------------------------------------------------------------------------- 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | -------------------------------------------------------------------------------- /.idea/misc.xml: -------------------------------------------------------------------------------- 1 | 2 | 3 | 4 | 5 | 7 | -------------------------------------------------------------------------------- /.idea/LF-YOLO.iml: -------------------------------------------------------------------------------- 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | -------------------------------------------------------------------------------- /weights/download_weights.sh: -------------------------------------------------------------------------------- 1 | #!/bin/bash 2 | # Download latest models from https://github.com/ultralytics/yolov3/releases 3 | # Usage: 4 | # $ bash weights/download_weights.sh 5 | 6 | python - <=3.2.2 5 | numpy>=1.18.5 6 | opencv-python>=4.1.2 7 | Pillow 8 | PyYAML>=5.3.1 9 | scipy>=1.4.1 10 | torch>=1.7.0 11 | torchvision>=0.8.1 12 | tqdm>=4.41.0 13 | 14 | # logging ------------------------------------- 15 | tensorboard>=2.4.1 16 | # wandb 17 | 18 | # plotting ------------------------------------ 19 | seaborn>=0.11.0 20 | pandas 21 | 22 | # export -------------------------------------- 23 | # coremltools>=4.1 24 | # onnx>=1.8.1 25 | # scikit-learn==0.19.2 # for coreml quantization 26 | 27 | # extras -------------------------------------- 28 | thop # FLOPS computation 29 | pycocotools>=2.0 # COCO mAP 30 | -------------------------------------------------------------------------------- /data/voc.yaml: -------------------------------------------------------------------------------- 1 | # PASCAL VOC dataset http://host.robots.ox.ac.uk/pascal/VOC/ 2 | # Train command: python train.py --data voc.yaml 3 | # Default dataset location is next to /yolov3: 4 | # /parent_folder 5 | # /VOC 6 | # /yolov3 7 | 8 | 9 | # download command/URL (optional) 10 | download: bash data/scripts/get_voc.sh 11 | 12 | # train and val data as 1) directory: path/images/, 2) file: path/images.txt, or 3) list: [path1/images/, path2/images/] 13 | train: ../VOC/images/train/ # 16551 images 14 | val: ../VOC/images/val/ # 4952 images 15 | 16 | # number of classes 17 | nc: 20 18 | 19 | # class names 20 | names: [ 'aeroplane', 'bicycle', 'bird', 'boat', 'bottle', 'bus', 'car', 'cat', 'chair', 'cow', 'diningtable', 'dog', 21 | 'horse', 'motorbike', 'person', 'pottedplant', 'sheep', 'sofa', 'train', 'tvmonitor' ] 22 | -------------------------------------------------------------------------------- /utils/aws/mime.sh: -------------------------------------------------------------------------------- 1 | # AWS EC2 instance startup 'MIME' script https://aws.amazon.com/premiumsupport/knowledge-center/execute-user-data-ec2/ 2 | # This script will run on every instance restart, not only on first start 3 | # --- DO NOT COPY ABOVE COMMENTS WHEN PASTING INTO USERDATA --- 4 | 5 | Content-Type: multipart/mixed; boundary="//" 6 | MIME-Version: 1.0 7 | 8 | --// 9 | Content-Type: text/cloud-config; charset="us-ascii" 10 | MIME-Version: 1.0 11 | Content-Transfer-Encoding: 7bit 12 | Content-Disposition: attachment; filename="cloud-config.txt" 13 | 14 | #cloud-config 15 | cloud_final_modules: 16 | - [scripts-user, always] 17 | 18 | --// 19 | Content-Type: text/x-shellscript; charset="us-ascii" 20 | MIME-Version: 1.0 21 | Content-Transfer-Encoding: 7bit 22 | Content-Disposition: attachment; filename="userdata.txt" 23 | 24 | #!/bin/bash 25 | # --- paste contents of userdata.sh here --- 26 | --// 27 | -------------------------------------------------------------------------------- /data/argoverse_hd.yaml: -------------------------------------------------------------------------------- 1 | # Argoverse-HD dataset (ring-front-center camera) http://www.cs.cmu.edu/~mengtial/proj/streaming/ 2 | # Train command: python train.py --data argoverse_hd.yaml 3 | # Default dataset location is next to /yolov5: 4 | # /parent_folder 5 | # /argoverse 6 | # /yolov5 7 | 8 | 9 | # download command/URL (optional) 10 | download: bash data/scripts/get_argoverse_hd.sh 11 | 12 | # train and val data as 1) directory: path/images/, 2) file: path/images.txt, or 3) list: [path1/images/, path2/images/] 13 | train: ../argoverse/Argoverse-1.1/images/train/ # 39384 images 14 | val: ../argoverse/Argoverse-1.1/images/val/ # 15062 iamges 15 | test: ../argoverse/Argoverse-1.1/images/test/ # Submit to: https://eval.ai/web/challenges/challenge-page/800/overview 16 | 17 | # number of classes 18 | nc: 8 19 | 20 | # class names 21 | names: [ 'person', 'bicycle', 'car', 'motorcycle', 'bus', 'truck', 'traffic_light', 'stop_sign' ] 22 | -------------------------------------------------------------------------------- /utils/wandb_logging/log_dataset.py: -------------------------------------------------------------------------------- 1 | import argparse 2 | 3 | import yaml 4 | 5 | from wandb_utils import WandbLogger 6 | 7 | WANDB_ARTIFACT_PREFIX = 'wandb-artifact://' 8 | 9 | 10 | def create_dataset_artifact(opt): 11 | with open(opt.data) as f: 12 | data = yaml.load(f, Loader=yaml.SafeLoader) # data dict 13 | logger = WandbLogger(opt, '', None, data, job_type='Dataset Creation') 14 | 15 | 16 | if __name__ == '__main__': 17 | parser = argparse.ArgumentParser() 18 | parser.add_argument('--data', type=str, default='data/coco128.yaml', help='data.yaml path') 19 | parser.add_argument('--single-cls', action='store_true', help='train as single-class dataset') 20 | parser.add_argument('--project', type=str, default='YOLOv5', help='name of W&B Project') 21 | opt = parser.parse_args() 22 | opt.resume = False # Explicitly disallow resume check for dataset upload job 23 | 24 | create_dataset_artifact(opt) 25 | -------------------------------------------------------------------------------- /utils/google_app_engine/Dockerfile: -------------------------------------------------------------------------------- 1 | FROM gcr.io/google-appengine/python 2 | 3 | # Create a virtualenv for dependencies. This isolates these packages from 4 | # system-level packages. 5 | # Use -p python3 or -p python3.7 to select python version. Default is version 2. 6 | RUN virtualenv /env -p python3 7 | 8 | # Setting these environment variables are the same as running 9 | # source /env/bin/activate. 10 | ENV VIRTUAL_ENV /env 11 | ENV PATH /env/bin:$PATH 12 | 13 | RUN apt-get update && apt-get install -y python-opencv 14 | 15 | # Copy the application's requirements.txt and run pip to install all 16 | # dependencies into the virtualenv. 17 | ADD requirements.txt /app/requirements.txt 18 | RUN pip install -r /app/requirements.txt 19 | 20 | # Add the application source code. 21 | ADD . /app 22 | 23 | # Run a WSGI server to serve the application. gunicorn must be declared as 24 | # a dependency in requirements.txt. 25 | CMD gunicorn -b :$PORT main:app 26 | -------------------------------------------------------------------------------- /data/hyp.finetune.yaml: -------------------------------------------------------------------------------- 1 | # Hyperparameters for VOC finetuning 2 | # python train.py --batch 64 --weights yolov5m.pt --data voc.yaml --img 512 --epochs 50 3 | # See tutorials for hyperparameter evolution https://github.com/ultralytics/yolov5#tutorials 4 | 5 | 6 | # Hyperparameter Evolution Results 7 | # Generations: 306 8 | # P R mAP.5 mAP.5:.95 box obj cls 9 | # Metrics: 0.6 0.936 0.896 0.684 0.0115 0.00805 0.00146 10 | 11 | lr0: 0.0032 12 | lrf: 0.12 13 | momentum: 0.843 14 | weight_decay: 0.00036 15 | warmup_epochs: 2.0 16 | warmup_momentum: 0.5 17 | warmup_bias_lr: 0.05 18 | box: 0.0296 19 | cls: 0.243 20 | cls_pw: 0.631 21 | obj: 0.301 22 | obj_pw: 0.911 23 | iou_t: 0.2 24 | anchor_t: 2.91 25 | # anchors: 3.63 26 | fl_gamma: 0.0 27 | hsv_h: 0.0138 28 | hsv_s: 0.664 29 | hsv_v: 0.464 30 | degrees: 0.373 31 | translate: 0.245 32 | scale: 0.898 33 | shear: 0.602 34 | perspective: 0.0 35 | flipud: 0.00856 36 | fliplr: 0.5 37 | mosaic: 1.0 38 | mixup: 0.243 39 | -------------------------------------------------------------------------------- /data/scripts/get_coco.sh: -------------------------------------------------------------------------------- 1 | #!/bin/bash 2 | # COCO 2017 dataset http://cocodataset.org 3 | # Download command: bash data/scripts/get_coco.sh 4 | # Train command: python train.py --data coco.yaml 5 | # Default dataset location is next to /yolov3: 6 | # /parent_folder 7 | # /coco 8 | # /yolov3 9 | 10 | # Download/unzip labels 11 | d='../' # unzip directory 12 | url=https://github.com/ultralytics/yolov5/releases/download/v1.0/ 13 | f='coco2017labels.zip' # or 'coco2017labels-segments.zip', 68 MB 14 | echo 'Downloading' $url$f ' ...' 15 | curl -L $url$f -o $f && unzip -q $f -d $d && rm $f & # download, unzip, remove in background 16 | 17 | # Download/unzip images 18 | d='../coco/images' # unzip directory 19 | url=http://images.cocodataset.org/zips/ 20 | f1='train2017.zip' # 19G, 118k images 21 | f2='val2017.zip' # 1G, 5k images 22 | f3='test2017.zip' # 7G, 41k images (optional) 23 | for f in $f1 $f2; do 24 | echo 'Downloading' $url$f '...' 25 | curl -L $url$f -o $f && unzip -q $f -d $d && rm $f & # download, unzip, remove in background 26 | done 27 | wait # finish background tasks 28 | -------------------------------------------------------------------------------- /utils/aws/resume.py: -------------------------------------------------------------------------------- 1 | # Resume all interrupted trainings in yolov5/ dir including DDP trainings 2 | # Usage: $ python utils/aws/resume.py 3 | 4 | import os 5 | import sys 6 | from pathlib import Path 7 | 8 | import torch 9 | import yaml 10 | 11 | sys.path.append('./') # to run '$ python *.py' files in subdirectories 12 | 13 | port = 0 # --master_port 14 | path = Path('').resolve() 15 | for last in path.rglob('*/**/last.pt'): 16 | ckpt = torch.load(last) 17 | if ckpt['optimizer'] is None: 18 | continue 19 | 20 | # Load opt.yaml 21 | with open(last.parent.parent / 'opt.yaml') as f: 22 | opt = yaml.load(f, Loader=yaml.SafeLoader) 23 | 24 | # Get device count 25 | d = opt['device'].split(',') # devices 26 | nd = len(d) # number of devices 27 | ddp = nd > 1 or (nd == 0 and torch.cuda.device_count() > 1) # distributed data parallel 28 | 29 | if ddp: # multi-GPU 30 | port += 1 31 | cmd = f'python -m torch.distributed.launch --nproc_per_node {nd} --master_port {port} train.py --resume {last}' 32 | else: # single-GPU 33 | cmd = f'python train.py --resume {last}' 34 | 35 | cmd += ' > /dev/null 2>&1 &' # redirect output to dev/null and run in daemon thread 36 | print(cmd) 37 | os.system(cmd) 38 | -------------------------------------------------------------------------------- /utils/aws/userdata.sh: -------------------------------------------------------------------------------- 1 | #!/bin/bash 2 | # AWS EC2 instance startup script https://docs.aws.amazon.com/AWSEC2/latest/UserGuide/user-data.html 3 | # This script will run only once on first instance start (for a re-start script see mime.sh) 4 | # /home/ubuntu (ubuntu) or /home/ec2-user (amazon-linux) is working dir 5 | # Use >300 GB SSD 6 | 7 | cd home/ubuntu 8 | if [ ! -d yolov5 ]; then 9 | echo "Running first-time script." # install dependencies, download COCO, pull Docker 10 | git clone https://github.com/ultralytics/yolov5 && sudo chmod -R 777 yolov5 11 | cd yolov5 12 | bash data/scripts/get_coco.sh && echo "Data done." & 13 | sudo docker pull ultralytics/yolov5:latest && echo "Docker done." & 14 | python -m pip install --upgrade pip && pip install -r requirements.txt && python detect.py && echo "Requirements done." & 15 | wait && echo "All tasks done." # finish background tasks 16 | else 17 | echo "Running re-start script." # resume interrupted runs 18 | i=0 19 | list=$(sudo docker ps -qa) # container list i.e. $'one\ntwo\nthree\nfour' 20 | while IFS= read -r id; do 21 | ((i++)) 22 | echo "restarting container $i: $id" 23 | sudo docker start $id 24 | # sudo docker exec -it $id python train.py --resume # single-GPU 25 | sudo docker exec -d $id python utils/aws/resume.py # multi-scenario 26 | done <<<"$list" 27 | fi 28 | -------------------------------------------------------------------------------- /models/yolov3-tiny.yaml: -------------------------------------------------------------------------------- 1 | # parameters 2 | nc: 80 # number of classes 3 | depth_multiple: 1.0 # model depth multiple 4 | width_multiple: 1.0 # layer channel multiple 5 | 6 | # anchors 7 | anchors: 8 | - [10,14, 23,27, 37,58] # P4/16 9 | - [81,82, 135,169, 344,319] # P5/32 10 | 11 | # YOLOv3-tiny backbone 12 | backbone: 13 | # [from, number, module, args] 14 | [[-1, 1, Conv, [16, 3, 1]], # 0 15 | [-1, 1, nn.MaxPool2d, [2, 2, 0]], # 1-P1/2 16 | [-1, 1, Conv, [32, 3, 1]], 17 | [-1, 1, nn.MaxPool2d, [2, 2, 0]], # 3-P2/4 18 | [-1, 1, Conv, [64, 3, 1]], 19 | [-1, 1, nn.MaxPool2d, [2, 2, 0]], # 5-P3/8 20 | [-1, 1, Conv, [128, 3, 1]], 21 | [-1, 1, nn.MaxPool2d, [2, 2, 0]], # 7-P4/16 22 | [-1, 1, Conv, [256, 3, 1]], 23 | [-1, 1, nn.MaxPool2d, [2, 2, 0]], # 9-P5/32 24 | [-1, 1, Conv, [512, 3, 1]], 25 | [-1, 1, nn.ZeroPad2d, [[0, 1, 0, 1]]], # 11 26 | [-1, 1, nn.MaxPool2d, [2, 1, 0]], # 12 27 | ] 28 | 29 | # YOLOv3-tiny head 30 | head: 31 | [[-1, 1, Conv, [1024, 3, 1]], 32 | [-1, 1, Conv, [256, 1, 1]], 33 | [-1, 1, Conv, [512, 3, 1]], # 15 (P5/32-large) 34 | 35 | [-2, 1, Conv, [128, 1, 1]], 36 | [-1, 1, nn.Upsample, [None, 2, 'nearest']], 37 | [[-1, 8], 1, Concat, [1]], # cat backbone P4 38 | [-1, 1, Conv, [256, 3, 1]], # 19 (P4/16-medium) 39 | 40 | [[19, 15], 1, Detect, [nc, anchors]], # Detect(P4, P5) 41 | ] 42 | -------------------------------------------------------------------------------- /models/yolov3-tiny-SPPD.yaml: -------------------------------------------------------------------------------- 1 | # parameters 2 | nc: 80 # number of classes 3 | depth_multiple: 1.0 # model depth multiple 4 | width_multiple: 1.0 # layer channel multiple 5 | 6 | # anchors 7 | anchors: 8 | - [10,14, 23,27, 37,58] # P4/16 9 | - [81,82, 135,169, 344,319] # P5/32 10 | 11 | # YOLOv3-tiny backbone 12 | backbone: 13 | # [from, number, module, args] 14 | [[-1, 1, Conv, [16, 3, 1]], # 0 15 | [-1, 1, nn.MaxPool2d, [2, 2, 0]], # 1-P1/2 16 | [-1, 1, Conv, [32, 3, 1]], 17 | [-1, 1, nn.MaxPool2d, [2, 2, 0]], # 3-P2/4 18 | [-1, 1, Conv, [64, 3, 1]], 19 | [-1, 1, nn.MaxPool2d, [2, 2, 0]], # 5-P3/8 20 | [-1, 1, Conv, [128, 3, 1]], 21 | [-1, 1, nn.MaxPool2d, [2, 2, 0]], # 7-P4/16 22 | [-1, 1, Conv, [256, 3, 1]], 23 | [-1, 1, nn.MaxPool2d, [2, 2, 0]], # 9-P5/32 24 | [-1, 1, Conv, [512, 3, 1]], 25 | [-1, 1, nn.ZeroPad2d, [[0, 1, 0, 1]]], # 11 26 | [-1, 1, nn.MaxPool2d, [2, 1, 0]], # 12 27 | ] 28 | 29 | # YOLOv3-tiny head 30 | head: 31 | [[-1, 1, Conv, [1024, 3, 1]], 32 | [-1, 1, SPPDilated, [6144]], 33 | [-1, 1, Conv, [256, 1, 1]], 34 | [-1, 1, Conv, [512, 3, 1]], # 15 (P5/32-large) 35 | 36 | [-2, 1, Conv, [128, 1, 1]], 37 | [-1, 1, nn.Upsample, [None, 2, 'nearest']], 38 | [[-1, 8], 1, Concat, [1]], # cat backbone P4 39 | [-1, 1, Conv, [256, 3, 1]], # 19 (P4/16-medium) 40 | 41 | [[19, 15], 1, Detect, [nc, anchors]], # Detect(P4, P5) 42 | ] 43 | -------------------------------------------------------------------------------- /models/yolov3-tiny-spp.yaml: -------------------------------------------------------------------------------- 1 | # parameters 2 | nc: 80 # number of classes 3 | depth_multiple: 1.0 # model depth multiple 4 | width_multiple: 1.0 # layer channel multiple 5 | 6 | # anchors 7 | anchors: 8 | - [10,14, 23,27, 37,58] # P4/16 9 | - [81,82, 135,169, 344,319] # P5/32 10 | 11 | # YOLOv3-tiny backbone 12 | backbone: 13 | # [from, number, module, args] 14 | [[-1, 1, Conv, [16, 3, 1]], # 0 15 | [-1, 1, nn.MaxPool2d, [2, 2, 0]], # 1-P1/2 16 | [-1, 1, Conv, [32, 3, 1]], 17 | [-1, 1, nn.MaxPool2d, [2, 2, 0]], # 3-P2/4 18 | [-1, 1, Conv, [64, 3, 1]], 19 | [-1, 1, nn.MaxPool2d, [2, 2, 0]], # 5-P3/8 20 | [-1, 1, Conv, [128, 3, 1]], 21 | [-1, 1, nn.MaxPool2d, [2, 2, 0]], # 7-P4/16 22 | [-1, 1, Conv, [256, 3, 1]], 23 | [-1, 1, nn.MaxPool2d, [2, 2, 0]], # 9-P5/32 24 | [-1, 1, Conv, [512, 3, 1]], 25 | [-1, 1, nn.ZeroPad2d, [[0, 1, 0, 1]]], # 11 26 | [-1, 1, nn.MaxPool2d, [2, 1, 0]], # 12 27 | ] 28 | 29 | # YOLOv3-tiny head 30 | head: 31 | [[-1, 1, Conv, [1024, 3, 1]], 32 | [-1, 1, SPP, [512, [5, 9, 13]]], 33 | [-1, 1, Conv, [256, 1, 1]], 34 | [-1, 1, Conv, [512, 3, 1]], # 15 (P5/32-large) 35 | 36 | [-2, 1, Conv, [128, 1, 1]], 37 | [-1, 1, nn.Upsample, [None, 2, 'nearest']], 38 | [[-1, 8], 1, Concat, [1]], # cat backbone P4 39 | [-1, 1, Conv, [256, 3, 1]], # 19 (P4/16-medium) 40 | 41 | [[19, 15], 1, Detect, [nc, anchors]], # Detect(P4, P5) 42 | ] 43 | -------------------------------------------------------------------------------- /data/coco128.yaml: -------------------------------------------------------------------------------- 1 | # COCO 2017 dataset http://cocodataset.org - first 128 training images 2 | # Train command: python train.py --data coco128.yaml 3 | # Default dataset location is next to /yolov3: 4 | # /parent_folder 5 | # /coco128 6 | # /yolov3 7 | 8 | 9 | # download command/URL (optional) 10 | download: https://github.com/ultralytics/yolov5/releases/download/v1.0/coco128.zip 11 | 12 | # train and val data as 1) directory: path/images/, 2) file: path/images.txt, or 3) list: [path1/images/, path2/images/] 13 | train: ../coco128/images/train2017/ # 128 images 14 | val: ../coco128/images/train2017/ # 128 images 15 | 16 | # number of classes 17 | nc: 80 18 | 19 | # class names 20 | names: [ 'person', 'bicycle', 'car', 'motorcycle', 'airplane', 'bus', 'train', 'truck', 'boat', 'traffic light', 21 | 'fire hydrant', 'stop sign', 'parking meter', 'bench', 'bird', 'cat', 'dog', 'horse', 'sheep', 'cow', 22 | 'elephant', 'bear', 'zebra', 'giraffe', 'backpack', 'umbrella', 'handbag', 'tie', 'suitcase', 'frisbee', 23 | 'skis', 'snowboard', 'sports ball', 'kite', 'baseball bat', 'baseball glove', 'skateboard', 'surfboard', 24 | 'tennis racket', 'bottle', 'wine glass', 'cup', 'fork', 'knife', 'spoon', 'bowl', 'banana', 'apple', 25 | 'sandwich', 'orange', 'broccoli', 'carrot', 'hot dog', 'pizza', 'donut', 'cake', 'chair', 'couch', 26 | 'potted plant', 'bed', 'dining table', 'toilet', 'tv', 'laptop', 'mouse', 'remote', 'keyboard', 'cell phone', 27 | 'microwave', 'oven', 'toaster', 'sink', 'refrigerator', 'book', 'clock', 'vase', 'scissors', 'teddy bear', 28 | 'hair drier', 'toothbrush' ] 29 | -------------------------------------------------------------------------------- /models/yolov3.yaml: -------------------------------------------------------------------------------- 1 | # parameters 2 | nc: 80 # number of classes 3 | depth_multiple: 1.0 # model depth multiple 4 | width_multiple: 1.0 # layer channel multiple 5 | 6 | # anchors 7 | anchors: 8 | - [10,13, 16,30, 33,23] # P3/8 9 | - [30,61, 62,45, 59,119] # P4/16 10 | - [116,90, 156,198, 373,326] # P5/32 11 | 12 | # darknet53 backbone 13 | backbone: 14 | # [from, number, module, args] 15 | [[-1, 1, Conv, [32, 3, 1]], # 0 16 | [-1, 1, Conv, [64, 3, 2]], # 1-P1/2 17 | [-1, 1, Bottleneck, [64]], 18 | [-1, 1, Conv, [128, 3, 2]], # 3-P2/4 19 | [-1, 2, Bottleneck, [128]], 20 | [-1, 1, Conv, [256, 3, 2]], # 5-P3/8 21 | [-1, 8, Bottleneck, [256]], 22 | [-1, 1, Conv, [512, 3, 2]], # 7-P4/16 23 | [-1, 8, Bottleneck, [512]], 24 | [-1, 1, Conv, [1024, 3, 2]], # 9-P5/32 25 | [-1, 4, Bottleneck, [1024]], # 10 26 | ] 27 | 28 | # YOLOv3 head 29 | head: 30 | [[-1, 1, Bottleneck, [1024, False]], 31 | [-1, 1, Conv, [512, [1, 1]]], 32 | [-1, 1, Conv, [1024, 3, 1]], 33 | [-1, 1, Conv, [512, 1, 1]], 34 | [-1, 1, Conv, [1024, 3, 1]], # 15 (P5/32-large) 35 | 36 | 37 | [-2, 1, Conv, [256, 1, 1]], 38 | [-1, 1, nn.Upsample, [None, 2, 'nearest']], 39 | [[-1, 8], 1, Concat, [1]], # cat backbone P4 40 | [-1, 1, Bottleneck, [512, False]], 41 | [-1, 1, Bottleneck, [512, False]], 42 | [-1, 1, Conv, [256, 1, 1]], 43 | [-1, 1, Conv, [512, 3, 1]], # 22 (P4/16-medium) 44 | 45 | [-2, 1, Conv, [128, 1, 1]], 46 | [-1, 1, nn.Upsample, [None, 2, 'nearest']], 47 | [[-1, 6], 1, Concat, [1]], # cat backbone P3 48 | [-1, 1, Bottleneck, [256, False]], 49 | [-1, 2, Bottleneck, [256, False]], # 27 (P3/8-small) 50 | 51 | [[27, 22, 15], 1, Detect, [nc, anchors]], # Detect(P3, P4, P5) 52 | ] 53 | -------------------------------------------------------------------------------- /models/yolov3-spp.yaml: -------------------------------------------------------------------------------- 1 | # parameters 2 | nc: 80 # number of classes 3 | depth_multiple: 1.0 # model depth multiple 4 | width_multiple: 1.0 # layer channel multiple 5 | 6 | # anchors 7 | anchors: 8 | - [10,13, 16,30, 33,23] # P3/8 9 | - [30,61, 62,45, 59,119] # P4/16 10 | - [116,90, 156,198, 373,326] # P5/32 11 | 12 | # darknet53 backbone 13 | backbone: 14 | # [from, number, module, args] 15 | [[-1, 1, Conv, [32, 3, 1]], # 0 16 | [-1, 1, Conv, [64, 3, 2]], # 1-P1/2 17 | [-1, 1, Bottleneck, [64]], 18 | [-1, 1, Conv, [128, 3, 2]], # 3-P2/4 19 | [-1, 2, Bottleneck, [128]], 20 | [-1, 1, Conv, [256, 3, 2]], # 5-P3/8 21 | [-1, 8, Bottleneck, [256]], 22 | [-1, 1, Conv, [512, 3, 2]], # 7-P4/16 23 | [-1, 8, Bottleneck, [512]], 24 | [-1, 1, Conv, [1024, 3, 2]], # 9-P5/32 25 | [-1, 4, Bottleneck, [1024]], # 10 26 | ] 27 | 28 | # YOLOv3-SPP head 29 | head: 30 | [[-1, 1, Bottleneck, [1024, False]], 31 | [-1, 1, SPP, [512, [5, 9, 13]]], 32 | [-1, 1, Conv, [1024, 3, 1]], 33 | [-1, 1, Conv, [512, 1, 1]], 34 | [-1, 1, Conv, [1024, 3, 1]], # 15 (P5/32-large) 35 | 36 | [-2, 1, Conv, [256, 1, 1]], 37 | [-1, 1, nn.Upsample, [None, 2, 'nearest']], 38 | [[-1, 8], 1, Concat, [1]], # cat backbone P4 39 | [-1, 1, Bottleneck, [512, False]], 40 | [-1, 1, Bottleneck, [512, False]], 41 | [-1, 1, Conv, [256, 1, 1]], 42 | [-1, 1, Conv, [512, 3, 1]], # 22 (P4/16-medium) 43 | 44 | [-2, 1, Conv, [128, 1, 1]], 45 | [-1, 1, nn.Upsample, [None, 2, 'nearest']], 46 | [[-1, 6], 1, Concat, [1]], # cat backbone P3 47 | [-1, 1, Bottleneck, [256, False]], 48 | [-1, 2, Bottleneck, [256, False]], # 27 (P3/8-small) 49 | 50 | [[27, 22, 15], 1, Detect, [nc, anchors]], # Detect(P3, P4, P5) 51 | ] 52 | -------------------------------------------------------------------------------- /data/coco.yaml: -------------------------------------------------------------------------------- 1 | # COCO 2017 dataset http://cocodataset.org 2 | # Train command: python train.py --data coco.yaml 3 | # Default dataset location is next to /yolov3: 4 | # /parent_folder 5 | # /coco 6 | # /yolov3 7 | 8 | 9 | # download command/URL (optional) 10 | download: bash data/scripts/get_coco.sh 11 | 12 | # train and val data as 1) directory: path/images/, 2) file: path/images.txt, or 3) list: [path1/images/, path2/images/] 13 | train: ../coco/train2017.txt # 118287 images 14 | val: ../coco/val2017.txt # 5000 images 15 | test: ../coco/test-dev2017.txt # 20288 of 40670 images, submit to https://competitions.codalab.org/competitions/20794 16 | 17 | # number of classes 18 | nc: 80 19 | 20 | # class names 21 | names: [ 'person', 'bicycle', 'car', 'motorcycle', 'airplane', 'bus', 'train', 'truck', 'boat', 'traffic light', 22 | 'fire hydrant', 'stop sign', 'parking meter', 'bench', 'bird', 'cat', 'dog', 'horse', 'sheep', 'cow', 23 | 'elephant', 'bear', 'zebra', 'giraffe', 'backpack', 'umbrella', 'handbag', 'tie', 'suitcase', 'frisbee', 24 | 'skis', 'snowboard', 'sports ball', 'kite', 'baseball bat', 'baseball glove', 'skateboard', 'surfboard', 25 | 'tennis racket', 'bottle', 'wine glass', 'cup', 'fork', 'knife', 'spoon', 'bowl', 'banana', 'apple', 26 | 'sandwich', 'orange', 'broccoli', 'carrot', 'hot dog', 'pizza', 'donut', 'cake', 'chair', 'couch', 27 | 'potted plant', 'bed', 'dining table', 'toilet', 'tv', 'laptop', 'mouse', 'remote', 'keyboard', 'cell phone', 28 | 'microwave', 'oven', 'toaster', 'sink', 'refrigerator', 'book', 'clock', 'vase', 'scissors', 'teddy bear', 29 | 'hair drier', 'toothbrush' ] 30 | 31 | # Print classes 32 | # with open('data/coco.yaml') as f: 33 | # d = yaml.load(f, Loader=yaml.FullLoader) # dict 34 | # for i, x in enumerate(d['names']): 35 | # print(i, x) 36 | -------------------------------------------------------------------------------- /models/LF-YOLO-0.5.yaml: -------------------------------------------------------------------------------- 1 | # parameters 2 | nc: 80 # number of classes 3 | depth_multiple: 1.0 # model depth multiple 4 | width_multiple: 1.0 # layer channel multiple 5 | 6 | # anchors 7 | anchors: 8 | - [10,13, 16,30, 33,23] # P3/8 9 | - [30,61, 62,45, 59,119] # P4/16 10 | - [116,90, 156,198, 373,326] # P5/32 11 | 12 | # darknet53 backbone 13 | backbone: 14 | # [from, number, module, args] 15 | [[-1, 1, Conv, [16, 3, 1]], # 0 16 | [-1, 1, nn.MaxPool2d, [3, 2, 1]], 17 | [-1, 1, EFE, [16, 1, 1]], 18 | [-1, 1, nn.MaxPool2d, [3, 2, 1]], 19 | [-1, 1, EFE, [32, 1, 1]], 20 | [-1, 1, EFE, [32, 1, 1]], 21 | [-1, 1, nn.MaxPool2d, [3, 2, 1]], 22 | [-1, 1, EFE, [64, 1, 1]], 23 | [-1, 1, EFE, [64, 1, 1]], 24 | [-1, 1, EFE, [64, 1, 1]], 25 | [-1, 1, EFE, [64, 1, 1]], # 10 26 | [-1, 1, nn.MaxPool2d, [3, 2, 1]], 27 | [-1, 1, EFE, [128, 1, 1]], 28 | [-1, 1, EFE, [128, 1, 1]], 29 | [-1, 1, EFE, [128, 1, 1]], 30 | [-1, 1, EFE, [128, 1, 1]], # 15 31 | [-1, 1, nn.MaxPool2d, [3, 2, 1]], 32 | [-1, 1, EFE, [256, 1, 1]], 33 | [-1, 1, EFE, [256, 1, 1]], # 18 34 | ] 35 | 36 | # YOLOv3-SPP head 37 | head: 38 | [[-1, 1, RMF, [1536]], 39 | [-1, 1, Conv, [256, 1, 1]], 40 | [-1, 1, GhostModule, [512]], 41 | [-1, 1, Conv, [128, 1, 1]], # 22 (P5/32-large) 42 | 43 | [-4, 1, Conv, [128, 1, 1]], 44 | [-1, 1, nn.Upsample, [None, 2, 'nearest']], 45 | [[-1, 15], 1, Concat, [1]], # cat backbone P4 46 | [-1, 1, Conv, [128, 1, 1]], 47 | [-1, 1, GhostModule, [256]], 48 | [-1, 1, Conv, [128, 1, 1]], # 28 (P4/16-medium) 49 | 50 | [-4, 1, Conv, [64, 1, 1]], 51 | [-1, 1, nn.Upsample, [None, 2, 'nearest']], 52 | [[-1, 10], 1, Concat, [1]], # cat backbone P3 53 | [-1, 1, Conv, [64, 1, 1]], 54 | [-1, 1, GhostModule, [128]], 55 | [-1, 1, Conv, [128, 1, 1]], # 34 (P3/8-small) 56 | [[34, 28, 22], 1, Detect, [nc, anchors]], # Detect(P3, P4, P5) 57 | ] -------------------------------------------------------------------------------- /models/LF-YOLO-0.75.yaml: -------------------------------------------------------------------------------- 1 | # parameters 2 | nc: 80 # number of classes 3 | depth_multiple: 1.0 # model depth multiple 4 | width_multiple: 1.0 # layer channel multiple 5 | 6 | # anchors 7 | anchors: 8 | - [10,13, 16,30, 33,23] # P3/8 9 | - [30,61, 62,45, 59,119] # P4/16 10 | - [116,90, 156,198, 373,326] # P5/32 11 | 12 | # darknet53 backbone 13 | backbone: 14 | # [from, number, module, args] 15 | [[-1, 1, Conv, [16, 3, 1]], # 0 16 | [-1, 1, nn.MaxPool2d, [3, 2, 1]], 17 | [-1, 1, EFE, [24, 1, 1]], 18 | [-1, 1, nn.MaxPool2d, [3, 2, 1]], 19 | [-1, 1, EFE, [48, 1, 1]], 20 | [-1, 1, EFE, [48, 1, 1]], 21 | [-1, 1, nn.MaxPool2d, [3, 2, 1]], 22 | [-1, 1, EFE, [96, 1, 1]], 23 | [-1, 1, EFE, [96, 1, 1]], 24 | [-1, 1, EFE, [96, 1, 1]], 25 | [-1, 1, EFE, [96, 1, 1]], # 10 26 | [-1, 1, nn.MaxPool2d, [3, 2, 1]], 27 | [-1, 1, EFE, [192, 1, 1]], 28 | [-1, 1, EFE, [192, 1, 1]], 29 | [-1, 1, EFE, [192, 1, 1]], 30 | [-1, 1, EFE, [192, 1, 1]], # 15 31 | [-1, 1, nn.MaxPool2d, [3, 2, 1]], 32 | [-1, 1, EFE, [384, 1, 1]], 33 | [-1, 1, EFE, [384, 1, 1]], # 18 34 | ] 35 | 36 | # YOLOv3-SPP head 37 | head: 38 | [[-1, 1, RMF, [2304]], 39 | [-1, 1, Conv, [384, 1, 1]], 40 | [-1, 1, GhostModule, [768]], 41 | [-1, 1, Conv, [192, 1, 1]], # 22 (P5/32-large) 42 | 43 | [-4, 1, Conv, [192, 1, 1]], 44 | [-1, 1, nn.Upsample, [None, 2, 'nearest']], 45 | [[-1, 15], 1, Concat, [1]], # cat backbone P4 46 | [-1, 1, Conv, [192, 1, 1]], 47 | [-1, 1, GhostModule, [384]], 48 | [-1, 1, Conv, [192, 1, 1]], # 28 (P4/16-medium) 49 | 50 | [-4, 1, Conv, [96, 1, 1]], 51 | [-1, 1, nn.Upsample, [None, 2, 'nearest']], 52 | [[-1, 10], 1, Concat, [1]], # cat backbone P3 53 | [-1, 1, Conv, [96, 1, 1]], 54 | [-1, 1, GhostModule, [192]], 55 | [-1, 1, Conv, [192, 1, 1]], # 34 (P3/8-small) 56 | [[34, 28, 22], 1, Detect, [nc, anchors]], # Detect(P3, P4, P5) 57 | ] 58 | -------------------------------------------------------------------------------- /models/LF-YOLO.yaml: -------------------------------------------------------------------------------- 1 | # parameters 2 | nc: 80 # number of classes 3 | depth_multiple: 1.0 # model depth multiple 4 | width_multiple: 1.0 # layer channel multiple 5 | 6 | # anchors 7 | anchors: 8 | - [10,13, 16,30, 33,23] # P3/8 9 | - [30,61, 62,45, 59,119] # P4/16 10 | - [116,90, 156,198, 373,326] # P5/32 11 | 12 | # darknet53 backbone 13 | backbone: 14 | # [from, number, module, args] 15 | [[-1, 1, Conv, [16, 3, 1]], # 0 16 | [-1, 1, nn.MaxPool2d, [3, 2, 1]], 17 | [-1, 1, EFE, [32, 1, 1]], 18 | [-1, 1, nn.MaxPool2d, [3, 2, 1]], 19 | [-1, 1, EFE, [64, 1, 1]], 20 | [-1, 1, EFE, [64, 1, 1]], 21 | [-1, 1, nn.MaxPool2d, [3, 2, 1]], 22 | [-1, 1, EFE, [128, 1, 1]], 23 | [-1, 1, EFE, [128, 1, 1]], 24 | [-1, 1, EFE, [128, 1, 1]], 25 | [-1, 1, EFE, [128, 1, 1]], # 10 26 | [-1, 1, nn.MaxPool2d, [3, 2, 1]], 27 | [-1, 1, EFE, [256, 1, 1]], 28 | [-1, 1, EFE, [256, 1, 1]], 29 | [-1, 1, EFE, [256, 1, 1]], 30 | [-1, 1, EFE, [256, 1, 1]], # 15 31 | [-1, 1, nn.MaxPool2d, [3, 2, 1]], 32 | [-1, 1, EFE, [512, 1, 1]], 33 | [-1, 1, EFE, [512, 1, 1]], # 18 34 | ] 35 | 36 | # YOLOv3-SPP head 37 | head: 38 | [[-1, 1, RMF, [3072]], 39 | [-1, 1, Conv, [512, 1, 1]], 40 | [-1, 1, GhostModule, [1024]], 41 | [-1, 1, Conv, [256, 1, 1]], # 22 (P5/32-large) 42 | 43 | [-4, 1, Conv, [256, 1, 1]], 44 | [-1, 1, nn.Upsample, [None, 2, 'nearest']], 45 | [[-1, 15], 1, Concat, [1]], # cat backbone P4 46 | [-1, 1, Conv, [256, 1, 1]], 47 | [-1, 1, GhostModule, [512]], 48 | [-1, 1, Conv, [256, 1, 1]], # 28 (P4/16-medium) 49 | 50 | [-4, 1, Conv, [128, 1, 1]], 51 | [-1, 1, nn.Upsample, [None, 2, 'nearest']], 52 | [[-1, 10], 1, Concat, [1]], # cat backbone P3 53 | [-1, 1, Conv, [128, 1, 1]], 54 | [-1, 1, GhostModule, [256]], 55 | [-1, 1, Conv, [256, 1, 1]], # 34 (P3/8-small) 56 | [[34, 28, 22], 1, Detect, [nc, anchors]], # Detect(P3, P4, P5) 57 | ] 58 | -------------------------------------------------------------------------------- /models/LF-YOLO-1.25.yaml: -------------------------------------------------------------------------------- 1 | # parameters 2 | nc: 80 # number of classes 3 | depth_multiple: 1.0 # model depth multiple 4 | width_multiple: 1.0 # layer channel multiple 5 | 6 | # anchors 7 | anchors: 8 | - [10,13, 16,30, 33,23] # P3/8 9 | - [30,61, 62,45, 59,119] # P4/16 10 | - [116,90, 156,198, 373,326] # P5/32 11 | 12 | # darknet53 backbone 13 | backbone: 14 | # [from, number, module, args] 15 | [[-1, 1, Conv, [16, 3, 1]], # 0 16 | [-1, 1, nn.MaxPool2d, [3, 2, 1]], 17 | [-1, 1, EFE, [40, 1, 1]], 18 | [-1, 1, nn.MaxPool2d, [3, 2, 1]], 19 | [-1, 1, EFE, [80, 1, 1]], 20 | [-1, 1, EFE, [80, 1, 1]], 21 | [-1, 1, nn.MaxPool2d, [3, 2, 1]], 22 | [-1, 1, EFE, [160, 1, 1]], 23 | [-1, 1, EFE, [160, 1, 1]], 24 | [-1, 1, EFE, [160, 1, 1]], 25 | [-1, 1, EFE, [160, 1, 1]], # 10 26 | [-1, 1, nn.MaxPool2d, [3, 2, 1]], 27 | [-1, 1, EFE, [320, 1, 1]], 28 | [-1, 1, EFE, [320, 1, 1]], 29 | [-1, 1, EFE, [320, 1, 1]], 30 | [-1, 1, EFE, [320, 1, 1]], # 15 31 | [-1, 1, nn.MaxPool2d, [3, 2, 1]], 32 | [-1, 1, EFE, [640, 1, 1]], 33 | [-1, 1, EFE, [640, 1, 1]], # 18 34 | ] 35 | 36 | # YOLOv3-SPP head 37 | head: 38 | [[-1, 1, RMF, [3840]], 39 | [-1, 1, Conv, [640, 1, 1]], 40 | [-1, 1, GhostModule, [1280]], 41 | [-1, 1, Conv, [320, 1, 1]], # 22 (P5/32-large) 42 | 43 | [-4, 1, Conv, [320, 1, 1]], 44 | [-1, 1, nn.Upsample, [None, 2, 'nearest']], 45 | [[-1, 15], 1, Concat, [1]], # cat backbone P4 46 | [-1, 1, Conv, [320, 1, 1]], 47 | [-1, 1, GhostModule, [640]], 48 | [-1, 1, Conv, [320, 1, 1]], # 28 (P4/16-medium) 49 | 50 | [-4, 1, Conv, [160, 1, 1]], 51 | [-1, 1, nn.Upsample, [None, 2, 'nearest']], 52 | [[-1, 10], 1, Concat, [1]], # cat backbone P3 53 | [-1, 1, Conv, [160, 1, 1]], 54 | [-1, 1, GhostModule, [320]], 55 | [-1, 1, Conv, [320, 1, 1]], # 34 (P3/8-small) 56 | [[34, 28, 22], 1, Detect, [nc, anchors]], # Detect(P3, P4, P5) 57 | ] 58 | -------------------------------------------------------------------------------- /Dockerfile: -------------------------------------------------------------------------------- 1 | # Start FROM Nvidia PyTorch image https://ngc.nvidia.com/catalog/containers/nvidia:pytorch 2 | FROM nvcr.io/nvidia/pytorch:21.03-py3 3 | 4 | # Install linux packages 5 | RUN apt update && apt install -y zip htop screen libgl1-mesa-glx 6 | 7 | # Install python dependencies 8 | COPY requirements.txt . 9 | RUN python -m pip install --upgrade pip 10 | RUN pip uninstall -y nvidia-tensorboard nvidia-tensorboard-plugin-dlprof 11 | RUN pip install --no-cache -r requirements.txt coremltools onnx gsutil notebook 12 | 13 | # Create working directory 14 | RUN mkdir -p /usr/src/app 15 | WORKDIR /usr/src/app 16 | 17 | # Copy contents 18 | COPY . /usr/src/app 19 | 20 | # Set environment variables 21 | ENV HOME=/usr/src/app 22 | 23 | 24 | # --------------------------------------------------- Extras Below --------------------------------------------------- 25 | 26 | # Build and Push 27 | # t=ultralytics/yolov3:latest && sudo docker build -t $t . && sudo docker push $t 28 | # for v in {300..303}; do t=ultralytics/coco:v$v && sudo docker build -t $t . && sudo docker push $t; done 29 | 30 | # Pull and Run 31 | # t=ultralytics/yolov3:latest && sudo docker pull $t && sudo docker run -it --ipc=host --gpus all $t 32 | 33 | # Pull and Run with local directory access 34 | # t=ultralytics/yolov3:latest && sudo docker pull $t && sudo docker run -it --ipc=host --gpus all -v "$(pwd)"/coco:/usr/src/coco $t 35 | 36 | # Kill all 37 | # sudo docker kill $(sudo docker ps -q) 38 | 39 | # Kill all image-based 40 | # sudo docker kill $(sudo docker ps -qa --filter ancestor=ultralytics/yolov5:latest) 41 | 42 | # Bash into running container 43 | # sudo docker exec -it 5a9b5863d93d bash 44 | 45 | # Bash into stopped container 46 | # id=$(sudo docker ps -qa) && sudo docker start $id && sudo docker exec -it $id bash 47 | 48 | # Send weights to GCP 49 | # python -c "from utils.general import *; strip_optimizer('runs/train/exp0_*/weights/best.pt', 'tmp.pt')" && gsutil cp tmp.pt gs://*.pt 50 | 51 | # Clean up 52 | # docker system prune -a --volumes 53 | -------------------------------------------------------------------------------- /data/hyp.scratch.yaml: -------------------------------------------------------------------------------- 1 | # Hyperparameters for COCO training from scratch 2 | # python train.py --batch 40 --cfg yolov5m.yaml --weights '' --data coco.yaml --img 640 --epochs 300 3 | # See tutorials for hyperparameter evolution https://github.com/ultralytics/yolov5#tutorials 4 | 5 | 6 | #lr0: 0.01 # initial learning rate (SGD=1E-2, Adam=1E-3) 7 | #lrf: 0.2 # final OneCycleLR learning rate (lr0 * lrf) 8 | #momentum: 0.937 # SGD momentum/Adam beta1 9 | #weight_decay: 0.0005 # optimizer weight decay 5e-4 10 | #warmup_epochs: 3.0 # warmup epochs (fractions ok) 11 | #warmup_momentum: 0.8 # warmup initial momentum 12 | #warmup_bias_lr: 0.1 # warmup initial bias lr 13 | #box: 0.05 # box loss gain 14 | #cls: 0.5 # cls loss gain 15 | #cls_pw: 1.0 # cls BCELoss positive_weight 16 | #obj: 1.0 # obj loss gain (scale with pixels) 17 | #obj_pw: 1.0 # obj BCELoss positive_weight 18 | #iou_t: 0.20 # IoU training threshold 19 | #anchor_t: 4.0 # anchor-multiple threshold 20 | ## anchors: 3 # anchors per output layer (0 to ignore) 21 | #fl_gamma: 0.0 # focal loss gamma (efficientDet default gamma=1.5) 22 | #hsv_h: 0.015 # image HSV-Hue augmentation (fraction) 23 | #hsv_s: 0.7 # image HSV-Saturation augmentation (fraction) 24 | #hsv_v: 0.4 # image HSV-Value augmentation (fraction) 25 | #degrees: 0.0 # image rotation (+/- deg) 26 | #translate: 0.0 # image translation (+/- fraction) 27 | #scale: 0.0 # image scale (+/- gain) 28 | #shear: 0.0 # image shear (+/- deg) 29 | #perspective: 0.0 # image perspective (+/- fraction), range 0-0.001 30 | #flipud: 0.0 # image flip up-down (probability) 31 | #fliplr: 0.0 # image flip left-right (probability) 32 | #mosaic: 0.5 # image mosaic (probability) 33 | #mixup: 0.0 # image mixup (probability) 34 | 35 | lr0: 0.01 36 | lrf: 0.2 37 | momentum: 0.937 38 | weight_decay: 0.0005 39 | warmup_epochs: 3.0 40 | warmup_momentum: 0.8 41 | warmup_bias_lr: 0.1 42 | box: 0.05 43 | cls: 0.5 44 | cls_pw: 1.0 45 | obj: 1.0 46 | obj_pw: 1.0 47 | iou_t: 0.2 48 | anchor_t: 4.0 49 | fl_gamma: 0.0 50 | hsv_h: 0.015 51 | hsv_s: 0.7 52 | hsv_v: 0.4 53 | degrees: 0.0 54 | translate: 0.1 55 | scale: 0.5 56 | shear: 0.0 57 | perspective: 0.0 58 | flipud: 0.0 59 | fliplr: 0.0 60 | mosaic: 1.0 61 | mixup: 0.0 62 | -------------------------------------------------------------------------------- /data/scripts/get_argoverse_hd.sh: -------------------------------------------------------------------------------- 1 | #!/bin/bash 2 | # Argoverse-HD dataset (ring-front-center camera) http://www.cs.cmu.edu/~mengtial/proj/streaming/ 3 | # Download command: bash data/scripts/get_argoverse_hd.sh 4 | # Train command: python train.py --data argoverse_hd.yaml 5 | # Default dataset location is next to /yolov5: 6 | # /parent_folder 7 | # /argoverse 8 | # /yolov5 9 | 10 | # Download/unzip images 11 | d='../argoverse/' # unzip directory 12 | mkdir $d 13 | url=https://argoverse-hd.s3.us-east-2.amazonaws.com/ 14 | f=Argoverse-HD-Full.zip 15 | curl -L $url$f -o $f && unzip -q $f -d $d && rm $f &# download, unzip, remove in background 16 | wait # finish background tasks 17 | 18 | cd ../argoverse/Argoverse-1.1/ 19 | ln -s tracking images 20 | 21 | cd ../Argoverse-HD/annotations/ 22 | 23 | python3 - "$@" <train.txt 82 | cat 2007_train.txt 2007_val.txt 2007_test.txt 2012_train.txt 2012_val.txt >train.all.txt 83 | 84 | python3 - "$@" < 1E6 # check 41 | except Exception as e: # GCP 42 | print(f'Download error: {e}') 43 | assert redundant, 'No secondary mirror' 44 | url = f'https://storage.googleapis.com/{repo}/ckpt/{name}' 45 | print(f'Downloading {url} to {file}...') 46 | os.system(f'curl -L {url} -o {file}') # torch.hub.download_url_to_file(url, weights) 47 | finally: 48 | if not file.exists() or file.stat().st_size < 1E6: # check 49 | file.unlink(missing_ok=True) # remove partial downloads 50 | print(f'ERROR: Download failure: {msg}') 51 | print('') 52 | return 53 | 54 | 55 | def gdrive_download(id='16TiPfZj7htmTyhntwcZyEEAejOUxuT6m', file='tmp.zip'): 56 | # Downloads a file from Google Drive. from yolov3.utils.google_utils import *; gdrive_download() 57 | t = time.time() 58 | file = Path(file) 59 | cookie = Path('cookie') # gdrive cookie 60 | print(f'Downloading https://drive.google.com/uc?export=download&id={id} as {file}... ', end='') 61 | file.unlink(missing_ok=True) # remove existing file 62 | cookie.unlink(missing_ok=True) # remove existing cookie 63 | 64 | # Attempt file download 65 | out = "NUL" if platform.system() == "Windows" else "/dev/null" 66 | os.system(f'curl -c ./cookie -s -L "drive.google.com/uc?export=download&id={id}" > {out}') 67 | if os.path.exists('cookie'): # large file 68 | s = f'curl -Lb ./cookie "drive.google.com/uc?export=download&confirm={get_token()}&id={id}" -o {file}' 69 | else: # small file 70 | s = f'curl -s -L -o {file} "drive.google.com/uc?export=download&id={id}"' 71 | r = os.system(s) # execute, capture return 72 | cookie.unlink(missing_ok=True) # remove existing cookie 73 | 74 | # Error check 75 | if r != 0: 76 | file.unlink(missing_ok=True) # remove partial 77 | print('Download error ') # raise Exception('Download error') 78 | return r 79 | 80 | # Unzip if archive 81 | if file.suffix == '.zip': 82 | print('unzipping... ', end='') 83 | os.system(f'unzip -q {file}') # unzip 84 | file.unlink() # remove zip to free space 85 | 86 | print(f'Done ({time.time() - t:.1f}s)') 87 | return r 88 | 89 | 90 | def get_token(cookie="./cookie"): 91 | with open(cookie) as f: 92 | for line in f: 93 | if "download" in line: 94 | return line.split()[-1] 95 | return "" 96 | 97 | # def upload_blob(bucket_name, source_file_name, destination_blob_name): 98 | # # Uploads a file to a bucket 99 | # # https://cloud.google.com/storage/docs/uploading-objects#storage-upload-object-python 100 | # 101 | # storage_client = storage.Client() 102 | # bucket = storage_client.get_bucket(bucket_name) 103 | # blob = bucket.blob(destination_blob_name) 104 | # 105 | # blob.upload_from_filename(source_file_name) 106 | # 107 | # print('File {} uploaded to {}.'.format( 108 | # source_file_name, 109 | # destination_blob_name)) 110 | # 111 | # 112 | # def download_blob(bucket_name, source_blob_name, destination_file_name): 113 | # # Uploads a blob from a bucket 114 | # storage_client = storage.Client() 115 | # bucket = storage_client.get_bucket(bucket_name) 116 | # blob = bucket.blob(source_blob_name) 117 | # 118 | # blob.download_to_filename(destination_file_name) 119 | # 120 | # print('Blob {} downloaded to {}.'.format( 121 | # source_blob_name, 122 | # destination_file_name)) 123 | -------------------------------------------------------------------------------- /models/experimental.py: -------------------------------------------------------------------------------- 1 | # YOLOv3 experimental modules 2 | 3 | import numpy as np 4 | import torch 5 | import torch.nn as nn 6 | 7 | from models.common import Conv, DWConv 8 | from utils.google_utils import attempt_download 9 | 10 | 11 | class CrossConv(nn.Module): 12 | # Cross Convolution Downsample 13 | def __init__(self, c1, c2, k=3, s=1, g=1, e=1.0, shortcut=False): 14 | # ch_in, ch_out, kernel, stride, groups, expansion, shortcut 15 | super(CrossConv, self).__init__() 16 | c_ = int(c2 * e) # hidden channels 17 | self.cv1 = Conv(c1, c_, (1, k), (1, s)) 18 | self.cv2 = Conv(c_, c2, (k, 1), (s, 1), g=g) 19 | self.add = shortcut and c1 == c2 20 | 21 | def forward(self, x): 22 | return x + self.cv2(self.cv1(x)) if self.add else self.cv2(self.cv1(x)) 23 | 24 | 25 | class Sum(nn.Module): 26 | # Weighted sum of 2 or more layers https://arxiv.org/abs/1911.09070 27 | def __init__(self, n, weight=False): # n: number of inputs 28 | super(Sum, self).__init__() 29 | self.weight = weight # apply weights boolean 30 | self.iter = range(n - 1) # iter object 31 | if weight: 32 | self.w = nn.Parameter(-torch.arange(1., n) / 2, requires_grad=True) # layer weights 33 | 34 | def forward(self, x): 35 | y = x[0] # no weight 36 | if self.weight: 37 | w = torch.sigmoid(self.w) * 2 38 | for i in self.iter: 39 | y = y + x[i + 1] * w[i] 40 | else: 41 | for i in self.iter: 42 | y = y + x[i + 1] 43 | return y 44 | 45 | 46 | class GhostConv(nn.Module): 47 | # Ghost Convolution https://github.com/huawei-noah/ghostnet 48 | def __init__(self, c1, c2, k=1, s=1, g=1, act=True): # ch_in, ch_out, kernel, stride, groups 49 | super(GhostConv, self).__init__() 50 | c_ = c2 // 2 # hidden channels 51 | self.cv1 = Conv(c1, c_, k, s, None, g, act) 52 | self.cv2 = Conv(c_, c_, 5, 1, None, c_, act) 53 | 54 | def forward(self, x): 55 | y = self.cv1(x) 56 | return torch.cat([y, self.cv2(y)], 1) 57 | 58 | 59 | class GhostBottleneck(nn.Module): 60 | # Ghost Bottleneck https://github.com/huawei-noah/ghostnet 61 | def __init__(self, c1, c2, k=3, s=1): # ch_in, ch_out, kernel, stride 62 | super(GhostBottleneck, self).__init__() 63 | c_ = c2 // 2 64 | self.conv = nn.Sequential(GhostConv(c1, c_, 1, 1), # pw 65 | DWConv(c_, c_, k, s, act=False) if s == 2 else nn.Identity(), # dw 66 | GhostConv(c_, c2, 1, 1, act=False)) # pw-linear 67 | self.shortcut = nn.Sequential(DWConv(c1, c1, k, s, act=False), 68 | Conv(c1, c2, 1, 1, act=False)) if s == 2 else nn.Identity() 69 | 70 | def forward(self, x): 71 | return self.conv(x) + self.shortcut(x) 72 | 73 | 74 | class MixConv2d(nn.Module): 75 | # Mixed Depthwise Conv https://arxiv.org/abs/1907.09595 76 | def __init__(self, c1, c2, k=(1, 3), s=1, equal_ch=True): 77 | super(MixConv2d, self).__init__() 78 | groups = len(k) 79 | if equal_ch: # equal c_ per group 80 | i = torch.linspace(0, groups - 1E-6, c2).floor() # c2 indices 81 | c_ = [(i == g).sum() for g in range(groups)] # intermediate channels 82 | else: # equal weight.numel() per group 83 | b = [c2] + [0] * groups 84 | a = np.eye(groups + 1, groups, k=-1) 85 | a -= np.roll(a, 1, axis=1) 86 | a *= np.array(k) ** 2 87 | a[0] = 1 88 | c_ = np.linalg.lstsq(a, b, rcond=None)[0].round() # solve for equal weight indices, ax = b 89 | 90 | self.m = nn.ModuleList([nn.Conv2d(c1, int(c_[g]), k[g], s, k[g] // 2, bias=False) for g in range(groups)]) 91 | self.bn = nn.BatchNorm2d(c2) 92 | self.act = nn.LeakyReLU(0.1, inplace=True) 93 | 94 | def forward(self, x): 95 | return x + self.act(self.bn(torch.cat([m(x) for m in self.m], 1))) 96 | 97 | 98 | class Ensemble(nn.ModuleList): 99 | # Ensemble of models 100 | def __init__(self): 101 | super(Ensemble, self).__init__() 102 | 103 | def forward(self, x, augment=False): 104 | y = [] 105 | for module in self: 106 | y.append(module(x, augment)[0]) 107 | # y = torch.stack(y).max(0)[0] # max ensemble 108 | # y = torch.stack(y).mean(0) # mean ensemble 109 | y = torch.cat(y, 1) # nms ensemble 110 | return y, None # inference, train output 111 | 112 | 113 | def attempt_load(weights, map_location=None): 114 | # Loads an ensemble of models weights=[a,b,c] or a single model weights=[a] or weights=a 115 | model = Ensemble() 116 | for w in weights if isinstance(weights, list) else [weights]: 117 | attempt_download(w) 118 | ckpt = torch.load(w, map_location=map_location) # load 119 | model.append(ckpt['ema' if ckpt.get('ema') else 'model'].float().fuse().eval()) # FP32 model 120 | 121 | # Compatibility updates 122 | for m in model.modules(): 123 | if type(m) in [nn.Hardswish, nn.LeakyReLU, nn.ReLU, nn.ReLU6, nn.SiLU]: 124 | m.inplace = True # pytorch 1.7.0 compatibility 125 | elif type(m) is Conv: 126 | m._non_persistent_buffers_set = set() # pytorch 1.6.0 compatibility 127 | 128 | if len(model) == 1: 129 | return model[-1] # return model 130 | else: 131 | print('Ensemble created with %s\n' % weights) 132 | for k in ['names', 'stride']: 133 | setattr(model, k, getattr(model[-1], k)) 134 | return model # return ensemble 135 | -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | This project is based on [ultralytics/yolov3](https://github.com/ultralytics/yolov3). 2 | 3 | LF-YOLO (Lighter and Faster YOLO) is used to detect defect of X-ray weld image. The related paper is available [here](https://ieeexplore.ieee.org/document/10054502). 4 | 5 | 6 | 7 | ## Download 8 | 9 | ```download 10 | $ git clone https://github.com/lmomoy/LF-YOLO 11 | ``` 12 | ## Train 13 | We provide multiple versions of LF-YOLO with different widths. 14 | 15 | ```train 16 | $ python train.py --data coco.yaml --cfg LF-YOLO.yaml --weights '' --batch-size 1 17 | LF-YOLO-1.25.yaml 1 18 | LF-YOLO-0.75.yaml 1 19 | LF-YOLO-0.5.yaml 1 20 | ``` 21 | 22 | ## Results 23 | We test LF-YOLO on our weld defect image dataset. Other methods are trained and tested based on [MMDetection](https://github.com/open-mmlab/mmdetection). 24 | 25 | Model |size (pixels) |mAP50test
|params (M) |FLOPS (B) 26 | --- |--- |--- |--- |--- 27 | Cascasde-RCNN (ResNet50) |(1333, 800) |90.0 |68.9 |243.2 28 | Cascasde-RCNN (ResNet101) |(1333, 800) |90.7 |87.9 |323.1 29 | Faster-RCNN (ResNet50) |(1333, 800) |90.1 |41.1 |215.4 30 | Faster-RCNN (ResNet101) |(1333, 800) |92.2 |60.1 |295.3 31 | Dynamic-RCNN (ResNet50) |(1333, 800) |90.3 |41.1 |215.4 32 | RetinaNet (ResNet50) |(1333, 800) |80.0 |36.2 |205.2 33 | VFNet (ResNet50) |(1333, 800) |87.0 |32.5 |197.8 34 | VFNet (ResNet101) |(1333, 800) |87.2 |51.5 |277.7 35 | Reppoints (ResNet101) |(1333, 800) |82.7 |36.6 |199.0 36 | SSD300 (VGGNet) |300 |88.1 |24.0 |30.6 37 | YOLOv3 (Darknet52) |416 |91.0 |62.0 |33.1 38 | SSD (MobileNet v2) |320 |82.3 |3.1 |0.7 39 | YOLOv3 (MobileNet v2) |320 |90.2 |3.7 |1.6 40 | LF-YOLO-0.5 |320 |90.7 |1.8 |1.1 41 | LF-YOLO |320 |92.9 |7.3 |4.0 42 | 43 | 44 | We test our model on public dataset MS COCO, and it also achieves competitive results. 45 | 46 | Model |size (pixels) |mAP50test
|params (M) |FLOPS (B) 47 | --- |--- |--- |--- |--- 48 | YOLOv3-tiny |640 |34.8 |8.8 |13.2 49 | YOLOv3 |320 |51.5 |39.0 |61.9 50 | SSD |300 |41.2 |35.2 |34.3 51 | SSD |512 |46.5 |99.5 |34.3 52 | Faster R-CNN (VGG16) |shorter size: 800 |43.9 |- |278.0 53 | R-FCN (ResNet50) |shorter size: 800 |49.0 |- |133.0 54 | R-FCN (ResNet101) |shorter size: 800 |52.9 |- |206.0 55 | LF-YOLO |640 |47.8 |7.4 |17.1 56 | 57 | 58 | 59 | 60 | [comment]: <> (
) 61 | 62 | [comment]: <> ( Table Notes (click to expand)) 63 | 64 | [comment]: <> ( * APtest denotes COCO [test-dev2017](http://cocodataset.org/#upload) server results, all other AP results denote val2017 accuracy. ) 65 | 66 | [comment]: <> ( * AP values are for single-model single-scale unless otherwise noted. **Reproduce mAP** by `python test.py --data coco.yaml --img 640 --conf 0.001 --iou 0.65` ) 67 | 68 | [comment]: <> ( * SpeedGPU averaged over 5000 COCO val2017 images using a GCP [n1-standard-16](https://cloud.google.com/compute/docs/machine-types#n1_standard_machine_types) V100 instance, and includes FP16 inference, postprocessing and NMS. **Reproduce speed** by `python test.py --data coco.yaml --img 640 --conf 0.25 --iou 0.45` ) 69 | 70 | [comment]: <> ( * All checkpoints are trained to 300 epochs with default settings and hyperparameters (no autoaugmentation). ) 71 | 72 | [comment]: <> (
) 73 | 74 | 75 | ## Requirements 76 | 77 | Python 3.8 or later with all [requirements.txt](https://github.com/ultralytics/yolov3/blob/master/requirements.txt) dependencies installed, including `torch>=1.7`. To install run: 78 | ```bash 79 | $ pip install -r requirements.txt 80 | ``` 81 | ## Inference 82 | ```bash 83 | $ python detect.py --source data/images --weights LF-YOLO.pt --conf 0.25 84 | ``` 85 | 86 | ## Citation 87 | Please consider citing my work as follows if it is helpful for you. 88 | ``` 89 | @article{liu2023lf, 90 | title={LF-YOLO: A lighter and faster yolo for weld defect detection of X-ray image}, 91 | author={Liu, Moyun and Chen, Youping and Xie, Jingming and He, Lei and Zhang, Yang}, 92 | journal={IEEE Sensors Journal}, 93 | volume={23}, 94 | number={7}, 95 | pages={7430--7439}, 96 | year={2023}, 97 | publisher={IEEE} 98 | } 99 | 100 | @article{liu2021lf, 101 | title={LF-YOLO: A Lighter and Faster YOLO for Weld Defect Detection of X-ray Image}, 102 | author={Liu, Moyun and Chen, Youping and He, Lei and Zhang, Yang and Xie, Jingming}, 103 | journal={arXiv preprint arXiv:2110.15045}, 104 | year={2021} 105 | } 106 | ``` 107 | 108 | -------------------------------------------------------------------------------- /utils/autoanchor.py: -------------------------------------------------------------------------------- 1 | # Auto-anchor utils 2 | 3 | import numpy as np 4 | import torch 5 | import yaml 6 | from scipy.cluster.vq import kmeans 7 | from tqdm import tqdm 8 | 9 | from utils.general import colorstr 10 | 11 | 12 | def check_anchor_order(m): 13 | # Check anchor order against stride order for YOLOv3 Detect() module m, and correct if necessary 14 | a = m.anchor_grid.prod(-1).view(-1) # anchor area 15 | da = a[-1] - a[0] # delta a 16 | ds = m.stride[-1] - m.stride[0] # delta s 17 | if da.sign() != ds.sign(): # same order 18 | print('Reversing anchor order') 19 | m.anchors[:] = m.anchors.flip(0) 20 | m.anchor_grid[:] = m.anchor_grid.flip(0) 21 | 22 | 23 | def check_anchors(dataset, model, thr=4.0, imgsz=640): 24 | # Check anchor fit to data, recompute if necessary 25 | prefix = colorstr('autoanchor: ') 26 | print(f'\n{prefix}Analyzing anchors... ', end='') 27 | m = model.module.model[-1] if hasattr(model, 'module') else model.model[-1] # Detect() 28 | shapes = imgsz * dataset.shapes / dataset.shapes.max(1, keepdims=True) 29 | scale = np.random.uniform(0.9, 1.1, size=(shapes.shape[0], 1)) # augment scale 30 | wh = torch.tensor(np.concatenate([l[:, 3:5] * s for s, l in zip(shapes * scale, dataset.labels)])).float() # wh 31 | 32 | def metric(k): # compute metric 33 | r = wh[:, None] / k[None] 34 | x = torch.min(r, 1. / r).min(2)[0] # ratio metric 35 | best = x.max(1)[0] # best_x 36 | aat = (x > 1. / thr).float().sum(1).mean() # anchors above threshold 37 | bpr = (best > 1. / thr).float().mean() # best possible recall 38 | return bpr, aat 39 | 40 | anchors = m.anchor_grid.clone().cpu().view(-1, 2) # current anchors 41 | bpr, aat = metric(anchors) 42 | print(f'anchors/target = {aat:.2f}, Best Possible Recall (BPR) = {bpr:.4f}', end='') 43 | if bpr < 0.98: # threshold to recompute 44 | print('. Attempting to improve anchors, please wait...') 45 | na = m.anchor_grid.numel() // 2 # number of anchors 46 | try: 47 | anchors = kmean_anchors(dataset, n=na, img_size=imgsz, thr=thr, gen=1000, verbose=False) 48 | except Exception as e: 49 | print(f'{prefix}ERROR: {e}') 50 | new_bpr = metric(anchors)[0] 51 | if new_bpr > bpr: # replace anchors 52 | anchors = torch.tensor(anchors, device=m.anchors.device).type_as(m.anchors) 53 | m.anchor_grid[:] = anchors.clone().view_as(m.anchor_grid) # for inference 54 | m.anchors[:] = anchors.clone().view_as(m.anchors) / m.stride.to(m.anchors.device).view(-1, 1, 1) # loss 55 | check_anchor_order(m) 56 | print(f'{prefix}New anchors saved to model. Update model *.yaml to use these anchors in the future.') 57 | else: 58 | print(f'{prefix}Original anchors better than new anchors. Proceeding with original anchors.') 59 | print('') # newline 60 | 61 | 62 | def kmean_anchors(path='./data/coco128.yaml', n=9, img_size=640, thr=4.0, gen=1000, verbose=True): 63 | """ Creates kmeans-evolved anchors from training dataset 64 | 65 | Arguments: 66 | path: path to dataset *.yaml, or a loaded dataset 67 | n: number of anchors 68 | img_size: image size used for training 69 | thr: anchor-label wh ratio threshold hyperparameter hyp['anchor_t'] used for training, default=4.0 70 | gen: generations to evolve anchors using genetic algorithm 71 | verbose: print all results 72 | 73 | Return: 74 | k: kmeans evolved anchors 75 | 76 | Usage: 77 | from utils.autoanchor import *; _ = kmean_anchors() 78 | """ 79 | thr = 1. / thr 80 | prefix = colorstr('autoanchor: ') 81 | 82 | def metric(k, wh): # compute metrics 83 | r = wh[:, None] / k[None] 84 | x = torch.min(r, 1. / r).min(2)[0] # ratio metric 85 | # x = wh_iou(wh, torch.tensor(k)) # iou metric 86 | return x, x.max(1)[0] # x, best_x 87 | 88 | def anchor_fitness(k): # mutation fitness 89 | _, best = metric(torch.tensor(k, dtype=torch.float32), wh) 90 | return (best * (best > thr).float()).mean() # fitness 91 | 92 | def print_results(k): 93 | k = k[np.argsort(k.prod(1))] # sort small to large 94 | x, best = metric(k, wh0) 95 | bpr, aat = (best > thr).float().mean(), (x > thr).float().mean() * n # best possible recall, anch > thr 96 | print(f'{prefix}thr={thr:.2f}: {bpr:.4f} best possible recall, {aat:.2f} anchors past thr') 97 | print(f'{prefix}n={n}, img_size={img_size}, metric_all={x.mean():.3f}/{best.mean():.3f}-mean/best, ' 98 | f'past_thr={x[x > thr].mean():.3f}-mean: ', end='') 99 | for i, x in enumerate(k): 100 | print('%i,%i' % (round(x[0]), round(x[1])), end=', ' if i < len(k) - 1 else '\n') # use in *.cfg 101 | return k 102 | 103 | if isinstance(path, str): # *.yaml file 104 | with open(path) as f: 105 | data_dict = yaml.load(f, Loader=yaml.SafeLoader) # model dict 106 | from utils.datasets import LoadImagesAndLabels 107 | dataset = LoadImagesAndLabels(data_dict['train'], augment=True, rect=True) 108 | else: 109 | dataset = path # dataset 110 | 111 | # Get label wh 112 | shapes = img_size * dataset.shapes / dataset.shapes.max(1, keepdims=True) 113 | wh0 = np.concatenate([l[:, 3:5] * s for s, l in zip(shapes, dataset.labels)]) # wh 114 | 115 | # Filter 116 | i = (wh0 < 3.0).any(1).sum() 117 | if i: 118 | print(f'{prefix}WARNING: Extremely small objects found. {i} of {len(wh0)} labels are < 3 pixels in size.') 119 | wh = wh0[(wh0 >= 2.0).any(1)] # filter > 2 pixels 120 | # wh = wh * (np.random.rand(wh.shape[0], 1) * 0.9 + 0.1) # multiply by random scale 0-1 121 | 122 | # Kmeans calculation 123 | print(f'{prefix}Running kmeans for {n} anchors on {len(wh)} points...') 124 | s = wh.std(0) # sigmas for whitening 125 | k, dist = kmeans(wh / s, n, iter=30) # points, mean distance 126 | assert len(k) == n, print(f'{prefix}ERROR: scipy.cluster.vq.kmeans requested {n} points but returned only {len(k)}') 127 | k *= s 128 | wh = torch.tensor(wh, dtype=torch.float32) # filtered 129 | wh0 = torch.tensor(wh0, dtype=torch.float32) # unfiltered 130 | k = print_results(k) 131 | 132 | # Plot 133 | # k, d = [None] * 20, [None] * 20 134 | # for i in tqdm(range(1, 21)): 135 | # k[i-1], d[i-1] = kmeans(wh / s, i) # points, mean distance 136 | # fig, ax = plt.subplots(1, 2, figsize=(14, 7), tight_layout=True) 137 | # ax = ax.ravel() 138 | # ax[0].plot(np.arange(1, 21), np.array(d) ** 2, marker='.') 139 | # fig, ax = plt.subplots(1, 2, figsize=(14, 7)) # plot wh 140 | # ax[0].hist(wh[wh[:, 0]<100, 0],400) 141 | # ax[1].hist(wh[wh[:, 1]<100, 1],400) 142 | # fig.savefig('wh.png', dpi=200) 143 | 144 | # Evolve 145 | npr = np.random 146 | f, sh, mp, s = anchor_fitness(k), k.shape, 0.9, 0.1 # fitness, generations, mutation prob, sigma 147 | pbar = tqdm(range(gen), desc=f'{prefix}Evolving anchors with Genetic Algorithm:') # progress bar 148 | for _ in pbar: 149 | v = np.ones(sh) 150 | while (v == 1).all(): # mutate until a change occurs (prevent duplicates) 151 | v = ((npr.random(sh) < mp) * npr.random() * npr.randn(*sh) * s + 1).clip(0.3, 3.0) 152 | kg = (k.copy() * v).clip(min=2.0) 153 | fg = anchor_fitness(kg) 154 | if fg > f: 155 | f, k = fg, kg.copy() 156 | pbar.desc = f'{prefix}Evolving anchors with Genetic Algorithm: fitness = {f:.4f}' 157 | if verbose: 158 | print_results(k) 159 | 160 | return print_results(k) 161 | -------------------------------------------------------------------------------- /utils/metrics.py: -------------------------------------------------------------------------------- 1 | # Model validation metrics 2 | 3 | from pathlib import Path 4 | 5 | import matplotlib.pyplot as plt 6 | import numpy as np 7 | import torch 8 | 9 | from . import general 10 | 11 | 12 | def fitness(x): 13 | # Model fitness as a weighted combination of metrics 14 | w = [0.0, 0.0, 1.0, 0.0] # weights for [P, R, mAP@0.5, mAP@0.5:0.95] 15 | return (x[:, :4] * w).sum(1) 16 | 17 | 18 | def ap_per_class(tp, conf, pred_cls, target_cls, plot=False, save_dir='.', names=()): 19 | """ Compute the average precision, given the recall and precision curves. 20 | Source: https://github.com/rafaelpadilla/Object-Detection-Metrics. 21 | # Arguments 22 | tp: True positives (nparray, nx1 or nx10). 23 | conf: Objectness value from 0-1 (nparray). 24 | pred_cls: Predicted object classes (nparray). 25 | target_cls: True object classes (nparray). 26 | plot: Plot precision-recall curve at mAP@0.5 27 | save_dir: Plot save directory 28 | # Returns 29 | The average precision as computed in py-faster-rcnn. 30 | """ 31 | 32 | # Sort by objectness 33 | i = np.argsort(-conf) 34 | tp, conf, pred_cls = tp[i], conf[i], pred_cls[i] 35 | 36 | # Find unique classes 37 | unique_classes = np.unique(target_cls) 38 | nc = unique_classes.shape[0] # number of classes, number of detections 39 | 40 | # Create Precision-Recall curve and compute AP for each class 41 | px, py = np.linspace(0, 1, 1000), [] # for plotting 42 | ap, p, r = np.zeros((nc, tp.shape[1])), np.zeros((nc, 1000)), np.zeros((nc, 1000)) 43 | for ci, c in enumerate(unique_classes): 44 | i = pred_cls == c 45 | n_l = (target_cls == c).sum() # number of labels 46 | n_p = i.sum() # number of predictions 47 | 48 | if n_p == 0 or n_l == 0: 49 | continue 50 | else: 51 | # Accumulate FPs and TPs 52 | fpc = (1 - tp[i]).cumsum(0) 53 | tpc = tp[i].cumsum(0) 54 | 55 | # Recall 56 | recall = tpc / (n_l + 1e-16) # recall curve 57 | r[ci] = np.interp(-px, -conf[i], recall[:, 0], left=0) # negative x, xp because xp decreases 58 | 59 | # Precision 60 | precision = tpc / (tpc + fpc) # precision curve 61 | p[ci] = np.interp(-px, -conf[i], precision[:, 0], left=1) # p at pr_score 62 | 63 | # AP from recall-precision curve 64 | for j in range(tp.shape[1]): 65 | ap[ci, j], mpre, mrec = compute_ap(recall[:, j], precision[:, j]) 66 | if plot and j == 0: 67 | py.append(np.interp(px, mrec, mpre)) # precision at mAP@0.5 68 | 69 | # Compute F1 (harmonic mean of precision and recall) 70 | f1 = 2 * p * r / (p + r + 1e-16) 71 | if plot: 72 | plot_pr_curve(px, py, ap, Path(save_dir) / 'PR_curve.png', names) 73 | plot_mc_curve(px, f1, Path(save_dir) / 'F1_curve.png', names, ylabel='F1') 74 | plot_mc_curve(px, p, Path(save_dir) / 'P_curve.png', names, ylabel='Precision') 75 | plot_mc_curve(px, r, Path(save_dir) / 'R_curve.png', names, ylabel='Recall') 76 | 77 | i = f1.mean(0).argmax() # max F1 index 78 | return p[:, i], r[:, i], ap, f1[:, i], unique_classes.astype('int32') 79 | 80 | 81 | def compute_ap(recall, precision): 82 | """ Compute the average precision, given the recall and precision curves 83 | # Arguments 84 | recall: The recall curve (list) 85 | precision: The precision curve (list) 86 | # Returns 87 | Average precision, precision curve, recall curve 88 | """ 89 | 90 | # Append sentinel values to beginning and end 91 | mrec = np.concatenate(([0.], recall, [recall[-1] + 0.01])) 92 | mpre = np.concatenate(([1.], precision, [0.])) 93 | 94 | # Compute the precision envelope 95 | mpre = np.flip(np.maximum.accumulate(np.flip(mpre))) 96 | 97 | # Integrate area under curve 98 | method = 'interp' # methods: 'continuous', 'interp' 99 | if method == 'interp': 100 | x = np.linspace(0, 1, 101) # 101-point interp (COCO) 101 | ap = np.trapz(np.interp(x, mrec, mpre), x) # integrate 102 | else: # 'continuous' 103 | i = np.where(mrec[1:] != mrec[:-1])[0] # points where x axis (recall) changes 104 | ap = np.sum((mrec[i + 1] - mrec[i]) * mpre[i + 1]) # area under curve 105 | 106 | return ap, mpre, mrec 107 | 108 | 109 | class ConfusionMatrix: 110 | # Updated version of https://github.com/kaanakan/object_detection_confusion_matrix 111 | def __init__(self, nc, conf=0.25, iou_thres=0.45): 112 | self.matrix = np.zeros((nc + 1, nc + 1)) 113 | self.nc = nc # number of classes 114 | self.conf = conf 115 | self.iou_thres = iou_thres 116 | 117 | def process_batch(self, detections, labels): 118 | """ 119 | Return intersection-over-union (Jaccard index) of boxes. 120 | Both sets of boxes are expected to be in (x1, y1, x2, y2) format. 121 | Arguments: 122 | detections (Array[N, 6]), x1, y1, x2, y2, conf, class 123 | labels (Array[M, 5]), class, x1, y1, x2, y2 124 | Returns: 125 | None, updates confusion matrix accordingly 126 | """ 127 | detections = detections[detections[:, 4] > self.conf] 128 | gt_classes = labels[:, 0].int() 129 | detection_classes = detections[:, 5].int() 130 | iou = general.box_iou(labels[:, 1:], detections[:, :4]) 131 | 132 | x = torch.where(iou > self.iou_thres) 133 | if x[0].shape[0]: 134 | matches = torch.cat((torch.stack(x, 1), iou[x[0], x[1]][:, None]), 1).cpu().numpy() 135 | if x[0].shape[0] > 1: 136 | matches = matches[matches[:, 2].argsort()[::-1]] 137 | matches = matches[np.unique(matches[:, 1], return_index=True)[1]] 138 | matches = matches[matches[:, 2].argsort()[::-1]] 139 | matches = matches[np.unique(matches[:, 0], return_index=True)[1]] 140 | else: 141 | matches = np.zeros((0, 3)) 142 | 143 | n = matches.shape[0] > 0 144 | m0, m1, _ = matches.transpose().astype(np.int16) 145 | for i, gc in enumerate(gt_classes): 146 | j = m0 == i 147 | if n and sum(j) == 1: 148 | self.matrix[gc, detection_classes[m1[j]]] += 1 # correct 149 | else: 150 | self.matrix[self.nc, gc] += 1 # background FP 151 | 152 | if n: 153 | for i, dc in enumerate(detection_classes): 154 | if not any(m1 == i): 155 | self.matrix[dc, self.nc] += 1 # background FN 156 | 157 | def matrix(self): 158 | return self.matrix 159 | 160 | def plot(self, save_dir='', names=()): 161 | try: 162 | import seaborn as sn 163 | 164 | array = self.matrix / (self.matrix.sum(0).reshape(1, self.nc + 1) + 1E-6) # normalize 165 | array[array < 0.005] = np.nan # don't annotate (would appear as 0.00) 166 | 167 | fig = plt.figure(figsize=(12, 9), tight_layout=True) 168 | sn.set(font_scale=1.0 if self.nc < 50 else 0.8) # for label size 169 | labels = (0 < len(names) < 99) and len(names) == self.nc # apply names to ticklabels 170 | sn.heatmap(array, annot=self.nc < 30, annot_kws={"size": 8}, cmap='Blues', fmt='.2f', square=True, 171 | xticklabels=names + ['background FP'] if labels else "auto", 172 | yticklabels=names + ['background FN'] if labels else "auto").set_facecolor((1, 1, 1)) 173 | fig.axes[0].set_xlabel('True') 174 | fig.axes[0].set_ylabel('Predicted') 175 | fig.savefig(Path(save_dir) / 'confusion_matrix.png', dpi=250) 176 | except Exception as e: 177 | pass 178 | 179 | def print(self): 180 | for i in range(self.nc + 1): 181 | print(' '.join(map(str, self.matrix[i]))) 182 | 183 | 184 | # Plots ---------------------------------------------------------------------------------------------------------------- 185 | 186 | def plot_pr_curve(px, py, ap, save_dir='pr_curve.png', names=()): 187 | # Precision-recall curve 188 | fig, ax = plt.subplots(1, 1, figsize=(9, 6), tight_layout=True) 189 | py = np.stack(py, axis=1) 190 | 191 | if 0 < len(names) < 21: # display per-class legend if < 21 classes 192 | for i, y in enumerate(py.T): 193 | ax.plot(px, y, linewidth=1, label=f'{names[i]} {ap[i, 0]:.3f}') # plot(recall, precision) 194 | else: 195 | ax.plot(px, py, linewidth=1, color='grey') # plot(recall, precision) 196 | 197 | ax.plot(px, py.mean(1), linewidth=3, color='blue', label='all classes %.3f mAP@0.5' % ap[:, 0].mean()) 198 | ax.set_xlabel('Recall') 199 | ax.set_ylabel('Precision') 200 | ax.set_xlim(0, 1) 201 | ax.set_ylim(0, 1) 202 | plt.legend(bbox_to_anchor=(1.04, 1), loc="upper left") 203 | fig.savefig(Path(save_dir), dpi=250) 204 | 205 | 206 | def plot_mc_curve(px, py, save_dir='mc_curve.png', names=(), xlabel='Confidence', ylabel='Metric'): 207 | # Metric-confidence curve 208 | fig, ax = plt.subplots(1, 1, figsize=(9, 6), tight_layout=True) 209 | 210 | if 0 < len(names) < 21: # display per-class legend if < 21 classes 211 | for i, y in enumerate(py): 212 | ax.plot(px, y, linewidth=1, label=f'{names[i]}') # plot(confidence, metric) 213 | else: 214 | ax.plot(px, py.T, linewidth=1, color='grey') # plot(confidence, metric) 215 | 216 | y = py.mean(0) 217 | ax.plot(px, y, linewidth=3, color='blue', label=f'all classes {y.max():.2f} at {px[y.argmax()]:.3f}') 218 | ax.set_xlabel(xlabel) 219 | ax.set_ylabel(ylabel) 220 | ax.set_xlim(0, 1) 221 | ax.set_ylim(0, 1) 222 | plt.legend(bbox_to_anchor=(1.04, 1), loc="upper left") 223 | fig.savefig(Path(save_dir), dpi=250) 224 | -------------------------------------------------------------------------------- /utils/loss.py: -------------------------------------------------------------------------------- 1 | # Loss functions 2 | 3 | import torch 4 | import torch.nn as nn 5 | 6 | from utils.general import bbox_iou 7 | from utils.torch_utils import is_parallel 8 | 9 | 10 | def smooth_BCE(eps=0.1): # https://github.com/ultralytics/yolov3/issues/238#issuecomment-598028441 11 | # return positive, negative label smoothing BCE targets 12 | return 1.0 - 0.5 * eps, 0.5 * eps 13 | 14 | 15 | class BCEBlurWithLogitsLoss(nn.Module): 16 | # BCEwithLogitLoss() with reduced missing label effects. 17 | def __init__(self, alpha=0.05): 18 | super(BCEBlurWithLogitsLoss, self).__init__() 19 | self.loss_fcn = nn.BCEWithLogitsLoss(reduction='none') # must be nn.BCEWithLogitsLoss() 20 | self.alpha = alpha 21 | 22 | def forward(self, pred, true): 23 | loss = self.loss_fcn(pred, true) 24 | pred = torch.sigmoid(pred) # prob from logits 25 | dx = pred - true # reduce only missing label effects 26 | # dx = (pred - true).abs() # reduce missing label and false label effects 27 | alpha_factor = 1 - torch.exp((dx - 1) / (self.alpha + 1e-4)) 28 | loss *= alpha_factor 29 | return loss.mean() 30 | 31 | 32 | class FocalLoss(nn.Module): 33 | # Wraps focal loss around existing loss_fcn(), i.e. criteria = FocalLoss(nn.BCEWithLogitsLoss(), gamma=1.5) 34 | def __init__(self, loss_fcn, gamma=1.5, alpha=0.25): 35 | super(FocalLoss, self).__init__() 36 | self.loss_fcn = loss_fcn # must be nn.BCEWithLogitsLoss() 37 | self.gamma = gamma 38 | self.alpha = alpha 39 | self.reduction = loss_fcn.reduction 40 | self.loss_fcn.reduction = 'none' # required to apply FL to each element 41 | 42 | def forward(self, pred, true): 43 | loss = self.loss_fcn(pred, true) 44 | # p_t = torch.exp(-loss) 45 | # loss *= self.alpha * (1.000001 - p_t) ** self.gamma # non-zero power for gradient stability 46 | 47 | # TF implementation https://github.com/tensorflow/addons/blob/v0.7.1/tensorflow_addons/losses/focal_loss.py 48 | pred_prob = torch.sigmoid(pred) # prob from logits 49 | p_t = true * pred_prob + (1 - true) * (1 - pred_prob) 50 | alpha_factor = true * self.alpha + (1 - true) * (1 - self.alpha) 51 | modulating_factor = (1.0 - p_t) ** self.gamma 52 | loss *= alpha_factor * modulating_factor 53 | 54 | if self.reduction == 'mean': 55 | return loss.mean() 56 | elif self.reduction == 'sum': 57 | return loss.sum() 58 | else: # 'none' 59 | return loss 60 | 61 | 62 | class QFocalLoss(nn.Module): 63 | # Wraps Quality focal loss around existing loss_fcn(), i.e. criteria = FocalLoss(nn.BCEWithLogitsLoss(), gamma=1.5) 64 | def __init__(self, loss_fcn, gamma=1.5, alpha=0.25): 65 | super(QFocalLoss, self).__init__() 66 | self.loss_fcn = loss_fcn # must be nn.BCEWithLogitsLoss() 67 | self.gamma = gamma 68 | self.alpha = alpha 69 | self.reduction = loss_fcn.reduction 70 | self.loss_fcn.reduction = 'none' # required to apply FL to each element 71 | 72 | def forward(self, pred, true): 73 | loss = self.loss_fcn(pred, true) 74 | 75 | pred_prob = torch.sigmoid(pred) # prob from logits 76 | alpha_factor = true * self.alpha + (1 - true) * (1 - self.alpha) 77 | modulating_factor = torch.abs(true - pred_prob) ** self.gamma 78 | loss *= alpha_factor * modulating_factor 79 | 80 | if self.reduction == 'mean': 81 | return loss.mean() 82 | elif self.reduction == 'sum': 83 | return loss.sum() 84 | else: # 'none' 85 | return loss 86 | 87 | 88 | class ComputeLoss: 89 | # Compute losses 90 | def __init__(self, model, autobalance=False): 91 | super(ComputeLoss, self).__init__() 92 | device = next(model.parameters()).device # get model device 93 | h = model.hyp # hyperparameters 94 | 95 | # Define criteria 96 | BCEcls = nn.BCEWithLogitsLoss(pos_weight=torch.tensor([h['cls_pw']], device=device)) 97 | BCEobj = nn.BCEWithLogitsLoss(pos_weight=torch.tensor([h['obj_pw']], device=device)) 98 | 99 | # Class label smoothing https://arxiv.org/pdf/1902.04103.pdf eqn 3 100 | self.cp, self.cn = smooth_BCE(eps=h.get('label_smoothing', 0.0)) # positive, negative BCE targets 101 | 102 | # Focal loss 103 | g = h['fl_gamma'] # focal loss gamma 104 | if g > 0: 105 | BCEcls, BCEobj = FocalLoss(BCEcls, g), FocalLoss(BCEobj, g) 106 | 107 | det = model.module.model[-1] if is_parallel(model) else model.model[-1] # Detect() module 108 | self.balance = {3: [4.0, 1.0, 0.4]}.get(det.nl, [4.0, 1.0, 0.25, 0.06, .02]) # P3-P7 109 | self.ssi = list(det.stride).index(16) if autobalance else 0 # stride 16 index 110 | self.BCEcls, self.BCEobj, self.gr, self.hyp, self.autobalance = BCEcls, BCEobj, model.gr, h, autobalance 111 | for k in 'na', 'nc', 'nl', 'anchors': 112 | setattr(self, k, getattr(det, k)) 113 | 114 | def __call__(self, p, targets): # predictions, targets, model 115 | device = targets.device 116 | lcls, lbox, lobj = torch.zeros(1, device=device), torch.zeros(1, device=device), torch.zeros(1, device=device) 117 | tcls, tbox, indices, anchors = self.build_targets(p, targets) # targets 118 | 119 | # Losses 120 | for i, pi in enumerate(p): # layer index, layer predictions 121 | b, a, gj, gi = indices[i] # image, anchor, gridy, gridx 122 | tobj = torch.zeros_like(pi[..., 0], device=device) # target obj 123 | 124 | n = b.shape[0] # number of targets 125 | if n: 126 | ps = pi[b, a, gj, gi] # prediction subset corresponding to targets 127 | 128 | # Regression 129 | pxy = ps[:, :2].sigmoid() * 2. - 0.5 130 | pwh = (ps[:, 2:4].sigmoid() * 2) ** 2 * anchors[i] 131 | pbox = torch.cat((pxy, pwh), 1) # predicted box 132 | iou = bbox_iou(pbox.T, tbox[i], x1y1x2y2=False, CIoU=True) # iou(prediction, target) 133 | lbox += (1.0 - iou).mean() # iou loss 134 | 135 | # Objectness 136 | tobj[b, a, gj, gi] = (1.0 - self.gr) + self.gr * iou.detach().clamp(0).type(tobj.dtype) # iou ratio 137 | 138 | # Classification 139 | if self.nc > 1: # cls loss (only if multiple classes) 140 | t = torch.full_like(ps[:, 5:], self.cn, device=device) # targets 141 | t[range(n), tcls[i]] = self.cp 142 | lcls += self.BCEcls(ps[:, 5:], t) # BCE 143 | 144 | # Append targets to text file 145 | # with open('targets.txt', 'a') as file: 146 | # [file.write('%11.5g ' * 4 % tuple(x) + '\n') for x in torch.cat((txy[i], twh[i]), 1)] 147 | 148 | obji = self.BCEobj(pi[..., 4], tobj) 149 | lobj += obji * self.balance[i] # obj loss 150 | if self.autobalance: 151 | self.balance[i] = self.balance[i] * 0.9999 + 0.0001 / obji.detach().item() 152 | 153 | if self.autobalance: 154 | self.balance = [x / self.balance[self.ssi] for x in self.balance] 155 | lbox *= self.hyp['box'] 156 | lobj *= self.hyp['obj'] 157 | lcls *= self.hyp['cls'] 158 | bs = tobj.shape[0] # batch size 159 | 160 | loss = lbox + lobj + lcls 161 | return loss * bs, torch.cat((lbox, lobj, lcls, loss)).detach() 162 | 163 | def build_targets(self, p, targets): 164 | # Build targets for compute_loss(), input targets(image,class,x,y,w,h) 165 | na, nt = self.na, targets.shape[0] # number of anchors, targets 166 | tcls, tbox, indices, anch = [], [], [], [] 167 | gain = torch.ones(7, device=targets.device) # normalized to gridspace gain 168 | ai = torch.arange(na, device=targets.device).float().view(na, 1).repeat(1, nt) # same as .repeat_interleave(nt) 169 | targets = torch.cat((targets.repeat(na, 1, 1), ai[:, :, None]), 2) # append anchor indices 170 | 171 | g = 0.5 # bias 172 | off = torch.tensor([[0, 0], 173 | # [1, 0], [0, 1], [-1, 0], [0, -1], # j,k,l,m 174 | # [1, 1], [1, -1], [-1, 1], [-1, -1], # jk,jm,lk,lm 175 | ], device=targets.device).float() * g # offsets 176 | 177 | for i in range(self.nl): 178 | anchors = self.anchors[i] 179 | gain[2:6] = torch.tensor(p[i].shape)[[3, 2, 3, 2]] # xyxy gain 180 | 181 | # Match targets to anchors 182 | t = targets * gain 183 | if nt: 184 | # Matches 185 | r = t[:, :, 4:6] / anchors[:, None] # wh ratio 186 | j = torch.max(r, 1. / r).max(2)[0] < self.hyp['anchor_t'] # compare 187 | # j = wh_iou(anchors, t[:, 4:6]) > model.hyp['iou_t'] # iou(3,n)=wh_iou(anchors(3,2), gwh(n,2)) 188 | t = t[j] # filter 189 | 190 | # Offsets 191 | gxy = t[:, 2:4] # grid xy 192 | gxi = gain[[2, 3]] - gxy # inverse 193 | j, k = ((gxy % 1. < g) & (gxy > 1.)).T 194 | l, m = ((gxi % 1. < g) & (gxi > 1.)).T 195 | j = torch.stack((torch.ones_like(j),)) 196 | t = t.repeat((off.shape[0], 1, 1))[j] 197 | offsets = (torch.zeros_like(gxy)[None] + off[:, None])[j] 198 | else: 199 | t = targets[0] 200 | offsets = 0 201 | 202 | # Define 203 | b, c = t[:, :2].long().T # image, class 204 | gxy = t[:, 2:4] # grid xy 205 | gwh = t[:, 4:6] # grid wh 206 | gij = (gxy - offsets).long() 207 | gi, gj = gij.T # grid xy indices 208 | 209 | # Append 210 | a = t[:, 6].long() # anchor indices 211 | indices.append((b, a, gj.clamp_(0, gain[3] - 1), gi.clamp_(0, gain[2] - 1))) # image, anchor, grid indices 212 | tbox.append(torch.cat((gxy - gij, gwh), 1)) # box 213 | anch.append(anchors[a]) # anchors 214 | tcls.append(c) # class 215 | 216 | return tcls, tbox, indices, anch 217 | -------------------------------------------------------------------------------- /detect.py: -------------------------------------------------------------------------------- 1 | import argparse 2 | import time 3 | from pathlib import Path 4 | 5 | import cv2 6 | import torch 7 | import torch.backends.cudnn as cudnn 8 | from numpy import random 9 | 10 | from models.experimental import attempt_load 11 | from utils.datasets import LoadStreams, LoadImages 12 | from utils.general import check_img_size, check_requirements, check_imshow, non_max_suppression, apply_classifier, \ 13 | scale_coords, xyxy2xywh, strip_optimizer, set_logging, increment_path 14 | from utils.plots import plot_one_box 15 | from utils.torch_utils import select_device, load_classifier, time_synchronized 16 | 17 | import os 18 | import numpy as np 19 | 20 | 21 | def make_dirs(path): 22 | if os.path.exists(path) is False: 23 | os.makedirs(path) 24 | 25 | 26 | def feature_map(intermediate_features, imgname): 27 | dst = 'features+/' 28 | therd_size = 320 29 | index = 0 30 | for layer, j in intermediate_features.items(): 31 | index += 1 32 | layer = str(index) 33 | features = j[0] 34 | iter_range = features.shape[0] 35 | 36 | resolution = j.shape[2] 37 | 38 | add_img = np.zeros(((resolution, resolution, 3))) 39 | for i in range(iter_range): 40 | 41 | channel = iter_range 42 | # plt.imshow(x[0].data.numpy()[0,i,:,:],cmap='jet') 43 | 44 | feature = features.data.cpu().numpy() 45 | feature_img = feature[i, :, :] 46 | feature_img = np.asarray(feature_img * 255, dtype=np.uint8) 47 | 48 | dst_path = os.path.join(dst, imgname) 49 | 50 | make_dirs(dst_path) 51 | feature_img = cv2.applyColorMap(feature_img, cv2.COLORMAP_JET) 52 | if feature_img.shape[0] < therd_size: 53 | dst_file = os.path.join(dst_path, str(layer) + '/') 54 | make_dirs(dst_file) 55 | dst_file = os.path.join(dst_file, str(i) + '.png') 56 | cv2.imwrite(dst_file, feature_img) 57 | 58 | tmp_file = os.path.join(dst_path, str(layer) + '/') 59 | make_dirs(tmp_file) 60 | tmp_file = os.path.join(tmp_file, str(i) + '_' + str(therd_size) + '.png') 61 | tmp_img = feature_img.copy() 62 | # tmp_img = cv2.resize(tmp_img, (therd_size, therd_size), interpolation=cv2.INTER_NEAREST) 63 | add_img = tmp_img/channel + add_img 64 | cv2.imwrite(tmp_file, tmp_img) 65 | add_img.astype(int) 66 | add_file = os.path.join(dst, imgname, str(layer) + '/') 67 | make_dirs(add_file) 68 | add_file = os.path.join(add_file, 'add' + '.png') 69 | cv2.imwrite(add_file, add_img) 70 | 71 | 72 | def detect(save_img=False): 73 | source, weights, view_img, save_txt, imgsz = opt.source, opt.weights, opt.view_img, opt.save_txt, opt.img_size 74 | save_img = not opt.nosave and not source.endswith('.txt') # save inference images 75 | webcam = source.isnumeric() or source.endswith('.txt') or source.lower().startswith( 76 | ('rtsp://', 'rtmp://', 'http://', 'https://')) 77 | 78 | # Directories 79 | save_dir = Path(increment_path(Path(opt.project) / opt.name, exist_ok=opt.exist_ok)) # increment run 80 | (save_dir / 'labels' if save_txt else save_dir).mkdir(parents=True, exist_ok=True) # make dir 81 | 82 | # Initialize 83 | set_logging() 84 | device = select_device(opt.device) 85 | half = device.type != 'cpu' # half precision only supported on CUDA 86 | 87 | # Load model 88 | model = attempt_load(weights, map_location=device) # load FP32 model 89 | stride = int(model.stride.max()) # model stride 90 | imgsz = check_img_size(imgsz, s=stride) # check img_size 91 | if half: 92 | model.half() # to FP16 93 | 94 | # Second-stage classifier 95 | classify = False 96 | if classify: 97 | modelc = load_classifier(name='resnet101', n=2) # initialize 98 | modelc.load_state_dict(torch.load('weights/resnet101.pt', map_location=device)['model']).to(device).eval() 99 | 100 | # Set Dataloader 101 | vid_path, vid_writer = None, None 102 | if webcam: 103 | view_img = check_imshow() 104 | cudnn.benchmark = True # set True to speed up constant image size inference 105 | dataset = LoadStreams(source, img_size=imgsz, stride=stride) 106 | else: 107 | dataset = LoadImages(source, img_size=imgsz, stride=stride) 108 | 109 | # Get names and colors 110 | names = model.module.names if hasattr(model, 'module') else model.names 111 | colors = [[random.randint(0, 255) for _ in range(3)] for _ in names] 112 | 113 | # Run inference 114 | if device.type != 'cpu': 115 | model(torch.zeros(1, 3, imgsz, imgsz).to(device).type_as(next(model.parameters()))) # run once 116 | t0 = time.time() 117 | for path, img, im0s, vid_cap in dataset: 118 | img = torch.from_numpy(img).to(device) 119 | img = img.half() if half else img.float() # uint8 to fp16/32 120 | img /= 255.0 # 0 - 255 to 0.0 - 1.0 121 | if img.ndimension() == 3: 122 | img = img.unsqueeze(0) 123 | 124 | imgname = path.split('/')[-1] 125 | # Inference 126 | t1 = time_synchronized() 127 | pred = model(img, augment=opt.augment, imgname=imgname)[0] 128 | 129 | # Apply NMS 130 | pred = non_max_suppression(pred, opt.conf_thres, opt.iou_thres, classes=opt.classes, agnostic=opt.agnostic_nms) 131 | t2 = time_synchronized() 132 | 133 | # Apply Classifier 134 | if classify: 135 | pred = apply_classifier(pred, modelc, img, im0s) 136 | 137 | # Process detections 138 | for i, det in enumerate(pred): # detections per image 139 | if webcam: # batch_size >= 1 140 | p, s, im0, frame = path[i], '%g: ' % i, im0s[i].copy(), dataset.count 141 | else: 142 | p, s, im0, frame = path, '', im0s, getattr(dataset, 'frame', 0) 143 | 144 | p = Path(p) # to Path 145 | save_path = str(save_dir / p.name) # img.jpg 146 | txt_path = str(save_dir / 'labels' / p.stem) + ('' if dataset.mode == 'image' else f'_{frame}') # img.txt 147 | s += '%gx%g ' % img.shape[2:] # print string 148 | gn = torch.tensor(im0.shape)[[1, 0, 1, 0]] # normalization gain whwh 149 | if len(det): 150 | # Rescale boxes from img_size to im0 size 151 | det[:, :4] = scale_coords(img.shape[2:], det[:, :4], im0.shape).round() 152 | 153 | # Print results 154 | for c in det[:, -1].unique(): 155 | n = (det[:, -1] == c).sum() # detections per class 156 | s += f"{n} {names[int(c)]}{'s' * (n > 1)}, " # add to string 157 | 158 | # Write results 159 | for *xyxy, conf, cls in reversed(det): 160 | if save_txt: # Write to file 161 | xywh = (xyxy2xywh(torch.tensor(xyxy).view(1, 4)) / gn).view(-1).tolist() # normalized xywh 162 | line = (cls, *xywh, conf) if opt.save_conf else (cls, *xywh) # label format 163 | with open(txt_path + '.txt', 'a') as f: 164 | f.write(('%g ' * len(line)).rstrip() % line + '\n') 165 | 166 | if save_img or view_img: # Add bbox to image 167 | # label = f'{names[int(cls)]} {conf:.2f}' 168 | label = f'{names[int(cls)]}' 169 | plot_one_box(xyxy, im0, label=label, color=colors[int(cls)], line_thickness=3) 170 | 171 | # Print time (inference + NMS) 172 | print(f'{s}Done. ({t2 - t1:.3f}s)') 173 | 174 | # Stream results 175 | if view_img: 176 | cv2.imshow(str(p), im0) 177 | cv2.waitKey(1) # 1 millisecond 178 | 179 | # Save results (image with detections) 180 | if save_img: 181 | if dataset.mode == 'image': 182 | cv2.imwrite(save_path, im0) 183 | else: # 'video' or 'stream' 184 | if vid_path != save_path: # new video 185 | vid_path = save_path 186 | if isinstance(vid_writer, cv2.VideoWriter): 187 | vid_writer.release() # release previous video writer 188 | if vid_cap: # video 189 | fps = vid_cap.get(cv2.CAP_PROP_FPS) 190 | w = int(vid_cap.get(cv2.CAP_PROP_FRAME_WIDTH)) 191 | h = int(vid_cap.get(cv2.CAP_PROP_FRAME_HEIGHT)) 192 | else: # stream 193 | fps, w, h = 30, im0.shape[1], im0.shape[0] 194 | save_path += '.mp4' 195 | vid_writer = cv2.VideoWriter(save_path, cv2.VideoWriter_fourcc(*'mp4v'), fps, (w, h)) 196 | vid_writer.write(im0) 197 | 198 | if save_txt or save_img: 199 | s = f"\n{len(list(save_dir.glob('labels/*.txt')))} labels saved to {save_dir / 'labels'}" if save_txt else '' 200 | print(f"Results saved to {save_dir}{s}") 201 | 202 | print(f'Done. ({time.time() - t0:.3f}s)') 203 | 204 | 205 | if __name__ == '__main__': 206 | parser = argparse.ArgumentParser() 207 | parser.add_argument('--weights', nargs='+', type=str, default='', help='model.pt path(s)') 208 | parser.add_argument('--source', type=str, default='', help='source') # file/folder, 0 for webcam 209 | parser.add_argument('--img-size', type=int, default=640, help='inference size (pixels)') 210 | parser.add_argument('--conf-thres', type=float, default=0.25, help='object confidence threshold') 211 | parser.add_argument('--iou-thres', type=float, default=0.45, help='IOU threshold for NMS') 212 | parser.add_argument('--device', default='', help='cuda device, i.e. 0 or 0,1,2,3 or cpu') 213 | parser.add_argument('--view-img', action='store_true', help='display results') 214 | parser.add_argument('--save-txt', action='store_true', help='save results to *.txt') 215 | parser.add_argument('--save-conf', action='store_true', help='save confidences in --save-txt labels') 216 | parser.add_argument('--nosave', action='store_true', help='do not save images/videos') 217 | parser.add_argument('--classes', nargs='+', type=int, help='filter by class: --class 0, or --class 0 2 3') 218 | parser.add_argument('--agnostic-nms', action='store_true', help='class-agnostic NMS') 219 | parser.add_argument('--augment', action='store_true', help='augmented inference') 220 | parser.add_argument('--update', action='store_true', help='update all models') 221 | parser.add_argument('--project', default='runs/detect', help='save results to project/name') 222 | parser.add_argument('--name', default='exp', help='save results to project/name') 223 | parser.add_argument('--exist-ok', action='store_true', help='existing project/name ok, do not increment') 224 | opt = parser.parse_args() 225 | print(opt) 226 | check_requirements(exclude=('pycocotools', 'thop')) 227 | 228 | with torch.no_grad(): 229 | if opt.update: # update all models (to fix SourceChangeWarning) 230 | for opt.weights in ['yolov3.pt', 'yolov3-spp.pt', 'yolov3-tiny.pt']: 231 | detect() 232 | strip_optimizer(opt.weights) 233 | else: 234 | detect() 235 | -------------------------------------------------------------------------------- /models/yolo.py: -------------------------------------------------------------------------------- 1 | # YOLOv3 YOLO-specific modules 2 | 3 | import argparse 4 | import logging 5 | import sys 6 | from copy import deepcopy 7 | 8 | sys.path.append('./') # to run '$ python *.py' files in subdirectories 9 | logger = logging.getLogger(__name__) 10 | 11 | from models.common import * 12 | from models.experimental import * 13 | from utils.autoanchor import check_anchor_order 14 | from utils.general import make_divisible, check_file, set_logging 15 | from utils.torch_utils import time_synchronized, fuse_conv_and_bn, model_info, scale_img, initialize_weights, \ 16 | select_device, copy_attr 17 | 18 | from detect import feature_map 19 | 20 | try: 21 | import thop # for FLOPS computation 22 | except ImportError: 23 | thop = None 24 | 25 | 26 | class Detect(nn.Module): 27 | stride = None # strides computed during build 28 | export = False # onnx export 29 | 30 | def __init__(self, nc=80, anchors=(), ch=()): # detection layer 31 | super(Detect, self).__init__() 32 | self.nc = nc # number of classes 33 | self.no = nc + 5 # number of outputs per anchor 34 | self.nl = len(anchors) # number of detection layers 35 | self.na = len(anchors[0]) // 2 # number of anchors 36 | self.grid = [torch.zeros(1)] * self.nl # init grid 37 | a = torch.tensor(anchors).float().view(self.nl, -1, 2) 38 | self.register_buffer('anchors', a) # shape(nl,na,2) 39 | self.register_buffer('anchor_grid', a.clone().view(self.nl, 1, -1, 1, 1, 2)) # shape(nl,1,na,1,1,2) 40 | self.m = nn.ModuleList(nn.Conv2d(x, self.no * self.na, 1) for x in ch) # output conv 41 | 42 | def forward(self, x): 43 | # x = x.copy() # for profiling 44 | z = [] # inference output 45 | self.training |= self.export 46 | for i in range(self.nl): 47 | x[i] = self.m[i](x[i]) # conv 48 | bs, _, ny, nx = x[i].shape # x(bs,255,20,20) to x(bs,3,20,20,85) 49 | x[i] = x[i].view(bs, self.na, self.no, ny, nx).permute(0, 1, 3, 4, 2).contiguous() 50 | 51 | if not self.training: # inference 52 | if self.grid[i].shape[2:4] != x[i].shape[2:4]: 53 | self.grid[i] = self._make_grid(nx, ny).to(x[i].device) 54 | 55 | y = x[i].sigmoid() 56 | y[..., 0:2] = (y[..., 0:2] * 2. - 0.5 + self.grid[i]) * self.stride[i] # xy 57 | y[..., 2:4] = (y[..., 2:4] * 2) ** 2 * self.anchor_grid[i] # wh 58 | z.append(y.view(bs, -1, self.no)) 59 | 60 | return x if self.training else (torch.cat(z, 1), x) 61 | 62 | @staticmethod 63 | def _make_grid(nx=20, ny=20): 64 | yv, xv = torch.meshgrid([torch.arange(ny), torch.arange(nx)]) 65 | return torch.stack((xv, yv), 2).view((1, 1, ny, nx, 2)).float() 66 | 67 | 68 | class Model(nn.Module): 69 | def __init__(self, cfg='yolov3.yaml', ch=3, nc=None, anchors=None): # model, input channels, number of classes 70 | super(Model, self).__init__() 71 | if isinstance(cfg, dict): 72 | self.yaml = cfg # model dict 73 | else: # is *.yaml 74 | import yaml # for torch hub 75 | self.yaml_file = Path(cfg).name 76 | with open(cfg) as f: 77 | self.yaml = yaml.load(f, Loader=yaml.SafeLoader) # model dict 78 | 79 | # Define model 80 | ch = self.yaml['ch'] = self.yaml.get('ch', ch) # input channels 81 | if nc and nc != self.yaml['nc']: 82 | logger.info(f"Overriding model.yaml nc={self.yaml['nc']} with nc={nc}") 83 | self.yaml['nc'] = nc # override yaml value 84 | if anchors: 85 | logger.info(f'Overriding model.yaml anchors with anchors={anchors}') 86 | self.yaml['anchors'] = round(anchors) # override yaml value 87 | self.model, self.save = parse_model(deepcopy(self.yaml), ch=[ch]) # model, savelist 88 | self.names = [str(i) for i in range(self.yaml['nc'])] # default names 89 | # print([x.shape for x in self.forward(torch.zeros(1, ch, 64, 64))]) 90 | 91 | # Build strides, anchors 92 | m = self.model[-1] # Detect() 93 | if isinstance(m, Detect): 94 | s = 256 # 2x min stride 95 | m.stride = torch.tensor([s / x.shape[-2] for x in self.forward(torch.zeros(1, ch, s, s))]) # forward 96 | m.anchors /= m.stride.view(-1, 1, 1) 97 | check_anchor_order(m) 98 | self.stride = m.stride 99 | self._initialize_biases() # only run once 100 | # print('Strides: %s' % m.stride.tolist()) 101 | 102 | # Init weights, biases 103 | initialize_weights(self) 104 | self.info() 105 | logger.info('') 106 | 107 | def forward(self, x, augment=False, profile=False, imgname=''): 108 | if augment: 109 | img_size = x.shape[-2:] # height, width 110 | s = [1, 0.83, 0.67] # scales 111 | f = [None, 3, None] # flips (2-ud, 3-lr) 112 | y = [] # outputs 113 | for si, fi in zip(s, f): 114 | xi = scale_img(x.flip(fi) if fi else x, si, gs=int(self.stride.max())) 115 | yi = self.forward_once(xi)[0] # forward 116 | # cv2.imwrite(f'img_{si}.jpg', 255 * xi[0].cpu().numpy().transpose((1, 2, 0))[:, :, ::-1]) # save 117 | yi[..., :4] /= si # de-scale 118 | if fi == 2: 119 | yi[..., 1] = img_size[0] - yi[..., 1] # de-flip ud 120 | elif fi == 3: 121 | yi[..., 0] = img_size[1] - yi[..., 0] # de-flip lr 122 | y.append(yi) 123 | return torch.cat(y, 1), None # augmented inference, train 124 | else: 125 | return self.forward_once(x, profile, imgname) # single-scale inference, train 126 | 127 | def forward_once(self, x, profile=False, imgname=''): 128 | y, dt = [], [] # outputs 129 | intermediate_features = {} 130 | i = 0 131 | for m in self.model: 132 | i += 1 133 | if m.f != -1: # if not from previous layer 134 | 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 135 | 136 | if profile: 137 | o = thop.profile(m, inputs=(x,), verbose=False)[0] / 1E9 * 2 if thop else 0 # FLOPS 138 | t = time_synchronized() 139 | for _ in range(10): 140 | _ = m(x) 141 | dt.append((time_synchronized() - t) * 100) 142 | print('%10.1f%10.0f%10.1fms %-40s' % (o, m.np, dt[-1], m.type)) 143 | 144 | x = m(x) # run 145 | # if i in range(0, 8): 146 | # intermediate_features[m] = x 147 | y.append(x if m.i in self.save else None) # save output 148 | 149 | if profile: 150 | print('%.1fms total' % sum(dt)) 151 | feature_map(intermediate_features, imgname) 152 | return x 153 | 154 | 155 | def _initialize_biases(self, cf=None): # initialize biases into Detect(), cf is class frequency 156 | # https://arxiv.org/abs/1708.02002 section 3.3 157 | # cf = torch.bincount(torch.tensor(np.concatenate(dataset.labels, 0)[:, 0]).long(), minlength=nc) + 1. 158 | m = self.model[-1] # Detect() module 159 | for mi, s in zip(m.m, m.stride): # from 160 | b = mi.bias.view(m.na, -1) # conv.bias(255) to (3,85) 161 | b.data[:, 4] += math.log(8 / (640 / s) ** 2) # obj (8 objects per 640 image) 162 | b.data[:, 5:] += math.log(0.6 / (m.nc - 0.99)) if cf is None else torch.log(cf / cf.sum()) # cls 163 | mi.bias = torch.nn.Parameter(b.view(-1), requires_grad=True) 164 | 165 | def _print_biases(self): 166 | m = self.model[-1] # Detect() module 167 | for mi in m.m: # from 168 | b = mi.bias.detach().view(m.na, -1).T # conv.bias(255) to (3,85) 169 | print(('%6g Conv2d.bias:' + '%10.3g' * 6) % (mi.weight.shape[1], *b[:5].mean(1).tolist(), b[5:].mean())) 170 | 171 | # def _print_weights(self): 172 | # for m in self.model.modules(): 173 | # if type(m) is Bottleneck: 174 | # print('%10.3g' % (m.w.detach().sigmoid() * 2)) # shortcut weights 175 | 176 | def fuse(self): # fuse model Conv2d() + BatchNorm2d() layers 177 | print('Fusing layers... ') 178 | for m in self.model.modules(): 179 | if type(m) is Conv and hasattr(m, 'bn'): 180 | m.conv = fuse_conv_and_bn(m.conv, m.bn) # update conv 181 | delattr(m, 'bn') # remove batchnorm 182 | m.forward = m.fuseforward # update forward 183 | self.info() 184 | return self 185 | 186 | def nms(self, mode=True): # add or remove NMS module 187 | present = type(self.model[-1]) is NMS # last layer is NMS 188 | if mode and not present: 189 | print('Adding NMS... ') 190 | m = NMS() # module 191 | m.f = -1 # from 192 | m.i = self.model[-1].i + 1 # index 193 | self.model.add_module(name='%s' % m.i, module=m) # add 194 | self.eval() 195 | elif not mode and present: 196 | print('Removing NMS... ') 197 | self.model = self.model[:-1] # remove 198 | return self 199 | 200 | def autoshape(self): # add autoShape module 201 | print('Adding autoShape... ') 202 | m = autoShape(self) # wrap model 203 | copy_attr(m, self, include=('yaml', 'nc', 'hyp', 'names', 'stride'), exclude=()) # copy attributes 204 | return m 205 | 206 | def info(self, verbose=False, img_size=640): # print model information 207 | model_info(self, verbose, img_size) 208 | 209 | 210 | def parse_model(d, ch): # model_dict, input_channels(3) 211 | logger.info('\n%3s%18s%3s%10s %-40s%-30s' % ('', 'from', 'n', 'params', 'module', 'arguments')) 212 | anchors, nc, gd, gw = d['anchors'], d['nc'], d['depth_multiple'], d['width_multiple'] 213 | na = (len(anchors[0]) // 2) if isinstance(anchors, list) else anchors # number of anchors 214 | no = na * (nc + 5) # number of outputs = anchors * (classes + 5) 215 | 216 | layers, save, c2 = [], [], ch[-1] # layers, savelist, ch out 217 | for i, (f, n, m, args) in enumerate(d['backbone'] + d['head']): # from, number, module, args 218 | m = eval(m) if isinstance(m, str) else m # eval strings 219 | for j, a in enumerate(args): 220 | try: 221 | args[j] = eval(a) if isinstance(a, str) else a # eval strings 222 | except: 223 | pass 224 | 225 | n = max(round(n * gd), 1) if n > 1 else n # depth gain 226 | if m in [Conv, GhostConv, Bottleneck, GhostBottleneck, SPP, DWConv, MixConv2d, Focus, CrossConv, BottleneckCSP, 227 | C3, C3TR, RMF, EFE, MySpp, GhostModule]: 228 | c1, c2 = ch[f], args[0] 229 | if c2 != no: # if not output 230 | c2 = make_divisible(c2 * gw, 8) 231 | 232 | args = [c1, c2, *args[1:]] 233 | if m in [BottleneckCSP, C3, C3TR]: 234 | args.insert(2, n) # number of repeats 235 | n = 1 236 | elif m is nn.BatchNorm2d: 237 | args = [ch[f]] 238 | elif m is Concat: 239 | c2 = sum([ch[x] for x in f]) 240 | elif m is Detect: 241 | args.append([ch[x] for x in f]) 242 | if isinstance(args[1], int): # number of anchors 243 | args[1] = [list(range(args[1] * 2))] * len(f) 244 | elif m is Contract: 245 | c2 = ch[f] * args[0] ** 2 246 | elif m is Expand: 247 | c2 = ch[f] // args[0] ** 2 248 | else: 249 | c2 = ch[f] 250 | 251 | m_ = nn.Sequential(*[m(*args) for _ in range(n)]) if n > 1 else m(*args) # module 252 | t = str(m)[8:-2].replace('__main__.', '') # module type 253 | np = sum([x.numel() for x in m_.parameters()]) # number params 254 | m_.i, m_.f, m_.type, m_.np = i, f, t, np # attach index, 'from' index, type, number params 255 | logger.info('%3s%18s%3s%10.0f %-40s%-30s' % (i, f, n, np, t, args)) # print 256 | save.extend(x % i for x in ([f] if isinstance(f, int) else f) if x != -1) # append to savelist 257 | layers.append(m_) 258 | if i == 0: 259 | ch = [] 260 | ch.append(c2) 261 | return nn.Sequential(*layers), sorted(save) 262 | 263 | 264 | if __name__ == '__main__': 265 | parser = argparse.ArgumentParser() 266 | parser.add_argument('--cfg', type=str, default='yolov3.yaml', help='model.yaml') 267 | parser.add_argument('--device', default='', help='cuda device, i.e. 0 or 0,1,2,3 or cpu') 268 | opt = parser.parse_args() 269 | opt.cfg = check_file(opt.cfg) # check file 270 | set_logging() 271 | device = select_device(opt.device) 272 | 273 | # Create model 274 | model = Model(opt.cfg).to(device) 275 | model.train() 276 | 277 | # Profile 278 | # img = torch.rand(8 if torch.cuda.is_available() else 1, 3, 640, 640).to(device) 279 | # y = model(img, profile=True) 280 | 281 | # Tensorboard 282 | # from torch.utils.tensorboard import SummaryWriter 283 | # tb_writer = SummaryWriter() 284 | # print("Run 'tensorboard --logdir=models/runs' to view tensorboard at http://localhost:6006/") 285 | # tb_writer.add_graph(model.model, img) # add model to tensorboard 286 | # tb_writer.add_image('test', img[0], dataformats='CWH') # add model to tensorboard 287 | -------------------------------------------------------------------------------- /utils/torch_utils.py: -------------------------------------------------------------------------------- 1 | # YOLOv3 PyTorch utils 2 | 3 | import datetime 4 | import logging 5 | import math 6 | import os 7 | import platform 8 | import subprocess 9 | import time 10 | from contextlib import contextmanager 11 | from copy import deepcopy 12 | from pathlib import Path 13 | 14 | import torch 15 | import torch.backends.cudnn as cudnn 16 | import torch.nn as nn 17 | import torch.nn.functional as F 18 | import torchvision 19 | 20 | from torchstat import stat 21 | 22 | try: 23 | import thop # for FLOPS computation 24 | except ImportError: 25 | thop = None 26 | logger = logging.getLogger(__name__) 27 | 28 | 29 | @contextmanager 30 | def torch_distributed_zero_first(local_rank: int): 31 | """ 32 | Decorator to make all processes in distributed training wait for each local_master to do something. 33 | """ 34 | if local_rank not in [-1, 0]: 35 | torch.distributed.barrier() 36 | yield 37 | if local_rank == 0: 38 | torch.distributed.barrier() 39 | 40 | 41 | def init_torch_seeds(seed=0): 42 | # Speed-reproducibility tradeoff https://pytorch.org/docs/stable/notes/randomness.html 43 | torch.manual_seed(seed) 44 | if seed == 0: # slower, more reproducible 45 | cudnn.benchmark, cudnn.deterministic = False, True 46 | else: # faster, less reproducible 47 | cudnn.benchmark, cudnn.deterministic = True, False 48 | 49 | 50 | def date_modified(path=__file__): 51 | # return human-readable file modification date, i.e. '2021-3-26' 52 | t = datetime.datetime.fromtimestamp(Path(path).stat().st_mtime) 53 | return f'{t.year}-{t.month}-{t.day}' 54 | 55 | 56 | def git_describe(path=Path(__file__).parent): # path must be a directory 57 | # return human-readable git description, i.e. v5.0-5-g3e25f1e https://git-scm.com/docs/git-describe 58 | s = f'git -C {path} describe --tags --long --always' 59 | try: 60 | return subprocess.check_output(s, shell=True, stderr=subprocess.STDOUT).decode()[:-1] 61 | except subprocess.CalledProcessError as e: 62 | return '' # not a git repository 63 | 64 | 65 | def select_device(device='', batch_size=None): 66 | # device = 'cpu' or '0' or '0,1,2,3' 67 | s = f'YOLOv3 🚀 {git_describe() or date_modified()} torch {torch.__version__} ' # string 68 | cpu = device.lower() == 'cpu' 69 | if cpu: 70 | os.environ['CUDA_VISIBLE_DEVICES'] = '-1' # force torch.cuda.is_available() = False 71 | elif device: # non-cpu device requested 72 | os.environ['CUDA_VISIBLE_DEVICES'] = device # set environment variable 73 | assert torch.cuda.is_available(), f'CUDA unavailable, invalid device {device} requested' # check availability 74 | 75 | cuda = not cpu and torch.cuda.is_available() 76 | if cuda: 77 | n = torch.cuda.device_count() 78 | if n > 1 and batch_size: # check that batch_size is compatible with device_count 79 | assert batch_size % n == 0, f'batch-size {batch_size} not multiple of GPU count {n}' 80 | space = ' ' * len(s) 81 | for i, d in enumerate(device.split(',') if device else range(n)): 82 | p = torch.cuda.get_device_properties(i) 83 | s += f"{'' if i == 0 else space}CUDA:{d} ({p.name}, {p.total_memory / 1024 ** 2}MB)\n" # bytes to MB 84 | else: 85 | s += 'CPU\n' 86 | 87 | logger.info(s.encode().decode('ascii', 'ignore') if platform.system() == 'Windows' else s) # emoji-safe 88 | return torch.device('cuda:0' if cuda else 'cpu') 89 | 90 | 91 | def time_synchronized(): 92 | # pytorch-accurate time 93 | if torch.cuda.is_available(): 94 | torch.cuda.synchronize() 95 | return time.time() 96 | 97 | 98 | def profile(x, ops, n=100, device=None): 99 | # profile a pytorch module or list of modules. Example usage: 100 | # x = torch.randn(16, 3, 640, 640) # input 101 | # m1 = lambda x: x * torch.sigmoid(x) 102 | # m2 = nn.SiLU() 103 | # profile(x, [m1, m2], n=100) # profile speed over 100 iterations 104 | 105 | device = device or torch.device('cuda:0' if torch.cuda.is_available() else 'cpu') 106 | x = x.to(device) 107 | x.requires_grad = True 108 | print(torch.__version__, device.type, torch.cuda.get_device_properties(0) if device.type == 'cuda' else '') 109 | print(f"\n{'Params':>12s}{'GFLOPS':>12s}{'forward (ms)':>16s}{'backward (ms)':>16s}{'input':>24s}{'output':>24s}") 110 | for m in ops if isinstance(ops, list) else [ops]: 111 | m = m.to(device) if hasattr(m, 'to') else m # device 112 | m = m.half() if hasattr(m, 'half') and isinstance(x, torch.Tensor) and x.dtype is torch.float16 else m # type 113 | dtf, dtb, t = 0., 0., [0., 0., 0.] # dt forward, backward 114 | try: 115 | flops = thop.profile(m, inputs=(x,), verbose=False)[0] / 1E9 * 2 # GFLOPS 116 | except: 117 | flops = 0 118 | 119 | for _ in range(n): 120 | t[0] = time_synchronized() 121 | y = m(x) 122 | t[1] = time_synchronized() 123 | try: 124 | _ = y.sum().backward() 125 | t[2] = time_synchronized() 126 | except: # no backward method 127 | t[2] = float('nan') 128 | dtf += (t[1] - t[0]) * 1000 / n # ms per op forward 129 | dtb += (t[2] - t[1]) * 1000 / n # ms per op backward 130 | 131 | s_in = tuple(x.shape) if isinstance(x, torch.Tensor) else 'list' 132 | s_out = tuple(y.shape) if isinstance(y, torch.Tensor) else 'list' 133 | p = sum(list(x.numel() for x in m.parameters())) if isinstance(m, nn.Module) else 0 # parameters 134 | print(f'{p:12}{flops:12.4g}{dtf:16.4g}{dtb:16.4g}{str(s_in):>24s}{str(s_out):>24s}') 135 | 136 | 137 | def is_parallel(model): 138 | return type(model) in (nn.parallel.DataParallel, nn.parallel.DistributedDataParallel) 139 | 140 | 141 | def intersect_dicts(da, db, exclude=()): 142 | # Dictionary intersection of matching keys and shapes, omitting 'exclude' keys, using da values 143 | 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} 144 | 145 | 146 | def initialize_weights(model): 147 | for m in model.modules(): 148 | t = type(m) 149 | if t is nn.Conv2d: 150 | pass # nn.init.kaiming_normal_(m.weight, mode='fan_out', nonlinearity='relu') 151 | elif t is nn.BatchNorm2d: 152 | m.eps = 1e-3 153 | m.momentum = 0.03 154 | elif t in [nn.Hardswish, nn.LeakyReLU, nn.ReLU, nn.ReLU6]: 155 | m.inplace = True 156 | 157 | 158 | def find_modules(model, mclass=nn.Conv2d): 159 | # Finds layer indices matching module class 'mclass' 160 | return [i for i, m in enumerate(model.module_list) if isinstance(m, mclass)] 161 | 162 | 163 | def sparsity(model): 164 | # Return global model sparsity 165 | a, b = 0., 0. 166 | for p in model.parameters(): 167 | a += p.numel() 168 | b += (p == 0).sum() 169 | return b / a 170 | 171 | 172 | def prune(model, amount=0.3): 173 | # Prune model to requested global sparsity 174 | import torch.nn.utils.prune as prune 175 | print('Pruning model... ', end='') 176 | for name, m in model.named_modules(): 177 | if isinstance(m, nn.Conv2d): 178 | prune.l1_unstructured(m, name='weight', amount=amount) # prune 179 | prune.remove(m, 'weight') # make permanent 180 | print(' %.3g global sparsity' % sparsity(model)) 181 | 182 | 183 | def fuse_conv_and_bn(conv, bn): 184 | # Fuse convolution and batchnorm layers https://tehnokv.com/posts/fusing-batchnorm-and-conv/ 185 | fusedconv = nn.Conv2d(conv.in_channels, 186 | conv.out_channels, 187 | kernel_size=conv.kernel_size, 188 | stride=conv.stride, 189 | padding=conv.padding, 190 | groups=conv.groups, 191 | bias=True).requires_grad_(False).to(conv.weight.device) 192 | 193 | # prepare filters 194 | w_conv = conv.weight.clone().view(conv.out_channels, -1) 195 | w_bn = torch.diag(bn.weight.div(torch.sqrt(bn.eps + bn.running_var))) 196 | fusedconv.weight.copy_(torch.mm(w_bn, w_conv).view(fusedconv.weight.shape)) 197 | 198 | # prepare spatial bias 199 | b_conv = torch.zeros(conv.weight.size(0), device=conv.weight.device) if conv.bias is None else conv.bias 200 | b_bn = bn.bias - bn.weight.mul(bn.running_mean).div(torch.sqrt(bn.running_var + bn.eps)) 201 | fusedconv.bias.copy_(torch.mm(w_bn, b_conv.reshape(-1, 1)).reshape(-1) + b_bn) 202 | 203 | return fusedconv 204 | 205 | 206 | def model_info(model, verbose=False, img_size=640): 207 | # Model information. img_size may be int or list, i.e. img_size=640 or img_size=[640, 320] 208 | n_p = sum(x.numel() for x in model.parameters()) # number parameters 209 | n_g = sum(x.numel() for x in model.parameters() if x.requires_grad) # number gradients 210 | if verbose: 211 | print('%5s %40s %9s %12s %20s %10s %10s' % ('layer', 'name', 'gradient', 'parameters', 'shape', 'mu', 'sigma')) 212 | for i, (name, p) in enumerate(model.named_parameters()): 213 | name = name.replace('module_list.', '') 214 | print('%5g %40s %9s %12g %20s %10.3g %10.3g' % 215 | (i, name, p.requires_grad, p.numel(), list(p.shape), p.mean(), p.std())) 216 | 217 | try: # FLOPS 218 | from thop import profile 219 | stride = max(int(model.stride.max()), 32) if hasattr(model, 'stride') else 32 220 | img = torch.zeros((1, model.yaml.get('ch', 3), stride, stride), device=next(model.parameters()).device) # input 221 | flops = profile(deepcopy(model), inputs=(img,), verbose=False)[0] / 1E9 * 2 # stride GFLOPS 222 | img_size = img_size if isinstance(img_size, list) else [img_size, img_size] # expand if int/float 223 | fs = ', %.1f GFLOPS' % (flops * img_size[0] / stride * img_size[1] / stride) # 640x640 GFLOPS 224 | except (ImportError, Exception): 225 | fs = '' 226 | 227 | logger.info(f"Model Summary: {len(list(model.modules()))} layers, {n_p} parameters, {n_g} gradients{fs}") 228 | 229 | 230 | def load_classifier(name='resnet101', n=2): 231 | # Loads a pretrained model reshaped to n-class output 232 | model = torchvision.models.__dict__[name](pretrained=True) 233 | 234 | # ResNet model properties 235 | # input_size = [3, 224, 224] 236 | # input_space = 'RGB' 237 | # input_range = [0, 1] 238 | # mean = [0.485, 0.456, 0.406] 239 | # std = [0.229, 0.224, 0.225] 240 | 241 | # Reshape output to n classes 242 | filters = model.fc.weight.shape[1] 243 | model.fc.bias = nn.Parameter(torch.zeros(n), requires_grad=True) 244 | model.fc.weight = nn.Parameter(torch.zeros(n, filters), requires_grad=True) 245 | model.fc.out_features = n 246 | return model 247 | 248 | 249 | def scale_img(img, ratio=1.0, same_shape=False, gs=32): # img(16,3,256,416) 250 | # scales img(bs,3,y,x) by ratio constrained to gs-multiple 251 | if ratio == 1.0: 252 | return img 253 | else: 254 | h, w = img.shape[2:] 255 | s = (int(h * ratio), int(w * ratio)) # new size 256 | img = F.interpolate(img, size=s, mode='bilinear', align_corners=False) # resize 257 | if not same_shape: # pad/crop img 258 | h, w = [math.ceil(x * ratio / gs) * gs for x in (h, w)] 259 | return F.pad(img, [0, w - s[1], 0, h - s[0]], value=0.447) # value = imagenet mean 260 | 261 | 262 | def copy_attr(a, b, include=(), exclude=()): 263 | # Copy attributes from b to a, options to only include [...] and to exclude [...] 264 | for k, v in b.__dict__.items(): 265 | if (len(include) and k not in include) or k.startswith('_') or k in exclude: 266 | continue 267 | else: 268 | setattr(a, k, v) 269 | 270 | 271 | class ModelEMA: 272 | """ Model Exponential Moving Average from https://github.com/rwightman/pytorch-image-models 273 | Keep a moving average of everything in the model state_dict (parameters and buffers). 274 | This is intended to allow functionality like 275 | https://www.tensorflow.org/api_docs/python/tf/train/ExponentialMovingAverage 276 | A smoothed version of the weights is necessary for some training schemes to perform well. 277 | This class is sensitive where it is initialized in the sequence of model init, 278 | GPU assignment and distributed training wrappers. 279 | """ 280 | 281 | def __init__(self, model, decay=0.9999, updates=0): 282 | # Create EMA 283 | self.ema = deepcopy(model.module if is_parallel(model) else model).eval() # FP32 EMA 284 | # if next(model.parameters()).device.type != 'cpu': 285 | # self.ema.half() # FP16 EMA 286 | self.updates = updates # number of EMA updates 287 | self.decay = lambda x: decay * (1 - math.exp(-x / 2000)) # decay exponential ramp (to help early epochs) 288 | for p in self.ema.parameters(): 289 | p.requires_grad_(False) 290 | 291 | def update(self, model): 292 | # Update EMA parameters 293 | with torch.no_grad(): 294 | self.updates += 1 295 | d = self.decay(self.updates) 296 | 297 | msd = model.module.state_dict() if is_parallel(model) else model.state_dict() # model state_dict 298 | for k, v in self.ema.state_dict().items(): 299 | if v.dtype.is_floating_point: 300 | v *= d 301 | v += (1. - d) * msd[k].detach() 302 | 303 | def update_attr(self, model, include=(), exclude=('process_group', 'reducer')): 304 | # Update EMA attributes 305 | copy_attr(self.ema, model, include, exclude) 306 | -------------------------------------------------------------------------------- /utils/wandb_logging/wandb_utils.py: -------------------------------------------------------------------------------- 1 | import json 2 | import sys 3 | from pathlib import Path 4 | 5 | import torch 6 | import yaml 7 | from tqdm import tqdm 8 | 9 | sys.path.append(str(Path(__file__).parent.parent.parent)) # add utils/ to path 10 | from utils.datasets import LoadImagesAndLabels 11 | from utils.datasets import img2label_paths 12 | from utils.general import colorstr, xywh2xyxy, check_dataset 13 | 14 | try: 15 | import wandb 16 | from wandb import init, finish 17 | except ImportError: 18 | wandb = None 19 | 20 | WANDB_ARTIFACT_PREFIX = 'wandb-artifact://' 21 | 22 | 23 | def remove_prefix(from_string, prefix=WANDB_ARTIFACT_PREFIX): 24 | return from_string[len(prefix):] 25 | 26 | 27 | def check_wandb_config_file(data_config_file): 28 | wandb_config = '_wandb.'.join(data_config_file.rsplit('.', 1)) # updated data.yaml path 29 | if Path(wandb_config).is_file(): 30 | return wandb_config 31 | return data_config_file 32 | 33 | 34 | def get_run_info(run_path): 35 | run_path = Path(remove_prefix(run_path, WANDB_ARTIFACT_PREFIX)) 36 | run_id = run_path.stem 37 | project = run_path.parent.stem 38 | model_artifact_name = 'run_' + run_id + '_model' 39 | return run_id, project, model_artifact_name 40 | 41 | 42 | def check_wandb_resume(opt): 43 | process_wandb_config_ddp_mode(opt) if opt.global_rank not in [-1, 0] else None 44 | if isinstance(opt.resume, str): 45 | if opt.resume.startswith(WANDB_ARTIFACT_PREFIX): 46 | if opt.global_rank not in [-1, 0]: # For resuming DDP runs 47 | run_id, project, model_artifact_name = get_run_info(opt.resume) 48 | api = wandb.Api() 49 | artifact = api.artifact(project + '/' + model_artifact_name + ':latest') 50 | modeldir = artifact.download() 51 | opt.weights = str(Path(modeldir) / "last.pt") 52 | return True 53 | return None 54 | 55 | 56 | def process_wandb_config_ddp_mode(opt): 57 | with open(opt.data) as f: 58 | data_dict = yaml.load(f, Loader=yaml.SafeLoader) # data dict 59 | train_dir, val_dir = None, None 60 | if isinstance(data_dict['train'], str) and data_dict['train'].startswith(WANDB_ARTIFACT_PREFIX): 61 | api = wandb.Api() 62 | train_artifact = api.artifact(remove_prefix(data_dict['train']) + ':' + opt.artifact_alias) 63 | train_dir = train_artifact.download() 64 | train_path = Path(train_dir) / 'data/images/' 65 | data_dict['train'] = str(train_path) 66 | 67 | if isinstance(data_dict['val'], str) and data_dict['val'].startswith(WANDB_ARTIFACT_PREFIX): 68 | api = wandb.Api() 69 | val_artifact = api.artifact(remove_prefix(data_dict['val']) + ':' + opt.artifact_alias) 70 | val_dir = val_artifact.download() 71 | val_path = Path(val_dir) / 'data/images/' 72 | data_dict['val'] = str(val_path) 73 | if train_dir or val_dir: 74 | ddp_data_path = str(Path(val_dir) / 'wandb_local_data.yaml') 75 | with open(ddp_data_path, 'w') as f: 76 | yaml.dump(data_dict, f) 77 | opt.data = ddp_data_path 78 | 79 | 80 | class WandbLogger(): 81 | def __init__(self, opt, name, run_id, data_dict, job_type='Training'): 82 | # Pre-training routine -- 83 | self.job_type = job_type 84 | self.wandb, self.wandb_run, self.data_dict = wandb, None if not wandb else wandb.run, data_dict 85 | # It's more elegant to stick to 1 wandb.init call, but useful config data is overwritten in the WandbLogger's wandb.init call 86 | if isinstance(opt.resume, str): # checks resume from artifact 87 | if opt.resume.startswith(WANDB_ARTIFACT_PREFIX): 88 | run_id, project, model_artifact_name = get_run_info(opt.resume) 89 | model_artifact_name = WANDB_ARTIFACT_PREFIX + model_artifact_name 90 | assert wandb, 'install wandb to resume wandb runs' 91 | # Resume wandb-artifact:// runs here| workaround for not overwriting wandb.config 92 | self.wandb_run = wandb.init(id=run_id, project=project, resume='allow') 93 | opt.resume = model_artifact_name 94 | elif self.wandb: 95 | self.wandb_run = wandb.init(config=opt, 96 | resume="allow", 97 | project='YOLOv5' if opt.project == 'runs/train' else Path(opt.project).stem, 98 | name=name, 99 | job_type=job_type, 100 | id=run_id) if not wandb.run else wandb.run 101 | if self.wandb_run: 102 | if self.job_type == 'Training': 103 | if not opt.resume: 104 | wandb_data_dict = self.check_and_upload_dataset(opt) if opt.upload_dataset else data_dict 105 | # Info useful for resuming from artifacts 106 | self.wandb_run.config.opt = vars(opt) 107 | self.wandb_run.config.data_dict = wandb_data_dict 108 | self.data_dict = self.setup_training(opt, data_dict) 109 | if self.job_type == 'Dataset Creation': 110 | self.data_dict = self.check_and_upload_dataset(opt) 111 | else: 112 | prefix = colorstr('wandb: ') 113 | print(f"{prefix}Install Weights & Biases for YOLOv5 logging with 'pip install wandb' (recommended)") 114 | 115 | def check_and_upload_dataset(self, opt): 116 | assert wandb, 'Install wandb to upload dataset' 117 | check_dataset(self.data_dict) 118 | config_path = self.log_dataset_artifact(opt.data, 119 | opt.single_cls, 120 | 'YOLOv5' if opt.project == 'runs/train' else Path(opt.project).stem) 121 | print("Created dataset config file ", config_path) 122 | with open(config_path) as f: 123 | wandb_data_dict = yaml.load(f, Loader=yaml.SafeLoader) 124 | return wandb_data_dict 125 | 126 | def setup_training(self, opt, data_dict): 127 | self.log_dict, self.current_epoch, self.log_imgs = {}, 0, 16 # Logging Constants 128 | self.bbox_interval = opt.bbox_interval 129 | if isinstance(opt.resume, str): 130 | modeldir, _ = self.download_model_artifact(opt) 131 | if modeldir: 132 | self.weights = Path(modeldir) / "last.pt" 133 | config = self.wandb_run.config 134 | opt.weights, opt.save_period, opt.batch_size, opt.bbox_interval, opt.epochs, opt.hyp = str( 135 | self.weights), config.save_period, config.total_batch_size, config.bbox_interval, config.epochs, \ 136 | config.opt['hyp'] 137 | data_dict = dict(self.wandb_run.config.data_dict) # eliminates the need for config file to resume 138 | if 'val_artifact' not in self.__dict__: # If --upload_dataset is set, use the existing artifact, don't download 139 | self.train_artifact_path, self.train_artifact = self.download_dataset_artifact(data_dict.get('train'), 140 | opt.artifact_alias) 141 | self.val_artifact_path, self.val_artifact = self.download_dataset_artifact(data_dict.get('val'), 142 | opt.artifact_alias) 143 | self.result_artifact, self.result_table, self.val_table, self.weights = None, None, None, None 144 | if self.train_artifact_path is not None: 145 | train_path = Path(self.train_artifact_path) / 'data/images/' 146 | data_dict['train'] = str(train_path) 147 | if self.val_artifact_path is not None: 148 | val_path = Path(self.val_artifact_path) / 'data/images/' 149 | data_dict['val'] = str(val_path) 150 | self.val_table = self.val_artifact.get("val") 151 | self.map_val_table_path() 152 | if self.val_artifact is not None: 153 | self.result_artifact = wandb.Artifact("run_" + wandb.run.id + "_progress", "evaluation") 154 | self.result_table = wandb.Table(["epoch", "id", "prediction", "avg_confidence"]) 155 | if opt.bbox_interval == -1: 156 | self.bbox_interval = opt.bbox_interval = (opt.epochs // 10) if opt.epochs > 10 else 1 157 | return data_dict 158 | 159 | def download_dataset_artifact(self, path, alias): 160 | if isinstance(path, str) and path.startswith(WANDB_ARTIFACT_PREFIX): 161 | dataset_artifact = wandb.use_artifact(remove_prefix(path, WANDB_ARTIFACT_PREFIX) + ":" + alias) 162 | assert dataset_artifact is not None, "'Error: W&B dataset artifact doesn\'t exist'" 163 | datadir = dataset_artifact.download() 164 | return datadir, dataset_artifact 165 | return None, None 166 | 167 | def download_model_artifact(self, opt): 168 | if opt.resume.startswith(WANDB_ARTIFACT_PREFIX): 169 | model_artifact = wandb.use_artifact(remove_prefix(opt.resume, WANDB_ARTIFACT_PREFIX) + ":latest") 170 | assert model_artifact is not None, 'Error: W&B model artifact doesn\'t exist' 171 | modeldir = model_artifact.download() 172 | epochs_trained = model_artifact.metadata.get('epochs_trained') 173 | total_epochs = model_artifact.metadata.get('total_epochs') 174 | assert epochs_trained < total_epochs, 'training to %g epochs is finished, nothing to resume.' % ( 175 | total_epochs) 176 | return modeldir, model_artifact 177 | return None, None 178 | 179 | def log_model(self, path, opt, epoch, fitness_score, best_model=False): 180 | model_artifact = wandb.Artifact('run_' + wandb.run.id + '_model', type='model', metadata={ 181 | 'original_url': str(path), 182 | 'epochs_trained': epoch + 1, 183 | 'save period': opt.save_period, 184 | 'project': opt.project, 185 | 'total_epochs': opt.epochs, 186 | 'fitness_score': fitness_score 187 | }) 188 | model_artifact.add_file(str(path / 'last.pt'), name='last.pt') 189 | wandb.log_artifact(model_artifact, 190 | aliases=['latest', 'epoch ' + str(self.current_epoch), 'best' if best_model else '']) 191 | print("Saving model artifact on epoch ", epoch + 1) 192 | 193 | def log_dataset_artifact(self, data_file, single_cls, project, overwrite_config=False): 194 | with open(data_file) as f: 195 | data = yaml.load(f, Loader=yaml.SafeLoader) # data dict 196 | nc, names = (1, ['item']) if single_cls else (int(data['nc']), data['names']) 197 | names = {k: v for k, v in enumerate(names)} # to index dictionary 198 | self.train_artifact = self.create_dataset_table(LoadImagesAndLabels( 199 | data['train']), names, name='train') if data.get('train') else None 200 | self.val_artifact = self.create_dataset_table(LoadImagesAndLabels( 201 | data['val']), names, name='val') if data.get('val') else None 202 | if data.get('train'): 203 | data['train'] = WANDB_ARTIFACT_PREFIX + str(Path(project) / 'train') 204 | if data.get('val'): 205 | data['val'] = WANDB_ARTIFACT_PREFIX + str(Path(project) / 'val') 206 | path = data_file if overwrite_config else '_wandb.'.join(data_file.rsplit('.', 1)) # updated data.yaml path 207 | data.pop('download', None) 208 | with open(path, 'w') as f: 209 | yaml.dump(data, f) 210 | 211 | if self.job_type == 'Training': # builds correct artifact pipeline graph 212 | self.wandb_run.use_artifact(self.val_artifact) 213 | self.wandb_run.use_artifact(self.train_artifact) 214 | self.val_artifact.wait() 215 | self.val_table = self.val_artifact.get('val') 216 | self.map_val_table_path() 217 | else: 218 | self.wandb_run.log_artifact(self.train_artifact) 219 | self.wandb_run.log_artifact(self.val_artifact) 220 | return path 221 | 222 | def map_val_table_path(self): 223 | self.val_table_map = {} 224 | print("Mapping dataset") 225 | for i, data in enumerate(tqdm(self.val_table.data)): 226 | self.val_table_map[data[3]] = data[0] 227 | 228 | def create_dataset_table(self, dataset, class_to_id, name='dataset'): 229 | # TODO: Explore multiprocessing to slpit this loop parallely| This is essential for speeding up the the logging 230 | artifact = wandb.Artifact(name=name, type="dataset") 231 | img_files = tqdm([dataset.path]) if isinstance(dataset.path, str) and Path(dataset.path).is_dir() else None 232 | img_files = tqdm(dataset.img_files) if not img_files else img_files 233 | for img_file in img_files: 234 | if Path(img_file).is_dir(): 235 | artifact.add_dir(img_file, name='data/images') 236 | labels_path = 'labels'.join(dataset.path.rsplit('images', 1)) 237 | artifact.add_dir(labels_path, name='data/labels') 238 | else: 239 | artifact.add_file(img_file, name='data/images/' + Path(img_file).name) 240 | label_file = Path(img2label_paths([img_file])[0]) 241 | artifact.add_file(str(label_file), 242 | name='data/labels/' + label_file.name) if label_file.exists() else None 243 | table = wandb.Table(columns=["id", "train_image", "Classes", "name"]) 244 | class_set = wandb.Classes([{'id': id, 'name': name} for id, name in class_to_id.items()]) 245 | for si, (img, labels, paths, shapes) in enumerate(tqdm(dataset)): 246 | height, width = shapes[0] 247 | labels[:, 2:] = (xywh2xyxy(labels[:, 2:].view(-1, 4))) * torch.Tensor([width, height, width, height]) 248 | box_data, img_classes = [], {} 249 | for cls, *xyxy in labels[:, 1:].tolist(): 250 | cls = int(cls) 251 | box_data.append({"position": {"minX": xyxy[0], "minY": xyxy[1], "maxX": xyxy[2], "maxY": xyxy[3]}, 252 | "class_id": cls, 253 | "box_caption": "%s" % (class_to_id[cls]), 254 | "scores": {"acc": 1}, 255 | "domain": "pixel"}) 256 | img_classes[cls] = class_to_id[cls] 257 | boxes = {"ground_truth": {"box_data": box_data, "class_labels": class_to_id}} # inference-space 258 | table.add_data(si, wandb.Image(paths, classes=class_set, boxes=boxes), json.dumps(img_classes), 259 | Path(paths).name) 260 | artifact.add(table, name) 261 | return artifact 262 | 263 | def log_training_progress(self, predn, path, names): 264 | if self.val_table and self.result_table: 265 | class_set = wandb.Classes([{'id': id, 'name': name} for id, name in names.items()]) 266 | box_data = [] 267 | total_conf = 0 268 | for *xyxy, conf, cls in predn.tolist(): 269 | if conf >= 0.25: 270 | box_data.append( 271 | {"position": {"minX": xyxy[0], "minY": xyxy[1], "maxX": xyxy[2], "maxY": xyxy[3]}, 272 | "class_id": int(cls), 273 | "box_caption": "%s %.3f" % (names[cls], conf), 274 | "scores": {"class_score": conf}, 275 | "domain": "pixel"}) 276 | total_conf = total_conf + conf 277 | boxes = {"predictions": {"box_data": box_data, "class_labels": names}} # inference-space 278 | id = self.val_table_map[Path(path).name] 279 | self.result_table.add_data(self.current_epoch, 280 | id, 281 | wandb.Image(self.val_table.data[id][1], boxes=boxes, classes=class_set), 282 | total_conf / max(1, len(box_data)) 283 | ) 284 | 285 | def log(self, log_dict): 286 | if self.wandb_run: 287 | for key, value in log_dict.items(): 288 | self.log_dict[key] = value 289 | 290 | def end_epoch(self, best_result=False): 291 | if self.wandb_run: 292 | wandb.log(self.log_dict) 293 | self.log_dict = {} 294 | if self.result_artifact: 295 | train_results = wandb.JoinedTable(self.val_table, self.result_table, "id") 296 | self.result_artifact.add(train_results, 'result') 297 | wandb.log_artifact(self.result_artifact, aliases=['latest', 'epoch ' + str(self.current_epoch), 298 | ('best' if best_result else '')]) 299 | self.result_table = wandb.Table(["epoch", "id", "prediction", "avg_confidence"]) 300 | self.result_artifact = wandb.Artifact("run_" + wandb.run.id + "_progress", "evaluation") 301 | 302 | def finish_run(self): 303 | if self.wandb_run: 304 | if self.log_dict: 305 | wandb.log(self.log_dict) 306 | wandb.run.finish() 307 | -------------------------------------------------------------------------------- /test.py: -------------------------------------------------------------------------------- 1 | import argparse 2 | import json 3 | import os 4 | from pathlib import Path 5 | from threading import Thread 6 | 7 | import numpy as np 8 | import torch 9 | import yaml 10 | from tqdm import tqdm 11 | 12 | from models.experimental import attempt_load 13 | from utils.datasets import create_dataloader 14 | from utils.general import coco80_to_coco91_class, check_dataset, check_file, check_img_size, check_requirements, \ 15 | box_iou, non_max_suppression, scale_coords, xyxy2xywh, xywh2xyxy, set_logging, increment_path, colorstr 16 | from utils.metrics import ap_per_class, ConfusionMatrix 17 | from utils.plots import plot_images, output_to_target, plot_study_txt 18 | from utils.torch_utils import select_device, time_synchronized 19 | 20 | 21 | def test(data, 22 | weights=None, 23 | batch_size=32, 24 | imgsz=640, 25 | conf_thres=0.001, 26 | iou_thres=0.6, # for NMS 27 | save_json=False, 28 | single_cls=False, 29 | augment=False, 30 | verbose=False, 31 | model=None, 32 | dataloader=None, 33 | save_dir=Path(''), # for saving images 34 | save_txt=False, # for auto-labelling 35 | save_hybrid=False, # for hybrid auto-labelling 36 | save_conf=False, # save auto-label confidences 37 | plots=True, 38 | wandb_logger=None, 39 | compute_loss=None, 40 | half_precision=True, 41 | is_coco=False): 42 | # Initialize/load model and set device 43 | training = model is not None 44 | if training: # called by train.py 45 | device = next(model.parameters()).device # get model device 46 | 47 | else: # called directly 48 | set_logging() 49 | device = select_device(opt.device, batch_size=batch_size) 50 | 51 | # Directories 52 | save_dir = Path(increment_path(Path(opt.project) / opt.name, exist_ok=opt.exist_ok)) # increment run 53 | (save_dir / 'labels' if save_txt else save_dir).mkdir(parents=True, exist_ok=True) # make dir 54 | 55 | # Load model 56 | model = attempt_load(weights, map_location=device) # load FP32 model 57 | gs = max(int(model.stride.max()), 32) # grid size (max stride) 58 | imgsz = check_img_size(imgsz, s=gs) # check img_size 59 | 60 | # Multi-GPU disabled, incompatible with .half() https://github.com/ultralytics/yolov5/issues/99 61 | # if device.type != 'cpu' and torch.cuda.device_count() > 1: 62 | # model = nn.DataParallel(model) 63 | 64 | # Half 65 | half = device.type != 'cpu' and half_precision # half precision only supported on CUDA 66 | if half: 67 | model.half() 68 | 69 | # Configure 70 | model.eval() 71 | if isinstance(data, str): 72 | is_coco = data.endswith('coco.yaml') 73 | with open(data) as f: 74 | data = yaml.load(f, Loader=yaml.SafeLoader) 75 | check_dataset(data) # check 76 | nc = 1 if single_cls else int(data['nc']) # number of classes 77 | iouv = torch.linspace(0.5, 0.95, 10).to(device) # iou vector for mAP@0.5:0.95 78 | niou = iouv.numel() 79 | 80 | # Logging 81 | log_imgs = 0 82 | if wandb_logger and wandb_logger.wandb: 83 | log_imgs = min(wandb_logger.log_imgs, 100) 84 | # Dataloader 85 | if not training: 86 | if device.type != 'cpu': 87 | model(torch.zeros(1, 3, imgsz, imgsz).to(device).type_as(next(model.parameters()))) # run once 88 | task = opt.task if opt.task in ('train', 'val', 'test') else 'val' # path to train/val/test images 89 | dataloader = create_dataloader(data[task], imgsz, batch_size, gs, opt, pad=0.5, rect=True, 90 | prefix=colorstr(f'{task}: '))[0] 91 | 92 | seen = 0 93 | confusion_matrix = ConfusionMatrix(nc=nc) 94 | names = {k: v for k, v in enumerate(model.names if hasattr(model, 'names') else model.module.names)} 95 | coco91class = coco80_to_coco91_class() 96 | s = ('%20s' + '%12s' * 6) % ('Class', 'Images', 'Labels', 'P', 'R', 'mAP@.5', 'mAP@.5:.95') 97 | p, r, f1, mp, mr, map50, map, t0, t1 = 0., 0., 0., 0., 0., 0., 0., 0., 0. 98 | loss = torch.zeros(3, device=device) 99 | jdict, stats, ap, ap_class, wandb_images = [], [], [], [], [] 100 | for batch_i, (img, targets, paths, shapes) in enumerate(tqdm(dataloader, desc=s)): 101 | img = img.to(device, non_blocking=True) 102 | img = img.half() if half else img.float() # uint8 to fp16/32 103 | img /= 255.0 # 0 - 255 to 0.0 - 1.0 104 | targets = targets.to(device) 105 | nb, _, height, width = img.shape # batch size, channels, height, width 106 | 107 | with torch.no_grad(): 108 | # Run model 109 | t = time_synchronized() 110 | out, train_out = model(img, augment=augment) # inference and training outputs 111 | t0 += time_synchronized() - t 112 | 113 | # Compute loss 114 | if compute_loss: 115 | loss += compute_loss([x.float() for x in train_out], targets)[1][:3] # box, obj, cls 116 | 117 | # Run NMS 118 | targets[:, 2:] *= torch.Tensor([width, height, width, height]).to(device) # to pixels 119 | lb = [targets[targets[:, 0] == i, 1:] for i in range(nb)] if save_hybrid else [] # for autolabelling 120 | t = time_synchronized() 121 | out = non_max_suppression(out, conf_thres=conf_thres, iou_thres=iou_thres, labels=lb, multi_label=True) 122 | t1 += time_synchronized() - t 123 | 124 | # Statistics per image 125 | for si, pred in enumerate(out): 126 | labels = targets[targets[:, 0] == si, 1:] 127 | nl = len(labels) 128 | tcls = labels[:, 0].tolist() if nl else [] # target class 129 | path = Path(paths[si]) 130 | seen += 1 131 | 132 | if len(pred) == 0: 133 | if nl: 134 | stats.append((torch.zeros(0, niou, dtype=torch.bool), torch.Tensor(), torch.Tensor(), tcls)) 135 | continue 136 | 137 | # Predictions 138 | predn = pred.clone() 139 | scale_coords(img[si].shape[1:], predn[:, :4], shapes[si][0], shapes[si][1]) # native-space pred 140 | 141 | # Append to text file 142 | if save_txt: 143 | gn = torch.tensor(shapes[si][0])[[1, 0, 1, 0]] # normalization gain whwh 144 | for *xyxy, conf, cls in predn.tolist(): 145 | xywh = (xyxy2xywh(torch.tensor(xyxy).view(1, 4)) / gn).view(-1).tolist() # normalized xywh 146 | line = (cls, *xywh, conf) if save_conf else (cls, *xywh) # label format 147 | with open(save_dir / 'labels' / (path.stem + '.txt'), 'a') as f: 148 | f.write(('%g ' * len(line)).rstrip() % line + '\n') 149 | 150 | # W&B logging - Media Panel Plots 151 | if len(wandb_images) < log_imgs and wandb_logger.current_epoch > 0: # Check for test operation 152 | if wandb_logger.current_epoch % wandb_logger.bbox_interval == 0: 153 | box_data = [{"position": {"minX": xyxy[0], "minY": xyxy[1], "maxX": xyxy[2], "maxY": xyxy[3]}, 154 | "class_id": int(cls), 155 | "box_caption": "%s %.3f" % (names[cls], conf), 156 | "scores": {"class_score": conf}, 157 | "domain": "pixel"} for *xyxy, conf, cls in pred.tolist()] 158 | boxes = {"predictions": {"box_data": box_data, "class_labels": names}} # inference-space 159 | wandb_images.append(wandb_logger.wandb.Image(img[si], boxes=boxes, caption=path.name)) 160 | wandb_logger.log_training_progress(predn, path, names) if wandb_logger and wandb_logger.wandb_run else None 161 | 162 | # Append to pycocotools JSON dictionary 163 | if save_json: 164 | # [{"image_id": 42, "category_id": 18, "bbox": [258.15, 41.29, 348.26, 243.78], "score": 0.236}, ... 165 | image_id = int(path.stem) if path.stem.isnumeric() else path.stem 166 | 167 | box = xyxy2xywh(predn[:, :4]) # xywh 168 | box[:, :2] -= box[:, 2:] / 2 # xy center to top-left corner 169 | for p, b in zip(pred.tolist(), box.tolist()): 170 | jdict.append({'image_id': image_id, 171 | 'category_id': coco91class[int(p[5])] if is_coco else int(p[5]), 172 | 'bbox': [round(x, 3) for x in b], 173 | 'score': round(p[4], 5)}) 174 | 175 | # Assign all predictions as incorrect 176 | correct = torch.zeros(pred.shape[0], niou, dtype=torch.bool, device=device) 177 | if nl: 178 | detected = [] # target indices 179 | tcls_tensor = labels[:, 0] 180 | 181 | # target boxes 182 | tbox = xywh2xyxy(labels[:, 1:5]) 183 | scale_coords(img[si].shape[1:], tbox, shapes[si][0], shapes[si][1]) # native-space labels 184 | if plots: 185 | confusion_matrix.process_batch(predn, torch.cat((labels[:, 0:1], tbox), 1)) 186 | 187 | # Per target class 188 | for cls in torch.unique(tcls_tensor): 189 | ti = (cls == tcls_tensor).nonzero(as_tuple=False).view(-1) # prediction indices 190 | pi = (cls == pred[:, 5]).nonzero(as_tuple=False).view(-1) # target indices 191 | 192 | # Search for detections 193 | if pi.shape[0]: 194 | # Prediction to target ious 195 | ious, i = box_iou(predn[pi, :4], tbox[ti]).max(1) # best ious, indices 196 | 197 | # Append detections 198 | detected_set = set() 199 | for j in (ious > iouv[0]).nonzero(as_tuple=False): 200 | d = ti[i[j]] # detected target 201 | if d.item() not in detected_set: 202 | detected_set.add(d.item()) 203 | detected.append(d) 204 | correct[pi[j]] = ious[j] > iouv # iou_thres is 1xn 205 | if len(detected) == nl: # all targets already located in image 206 | break 207 | 208 | # Append statistics (correct, conf, pcls, tcls) 209 | stats.append((correct.cpu(), pred[:, 4].cpu(), pred[:, 5].cpu(), tcls)) 210 | 211 | # Plot images 212 | if plots and batch_i < 3: 213 | f = save_dir / f'test_batch{batch_i}_labels.jpg' # labels 214 | Thread(target=plot_images, args=(img, targets, paths, f, names), daemon=True).start() 215 | f = save_dir / f'test_batch{batch_i}_pred.jpg' # predictions 216 | Thread(target=plot_images, args=(img, output_to_target(out), paths, f, names), daemon=True).start() 217 | 218 | # Compute statistics 219 | stats = [np.concatenate(x, 0) for x in zip(*stats)] # to numpy 220 | if len(stats) and stats[0].any(): 221 | p, r, ap, f1, ap_class = ap_per_class(*stats, plot=plots, save_dir=save_dir, names=names) 222 | ap50, ap = ap[:, 0], ap.mean(1) # AP@0.5, AP@0.5:0.95 223 | mp, mr, map50, map = p.mean(), r.mean(), ap50.mean(), ap.mean() 224 | nt = np.bincount(stats[3].astype(np.int64), minlength=nc) # number of targets per class 225 | else: 226 | nt = torch.zeros(1) 227 | 228 | # Print results 229 | pf = '%20s' + '%12i' * 2 + '%12.3g' * 4 # print format 230 | print(pf % ('all', seen, nt.sum(), mp, mr, map50, map)) 231 | 232 | # Print results per class 233 | if (verbose or (nc < 50 and not training)) and nc > 1 and len(stats): 234 | for i, c in enumerate(ap_class): 235 | print(pf % (names[c], seen, nt[c], p[i], r[i], ap50[i], ap[i])) 236 | 237 | # Print speeds 238 | t = tuple(x / seen * 1E3 for x in (t0, t1, t0 + t1)) + (imgsz, imgsz, batch_size) # tuple 239 | if not training: 240 | print('Speed: %.1f/%.1f/%.1f ms inference/NMS/total per %gx%g image at batch-size %g' % t) 241 | 242 | # Plots 243 | if plots: 244 | confusion_matrix.plot(save_dir=save_dir, names=list(names.values())) 245 | if wandb_logger and wandb_logger.wandb: 246 | val_batches = [wandb_logger.wandb.Image(str(f), caption=f.name) for f in sorted(save_dir.glob('test*.jpg'))] 247 | wandb_logger.log({"Validation": val_batches}) 248 | if wandb_images: 249 | wandb_logger.log({"Bounding Box Debugger/Images": wandb_images}) 250 | 251 | # Save JSON 252 | if save_json and len(jdict): 253 | w = Path(weights[0] if isinstance(weights, list) else weights).stem if weights is not None else '' # weights 254 | anno_json = '../coco/annotations/instances_val2017.json' # annotations json 255 | pred_json = str(save_dir / f"{w}_predictions.json") # predictions json 256 | print('\nEvaluating pycocotools mAP... saving %s...' % pred_json) 257 | with open(pred_json, 'w') as f: 258 | json.dump(jdict, f) 259 | 260 | try: # https://github.com/cocodataset/cocoapi/blob/master/PythonAPI/pycocoEvalDemo.ipynb 261 | from pycocotools.coco import COCO 262 | from pycocotools.cocoeval import COCOeval 263 | 264 | anno = COCO(anno_json) # init annotations api 265 | pred = anno.loadRes(pred_json) # init predictions api 266 | eval = COCOeval(anno, pred, 'bbox') 267 | if is_coco: 268 | eval.params.imgIds = [int(Path(x).stem) for x in dataloader.dataset.img_files] # image IDs to evaluate 269 | eval.evaluate() 270 | eval.accumulate() 271 | eval.summarize() 272 | map, map50 = eval.stats[:2] # update results (mAP@0.5:0.95, mAP@0.5) 273 | except Exception as e: 274 | print(f'pycocotools unable to run: {e}') 275 | 276 | # Return results 277 | model.float() # for training 278 | if not training: 279 | s = f"\n{len(list(save_dir.glob('labels/*.txt')))} labels saved to {save_dir / 'labels'}" if save_txt else '' 280 | print(f"Results saved to {save_dir}{s}") 281 | maps = np.zeros(nc) + map 282 | for i, c in enumerate(ap_class): 283 | maps[c] = ap[i] 284 | return (mp, mr, map50, map, *(loss.cpu() / len(dataloader)).tolist()), maps, t 285 | 286 | 287 | if __name__ == '__main__': 288 | parser = argparse.ArgumentParser(prog='test.py') 289 | parser.add_argument('--weights', nargs='+', type=str, default='', help='model.pt path(s)') 290 | parser.add_argument('--data', type=str, default='data/coco.yaml', help='*.data path') 291 | parser.add_argument('--batch-size', type=int, default=1, help='size of each image batch') 292 | parser.add_argument('--img-size', type=int, default=640, help='inference size (pixels)') 293 | parser.add_argument('--conf-thres', type=float, default=0.001, help='object confidence threshold') 294 | parser.add_argument('--iou-thres', type=float, default=0.6, help='IOU threshold for NMS') 295 | parser.add_argument('--task', default='val', help='train, val, test, speed or study') 296 | parser.add_argument('--device', default='', help='cuda device, i.e. 0 or 0,1,2,3 or cpu') 297 | parser.add_argument('--single-cls', action='store_true', help='treat as single-class dataset') 298 | parser.add_argument('--augment', action='store_true', help='augmented inference') 299 | parser.add_argument('--verbose', action='store_true', help='report mAP by class') 300 | parser.add_argument('--save-txt', action='store_true', help='save results to *.txt') 301 | parser.add_argument('--save-hybrid', action='store_true', help='save label+prediction hybrid results to *.txt') 302 | parser.add_argument('--save-conf', action='store_true', help='save confidences in --save-txt labels') 303 | parser.add_argument('--save-json', action='store_true', help='save a cocoapi-compatible JSON results file') 304 | parser.add_argument('--project', default='runs/test', help='save to project/name') 305 | parser.add_argument('--name', default='exp', help='save to project/name') 306 | parser.add_argument('--exist-ok', action='store_true', help='existing project/name ok, do not increment') 307 | opt = parser.parse_args() 308 | opt.save_json |= opt.data.endswith('coco.yaml') 309 | opt.data = check_file(opt.data) # check file 310 | print(opt) 311 | check_requirements() 312 | 313 | if opt.task in ('train', 'val', 'test'): # run normally 314 | test(opt.data, 315 | opt.weights, 316 | opt.batch_size, 317 | opt.img_size, 318 | opt.conf_thres, 319 | opt.iou_thres, 320 | opt.save_json, 321 | opt.single_cls, 322 | opt.augment, 323 | opt.verbose, 324 | save_txt=opt.save_txt | opt.save_hybrid, 325 | save_hybrid=opt.save_hybrid, 326 | save_conf=opt.save_conf, 327 | ) 328 | 329 | elif opt.task == 'speed': # speed benchmarks 330 | for w in opt.weights: 331 | test(opt.data, w, opt.batch_size, opt.img_size, 0.25, 0.45, save_json=False, plots=False) 332 | 333 | elif opt.task == 'study': # run over a range of settings and save/plot 334 | # python test.py --task study --data coco.yaml --iou 0.7 --weights yolov3.pt yolov3-spp.pt yolov3-tiny.pt 335 | x = list(range(256, 1536 + 128, 128)) # x axis (image sizes) 336 | for w in opt.weights: 337 | f = f'study_{Path(opt.data).stem}_{Path(w).stem}.txt' # filename to save to 338 | y = [] # y axis 339 | for i in x: # img-size 340 | print(f'\nRunning {f} point {i}...') 341 | r, _, t = test(opt.data, w, opt.batch_size, i, opt.conf_thres, opt.iou_thres, opt.save_json, 342 | plots=False) 343 | y.append(r + t) # results and times 344 | np.savetxt(f, y, fmt='%10.4g') # save 345 | os.system('zip -r study.zip study_*.txt') 346 | plot_study_txt(x=x) # plot 347 | -------------------------------------------------------------------------------- /utils/plots.py: -------------------------------------------------------------------------------- 1 | # Plotting utils 2 | 3 | import glob 4 | import math 5 | import os 6 | import random 7 | from copy import copy 8 | from pathlib import Path 9 | 10 | import cv2 11 | import matplotlib 12 | import matplotlib.pyplot as plt 13 | import numpy as np 14 | import pandas as pd 15 | import seaborn as sns 16 | import torch 17 | import yaml 18 | from PIL import Image, ImageDraw, ImageFont 19 | from scipy.signal import butter, filtfilt 20 | 21 | from utils.general import xywh2xyxy, xyxy2xywh 22 | from utils.metrics import fitness 23 | 24 | # Settings 25 | matplotlib.rc('font', **{'size': 11}) 26 | matplotlib.use('Agg') # for writing to files only 27 | 28 | 29 | def color_list(): 30 | # Return first 10 plt colors as (r,g,b) https://stackoverflow.com/questions/51350872/python-from-color-name-to-rgb 31 | def hex2rgb(h): 32 | return tuple(int(h[1 + i:1 + i + 2], 16) for i in (0, 2, 4)) 33 | 34 | return [hex2rgb(h) for h in matplotlib.colors.TABLEAU_COLORS.values()] # or BASE_ (8), CSS4_ (148), XKCD_ (949) 35 | 36 | 37 | def hist2d(x, y, n=100): 38 | # 2d histogram used in labels.png and evolve.png 39 | xedges, yedges = np.linspace(x.min(), x.max(), n), np.linspace(y.min(), y.max(), n) 40 | hist, xedges, yedges = np.histogram2d(x, y, (xedges, yedges)) 41 | xidx = np.clip(np.digitize(x, xedges) - 1, 0, hist.shape[0] - 1) 42 | yidx = np.clip(np.digitize(y, yedges) - 1, 0, hist.shape[1] - 1) 43 | return np.log(hist[xidx, yidx]) 44 | 45 | 46 | def butter_lowpass_filtfilt(data, cutoff=1500, fs=50000, order=5): 47 | # https://stackoverflow.com/questions/28536191/how-to-filter-smooth-with-scipy-numpy 48 | def butter_lowpass(cutoff, fs, order): 49 | nyq = 0.5 * fs 50 | normal_cutoff = cutoff / nyq 51 | return butter(order, normal_cutoff, btype='low', analog=False) 52 | 53 | b, a = butter_lowpass(cutoff, fs, order=order) 54 | return filtfilt(b, a, data) # forward-backward filter 55 | 56 | 57 | def plot_one_box(x, img, color=None, label=None, line_thickness=3): 58 | # Plots one bounding box on image img 59 | tl = line_thickness or round(0.002 * (img.shape[0] + img.shape[1]) / 2) + 1 # line/font thickness 60 | color = color or [random.randint(0, 255) for _ in range(3)] 61 | c1, c2 = (int(x[0]), int(x[1])), (int(x[2]), int(x[3])) 62 | cv2.rectangle(img, c1, c2, color, thickness=tl, lineType=cv2.LINE_AA) 63 | if label: 64 | tf = max(tl - 1, 1) # font thickness 65 | t_size = cv2.getTextSize(label, 0, fontScale=tl / 3, thickness=tf)[0] 66 | c2 = c1[0] + t_size[0], c1[1] - t_size[1] - 3 67 | cv2.rectangle(img, c1, c2, color, -1, cv2.LINE_AA) # filled 68 | cv2.putText(img, label, (c1[0], c1[1] - 2), 0, tl / 3, [225, 255, 255], thickness=tf, lineType=cv2.LINE_AA) 69 | 70 | 71 | def plot_one_box_PIL(box, img, color=None, label=None, line_thickness=None): 72 | img = Image.fromarray(img) 73 | draw = ImageDraw.Draw(img) 74 | line_thickness = line_thickness or max(int(min(img.size) / 200), 2) 75 | draw.rectangle(box, width=line_thickness, outline=tuple(color)) # plot 76 | if label: 77 | fontsize = max(round(max(img.size) / 40), 12) 78 | font = ImageFont.truetype("Arial.ttf", fontsize) 79 | txt_width, txt_height = font.getsize(label) 80 | draw.rectangle([box[0], box[1] - txt_height + 4, box[0] + txt_width, box[1]], fill=tuple(color)) 81 | draw.text((box[0], box[1] - txt_height + 1), label, fill=(255, 255, 255), font=font) 82 | return np.asarray(img) 83 | 84 | 85 | def plot_wh_methods(): # from utils.plots import *; plot_wh_methods() 86 | # Compares the two methods for width-height anchor multiplication 87 | # https://github.com/ultralytics/yolov3/issues/168 88 | x = np.arange(-4.0, 4.0, .1) 89 | ya = np.exp(x) 90 | yb = torch.sigmoid(torch.from_numpy(x)).numpy() * 2 91 | 92 | fig = plt.figure(figsize=(6, 3), tight_layout=True) 93 | plt.plot(x, ya, '.-', label='YOLOv3') 94 | plt.plot(x, yb ** 2, '.-', label='YOLOv5 ^2') 95 | plt.plot(x, yb ** 1.6, '.-', label='YOLOv5 ^1.6') 96 | plt.xlim(left=-4, right=4) 97 | plt.ylim(bottom=0, top=6) 98 | plt.xlabel('input') 99 | plt.ylabel('output') 100 | plt.grid() 101 | plt.legend() 102 | fig.savefig('comparison.png', dpi=200) 103 | 104 | 105 | def output_to_target(output): 106 | # Convert model output to target format [batch_id, class_id, x, y, w, h, conf] 107 | targets = [] 108 | for i, o in enumerate(output): 109 | for *box, conf, cls in o.cpu().numpy(): 110 | targets.append([i, cls, *list(*xyxy2xywh(np.array(box)[None])), conf]) 111 | return np.array(targets) 112 | 113 | 114 | def plot_images(images, targets, paths=None, fname='images.jpg', names=None, max_size=640, max_subplots=16): 115 | # Plot image grid with labels 116 | 117 | if isinstance(images, torch.Tensor): 118 | images = images.cpu().float().numpy() 119 | if isinstance(targets, torch.Tensor): 120 | targets = targets.cpu().numpy() 121 | 122 | # un-normalise 123 | if np.max(images[0]) <= 1: 124 | images *= 255 125 | 126 | tl = 3 # line thickness 127 | tf = max(tl - 1, 1) # font thickness 128 | bs, _, h, w = images.shape # batch size, _, height, width 129 | bs = min(bs, max_subplots) # limit plot images 130 | ns = np.ceil(bs ** 0.5) # number of subplots (square) 131 | 132 | # Check if we should resize 133 | scale_factor = max_size / max(h, w) 134 | if scale_factor < 1: 135 | h = math.ceil(scale_factor * h) 136 | w = math.ceil(scale_factor * w) 137 | 138 | colors = color_list() # list of colors 139 | mosaic = np.full((int(ns * h), int(ns * w), 3), 255, dtype=np.uint8) # init 140 | for i, img in enumerate(images): 141 | if i == max_subplots: # if last batch has fewer images than we expect 142 | break 143 | 144 | block_x = int(w * (i // ns)) 145 | block_y = int(h * (i % ns)) 146 | 147 | img = img.transpose(1, 2, 0) 148 | if scale_factor < 1: 149 | img = cv2.resize(img, (w, h)) 150 | 151 | mosaic[block_y:block_y + h, block_x:block_x + w, :] = img 152 | if len(targets) > 0: 153 | image_targets = targets[targets[:, 0] == i] 154 | boxes = xywh2xyxy(image_targets[:, 2:6]).T 155 | classes = image_targets[:, 1].astype('int') 156 | labels = image_targets.shape[1] == 6 # labels if no conf column 157 | conf = None if labels else image_targets[:, 6] # check for confidence presence (label vs pred) 158 | 159 | if boxes.shape[1]: 160 | if boxes.max() <= 1.01: # if normalized with tolerance 0.01 161 | boxes[[0, 2]] *= w # scale to pixels 162 | boxes[[1, 3]] *= h 163 | elif scale_factor < 1: # absolute coords need scale if image scales 164 | boxes *= scale_factor 165 | boxes[[0, 2]] += block_x 166 | boxes[[1, 3]] += block_y 167 | for j, box in enumerate(boxes.T): 168 | cls = int(classes[j]) 169 | color = colors[cls % len(colors)] 170 | cls = names[cls] if names else cls 171 | if labels or conf[j] > 0.25: # 0.25 conf thresh 172 | label = '%s' % cls if labels else '%s %.1f' % (cls, conf[j]) 173 | plot_one_box(box, mosaic, label=label, color=color, line_thickness=tl) 174 | 175 | # Draw image filename labels 176 | if paths: 177 | label = Path(paths[i]).name[:40] # trim to 40 char 178 | t_size = cv2.getTextSize(label, 0, fontScale=tl / 3, thickness=tf)[0] 179 | cv2.putText(mosaic, label, (block_x + 5, block_y + t_size[1] + 5), 0, tl / 3, [220, 220, 220], thickness=tf, 180 | lineType=cv2.LINE_AA) 181 | 182 | # Image border 183 | cv2.rectangle(mosaic, (block_x, block_y), (block_x + w, block_y + h), (255, 255, 255), thickness=3) 184 | 185 | if fname: 186 | r = min(1280. / max(h, w) / ns, 1.0) # ratio to limit image size 187 | mosaic = cv2.resize(mosaic, (int(ns * w * r), int(ns * h * r)), interpolation=cv2.INTER_AREA) 188 | # cv2.imwrite(fname, cv2.cvtColor(mosaic, cv2.COLOR_BGR2RGB)) # cv2 save 189 | Image.fromarray(mosaic).save(fname) # PIL save 190 | return mosaic 191 | 192 | 193 | def plot_lr_scheduler(optimizer, scheduler, epochs=300, save_dir=''): 194 | # Plot LR simulating training for full epochs 195 | optimizer, scheduler = copy(optimizer), copy(scheduler) # do not modify originals 196 | y = [] 197 | for _ in range(epochs): 198 | scheduler.step() 199 | y.append(optimizer.param_groups[0]['lr']) 200 | plt.plot(y, '.-', label='LR') 201 | plt.xlabel('epoch') 202 | plt.ylabel('LR') 203 | plt.grid() 204 | plt.xlim(0, epochs) 205 | plt.ylim(0) 206 | plt.savefig(Path(save_dir) / 'LR.png', dpi=200) 207 | plt.close() 208 | 209 | 210 | def plot_test_txt(): # from utils.plots import *; plot_test() 211 | # Plot test.txt histograms 212 | x = np.loadtxt('test.txt', dtype=np.float32) 213 | box = xyxy2xywh(x[:, :4]) 214 | cx, cy = box[:, 0], box[:, 1] 215 | 216 | fig, ax = plt.subplots(1, 1, figsize=(6, 6), tight_layout=True) 217 | ax.hist2d(cx, cy, bins=600, cmax=10, cmin=0) 218 | ax.set_aspect('equal') 219 | plt.savefig('hist2d.png', dpi=300) 220 | 221 | fig, ax = plt.subplots(1, 2, figsize=(12, 6), tight_layout=True) 222 | ax[0].hist(cx, bins=600) 223 | ax[1].hist(cy, bins=600) 224 | plt.savefig('hist1d.png', dpi=200) 225 | 226 | 227 | def plot_targets_txt(): # from utils.plots import *; plot_targets_txt() 228 | # Plot targets.txt histograms 229 | x = np.loadtxt('targets.txt', dtype=np.float32).T 230 | s = ['x targets', 'y targets', 'width targets', 'height targets'] 231 | fig, ax = plt.subplots(2, 2, figsize=(8, 8), tight_layout=True) 232 | ax = ax.ravel() 233 | for i in range(4): 234 | ax[i].hist(x[i], bins=100, label='%.3g +/- %.3g' % (x[i].mean(), x[i].std())) 235 | ax[i].legend() 236 | ax[i].set_title(s[i]) 237 | plt.savefig('targets.jpg', dpi=200) 238 | 239 | 240 | def plot_study_txt(path='', x=None): # from utils.plots import *; plot_study_txt() 241 | # Plot study.txt generated by test.py 242 | fig, ax = plt.subplots(2, 4, figsize=(10, 6), tight_layout=True) 243 | # ax = ax.ravel() 244 | 245 | fig2, ax2 = plt.subplots(1, 1, figsize=(8, 4), tight_layout=True) 246 | # for f in [Path(path) / f'study_coco_{x}.txt' for x in ['yolov3-tiny', 'yolov3', 'yolov3-spp', 'yolov5l']]: 247 | for f in sorted(Path(path).glob('study*.txt')): 248 | y = np.loadtxt(f, dtype=np.float32, usecols=[0, 1, 2, 3, 7, 8, 9], ndmin=2).T 249 | x = np.arange(y.shape[1]) if x is None else np.array(x) 250 | s = ['P', 'R', 'mAP@.5', 'mAP@.5:.95', 't_inference (ms/img)', 't_NMS (ms/img)', 't_total (ms/img)'] 251 | # for i in range(7): 252 | # ax[i].plot(x, y[i], '.-', linewidth=2, markersize=8) 253 | # ax[i].set_title(s[i]) 254 | 255 | j = y[3].argmax() + 1 256 | ax2.plot(y[6, 1:j], y[3, 1:j] * 1E2, '.-', linewidth=2, markersize=8, 257 | label=f.stem.replace('study_coco_', '').replace('yolo', 'YOLO')) 258 | 259 | ax2.plot(1E3 / np.array([209, 140, 97, 58, 35, 18]), [34.6, 40.5, 43.0, 47.5, 49.7, 51.5], 260 | 'k.-', linewidth=2, markersize=8, alpha=.25, label='EfficientDet') 261 | 262 | ax2.grid(alpha=0.2) 263 | ax2.set_yticks(np.arange(20, 60, 5)) 264 | ax2.set_xlim(0, 57) 265 | ax2.set_ylim(15, 55) 266 | ax2.set_xlabel('GPU Speed (ms/img)') 267 | ax2.set_ylabel('COCO AP val') 268 | ax2.legend(loc='lower right') 269 | plt.savefig(str(Path(path).name) + '.png', dpi=300) 270 | 271 | 272 | def plot_labels(labels, names=(), save_dir=Path(''), loggers=None): 273 | # plot dataset labels 274 | print('Plotting labels... ') 275 | c, b = labels[:, 0], labels[:, 1:].transpose() # classes, boxes 276 | nc = int(c.max() + 1) # number of classes 277 | colors = color_list() 278 | x = pd.DataFrame(b.transpose(), columns=['x', 'y', 'width', 'height']) 279 | 280 | # seaborn correlogram 281 | sns.pairplot(x, corner=True, diag_kind='auto', kind='hist', diag_kws=dict(bins=50), plot_kws=dict(pmax=0.9)) 282 | plt.savefig(save_dir / 'labels_correlogram.jpg', dpi=200) 283 | plt.close() 284 | 285 | # matplotlib labels 286 | matplotlib.use('svg') # faster 287 | ax = plt.subplots(2, 2, figsize=(8, 8), tight_layout=True)[1].ravel() 288 | ax[0].hist(c, bins=np.linspace(0, nc, nc + 1) - 0.5, rwidth=0.8) 289 | ax[0].set_ylabel('instances') 290 | if 0 < len(names) < 30: 291 | ax[0].set_xticks(range(len(names))) 292 | ax[0].set_xticklabels(names, rotation=90, fontsize=10) 293 | else: 294 | ax[0].set_xlabel('classes') 295 | sns.histplot(x, x='x', y='y', ax=ax[2], bins=50, pmax=0.9) 296 | sns.histplot(x, x='width', y='height', ax=ax[3], bins=50, pmax=0.9) 297 | 298 | # rectangles 299 | labels[:, 1:3] = 0.5 # center 300 | labels[:, 1:] = xywh2xyxy(labels[:, 1:]) * 2000 301 | img = Image.fromarray(np.ones((2000, 2000, 3), dtype=np.uint8) * 255) 302 | for cls, *box in labels[:1000]: 303 | ImageDraw.Draw(img).rectangle(box, width=1, outline=colors[int(cls) % 10]) # plot 304 | ax[1].imshow(img) 305 | ax[1].axis('off') 306 | 307 | for a in [0, 1, 2, 3]: 308 | for s in ['top', 'right', 'left', 'bottom']: 309 | ax[a].spines[s].set_visible(False) 310 | plt.savefig(save_dir / 'labels.jpg', dpi=200) 311 | matplotlib.use('Agg') 312 | plt.close() 313 | 314 | # loggers 315 | for k, v in loggers.items() or {}: 316 | if k == 'wandb' and v: 317 | v.log({"Labels": [v.Image(str(x), caption=x.name) for x in save_dir.glob('*labels*.jpg')]}, commit=False) 318 | 319 | 320 | def plot_evolution(yaml_file='data/hyp.finetune.yaml'): # from utils.plots import *; plot_evolution() 321 | # Plot hyperparameter evolution results in evolve.txt 322 | with open(yaml_file) as f: 323 | hyp = yaml.load(f, Loader=yaml.SafeLoader) 324 | x = np.loadtxt('evolve.txt', ndmin=2) 325 | f = fitness(x) 326 | # weights = (f - f.min()) ** 2 # for weighted results 327 | plt.figure(figsize=(10, 12), tight_layout=True) 328 | matplotlib.rc('font', **{'size': 8}) 329 | for i, (k, v) in enumerate(hyp.items()): 330 | y = x[:, i + 7] 331 | # mu = (y * weights).sum() / weights.sum() # best weighted result 332 | mu = y[f.argmax()] # best single result 333 | plt.subplot(6, 5, i + 1) 334 | plt.scatter(y, f, c=hist2d(y, f, 20), cmap='viridis', alpha=.8, edgecolors='none') 335 | plt.plot(mu, f.max(), 'k+', markersize=15) 336 | plt.title('%s = %.3g' % (k, mu), fontdict={'size': 9}) # limit to 40 characters 337 | if i % 5 != 0: 338 | plt.yticks([]) 339 | print('%15s: %.3g' % (k, mu)) 340 | plt.savefig('evolve.png', dpi=200) 341 | print('\nPlot saved as evolve.png') 342 | 343 | 344 | def profile_idetection(start=0, stop=0, labels=(), save_dir=''): 345 | # Plot iDetection '*.txt' per-image logs. from utils.plots import *; profile_idetection() 346 | ax = plt.subplots(2, 4, figsize=(12, 6), tight_layout=True)[1].ravel() 347 | s = ['Images', 'Free Storage (GB)', 'RAM Usage (GB)', 'Battery', 'dt_raw (ms)', 'dt_smooth (ms)', 'real-world FPS'] 348 | files = list(Path(save_dir).glob('frames*.txt')) 349 | for fi, f in enumerate(files): 350 | try: 351 | results = np.loadtxt(f, ndmin=2).T[:, 90:-30] # clip first and last rows 352 | n = results.shape[1] # number of rows 353 | x = np.arange(start, min(stop, n) if stop else n) 354 | results = results[:, x] 355 | t = (results[0] - results[0].min()) # set t0=0s 356 | results[0] = x 357 | for i, a in enumerate(ax): 358 | if i < len(results): 359 | label = labels[fi] if len(labels) else f.stem.replace('frames_', '') 360 | a.plot(t, results[i], marker='.', label=label, linewidth=1, markersize=5) 361 | a.set_title(s[i]) 362 | a.set_xlabel('time (s)') 363 | # if fi == len(files) - 1: 364 | # a.set_ylim(bottom=0) 365 | for side in ['top', 'right']: 366 | a.spines[side].set_visible(False) 367 | else: 368 | a.remove() 369 | except Exception as e: 370 | print('Warning: Plotting error for %s; %s' % (f, e)) 371 | 372 | ax[1].legend() 373 | plt.savefig(Path(save_dir) / 'idetection_profile.png', dpi=200) 374 | 375 | 376 | def plot_results_overlay(start=0, stop=0): # from utils.plots import *; plot_results_overlay() 377 | # Plot training 'results*.txt', overlaying train and val losses 378 | s = ['train', 'train', 'train', 'Precision', 'mAP@0.5', 'val', 'val', 'val', 'Recall', 'mAP@0.5:0.95'] # legends 379 | t = ['Box', 'Objectness', 'Classification', 'P-R', 'mAP-F1'] # titles 380 | for f in sorted(glob.glob('results*.txt') + glob.glob('../../Downloads/results*.txt')): 381 | results = np.loadtxt(f, usecols=[2, 3, 4, 8, 9, 12, 13, 14, 10, 11], ndmin=2).T 382 | n = results.shape[1] # number of rows 383 | x = range(start, min(stop, n) if stop else n) 384 | fig, ax = plt.subplots(1, 5, figsize=(14, 3.5), tight_layout=True) 385 | ax = ax.ravel() 386 | for i in range(5): 387 | for j in [i, i + 5]: 388 | y = results[j, x] 389 | ax[i].plot(x, y, marker='.', label=s[j]) 390 | # y_smooth = butter_lowpass_filtfilt(y) 391 | # ax[i].plot(x, np.gradient(y_smooth), marker='.', label=s[j]) 392 | 393 | ax[i].set_title(t[i]) 394 | ax[i].legend() 395 | ax[i].set_ylabel(f) if i == 0 else None # add filename 396 | fig.savefig(f.replace('.txt', '.png'), dpi=200) 397 | 398 | 399 | def plot_results(start=0, stop=0, bucket='', id=(), labels=(), save_dir=''): 400 | # Plot training 'results*.txt'. from utils.plots import *; plot_results(save_dir='runs/train/exp') 401 | fig, ax = plt.subplots(2, 5, figsize=(12, 6), tight_layout=True) 402 | ax = ax.ravel() 403 | s = ['Box', 'Objectness', 'Classification', 'Precision', 'Recall', 404 | 'val Box', 'val Objectness', 'val Classification', 'mAP@0.5', 'mAP@0.5:0.95'] 405 | if bucket: 406 | # files = ['https://storage.googleapis.com/%s/results%g.txt' % (bucket, x) for x in id] 407 | files = ['results%g.txt' % x for x in id] 408 | c = ('gsutil cp ' + '%s ' * len(files) + '.') % tuple('gs://%s/results%g.txt' % (bucket, x) for x in id) 409 | os.system(c) 410 | else: 411 | files = list(Path(save_dir).glob('results*.txt')) 412 | assert len(files), 'No results.txt files found in %s, nothing to plot.' % os.path.abspath(save_dir) 413 | for fi, f in enumerate(files): 414 | try: 415 | results = np.loadtxt(f, usecols=[2, 3, 4, 8, 9, 12, 13, 14, 10, 11], ndmin=2).T 416 | n = results.shape[1] # number of rows 417 | x = range(start, min(stop, n) if stop else n) 418 | for i in range(10): 419 | y = results[i, x] 420 | if i in [0, 1, 2, 5, 6, 7]: 421 | y[y == 0] = np.nan # don't show zero loss values 422 | # y /= y[0] # normalize 423 | label = labels[fi] if len(labels) else f.stem 424 | ax[i].plot(x, y, marker='.', label=label, linewidth=2, markersize=8) 425 | ax[i].set_title(s[i]) 426 | # if i in [5, 6, 7]: # share train and val loss y axes 427 | # ax[i].get_shared_y_axes().join(ax[i], ax[i - 5]) 428 | except Exception as e: 429 | print('Warning: Plotting error for %s; %s' % (f, e)) 430 | 431 | ax[1].legend() 432 | fig.savefig(Path(save_dir) / 'results.png', dpi=200) 433 | -------------------------------------------------------------------------------- /models/common.py: -------------------------------------------------------------------------------- 1 | # YOLOv3 common modules 2 | 3 | import math 4 | from copy import copy 5 | from pathlib import Path 6 | 7 | import numpy as np 8 | import pandas as pd 9 | import requests 10 | import torch 11 | import torch.nn as nn 12 | from PIL import Image 13 | from torch.cuda import amp 14 | 15 | from utils.datasets import letterbox 16 | from utils.general import non_max_suppression, make_divisible, scale_coords, increment_path, xyxy2xywh 17 | from utils.plots import color_list, plot_one_box 18 | from utils.torch_utils import time_synchronized 19 | 20 | 21 | def autopad(k, p=None): # kernel, padding 22 | # Pad to 'same' 23 | if p is None: 24 | p = k // 2 if isinstance(k, int) else [x // 2 for x in k] # auto-pad 25 | return p 26 | 27 | 28 | def DWConv(c1, c2, k=1, s=1, act=True): 29 | # Depthwise convolution 30 | return Conv(c1, c2, k, s, g=math.gcd(c1, c2), act=act) 31 | 32 | 33 | class Conv(nn.Module): 34 | # Standard convolution 35 | def __init__(self, c1, c2, k=1, s=1, p=None, g=1, act=True): # ch_in, ch_out, kernel, stride, padding, groups 36 | super(Conv, self).__init__() 37 | self.conv = nn.Conv2d(c1, c2, k, s, autopad(k, p), groups=g, bias=False) 38 | self.bn = nn.BatchNorm2d(c2) 39 | self.act = nn.LeakyReLU(0.1) if act is True else (act if isinstance(act, nn.Module) else nn.Identity()) 40 | 41 | def forward(self, x): 42 | return self.act(self.bn(self.conv(x))) 43 | 44 | def fuseforward(self, x): 45 | return self.act(self.conv(x)) 46 | 47 | 48 | class TransformerLayer(nn.Module): 49 | # Transformer layer https://arxiv.org/abs/2010.11929 (LayerNorm layers removed for better performance) 50 | def __init__(self, c, num_heads): 51 | super().__init__() 52 | self.q = nn.Linear(c, c, bias=False) 53 | self.k = nn.Linear(c, c, bias=False) 54 | self.v = nn.Linear(c, c, bias=False) 55 | self.ma = nn.MultiheadAttention(embed_dim=c, num_heads=num_heads) 56 | self.fc1 = nn.Linear(c, c, bias=False) 57 | self.fc2 = nn.Linear(c, c, bias=False) 58 | 59 | def forward(self, x): 60 | x = self.ma(self.q(x), self.k(x), self.v(x))[0] + x 61 | x = self.fc2(self.fc1(x)) + x 62 | return x 63 | 64 | 65 | class TransformerBlock(nn.Module): 66 | # Vision Transformer https://arxiv.org/abs/2010.11929 67 | def __init__(self, c1, c2, num_heads, num_layers): 68 | super().__init__() 69 | self.conv = None 70 | if c1 != c2: 71 | self.conv = Conv(c1, c2) 72 | self.linear = nn.Linear(c2, c2) # learnable position embedding 73 | self.tr = nn.Sequential(*[TransformerLayer(c2, num_heads) for _ in range(num_layers)]) 74 | self.c2 = c2 75 | 76 | def forward(self, x): 77 | if self.conv is not None: 78 | x = self.conv(x) 79 | b, _, w, h = x.shape 80 | p = x.flatten(2) 81 | p = p.unsqueeze(0) 82 | p = p.transpose(0, 3) 83 | p = p.squeeze(3) 84 | e = self.linear(p) 85 | x = p + e 86 | 87 | x = self.tr(x) 88 | x = x.unsqueeze(3) 89 | x = x.transpose(0, 3) 90 | x = x.reshape(b, self.c2, w, h) 91 | return x 92 | 93 | 94 | class Bottleneck(nn.Module): 95 | # Standard bottleneck 96 | def __init__(self, c1, c2, shortcut=True, g=1, e=0.5): # ch_in, ch_out, shortcut, groups, expansion 97 | super(Bottleneck, self).__init__() 98 | c_ = int(c2 * e) # hidden channels 99 | self.cv1 = Conv(c1, c_, 1, 1) 100 | self.cv2 = Conv(c_, c2, 3, 1, g=g) 101 | self.add = shortcut and c1 == c2 102 | 103 | def forward(self, x): 104 | return x + self.cv2(self.cv1(x)) if self.add else self.cv2(self.cv1(x)) 105 | 106 | 107 | class BottleneckCSP(nn.Module): 108 | # CSP Bottleneck https://github.com/WongKinYiu/CrossStagePartialNetworks 109 | def __init__(self, c1, c2, n=1, shortcut=True, g=1, e=0.5): # ch_in, ch_out, number, shortcut, groups, expansion 110 | super(BottleneckCSP, self).__init__() 111 | c_ = int(c2 * e) # hidden channels 112 | self.cv1 = Conv(c1, c_, 1, 1) 113 | self.cv2 = nn.Conv2d(c1, c_, 1, 1, bias=False) 114 | self.cv3 = nn.Conv2d(c_, c_, 1, 1, bias=False) 115 | self.cv4 = Conv(2 * c_, c2, 1, 1) 116 | self.bn = nn.BatchNorm2d(2 * c_) # applied to cat(cv2, cv3) 117 | self.act = nn.LeakyReLU(0.1, inplace=True) 118 | self.m = nn.Sequential(*[Bottleneck(c_, c_, shortcut, g, e=1.0) for _ in range(n)]) 119 | 120 | def forward(self, x): 121 | y1 = self.cv3(self.m(self.cv1(x))) 122 | y2 = self.cv2(x) 123 | return self.cv4(self.act(self.bn(torch.cat((y1, y2), dim=1)))) 124 | 125 | 126 | class C3(nn.Module): 127 | # CSP Bottleneck with 3 convolutions 128 | def __init__(self, c1, c2, n=1, shortcut=True, g=1, e=0.5): # ch_in, ch_out, number, shortcut, groups, expansion 129 | super(C3, self).__init__() 130 | c_ = int(c2 * e) # hidden channels 131 | self.cv1 = Conv(c1, c_, 1, 1) 132 | self.cv2 = Conv(c1, c_, 1, 1) 133 | self.cv3 = Conv(2 * c_, c2, 1) # act=FReLU(c2) 134 | self.m = nn.Sequential(*[Bottleneck(c_, c_, shortcut, g, e=1.0) for _ in range(n)]) 135 | # self.m = nn.Sequential(*[CrossConv(c_, c_, 3, 1, g, 1.0, shortcut) for _ in range(n)]) 136 | 137 | def forward(self, x): 138 | return self.cv3(torch.cat((self.m(self.cv1(x)), self.cv2(x)), dim=1)) 139 | 140 | 141 | class C3TR(C3): 142 | # C3 module with TransformerBlock() 143 | def __init__(self, c1, c2, n=1, shortcut=True, g=1, e=0.5): 144 | super().__init__(c1, c2, n, shortcut, g, e) 145 | c_ = int(c2 * e) 146 | self.m = TransformerBlock(c_, c_, 4, n) 147 | 148 | 149 | class SPP(nn.Module): 150 | # Spatial pyramid pooling layer used in YOLOv3-SPP 151 | def __init__(self, c1, c2, k=(5, 9, 13)): 152 | super(SPP, self).__init__() 153 | c_ = c1 // 2 # hidden channels 154 | self.cv1 = Conv(c1, c_, 1, 1) 155 | self.cv2 = Conv(c_ * (len(k) + 1), c2, 1, 1) 156 | self.m = nn.ModuleList([nn.MaxPool2d(kernel_size=x, stride=1, padding=x // 2) for x in k]) 157 | 158 | def forward(self, x): 159 | x = self.cv1(x) 160 | x = self.cv2(torch.cat([x] + [m(x) for m in self.m], 1)) 161 | return x 162 | 163 | 164 | class GDConv(nn.Module): 165 | def __init__(self, inp, oup, kernel_size=1, ratio=2, dw_size=3, stride=1, relu=True, dialte=1): 166 | super(GDConv, self).__init__() 167 | self.oup = oup 168 | # ratio = oup 169 | init_channels = math.ceil(oup / ratio) 170 | # init_channels = 8 171 | new_channels = init_channels*(ratio-1) 172 | # new_channels = oup - init_channels 173 | 174 | self.primary_conv = nn.Sequential( 175 | nn.Conv2d(inp, init_channels, kernel_size, stride, kernel_size//2, bias=False), 176 | nn.BatchNorm2d(init_channels), 177 | nn.ReLU(inplace=True) if relu else nn.Sequential(), 178 | ) 179 | 180 | self.cheap_operation = nn.Sequential( 181 | nn.Conv2d(init_channels, new_channels, dw_size, 1, dialte, groups=init_channels, dilation=dialte, bias=False), 182 | nn.BatchNorm2d(new_channels), 183 | nn.ReLU(inplace=True) if relu else nn.Sequential(), 184 | ) 185 | 186 | def forward(self, x): 187 | x1 = self.primary_conv(x) 188 | x2 = self.cheap_operation(x1) 189 | out = torch.cat([x1, x2], dim=1) 190 | return out[:, :self.oup, :, :] 191 | 192 | 193 | class RMF(nn.Module): 194 | def __init__(self, inp, oup): 195 | super(RMF, self).__init__() 196 | ratio = 12 197 | oup = oup//ratio 198 | self.Dilation0_1 = GDConv(inp, oup, dialte=1) 199 | self.Dilation0_3 = GDConv(inp, oup, dialte=3) 200 | self.Dilation0_5 = GDConv(inp, oup, dialte=5) 201 | 202 | self.SPPmax5 = nn.MaxPool2d(kernel_size=5, stride=1, padding=2) 203 | self.Dilation1_1 = GDConv(inp, oup, dialte=1) 204 | self.Dilation1_3 = GDConv(inp, oup, dialte=3) 205 | self.Dilation1_5 = GDConv(inp, oup, dialte=5) 206 | 207 | self.SPPmax9 = nn.MaxPool2d(kernel_size=9, stride=1, padding=4) 208 | self.Dilation2_1 = GDConv(inp, oup, dialte=1) 209 | self.Dilation2_3 = GDConv(inp, oup, dialte=3) 210 | self.Dilation2_5 = GDConv(inp, oup, dialte=5) 211 | 212 | self.SPPmax13 = nn.MaxPool2d(kernel_size=13, stride=1, padding=6) 213 | self.Dilation3_1 = GDConv(inp, oup, dialte=1) 214 | self.Dilation3_3 = GDConv(inp, oup, dialte=3) 215 | self.Dilation3_5 = GDConv(inp, oup, dialte=5) 216 | 217 | def forward(self, x): 218 | x_d0_1 = self.Dilation0_1(x) 219 | x_d0_3 = self.Dilation0_3(x) 220 | x_d0_5 = self.Dilation0_5(x) 221 | x_d0 = torch.cat((x_d0_1, x_d0_3, x_d0_5), 1) 222 | 223 | x_spp5 = self.SPPmax5(x) 224 | x_d1_1 = self.Dilation1_1(x_spp5) 225 | x_d1_3 = self.Dilation1_3(x_spp5) 226 | x_d1_5 = self.Dilation1_5(x_spp5) 227 | x_d1 = torch.cat((x_d1_1, x_d1_3, x_d1_5), 1) 228 | 229 | x_spp9 = self.SPPmax9(x) 230 | x_d2_1 = self.Dilation2_1(x_spp9) 231 | x_d2_3 = self.Dilation2_3(x_spp9) 232 | x_d2_5 = self.Dilation2_5(x_spp9) 233 | x_d2 = torch.cat((x_d2_1, x_d2_3, x_d2_5), 1) 234 | 235 | x_spp13 = self.SPPmax13(x) 236 | x_d3_1 = self.Dilation3_1(x_spp13) 237 | x_d3_3 = self.Dilation3_3(x_spp13) 238 | x_d3_5 = self.Dilation3_5(x_spp13) 239 | x_d3 = torch.cat((x_d3_1, x_d3_3, x_d3_5), 1) 240 | 241 | xend = torch.cat((x_d0, x_d1, x_d2, x_d3), 1) 242 | return xend 243 | 244 | 245 | class MySpp(nn.Module): 246 | def __init__(self, inp, oup): 247 | super(MySpp, self).__init__() 248 | input = inp 249 | self.SPP1 = nn.MaxPool2d(kernel_size=5, stride=1, padding=2) 250 | self.SPP2 = nn.MaxPool2d(kernel_size=9, stride=1, padding=4) 251 | self.SPP3 = nn.MaxPool2d(kernel_size=13, stride=1, padding=6) 252 | 253 | def forward(self, x): 254 | x1 = self.SPP1(x) 255 | x2 = self.SPP2(x) 256 | x3 = self.SPP3(x) 257 | xend = torch.cat((x, x1, x2, x3), 1) 258 | 259 | return xend 260 | 261 | 262 | class GhostModule(nn.Module): 263 | def __init__(self, inp, oup, kernel_size=1, ratio=2, dw_size=3, stride=1, relu=True): 264 | super(GhostModule, self).__init__() 265 | self.oup = oup 266 | # ratio = oup 267 | init_channels = math.ceil(oup / ratio) 268 | # init_channels = 8 269 | new_channels = init_channels*(ratio-1) 270 | # new_channels = oup - init_channels 271 | 272 | self.primary_conv = nn.Sequential( 273 | nn.Conv2d(inp, init_channels, kernel_size, stride, kernel_size//2, bias=False), 274 | nn.BatchNorm2d(init_channels), 275 | nn.ReLU(inplace=True) if relu else nn.Sequential(), 276 | ) 277 | 278 | self.cheap_operation = nn.Sequential( 279 | nn.Conv2d(init_channels, new_channels, dw_size, 1, dw_size//2, groups=init_channels, bias=False), 280 | nn.BatchNorm2d(new_channels), 281 | nn.ReLU(inplace=True) if relu else nn.Sequential(), 282 | ) 283 | 284 | def forward(self, x): 285 | x1 = self.primary_conv(x) 286 | x2 = self.cheap_operation(x1) 287 | out = torch.cat([x1, x2], dim=1) 288 | return out[:, :self.oup, :, :] 289 | 290 | 291 | class CspGhost(nn.Module): 292 | 293 | def __init__(self, in_channels, op_channels): 294 | super(CspGhost, self).__init__() 295 | part_ratio = 0.25 296 | self.part1_chnls = int(in_channels * part_ratio) 297 | self.part2_chnls = in_channels - self.part1_chnls 298 | self.num_layers = op_channels - self.part1_chnls 299 | self.dense = GhostModule(self.part2_chnls, self.num_layers) 300 | 301 | def forward(self, x): 302 | part1 = x[:, :self.part1_chnls, :, :] 303 | part2 = x[:, self.part1_chnls:, :, :] 304 | part2 = self.dense(part2) 305 | # part2 = self.transtion(part2) 306 | out = torch.cat((part1, part2), 1) 307 | return out 308 | 309 | 310 | class EFE(nn.Module): 311 | def __init__(self, inp, oup, k=1, s=1, channel_change=True): 312 | super(EFE, self).__init__() 313 | self.conv1x1_1 = nn.Sequential(nn.Conv2d(inp, oup, kernel_size=k, stride=s, padding=0), 314 | nn.BatchNorm2d(oup, momentum=0.03, eps=1E-4)) 315 | self.Ghost = CspGhost(oup, oup*2) 316 | self.conv1x1_2 = nn.Sequential(nn.Conv2d(oup*2, oup, kernel_size=k, stride=s, padding=0), 317 | nn.BatchNorm2d(oup, momentum=0.03, eps=1E-4)) 318 | 319 | def forward(self, x): 320 | x = self.conv1x1_1(x) 321 | residual = x 322 | x = self.Ghost(x) 323 | x = self.conv1x1_2(x) 324 | x = residual + x 325 | 326 | return x 327 | 328 | 329 | class Focus(nn.Module): 330 | # Focus wh information into c-space 331 | def __init__(self, c1, c2, k=1, s=1, p=None, g=1, act=True): # ch_in, ch_out, kernel, stride, padding, groups 332 | super(Focus, self).__init__() 333 | self.conv = Conv(c1 * 4, c2, k, s, p, g, act) 334 | # self.contract = Contract(gain=2) 335 | 336 | def forward(self, x): # x(b,c,w,h) -> y(b,4c,w/2,h/2) 337 | return self.conv(torch.cat([x[..., ::2, ::2], x[..., 1::2, ::2], x[..., ::2, 1::2], x[..., 1::2, 1::2]], 1)) 338 | # return self.conv(self.contract(x)) 339 | 340 | 341 | class Contract(nn.Module): 342 | # Contract width-height into channels, i.e. x(1,64,80,80) to x(1,256,40,40) 343 | def __init__(self, gain=2): 344 | super().__init__() 345 | self.gain = gain 346 | 347 | def forward(self, x): 348 | N, C, H, W = x.size() # assert (H / s == 0) and (W / s == 0), 'Indivisible gain' 349 | s = self.gain 350 | x = x.view(N, C, H // s, s, W // s, s) # x(1,64,40,2,40,2) 351 | x = x.permute(0, 3, 5, 1, 2, 4).contiguous() # x(1,2,2,64,40,40) 352 | return x.view(N, C * s * s, H // s, W // s) # x(1,256,40,40) 353 | 354 | 355 | class Expand(nn.Module): 356 | # Expand channels into width-height, i.e. x(1,64,80,80) to x(1,16,160,160) 357 | def __init__(self, gain=2): 358 | super().__init__() 359 | self.gain = gain 360 | 361 | def forward(self, x): 362 | N, C, H, W = x.size() # assert C / s ** 2 == 0, 'Indivisible gain' 363 | s = self.gain 364 | x = x.view(N, s, s, C // s ** 2, H, W) # x(1,2,2,16,80,80) 365 | x = x.permute(0, 3, 4, 1, 5, 2).contiguous() # x(1,16,80,2,80,2) 366 | return x.view(N, C // s ** 2, H * s, W * s) # x(1,16,160,160) 367 | 368 | 369 | class Concat(nn.Module): 370 | # Concatenate a list of tensors along dimension 371 | def __init__(self, dimension=1): 372 | super(Concat, self).__init__() 373 | self.d = dimension 374 | 375 | def forward(self, x): 376 | return torch.cat(x, self.d) 377 | 378 | 379 | class NMS(nn.Module): 380 | # Non-Maximum Suppression (NMS) module 381 | conf = 0.25 # confidence threshold 382 | iou = 0.45 # IoU threshold 383 | classes = None # (optional list) filter by class 384 | 385 | def __init__(self): 386 | super(NMS, self).__init__() 387 | 388 | def forward(self, x): 389 | return non_max_suppression(x[0], conf_thres=self.conf, iou_thres=self.iou, classes=self.classes) 390 | 391 | 392 | class autoShape(nn.Module): 393 | # input-robust model wrapper for passing cv2/np/PIL/torch inputs. Includes preprocessing, inference and NMS 394 | conf = 0.25 # NMS confidence threshold 395 | iou = 0.45 # NMS IoU threshold 396 | classes = None # (optional list) filter by class 397 | 398 | def __init__(self, model): 399 | super(autoShape, self).__init__() 400 | self.model = model.eval() 401 | 402 | def autoshape(self): 403 | print('autoShape already enabled, skipping... ') # model already converted to model.autoshape() 404 | return self 405 | 406 | @torch.no_grad() 407 | def forward(self, imgs, size=640, augment=False, profile=False): 408 | # Inference from various sources. For height=640, width=1280, RGB images example inputs are: 409 | # filename: imgs = 'data/samples/zidane.jpg' 410 | # URI: = 'https://github.com/ultralytics/yolov5/releases/download/v1.0/zidane.jpg' 411 | # OpenCV: = cv2.imread('image.jpg')[:,:,::-1] # HWC BGR to RGB x(640,1280,3) 412 | # PIL: = Image.open('image.jpg') # HWC x(640,1280,3) 413 | # numpy: = np.zeros((640,1280,3)) # HWC 414 | # torch: = torch.zeros(16,3,320,640) # BCHW (scaled to size=640, 0-1 values) 415 | # multiple: = [Image.open('image1.jpg'), Image.open('image2.jpg'), ...] # list of images 416 | 417 | t = [time_synchronized()] 418 | p = next(self.model.parameters()) # for device and type 419 | if isinstance(imgs, torch.Tensor): # torch 420 | with amp.autocast(enabled=p.device.type != 'cpu'): 421 | return self.model(imgs.to(p.device).type_as(p), augment, profile) # inference 422 | 423 | # Pre-process 424 | n, imgs = (len(imgs), imgs) if isinstance(imgs, list) else (1, [imgs]) # number of images, list of images 425 | shape0, shape1, files = [], [], [] # image and inference shapes, filenames 426 | for i, im in enumerate(imgs): 427 | f = f'image{i}' # filename 428 | if isinstance(im, str): # filename or uri 429 | im, f = np.asarray(Image.open(requests.get(im, stream=True).raw if im.startswith('http') else im)), im 430 | elif isinstance(im, Image.Image): # PIL Image 431 | im, f = np.asarray(im), getattr(im, 'filename', f) or f 432 | files.append(Path(f).with_suffix('.jpg').name) 433 | if im.shape[0] < 5: # image in CHW 434 | im = im.transpose((1, 2, 0)) # reverse dataloader .transpose(2, 0, 1) 435 | im = im[:, :, :3] if im.ndim == 3 else np.tile(im[:, :, None], 3) # enforce 3ch input 436 | s = im.shape[:2] # HWC 437 | shape0.append(s) # image shape 438 | g = (size / max(s)) # gain 439 | shape1.append([y * g for y in s]) 440 | imgs[i] = im # update 441 | shape1 = [make_divisible(x, int(self.stride.max())) for x in np.stack(shape1, 0).max(0)] # inference shape 442 | x = [letterbox(im, new_shape=shape1, auto=False)[0] for im in imgs] # pad 443 | x = np.stack(x, 0) if n > 1 else x[0][None] # stack 444 | x = np.ascontiguousarray(x.transpose((0, 3, 1, 2))) # BHWC to BCHW 445 | x = torch.from_numpy(x).to(p.device).type_as(p) / 255. # uint8 to fp16/32 446 | t.append(time_synchronized()) 447 | 448 | with amp.autocast(enabled=p.device.type != 'cpu'): 449 | # Inference 450 | y = self.model(x, augment, profile)[0] # forward 451 | t.append(time_synchronized()) 452 | 453 | # Post-process 454 | y = non_max_suppression(y, conf_thres=self.conf, iou_thres=self.iou, classes=self.classes) # NMS 455 | for i in range(n): 456 | scale_coords(shape1, y[i][:, :4], shape0[i]) 457 | 458 | t.append(time_synchronized()) 459 | return Detections(imgs, y, files, t, self.names, x.shape) 460 | 461 | 462 | class Detections: 463 | # detections class for YOLOv3 inference results 464 | def __init__(self, imgs, pred, files, times=None, names=None, shape=None): 465 | super(Detections, self).__init__() 466 | d = pred[0].device # device 467 | gn = [torch.tensor([*[im.shape[i] for i in [1, 0, 1, 0]], 1., 1.], device=d) for im in imgs] # normalizations 468 | self.imgs = imgs # list of images as numpy arrays 469 | self.pred = pred # list of tensors pred[0] = (xyxy, conf, cls) 470 | self.names = names # class names 471 | self.files = files # image filenames 472 | self.xyxy = pred # xyxy pixels 473 | self.xywh = [xyxy2xywh(x) for x in pred] # xywh pixels 474 | self.xyxyn = [x / g for x, g in zip(self.xyxy, gn)] # xyxy normalized 475 | self.xywhn = [x / g for x, g in zip(self.xywh, gn)] # xywh normalized 476 | self.n = len(self.pred) # number of images (batch size) 477 | self.t = tuple((times[i + 1] - times[i]) * 1000 / self.n for i in range(3)) # timestamps (ms) 478 | self.s = shape # inference BCHW shape 479 | 480 | def display(self, pprint=False, show=False, save=False, render=False, save_dir=''): 481 | colors = color_list() 482 | for i, (img, pred) in enumerate(zip(self.imgs, self.pred)): 483 | str = f'image {i + 1}/{len(self.pred)}: {img.shape[0]}x{img.shape[1]} ' 484 | if pred is not None: 485 | for c in pred[:, -1].unique(): 486 | n = (pred[:, -1] == c).sum() # detections per class 487 | str += f"{n} {self.names[int(c)]}{'s' * (n > 1)}, " # add to string 488 | if show or save or render: 489 | for *box, conf, cls in pred: # xyxy, confidence, class 490 | label = f'{self.names[int(cls)]} {conf:.2f}' 491 | plot_one_box(box, img, label=label, color=colors[int(cls) % 10]) 492 | img = Image.fromarray(img.astype(np.uint8)) if isinstance(img, np.ndarray) else img # from np 493 | if pprint: 494 | print(str.rstrip(', ')) 495 | if show: 496 | img.show(self.files[i]) # show 497 | if save: 498 | f = self.files[i] 499 | img.save(Path(save_dir) / f) # save 500 | print(f"{'Saved' * (i == 0)} {f}", end=',' if i < self.n - 1 else f' to {save_dir}\n') 501 | if render: 502 | self.imgs[i] = np.asarray(img) 503 | 504 | def print(self): 505 | self.display(pprint=True) # print results 506 | print(f'Speed: %.1fms pre-process, %.1fms inference, %.1fms NMS per image at shape {tuple(self.s)}' % self.t) 507 | 508 | def show(self): 509 | self.display(show=True) # show results 510 | 511 | def save(self, save_dir='runs/hub/exp'): 512 | save_dir = increment_path(save_dir, exist_ok=save_dir != 'runs/hub/exp') # increment save_dir 513 | Path(save_dir).mkdir(parents=True, exist_ok=True) 514 | self.display(save=True, save_dir=save_dir) # save results 515 | 516 | def render(self): 517 | self.display(render=True) # render results 518 | return self.imgs 519 | 520 | def pandas(self): 521 | # return detections as pandas DataFrames, i.e. print(results.pandas().xyxy[0]) 522 | new = copy(self) # return copy 523 | ca = 'xmin', 'ymin', 'xmax', 'ymax', 'confidence', 'class', 'name' # xyxy columns 524 | cb = 'xcenter', 'ycenter', 'width', 'height', 'confidence', 'class', 'name' # xywh columns 525 | for k, c in zip(['xyxy', 'xyxyn', 'xywh', 'xywhn'], [ca, ca, cb, cb]): 526 | a = [[x[:5] + [int(x[5]), self.names[int(x[5])]] for x in x.tolist()] for x in getattr(self, k)] # update 527 | setattr(new, k, [pd.DataFrame(x, columns=c) for x in a]) 528 | return new 529 | 530 | def tolist(self): 531 | # return a list of Detections objects, i.e. 'for result in results.tolist():' 532 | x = [Detections([self.imgs[i]], [self.pred[i]], self.names, self.s) for i in range(self.n)] 533 | for d in x: 534 | for k in ['imgs', 'pred', 'xyxy', 'xyxyn', 'xywh', 'xywhn']: 535 | setattr(d, k, getattr(d, k)[0]) # pop out of list 536 | return x 537 | 538 | def __len__(self): 539 | return self.n 540 | 541 | 542 | class Classify(nn.Module): 543 | # Classification head, i.e. x(b,c1,20,20) to x(b,c2) 544 | def __init__(self, c1, c2, k=1, s=1, p=None, g=1): # ch_in, ch_out, kernel, stride, padding, groups 545 | super(Classify, self).__init__() 546 | self.aap = nn.AdaptiveAvgPool2d(1) # to x(b,c1,1,1) 547 | self.conv = nn.Conv2d(c1, c2, k, s, autopad(k, p), groups=g) # to x(b,c2,1,1) 548 | self.flat = nn.Flatten() 549 | 550 | def forward(self, x): 551 | z = torch.cat([self.aap(y) for y in (x if isinstance(x, list) else [x])], 1) # cat if list 552 | return self.flat(self.conv(z)) # flatten to x(b,c2) 553 | --------------------------------------------------------------------------------