Update 4 main ops for paths and .run()

.github/workflows/ci-testing.yml

@@ -74,5 +74,5 @@ jobs:
 
           python hubconf.py  # hub
           python models/yolo.py --cfg ${{ matrix.model }}.yaml  # inspect
-          python models/export.py --img 128 --batch 1 --weights ${{ matrix.model }}.pt  # export
+          python export.py --img 128 --batch 1 --weights ${{ matrix.model }}.pt  # export
         shell: bash

.github/workflows/greetings.yml

@@ -52,5 +52,5 @@ jobs:
 
             ![CI CPU testing](https://github.com/ultralytics/yolov5/workflows/CI%20CPU%20testing/badge.svg)
 
-            If this badge is green, all [YOLOv5 GitHub Actions](https://github.com/ultralytics/yolov5/actions) Continuous Integration (CI) tests are currently passing. CI tests verify correct operation of YOLOv5 training ([train.py](https://github.com/ultralytics/yolov5/blob/master/train.py)), testing ([test.py](https://github.com/ultralytics/yolov5/blob/master/test.py)), inference ([detect.py](https://github.com/ultralytics/yolov5/blob/master/detect.py)) and export ([export.py](https://github.com/ultralytics/yolov5/blob/master/models/export.py)) on MacOS, Windows, and Ubuntu every 24 hours and on every commit.
+            If this badge is green, all [YOLOv5 GitHub Actions](https://github.com/ultralytics/yolov5/actions) Continuous Integration (CI) tests are currently passing. CI tests verify correct operation of YOLOv5 training ([train.py](https://github.com/ultralytics/yolov5/blob/master/train.py)), testing ([test.py](https://github.com/ultralytics/yolov5/blob/master/test.py)), inference ([detect.py](https://github.com/ultralytics/yolov5/blob/master/detect.py)) and export ([export.py](https://github.com/ultralytics/yolov5/blob/master/export.py)) on MacOS, Windows, and Ubuntu every 24 hours and on every commit.
 

detect.py

@@ -1,11 +1,21 @@
+"""Run inference with a YOLOv5 model on images, videos, directories, streams
+
+Usage:
+    $ python path/to/detect.py --source path/to/img.jpg --weights yolov5s.pt --img 640
+"""
+
 import argparse
+import sys
 import time
 from pathlib import Path
 
 import cv2
 import torch
 import torch.backends.cudnn as cudnn
 
+FILE = Path(__file__).absolute()
+sys.path.append(FILE.parents[0].as_posix())  # add yolov5/ to path
+
 from models.experimental import attempt_load
 from utils.datasets import LoadStreams, LoadImages
 from utils.general import check_img_size, check_requirements, check_imshow, colorstr, non_max_suppression, \
@@ -15,30 +25,30 @@
 
 
 @torch.no_grad()
-def detect(weights='yolov5s.pt',  # model.pt path(s)
-           source='data/images',  # file/dir/URL/glob, 0 for webcam
-           imgsz=640,  # inference size (pixels)
-           conf_thres=0.25,  # confidence threshold
-           iou_thres=0.45,  # NMS IOU threshold
-           max_det=1000,  # maximum detections per image
-           device='',  # cuda device, i.e. 0 or 0,1,2,3 or cpu
-           view_img=False,  # show results
-           save_txt=False,  # save results to *.txt
-           save_conf=False,  # save confidences in --save-txt labels
-           save_crop=False,  # save cropped prediction boxes
-           nosave=False,  # do not save images/videos
-           classes=None,  # filter by class: --class 0, or --class 0 2 3
-           agnostic_nms=False,  # class-agnostic NMS
-           augment=False,  # augmented inference
-           update=False,  # update all models
-           project='runs/detect',  # save results to project/name
-           name='exp',  # save results to project/name
-           exist_ok=False,  # existing project/name ok, do not increment
-           line_thickness=3,  # bounding box thickness (pixels)
-           hide_labels=False,  # hide labels
-           hide_conf=False,  # hide confidences
-           half=False,  # use FP16 half-precision inference
-           ):
+def run(weights='yolov5s.pt',  # model.pt path(s)
+        source='data/images',  # file/dir/URL/glob, 0 for webcam
+        imgsz=640,  # inference size (pixels)
+        conf_thres=0.25,  # confidence threshold
+        iou_thres=0.45,  # NMS IOU threshold
+        max_det=1000,  # maximum detections per image
+        device='',  # cuda device, i.e. 0 or 0,1,2,3 or cpu
+        view_img=False,  # show results
+        save_txt=False,  # save results to *.txt
+        save_conf=False,  # save confidences in --save-txt labels
+        save_crop=False,  # save cropped prediction boxes
+        nosave=False,  # do not save images/videos
+        classes=None,  # filter by class: --class 0, or --class 0 2 3
+        agnostic_nms=False,  # class-agnostic NMS
+        augment=False,  # augmented inference
+        update=False,  # update all models
+        project='runs/detect',  # save results to project/name
+        name='exp',  # save results to project/name
+        exist_ok=False,  # existing project/name ok, do not increment
+        line_thickness=3,  # bounding box thickness (pixels)
+        hide_labels=False,  # hide labels
+        hide_conf=False,  # hide confidences
+        half=False,  # use FP16 half-precision inference
+        ):
     save_img = not nosave and not source.endswith('.txt')  # save inference images
     webcam = source.isnumeric() or source.endswith('.txt') or source.lower().startswith(
         ('rtsp://', 'rtmp://', 'http://', 'https://'))
@@ -204,7 +214,7 @@ def parse_opt():
 def main(opt):
     print(colorstr('detect: ') + ', '.join(f'{k}={v}' for k, v in vars(opt).items()))
     check_requirements(exclude=('tensorboard', 'thop'))
-    detect(**vars(opt))
+    run(**vars(opt))
 
 
 if __name__ == "__main__":

models/export.py → export.py

@@ -1,7 +1,7 @@
 """Export a YOLOv5 *.pt model to TorchScript, ONNX, CoreML formats
 
 Usage:
-    $ python path/to/models/export.py --weights yolov5s.pt --img 640 --batch 1
+    $ python path/to/export.py --weights yolov5s.pt --img 640 --batch 1
 """
 
 import argparse
@@ -14,7 +14,7 @@
 from torch.utils.mobile_optimizer import optimize_for_mobile
 
 FILE = Path(__file__).absolute()
-sys.path.append(FILE.parents[1].as_posix())  # add yolov5/ to path
+sys.path.append(FILE.parents[0].as_posix())  # add yolov5/ to path
 
 from models.common import Conv
 from models.yolo import Detect
@@ -24,19 +24,19 @@
 from utils.torch_utils import select_device
 
 
-def export(weights='./yolov5s.pt',  # weights path
-           img_size=(640, 640),  # image (height, width)
-           batch_size=1,  # batch size
-           device='cpu',  # cuda device, i.e. 0 or 0,1,2,3 or cpu
-           include=('torchscript', 'onnx', 'coreml'),  # include formats
-           half=False,  # FP16 half-precision export
-           inplace=False,  # set YOLOv5 Detect() inplace=True
-           train=False,  # model.train() mode
-           optimize=False,  # TorchScript: optimize for mobile
-           dynamic=False,  # ONNX: dynamic axes
-           simplify=False,  # ONNX: simplify model
-           opset_version=12,  # ONNX: opset version
-           ):
+def run(weights='./yolov5s.pt',  # weights path
+        img_size=(640, 640),  # image (height, width)
+        batch_size=1,  # batch size
+        device='cpu',  # cuda device, i.e. 0 or 0,1,2,3 or cpu
+        include=('torchscript', 'onnx', 'coreml'),  # include formats
+        half=False,  # FP16 half-precision export
+        inplace=False,  # set YOLOv5 Detect() inplace=True
+        train=False,  # model.train() mode
+        optimize=False,  # TorchScript: optimize for mobile
+        dynamic=False,  # ONNX: dynamic axes
+        simplify=False,  # ONNX: simplify model
+        opset_version=12,  # ONNX: opset version
+        ):
     t = time.time()
     include = [x.lower() for x in include]
     img_size *= 2 if len(img_size) == 1 else 1  # expand
@@ -165,7 +165,7 @@ def parse_opt():
 def main(opt):
     set_logging()
     print(colorstr('export: ') + ', '.join(f'{k}={v}' for k, v in vars(opt).items()))
-    export(**vars(opt))
+    run(**vars(opt))
 
 
 if __name__ == "__main__":

test.py

@@ -1,6 +1,13 @@
+"""Test a trained YOLOv5 model accuracy on a custom dataset
+
+Usage:
+    $ python path/to/test.py --data coco128.yaml --weights yolov5s.pt --img 640
+"""
+
 import argparse
 import json
 import os
+import sys
 from pathlib import Path
 from threading import Thread
 
@@ -9,6 +16,9 @@
 import yaml
 from tqdm import tqdm
 
+FILE = Path(__file__).absolute()
+sys.path.append(FILE.parents[0].as_posix())  # add yolov5/ to path
+
 from models.experimental import attempt_load
 from utils.datasets import create_dataloader
 from utils.general import coco80_to_coco91_class, check_dataset, check_file, check_img_size, check_requirements, \
@@ -19,32 +29,32 @@
 
 
 @torch.no_grad()
-def test(data,
-         weights=None,  # model.pt path(s)
-         batch_size=32,  # batch size
-         imgsz=640,  # inference size (pixels)
-         conf_thres=0.001,  # confidence threshold
-         iou_thres=0.6,  # NMS IoU threshold
-         task='val',  # train, val, test, speed or study
-         device='',  # cuda device, i.e. 0 or 0,1,2,3 or cpu
-         single_cls=False,  # treat as single-class dataset
-         augment=False,  # augmented inference
-         verbose=False,  # verbose output
-         save_txt=False,  # save results to *.txt
-         save_hybrid=False,  # save label+prediction hybrid results to *.txt
-         save_conf=False,  # save confidences in --save-txt labels
-         save_json=False,  # save a cocoapi-compatible JSON results file
-         project='runs/test',  # save to project/name
-         name='exp',  # save to project/name
-         exist_ok=False,  # existing project/name ok, do not increment
-         half=True,  # use FP16 half-precision inference
-         model=None,
-         dataloader=None,
-         save_dir=Path(''),
-         plots=True,
-         wandb_logger=None,
-         compute_loss=None,
-         ):
+def run(data,
+        weights=None,  # model.pt path(s)
+        batch_size=32,  # batch size
+        imgsz=640,  # inference size (pixels)
+        conf_thres=0.001,  # confidence threshold
+        iou_thres=0.6,  # NMS IoU threshold
+        task='val',  # train, val, test, speed or study
+        device='',  # cuda device, i.e. 0 or 0,1,2,3 or cpu
+        single_cls=False,  # treat as single-class dataset
+        augment=False,  # augmented inference
+        verbose=False,  # verbose output
+        save_txt=False,  # save results to *.txt
+        save_hybrid=False,  # save label+prediction hybrid results to *.txt
+        save_conf=False,  # save confidences in --save-txt labels
+        save_json=False,  # save a cocoapi-compatible JSON results file
+        project='runs/test',  # save to project/name
+        name='exp',  # save to project/name
+        exist_ok=False,  # existing project/name ok, do not increment
+        half=True,  # use FP16 half-precision inference
+        model=None,
+        dataloader=None,
+        save_dir=Path(''),
+        plots=True,
+        wandb_logger=None,
+        compute_loss=None,
+        ):
     # Initialize/load model and set device
     training = model is not None
     if training:  # called by train.py
@@ -327,12 +337,12 @@ def main(opt):
     check_requirements(exclude=('tensorboard', 'thop'))
 
     if opt.task in ('train', 'val', 'test'):  # run normally
-        test(**vars(opt))
+        run(**vars(opt))
 
     elif opt.task == 'speed':  # speed benchmarks
         for w in opt.weights if isinstance(opt.weights, list) else [opt.weights]:
-            test(opt.data, weights=w, batch_size=opt.batch_size, imgsz=opt.imgsz, conf_thres=.25, iou_thres=.45,
-                 save_json=False, plots=False)
+            run(opt.data, weights=w, batch_size=opt.batch_size, imgsz=opt.imgsz, conf_thres=.25, iou_thres=.45,
+                save_json=False, plots=False)
 
     elif opt.task == 'study':  # run over a range of settings and save/plot
         # python test.py --task study --data coco.yaml --iou 0.7 --weights yolov5s.pt yolov5m.pt yolov5l.pt yolov5x.pt
@@ -342,8 +352,8 @@ def main(opt):
             y = []  # y axis
             for i in x:  # img-size
                 print(f'\nRunning {f} point {i}...')
-                r, _, t = test(opt.data, weights=w, batch_size=opt.batch_size, imgsz=i, conf_thres=opt.conf_thres,
-                               iou_thres=opt.iou_thres, save_json=opt.save_json, plots=False)
+                r, _, t = run(opt.data, weights=w, batch_size=opt.batch_size, imgsz=i, conf_thres=opt.conf_thres,
+                              iou_thres=opt.iou_thres, save_json=opt.save_json, plots=False)
                 y.append(r + t)  # results and times
             np.savetxt(f, y, fmt='%10.4g')  # save
         os.system('zip -r study.zip study_*.txt')

train.py

@@ -1,8 +1,15 @@
+"""Train a YOLOv5 model on a custom dataset
+
+Usage:
+    $ python path/to/train.py --data coco128.yaml --weights yolov5s.pt --img 640
+"""
+
 import argparse
 import logging
 import math
 import os
 import random
+import sys
 import time
 import warnings
 from copy import deepcopy
@@ -22,6 +29,9 @@
 from torch.utils.tensorboard import SummaryWriter
 from tqdm import tqdm
 
+FILE = Path(__file__).absolute()
+sys.path.append(FILE.parents[0].as_posix())  # add yolov5/ to path
+
 import test  # for end-of-epoch mAP
 from models.experimental import attempt_load
 from models.yolo import Model
@@ -89,7 +99,7 @@ def train(hyp,  # path/to/hyp.yaml or hyp dictionary
         # W&B
         opt.hyp = hyp  # add hyperparameters
         run_id = torch.load(weights).get('wandb_id') if weights.endswith('.pt') and os.path.isfile(weights) else None
-        run_id = run_id if opt.resume else None # start fresh run if transfer learning
+        run_id = run_id if opt.resume else None  # start fresh run if transfer learning
         wandb_logger = WandbLogger(opt, save_dir.stem, run_id, data_dict)
         loggers['wandb'] = wandb_logger.wandb
         if loggers['wandb']:
@@ -375,18 +385,18 @@ def train(hyp,  # path/to/hyp.yaml or hyp dictionary
             final_epoch = epoch + 1 == epochs
             if not notest or final_epoch:  # Calculate mAP
                 wandb_logger.current_epoch = epoch + 1
-                results, maps, _ = test.test(data_dict,
-                                             batch_size=batch_size // WORLD_SIZE * 2,
-                                             imgsz=imgsz_test,
-                                             model=ema.ema,
-                                             single_cls=single_cls,
-                                             dataloader=testloader,
-                                             save_dir=save_dir,
-                                             save_json=is_coco and final_epoch,
-                                             verbose=nc < 50 and final_epoch,
-                                             plots=plots and final_epoch,
-                                             wandb_logger=wandb_logger,
-                                             compute_loss=compute_loss)
+                results, maps, _ = test.run(data_dict,
+                                            batch_size=batch_size // WORLD_SIZE * 2,
+                                            imgsz=imgsz_test,
+                                            model=ema.ema,
+                                            single_cls=single_cls,
+                                            dataloader=testloader,
+                                            save_dir=save_dir,
+                                            save_json=is_coco and final_epoch,
+                                            verbose=nc < 50 and final_epoch,
+                                            plots=plots and final_epoch,
+                                            wandb_logger=wandb_logger,
+                                            compute_loss=compute_loss)
 
             # Write
             with open(results_file, 'a') as f:
@@ -443,17 +453,17 @@ def train(hyp,  # path/to/hyp.yaml or hyp dictionary
         if not evolve:
             if is_coco:  # COCO dataset
                 for m in [last, best] if best.exists() else [last]:  # speed, mAP tests
-                    results, _, _ = test.test(data,
-                                              batch_size=batch_size // WORLD_SIZE * 2,
-                                              imgsz=imgsz_test,
-                                              conf_thres=0.001,
-                                              iou_thres=0.7,
-                                              model=attempt_load(m, device).half(),
-                                              single_cls=single_cls,
-                                              dataloader=testloader,
-                                              save_dir=save_dir,
-                                              save_json=True,
-                                              plots=False)
+                    results, _, _ = test.run(data,
+                                             batch_size=batch_size // WORLD_SIZE * 2,
+                                             imgsz=imgsz_test,
+                                             conf_thres=0.001,
+                                             iou_thres=0.7,
+                                             model=attempt_load(m, device).half(),
+                                             single_cls=single_cls,
+                                             dataloader=testloader,
+                                             save_dir=save_dir,
+                                             save_json=True,
+                                             plots=False)
 
             # Strip optimizers
             for f in last, best:

tutorial.ipynb

@@ -1125,7 +1125,7 @@
         "\n",
         "![CI CPU testing](https://github.com/ultralytics/yolov5/workflows/CI%20CPU%20testing/badge.svg)\n",
         "\n",
-        "If this badge is green, all [YOLOv5 GitHub Actions](https://github.com/ultralytics/yolov5/actions) Continuous Integration (CI) tests are currently passing. CI tests verify correct operation of YOLOv5 training ([train.py](https://github.com/ultralytics/yolov5/blob/master/train.py)), testing ([test.py](https://github.com/ultralytics/yolov5/blob/master/test.py)), inference ([detect.py](https://github.com/ultralytics/yolov5/blob/master/detect.py)) and export ([export.py](https://github.com/ultralytics/yolov5/blob/master/models/export.py)) on MacOS, Windows, and Ubuntu every 24 hours and on every commit.\n"
+        "If this badge is green, all [YOLOv5 GitHub Actions](https://github.com/ultralytics/yolov5/actions) Continuous Integration (CI) tests are currently passing. CI tests verify correct operation of YOLOv5 training ([train.py](https://github.com/ultralytics/yolov5/blob/master/train.py)), testing ([test.py](https://github.com/ultralytics/yolov5/blob/master/test.py)), inference ([detect.py](https://github.com/ultralytics/yolov5/blob/master/detect.py)) and export ([export.py](https://github.com/ultralytics/yolov5/blob/master/export.py)) on MacOS, Windows, and Ubuntu every 24 hours and on every commit.\n"
       ]
     },
     {
@@ -1212,7 +1212,7 @@
         "  done\n",
         "  python hubconf.py  # hub\n",
         "  python models/yolo.py --cfg $m.yaml  # inspect\n",
-        "  python models/export.py --weights $m.pt --img 640 --batch 1  # export\n",
+        "  python export.py --weights $m.pt --img 640 --batch 1  # export\n",
         "done"
       ],
       "execution_count": null,