Update 4 main ops for paths and .run() (#3715)

* Add yolov5/ to path

* rename functions to run()

* cleanup

* rename fix

* CI fix

* cleanup find models/export.py

.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

@@ -1,4 +1,11 @@
+"""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
 
@@ -6,6 +13,9 @@ 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, \
@@ -15,7 +25,7 @@ from utils.torch_utils import select_device, load_classifier, time_synchronized
 
 
 @torch.no_grad()
-def detect(weights='yolov5s.pt',  # model.pt path(s)
+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
@@ -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__":

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 @@ import torch.nn as nn
 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
@@ -24,7 +24,7 @@ from utils.general import colorstr, check_img_size, check_requirements, file_siz
 from utils.torch_utils import select_device
 
 
-def export(weights='./yolov5s.pt',  # weights path
+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
@@ -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 torch
 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, \
@@ -19,7 +29,7 @@ from utils.torch_utils import select_device, time_synchronized
 
 
 @torch.no_grad()
-def test(data,
+def run(data,
         weights=None,  # model.pt path(s)
         batch_size=32,  # batch size
         imgsz=640,  # inference size (pixels)
@@ -327,11 +337,11 @@ 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,
+            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
@@ -342,7 +352,7 @@ 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,
+                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

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.nn.parallel import DistributedDataParallel as DDP
 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
@@ -375,7 +385,7 @@ 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,
+                results, maps, _ = test.run(data_dict,
                                             batch_size=batch_size // WORLD_SIZE * 2,
                                             imgsz=imgsz_test,
                                             model=ema.ema,
@@ -443,7 +453,7 @@ 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,
+                    results, _, _ = test.run(data,
                                              batch_size=batch_size // WORLD_SIZE * 2,
                                              imgsz=imgsz_test,
                                              conf_thres=0.001,

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,