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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
pull/3720/head
Glenn Jocher 2021-06-21 17:25:04 +02:00 committed by GitHub
parent 75c0ff43af
commit 1f69d12591
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7 changed files with 130 additions and 100 deletions
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2
.github/workflows/ci-testing.yml vendored
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@ -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

2
.github/workflows/greetings.yml vendored
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@ -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.

14
detect.py
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@ -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__":

8
models/export.py → export.py
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@ -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__":

18
test.py
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@ -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

14
train.py
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@ -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,

4
tutorial.ipynb vendored
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@ -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,