退购1.1定位算法
This commit is contained in:
jiajie555
97
tests/test_cli.py
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97
tests/test_cli.py
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# Ultralytics YOLO 🚀, AGPL-3.0 license
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import subprocess
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from pathlib import Path
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from ultralytics.yolo.utils import LINUX, ONLINE, ROOT, SETTINGS
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MODEL = Path(SETTINGS['weights_dir']) / 'yolov8n'
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CFG = 'yolov8n'
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def run(cmd):
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# Run a subprocess command with check=True
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subprocess.run(cmd.split(), check=True)
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def test_special_modes():
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run('yolo checks')
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run('yolo settings')
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run('yolo help')
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# Train checks ---------------------------------------------------------------------------------------------------------
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def test_train_det():
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run(f'yolo train detect model={CFG}.yaml data=coco8.yaml imgsz=32 epochs=1 v5loader')
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def test_train_seg():
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run(f'yolo train segment model={CFG}-seg.yaml data=coco8-seg.yaml imgsz=32 epochs=1')
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def test_train_cls():
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run(f'yolo train classify model={CFG}-cls.yaml data=imagenet10 imgsz=32 epochs=1')
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def test_train_pose():
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run(f'yolo train pose model={CFG}-pose.yaml data=coco8-pose.yaml imgsz=32 epochs=1')
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# Val checks -----------------------------------------------------------------------------------------------------------
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def test_val_detect():
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run(f'yolo val detect model={MODEL}.pt data=coco8.yaml imgsz=32')
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def test_val_segment():
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run(f'yolo val segment model={MODEL}-seg.pt data=coco8-seg.yaml imgsz=32')
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def test_val_classify():
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run(f'yolo val classify model={MODEL}-cls.pt data=imagenet10 imgsz=32')
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def test_val_pose():
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run(f'yolo val pose model={MODEL}-pose.pt data=coco8-pose.yaml imgsz=32')
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# Predict checks -------------------------------------------------------------------------------------------------------
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def test_predict_detect():
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run(f"yolo predict model={MODEL}.pt source={ROOT / 'assets'} imgsz=32 save save_crop save_txt")
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if ONLINE:
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run(f'yolo predict model={MODEL}.pt source=https://ultralytics.com/images/bus.jpg imgsz=32')
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run(f'yolo predict model={MODEL}.pt source=https://ultralytics.com/assets/decelera_landscape_min.mov imgsz=32')
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run(f'yolo predict model={MODEL}.pt source=https://ultralytics.com/assets/decelera_portrait_min.mov imgsz=32')
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def test_predict_segment():
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run(f"yolo predict model={MODEL}-seg.pt source={ROOT / 'assets'} imgsz=32 save save_txt")
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def test_predict_classify():
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run(f"yolo predict model={MODEL}-cls.pt source={ROOT / 'assets'} imgsz=32 save save_txt")
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def test_predict_pose():
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run(f"yolo predict model={MODEL}-pose.pt source={ROOT / 'assets'} imgsz=32 save save_txt")
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# Export checks --------------------------------------------------------------------------------------------------------
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def test_export_detect_torchscript():
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run(f'yolo export model={MODEL}.pt format=torchscript')
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def test_export_segment_torchscript():
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run(f'yolo export model={MODEL}-seg.pt format=torchscript')
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def test_export_classify_torchscript():
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run(f'yolo export model={MODEL}-cls.pt format=torchscript')
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def test_export_classify_pose():
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run(f'yolo export model={MODEL}-pose.pt format=torchscript')
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def test_export_detect_edgetpu(enabled=False):
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if enabled and LINUX:
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run(f'yolo export model={MODEL}.pt format=edgetpu')
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125
tests/test_engine.py
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125
tests/test_engine.py
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# Ultralytics YOLO 🚀, AGPL-3.0 license
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from pathlib import Path
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from ultralytics import YOLO
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from ultralytics.yolo.cfg import get_cfg
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from ultralytics.yolo.engine.exporter import Exporter
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from ultralytics.yolo.utils import DEFAULT_CFG, ROOT, SETTINGS
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from ultralytics.yolo.v8 import classify, detect, segment
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CFG_DET = 'yolov8n.yaml'
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CFG_SEG = 'yolov8n-seg.yaml'
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CFG_CLS = 'squeezenet1_0'
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CFG = get_cfg(DEFAULT_CFG)
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MODEL = Path(SETTINGS['weights_dir']) / 'yolov8n'
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SOURCE = ROOT / 'assets'
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def test_func(model=None):
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print('callback test passed')
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def test_export():
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exporter = Exporter()
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exporter.add_callback('on_export_start', test_func)
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assert test_func in exporter.callbacks['on_export_start'], 'callback test failed'
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f = exporter(model=YOLO(CFG_DET).model)
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YOLO(f)(SOURCE) # exported model inference
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def test_detect():
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overrides = {'data': 'coco8.yaml', 'model': CFG_DET, 'imgsz': 32, 'epochs': 1, 'save': False}
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CFG.data = 'coco8.yaml'
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# Trainer
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trainer = detect.DetectionTrainer(overrides=overrides)
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trainer.add_callback('on_train_start', test_func)
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assert test_func in trainer.callbacks['on_train_start'], 'callback test failed'
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trainer.train()
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# Validator
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val = detect.DetectionValidator(args=CFG)
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val.add_callback('on_val_start', test_func)
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assert test_func in val.callbacks['on_val_start'], 'callback test failed'
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val(model=trainer.best) # validate best.pt
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# Predictor
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pred = detect.DetectionPredictor(overrides={'imgsz': [64, 64]})
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pred.add_callback('on_predict_start', test_func)
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assert test_func in pred.callbacks['on_predict_start'], 'callback test failed'
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result = pred(source=SOURCE, model=f'{MODEL}.pt')
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assert len(result), 'predictor test failed'
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overrides['resume'] = trainer.last
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trainer = detect.DetectionTrainer(overrides=overrides)
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try:
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trainer.train()
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except Exception as e:
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print(f'Expected exception caught: {e}')
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return
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Exception('Resume test failed!')
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def test_segment():
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overrides = {'data': 'coco8-seg.yaml', 'model': CFG_SEG, 'imgsz': 32, 'epochs': 1, 'save': False}
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CFG.data = 'coco8-seg.yaml'
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CFG.v5loader = False
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# YOLO(CFG_SEG).train(**overrides) # works
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# trainer
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trainer = segment.SegmentationTrainer(overrides=overrides)
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trainer.add_callback('on_train_start', test_func)
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assert test_func in trainer.callbacks['on_train_start'], 'callback test failed'
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trainer.train()
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# Validator
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val = segment.SegmentationValidator(args=CFG)
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val.add_callback('on_val_start', test_func)
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assert test_func in val.callbacks['on_val_start'], 'callback test failed'
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val(model=trainer.best) # validate best.pt
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# Predictor
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pred = segment.SegmentationPredictor(overrides={'imgsz': [64, 64]})
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pred.add_callback('on_predict_start', test_func)
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assert test_func in pred.callbacks['on_predict_start'], 'callback test failed'
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result = pred(source=SOURCE, model=f'{MODEL}-seg.pt')
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assert len(result), 'predictor test failed'
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# Test resume
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overrides['resume'] = trainer.last
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trainer = segment.SegmentationTrainer(overrides=overrides)
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try:
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trainer.train()
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except Exception as e:
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print(f'Expected exception caught: {e}')
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return
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Exception('Resume test failed!')
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def test_classify():
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overrides = {'data': 'imagenet10', 'model': 'yolov8n-cls.yaml', 'imgsz': 32, 'epochs': 1, 'save': False}
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CFG.data = 'imagenet10'
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CFG.imgsz = 32
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# YOLO(CFG_SEG).train(**overrides) # works
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# Trainer
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trainer = classify.ClassificationTrainer(overrides=overrides)
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trainer.add_callback('on_train_start', test_func)
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assert test_func in trainer.callbacks['on_train_start'], 'callback test failed'
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trainer.train()
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# Validator
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val = classify.ClassificationValidator(args=CFG)
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val.add_callback('on_val_start', test_func)
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assert test_func in val.callbacks['on_val_start'], 'callback test failed'
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val(model=trainer.best)
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# Predictor
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pred = classify.ClassificationPredictor(overrides={'imgsz': [64, 64]})
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pred.add_callback('on_predict_start', test_func)
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assert test_func in pred.callbacks['on_predict_start'], 'callback test failed'
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result = pred(source=SOURCE, model=trainer.best)
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assert len(result), 'predictor test failed'
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248
tests/test_python.py
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248
tests/test_python.py
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# Ultralytics YOLO 🚀, AGPL-3.0 license
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from pathlib import Path
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import cv2
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import numpy as np
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import torch
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from PIL import Image
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from ultralytics import YOLO
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from ultralytics.yolo.data.build import load_inference_source
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from ultralytics.yolo.utils import LINUX, ONLINE, ROOT, SETTINGS
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MODEL = Path(SETTINGS['weights_dir']) / 'yolov8n.pt'
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CFG = 'yolov8n.yaml'
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SOURCE = ROOT / 'assets/bus.jpg'
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SOURCE_GREYSCALE = Path(f'{SOURCE.parent / SOURCE.stem}_greyscale.jpg')
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SOURCE_RGBA = Path(f'{SOURCE.parent / SOURCE.stem}_4ch.png')
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# Convert SOURCE to greyscale and 4-ch
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im = Image.open(SOURCE)
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im.convert('L').save(SOURCE_GREYSCALE) # greyscale
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im.convert('RGBA').save(SOURCE_RGBA) # 4-ch PNG with alpha
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def test_model_forward():
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model = YOLO(CFG)
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model(SOURCE)
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def test_model_info():
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model = YOLO(CFG)
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model.info()
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model = YOLO(MODEL)
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model.info(verbose=True)
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def test_model_fuse():
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model = YOLO(CFG)
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model.fuse()
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model = YOLO(MODEL)
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model.fuse()
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def test_predict_dir():
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model = YOLO(MODEL)
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model(source=ROOT / 'assets')
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def test_predict_img():
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model = YOLO(MODEL)
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seg_model = YOLO('yolov8n-seg.pt')
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cls_model = YOLO('yolov8n-cls.pt')
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pose_model = YOLO('yolov8n-pose.pt')
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im = cv2.imread(str(SOURCE))
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assert len(model(source=Image.open(SOURCE), save=True, verbose=True)) == 1 # PIL
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assert len(model(source=im, save=True, save_txt=True)) == 1 # ndarray
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assert len(model(source=[im, im], save=True, save_txt=True)) == 2 # batch
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assert len(list(model(source=[im, im], save=True, stream=True))) == 2 # stream
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assert len(model(torch.zeros(320, 640, 3).numpy())) == 1 # tensor to numpy
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batch = [
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str(SOURCE), # filename
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Path(SOURCE), # Path
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'https://ultralytics.com/images/zidane.jpg' if ONLINE else SOURCE, # URI
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cv2.imread(str(SOURCE)), # OpenCV
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Image.open(SOURCE), # PIL
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np.zeros((320, 640, 3))] # numpy
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assert len(model(batch, visualize=True)) == len(batch) # multiple sources in a batch
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# Test tensor inference
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im = cv2.imread(str(SOURCE)) # OpenCV
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t = cv2.resize(im, (32, 32))
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t = torch.from_numpy(t.transpose((2, 0, 1)))
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t = torch.stack([t, t, t, t])
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results = model(t, visualize=True)
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assert len(results) == t.shape[0]
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results = seg_model(t, visualize=True)
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assert len(results) == t.shape[0]
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results = cls_model(t, visualize=True)
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assert len(results) == t.shape[0]
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results = pose_model(t, visualize=True)
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assert len(results) == t.shape[0]
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def test_predict_grey_and_4ch():
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model = YOLO(MODEL)
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for f in SOURCE_RGBA, SOURCE_GREYSCALE:
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for source in Image.open(f), cv2.imread(str(f)), f:
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model(source, save=True, verbose=True)
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def test_val():
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model = YOLO(MODEL)
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model.val(data='coco8.yaml', imgsz=32)
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def test_val_scratch():
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model = YOLO(CFG)
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model.val(data='coco8.yaml', imgsz=32)
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def test_amp():
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if torch.cuda.is_available():
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from ultralytics.yolo.engine.trainer import check_amp
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model = YOLO(MODEL).model.cuda()
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assert check_amp(model)
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def test_train_scratch():
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model = YOLO(CFG)
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model.train(data='coco8.yaml', epochs=1, imgsz=32)
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model(SOURCE)
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def test_train_pretrained():
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model = YOLO(MODEL)
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model.train(data='coco8.yaml', epochs=1, imgsz=32)
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model(SOURCE)
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def test_export_torchscript():
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model = YOLO(MODEL)
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f = model.export(format='torchscript')
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YOLO(f)(SOURCE) # exported model inference
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def test_export_torchscript_scratch():
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model = YOLO(CFG)
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f = model.export(format='torchscript')
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YOLO(f)(SOURCE) # exported model inference
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def test_export_onnx():
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model = YOLO(MODEL)
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f = model.export(format='onnx')
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YOLO(f)(SOURCE) # exported model inference
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def test_export_openvino():
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model = YOLO(MODEL)
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f = model.export(format='openvino')
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YOLO(f)(SOURCE) # exported model inference
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def test_export_coreml(): # sourcery skip: move-assign
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model = YOLO(MODEL)
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model.export(format='coreml')
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# if MACOS:
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# YOLO(f)(SOURCE) # model prediction only supported on macOS
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def test_export_tflite(enabled=False):
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# TF suffers from install conflicts on Windows and macOS
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if enabled and LINUX:
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model = YOLO(MODEL)
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f = model.export(format='tflite')
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YOLO(f)(SOURCE)
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def test_export_pb(enabled=False):
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# TF suffers from install conflicts on Windows and macOS
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if enabled and LINUX:
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model = YOLO(MODEL)
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f = model.export(format='pb')
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YOLO(f)(SOURCE)
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def test_export_paddle(enabled=False):
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# Paddle protobuf requirements conflicting with onnx protobuf requirements
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if enabled:
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model = YOLO(MODEL)
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model.export(format='paddle')
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def test_all_model_yamls():
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for m in list((ROOT / 'models').rglob('yolo*.yaml')):
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YOLO(m.name)
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def test_workflow():
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model = YOLO(MODEL)
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model.train(data='coco8.yaml', epochs=1, imgsz=32)
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model.val()
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model.predict(SOURCE)
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model.export(format='onnx') # export a model to ONNX format
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def test_predict_callback_and_setup():
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# test callback addition for prediction
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def on_predict_batch_end(predictor): # results -> List[batch_size]
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path, im0s, _, _ = predictor.batch
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# print('on_predict_batch_end', im0s[0].shape)
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im0s = im0s if isinstance(im0s, list) else [im0s]
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bs = [predictor.dataset.bs for _ in range(len(path))]
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predictor.results = zip(predictor.results, im0s, bs)
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model = YOLO(MODEL)
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model.add_callback('on_predict_batch_end', on_predict_batch_end)
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dataset = load_inference_source(source=SOURCE)
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bs = dataset.bs # noqa access predictor properties
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results = model.predict(dataset, stream=True) # source already setup
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for _, (result, im0, bs) in enumerate(results):
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print('test_callback', im0.shape)
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print('test_callback', bs)
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boxes = result.boxes # Boxes object for bbox outputs
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print(boxes)
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def test_result():
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model = YOLO('yolov8n-pose.pt')
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res = model([SOURCE, SOURCE])
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res[0].plot(conf=True, boxes=False)
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res[0].plot(pil=True)
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res[0] = res[0].cpu().numpy()
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print(res[0].path, res[0].keypoints)
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model = YOLO('yolov8n-seg.pt')
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res = model([SOURCE, SOURCE])
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res[0].plot(conf=True, boxes=False, masks=True)
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res[0].plot(pil=True)
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res[0] = res[0].cpu().numpy()
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print(res[0].path, res[0].masks.data)
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model = YOLO('yolov8n.pt')
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res = model(SOURCE)
|
||||
res[0].plot(pil=True)
|
||||
res[0].plot()
|
||||
res[0] = res[0].cpu().numpy()
|
||||
print(res[0].path)
|
||||
|
||||
model = YOLO('yolov8n-cls.pt')
|
||||
res = model(SOURCE)
|
||||
res[0].plot(probs=False)
|
||||
res[0].plot(pil=True)
|
||||
res[0].plot()
|
||||
res[0] = res[0].cpu().numpy()
|
||||
print(res[0].path)
|
||||
|
||||
|
||||
def test_track():
|
||||
im = cv2.imread(str(SOURCE))
|
||||
model = YOLO(MODEL)
|
||||
seg_model = YOLO('yolov8n-seg.pt')
|
||||
pose_model = YOLO('yolov8n-pose.pt')
|
||||
model.track(source=im)
|
||||
seg_model.track(source=im)
|
||||
pose_model.track(source=im)
|
||||
Reference in New Issue
Block a user