Spaces:
Running
Running
File size: 5,722 Bytes
2793310 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 | # Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
import sys
from unittest import mock
import torch
from tests import MODEL
from ultralytics import YOLO
from ultralytics.cfg import get_cfg
from ultralytics.engine.exporter import Exporter
from ultralytics.models.yolo import classify, detect, segment
from ultralytics.utils import ASSETS, DEFAULT_CFG, WEIGHTS_DIR
def test_func(*args):
"""Test function callback for evaluating YOLO model performance metrics."""
print("callback test passed")
def test_export():
"""Test model exporting functionality by adding a callback and verifying its execution."""
exporter = Exporter()
exporter.add_callback("on_export_start", test_func)
assert test_func in exporter.callbacks["on_export_start"], "callback test failed"
f = exporter(model=YOLO("yolo11n.yaml").model)
YOLO(f)(ASSETS) # exported model inference
def test_detect():
"""Test YOLO object detection training, validation, and prediction functionality."""
overrides = {"data": "coco8.yaml", "model": "yolo11n.yaml", "imgsz": 32, "epochs": 1, "save": False}
cfg = get_cfg(DEFAULT_CFG)
cfg.data = "coco8.yaml"
cfg.imgsz = 32
# Trainer
trainer = detect.DetectionTrainer(overrides=overrides)
trainer.add_callback("on_train_start", test_func)
assert test_func in trainer.callbacks["on_train_start"], "callback test failed"
trainer.train()
# Validator
val = detect.DetectionValidator(args=cfg)
val.add_callback("on_val_start", test_func)
assert test_func in val.callbacks["on_val_start"], "callback test failed"
val(model=trainer.best) # validate best.pt
# Predictor
pred = detect.DetectionPredictor(overrides={"imgsz": [64, 64]})
pred.add_callback("on_predict_start", test_func)
assert test_func in pred.callbacks["on_predict_start"], "callback test failed"
# Confirm there is no issue with sys.argv being empty
with mock.patch.object(sys, "argv", []):
result = pred(source=ASSETS, model=MODEL)
assert len(result), "predictor test failed"
# Test resume functionality
overrides["resume"] = trainer.last
trainer = detect.DetectionTrainer(overrides=overrides)
try:
trainer.train()
except Exception as e:
print(f"Expected exception caught: {e}")
return
raise Exception("Resume test failed!")
def test_segment():
"""Test image segmentation training, validation, and prediction pipelines using YOLO models."""
overrides = {
"data": "coco8-seg.yaml",
"model": "yolo11n-seg.yaml",
"imgsz": 32,
"epochs": 1,
"save": False,
"mask_ratio": 1,
"overlap_mask": False,
}
cfg = get_cfg(DEFAULT_CFG)
cfg.data = "coco8-seg.yaml"
cfg.imgsz = 32
# Trainer
trainer = segment.SegmentationTrainer(overrides=overrides)
trainer.add_callback("on_train_start", test_func)
assert test_func in trainer.callbacks["on_train_start"], "callback test failed"
trainer.train()
# Validator
val = segment.SegmentationValidator(args=cfg)
val.add_callback("on_val_start", test_func)
assert test_func in val.callbacks["on_val_start"], "callback test failed"
val(model=trainer.best) # validate best.pt
# Predictor
pred = segment.SegmentationPredictor(overrides={"imgsz": [64, 64]})
pred.add_callback("on_predict_start", test_func)
assert test_func in pred.callbacks["on_predict_start"], "callback test failed"
result = pred(source=ASSETS, model=WEIGHTS_DIR / "yolo11n-seg.pt")
assert len(result), "predictor test failed"
# Test resume functionality
overrides["resume"] = trainer.last
trainer = segment.SegmentationTrainer(overrides=overrides)
try:
trainer.train()
except Exception as e:
print(f"Expected exception caught: {e}")
return
raise Exception("Resume test failed!")
def test_classify():
"""Test image classification including training, validation, and prediction phases."""
overrides = {"data": "imagenet10", "model": "yolo11n-cls.yaml", "imgsz": 32, "epochs": 1, "save": False}
cfg = get_cfg(DEFAULT_CFG)
cfg.data = "imagenet10"
cfg.imgsz = 32
# Trainer
trainer = classify.ClassificationTrainer(overrides=overrides)
trainer.add_callback("on_train_start", test_func)
assert test_func in trainer.callbacks["on_train_start"], "callback test failed"
trainer.train()
# Validator
val = classify.ClassificationValidator(args=cfg)
val.add_callback("on_val_start", test_func)
assert test_func in val.callbacks["on_val_start"], "callback test failed"
val(model=trainer.best)
# Predictor
pred = classify.ClassificationPredictor(overrides={"imgsz": [64, 64]})
pred.add_callback("on_predict_start", test_func)
assert test_func in pred.callbacks["on_predict_start"], "callback test failed"
result = pred(source=ASSETS, model=trainer.best)
assert len(result), "predictor test failed"
def test_nan_recovery():
"""Test NaN loss detection and recovery during training."""
nan_injected = [False]
def inject_nan(trainer):
"""Inject NaN into loss during batch processing to test recovery mechanism."""
if trainer.epoch == 1 and trainer.tloss is not None and not nan_injected[0]:
trainer.tloss *= torch.tensor(float("nan"))
nan_injected[0] = True
overrides = {"data": "coco8.yaml", "model": "yolo11n.yaml", "imgsz": 32, "epochs": 3}
trainer = detect.DetectionTrainer(overrides=overrides)
trainer.add_callback("on_train_batch_end", inject_nan)
trainer.train()
assert nan_injected[0], "NaN injection failed"
|