File size: 3,494 Bytes
8f17d1c |
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 |
# Ultralytics YOLO 🚀, AGPL-3.0 license
import pytest
import torch
from ultralytics import YOLO
from ultralytics.utils import ASSETS, WEIGHTS_DIR, checks
CUDA_IS_AVAILABLE = checks.cuda_is_available()
CUDA_DEVICE_COUNT = checks.cuda_device_count()
MODEL = WEIGHTS_DIR / "path with spaces" / "yolov8n.pt" # test spaces in path
DATA = "coco8.yaml"
BUS = ASSETS / "bus.jpg"
def test_checks():
"""Validate CUDA settings against torch CUDA functions."""
assert torch.cuda.is_available() == CUDA_IS_AVAILABLE
assert torch.cuda.device_count() == CUDA_DEVICE_COUNT
@pytest.mark.skipif(not CUDA_IS_AVAILABLE, reason="CUDA is not available")
def test_train():
"""Test model training on a minimal dataset."""
device = 0 if CUDA_DEVICE_COUNT == 1 else [0, 1]
YOLO(MODEL).train(data=DATA, imgsz=64, epochs=1, device=device) # requires imgsz>=64
@pytest.mark.slow
@pytest.mark.skipif(not CUDA_IS_AVAILABLE, reason="CUDA is not available")
def test_predict_multiple_devices():
"""Validate model prediction on multiple devices."""
model = YOLO("yolov8n.pt")
model = model.cpu()
assert str(model.device) == "cpu"
_ = model(BUS) # CPU inference
assert str(model.device) == "cpu"
model = model.to("cuda:0")
assert str(model.device) == "cuda:0"
_ = model(BUS) # CUDA inference
assert str(model.device) == "cuda:0"
model = model.cpu()
assert str(model.device) == "cpu"
_ = model(BUS) # CPU inference
assert str(model.device) == "cpu"
model = model.cuda()
assert str(model.device) == "cuda:0"
_ = model(BUS) # CUDA inference
assert str(model.device) == "cuda:0"
@pytest.mark.skipif(not CUDA_IS_AVAILABLE, reason="CUDA is not available")
def test_autobatch():
"""Check batch size for YOLO model using autobatch."""
from ultralytics.utils.autobatch import check_train_batch_size
check_train_batch_size(YOLO(MODEL).model.cuda(), imgsz=128, amp=True)
@pytest.mark.slow
@pytest.mark.skipif(not CUDA_IS_AVAILABLE, reason="CUDA is not available")
def test_utils_benchmarks():
"""Profile YOLO models for performance benchmarks."""
from ultralytics.utils.benchmarks import ProfileModels
# Pre-export a dynamic engine model to use dynamic inference
YOLO(MODEL).export(format="engine", imgsz=32, dynamic=True, batch=1)
ProfileModels([MODEL], imgsz=32, half=False, min_time=1, num_timed_runs=3, num_warmup_runs=1).profile()
@pytest.mark.skipif(not CUDA_IS_AVAILABLE, reason="CUDA is not available")
def test_predict_sam():
"""Test SAM model prediction with various prompts."""
from ultralytics import SAM
from ultralytics.models.sam import Predictor as SAMPredictor
# Load a model
model = SAM(WEIGHTS_DIR / "sam_b.pt")
# Display model information (optional)
model.info()
# Run inference
model(BUS, device=0)
# Run inference with bboxes prompt
model(BUS, bboxes=[439, 437, 524, 709], device=0)
# Run inference with points prompt
model(ASSETS / "zidane.jpg", points=[900, 370], labels=[1], device=0)
# Create SAMPredictor
overrides = dict(conf=0.25, task="segment", mode="predict", imgsz=1024, model=WEIGHTS_DIR / "mobile_sam.pt")
predictor = SAMPredictor(overrides=overrides)
# Set image
predictor.set_image(ASSETS / "zidane.jpg") # set with image file
# predictor(bboxes=[439, 437, 524, 709])
# predictor(points=[900, 370], labels=[1])
# Reset image
predictor.reset_image()
|