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121 lines
4.9 KiB
121 lines
4.9 KiB
# Ultralytics YOLO 🚀, AGPL-3.0 license |
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import subprocess |
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import pytest |
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from PIL import Image |
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from tests import CUDA_DEVICE_COUNT, CUDA_IS_AVAILABLE |
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from ultralytics.cfg import TASK2DATA, TASK2MODEL, TASKS |
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from ultralytics.utils import ASSETS, WEIGHTS_DIR, checks |
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from ultralytics.utils.torch_utils import TORCH_1_9 |
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# Constants |
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TASK_MODEL_DATA = [(task, WEIGHTS_DIR / TASK2MODEL[task], TASK2DATA[task]) for task in TASKS] |
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MODELS = [WEIGHTS_DIR / TASK2MODEL[task] for task in TASKS] |
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def run(cmd): |
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"""Execute a shell command using subprocess.""" |
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subprocess.run(cmd.split(), check=True) |
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def test_special_modes(): |
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"""Test various special command-line modes for YOLO functionality.""" |
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run("yolo help") |
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run("yolo checks") |
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run("yolo version") |
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run("yolo settings reset") |
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run("yolo cfg") |
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@pytest.mark.parametrize("task,model,data", TASK_MODEL_DATA) |
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def test_train(task, model, data): |
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"""Test YOLO training for different tasks, models, and datasets.""" |
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run(f"yolo train {task} model={model} data={data} imgsz=32 epochs=1 cache=disk") |
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@pytest.mark.parametrize("task,model,data", TASK_MODEL_DATA) |
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def test_val(task, model, data): |
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"""Test YOLO validation process for specified task, model, and data using a shell command.""" |
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run(f"yolo val {task} model={model} data={data} imgsz=32 save_txt save_json") |
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@pytest.mark.parametrize("task,model,data", TASK_MODEL_DATA) |
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def test_predict(task, model, data): |
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"""Test YOLO prediction on provided sample assets for specified task and model.""" |
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run(f"yolo predict model={model} source={ASSETS} imgsz=32 save save_crop save_txt") |
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@pytest.mark.parametrize("model", MODELS) |
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def test_export(model): |
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"""Test exporting a YOLO model to TorchScript format.""" |
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run(f"yolo export model={model} format=torchscript imgsz=32") |
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def test_rtdetr(task="detect", model="yolov8n-rtdetr.yaml", data="coco8.yaml"): |
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"""Test the RTDETR functionality within Ultralytics for detection tasks using specified model and data.""" |
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# Warning: must use imgsz=640 (note also add coma, spaces, fraction=0.25 args to test single-image training) |
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run(f"yolo train {task} model={model} data={data} --imgsz= 160 epochs =1, cache = disk fraction=0.25") |
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run(f"yolo predict {task} model={model} source={ASSETS / 'bus.jpg'} imgsz=160 save save_crop save_txt") |
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if TORCH_1_9: |
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run(f"yolo predict {task} model='rtdetr-l.pt' source={ASSETS / 'bus.jpg'} imgsz=160 save save_crop save_txt") |
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@pytest.mark.skipif(checks.IS_PYTHON_3_12, reason="MobileSAM with CLIP is not supported in Python 3.12") |
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def test_fastsam(task="segment", model=WEIGHTS_DIR / "FastSAM-s.pt", data="coco8-seg.yaml"): |
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"""Test FastSAM model for segmenting objects in images using various prompts within Ultralytics.""" |
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source = ASSETS / "bus.jpg" |
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run(f"yolo segment val {task} model={model} data={data} imgsz=32") |
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run(f"yolo segment predict model={model} source={source} imgsz=32 save save_crop save_txt") |
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from ultralytics import FastSAM |
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from ultralytics.models.sam import Predictor |
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# Create a FastSAM model |
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sam_model = FastSAM(model) # or FastSAM-x.pt |
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# Run inference on an image |
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for s in (source, Image.open(source)): |
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everything_results = sam_model(s, device="cpu", retina_masks=True, imgsz=320, conf=0.4, iou=0.9) |
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# Remove small regions |
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new_masks, _ = Predictor.remove_small_regions(everything_results[0].masks.data, min_area=20) |
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# Run inference with bboxes and points and texts prompt at the same time |
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sam_model(source, bboxes=[439, 437, 524, 709], points=[[200, 200]], labels=[1], texts="a photo of a dog") |
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def test_mobilesam(): |
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"""Test MobileSAM segmentation with point prompts using Ultralytics.""" |
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from ultralytics import SAM |
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# Load the model |
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model = SAM(WEIGHTS_DIR / "mobile_sam.pt") |
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# Source |
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source = ASSETS / "zidane.jpg" |
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# Predict a segment based on a 1D point prompt and 1D labels. |
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model.predict(source, points=[900, 370], labels=[1]) |
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# Predict a segment based on 3D points and 2D labels (multiple points per object). |
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model.predict(source, points=[[[900, 370], [1000, 100]]], labels=[[1, 1]]) |
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# Predict a segment based on a box prompt |
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model.predict(source, bboxes=[439, 437, 524, 709], save=True) |
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# Predict all |
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# model(source) |
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# Slow Tests ----------------------------------------------------------------------------------------------------------- |
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@pytest.mark.slow |
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@pytest.mark.parametrize("task,model,data", TASK_MODEL_DATA) |
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@pytest.mark.skipif(not CUDA_IS_AVAILABLE, reason="CUDA is not available") |
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@pytest.mark.skipif(CUDA_DEVICE_COUNT < 2, reason="DDP is not available") |
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def test_train_gpu(task, model, data): |
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"""Test YOLO training on GPU(s) for various tasks and models.""" |
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run(f"yolo train {task} model={model} data={data} imgsz=32 epochs=1 device=0") # single GPU |
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run(f"yolo train {task} model={model} data={data} imgsz=32 epochs=1 device=0,1") # multi GPU
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