feat: HSAP platform v2 — modular navigation, quality review, audit log, world model simulation
Major changes: - New frontend (platform/web/): Vite + React 18 + TypeScript + Tailwind - 4-module navigation: 数据送标 / 模型管理 / 车队管理 / 系统管理 - Data catalog with charts (DMS/ADAS/Lane 3-tab view) - Quality review workflow (标注质检): Good/Fine/Bad scoring with auto-advance - Audit enhancements: batch operations, rejection categories, Feishu notifications - Operation audit log (操作日志) - World model simulation studio (仿真工坊) - Dataset version management with snapshots and diff - ADAS 7-class dataset integration (138K images organized + compressed) - User management with Feishu integration and pagination - CRUD/search/filter on all pages, card layout redesign - PIL-optimized image overlay rendering - Auto-snapshot on build, in_review workflow stage - Removed embedded algorithm code (now in workspace)
This commit is contained in:
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algorithms/dms_yolo/code.embedded.bak/tests/test_cuda.py
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algorithms/dms_yolo/code.embedded.bak/tests/test_cuda.py
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# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
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import os
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from itertools import product
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from pathlib import Path
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import pytest
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import torch
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from tests import CUDA_DEVICE_COUNT, CUDA_IS_AVAILABLE, MODEL, SOURCE
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from ultralytics import YOLO
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from ultralytics.cfg import TASK2DATA, TASK2MODEL, TASKS
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from ultralytics.utils import ASSETS, IS_JETSON, WEIGHTS_DIR
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from ultralytics.utils.autodevice import GPUInfo
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from ultralytics.utils.checks import check_amp
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from ultralytics.utils.torch_utils import TORCH_1_13
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# Try to find idle devices if CUDA is available
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DEVICES = []
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if CUDA_IS_AVAILABLE:
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if IS_JETSON:
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DEVICES = [0] # NVIDIA Jetson only has one GPU and does not fully support pynvml library
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else:
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gpu_info = GPUInfo()
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gpu_info.print_status()
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autodevice_fraction = __import__("os").environ.get("YOLO_AUTODEVICE_FRACTION_FREE", 0.3)
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if idle_gpus := gpu_info.select_idle_gpu(
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count=2,
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min_memory_fraction=autodevice_fraction,
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min_util_fraction=autodevice_fraction,
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):
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DEVICES = idle_gpus
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def test_checks():
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"""Validate CUDA settings against torch CUDA functions."""
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assert torch.cuda.is_available() == CUDA_IS_AVAILABLE
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assert torch.cuda.device_count() == CUDA_DEVICE_COUNT
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@pytest.mark.skipif(not DEVICES, reason="No CUDA devices available")
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def test_amp():
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"""Test AMP training checks."""
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model = YOLO("yolo26n.pt").model.to(f"cuda:{DEVICES[0]}")
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assert check_amp(model)
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@pytest.mark.slow
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@pytest.mark.skipif(not DEVICES, reason="No CUDA devices available")
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@pytest.mark.parametrize(
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"task, dynamic, int8, half, batch, simplify, nms",
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[ # generate all combinations except for exclusion cases
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(task, dynamic, int8, half, batch, simplify, nms)
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for task, dynamic, int8, half, batch, simplify, nms in product(
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TASKS, [True, False], [False], [False], [1, 2], [True, False], [True, False]
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)
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if not (
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(int8 and half) or (task == "classify" and nms) or (task == "obb" and nms and (not TORCH_1_13 or IS_JETSON))
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)
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],
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)
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def test_export_onnx_matrix(task, dynamic, int8, half, batch, simplify, nms):
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"""Test YOLO exports to ONNX format with various configurations and parameters."""
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file = YOLO(TASK2MODEL[task]).export(
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format="onnx",
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imgsz=32,
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dynamic=dynamic,
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int8=int8,
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half=half,
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batch=batch,
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simplify=simplify,
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nms=nms,
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device=DEVICES[0],
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# opset=20 if nms else None, # fix ONNX Runtime errors with NMS
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)
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YOLO(file)([SOURCE] * batch, imgsz=64 if dynamic else 32, device=DEVICES[0]) # exported model inference
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Path(file).unlink() # cleanup
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@pytest.mark.slow
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@pytest.mark.skipif(not DEVICES, reason="No CUDA devices available")
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@pytest.mark.parametrize(
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"task, dynamic, int8, half, batch",
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[ # generate all combinations but exclude those where both int8 and half are True
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(task, dynamic, int8, half, batch)
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# Note: tests reduced below pending compute availability expansion as GPU CI runner utilization is high
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# for task, dynamic, int8, half, batch in product(TASKS, [True, False], [True, False], [True, False], [1, 2])
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for task, dynamic, int8, half, batch in product(TASKS, [True], [True], [False], [2])
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if not (int8 and half) # exclude cases where both int8 and half are True
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],
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)
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def test_export_engine_matrix(task, dynamic, int8, half, batch):
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"""Test YOLO model export to TensorRT format for various configurations and run inference."""
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import tensorrt as trt
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is_trt10 = int(trt.__version__.split(".", 1)[0]) >= 10
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if is_trt10 and int8 and dynamic:
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pytest.skip("YOLO26 INT8+dynamic export requires explicit quantization on TensorRT 10+")
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file = YOLO(TASK2MODEL[task]).export(
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format="engine",
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imgsz=32,
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dynamic=dynamic,
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int8=int8,
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half=half,
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batch=batch,
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data=TASK2DATA[task],
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workspace=1, # reduce workspace GB for less resource utilization during testing
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simplify=True,
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device=DEVICES[0],
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)
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YOLO(file)([SOURCE] * batch, imgsz=64 if dynamic else 32, device=DEVICES[0]) # exported model inference
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Path(file).unlink() # cleanup
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Path(file).with_suffix(".cache").unlink(missing_ok=True) if int8 else None # cleanup INT8 cache
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@pytest.mark.skipif(not DEVICES, reason="No CUDA devices available")
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def test_train():
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"""Test model training on a minimal dataset using available CUDA devices."""
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device = tuple(DEVICES) if len(DEVICES) > 1 else DEVICES[0]
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# NVIDIA Jetson only has one GPU and therefore skipping checks
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if not IS_JETSON:
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results = YOLO(MODEL).train(data="coco8.yaml", imgsz=64, epochs=1, device=device, batch=15, compile=True)
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results = YOLO(MODEL).train(data="coco128.yaml", imgsz=64, epochs=1, device=device, batch=15, val=False)
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visible = eval(os.environ["CUDA_VISIBLE_DEVICES"])
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assert visible == device, f"Passed GPUs '{device}', but used GPUs '{visible}'"
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# Note DDP training returns None, single-GPU returns metrics
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assert (results is None) if len(DEVICES) > 1 else (results is not None)
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@pytest.mark.slow
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@pytest.mark.skipif(not DEVICES, reason="No CUDA devices available")
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def test_predict_multiple_devices():
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"""Validate model prediction consistency across CPU and CUDA devices."""
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model = YOLO("yolo26n.pt")
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# Test CPU
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model = model.cpu()
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assert str(model.device) == "cpu"
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_ = model(SOURCE)
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assert str(model.device) == "cpu"
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# Test CUDA
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cuda_device = f"cuda:{DEVICES[0]}"
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model = model.to(cuda_device)
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assert str(model.device) == cuda_device
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_ = model(SOURCE)
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assert str(model.device) == cuda_device
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# Test CPU again
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model = model.cpu()
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assert str(model.device) == "cpu"
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_ = model(SOURCE)
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assert str(model.device) == "cpu"
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# Test CUDA again
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model = model.to(cuda_device)
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assert str(model.device) == cuda_device
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_ = model(SOURCE)
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assert str(model.device) == cuda_device
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@pytest.mark.skipif(not DEVICES, reason="No CUDA devices available")
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def test_autobatch():
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"""Check optimal batch size for YOLO model training using autobatch utility."""
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from ultralytics.utils.autobatch import check_train_batch_size
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check_train_batch_size(YOLO(MODEL).model.to(f"cuda:{DEVICES[0]}"), imgsz=128, amp=True)
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@pytest.mark.slow
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@pytest.mark.skipif(not DEVICES, reason="No CUDA devices available")
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def test_utils_benchmarks():
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"""Profile YOLO models for performance benchmarks."""
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from ultralytics.utils.benchmarks import ProfileModels
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# Pre-export a dynamic engine model to use dynamic inference
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YOLO(MODEL).export(format="engine", imgsz=32, dynamic=True, batch=1, device=DEVICES[0])
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ProfileModels(
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[MODEL],
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imgsz=32,
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half=False,
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min_time=1,
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num_timed_runs=3,
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num_warmup_runs=1,
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device=DEVICES[0],
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).run()
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@pytest.mark.skipif(not DEVICES, reason="No CUDA devices available")
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def test_predict_sam():
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"""Test SAM model predictions using different prompts."""
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from ultralytics import SAM
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from ultralytics.models.sam import Predictor as SAMPredictor
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model = SAM(WEIGHTS_DIR / "sam2.1_b.pt")
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model.info()
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# Run inference with various prompts
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model(SOURCE, device=DEVICES[0])
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model(SOURCE, bboxes=[439, 437, 524, 709], device=DEVICES[0])
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model(ASSETS / "zidane.jpg", points=[900, 370], device=DEVICES[0])
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model(ASSETS / "zidane.jpg", points=[900, 370], labels=[1], device=DEVICES[0])
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model(ASSETS / "zidane.jpg", points=[[900, 370]], labels=[1], device=DEVICES[0])
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model(ASSETS / "zidane.jpg", points=[[400, 370], [900, 370]], labels=[1, 1], device=DEVICES[0])
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model(ASSETS / "zidane.jpg", points=[[[900, 370], [1000, 100]]], labels=[[1, 1]], device=DEVICES[0])
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# Test predictor
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predictor = SAMPredictor(
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overrides=dict(
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conf=0.25,
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task="segment",
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mode="predict",
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imgsz=1024,
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model=WEIGHTS_DIR / "mobile_sam.pt",
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device=DEVICES[0],
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)
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)
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predictor.set_image(ASSETS / "zidane.jpg")
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# predictor(bboxes=[439, 437, 524, 709])
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# predictor(points=[900, 370], labels=[1])
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predictor.reset_image()
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