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222 lines
8.8 KiB
222 lines
8.8 KiB
# Ultralytics YOLO 🚀, AGPL-3.0 license |
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import shutil |
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import uuid |
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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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from tests import 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 ( |
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IS_RASPBERRYPI, |
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LINUX, |
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MACOS, |
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WINDOWS, |
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checks, |
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) |
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from ultralytics.utils.torch_utils import TORCH_1_9, TORCH_1_13 |
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def test_export_torchscript(): |
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"""Test YOLO model exporting to TorchScript format for compatibility and correctness.""" |
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file = YOLO(MODEL).export(format="torchscript", optimize=False, imgsz=32) |
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YOLO(file)(SOURCE, imgsz=32) # exported model inference |
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def test_export_onnx(): |
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"""Test YOLO model export to ONNX format with dynamic axes.""" |
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file = YOLO(MODEL).export(format="onnx", dynamic=True, imgsz=32) |
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YOLO(file)(SOURCE, imgsz=32) # exported model inference |
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@pytest.mark.skipif(not TORCH_1_13, reason="OpenVINO requires torch>=1.13") |
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def test_export_openvino(): |
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"""Test YOLO exports to OpenVINO format for model inference compatibility.""" |
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file = YOLO(MODEL).export(format="openvino", imgsz=32) |
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YOLO(file)(SOURCE, imgsz=32) # exported model inference |
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@pytest.mark.slow |
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@pytest.mark.skipif(not TORCH_1_13, reason="OpenVINO requires torch>=1.13") |
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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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for task, dynamic, int8, half, batch in product(TASKS, [True, False], [True, False], [True, False], [1, 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_openvino_matrix(task, dynamic, int8, half, batch): |
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"""Test YOLO model exports to OpenVINO under various configuration matrix conditions.""" |
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file = YOLO(TASK2MODEL[task]).export( |
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format="openvino", |
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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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) |
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if WINDOWS: |
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# Use unique filenames due to Windows file permissions bug possibly due to latent threaded use |
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# See https://github.com/ultralytics/ultralytics/actions/runs/8957949304/job/24601616830?pr=10423 |
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file = Path(file) |
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file = file.rename(file.with_stem(f"{file.stem}-{uuid.uuid4()}")) |
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YOLO(file)([SOURCE] * batch, imgsz=64 if dynamic else 32) # exported model inference |
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shutil.rmtree(file, ignore_errors=True) # retry in case of potential lingering multi-threaded file usage errors |
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@pytest.mark.slow |
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@pytest.mark.parametrize( |
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"task, dynamic, int8, half, batch, simplify", product(TASKS, [True, False], [False], [False], [1, 2], [True, False]) |
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) |
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def test_export_onnx_matrix(task, dynamic, int8, half, batch, simplify): |
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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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) |
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YOLO(file)([SOURCE] * batch, imgsz=64 if dynamic else 32) # exported model inference |
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Path(file).unlink() # cleanup |
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@pytest.mark.slow |
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@pytest.mark.parametrize("task, dynamic, int8, half, batch", product(TASKS, [False], [False], [False], [1, 2])) |
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def test_export_torchscript_matrix(task, dynamic, int8, half, batch): |
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"""Tests YOLO model exports to TorchScript format under varied configurations.""" |
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file = YOLO(TASK2MODEL[task]).export( |
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format="torchscript", |
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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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) |
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YOLO(file)([SOURCE] * 3, imgsz=64 if dynamic else 32) # exported model inference at batch=3 |
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Path(file).unlink() # cleanup |
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@pytest.mark.slow |
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@pytest.mark.skipif(not MACOS, reason="CoreML inference only supported on macOS") |
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@pytest.mark.skipif(not TORCH_1_9, reason="CoreML>=7.2 not supported with PyTorch<=1.8") |
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@pytest.mark.skipif(checks.IS_PYTHON_3_12, reason="CoreML not supported in Python 3.12") |
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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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for task, dynamic, int8, half, batch in product(TASKS, [False], [True, False], [True, False], [1]) |
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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_coreml_matrix(task, dynamic, int8, half, batch): |
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"""Test YOLO exports to CoreML format with various parameter configurations.""" |
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file = YOLO(TASK2MODEL[task]).export( |
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format="coreml", |
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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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) |
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YOLO(file)([SOURCE] * batch, imgsz=32) # exported model inference at batch=3 |
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shutil.rmtree(file) # cleanup |
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@pytest.mark.slow |
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@pytest.mark.skipif(not checks.IS_PYTHON_MINIMUM_3_10, reason="TFLite export requires Python>=3.10") |
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@pytest.mark.skipif(not LINUX, reason="Test disabled as TF suffers from install conflicts on Windows and macOS") |
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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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for task, dynamic, int8, half, batch in product(TASKS, [False], [True, False], [True, False], [1]) |
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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_tflite_matrix(task, dynamic, int8, half, batch): |
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"""Test YOLO exports to TFLite format considering various export configurations.""" |
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file = YOLO(TASK2MODEL[task]).export( |
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format="tflite", |
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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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) |
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YOLO(file)([SOURCE] * batch, imgsz=32) # exported model inference at batch=3 |
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Path(file).unlink() # cleanup |
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@pytest.mark.skipif(not TORCH_1_9, reason="CoreML>=7.2 not supported with PyTorch<=1.8") |
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@pytest.mark.skipif(WINDOWS, reason="CoreML not supported on Windows") # RuntimeError: BlobWriter not loaded |
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@pytest.mark.skipif(IS_RASPBERRYPI, reason="CoreML not supported on Raspberry Pi") |
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@pytest.mark.skipif(checks.IS_PYTHON_3_12, reason="CoreML not supported in Python 3.12") |
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def test_export_coreml(): |
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"""Test YOLO exports to CoreML format, optimized for macOS only.""" |
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if MACOS: |
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file = YOLO(MODEL).export(format="coreml", imgsz=32) |
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YOLO(file)(SOURCE, imgsz=32) # model prediction only supported on macOS for nms=False models |
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else: |
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YOLO(MODEL).export(format="coreml", nms=True, imgsz=32) |
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@pytest.mark.skipif(not checks.IS_PYTHON_MINIMUM_3_10, reason="TFLite export requires Python>=3.10") |
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@pytest.mark.skipif(not LINUX, reason="Test disabled as TF suffers from install conflicts on Windows and macOS") |
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def test_export_tflite(): |
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"""Test YOLO exports to TFLite format under specific OS and Python version conditions.""" |
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model = YOLO(MODEL) |
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file = model.export(format="tflite", imgsz=32) |
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YOLO(file)(SOURCE, imgsz=32) |
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@pytest.mark.skipif(True, reason="Test disabled") |
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@pytest.mark.skipif(not LINUX, reason="TF suffers from install conflicts on Windows and macOS") |
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def test_export_pb(): |
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"""Test YOLO exports to TensorFlow's Protobuf (*.pb) format.""" |
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model = YOLO(MODEL) |
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file = model.export(format="pb", imgsz=32) |
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YOLO(file)(SOURCE, imgsz=32) |
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@pytest.mark.skipif(True, reason="Test disabled as Paddle protobuf and ONNX protobuf requirements conflict.") |
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def test_export_paddle(): |
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"""Test YOLO exports to Paddle format, noting protobuf conflicts with ONNX.""" |
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YOLO(MODEL).export(format="paddle", imgsz=32) |
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@pytest.mark.slow |
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@pytest.mark.skipif(IS_RASPBERRYPI, reason="MNN not supported on Raspberry Pi") |
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def test_export_mnn(): |
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"""Test YOLO exports to MNN format (WARNING: MNN test must precede NCNN test or CI error on Windows).""" |
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file = YOLO(MODEL).export(format="mnn", imgsz=32) |
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YOLO(file)(SOURCE, imgsz=32) # exported model inference |
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@pytest.mark.slow |
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def test_export_ncnn(): |
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"""Test YOLO exports to NCNN format.""" |
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file = YOLO(MODEL).export(format="ncnn", imgsz=32) |
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YOLO(file)(SOURCE, imgsz=32) # exported model inference |
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@pytest.mark.skipif(not LINUX or MACOS, reason="Skipping test on Windows and Macos") |
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def test_export_imx500_ptq(): |
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"""Test YOLOv8n exports to imx500 format.""" |
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model = YOLO("yolov8n.pt") |
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file = model.export(format="imx500", imgsz=32, gptq=False) |
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YOLO(file)(SOURCE, imgsz=32) |
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@pytest.mark.skipif(IS_RASPBERRYPI or not LINUX or MACOS, reason="Skipping test on Raspberry Pi and Windows") |
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def test_export_imx500_gptq(): |
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"""Test YOLOv8n exports to imx500 format with gptq.""" |
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model = YOLO("yolov8n.pt") |
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file = model.export(format="imx500", imgsz=32, gptq=True) |
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YOLO(file)(SOURCE, imgsz=32)
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