68 lines
1.7 KiB
Python
68 lines
1.7 KiB
Python
# Copyright (c) OpenMMLab. All rights reserved.
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import os.path as osp
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import tempfile
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import mmengine
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import pytest
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import torch
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import torch.nn as nn
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from mmdeploy.utils import Backend
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from mmdeploy.utils.test import backend_checker
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onnx_file = tempfile.NamedTemporaryFile(suffix='.onnx').name
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test_img = torch.rand([1, 3, 8, 8])
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@pytest.mark.skip(reason='This a not test class but a utility class.')
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class TestModel(nn.Module):
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def __init__(self):
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super().__init__()
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def forward(self, x):
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return x * 0.5
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test_model = TestModel().eval()
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def generate_onnx_file(model):
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with torch.no_grad():
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torch.onnx.export(
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model,
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test_img,
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onnx_file,
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output_names=['output'],
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input_names=['input'],
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keep_initializers_as_inputs=True,
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do_constant_folding=True,
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verbose=False,
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opset_version=11)
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assert osp.exists(onnx_file)
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def get_deploy_cfg():
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deploy_cfg = mmengine.Config(
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dict(
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backend_config=dict(
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type='rknn',
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common_config=dict(),
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quantization_config=dict(do_quantization=False, dataset=None),
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input_size_list=[[3, 8, 8]])))
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return deploy_cfg
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@backend_checker(Backend.RKNN)
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def test_onnx2rknn():
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from mmdeploy.backend.rknn.onnx2rknn import onnx2rknn
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model = test_model
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generate_onnx_file(model)
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work_dir, _ = osp.split(onnx_file)
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rknn_file = onnx_file.replace('.onnx', '.rknn')
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deploy_cfg = get_deploy_cfg()
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onnx2rknn(onnx_file, rknn_file, deploy_cfg)
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assert osp.exists(work_dir)
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assert osp.exists(rknn_file)
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