mirror of
https://github.com/open-mmlab/mmdeploy.git
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7cb4b9b18a
* finish framework * add autotvm and auto-scheduler tuner * add python deploy api * add SDK net(WIP * add sdk support * support det, support vm * fix vm sdk * support two stage detector * add instance seg support * add docstring * update docs and ut * add quantize * update doc * update docs * synchronize stream * support dlpack * remove submodule * fix stride * add alignment * support dlpack * remove submodule * replace exclusive_scan * add backend check * add build script * fix comment * add ci * fix ci * ci fix2 * update build script * update ci * add pytest * update sed command * update sed again * add xgboost * remove tvm ut * update ansor runner * add stream sync * fix topk * sync default stream * fix tvm net * fix window
106 lines
2.9 KiB
Python
106 lines
2.9 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 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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self.conv = torch.nn.Conv2d(3, 8, 3, 1, 1)
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def forward(self, x):
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return self.conv(x)
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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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@backend_checker(Backend.TVM)
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def test_onnx2tvm():
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from mmdeploy.apis.tvm import from_onnx, get_library_ext
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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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file_name = osp.splitext(onnx_file)[0]
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ext = get_library_ext()
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lib_path = osp.join(work_dir, file_name + ext)
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bytecode_path = osp.join(work_dir, file_name + '.code')
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log_file = osp.join(work_dir, file_name + '.log')
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shape = {'input': test_img.shape}
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dtype = {'input': 'float32'}
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target = 'llvm'
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# test default tuner
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tuner_dict = dict(type='DefaultTuner', target=target)
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from_onnx(onnx_file, lib_path, shape=shape, dtype=dtype, tuner=tuner_dict)
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assert osp.exists(lib_path)
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# test autotvm
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lib_path = osp.join(work_dir, file_name + '_autotvm' + ext)
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bytecode_path = osp.join(work_dir, file_name + '_autotvm.code')
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log_file = osp.join(work_dir, file_name + '_autotvm.log')
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tuner_dict = dict(
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type='AutoTVMTuner',
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target=target,
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log_file=log_file,
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n_trial=1,
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tuner=dict(type='XGBTuner'))
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from_onnx(
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onnx_file,
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lib_path,
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use_vm=True,
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bytecode_file=bytecode_path,
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shape=shape,
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dtype=dtype,
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tuner=tuner_dict)
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assert osp.exists(lib_path)
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assert osp.exists(bytecode_path)
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# test ansor
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lib_path = osp.join(work_dir, file_name + '_ansor' + ext)
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bytecode_path = osp.join(work_dir, file_name + '_ansor.code')
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log_file = osp.join(work_dir, file_name + '_ansor.log')
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tuner_dict = dict(
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type='AutoScheduleTuner',
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target=target,
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log_file=log_file,
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num_measure_trials=2)
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from_onnx(
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onnx_file,
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lib_path,
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use_vm=True,
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bytecode_file=bytecode_path,
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shape=shape,
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dtype=dtype,
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tuner=tuner_dict)
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assert osp.exists(lib_path)
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assert osp.exists(bytecode_path)
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