mirror of
https://github.com/open-mmlab/mmdeploy.git
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792c27b054
* add acl backend * support dynamic batch size and dynamic image size * add preliminary ascend backend * support dtypes other than float * support dynamic_dims in SDK * fix dynamic batch size * better error handling * remove debug info * [WIP] dynamic shape support * fix static shape * fix dynamic batch size * add retinanet support * fix dynamic image size * fix dynamic image size * fix dynamic dims * fix dynamic dims * simplify config files * fix yolox support * fix negative index * support faster rcnn * add seg config * update benchmark * fix onnx2ascend dynamic shape * update docstring and benchmark * add unit test, update documents * fix wrapper * fix ut * fix for vit * error handling * context handling & multi-device support * build with stub libraries * add ci * fix lint * fix lint * update doc ref * fix typo * down with `target_link_directories` * setup python * makedir * fix ci * fix ci * remove verbose logs * fix UBs * export Error * fix lint * update checkenv Co-authored-by: grimoire <yaoqian@sensetime.com>
72 lines
1.7 KiB
Python
72 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 mmcv
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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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dynamic_axes = {
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'input': {
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0: 'batch',
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2: 'width',
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3: 'height'
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},
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'output': {
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0: 'batch'
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}
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}
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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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dynamic_axes=dynamic_axes)
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assert osp.exists(onnx_file)
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@backend_checker(Backend.ASCEND)
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def test_onnx2ascend():
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from mmdeploy.apis.ascend import from_onnx
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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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om_path = osp.join(work_dir, file_name + '.om')
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model_inputs = mmcv.Config(
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dict(
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dynamic_batch_size=[1, 2, 4],
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input_shapes=dict(input=[-1, 3, 224, 224])))
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from_onnx(onnx_file, work_dir, model_inputs)
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assert osp.exists(work_dir)
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assert osp.exists(om_path)
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