87 lines
3.6 KiB
Markdown
87 lines
3.6 KiB
Markdown
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# 为推理 ops 添加测试单元
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本教程介绍如何为后端 ops 添加单元测试。在 backend_ops 目录下添加自定义 op 时，需要添加相应的测试单元。op 的单元测试在 `test/test_ops/test_ops.py` 中。
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添加新的自定义 op 后，需要重新编译，引用 [build.md](../01-how-to-build/build_from_source.md) 。
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## ops 单元测试样例
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```python
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@pytest.mark.parametrize('backend', [TEST_TENSORRT, TEST_ONNXRT]) # 1.1 backend test class
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@pytest.mark.parametrize('pool_h,pool_w,spatial_scale,sampling_ratio', # 1.2 set parameters of op
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[(2, 2, 1.0, 2), (4, 4, 2.0, 4)]) # [（# Examples of op test parameters）,...]
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def test_roi_align(backend,
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pool_h, # set parameters of op
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pool_w,
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spatial_scale,
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sampling_ratio,
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input_list=None,
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save_dir=None):
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backend.check_env()
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if input_list is None:
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input = torch.rand(1, 1, 16, 16, dtype=torch.float32) # 1.3 op input data initialization
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single_roi = torch.tensor([[0, 0, 0, 4, 4]], dtype=torch.float32)
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else:
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input = torch.tensor(input_list[0], dtype=torch.float32)
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single_roi = torch.tensor(input_list[1], dtype=torch.float32)
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from mmcv.ops import roi_align
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def wrapped_function(torch_input, torch_rois): # 1.4 initialize op model to be tested
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return roi_align(torch_input, torch_rois, (pool_w, pool_h),
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spatial_scale, sampling_ratio, 'avg', True)
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wrapped_model = WrapFunction(wrapped_function).eval()
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with RewriterContext(cfg={}, backend=backend.backend_name, opset=11): # 1.5 call the backend test class interface
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backend.run_and_validate(
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wrapped_model, [input, single_roi],
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'roi_align',
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input_names=['input', 'rois'],
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output_names=['roi_feat'],
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save_dir=save_dir)
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```
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mmdeploy 支持的模型有两种格式：
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- torch 模型：参考 roi_align 单元测试，必须要求 op 相关 Python 代码
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- onnx 模型：参考 multi_level_roi_align 单元测试，需要调用 onnx api 进行构建
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调用 `run_and_validate` 即可运行
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```python
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def run_and_validate(self,
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model,
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input_list,
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model_name='tmp',
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tolerate_small_mismatch=False,
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do_constant_folding=True,
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dynamic_axes=None,
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output_names=None,
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input_names=None,
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expected_result=None,
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save_dir=None):
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```
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#### Parameter Description
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| 参数 | 说明 |
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| :---------------------: | :-----------------------------------: |
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| model | 要测试的输入模型 |
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| input_list | 测试数据列表，映射到input_names的顺序 |
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| tolerate_small_mismatch | 是否允许验证结果出现精度误差 |
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| do_constant_folding | 是否使用常量折叠 |
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| output_names | 输出节点名字 |
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| input_names | 输入节点名字 |
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| expected_result | 期望的 ground truth |
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| save_dir | 结果保存目录 |
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## 测试模型
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用 `pytest` 调用 ops 测试
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```bash
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pytest tests/test_ops/test_ops.py::test_XXXX
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```
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