在训练完pytorch模型后,往往根据实际项目的需要将torch模型转为onnx模型,而转换完后可能会存在一个问题,即自己转换的onnx模型是否转换正确,模型精度是否与torch模型一致。本文以retinaface训练的torch模型为例,默认torch模型已经转换为onnx模型,验证该转换onnx模型是否与torch模型预测精度一致。具体代码示例如下: ...pytor...
ort_outs = ort_session.run(None, ort_inputs) # compare ONNX Runtime and PyTorch results np.testing.assert_allclose(to_numpy(torch_out), ort_outs[0], rtol=1e-03, atol=1e-05) print("Exported model has been tested with ONNXRuntime, and the result looks good!") 1. 2. 3. 4. ...
In contrast to the torch.allclose and torch.eq that we are currently using throughout the test suite, by default torch.testing.assert_close also checks for matching dtype's, device's and stride()'s. These checks can be relaxed by setting check_(dtype|device|stride)=False, but this should...
FAILED tests/kernel/wave/runtime/cache_test.py::testSameSizeDifferentBlock - AssertionError: The values for attribute 'dtype' do not match: torch.float32 != torch.float16. https://github.com/iree-org/iree-turbine/actions/runs/12915847540/job/36018656310#step:8:330 ScottTodd approved these ...
() assert torch.allclose(local_out, ddp_all_out.cpu(), atol = 1e-3), 'output is not the same' # validate gradients of first expert is the same for local vs distributed get_first_expert_grad = lambda t: t.experts[0].net[0].weight.grad assert torch.allclose( get_first_expert_...