Feature Request

System information

ONNX version (you are using): The latest main branch.

What is the problem that this feature solves?

To enhance robustness of node test data, ONNX CIs should have some ways to validate updated/uploaded input.pb/output.pb and ONNX models. Currently at least ONNX models have been covered by this PR: onnx/onnx#3855. However,

我发现很多人在使用voc格式的数据集时，和我遇到了同样的问题，训练时AP一直为0，
今早，仔细检查后，我也找到了真正的原因，主要是数据加载的地方出现了问题，还是我们自己太不仔细了

解决流程思路： 解决YOLOX训练时AP为0

请问可以直接training tmfile出来吗? 因为tengine-convert-tool covert 会有error

tengine-lite library version: 1.4-dev
Get input tensor failed

或是有例子能training出下面tmfile 呢?
![Screenshot from 2021-05-27 07-01-46](https://user-images.githubusercontent.com/40915044/11

I have a simple regression task (using a LightGBMRegressor) where I want to penalize negative predictions more than positive ones. Is there a way to achieve this with the default regression LightGBM objectives (see https://lightgbm.readthedocs.io/en/latest/Parameters.html)? If not, is it somehow possible to define (many example for default LightGBM model) and pass a custom regression objective?

The latest release of openvino has changed the inference engine API, providing a new API that takes advantage of the full potential of Intel's latest version of IR (IR 11). More information can be found at this link. We should adapt nebullvm's OpenVinoInferenceLearner to their latest API.

Usef

I am trying to convert a custom pytorch model to tensorflow, I am abe to convert pytorch to onnx but converting onnx to tensorflow gives issue.

The code snippets are as follows-

pytorch to onnx

net = custom pytorch model
net.load_state_dict("pre-trained model")
dummyInput = np.random.uniform(0,1,(1,8,3,256,256))
dummyInput = Variable(torch.FloatTensor(dummyInput))
torch.onnx.export(ne