Hi, I wanted to convert the pretrained SimSwap 512 .pth model to .onnx file format. I'm not so much into Python, so I don't really know what to do. From what I understand, the code to do so looks something like this: import io import num...
I trained a ProGAN agent usingthisPyTorch reimplementation, and I saved the agent as a.pth. Now I need to convert the agent into the.onnxformat, which I am doing using this scipt: fromtorch.autogradimportVariableimporttorch.onnximporttorchvisionimporttorch device = torch.device("cuda") dummy...
Convert model frompthtoonnx(Using method from MMDeploy) Be able to inference the model using ONNX Simplify model using onnx-sim Convert ONNX model from simplify version to ncnn Got a error The issue might cause from NCNN layer doesn't support efficientnetv2 layer. Do anyone have a suggest...
当我放弃tensorRT转为onnx后 可以正常转换 但是在实例化Detector后 接口层被阻塞在那里 并没有往下执行 When I gave up tensorRT and switched to onnx, I could convert normally, but after instantiating the Detector, the interface layer was blocked there and did not go down. 也没有错误信息 除了几个...
['img']}]} Registry:{'keep_ratio': True} Registry:{} Registry:{'pad_to_square': True, 'pad_val': {'img': (114.0, 114.0, 114.0)}} Registry:{} Registry:{'keys': ['img']} 2022-07-21 09:57:54,053 - mmdeploy - WARNING - DeprecationWarning: get_onnx_config will be ...
Convert the HF model to GGUF model: python llama.cpp/convert.py vicuna-hf \ --outfile vicuna-13b-v1.5.gguf \ --outtype q8_0 In this case we're also quantizing the model to 8 bit by setting --outtype q8_0. Quantizing helps improve inference speed, but it can ...
Bump. I still think this needs better or more-obvious documentation. I personally don't know when to use which. Also, is it better to convert straight from original pth or does it produce the same result as converting from HF safetensors?