input_x,'model_onnx.pt',export_params=True, opset_version=11, do_constant_folding=True, input_names = ['input_ids', 'attention_mask'], output_names = ['output'], dynamic_axes= { 'input_ids' : {0 : 'batch_size', 1:'length'},'attention_mask' : {0 : 'batch_size'...
为了解决PyTorch转换为ONNX格式时的input和output name报错,我们需要在导出模型时设置正确的名称。下面是一个示例代码: importtorchimporttorch.onnxasonnx# 假设我们有一个PyTorch模型modelmodel=...# 设置模型的输入和输出名称input_names=["input"]output_names=["output"]# 导出模型为ONNX格式onnx.export(model,...
y = model(x) torch.onnx.export( model, x, 'debug.onnx', input_names=['input'], output_names=['output'], ) 执行这段代码,我们将得到一个这样的onnx: 假设我想知道self.linear2变成了什么,那么我们就在forward中self.linear2前面或者后面插入一个自定义算子: class MyNet(nn.Module): def __i...
dummy_model_input = tokenizer("This is a sample", return_tensors="pt") # export torch.onnx.export( model, tuple(dummy_model_input.values()), f="torch-model.onnx", input_names=['input_ids', 'attention_mask'], output_names=['logits'], dynamic_axes={'input_ids': {0: 'batch_si...
= torchvision.models.alexnet(pretrained=True).cuda()input_names = [ "actual_input_1" ] + [ "learned_%d" % i for i in range(16) ]output_names = [ "output1" ]torch.onnx.export(model, dummy_input, "alexnet.onnx", verbose=True, input_names=input_names, output_names=output_names)...
torch_out= torch.onnx._export(net, inputs, output_onnx, export_params=True, verbose=False, input_names=input_names, output_names=output_names, opset_version=11) opset=11或更高 影响 upsample,会将其变为 resize。opset_version = 11解决了上面的警告。
torch.onnx.export( model, (b_input_ids,token_types,b_input_mask), "model.onnx", input_names=['input_ids','token_type_ids', 'attention_mask'], output_names=["logits"], dynamic_axes= { 'input_ids': {0: 'batch_size'},
torch.onnx.export(model, dummy_input,'simple_nn.onnx', input_names=['input'], output_names=['output']) print('Model converted to ONNX and saved to simple_nn.onnx') definference(): # 数据加载和预处理 transform=transforms.Compose([ ...
torch.onnx.export( model = model, args = (input_ids, attention_mask), f = "ai11.onnx", opset_version=11, input_names=input_names, output_names=output_names) 1. 2. 3. 4. 5. 6. 7. 8. 9. 10. 11. 12. 13. 14. 15. ...
torch.onnx.export(model,dummy_input,"yolov3.onnx",input_names=['images'],output_names=['outTensors'],export_params=True,training=False) File "/home/xyj/.local/lib/python3.6/site-packages/torch/onnx/__init__.py", line 25, in export ...