The saved .pth or .pt file can be restored by building a model using PyTorch and then loading the weight. Then you can export the ONNX model. The following is an example.

import torch
import torch.onnx
import torchvision.models as models
# Set the CPU to be used to export the model.
device = torch.device("cpu") 
 
def convert():
# The model definition comes from the torchvision. The model file generated in the example is based on the ResNet-50 model.
    model = models.resnet50(pretrained = False)  
    resnet50_model = torch.load('resnet50.pth', map_location='cpu')
    model.load_state_dict(resnet50_model) 
 
    batch_size = 1 # Size of the batch processing
    input_shape = (3, 224, 224) # Input data. Replace it with the actual shape.

    # Set the model to inference mode.
    model.eval()

    dummy_input = torch.randn(batch_size, *input_shape) # Define the input shape.
    torch.onnx.export(model, 
                      dummy_input, 
                      "resnet50_official.onnx", 
                      input_names = ["input"], # Construct the input name.
                      output_names = ["output"], # Construct the output name.
                      opset_version=11, # Currently, the ATC tool supports only opset_version=11.
                      dynamic_axes={"input":{0:"batch_size"}, "output":{0:"batch_size"}}) # Dynamic axes of the output is supported.
                      ) 
     
if __name__ == "__main__":
    convert()

Before exporting the ONNX model using the .pth.tar file, you need to check the saved information. Sometimes, the saved node name may be different from the node name in the model definition. For example, a prefix and suffix may be added. During the conversion, you can modify the node name. The following is an example of the conversion.

import torch
import torch.onnx
from collections import OrderedDict
import mobilenet

# In this example, when the .pth.tar file is saved, the prefix module is added to the node name. Delete it by traversing.
def proc_nodes_module(checkpoint, AttrName):
    new_state_dict = OrderedDict()
    for key, value in checkpoint[AttrName].items():
        if key == "module.features.0.0.weight":
            print(value)
        if(key[0:7] == "module."):
            name = key[7:]
        else:
            name = key[0:]

        new_state_dict[name] = value
    return new_state_dict

def convert():
    checkpoint = torch.load("./mobilenet_cpu.pth.tar", map_location=torch.device('cpu'))
    checkpoint['state_dict'] = proc_nodes_module(checkpoint,'state_dict')
    model = mobilenet.mobilenet_v2(pretrained = False)
    model.load_state_dict(checkpoint['state_dict'])
    model.eval()
    input_names = ["actual_input_1"]
    output_names = ["output1"]
    dummy_input = torch.randn(1, 3, 224, 224)
    torch.onnx.export(model, dummy_input, "mobilenetV2_npu.onnx", input_names = input_names, output_names = output_names, opset_version=11)

if __name__ == "__main__":
    convert()