C++ Inferencing using Torchscript Exported Torchvision model Erorr

🐛** C++ Inferencing using Torchscript Exported Torchvision model Erorr

I'm trying to use this approach to make my model (Mobilenetv3 small) using Torchvison models, In train and validation phase (python) worked Whiteout any problem but after saving Torchscript to use in c++ inference, got this error:

terminate` called after throwing an instance of 'torch::jit::ErrorReport'
  what():  
Unknown type name 'NoneType':
Serialized   File "code/__torch__/torch/nn/modules/linear.py", line 6
  training : bool
  _is_full_backward_hook : Optional[bool]
  def forward(self: __torch__.torch.nn.modules.linear.Identity) -> NoneType:
                                                                   ~~~~~~~~ <--- HERE
    return None
class Linear(Module):

Aborted (core dumped)

My simplified Torchscript exporting code:

import sys
import time
from pathlib import Path

import torch
import torch.nn as nn
from model import initialize_model,BSConv_init
num_classes=14
device = torch.device('cpu')
model = models.mobilenet_v3_small(pretrained=use_pretrained)
num_ftrs = model.classifier[3].in_features
model.classifier[3] = nn.Linear(num_ftrs, num_classes)
model = model.to(device)
checkpoint = torch.load('checkpoint/best_model_MobBsconv_ckpt.t7', map_location=device)  
model.load_state_dict(checkpoint['model'])

# Input
img = torch.rand(1, 3, 224, 224).to(device)
model.eval()
ts = torch.jit.trace(model, img, strict=False)
ts.save("traced_mob_bsconv_model.pt")

this exporting script run successfully, but using c++ produce error.
this is my simpilified C++ code that works for other models:

try{ this->module = torch::jit::load(ModelAddress); }catch (const c10::Error& e) { std::cerr << "error loading the model: " << e.what() << std::endl; std::exit(EXIT_FAILURE); } half_ = (device_ != torch::kCPU); this->module.to(device_); if (half_) { module.to(torch::kHalf); } torch::NoGradGuard no_grad; module.eval();
Even got error until this initializing, but my other exported models work fine at forward and ... .
I'm confused and need help.

Environment

env 1: System which trained and export torchscript (by above code):
OS: Ubuntu 18.04.5 LTS (x86_64)
GCC version: (Ubuntu 7.5.0-3ubuntu1~18.04) 7.5.0
Clang version: 6.0.0-1ubuntu2 (tags/RELEASE_600/final)
CMake version: version 3.10.2
Libc version: glibc-2.25

Python version: 3.6.9 (default, Jul 17 2020, 12:50:27) [GCC 8.4.0] (64-bit runtime)
Python platform: Linux-5.4.0-48-generic-x86_64-with-Ubuntu-18.04-bionic
Is CUDA available: True
CUDA runtime version: Could not collect
GPU models and configuration: GPU 0: GeForce GTX 1060 6GB
Nvidia driver version: 450.66
cuDNN version: /usr/lib/x86_64-linux-gnu/libcudnn.so.7.6.5
HIP runtime version: N/A
MIOpen runtime version: N/A

Versions of relevant libraries:
[pip3] numpy==1.19.4
[pip3] torch==1.9.0
[pip3] torchvision==0.10.0

env 2: system which run c++ code and got error:

OS: Ubuntu 18.04.4 LTS (x86_64)
GCC version: (Ubuntu 7.5.0-3ubuntu1~18.04) 7.5.0
Clang version: Could not collect
CMake version: version 3.10.2
Libc version: glibc-2.15

Python version: 2.7.17 (default, Jul 20 2020, 15:37:01) [GCC 7.5.0] (64-bit runtime)
Python platform: Linux-5.4.0-42-generic-x86_64-with-Ubuntu-18.04-bionic
Is CUDA available: N/A
CUDA runtime version: Could not collect
GPU models and configuration: Could not collect
Nvidia driver version: Could not collect
cuDNN version: Could not collect
HIP runtime version: N/A
MIOpen runtime version: N/A

Versions of relevant libraries:
[pip3] numpy==1.18.1

Additional context

Start with the TorchScript export in the Python snippet and the C++ torch::jit::load call, then compare the listed PyTorch, torchvision, compiler, and system environments. Reproduce the reported Unknown type name 'NoneType' error during model loading and determine the relevant compatibility boundary. Done means documenting or addressing why this exported model fails to load in the stated C++ environment.