C++ Inferencing using Torchscript Exported Torchvision model Erorr

[Yasin40]

🐛** 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

[fmassa]

Hi,

Was the version of PyTorch and torchvision used to run on the second environment the same as in the machine used to export the model?

[Yasin40]

Hi,

Was the version of PyTorch and torchvision used to run on the second environment the same as in the machine used to export the model?

Thanks for attention. In second environment i use model to c++ inference with libtorch. i was tested libtorch 1.9.0 and latest(Preview nightly) cpu version. but Torch & torchvision version is:
PyTorch Version: 1.9.0+cu102
Torchvision Version: 0.10.0+cu102

[fmassa]

@eellison who would be a good POC from the team to have a look?

[fmassa]

Also, @Yasin40 have you tried re-exporting the model in Python and running it again in C++?

[Yasin40]

Also, @Yasin40 have you tried re-exporting the model in Python and running it again in C++?

Yes. Also i tested in this way:

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=False)
model = model.to(device)
checkpoint = torch.load('mobilenet_v3_small-047dcff4.pth', map_location=device)  # load FP32 model
model.load_state_dict(checkpoint)
img = torch.rand(1, 3, 224, 224).to(device)
model.eval()
ts = torch.jit.trace(model, img, strict=False)
ts.save("traced_mobnet__pt_model.pt")

But got same error.

[eellison]

frontend related error cc @gmagogsfm

[Yasin40]

anyone can help me?
I tested resnet18 from torchvision model instead mobilenetv3 and works.
Whats problem about mobilenetv3 and Linear?

[datumbox]

@Yasin40 This looks like an error on PyTorch's nn module. The reason why MobileNetV3 is only affected is because it uses the Identity module which seems to have issue.

It might be worth filing this on PyTorch core.

[Yasin40]

Anyone can help me?