alantess / learntorch

Tutorials for PyTorch C++

learntorch

Tutorials for PyTorch C++

learntorch's Issues

Values are different in Pytorch and C++

Hi, Thanks for the amazing tutorials.
I am solving 1D pde and tried to call that model in C++. I have done this successfully but the values i am predicting in Pytorch and in C++ are different.

#include <torch/script.h>
#include
#include
int main(int argc, const char* argv[])
{
if (argc != 2) {
std::cerr << "usage: main \n";
return -1;
}

try {
// Deserialize the ScriptModule from a file using torch::jit::load().
torch::jit::script::Module module;
module = torch::jit::load("/home/sunny/neural/diffusion.pth");
// Create a vector of inputs.
double x;
std::cout<<"Please enter the value of x ";
std::cin>>x;
double t;
std::cout<<"Please enter the value of t ";
std::cin>>t;

std::vector<torch::jit::IValue> inputs;
inputs.push_back(torch::tensor({{x,t}}));
// Execute the model and turn its output into a tensor.
torch::Tensor output = module.forward((inputs)).toTensor();

std::cout << output << std::endl;

}
catch (const c10::Error& e) {
std::cerr << "error loading the model\n";
return -1;
}

}

I used the same method you mentioned to save the model. Can you tell me what i am doing wrong?

Error while loading a FasterRCNN model in C++

Hello,
did you try as well to load a FasterRCNN model in C++ ? a simple code for models.py would be:

import torch
import torchvision
from torchvision import models
from torchvision.models.detection.faster_rcnn import FastRCNNPredictor
# An instance of your model.
#model = torchvision.models.resnet18()
model = models.detection.fasterrcnn_resnet50_fpn(pretrained=True)
script_model = torch.jit.script(model)
script_model.save("model.pt")

This does save the model.pt. But upon loading the model in C++,

#include <torch/script.h> // One-stop header.
#include <iostream>
#include <memory>
int main(int argc, const char* argv[]) {
  if (argc != 2) {
    std::cerr << "usage: example-app <path-to-exported-script-module>\n";
    return -1;
  }
    torch::jit::script::Module module;
  try {
    // Deserialize the ScriptModule from a file using torch::jit::load().
    module = torch::jit::load(argv[1]);
  }
  catch (const c10::Error& e) {
    std::cerr << "error loading the model\n";
    return -1;
  }
  std::cout << "ok\n";
}

There is an error which says:

 ./example-app ../model.pt 
terminate called after throwing an instance of 'torch::jit::ErrorReport'
  what():  
Unknown builtin op: torchvision::nms.
Could not find any similar ops to torchvision::nms. This op may not exist or may not be currently supported in TorchScript.
:
  File "/home/ubuntu/Documents/python-virtual-environments/env/lib/python3.8/site-packages/torchvision/ops/boxes.py", line 35
    """
    _assert_has_ops()
    return torch.ops.torchvision.nms(boxes, scores, iou_threshold)
           ~~~~~~~~~~~~~~~~~~~~~~~~~ <--- HERE
Serialized   File "code/__torch__/torchvision/ops/boxes.py", line 148
  _61 = __torch__.torchvision.extension._assert_has_ops
  _62 = _61()
  _63 = ops.torchvision.nms(boxes, scores, iou_threshold)
        ~~~~~~~~~~~~~~~~~~~ <--- HERE
  return _63
'nms' is being compiled since it was called from '_batched_nms_vanilla'
  File "/home/ubuntu/Documents/python-virtual-environments/env/lib/python3.8/site-packages/torchvision/ops/boxes.py", line 102
    for class_id in torch.unique(idxs):
        curr_indices = torch.where(idxs == class_id)[0]
        curr_keep_indices = nms(boxes[curr_indices], scores[curr_indices], iou_threshold)
                            ~~~ <--- HERE
        keep_mask[curr_indices[curr_keep_indices]] = True
    keep_indices = torch.where(keep_mask)[0]
Serialized   File "code/__torch__/torchvision/ops/boxes.py", line 77
    _28 = torch.index(boxes, _27)
    _29 = annotate(List[Optional[Tensor]], [curr_indices])
    curr_keep_indices = __torch__.torchvision.ops.boxes.nms(_28, torch.index(scores, _29), iou_threshold, )
    ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ <--- HERE
    _30 = annotate(List[Optional[Tensor]], [curr_keep_indices])
    _31 = torch.index(curr_indices, _30)
'_batched_nms_vanilla' is being compiled since it was called from 'batched_nms'
Serialized   File "code/__torch__/torchvision/ops/boxes.py", line 35
    idxs: Tensor,
    iou_threshold: float) -> Tensor:
  _9 = __torch__.torchvision.ops.boxes._batched_nms_vanilla
  ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ <--- HERE
  _10 = __torch__.torchvision.ops.boxes._batched_nms_coordinate_trick
  if torch.gt(torch.numel(boxes), 4000):
'batched_nms' is being compiled since it was called from 'RegionProposalNetwork.filter_proposals'
Serialized   File "code/__torch__/torchvision/models/detection/rpn.py", line 72
    _11 = __torch__.torchvision.ops.boxes.clip_boxes_to_image
    _12 = __torch__.torchvision.ops.boxes.remove_small_boxes
    _13 = __torch__.torchvision.ops.boxes.batched_nms
    ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ <--- HERE
    num_images = (torch.size(proposals))[0]
    device = ops.prim.device(proposals)
'RegionProposalNetwork.filter_proposals' is being compiled since it was called from 'RegionProposalNetwork.forward'
  File "/home/ubuntu/Documents/python-virtual-environments/env/lib/python3.8/site-packages/torchvision/models/detection/rpn.py", line 356
        proposals = self.box_coder.decode(pred_bbox_deltas.detach(), anchors)
        proposals = proposals.view(num_images, -1, 4)
        boxes, scores = self.filter_proposals(proposals, objectness, images.image_sizes, num_anchors_per_level)
                        ~~~~~~~~~~~~~~~~~~~~~ <--- HERE
    
        losses = {}
Serialized   File "code/__torch__/torchvision/models/detection/rpn.py", line 43
    proposals0 = torch.view(proposals, [num_images, -1, 4])
    image_sizes = images.image_sizes
    _8 = (self).filter_proposals(proposals0, objectness0, image_sizes, num_anchors_per_level, )
                                                                       ~~~~~~~~~~~~~~~~~~~~~ <--- HERE
    boxes, scores, = _8
    losses = annotate(Dict[str, Tensor], {})

Aborted (core dumped)

There was a similar error reported here:
pytorch/pytorch#17054
One of the solution was to use the Nightly build. I tried that as well but no difference.
Could you please help me out on this ?

Can't feed my model with my image data tensor

I'm making a vector with full of my tensor images and pass to my model but it doesn't work. I did use your codes. Where is the problem?
Code: https://stackoverflow.com/questions/70947854/can't-feed-my-model-with-my-image-data-tensor please
@alantess

CMakeFiles/main.dir/main.cpp.o: in function `main': main.cpp:(.text.startup+0x133): undefined reference to `cv::imshow(std::string const&, cv::_InputArray const&)'