I may find where cause these problems.
In pytorch, after transforming numpy to tensor, the image value doesn’t change expect int16 to float32:
test_data = np.asarray(nda[110]) # int16
test_data = torch.from_numpy(test_data)
test_data = torch.tensor(test_data,dtype=torch.float32) #to float32
test_data=torch.unsqueeze(test_data,0)
test_data=torch.unsqueeze(test_data,1)
But in libtorch, i implement the same progress with code:
torch::Tensor tImage = torch::from_blob(src.data, { 240,240,1 }, torch::kFloat32);
tImage = tImage.permute({ 2,0,1 });
tImage = tImage.div(255);
tImage = tImage.unsqueeze(0);
cout << "prob:" << tImage.data() << endl;
vector<torch::jit::IValue> inputs; //def an input
inputs.push_back(torch::ones({ 1,1,240,240 }).toType(torch::kFloat32));
and i get different values:
i don’t exactly know whether is the key point or not. i need help.
Edit: those C++ output vaules are the result of ‘torch.div(255)’. i think it is not the key point.