Hi,
I’m working on a project which converts a pre-trained model from TF to libtorch. But I have experienced some issue with the final performance. It’s a huge drop. After some debugging work, I found the major problem is caused by the Conv2d layers in the network. For example, the output of the layer before the first Conv2d layer is the same as in Tensorflow. However, the output tensor of Conv2d is completely different from that in TF. I’m so confused. I have put some example code below.
Tensor loadTensorWeightFromH5(std::string name)
{
    Tensor output = readH5ToTensor(name);
    if (output.dim() == 4)
        // Convert TF weight [kernel_h, kernel_w, channel_in, channel_out] 
        // to [channel_out, channel_in, kernel_h, kernel_w]
        output = output.permute({ 3, 2, 0, 1 }); 
    return output;
}

struct Net : nn::Module
{
    Net()
        :mConv0(nn::Conv2dOptions(3, 32, 1).padding(0).stride(1)),
        mBN0(nn::BatchNormOptions(32))
    {
        register_module("Conv0", mConv0);
        register_module("BN0", mBN0);

        mConv0->weight = loadTensorWeightFromH5("Conv0_weights:0");
        mConv0->bias = loadTensorWeightFromH5("Conv0_bias:0");
        mBN0->weight = loadTensorWeightFromH5("BN0_gamma:0");
        mBN0->bias = loadTensorWeightFromH5("BN0_beta:0");
        mBN0->running_mean = loadTensorWeightFromH5("BN0_moving_mean:0");
        mBN0->running_var = loadTensorWeightFromH5("BN0_moving_variance:0");
    }

    Tensor forword(Tensor & input)
    {
        // Convert NHWC to NCHW
        auto x = input.permute({ 0, 3, 1, 2 });

        x = mConv0->forward(x);

        x = mBN0->forward(x);

        x = torch::relu(x);

        // Convert NCHW to NHWC
        x = x.permute({ 0, 2, 3, 1 });

        return x;
    }

    nn::Conv2d mConv0;
    nn::BatchNorm mBN0;
}


Could anyone help?