I have a torch model, that simply contains a conv1d (intended to be used to implement a STFT fourier transform).The model works fine in torch, and traced using torch.jit.load in Python.
When I attempt to use the model on iOS with libtorch via typescript (https://www.npmjs.com/package/react-native-pytorch-core), I do not get the intended output.
The first output channel is correct (i.e. in this case it equals the first 2048 samples dot product with the convolution), but the remainder output channels, which should correspond with the kernel sliding along the signal (in time) are the same as the first one!)
In Python / torch...
import torchclass Model(torch.nn.Module): def __init__(self): n_fft = 2048 hop_length = 512 self.conv = torch.nn.Conv1d(in_channels=1, out_channels=n_fft // 2 + 1, kernel_size=n_fft, stride=hop_length, padding=0, dilation=1, groups=1, bias=False) def forward(self, x): return self.conv(x)model = Model();torch.jit.script(model)._save_for_lite_interpreter('model.ptl')In inference, react native typescript
import { torch } from 'react-native-pytorch-core';let x = torch.linspace(0,1,96000).unsqueeze(0);model.forward(x).then((e) => { console.log(e.shape) // this the correct shape [1, 184, 1025] [batch, time, bin] let data = e.data(); data[0,0,0] == data[0,0,1] // this is false, as expected data[0,0,0] == data[0,1,0] // this is true, for any time step. NOT expected, if the convolution kernel sliding is correct});