Adding tl.Linear() in autoencoder model

Hi ,

The following model works without the tl layer:

class Autoencoder(nn.Module):
    def __init__(self):
        super(
            Autoencoder, self
        ).__init__()  # This should apply Uniform random values to weights and biases.

        self.encoder = nn.Sequential(
            nn.Conv1d(in_channels, out_channels, kernel_size),
            nn.ReLU(True),
            nn.Conv1d(out_channels, in_channels, kernel_size),
            nn.ReLU(True),
            tl.Linear(2),
        )

        self.decoder = nn.Sequential(
            nn.ConvTranspose1d(in_channels, out_channels, kernel_size),
            nn.ReLU(True),
            nn.ConvTranspose1d(out_channels, in_channels, kernel_size),
        )

    def forward(self, x):
        encoded = self.encoder(x)
        decoded = self.decoder(encoded)
        return decoded, encoded

I have added tl.Linear(2) as the compressed layer between the encoder and decoder.

Autoencoder_tl = tl.build(Autoencoder(), Train_x_t_e) will throw this error:

Train_x_t_e.shape ---> torch.Size([210, 719, 11])

RuntimeErrorTraceback (most recent call last)
in ----> 1 Autoencoder_tl = tl.build(Autoencoder(), Train_x_t_e) ~/.local/lib/python3.7/site-packages/torchlayers/__init__.py in build(module, *args, **kwargs) 65 with torch.no_grad(): 66 module.eval() ---> 67 module(*args, **kwargs) 68 module.train() 69 module = torch_compile(module) /opt/conda/lib/python3.7/site-packages/torch/nn/modules/module.py in __call__(self, *input, **kwargs) 548 result = self._slow_forward(*input, **kwargs) 549 else: --> 550 result = self.forward(*input, **kwargs) 551 for hook in self._forward_hooks.values(): 552 hook_result = hook(self, input, result) in forward(self, x) 18 19 def forward(self,x): ---> 20 encoded = self.encoder(x) 21 decoded = self.decoder(encoded) 22 return decoded, encoded /opt/conda/lib/python3.7/site-packages/torch/nn/modules/module.py in __call__(self, *input, **kwargs) 548 result = self._slow_forward(*input, **kwargs) 549 else: --> 550 result = self.forward(*input, **kwargs) 551 for hook in self._forward_hooks.values(): 552 hook_result = hook(self, input, result) /opt/conda/lib/python3.7/site-packages/torch/nn/modules/container.py in forward(self, input) 98 def forward(self, input): 99 for module in self: --> 100 input = module(input) 101 return input 102 /opt/conda/lib/python3.7/site-packages/torch/nn/modules/module.py in __call__(self, *input, **kwargs) 548 result = self._slow_forward(*input, **kwargs) 549 else: --> 550 result = self.forward(*input, **kwargs) 551 for hook in self._forward_hooks.values(): 552 hook_result = hook(self, input, result) ~/.local/lib/python3.7/site-packages/torchlayers/_dev_utils/infer.py in forward(self, inputs, *args, **kwargs) 212 infered_module = getattr(self, module) 213 --> 214 return infered_module(inputs, *args, **kwargs) 215 216 return forward /opt/conda/lib/python3.7/site-packages/torch/nn/modules/module.py in __call__(self, *input, **kwargs) 548 result = self._slow_forward(*input, **kwargs) 549 else: --> 550 result = self.forward(*input, **kwargs) 551 for hook in self._forward_hooks.values(): 552 hook_result = hook(self, input, result) /opt/conda/lib/python3.7/site-packages/torch/nn/modules/linear.py in forward(self, input) 85 86 def forward(self, input): ---> 87 return F.linear(input, self.weight, self.bias) 88 89 def extra_repr(self): /opt/conda/lib/python3.7/site-packages/torch/nn/functional.py in linear(input, weight, bias) 1610 ret = torch.addmm(bias, input, weight.t()) 1611 else: -> 1612 output = input.matmul(weight.t()) 1613 if bias is not None: 1614 output += bias RuntimeError: size mismatch, m1: [150990 x 7], m2: [719 x 2] at /pytorch/aten/src/TH/generic/THTensorMath.cpp:41

szymonmaszke / torchlayers

Adding tl.Linear() in autoencoder model #9