Failed to conversion a convolution when padding argument is 'valid' or default

[kokeshing]

First of all, thank you for developing this great project!

I found an event that failed in the convolution conversion when its padding argument is 'valid'(nn.Conv) or default(F.conv).

UserWarning: Conversion exception on node 'Conv1d': The `padding` argument must be a tuple of 2 integers. Received: v
    raise Exception(f'Failed conversion: {self.original_node}')
Exception: Failed conversion: Conv1d(128, 128, kernel_size=(1,), stride=(1,), padding=valid)

UserWarning: Validation exception on node 'conv1d': Failed conversion: <built-in method conv1d of type object at 0x105101780>
Exception: Failed conversion: <built-in method conv1d of type object at 0x122109780>

ValueError: `padding` should have two elements. Received: valid.
    raise Exception(f'Failed conversion: {self.original_node}')
Exception: Failed conversion: Conv2d(128, 128, kernel_size=(1, 1), stride=(1, 1), padding=valid)

UserWarning: Validation exception on node 'ErrorModel': Failed conversion: <built-in method conv2d of type object at 0x122101980>
Exception: Failed conversion: <built-in method conv2d of type object at 0x122101510>

The code to be reproduced is as follows:

import nobuco
import torch
import torch.nn as nn
import torch.nn.functional as F
from nobuco import ChannelOrder


class Model(nn.Module):
    def __init__(self):
        super().__init__()

        self.conv0_weight = nn.Parameter(torch.randn(128, 3, 3, 3))
        self.conv0_bias = nn.Parameter(torch.randn(128))

        self.conv1 = nn.Conv2d(128, 128, 1, 1, padding=0)

        self.conv2_weight = nn.Parameter(torch.randn(128, 128, 3))
        self.conv2_bias = nn.Parameter(torch.randn(128))

        self.conv3 = nn.Conv1d(128, 128, 1, 1, padding=0)

    def forward(self, x):
        x = F.conv2d(x, self.conv0_weight, self.conv0_bias, padding="same")
        x = F.relu(x)
        x = self.conv1(x)
        x = F.relu(x)

        x = x.reshape(x.shape[0], x.shape[1], -1)
        x = F.conv1d(x, self.conv2_weight, self.conv2_bias, padding="same")
        x = F.relu(x)
        x = self.conv3(x)
        x = F.relu(x)

        return x


class ErrorModel(nn.Module):
    def __init__(self):
        super().__init__()

        self.conv0_weight = nn.Parameter(torch.randn(128, 3, 3, 3))
        self.conv0_bias = nn.Parameter(torch.randn(128))

        self.conv1 = nn.Conv2d(128, 128, 1, 1, "valid")

        self.conv2_weight = nn.Parameter(torch.randn(128, 128, 3))
        self.conv2_bias = nn.Parameter(torch.randn(128))

        self.conv3 = nn.Conv1d(128, 128, 1, 1, "valid")

    def forward(self, x):
        x = F.conv2d(x, self.conv0_weight, self.conv0_bias)
        x = F.relu(x)
        x = self.conv1(x)
        x = F.relu(x)

        x = x.reshape(x.shape[0], x.shape[1], -1)
        x = F.conv1d(x, self.conv2_weight, self.conv2_bias)
        x = F.relu(x)
        x = self.conv3(x)
        x = F.relu(x)

        return x


def main():
    dummy_image = torch.rand(size=(1, 3, 64, 64))

    model = Model().eval()
    _ = nobuco.pytorch_to_keras(
        model,
        args=[dummy_image],
        kwargs=None,
        inputs_channel_order=ChannelOrder.TENSORFLOW,
        outputs_channel_order=ChannelOrder.TENSORFLOW,
    )

    error_model = ErrorModel().eval()
    _ = nobuco.pytorch_to_keras(
        error_model,
        args=[dummy_image],
        kwargs=None,
        inputs_channel_order=ChannelOrder.TENSORFLOW,
        outputs_channel_order=ChannelOrder.TENSORFLOW,
    )  # This will raise an error


if __name__ == "__main__":
    main()

[kokeshing]

I have investigated and submitted #32. Please confirm.

[AlexanderLutsenko]

Awesome, thank you!