I’m new to Pytorch and trying to implement the BKNet network from the article “Facial smile detection using convolutional neural networks”.

I wrote the following Neural network code:

```
model = nn.Sequential(nn.Conv2d(32, 32, kernel_size=(3,3), stride=1),
nn.Conv2d(32, 32, kernel_size=(3,3), stride=1),
nn.MaxPool2d(kernel_size=(2,2), stride=2),
nn.ReLU(),
nn.Conv2d(64, 64, kernel_size=(3,3), stride=1),
nn.Conv2d(64, 64, kernel_size=(3,3), stride=1),
nn.MaxPool2d(kernel_size=(2,2), stride=2),
nn.ReLU(),
nn.Conv2d(128, 128, kernel_size=(3,3), stride=1),
nn.Conv2d(128, 128, kernel_size=(3,3), stride=1),
nn.Conv2d(128, 128, kernel_size=(3,3), stride=1),
nn.MaxPool2d(kernel_size=(2,2), stride=2),
nn.ReLU(),
nn.Conv2d(256, 256, kernel_size=(3,3), stride=1),
nn.Conv2d(256, 256, kernel_size=(3,3), stride=1),
nn.Conv2d(256, 256, kernel_size=(3,3), stride=1),
nn.MaxPool2d(kernel_size=(2,2), stride=2),
nn.ReLU(),
nn.Linear(256,256),
nn.Linear(256,256),
nn.ReLU(),
nn.Linear(256,2),
nn.LogSoftmax(dim=1))
```

And trained it using the training code here and an image database with many images of size 178x218, which are transformed in the following way:

```
data_transforms = {
'train': transforms.Compose([
transforms.Resize(178),
transforms.CenterCrop(178),
transforms.RandomHorizontalFlip(),
transforms.ToTensor(),
transforms.Normalize([0.5, 0.5, 0.5], [0.5, 0.5, 0.5])
]),
'val': transforms.Compose([
transforms.Resize(178),
transforms.CenterCrop(178),
transforms.ToTensor(),
transforms.Normalize([0.5, 0.5, 0.5], [0.5, 0.5, 0.5])
]),
}
```

But I’m getting the following error:

RuntimeError: Given groups=1, weight of size 32 32 3 3, expected input[32, 3, 178, 178] to have 32 channels, but got 3 channels instead

What have I done wrong?