Flatten layer of PyTorch build by sequential container

The fastest way to flatten the layer is not to create the new module and to add that module to the main via main.add_module('flatten', Flatten()).

class Flatten(nn.Module):
    def forward(self, input):
        return input.view(input.size(0), -1)

Instead, just a simple, out = inp.reshape(inp.size(0), -1) inside forward of your model is faster as I showed in here.

This might not be exactly what you are looking for, but you can simply create your own nn.Module that flattens any input, which you can then add to the nn.Sequential() object:

class Flatten(nn.Module):
    def forward(self, x):
        return x.view(x.size()[0], -1)

The x.size()[0] will select the batch dim, and -1 will compute all remaining dims to fit the number of elements, thereby flattening any tensor/Variable.

And using it in nn.Sequential:

main = nn.Sequential()
self._conv_block(main, 'conv_0', 3, 6, 5)
main.add_module('max_pool_0_2_2', nn.MaxPool2d(2,2))
self._conv_block(main, 'conv_1', 6, 16, 3)
main.add_module('max_pool_1_2_2', nn.MaxPool2d(2,2)) 
main.add_module('flatten', Flatten())