Flatten layer of PyTorch build by sequential container
I am trying to build a cnn by sequential container of PyTorch, my problem is I cannot figure out how to flatten the layer.

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', make_it_flatten)


What should I put in the "make_it_flatten"?
I tried to flatten the main but it do not work, main do not exist something call view

main = main.view(-1, 16*3*3)


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())

