While coding a GAN and I encountered an error saying `'NoneType' object is not callable`. Please explain this error and some possible solutions? - deep-learning

Question

I was trying to create a Generative Adverserial Network using PyTorch. I coded the discriminator block and printed the summary. After that, I moved to create Generator block. I defined forward() function and reshaped the input noise dimensions from (batch_size, noise_dim) to (batch_size, channel, height, width). While running the code for getting summary, the error popped saying 'NoneType' object is not callable. I searched stackoverflow and other places but my problem didn't resolved.
I first created a generator block function with the following code:
def get_gen_block(in_channels, out_channels, kernel_size, stride, final_block = False):
if final_block == True:
return nn.Sequential(
nn.ConvTranspose2d(in_channels, out_channels, kernel_size, stride),
nn.Tanh()
)
return nn.Sequential(
nn.ConvTranspose2d(in_channels, out_channels, kernel_size, stride),
nn.BatchNorm2d(out_channels),
nn.ReLU()
)
Then I defined a class for generator block to create several class and defined forward() function linke this:
class Generator(nn.Module):
def __init__(self, noise_dim):
super(Generator, self).__init__()
self.noise_dim = noise_dim
self.block_1 = get_gen_block(noise_dim, 256, (3, 3), 2)
self.block_2 = get_gen_block(256, 128, (4, 4), 1)
self.block_3 = get_gen_block(128, 64, (3, 3), 2)
self.block_4 = get_gen_block(64, 1, (4, 4), 2, final_block=True)
def forward(self, r_noise_vec):
x = r_noise_vec.view(-1, self.noise_dim, 1, 1)
x1 = self.block_1(x)
x2 = self.block_2(x1)
x3 = self.block_3(x2)
x4 = self.block_4(x3)
return x4
After this, when I was printing summary for the generator, this error occured pointing to the line 'x1 = self.block_1(x)' saying 'NoneType' object is not callable. Anyone please help me in resolving this issue.

Answer 1

Please check your get_gen_block function, looks like you missed else: branch or messed up the indentation and when final_block = False it returns None instead of
return nn.Sequential(
nn.ConvTranspose2d(in_channels, out_channels, kernel_size, stride),
nn.BatchNorm2d(out_channels),
nn.ReLU()
)
if cond:
return module1
return module2
Always returns module1 when condition is met, otherwise None.
I think you wanted this
if cond:
return module1
return module2
when condition is met return module1 otherwise module2. and now compare the indentation.