Daniel Voigt Godoy - Deep Learning with PyTorch Step-by-Step A Beginner’s Guide-leanpub

Let’s see what the Inception module looks like in code:
class Inception(nn.Module):
def __init__(self, in_channels):
super(Inception, self).__init__()
# in_channels@HxW -> 2@HxW
self.branch1x1_1 = nn.Conv2d(in_channels,2,kernel_size=1)
# in_channels@HxW -> 2@HxW -> 3@HxW
self.branch5x5_1 = nn.Conv2d(in_channels,2,kernel_size=1) 1
self.branch5x5_2 = nn.Conv2d(2, 3, kernel_size=5, padding=2)
# in_channels@HxW -> 2@HxW -> 3@HxW
self.branch3x3_1 = nn.Conv2d(in_channels,2,kernel_size=1) 1
self.branch3x3_2 = nn.Conv2d(2, 3, kernel_size=3, padding=1)
# in_channels@HxW -> in_channels@HxW -> 1@HxW
self.branch_pool_1 = nn.AvgPool2d(
kernel_size=3, stride=1, padding=1
)
self.branch_pool_2 = nn.Conv2d(
1
in_channels, 2, kernel_size=1
)
def forward(self, x):
# Produces 2 channels
branch1x1 = self.branch1x1_1(x)
# Produces 3 channels
branch5x5 = self.branch5x5_1(x)
branch5x5 = self.branch5x5_2(branch5x5)
# Produces 3 channels
branch3x3 = self.branch3x3_1(x)
branch3x3 = self.branch3x3_2(branch3x3)
# Produces 2 channels
branch_pool = self.branch_pool_1(x)
branch_pool = self.branch_pool_2(branch_pool)
# Concatenates all channels together (10)
outputs = torch.cat([branch1x1, branch5x5,
branch3x3, branch_pool], 1)
return outputs
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1
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1 Dimension reduction with 1x1 convolution
2 Stacking / concatenating channels
530 | Chapter 7: Transfer Learning