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@Sivaram46
Created June 12, 2021 17:35
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class Dense_Layer(nn.Module):
def __init__(self, in_channels, growthrate, bn_size):
super(Dense_Layer, self).__init__()
self.bn1 = nn.BatchNorm2d(in_channels)
self.conv1 = nn.Conv2d(
in_channels, bn_size * growthrate, kernel_size=1, bias=False
)
self.bn2 = nn.BatchNorm2d(bn_size * growthrate)
self.conv2 = nn.Conv2d(
bn_size * growthrate, growthrate, kernel_size=3, padding=1, bias=False
)
def forward(self, prev_features):
out1 = torch.cat(prev_features, dim=1)
out1 = self.conv1(F.relu(self.bn1(out1)))
out2 = self.conv2(F.relu(self.bn2(out1)))
return out2
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