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class Dense_Block(nn.ModuleDict): | |
def __init__(self, n_layers, in_channels, growthrate, bn_size): | |
""" | |
A Dense block consists of `n_layers` of `Dense_Layer` | |
Parameters | |
---------- | |
n_layers: Number of dense layers to be stacked | |
in_channels: Number of input channels for first layer in the block | |
growthrate: Growth rate (k) as mentioned in DenseNet paper | |
bn_size: Multiplicative factor for # of bottleneck layers | |
""" | |
super(Dense_Block, self).__init__() | |
layers = dict() | |
for i in range(n_layers): | |
layer = Dense_Layer(in_channels + i * growthrate, growthrate, bn_size) | |
layers['dense{}'.format(i)] = layer | |
self.block = nn.ModuleDict(layers) | |
def forward(self, features): | |
if(isinstance(features, torch.Tensor)): | |
features = [features] | |
for _, layer in self.block.items(): | |
new_features = layer(features) | |
features.append(new_features) | |
return torch.cat(features, dim=1) |
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