136 lines
4.3 KiB
Python
136 lines
4.3 KiB
Python
'''DLA in PyTorch.
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Reference:
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Deep Layer Aggregation. https://arxiv.org/abs/1707.06484
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'''
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import torch
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import torch.nn as nn
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import torch.nn.functional as F
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class BasicBlock(nn.Module):
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expansion = 1
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def __init__(self, in_planes, planes, stride=1):
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super(BasicBlock, self).__init__()
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self.conv1 = nn.Conv2d(
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in_planes, planes, kernel_size=3, stride=stride, padding=1, bias=False)
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self.bn1 = nn.BatchNorm2d(planes)
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self.conv2 = nn.Conv2d(planes, planes, kernel_size=3,
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stride=1, padding=1, bias=False)
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self.bn2 = nn.BatchNorm2d(planes)
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self.shortcut = nn.Sequential()
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if stride != 1 or in_planes != self.expansion*planes:
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self.shortcut = nn.Sequential(
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nn.Conv2d(in_planes, self.expansion*planes,
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kernel_size=1, stride=stride, bias=False),
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nn.BatchNorm2d(self.expansion*planes)
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)
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def forward(self, x):
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out = F.relu(self.bn1(self.conv1(x)))
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out = self.bn2(self.conv2(out))
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out += self.shortcut(x)
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out = F.relu(out)
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return out
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class Root(nn.Module):
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def __init__(self, in_channels, out_channels, kernel_size=1):
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super(Root, self).__init__()
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self.conv = nn.Conv2d(
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in_channels, out_channels, kernel_size,
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stride=1, padding=(kernel_size - 1) // 2, bias=False)
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self.bn = nn.BatchNorm2d(out_channels)
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def forward(self, xs):
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x = torch.cat(xs, 1)
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out = F.relu(self.bn(self.conv(x)))
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return out
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class Tree(nn.Module):
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def __init__(self, block, in_channels, out_channels, level=1, stride=1):
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super(Tree, self).__init__()
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self.level = level
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if level == 1:
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self.root = Root(2*out_channels, out_channels)
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self.left_node = block(in_channels, out_channels, stride=stride)
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self.right_node = block(out_channels, out_channels, stride=1)
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else:
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self.root = Root((level+2)*out_channels, out_channels)
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for i in reversed(range(1, level)):
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subtree = Tree(block, in_channels, out_channels,
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level=i, stride=stride)
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self.__setattr__('level_%d' % i, subtree)
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self.prev_root = block(in_channels, out_channels, stride=stride)
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self.left_node = block(out_channels, out_channels, stride=1)
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self.right_node = block(out_channels, out_channels, stride=1)
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def forward(self, x):
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xs = [self.prev_root(x)] if self.level > 1 else []
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for i in reversed(range(1, self.level)):
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level_i = self.__getattr__('level_%d' % i)
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x = level_i(x)
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xs.append(x)
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x = self.left_node(x)
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xs.append(x)
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x = self.right_node(x)
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xs.append(x)
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out = self.root(xs)
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return out
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class DLA(nn.Module):
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def __init__(self, block=BasicBlock, num_classes=10):
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super(DLA, self).__init__()
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self.base = nn.Sequential(
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nn.Conv2d(3, 16, kernel_size=3, stride=1, padding=1, bias=False),
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nn.BatchNorm2d(16),
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nn.ReLU(True)
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)
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self.layer1 = nn.Sequential(
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nn.Conv2d(16, 16, kernel_size=3, stride=1, padding=1, bias=False),
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nn.BatchNorm2d(16),
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nn.ReLU(True)
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)
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self.layer2 = nn.Sequential(
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nn.Conv2d(16, 32, kernel_size=3, stride=1, padding=1, bias=False),
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nn.BatchNorm2d(32),
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nn.ReLU(True)
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)
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self.layer3 = Tree(block, 32, 64, level=1, stride=1)
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self.layer4 = Tree(block, 64, 128, level=2, stride=2)
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self.layer5 = Tree(block, 128, 256, level=2, stride=2)
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self.layer6 = Tree(block, 256, 512, level=1, stride=2)
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self.linear = nn.Linear(512, num_classes)
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def forward(self, x):
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out = self.base(x)
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out = self.layer1(out)
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out = self.layer2(out)
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out = self.layer3(out)
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out = self.layer4(out)
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out = self.layer5(out)
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out = self.layer6(out)
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out = F.avg_pool2d(out, 4)
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out = out.view(out.size(0), -1)
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out = self.linear(out)
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return out
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def test():
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net = DLA()
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print(net)
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x = torch.randn(1, 3, 32, 32)
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y = net(x)
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print(y.size())
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if __name__ == '__main__':
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test()
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