mirror of
https://github.com/AntoineHX/BU_Stoch_pool.git
synced 2025-05-03 17:20:45 +02:00
98 lines
3.5 KiB
Python
98 lines
3.5 KiB
Python
'''Dual Path Networks in PyTorch.'''
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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 Bottleneck(nn.Module):
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def __init__(self, last_planes, in_planes, out_planes, dense_depth, stride, first_layer):
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super(Bottleneck, self).__init__()
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self.out_planes = out_planes
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self.dense_depth = dense_depth
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self.conv1 = nn.Conv2d(last_planes, in_planes, kernel_size=1, bias=False)
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self.bn1 = nn.BatchNorm2d(in_planes)
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self.conv2 = nn.Conv2d(in_planes, in_planes, kernel_size=3, stride=stride, padding=1, groups=32, bias=False)
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self.bn2 = nn.BatchNorm2d(in_planes)
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self.conv3 = nn.Conv2d(in_planes, out_planes+dense_depth, kernel_size=1, bias=False)
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self.bn3 = nn.BatchNorm2d(out_planes+dense_depth)
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self.shortcut = nn.Sequential()
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if first_layer:
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self.shortcut = nn.Sequential(
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nn.Conv2d(last_planes, out_planes+dense_depth, kernel_size=1, stride=stride, bias=False),
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nn.BatchNorm2d(out_planes+dense_depth)
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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 = F.relu(self.bn2(self.conv2(out)))
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out = self.bn3(self.conv3(out))
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x = self.shortcut(x)
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d = self.out_planes
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out = torch.cat([x[:,:d,:,:]+out[:,:d,:,:], x[:,d:,:,:], out[:,d:,:,:]], 1)
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out = F.relu(out)
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return out
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class DPN(nn.Module):
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def __init__(self, cfg):
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super(DPN, self).__init__()
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in_planes, out_planes = cfg['in_planes'], cfg['out_planes']
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num_blocks, dense_depth = cfg['num_blocks'], cfg['dense_depth']
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self.conv1 = nn.Conv2d(3, 64, kernel_size=3, stride=1, padding=1, bias=False)
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self.bn1 = nn.BatchNorm2d(64)
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self.last_planes = 64
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self.layer1 = self._make_layer(in_planes[0], out_planes[0], num_blocks[0], dense_depth[0], stride=1)
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self.layer2 = self._make_layer(in_planes[1], out_planes[1], num_blocks[1], dense_depth[1], stride=2)
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self.layer3 = self._make_layer(in_planes[2], out_planes[2], num_blocks[2], dense_depth[2], stride=2)
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self.layer4 = self._make_layer(in_planes[3], out_planes[3], num_blocks[3], dense_depth[3], stride=2)
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self.linear = nn.Linear(out_planes[3]+(num_blocks[3]+1)*dense_depth[3], 10)
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def _make_layer(self, in_planes, out_planes, num_blocks, dense_depth, stride):
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strides = [stride] + [1]*(num_blocks-1)
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layers = []
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for i,stride in enumerate(strides):
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layers.append(Bottleneck(self.last_planes, in_planes, out_planes, dense_depth, stride, i==0))
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self.last_planes = out_planes + (i+2) * dense_depth
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return nn.Sequential(*layers)
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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.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 = 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 DPN26():
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cfg = {
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'in_planes': (96,192,384,768),
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'out_planes': (256,512,1024,2048),
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'num_blocks': (2,2,2,2),
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'dense_depth': (16,32,24,128)
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}
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return DPN(cfg)
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def DPN92():
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cfg = {
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'in_planes': (96,192,384,768),
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'out_planes': (256,512,1024,2048),
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'num_blocks': (3,4,20,3),
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'dense_depth': (16,32,24,128)
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}
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return DPN(cfg)
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def test():
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net = DPN92()
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x = torch.randn(1,3,32,32)
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y = net(x)
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print(y)
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# test()
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