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https://github.com/AntoineHX/BU_Stoch_pool.git
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187 lines
6.9 KiB
Python
187 lines
6.9 KiB
Python
'''ResNet in PyTorch.
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For Pre-activation ResNet, see 'preact_resnet.py'.
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Reference:
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[1] Kaiming He, Xiangyu Zhang, Shaoqing Ren, Jian Sun
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Deep Residual Learning for Image Recognition. arXiv:1512.03385
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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 Bottleneck(nn.Module):
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expansion = 4
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def __init__(self, in_planes, planes, stride=1):
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super(Bottleneck, self).__init__()
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self.conv1 = nn.Conv2d(in_planes, planes, kernel_size=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=stride, padding=1, bias=False)
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self.bn2 = nn.BatchNorm2d(planes)
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self.conv3 = nn.Conv2d(planes, self.expansion *
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planes, kernel_size=1, bias=False)
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self.bn3 = nn.BatchNorm2d(self.expansion*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 = F.relu(self.bn2(self.conv2(out)))
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out = self.bn3(self.conv3(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 ResNet(nn.Module):
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def __init__(self, block, num_blocks, num_classes=10,stoch=False):
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super(ResNet, self).__init__()
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self.in_planes = 64
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self.conv1 = nn.Conv2d(3, 64, kernel_size=3,
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stride=1, padding=1, bias=False)
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self.conv2 = nn.Conv2d(512, 512, kernel_size=3,
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stride=1, padding=1, bias=True)
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self.bn1 = nn.BatchNorm2d(64)
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self.layer1 = self._make_layer(block, 64, num_blocks[0], stride=1)
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self.layer2 = self._make_layer(block, 128, num_blocks[1], stride=2)
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self.layer3 = self._make_layer(block, 256, num_blocks[2], stride=2)
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self.layer4 = self._make_layer(block, 512, num_blocks[3], stride=2)
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self.linear = nn.Linear(512*block.expansion, num_classes)
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self.stoch = stoch
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def _make_layer(self, block, planes, num_blocks, stride):
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strides = [stride] + [1]*(num_blocks-1)
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layers = []
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for stride in strides:
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layers.append(block(self.in_planes, planes, stride))
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self.in_planes = planes * block.expansion
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return nn.Sequential(*layers)
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def myconv2d_avg(self, input, weight, bias=None, stride=1, padding=0, dilation=1, groups=1,size=2):
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batch_size, in_channels, in_h, in_w = input.shape
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out_channels, in_channels, kh, kw = weight.shape
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out_h = (in_h+2*padding)-2*(int(kh)/2)
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out_w = (in_w+2*padding)-2*(int(kw)/2)
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unfold = torch.nn.Unfold(kernel_size=(kh, kw), dilation=dilation, padding=padding, stride=stride)
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inp_unf = unfold(input).view(batch_size,in_channels*kh*kw,out_h,out_w)
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sel_h = torch.LongTensor(out_h/size,out_w/size).random_(0, size)#.cuda()
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rng_h = sel_h + torch.arange(0,out_h,size).long()#.cuda()
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sel_w = torch.LongTensor(out_h/size,out_w/size).random_(0, size)#.cuda()
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rng_w = sel_w+torch.arange(0,out_w,size).long()#.cuda()
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inp_unf = inp_unf[:,:,rng_h,rng_w].view(batch_size,in_channels*kh*kw,out_h/size*out_w/size)
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#unfold_avg = torch.nn.Unfold(kernel_size=(1, 1), dilation=1, padding=0, stride=2)
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if bias is None:
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out_unf = inp_unf.transpose(1, 2).matmul(weight.view(weight.size(0), -1).t()).transpose(1, 2)
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else:
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out_unf = (inp_unf.transpose(1, 2).matmul(weight.view(weight.size(0), -1).t()) + bias).transpose(1, 2)
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out = out_unf.view(batch_size, out_channels, out_h/size, out_w/size).contiguous()
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return out
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def savg_pool2d(self,x,size,locx=-1,locy=-1):
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b,c,h,w = x.shape
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if locx==-1:
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selh = torch.LongTensor(h/size,w/size).random_(0, size)
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else:
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selh = torch.ones(h/size,w/size).long()*loc
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rngh = torch.arange(0,h,size).long().view(h/size,1).repeat(1,w/size).view(h/size,w/size)
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selx = (selh+rngh).repeat(b,c,1,1)
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if locy==-1:
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selw = torch.LongTensor(h/size,w/size).random_(0, size)
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else:
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selw = torch.ones(h/size,w/size).long()*loc
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rngw = torch.arange(0,w,size).long().view(1,h/size).repeat(h/size,1).view(h/size,w/size)
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sely = (selw+rngw).repeat(b,c,1,1)
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bv, cv ,hv, wv = torch.meshgrid([torch.arange(0,b), torch.arange(0,c),torch.arange(0,h/size),torch.arange(0,w/size)])
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#x=x.view(b,c,h*w)
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newx = x[bv,cv, selx, sely]
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#ghdh
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return newx
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def forward(self, x ,stoch = True):
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#if self.training==False:
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# stoch=False
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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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if self.stoch and stoch:
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out = F.relu(self.myconv2d_avg(out, self.conv2.weight, bias=self.conv2.bias,padding=1,size=4))
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#out = F.avg_pool2d(out, 2)
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else:
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out = F.relu(self.myconv2d_avg(out, self.conv2.weight, bias=self.conv2.bias,padding=1,size=1))
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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 MyResNet18(stoch=False):
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return ResNet(BasicBlock, [2, 2, 2, 2],stoch=stoch)
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def ResNet34():
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return ResNet(BasicBlock, [3, 4, 6, 3])
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def MyResNet50():
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return ResNet(Bottleneck, [3, 4, 6, 3])
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def ResNet101():
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return ResNet(Bottleneck, [3, 4, 23, 3])
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def ResNet152():
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return ResNet(Bottleneck, [3, 8, 36, 3])
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def test():
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net = ResNet18()
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y = net(torch.randn(1, 3, 32, 32))
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print(y.size())
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# test()
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