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