--- a
+++ b/CellGraph/resnet.py
@@ -0,0 +1,160 @@
+'''
+Properly implemented ResNet-s for CIFAR10 as described in paper [1].
+
+The implementation and structure of this file is hugely influenced by [2]
+which is implemented for ImageNet and doesn't have option A for identity.
+Moreover, most of the implementations on the web is copy-paste from
+torchvision's resnet and has wrong number of params.
+
+Proper ResNet-s for CIFAR10 (for fair comparision and etc.) has following
+number of layers and parameters:
+
+name      | layers | params
+ResNet20  |    20  | 0.27M
+ResNet32  |    32  | 0.46M
+ResNet44  |    44  | 0.66M
+ResNet56  |    56  | 0.85M
+ResNet110 |   110  |  1.7M
+ResNet1202|  1202  | 19.4m
+
+which this implementation indeed has.
+
+Reference:
+[1] Kaiming He, Xiangyu Zhang, Shaoqing Ren, Jian Sun
+    Deep Residual Learning for Image Recognition. arXiv:1512.03385
+[2] https://github.com/pytorch/vision/blob/master/torchvision/models/resnet.py
+
+If you use this implementation in you work, please don't forget to mention the
+author, Yerlan Idelbayev.
+'''
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+import torch.nn.init as init
+
+from torch.autograd import Variable
+
+__all__ = ['ResNet', 'resnet20', 'resnet32', 'resnet44', 'resnet56', 'resnet110', 'resnet1202']
+
+def _weights_init(m):
+    classname = m.__class__.__name__
+    print(classname)
+    if isinstance(m, nn.Linear) or isinstance(m, nn.Conv2d):
+        init.kaiming_normal(m.weight)
+
+class LambdaLayer(nn.Module):
+    def __init__(self, lambd):
+        super(LambdaLayer, self).__init__()
+        self.lambd = lambd
+
+    def forward(self, x):
+        return self.lambd(x)
+
+
+class BasicBlock(nn.Module):
+    expansion = 1
+
+    def __init__(self, in_planes, planes, stride=1, option='A'):
+        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 != planes:
+            if option == 'A':
+                """
+                For CIFAR10 ResNet paper uses option A.
+                """
+                self.shortcut = LambdaLayer(lambda x:
+                                            F.pad(x[:, :, ::2, ::2], (0, 0, 0, 0, planes//4, planes//4), "constant", 0))
+            elif option == 'B':
+                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 ResNet(nn.Module):
+    def __init__(self, block, num_blocks, num_classes=10):
+        super(ResNet, self).__init__()
+        self.in_planes = 16
+
+        self.conv1 = nn.Conv2d(3, 16, kernel_size=3, stride=1, padding=1, bias=False)
+        self.bn1 = nn.BatchNorm2d(16)
+        self.layer1 = self._make_layer(block, 16, num_blocks[0], stride=1)
+        self.layer2 = self._make_layer(block, 32, num_blocks[1], stride=2)
+        self.layer3 = self._make_layer(block, 64, num_blocks[2], stride=2)
+        self.linear = nn.Linear(64, num_classes)
+
+        self.apply(_weights_init)
+
+    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 forward(self, x):
+        out = F.relu(self.bn1(self.conv1(x)))
+        out = self.layer1(out)
+        out = self.layer2(out)
+        out = self.layer3(out)
+        out = F.avg_pool2d(out, out.size()[3])
+        out = out.view(out.size(0), -1)
+        return out
+
+
+
+def resnet20():
+    return ResNet(BasicBlock, [3, 3, 3])
+
+
+def resnet32():
+    return ResNet(BasicBlock, [5, 5, 5])
+
+
+def resnet44():
+    return ResNet(BasicBlock, [7, 7, 7])
+
+
+def resnet56():
+    return ResNet(BasicBlock, [9, 9, 9])
+
+
+def resnet110():
+    return ResNet(BasicBlock, [18, 18, 18])
+
+
+def resnet1202():
+    return ResNet(BasicBlock, [200, 200, 200])
+
+
+def test(net):
+    import numpy as np
+    total_params = 0
+
+    for x in filter(lambda p: p.requires_grad, net.parameters()):
+        total_params += np.prod(x.data.numpy().shape)
+    print("Total number of params", total_params)
+    print("Total layers", len(list(filter(lambda p: p.requires_grad and len(p.data.size())>1, net.parameters()))))
+
+
+if __name__ == "__main__":
+    for net_name in __all__:
+        if net_name.startswith('resnet'):
+            print(net_name)
+            test(globals()[net_name]())
+            print()