--- a
+++ b/CellGraph/resnet_custom.py
@@ -0,0 +1,316 @@
+# modified from Pytorch official resnet.py
+# oops
+import torch.nn as nn
+import torch.utils.model_zoo as model_zoo
+import torch
+from torchsummary import summary
+import torch.nn.functional as F
+
+__all__ = ['ResNet', 'resnet18', 'resnet34', 'resnet50', 'resnet101',
+           'resnet152']
+
+
+model_urls = {
+    'resnet18': 'https://download.pytorch.org/models/resnet18-5c106cde.pth',
+    'resnet34': 'https://download.pytorch.org/models/resnet34-333f7ec4.pth',
+    'resnet50': 'https://download.pytorch.org/models/resnet50-19c8e357.pth',
+    'resnet101': 'https://download.pytorch.org/models/resnet101-5d3b4d8f.pth',
+    'resnet152': 'https://download.pytorch.org/models/resnet152-b121ed2d.pth',
+}
+
+
+def conv3x3(in_planes, out_planes, stride=1):
+    """3x3 convolution with padding"""
+    return nn.Conv2d(in_planes, out_planes, kernel_size=3, stride=stride,
+                     padding=1, bias=False)
+
+
+class BasicBlock(nn.Module):
+    expansion = 1
+
+    def __init__(self, inplanes, planes, stride=1, downsample=None):
+        super(BasicBlock, self).__init__()
+        self.conv1 = conv3x3(inplanes, planes, stride)
+        # self.bn1 = nn.BatchNorm2d(planes)
+        self.relu = nn.ReLU(inplace=True)
+        self.conv2 = conv3x3(planes, planes)
+        # self.bn2 = nn.BatchNorm2d(planes)
+        self.downsample = downsample
+        self.stride = stride
+
+    def forward(self, x):
+        residual = x
+
+        out = self.conv1(x)
+        # out = self.bn1(out)
+        out = self.relu(out)
+
+        out = self.conv2(out)
+        # out = self.bn2(out)
+
+        if self.downsample is not None:
+            residual = self.downsample(x)
+
+        out += residual
+        out = self.relu(out)
+
+        return out
+
+
+class Bottleneck(nn.Module):
+    expansion = 4
+
+    def __init__(self, inplanes, planes, stride=1, downsample=None):
+        super(Bottleneck, self).__init__()
+        self.conv1 = nn.Conv2d(inplanes, 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, planes * self.expansion, kernel_size=1, bias=False)
+        # self.bn3 = nn.BatchNorm2d(planes * self.expansion)
+        self.relu = nn.ReLU(inplace=True)
+        self.downsample = downsample
+        self.stride = stride
+
+    def forward(self, x):
+        residual = x
+
+        out = self.conv1(x)
+        # out = self.bn1(out)
+        out = self.relu(out)
+
+        out = self.conv2(out)
+        # out = self.bn2(out)
+        out = self.relu(out)
+
+        out = self.conv3(out)
+        # out = self.bn3(out)
+
+        if self.downsample is not None:
+            residual = self.downsample(x)
+
+        out += residual
+        out = self.relu(out)
+
+        return out
+
+class LayerNorm(nn.Module):
+    def __init__(self):
+        super(LayerNorm, self).__init__()
+        
+    def forward(self, x):
+        return F.layer_norm(x, x.size()[1:])
+
+class Bottleneck_LN(nn.Module):
+    expansion = 4
+
+    def __init__(self, inplanes, planes, stride=1, downsample=None):
+        super(Bottleneck_LN, self).__init__()
+        self.conv1 = nn.Conv2d(inplanes, planes, kernel_size=1, bias=False)
+        self.conv2 = nn.Conv2d(planes, planes, kernel_size=3, stride=stride,
+                               padding=1, bias=False)
+        self.conv3 = nn.Conv2d(planes, planes * self.expansion, kernel_size=1, bias=False)
+        self.relu = nn.ReLU(inplace=True)
+        self.ln = LayerNorm()
+        self.downsample = downsample
+        self.stride = stride
+
+    def forward(self, x):
+        residual = x
+
+        out = self.conv1(x)
+        # out = F.layer_norm(out, out.size()[1:])
+        out = self.ln(out)
+        out = self.relu(out)
+
+        out = self.conv2(out)
+        # out = F.layer_norm(out, out.size()[1:])
+        out = self.ln(out)
+        out = self.relu(out)
+
+        out = self.conv3(out)
+        # out = F.layer_norm(out, out.size()[1:])
+        out = self.ln(out)
+
+        if self.downsample is not None:
+            residual = self.downsample(x)
+            # residual = F.layer_norm(residual, residual.size()[1:])
+            residual = self.ln(residual)
+
+        out += residual
+        out = self.relu(out)
+
+        return out
+
+
+class ResNet(nn.Module):
+
+    def __init__(self, block, layers
+                 # num_classes=1000
+                 ):
+        self.inplanes = 64
+        super(ResNet, self).__init__()
+        self.conv1 = nn.Conv2d(3, 64, kernel_size=7, stride=2, padding=3,
+                               bias=False)
+        # self.bn1 = nn.BatchNorm2d(64)
+        self.relu = nn.ReLU(inplace=True)
+        self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)
+        self.layer1 = self._make_layer(block, 64, layers[0])
+        self.layer2 = self._make_layer(block, 128, layers[1], stride=2)
+        self.layer3 = self._make_layer(block, 256, layers[2], stride=2)
+        # self.layer4 = self._make_layer(block, 512, layers[3], stride=2)
+        self.avgpool = nn.AdaptiveAvgPool2d(1) # was 7, if have layer4
+        # remove the final fc
+        # self.fc = nn.Linear(512 * block.expansion, num_classes)
+
+        for m in self.modules():
+            if isinstance(m, nn.Conv2d):
+                nn.init.kaiming_normal_(m.weight, mode='fan_out', nonlinearity='relu')
+            elif isinstance(m, nn.BatchNorm2d):
+                nn.init.constant_(m.weight, 1)
+                nn.init.constant_(m.bias, 0)
+
+    def _make_layer(self, block, planes, blocks, stride=1):
+        downsample = None
+        if stride != 1 or self.inplanes != planes * block.expansion:
+            downsample = nn.Sequential(
+                nn.Conv2d(self.inplanes, planes * block.expansion,
+                          kernel_size=1, stride=stride, bias=False),
+                # nn.BatchNorm2d(planes * block.expansion),
+            )
+
+        layers = []
+        layers.append(block(self.inplanes, planes, stride, downsample))
+        self.inplanes = planes * block.expansion
+        for i in range(1, blocks):
+            layers.append(block(self.inplanes, planes))
+
+        return nn.Sequential(*layers)
+
+    def forward(self, x):
+        x = self.conv1(x)
+        # x = self.bn1(x)
+        x = self.relu(x)
+        x = self.maxpool(x)
+
+        x = self.layer1(x)
+        x = self.layer2(x)
+        x = self.layer3(x)
+        # x = self.layer4(x)
+
+        x = self.avgpool(x)
+        x = x.view(x.size(0), -1)
+        # x = self.fc(x)
+
+        return x
+
+
+def resnet18(pretrained=False, **kwargs):
+    """Constructs a ResNet-18 model.
+    Args:
+        pretrained (bool): If True, returns a model pre-trained on ImageNet
+    """
+    model = ResNet(BasicBlock, [2, 2, 2, 2], **kwargs)
+    if pretrained:
+        model = neq_load(model, 'resnet18')
+        # model.load_state_dict(model_zoo.load_url(model_urls['resnet18']))
+    return model
+
+
+def resnet34(pretrained=False, **kwargs):
+    """Constructs a ResNet-34 model.
+    Args:
+        pretrained (bool): If True, returns a model pre-trained on ImageNet
+    """
+    model = ResNet(BasicBlock, [3, 4, 6, 3], **kwargs)
+    if pretrained:
+        model = neq_load(model, 'resnet34')
+        # model.load_state_dict(model_zoo.load_url(model_urls['resnet34']))
+    return model
+
+
+def resnet50(pretrained=False, **kwargs):
+    """Constructs a ResNet-50 model.
+    Args:
+        pretrained (bool): If True, returns a model pre-trained on ImageNet
+    """
+    model = ResNet(Bottleneck, [3, 4, 6, 3], **kwargs)
+    if pretrained:
+        model = neq_load(model, 'resnet50')
+        # model.load_state_dict(model_zoo.load_url(model_urls['resnet50']))
+    return model
+
+def resnet50_ln(pretrained=False):
+    """Constructs a ResNet-50 model.
+    Args:
+        pretrained (bool): If True, returns a model pre-trained on ImageNet
+    """
+    model = ResNet(Bottleneck_LN, [3, 4, 6, 3], **kwargs)
+    
+    if pretrained:
+        model = neq_load(model, 'resnet50')
+        # model.load_state_dict(model_zoo.load_url(model_urls['resnet50']))
+    return model
+
+
+def resnet101(pretrained=False, **kwargs):
+    """Constructs a ResNet-101 model.
+    Args:
+        pretrained (bool): If True, returns a model pre-trained on ImageNet
+    """
+    model = ResNet(Bottleneck, [3, 4, 23, 3], **kwargs)
+    if pretrained:
+        model = neq_load(model, 'resnet101')
+        # model.load_state_dict(model_zoo.load_url(model_urls['resnet101']))
+    return model
+
+def resnet101_wide(pretrained=False, ln=False):
+    """Constructs a ResNet-101 model.
+    Args:
+        pretrained (bool): If True, returns a model pre-trained on ImageNet
+    """
+    if ln:
+        model = ResNet_Wide_LN(Bottleneck_LN, Bottleneck_Wide_LN, [3, 4, 46, 3])
+    else:
+        model = ResNet_Wide(Bottleneck, Bottleneck_Wide, [3, 4, 46, 3])
+    
+    if pretrained:
+        model = neq_load(model, 'resnet101')
+        # model.load_state_dict(model_zoo.load_url(model_urls['resnet101']))
+    return model
+
+
+def resnet152(pretrained=False, **kwargs):
+    """Constructs a ResNet-152 model.
+    Args:
+        pretrained (bool): If True, returns a model pre-trained on ImageNet
+    """
+    model = ResNet(Bottleneck, [3, 8, 36, 3], **kwargs)
+    if pretrained:
+        model = neq_load(model, 'resnet152')
+        # model.load_state_dict(model_zoo.load_url(model_urls['resnet152']))
+    return model
+
+
+def neq_load(model, name):
+    # load pre-trained model in a not-equal way
+    # when new model has been modified
+    pretrained_dict = model_zoo.load_url(model_urls[name])
+    model_dict = model.state_dict()
+    pretrained_dict = {k: v for k, v in pretrained_dict.items() if k in model_dict}
+    model_dict.update(pretrained_dict)
+    model.load_state_dict(model_dict)
+    return model
+
+if __name__ == '__main__':
+    # model = resnet50_wide(pretrained = False)
+    model = resnet50_wide(ln=True)
+    print(model)
+    # summary(model, (3,64,64))
+    x = torch.rand(49, 3, 64, 64)
+    x = model(x).squeeze()
+    print(x.shape)
+    print(len(x.shape))
+
+