# 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))