## Adapted from https://github.com/pytorch/vision/blob/master/torchvision/models/resnet.py

from collections import OrderedDict

import torch
import torch.nn as nn
from utils import *
from utils.convert_weight import convert_ckpt

torch.hub.set_dir('/cmlscratch/manlis/hub')

model_urls = {
    'resnet18': 'https://download.pytorch.org/models/resnet18-f37072fd.pth',
    'resnet34': 'https://download.pytorch.org/models/resnet34-b627a593.pth',
    'resnet50': 'https://download.pytorch.org/models/resnet50-0676ba61.pth',
    'resnet101': 'https://download.pytorch.org/models/resnet101-63fe2227.pth',
    'resnet152': 'https://download.pytorch.org/models/resnet152-394f9c45.pth',
    'resnext50_32x4d': 'https://download.pytorch.org/models/resnext50_32x4d-7cdf4587.pth',
    'resnext101_32x8d': 'https://download.pytorch.org/models/resnext101_32x8d-8ba56ff5.pth',
    'wide_resnet50_2': 'https://download.pytorch.org/models/wide_resnet50_2-95faca4d.pth',
    'wide_resnet101_2': 'https://download.pytorch.org/models/wide_resnet101_2-32ee1156.pth',
}

def conv3x3(in_planes, out_planes, stride=1, groups=1, dilation=1):
    """3x3 convolution with padding"""
    return nn.Conv2d(in_planes, out_planes, kernel_size=3, stride=stride,
                     padding=dilation, groups=groups, bias=False, dilation=dilation)


def conv1x1(in_planes, out_planes, stride=1):
    """1x1 convolution"""
    return nn.Conv2d(in_planes, out_planes, kernel_size=1, stride=stride, bias=False)


class BasicBlock(nn.Module):
    expansion = 1
    __constants__ = ['downsample']

    def __init__(self, inplanes, planes, stride=1, downsample=None, groups=1,
                 base_width=64, dilation=1, norm_layer=None, momentum=0.1):
        super(BasicBlock, self).__init__()
        if norm_layer is None:
            norm_layer = nn.BatchNorm2d
        if groups != 1 or base_width != 64:
            raise ValueError('BasicBlock only supports groups=1 and base_width=64')
        if dilation > 1:
            raise NotImplementedError("Dilation > 1 not supported in BasicBlock")
        # Both self.conv1 and self.downsample layers downsample the input when stride != 1
        self.conv1 = conv3x3(inplanes, planes, stride)
        self.clean_bn1 = norm_layer(planes, momentum=momentum)
        self.adv_bn1 = norm_layer(planes, momentum=momentum)
        self.relu = nn.ReLU(inplace=True)
        self.conv2 = conv3x3(planes, planes)
        self.clean_bn2 = norm_layer(planes, momentum=momentum)
        self.adv_bn2 = norm_layer(planes, momentum=momentum)
        self.downsample = downsample
        self.stride = stride

    def forward(self, x, tag='clean'):
        identity = x

        out = self.conv1(x)
        if tag == 'clean':
            out = self.clean_bn1(out)
        else:
            out = self.adv_bn1(out)
        out = self.relu(out)

        out = self.conv2(out)
        if tag == 'clean':
            out = self.clean_bn2(out)
        else:
            out = self.adv_bn2(out)

        if self.downsample is not None:
            identity = self.downsample.conv(x)
            if tag == 'clean':
                identity = self.downsample.clean_bn(identity)
            else:
                identity = self.downsample.adv_bn(identity)

        out += identity
        out = self.relu(out)

        return out


class Bottleneck(nn.Module):
    expansion = 4
    __constants__ = ['downsample']

    def __init__(self, inplanes, planes, stride=1, downsample=None, groups=1,
                 base_width=64, dilation=1, norm_layer=None, momentum=0.1):
        super(Bottleneck, self).__init__()
        if norm_layer is None:
            norm_layer = nn.BatchNorm2d
        width = int(planes * (base_width / 64.)) * groups
        # Both self.conv2 and self.downsample layers downsample the input when stride != 1
        self.conv1 = conv1x1(inplanes, width)
        self.clean_bn1 = norm_layer(width, momentum=momentum)
        self.adv_bn1 = norm_layer(width, momentum=momentum)
        self.conv2 = conv3x3(width, width, stride, groups, dilation)
        self.clean_bn2 = norm_layer(width, momentum=momentum)
        self.adv_bn2 = norm_layer(width, momentum=momentum)
        self.conv3 = conv1x1(width, planes * self.expansion)
        self.clean_bn3 = norm_layer(planes * self.expansion, momentum=momentum)
        self.adv_bn3 = norm_layer(planes * self.expansion, momentum=momentum)
        self.relu = nn.ReLU(inplace=True)
        self.downsample = downsample
        self.stride = stride

    def forward(self, x, tag='clean'):
        identity = x

        out = self.conv1(x)
        if tag == 'clean':
            out = self.clean_bn1(out)
        else:
            out = self.adv_bn1(out)
        out = self.relu(out)

        out = self.conv2(out)
        if tag == 'clean':
            out = self.clean_bn2(out)
        else:
            out = self.adv_bn2(out)
        out = self.relu(out)

        out = self.conv3(out)
        if tag == 'clean':
            out = self.clean_bn3(out)
        else:
            out = self.adv_bn3(out)

        if self.downsample is not None:
            identity = self.downsample.conv(x)
            if tag == 'clean':
                identity = self.downsample.clean_bn(identity)
            else:
                identity = self.downsample.adv_bn(identity)

        out += identity
        out = self.relu(out)

        return out
        

class Head(nn.Module):
    def __init__(self, layers, cut, num_classes):
        super(Head, self).__init__()
        block_num = []
        for i, layer in enumerate(layers):
            block_num.append(len(layer))
            for j, block in enumerate(layer):
                setattr(self, 'layer{}_{}'.format(str(int(cut)+i+1), str(j)), block)
        self.layer_num = len(layers)
        self.block_num = block_num
        self.cut = cut
        self.avgpool = nn.AdaptiveAvgPool2d((1, 1))
        self.flatten = nn.Flatten()
        self.fc = nn.Linear(512 * block.expansion, num_classes)

    def forward(self, x, tag='clean'):
        for i in range(self.layer_num):
            for j in range(self.block_num[i]):
                m = getattr(self, 'layer{}_{}'.format(str(int(self.cut)+i+1), str(j)))
                x = m(x, tag)
        x = self.avgpool(x)
        x = self.flatten(x)
        x = self.fc(x)
        return x


class ResNet(nn.Module):
    '''
        Additional args:
            train_all (bool): Fix the first few layers during training if train_all==False. 
            cut (int): the index of layer that cut between 'feature extractor (g^{1, l})'(input -> layer{cut-1}) 
                       and 'downstream layers (g^{l+1, L})'(layer{cut} -> output)
    '''
    def __init__(self, block, layers, num_classes=1000, train_all=False, zero_init_residual=False,
                 groups=1, width_per_group=64, replace_stride_with_dilation=None, norm_layer=None, cut=2):
        super(ResNet, self).__init__()
        if norm_layer is None:
            norm_layer = nn.BatchNorm2d
        self._norm_layer = norm_layer
        self.inplanes = 64
        self.dilation = 1
        if replace_stride_with_dilation is None:
            # each element in the tuple indicates if we should replace
            # the 2x2 stride with a dilated convolution instead
            replace_stride_with_dilation = [False, False, False]
        if len(replace_stride_with_dilation) != 3:
            raise ValueError("replace_stride_with_dilation should be None "
                             "or a 3-element tuple, got {}".format(replace_stride_with_dilation))
        self.groups = groups
        self.base_width = width_per_group

        if num_classes == 1000:
            conv1 = nn.Conv2d(3, self.inplanes, kernel_size=7, stride=2, padding=3,
                                   bias=False)
        elif num_classes == 10:
            conv1 = nn.Conv2d(3, self.inplanes, kernel_size=3, stride=1, padding=1,
                                   bias=False)
        bn1 = norm_layer(self.inplanes)
        relu = nn.ReLU(inplace=True)
        maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)
        layer_list = []
        layer_list.append(self._make_layer(block, 64, layers[0]))
        layer_list.append(self._make_layer(block, 128, layers[1], stride=2,
                          dilate=replace_stride_with_dilation[0]))
        layer_list.append(self._make_layer(block, 256, layers[2], stride=2,
                          dilate=replace_stride_with_dilation[1]))
        layer_list.append(self._make_layer(block, 512, layers[3], stride=2,
                          dilate=replace_stride_with_dilation[2]))
        self.cut = cut

        # first few layers (g^{1, l}) for extracting features
        feature_x = []
        feature_x.append(conv1)
        feature_x.append(bn1)
        feature_x.append(relu)
        if num_classes == 1000:
            feature_x.append(maxpool)
        for i in range(self.cut):
            layer = layer_list[i]
            feature_x.append(nn.Sequential(*layer))
        self.feature_x = nn.Sequential(*feature_x)

        # fix the g^{1, l} when training with advbn
        if not train_all:
            for params in getattr(self, 'feature_x').parameters():
               params.requires_grad = False
            for m in self.feature_x.modules():
               if isinstance(m, (nn.BatchNorm2d, nn.GroupNorm)):
                   m.eval()

        # downstream layers for deeper feature extraction and classification
        head = []
        if self.cut + 1 <= 4:
            for i in range(self.cut, 4):
                layer = layer_list[i]
                head.append(layer)
        self.head = Head(head, cut, num_classes)

        # parameter initialization
        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.GroupNorm)):
                nn.init.constant_(m.weight, 1)
                nn.init.constant_(m.bias, 0)
        if zero_init_residual:
            for m in self.modules():
                if isinstance(m, Bottleneck):
                    nn.init.constant_(m.bn3.weight, 0)
                elif isinstance(m, BasicBlock):
                    nn.init.constant_(m.bn2.weight, 0)

    def _make_layer(self, block, planes, blocks, stride=1, dilate=False):
        norm_layer = self._norm_layer
        downsample = None
        previous_dilation = self.dilation
        if dilate:
            self.dilation *= stride
            stride = 1
        if stride != 1 or self.inplanes != planes * block.expansion:
            downsample = nn.Sequential(OrderedDict([
                ('conv', conv1x1(self.inplanes, planes * block.expansion, stride)),
                ('clean_bn', norm_layer(planes * block.expansion)),
                ('adv_bn', norm_layer(planes * block.expansion)),
            ]))

        layers = []
        layers.append(block(self.inplanes, planes, stride, downsample, self.groups,
                            self.base_width, previous_dilation, norm_layer))
        self.inplanes = planes * block.expansion
        for _ in range(1, blocks):
            layers.append(block(self.inplanes, planes, groups=self.groups,
                                base_width=self.base_width, dilation=self.dilation,
                                norm_layer=norm_layer))

        return layers

    def _forward_impl(self, x, stage, tag='clean'):
        if stage == 'feature_x':
            x = self.feature_x(x)
        elif stage == 'head':
            x = self.head(x, tag)
        else:
            x = self.feature_x(x)
            x = self.head(x, tag)
        return x

    def forward(self, x, stage, tag='clean'):
        return self._forward_impl(x, stage, tag)
        

def _resnet(arch, block, layers, pretrained, progress, **kwargs):
    model = ResNet(block, layers, **kwargs)
    if pretrained:
        kv = torch.hub.load_state_dict_from_url(model_urls[arch])
        nkv = convert_ckpt(model, kv, 'adv_bn')
        model.load_state_dict(nkv)
    return model


def resnet18(pretrained=False, progress=True, **kwargs):
    r"""ResNet-18 model from
    `"Deep Residual Learning for Image Recognition" <https://arxiv.org/pdf/1512.03385.pdf>`_

    Args:
        pretrained (bool): If True, returns a model pre-trained on ImageNet
        progress (bool): If True, displays a progress bar of the download to stderr
    """
    return _resnet('resnet18', BasicBlock, [2, 2, 2, 2], pretrained, progress,
                   **kwargs)


def resnet34(pretrained=False, progress=True, **kwargs):
    r"""ResNet-34 model from
    `"Deep Residual Learning for Image Recognition" <https://arxiv.org/pdf/1512.03385.pdf>`_

    Args:
        pretrained (bool): If True, returns a model pre-trained on ImageNet
        progress (bool): If True, displays a progress bar of the download to stderr
    """
    return _resnet('resnet34', BasicBlock, [3, 4, 6, 3], pretrained, progress,
                   **kwargs)


def resnet50(pretrained=False, progress=True, **kwargs):
    r"""ResNet-50 model from
    `"Deep Residual Learning for Image Recognition" <https://arxiv.org/pdf/1512.03385.pdf>`_

    Args:
        pretrained (bool): If True, returns a model pre-trained on ImageNet
        progress (bool): If True, displays a progress bar of the download to stderr
    """
    return _resnet('resnet50', Bottleneck, [3, 4, 6, 3], pretrained, progress,
                   **kwargs)


def resnet101(pretrained=False, progress=True, **kwargs):
    r"""ResNet-101 model from
    `"Deep Residual Learning for Image Recognition" <https://arxiv.org/pdf/1512.03385.pdf>`_

    Args:
        pretrained (bool): If True, returns a model pre-trained on ImageNet
        progress (bool): If True, displays a progress bar of the download to stderr
    """
    return _resnet('resnet101', Bottleneck, [3, 4, 23, 3], pretrained, progress,
                   **kwargs)


def resnet152(pretrained=False, progress=True, **kwargs):
    r"""ResNet-152 model from
    `"Deep Residual Learning for Image Recognition" <https://arxiv.org/pdf/1512.03385.pdf>`_

    Args:
        pretrained (bool): If True, returns a model pre-trained on ImageNet
        progress (bool): If True, displays a progress bar of the download to stderr
    """
    return _resnet('resnet152', Bottleneck, [3, 8, 36, 3], pretrained, progress,
                   **kwargs)

