'''ResNet in PyTorch.

BasicBlock and Bottleneck module is from the original ResNet paper:
[1] Kaiming He, Xiangyu Zhang, Shaoqing Ren, Jian Sun
    Deep Residual Learning for Image Recognition. arXiv:1512.03385

PreActBlock and PreActBottleneck module is from the later paper:
[2] Kaiming He, Xiangyu Zhang, Shaoqing Ren, Jian Sun
    Identity Mappings in Deep Residual Networks. arXiv:1603.05027
'''
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.nn.init as init
from torch.autograd import Variable
from torch.nn.parameter import Parameter
import math
from torch._six import container_abcs
from itertools import repeat

def _ntuple(n):
    def parse(x):
        if isinstance(x, container_abcs.Iterable):
            return x
        return tuple(repeat(x, n))
    return parse

_single = _ntuple(1)
_pair = _ntuple(2)
_triple = _ntuple(3)
_quadruple = _ntuple(4)

def EW(w, T):
    if w is None:
        return w
    w_exp = (w * T).exp()
    return (w_exp / w_exp.max()) * w

class Conv2d_EW(nn.Module):
    
    def __init__(self, in_channels, out_channels, kernel_size, stride=1, padding = 0, dilation = 1, groups = 1, bias = True):
        super(Conv2d_EW, self).__init__()
        
        self.in_channels = in_channels
        self.out_channels = out_channels
        self.kernel_size = _pair(kernel_size)
        self.stride = _pair(stride)
        self.padding = _pair(padding)
        self.dilation = _pair(dilation)
        self.groups = groups
        self.EW_T = None
        
        self.weight = Parameter(torch.Tensor(
                out_channels, in_channels // groups, *self.kernel_size))
                
        if bias:
            self.bias = Parameter(torch.Tensor(out_channels))
        else:
            self.register_parameter('bias', None)

        self.reset_parameters()

    def reset_parameters(self):
        init.kaiming_uniform_(self.weight, a=math.sqrt(5))
        if self.bias is not None:
            fan_in, _ = init._calculate_fan_in_and_fan_out(self.weight)
            bound = 1 / math.sqrt(fan_in)
            init.uniform_(self.bias, -bound, bound)

    def forward(self, input):
        if self.EW_T is None:
            return F.conv2d(input, self.weight, self.bias, self.stride,
                        self.padding, self.dilation, self.groups)
        else:
            return F.conv2d(input, EW(self.weight, self.EW_T), EW(self.bias, self.EW_T), 
                        self.stride, self.padding, self.dilation, self.groups)


class Linear_EW(nn.Module):
    def __init__(self, in_features, out_features, bias=True):
        super(Linear_EW, self).__init__()
        self.in_features = in_features
        self.out_features = out_features
        self.EW_T = None
        self.weight = Parameter(torch.Tensor(out_features, in_features))
        if bias:
            self.bias = Parameter(torch.Tensor(out_features))
        else:
            self.register_parameter('bias', None)
        self.reset_parameters()

    def reset_parameters(self):
        init.kaiming_uniform_(self.weight, a=math.sqrt(5))
        if self.bias is not None:
            fan_in, _ = init._calculate_fan_in_and_fan_out(self.weight)
            bound = 1 / math.sqrt(fan_in)
            init.uniform_(self.bias, -bound, bound)

    def forward(self, input):
        if self.EW_T is None:
            return F.linear(input, self.weight, self.bias)
        else:
            return F.linear(input, EW(self.weight, self.EW_T), EW(self.bias, self.EW_T))


def conv3x3_EW(in_planes, out_planes, stride=1):
    return Conv2d_EW(in_planes, out_planes, kernel_size=3, stride=stride, padding=1, bias=False)


class BasicBlock(nn.Module):
    expansion = 1

    def __init__(self, in_planes, planes, stride=1):
        super(BasicBlock, self).__init__()
        self.conv1 = conv3x3_EW(in_planes, planes, stride)
        self.bn1 = nn.BatchNorm2d(planes)
        self.conv2 = conv3x3_EW(planes, planes)
        self.bn2 = nn.BatchNorm2d(planes)

        self.shortcut = nn.Sequential()
        if stride != 1 or in_planes != self.expansion * planes:
            self.shortcut = nn.Sequential(
                Conv2d_EW(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 PreActBlock(nn.Module):
    '''Pre-activation version of the BasicBlock.'''
    expansion = 1

    def __init__(self, in_planes, planes, stride=1):
        super(PreActBlock, self).__init__()
        self.bn1 = nn.BatchNorm2d(in_planes)
        self.conv1 = conv3x3_EW(in_planes, planes, stride)
        self.bn2 = nn.BatchNorm2d(planes)
        self.conv2 = conv3x3_EW(planes, planes)

        if stride != 1 or in_planes != self.expansion * planes:
            self.shortcut = nn.Sequential(
                Conv2d_EW(in_planes, self.expansion * planes,
                          kernel_size=1, stride=stride, bias=False)
            )

    def forward(self, x):
        out = F.relu(self.bn1(x))
        shortcut = self.shortcut(out) if hasattr(self, 'shortcut') else x
        out = self.conv1(out)
        out = self.conv2(F.relu(self.bn2(out)))
        out += shortcut
        return out


class Bottleneck(nn.Module):
    expansion = 4

    def __init__(self, in_planes, planes, stride=1):
        super(Bottleneck, self).__init__()
        self.conv1 = Conv2d_EW(in_planes, planes, kernel_size=1, bias=False)
        self.bn1 = nn.BatchNorm2d(planes)
        self.conv2 = Conv2d_EW(planes, planes, kernel_size=3, stride=stride, padding=1, bias=False)
        self.bn2 = nn.BatchNorm2d(planes)
        self.conv3 = Conv2d_EW(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(
                Conv2d_EW(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 PreActBottleneck(nn.Module):
    '''Pre-activation version of the original Bottleneck module.'''
    expansion = 4

    def __init__(self, in_planes, planes, stride=1):
        super(PreActBottleneck, self).__init__()
        self.bn1 = nn.BatchNorm2d(in_planes)
        self.conv1 = Conv2d_EW(in_planes, planes, kernel_size=1, bias=False)
        self.bn2 = nn.BatchNorm2d(planes)
        self.conv2 = Conv2d_EW(planes, planes, kernel_size=3, stride=stride, padding=1, bias=False)
        self.bn3 = nn.BatchNorm2d(planes)
        self.conv3 = Conv2d_EW(planes, self.expansion * planes, kernel_size=1, bias=False)

        if stride != 1 or in_planes != self.expansion * planes:
            self.shortcut = nn.Sequential(
                Conv2d_EW(in_planes, self.expansion * planes,
                          kernel_size=1, stride=stride, bias=False)
            )

    def forward(self, x):
        out = F.relu(self.bn1(x))
        shortcut = self.shortcut(out) if hasattr(self, 'shortcut') else x
        out = self.conv1(out)
        out = self.conv2(F.relu(self.bn2(out)))
        out = self.conv3(F.relu(self.bn3(out)))
        out += shortcut
        return out


class ResNet(nn.Module):
    def __init__(self, block, num_blocks, num_classes=10):
        super(ResNet, self).__init__()
        self.in_planes = 64

        self.conv1 = conv3x3_EW(3, 64)
        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 = Linear_EW(512 * block.expansion, num_classes)

    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 freeze_hidden_layers(self):
        self._freeze_layer(self.conv1)
        self._freeze_layer(self.bn1)
        self._freeze_layer(self.layer1)
        self._freeze_layer(self.layer2)
        self._freeze_layer(self.layer3)
        self._freeze_layer(self.layer4)

    def unfreeze_model(self):
        self._freeze_layer(self.conv1, freeze=False)
        self._freeze_layer(self.bn1, freeze=False)
        self._freeze_layer(self.layer1, freeze=False)
        self._freeze_layer(self.layer2, freeze=False)
        self._freeze_layer(self.layer3, freeze=False)
        self._freeze_layer(self.layer4, freeze=False)
        self._freeze_layer(self.linear, freeze=False)

    def embed_in_n_layer(self, n):
        self._freeze_layer(self.conv1)
        self._freeze_layer(self.bn1)
        if n == 1:
            self._freeze_layer(self.layer1)
        elif n == 2:
            self._freeze_layer(self.layer2)
        elif n == 3:
            self._freeze_layer(self.layer3)
        elif n == 4:
            self._freeze_layer(self.layer4)
        else:
            self._freeze_layer(self.linear)

    def _freeze_layer(self, layer, freeze=True):
        if freeze:
            for p in layer.parameters():
                p.requires_grad = False
        else:
            for p in layer.parameters():
                p.requires_grad = True
    
    def feature(self, x, stage_id):
        out = F.relu(self.bn1(self.conv1(x)))
        out = self.layer1(out)
        if stage_id == 1:
            return out
        out = self.layer2(out)
        if stage_id == 2:
            return out
        out = self.layer3(out)
        if stage_id == 3:
            return out
        out = self.layer4(out)
        return out
        

    def forward(self, x, stage_id=0):
        out = F.relu(self.bn1(self.conv1(x)))
        out = self.layer1(out)
        if stage_id == 1:
            return out
        out = self.layer2(out)
        if stage_id == 2:
            return out
        out = self.layer3(out)
        if stage_id == 3:
            return out
        out = self.layer4(out)
        if stage_id == 4:
            return out
        out = F.avg_pool2d(out, 4)
        out = out.view(out.size(0), -1)
        if stage_id == 5:
            return out
        out = self.linear(out)
        return out


def ResNet18(num_classes=10):
    return ResNet(PreActBlock, [2, 2, 2, 2], num_classes)

def ResNet18_Basic(num_classes=10):
    return ResNet(BasicBlock, [2, 2, 2, 2], num_classes)


def ResNet34(num_classes=10):
    return ResNet(BasicBlock, [3, 4, 6, 3], num_classes)


def ResNet50(num_classes=10):
    return ResNet(Bottleneck, [3, 4, 6, 3], num_classes)


def ResNet101(num_classes=10):
    return ResNet(Bottleneck, [3, 4, 23, 3], num_classes)


def ResNet152():
    return ResNet(Bottleneck, [3, 8, 36, 3])


def test():
    net = ResNet18()
    y = net(Variable(torch.randn(1, 3, 32, 32)))
    print(y.size())

# test()
