"""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
Original code is from https://github.com/kuangliu/pytorch-cifar/blob/master/models/resnet.py
"""
import math
import os

import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.autograd import Variable
from torch.nn.parameter import Parameter


def conv3x3(in_planes, out_planes, stride=1):
    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, in_planes, planes, stride=1):
        super(BasicBlock, self).__init__()
        self.conv1 = conv3x3(in_planes, planes, stride)
        self.bn1 = nn.BatchNorm2d(planes)
        self.conv2 = conv3x3(planes, planes)
        self.bn2 = nn.BatchNorm2d(planes)

        self.shortcut = nn.Sequential()
        if stride != 1 or in_planes != self.expansion * planes:
            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 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(in_planes, planes, stride)
        self.bn2 = nn.BatchNorm2d(planes)
        self.conv2 = conv3x3(planes, planes)

        if stride != 1 or in_planes != self.expansion * planes:
            self.shortcut = nn.Sequential(
                nn.Conv2d(
                    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 = nn.Conv2d(in_planes, 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, 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(
                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 = 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 = nn.Conv2d(in_planes, planes, kernel_size=1, bias=False)
        self.bn2 = nn.BatchNorm2d(planes)
        self.conv2 = nn.Conv2d(
            planes, planes, kernel_size=3, stride=stride, padding=1, bias=False
        )
        self.bn3 = nn.BatchNorm2d(planes)
        self.conv3 = nn.Conv2d(
            planes, self.expansion * planes, kernel_size=1, bias=False
        )

        if stride != 1 or in_planes != self.expansion * planes:
            self.shortcut = nn.Sequential(
                nn.Conv2d(
                    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(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 = nn.Linear(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 forward(self, x):
        out = F.relu(self.bn1(self.conv1(x)))
        out = self.layer1(out)
        out = self.layer2(out)
        out = self.layer3(out)
        out = self.layer4(out)
        out = F.avg_pool2d(out, 4)
        out = out.view(out.size(0), -1)
        y = self.linear(out)
        return y

    # function to extact the multiple features
    def feature_list(self, x):
        out_list = []
        out = F.relu(self.bn1(self.conv1(x)))
        out_list.append(out)
        out = self.layer1(out)
        out_list.append(out)
        out = self.layer2(out)
        out_list.append(out)
        out = self.layer3(out)
        out_list.append(out)
        out = self.layer4(out)
        out_list.append(out)
        out = F.avg_pool2d(out, 4)
        out = out.view(out.size(0), -1)
        y = self.linear(out)
        return y, out_list

    # function to extact a specific feature
    def intermediate_forward(self, x, layer_index):
        out = F.relu(self.bn1(self.conv1(x)))
        if layer_index == 1:
            out = self.layer1(out)
        elif layer_index == 2:
            out = self.layer1(out)
            out = self.layer2(out)
        elif layer_index == 3:
            out = self.layer1(out)
            out = self.layer2(out)
            out = self.layer3(out)
        elif layer_index == 4:
            out = self.layer1(out)
            out = self.layer2(out)
            out = self.layer3(out)
            out = self.layer4(out)
        return out

    # function to extact the penultimate features
    def penultimate_forward(self, x):
        out = F.relu(self.bn1(self.conv1(x)))
        out = self.layer1(out)
        out = self.layer2(out)
        out = self.layer3(out)
        penultimate = self.layer4(out)
        out = F.avg_pool2d(penultimate, 4)
        out = out.view(out.size(0), -1)
        y = self.linear(out)
        return y, penultimate


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


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


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


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


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()
