##+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
## Created by: Yaoyao Liu
## Modified from: https://github.com/hshustc/CVPR19_Incremental_Learning
## Max Planck Institute for Informatics
## yaoyao.liu@mpi-inf.mpg.de
## Copyright (c) 2021
##
## This source code is licensed under the MIT-style license found in the
## LICENSE file in the root directory of this source tree
##+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
import torch.nn as nn
import math
import torch.utils.model_zoo as model_zoo
import models.modified_linear as modified_linear
from utils.incremental.conv2d_mtl import Conv2dMtl

def conv3x3mtl(in_planes, out_planes, stride=1):
    """3x3 convolution with padding"""
    return Conv2dMtl(in_planes, out_planes, kernel_size=3, stride=stride,
                     padding=1, bias=False)


class BasicBlockMtl(nn.Module):
    expansion = 1

    def __init__(self, inplanes, planes, stride=1, downsample=None, last=False):
        super(BasicBlockMtl, self).__init__()
        self.conv1 = conv3x3mtl(inplanes, planes, stride)
        self.bn1 = nn.BatchNorm2d(planes)
        self.relu = nn.ReLU(inplace=True)
        self.conv2 = conv3x3mtl(planes, planes)
        self.bn2 = nn.BatchNorm2d(planes)
        self.downsample = downsample
        self.stride = stride
        self.last = last

    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
        if not self.last: 
            out = self.relu(out)

        return out

class ResNetMtl(nn.Module):

    def __init__(self, block, layers, num_classes=1000):
        self.inplanes = 64
        super(ResNetMtl, self).__init__()
        self.conv1 = Conv2dMtl(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, last_phase=True)
        self.avgpool = nn.AvgPool2d(7, stride=1)
        self.fc = modified_linear.CosineLinear(512 * block.expansion, num_classes)

        for m in self.modules():
            if isinstance(m, Conv2dMtl):
                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, last_phase=False):
        downsample = None
        if stride != 1 or self.inplanes != planes * block.expansion:
            downsample = nn.Sequential(
                Conv2dMtl(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
        if last_phase:
            for i in range(1, blocks-1):
                layers.append(block(self.inplanes, planes))
            layers.append(block(self.inplanes, planes, last=True))
        else: 
            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 resnetmtl18(pretrained=False, **kwargs):
    model = ResNetMtl(BasicBlockMtl, [2, 2, 2, 2], **kwargs)
    return model

def resnetmtl34(pretrained=False, **kwargs):
    model = ResNetMtl(BasicBlockMtl, [3, 4, 6, 3], **kwargs)
    return model
