"""
ResNet model architecture for RRAM based CIM inference
"""

import torch.nn as nn
import torch.nn.functional as F
from torch.nn import init
from .quant import RRAMConv2d, QConv2d, QLinear


class BasicBlock(nn.Module):
    expansion = 1
    def __init__(self, in_planes, planes, stride=1, wbit=4, abit=4, alpha_init=10, wqmode='symm', ADCprecision=5, cellBit=2, subArray=128, sensitive_lv=0):
        super(BasicBlock, self).__init__()
        self.conv1 = RRAMConv2d(in_planes, planes, kernel_size=3, stride=stride, padding=1, bias=False,
                                wl_input=abit,wl_weight=wbit, subArray=subArray, inference=1, cellBit=cellBit, ADCprecision=ADCprecision, sensitive_lv=sensitive_lv)
        self.bn1 = nn.BatchNorm2d(planes)
        self.relu1 = nn.ReLU(inplace=True)
        
        self.conv2 = RRAMConv2d(planes, planes, kernel_size=3, stride=1, padding=1, bias=False,
                                wl_input=abit,wl_weight=wbit, subArray=subArray, inference=1, cellBit=cellBit, ADCprecision=ADCprecision, sensitive_lv=sensitive_lv)
        self.bn2 = nn.BatchNorm2d(planes)
        self.relu2 = nn.ReLU(inplace=True)
        
        self.shortcut = nn.Sequential()

        if stride != 1 or in_planes != self.expansion*planes:
            self.shortcut = nn.Sequential(
                QConv2d(in_planes, self.expansion*planes, kernel_size=1, stride=stride, bias=False, wbit=wbit, abit=abit, alpha_init=alpha_init, wqmode=wqmode),      
                nn.BatchNorm2d(self.expansion*planes)
            )

    def forward(self, x):
        out = self.conv1(x)
        out = self.bn1(out)
        out = self.relu1(out)
        
        out = self.conv2(out)
        out = self.bn2(out)
        out += self.shortcut(x)
        out = self.relu2(out)

        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 ResNet(nn.Module):
    def __init__(self, block, num_blocks, num_classes=10, wbit=4, abit=4, alpha_init=10, wqmode='symm', 
                ADCprecision=5, cellBit=2, subArray=128, sensitive_lv=0):
        super(ResNet, self).__init__()
        self.in_planes = 64
        self.conv1 = QConv2d(3, 64, kernel_size=3, stride=1, padding=1, bias=False, wbit=wbit, abit=32, wqmode=wqmode)
        self.bn1 = nn.BatchNorm2d(64) 
        self.relu0 = nn.ReLU(inplace=True)

        self.layer1 = self._make_layer(block, 64, num_blocks[0], stride=1, wbit=wbit, abit=abit, alpha_init=alpha_init, wqmode=wqmode,
                                    subArray=subArray, cellBit=cellBit, ADCprecision=ADCprecision, sensitive_lv=sensitive_lv)
        self.layer2 = self._make_layer(block, 128, num_blocks[1], stride=2, wbit=wbit, abit=abit, alpha_init=alpha_init, wqmode=wqmode,
                                    subArray=subArray, cellBit=cellBit, ADCprecision=ADCprecision, sensitive_lv=sensitive_lv)
        self.layer3 = self._make_layer(block, 256, num_blocks[2], stride=2, wbit=wbit, abit=abit, alpha_init=alpha_init, wqmode=wqmode,
                                    subArray=subArray, cellBit=cellBit, ADCprecision=ADCprecision, sensitive_lv=sensitive_lv)
        self.layer4 = self._make_layer(block, 512, num_blocks[3], stride=2, wbit=wbit, abit=abit, alpha_init=alpha_init, wqmode=wqmode,
                                    subArray=subArray, cellBit=cellBit, ADCprecision=ADCprecision, sensitive_lv=sensitive_lv)
        
        self.linear = QLinear(512*block.expansion, num_classes, wbit=wbit, abit=abit)

    def _make_layer(self, block, planes, num_blocks, stride, wbit=4, abit=4, alpha_init=10, wqmode='symm',
                    ADCprecision=5, cellBit=2, subArray=128, sensitive_lv=0):
        strides = [stride] + [1]*(num_blocks-1)
        layers = []
        for stride in strides:
            layers.append(block(self.in_planes, planes, stride, wbit=wbit, abit=abit, alpha_init=alpha_init, wqmode=wqmode,
                        subArray=subArray, cellBit=cellBit, ADCprecision=ADCprecision, sensitive_lv=sensitive_lv))
            self.in_planes = planes * block.expansion
        return nn.Sequential(*layers)

    def forward(self, x):
        out = self.conv1(x)

        out = self.relu0(self.bn1(out))
        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)
        out = self.linear(out)

        return out

class resnet18_quant_eval:
    base = ResNet
    args = list()
    kwargs = {'block': BasicBlock, 'num_blocks': [2, 2, 2, 2]}

