import math
import torch.nn as nn
import pdb  # 파이썬 디버거


# Conv2D (3,3)
def conv3x3(in_planes, out_planes, stride=1):
    return nn.Conv2d(in_planes, out_planes, kernel_size=3, stride=stride,
                     padding=1, bias=False)


# Conv2D (1,1) + BatchNorm2D
def downsample_basic_block( inplanes, outplanes, stride ):
    return  nn.Sequential(
                nn.Conv2d(inplanes, outplanes, kernel_size=1, stride=stride, bias=False),
                nn.BatchNorm2d(outplanes),
            )

# AvgPool2D + Conv2D (1,1) + BatchNorm2D
def downsample_basic_block_v2( inplanes, outplanes, stride ):
    return  nn.Sequential(
                nn.AvgPool2d(kernel_size=stride, stride=stride, ceil_mode=True, count_include_pad=False),
                nn.Conv2d(inplanes, outplanes, kernel_size=1, stride=1, bias=False),
                nn.BatchNorm2d(outplanes),
            )



# 기본 블럭 2D
class BasicBlock(nn.Module):
    expansion = 1

    def __init__(self, inplanes, planes, stride=1, downsample=None, relu_type = 'relu' ):
        super(BasicBlock, self).__init__()

        # relu_type 변수 값이 'relu','prelu' 인지 확인, 아니면 AssertionError 메시지를 띄움
        assert relu_type in ['relu','prelu']  # 원하는 조건의 변수값을 보증하기 위해 사용

        self.conv1 = conv3x3(inplanes, planes, stride)  # Conv2D (3,3)
        self.bn1 = nn.BatchNorm2d(planes)  # BatchNorm2D

        # type of ReLU is an input option
        if relu_type == 'relu':  # ReLU
            self.relu1 = nn.ReLU(inplace=True)
            self.relu2 = nn.ReLU(inplace=True)
        elif relu_type == 'prelu':  # PReLU
            self.relu1 = nn.PReLU(num_parameters=planes)
            self.relu2 = nn.PReLU(num_parameters=planes)
        else:
            raise Exception('relu type not implemented')  # 에러 발생시키기
        # --------

        self.conv2 = conv3x3(planes, planes)  # Conv2D (3,3)
        self.bn2 = nn.BatchNorm2d(planes)  # BatchNorm2D
        
        self.downsample = downsample
        self.stride = stride

    # 모델이 학습데이터를 입력받아서 forward propagation 진행
    def forward(self, x):
        residual = x
        out = self.conv1(x)
        out = self.bn1(out)
        out = self.relu1(out)
        out = self.conv2(out)
        out = self.bn2(out)
        if self.downsample is not None:
            residual = self.downsample(x)

        out += residual
        out = self.relu2(out)

        return out


# 레즈넷 2D
class ResNet(nn.Module):

    def __init__(self, block, layers, num_classes=1000, relu_type = 'relu', gamma_zero = False, avg_pool_downsample = False):
        self.inplanes = 64
        self.relu_type = relu_type
        self.gamma_zero = gamma_zero
        self.downsample_block = downsample_basic_block_v2 if avg_pool_downsample else downsample_basic_block  # AvgPool2D 적용하면 v2 아니면 v1

        super(ResNet, self).__init__()
        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)
        self.avgpool = nn.AdaptiveAvgPool2d(1)

        # default init
        for m in self.modules():
            if isinstance(m, nn.Conv2d):  # Conv2D 인스턴스인가
                n = m.kernel_size[0] * m.kernel_size[1] * m.out_channels
                m.weight.data.normal_(0, math.sqrt(2. / n))
            elif isinstance(m, nn.BatchNorm2d):  # BatchNrom2D 인스턴스인가
                m.weight.data.fill_(1)
                m.bias.data.zero_()
                #nn.init.ones_(m.weight)
                #nn.init.zeros_(m.bias)

        if self.gamma_zero:
            for m in self.modules():
                if isinstance(m, BasicBlock ):  # 기본 블럭 인스턴스인가
                    m.bn2.weight.data.zero_()

    # 레이어 생성
    def _make_layer(self, block, planes, blocks, stride=1):


        downsample = None
        if stride != 1 or self.inplanes != planes * block.expansion:
            downsample = self.downsample_block( inplanes = self.inplanes, 
                                                 outplanes = planes * block.expansion, 
                                                 stride = stride )  # (AvgPool2D) + Conv2D (1,1) + BatchNorm2D

        layers = []
        layers.append(block(self.inplanes, planes, stride, downsample, relu_type = self.relu_type))
        self.inplanes = planes * block.expansion
        for i in range(1, blocks):
            layers.append(block(self.inplanes, planes, relu_type = self.relu_type))

        return nn.Sequential(*layers)  # 설정한 레이어 반환

    # 모델이 학습데이터를 입력받아서 forward propagation 진행
    def forward(self, 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)
        return x