# -*- coding: utf-8 -*-
'''
update: 2021/5/18    lym
'''
import torch
import torch.nn.functional as F
import torch.nn as nn
import math
import torch.utils.model_zoo as model_zoo
from torchsummary import summary

__all__ = ['ResNet', 'resnet18', 'resnet34', 'resnet50', 'resnet101',
           'resnet152']

model_urls = {
    'resnet18': 'https://download.pytorch.org/models/resnet18-5c106cde.pth',
    'resnet34': 'https://download.pytorch.org/models/resnet34-333f7ec4.pth',
    'resnet50': 'https://download.pytorch.org/models/resnet50-19c8e357.pth',
    'resnet101': 'https://download.pytorch.org/models/resnet101-5d3b4d8f.pth',
    'resnet152': 'https://download.pytorch.org/models/resnet152-b121ed1d.pth',
}

#Mish - "Mish: A Self Regularized Non-Monotonic Neural Activation Function"
#https://arxiv.org/abs/1908.08681v1
#implemented for PyTorch / FastAI by lessw2020 
#github: https://github.com/lessw2020/mish

class Mish(nn.Module):
    def __init__(self):
        super().__init__()

    def forward(self, x):
        #inlining this saves 1 second per epoch (V100 GPU) vs having a temp x and then returning x(!)
        return x *( torch.tanh(F.softplus(x)))

# or: ELU+init (a=0.54; gain=1.55)
act_fn = Mish()#nn.ReLU(inplace=True)

class Attention(nn.Module):
    def __init__(self, feature_dim, step_dim, bias=True, **kwargs):
        super(Attention, self).__init__(**kwargs)
        
        self.supports_masking = True

        self.bias = bias
        self.feature_dim = feature_dim
        self.step_dim = step_dim
        self.features_dim = 0
        
        weight = torch.zeros(feature_dim, 1)
        nn.init.kaiming_uniform_(weight)
        self.weight = nn.Parameter(weight)
        
        if bias:
            self.b = nn.Parameter(torch.zeros(step_dim))
        
    def forward(self, x, mask=None):
        feature_dim = self.feature_dim 
        step_dim = self.step_dim

        eij = torch.mm(
            x.contiguous().view(-1, feature_dim), 
            self.weight
        ).view(-1, step_dim)
        
        if self.bias:
            eij = eij + self.b
            
        eij = torch.tanh(eij)
        a = torch.exp(eij)
        
        if mask is not None:
            a = a * mask

        a = a / (torch.sum(a, 1, keepdim=True) + 1e-10)

        weighted_input = x * torch.unsqueeze(a, -1)
        return torch.sum(weighted_input, 1)

def conv3x3(in_planes, out_planes, stride=1):
    """3x3 convolution with padding"""
    return nn.Conv1d(in_planes, out_planes, kernel_size=7, stride=stride,
                     padding=3, bias=False)


class BasicBlock(nn.Module):
    expansion = 1

    def __init__(self, inplanes, planes, stride=1, downsample=None):
        super(BasicBlock, self).__init__()
        self.conv1 = conv3x3(inplanes, planes, stride)
        self.bn1 = nn.BatchNorm1d(planes)
        self.relu = nn.ReLU(inplace=True)
        self.conv2 = conv3x3(planes, planes)
        self.bn2 = nn.BatchNorm1d(planes)
        self.downsample = downsample
        self.stride = stride
        self.dropout = nn.Dropout(.2)

    def forward(self, x):
        residual = x

        out = self.conv1(x)
        out = self.bn1(out)
        out = self.relu(out)
        out = self.dropout(out)
        out = self.conv2(out)
        out = self.bn2(out)

        if self.downsample is not None:
            residual = self.downsample(x)

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

        return out


class Bottleneck(nn.Module):
    expansion = 4

    def __init__(self, inplanes, planes, stride=1, downsample=None):
        super(Bottleneck, self).__init__()
        self.conv1 = nn.Conv1d(inplanes, planes, kernel_size=7, bias=False, padding=3)
        self.bn1 = nn.BatchNorm1d(planes)
        self.conv2 = nn.Conv1d(planes, planes, kernel_size=11, stride=stride,
                               padding=5, bias=False)
        self.bn2 = nn.BatchNorm1d(planes)
        self.conv3 = nn.Conv1d(planes, planes * 4, kernel_size=7, bias=False, padding=3)
        self.bn3 = nn.BatchNorm1d(planes * 4)
        self.relu = nn.ReLU(inplace=True)
        self.downsample = downsample
        self.stride = stride
        self.dropout = nn.Dropout(.2)

    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)
        out = self.relu(out)
        out = self.dropout(out)

        out = self.conv3(out)
        out = self.bn3(out)

        if self.downsample is not None:
            residual = self.downsample(x)

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

        return out


class ResNet(nn.Module):

    def __init__(self, in_channels, block, layers, num_classes=27):
        self.inplanes = 64
        super(ResNet, self).__init__()
        self.conv1 = nn.Conv1d(in_channels, 64, kernel_size=15, stride=2, padding=7, bias=False)  #12   6dao lian
        self.bn1 = nn.BatchNorm1d(64)
        self.relu = nn.ReLU(inplace=True)
        self.maxpool = nn.MaxPool1d(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)
        self.avgpool = nn.AdaptiveAvgPool1d(1)

        self.lstm = nn.LSTM(80, 512, bidirectional=True, batch_first=True)
        self.attention_layer = Attention(1024, 512)

        self.fc = nn.Linear(512 * block.expansion, num_classes)

        for m in self.modules():
            if isinstance(m, nn.Conv1d):
                n = m.kernel_size[0] * m.kernel_size[0] * m.out_channels
                m.weight.data.normal_(0, math.sqrt(2. / n))
            elif isinstance(m, nn.BatchNorm1d):
                m.weight.data.fill_(1)
                m.bias.data.zero_()

    def _make_layer(self, block, planes, blocks, stride=1):
        downsample = None
        if stride != 1 or self.inplanes != planes * block.expansion:
            downsample = nn.Sequential(
                nn.Conv1d(self.inplanes, planes * block.expansion,
                          kernel_size=1, stride=stride, bias=False),
                nn.BatchNorm1d(planes * block.expansion),
            )

        layers = []
        layers.append(block(self.inplanes, planes, stride, downsample))
        self.inplanes = planes * block.expansion
        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)
        # print(x.shape)
        # max_pooled = F.adaptive_max_pool1d(out, 1)
        # avg_pooled = F.adaptive_avg_pool1d(out, 1)
        # out = torch.cat([max_pooled, avg_pooled], dim=1)
        x = F.adaptive_max_pool1d(x, 1)#self.avgpool(x)
        # x,_ = self.lstm(x)
        # x = self.attention_layer(x)

        # print(x.shape)
        x = x.view(x.size(0), -1)
        # print(x.shape)
        x = self.fc(x)
        # print('x.shape:', x.shape)
        return x


def resnet18(pretrained=False, **kwargs):
    """Constructs a ResNet-18 model.

    Args:
        pretrained (bool): If True, returns a model pre-trained on ImageNet
    """
    model = ResNet(BasicBlock, [2, 2, 2, 2], **kwargs)
    # if pretrained:
    #     model.load_state_dict(model_zoo.load_url(model_urls['resnet18']))
    return model


def resnet34(pretrained=False, **kwargs):
    """Constructs a ResNet-34 model.

    Args:
        pretrained (bool): If True, returns a model pre-trained on ImageNet
    """
    model = ResNet(BasicBlock, [3, 4, 6, 3], **kwargs)
    # if pretrained:
    #     model.load_state_dict(model_zoo.load_url(model_urls['resnet34']))
    return model


# def resnet50(pretrained=False, **kwargs):
#     """Constructs a ResNet-50 model.
#
#     Args:
#         pretrained (bool): If True, returns a model pre-trained on ImageNet
#     """
#     model = ResNet(Bottleneck, [3, 4, 6, 3], **kwargs)
#     if pretrained:
#         print('----------------downloading----------------')
#         model.load_state_dict(model_zoo.load_url(model_urls['resnet50']))
#     return model

def resnet50(in_channels, **kwargs):
    """Constructs a ResNet-50 model.

    Args:
        pretrained (bool): If True, returns a model pre-trained on ImageNet
    """
    model = ResNet(in_channels,Bottleneck, [3, 4, 6, 3], **kwargs)
    # if pretrained:
    #     print('----------------downloading----------------')
    #     model.load_state_dict(model_zoo.load_url(model_urls['resnet50']))
    return model


def resnet101(pretrained=False, **kwargs):
    """Constructs a ResNet-101 model.

    Args:
        pretrained (bool): If True, returns a model pre-trained on ImageNet
    """
    model = ResNet(Bottleneck, [3, 4, 23, 3], **kwargs)
    # if pretrained:
    #     model.load_state_dict(model_zoo.load_url(model_urls['resnet101']))
    return model


def resnet152(pretrained=False, **kwargs):
    """Constructs a ResNet-152 model.

    Args:
        pretrained (bool): If True, returns a model pre-trained on ImageNet
    """
    model = ResNet(Bottleneck, [3, 8, 36, 3], **kwargs)
    # if pretrained:
    #     model.load_state_dict(model_zoo.load_url(model_urls['resnet152']))
    return model


if __name__ == '__main__':

    in_channels = 6
    x = torch.randn(2, in_channels, 72000)
    # x = torch.randn(1, 2560*6, 12)
    # net = resnet18()
    # net = resnet34()
    net = resnet50(in_channels)
    # net = resnet101()
    # net = resnet152()
    y = net(x)

    summary(net, (in_channels, 72000))

    # print(net)
    print(x.shape)
    print(y.shape)
