import torch
import torch.nn as nn
import torch.nn.functional as F

class ConvNet(nn.Module):
    ''' 网络结构和cvpr2020的 M-ADA 方法一致 '''
    def __init__(self, imdim=3):
        super(ConvNet, self).__init__()

        self.conv1 = nn.Conv2d(imdim, 64, kernel_size=5, stride=1, padding=0)
        self.mp = nn.MaxPool2d(2)
        self.relu1 = nn.ReLU(inplace=True)
        self.conv2 = nn.Conv2d(64, 128, kernel_size=5, stride=1, padding=0)
        self.relu2 = nn.ReLU(inplace=True)
        self.fc1 = nn.Linear(128*5*5, 1024)
        self.relu3 = nn.ReLU(inplace=True)
        self.fc2 = nn.Linear(1024, 1024)
        self.relu4 = nn.ReLU(inplace=True)
        
        self.cls_head_src = nn.Linear(1024, 10)
        # self.cls_head_tgt = nn.Linear(1024, 10)
        # self.pro_head = nn.Linear(1024, 128)

    def forward(self, x, mode='fc'):
        if mode == 'c':
            out4 = self.relu4(x)
            p = self.cls_head_src(out4)
            return p
        elif mode == 'fc':
            in_size = x.size(0)
            out1 = self.mp(self.relu1(self.conv1(x)))
            out2 = self.mp(self.relu2(self.conv2(out1)))
            out2 = out2.view(in_size, -1)
            out3 = self.relu3(self.fc1(out2))
            out4_worelu = self.fc2(out3)
            out4 = self.relu4(out4_worelu)
            p = self.cls_head_src(out4)
            return p, out4_worelu

        # if mode == 'test':
        #     p = self.cls_head_src(out4)
        #     return p
        # elif mode == 'train':
        #     p = self.cls_head_src(out4)
        #     # z = self.pro_head(out4)
        #     # z = F.normalize(z)
        #     return p,out4_worelu
        # elif mode == 'p_f':
        #     p = self.cls_head_src(out4)
        #     return p, out4
        #elif mode == 'target':
        #    p = self.cls_head_tgt(out4)
        #    z = self.pro_head(out4)
        #    z = F.normalize(z)
        #    return p,z
    
class ConvNetVis(nn.Module):
    ''' 方便可视化，特征提取器输出2-d特征
    '''
    def __init__(self, imdim=3):
        super(ConvNetVis, self).__init__()

        self.conv1 = nn.Conv2d(imdim, 64, kernel_size=5, stride=1, padding=0)
        self.mp = nn.MaxPool2d(2)
        self.relu1 = nn.ReLU(inplace=True)
        self.conv2 = nn.Conv2d(64, 128, kernel_size=5, stride=1, padding=0)
        self.relu2 = nn.ReLU(inplace=True)
        self.fc1 = nn.Linear(128*5*5, 1024)
        self.relu3 = nn.ReLU(inplace=True)
        self.fc2 = nn.Linear(1024, 2)
        self.relu4 = nn.ReLU(inplace=True)
        
        self.cls_head_src = nn.Linear(2, 10)
        self.cls_head_tgt = nn.Linear(2, 10)
        self.pro_head = nn.Linear(2, 128)

    def forward(self, x, mode='test'):

        in_size = x.size(0)
        out1 = self.mp(self.relu1(self.conv1(x)))
        out2 = self.mp(self.relu2(self.conv2(out1)))
        out2 = out2.view(in_size, -1)
        out3 = self.relu3(self.fc1(out2))
        out4 = self.relu4(self.fc2(out3))
        
        if mode == 'test':
            p = self.cls_head_src(out4)
            return p
        elif mode == 'train':
            p = self.cls_head_src(out4)
            z = self.pro_head(out4)
            z = F.normalize(z)
            return p,z
        elif mode == 'p_f':
            p = self.cls_head_src(out4)
            return p, out4
        #elif mode == 'target':
        #    p = self.cls_head_tgt(out4)
        #    z = self.pro_head(out4)
        #    z = F.normalize(z)
        #    return p,z