import torch
import torch.nn as nn
import torch.nn.functional as F
# https://github.com/samcw/ResNet18-Pytorch
class ResBlock(nn.Module):
    def __init__(self, inchannel, outchannel, stride=1):
        super(ResBlock, self).__init__()
        self.left = nn.Sequential(
            nn.Conv1d(inchannel, outchannel, kernel_size=3, stride=stride, padding=1, bias=False),
            nn.BatchNorm1d(outchannel),
            nn.ReLU(inplace=True),
            nn.Conv1d(outchannel, outchannel, kernel_size=3, stride=1, padding=1, bias=False),
            nn.BatchNorm1d(outchannel)
        )
        self.shortcut = nn.Sequential()
        if stride != 1 or inchannel != outchannel:
            self.shortcut = nn.Sequential(
                nn.Conv1d(inchannel, outchannel, kernel_size=1, stride=stride, bias=False),
                nn.BatchNorm1d(outchannel)
            )
            
    def forward(self, x):
        out = self.left(x)
        out = out + self.shortcut(x)
        out = F.relu(out)
        
        return out
    
class ResNet18(nn.Module):
    def __init__(self, args):
        super(ResNet18, self).__init__()
        self.inchannel = 64
        self.conv1 = nn.Sequential(
            nn.Conv1d(1, 64, kernel_size=3, stride=1, padding=1, bias=False),
            nn.BatchNorm1d(64),
            nn.ReLU()
        )
        self.layer1 = self.make_layer(ResBlock, 64, 2, stride=1)
        self.layer2 = self.make_layer(ResBlock, 128, 2, stride=2)
        self.layer3 = self.make_layer(ResBlock, 256, 2, stride=2)        
        self.layer4 = self.make_layer(ResBlock, 512, 2, stride=2)        
        self.pred_layer = nn.Sequential(
        nn.Linear(512, 512),
        nn.SiLU(),
        nn.Dropout(p=0.3),
        nn.Linear(512, 1),
    )
        if getattr(args, 'mean_label', False):
          self.pred_layer[3].bias.data.fill_(args.mean_label)

    def make_layer(self, block, channels, num_blocks, stride):
        strides = [stride] + [1] * (num_blocks - 1)
        layers = []
        for stride in strides:
            layers.append(block(self.inchannel, channels, stride))
            self.inchannel = channels
        return nn.Sequential(*layers)
    
    def forward(self, x):
        x = x.unsqueeze(1)
        out = self.conv1(x)
        out = F.max_pool1d(out, 3, 2, 1)
        out = self.layer1(out)
        out = self.layer2(out)
        out = self.layer3(out)
        out = self.layer4(out)
        out = out.mean(-1)
        out = self.pred_layer(out)
        return out