#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Tue Jul  9 11:13:16 2019

@author: laura
"""

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


# ------------------------------------------------------------------------------------------
# ------------------------------------------------------------------------------------------

class CNN(nn.Module):
  def __init__(self, num_classes=2):
        super(CNN, self).__init__()
        self.layer1 = nn.Sequential(
            nn.Conv2d(1, 16, kernel_size=5, stride=1, padding=2),
            nn.Dropout2d(p=0.0),
            nn.BatchNorm2d(16),
            nn.ReLU())
        self.layer2 = nn.Sequential(
            nn.Conv2d(16, 32, kernel_size=5, stride=1, padding=2),
            nn.Dropout2d(p=0.0),
            nn.BatchNorm2d(32),
            nn.ReLU(),
            nn.MaxPool2d(kernel_size=2, stride=2))
        self.layer3 = nn.Sequential(
            nn.Conv2d(32, 32, kernel_size=5, stride=1, padding=2),
            nn.Dropout2d(p=0.0),
            nn.BatchNorm2d(32),
            nn.ReLU())
        self.layer4 = nn.Sequential(
            nn.Conv2d(32, 32, kernel_size=5, stride=1, padding=2),
            nn.Dropout2d(p=0.0),
            nn.BatchNorm2d(32),
            nn.ReLU())
        self.layer5 = nn.Sequential(
            nn.Conv2d(32, 32, kernel_size=5, stride=1, padding=2),
            nn.Dropout2d(p=0.0),
            nn.BatchNorm2d(32),
            nn.ReLU(),
            nn.MaxPool2d(kernel_size=2, stride =2))
        self.gap = nn.AdaptiveAvgPool2d((1,1))
        self.fc = nn.Sequential(
            nn.Linear(32, num_classes))

  def forward(self, x):
        out = self.layer1(x)
        out = self.layer2(out)
        out = self.layer3(out)
        out = self.layer4(out)
        out = self.layer5(out)
        out = self.gap(out)
        out = out.reshape(out.size(0), -1)
        out = self.fc(out)
        return out
    
# ------------------------------------------------------------------------------------------
# ------------------------------------------------------------------------------------------
        
class CNN1(nn.Module):
  def __init__(self, num_classes=2):
        super(CNN1, self).__init__()
        self.layer1 = nn.Sequential(
            nn.Conv2d(1, 16, kernel_size=5, stride=1, padding=2),
            nn.Dropout2d(p=0.2),
            nn.BatchNorm2d(16),
            nn.ReLU())
        self.layer2 = nn.Sequential(
            nn.Conv2d(16, 32, kernel_size=5, stride=1, padding=2),
            nn.Dropout2d(p=0.0),
            nn.BatchNorm2d(32),
            nn.ReLU(),
            nn.MaxPool2d(kernel_size=2, stride=2))
        self.layer3 = nn.Sequential(
            nn.Conv2d(32, 32, kernel_size=5, stride=1, padding=2),
            nn.Dropout2d(p=0.2),
            nn.BatchNorm2d(32),
            nn.ReLU(),
            nn.MaxPool2d(kernel_size=2, stride =2))
        self.layer4 = nn.Sequential(
            nn.Conv2d(32, 32, kernel_size=5, stride=1, padding=2),
            nn.Dropout2d(p=0.0),
            nn.BatchNorm2d(32),
            nn.ReLU())
        self.layer5 = nn.Sequential(
            nn.Conv2d(32, 32, kernel_size=5, stride=1, padding=2),
            nn.Dropout2d(p=0.2),
            nn.BatchNorm2d(32),
            nn.ReLU(),
            nn.MaxPool2d(kernel_size=2, stride =2))
        self.gap = nn.AdaptiveAvgPool2d((1,1))
        self.fc = nn.Sequential(
            nn.Linear(32, num_classes))

  def forward(self, x):
        out = self.layer1(x)
        out = self.layer2(out)
        out = self.layer3(out)
        out = self.layer4(out)
        out = self.layer5(out)
        out = self.gap(out)
        out = out.reshape(out.size(0), -1)
        out = self.fc(out)
        return out
    
# ------------------------------------------------------------------------------------------
# ------------------------------------------------------------------------------------------

class CNN2(nn.Module):
  def __init__(self, num_classes=2):
        super(CNN2, self).__init__()
        self.layer1 = nn.Sequential(
            nn.Conv2d(1, 16, kernel_size=5, stride=1, padding=2),
            nn.Dropout2d(p=0.2),
            nn.BatchNorm2d(16),
            nn.ReLU())
        self.layer2 = nn.Sequential(
            nn.Conv2d(16, 32, kernel_size=5, stride=1, padding=2),
            nn.Dropout2d(p=0.2),
            nn.BatchNorm2d(32),
            nn.ReLU(),
            nn.MaxPool2d(kernel_size=2, stride=2))
        self.layer3 = nn.Sequential(
            nn.Conv2d(32, 32, kernel_size=5, stride=1, padding=2),
            nn.Dropout2d(p=0.2),
            nn.BatchNorm2d(32),
            nn.ReLU(),
            nn.MaxPool2d(kernel_size=2, stride =2))
        self.layer4 = nn.Sequential(
            nn.Conv2d(32, 32, kernel_size=5, stride=1, padding=2),
            nn.Dropout2d(p=0.2),
            nn.BatchNorm2d(32),
            nn.ReLU())
        self.layer5 = nn.Sequential(
            nn.Conv2d(32, 32, kernel_size=5, stride=1, padding=2),
            nn.Dropout2d(p=0.2),
            nn.BatchNorm2d(32),
            nn.ReLU(),
            nn.MaxPool2d(kernel_size=2, stride =2))
        self.gap = nn.AdaptiveAvgPool2d((1,1))
        self.fc = nn.Sequential(
            nn.Linear(32, num_classes))

  def forward(self, x):
        out = self.layer1(x)
        out = self.layer2(out)
        out = self.layer3(out)
        out = self.layer4(out)
        out = self.layer5(out)
        out = self.gap(out)
        out = out.reshape(out.size(0), -1)
        out = self.fc(out)
        return out        
    
# ------------------------------------------------------------------------------------------
# ------------------------------------------------------------------------------------------
 
def conv(in_channels, out_channels):
    return nn.Sequential(
            nn.Conv2d(in_channels, out_channels, kernel_size =3, stride = 1, padding = 1, bias = False),
            nn.BatchNorm2d(out_channels),
            nn.ReLU()) 

      
class ResBlock(nn.Module):
    def __init__(self,out_channels):
        super().__init__()
        self.conv1 = conv(out_channels, out_channels)
        self.conv2 = conv(out_channels, out_channels)
    def forward(self,x): return x + self.conv2(self.conv1(x))
    

def conv_and_res(in_channels, out_channels): return nn.Sequential(conv(in_channels, out_channels), ResBlock(out_channels))


class my_ResNet(nn.Module):
    def __init__ (self,num_classes =2):
        super(ResNet, self).__init__()
        self.layer1 = conv_and_res(1,8)
        self.layer2 = conv_and_res(8,16)
        self.layer3 = conv_and_res(16,32)
        self.layer4 = conv_and_res(32,16)
        self.layer5 = conv(16,16)
        self.gap = nn.AdaptiveAvgPool2d((1,1))
        self.fc = nn.Linear(16, num_classes)
    def forward(self, x):
        out = self.layer1(x)
        out = self.layer2(out)
        out = self.layer3(out)
        out = self.layer4(out)
        out = self.layer5(out)
        out = self.gap(out)
        out = out.reshape(out.size(0), -1)
        out = self.fc(out)
        return out   
    
    
# ------------------------------------------------------------------------------------------
# ------------------------------------------------------------------------------------------


class BasicBlock(nn.Module):
    expansion = 1

    def __init__(self, in_planes, planes, stride=1):
        super(BasicBlock, self).__init__()
        self.conv1 = nn.Conv2d(in_planes, planes, kernel_size=3, stride=stride, padding=1, bias=False)
        self.bn1 = nn.BatchNorm2d(planes)
        self.conv2 = nn.Conv2d(planes, planes, kernel_size=3, stride=1, padding=1, bias=False)
        self.bn2 = nn.BatchNorm2d(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 = self.bn2(self.conv2(out))
        out += self.shortcut(x)
        out = F.relu(out)
        return out



class ResNet(nn.Module):
    def __init__(self, block, num_blocks, num_classes=2):
        super(ResNet, self).__init__()
        self.in_planes = 64

        self.conv1 = nn.Conv2d(1, 64, kernel_size=3, stride=1, padding=1, bias=False)
        self.bn1 = nn.BatchNorm2d(64)
        self.maxpooling = nn.MaxPool2d(kernel_size=2, stride =2)
        self.layer1 = self._make_layer(block, 64, num_blocks[0], stride=1)
        self.layer2 = self._make_layer(block, 128, num_blocks[1], stride=2)
        self.layer3 = self._make_layer(block, 256, num_blocks[2], stride=2)
        self.layer4 = self._make_layer(block, 512, num_blocks[3], stride=2)
        self.gap = nn.AdaptiveAvgPool2d((1,1))
        self.fc = nn.Linear(512*block.expansion, num_classes)

    def _make_layer(self, block, planes, num_blocks, stride):
        strides = [stride] + [1]*(num_blocks-1)
        layers = []
        for stride in strides:
            layers.append(block(self.in_planes, planes, stride))
            self.in_planes = planes * block.expansion
        return nn.Sequential(*layers)

    def forward(self, x):
        out = F.relu(self.bn1(self.conv1(x)))
        out = self.layer1(out)
        out = self.layer2(out)
        out = self.layer3(out)
        out = self.layer4(out)
        out = self.gap(out)
        out = out.reshape(out.size(0), -1)
        out = self.fc(out)
        return out


def ResNet18():
    return ResNet(BasicBlock, [2,2,2,2])

def ResNet34():
    return ResNet(BasicBlock, [3,4,6,3])
