<reponame>caidao22/anode
import torch
import torch.nn as nn
import torch.nn.functional as F
import numpy as np
import math

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 BasicBlock2(nn.Module):
    expansion = 1

    def __init__(self, dim):
        super(BasicBlock2, self).__init__()
        in_planes = dim
        planes = dim
        stride = 1
        self.nfe = 0
        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()

    def forward(self,t, x):
        self.nfe += 1
        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 = 10, ODEBlock_ = None):
        super(ResNet, self).__init__()
        self.in_planes = 64
        self.ODEBlock = ODEBlock_

        self.conv1 = nn.Conv2d(3, 64, kernel_size=3, stride=1, padding=1, bias=False)
        self.bn1 = nn.BatchNorm2d(64)
        self.layer1_1 = self._make_layer(64, 1, stride=1)
        self.layer1_2 = self._make_layer2(64, num_blocks[0]-1, stride=1)

        self.layer2_1 = self._make_layer(128, 1, stride=2)
        self.layer2_2 = self._make_layer2(128, num_blocks[1]-1, stride=1)

        self.layer3_1 = self._make_layer(256, 1, stride=2)
        self.layer3_2 = self._make_layer2(256, num_blocks[2]-1, stride=1)

        self.layer4_1 = self._make_layer(512, 1, stride=2)
        self.layer4_2 = self._make_layer2(512, num_blocks[3]-1, stride=1)
        self.linear = nn.Linear(512 * block.expansion, num_classes)

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

    def _make_layer2(self, planes, num_blocks, stride):
        strides = [stride] + [1]*(num_blocks-1)
        layers = []
        for stride in strides:
            layers.append(self.ODEBlock(BasicBlock2(self.in_planes)))
        return nn.Sequential(*layers)

    def forward(self, x):
        out = F.relu(self.bn1(self.conv1(x)))
        out = self.layer1_1(out)
        out = self.layer1_2(out)
        out = self.layer2_1(out)
        out = self.layer2_2(out)
        out = self.layer3_1(out)
        out = self.layer3_2(out)
        out = self.layer4_1(out)
        out = self.layer4_2(out)
        out = F.avg_pool2d(out, 4)
        out = out.view(out.size(0), -1)
        out = self.linear(out)
        return out

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

def lr_schedule(lr, epoch):
    optim_factor = 0
    if epoch > 250:
        optim_factor = 2
    elif epoch > 150:
        optim_factor = 1

    return lr / math.pow(10, (optim_factor))


import torch
import torch.nn as nn
import torch.nn.functional as F
import numpy as np
import math

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 BasicBlock2(nn.Module):
    expansion = 1

    def __init__(self, dim):
        super(BasicBlock2, self).__init__()
        in_planes = dim
        planes = dim
        stride = 1
        self.nfe = 0
        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()

    def forward(self,t, x):
        self.nfe += 1
        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 = 10, ODEBlock_ = None):
        super(ResNet, self).__init__()
        self.in_planes = 64
        self.ODEBlock = ODEBlock_

        self.conv1 = nn.Conv2d(3, 64, kernel_size=3, stride=1, padding=1, bias=False)
        self.bn1 = nn.BatchNorm2d(64)
        self.layer1_1 = self._make_layer(64, 1, stride=1)
        self.layer1_2 = self._make_layer2(64, num_blocks[0]-1, stride=1)

        self.layer2_1 = self._make_layer(128, 1, stride=2)
        self.layer2_2 = self._make_layer2(128, num_blocks[1]-1, stride=1)

        self.layer3_1 = self._make_layer(256, 1, stride=2)
        self.layer3_2 = self._make_layer2(256, num_blocks[2]-1, stride=1)

        self.layer4_1 = self._make_layer(512, 1, stride=2)
        self.layer4_2 = self._make_layer2(512, num_blocks[3]-1, stride=1)
        self.linear = nn.Linear(512 * block.expansion, num_classes)

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

    def _make_layer2(self, planes, num_blocks, stride):
        strides = [stride] + [1]*(num_blocks-1)
        layers = []
        for stride in strides:
            layers.append(self.ODEBlock(BasicBlock2(self.in_planes)))
        return nn.Sequential(*layers)

    def forward(self, x):
        out = F.relu(self.bn1(self.conv1(x)))
        out = self.layer1_1(out)
        out = self.layer1_2(out)
        out = self.layer2_1(out)
        out = self.layer2_2(out)
        out = self.layer3_1(out)
        out = self.layer3_2(out)
        out = self.layer4_1(out)
        out = self.layer4_2(out)
        out = F.avg_pool2d(out, 4)
        out = out.view(out.size(0), -1)
        out = self.linear(out)
        return out

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

def lr_schedule(lr, epoch):
    optim_factor = 0
    if epoch > 250:
        optim_factor = 2
    elif epoch > 150:
        optim_factor = 1

    return lr / math.pow(10, (optim_factor))
#out += self.shortcut(x)