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resnet_bayes.py
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resnet_bayes.py
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#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Sat Jun 6 22:46:26 2020
@author: pc-3
"""
# -*- coding: utf-8 -*-
import torch
import torch.nn as nn
import torch.nn.functional as F
import numpy as np
class Linear(nn.Module):
def __init__(self, in_features, out_features):
super(Linear, self).__init__()
self.w = nn.Parameter(torch.randn(in_features, out_features))
def forward(self, x):
x = x.mm(self.w)
return x
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 Bottleneck(nn.Module):
expansion = 4
def __init__(self, in_planes, planes, stride=1):
super(Bottleneck, self).__init__()
self.conv1 = nn.Conv2d(in_planes, planes, kernel_size=1, bias=False)
self.bn1 = nn.BatchNorm2d(planes)
self.conv2 = nn.Conv2d(planes, planes, kernel_size=3, stride=stride, padding=1, bias=False)
self.bn2 = nn.BatchNorm2d(planes)
self.conv3 = nn.Conv2d(planes, self.expansion*planes, kernel_size=1, bias=False)
self.bn3 = nn.BatchNorm2d(self.expansion*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 = F.relu(self.bn2(self.conv2(out)))
out = self.bn3(self.conv3(out))
out += self.shortcut(x)
out = F.relu(out)
return out
class ResNet(nn.Module):
def __init__(self, block, num_blocks, num_classes):
super(ResNet, self).__init__()
self.in_planes = 64
self.conv1 = nn.Conv2d(3, 64, kernel_size=3, stride=1, padding=0, bias=False)
self.bn1 = nn.BatchNorm2d(64)
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.bayes_linear = nn.Linear(512*block.expansion, num_classes)
self.avgpool = nn.AdaptiveAvgPool2d((1, 1))
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.avgpool(out)
out_1 = out.view(out.size(0), -1)
out_2 = self.bayes_linear(out_1)
out_2 = out_2.reshape(out_2.size(0),10,10)
out_2 = F.softmax(out_2, dim=2)
return out_2.float()
class ResNet_F(nn.Module):
def __init__(self, block, num_blocks, num_classes):
super(ResNet_F, self).__init__()
self.in_planes = 64
self.conv1 = nn.Conv2d(1, 64, kernel_size=3, stride=1, padding=0, bias=False)
self.bn1 = nn.BatchNorm2d(64)
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.bayes_linear = nn.Linear(512 * block.expansion, num_classes)
self.avgpool = nn.AdaptiveAvgPool2d((1, 1))
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.avgpool(out)
out_1 = out.view(out.size(0), -1)
out_2 = self.bayes_linear(out_1)
out_2 = out_2.reshape(out_2.size(0),10,10)
out_2 = F.softmax(out_2, dim=2)
return out_2.float()
def ResNet18_F(num_classes):
return ResNet_F(BasicBlock, [2,2,2,2], num_classes)
def ResNet18(num_classes):
return ResNet(BasicBlock, [2,2,2,2], num_classes)
def ResNet34(num_classes):
return ResNet(BasicBlock, [3,4,6,3], num_classes)
def ResNet50(num_classes):
return ResNet(Bottleneck, [3,4,6,3], num_classes)
def ResNet101(num_classes):
return ResNet(Bottleneck, [3,4,23,3], num_classes)
def ResNet152(num_classes):
return ResNet(Bottleneck, [3,8,36,3], num_classes)