实例介绍
【实例简介】
【实例截图】
【实例截图】
【核心代码】
from __future__ import print_function
import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
# PyTorch implementation of Dilated Residual Network
def conv3x3(planes):
''' 3x3 convolution '''
return nn.Conv2d(planes, planes, kernel_size=(3,3), padding=(1,1), bias=False)
class ResBasicBlock(nn.Module):
''' basic Conv2D Block for ResNet '''
def __init__(self, planes):
super(ResBasicBlock, self).__init__()
self.bn1 = nn.BatchNorm2d(planes)
self.re1 = nn.ReLU(inplace=True)
self.cnn1 = conv3x3(planes)
self.bn2 = nn.BatchNorm2d(planes)
self.re2 = nn.ReLU(inplace=True)
self.cnn2 = conv3x3(planes)
def forward(self, x):
residual = x
x = self.cnn2(self.re2(self.bn2(self.cnn1(self.re1(self.bn1(x))))))
x = residual
return x
class SpoofSmallResNet256_400(nn.Module):
''' small ResNet for 256 by 400 feature map (same NN as SpoofSmallResNet257_400) '''
def __init__(self, num_classes, binary=False, resnet_blocks=1, input_size=(1,256,400)):
super(SpoofSmallResNet256_400, self).__init__()
self.binary = binary
self.expansion = nn.Conv2d(1, 8, kernel_size=(3,3), padding=(1,1))
## block 1
layers = []
for i in range(resnet_blocks):
layers.append(ResBasicBlock(8))
self.block1 = nn.Sequential(*layers)
self.mp1 = nn.MaxPool2d(kernel_size=(2,2))
self.cnn1 = nn.Conv2d(8, 16, kernel_size=(3,3), dilation=(2,2))
## block 2
layers = []
for i in range(resnet_blocks):
layers.append(ResBasicBlock(16))
self.block2 = nn.Sequential(*layers)
self.mp2 = nn.MaxPool2d(kernel_size=(2,2))
self.cnn2 = nn.Conv2d(16, 32, kernel_size=(3,3), dilation=(4,4))
## block 3
layers = []
for i in range(resnet_blocks):
layers.append(ResBasicBlock(32))
self.block3 = nn.Sequential(*layers)
self.mp3 = nn.MaxPool2d(kernel_size=(2,2))
self.cnn3 = nn.Conv2d(32, 64, kernel_size=(3,3), dilation=(4,4))
## block 4
layers = []
for i in range(resnet_blocks):
layers.append(ResBasicBlock(64))
self.block4 = nn.Sequential(*layers)
self.mp4 = nn.MaxPool2d(kernel_size=(1,2))
self.cnn4 = nn.Conv2d(64, 64, kernel_size=(3,3), dilation=(8,8))
self.flat_feats = 64*3*2
self.fc = nn.Linear(self.flat_feats, 100)
self.bn = nn.BatchNorm1d(100)
self.re = nn.ReLU(inplace=True)
self.fc_out = nn.Linear(100, num_classes)
## Weights initialization
for m in self.modules():
if isinstance(m, nn.Conv2d or nn.Linear):
nn.init.kaiming_normal_(m.weight, mode='fan_out', nonlinearity='relu')
elif isinstance(m, nn.BatchNorm2d or nn.BatchNorm1d):
nn.init.constant_(m.weight, 1)
nn.init.constant_(m.bias, 0)
def forward(self, x):
x = self.expansion(x)
## block 1
x = self.cnn1(self.mp1(self.block1(x)))
#print(x.size())
## block 2
x = self.cnn2(self.mp2(self.block2(x)))
#print(x.size())
## block 3
x = self.cnn3(self.mp3(self.block3(x)))
#print(x.size())
## block 4
x = self.cnn4(self.mp4(self.block4(x)))
#print(x.size())
## FC
x = self.fc_out(self.re(self.bn(self.fc(x.view(-1, self.flat_feats)))))
#print(x.size())
if self.binary: return x
else: return F.log_softmax(x, dim=-1) # take log-softmax over C classes
class SpoofSmallResNet257_400(nn.Module):
''' small ResNet (less GPU memory) for 257 by 400 feature map '''
def __init__(self, num_classes, resnet_blocks=1, focal_loss=False, input_size=(1,257,400)):
super(SpoofSmallResNet257_400, self).__init__()
self.focal_loss = focal_loss
self.expansion = nn.Conv2d(1, 8, kernel_size=(3,3), padding=(1,1))
## block 1
layers = []
for i in range(resnet_blocks):
layers.append(ResBasicBlock(8))
self.block1 = nn.Sequential(*layers)
self.mp1 = nn.MaxPool2d(kernel_size=(2,2))
self.cnn1 = nn.Conv2d(8, 16, kernel_size=(3,3), dilation=(2,2))
## block 2
layers = []
for i in range(resnet_blocks):
layers.append(ResBasicBlock(16))
self.block2 = nn.Sequential(*layers)
self.mp2 = nn.MaxPool2d(kernel_size=(2,2))
self.cnn2 = nn.Conv2d(16, 32, kernel_size=(3,3), dilation=(4,4))
## block 3
layers = []
for i in range(resnet_blocks):
layers.append(ResBasicBlock(32))
self.block3 = nn.Sequential(*layers)
self.mp3 = nn.MaxPool2d(kernel_size=(2,2))
self.cnn3 = nn.Conv2d(32, 64, kernel_size=(3,3), dilation=(4,4))
## block 4
layers = []
for i in range(resnet_blocks):
layers.append(ResBasicBlock(64))
self.block4 = nn.Sequential(*layers)
self.mp4 = nn.MaxPool2d(kernel_size=(1,2))
self.cnn4 = nn.Conv2d(64, 64, kernel_size=(3,3), dilation=(8,8))
self.flat_feats = 64*3*2
self.fc = nn.Linear(self.flat_feats, 100)
self.bn = nn.BatchNorm1d(100)
self.re = nn.ReLU(inplace=True)
self.fc_out = nn.Linear(100, num_classes)
## Weights initialization
for m in self.modules():
if isinstance(m, nn.Conv2d or nn.Linear):
nn.init.kaiming_normal_(m.weight, mode='fan_out', nonlinearity='relu')
elif isinstance(m, nn.BatchNorm2d or nn.BatchNorm1d):
nn.init.constant_(m.weight, 1)
nn.init.constant_(m.bias, 0)
def forward(self, x):
x = self.expansion(x)
## block 1
x = self.cnn1(self.mp1(self.block1(x)))
#print(x.size())
## block 2
x = self.cnn2(self.mp2(self.block2(x)))
#print(x.size())
## block 3
x = self.cnn3(self.mp3(self.block3(x)))
#print(x.size())
## block 4
x = self.cnn4(self.mp4(self.block4(x)))
#print(x.size())
## FC
x = self.fc_out(self.re(self.bn(self.fc(x.view(-1, self.flat_feats)))))
#print(x.size())
if self.focal_loss: return x
else: return F.log_softmax(x, dim=-1) # take log-softmax over C classes
class SpoofResNet30_400(nn.Module):
''' primative ResNet for 30 by 400 feature map '''
def __init__(self, num_classes, resnet_blocks=1, input_size=(1,30,400)):
super(SpoofResNet30_400, self).__init__()
self.expansion = nn.Conv2d(1, 16, kernel_size=(3,3), padding=(1,1))
## block 1
layers = []
for i in range(resnet_blocks):
layers.append(ResBasicBlock(16))
self.block1 = nn.Sequential(*layers)
self.mp1 = nn.MaxPool2d(kernel_size=(1,1))
self.cnn1 = nn.Conv2d(16, 32, kernel_size=(3,3), dilation=(1,2))
## block 2
layers = []
for i in range(resnet_blocks):
layers.append(ResBasicBlock(32))
self.block2 = nn.Sequential(*layers)
self.mp2 = nn.MaxPool2d(kernel_size=(1,1))
self.cnn2 = nn.Conv2d(32, 32, kernel_size=(3,3), dilation=(2,4))
## block 3
layers = []
for i in range(resnet_blocks):
layers.append(ResBasicBlock(32))
self.block3 = nn.Sequential(*layers)
self.mp3 = nn.MaxPool2d(kernel_size=(1,2))
self.cnn3 = nn.Conv2d(32, 32, kernel_size=(3,3), dilation=(2,4))
## block 4
layers = []
for i in range(resnet_blocks):
layers.append(ResBasicBlock(32))
self.block4 = nn.Sequential(*layers)
self.mp4 = nn.MaxPool2d(kernel_size=(1,2))
self.cnn4 = nn.Conv2d(32, 32, kernel_size=(3,3), dilation=(4,8))
## block 5
layers = []
for i in range(resnet_blocks):
layers.append(ResBasicBlock(32))
self.block5 = nn.Sequential(*layers)
self.mp5 = nn.MaxPool2d(kernel_size=(1,4))
self.cnn5 = nn.Conv2d(32, 32, kernel_size=(3,3), dilation=(4,8))
self.flat_feats = 32*4*3
self.fc = nn.Linear(self.flat_feats, 100)
self.bn = nn.BatchNorm1d(100)
self.re = nn.ReLU(inplace=True)
self.fc_out = nn.Linear(100, num_classes)
## Weights initialization
for m in self.modules():
if isinstance(m, nn.Conv2d or nn.Linear):
nn.init.kaiming_normal_(m.weight, mode='fan_out', nonlinearity='relu')
elif isinstance(m, nn.BatchNorm2d or nn.BatchNorm1d):
nn.init.constant_(m.weight, 1)
nn.init.constant_(m.bias, 0)
def forward(self, x):
x = self.expansion(x)
## block 1
x = self.cnn1(self.mp1(self.block1(x)))
#print(x.size())
## block 2
x = self.cnn2(self.mp2(self.block2(x)))
#print(x.size())
## block 3
x = self.cnn3(self.mp3(self.block3(x)))
#print(x.size())
## block 4
x = self.cnn4(self.mp4(self.block4(x)))
#print(x.size())
## block 5
x = self.cnn5(self.mp5(self.block5(x)))
#print(x.size())
## FC
x = self.fc_out(self.re(self.bn(self.fc(x.view(-1, self.flat_feats)))))
#print(x.size())
return F.log_softmax(x, dim=-1) # take log-softmax over C classes
def predict(self, x):
raise NotImplementedError()
class SpoofResNet257_500(nn.Module):
''' primiative ResNet for feature map 257 by 500 '''
def __init__(self, num_classes, resnet_blocks=1, input_size=(1,257,500)):
super(SpoofResNet257_500, self).__init__()
self.expansion = nn.Conv2d(1, 16, kernel_size=(3,3), padding=(1,1))
## block 1
layers = []
for i in range(resnet_blocks):
layers.append(ResBasicBlock(16))
self.block1 = nn.Sequential(*layers)
self.mp1 = nn.MaxPool2d(kernel_size=(1,1))
self.cnn1 = nn.Conv2d(16, 32, kernel_size=(3,3), dilation=(2,2))
## block 2
layers = []
for i in range(resnet_blocks):
layers.append(ResBasicBlock(32))
self.block2 = nn.Sequential(*layers)
self.mp2 = nn.MaxPool2d(kernel_size=(1,1))
self.cnn2 = nn.Conv2d(32, 32, kernel_size=(3,3), dilation=(4,4))
## block 3
layers = []
for i in range(resnet_blocks):
layers.append(ResBasicBlock(32))
self.block3 = nn.Sequential(*layers)
self.mp3 = nn.MaxPool2d(kernel_size=(2,2))
self.cnn3 = nn.Conv2d(32, 32, kernel_size=(3,3), dilation=(4,4))
## block 4
layers = []
for i in range(resnet_blocks):
layers.append(ResBasicBlock(32))
self.block4 = nn.Sequential(*layers)
self.mp4 = nn.MaxPool2d(kernel_size=(2,2))
self.cnn4 = nn.Conv2d(32, 32, kernel_size=(3,3), dilation=(8,8))
## block 5
layers = []
for i in range(resnet_blocks):
layers.append(ResBasicBlock(32))
self.block5 = nn.Sequential(*layers)
self.mp5 = nn.MaxPool2d(kernel_size=(2,4))
self.cnn5 = nn.Conv2d(32, 32, kernel_size=(3,3), dilation=(8,11)) # change dilation rate from (8,8) to (8,11)
self.flat_feats = 32*4*3
self.fc = nn.Linear(self.flat_feats, 100)
self.bn = nn.BatchNorm1d(100)
self.re = nn.ReLU(inplace=True)
self.fc_out = nn.Linear(100, num_classes)
## Weights initialization
for m in self.modules():
if isinstance(m, nn.Conv2d or nn.Linear):
nn.init.kaiming_normal_(m.weight, mode='fan_out', nonlinearity='relu')
elif isinstance(m, nn.BatchNorm2d or nn.BatchNorm1d):
nn.init.constant_(m.weight, 1)
nn.init.constant_(m.bias, 0)
def forward(self, x):
##print(x.size())
x = self.expansion(x)
## block 1
x = self.cnn1(self.mp1(self.block1(x)))
##print(x.size())
## block 2
x = self.cnn2(self.mp2(self.block2(x)))
##print(x.size())
## block 3
x = self.cnn3(self.mp3(self.block3(x)))
##print(x.size())
## block 4
x = self.cnn4(self.mp4(self.block4(x)))
##print(x.size())
## block 5
x = self.cnn5(self.mp5(self.block5(x)))
##print(x.size())
## FC
x = self.fc_out(self.re(self.bn(self.fc(x.view(-1, self.flat_feats)))))
##print(x.size())
return F.log_softmax(x, dim=-1) # take log-softmax over C classes
def predict(self, x):
raise NotImplementedError()
class SpoofResNet257_400(nn.Module):
''' primative ResNet for 257 by 400 feature map '''
def __init__(self, num_classes, resnet_blocks=1, input_size=(1,257,400)):
super(SpoofResNet257_400, self).__init__()
self.expansion = nn.Conv2d(1, 16, kernel_size=(3,3), padding=(1,1))
## block 1
layers = []
for i in range(resnet_blocks):
layers.append(ResBasicBlock(16))
self.block1 = nn.Sequential(*layers)
self.mp1 = nn.MaxPool2d(kernel_size=(1,1))
self.cnn1 = nn.Conv2d(16, 32, kernel_size=(3,3), dilation=(2,2))
## block 2
layers = []
for i in range(resnet_blocks):
layers.append(ResBasicBlock(32))
self.block2 = nn.Sequential(*layers)
self.mp2 = nn.MaxPool2d(kernel_size=(1,1))
self.cnn2 = nn.Conv2d(32, 32, kernel_size=(3,3), dilation=(4,4))
## block 3
layers = []
for i in range(resnet_blocks):
layers.append(ResBasicBlock(32))
self.block3 = nn.Sequential(*layers)
self.mp3 = nn.MaxPool2d(kernel_size=(2,2))
self.cnn3 = nn.Conv2d(32, 32, kernel_size=(3,3), dilation=(4,4))
## block 4
layers = []
for i in range(resnet_blocks):
layers.append(ResBasicBlock(32))
self.block4 = nn.Sequential(*layers)
self.mp4 = nn.MaxPool2d(kernel_size=(2,2))
self.cnn4 = nn.Conv2d(32, 32, kernel_size=(3,3), dilation=(8,8))
## block 5
layers = []
for i in range(resnet_blocks):
layers.append(ResBasicBlock(32))
self.block5 = nn.Sequential(*layers)
self.mp5 = nn.MaxPool2d(kernel_size=(2,4))
self.cnn5 = nn.Conv2d(32, 32, kernel_size=(3,3), dilation=(8,8))
self.flat_feats = 32*4*3
self.fc = nn.Linear(self.flat_feats, 100)
self.bn = nn.BatchNorm1d(100)
self.re = nn.ReLU(inplace=True)
self.fc_out = nn.Linear(100, num_classes)
## Weights initialization
for m in self.modules():
if isinstance(m, nn.Conv2d or nn.Linear):
nn.init.kaiming_normal_(m.weight, mode='fan_out', nonlinearity='relu')
elif isinstance(m, nn.BatchNorm2d or nn.BatchNorm1d):
nn.init.constant_(m.weight, 1)
nn.init.constant_(m.bias, 0)
def forward(self, x):
x = self.expansion(x)
## block 1
x = self.cnn1(self.mp1(self.block1(x)))
##print(x.size())
## block 2
x = self.cnn2(self.mp2(self.block2(x)))
##print(x.size())
## block 3
x = self.cnn3(self.mp3(self.block3(x)))
##print(x.size())
## block 4
x = self.cnn4(self.mp4(self.block4(x)))
##print(x.size())
## block 5
x = self.cnn5(self.mp5(self.block5(x)))
##print(x.size())
## FC
x = self.fc_out(self.re(self.bn(self.fc(x.view(-1, self.flat_feats)))))
##print(x.size())
return F.log_softmax(x, dim=-1) # take log-softmax over C classes
def predict(self, x):
raise NotImplementedError()
标签： PyTorch
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