File size: 6,788 Bytes
d3cde70 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 |
import math
import torch
import torch.nn as nn
import torch.nn.init as init
import torch.nn.functional as F
import torch.utils.model_zoo as model_zoo
class HiddenLayer(nn.Module):
def __init__(self, input_size, output_size):
super(HiddenLayer, self).__init__()
self.fc = nn.Linear(input_size, output_size)
self.relu = nn.ReLU()
def forward(self, x):
return self.relu(self.fc(x))
class VNet(nn.Module):
def __init__(self, hidden_size=100, num_layers=1):
super(VNet, self).__init__()
self.first_hidden_layer = HiddenLayer(1, hidden_size)
self.rest_hidden_layers = nn.Sequential(*[HiddenLayer(hidden_size, hidden_size) for _ in range(num_layers - 1)])
self.output_layer = nn.Linear(hidden_size, 1)
def forward(self, x):
x = self.first_hidden_layer(x)
x = self.rest_hidden_layers(x)
x = self.output_layer(x)
return torch.sigmoid(x)
class CNN(nn.Module):
def __init__(self, input_channel=3, n_outputs=10, dropout_rate=0.25):
self.dropout_rate = dropout_rate
super(CNN, self).__init__()
#block1
self.conv1 = nn.Conv2d(input_channel, 128, kernel_size=3, stride=1, padding=1)
self.bn1=nn.BatchNorm2d(128)
self.conv2 = nn.Conv2d(128, 128, kernel_size=3, stride=1, padding=1)
self.bn2=nn.BatchNorm2d(128)
self.conv3 = nn.Conv2d(128, 128, kernel_size=3, stride=1, padding=1)
self.bn3=nn.BatchNorm2d(128)
#block2
self.conv4 = nn.Conv2d(128, 256, kernel_size=3, stride=1, padding=1)
self.bn4=nn.BatchNorm2d(256)
self.conv5 = nn.Conv2d(256, 256, kernel_size=3, stride=1, padding=1)
self.bn5=nn.BatchNorm2d(256)
self.conv6 = nn.Conv2d(256, 256, kernel_size=3, stride=1, padding=1)
self.bn6=nn.BatchNorm2d(256)
#block3
self.conv7 = nn.Conv2d(256, 512, kernel_size=3, stride=1, padding=0)
self.bn7=nn.BatchNorm2d(512)
self.conv8 = nn.Conv2d(512, 256, kernel_size=3, stride=1, padding=0)
self.bn8=nn.BatchNorm2d(256)
self.conv9 = nn.Conv2d(256, 128, kernel_size=3, stride=1, padding=0)
self.bn9=nn.BatchNorm2d(128)
self.pool = nn.MaxPool2d(2, 2)
self.avgpool = nn.AvgPool2d(kernel_size=2)
self.fc=nn.Linear(128,n_outputs)
for m in self.modules():
if isinstance(m, nn.Conv2d):
nn.init.kaiming_normal_(m.weight, mode='fan_out')
elif isinstance(m, nn.BatchNorm2d):
nn.init.constant_(m.weight, 1)
nn.init.constant_(m.bias, 0)
def forward(self, x):
#block1
x=F.leaky_relu(self.bn1(self.conv1(x)), negative_slope=0.01)
x=F.leaky_relu(self.bn2(self.conv2(x)), negative_slope=0.01)
x=F.leaky_relu(self.bn3(self.conv3(x)), negative_slope=0.01)
x=self.pool(x)
x=F.dropout2d(x, p=self.dropout_rate)
#block2
x=F.leaky_relu(self.bn4(self.conv4(x)), negative_slope=0.01)
x=F.leaky_relu(self.bn5(self.conv5(x)), negative_slope=0.01)
x=F.leaky_relu(self.bn6(self.conv6(x)), negative_slope=0.01)
x=self.pool(x)
x=F.dropout2d(x, p=self.dropout_rate)
#block3
x=F.leaky_relu(self.bn7(self.conv7(x)), negative_slope=0.01)
x=F.leaky_relu(self.bn8(self.conv8(x)), negative_slope=0.01)
x=F.leaky_relu(self.bn9(self.conv9(x)), negative_slope=0.01)
x=self.avgpool(x)
x = x.view(x.size(0), x.size(1))
x=self.fc(x)
return x
def conv3x3(in_planes, out_planes, stride=1):
"""3x3 convolution with padding"""
return nn.Conv2d(in_planes, out_planes, kernel_size=3, stride=stride,
padding=1, bias=False)
class BasicBlock(nn.Module):
expansion = 1
def __init__(self, inplanes, planes, stride=1, downsample=None):
super(BasicBlock, self).__init__()
self.conv1 = conv3x3(inplanes, planes, stride)
self.bn1 = nn.BatchNorm2d(planes)
self.relu = nn.ReLU(inplace=True)
self.conv2 = conv3x3(planes, planes)
self.bn2 = nn.BatchNorm2d(planes)
self.downsample = downsample
self.stride = stride
def forward(self, x):
residual = x
out = self.conv1(x)
out = self.bn1(out)
out = self.relu(out)
out = self.conv2(out)
out = self.bn2(out)
if self.downsample is not None:
residual = self.downsample(x)
out += residual
out = self.relu(out)
return out
class ResNet(nn.Module):
def __init__(self, block, layers, num_classes=14):
self.inplanes = 64
super(ResNet, self).__init__()
self.conv1 = nn.Conv2d(3, 64, kernel_size=7, stride=2, padding=3, bias=False)
self.bn1 = nn.BatchNorm2d(64)
self.relu = nn.ReLU(inplace=True)
self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)
self.layer1 = self._make_layer(block, 64, layers[0])
self.layer2 = self._make_layer(block, 128, layers[1], stride=2)
self.layer3 = self._make_layer(block, 256, layers[2], stride=2)
self.layer4 = self._make_layer(block, 512, layers[3], stride=2)
self.avgpool = nn.AvgPool2d(7, stride=1)
self.fc = nn.Linear(512 * block.expansion, num_classes)
for m in self.modules():
if isinstance(m, nn.Conv2d):
nn.init.kaiming_normal_(m.weight, mode='fan_out')
elif isinstance(m, nn.BatchNorm2d):
nn.init.constant_(m.weight, 1)
nn.init.constant_(m.bias, 0)
def _make_layer(self, block, planes, blocks, stride=1):
downsample = None
if stride != 1 or self.inplanes != planes * block.expansion:
downsample = nn.Sequential(
nn.Conv2d(self.inplanes, planes * block.expansion,
kernel_size=1, stride=stride, bias=False),
nn.BatchNorm2d(planes * block.expansion),
)
layers = []
layers.append(block(self.inplanes, planes, stride, downsample))
self.inplanes = planes * block.expansion
for i in range(1, blocks):
layers.append(block(self.inplanes, planes))
return nn.Sequential(*layers)
def forward(self, x):
x = self.conv1(x)
x = self.bn1(x)
x = self.relu(x)
x = self.maxpool(x)
x = self.layer1(x)
x = self.layer2(x)
x = self.layer3(x)
x = self.layer4(x)
x = self.avgpool(x)
x = x.view(x.size(0), -1)
x = self.fc(x)
return x
def resnet18(pretrained=False, **kwargs):
model = ResNet(BasicBlock, [2, 2, 2, 2], **kwargs)
return model |