File size: 2,381 Bytes
6c267c2 |
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 |
import torch
import torch.nn as nn
import torch.nn.functional as F
class ConvBNBlock(nn.Module):
def __init__(self, in_planes, planes, stride=1, p=0.0):
super(ConvBNBlock, self).__init__()
self.dropout_prob = p
self.conv_bn_block = nn.Sequential(
nn.Conv2d(in_planes, planes, kernel_size=3, stride=stride, padding=1, bias=False),
nn.BatchNorm2d(planes)
)
self.drop_out = nn.Dropout2d(p=self.dropout_prob)
def forward(self, x):
out =F.relu(self.drop_out(self.conv_bn_block(x)) )
return out
class TransitionBlock(nn.Module):
def __init__(self, in_planes, planes, stride=1, p=0.0):
super(TransitionBlock, self).__init__()
self.p = p
self.transition_block = nn.Sequential(
nn.Conv2d(in_planes, planes, kernel_size=3, stride=stride, padding=1, bias=False),
nn.BatchNorm2d(planes),
nn.ReLU(inplace=True),
nn.MaxPool2d(2, 2),
nn.Dropout2d(p=self.p)
)
def forward(self, x):
x = self.transition_block(x)
return x
class ResBlock(nn.Module):
def __init__(self, in_planes, planes, stride=1, p=0.0):
super(ResBlock, self).__init__()
self.p = p
self.transition_block = TransitionBlock(in_planes, planes, stride, p)
self.conv_block1 = ConvBNBlock(planes, planes, stride, p)
self.conv_block2 = ConvBNBlock(planes, planes, stride, p)
def forward(self, x):
x = self.transition_block(x)
r = self.conv_block2(self.conv_block1(x))
out = x + r
return out
class CustomResNet(nn.Module):
def __init__(self, p=0.0, num_classes=10):
super(CustomResNet, self).__init__()
self.in_planes = 64
self.p = p
self.conv = ConvBNBlock(3, 64, 1, p)
self.layer1 = ResBlock(64, 128, 1, p)
self.layer2 = TransitionBlock(128, 256, 1, p)
self.layer3 = ResBlock(256, 512, 1, p)
self.max_pool = nn.MaxPool2d(4, 4)
self.linear = nn.Linear(512, num_classes)
def forward(self, x):
out = self.conv(x)
out = self.layer1(out)
out = self.layer2(out)
out = self.layer3(out)
out = self.max_pool(out)
out = out.view(out.size(0), -1)
out = self.linear(out)
return F.log_softmax(out, dim=1)
|