Spaces:
Sleeping
Sleeping
File size: 2,158 Bytes
4937055 |
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.nn as nn
import torch.nn.functional as F
class ResBlock(nn.Module):
def __init__(self, in_channels, out_channels):
super(ResBlock, self).__init__()
self.convblock1 = nn.Sequential(
nn.Conv2d(in_channels, out_channels,kernel_size=(3,3), stride = 1, padding = 1,bias=False),
nn.BatchNorm2d(out_channels),
nn.ReLU(),
nn.Conv2d(out_channels, out_channels,kernel_size=(3,3), stride = 1, padding = 1,bias=False),
nn.BatchNorm2d(out_channels),
nn.ReLU()
)
def forward(self, x):
x = self.convblock1(x)
return x
class MyResNet(nn.Module):
def __init__(self):
super(MyResNet,self).__init__()
self.prep_layer = nn.Sequential(
nn.Conv2d(3, 64, kernel_size=3, stride=1, padding=1,bias=True),
nn.BatchNorm2d(64),
nn.ReLU(),
)
self.layer1 = nn.Sequential(
nn.Conv2d(64, 128, kernel_size=3, stride=1, padding=1,bias=True),
nn.MaxPool2d(2,2),
nn.BatchNorm2d(128),
nn.ReLU(),
)
self.resblock1 = ResBlock(128, 128)
self.layer2 = nn.Sequential(
nn.Conv2d(128, 256, kernel_size=3, stride=1, padding=1,bias=True),
nn.MaxPool2d(kernel_size=2),
nn.BatchNorm2d(256),
nn.ReLU(),
)
self.layer3 = nn.Sequential(
nn.Conv2d(256, 512, kernel_size=3, stride=1, padding=1,bias=True),
nn.MaxPool2d(kernel_size=2),
nn.BatchNorm2d(512),
nn.ReLU(),
)
self.resblock2 = ResBlock(512, 512)
self.maxpool = nn.MaxPool2d(kernel_size=4)
self.fc = nn.Linear(512, 10)
def forward(self, x):
out = self.prep_layer(x)
out = self.layer1(out)
res1 = self.resblock1(out)
out = out + res1
out = self.layer2(out)
out = self.layer3(out)
res2 = self.resblock2(out)
out = out + res2
out = self.maxpool(out)
out = out.view(out.size(0), -1)
out = self.fc(out)
return F.log_softmax(out,dim = -1) |