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import torch
from torch import nn
from torch.nn import functional as F
from typing import Any,List,Tuple,Dict
class Net(nn.Module):
def __init__(self,config:Dict):
super(Net,self).__init__()
DROPOUT= config.get('dropout_rate',0.01)
BIAS = config.get('bias',False)
self.conv1 = nn.Sequential(
nn.Conv2d(in_channels=1,out_channels=8,kernel_size=3,stride=1,padding=1,bias=BIAS),
nn.ReLU(),
nn.BatchNorm2d(8),
nn.Dropout2d(p=DROPOUT),
nn.Conv2d(in_channels=8,out_channels=10,kernel_size=3,stride=1,padding=1,bias=BIAS),
nn.ReLU(),
nn.BatchNorm2d(10),
nn.Dropout2d(p=DROPOUT),
nn.Conv2d(in_channels=10,out_channels=10,kernel_size=3,stride=1,padding=1,bias=BIAS),
nn.ReLU(),
nn.BatchNorm2d(10),
nn.Dropout2d(p=DROPOUT),
)
self.trans1 = nn.Sequential(
nn.MaxPool2d( kernel_size =2 , stride =2 , padding =1 ),
nn.Conv2d(in_channels=10,out_channels=8,kernel_size=1,bias=BIAS,padding=1),
)
self.conv2 =nn.Sequential(
nn.Conv2d(in_channels=8,out_channels=10,kernel_size=3,stride=1,padding=1,bias=BIAS),
nn.BatchNorm2d(10),
nn.ReLU(),
nn.Dropout2d(p=DROPOUT),
nn.Conv2d(in_channels=10,out_channels=12,kernel_size=3,stride=1,padding=1,bias=BIAS),
nn.BatchNorm2d(12),
nn.ReLU(),
nn.Dropout2d(p=DROPOUT),
nn.Conv2d(in_channels=12,out_channels=12,kernel_size=3,stride=1,padding=1,bias=BIAS),
nn.BatchNorm2d(12),
nn.ReLU(),
nn.Dropout2d(p=DROPOUT),
)
self.trans2 = nn.Sequential(
nn.MaxPool2d( kernel_size =2 , stride =2 , padding =1 ),
nn.Conv2d(in_channels=12,out_channels=8,kernel_size=1,bias=BIAS),
nn.BatchNorm2d(8),
)
self.conv3 = nn.Sequential(
nn.Conv2d(in_channels=8,out_channels=10,kernel_size=3,stride=1,padding=1,bias=BIAS),
nn.BatchNorm2d(10),
nn.ReLU(),
nn.Dropout2d(p=DROPOUT),
nn.Conv2d(in_channels=10,out_channels=12,kernel_size=3,stride=1,padding=1,bias=BIAS),
nn.ReLU(),
nn.BatchNorm2d(12),
nn.Dropout2d(p=DROPOUT),
)
self.trans3 = nn.Sequential(
nn.Conv2d(in_channels=12,out_channels=10,kernel_size=1,bias=BIAS),
nn.MaxPool2d( kernel_size =2 , stride =2 , padding =0 ),
nn.BatchNorm2d(10),
)
self.out4 = nn.Sequential(
nn.Conv2d(in_channels=10 ,out_channels=10, kernel_size=3,stride=1,padding=1,bias=BIAS),
nn.AvgPool2d(kernel_size=3) #(1*1*10)
)
def forward(self,x):
x = self.trans1( self.conv1(x) )
x = self.trans2( self.conv2(x) )
x = self.trans3( self.conv3(x) )
x = self.out4(x)
x = x.view(-1,10)
return F.log_softmax(x,dim=1)
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