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model.py
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model.py
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import torch
import torch.nn as nn
class Alexnet(nn.Module):
def __init__(self,num_classes=1000,init_weights=False):
super(Alexnet,self).__init__()
self.features = nn.Sequential(
nn.Conv2d(3,48,kernel_size=11,stride=4,padding=2),# 53.25 53.25 48
nn.ReLU(inplace=True),
nn.MaxPool2d(kernel_size=3,stride=2),# 27 27 48
nn.Conv2d(48,128,kernel_size=5,padding=2),
nn.ReLU(inplace=True),
nn.MaxPool2d(kernel_size=3,stride=2),
nn.Conv2d(128,192,kernel_size=3,padding=1),
nn.ReLU(inplace=True),
nn.Conv2d(192,192,kernel_size=3,padding=1),
nn.ReLU(inplace=True),
nn.Conv2d(192,128,kernel_size=3,stride=2,padding=1),
nn.ReLU(inplace=True),
nn.MaxPool2d(kernel_size=3,stride=2)
)
self.classifer = nn.Sequential(
nn.Dropout(p=0.5),
nn.Linear(128*6*6,2048),
nn.ReLU(inplace=True),
nn.Dropout(p=0.5),
nn.Linear(2048,2048),
nn.ReLU(inplace=True),
nn.Linear(2048,num_classes)
)
if init_weights:
self.initialize_weights()
def forward(self,x):
x = self.features(x)
x = torch.flatten(x,start_dim=1)
x=self.classifer(x)
return x
def initialize_weights(self):
for m in self.modules():
if isinstance(m,nn.Conv2d):
nn.init.kaiming_normal_(m.weight,mode="fan_out",nonlinearity='relu')
if m.bias is not None:
nn.init.constant_(m.bias,0)
elif isinstance(m,nn.Linear):
nn.init.normal_(m.weight,0,0.01)
nn.init.constant_(m.bisa,0)