2015顶峰 何凯明
from streamlit.testing.v1.element_tree import SpecialBlock
from torch import nn
from torch.testing._internal.common_nn import output_size
def CommonBlock(param, param1, param2):
pass
def SpecialBlockBlock(param, param1, param2):
pass
class ResNet18(nn.Module):
def __init__(self,classes_num):
super(ResNet18,self).__init__()
self.prepare = nn.Sequential(
nn.Conv2d(3,64,7,2,3),
nn.BatchNorm2d(64),
nn.BatchNorm2d(64),
nn.ReLU(inplace=True),
nn.MaxPool2d(3,2,1)
)
self.layer1 = nn.Sequential(
CommonBlock(64,64,1),
CommonBlock(64,64,1)
)
self.layer2 = nn.Sequential(
SpecialBlockBlock(64,128,[2,1]),
CommonBlock(64,128,1)
)
self.layer3 = nn.Sequential(
SpecialBlock(128,256,[2,1]),
CommonBlock(256,256,1)
)
self.layer4 = nn.Sequential(
SpecialBlock(256,512,[2,1]),
CommonBlock(512,512,1)
)
self.pool = nn.AdaptiveAvgPool2d(output_size)
self.fc = nn.Sequential(
# nn.Dropout(p=0.5),
# nn.Linear(512,256),
# nn.ReLU(inplace=True),
# nn.Dropout(p=0.5),
nn.Linear(512,classes_num)
)
def forward(self,x):
x = self.prepare(x)
x = self.layer1(x)
x = self.layer2(x)
x = self.layer3(x)
x = self.pool(x)
x = x.reshape(x.shape[0],-1)
x = self.fc(x)
return x
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