c3d_main.py 文件源码

python
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项目:c3d_pytorch 作者: whitesnowdrop 项目源码 文件源码
def __init__(self):
        super(C3D, self).__init__()
        self.group1 = nn.Sequential(
            nn.Conv3d(3, 64, kernel_size=3, padding=1),
            nn.ReLU(),
            nn.MaxPool3d(kernel_size=(1, 2, 2), stride=(1, 2, 2)))
        #init.xavier_normal(self.group1.state_dict()['weight'])
        self.group2 = nn.Sequential(
            nn.Conv3d(64, 128, kernel_size=3, padding=1),
            nn.ReLU(),
            nn.MaxPool3d(kernel_size=(2, 2, 2), stride=(2, 2, 2)))
        #init.xavier_normal(self.group2.state_dict()['weight'])
        self.group3 = nn.Sequential(
            nn.Conv3d(128, 256, kernel_size=3, padding=1),
            nn.ReLU(),
            nn.Conv3d(256, 256, kernel_size=3, padding=1),
            nn.ReLU(),
            nn.MaxPool3d(kernel_size=(2, 2, 2), stride=(2, 2, 2)))
        #init.xavier_normal(self.group3.state_dict()['weight'])
        self.group4 = nn.Sequential(
            nn.Conv3d(256, 512, kernel_size=3, padding=1),
            nn.ReLU(),
            nn.Conv3d(512, 512, kernel_size=3, padding=1),
            nn.ReLU(),
            nn.MaxPool3d(kernel_size=(2, 2, 2), stride=(2, 2, 2)))
        #init.xavier_normal(self.group4.state_dict()['weight'])
        self.group5 = nn.Sequential(
            nn.Conv3d(512, 512, kernel_size=3, padding=1),
            nn.ReLU(),
            nn.Conv3d(512, 512, kernel_size=3, padding=1),
            nn.ReLU(),
            nn.MaxPool3d(kernel_size=(2, 2, 2), stride=(2, 2, 2)))
        #init.xavier_normal(self.group5.state_dict()['weight'])

        self.fc1 = nn.Sequential(
            nn.Linear(512 * 3 * 3, 2048),               #
            nn.ReLU(),
            nn.Dropout(0.5))
        #init.xavier_normal(self.fc1.state_dict()['weight'])
        self.fc2 = nn.Sequential(
            nn.Linear(2048, 2048),
            nn.ReLU(),
            nn.Dropout(0.5))
        #init.xavier_normal(self.fc2.state_dict()['weight'])
        self.fc3 = nn.Sequential(
            nn.Linear(2048, 32))           #101

        self._features = nn.Sequential(
            self.group1,
            self.group2,
            self.group3,
            self.group4,
            self.group5
        )

        self._classifier = nn.Sequential(
            self.fc1,
            self.fc2
        )
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