Thank you for your reply. I will give the structure of my network.
def __init__(self,input_channel,output_channel):
        super(Net, self).__init__()

        self.convD0 = nn.Sequential(nn.Conv2d(input_channel, 64, 5,1,2),
                                    nn.ReLU(),
                                    nn.BatchNorm2d(64)
                                    )

        self.convD1 = nn.Sequential(nn.Conv2d(64, 128, 3,1,1),
                                    nn.ReLU(),
                                    nn.BatchNorm2d(128),
                                    nn.MaxPool2d([2,1]))
        self.convD2 = nn.Sequential(nn.Conv2d(128, 256, 3,1,1),
                                    nn.ReLU(),
                                    nn.BatchNorm2d(256))
        self.convD3 = nn.Sequential(nn.Conv2d(256,128,5,1,[0,2]),
                                    nn.ReLU(),
                                    nn.BatchNorm2d(128))
        self.convD4 = nn.Sequential(nn.Conv2d(128,64,3,1,[0,1]),
                                    nn.ReLU(),
                                    nn.BatchNorm2d(64))
        self.convD5 = nn.Sequential(nn.Conv2d(64,32,5,1,[0,2]),
                                    nn.ReLU(),
                                    nn.BatchNorm2d(32))
        self.convD6 = nn.Sequential(nn.Conv2d(32,16,3,1,1),
                                    nn.ReLU(),
                                    nn.BatchNorm2d(16))       
        self.convD7 = nn.Sequential(nn.Conv2d(16,output_channel,3,1,1),
                                    nn.ReLU()
                                    )      
def forward(self, inputs):
        x0 = inputs.unsqueeze(dim=1)  
        x1_d = self.convD0(x0)
        x2_d = self.convD1(x1_d)
        x3_d = self.convD2(x2_d)
        x4_d = self.convD3(x3_d)
        x5_d = self.convD4(x4_d)
        x6_d = self.convD5(x5_d)
        x7_d = self.convD6(x6_d)
        x8_d = self.convD7(x7_d)
        output = x8_d.squeeze()      
       return output 

 The shape of input is [batch_size,140,33 ],  and, the value of the input range from 1.3280e-16 to 5.4188e-08, which are some very small value.  Toward to the output, the shape of it  is [batch_size, 60,33], the discrete value of output range from 0 to 140. 
 In order to get the output's shape, I use the MaxPool2d([2,1]) behind convD1 to obtain the half size of height. Then I can get the final size of output by using the convD3、convD4 and convD5. However, the value of MaxPool2D layer gets the NAN, when I use [2,1] as parameter. To check the parameter, I try to set the parameter as [2,2], which can obtain the normal value like 0.39.
 Can you give me some advise about the struture of my network to get the ideal output? Thank you.