Thank you for reply.
this my code now:
class Net(nn.Module):

    def __init__(self):
        super(Net, self).__init__()
        self.conv1 = nn.Conv2d(3, 320, kernel_size=5)
        self.conv2 = nn.Conv2d(320, 640, kernel_size=5)
        self.conv3 = nn.Conv2d(640, 1024, kernel_size=5)
        self.dropout = nn.Dropout2d()
        self.fc1 = nn.Linear(64512, 500)
        self.fc2 = nn.Linear(500, 250)
        self.fc3 = nn.Linear(250, 2)

    def forward(self, x):    
        x = F.relu(F.max_pool2d(self.conv1(x), 2))          
        x = F.relu(F.max_pool2d(self.dropout(self.conv2(x)), 2))
        x = F.relu(F.max_pool2d(self.dropout(self.conv3(x)), 2))
        x = x.view(-1, 64512 )    
        x = F.relu(self.fc1(x))	
        x = F.dropout(x, training=self.training)
        x = F.relu(self.fc2(x))
        # 50 -> 10
        x = self.fc3(x)       
        # transform to logits
        return F.log_softmax(x)

and my output:
Net(
  (conv1): Conv2d(3, 320, kernel_size=(5, 5), stride=(1, 1))
  (conv2): Conv2d(320, 640, kernel_size=(5, 5), stride=(1, 1))
  (conv3): Conv2d(640, 1024, kernel_size=(5, 5), stride=(1, 1))
  (dropout): Dropout2d(p=0.5)
  (fc1): Linear(in_features=64512, out_features=500, bias=True)
  (fc2): Linear(in_features=500, out_features=250, bias=True)
  (fc3): Linear(in_features=250, out_features=2, bias=True)
)
testcnn.py:50: UserWarning: Implicit dimension choice for log_softmax has been deprecated. Change the call to include dim=X as an argument.
  return F.log_softmax(x)
[1,   100] loss: 0.69665333777666094139
[1,   200] loss: 0.00708028078079223598
[1,   300] loss: 0.00437617301940917969
[1,   400] loss: 0.00268048048019409180
[1,   500] loss: 0.00068682432174682617
[1,   600] loss: 0.00131387710571289067
[1,   700] loss: 0.00132138729095458989
[1,   800] loss: 0.00043724775314331056
[1,   900] loss: 0.00070947408676147461
[1,  1000] loss: 0.00035747289657592771
[1,  1100] loss: 0.00042194128036499023
[1,  1200] loss: 0.00050359964370727539
[1,  1300] loss: 1.56638206690549841582
[1,  1400] loss: 0.00618010759353637695
[1,  1500] loss: 0.00646384358406066912
[1,  1600] loss: 0.00293265581130981463
[1,  1700] loss: 0.00214604139328002921
[1,  1800] loss: 0.00116072893142700191
[1,  1900] loss: 0.00060119628906249998
[1,  2000] loss: 0.00109928846359252930
[1,  2100] loss: 0.00142927765846252437
[1,  2200] loss: 0.00041127204895019531
[1,  2300] loss: 0.00112100839614868173
[1,  2400] loss: 0.00148788094520568848
[1,  2500] loss: 0.00093175768852233882
[1,  2600] loss: 0.00042861700057983398
[1,  2700] loss: 0.00033172369003295896
[1,  2800] loss: 0.00043708086013793945
[1,  2900] loss: 0.00052500963211059575
Finished Training
Correct: 49 %