What card and cudnn version are you using? I could not repro on P100 and V100 with the following script
import torch
import torch.nn as nn
import numpy as np

class MyModel(nn.Module):
    def __init__(self):
       super(MyModel, self).__init__()
       self.conv1 = nn.Conv3d(1, 3, 3, padding=1)
        
       self.conv2 = nn.Conv3d(3, 128, 3, padding=1, stride=2)
        
       self.conv3 = nn.Conv3d(128, 128, 3, padding=1, stride=2)
       self.conv4 = nn.Conv3d(128, 256, 3, padding=1, stride=1)
       self.conv5 = nn.Conv3d(256, 512, 3, padding=1, stride=2)
       self.conv6 = nn.Conv3d(512, 1024, 3, padding=1, stride=1)
       self.conv7 = nn.Conv3d(1024, 1024, 3, padding=1, stride=1)
        
        
        
        
        
       self.deconv1 = nn.ConvTranspose3d(1024, 1024, 3, 1, padding=1,  output_padding=0 )
       self.deconv2 = nn.ConvTranspose3d(1024, 512, 3, 2, padding=1, output_padding=0)
       self.deconv3 = nn.ConvTranspose3d(512, 256, 3, 2, padding=1, output_padding=0)
       self.deconv4 = nn.ConvTranspose3d(256, 128, 3, 1, padding=1, output_padding=0)
       self.deconv5 = nn.ConvTranspose3d(128, 128, 3, 2, padding=1, output_padding=1)
       self.deconv6 = nn.ConvTranspose3d(128, 2, 3, 1, padding=1, output_padding=0)

    def forward(self,x):
       x = self.conv1(x)
       x = self.conv2(x)
       x = self.conv3(x)
       x = self.conv4(x)
       x = self.conv5(x)
       x = self.conv6(x)
       x = self.conv7(x)
       print(x.size())
       x = self.deconv1(x)
       x = self.deconv2(x)
       x = self.deconv3(x)
       x = self.deconv4(x)
       x = self.deconv5(x)
       x = self.deconv6(x)
       return x



model = MyModel().cuda()
arr = np.random.randn(1, 64, 64, 64)
input = (torch.from_numpy(arr).float().unsqueeze(1).cuda())
print(input.shape)
output = model(input)
torch.cuda.synchronize()
print(output.shape)