hnhuang commented on issue #7582: Gluon GPU memory efficiency
URL: 
https://github.com/apache/incubator-mxnet/issues/7582#issuecomment-324477834
 
 
   If I only use vgg+fcn, then I can set batch_size = 64 for tf+keras and 
gluon. Here is the code:
   
   ```
   def get_vgg16(max):
       #net = nn.HybridSequential()
       net = nn.Sequential()
       with net.name_scope():
           # Block 1
           net.add(nn.Conv3D(64, kernel_size = 3, padding = 1, 
activation='relu'))
           net.add(nn.Conv3D(64, kernel_size = 3, padding = 1, 
activation='relu'))
           net.add(nn.MaxPool3D(pool_size=(2, 2, 2), strides = (2, 2, 2)))
           # Block 2
           net.add(nn.Conv3D(128, kernel_size = 3, padding = 1, 
activation='relu'))
           net.add(nn.Conv3D(128, kernel_size = 3, padding = 1, 
activation='relu'))
           net.add(nn.MaxPool3D(pool_size=(2, 2, 2), strides = (2, 2, 2)))
           # Block 3
           net.add(nn.Conv3D(256, kernel_size = 3, padding = 1, 
activation='relu'))
           net.add(nn.Conv3D(256, kernel_size = 3, padding = 1, 
activation='relu'))
           net.add(nn.Conv3D(256, kernel_size = 3, padding = 1, 
activation='relu'))
           net.add(nn.MaxPool3D(pool_size=(2, 2, 2), strides = (2, 2, 2)))
           # Block 4
           net.add(nn.Conv3D(512, kernel_size = 3, padding = 1, 
activation='relu'))
           net.add(nn.Conv3D(512, kernel_size = 3, padding = 1, 
activation='relu'))
           net.add(nn.Conv3D(512, kernel_size = 3, padding = 1, 
activation='relu'))
           net.add(nn.MaxPool3D(pool_size=(2, 2, 2), strides = (2, 2, 2)))
           # Block 5
           net.add(nn.Conv3D(512, kernel_size = 3, padding = 1, 
activation='relu'))
           net.add(nn.Conv3D(512, kernel_size = 3, padding = 1, 
activation='relu'))
           # Block 6
           net.add(nn.Conv3DTranspose(512, kernel_size = 4, strides = (4, 4, 
4), activation='relu'))
           # Block 7
           net.add(nn.Conv3D(256, kernel_size = 3, padding = 1, 
activation='relu'))
           net.add(nn.Conv3D(4096, kernel_size = 1, padding = 0, 
activation='relu'))
           net.add(nn.Conv3D(4096, kernel_size = 1, padding = 0, 
activation='relu'))
           net.add(nn.Conv3D(max, kernel_size = 1, activation='sigmoid'))
           return net
   ```

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