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Posted to commits@mxnet.apache.org by GitBox <gi...@apache.org> on 2018/04/10 20:08:07 UTC
[GitHub] hnhuang commented on issue #7582: Gluon GPU memory efficiency
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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