def lenet():
model = df.Sequential()
model.add(df.Reshape(-1, 1, 28, 28))
model.add(df.SpatialConvolution(1, 32, 5, 5, 1, 1, with_bias=False))
model.add(df.BatchNormalization(32))
model.add(df.ReLU())
model.add(df.SpatialMaxPooling(2, 2))
model.add(df.SpatialConvolution(32, 64, 5, 5, 1, 1, with_bias=False))
model.add(df.BatchNormalization(64))
model.add(df.ReLU())
model.add(df.SpatialMaxPooling(2, 2))
model.add(df.Reshape(-1, 4 * 4 * 64))
model.add(df.Linear(4 * 4 * 64, 100, with_bias=False))
model.add(df.BatchNormalization(100))
model.add(df.ReLU())
model.add(df.Dropout(0.5))
model.add(df.Linear(100, 10))
model.add(df.SoftMax())
return model
def lenet_cudnn():
model = df.Sequential()
model.add(df.Reshape(-1, 1, 28, 28))
model.add(
df.SpatialConvolutionCUDNN(1,
32,
5,
5,
1,
1,
border='same',
with_bias=False))
model.add(df.BatchNormalization(32))
model.add(df.ReLU())
model.add(df.SpatialMaxPoolingCUDNN(2, 2))
model.add(
df.SpatialConvolutionCUDNN(32,
64,
5,
5,
1,
1,
border='same',
with_bias=False))
model.add(df.BatchNormalization(64))
model.add(df.ReLU())
model.add(df.SpatialMaxPoolingCUDNN(2, 2))
model.add(df.Reshape(-1, 7 * 7 * 64))
model.add(df.Linear(7 * 7 * 64, 100, with_bias=False))
model.add(df.BatchNormalization(100))
model.add(df.ReLU())
model.add(df.Dropout(0.5))
model.add(df.Linear(100, 10))
model.add(df.SoftMax())
return model
def model_head(fully_conv=True):
if fully_conv:
return [
df.SpatialConvolutionCUDNN(512, 4096, 7, 7, border='valid'),
df.ReLU(),
df.Dropout(0.5),
df.SpatialConvolutionCUDNN(4096, 4096, 1, 1, border='valid'),
df.ReLU(),
df.Dropout(0.5),
df.SpatialConvolutionCUDNN(4096, 1000, 1, 1, border='valid'),
df.SpatialSoftMaxCUDNN(),
]
else:
return [
df.Reshape(-1, 512 * 7 * 7),
df.Linear(512 * 7 * 7, 4096),
df.ReLU(),
df.Dropout(0.5),
df.Linear(4096, 4096),
df.ReLU(),
df.Dropout(0.5),
df.Linear(4096, 1000),
df.SoftMax()
]
