def mknet():
return df.Sequential( # 48x70 (HxW)
df.SpatialConvolution(3, 24, (3, 3)), # 46x68
df.BatchNormalization(24),
df.ReLU(),
df.SpatialConvolution(24, 24, (3, 3)), # 44x66
df.BatchNormalization(24),
df.SpatialMaxPooling((2, 3)), # 22x22
df.ReLU(),
df.SpatialConvolution(24, 48, (3, 3)), # 20x20
df.BatchNormalization(48),
df.ReLU(),
df.SpatialConvolution(48, 48, (3, 3)), # 18x18
df.BatchNormalization(48),
df.SpatialMaxPooling((2, 2)), # 9x9
df.ReLU(),
df.SpatialConvolution(48, 64, (3, 3)), # 7x7
df.BatchNormalization(64),
df.ReLU(),
df.SpatialConvolution(64, 64, (3, 3)), # 5x5
df.BatchNormalization(64),
df.ReLU(),
df.Dropout(0.2),
Flatten(),
df.Linear(64 * 5 * 5, 512),
df.ReLU(),
df.Dropout(0.5),
df.Linear(512, 2, init=df.init.normal(0.01)),
Biternion())
def mknet_gpu(*outlayers):
return df.Sequential( # 3@46
df.SpatialConvolutionCUDNN(3, 24, 3, 3), # -> 24@44
df.BatchNormalization(24),
df.ReLU(),
df.SpatialConvolutionCUDNN(24, 24, 3, 3), # -> 24@42
df.BatchNormalization(24),
df.SpatialMaxPoolingCUDNN(2, 2), # -> 24@21
df.ReLU(),
df.SpatialConvolutionCUDNN(24, 48, 3, 3), # -> 48@19
df.BatchNormalization(48),
df.ReLU(),
df.SpatialConvolutionCUDNN(48, 48, 3, 3), # -> 48@17
df.BatchNormalization(48),
df.SpatialMaxPooling(2, 2), # -> 48@9
df.ReLU(),
df.SpatialConvolutionCUDNN(48, 64, 3, 3), # -> 48@7
df.BatchNormalization(64),
df.ReLU(),
df.SpatialConvolutionCUDNN(64, 64, 3, 3), # -> 48@5
df.BatchNormalization(64),
df.ReLU(),
df.Dropout(0.2),
Flatten(),
df.Linear(64 * 5 * 5, 512),
df.ReLU(),
df.Dropout(0.5),
*outlayers)
def __init__(self, weightsname, *unused, **unused_kw):
self._net = df.Sequential( # 48x70 (HxW)
df.SpatialConvolution(3, 24, (3, 3)), # 46x68
df.BatchNormalization(24),
df.ReLU(),
df.SpatialConvolution(24, 24, (3, 3)), # 44x66
df.BatchNormalization(24),
df.SpatialMaxPooling((2, 3)), # 22x22
df.ReLU(),
df.SpatialConvolution(24, 48, (3, 3)), # 20x20
df.BatchNormalization(48),
df.ReLU(),
df.SpatialConvolution(48, 48, (3, 3)), # 18x18
df.BatchNormalization(48),
df.SpatialMaxPooling((2, 2)), # 9x9
df.ReLU(),
df.SpatialConvolution(48, 64, (3, 3)), # 7x7
df.BatchNormalization(64),
df.ReLU(),
df.SpatialConvolution(64, 64, (3, 3)), # 5x5
df.BatchNormalization(64),
df.ReLU(),
df.Dropout(0.2),
Flatten(),
df.Linear(64 * 5 * 5, 512),
df.ReLU(),
df.Dropout(0.5),
df.Linear(512, 2, init=df.init.normal(0.01)),
Biternion())
self._net.__setstate__(np.load(weightsname))
self._net.evaluate()
self._aug = AugmentationPipeline(None, None, Cropper((48, 70)))
def mknet():
return df.Sequential( # 184x76
df.SpatialConvolution(3, 24, (3, 3)), # 182x74
df.BatchNormalization(24),
df.ReLU(),
df.SpatialConvolution(24, 24, (3, 3)), # 180x72
df.SpatialMaxPooling((3, 3)), # 60x24
df.BatchNormalization(24),
df.ReLU(),
df.SpatialConvolution(24, 48, (3, 3)), # 58x22
df.BatchNormalization(48),
df.ReLU(),
df.SpatialConvolution(48, 48, (3, 3)), # 56x20
df.SpatialMaxPooling((2, 2)), # 28x10
df.BatchNormalization(48),
df.ReLU(),
df.SpatialConvolution(48, 64, (3, 3)), # 26x8
df.BatchNormalization(64),
df.ReLU(),
df.SpatialConvolution(64, 64, (3, 3)), # 24x6
df.SpatialMaxPooling((2, 2)), # 12x3
df.BatchNormalization(64),
df.ReLU(),
df.SpatialConvolution(64, 64, (3, 2)), # 10x2
df.BatchNormalization(64),
df.ReLU(),
df.Dropout(0.2),
Flatten(),
df.Linear(64 * 10 * 2, 512),
df.ReLU(),
df.Dropout(0.5),
df.Linear(512, 2, init=df.init.normal(0.01)),
Biternion())
def mknet():
return df.Sequential( # 3@46
df.SpatialConvolution(3, 24, (3, 3)), # -> 24@44
df.BatchNormalization(24),
df.ReLU(),
df.SpatialConvolution(24, 24, (3, 3)), # -> 24@42
df.BatchNormalization(24),
df.SpatialMaxPooling((2, 2), ignore_border=False), # -> 24@21
df.ReLU(),
df.SpatialConvolution(24, 48, (3, 3)), # -> 48@19
df.BatchNormalization(48),
df.ReLU(),
df.SpatialConvolution(48, 48, (3, 3)), # -> 48@17
df.BatchNormalization(48),
df.SpatialMaxPooling((2, 2), ignore_border=False), # -> 48@9
df.ReLU(),
df.SpatialConvolution(48, 64, (3, 3)), # -> 64@7
df.BatchNormalization(64),
df.ReLU(),
df.SpatialConvolution(64, 64, (3, 3)), # -> 64@5
df.BatchNormalization(64),
df.ReLU(),
df.Dropout(0.2),
Flatten(),
df.Linear(64 * 5 * 5, 512),
df.ReLU(),
df.Dropout(0.5),
df.Linear(512, 2, init=df.init.normal(0.01)),
Biternion())
def __init__(self, weightsname, *unused, **unused_kw):
self._net = df.Sequential( # 3@46
df.SpatialConvolution( 3, 24, (3, 3)), # -> 24@44
df.BatchNormalization(24),
df.ReLU(),
df.SpatialConvolution(24, 24, (3, 3)), # -> 24@42
df.BatchNormalization(24),
df.SpatialMaxPooling((2, 2), ignore_border=False), # -> 24@21
df.ReLU(),
df.SpatialConvolution(24, 48, (3, 3)), # -> 48@19
df.BatchNormalization(48),
df.ReLU(),
df.SpatialConvolution(48, 48, (3, 3)), # -> 48@17
df.BatchNormalization(48),
df.SpatialMaxPooling((2, 2), ignore_border=False), # -> 48@9
df.ReLU(),
df.SpatialConvolution(48, 64, (3, 3)), # -> 64@7
df.BatchNormalization(64),
df.ReLU(),
df.SpatialConvolution(64, 64, (3, 3)), # -> 64@5
df.BatchNormalization(64),
df.ReLU(),
df.Dropout(0.2),
Flatten(),
df.Linear(64*5*5, 512),
df.ReLU(),
df.Dropout(0.5),
df.Linear(512, 2, init=df.init.normal(0.01)),
Biternion()
)
self._net.__setstate__(np.load(weightsname))
self._net.evaluate()
self._aug = AugmentationPipeline(None, None, Cropper((46, 46)))
def __init__(self, weightsname, *unused, **unused_kw):
self._net = df.Sequential( # 184x76
df.SpatialConvolution(3, 24, (3, 3)), # 182x74
df.BatchNormalization(24),
df.ReLU(),
df.SpatialConvolution(24, 24, (3, 3)), # 180x72
df.SpatialMaxPooling((3, 3)), # 60x24
df.BatchNormalization(24),
df.ReLU(),
df.SpatialConvolution(24, 48, (3, 3)), # 58x22
df.BatchNormalization(48),
df.ReLU(),
df.SpatialConvolution(48, 48, (3, 3)), # 56x20
df.SpatialMaxPooling((2, 2)), # 28x10
df.BatchNormalization(48),
df.ReLU(),
df.SpatialConvolution(48, 64, (3, 3)), # 26x8
df.BatchNormalization(64),
df.ReLU(),
df.SpatialConvolution(64, 64, (3, 3)), # 24x6
df.SpatialMaxPooling((2, 2)), # 12x3
df.BatchNormalization(64),
df.ReLU(),
df.SpatialConvolution(64, 64, (3, 2)), # 10x2
df.BatchNormalization(64),
df.ReLU(),
df.Dropout(0.2),
Flatten(),
df.Linear(64 * 10 * 2, 512),
df.ReLU(),
df.Dropout(0.5),
df.Linear(512, 2, init=df.init.normal(0.01)),
Biternion())
self._net.__setstate__(np.load(weightsname))
self._net.evaluate()
self._aug = AugmentationPipeline(None, None, Cropper((184, 76)))
def mknet(self, *outlayers):
return df.Sequential(
#df.SpatialConvolutionCUDNN(3,24,(3,3)),
df.SpatialConvolution(3, 24, (3, 3)),
df.BatchNormalization(24),
df.ReLU(),
#df.SpatialConvolutionCUDNN(24,24,(3,3)),
df.SpatialConvolution(24, 24, (3, 3)),
df.BatchNormalization(24),
#df.SpatialMaxPoolingCUDNN(2,2),
df.SpatialMaxPooling((2, 2)),
#df.MaxPooling(2,2),
df.ReLU(),
#df.SpatialConvolutionCUDNN(24,48,(3,3)),
df.SpatialConvolution(24, 48, (3, 3)),
df.BatchNormalization(48),
df.ReLU(),
# df.PoolingCUDNN()?
df.SpatialMaxPoolingCUDNN(48, 48, 3, 3),
#df.SpatialConvolution(48,48,(3,3)),
df.BatchNormalization(48),
df.SpatialMaxPooling((2, 2)),
df.ReLU(),
#df.SpatialConvolutionCUDNN(48,64,(3,3)),
df.SpatialConvolution(48, 64, (3, 3)),
df.BatchNormalization(64),
df.ReLU(),
#df.SpatialConvolutionCUDNN(64,64,(3,3)),
df.SpatialConvolution(64, 64, (3, 3)),
df.BatchNormalization(64),
df.ReLU(),
df.Dropout(0.2),
Flatten(),
df.Linear(64 * 5 * 5, 512),
df.ReLU(),
df.Dropout(0.5),
*outlayers)
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()
]
def nnet():
model = df.Sequential()
model.add(df.AddConstant(1.0))
model.add(df.Log())
model.add(df.BatchNormalization(93))
model.add(df.Dropout(0.1))
model.add(df.Linear(93, 512))
model.add(df.BatchNormalization(512))
model.add(df.ReLU())
model.add(df.Dropout(0.5))
model.add(df.Linear(512, 512))
model.add(df.BatchNormalization(512))
model.add(df.ReLU())
model.add(df.Dropout(0.5))
model.add(df.Linear(512, 512))
model.add(df.BatchNormalization(512))
model.add(df.ReLU())
model.add(df.Dropout(0.5))
model.add(df.Linear(512, 9))
model.add(df.SoftMax())
return model
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
