def res_unit(x, scope_name, rng, dn=False, test=False):
C = x.shape[1]
with nn.parameter_scope(scope_name):
# Conv -> BN -> BinaryTanh
with nn.parameter_scope("conv1"):
h = PF.binary_connect_convolution(x, C / 2, kernel=(1, 1), pad=(0, 0),
with_bias=False)
h = PF.batch_normalization(h, batch_stat=not test)
h = F.binary_tanh(h)
# Conv -> BN -> BinaryTanh
with nn.parameter_scope("conv2"):
h = PF.binary_connect_convolution(h, C / 2, kernel=(3, 3), pad=(1, 1),
with_bias=False)
h = PF.batch_normalization(h, batch_stat=not test)
h = F.binary_tanh(h)
# Conv -> BN
with nn.parameter_scope("conv3"):
h = PF.binary_connect_convolution(h, C, kernel=(1, 1), pad=(0, 0),
with_bias=False)
h = PF.batch_normalization(h, batch_stat=not test)
# Residual -> BinaryTanh
h = F.binary_tanh(h + x)
# Maxpooling
if dn:
h = F.max_pooling(h, kernel=(2, 2), stride=(2, 2))
return h
def network01E(x, y, test=False):
# Input:x -> 1,64,48
# BinaryConnectConvolution -> 64,60,44
h = PF.binary_connect_convolution(x,
64, (5, 5), (0, 0),
name='BinaryConnectConvolution')
# MaxPooling -> 64,30,22
h = F.max_pooling(h, (2, 2), (2, 2))
# BatchNormalization
h = PF.batch_normalization(h, (1, ),
0.5,
0.01,
not test,
name='BatchNormalization')
# BinarySigmoid
h = F.binary_sigmoid(h)
# BinaryConnectConvolution_2 -> 64,26,18
h = PF.binary_connect_convolution(h,
64, (5, 5), (0, 0),
name='BinaryConnectConvolution_2')
# MaxPooling_2 -> 64,13,9
h = F.max_pooling(h, (2, 2), (2, 2))
# BatchNormalization_2
h = PF.batch_normalization(h, (1, ),
0.5,
0.01,
not test,
name='BatchNormalization_2')
# BinarySigmoid_2
h = F.binary_sigmoid(h)
# BinaryConnectAffine -> 512
h = PF.binary_connect_affine(h, (512, ), name='BinaryConnectAffine')
# BatchNormalization_3
h = PF.batch_normalization(h, (1, ),
0.5,
0.01,
not test,
name='BatchNormalization_3')
# BinarySigmoid_3
h = F.binary_sigmoid(h)
# BinaryConnectAffine_2 -> 10
h = PF.binary_connect_affine(h, (10, ), name='BinaryConnectAffine_2')
# BatchNormalization_4
h = PF.batch_normalization(h, (1, ),
0.5,
0.01,
not test,
name='BatchNormalization_4')
# Softmax
h = F.softmax(h)
# CategoricalCrossEntropy -> 1
# h = F.categorical_cross_entropy(h, y)
return h
def res_unit(x, scope):
C = x.shape[1]
with nn.parameter_scope(scope):
with nn.parameter_scope('conv1'):
h = F.elu(bn(PF.binary_connect_convolution(
x, C / 2, (1, 1), with_bias=False)))
with nn.parameter_scope('conv2'):
h = F.elu(
bn(PF.binary_connect_convolution(h, C / 2, (3, 3), pad=(1, 1), with_bias=False)))
with nn.parameter_scope('conv3'):
h = bn(PF.binary_connect_convolution(
h, C, (1, 1), with_bias=False))
return F.elu(x + h)
def res_unit(x, scope):
C = x.shape[1]
with nn.parameter_scope(scope):
with nn.parameter_scope('conv1'):
h = F.elu(bn(PF.binary_connect_convolution(
x, C / 2, (1, 1), with_bias=False)))
with nn.parameter_scope('conv2'):
h = F.elu(
bn(PF.binary_connect_convolution(h, C / 2, (3, 3), pad=(1, 1), with_bias=False)))
with nn.parameter_scope('conv3'):
h = bn(PF.binary_connect_convolution(
h, C, (1, 1), with_bias=False))
return F.elu(x + h)
def mnist_binary_connect_lenet_prediction(image, test=False):
"""
Construct LeNet for MNIST (BinaryNet version).
"""
with nn.parameter_scope("conv1"):
c1 = PF.binary_connect_convolution(image, 16, (5, 5))
c1 = PF.batch_normalization(c1, batch_stat=not test)
c1 = F.elu(F.average_pooling(c1, (2, 2)))
with nn.parameter_scope("conv2"):
c2 = PF.binary_connect_convolution(c1, 16, (5, 5))
c2 = PF.batch_normalization(c2, batch_stat=not test)
c2 = F.elu(F.average_pooling(c2, (2, 2)))
with nn.parameter_scope("fc3"):
c3 = PF.binary_connect_affine(c2, 50)
c3 = PF.batch_normalization(c3, batch_stat=not test)
c3 = F.elu(c3)
with nn.parameter_scope("fc4"):
c4 = PF.binary_connect_affine(c3, 10)
c4 = PF.batch_normalization(c4, batch_stat=not test)
return c4
def mnist_binary_connect_lenet_prediction(image, test=False):
"""
Construct LeNet for MNIST (BinaryNet version).
"""
with nn.parameter_scope("conv1"):
c1 = PF.binary_connect_convolution(image, 16, (5, 5))
c1 = PF.batch_normalization(c1, batch_stat=not test)
c1 = F.elu(F.average_pooling(c1, (2, 2)))
with nn.parameter_scope("conv2"):
c2 = PF.binary_connect_convolution(c1, 16, (5, 5))
c2 = PF.batch_normalization(c2, batch_stat=not test)
c2 = F.elu(F.average_pooling(c2, (2, 2)))
with nn.parameter_scope("fc3"):
c3 = PF.binary_connect_affine(c2, 50)
c3 = PF.batch_normalization(c3, batch_stat=not test)
c3 = F.elu(c3)
with nn.parameter_scope("fc4"):
c4 = PF.binary_connect_affine(c3, 10)
c4 = PF.batch_normalization(c4, batch_stat=not test)
return c4
def mnist_binary_connect_resnet_prediction(image, test=False):
"""
Construct ResNet for MNIST (BinaryNet version).
"""
def bn(x):
return PF.batch_normalization(x, batch_stat=not test)
def res_unit(x, scope):
C = x.shape[1]
with nn.parameter_scope(scope):
with nn.parameter_scope('conv1'):
h = F.elu(
bn(
PF.binary_connect_convolution(x,
C / 2, (1, 1),
with_bias=False)))
with nn.parameter_scope('conv2'):
h = F.elu(
bn(
PF.binary_connect_convolution(h,
C / 2, (3, 3),
pad=(1, 1),
with_bias=False)))
with nn.parameter_scope('conv3'):
h = bn(
PF.binary_connect_convolution(h,
C, (1, 1),
with_bias=False))
return F.elu(x + h)
# Conv1 --> 64 x 32 x 32
with nn.parameter_scope("conv1"):
c1 = F.elu(
bn(
PF.binary_connect_convolution(image,
64, (3, 3),
pad=(3, 3),
with_bias=False)))
# Conv2 --> 64 x 16 x 16
c2 = F.max_pooling(res_unit(c1, "conv2"), (2, 2))
# Conv3 --> 64 x 8 x 8
c3 = F.max_pooling(res_unit(c2, "conv3"), (2, 2))
# Conv4 --> 64 x 8 x 8
c4 = res_unit(c3, "conv4")
# Conv5 --> 64 x 4 x 4
c5 = F.max_pooling(res_unit(c4, "conv5"), (2, 2))
# Conv5 --> 64 x 4 x 4
c6 = res_unit(c5, "conv6")
pl = F.average_pooling(c6, (4, 4))
with nn.parameter_scope("classifier"):
y = bn(PF.binary_connect_affine(pl, 10))
return y
def mnist_binary_connect_resnet_prediction(image, test=False):
"""
Construct ResNet for MNIST (BinaryNet version).
"""
def bn(x):
return PF.batch_normalization(x, batch_stat=not test)
def res_unit(x, scope):
C = x.shape[1]
with nn.parameter_scope(scope):
with nn.parameter_scope('conv1'):
h = F.elu(bn(PF.binary_connect_convolution(
x, C / 2, (1, 1), with_bias=False)))
with nn.parameter_scope('conv2'):
h = F.elu(
bn(PF.binary_connect_convolution(h, C / 2, (3, 3), pad=(1, 1), with_bias=False)))
with nn.parameter_scope('conv3'):
h = bn(PF.binary_connect_convolution(
h, C, (1, 1), with_bias=False))
return F.elu(x + h)
# Conv1 --> 64 x 32 x 32
with nn.parameter_scope("conv1"):
c1 = F.elu(
bn(PF.binary_connect_convolution(image, 64, (3, 3), pad=(3, 3), with_bias=False)))
# Conv2 --> 64 x 16 x 16
c2 = F.max_pooling(res_unit(c1, "conv2"), (2, 2))
# Conv3 --> 64 x 8 x 8
c3 = F.max_pooling(res_unit(c2, "conv3"), (2, 2))
# Conv4 --> 64 x 8 x 8
c4 = res_unit(c3, "conv4")
# Conv5 --> 64 x 4 x 4
c5 = F.max_pooling(res_unit(c4, "conv5"), (2, 2))
# Conv5 --> 64 x 4 x 4
c6 = res_unit(c5, "conv6")
pl = F.average_pooling(c6, (4, 4))
with nn.parameter_scope("classifier"):
y = bn(PF.binary_connect_affine(pl, 10))
return y
def cifar10_binary_net_resnet23_prediction(image, maps=64,
test=False):
"""
Construct BianryNet using resnet23. Binary Net binaries weights and activations.
References:
Courbariaux Matthieu, Bengio Yoshua, David Jean-Pierre,
"BinaryConnect: Training Deep Neural Networks with binary weights during propagations",
Advances in Neural Information Processing Systems 28 (NIPS 2015)
"""
# Residual Unit
def res_unit(x, scope_name, rng, dn=False, test=False):
C = x.shape[1]
with nn.parameter_scope(scope_name):
# Conv -> BN -> BinaryTanh
with nn.parameter_scope("conv1"):
h = PF.binary_connect_convolution(x, C / 2, kernel=(1, 1), pad=(0, 0),
with_bias=False)
h = PF.batch_normalization(h, batch_stat=not test)
h = F.binary_tanh(h)
# Conv -> BN -> BinaryTanh
with nn.parameter_scope("conv2"):
h = PF.binary_connect_convolution(h, C / 2, kernel=(3, 3), pad=(1, 1),
with_bias=False)
h = PF.batch_normalization(h, batch_stat=not test)
h = F.binary_tanh(h)
# Conv -> BN
with nn.parameter_scope("conv3"):
h = PF.binary_connect_convolution(h, C, kernel=(1, 1), pad=(0, 0),
with_bias=False)
h = PF.batch_normalization(h, batch_stat=not test)
# Residual -> BinaryTanh
h = F.binary_tanh(h + x)
# Maxpooling
if dn:
h = F.max_pooling(h, kernel=(2, 2), stride=(2, 2))
return h
ncls = 10
# Conv -> BN -> Binary_tanh
with nn.parameter_scope("conv1"):
# Preprocess
image /= 255.0
if not test:
image = F.image_augmentation(image, contrast=1.0,
angle=0.25,
flip_lr=True)
image.need_grad = False
h = PF.binary_connect_convolution(image, maps, kernel=(3, 3), pad=(1, 1),
with_bias=False)
h = PF.batch_normalization(h, batch_stat=not test)
h = F.binary_tanh(h)
h = res_unit(h, "conv2", False) # -> 32x32
h = res_unit(h, "conv3", True) # -> 16x16
h = res_unit(h, "conv4", False) # -> 16x16
h = res_unit(h, "conv5", True) # -> 8x8
h = res_unit(h, "conv6", False) # -> 8x8
h = res_unit(h, "conv7", True) # -> 4x4
h = res_unit(h, "conv8", False) # -> 4x4
h = F.average_pooling(h, kernel=(4, 4)) # -> 1x1
pred = PF.binary_connect_affine(h, ncls)
return pred
def cifar10_binary_net_resnet23_prediction(image, maps=64, test=False):
"""
Construct BianryNet using resnet23.
"""
# Residual Unit
def res_unit(x, scope_name, dn=False):
C = x.shape[1]
with nn.parameter_scope(scope_name):
# Conv -> BN -> BinaryTanh
with nn.parameter_scope("conv1"):
h = PF.binary_connect_convolution(x,
C // 2,
kernel=(1, 1),
pad=(0, 0),
with_bias=False)
h = PF.batch_normalization(h, batch_stat=not test)
h = F.binary_tanh(h)
# Conv -> BN -> BinaryTanh
with nn.parameter_scope("conv2"):
h = PF.binary_connect_convolution(h,
C // 2,
kernel=(3, 3),
pad=(1, 1),
with_bias=False)
h = PF.batch_normalization(h, batch_stat=not test)
h = F.binary_tanh(h)
# Conv -> BN
with nn.parameter_scope("conv3"):
h = PF.binary_connect_convolution(h,
C,
kernel=(1, 1),
pad=(0, 0),
with_bias=False)
h = PF.batch_normalization(h, batch_stat=not test)
# Residual -> BinaryTanh
h = F.binary_tanh(h + x)
# Maxpooling
if dn:
h = F.max_pooling(h, kernel=(2, 2), stride=(2, 2))
return h
ncls = 10
# Conv -> BN -> Binary_tanh
with nn.parameter_scope("conv1"):
# Preprocess
image /= 255.0
if not test:
image = F.image_augmentation(image,
contrast=1.0,
angle=0.25,
flip_lr=True)
image.need_grad = False
h = PF.binary_connect_convolution(image,
maps,
kernel=(3, 3),
pad=(1, 1),
with_bias=False)
h = PF.batch_normalization(h, batch_stat=not test)
h = F.binary_tanh(h)
h = res_unit(h, "conv2", False) # -> 32x32
h = res_unit(h, "conv3", True) # -> 16x16
h = res_unit(h, "conv4", False) # -> 16x16
h = res_unit(h, "conv5", True) # -> 8x8
h = res_unit(h, "conv6", False) # -> 8x8
h = res_unit(h, "conv7", True) # -> 4x4
h = res_unit(h, "conv8", False) # -> 4x4
h = F.average_pooling(h, kernel=(4, 4)) # -> 1x1
pred = PF.binary_connect_affine(h, ncls)
return pred