def Block(net, name, nb_filters, stride):
split = net.add(layer.Split(name + "-split", 2))
if stride > 1:
net.add(layer.Conv2D(name + "-br2-conv", nb_filters, 1, stride, pad=0),
split)
br2bn = net.add(layer.BatchNormalization(name + "-br2-bn"))
net.add(layer.Conv2D(name + "-br1-conv1", nb_filters, 3, stride, pad=1),
split)
net.add(layer.BatchNormalization(name + "-br1-bn1"))
net.add(layer.Activation(name + "-br1-relu"))
net.add(layer.Conv2D(name + "-br1-conv2", nb_filters, 3, 1, pad=1))
br1bn2 = net.add(layer.BatchNormalization(name + "-br1-bn2"))
if stride > 1:
net.add(layer.Merge(name + "-merge"), [br1bn2, br2bn])
else:
net.add(layer.Merge(name + "-merge"), [br1bn2, split])
def create_net(input_shape, use_cpu=False):
if use_cpu:
layer.engine = 'singacpp'
net = ffnet.FeedForwardNet(loss.SoftmaxCrossEntropy(), metric.Accuracy())
ConvBnReLUPool(net, 'conv1', 32, input_shape)
ConvBnReLUPool(net, 'conv2', 64)
ConvBnReLUPool(net, 'conv3', 128)
ConvBnReLUPool(net, 'conv4', 128)
ConvBnReLUPool(net, 'conv5', 256)
ConvBnReLUPool(net, 'conv6', 256)
ConvBnReLUPool(net, 'conv7', 512)
ConvBnReLUPool(net, 'conv8', 512)
net.add(layer.Flatten('flat'))
net.add(layer.Dense('ip1', 256))
net.add(layer.BatchNormalization('bn1'))
net.add(layer.Activation('relu1'))
net.add(layer.Dropout('dropout1', 0.2))
net.add(layer.Dense('ip2', 16))
net.add(layer.BatchNormalization('bn2'))
net.add(layer.Activation('relu2'))
net.add(layer.Dropout('dropout2', 0.2))
net.add(layer.Dense('ip3', 2))
print 'Parameter intialization............'
for (p, name) in zip(net.param_values(), net.param_names()):
print name, p.shape
if 'mean' in name or 'beta' in name:
p.set_value(0.0)
elif 'var' in name:
p.set_value(1.0)
elif 'gamma' in name:
initializer.uniform(p, 0, 1)
elif len(p.shape) > 1:
if 'conv' in name:
initializer.gaussian(p, 0, p.size())
else:
p.gaussian(0, 0.02)
else:
p.set_value(0)
print name, p.l1()
return net
def create_net(use_cpu=False):
if use_cpu:
layer.engine = 'singacpp'
net = ffnet.FeedForwardNet(loss.SoftmaxCrossEntropy(), metric.Accuracy())
ConvBnReLU(net, 'conv1_1', 64, (3, 32, 32))
net.add(layer.Dropout('drop1', 0.3))
ConvBnReLU(net, 'conv1_2', 64)
net.add(layer.MaxPooling2D('pool1', 2, 2, border_mode='valid'))
ConvBnReLU(net, 'conv2_1', 128)
net.add(layer.Dropout('drop2_1', 0.4))
ConvBnReLU(net, 'conv2_2', 128)
net.add(layer.MaxPooling2D('pool2', 2, 2, border_mode='valid'))
ConvBnReLU(net, 'conv3_1', 256)
net.add(layer.Dropout('drop3_1', 0.4))
ConvBnReLU(net, 'conv3_2', 256)
net.add(layer.Dropout('drop3_2', 0.4))
ConvBnReLU(net, 'conv3_3', 256)
net.add(layer.MaxPooling2D('pool3', 2, 2, border_mode='valid'))
ConvBnReLU(net, 'conv4_1', 512)
net.add(layer.Dropout('drop4_1', 0.4))
ConvBnReLU(net, 'conv4_2', 512)
net.add(layer.Dropout('drop4_2', 0.4))
ConvBnReLU(net, 'conv4_3', 512)
net.add(layer.MaxPooling2D('pool4', 2, 2, border_mode='valid'))
ConvBnReLU(net, 'conv5_1', 512)
net.add(layer.Dropout('drop5_1', 0.4))
ConvBnReLU(net, 'conv5_2', 512)
net.add(layer.Dropout('drop5_2', 0.4))
ConvBnReLU(net, 'conv5_3', 512)
net.add(layer.MaxPooling2D('pool5', 2, 2, border_mode='valid'))
net.add(layer.Flatten('flat'))
net.add(layer.Dropout('drop_flat', 0.5))
net.add(layer.Dense('ip1', 512))
net.add(layer.BatchNormalization('batchnorm_ip1'))
net.add(layer.Activation('relu_ip1'))
net.add(layer.Dropout('drop_ip2', 0.5))
net.add(layer.Dense('ip2', 10))
print 'Start intialization............'
for (p, name) in zip(net.param_values(), net.param_names()):
print name, p.shape
if 'mean' in name or 'beta' in name:
p.set_value(0.0)
elif 'var' in name:
p.set_value(1.0)
elif 'gamma' in name:
initializer.uniform(p, 0, 1)
elif len(p.shape) > 1:
if 'conv' in name:
initializer.gaussian(p, 0, 3 * 3 * p.shape[0])
else:
p.gaussian(0, 0.02)
else:
p.set_value(0)
print name, p.l1()
return net
def ConvBnReLU(net, name, nb_filers, sample_shape=None):
net.add(
layer.Conv2D(name + '_1',
nb_filers,
3,
1,
pad=1,
input_sample_shape=sample_shape))
net.add(layer.BatchNormalization(name + '_2'))
net.add(layer.Activation(name + '_3'))
def ConvBnReLUPool(net, name, nb_filers, sample_shape=None):
net.add(
layer.Conv2D(name + '_conv',
nb_filers,
3,
1,
pad=1,
input_sample_shape=sample_shape))
net.add(layer.BatchNormalization(name + '_bn'))
net.add(layer.Activation(name + '_relu'))
net.add(layer.MaxPooling2D(name + '_pool', 2, 2, border_mode='valid'))
def create_net(use_cpu=False):
if use_cpu:
layer.engine = 'singacpp'
net = ffnet.FeedForwardNet(loss.SoftmaxCrossEntropy(), metric.Accuracy())
net.add(
layer.Conv2D("conv1", 16, 3, 1, pad=1, input_sample_shape=(3, 32, 32)))
net.add(layer.BatchNormalization("bn1"))
net.add(layer.Activation("relu1"))
Block(net, "2a", 16, 1)
Block(net, "2b", 16, 1)
Block(net, "2c", 16, 1)
Block(net, "3a", 32, 2)
Block(net, "3b", 32, 1)
Block(net, "3c", 32, 1)
Block(net, "4a", 64, 2)
Block(net, "4b", 64, 1)
Block(net, "4c", 64, 1)
net.add(layer.AvgPooling2D("pool4", 8, 8, border_mode='valid'))
net.add(layer.Flatten('flat'))
net.add(layer.Dense('ip5', 10))
print 'Start intialization............'
for (p, name) in zip(net.param_values(), net.param_names()):
# print name, p.shape
if 'mean' in name or 'beta' in name:
p.set_value(0.0)
elif 'var' in name:
p.set_value(1.0)
elif 'gamma' in name:
initializer.uniform(p, 0, 1)
elif len(p.shape) > 1:
if 'conv' in name:
# initializer.gaussian(p, 0, math.sqrt(2.0/p.shape[1]))
initializer.gaussian(p, 0, 9.0 * p.shape[0])
else:
initializer.uniform(p, p.shape[0], p.shape[1])
else:
p.set_value(0)
# print name, p.l1()
return net
def build_net(self):
if self.use_cpu:
layer.engine = 'singacpp'
else:
layer.engine = 'cudnn'
self.net = ffnet.FeedForwardNet(loss.SoftmaxCrossEntropy(),
metric.Accuracy())
self.net.add(
Reshape('reshape1', (self.vocab_size, ),
input_sample_shape=(self.maxlen, self.vocab_size)))
self.net.add(layer.Dense('embed',
self.embed_size)) # output: (embed_size, )
self.net.add(layer.Dropout('dropout'))
self.net.add(Reshape('reshape2', (1, self.maxlen, self.embed_size)))
self.net.add(
layer.Conv2D('conv',
self.filters, (self.kernel_size, self.embed_size),
border_mode='valid')) # output: (filter, embed_size)
if self.use_cpu == False:
self.net.add(layer.BatchNormalization('batchNorm'))
self.net.add(layer.Activation('activ')) # output: (filter, embed_size)
self.net.add(layer.MaxPooling2D('max', stride=self.pool_size))
self.net.add(layer.Flatten('flatten'))
self.net.add(layer.Dense('dense', 2))
def create_net(input_shape, use_cpu=False):
if use_cpu:
layer.engine = 'singacpp'
net = ffnet.FeedForwardNet(loss.SoftmaxCrossEntropy(), metric.Accuracy())
net.add(
layer.Conv2D('conv1',
nb_kernels=32,
kernel=7,
stride=3,
pad=1,
input_sample_shape=input_shape))
net.add(layer.Activation('relu1'))
net.add(layer.BatchNormalization('bn1'))
net.add(layer.MaxPooling2D('pool1', 2, 2, border_mode='valid'))
net.add(layer.Conv2D('conv2', nb_kernels=64, kernel=5, stride=3))
net.add(layer.Activation('relu2'))
net.add(layer.BatchNormalization('bn2'))
net.add(layer.MaxPooling2D('pool2', 2, 2, border_mode='valid'))
net.add(layer.Conv2D('conv3', nb_kernels=128, kernel=3, stride=1, pad=2))
net.add(layer.Activation('relu3'))
net.add(layer.BatchNormalization('bn3'))
net.add(layer.MaxPooling2D('pool3', 2, 2, border_mode='valid'))
net.add(layer.Conv2D('conv4', nb_kernels=256, kernel=3, stride=1))
net.add(layer.Activation('relu4'))
net.add(layer.BatchNormalization('bn4'))
net.add(layer.MaxPooling2D('pool4', 2, 2, border_mode='valid'))
net.add(layer.Conv2D('conv5', nb_kernels=512, kernel=3, stride=1, pad=1))
net.add(layer.Activation('relu5'))
net.add(layer.BatchNormalization('bn5'))
net.add(layer.MaxPooling2D('pool5', 2, 2, border_mode='valid'))
net.add(layer.Flatten('flat'))
net.add(layer.Dense('ip6', 2048))
net.add(layer.Activation('relu6'))
net.add(layer.BatchNormalization('bn6'))
net.add(layer.Dropout('dropout6', 0.5))
net.add(layer.Dense('ip7', 128))
net.add(layer.Activation('relu7'))
net.add(layer.BatchNormalization('bn7'))
net.add(layer.Dropout('dropout7', 0.2))
net.add(layer.Dense('ip8', 2))
print 'Parameter intialization............'
for (p, name) in zip(net.param_values(), net.param_names()):
print name, p.shape
if 'mean' in name or 'beta' in name:
p.set_value(0.0)
elif 'var' in name:
p.set_value(1.0)
elif 'gamma' in name:
initializer.uniform(p, 0, 1)
elif len(p.shape) > 1:
if 'conv' in name:
initializer.gaussian(p, 0, p.size())
else:
p.gaussian(0, 0.02)
else:
p.set_value(0)
print name, p.l1()
return net
def test_batch_normalization(self):
in_sample_shape = (3, 224, 224)
bn = layer.BatchNormalization('bn', input_sample_shape=in_sample_shape)
out_sample_shape = bn.get_output_sample_shape()
self.check_shape(out_sample_shape, in_sample_shape)
def create_net(input_shape, use_cpu=False):
if use_cpu:
layer.engine = 'singacpp'
net = ffnet.FeedForwardNet(loss.SoftmaxCrossEntropy(), metric.Accuracy())
ConvBnReLU(net, 'conv1_1', 64, input_shape)
#net.add(layer.Dropout('drop1', 0.3))
net.add(layer.MaxPooling2D('pool0', 2, 2, border_mode='valid'))
ConvBnReLU(net, 'conv1_2', 128)
net.add(layer.MaxPooling2D('pool1', 2, 2, border_mode='valid'))
ConvBnReLU(net, 'conv2_1', 128)
net.add(layer.Dropout('drop2_1', 0.4))
ConvBnReLU(net, 'conv2_2', 128)
net.add(layer.MaxPooling2D('pool2', 2, 2, border_mode='valid'))
ConvBnReLU(net, 'conv3_1', 256)
net.add(layer.Dropout('drop3_1', 0.4))
ConvBnReLU(net, 'conv3_2', 256)
net.add(layer.Dropout('drop3_2', 0.4))
ConvBnReLU(net, 'conv3_3', 256)
net.add(layer.MaxPooling2D('pool3', 2, 2, border_mode='valid'))
ConvBnReLU(net, 'conv4_1', 256)
net.add(layer.Dropout('drop4_1', 0.4))
ConvBnReLU(net, 'conv4_2', 256)
net.add(layer.Dropout('drop4_2', 0.4))
ConvBnReLU(net, 'conv4_3', 256)
net.add(layer.MaxPooling2D('pool4', 2, 2, border_mode='valid'))
ConvBnReLU(net, 'conv5_1', 512)
net.add(layer.Dropout('drop5_1', 0.4))
ConvBnReLU(net, 'conv5_2', 512)
net.add(layer.Dropout('drop5_2', 0.4))
ConvBnReLU(net, 'conv5_3', 512)
net.add(layer.MaxPooling2D('pool5', 2, 2, border_mode='valid'))
#ConvBnReLU(net, 'conv6_1', 512)
#net.add(layer.Dropout('drop6_1', 0.4))
#ConvBnReLU(net, 'conv6_2', 512)
#net.add(layer.Dropout('drop6_2', 0.4))
#ConvBnReLU(net, 'conv6_3', 512)
#net.add(layer.MaxPooling2D('pool6', 2, 2, border_mode='valid'))
#ConvBnReLU(net, 'conv7_1', 512)
#net.add(layer.Dropout('drop7_1', 0.4))
#ConvBnReLU(net, 'conv7_2', 512)
#net.add(layer.Dropout('drop7_2', 0.4))
#ConvBnReLU(net, 'conv7_3', 512)
#net.add(layer.MaxPooling2D('pool7', 2, 2, border_mode='valid'))
net.add(layer.Flatten('flat'))
net.add(layer.Dense('ip1', 256))
net.add(layer.BatchNormalization('bn1'))
net.add(layer.Activation('relu1'))
net.add(layer.Dropout('dropout1', 0.2))
net.add(layer.Dense('ip2', 16))
net.add(layer.BatchNormalization('bn2'))
net.add(layer.Activation('relu2'))
net.add(layer.Dropout('dropout2', 0.2))
net.add(layer.Dense('ip3', 2))
print 'Parameter intialization............'
for (p, name) in zip(net.param_values(), net.param_names()):
print name, p.shape
if 'mean' in name or 'beta' in name:
p.set_value(0.0)
elif 'var' in name:
p.set_value(1.0)
elif 'gamma' in name:
initializer.uniform(p, 0, 1)
elif len(p.shape) > 1:
if 'conv' in name:
initializer.gaussian(p, 0, p.size())
else:
p.gaussian(0, 0.02)
else:
p.set_value(0)
print name, p.l1()
return net
