hacker99 created SINGA-248:
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Summary: bug in checkpoint size in vgg-16 model
Key: SINGA-248
URL: https://issues.apache.org/jira/browse/SINGA-248
Project: Singa
Issue Type: Bug
Environment: ubuntu 14.04
Reporter: hacker99
i created vgg-16 net, then saved it with python interface
(python/dragon/net.py) net.save('model.bin'),then find model.bin is about
1.5GB.but same model in caffe just 528MB. can anyone may explain why?very
appreciate.
vgg-16 code :
from dragon import layer
from dragon import initializer
from dragon import metric
from dragon import loss
from dragon import net as ffnet
def ConvReLU(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.Activation(name + '_3'))
def create_net(use_cpu=False):
if use_cpu:
layer.engine = 'dragoncpp'
net = ffnet.FeedForwardNet(loss.SoftmaxCrossEntropy(), metric.Accuracy())
ConvReLU(net, 'conv1_1', 64, (3, 224, 224))
ConvReLU(net, 'conv1_2', 64)
net.add(layer.MaxPooling2D('pool1', 2, 2, border_mode='valid'))
ConvReLU(net, 'conv2_1', 128)
ConvReLU(net, 'conv2_2', 128)
net.add(layer.MaxPooling2D('pool2', 2, 2, border_mode='valid'))
ConvReLU(net, 'conv3_1', 256)
ConvReLU(net, 'conv3_2', 256)
ConvReLU(net, 'conv3_3', 256)
net.add(layer.MaxPooling2D('pool3', 2, 2, border_mode='valid'))
ConvReLU(net, 'conv4_1', 512)
ConvReLU(net, 'conv4_2', 512)
ConvReLU(net, 'conv4_3', 512)
net.add(layer.MaxPooling2D('pool4', 2, 2, border_mode='valid'))
ConvReLU(net, 'conv5_1', 512)
ConvReLU(net, 'conv5_2', 512)
ConvReLU(net, 'conv5_3', 512)
net.add(layer.MaxPooling2D('pool5', 2, 2, border_mode='valid'))
net.add(layer.Flatten('flat'))
net.add(layer.Dense('ip1', 4096))
net.add(layer.Dropout('drop_ip1', 0.5))
net.add(layer.Activation('relu_ip1'))
net.add(layer.Dense('ip2', 4096))
net.add(layer.Activation('relu_ip2'))
net.add(layer.Dropout('drop_ip2', 0.5))
#net.add(layer.BatchNormalization('batchnorm_ip1'))
net.add(layer.Dense('ip3', 1000))
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
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