我希望您一切都好，我尝试运行从此处下载的python代码：“ https://github.com/may0324/DeepCompression-caffe/tree/master/examples/mnist”
我正在使用Ubuntu 16.04，python（2.7,3.5），
import sys
import os
sparse_ratio_vec = [0.33, 0.8, 0.9, 0.8] #sparse ratio of each layer
iters = [500, 1000, 10500, 11000, 500] #max iteration of each stage
def generate_data_layer():
data_layer_str = '''
name: "LeNet"
layer {
name: "mnist"
type: "Data"
top: "data"
top: "label"
include {
phase: TRAIN
}
transform_param {
scale: 0.00390625
}
data_param {
source: "examples/mnist/mnist_train_lmdb"
batch_size: 64
backend: LMDB
}
}
layer {
name: "mnist"
type: "Data"
top: "data"
top: "label"
include {
phase: TEST
}
transform_param {
scale: 0.00390625
}
data_param {
source: "examples/mnist/mnist_test_lmdb"
batch_size: 100
backend: LMDB
}
}
'''
return data_layer_str
def generate_cmp_conv_layer(kernel_size, kernel_num, stride, layer_name, bottom, top, filler="xavier", sparse_ratio=0, class_num=256, quantize_term="false"):
tmp =''
if filler == 'gaussian':
tmp = ''' std: 0.01
'''
conv_layer_str = '''
layer {
name: "%s"
type: "CmpConvolution"
bottom: "%s"
top: "%s"
param {
lr_mult: 1
}
param {
lr_mult: 2
}
convolution_param {
num_output: %d
kernel_size: %d
stride: %d
sparse_ratio: %f
class_num: %d
quantize_term: %s
weight_filler {
type: "%s"
''' %(layer_name, bottom, top, kernel_num, kernel_size, stride, sparse_ratio, class_num, quantize_term, filler) + tmp + '''
}
bias_filler {
type: "constant"
}
}
}
'''
return conv_layer_str
def generate_cmp_fc_layer(kernel_num, layer_name, bottom, top, filler="xavier", sparse_ratio=0, class_num=256, quantize_term="false"):
tmp =''
if filler == 'gaussian':
tmp = ''' std: 0.01
'''
fc_layer_str = '''
layer {
name: "%s"
type: "CmpInnerProduct"
bottom: "%s"
top: "%s"
param {
lr_mult: 1
}
param {
lr_mult: 2
}
inner_product_param {
num_output: %d
sparse_ratio: %f
class_num: %d
quantize_term: %s
weight_filler {
type: "%s"
''' %(layer_name, bottom, top, kernel_num, sparse_ratio, class_num, quantize_term, filler ) + tmp + '''
}
bias_filler {
type: "constant"
}
}
}'''
return fc_layer_str
def generate_pooling_layer(kernel_size, stride, pool_type, layer_name, bottom, top):
pool_layer_str = '''
layer {
name: "%s"
type: "Pooling"
bottom: "%s"
top: "%s"
pooling_param {
pool: %s
kernel_size: %d
stride: %d
}
}'''%(layer_name, bottom, top, pool_type, kernel_size, stride)
return pool_layer_str
def generate_activation_layer(layer_name, bottom, top, act_type="ReLU"):
act_layer_str = '''
layer {
name: "%s"
type: "%s"
bottom: "%s"
top: "%s"
}'''%(layer_name, act_type, bottom, top)
return act_layer_str
def generate_softmax_loss(bottom):
softmax_loss_str = '''
layer {
name: "loss"
type: "SoftmaxWithLoss"
bottom: "%s"
bottom: "label"
top: "loss"
}
layer {
name: "accuracy"
type: "Accuracy"
bottom: "%s"
bottom: "label"
top: "accuracy"
include {
phase: TEST
}
}
'''%(bottom, bottom)
return softmax_loss_str
def generate_lenet(stage):
if stage<1:
return ''
network_str = generate_data_layer()
if stage == 5: #last stage do weight quantization
quantize_term = "true"
else:
quantize_term = "false"
ratio = sparse_ratio_vec[0]
network_str += generate_cmp_conv_layer(5,20,1,"conv1","data","conv1","xavier",ratio,256,quantize_term)
network_str += generate_pooling_layer(2,2,"MAX","pool1","conv1","pool1")
if stage >= 2:
ratio = sparse_ratio_vec[1]
else:
ratio = 0
network_str += generate_cmp_conv_layer(5,50,1,"conv2","pool1","conv2","xavier",ratio,256,quantize_term)
network_str += generate_pooling_layer(2,2,"MAX","pool2","conv2","pool2")
if stage >= 3:
ratio = sparse_ratio_vec[2]
else:
ratio = 0
network_str += generate_cmp_fc_layer(500,"fc1","pool2","fc1","xavier",ratio,32,quantize_term)
network_str += generate_activation_layer("relu1", "fc1", "fc1", "ReLU")
if stage >= 4:
ratio = sparse_ratio_vec[3]
else:
ratio = 0
network_str += generate_cmp_fc_layer(10,"fc2","fc1","fc2","xavier",ratio,32,quantize_term)
network_str += generate_softmax_loss("fc2")
protoname = 'examples/mnist/lenet_train_test_compress_stage%d.prototxt' %stage
fp = open(protoname, 'w')
fp.write(network_str)
fp.close()
def generate_solver(stage, max_iter):
solver_str = '''
# The train/test net protocol buffer definition
net: "examples/mnist/lenet_train_test_compress_stage%d.prototxt"
# test_iter specifies how many forward passes the test should carry out.
# In the case of MNIST, we have test batch size 100 and 100 test iterations,
# covering the full 10,000 testing images.
test_iter: 100
# Carry out testing every 500 training iterations.
test_interval: 500
# The base learning rate, momentum and the weight decay of the network.
base_lr: 0.001#0.01
momentum: 0.9
weight_decay: 0.0005
# The learning rate policy
lr_policy: "inv"
gamma: 0.0001
power: 0.75
# Display every 100 iterations
display: 100
# The maximum number of iterations
max_iter: %d
# snapshot intermediate results
snapshot: 500
snapshot_prefix: "examples/mnist/lenet_finetune_stage%d"
# solver mode: CPU or GPU
solver_mode: CPU
''' %(stage, max_iter, stage )
protoname = 'examples/mnist/lenet_solver_stage%d.prototxt' %stage
fp = open(protoname,'w')
fp.write(solver_str)
fp.close()
if __name__ == '__main__':
max_stage = 5
for s in range(0,max_stage):
generate_lenet(s+1)
generate_solver(s+1,iters[s])
if s==0:
modelfile = "lenet_iter_10000.caffemodel" #initial model
else:
modelfile = "lenet_finetune_stage%d_iter_%d.caffemodel" %(s, iters[s-1]) #model of last stage
cmd = "./build/tools/caffe train --solver=examples/mnist/lenet_solver_stage%d.prototxt --weights=examples/mnist/%s " %(s+1, modelfile)
#print cmd
os.system(cmd)
当我尝试运行以上代码时，出现此错误：
[libprotobuf ERROR google/protobuf/text_format.cc:274] Error parsing text-format caffe.NetParameter: 54:17: Message type "caffe.ConvolutionParameter" has no field named "sparse_ratio". F0213 15:05:57.959002 4726 upgrade_proto.cpp:79] Check failed: ReadProtoFromTextFile(param_file, param) Failed to parse NetParameter file: examples/mnist/lenet_train_test_compress_stage5.prototxt
我想找出问题或解决问题。 我正在使用此代码修剪lenet 5
答案 0 :(得分：0)
您的Caffe版本没有sparse_ratio作为其inner_product_param或其convolution_param的一部分（请参阅caffe.proto中的这些定义）。
似乎您要运行的特定代码需要它自己的caffe版本，其caffe.proto定义稍有不同。