目錄
Pytorch_模型轉Caffe(一)
1.Caffe簡介
2.Caffe進行目標檢測任務
- 利用ssd進行目標檢測任務,主要步驟如下(重點是模型的移植)
3.Caffe五大組件
4.caffemodel
- 包含了prototxt(除了solver.prototxt) 和 weights bias
prototxt 以文本的方式存儲網絡結構 - 通過創建
caffe_pb2.NetParameter()
對象,獲取caffemodel內容
model = caffe_pb2.NetParameter()
f = open(caffemodel_filename, 'rb')
model.ParseFromString(f.read())
- 循環獲取每個layer下的參數
model.layer是每層的信息
## 逐個解析prototxt 內容 但有點復雜
for i,layer in enumerate(Tarpa_model.layer):
tops = layer.top
bottoms = layer.bottom
top_str = ''
bottom_str =''
transform_param_str = ''
data_param_str = ''
annotated_data_param_str=''
for top in layer.top:
top_str += '\ttop:"{}"\n'.format(top)
for bottom in layer.bottom:
bottom_str += '\tbottom:"{}"\n'.format(bottom)
# transform
if str(layer.transform_param)!='':
transform_param_str = str(layer.transform_param).split('\n')
new_str_trans =''
for item in transform_param_str:
new_str_trans += '\t\t'+str(item) + '\n' if item!='' else ''
# print(new_str_trans)
transform_param_str = '\t' +'transform_param {\n'+ new_str_trans +'\t}'+'\n'
# data_param
if str(layer.data_param) != '':
data_param_str = str(layer.data_param).split('\n')
new_str_data_param =''
for item in data_param_str:
new_str_data_param += '\t\t'+str(item) + '\n' if item!='' else ''
data_param_str = '\t' +'data_param {\n'+ new_str_data_param +'\t}'+'\n'
# annotated_data_param
if str(layer.annotated_data_param) != '':
annotated_data_param_str = str(layer.annotated_data_param).split('\n')
new_str_annotated_data_param =''
for item in annotated_data_param_str:
new_str_annotated_data_param += '\t\t'+str(item) + '\n' if item!='' else ''
annotated_data_param_str = '\t' +'annotated_data_param {\n'+ new_str_annotated_data_param +'\t}'+'\n'
- 解析后的部分結果
### train.prototxt 卷積層
layer {
name: "conv1_2"
type: "Convolution"
bottom: "conv1_1"
top: "conv1_2"
param {
lr_mult: 1.0
decay_mult: 1.0
}
param {
lr_mult: 2.0
decay_mult: 0.0
}
convolution_param {
num_output: 64
pad: 1
kernel_size: 3
weight_filler {
type: "xavier"
}
bias_filler {
type: "constant"
value: 0.0
}
}
}
5.通過caffemodel解析train.prototxt
- 旨在學習了解caffemodel中的數據存儲結構
采用剔除法,先保存所有layer,之后刪除blobs和其他無用信息
import caffe.proto.caffe_pb2 as caffe_pb2
caffemodel_filename = src_path + '/***.caffemodel'
Tarpa_model = caffe_pb2.NetParameter()
f = open(caffemodel_filename, 'rb')
Tarpa_model.ParseFromString(f.read())
f.close()
print(Tarpa_model.name)
print(Tarpa_model.input)
# print(Tarpa_model.layer)
# print(type(Tarpa_model.layer))
f = open('_caffemodel_.log','w')
f.write('name: "{}"'.format(Tarpa_model.name)+'\n')
for i,layer in enumerate(Tarpa_model.layer):
transform_param_str = str(layer).split('\n')
new_str_trans =''
comtinue_flag = 0
for item in transform_param_str:
if item == 'phase: TRAIN':
continue
if comtinue_flag and '}'in item:
continue
comtinue_flag = 0
if 'blobs' in item or 'data:'in item or 'shape'in item or 'dim:'in item:
comtinue_flag = 1
continue
new_str_trans += '\t'+str(item) + '\n' if item!='' else ''
layer_str = 'layer {' +'\n'+\
new_str_trans+\
'}'+'\n'
f.write(str(layer_str))
print(i)
# if i ==2:
# break
f.close()
6.caffemodel解析現存問題
在生成.prototxt后可以看出有很多split字段,暫未得到解決
layer {
name: "data_data_0_split"
type: "Split"
bottom: "data"
top: "data_data_0_split_0"
top: "data_data_0_split_1"
top: "data_data_0_split_2"
top: "data_data_0_split_3"
top: "data_data_0_split_4"
top: "data_data_0_split_5"
top: "data_data_0_split_6"
top: "data_data_0_split_7"
}