简介

分类

模型转换

网络参数转到MAT 文件

keras权重到mat

可知直接用matlab读取hdf5文件, 也可以通过如下脚本 keras2mat.py 转换.

#!/usr/bin/env python2 #-*- coding: utf-8 -*- # Author: # ---------<Zhi Liu> # ---------<Xidian University> # ---------<zhiliu.mind@gmail.com> # ---------<https://iridescent.blog.csdn.net/> # ---------<https://www.linkedin.com/in/zhiliuln> # ---------<2019/08/16> import os import scipy.io as sio from ssd import SSD300 modelW = '[YOUR WEIGHT PATH in hdf5]' input_shape = (6, 4000, 1) NUM_CLASSES = 11 model = SSD300(input_shape, num_classes=NUM_CLASSES) model.load_weights(modelW, by_name=True) output_dir = './' netpars = {} for w in model.get_weights(): print(w.shape) for layer in model.layers: wb = model.get_layer(layer.name).get_weights() print(layer.name, len(wb)) if len(wb) == 0: netpars[layer.name + 'v'] = wb if len(wb) > 0: netpars[layer.name + 'w'] = wb[0] if len(wb) > 1: netpars[layer.name + 'b'] = wb[1] sio.savemat(output_dir + 'keras_ssd.mat', {'netpars': netpars})

caffe 到 mat

可以使用caffe的matlab接口, 也可以使用如下脚本 caffe2mat 转换

#!/usr/bin/env python2 #-*- coding: utf-8 -*- # Author: # ---------<Zhi Liu> # ---------<Xidian University> # ---------<zhiliu.mind@gmail.com> # ---------<https://iridescent.blog.csdn.net/> # ---------<https://www.linkedin.com/in/zhiliuln> # ---------<2017/10/06> import os import scipy.io as sio import caffe # specify model file -->'deploy.prototxt' and weights file '*.caffemodel' # net_model = '../../model/alexnet/deploy.prototxt' # net_weights = '../../model/alexnet/bvlc_alexnet.caffemodel' output_dir = './mat/' net_model = '../../model/ssd/deploy.prototxt' net_weights = '../../model/ssd/VGG_INST25_SSD_300x300_iter_50000.caffemodel' def GetFileNameAndExt(filename): import os (filepath, tempfilename) = os.path.split(filename) (shotname, extension) = os.path.splitext(tempfilename) return shotname, extension def load(net_model, net_weights, output_dir): name, ext = GetFileNameAndExt(net_weights) # Load the net caffe.set_mode_cpu() # You may need to train this caffemodel first # There should be script to help you do the training net = caffe.Net(net_model, net_weights, caffe.TEST) netpars = {} # 遍历每一层 for param_name in net.params.keys(): # 权重参数 weight = net.params[param_name][0].data netpars[param_name + 'w'] = weight if len(net.params[param_name]) > 1: # 偏置参数 bias = net.params[param_name][1].data netpars[param_name + 'b'] = bias biasshape = bias.shape else: biasshape = [] print(param_name, len(net.params[param_name]), 'weights:', weight.shape, 'bias:', biasshape) sio.savemat(output_dir + name + '.mat', {'netpars': netpars}) if __name__ == "__main__": if os.path.exists(output_dir) == False: os.mkdir(output_dir) load(net_model, net_weights, output_dir) print("========Done!=========")