模型量化的本质就是将模型中的参数按照一定的规则 把32位或者64位浮点数 转化位16位浮点数或者8位定点数。这里我用keras和numpy实现了16位和8位的量化,未考虑量化的科学合理性,仅仅是搞清楚量化本质的一次实验。
量化 """ #coding:utf-8 __project_ = 'TF2learning' __file_name__ = 'quantization' __author__ = 'qilibin' __time__ = '2021/3/17 9:18' __product_name = PyCharm """ import h5py import pandas as pd import numpy as np ''' 读取原来的只包含权重的H5模型,按层遍历,对每层的每个权重进行16位或8位量化,将量化后的权重数值重新保存在H5文件中 ''' def quantization16bit(old_model_path,new_model_path,bit_num): ''' :param old_model_path: 未量化的模型路径 模型是只保存了权重未保存网络结构 :param new_model_path: 量化过后的模型路径 :param bit_num: 量化位数 :return: ''' f = h5py.File(old_model_path,'r') f2 = h5py.File(new_model_path,'w') for layer in f.keys(): # layer : 层的名称 print (layer) # # 每层里面的权重名称 有的层没有参数 # name_of_weight_of_layer = f[layer].attrs['weight_names'] # # 有的层是没有参数的 比如 relu # length = len(name_of_weight_of_layer) length = len(list(f[layer].keys())) if length > 0: g1 = f2.create_group(layer) g1.attrs["weight_names"] = layer g2 = g1.create_group(layer) for weight in f[layer][layer].keys(): print ("wieght name is :" + weight) oldparam = f[layer][layer][weight][:] print ('-----------------------------------------old-----------------------') print (oldparam) if type(oldparam) == np.ndarray: if bit_num == 16: newparam = np.float16(oldparam) if bit_num == 8: min_val = np.min(oldparam) max_val = np.max(oldparam) oldparam = np.round((oldparam - min_val) / (max_val - min_val) * 255) newparam = np.uint8(oldparam) else: newparam = oldparam print ('-----------------------------------------new-----------------------') #print (newparam) #f[key][key][weight_name][:] = newparam 在原来模型的基础上修改 行不通 if bit_num == 16: d = g2.create_dataset(weight, data=newparam,dtype=np.float16) if bit_num == 8: d = g2.create_dataset(weight, data=newparam, dtype=np.uint8) else: g1 = f2.create_group(layer) g1.attrs["weight_names"] = layer f.close() f2.close() old_model_path = './model_0_.h5' new_model_path = './new_model.h5' quantization16bit(old_model_path,new_model_path,8) # print (f['batch_normalization']['batch_normalization']['gamma:0'][:])
检查量化后的文件
""" #coding:utf-8 __project_ = 'TF2learning' __file_name__ = 'readNewMoDel' __author__ = 'qilibin' __time__ = '2021/3/17 13:27' __product_name = PyCharm """ ''' 用来打印量化之后的模型 查看其各个权重的参数 ''' import h5py modelpath = './new_model.h5' #modelpath = './model_0_.h5' f = h5py.File(modelpath,'r') for layer in f.keys(): # key : 层的名称 print ("layer name is :"+layer) # 有些层是没有参数的 比如relu length = len(list(f[layer].keys())) #print (length) if length > 0: for weight in f[layer][layer].keys(): print("wieght name is :" + weight) param = f[layer][layer][weight][:] print(param) f.close() # print (f['batch_normalization']['batch_normalization']['gamma:0'][:])
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