def searchMoea(net_path, save_path, upper_limit=None, lower_limit=None):
"""多目标优化搜索,输入为预训练模型的权重所在路径，输出为遗传算法搜索结果的.csv保存的目录路径"""
"""==============================环境参数设置和问题类的初始化==========================="""
# get options
opt = BaseOptions().parse()
if not (upper_limit is None):
opt.max_rate = upper_limit
if not (lower_limit is None):
opt.min_rate = lower_limit
# basic settings
os.environ["CUDA_VISIBLE_DEVICES"] = str(opt.gpu_ids)[1:-1]
if torch.cuda.is_available():
device = "cuda"
torch.backends.cudnn.benchmark = False
else:
device = "cpu"
##################### Get Dataloader ####################
dataloader_train, dataloader_val = custom_get_dataloaders(opt)
# dummy_input is sample input of dataloaders
if hasattr(dataloader_val, "dataset"):
dummy_input = dataloader_val.dataset.__getitem__(0)
dummy_input = dummy_input[0]
dummy_input = dummy_input.unsqueeze(0)
else:
# for imagenet dali loader
dummy_input = torch.rand(1, 3, 224, 224)
##################### Create Baseline Model ####################
net = ModelWrapper(opt)
# 预训练模型的权重导入
load(net, net_path)
flops_before, params_before = model_summary(net.get_compress_part(),
dummy_input)
compression_scheduler = distiller.file_config(net.get_compress_part(),
net.optimizer,
opt.compress_schedule_path)
num_layer = len(compression_scheduler.policies[1])
##################### 定义问题，并进行遗传算法 ####################
args = dict()
args["dataloader_train"] = dataloader_train
args["dataloader_val"] = dataloader_val
args["dummy_input"] = dummy_input
args["flops_before"] = flops_before
args["device"] = device
args["num_layer"] = num_layer
problem = MoeaProblem(opt, net_path, args)
"""==============================种群设置==========================="""
Encoding = 'RI' #编码方式,此处为整数实数混合编码
NIND = 10 #种群规模
Field = gp.crtfld(Encoding, problem.varTypes, problem.ranges,
problem.borders) #创建区域描述器
population = gp.Population(Encoding, Field, NIND)
"""===========================算法参数设置=========================="""
#myAlgorithm = gp.moea_awGA_templet(problem, population)
#myAlgorithm = gp.moea_MOEAD_DE_templet(problem, population)
#myAlgorithm = gp.moea_RVEA_RES_templet(problem, population)
myAlgorithm = gp.moea_NSGA2_archive_templet(problem, population)
#实例化算法模板
myAlgorithm.MAXGEN = 100 # 最大进化代数
#myAlgorithm.mutOper.F = 0.5 # 差分进化中的参数F
#myAlgorithm.recOper.XOVR = 0.7 # 设置交叉概率
myAlgorithm.logTras = 10 # 设置每隔多少代记录日志，若设置成0则表示不记录日志
myAlgorithm.verbose = True # 设置是否打印输出日志信息
myAlgorithm.drawing = 1 # 设置绘图方式（0：不绘图；1：绘制结果图；2：绘制目标空间过程动画)
"""==========================根据先验知识创建先知种群==============="""
#data = pd.read_csv('/eagle_eye/search_results/old_dataset/pop_get_search_result_moea2/Phen.csv')
#well_config=np.array(data)[5:,:]
#prophetpop=gp.Population(Encoding,Field,5,well_config)# 实例化种群对象
#myAlgorithm.call_aimFunc(prophetpop)# 计算先知种群的目标函数值及约束（假如有约束）
#myAlgorithm.call_aimFunc()# 计算先知种群的目标函数值及约束（假如有约束）
"""==========================调用算法模板进行种群进化==============="""
#[BestIndi, population] = myAlgorithm.run(prophetpop) # 执行带先验种群的算法模板，得到最优个体以及最后一代种群
[BestIndi, population] = myAlgorithm.run() # 执行算法模板，得到最优个体以及最后一代种群
BestIndi.save(os.path.join(save_path, "BestIndi")) # 把最优个体的信息保存到文件中
population.save(os.path.join(save_path, "Population")) # save population
"""=================================输出结果======================="""
print('评价次数：%s' % myAlgorithm.evalsNum)
print('时间已过%s秒' % myAlgorithm.passTime)
if BestIndi.sizes != 0:
print('最优的目标函数值为： ')
print(BestIndi.ObjV[0, :])
print('最优的控制变量值为：')
print(BestIndi.Phen[0, :])
return BestIndi.ObjV[0, :], BestIndi.Phen[0, :]
else:
print('没找到可行解。')
return -1, -1
def randomSearch(net_path,save_path,upper_limit=None,lower_limit=None):
"""随机搜索，需要指定需要保留压缩方案的上下阈值"""
"""==============================环境参数设置==========================="""
# get options
opt = BaseOptions().parse()
if not (upper_limit is None):
opt.max_rate=upper_limit
if not (lower_limit is None):
opt.min_rate = lower_limit
# basic settings
os.environ["CUDA_VISIBLE_DEVICES"] = str(opt.gpu_ids)[1:-1]
if torch.cuda.is_available():
device = "cuda"
torch.backends.cudnn.benchmark = False
else:
device = "cpu"
##################### Get Dataloader ####################
dataloader_train, dataloader_val = custom_get_dataloaders(opt)
# dummy_input is sample input of dataloaders
if hasattr(dataloader_val, "dataset"):
dummy_input = dataloader_val.dataset.__getitem__(0)
dummy_input = dummy_input[0]
dummy_input = dummy_input.unsqueeze(0)
else:
# for imagenet dali loader
dummy_input = torch.rand(1, 3, 224, 224)
##################### Create Baseline Model ####################
net = ModelWrapper(opt)
# 预训练模型的权重导入
load(net, net_path)
flops_before, params_before = model_summary(net.get_compress_part(), dummy_input)
compression_scheduler = distiller.file_config(
net.get_compress_part(), net.optimizer, opt.compress_schedule_path
)
num_layer = len(compression_scheduler.policies[1])
##################### Pruning Strategy Generation ###############
compression_scheduler = distiller.file_config(
net.get_compress_part(), net.optimizer, opt.compress_schedule_path
)
output=[]
while len(output)<10:
# 随机产生压缩向量
channel_config = []
for i in range(num_layer):
channel_config.append(random.uniform(opt.min_rate, opt.max_rate))
compression_scheduler = setCompressionScheduler(
compression_scheduler, channel_config
)
###### Adaptive-BN-based Candidate Evaluation of Pruning Strategy ###
thinning(net, compression_scheduler, input_tensor=dummy_input)
flops_after, params_after = model_summary(net.get_compress_part(), dummy_input)
ratio = flops_after / flops_before
if upper_limit>=ratio>=lower_limit:
net = net.to(device)
net.parallel(opt.gpu_ids)
net.get_compress_part().train()
with torch.no_grad():
for index, sample in enumerate(dataloader_train):
_ = net.get_loss(sample)
if index > 100:
break
strategy_score = net.get_eval_scores(dataloader_val)["accuracy"]
channel_config.insert(0,strategy_score)
channel_config.insert(0,ratio)
output.append(channel_config)
del net
net = ModelWrapper(opt)
# 预训练模型的权重导入
load(net, net_path)
columns=["acc","ratio","vars"]
columns+=[""]*(num_layer-1)
pd.DataFrame(output,columns=columns).to_csv(save_path)