使用CPU的话，test.py文件应该如何修改

[oSallyo]

没有gpu，不能用pynvml包，请问test.py代码应该如何修改

[yuciton]

我也是只有cpu，然后我用GitHub Copilot询问修改后可以运行，它说在代码中，pynvml库被用于监控GPU内存使用情况。如果想在使用CPU时修改代码，可以简单地移除与pynvml相关的代码，因为在CPU模式下，不需要监控GPU内存。除此之外还有一个大问题就是gym的版本修改等，你可以看另一个问题，有大佬发了教程。 目前修改完test-cpu如下： import copy import json import os import random import time as time

import gym import pandas as pd import torch import numpy as np

import PPO_model from env.load_data import nums_detec

def setup_seed(seed): torch.manual_seed(seed) torch.cuda.manual_seed_all(seed) np.random.seed(seed) random.seed(seed) torch.backends.cudnn.deterministic = True

def tabulate_rows(rows, headers): if rows: return tabulate.tabulate(rows, headers=headers) else: print("No data to tabulate.") def main():

PyTorch initialization

device = torch.device("cpu")
torch.set_default_dtype(torch.float32)
torch.set_default_device(device)
print("PyTorch device: ", device.type)
torch.set_printoptions(precision=None, threshold=np.inf, edgeitems=None, linewidth=None, profile=None, sci_mode=False)

# Load config and init objects
# ... rest of your code ...
# Load config and init objects
with open("./config.json", 'r') as load_f:
    load_dict = json.load(load_f)
env_paras = load_dict["env_paras"]
model_paras = load_dict["model_paras"]
train_paras = load_dict["train_paras"]
test_paras = load_dict["test_paras"]
env_paras["device"] = device
model_paras["device"] = device
env_test_paras = copy.deepcopy(env_paras)
num_ins = test_paras["num_ins"]
if test_paras["sample"]:
    env_test_paras["batch_size"] = test_paras["num_sample"]
else:
    env_test_paras["batch_size"] = 1
model_paras["actor_in_dim"] = model_paras["out_size_ma"] * 2 + model_paras["out_size_ope"] * 2
model_paras["critic_in_dim"] = model_paras["out_size_ma"] + model_paras["out_size_ope"]

data_path = "./data_test/{0}/".format(test_paras["data_path"])
test_files = os.listdir(data_path)
test_files.sort(key=lambda x: x[:-4])
test_files = test_files[:num_ins]
mod_files = os.listdir('./model/')[:]

memories = PPO_model.Memory()
model = PPO_model.PPO(model_paras, train_paras)
rules = test_paras["rules"]
envs = []  # Store multiple environments

# Detect and add models to "rules"
if "DRL" in rules:
    for root, ds, fs in os.walk('./model/'):
        for f in fs:
            if f.endswith('.pt'):
                rules.append(f)
if len(rules) != 1:
    if "DRL" in rules:
        rules.remove("DRL")

# Generate data files and fill in the header
str_time = time.strftime("%Y%m%d_%H%M%S", time.localtime(time.time()))
save_path = './save/test_{0}'.format(str_time)
os.makedirs(save_path)
writer = pd.ExcelWriter(
    '{0}/makespan_{1}.xlsx'.format(save_path, str_time))  # Makespan data storage path
writer_time = pd.ExcelWriter('{0}/time_{1}.xlsx'.format(save_path, str_time))  # time data storage path
file_name = [test_files[i] for i in range(num_ins)]
data_file = pd.DataFrame(file_name, columns=["file_name"])
data_file.to_excel(writer, sheet_name='Sheet1', index=False)
writer.save()
writer.close()
data_file.to_excel(writer_time, sheet_name='Sheet1', index=False)
writer_time.save()
writer_time.close()

# Rule-by-rule (model-by-model) testing
start = time.time()
for i_rules in range(len(rules)):
    rule = rules[i_rules]
    # Load trained model
    if rule.endswith('.pt'):
        if device.type == 'cuda':
            model_CKPT = torch.load('./model/' + mod_files[i_rules])
        else:
            model_CKPT = torch.load('./model/' + mod_files[i_rules], map_location='cpu')
        print('\nloading checkpoint:', mod_files[i_rules])
        model.policy.load_state_dict(model_CKPT)
        model.policy_old.load_state_dict(model_CKPT)
    print('rule:', rule)

    # Schedule instance by instance
    step_time_last = time.time()
    makespans = []
    times = []
    for i_ins in range(num_ins):
        test_file = data_path + test_files[i_ins]
        with open(test_file) as file_object:
            line = file_object.readlines()
            ins_num_jobs, ins_num_mas, _ = nums_detec(line)
        env_test_paras["num_jobs"] = ins_num_jobs
        env_test_paras["num_mas"] = ins_num_mas

        # Environment object already exists
        if len(envs) == num_ins:
            env = envs[i_ins]
        # Create environment object

        else:
            # DRL-S, each env contains multiple (=num_sample) copies of one instance
            if test_paras["sample"]:
                env = gym.make('fjsp-v0', case=[test_file] * test_paras["num_sample"],
                               env_paras=env_test_paras, data_source='file')
            # DRL-G, each env contains one instance
            else:
                env = gym.make('fjsp-v0', case=[test_file], env_paras=env_test_paras, data_source='file')
            envs.append(copy.deepcopy(env))
            print("Create env[{0}]".format(i_ins))

        # Schedule an instance/environment
        # DRL-S
        if test_paras["sample"]:
            makespan, time_re = schedule(env, model, memories, flag_sample=test_paras["sample"])
            makespans.append(torch.min(makespan))
            times.append(time_re)
        # DRL-G
        else:
            time_s = []
            makespan_s = []  # In fact, the results obtained by DRL-G do not change
            for j in range(test_paras["num_average"]):
                makespan, time_re = schedule(env, model, memories)
                makespan_s.append(makespan)
                time_s.append(time_re)
                env.reset()
            makespans.append(torch.mean(torch.tensor(makespan_s)))
            times.append(torch.mean(torch.tensor(time_s)))
        print("finish env {0}".format(i_ins))
    print("rule_spend_time: ", time.time() - step_time_last)

    # Save makespan and time data to files
    data = pd.DataFrame(torch.tensor(makespans).t().tolist(), columns=[rule])
    data.to_excel(writer, sheet_name='Sheet1', index=False, startcol=i_rules + 1)
    writer._save()
    writer._close()
    data = pd.DataFrame(torch.tensor(times).t().tolist(), columns=[rule])
    data.to_excel(writer_time, sheet_name='Sheet1', index=False, startcol=i_rules + 1)
    writer_time.save()
    writer_time.close()

    for env in envs:
        env.reset()

print("total_spend_time: ", time.time() - start)

def schedule(env, model, memories, flag_sample=False):

Get state and completion signal

state = env.state
dones = env.done_batch
done = False  # Unfinished at the beginning
last_time = time.time()
i = 0
while ~done:
    i += 1
    with torch.no_grad():
        actions = model.policy_old.act(state, memories, dones, flag_sample=flag_sample, flag_train=False)
    state, rewards, dones = env.step(actions)  # environment transit
    done = dones.all()
spend_time = time.time() - last_time  # The time taken to solve this environment (instance)
# print("spend_time: ", spend_time)

# Verify the solution
gantt_result = env.validate_gantt()[0]
if not gantt_result:
    print("Scheduling Error！！！！！！")
return copy.deepcopy(env.makespan_batch), spend_time

if name == 'main': main()

[yuciton]

我也是只有cpu，然后我用GitHub Copilot询问修改后可以运行，它说在代码中，库被用于监控GPU内存使用情况。如果想在使用CPU时修改代码，可以简单地移除与相关的代码，因为在CPU模式下，不需要监控GPU内存。除此之外还有一个大问题就是gym的版本修改等，你可以看另一个问题，有大佬发了教程。 目前修改完test-cpu如下： import copy import json import os import random import time as timepynvml``pynvml

导入健身房 导入熊猫作为 PD 导入火炬导入 numpy 作为 NP

从env.load_data导入nums_detec导入PPO_model

def setup_seed（种子）： torch.manual_seed（种子） torch.cuda.manual_seed_all（种子） np.random.seed（种子） random.seed（种子） torch.backends.cudnn.deterministic = True

def tabulate_rows（rows， headers）： if rows： return tabulate.tabulate（rows， headers=headers） else： print（“没有要制表的数据。 def main（）： # PyTorch 初始化 device = torch.device（“cpu”） torch.set_default_dtype（torch.float32） torch.set_default_device（device） print（“PyTorch device： ”， device.type） torch.set_printoptions（precision=None， threshold=np.inf， edgeitems=None， linewidth=None， profile=None， sci_mode=False）

# Load config and init objects
# ... rest of your code ...
# Load config and init objects
with open("./config.json", 'r') as load_f:
    load_dict = json.load(load_f)
env_paras = load_dict["env_paras"]
model_paras = load_dict["model_paras"]
train_paras = load_dict["train_paras"]
test_paras = load_dict["test_paras"]
env_paras["device"] = device
model_paras["device"] = device
env_test_paras = copy.deepcopy(env_paras)
num_ins = test_paras["num_ins"]
if test_paras["sample"]:
    env_test_paras["batch_size"] = test_paras["num_sample"]
else:
    env_test_paras["batch_size"] = 1
model_paras["actor_in_dim"] = model_paras["out_size_ma"] * 2 + model_paras["out_size_ope"] * 2
model_paras["critic_in_dim"] = model_paras["out_size_ma"] + model_paras["out_size_ope"]

data_path = "./data_test/{0}/".format(test_paras["data_path"])
test_files = os.listdir(data_path)
test_files.sort(key=lambda x: x[:-4])
test_files = test_files[:num_ins]
mod_files = os.listdir('./model/')[:]

memories = PPO_model.Memory()
model = PPO_model.PPO(model_paras, train_paras)
rules = test_paras["rules"]
envs = []  # Store multiple environments

# Detect and add models to "rules"
if "DRL" in rules:
    for root, ds, fs in os.walk('./model/'):
        for f in fs:
            if f.endswith('.pt'):
                rules.append(f)
if len(rules) != 1:
    if "DRL" in rules:
        rules.remove("DRL")

# Generate data files and fill in the header
str_time = time.strftime("%Y%m%d_%H%M%S", time.localtime(time.time()))
save_path = './save/test_{0}'.format(str_time)
os.makedirs(save_path)
writer = pd.ExcelWriter(
    '{0}/makespan_{1}.xlsx'.format(save_path, str_time))  # Makespan data storage path
writer_time = pd.ExcelWriter('{0}/time_{1}.xlsx'.format(save_path, str_time))  # time data storage path
file_name = [test_files[i] for i in range(num_ins)]
data_file = pd.DataFrame(file_name, columns=["file_name"])
data_file.to_excel(writer, sheet_name='Sheet1', index=False)
writer._save()
writer.close()
data_file.to_excel(writer_time, sheet_name='Sheet1', index=False)
writer_time._save()
writer_time.close()

# Rule-by-rule (model-by-model) testing
start = time.time()
for i_rules in range(len(rules)):
    rule = rules[i_rules]
    # Load trained model
    if rule.endswith('.pt'):
        if device.type == 'cuda':
            model_CKPT = torch.load('./model/' + mod_files[i_rules])
        else:
            model_CKPT = torch.load('./model/' + mod_files[i_rules], map_location='cpu')
        print('\nloading checkpoint:', mod_files[i_rules])
        model.policy.load_state_dict(model_CKPT)
        model.policy_old.load_state_dict(model_CKPT)
    print('rule:', rule)

    # Schedule instance by instance
    step_time_last = time.time()
    makespans = []
    times = []
    for i_ins in range(num_ins):
        test_file = data_path + test_files[i_ins]
        with open(test_file) as file_object:
            line = file_object.readlines()
            ins_num_jobs, ins_num_mas, _ = nums_detec(line)
        env_test_paras["num_jobs"] = ins_num_jobs
        env_test_paras["num_mas"] = ins_num_mas

        # Environment object already exists
        if len(envs) == num_ins:
            env = envs[i_ins]
        # Create environment object

        else:
            # DRL-S, each env contains multiple (=num_sample) copies of one instance
            if test_paras["sample"]:
                env = gym.make('fjsp-v0', case=[test_file] * test_paras["num_sample"],
                               env_paras=env_test_paras, data_source='file')
            # DRL-G, each env contains one instance
            else:
                env = gym.make('fjsp-v0', case=[test_file], env_paras=env_test_paras, data_source='file')
            envs.append(copy.deepcopy(env))
            print("Create env[{0}]".format(i_ins))

        # Schedule an instance/environment
        # DRL-S
        if test_paras["sample"]:
            makespan, time_re = schedule(env, model, memories, flag_sample=test_paras["sample"])
            makespans.append(torch.min(makespan))
            times.append(time_re)
        # DRL-G
        else:
            time_s = []
            makespan_s = []  # In fact, the results obtained by DRL-G do not change
            for j in range(test_paras["num_average"]):
                makespan, time_re = schedule(env, model, memories)
                makespan_s.append(makespan)
                time_s.append(time_re)
                env.reset()
            makespans.append(torch.mean(torch.tensor(makespan_s)))
            times.append(torch.mean(torch.tensor(time_s)))
        print("finish env {0}".format(i_ins))
    print("rule_spend_time: ", time.time() - step_time_last)

    # Save makespan and time data to files
    data = pd.DataFrame(torch.tensor(makespans).t().tolist(), columns=[rule])
    data.to_excel(writer, sheet_name='Sheet1', index=False, startcol=i_rules + 1)
    writer._save()
    writer._close()
    data = pd.DataFrame(torch.tensor(times).t().tolist(), columns=[rule])
    data.to_excel(writer_time, sheet_name='Sheet1', index=False, startcol=i_rules + 1)
    writer_time.save()
    writer_time.close()

    for env in envs:
        env.reset()

print("total_spend_time: ", time.time() - start)

def schedule(env, model, memories, flag_sample=False): # Get state and completion signal state = env.state dones = env.done_batch done = False # Unfinished at the beginning last_time = time.time() i = 0 while ~done: i += 1 with torch.no_grad(): actions = model.policy_old.act(state, memories, dones, flag_sample=flag_sample, flag_train=False) state, rewards, dones = env.step(actions) # environment transit done = dones.all() spend_time = time.time() - last_time # The time taken to solve this environment (instance) # print("spend_time: ", spend_time)

# Verify the solution
gantt_result = env.validate_gantt()[0]
if not gantt_result:
    print("Scheduling Error！！！！！！")
return copy.deepcopy(env.makespan_batch), spend_time

if name == 'main': main()

能运行创建环境，

但rule_spend_time之后遇到了这样的问题：ValueError: index must be as long as the number of data rows: len(index)=7 len(rows)=0

[yuciton]

求助大佬，哭泣