def main():
print(0)
args =parser.parse_args()
torch.manual_seed(666)
torch.backends.cudnn.deterministic = True
torch.backends.cudnn.benchmark = False
#no parrallel
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
print(1)
torch.cuda.set_device(0)
print(2)
train_data = SignDataset('../dataset/train.mat','../frame/',
args.window_size,
transforms.Compose([
transforms.ToTensor(),
transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
]))
vali_data = SignDataset('../dataset/validation.mat','../frame/',
args.window_size,
transforms.Compose([
transforms.ToTensor(),
transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
]))
train_loader = torch.utils.data.DataLoader(train_data,batch_size = args.batch_size,shuffle = True,num_workers = args.workers,pin_memory = True)
vali_loader = torch.utils.data.DataLoader(vali_data, batch_size=args.batch_size, shuffle=False, num_workers=args.workers, pin_memory=True)
D_xs = resnet_lstm(args.window_size)
D_xs.apply(weights_init)
args.cuda = True
if args.cuda:
print('lets use',torch.cuda.device_count(),"gpus")
D_xs = D_xs.cuda()
lr = args.learning_rate
D_xs_solver = optim.Adam(D_xs.parameters(),lr = lr)
BCE = nn.BCELoss().cuda()
e_shift =0
min_val_loss = 99999
no_improve_epoch = 0
now = datetime.now()
#log
log_path = '../log/lr_{}_time_{}'.format(args.learning_rate,now.strftime("%Y%m%d-%H%M%S"))
try:
os.mkdir(log_path)
except:
pass
#writer = SummaryWriter(log_path)
logger = Logger(log_path)
#train
mes_sum=0
n_iter = 0
for epoch in range(args.epochs):
print(1)
D_xs.train()
print('len of train_loader',len(train_loader))
for i,(data1,v1) in enumerate(train_loader):
if len(data1)<args.batch_size:
continue
n_iter+=2
D_xs.zero_grad()
x_1 =data1
x_1 = x_1.to(device)
# x_2 = data2
# x_2 = x_2.to(device)
vv_1 = v1.type(torch.FloatTensor)
vv_1= vv_1.cuda()
vv_1 = Variable(vv_1,requires_grad =False)
# vv_2 = v2.type(torch.FloatTensor)
# vv_2= vv_2.cuda()
# vv_2 = Variable(vv_2,requires_grad =False)
# score_2 = D_xs(x_2)
# v_loss=L1Loss(score_2,vv_2)
# v_loss.backward()
# D_xs_solver.step()
# mes_sum+=v_loss.item()
# print('nega:',v_loss.item())
score_1 = D_xs(x_1)
#print(score_1.shape)
v_loss=BCE(score_1,vv_1)
v_loss.backward()
D_xs_solver.step()
mes_sum+=v_loss.item()
pre = round(list(score_1))
print(pre)
# print(vv_1)
# print(score_1)
# print('loss:',v_loss.item())
if i%10 ==0:
print(vv_1)
print(score_1)
#writer.add_scalar('train/loss',mes_sum/10,n_iter)
info = { 'loss': v_loss.item()}
for tag, value in info.items():
logger.scalar_summary(tag, value, (i+epoch*1000))
for tag, value in D_xs.named_parameters():
tag = tag.replace('.','/')
logger.histo_summary(tag, value.data.cpu().numpy(), i)
logger.histo_summary(tag+'/grad', value.grad.data.cpu().numpy(), i)
print('epoch:[%2d] [%4d/%4d] loss: %.4f' % (epoch + e_shift, i, (len(train_data)/args.batch_size), mes_sum/10))
mes_sum=0
mse_sum = 0
D_xs.eval()
with torch.no_grad():
for i,(data1,v1) in enumerate(vali_loader):
x_1 =data1
x_1 = x_1.to(device)
vv_1 = v1.type(torch.FloatTensor)
vv_1= vv_1.cuda()
vv_1 = Variable(vv_1,requires_grad =False)
score=D_xs(x_1)
v_loss =BCE(score,vv_1)
mse_sum = mse_sum+v_loss.item()
val_loss = mse_sum/float(i+1)
print('epoch:[%2d] ,val_mse :%.6f '%(epoch+e_shift,val_loss))
#writer.add_scalar('Test/Loss',val_loss,n_iter)
info = { 'vali_loss': v_loss.item()}
for tag, value in info.items():
logger.scalar_summary(tag, value, epoch)
if val_loss < min_val_loss:
min_val_loss = val_loss
no_improve_epoch = 0
val_loss = round(val_loss,2)
torch.save(D_xs.state_dict(),'{}/epoch_{}_val_loss_{}.pth'.format(args.outf, epoch + e_shift, val_loss))
print("performance improve, saved the new model......")
else:
no_improve_epoch+=1
if no_improve_epoch>args.patiences:
print('stop training')
break
default='model.pth',
type=str,
help='pretrained model checkpoint')
parser.add_argument('--message',
default='message',
type=str,
help='pretrained model checkpoint')
parser.add_argument('--epochs', default=101, type=int, help='train epochs')
parser.add_argument('--train', default=True, type=bool, help='train')
args = parser.parse_args()
save_path = args.save_path + f'{args.message}_{time_str}'
if not os.path.exists(save_path):
os.mkdir(save_path)
logger = Logger(f'{save_path}/log.log')
logger.Print(args)
train_data, val_data, test_data = load_cisia_surf(train_size=args.batch_size,
test_size=args.test_size)
model = Model(pretrained=False, num_classes=2)
criterion = nn.CrossEntropyLoss()
optimizer = optim.SGD(model.parameters(),
lr=0.01,
momentum=0.9,
weight_decay=5e-4)
scheduler = lr_scheduler.ExponentialLR(optimizer, gamma=0.95)
if use_cuda:
model = model.cuda()
criterion = criterion.cuda()
def main():
os.environ["CUDA_DEVICE_ORDER"]="PCI_BUS_ID"
os.environ['CUDA_VISIBLE_DEVICES']='1'
# print(0)
args =parser.parse_args()
torch.manual_seed(666)
torch.backends.cudnn.deterministic = True
torch.backends.cudnn.benchmark = False
#device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
#print(1)
#torch.cuda.set_device(0)
#print(2)
train_data = TwoStream_dataset('../dataset/train.mat','../frame/','../optical_flow_matrix/',
args.window_size,
transforms.Compose([
transforms.ToTensor(),
transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
]))
vali_data = TwoStream_dataset('../dataset/validation.mat','../frame/','../optical_flow_matrix/',
args.window_size,
transforms.Compose([
transforms.ToTensor(),
transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
]))
train_loader = torch.utils.data.DataLoader(train_data,batch_size = args.batch_size,shuffle = True,num_workers = args.workers,pin_memory = True)
vali_loader = torch.utils.data.DataLoader(vali_data, batch_size=args.batch_size, shuffle=False, num_workers=args.workers, pin_memory=True)
D_xs = TwoStream_Fusion2()
D_xs.apply(weights_init)
args.cuda = True
if args.cuda:
print('lets use',torch.cuda.device_count(),"gpus")
D_xs = torch.nn.DataParallel(D_xs).cuda()
lr = args.learning_rate
D_xs_solver = optim.Adam(D_xs.parameters(),lr = lr)
BCE = nn.BCELoss().cuda()
e_shift =0
min_val_loss = 99999
max_accuracy = 0
no_improve_epoch = 0
now = datetime.now()
#log
log_path = '../log/lr_{}_time_{}'.format(args.learning_rate,now.strftime("%Y%m%d-%H%M%S"))
try:
os.mkdir(log_path)
except:
pass
#writer = SummaryWriter(log_path)
logger = Logger(log_path)
#train
mes_sum=0
n_iter = 0
for epoch in range(args.epochs):
print(1)
D_xs.train()
print('len of train_loader',len(train_loader))
train_epoch_score = 0
train_predictions = []
train_gt = []
for i,(data1,v1) in enumerate(train_loader):
if len(data1[0])<args.batch_size:
continue
n_iter+=2
D_xs.zero_grad()
x_1 =data1[0]
x_2 = data1[1]
x_1 = x_1.cuda()
x_2 = x_2.cuda()
# x_2 = data2
# x_2 = x_2.to(device)
vv_1 = v1.type(torch.FloatTensor)
vv_1= vv_1.cuda()
vv_1 = Variable(vv_1,requires_grad =False)
# vv_2 = v2.type(torch.FloatTensor)
# vv_2= vv_2.cuda()
# vv_2 = Variable(vv_2,requires_grad =False)
# score_2 = D_xs(x_2)
# v_loss=L1Loss(score_2,vv_2)
# v_loss.backward()
# D_xs_solver.step()
# mes_sum+=v_loss.item()
# print('nega:',v_loss.item())
score_1 = D_xs(x_1,x_2)
v_loss=BCE(score_1,vv_1)
v_loss.backward()
D_xs_solver.step()
mes_sum+=v_loss.item()
for item in score_1.tolist():
if item > 0.5:
train_predictions.append(1)
else:
train_predictions.append(0)
train_gt += vv_1.tolist()
# pre = round(list(score_1))
# print(pre)
# print(vv_1)
# print(score_1)
# print('loss:',v_loss.item())
if i%3 ==0:
#print(vv_1)
#print(score_1)
#writer.add_scalar('train/loss',mes_sum/10,n_iter)
info = { 'loss': v_loss.item()}
for tag, value in info.items():
logger.scalar_summary(tag, value, (i+epoch*1000))
# for tag, value in D_xs.named_parameters():
# tag = tag.replace('.','/')
# logger.histo_summary(tag, value.data.cpu().numpy(), i)
# logger.histo_summary(tag+'/grad', value.grad.data.cpu().numpy(), i)
print('epoch:[%2d] [%4d/%4d] loss: %.4f' % (epoch + e_shift, i, (len(train_data)/args.batch_size), mes_sum/10))
mes_sum=0
train_epoch_score = sum(i == j for i, j in zip(train_predictions, train_gt))
train_accuracy = train_epoch_score/(len(train_loader)*args.batch_size)
train_f1_score = f1_score(train_gt, train_predictions)
print("Train accuracy for epoch:[%2d]: %.4f" %(epoch, train_accuracy))
print("Train F1 score for epoch:[%2d]: %.4f" %(epoch, train_f1_score))
info2 = {'train_accuracy' : train_accuracy, "train_f1_score" : train_f1_score}
for tag, value in info2.items():
logger.scalar_summary(tag, value, epoch)
mse_sum = 0
D_xs.eval()
vali_epoch_score = 0
vali_predictions = []
vali_gt = []
with torch.no_grad():
for i,(data1,v1) in enumerate(vali_loader):
x_1 = data1[0]
x_2 = data1[1]
x_1 = x_1.cuda()
x_2 = x_2.cuda()
vv_1 = v1.type(torch.FloatTensor)
vv_1= vv_1.cuda()
vv_1 = Variable(vv_1,requires_grad =False)
score=D_xs(x_1,x_2)
v_loss =BCE(score,vv_1)
mse_sum = mse_sum+v_loss.item()
for item in score.tolist():
if item > 0.5:
vali_predictions.append(1)
else:
vali_predictions.append(0)
vali_gt += vv_1.tolist()
val_loss = mse_sum/float(i+1)
print('epoch:[%2d] ,val_mse :%.6f '%(epoch+e_shift,val_loss))
vali_epoch_score = sum(i == j for i, j in zip(vali_predictions, vali_gt))
vali_accuracy = vali_epoch_score/(len(vali_loader)*args.batch_size)
vali_f1_score = f1_score(vali_gt, vali_predictions)
#writer.add_scalar('Test/Loss',val_loss,n_iter)
print("validation accuracy for epoch:[%2d]: %.4f" %(epoch, vali_accuracy))
print("validation F1 score for epoch:[%2d]: %.4f" %(epoch, vali_f1_score))
info3 = { 'vali_loss': mse_sum, 'vali_accuracy': vali_accuracy, 'vali_f1_score' : vali_f1_score}
for tag, value in info3.items():
logger.scalar_summary(tag, value, epoch)
if max_accuracy < vali_accuracy:
max_accuracy = vali_accuracy
no_improve_epoch = 0
max_accuracy = round(max_accuracy,4)
torch.save(D_xs.state_dict(),'{}/epoch_{}_max_accuracy_{}.pth'.format(args.outf, epoch + e_shift, max_accuracy))
print("performance improve, saved the new model......")
else:
no_improve_epoch+=1
if no_improve_epoch>args.patiences:
print('stop training')
break
import math
import os
import json
from loger import Logger
import numpy as np
import copy
import matplotlib.pyplot as plt
from matplotlib.pyplot import MultipleLocator
SRC_NUMBER = 1
DST_NUMBER = 1
UAV_COUNTS = 10
UAV_BATCH = 10
CONFIG_PATH = "./config.json"
logger = Logger('log_' + time.strftime("%Y-%m-%d %H:%M:%S", time.localtime()))
CONFIG = json.loads(open(CONFIG_PATH, "r").read())
destination = CONFIG['width'] * CONFIG['height']
turns_of_change = 30
rate_of_change = 0.02
logger.openLogger = False
MOVE_RECORD = dict()
def run():
global turns_of_change
global rate_of_change
global destination
time_swap = 1
MAP_RECORD = []
#coding=utf-8
import requests, xlrd, pymysql, time, sys
# 导入需要用到的模块
from xlutils import copy
import requests
from loger import Logger
logger = Logger(logger='AutoDemo').getloger()
class AutoDemo:
# 从xlutils模块中导入copy这个函数
def __init__(self):
self.autodemo = AutoDemo()
def readExcel(self, file_path):
'''
读取excel测试用例的函数
:param file_path:传入一个excel文件，或者文件的绝对路径
:return:返回这个excel第一个sheet页中的所有测试用例的list
'''
try:
logger.info('start read case file !')
book = xlrd.open_workbook(file_path) # 打开excel
except Exception as e:
# 如果路径不在或者excel不正确，返回报错信息
print('路径不在或者excel不正确', e)
logger.error(u'路径不在或者excel不正确')
return e
else:
import stradegy import pathTree import numpy as np import UAV import time import random from loger import Logger logger = Logger() """ uav_1 = UAV.UAV(1,'./config.json',logger) global_map = np.random.randn(uav_1.config['width'],uav_1.config['height']) global_map = (np.multiply(global_map,255.0/(2.58*2)) + 127.5).astype(np.uint8) uav_1.initialize_global_information(global_map) uav_1.update_all_level_maps() uav_1.position[0] = 0 uav_1.position[1] = 0 uav_2 = UAV.UAV(2,'./config.json',logger) uav_2.initialize_global_information(global_map) uav_2.update_all_level_maps() uav_2.position[0] = 0 uav_2.position[1] = 0 uav_3 = UAV.UAV(3,'./config.json',logger) uav_3.initialize_global_information(global_map) uav_3.update_all_level_maps() uav_3.position[0] = 0 uav_3.position[1] = 0
timeArray = time.localtime(time.time())
otherStyleTime = time.strftime("%Y-%m-%d %H:%M:%S", timeArray)
decoded["lastsynctime"] = otherStyleTime
decoded["isLastsyncSuccess"] = "True"
try:
fss = open("status.list", "w")
except Exception, arg:
crlog.error(arg)
crlog.error("write status.list failed, pls check it!")
crlog.critical("exit")
sys.exit(1)
fss.truncate()
fss.write(json.dumps(decoded).replace('\'', '\"')) #将单引号替换双引号
fss.close()
# 任务结束进入休眠
crlog.info("current task is over.")
sleep()
return 0
#测试执行顺序列表
loadlogdir() # 初始化日志目录
crlog = Logger(logdir) #实例化日志管理类
my = Hparser() #实例化解析类
pconfig()
isFirstrun()
