def run(turn, stf_info=None, name="", filename="log_wifi.txt"):
stf = False
device_params = {}
# path to save logs
path = os.path.dirname(os.path.abspath(__file__))
path = os.path.join(path, "..", "..", "qa", "reports", "")
# instantiate logger to save a report file
logger = Logger(filename, path, name)
# initiate connection to device(s)
controller = PhoneControl()
# if a serial was received, run test only for that single serial
if stf_info is not None:
serials = [stf_info['remote_serial']]
device_params = utils.get_device_data(stf_info['device'])
stf = True
else:
# read the serials
serials = controller.read_serials()
for i in range(len(serials)):
logger.write_log(" Device {} = {}".format(i+1, serials))
# init device with serial
controller.init_device(serials[i])
# get the device parameters from the device version json
if not stf:
device_params = utils.get_device_data(serials[i])
logger.write_log("Script Turn Wifi " + turn + " ---------")
try:
action(turn, logger, controller, device_params)
time.sleep(3)
except Exception as ex:
logger.error_log(ex.message)
def run_framework():
cwd_path = os.path.dirname(os.path.abspath(__file__))
path = os.path.join(cwd_path, "qa", "reports", "")
logger = Logger("logger_suite.txt", path)
# here all the suites specified will be ran, to execute all test cases
# Wifi Suite
WifiSuite.run_suite()
# Dialer Suite
DialerSuite.run_suite()
# Calculator Suite
CalculatorSuite.run_suite()
# Twilio Suite
twilio.run_suite()
logger.end_suit()
def main():
"""
Метод запускает процедуру проверки веток на соответствие
условиям удаления/оповещения и выполнении действий
Возвращает void
"""
logger = Logger(config.LOG_FILE)
logger.Write("Start")
branch_handler = BranchHandler()
branch_handler.Handle()
logger.Write("Stop")
logger.Close()
def run(filename, path):
logger = Logger(filename, path)
controller = PhoneControl(3)
serial = controller.read_serial()
logger.write_log("1st Device on List = {}".format(serial))
controller.init_device()
logger.write_log("Script Turn Wifi On---------")
try:
action(logger, controller)
time.sleep(3)
except Exception as ex:
logger.error_log(ex.message)
def run(number_to=None, number_from=None, filename="log_twilio.txt"):
# path to save logs
path = os.path.dirname(os.path.abspath(__file__))
path = os.path.join(path, "..", "..", "qa", "reports", "")
# instantiate logger to save a report file
logger = Logger(filename, path)
logger.write_log("Script Twilio Voice Mail ---------")
try:
action(logger, number_to, number_from)
except Exception as ex:
logger.end_log(ex.message)
def prefetch_test(opt):
os.environ['CUDA_VISIBLE_DEVICES'] = opt.gpus_str
Dataset = dataset_factory[opt.dataset]
opt = opts().update_dataset_info_and_set_heads(opt, Dataset)
print(opt)
Logger(opt)
Detector = detector_factory[opt.task]
# split = 'val'if not opt.trainval else 'test'
detector = Detector(opt)
datasets = build_dataset(Dataset, opt, is_train=False)
datasets = datasets[0]
data_loader = torch.utils.data.DataLoader(PrefetchDataset(
opt, datasets, detector.pre_process),
batch_size=1,
shuffle=False,
num_workers=0,
pin_memory=True)
results = {}
num_iters = len(datasets)
bar = Bar('{}'.format(opt.exp_id), max=num_iters)
time_stats = ['tot', 'load', 'pre', 'net', 'dec', 'post', 'merge']
avg_time_stats = {t: AverageMeter() for t in time_stats}
for ind, (img_id, pre_processed_images) in enumerate(data_loader):
ret = detector.run(pre_processed_images)
results[img_id.numpy().astype(np.int32)[0]] = ret['results']
Bar.suffix = '[{0}/{1}]|Tot: {total:} |ETA: {eta:} '.format(
ind, num_iters, total=bar.elapsed_td, eta=bar.eta_td)
for t in avg_time_stats:
avg_time_stats[t].update(ret[t])
Bar.suffix = Bar.suffix + '|{} {tm.val:.3f}s ({tm.avg:.3f}s) '.format(
t, tm=avg_time_stats[t])
bar.next()
bar.finish()
for t in avg_time_stats:
print('|{} {tm.val:.3f}s ({tm.avg:.3f}s) '.format(
t, tm=avg_time_stats[t]))
datasets.run_eval(results, opt.save_dir)
def run(turn, filename="log_wifi.txt"):
# path to save logs
path = os.path.dirname(os.path.abspath(__file__))
path = os.path.join(path, "..", "..", "qa", "reports", "")
# instantiate logger to save a report file
logger = Logger(filename, path)
# initiate connection to device(s)
controller = PhoneControl()
# read the serials
serials = controller.read_serials()
for i in range(len(serials)):
logger.write_log(" Device {} = {}".format(i + 1, serials))
# init device with serial
controller.init_device(serials[i])
# get the device parameters from the device version dictionary
device_params = utils.get_device_data(serials[i])
logger.write_log("Script Turn Wifi " + turn + " ---------")
try:
action(turn, logger, controller, device_params)
time.sleep(3)
except Exception as ex:
logger.error_log(ex.message)
def test(opt):
os.environ['CUDA_VISIBLE_DEVICES'] = opt.gpus_str
Dataset = dataset_factory[opt.dataset]
opt = opts().update_dataset_info_and_set_heads(opt, Dataset)
print(opt)
Logger(opt)
Detector = detector_factory[opt.task]
split = 'val' if not opt.trainval else 'test'
dataset = Dataset(opt, split)
detector = Detector(opt)
results = {}
num_iters = len(dataset)
bar = Bar('{}'.format(opt.exp_id), max=num_iters)
time_stats = ['tot', 'load', 'pre', 'net', 'dec', 'post', 'merge']
avg_time_stats = {t: AverageMeter() for t in time_stats}
for ind in range(num_iters):
img_id = dataset.images[ind]
img_info = dataset.coco.loadImgs(ids=[img_id])[0]
img_path = os.path.join(dataset.img_dir, img_info['file_name'])
if opt.task == 'ddd':
ret = detector.run(img_path, img_info['calib'])
else:
ret = detector.run(img_path)
results[img_id] = ret['results']
Bar.suffix = '[{0}/{1}]|Tot: {total:} |ETA: {eta:} '.format(
ind, num_iters, total=bar.elapsed_td, eta=bar.eta_td)
for t in avg_time_stats:
avg_time_stats[t].update(ret[t])
Bar.suffix = Bar.suffix + '|{} {:.3f} '.format(
t, avg_time_stats[t].avg)
bar.next()
bar.finish()
dataset.run_eval(results, opt.save_dir)
def run(a, op, b, filename="log_calculator.txt"):
# path to save logs
cwd_path = os.path.dirname(os.path.abspath(__file__))
path = os.path.join(cwd_path, "..", "..", "qa", "reports", "")
logger = Logger(filename, path)
controller = PhoneControl(1)
serials = controller.read_serials()
for i in range(len(serials)):
logger.write_log(" Device {} = {}".format(i + 1, serials))
controller.init_device(serials[i])
device_params = utils.get_device_data(serials[i])
logger.write_log("Script UI Calculator -----------------")
try:
calculator = Calculator(a, op, b)
if calculator.valid:
logger.write_log("Operation: " + a + op + b)
action(logger, controller, calculator, device_params)
else:
logger.error_log("Validation of Inputs Failed")
except SyntaxError as ex:
logger.end_log(ex.message)
except ValueError as ex:
logger.end_log(ex.message)
except TypeError as ex:
logger.end_log(ex.message)
except ZeroDivisionError as ex:
logger.end_log(ex.message)
except Exception as ex:
logger.error_log(ex.message)
def run(number=None, stf_info=None, name="", filename="log_dialer.txt"):
stf = False
device_params = {}
# path to save logs
path = os.path.dirname(os.path.abspath(__file__))
path = os.path.join(path, "..", "..", "qa", "reports", "")
logger = Logger(filename, path, name)
controller = PhoneControl()
if stf_info is not None:
serials = [stf_info['remote_serial']]
device_params = utils.get_device_data(stf_info['device'])
stf = True
else:
serials = controller.read_serials()
for i in range(len(serials)):
logger.write_log(" Device {} = {}".format(i + 1, serials))
controller.init_device(serials[i])
if not stf:
device_params = utils.get_device_data(serials[i])
logger.write_log("Script Dial Number---------")
if number is None:
n = raw_input("Enter the number to dial : ")
else:
n = number
try:
dialable, parsedNumber, msg = utils.validate_number(n)
if dialable:
logger.write_log(msg + " " + str(parsedNumber))
action(logger, controller, parsedNumber, device_params)
else:
logger.end_log("Invalid phone number")
except Exception as ex:
logger.error_log(ex.message)
def run(filename, path, number=None):
logger = Logger(filename, path)
controller = PhoneControl(3)
serial = controller.read_serial()
logger.write_log("1st Device on List = {}".format(serial))
controller.init_device()
logger.write_log("Script Dial Number---------")
if number is None:
n = raw_input("Enter the number to dial : ")
else:
n = number
try:
dialable, parsedNumber, msg = utils.validate_number(n)
if dialable:
logger.write_log(msg + " " + str(parsedNumber))
action(logger, controller, parsedNumber)
else:
logger.error_log("Invalid phone number")
except Exception as ex:
logger.error_log(ex.message)
def main(opt):
torch.manual_seed(opt.seed)
torch.backends.cudnn.benchmark = not opt.not_cuda_benchmark and not opt.test
# torch.backends.cudnn.enabled = False
Dataset = get_dataset(opt.dataset, opt.task)
opt = opts().update_dataset_info_and_set_heads(opt, Dataset)
print(opt)
logger = Logger(opt)
os.environ['CUDA_VISIBLE_DEVICES'] = opt.gpus_str
opt.device = torch.device('cuda' if opt.gpus[0] >= 0 else 'cpu')
print('Creating model...')
model = create_model(opt.arch, opt.heads, opt.head_conv, opt.houghnet,
opt.region_num, opt.vote_field_size)
optimizer = torch.optim.Adam(model.parameters(), opt.lr)
start_epoch = 0
if opt.load_model != '':
model, optimizer, start_epoch = load_model(model, opt.load_model,
optimizer, opt.resume,
opt.lr, opt.lr_step)
Trainer = train_factory[opt.task]
trainer = Trainer(opt, model, optimizer)
trainer.set_device(opt.gpus, opt.chunk_sizes, opt.device)
print('Setting up data...')
val_loader = torch.utils.data.DataLoader(Dataset(opt, 'val'),
batch_size=1,
shuffle=False,
num_workers=1,
pin_memory=True)
if opt.test:
_, preds = trainer.val(0, val_loader)
val_loader.dataset.run_eval(preds, opt.save_dir)
return
train_loader = torch.utils.data.DataLoader(
Dataset(opt, 'train'), #train
batch_size=opt.batch_size,
shuffle=True,
num_workers=opt.num_workers,
pin_memory=True,
drop_last=True)
print('Starting training...')
best = 1e10
for epoch in range(start_epoch + 1, opt.num_epochs + 1):
mark = epoch if opt.save_all else 'last'
log_dict_train, _ = trainer.train(epoch, train_loader)
logger.write('epoch: {} |'.format(epoch))
for k, v in log_dict_train.items():
logger.scalar_summary('train_{}'.format(k), v, epoch)
logger.write('{} {:8f} | '.format(k, v))
if opt.val_intervals > 0 and epoch % opt.val_intervals == 0:
save_model(os.path.join(opt.save_dir, 'model_{}.pth'.format(mark)),
epoch, model, optimizer)
with torch.no_grad():
log_dict_val, preds = trainer.val(epoch, val_loader)
for k, v in log_dict_val.items():
logger.scalar_summary('val_{}'.format(k), v, epoch)
logger.write('{} {:8f} | '.format(k, v))
if log_dict_val[opt.metric] < best:
best = log_dict_val[opt.metric]
save_model(os.path.join(opt.save_dir, 'model_best.pth'), epoch,
model)
else:
save_model(os.path.join(opt.save_dir, 'model_last.pth'), epoch,
model, optimizer)
logger.write('\n')
if epoch in opt.lr_step:
save_model(
os.path.join(opt.save_dir, 'model_{}.pth'.format(epoch)),
epoch, model, optimizer)
lr = opt.lr * (0.1**(opt.lr_step.index(epoch) + 1))
print('Drop LR to', lr)
for param_group in optimizer.param_groups:
param_group['lr'] = lr
logger.close()
def main(opt):
torch.manual_seed(
opt.seed
) # opt.seed: default=317 ；加上torch.manual_seed这个函数调用的话，打印出来的随机数每次都一样。
torch.backends.cudnn.benchmark = not opt.not_cuda_benchmark and not opt.test
Dataset = get_dataset(
opt.dataset, opt.task
) # opt.dataset = coco, opt.task = ctdet (| ddd | multi_pose | exdet)
opt = opts().update_dataset_info_and_set_heads(opt, Dataset)
print(opt)
logger = Logger(opt)
os.environ['CUDA_VISIBLE_DEVICES'] = opt.gpus_str
opt.device = torch.device('cuda' if opt.gpus[0] >= 0 else 'cpu')
print('Creating model...')
model = create_model(opt.arch, opt.heads, opt.head_conv)
optimizer = torch.optim.Adam(model.parameters(), opt.lr)
start_epoch = 0
if opt.load_model != '':
model, optimizer, start_epoch = load_model(model, opt.load_model,
optimizer, opt.resume,
opt.lr, opt.lr_step)
Trainer = train_factory[opt.task]
trainer = Trainer(opt, model, optimizer)
trainer.set_device(opt.gpus, opt.chunk_sizes, opt.device)
print('Setting up data...')
# val_loader = torch.utils.data.DataLoader(Dataset(opt, 'val'), batch_size=1, shuffle=False, num_workers=1,pin_memory=True) # modified by zy
val_loader = torch.utils.data.DataLoader(Dataset(opt, 'test'),
batch_size=1,
shuffle=False,
num_workers=1,
pin_memory=True)
if opt.test:
_, preds = trainer.val(0, val_loader)
val_loader.dataset.run_eval(preds, opt.save_dir)
return
train_loader = torch.utils.data.DataLoader(Dataset(opt, 'train'),
batch_size=opt.batch_size,
shuffle=True,
num_workers=opt.num_workers,
pin_memory=True,
drop_last=True)
output_choice_log = '/home/zy/zy/2new_network/CenterNet-master/output_choice.log'
if os.path.exists(output_choice_log):
os.remove(output_choice_log)
print('Starting training...')
best = 1e10
for epoch in range(start_epoch + 1, opt.num_epochs + 1):
mark = epoch if opt.save_all else 'last'
try:
log_dict_train, _ = trainer.train(
epoch, train_loader
) # !!!!!!!! train = self.run_epoch('train', epoch, data_loader)
except Exception as e: # 如果发生异常，那就返回预设的loss值
print('Error_train!!!', e)
print(traceback.format_exc())
continue
logger.write('epoch: {} |'.format(epoch))
for k, v in log_dict_train.items():
logger.scalar_summary('train_{}'.format(k), v, epoch)
logger.write('{} {:8f} | '.format(k, v))
if opt.val_intervals > 0 and epoch % opt.val_intervals == 0:
save_model(os.path.join(opt.save_dir, 'model_{}.pth'.format(mark)),
epoch, model, optimizer)
with torch.no_grad():
try:
log_dict_val, preds = trainer.val(epoch, val_loader)
except Exception as e: # 如果发生异常，那就返回预设的loss值
print('Error_train!!!', e)
print(traceback.format_exc())
continue
for k, v in log_dict_val.items():
logger.scalar_summary('val_{}'.format(k), v, epoch)
logger.write('{} {:8f} | '.format(k, v))
if log_dict_val[opt.metric] < best:
best = log_dict_val[opt.metric]
save_model(os.path.join(opt.save_dir, 'model_best.pth'), epoch,
model)
else:
save_model(os.path.join(opt.save_dir, 'model_last.pth'), epoch,
model, optimizer)
logger.write('\n')
if epoch in opt.lr_step:
save_model(
os.path.join(opt.save_dir, 'model_{}.pth'.format(epoch)),
epoch, model, optimizer)
lr = opt.lr * (0.1**(opt.lr_step.index(epoch) + 1))
print('Drop LR to', lr)
for param_group in optimizer.param_groups:
param_group['lr'] = lr
logger.close()
def main(opt):
torch.manual_seed(opt.seed)
torch.backends.cudnn.benchmark = not opt.not_cuda_benchmark and not opt.test
print('Setting up data...')
Dataset = get_dataset(opt.dataset, opt.task)
f = open(opt.data_cfg)
data_config = json.load(f)
trainset_paths = data_config['train']
dataset_root = data_config['root']
f.close()
transforms = T.Compose([T.ToTensor()])
dataset = Dataset(opt,
dataset_root,
trainset_paths, (1088, 608),
augment=True,
transforms=transforms)
opt = opts().update_dataset_info_and_set_heads(opt, dataset)
print(opt)
logger = Logger(opt)
os.environ['CUDA_VISIBLE_DEVICES'] = opt.gpus_str
opt.device = torch.device('cuda' if opt.gpus[0] >= 0 else 'cpu')
print('Creating model...')
model = create_model(opt.arch, opt.heads, opt.head_conv)
optimizer = torch.optim.Adam(model.parameters(), opt.lr)
start_epoch = 0
# Get dataloader
train_loader = torch.utils.data.DataLoader(dataset,
batch_size=opt.batch_size,
shuffle=True,
num_workers=opt.num_workers,
pin_memory=True,
drop_last=True)
print('Starting training...')
Trainer = train_factory[opt.task]
trainer = Trainer(opt, model, optimizer)
trainer.set_device(opt.gpus, opt.chunk_sizes, opt.device)
if opt.load_model != '':
model, optimizer, start_epoch = load_model(model, opt.load_model,
trainer.optimizer,
opt.resume, opt.lr,
opt.lr_step)
best = 1e10
for epoch in range(start_epoch + 1, opt.num_epochs + 1):
mark = epoch if opt.save_all else 'last'
log_dict_train, _ = trainer.train(epoch, train_loader)
logger.write('epoch: {} |'.format(epoch))
for k, v in log_dict_train.items():
logger.scalar_summary('train_{}'.format(k), v, epoch)
logger.write('{} {:8f} | '.format(k, v))
if opt.val_intervals > 0 and epoch % opt.val_intervals == 0:
save_model(os.path.join(opt.save_dir, 'model_{}.pth'.format(mark)),
epoch, model, optimizer)
else:
save_model(os.path.join(opt.save_dir, 'model_last.pth'), epoch,
model, optimizer)
logger.write('\n')
if epoch in opt.lr_step:
save_model(
os.path.join(opt.save_dir, 'model_{}.pth'.format(epoch)),
epoch, model, optimizer)
lr = opt.lr * (0.1**(opt.lr_step.index(epoch) + 1))
print('Drop LR to', lr)
for param_group in optimizer.param_groups:
param_group['lr'] = lr
if epoch % 5 == 0 or epoch >= 25:
save_model(
os.path.join(opt.save_dir, 'model_{}.pth'.format(epoch)),
epoch, model, optimizer)
logger.close()
def main(opt):
torch.manual_seed(opt.seed)
torch.backends.cudnn.benchmark = not opt.not_cuda_benchmark and not opt.test
Dataset = get_dataset(opt.dataset)
opt = opts().update_dataset_info_and_set_heads(opt, Dataset)
print(opt)
if not opt.not_set_cuda_env:
os.environ["CUDA_VISIBLE_DEVICES"] = opt.gpus_str
opt.device = torch.device("cuda" if opt.gpus[0] >= 0 else "cpu")
logger = Logger(opt)
print("Creating model...")
model = create_model(opt.arch, opt.heads, opt.head_conv, opt=opt)
optimizer = get_optimizer(opt, model)
start_epoch = 0
if opt.load_model != "":
model, optimizer, start_epoch = load_model(model, opt.load_model, opt, optimizer)
trainer = Trainer(opt, model, optimizer)
trainer.set_device(opt.gpus, opt.chunk_sizes, opt.device)
if opt.val_intervals < opt.num_epochs or opt.test:
print("Setting up validation data...")
val_loader = torch.utils.data.DataLoader(
Dataset(opt, "val"), batch_size=1, shuffle=False, num_workers=1, pin_memory=True
)
if opt.test:
_, preds = trainer.val(0, val_loader)
val_loader.dataset.run_eval(preds, opt.save_dir)
return
print("Setting up train data...")
train_loader = torch.utils.data.DataLoader(
Dataset(opt, "train"),
batch_size=opt.batch_size,
shuffle=True,
num_workers=opt.num_workers,
pin_memory=True,
drop_last=True,
)
print("Starting training...")
for epoch in range(start_epoch + 1, opt.num_epochs + 1):
mark = epoch if opt.save_all else "last"
log_dict_train, _ = trainer.train(epoch, train_loader)
logger.write("epoch: {} |".format(epoch))
for k, v in log_dict_train.items():
logger.scalar_summary("train_{}".format(k), v, epoch)
logger.write("{} {:8f} | ".format(k, v))
if opt.val_intervals > 0 and epoch % opt.val_intervals == 0:
save_model(os.path.join(opt.save_dir, "model_{}.pth".format(mark)), epoch, model, optimizer)
with torch.no_grad():
log_dict_val, preds = trainer.val(epoch, val_loader)
if opt.eval_val:
val_loader.dataset.run_eval(preds, opt.save_dir)
for k, v in log_dict_val.items():
logger.scalar_summary("val_{}".format(k), v, epoch)
logger.write("{} {:8f} | ".format(k, v))
else:
save_model(os.path.join(opt.save_dir, "model_last.pth"), epoch, model, optimizer)
logger.write("\n")
if epoch in opt.save_point:
save_model(os.path.join(opt.save_dir, "model_{}.pth".format(epoch)), epoch, model, optimizer)
if epoch in opt.lr_step:
lr = opt.lr * (0.1 ** (opt.lr_step.index(epoch) + 1))
print("Drop LR to", lr)
for param_group in optimizer.param_groups:
param_group["lr"] = lr
logger.close()