def test_func(args):
"""
main function for testing
param args: global arguments
return: None
"""
print(args)
global network_type
if args.cuda:
print("=====> use gpu id: '{}'".format(args.gpus))
os.environ["CUDA_VISIBLE_DEVICES"] = args.gpus
if not torch.cuda.is_available():
raise Exception(
"no GPU found or wrong gpu id, please run without --cuda")
args.seed = random.randint(1, 10000)
print("Random Seed: ", args.seed)
torch.manual_seed(args.seed)
if args.cuda:
torch.cuda.manual_seed(args.seed)
print('=====> checking if processed cached_data_file exists')
if not os.path.isfile(args.inform_data_file):
dataCollect = CityscapesTrainInform(
args.data_dir,
args.classes,
train_set_file=args.dataset_list,
inform_data_file=args.inform_data_file
) #collect mean std, weigth_class information
data = dataCollect.collectDataAndSave()
if data is None:
print("error while pickling data, please check")
exit(-1)
else:
data = pickle.load(open(args.inform_data_file, "rb"))
M = args.M
N = args.N
model = CGNet.Context_Guided_Network(classes=args.classes, M=M, N=N)
network_type = "CGNet"
print("Arch: CGNet")
# define optimization criteria
weight = torch.from_numpy(
data['classWeights']) # convert the numpy array to torch
if args.cuda:
weight = weight.cuda()
criteria = CrossEntropyLoss2d(weight) #weight
if args.cuda:
model = model.cuda() # using GPU for inference
criteria = criteria.cuda()
cudnn.benchmark = True
print('Dataset statistics')
print('mean and std: ', data['mean'], data['std'])
print('classWeights: ', data['classWeights'])
if args.save_seg_dir:
if not os.path.exists(args.save_seg_dir):
os.makedirs(args.save_seg_dir)
# validation set
testLoader = torch.utils.data.DataLoader(CityscapesTestDataSet(
args.data_dir, args.test_data_list, mean=data['mean']),
batch_size=1,
shuffle=False,
num_workers=args.num_workers,
pin_memory=True)
if args.resume:
if os.path.isfile(args.resume):
print("=====> loading checkpoint '{}'".format(args.resume))
checkpoint = torch.load(args.resume)
#model.load_state_dict(checkpoint['model'])
model.load_state_dict(convert_state_dict(checkpoint['model']))
else:
print("=====> no checkpoint found at '{}'".format(args.resume))
print("=====> beginning testing")
print("test set length: ", len(testLoader))
test(args, testLoader, model)
def test_model(args):
"""
main function for testing
param args: global arguments
return: None
"""
print(args)
if args.cuda:
print("=====> use gpu id: '{}'".format(args.gpus))
os.environ["CUDA_VISIBLE_DEVICES"] = args.gpus
if not torch.cuda.is_available():
raise Exception(
"no GPU found or wrong gpu id, please run without --cuda")
# build the model
model = build_model(args.model, num_classes=args.classes)
if args.cuda:
model = model.cuda() # using GPU for inference
cudnn.benchmark = True
if args.save:
if not os.path.exists(args.save_seg_dir):
os.makedirs(args.save_seg_dir)
# load the test set
datas, testLoader = build_dataset_test(args.dataset, args.num_workers)
if not args.best:
if args.checkpoint:
if os.path.isfile(args.checkpoint):
print("=====> loading checkpoint '{}'".format(args.checkpoint))
checkpoint = torch.load(args.checkpoint)
#model.load_state_dict(checkpoint['model'])
model.load_state_dict(convert_state_dict(checkpoint['model']))
else:
print("=====> no checkpoint found at '{}'".format(
args.checkpoint))
raise FileNotFoundError("no checkpoint found at '{}'".format(
args.checkpoint))
print("=====> beginning validation")
print("validation set length: ", len(testLoader))
mIOU_val, per_class_iu = test(args, testLoader, model)
print(mIOU_val)
print(per_class_iu)
# Get the best test result among the last 10 model records.
else:
if args.checkpoint:
if os.path.isfile(args.checkpoint):
dirname, basename = os.path.split(args.checkpoint)
epoch = int(os.path.splitext(basename)[0].split('_')[1])
mIOU_val = []
per_class_iu = []
for i in range(epoch - 9, epoch + 1):
basename = 'model_' + str(i) + '.pth'
resume = os.path.join(dirname, basename)
checkpoint = torch.load(resume)
model.load_state_dict(checkpoint['model'])
print("=====> beginning test the" + basename)
print("validation set length: ", len(testLoader))
mIOU_val_0, per_class_iu_0 = test(args, testLoader, model)
mIOU_val.append(mIOU_val_0)
per_class_iu.append(per_class_iu_0)
index = list(range(epoch - 9, epoch + 1))[np.argmax(mIOU_val)]
print("The best mIoU among the last 10 models is", index)
print(mIOU_val)
per_class_iu = per_class_iu[np.argmax(mIOU_val)]
mIOU_val = np.max(mIOU_val)
print(mIOU_val)
print(per_class_iu)
else:
print("=====> no checkpoint found at '{}'".format(
args.checkpoint))
raise FileNotFoundError("no checkpoint found at '{}'".format(
args.checkpoint))
# Save the result
if not args.best:
model_path = os.path.splitext(os.path.basename(args.checkpoint))
args.logFile = 'test_' + model_path[0] + '.txt'
logFileLoc = os.path.join(os.path.dirname(args.checkpoint),
args.logFile)
else:
args.logFile = 'test_' + 'best' + str(index) + '.txt'
logFileLoc = os.path.join(os.path.dirname(args.checkpoint),
args.logFile)
# Save the result
if os.path.isfile(logFileLoc):
logger = open(logFileLoc, 'a')
else:
logger = open(logFileLoc, 'w')
logger.write("Mean IoU: %.4f" % mIOU_val)
logger.write("\nPer class IoU: ")
for i in range(len(per_class_iu)):
logger.write("%.4f\t" % per_class_iu[i])
logger.flush()
logger.close()
def test_func(args):
"""
main function for testing
param args: global arguments
return: None
"""
print(args)
global network_type
if args.cuda:
print("=====> use gpu id: '{}'".format(args.gpus))
os.environ["CUDA_VISIBLE_DEVICES"] = args.gpus
if not torch.cuda.is_available():
raise Exception(
"no GPU found or wrong gpu id, please run without --cuda")
device = 'cuda'
args.seed = random.randint(1, 10000)
print("Random Seed: ", args.seed)
torch.manual_seed(args.seed)
if args.cuda:
torch.cuda.manual_seed(args.seed)
print('=====> checking if processed cached_data_file exists')
if not os.path.isfile(args.inform_data_file):
dataCollect = CityscapesTrainInform(
args.data_dir,
args.classes,
train_set_file=args.dataset_list,
inform_data_file=args.inform_data_file
) #collect mean std, weigth_class information
data = dataCollect.collectDataAndSave()
if data is None:
print("error while pickling data, please check")
exit(-1)
else:
data = pickle.load(open(args.inform_data_file, "rb"))
M = args.M
N = args.N
model = MobileNetV3(model_mode="SMALL", num_classes=args.classes)
network_type = "MobileNetV3"
print("Arch: MobileNetV3")
if args.cuda:
model = model.to(device) # using GPU for inference
cudnn.benchmark = True
print('Dataset statistics')
print('mean and std: ', data['mean'], data['std'])
print('classWeights: ', data['classWeights'])
# validation set
testLoader = torch.utils.data.DataLoader(CityscapesTestDataSet(
args.data_dir, args.test_data_list, mean=data['mean']),
batch_size=1,
shuffle=False,
num_workers=args.num_workers,
pin_memory=True)
if args.resume:
if os.path.isfile(args.resume):
print("=====> loading checkpoint '{}'".format(args.resume))
checkpoint = torch.load(args.resume)
#model.load_state_dict(checkpoint['model'])
model.load_state_dict(convert_state_dict(checkpoint['model']))
else:
print("=====> no checkpoint found at '{}'".format(args.resume))
print("=====> beginning testing")
print("test set length: ", len(testLoader))
test(args, testLoader, model, device, data)
def test_func(args):
'''
Main function for testing
param args: global arguments
return: None
'''
print(args)
global network_type
if args.cuda:
print("=====> use gpu id: '{}'".format(args.gpus))
os.environ["CUDA_VISIBLE_DEVICES"] = args.gpus
if not torch.cuda.is_available():
raise Exception(
"No GPU found or Wrong gpu id, please run without --cuda")
args.seed = random.randint(1, 10000)
print("Random Seed: ", args.seed)
torch.manual_seed(args.seed)
if args.cuda:
torch.cuda.manual_seed(args.seed)
print('checking if processed cached_data_file exists or not')
if not os.path.isfile(args.cached_data_file):
dataCollect = StatisticalInformDataset(args.data_dir, args.classes,
args.cached_data_file)
data = dataCollect.collectDataAndSave()
if data is None:
print("error while pickling data, please check")
exit(-1)
else:
data = pickle.load(open(args.cached_data_file, "rb"))
M = args.M
N = args.N
# load the model
print('====> Building network')
model = net.Context_Guided_Network(classes=args.classes, M=M, N=N)
network_type = "CGNet"
print("Arch: CGNet")
if args.cuda:
#model = torch.nn.DataParallel(model).cuda() # multi-card testing
model = model.cuda() # single-card testing
print('Dataset statistics')
print('mean and std: ', data['mean'], data['std'])
print('classWeights: ', data['classWeights'])
if args.save_seg_dir:
if not os.path.exists(args.save_seg_dir):
os.makedirs(args.save_seg_dir)
testLoader = torch.utils.data.DataLoader(CityscapesTestDataSet(
args.data_dir, args.test_data_list, mean=data['mean']),
batch_size=1,
shuffle=False,
num_workers=args.num_workers,
pin_memory=True)
if args.cuda:
cudnn.benchmark = True
print("=====> load pretrained model")
if args.resume:
if os.path.isfile(args.resume):
print("=> loading checkpoint '{}'".format(args.resume))
checkpoint = torch.load(args.resume)
model.load_state_dict(convert_state_dict(checkpoint['model']))
else:
print("=> no checkpoint found at '{}'".format(args.resume))
# evaluate on test set
print("=====> beginning test")
print("test set length: ", len(testLoader))
test(args, testLoader, model)