def test_model(args):
"""
main function for testing
args:
args: global arguments
"""
print("=====> Check if the cached file exists ")
if not os.path.isfile(args.inform_data_file):
print("%s is not found" % (args.inform_data_file))
dataCollect = CamVidTrainInform(
args.data_dir,
args.classes,
train_set_file=args.dataset_list,
inform_data_file=args.inform_data_file
) #collect mean std, weigth_class information
datas = dataCollect.collectDataAndSave()
if datas is None:
print('Error while pickling data. Please check.')
exit(-1)
else:
print("%s exists" % (args.inform_data_file))
datas = pickle.load(open(args.inform_data_file, "rb"))
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)
cudnn.enabled = True
M = args.M
N = args.N
model = CGNet.Context_Guided_Network(classes=args.classes, M=M, N=N)
network_type = "CGNet"
print("=====> current architeture: CGNet_M%sN%s" % (M, N))
total_paramters = netParams(model)
print("the number of parameters: " + str(total_paramters))
print("data['classWeights']: ", datas['classWeights'])
weight = torch.from_numpy(datas['classWeights'])
print("=====> Dataset statistics")
print("mean and std: ", datas['mean'], datas['std'])
# define optimization criteria
criteria = CrossEntropyLoss2d(weight, args.ignore_label)
if args.cuda:
model = model.cuda()
criteria = criteria.cuda()
#load test set
train_transform = transforms.Compose([transforms.ToTensor()])
testLoader = data.DataLoader(CamVidValDataSet(args.data_dir,
args.test_data_list,
f_scale=1,
mean=datas['mean']),
batch_size=args.batch_size,
shuffle=True,
num_workers=args.num_workers,
pin_memory=True,
drop_last=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(convert_state_dict(checkpoint['model']))
model.load_state_dict(checkpoint['model'])
else:
print("=====> no checkpoint found at '{}'".format(args.resume))
cudnn.benchmark = True
print("=====> beginning test")
print("length of test set:", len(testLoader))
mIOU_val, per_class_iu = test(args, testLoader, model, criteria)
print(mIOU_val)
print(per_class_iu)
def train_model(args):
"""
args:
args: global arguments
"""
h, w = map(int, args.input_size.split(','))
input_size = (h, w)
print("=====> checking if inform_data_file exists")
if not os.path.isfile(args.inform_data_file):
print("%s is not found" % (args.inform_data_file))
dataCollect = CamVidTrainInform(
args.data_dir,
args.classes,
train_set_file=args.dataset_list,
inform_data_file=args.inform_data_file
) #collect mean std, weigth_class information
datas = dataCollect.collectDataAndSave()
if datas is None:
print("error while pickling data. Please check.")
exit(-1)
else:
print("find file: ", str(args.inform_data_file))
datas = pickle.load(open(args.inform_data_file, "rb"))
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)
cudnn.enabled = True
M = args.M
N = args.N
print("=====> building network")
model = CGNet.Context_Guided_Network(classes=args.classes, M=M, N=N)
network_type = "CGNet"
print("=====> current architeture: CGNet")
print("=====> computing network parameters")
total_paramters = netParams(model)
print("the number of parameters: " + str(total_paramters))
print("data['classWeights']: ", datas['classWeights'])
print('=====> Dataset statistics')
print('mean and std: ', datas['mean'], datas['std'])
# define optimization criteria
weight = torch.from_numpy(datas['classWeights'])
criteria = CrossEntropyLoss2d(weight, args.ignore_label)
if args.cuda:
criteria = criteria.cuda()
args.gpu_nums = 1
if torch.cuda.device_count() > 1:
print("torch.cuda.device_count()=", torch.cuda.device_count())
args.gpu_nums = torch.cuda.device_count()
model = torch.nn.DataParallel(model).cuda()
else:
print("single GPU for training")
model = model.cuda()
args.savedir = (args.savedir + args.dataset + '/' + network_type + "_M" +
str(M) + 'N' + str(N) + 'bs' + str(args.batch_size) +
'gpu' + str(args.gpu_nums) + "_" + str(args.train_type) +
'/')
if not os.path.exists(args.savedir):
os.makedirs(args.savedir)
#Data augmentation, compose the data with transforms
train_transform = transforms.Compose([transforms.ToTensor()])
trainLoader = data.DataLoader(CamVidDataSet(args.data_dir,
args.train_data_list,
crop_size=input_size,
scale=args.random_scale,
mirror=args.random_mirror,
mean=datas['mean']),
batch_size=args.batch_size,
shuffle=True,
num_workers=args.num_workers,
pin_memory=True,
drop_last=True)
valLoader = data.DataLoader(CamVidValDataSet(args.data_dir,
args.val_data_list,
f_scale=1,
mean=datas['mean']),
batch_size=1,
shuffle=True,
num_workers=args.num_workers,
pin_memory=True,
drop_last=True)
start_epoch = 0
if args.resume:
if os.path.isfile(args.resume):
checkpoint = torch.load(args.resume)
start_epoch = checkpoint['epoch']
model.load_state_dict(checkpoint['model'])
#model.load_state_dict(convert_state_dict(checkpoint['model']))
print("=====> loading checkpoint '{}' (epoch {})".format(
args.resume, checkpoint['epoch']))
else:
print("=====> no checkpoint found at '{}'".format(args.resume))
model.train()
cudnn.benchmark = True
logFileLoc = args.savedir + args.logFile
if os.path.isfile(logFileLoc):
logger = open(logFileLoc, 'a')
else:
logger = open(logFileLoc, 'w')
logger.write("Parameters: %s" % (str(total_paramters)))
logger.write(
"\n%s\t\t%s\t\t%s\t\t%s\t\t%s\t\t" %
('Epoch', 'Loss(Tr)', 'Loss(val)', 'mIOU (tr)', 'mIOU (val)'))
logger.flush()
optimizer = torch.optim.Adam(model.parameters(),
args.lr, (0.9, 0.999),
eps=1e-08,
weight_decay=5e-4)
print('=====> beginning training')
for epoch in range(start_epoch, args.max_epochs):
#training
lossTr, per_class_iu_tr, mIOU_tr, lr = train(args, trainLoader, model,
criteria, optimizer,
epoch)
#validation
if epoch % 50 == 0:
mIOU_val, per_class_iu = val(args, valLoader, model, criteria)
logger.write("\n%d\t\t%.4f\t\t%.4f\t\t%.4f\t\t%.7f" %
(epoch, lossTr, mIOU_tr, mIOU_val, lr))
logger.flush()
print("epoch: " + str(epoch) + ' Details')
print(
"\nEpoch No.: %d\tTrain Loss = %.4f\t mIOU(tr) = %.4f\t mIOU(val) = %.4f\t lr= %.6f"
% (epoch, lossTr, mIOU_tr, mIOU_val, lr))
else:
logger.write("\n%d\t\t%.4f\t\t%.4f\t\t%.7f" %
(epoch, lossTr, mIOU_tr, lr))
logger.flush()
print("Epoch : " + str(epoch) + ' Details')
print(
"\nEpoch No.: %d\tTrain Loss = %.4f\t mIOU(tr) = %.4f\t lr= %.6f"
% (epoch, lossTr, mIOU_tr, lr))
#save the model
model_file_name = args.savedir + '/model_' + str(epoch + 1) + '.pth'
state = {"epoch": epoch + 1, "model": model.state_dict()}
if epoch > args.max_epochs - 10:
torch.save(state, model_file_name)
elif not epoch % 20:
torch.save(state, model_file_name)
logger.close()