# fix random seed
rng = np.random.RandomState(37148)
# create instance of HED model
net = HED()
net.cuda()
# load the weights for the model
net.load_state_dict(torch.load(arg_Model))
# batch size
nBatch = 1
# make test list for infer
make_txt(arg_DataRoot,'test')
# create data loaders from dataset
testPath = os.path.join(arg_DataRoot, 'test.lst')
print(testPath)
# create data loaders from dataset
std = [0.229, 0.224, 0.225]
mean = [0.485, 0.456, 0.406]
# std=[0.229, 0.224, 0.225]
# mean=[0.185, 0.156, 0.106]
transform = transforms.Compose([
transforms.ToTensor(),
#transforms.Normalize(mean, std)
# max epoch
nEpoch = 150
# load the images dataset
dataRoot = 'data/dam_material_falloff/'
modelPath = 'model/vgg16.pth'
pretrain_bool = True
filter_bool = False
option = ''
valPath = dataRoot + 'val.lst'
trainPath = dataRoot + 'train.lst'
# write txt file
make_txt(dataRoot, 'train')
make_txt(dataRoot, 'val')
make_txt(dataRoot, 'test')
# create data loaders from dataset
std = [0.229, 0.224, 0.225]
mean = [0.485, 0.456, 0.406]
transform = transforms.Compose(
[transforms.ToTensor(),
transforms.Normalize(mean, std)])
targetTransform = transforms.Compose([transforms.ToTensor()])
#
# trans = transforms.Compose([
# transforms.RandomChoice([
# transforms.RandomRotation((0, 0)),