USE_CUDA = torch.cuda.is_available()
device = torch.device("cuda" if USE_CUDA else "cpu")
job_number = int(args.model) # get job number
pool = args.pool
HOME_PATH = '/gpfs/data/denizlab/Users/bz1030/data/OAI_processed/mix/'
summary_path = '/gpfs/data/denizlab/Users/bz1030/data/OAI_processed/'
model_file_path = '/gpfs/data/denizlab/Users/bz1030/KneeNet/KneeProject/model/model_torch/model_densenet/model_large_weights3/epoch_17.pth'
val = pd.read_csv(summary_path + 'test.csv').sample(
n=5).reset_index() # split train - test set.
tensor_transform_test = transforms.Compose([
CenterCrop(896),
transforms.ToTensor(),
lambda x: x.float(),
])
dataset_test = KneeGradingDataset(val,
HOME_PATH,
tensor_transform_test,
stage='val')
test_loader = data.DataLoader(dataset_test, batch_size=2)
print('Validation data:', len(dataset_test))
# Network
#net = DenseNet(121,True,pool)
net = dn.densenet121(pretrained=True)
net.classifier = nn.Sequential(nn.Dropout(0.4), nn.Linear(1024, 5))
print(net)
print('Number of model parameters: {}'.format(
sum([p.data.nelement() for p in net.parameters()])))
print('############### Model Finished ####################')
criterion = nn.CrossEntropyLoss()
if USE_CUDA:
state_dict = torch.load(model_file_path)
train = pd.read_csv(summary_path + 'train.csv')#.sample(n=50).reset_index()
val = pd.read_csv(summary_path + 'val.csv')#.reset_index() # split train - test set.
start_val = 0
tensor_transform_train = transforms.Compose([
RandomCrop(896),
transforms.ToTensor(),
lambda x: x.float(),
])
tensor_transform_val = transforms.Compose([
CenterCrop(896),
transforms.ToTensor(),
lambda x: x.float(),
])
dataset_train = KneeGradingDataset(train,HOME_PATH,tensor_transform_train,stage = 'train')
dataset_val = KneeGradingDataset(val,HOME_PATH,tensor_transform_val,stage = 'val')
train_loader = data.DataLoader(dataset_train,batch_size=8)
val_loader = data.DataLoader(dataset_val,batch_size=8)
print('Training data: ', len(dataset_train))
print('Validation data:', len(dataset_val))
net = resnet34(pretrained=True)
print(net)
net.avgpool = nn.AvgPool2d(28, 28)
net.fc = nn.Sequential(nn.Dropout(0.2), nn.Linear(512, 5)) # OULU's paper.
load_file = None
#'/gpfs/data/denizlab/Users/bz1030/KneeNet/KneeProject/model/model_torch/model_flatten_linear_layer/model_weights3/epoch_3.pth'
if load_file:
net.load_state_dict(torch.load(load_file))
start_epoch = 3
HOME_PATH = '/gpfs/data/denizlab/Users/bz1030/data/OAI_processed/mix/'
summary_path = '/gpfs/data/denizlab/Users/bz1030/data/OAI_processed/'
log_file_path = '/gpfs/data/denizlab/Users/bz1030/model/model_torch/model_flatten_linear_layer/train_log'
model_file_path = '/gpfs/data/denizlab/Users/bz1030/model/model_torch/model_flatten_linear_layer/model_weights'
test = pd.read_csv(summary_path + 'test.csv').sample(
n=40000).reset_index() # split train - test set.
start_test = 0
tensor_transform_test = transforms.Compose([
CenterCrop(896),
transforms.ToTensor(),
lambda x: x.float(),
])
dataset_test = KneeGradingDataset(test,
HOME_PATH,
tensor_transform_test,
stage='test')
test_loader = data.DataLoader(dataset_test, batch_size=10)
print('Test data:', len(dataset_test))
net = resnet34(pretrained=True)
net.avgpool = nn.AvgPool2d(7, 7)
net.fc = nn.Sequential(nn.Dropout(0.1), nn.Linear(8192, 5))
print(net)
# Network
net = nn.DataParallel(net)
optimizer = optim.Adam(net.parameters(), lr=0.0001, weight_decay=0)
net.load_state_dict(torch.load('model_weights/checkpoint_520.pth'))
net.eval()