def run_test(path_model):
model_name = 'PNASNET'
model_pretrained = True
path_data = '../../MURA-v1.0/'
path_root = '../../'
batch_size = 6
img_size = 354
crop_size = 331
target_mean = 0.5
target_std = 0.5
data_transform = DataTransform(no_bg=True, pad=True)
data_transform_valid = data_transform.get_valid(img_size=img_size,
crop_size=crop_size,
target_mean=target_mean,
target_std=target_std)
device = None
opts, _ = getopt.getopt(sys.argv[1:], "d:", ["device="])
for opt, arg in opts:
if opt in ("-d", "--device") and torch.cuda.is_available():
device = torch.device("cuda:" + str(arg))
if device is None:
print("GPU not found! Using CPU!")
device = torch.device("cpu")
print('NN architecture = ', model_name)
print("using data transforms: " + str(data_transform))
if os.path.exists(path_model + "-L.pth.tar"):
print('Testing the model with best valid-loss')
train_multiview.test(path_data=path_data,
path_root=path_root,
path_model=path_model + "-L",
model_name=model_name,
model_pretrained=model_pretrained,
batch_size=batch_size,
device=device,
transform=data_transform_valid)
if os.path.exists(path_model + "-A.pth.tar"):
print('Testing the model with best valid-auroc')
train_multiview.test(path_data=path_data,
path_root=path_root,
path_model=path_model + "-A",
model_name=model_name,
model_pretrained=model_pretrained,
batch_size=batch_size,
device=device,
transform=data_transform_valid)
def run_train():
timestamp = time.strftime("%Y%m%d") + '-' + time.strftime("%H%M%S")
model_name = 'INCEPTIONRESNETV2'
model_pretrained = True
path_data = '../../MURA-v1.0/'
path_root = '../../'
path_log = '../../trained_models/' + timestamp + '/tb'
batch_size = 24
epoch_num = 15
img_size = 299
crop_size = 299
target_mean = 0.5
target_std = 0.5
checkpoint = "../../trained_models/04037-09061-inceptionresnetv2-adam-nobgpad/m-20180518-015825-L.pth.tar"
path_model = '../../trained_models/' + timestamp + '/m-' + timestamp
data_transform = DataTransform(no_bg=True, pad=True)
data_transform_train = data_transform.get_train(img_size=img_size,
crop_size=crop_size,
target_mean=target_mean,
target_std=target_std)
data_transform_valid = data_transform.get_valid(img_size=img_size,
crop_size=crop_size,
target_mean=target_mean,
target_std=target_std)
device = None
opts, _ = getopt.getopt(sys.argv[1:], "d:", ["device="])
for opt, arg in opts:
if opt in ("-d", "--device") and torch.cuda.is_available():
device = torch.device("cuda:" + str(arg))
if device is None:
print("GPU not found! Using CPU!")
device = torch.device("cpu")
print('Training NN architecture = ', model_name)
print('Training Multiview')
train_multiview.train(path_data=path_data,
path_root=path_root,
path_log=path_log,
path_model=path_model,
model_name=model_name,
model_pretrained=model_pretrained,
batch_size=batch_size,
epoch_num=epoch_num,
checkpoint=checkpoint,
device=device,
transform_train=data_transform_train,
transform_valid=data_transform_valid,
optimizer_fn=optimizers.adam_optimizers_small)
print('NN architecture = ', model_name)
print("using data transforms: " + str(data_transform))
print('Testing the model with best valid-loss')
print('timestamp = ' + timestamp)
train_multiview.test(path_data=path_data,
path_root=path_root,
path_model=path_model + "-L",
model_name=model_name,
model_pretrained=model_pretrained,
batch_size=batch_size,
device=device,
transform=data_transform_valid)
print('Testing the model with best valid-auroc')
print('timestamp = ' + timestamp)
train_multiview.test(path_data=path_data,
path_root=path_root,
path_model=path_model + "-A",
model_name=model_name,
model_pretrained=model_pretrained,
batch_size=batch_size,
device=device,
transform=data_transform_valid)
def run_train():
timestamp = time.strftime("%Y%m%d") + '-' + time.strftime("%H%M%S")
model_name = 'SENET154-LARGE'
model_pretrained = True
path_data = '../../MURA-v1.0/'
path_root = '../../'
path_log = '../../trained_models/' + timestamp + '/tb'
batch_size = 16
epoch_num = 10
img_size = 256
crop_size = 256
target_mean = np.array([0.485, 0.456, 0.406])
target_std = np.array([0.229, 0.224, 0.225])
checkpoint = "../../trained_models/03926-09052-senet154large-adam-nobgpad/m-20180626-081034-L.pth.tar"
path_model = '../../trained_models/' + timestamp + '/m-' + timestamp
data_transform = DataTransform(no_bg=True, pad=True)
data_transform_train = data_transform.get_train(img_size=img_size,
crop_size=crop_size,
target_mean=target_mean,
target_std=target_std)
data_transform_valid = data_transform.get_valid(img_size=img_size,
crop_size=crop_size,
target_mean=target_mean,
target_std=target_std)
device = None
opts, _ = getopt.getopt(sys.argv[1:], "d:", ["device="])
for opt, arg in opts:
if opt in ("-d", "--device") and torch.cuda.is_available():
device = torch.device("cuda:" + str(arg))
if device is None:
print("GPU not found! Using CPU!")
device = torch.device("cpu")
print('Training NN architecture = ', model_name)
print('Training Multiview')
train_multiview.train(path_data=path_data,
path_root=path_root,
path_log=path_log,
path_model=path_model,
model_name=model_name,
model_pretrained=model_pretrained,
batch_size=batch_size,
epoch_num=epoch_num,
checkpoint=checkpoint,
device=device,
transform_train=data_transform_train,
transform_valid=data_transform_valid,
optimizer_fn=optimizers.adam_optimizers_small)
print('NN architecture = ', model_name)
print("using data transforms: " + str(data_transform))
print('Testing the model with best valid-loss')
print('timestamp = ' + timestamp)
train_multiview.test(path_data=path_data,
path_root=path_root,
path_model=path_model + "-L",
model_name=model_name,
model_pretrained=model_pretrained,
batch_size=batch_size,
device=device,
transform=data_transform_valid)
print('Testing the model with best valid-auroc')
print('timestamp = ' + timestamp)
train_multiview.test(path_data=path_data,
path_root=path_root,
path_model=path_model + "-A",
model_name=model_name,
model_pretrained=model_pretrained,
batch_size=batch_size,
device=device,
transform=data_transform_valid)
