import sys
sys.path.insert(1, 'fmri_cnn')
import common
train_dataset = common.TimeseriesDataset("train_set_class.pkl")
len(train_dataset)
train_dataset[10]
if __name__ == "__main__":
parser = argparse.ArgumentParser()
parser.add_argument(
'--data_folder',
required=True,
help='path to folder contaning all the data - train, validate and test'
)
parser.add_argument('--out_folder',
required=True,
help='path to folder where to save the model')
args = parser.parse_args()
train_dataset = common.TimeseriesDataset(
os.path.join(args.data_folder, "train_set_class.pkl"))
validate_dataset = common.TimeseriesDataset(
os.path.join(args.data_folder, "validate_set_class.pkl"))
train_loader = torch.utils.data.DataLoader(dataset=train_dataset,
batch_size=batch_size,
shuffle=True)
validate_loader = torch.utils.data.DataLoader(dataset=validate_dataset,
batch_size=batch_size,
shuffle=False)
#create object of the fMRI_CNN class
fMRI_CNN = common.fMRI_CNN()
fMRI_CNN = common.to_cuda(fMRI_CNN)
accuracy = (100 * float(correct) / total)
print("number of below:", num_of_below_avg, " number of average:",
num_of_avg, " number of above:", num_of_above_avg)
return accuracy
if __name__ == "__main__":
parser = argparse.ArgumentParser()
parser.add_argument(
'--data_folder',
required=True,
help='path to folder contaning all the data - train, validate and test'
)
parser.add_argument('--model',
required=True,
help='path to the model after training')
args = parser.parse_args()
test_dataset = common.TimeseriesDataset(
os.path.join(args.data_folder, "test_set_class.pkl"))
test_loader = torch.utils.data.DataLoader(dataset=test_dataset,
shuffle=False)
net = common.DeepFCNet()
#load the model
net = load_checkpoint(net, args.model)
net = common.to_cuda(net)
# Test the Model
testErr = testFunc(net, test_loader)
print(testErr)