from train import train, validation
from evaluation import evaluation
from Dataset import NACCDataset
from utils.config_utils import load_config
from utils.fs_utils import create_folder
from utils.Timer import Timer
# system
import argparse
import time
import pickle
# create folder to store checkpoints
create_folder('checkpoints')
checkPath = 'checkpoints/session_' + Timer.timeFilenameString()
create_folder(checkPath)
# create folder to store models for each epoch
create_folder("models")
modelPath = "models/session_" + Timer.timeFilenameString()
create_folder(modelPath)
# create folder to store log files
create_folder('logs')
logPath = 'logs/log_' + Timer.timeFilenameString()
# create folder to store history dictionary
create_folder("history")
hist_file = "history/session_" + Timer.timeFilenameString() + ".pkl"
from utils.Timer import Timer
from utils.AverageMeter import AverageMeter
from modules.model import Net
from Dataset import Dataset
from utils import viz_utils
from utils import torch_utils
from utils.fs_utils import create_folder
# system
import argparse
import time
create_folder('checkpoints')
folderPath = 'checkpoints/session_' + Timer.timeFilenameString() + '/'
create_folder(folderPath)
create_folder('log')
logPath = 'log/log_' + Timer.timeFilenameString()
def append_line_to_log(line='\n'):
with open(logPath, 'a') as f:
f.write(line + '\n')
torch.set_default_tensor_type('torch.cuda.FloatTensor')
params = load_config('config.yaml')
