# Global environment setup. import os # Arg parser from config import globalparser args = vars(globalparser.getparser().parse_args()) from config import globalconfig globalconfig.run(args, False) import glob import numpy as np def print_prompt(): available_data = np.array([]) datalist = glob.glob(os.environ['datapath'] + '/*') for p in datalist: if os.path.exists(p + '/info.json'): available_data = np.append(available_data, p) available_data = [x[x.rfind('/') + 1:] for x in available_data] print('Available datas:') print(available_data) modellist = glob.glob('./Loader/Model/*.py') modellist = [x[x.rfind('/') + 1:x.rfind('.py')] for x in modellist] print('Available models:') print(modellist) print_prompt()
# Global environment setup. import os # Arg parser from config import globalparser args = vars(globalparser.getparser().parse_args()) from config import globalconfig globalconfig.run(args) print('Train {} with {}.(Running on {})'.format(os.environ['savepath'], os.environ['datapath'], os.environ['device'])) import importlib # Essential network building blocks. model = importlib.import_module('Loader.Model.' + args['model']) transform = importlib.import_module('Loader.Transform.train_aug') # Data loader. from DataUtils import ImgFolder_5fold as data # Official packages. import torch.nn as nn import torch.optim as optim # 下面开始进行主干内容 from tools import datainfo info = datainfo.getdatainfo(os.environ['datapath']) models, params, modelinfo = model.load(info, args['continue'])
# Global environment setup. import os from config import globalconfig globalconfig.run() globalconfig.update_filename(__file__) # Essential network building blocks. from Networks.Nets import TwoLayerFC from Networks.Nets import ThreeLayerConvNet from Networks.Blocks import LinearReLU # Data loader. from DataUtils import cifar10 # Useful tools. from tools import train_and_check as mtool # Official packages. import torch import torch.nn as nn import torch.optim as optim # Training setup. os.environ['print_every'] = '10' os.environ['save_every'] = '1' TRAIN_EPOCHS=20 LEARNING_RATE=0.1 # GOT DATA train_dataloader, val_dataloader, test_dataloader, sample = cifar10.getdata()