90 lines
3.9 KiB
Python
90 lines
3.9 KiB
Python
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##################################################
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# Copyright (c) Xuanyi Dong [GitHub D-X-Y], 2019 #
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##################################################
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import os, sys, time, glob, random, argparse
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import numpy as np
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from copy import deepcopy
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import torch
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import torch.nn as nn
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import torch.nn.functional as F
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import torchvision.datasets as dset
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import torch.backends.cudnn as cudnn
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import torchvision.transforms as transforms
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from pathlib import Path
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lib_dir = (Path(__file__).parent / '..' / 'lib').resolve()
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if str(lib_dir) not in sys.path: sys.path.insert(0, str(lib_dir))
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from utils import AverageMeter, time_string, convert_secs2time
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from utils import print_log, obtain_accuracy
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from utils import Cutout, count_parameters_in_MB
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from nas import model_types as models
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from train_utils import main_procedure
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from train_utils_imagenet import main_procedure_imagenet
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from scheduler import load_config
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parser = argparse.ArgumentParser("Train-CNN")
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parser.add_argument('--data_path', type=str, help='Path to dataset')
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parser.add_argument('--dataset', type=str, choices=['imagenet', 'cifar10', 'cifar100'], help='Choose between Cifar10/100 and ImageNet.')
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parser.add_argument('--arch', type=str, choices=models.keys(), help='the searched model.')
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#
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parser.add_argument('--grad_clip', type=float, help='gradient clipping')
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parser.add_argument('--model_config', type=str , help='the model configuration')
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parser.add_argument('--init_channels', type=int , help='the initial number of channels')
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parser.add_argument('--layers', type=int , help='the number of layers.')
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# log
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parser.add_argument('--workers', type=int, default=2, help='number of data loading workers (default: 2)')
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parser.add_argument('--save_path', type=str, help='Folder to save checkpoints and log.')
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parser.add_argument('--print_freq', type=int, help='print frequency (default: 200)')
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parser.add_argument('--manualSeed', type=int, help='manual seed')
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args = parser.parse_args()
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if 'CUDA_VISIBLE_DEVICES' not in os.environ: print('Can not find CUDA_VISIBLE_DEVICES in os.environ')
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else : print('Find CUDA_VISIBLE_DEVICES={:}'.format(os.environ['CUDA_VISIBLE_DEVICES']))
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assert torch.cuda.is_available(), 'torch.cuda is not available'
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if args.manualSeed is None or args.manualSeed < 0:
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args.manualSeed = random.randint(1, 10000)
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random.seed(args.manualSeed)
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cudnn.benchmark = True
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cudnn.enabled = True
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torch.manual_seed(args.manualSeed)
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torch.cuda.manual_seed_all(args.manualSeed)
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def main():
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# Init logger
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#args.save_path = os.path.join(args.save_path, 'seed-{:}'.format(args.manualSeed))
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if not os.path.isdir(args.save_path):
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os.makedirs(args.save_path)
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log = open(os.path.join(args.save_path, 'seed-{:}-log.txt'.format(args.manualSeed)), 'w')
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print_log('Save Path : {:}'.format(args.save_path), log)
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state = {k: v for k, v in args._get_kwargs()}
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print_log(state, log)
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print_log("Random Seed : {:}".format(args.manualSeed), log)
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print_log("Python version : {:}".format(sys.version.replace('\n', ' ')), log)
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print_log("Torch version : {:}".format(torch.__version__), log)
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print_log("CUDA version : {:}".format(torch.version.cuda), log)
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print_log("cuDNN version : {:}".format(cudnn.version()), log)
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print_log("Num of GPUs : {:}".format(torch.cuda.device_count()), log)
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args.dataset = args.dataset.lower()
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config = load_config(args.model_config)
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genotype = models[args.arch]
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print_log('configuration : {:}'.format(config), log)
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print_log('genotype : {:}'.format(genotype), log)
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# clear GPU cache
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torch.cuda.empty_cache()
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if args.dataset == 'imagenet':
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main_procedure_imagenet(config, args.data_path, args, genotype, args.init_channels, args.layers, None, log)
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else:
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main_procedure(config, args.dataset, args.data_path, args, genotype, args.init_channels, args.layers, None, log)
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log.close()
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if __name__ == '__main__':
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main()
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