54 lines
2.0 KiB
Python
54 lines
2.0 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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# For evaluating the learned model
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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("Evaluate-CNN")
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parser.add_argument('--data_path', type=str, help='Path to dataset.')
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parser.add_argument('--checkpoint', type=str, help='Choose between Cifar10/100 and ImageNet.')
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args = parser.parse_args()
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assert torch.cuda.is_available(), 'torch.cuda is not available'
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def main():
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assert os.path.isdir( args.data_path ), 'invalid data-path : {:}'.format(args.data_path)
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assert os.path.isfile( args.checkpoint ), 'invalid checkpoint : {:}'.format(args.checkpoint)
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checkpoint = torch.load( args.checkpoint )
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xargs = checkpoint['args']
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config = load_config(xargs.model_config)
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genotype = models[xargs.arch]
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# clear GPU cache
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torch.cuda.empty_cache()
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if xargs.dataset == 'imagenet':
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main_procedure_imagenet(config, args.data_path, xargs, genotype, xargs.init_channels, xargs.layers, checkpoint['state_dict'], None)
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else:
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main_procedure(config, xargs.dataset, args.data_path, xargs, genotype, xargs.init_channels, xargs.layers, checkpoint['state_dict'], None)
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if __name__ == '__main__':
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main()
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