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fewshot_test.py
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fewshot_test.py
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from __future__ import print_function
import os
import argparse
import time
import sys
from pathlib import Path
lib_dir = (Path(__file__).parent / 'eval_lib').resolve()
if str(lib_dir) not in sys.path: sys.path.insert(0, str(lib_dir))
import torch
import torch.backends.cudnn as cudnn
from torch.utils.data import DataLoader
import logging
import random
import numpy as np
from rfs_models import model_dict, model_pool
from rfs_models.util import create_model, count_params
from rfs_dataset.mini_imagenet import ImageNet, MetaImageNet
from rfs_dataset.tiered_imagenet import TieredImageNet, MetaTieredImageNet
from rfs_dataset.cifar import CIFAR100, MetaCIFAR100
from rfs_dataset.transform_cfg import transforms_options, transforms_list
from eval.meta_eval import meta_test
from ptflops import get_model_complexity_info
def parse_option(argv):
parser = argparse.ArgumentParser('argument for training')
# load pretrained model
parser.add_argument('--model', type=str, default='augmentcnn', choices=model_pool)
parser.add_argument('--model_path', type=str, default=None, help='absolute path to .pth model')
# dataset
parser.add_argument('--dataset', type=str, default='miniImageNet', choices=['miniImageNet', 'tieredImageNet',
'CIFAR-FS', 'FC100'])
parser.add_argument('--transform', type=str, default='A', choices=transforms_list)
# meta setting
parser.add_argument('--n_test_runs', type=int, default=1000, metavar='N',
help='Number of test runs')
parser.add_argument('--n_ways', type=int, default=5, metavar='N',
help='Number of classes for doing each classification run')
parser.add_argument('--n_shots', type=int, default=1, metavar='N',
help='Number of shots in test')
parser.add_argument('--n_queries', type=int, default=50, metavar='N',
help='Number of query in test')
parser.add_argument('--n_aug_support_samples', default=5, type=int,
help='The number of augmented samples for each meta test sample')
parser.add_argument('--data_root', type=str, default='~/data', metavar='N',
help='Root dataset')
parser.add_argument('--num_workers', type=int, default=3, metavar='N',
help='Number of workers for dataloader')
parser.add_argument('--test_batch_size', type=int, default=1, metavar='test_batch_size',
help='Size of test batch)')
# specify architectures for DARTS space
parser.add_argument('--layers', type=int, default=4, help='number of layers')
parser.add_argument('--init_channels', type=int, default=28, help='num of init channels')
parser.add_argument('--genotype', type=str, default='', help='Cell genotype')
parser.add_argument('--aug_stemm', type=int, default=3, help='Stem multiplier during augmentation')
parser.add_argument('--aug_fsr', type=int, default=1, help='Feature scaling ratio during augmentation')
# Parameters for Logistic regression
parser.add_argument('--C', type=float, default=1.0, help='coefficient of Logistic Regression')
parser.add_argument('--nonorm', action='store_false', dest='norm', help='if normalize feature, default: True')
parser.add_argument('--seed', type=int, default=42, help='Random seed')
opt = parser.parse_args(argv)
if 'trainval' in opt.model_path:
opt.use_trainval = True
else:
opt.use_trainval = False
opt.data_root = os.path.join(opt.data_root,opt.dataset)
opt.data_aug = True
return opt
def prepare_seed(rand_seed):
random.seed(rand_seed)
np.random.seed(rand_seed)
torch.manual_seed(rand_seed)
torch.cuda.manual_seed(rand_seed)
torch.cuda.manual_seed_all(rand_seed)
def main(argv):
opt = parse_option(argv)
if __name__ == '__main__':
logging.basicConfig(filename= "rfs_results_{}shots.log".format(opt.n_shots),
format='%(asctime)s %(message)s',
filemode='w')
logger = logging.getLogger()
logger.setLevel(logging.DEBUG)
else:
logger = logging.getLogger(__name__)
prepare_seed(opt.seed)
# test loader
# args = opt
opt.batch_size = opt.test_batch_size
# args.n_aug_support_samples = 1
if opt.dataset == 'miniImageNet':
train_trans, test_trans = transforms_options[opt.transform]
meta_testloader = DataLoader(MetaImageNet(args=opt, partition='test',
train_transform=train_trans,
test_transform=test_trans,
fix_seed=False),
batch_size=opt.test_batch_size, shuffle=False, drop_last=False,
num_workers=opt.num_workers)
meta_valloader = DataLoader(MetaImageNet(args=opt, partition='val',
train_transform=train_trans,
test_transform=test_trans,
fix_seed=False),
batch_size=opt.test_batch_size, shuffle=False, drop_last=False,
num_workers=opt.num_workers)
if opt.use_trainval:
n_cls = 80
else:
n_cls = 64
elif opt.dataset == 'tieredImageNet':
train_trans, test_trans = transforms_options[opt.transform]
meta_testloader = DataLoader(MetaTieredImageNet(args=opt, partition='test',
train_transform=train_trans,
test_transform=test_trans,
fix_seed=False),
batch_size=opt.test_batch_size, shuffle=False, drop_last=False,
num_workers=opt.num_workers)
meta_valloader = DataLoader(MetaTieredImageNet(args=opt, partition='val',
train_transform=train_trans,
test_transform=test_trans,
fix_seed=False),
batch_size=opt.test_batch_size, shuffle=False, drop_last=False,
num_workers=opt.num_workers)
if opt.use_trainval:
n_cls = 448
else:
n_cls = 351
elif opt.dataset == 'CIFAR-FS' or opt.dataset == 'FC100':
train_trans, test_trans = transforms_options['D']
meta_testloader = DataLoader(MetaCIFAR100(args=opt, partition='test',
train_transform=train_trans,
test_transform=test_trans,
fix_seed=False),
batch_size=opt.test_batch_size, shuffle=False, drop_last=False,
num_workers=opt.num_workers)
meta_valloader = DataLoader(MetaCIFAR100(args=opt, partition='val',
train_transform=train_trans,
test_transform=test_trans,
fix_seed=False),
batch_size=opt.test_batch_size, shuffle=False, drop_last=False,
num_workers=opt.num_workers)
if opt.use_trainval:
n_cls = 80
else:
if opt.dataset == 'CIFAR-FS':
n_cls = 64
elif opt.dataset == 'FC100':
n_cls = 60
else:
raise NotImplementedError('dataset not supported: {}'.format(opt.dataset))
else:
raise NotImplementedError(opt.dataset)
support_xs, _, _, _ = next(iter(meta_testloader))
batch_size, _, channel, height, width = support_xs.size()
# Get input channel/size for creating augmentcnn model
if opt.model == 'augmentcnn':
assert height == width
opt.n_input_channels = channel
opt.input_size = height
# load model
model = create_model(opt.model, n_cls, opt.dataset, args=opt )
ckpt = torch.load(opt.model_path)
model.load_state_dict(ckpt['model'])
if torch.cuda.is_available():
model = model.cuda()
cudnn.benchmark = True
# Calculate model size & number of flops
logger.info('Number of parameters: {}'.format(count_params(model)))
macs, params = get_model_complexity_info(model, (channel, height, width), as_strings=True,
print_per_layer_stat=False, verbose=False)
logger.info('{:<30} {:<8}'.format('Computational complexity: ', macs))
logger.info('{:<30} {:<8}'.format('Number of parameters: ', params))
logger.info('Use norm:{}'.format(opt.norm))
start = time.time()
test_acc_feat, test_std_feat = meta_test(model, meta_testloader, use_logit=False, is_norm=opt.norm, C=opt.C)
test_time = time.time() - start
logger.info('test_acc_feat: {:.4f}, test_std: {:.4f}, time: {:.1f}'.format(test_acc_feat,
test_std_feat,
test_time))
if __name__ == '__main__':
main(sys.argv[1:])