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eval_fewshot.py
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eval_fewshot.py
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from __future__ import print_function
import os
import argparse
import socket
import time
import sys
import torch
import torch.backends.cudnn as cudnn
from torch.utils.data import DataLoader
from models import model_dict, model_pool
from models.util import create_model
from dataset.mini_imagenet import ImageNet, MetaImageNet
from dataset.tiered_imagenet import TieredImageNet, MetaTieredImageNet
from dataset.cifar import CIFAR100, MetaCIFAR100
from dataset.transform_cfg import transforms_options, transforms_list
from eval.meta_eval import meta_test
def parse_option():
hostname = socket.gethostname()
parser = argparse.ArgumentParser('argument for training')
# load pretrained model
parser.add_argument('--model', type=str, default='resnet12', 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=600, 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=15, 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)')
opt = parser.parse_args()
if 'trainval' in opt.model_path:
opt.use_trainval = True
else:
opt.use_trainval = False
# set the path according to the environment
if hostname.startswith('visiongpu'):
opt.data_root = '/data/vision/phillipi/rep-learn/{}'.format(opt.dataset)
opt.data_aug = True
elif hostname.startswith('instance'):
opt.data_root = '/mnt/globalssd/fewshot/{}'.format(opt.dataset)
opt.data_aug = True
elif opt.data_root != 'data':
opt.data_aug = True
else:
raise NotImplementedError('server invalid: {}'.format(hostname))
return opt
def main():
opt = parse_option()
# test loader
args = opt
args.batch_size = args.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)
# load model
model = create_model(opt.model, n_cls, opt.dataset)
ckpt = torch.load(opt.model_path)
model.load_state_dict(ckpt['model'])
if torch.cuda.is_available():
model = model.cuda()
cudnn.benchmark = True
# evalation
start = time.time()
val_acc, val_std = meta_test(model, meta_valloader)
val_time = time.time() - start
print('val_acc: {:.4f}, val_std: {:.4f}, time: {:.1f}'.format(val_acc, val_std,
val_time))
start = time.time()
val_acc_feat, val_std_feat = meta_test(model, meta_valloader, use_logit=False)
val_time = time.time() - start
print('val_acc_feat: {:.4f}, val_std: {:.4f}, time: {:.1f}'.format(val_acc_feat,
val_std_feat,
val_time))
start = time.time()
test_acc, test_std = meta_test(model, meta_testloader)
test_time = time.time() - start
print('test_acc: {:.4f}, test_std: {:.4f}, time: {:.1f}'.format(test_acc, test_std,
test_time))
start = time.time()
test_acc_feat, test_std_feat = meta_test(model, meta_testloader, use_logit=False)
test_time = time.time() - start
print('test_acc_feat: {:.4f}, test_std: {:.4f}, time: {:.1f}'.format(test_acc_feat,
test_std_feat,
test_time))
if __name__ == '__main__':
main()