def eval_datasets(model,
datasets=('oxford5k', 'paris6k', 'roxford5k', 'rparis6k'),
ms=False,
tta_gem_p=1.0,
logger=None):
model = model.eval()
data_root = os.path.join(get_root(), 'cirtorch')
scales = [1 / 2**(1 / 2), 1.0, 2**(1 / 2)] if ms else [1.0]
results = dict()
for dataset in datasets:
# prepare config structure for the test dataset
cfg = configdataset(dataset, os.path.join(data_root, 'test'))
images = [cfg['im_fname'](cfg, i) for i in range(cfg['n'])]
qimages = [cfg['qim_fname'](cfg, i) for i in range(cfg['nq'])]
bbxs = [tuple(cfg['gnd'][i]['bbx']) for i in range(cfg['nq'])]
tqdm_desc = cfg['dataset']
db_feats = extract_vectors(model,
images=images,
bbxs=None,
scales=scales,
tta_gem_p=tta_gem_p,
tqdm_desc=tqdm_desc)
query_feats = extract_vectors(model,
images=qimages,
bbxs=bbxs,
scales=scales,
tta_gem_p=tta_gem_p,
tqdm_desc=tqdm_desc)
scores = np.dot(db_feats, query_feats.T)
ranks = np.argsort(-scores, axis=0)
results[dataset] = compute_map_and_print(dataset,
ranks,
cfg['gnd'],
kappas=[1, 5, 10],
logger=logger)
return results
import numpy as np
import torch
from torch.utils.model_zoo import load_url
from torch.autograd import Variable
from torchvision import transforms
from cirtorch.networks.imageretrievalnet import init_network, extract_vectors
from cirtorch.datasets.datahelpers import cid2filename
from cirtorch.datasets.testdataset import configdataset
from cirtorch.utils.download import download_train, download_test
from cirtorch.utils.whiten import whitenlearn, whitenapply
from cirtorch.utils.evaluate import compute_map_and_print
from cirtorch.utils.general import get_root, get_data_root, htime
data_root = os.path.join(get_root(), 'cirtorch')
PRETRAINED = {
'retrievalSfM120k-vgg16-gem':
'http://cmp.felk.cvut.cz/cnnimageretrieval/data/networks/retrieval-SfM-120k/retrievalSfM120k-vgg16-gem-b4dcdc6.pth',
'retrievalSfM120k-resnet101-gem':
'http://cmp.felk.cvut.cz/cnnimageretrieval/data/networks/retrieval-SfM-120k/retrievalSfM120k-resnet101-gem-b80fb85.pth',
}
datasets_names = ['oxford5k', 'paris6k', 'roxford5k', 'rparis6k']
whitening_names = ['retrieval-SfM-30k', 'retrieval-SfM-120k']
parser = argparse.ArgumentParser(
description='PyTorch CNN Image Retrieval Testing')
# network