def eval_model(db, net, trfs, pooling='mean', gemp=3, detailed=False, whiten=None,
aqe=None, adba=None, threads=8, batch_size=16, save_feats=None,
load_feats=None, dbg=()):
""" Evaluate a trained model (network) on a given dataset.
The dataset is supposed to contain the evaluation code.
"""
print("\n>> Evaluation...")
query_db = db.get_query_db()
# load DB feats
bdescs = []
qdescs = []
bdescs = np.load(os.path.join(load_feats, 'feats.bdescs.npy'))
qdescs = bdescs
if whiten is not None:
bdescs = common.whiten_features(tonumpy(bdescs), net.pca, **whiten)
qdescs = common.whiten_features(tonumpy(qdescs), net.pca, **whiten)
if adba is not None:
bdescs = expand_descriptors(bdescs, **args.adba)
if aqe is not None:
qdescs = expand_descriptors(qdescs, db=bdescs, **args.aqe)
scores = matmul(qdescs, bdescs)
data_sorted = np.argsort(-scores)
del bdescs
del qdescs
return data_sorted
def expand_descriptors(descs, db=None, alpha=0, k=0):
assert k >= 0 and alpha >= 0, 'k and alpha must be non-negative'
if k == 0:
return descs
descs = tonumpy(descs)
n = descs.shape[0]
db_descs = tonumpy(db if db is not None else descs)
sim = matmul(descs, db_descs)
if db is None:
sim[np.diag_indices(n)] = 0
idx = np.argpartition(sim, int(-k), axis=1)[:, int(-k):]
descs_aug = np.zeros_like(descs)
for i in range(n):
new_q = np.vstack([db_descs[j, :] * sim[i, j]**alpha for j in idx[i]])
new_q = np.vstack([descs[i], new_q])
new_q = np.mean(new_q, axis=0)
descs_aug[i] = new_q / np.linalg.norm(new_q)
return descs_aug
def eval_model(db,
net,
trfs,
pooling='mean',
gemp=3,
detailed=False,
whiten=None,
aqe=None,
adba=None,
threads=8,
batch_size=16,
save_feats=None,
load_feats=None,
dbg=()):
""" Evaluate a trained model (network) on a given dataset.
The dataset is supposed to contain the evaluation code.
"""
print("\n>> Evaluation...")
query_db = db.get_query_db()
# extract DB feats
bdescs = []
qdescs = []
if not load_feats:
trfs_list = [trfs] if isinstance(trfs, str) else trfs
for trfs in trfs_list:
kw = dict(iscuda=net.iscuda,
threads=threads,
batch_size=batch_size,
same_size='Pad' in trfs or 'Crop' in trfs)
bdescs.append(
extract_image_features(db, trfs, net, desc="DB", **kw))
# extract query feats
qdescs.append(
bdescs[-1] if db is query_db else extract_image_features(
query_db, trfs, net, desc="query", **kw))
# pool from multiple transforms (scales)
bdescs = F.normalize(pool(bdescs, pooling, gemp), p=2, dim=1)
qdescs = F.normalize(pool(qdescs, pooling, gemp), p=2, dim=1)
else:
bdescs = np.load(os.path.join(load_feats, 'feats.bdescs.npy'))
if query_db is not db:
qdescs = np.load(os.path.join(load_feats, 'feats.qdescs.npy'))
else:
qdescs = bdescs
if save_feats:
mkdir(save_feats)
np.save(os.path.join(save_feats, 'feats.bdescs.npy'),
bdescs.cpu().numpy())
if query_db is not db:
np.save(os.path.join(save_feats, 'feats.qdescs.npy'),
qdescs.cpu().numpy())
if whiten is not None:
bdescs = common.whiten_features(tonumpy(bdescs), net.pca, **whiten)
qdescs = common.whiten_features(tonumpy(qdescs), net.pca, **whiten)
if adba is not None:
bdescs = expand_descriptors(bdescs, **args.adba)
if aqe is not None:
qdescs = expand_descriptors(qdescs, db=bdescs, **args.aqe)
scores = matmul(qdescs, bdescs)
del bdescs
del qdescs
res = {}
try:
aps = [
db.eval_query_AP(q, s)
for q, s in enumerate(tqdm.tqdm(scores, desc='AP'))
]
if not isinstance(aps[0], dict):
aps = [float(e) for e in aps]
if detailed:
res['APs'] = aps
# Queries with no relevants have an AP of -1
res['mAP'] = float(np.mean([e for e in aps if e >= 0]))
else:
modes = aps[0].keys()
for mode in modes:
apst = [float(e[mode]) for e in aps]
if detailed:
res['APs' + '-' + mode] = apst
# Queries with no relevants have an AP of -1
res['mAP' + '-' + mode] = float(
np.mean([e for e in apst if e >= 0]))
except NotImplementedError:
print(" AP not implemented!")
try:
tops = [
db.eval_query_top(q, s)
for q, s in enumerate(tqdm.tqdm(scores, desc='top1'))
]
if detailed:
res['tops'] = tops
for k in tops[0]:
res['top%d' % k] = float(np.mean([top[k] for top in tops]))
except NotImplementedError:
pass
return res
def test(db,
net,
trfs,
pooling='mean',
gemp=3,
detailed=False,
threads=8,
batch_size=16):
""" Evaluate a trained model (network) on a given dataset.
The dataset is supposed to contain the evaluation code.
"""
print("\n>> Evaluation...")
query_db = db.get_query_db()
# extract DB feats
bdescs = []
qdescs = []
trfs_list = [trfs] if isinstance(trfs, str) else trfs
for trfs in trfs_list:
kw = dict(iscuda=net.iscuda,
threads=threads,
batch_size=batch_size,
same_size='Pad' in trfs or 'Crop' in trfs)
bdescs.append(extract_image_features(db, trfs, net, desc="DB", **kw))
# extract query feats
qdescs.append(bdescs[-1] if db is query_db else extract_image_features(
query_db, trfs, net, desc="query", **kw))
# pool from multiple transforms (scales)
bdescs = F.normalize(pool(bdescs, pooling, gemp), p=2, dim=1)
qdescs = F.normalize(pool(qdescs, pooling, gemp), p=2, dim=1)
bdescs = tonumpy(bdescs)
qdescs = tonumpy(qdescs)
scores = matmul(qdescs, bdescs)
del bdescs
del qdescs
res = {}
try:
aps = [
db.eval_query_AP(q, s)
for q, s in enumerate(tqdm.tqdm(scores, desc='AP'))
]
if not isinstance(aps[0], dict):
aps = [float(e) for e in aps]
if detailed:
res['APs'] = aps
# Queries with no relevants have an AP of -1
res['mAP'] = float(np.mean([e for e in aps if e >= 0]))
else:
modes = aps[0].keys()
for mode in modes:
apst = [float(e[mode]) for e in aps]
if detailed:
res['APs' + '-' + mode] = apst
# Queries with no relevants have an AP of -1
res['mAP' + '-' + mode] = float(
np.mean([e for e in apst if e >= 0]))
except NotImplementedError:
print(" AP not implemented!")
#writer.add_scalar('mAP', res['mAP'], epoch)
return res
def extract_kapture_global_features(kapture_root_path: str,
net,
global_features_type: str,
trfs,
pooling='mean',
gemp=3,
whiten=None,
threads=8,
batch_size=16):
""" Extract features from trained model (network) on a given dataset.
"""
print(f'loading {kapture_root_path}')
with get_all_tar_handlers(kapture_root_path,
mode={
kapture.Keypoints: 'r',
kapture.Descriptors: 'r',
kapture.GlobalFeatures: 'a',
kapture.Matches: 'r'
}) as tar_handlers:
kdata = kapture_from_dir(kapture_root_path,
None,
skip_list=[
kapture.Keypoints, kapture.Descriptors,
kapture.Matches, kapture.Points3d,
kapture.Observations
],
tar_handlers=tar_handlers)
root = get_image_fullpath(kapture_root_path, image_filename=None)
assert kdata.records_camera is not None
imgs = [
image_name
for _, _, image_name in kapture.flatten(kdata.records_camera)
]
if kdata.global_features is None:
kdata.global_features = {}
if global_features_type in kdata.global_features:
imgs = [
image_name for image_name in imgs if image_name not in
kdata.global_features[global_features_type]
]
if len(imgs) == 0:
print('All global features are already extracted')
return
dataset = ImageList(img_list_path=None, root=root, imgs=imgs)
print(f'\nEvaluation on {dataset}')
# extract DB feats
bdescs = []
trfs_list = [trfs] if isinstance(trfs, str) else trfs
for trfs in trfs_list:
kw = dict(iscuda=net.iscuda,
threads=threads,
batch_size=batch_size,
same_size='Pad' in trfs or 'Crop' in trfs)
bdescs.append(
extract_image_features(dataset, trfs, net, desc="DB", **kw))
# pool from multiple transforms (scales)
bdescs = tonumpy(F.normalize(pool(bdescs, pooling, gemp), p=2, dim=1))
if whiten is not None:
bdescs = common.whiten_features(bdescs, net.pca, **whiten)
print('writing extracted global features')
os.umask(0o002)
gfeat_dtype = bdescs.dtype
gfeat_dsize = bdescs.shape[1]
if global_features_type not in kdata.global_features:
kdata.global_features[
global_features_type] = kapture.GlobalFeatures(
'dirtorch', gfeat_dtype, gfeat_dsize, 'L2')
global_features_config_absolute_path = get_feature_csv_fullpath(
kapture.GlobalFeatures, global_features_type,
kapture_root_path)
global_features_to_file(
global_features_config_absolute_path,
kdata.global_features[global_features_type])
else:
assert kdata.global_features[
global_features_type].dtype == gfeat_dtype
assert kdata.global_features[
global_features_type].dsize == gfeat_dsize
assert kdata.global_features[
global_features_type].metric_type == 'L2'
for i in tqdm.tqdm(range(dataset.nimg)):
image_name = dataset.get_key(i)
global_feature_fullpath = get_global_features_fullpath(
global_features_type, kapture_root_path, image_name,
tar_handlers)
gfeat_i = bdescs[i, :]
assert gfeat_i.shape == (gfeat_dsize, )
image_global_features_to_file(global_feature_fullpath, gfeat_i)
kdata.global_features[global_features_type].add(image_name)
del gfeat_i
del bdescs
if not global_features_check_dir(
kdata.global_features[global_features_type],
global_features_type, kapture_root_path, tar_handlers):
print(
'global feature extraction ended successfully but not all files were saved'
)
else:
print('Features extracted.')