def visualize(self):
import sklearn.metrics.pairwise as skp
metric = self.eval_metric
self.set_eval()
query_feats, query_pids, query_camids = self.extract_feats(
self.datamanager.query_loader)
gallery_feats, gallery_pids, gallery_camids = self.extract_feats(
self.datamanager.gallery_loader)
if metric == 'cosine':
distmat = skp.cosine_distances(
query_feats, gallery_feats
) # please note, it is cosine distance not similarity
elif metric == 'euclidean':
distmat = skp.euclidean_distances(query_feats, gallery_feats)
elif metric == 'hamming':
distmat = lightreid.utils.hamming_distance(query_feats,
gallery_feats)
dataset = [
self.datamanager.query_dataset.samples,
self.datamanager.gallery_dataset.samples
]
visualize_ranked_results(distmat,
dataset,
save_dir='./vis-results/',
topk=20,
mode='inter-camera',
show='all')
def visualize(self):
import sklearn.metrics.pairwise as skp
metric = self.eval_metric
self.set_eval()
for dataset_name, (
query_loader, gallery_loader
) in self.datamanager.query_gallery_loader_dict.items():
query_feats, query_pids, query_camids = self.extract_feats(
query_loader)
gallery_feats, gallery_pids, gallery_camids = self.extract_feats(
gallery_loader)
if metric == 'cosine':
distmat = skp.cosine_distances(
query_feats, gallery_feats
) # please note, it is cosine distance not similarity
sort = 'ascend'
elif metric == 'euclidean':
distmat = skp.euclidean_distances(query_feats, gallery_feats)
sort = 'ascend'
elif metric == 'hamming':
distmat = lightreid.utils.hamming_distance(
query_feats, gallery_feats)
sort = 'descend'
else:
assert 0, 'metric type error'
query_dataset, gallery_dataset = self.datamanager.query_gallery_dataset_dict[
dataset_name]
dataset = [query_dataset.samples, gallery_dataset.samples]
output_path = join(self.results_dir, 'visualize/',
'{}/'.format(dataset_name))
visualize_ranked_results(distmat,
dataset,
save_dir=output_path,
sort=sort,
topk=20,
mode='inter-camera',
show='all')