mirror of https://github.com/JDAI-CV/fast-reid.git
119 lines
3.9 KiB
Python
119 lines
3.9 KiB
Python
# encoding: utf-8
|
|
"""
|
|
@author: liaoxingyu
|
|
@contact: sherlockliao01@gmail.com
|
|
"""
|
|
import copy
|
|
import logging
|
|
from collections import OrderedDict
|
|
|
|
import numpy as np
|
|
import torch
|
|
import torch.nn.functional as F
|
|
from sklearn import metrics
|
|
|
|
from fastreid.utils import comm
|
|
from fastreid.utils.compute_dist import build_dist
|
|
from .evaluator import DatasetEvaluator
|
|
from .query_expansion import aqe
|
|
from .rank import evaluate_rank
|
|
from .roc import evaluate_roc
|
|
|
|
logger = logging.getLogger(__name__)
|
|
|
|
|
|
class ReidEvaluator(DatasetEvaluator):
|
|
def __init__(self, cfg, num_query, output_dir=None):
|
|
self.cfg = cfg
|
|
self._num_query = num_query
|
|
self._output_dir = output_dir
|
|
|
|
self.features = []
|
|
self.pids = []
|
|
self.camids = []
|
|
|
|
def reset(self):
|
|
self.features = []
|
|
self.pids = []
|
|
self.camids = []
|
|
|
|
def process(self, inputs, outputs):
|
|
self.pids.extend(inputs["targets"])
|
|
self.camids.extend(inputs["camids"])
|
|
self.features.append(outputs.cpu())
|
|
|
|
def evaluate(self):
|
|
if comm.get_world_size() > 1:
|
|
comm.synchronize()
|
|
features = comm.gather(self.features)
|
|
features = sum(features, [])
|
|
|
|
pids = comm.gather(self.pids)
|
|
pids = sum(pids, [])
|
|
|
|
camids = comm.gather(self.camids)
|
|
camids = sum(camids, [])
|
|
|
|
# fmt: off
|
|
if not comm.is_main_process(): return {}
|
|
# fmt: on
|
|
else:
|
|
features = self.features
|
|
pids = self.pids
|
|
camids = self.camids
|
|
|
|
features = torch.cat(features, dim=0)
|
|
# query feature, person ids and camera ids
|
|
query_features = features[:self._num_query]
|
|
query_pids = np.asarray(pids[:self._num_query])
|
|
query_camids = np.asarray(camids[:self._num_query])
|
|
|
|
# gallery features, person ids and camera ids
|
|
gallery_features = features[self._num_query:]
|
|
gallery_pids = np.asarray(pids[self._num_query:])
|
|
gallery_camids = np.asarray(camids[self._num_query:])
|
|
|
|
self._results = OrderedDict()
|
|
|
|
if self.cfg.TEST.AQE.ENABLED:
|
|
logger.info("Test with AQE setting")
|
|
qe_time = self.cfg.TEST.AQE.QE_TIME
|
|
qe_k = self.cfg.TEST.AQE.QE_K
|
|
alpha = self.cfg.TEST.AQE.ALPHA
|
|
query_features, gallery_features = aqe(query_features, gallery_features, qe_time, qe_k, alpha)
|
|
|
|
dist = build_dist(query_features, gallery_features, self.cfg.TEST.METRIC)
|
|
|
|
if self.cfg.TEST.RERANK.ENABLED:
|
|
logger.info("Test with rerank setting")
|
|
k1 = self.cfg.TEST.RERANK.K1
|
|
k2 = self.cfg.TEST.RERANK.K2
|
|
lambda_value = self.cfg.TEST.RERANK.LAMBDA
|
|
|
|
if self.cfg.TEST.METRIC == "cosine":
|
|
query_features = F.normalize(query_features, dim=1)
|
|
gallery_features = F.normalize(gallery_features, dim=1)
|
|
|
|
rerank_dist = build_dist(query_features, gallery_features, metric="jaccard", k1=k1, k2=k2)
|
|
dist = rerank_dist * (1 - lambda_value) + dist * lambda_value
|
|
|
|
cmc, all_AP, all_INP = evaluate_rank(dist, query_pids, gallery_pids, query_camids, gallery_camids)
|
|
|
|
mAP = np.mean(all_AP)
|
|
mINP = np.mean(all_INP)
|
|
for r in [1, 5, 10]:
|
|
self._results['Rank-{}'.format(r)] = cmc[r - 1] * 100
|
|
self._results['mAP'] = mAP * 100
|
|
self._results['mINP'] = mINP * 100
|
|
self._results["metric"] = (mAP + cmc[0]) / 2 * 100
|
|
|
|
if self.cfg.TEST.ROC_ENABLED:
|
|
scores, labels = evaluate_roc(dist, query_pids, gallery_pids, query_camids, gallery_camids)
|
|
fprs, tprs, thres = metrics.roc_curve(labels, scores)
|
|
|
|
for fpr in [1e-4, 1e-3, 1e-2]:
|
|
ind = np.argmin(np.abs(fprs - fpr))
|
|
self._results["TPR@FPR={:.0e}".format(fpr)] = tprs[ind]
|
|
|
|
return copy.deepcopy(self._results)
|