"""
BSD 3-Clause License
Copyright (c) Soumith Chintala 2016,
All rights reserved.
Redistribution and use in source and binary forms, with or without
modification, are permitted provided that the following conditions are met:
* Redistributions of source code must retain the above copyright notice, this
list of conditions and the following disclaimer.
* Redistributions in binary form must reproduce the above copyright notice,
this list of conditions and the following disclaimer in the documentation
and/or other materials provided with the distribution.
* Neither the name of the copyright holder nor the names of its
contributors may be used to endorse or promote products derived from
this software without specific prior written permission.
THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE
FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL
DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,
OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
Copyright 2020 Huawei Technologies Co., Ltd
Licensed under the BSD 3-Clause License (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
https://spdx.org/licenses/BSD-3-Clause.html
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License.
"""
from __future__ import print_function, absolute_import
import time
from collections import OrderedDict
import torch
from .evaluation_metrics import cmc, mean_ap
from .feature_extraction import extract_cnn_feature
from .utils.meters import AverageMeter
import os
def extract_features(model, data_loader, print_freq=10):
model.eval()
batch_time = AverageMeter()
data_time = AverageMeter()
features = OrderedDict()
labels = OrderedDict()
end = time.time()
for i, (imgs, fnames, pids, _) in enumerate(data_loader):
data_time.update(time.time() - end)
if os.environ['device'] == 'npu':
imgs = imgs.npu()
elif os.environ['device'] == 'gpu':
imgs = imgs.cuda()
outputs = extract_cnn_feature(model, imgs)
for fname, output, pid in zip(fnames, outputs, pids):
features[fname] = output
labels[fname] = pid
batch_time.update(time.time() - end)
end = time.time()
if (i + 1) % print_freq == 0:
print('Extract Features: [{}/{}]\t'
'Time {:.3f} ({:.3f})\t'
'Data {:.3f} ({:.3f})\t'
.format(i + 1, len(data_loader),
batch_time.val, batch_time.avg,
data_time.val, data_time.avg))
return features, labels
def pairwise_distance(query_features, gallery_features, query=None, gallery=None):
if query is None and gallery is None:
n = len(features)
x = torch.cat(list(features.values()))
x = x.view(n, -1)
dist = torch.pow(x, 2).sum(1) * 2
dist = dist.expand(n, n) - 2 * torch.mm(x, x.t())
return dist
x = torch.cat([query_features[f].unsqueeze(0) for f, _, _ in query], 0)
y = torch.cat([gallery_features[f].unsqueeze(0) for f, _, _ in gallery], 0)
m, n = x.size(0), y.size(0)
x = x.view(m, -1)
y = y.view(n, -1)
dist = torch.pow(x, 2).sum(1).unsqueeze(1).expand(m, n) + \
torch.pow(y, 2).sum(1).unsqueeze(1).expand(n, m).t()
dist.addmm_(1, -2, x, y.t())
return dist
def evaluate_all(distmat, query=None, gallery=None,
query_ids=None, gallery_ids=None,
query_cams=None, gallery_cams=None,
cmc_topk=(1, 5, 10)):
if query is not None and gallery is not None:
query_ids = [pid for _, pid, _ in query]
gallery_ids = [pid for _, pid, _ in gallery]
query_cams = [cam for _, _, cam in query]
gallery_cams = [cam for _, _, cam in gallery]
else:
assert (query_ids is not None and gallery_ids is not None
and query_cams is not None and gallery_cams is not None)
mAP = mean_ap(distmat, query_ids, gallery_ids, query_cams, gallery_cams)
print('Mean AP: {:4.1%}'.format(mAP))
cmc_configs = {
'market1501': dict(separate_camera_set=False,
single_gallery_shot=False,
first_match_break=True)}
cmc_scores = {name: cmc(distmat, query_ids, gallery_ids,
query_cams, gallery_cams, **params)
for name, params in cmc_configs.items()}
print('CMC Scores{:>12}'
.format('market1501'))
for k in cmc_topk:
print(' top-{:<4}{:12.1%}'
.format(k, cmc_scores['market1501'][k - 1]))
return cmc_scores['market1501'][0]
class Evaluator(object):
def __init__(self, model):
super(Evaluator, self).__init__()
self.model = model
def evaluate(self, query_loader, gallery_loader, query, gallery):
print('extracting query features\n')
query_features, _ = extract_features(self.model, query_loader)
print('extracting gallery features\n')
gallery_features, _ = extract_features(self.model, gallery_loader)
distmat = pairwise_distance(query_features, gallery_features, query, gallery)
return evaluate_all(distmat, query=query, gallery=gallery)