def test(model, queryloader, galleryloader, use_gpu, ranks=[1, 5, 10, 20]):
batch_time = AverageMeter()
model.eval()
with torch.no_grad():
qf, q_pids, q_camids, lqf = [], [], [], []
for batch_idx, (imgs, pids) in enumerate(queryloader):
if use_gpu: imgs = imgs.cuda()
end = time.time()
# qf.append(extract_feature(
# model, imgs, requires_norm=True, vectorize=True,use_pcb=args.use_pcb).cpu().data)
features,local_features = model(imgs)
# print('lqf shape',local_features.shape)
# print('qf shape',features.shape)
batch_time.update(time.time() - end)
features = features.data.cpu()
local_features = local_features.data.cpu()
qf.append(features)
lqf.append(local_features)
q_pids.extend(pids)
# q_camids.extend(camids)
qf = torch.cat(qf, 0)
lqf = torch.cat(lqf,0)
print('qf shape',qf.shape)
print('lqf shape',lqf.shape)
q_pids = np.asarray(q_pids)
#q_camids = np.asarray(q_camids)
print("Extracted features for query set, obtained {}-by-{} matrix".format(qf.size(0), qf.size(1)))
gf, g_pids, g_camids, lgf = [], [], [], []
end = time.time()
for batch_idx, (imgs, pids) in enumerate(galleryloader):
if use_gpu: imgs = imgs.cuda()
end = time.time()
# features, local_features = model(imgs)
# gf.append(extract_feature(
# model, imgs, requires_norm=True, vectorize=True,use_pcb=args.use_pcb).cpu().data)
features,local_features = model(imgs)
batch_time.update(time.time() - end)
features = features.data.cpu()
local_features = local_features.data.cpu()
gf.append(features)
lgf.append(local_features)
g_pids.extend(pids)
# g_camids.extend(camids)
gf = torch.cat(gf, 0)
# lgf = torch.cat(lgf,0)
print('gf shape',gf.shape)
# print('lgf shape',lgf.shape)
g_pids = np.asarray(g_pids)
# g_camids = np.asarray(g_camids)
print("Extracted features for gallery set, obtained {}-by-{} matrix".format(gf.size(0), gf.size(1)))
print("==> BatchTime(s)/BatchSize(img): {:.3f}/{}".format(batch_time.avg, args.test_batch))
# feature normlization
qf = 1. * qf / (torch.norm(qf, 2, dim = -1, keepdim=True).expand_as(qf) + 1e-12)
gf = 1. * gf / (torch.norm(gf, 2, dim = -1, keepdim=True).expand_as(gf) + 1e-12)
m, n = qf.size(0), gf.size(0)
distmat = torch.pow(qf, 2).sum(dim=1, keepdim=True).expand(m, n) + \
torch.pow(gf, 2).sum(dim=1, keepdim=True).expand(n, m).t()
print('distmat shape1',distmat.shape)
distmat.addmm_(1, -2, qf, gf.t())
print('distmat shape2',distmat.shape)
distmat = distmat.cpu().numpy()
# args.test_distance = 'global'(default)
if not args.test_distance== 'global':
print("Only using global branch")
from util.distance import low_memory_local_dist
#embed()
lqf = lqf.permute(0,2,1)
lgf = lgf.permute(0,2,1)
local_distmat = low_memory_local_dist(lqf.numpy(),lgf.numpy(),aligned= not args.unaligned)
if args.test_distance== 'local':
print("Only using local branch")
distmat = local_distmat
if args.test_distance == 'global_local':
print("Using global and local branches")
distmat = local_distmat+distmat
print("Computing CMC and mAP")
cmc, mAP = evaluate(distmat, q_pids, g_pids, q_camids, g_camids, use_metric_cuhk03=args.use_metric_cuhk03)
print("Results ----------")
print("mAP: {:.1%}".format(mAP))
print("CMC curve")
for r in ranks:
print("Rank-{:<3}: {:.1%}".format(r, cmc[r - 1]))
print("------------------")
# args.reranking = false(default)
if args.reranking:
from util.re_ranking import re_ranking
if args.test_distance == 'global':
print("Only using global branch for reranking")
distmat = re_ranking(qf,gf,k1=20, k2=6, lambda_value=0.3)
else:
local_qq_distmat = low_memory_local_dist(lqf.numpy(), lqf.numpy(),aligned= not args.unaligned)
local_gg_distmat = low_memory_local_dist(lgf.numpy(), lgf.numpy(),aligned= not args.unaligned)
local_dist = np.concatenate(
[np.concatenate([local_qq_distmat, local_distmat], axis=1),
np.concatenate([local_distmat.T, local_gg_distmat], axis=1)],
axis=0)
if args.test_distance == 'local':
print("Only using local branch for reranking")
distmat = re_ranking(qf,gf,k1=20,k2=6,lambda_value=0.3,local_distmat=local_dist,only_local=True)
elif args.test_distance == 'global_local':
print("Using global and local branches for reranking")
distmat = re_ranking(qf,gf,k1=20,k2=6,lambda_value=0.3,local_distmat=local_dist,only_local=False)
print("Computing CMC and mAP for re_ranking")
cmc, mAP = evaluate(distmat, q_pids, g_pids, q_camids, g_camids, use_metric_cuhk03=args.use_metric_cuhk03)
print("Results ----------")
print("mAP(RK): {:.1%}".format(mAP))
print("CMC curve(RK)")
for r in ranks:
print("Rank-{:<3}: {:.1%}".format(r, cmc[r - 1]))
print("------------------")
return mAP
def test(model, queryloader, galleryloader, use_gpu, ranks=[1, 5, 8]):
print('------开始测试------')
# batch_time = AverageMeter()
model.eval()
with torch.no_grad():
### 计算query集的features
qf,lqf = [], []# qf:query feature lqf:local query feature
i = 0
for batch_idx, imgs in enumerate(queryloader):
i += 1
if use_gpu: imgs = imgs.cuda()
# end = time.time()
# 使用model对图像进行特征提取
features, local_features = model(imgs)
# batch_time.update(time.time() - end)
features = features.data.cpu()# 使用cpu进行处理
local_features = local_features.data.cpu()
qf.append(features)
lqf.append(local_features)
# 对tensor进行拼接,axis=0表示进行竖向拼接
qf = torch.cat(qf, 0)
lqf = torch.cat(lqf, 0)
print("Extracted features for query set, obtained {}-by-{} matrix".format(qf.size(0), qf.size(1)))
### 计算gallery集的features
gf,lgf = [], []
# end = time.time()
for batch_idx, imgs in enumerate(galleryloader):
if use_gpu: imgs = imgs.cuda()
# end = time.time()
# 使用resnet50进行图像特征提取
features, local_features = model(imgs)
# batch_time.update(time.time() - end)
features = features.data.cpu()
local_features = local_features.data.cpu()
gf.append(features)
lgf.append(local_features)
# 打个断点，看一下gf
gf = torch.cat(gf, 0)
lgf = torch.cat(lgf, 0)
print("Extracted features for gallery set, obtained {}-by-{} matrix".format(gf.size(0), gf.size(1)))
# print("==> BatchTime(s)/BatchSize(img): {:.3f}/{}".format(batch_time.avg, 32))
# 下面这些是处理要点
# feature normlization 特征标准化
qf = 1. * qf / (torch.norm(qf, 2, dim=-1, keepdim=True).expand_as(qf) + 1e-12)
gf = 1. * gf / (torch.norm(gf, 2, dim=-1, keepdim=True).expand_as(gf) + 1e-12)
# 矩阵的行列数
m, n = qf.size(0), gf.size(0)
torch.pow(qf, 2).sum(dim=1, keepdim=True)
# 计算全局距离矩阵global distmat
# torch.pow(qf,2)：求矩阵中各元素的平方
distmat = torch.pow(qf, 2).sum(dim=1, keepdim=True).expand(m, n) + \
torch.pow(gf, 2).sum(dim=1, keepdim=True).expand(n, m).t()
distmat.addmm_(1, -2, qf, gf.t()) # 矩阵相乘
distmat = distmat.numpy()
# 使用local_distmat
from util.distance import low_memory_local_dist
lqf = lqf.permute(0, 2, 1)
lgf = lgf.permute(0, 2, 1)
local_distmat = low_memory_local_dist(lqf.numpy(), lgf.numpy(), aligned=True)
# Using global and local branches
distmat = local_distmat + distmat
# 归一化
distmat = distmat / 10
# 用于测试
mm, nn = distmat.shape[0], distmat.shape[1]
min = [1, 1, 1, 1, 1, 1, 1, 1] # min数组的大小应该等于mm
num = 0
for i in range(mm):
for j in range(nn):
if distmat[i][j] < min[i]:
min[i] = distmat[i][j]
# 这里的判定两object是否为同一object的distance阈值为经验值，还需进一步优化
if min[i] < 3.5:
# print("min[i] is",min[i])
num += 1
print('各图像之间的相似度为：\n',1 - distmat)
print('经多视角识别后的person_num为:', num)
print('------测试结束------')
def test(model, queryloader, galleryloader, use_gpu, ranks=[1, 5, 10, 20]):
batch_time = AverageMeter()
model.eval()
with torch.no_grad():
qf, q_pids, q_camids, lqf = [], [], [], []
for batch_idx, (imgs, pids, camids) in enumerate(queryloader):
if use_gpu: imgs = imgs.cuda()
end = time.time()
features, local_features = model(imgs)
batch_time.update(time.time() - end)
features = features.data.cpu()
local_features = local_features.data.cpu()
qf.append(features)
lqf.append(local_features)
q_pids.extend(pids)
q_camids.extend(camids)
qf = torch.cat(qf, 0)
lqf = torch.cat(lqf, 0)
q_pids = np.asarray(q_pids)
q_camids = np.asarray(q_camids)
print("Extracted features for query set, obtained {}-by-{} matrix".
format(qf.size(0), qf.size(1)))
gf, g_pids, g_camids, lgf = [], [], [], []
end = time.time()
for batch_idx, (imgs, pids, camids) in enumerate(galleryloader):
if use_gpu: imgs = imgs.cuda()
end = time.time()
features, local_features = model(imgs)
batch_time.update(time.time() - end)
features = features.data.cpu()
local_features = local_features.data.cpu()
gf.append(features)
lgf.append(local_features)
g_pids.extend(pids)
g_camids.extend(camids)
gf = torch.cat(gf, 0)
lgf = torch.cat(lgf, 0)
g_pids = np.asarray(g_pids)
g_camids = np.asarray(g_camids)
print("Extracted features for gallery set, obtained {}-by-{} matrix".
format(gf.size(0), gf.size(1)))
print("==> BatchTime(s)/BatchSize(img): {:.3f}/{}".format(
batch_time.avg, 4))
# feature normlization
qf = 1. * qf / (torch.norm(qf, 2, dim=-1, keepdim=True).expand_as(qf) +
1e-12)
gf = 1. * gf / (torch.norm(gf, 2, dim=-1, keepdim=True).expand_as(gf) +
1e-12)
m, n = qf.size(0), gf.size(0)
distmat = torch.pow(qf, 2).sum(dim=1, keepdim=True).expand(m, n) + \
torch.pow(gf, 2).sum(dim=1, keepdim=True).expand(n, m).t()
distmat.addmm_(1, -2, qf, gf.t())
distmat = distmat.numpy()
## Calculating global distance
print("Using global and local branches")
from util.distance import low_memory_local_dist
lqf = lqf.permute(0, 2, 1)
lgf = lgf.permute(0, 2, 1)
local_distmat = low_memory_local_dist(lqf.numpy(),
lgf.numpy(),
aligned=True)
distmat = local_distmat + distmat
print("Computing CMC and mAP")
cmc, mAP = evaluate(distmat, q_pids, g_pids, q_camids, g_camids)
print("Results ----------")
print("mAP: {:.1%}".format(mAP))
print("CMC curve")
for r in ranks:
print("Rank-{:<3}: {:.1%}".format(r, cmc[r - 1]))
print("------------------")
return distmat
def test(model, queryloader, galleryloader, use_gpu, ranks=[1, 5, 10, 20]):
print('------start testing------')
cmc1 = []
cmc2 = []
batch_time = AverageMeter()
model.eval()
with torch.no_grad():
qf, q_pids, q_camids, lqf = [], [], [], []
for batch_idx, (imgs, pids, camids) in enumerate(queryloader):
if use_gpu: imgs = imgs.cuda()
end = time.time()
features, local_features = model(imgs)
batch_time.update(time.time() - end)
features = features.data.cpu()
local_features = local_features.data.cpu()
qf.append(features)
lqf.append(local_features)
q_pids.extend(pids)
q_camids.extend(camids)
qf = torch.cat(qf, 0)
lqf = torch.cat(lqf, 0)
q_pids = np.asarray(q_pids)
q_camids = np.asarray(q_camids)
print("Extracted features for query set, obtained {}-by-{} matrix".
format(qf.size(0), qf.size(1)))
gf, g_pids, g_camids, lgf = [], [], [], []
end = time.time()
for batch_idx, (imgs, pids, camids) in enumerate(galleryloader):
if use_gpu: imgs = imgs.cuda()
end = time.time()
features, local_features = model(imgs)
batch_time.update(time.time() - end)
features = features.data.cpu()
local_features = local_features.data.cpu()
gf.append(features)
lgf.append(local_features)
g_pids.extend(pids)
g_camids.extend(camids)
gf = torch.cat(gf, 0)
lgf = torch.cat(lgf, 0)
g_pids = np.asarray(g_pids)
g_camids = np.asarray(g_camids)
print("Extracted features for gallery set, obtained {}-by-{} matrix".
format(gf.size(0), gf.size(1)))
print("==> BatchTime(s)/BatchSize(img): {:.3f}/{}".format(
batch_time.avg, args.test_batch))
# feature normlization
qf = 1. * qf / (torch.norm(qf, 2, dim=-1, keepdim=True).expand_as(qf) +
1e-12)
gf = 1. * gf / (torch.norm(gf, 2, dim=-1, keepdim=True).expand_as(gf) +
1e-12)
m, n = qf.size(0), gf.size(0)
distmat = torch.pow(qf, 2).sum(dim=1, keepdim=True).expand(m, n) + \
torch.pow(gf, 2).sum(dim=1, keepdim=True).expand(n, m).t()
distmat.addmm_(1, -2, qf, gf.t())
distmat = distmat.numpy()
if not args.test_distance == 'global':
print("Only using global branch")
from util.distance import low_memory_local_dist
lqf = lqf.permute(0, 2, 1)
lgf = lgf.permute(0, 2, 1)
local_distmat = low_memory_local_dist(lqf.numpy(),
lgf.numpy(),
aligned=not args.unaligned)
if args.test_distance == 'local':
print("Only using local branch")
distmat = local_distmat
if args.test_distance == 'global_local':
print("Using global and local branches")
distmat = local_distmat + distmat
print("Computing CMC and mAP")
cmc, mAP = evaluate(distmat,
q_pids,
g_pids,
q_camids,
g_camids,
use_metric_cuhk03=args.use_metric_cuhk03)
print("cms's shape: ", cmc.shape)
print("cms's type: ", cmc.dtype)
# cmc1 = []
# print("cmc2's shape: ",cmc2.shape)
print("Results ----------")
print("mAP: {:.1%}".format(mAP))
print("CMC curve")
for r in ranks:
print("Rank-{:<3}: {:.1%}".format(r, cmc[r - 1]))
cmc1.append(cmc[r - 1])
print("------------------")
# test matplot
# x = np.linspace(0,2*np.pi,50)
# y = np.sin(x)
# print("cmc2's shape: ",cmc2.shape)
# print(cmc2)
if args.reranking:
from util.re_ranking import re_ranking
if args.test_distance == 'global':
print("Only using global branch for reranking")
distmat = re_ranking(qf, gf, k1=20, k2=6, lambda_value=0.3)
else:
local_qq_distmat = low_memory_local_dist(
lqf.numpy(), lqf.numpy(), aligned=not args.unaligned)
local_gg_distmat = low_memory_local_dist(
lgf.numpy(), lgf.numpy(), aligned=not args.unaligned)
local_dist = np.concatenate([
np.concatenate([local_qq_distmat, local_distmat], axis=1),
np.concatenate([local_distmat.T, local_gg_distmat], axis=1)
],
axis=0)
if args.test_distance == 'local':
print("Only using local branch for reranking")
distmat = re_ranking(qf,
gf,
k1=20,
k2=6,
lambda_value=0.3,
local_distmat=local_dist,
only_local=True)
elif args.test_distance == 'global_local':
print("Using global and local branches for reranking")
distmat = re_ranking(qf,
gf,
k1=20,
k2=6,
lambda_value=0.3,
local_distmat=local_dist,
only_local=False)
print("Computing CMC and mAP for re_ranking")
cmc, mAP = evaluate(distmat,
q_pids,
g_pids,
q_camids,
g_camids,
use_metric_cuhk03=args.use_metric_cuhk03)
# cmc2 = []
print("Results ----------")
print("mAP(RK): {:.1%}".format(mAP))
print("CMC curve(RK)")
for r in ranks:
print("Rank-{:<3}: {:.1%}".format(r, cmc[r - 1]))
cmc2.append(cmc[r - 1])
print("------------------")
# matlpot
# print("----cmc2----",cmc2)
# print("cmc1's value------")
# print(cmc1)
plt.plot(ranks,
cmc1,
label='ranking',
color='red',
marker='o',
markersize=5)
plt.plot(ranks,
cmc2,
label='re-ranking',
color='blue',
marker='o',
markersize=5)
plt.ylabel('Accuracy')
plt.xlabel('Rank_num')
plt.title('Result of Ranking and Re-ranking(query_tank_cam=5)')
plt.legend()
plt.savefig('/home/gaoziqiang/tempt/tank_cam5.png')
plt.show()
return cmc[0]
def test(model, queryloader, galleryloader, use_gpu, ranks=[1, 5, 8]):
print('------start testing------')
cmc1 = []
cmc2 = []
batch_time = AverageMeter()
model.eval()
with torch.no_grad():
# 计算query集的features
qf, q_pids, q_camids, lqf = [], [], [], []
for batch_idx, (imgs, pids, camids) in enumerate(queryloader):
if use_gpu: imgs = imgs.cuda()
end = time.time()
features, local_features = model(imgs)
embed()
batch_time.update(time.time() - end)
features = features.data.cpu()
local_features = local_features.data.cpu()
qf.append(features)
lqf.append(local_features)
q_pids.extend(pids)
q_camids.extend(camids)
# embed()
# 对tensor进行拼接,axis=0表示进行竖向拼接
qf = torch.cat(qf, 0)
lqf = torch.cat(lqf, 0)
q_pids = np.asarray(q_pids)
q_camids = np.asarray(q_camids)
print("Extracted features for query set, obtained {}-by-{} matrix".
format(qf.size(0), qf.size(1)))
# 计算gallery集的features
gf, g_pids, g_camids, lgf = [], [], [], []
end = time.time()
for batch_idx, (imgs, pids, camids) in enumerate(galleryloader):
if use_gpu: imgs = imgs.cuda()
end = time.time()
features, local_features = model(imgs)
batch_time.update(time.time() - end)
features = features.data.cpu()
local_features = local_features.data.cpu()
gf.append(features)
lgf.append(local_features)
g_pids.extend(pids)
g_camids.extend(camids)
# 打个断点，看一下gf
# embed()
gf = torch.cat(gf, 0)
lgf = torch.cat(lgf, 0)
g_pids = np.asarray(g_pids)
g_camids = np.asarray(g_camids)
print("Extracted features for gallery set, obtained {}-by-{} matrix".
format(gf.size(0), gf.size(1)))
print("==> BatchTime(s)/BatchSize(img): {:.3f}/{}".format(
batch_time.avg, args.test_batch))
# 下面这些是处理要点
# feature normlization 特征标准化
qf = 1. * qf / (torch.norm(qf, 2, dim=-1, keepdim=True).expand_as(qf) +
1e-12)
gf = 1. * gf / (torch.norm(gf, 2, dim=-1, keepdim=True).expand_as(gf) +
1e-12)
# 这是啥
m, n = qf.size(0), gf.size(0)
distmat = torch.pow(qf, 2).sum(dim=1, keepdim=True).expand(m, n) + \
torch.pow(gf, 2).sum(dim=1, keepdim=True).expand(n, m).t()
distmat.addmm_(1, -2, qf, gf.t()) # 矩阵相乘
distmat = distmat.numpy()
# 用于测试
mm, nn = distmat.shape[0], distmat.shape[1]
min = [1, 1, 1, 1, 1, 1, 1, 1] # min数组的大小应该等于mm
num = 0
for i in range(mm):
for j in range(nn):
if distmat[i][j] < min[i]:
min[i] = distmat[i][j]
if min[i] < 0.4:
num += 1
print('经多视角识别后的person_num为:', num)
if not args.test_distance == 'global':
print("Only using global branch")
from util.distance import low_memory_local_dist
lqf = lqf.permute(0, 2, 1)
lgf = lgf.permute(0, 2, 1)
# 计算local_distmat
local_distmat = low_memory_local_dist(lqf.numpy(),
lgf.numpy(),
aligned=not args.unaligned)
if args.test_distance == 'local':
print("Only using local branch")
distmat = local_distmat
if args.test_distance == 'global_local':
print("Using global and local branches")
# total distmat = local_distmat + distmat(global)
distmat = local_distmat + distmat
print("Computing CMC and mAP")
# 打一个断点，对distmat进行排序
# embed()
cmc, mAP = evaluate(distmat,
q_pids,
g_pids,
q_camids,
g_camids,
use_metric_cuhk03=args.use_metric_cuhk03)
print("cms's shape: ", cmc.shape)
print("cms's type: ", cmc.dtype)
#cmc1 = []
print("------Results ----------")
print("mAP: {:.1%}".format(mAP))
print("CMC curve")
for r in ranks:
print("Rank-{:<3}: {:.1%}".format(r, cmc[r - 1]))
cmc1.append(cmc[r - 1])
print("------------------")
return cmc[0]