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 = [], [], []
for batch_idx, (imgs, pids, camids) in enumerate(queryloader):
if use_gpu: imgs = imgs.cuda()
end = time.time()
features = model(imgs)
batch_time.update(time.time() - end)
features = features.data.cpu()
qf.append(features)
q_pids.extend(pids)
q_camids.extend(camids)
qf = torch.cat(qf, 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 = [], [], []
end = time.time()
for batch_idx, (imgs, pids, camids) in enumerate(galleryloader):
if use_gpu: imgs = imgs.cuda()
end = time.time()
features = model(imgs)
batch_time.update(time.time() - end)
features = features.data.cpu()
gf.append(features)
g_pids.extend(pids)
g_camids.extend(camids)
gf = torch.cat(gf, 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))
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_(qf, gf.t(), beta=1, alpha=-2)
distmat = distmat.numpy()
# 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()) # ???!!! warning
# # torch.addmm_(qf, gf.t(), distmat, 1, -2) module 'torch' has no attribute 'addmm_'
# # # addmm_() takes 2 positional arguments but 4 were given
# # addmm_(Tensor mat1, Tensor mat2, *, Number beta, Number alpha)
# distmat = distmat.numpy()
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("------------------")
return cmc[0]
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, 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("Results ----------")
print("mAP: {:.1%}".format(mAP))
print("CMC curve")
for r in ranks:
print("Rank-{:<3}: {:.1%}".format(r, cmc[r - 1]))
print("------------------")
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 cmc[0]
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 = [], [], []
for batch_idx, (imgs, pids, camids) in enumerate(queryloader):
if use_gpu: imgs = imgs.cuda()
end = time.time()
features = model(imgs)
batch_time.update(time.time() - end)
features = features.data.cpu()
qf.append(features)
q_pids.extend(pids)
q_camids.extend(camids)
qf = torch.cat(qf, 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 = [], [], []
end = time.time()
for batch_idx, (imgs, pids, camids) in enumerate(galleryloader):
if use_gpu: imgs = imgs.cuda()
end = time.time()
features = model(imgs)
batch_time.update(time.time() - end)
features = features.data.cpu()
gf.append(features)
g_pids.extend(pids)
g_camids.extend(camids)
gf = torch.cat(gf, 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()
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("------------------")
if args.reranking:
from util.re_ranking import re_ranking
distmat = re_ranking(qf, gf, k1=20, k2=6, lambda_value=0.3)
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 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]