num_g = 1000
distmat = np.random.rand(num_q, num_g) * 20
q_pids = np.random.randint(0, num_q, size=num_q)
g_pids = np.random.randint(0, num_g, size=num_g)
q_camids = np.random.randint(0, 5, size=num_q)
g_camids = np.random.randint(0, 5, size=num_g)
end = time.time()
cmc, mAP = eval_market1501_wrap(distmat,
q_pids,
g_pids,
q_camids,
g_camids, 10)
elapsed_cython = time.time() - end
print("=> Cython evaluation")
print("consume time {:.5f} \n mAP is {} \n cmc is {}".format(elapsed_cython, mAP, cmc))
end = time.time()
cmc, mAP = eval_market1501(distmat,
q_pids,
g_pids,
q_camids,
g_camids, 10)
elapsed_python = time.time() - end
print("=> Python evaluation")
print("consume time {:.5f} \n mAP is {} \n cmc is {}".format(elapsed_python, mAP, cmc))
xtimes = elapsed_python / elapsed_cython
print("=> Conclusion: cython is {:.2f}x faster than python".format(xtimes))
from eval_metrics import eval_market1501
import numpy as np
import time
num_q = 100
num_g = 1000
distmat = np.random.rand(num_q, num_g) * 20
q_pids = np.random.randint(0, num_q, size=num_q)
g_pids = np.random.randint(0, num_g, size=num_g)
q_camids = np.random.randint(0, 5, size=num_q)
g_camids = np.random.randint(0, 5, size=num_g)
end = time.time()
cmc, mAP = eval_market1501_wrap(distmat, q_pids, g_pids, q_camids, g_camids,
10)
elapsed_cython = time.time() - end
print("=> Cython evaluation")
print("consume time {:.5f} \n mAP is {} \n cmc is {}".format(
elapsed_cython, mAP, cmc))
end = time.time()
cmc, mAP = eval_market1501(distmat, q_pids, g_pids, q_camids, g_camids, 10)
elapsed_python = time.time() - end
print("=> Python evaluation")
print("consume time {:.5f} \n mAP is {} \n cmc is {}".format(
elapsed_python, mAP, cmc))
xtimes = elapsed_python / elapsed_cython
print("=> Conclusion: cython is {:.2f}x faster than python".format(xtimes))
def test(model, queryloader, galleryloader, ranks=[1, 5, 10, 20]):
# 设定模型为测试模式
model.eval()
# with torch.no_grad():使with中的语句不会计算梯度，计算过程不会在反向传播中被记录
with torch.no_grad():
# 以下都是列表。qf存储全局特征；q_pids存储行人ID；q_camids存储摄像机ID；lqf存储局部特征
qf, q_pids, q_camids, lqf = [], [], [], []
for batch_idx, (imgs, pids, camids) in enumerate(queryloader):
features, local_features = model(imgs) # 计算全局特征与局部特征
qf.append(features)
lqf.append(local_features)
q_pids.extend(pids)
q_camids.extend(camids)
qf = torch.cat(qf, 0) # 将全局特征按列形成一个张量
lqf = torch.cat(lqf, 0) # 将局部特征按列形成一个张量
q_pids = np.asarray(q_pids) # 将行人ID列表转成np数组
q_camids = np.asarray(q_camids) # 将摄像机ID列表转成np数组
print("Extracted features for query set, obtained {}-by-{} matrix".
format(qf.size(0), qf.size(1)))
# 以下都是列表。gf存储全局特征；g_pids存储行人ID；g_camids存储摄像机ID；lgf存储局部特征
gf, g_pids, g_camids, lgf = [], [], [], []
for batch_idx, (imgs, pids, camids) in enumerate(galleryloader):
features, local_features = model(imgs) # 计算全局特征与局部特征
gf.append(features)
lgf.append(local_features)
g_pids.extend(pids)
g_camids.extend(camids)
gf = torch.cat(gf, 0) # 将全局特征按列形成一个张量
lgf = torch.cat(lgf, 0) # 将局部特征按列形成一个张量
g_pids = np.asarray(g_pids) # 将行人ID列表转成np数组
g_camids = np.asarray(g_camids) # 将摄像机ID列表转成np数组
print("Extracted features for gallery set, obtained {}-by-{} matrix".
format(gf.size(0), gf.size(1)))
# 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)
"""
计算query特征与gallery特征的距离矩阵
全局特征计算欧氏距离，矩阵A,B欧氏距离等于√(A^2 + (B^T)^2 - 2A(B^T))
局部特征使用局部对齐最小距离算法计算距离
"""
m, n = qf.size(0), gf.size(0)
# 计算A^2 + (B^T)^2
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()
# 计算A^2 + (B^T)^2 - 2A(B^T)
distmat.addmm_(1, -2, qf, gf.t())
# 计算√(A^2 + (B^T)^2 - 2A(B^T))
distance = distmat.clamp(min=1e-12).sqrt()
# 转换成numpy数组
global_distmat = distmat.numpy()
lqf = lqf.permute(0, 2, 1)
lgf = lgf.permute(0, 2, 1)
# 计算局部特征距离矩阵
local_distmat = low_memory_local_dist(lqf.numpy(),
lgf.numpy(),
aligned=aligned)
# 得到包含全局特征与局部特征的总距离矩阵
print("Using global and local branches")
distmat = local_distmat + global_distmat
print("Computing CMC and mAP")
# 使用评估函数计算cmc曲线中的Rank-N与mAP平均准确率均值
cmc, mAP = eval_market1501(distmat, q_pids, g_pids, q_camids, g_camids)
# 打印相关性能测试结果
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 0