def get_test_acc(model, image_datasets, dataloaders, use_gpu, max_rank=10):
gallery_path = image_datasets['gallery'].imgs
query_path = image_datasets['query'].imgs
try:
g_camid, g_pid = get_id(gallery_path)
q_camid, q_pid = get_id(query_path)
except:
g_camid, g_pid = get_id_CUHK(gallery_path)
q_camid, q_pid = get_id_CUHK(query_path)
if use_gpu:
model = model.cuda()
# Extract feature
g_feas = extract_feature(model, dataloaders['gallery'])
# print(g_feas.shape)
# print("Extracted features for gallery set, obtained {}-by-{} matrix".format(g_feas.size(0), g_feas.size(1)))
q_feas = extract_feature(model, dataloaders['query'])
# print("Extracted features for gallery set, obtained {}-by-{} matrix".format(q_feas.size(0), q_feas.size(1)))
distmat = np.matmul(q_feas.data.numpy(), np.transpose(
g_feas.data.numpy())) * (-1.0)
CMC, mAP = eval_market1501_wrap(distmat,
q_pid,
g_pid,
q_camid,
g_camid,
max_rank=10)
return CMC, mAP
def evaluate(distmat, q_pids, g_pids, q_camids, g_camids, max_rank=50, use_metric_cuhk03=False, use_cython=True):
if use_metric_cuhk03:
return eval_cuhk03(distmat, q_pids, g_pids, q_camids, g_camids, max_rank)
else:
if use_cython and CYTHON_EVAL_AVAI:
return eval_market1501_wrap(distmat, q_pids, g_pids, q_camids, g_camids, max_rank)
else:
return eval_market1501(distmat, q_pids, g_pids, q_camids, g_camids, max_rank)
def evaluate(distmat, q_pids, g_pids, q_camids, g_camids, max_rank=50, use_metric_cuhk03=False, use_cython=True):
if use_metric_cuhk03:
return eval_cuhk03(distmat, q_pids, g_pids, q_camids, g_camids, max_rank)
else:
if use_cython and CYTHON_EVAL_AVAI:
return eval_market1501_wrap(distmat, q_pids, g_pids, q_camids, g_camids, max_rank)
else:
return eval_market1501(distmat, q_pids, g_pids, q_camids, g_camids, max_rank)
def evaluate(distmat, q_pids, g_pids, q_camids, g_camids, max_rank=50, dataset_type='cuhk03', use_cython=True):
if dataset_type == 'cuhk03':
return eval_cuhk03(distmat, q_pids, g_pids, q_camids, g_camids, max_rank)
elif dataset_type == 'market1501':
if use_cython and CYTHON_EVAL_AVAI:
return eval_market1501_wrap(distmat, q_pids, g_pids, q_camids, g_camids, max_rank)
else:
return eval_market1501(distmat, q_pids, g_pids, q_camids, g_camids, max_rank)
else:
return eval_videotag(distmat, q_pids, g_pids, q_camids, g_camids, max_rank)
def evaluate(distmat,
q_pids,
g_pids,
q_camids,
g_camids,
max_rank=50,
use_metric_cuhk03=False,
use_cython=False):
if use_metric_cuhk03:
return eval_cuhk03(distmat, q_pids, g_pids, q_camids, g_camids,
max_rank)
else:
if not use_cython:
return eval_market1501(distmat, q_pids, g_pids, q_camids, g_camids,
max_rank)
else:
return eval_market1501_wrap(distmat, q_pids, g_pids, q_camids,
g_camids, max_rank)
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))
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))