mirror of https://github.com/JDAI-CV/fast-reid.git
121 lines
5.2 KiB
Python
121 lines
5.2 KiB
Python
import sys
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import timeit
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import numpy as np
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import os.path as osp
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sys.path.insert(0, osp.dirname(osp.abspath(__file__)) + '/../../..')
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from fastreid.evaluation import evaluate_rank
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from fastreid.evaluation import evaluate_roc
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"""
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Test the speed of cython-based evaluation code. The speed improvements
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can be much bigger when using the real reid data, which contains a larger
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amount of query and gallery images.
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Note: you might encounter the following error:
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'AssertionError: Error: all query identities do not appear in gallery'.
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This is normal because the inputs are random numbers. Just try again.
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"""
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print('*** Compare running time ***')
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setup = '''
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import sys
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import os.path as osp
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import numpy as np
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sys.path.insert(0, osp.dirname(osp.abspath(__file__)) + '/../../..')
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from fastreid.evaluation import evaluate_rank
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from fastreid.evaluation import evaluate_roc
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num_q = 30
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num_g = 300
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dim = 512
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max_rank = 5
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q_feats = np.random.rand(num_q, dim).astype(np.float32) * 20
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q_feats = q_feats / np.linalg.norm(q_feats, ord=2, axis=1, keepdims=True)
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g_feats = np.random.rand(num_g, dim).astype(np.float32) * 20
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g_feats = g_feats / np.linalg.norm(g_feats, ord=2, axis=1, keepdims=True)
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distmat = 1 - np.dot(q_feats, g_feats.transpose())
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q_pids = np.random.randint(0, num_q, size=num_q)
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g_pids = np.random.randint(0, num_g, size=num_g)
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q_camids = np.random.randint(0, 5, size=num_q)
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g_camids = np.random.randint(0, 5, size=num_g)
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'''
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print('=> Using CMC metric')
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pytime = timeit.timeit(
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'evaluate_rank(distmat, q_feats, g_feats, q_pids, g_pids, q_camids, g_camids, max_rank, use_distmat=True, use_cython=False)',
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setup=setup,
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number=20
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)
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cytime = timeit.timeit(
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'evaluate_rank(distmat, q_feats, g_feats, q_pids, g_pids, q_camids, g_camids, max_rank, use_distmat=True, use_cython=True)',
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setup=setup,
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number=20
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)
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print('Python time: {} s'.format(pytime))
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print('Cython time: {} s'.format(cytime))
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print('CMC Cython is {} times faster than python\n'.format(pytime / cytime))
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print('=> Using ROC metric')
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pytime = timeit.timeit(
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'evaluate_roc(distmat, q_feats, g_feats, q_pids, g_pids, q_camids, g_camids, use_cython=False)',
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setup=setup,
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number=20
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)
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cytime = timeit.timeit(
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'evaluate_roc(distmat, q_feats, g_feats, q_pids, g_pids, q_camids, g_camids, use_cython=True)',
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setup=setup,
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number=20
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)
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print('Python time: {} s'.format(pytime))
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print('Cython time: {} s'.format(cytime))
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print('ROC Cython is {} times faster than python\n'.format(pytime / cytime))
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print("=> Check precision")
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num_q = 30
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num_g = 300
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dim = 512
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max_rank = 5
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q_feats = np.random.rand(num_q, dim).astype(np.float32) * 20
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q_feats = q_feats / np.linalg.norm(q_feats, ord=2, axis=1, keepdims=True)
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g_feats = np.random.rand(num_g, dim).astype(np.float32) * 20
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g_feats = g_feats / np.linalg.norm(g_feats, ord=2, axis=1, keepdims=True)
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distmat = 1 - np.dot(q_feats, g_feats.transpose())
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q_pids = np.random.randint(0, num_q, size=num_q)
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g_pids = np.random.randint(0, num_g, size=num_g)
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q_camids = np.random.randint(0, 5, size=num_q)
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g_camids = np.random.randint(0, 5, size=num_g)
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cmc_py_d, mAP_py_d, mINP_py_d = evaluate_rank(distmat, q_feats, g_feats, q_pids, g_pids, q_camids, g_camids, max_rank,
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use_distmat=True, use_cython=False)
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cmc_py, mAP_py, mINP_py = evaluate_rank(distmat, q_feats, g_feats, q_pids, g_pids, q_camids, g_camids, max_rank,
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use_distmat=False, use_cython=False)
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np.testing.assert_allclose(cmc_py_d, cmc_py, rtol=1e-3, atol=1e-6)
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np.testing.assert_allclose(mAP_py_d, mAP_py, rtol=1e-3, atol=1e-6)
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np.testing.assert_allclose(mINP_py_d, mINP_py, rtol=1e-3, atol=1e-6)
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print('Results between distmat and features are the same in python!')
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cmc_cy_d, mAP_cy_d, mINP_cy_d = evaluate_rank(distmat, q_feats, g_feats, q_pids, g_pids, q_camids, g_camids, max_rank,
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use_distmat=True, use_cython=True)
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cmc_cy, mAP_cy, mINP_cy = evaluate_rank(distmat, q_feats, g_feats, q_pids, g_pids, q_camids, g_camids, max_rank,
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use_distmat=False, use_cython=True)
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np.testing.assert_allclose(cmc_cy_d, cmc_cy, rtol=1e-3, atol=1e-6)
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np.testing.assert_allclose(mAP_cy_d, mAP_cy, rtol=1e-3, atol=1e-6)
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np.testing.assert_allclose(mINP_cy_d, mINP_cy, rtol=1e-3, atol=1e-6)
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print('Results between distmat and features are the same in cython!')
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np.testing.assert_allclose(cmc_py, cmc_cy, rtol=1e-3, atol=1e-6)
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np.testing.assert_allclose(mAP_py, mAP_cy, rtol=1e-3, atol=1e-6)
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np.testing.assert_allclose(mINP_py, mINP_cy, rtol=1e-3, atol=1e-6)
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print('Rank results between python and cython are the same!')
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scores_cy, labels_cy = evaluate_roc(distmat, q_feats, g_feats, q_pids, g_pids, q_camids, g_camids)
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scores_py, labels_py = evaluate_roc(distmat, q_feats, g_feats, q_pids, g_pids, q_camids, g_camids,
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use_cython=False)
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np.testing.assert_allclose(scores_cy, scores_py, rtol=1e-3, atol=1e-6)
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np.testing.assert_allclose(labels_cy, labels_py, rtol=1e-3, atol=1e-6)
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print('ROC results between python and cython are the same!\n')
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print("=> Check exact values")
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print("mAP = {} \ncmc = {}\nmINP = {}\nScores = {}".format(np.array(mAP_cy), cmc_cy, np.array(mINP_cy), scores_cy))
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