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veri.log
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==========
Args:Namespace(arch='resnet50', batch_size=64, data_dir='/home/ljw/cluster-contrast/data', dataset='veri', dropout=0, epochs=50, eps=0.7, eps_gap=0.02, eval_step=10, features=0, height=224, iters=400, k1=30, k2=6, logs_dir='/home/ljw/cluster-contrast/logs', lr=0.00035, momentum=0.1, num_instances=16, pooling_type='gem', print_freq=10, seed=1, step_size=20, temp=0.05, use_hard=False, weight_decay=0.0005, width=224, workers=4)
==========
==> Load unlabeled dataset
self.dataset_dir /home/ljw/datasets/VeRi
=> VeRi loaded
Dataset statistics:
----------------------------------------
subset | # ids | # images | # cameras
----------------------------------------
train | 576 | 37778 | 20
query | 200 | 1678 | 19
gallery | 200 | 11579 | 19
----------------------------------------
pooling_type: gem
==> Create pseudo labels for unlabeled data
bruteForceKnn is deprecated; call bfKnn instead
Extract Features: [50/591] Time 0.090 (0.159) Data 0.000 (0.049)
Extract Features: [100/591] Time 0.090 (0.138) Data 0.000 (0.037)
Extract Features: [150/591] Time 0.090 (0.144) Data 0.000 (0.046)
Extract Features: [200/591] Time 0.090 (0.133) Data 0.000 (0.037)
Extract Features: [250/591] Time 0.099 (0.141) Data 0.000 (0.046)
Extract Features: [300/591] Time 0.097 (0.134) Data 0.000 (0.039)
Extract Features: [350/591] Time 0.090 (0.138) Data 0.000 (0.044)
Extract Features: [400/591] Time 0.090 (0.134) Data 0.000 (0.040)
Extract Features: [450/591] Time 0.090 (0.132) Data 0.000 (0.039)
Extract Features: [500/591] Time 0.090 (0.129) Data 0.000 (0.035)
Extract Features: [550/591] Time 0.111 (0.127) Data 0.020 (0.033)
Computing jaccard distance...
Jaccard distance computing time cost: 73.92079091072083
Clustering criterion: eps: 0.700
==> Statistics for epoch 0: 561 clusters
Epoch: [0][10/400] Time 0.306 (0.500) Data 0.000 (0.035) Loss 5.573 (5.716)
Epoch: [0][20/400] Time 0.304 (0.403) Data 0.000 (0.018) Loss 5.619 (5.794)
Epoch: [0][30/400] Time 0.310 (0.373) Data 0.000 (0.012) Loss 6.181 (5.826)
Epoch: [0][40/400] Time 0.311 (0.358) Data 0.000 (0.009) Loss 5.768 (5.841)
Epoch: [0][50/400] Time 0.304 (0.348) Data 0.000 (0.007) Loss 5.722 (5.889)
Epoch: [0][60/400] Time 0.307 (0.341) Data 0.000 (0.006) Loss 5.947 (5.883)
Epoch: [0][70/400] Time 0.306 (0.336) Data 0.000 (0.005) Loss 5.956 (5.888)
Epoch: [0][80/400] Time 0.307 (0.332) Data 0.000 (0.004) Loss 6.311 (5.898)
Epoch: [0][90/400] Time 0.305 (0.330) Data 0.000 (0.004) Loss 5.842 (5.907)
Epoch: [0][100/400] Time 0.304 (0.327) Data 0.000 (0.004) Loss 5.928 (5.917)
Epoch: [0][110/400] Time 0.306 (0.326) Data 0.000 (0.003) Loss 5.957 (5.914)
Epoch: [0][120/400] Time 0.305 (0.324) Data 0.000 (0.003) Loss 5.744 (5.913)
Epoch: [0][130/400] Time 0.305 (0.323) Data 0.000 (0.003) Loss 5.724 (5.905)
Epoch: [0][140/400] Time 0.304 (0.322) Data 0.000 (0.003) Loss 5.485 (5.886)
Epoch: [0][150/400] Time 0.305 (0.327) Data 0.000 (0.009) Loss 5.042 (5.818)
Epoch: [0][160/400] Time 0.305 (0.325) Data 0.000 (0.008) Loss 5.288 (5.801)
Epoch: [0][170/400] Time 0.305 (0.324) Data 0.000 (0.008) Loss 6.550 (5.793)
Epoch: [0][180/400] Time 0.305 (0.323) Data 0.000 (0.007) Loss 5.448 (5.810)
Epoch: [0][190/400] Time 0.304 (0.322) Data 0.000 (0.007) Loss 7.042 (5.818)
Epoch: [0][200/400] Time 0.309 (0.322) Data 0.000 (0.006) Loss 6.404 (5.847)
Epoch: [0][210/400] Time 0.305 (0.321) Data 0.000 (0.006) Loss 5.788 (5.859)
Epoch: [0][220/400] Time 0.305 (0.320) Data 0.000 (0.006) Loss 5.906 (5.857)
Epoch: [0][230/400] Time 0.305 (0.320) Data 0.000 (0.006) Loss 5.779 (5.877)
Epoch: [0][240/400] Time 0.309 (0.319) Data 0.000 (0.005) Loss 5.490 (5.880)
Epoch: [0][250/400] Time 0.304 (0.318) Data 0.000 (0.005) Loss 5.859 (5.876)
Epoch: [0][260/400] Time 0.310 (0.318) Data 0.000 (0.005) Loss 6.166 (5.889)
Epoch: [0][270/400] Time 0.309 (0.318) Data 0.000 (0.005) Loss 5.695 (5.895)
Epoch: [0][280/400] Time 0.304 (0.317) Data 0.000 (0.005) Loss 6.129 (5.906)
Epoch: [0][290/400] Time 0.306 (0.320) Data 0.000 (0.007) Loss 5.556 (5.899)
Epoch: [0][300/400] Time 0.305 (0.319) Data 0.000 (0.007) Loss 5.604 (5.901)
Epoch: [0][310/400] Time 0.306 (0.319) Data 0.000 (0.007) Loss 5.479 (5.893)
Epoch: [0][320/400] Time 0.305 (0.319) Data 0.000 (0.007) Loss 6.134 (5.912)
Epoch: [0][330/400] Time 0.316 (0.318) Data 0.000 (0.006) Loss 5.131 (5.909)
Epoch: [0][340/400] Time 0.325 (0.318) Data 0.000 (0.006) Loss 6.066 (5.907)
Epoch: [0][350/400] Time 0.324 (0.318) Data 0.000 (0.006) Loss 5.640 (5.893)
Epoch: [0][360/400] Time 0.321 (0.319) Data 0.000 (0.006) Loss 5.630 (5.884)
Epoch: [0][370/400] Time 0.324 (0.319) Data 0.000 (0.006) Loss 6.357 (5.883)
Epoch: [0][380/400] Time 0.323 (0.319) Data 0.000 (0.006) Loss 5.907 (5.878)
Epoch: [0][390/400] Time 0.327 (0.319) Data 0.000 (0.005) Loss 6.396 (5.873)
Epoch: [0][400/400] Time 0.323 (0.319) Data 0.000 (0.005) Loss 6.232 (5.872)
==> Create pseudo labels for unlabeled data
bruteForceKnn is deprecated; call bfKnn instead
Extract Features: [50/591] Time 0.096 (0.107) Data 0.000 (0.013)
Extract Features: [100/591] Time 0.095 (0.101) Data 0.000 (0.006)
Extract Features: [150/591] Time 0.094 (0.099) Data 0.000 (0.004)
Extract Features: [200/591] Time 0.096 (0.099) Data 0.000 (0.003)
Extract Features: [250/591] Time 0.095 (0.098) Data 0.000 (0.003)
Extract Features: [300/591] Time 0.096 (0.097) Data 0.000 (0.002)
Extract Features: [350/591] Time 0.095 (0.097) Data 0.000 (0.002)
Extract Features: [400/591] Time 0.095 (0.097) Data 0.000 (0.002)
Extract Features: [450/591] Time 0.096 (0.097) Data 0.000 (0.002)
Extract Features: [500/591] Time 0.095 (0.097) Data 0.000 (0.001)
Extract Features: [550/591] Time 0.095 (0.097) Data 0.000 (0.001)
Computing jaccard distance...
Jaccard distance computing time cost: 74.70889711380005
==> Statistics for epoch 1: 619 clusters
Epoch: [1][10/400] Time 0.305 (0.578) Data 0.000 (0.139) Loss 2.177 (1.751)
Epoch: [1][20/400] Time 0.306 (0.442) Data 0.000 (0.070) Loss 1.600 (1.670)
Epoch: [1][30/400] Time 0.309 (0.397) Data 0.000 (0.046) Loss 1.840 (1.599)
Epoch: [1][40/400] Time 0.310 (0.375) Data 0.000 (0.035) Loss 1.674 (1.577)
Epoch: [1][50/400] Time 0.307 (0.361) Data 0.000 (0.028) Loss 0.539 (1.465)
Epoch: [1][60/400] Time 0.306 (0.352) Data 0.000 (0.023) Loss 1.777 (1.462)
Epoch: [1][70/400] Time 0.316 (0.346) Data 0.000 (0.020) Loss 0.612 (1.411)
Epoch: [1][80/400] Time 0.306 (0.341) Data 0.000 (0.017) Loss 1.470 (1.441)
Epoch: [1][90/400] Time 0.307 (0.338) Data 0.000 (0.016) Loss 0.922 (1.410)
Epoch: [1][100/400] Time 0.305 (0.335) Data 0.000 (0.014) Loss 1.074 (1.388)
Epoch: [1][110/400] Time 0.308 (0.332) Data 0.000 (0.013) Loss 1.341 (1.394)
Epoch: [1][120/400] Time 0.311 (0.330) Data 0.000 (0.012) Loss 1.290 (1.406)
Epoch: [1][130/400] Time 0.308 (0.329) Data 0.000 (0.011) Loss 0.992 (1.390)
Epoch: [1][140/400] Time 0.306 (0.327) Data 0.000 (0.010) Loss 0.926 (1.372)
Epoch: [1][150/400] Time 0.304 (0.326) Data 0.000 (0.009) Loss 0.818 (1.353)
Epoch: [1][160/400] Time 0.305 (0.334) Data 0.000 (0.018) Loss 4.332 (1.412)
Epoch: [1][170/400] Time 0.317 (0.332) Data 0.000 (0.017) Loss 3.723 (1.478)
Epoch: [1][180/400] Time 0.304 (0.331) Data 0.000 (0.016) Loss 2.831 (1.578)
Epoch: [1][190/400] Time 0.305 (0.329) Data 0.000 (0.015) Loss 2.140 (1.652)
Epoch: [1][200/400] Time 0.303 (0.328) Data 0.000 (0.015) Loss 1.404 (1.726)
Epoch: [1][210/400] Time 0.305 (0.327) Data 0.000 (0.014) Loss 3.083 (1.781)
Epoch: [1][220/400] Time 0.315 (0.327) Data 0.000 (0.013) Loss 4.328 (1.845)
Epoch: [1][230/400] Time 0.321 (0.326) Data 0.000 (0.013) Loss 4.497 (1.903)
Epoch: [1][240/400] Time 0.325 (0.326) Data 0.000 (0.012) Loss 1.825 (1.954)
Epoch: [1][250/400] Time 0.325 (0.326) Data 0.000 (0.012) Loss 2.635 (1.983)
Epoch: [1][260/400] Time 0.324 (0.326) Data 0.000 (0.011) Loss 4.176 (2.023)
Epoch: [1][270/400] Time 0.327 (0.326) Data 0.000 (0.011) Loss 1.962 (2.058)
Epoch: [1][280/400] Time 0.324 (0.326) Data 0.000 (0.010) Loss 3.944 (2.109)
Epoch: [1][290/400] Time 0.325 (0.326) Data 0.000 (0.010) Loss 4.607 (2.156)
Epoch: [1][300/400] Time 0.322 (0.326) Data 0.000 (0.010) Loss 2.022 (2.174)
Epoch: [1][310/400] Time 0.327 (0.331) Data 0.000 (0.014) Loss 3.171 (2.198)
Epoch: [1][320/400] Time 0.323 (0.331) Data 0.000 (0.014) Loss 3.369 (2.227)
Epoch: [1][330/400] Time 0.321 (0.331) Data 0.000 (0.013) Loss 3.208 (2.249)
Epoch: [1][340/400] Time 0.325 (0.330) Data 0.000 (0.013) Loss 2.926 (2.274)
Epoch: [1][350/400] Time 0.325 (0.330) Data 0.000 (0.013) Loss 2.812 (2.305)
Epoch: [1][360/400] Time 0.324 (0.330) Data 0.000 (0.012) Loss 4.424 (2.349)
Epoch: [1][370/400] Time 0.323 (0.330) Data 0.000 (0.012) Loss 3.124 (2.386)
Epoch: [1][380/400] Time 0.327 (0.330) Data 0.000 (0.012) Loss 2.989 (2.399)
Epoch: [1][390/400] Time 0.326 (0.330) Data 0.000 (0.011) Loss 2.553 (2.417)
Epoch: [1][400/400] Time 0.325 (0.330) Data 0.000 (0.011) Loss 2.202 (2.425)
==> Create pseudo labels for unlabeled data
bruteForceKnn is deprecated; call bfKnn instead
Extract Features: [50/591] Time 0.095 (0.135) Data 0.000 (0.039)
Extract Features: [100/591] Time 0.095 (0.115) Data 0.000 (0.019)
Extract Features: [150/591] Time 0.095 (0.117) Data 0.000 (0.021)
Extract Features: [200/591] Time 0.095 (0.112) Data 0.000 (0.016)
Extract Features: [250/591] Time 0.095 (0.122) Data 0.000 (0.026)
Extract Features: [300/591] Time 0.310 (0.118) Data 0.218 (0.022)
Extract Features: [350/591] Time 0.095 (0.121) Data 0.000 (0.025)
Extract Features: [400/591] Time 0.096 (0.118) Data 0.000 (0.022)
Extract Features: [450/591] Time 0.097 (0.116) Data 0.000 (0.020)
Extract Features: [500/591] Time 0.096 (0.114) Data 0.000 (0.018)
Extract Features: [550/591] Time 0.095 (0.114) Data 0.000 (0.018)
Computing jaccard distance...
Jaccard distance computing time cost: 78.16139817237854
==> Statistics for epoch 2: 828 clusters
Epoch: [2][10/400] Time 0.311 (0.582) Data 0.000 (0.192) Loss 1.246 (0.786)
Epoch: [2][20/400] Time 0.309 (0.446) Data 0.000 (0.096) Loss 0.905 (0.818)
Epoch: [2][30/400] Time 0.312 (0.400) Data 0.000 (0.064) Loss 0.606 (0.899)
Epoch: [2][40/400] Time 0.306 (0.377) Data 0.000 (0.048) Loss 1.089 (0.906)
Epoch: [2][50/400] Time 0.308 (0.363) Data 0.000 (0.038) Loss 0.326 (0.851)
Epoch: [2][60/400] Time 0.312 (0.354) Data 0.000 (0.032) Loss 0.929 (0.902)
Epoch: [2][70/400] Time 0.308 (0.347) Data 0.000 (0.028) Loss 2.160 (0.916)
Epoch: [2][80/400] Time 0.306 (0.342) Data 0.000 (0.024) Loss 1.162 (0.902)
Epoch: [2][90/400] Time 0.307 (0.338) Data 0.000 (0.021) Loss 1.373 (0.889)
Epoch: [2][100/400] Time 0.307 (0.335) Data 0.000 (0.019) Loss 0.690 (0.871)
Epoch: [2][110/400] Time 0.306 (0.333) Data 0.000 (0.018) Loss 1.397 (0.856)
Epoch: [2][120/400] Time 0.314 (0.331) Data 0.000 (0.016) Loss 0.921 (0.851)
Epoch: [2][130/400] Time 0.307 (0.329) Data 0.000 (0.015) Loss 0.505 (0.842)
Epoch: [2][140/400] Time 0.310 (0.328) Data 0.000 (0.014) Loss 0.563 (0.827)
Epoch: [2][150/400] Time 0.305 (0.326) Data 0.000 (0.013) Loss 2.330 (0.831)
Epoch: [2][160/400] Time 0.308 (0.325) Data 0.000 (0.012) Loss 1.565 (0.826)
Epoch: [2][170/400] Time 0.305 (0.324) Data 0.000 (0.011) Loss 0.381 (0.808)
Epoch: [2][180/400] Time 0.306 (0.323) Data 0.000 (0.011) Loss 0.198 (0.793)
Epoch: [2][190/400] Time 0.329 (0.323) Data 0.000 (0.010) Loss 0.523 (0.772)
Epoch: [2][200/400] Time 0.327 (0.323) Data 0.002 (0.010) Loss 0.342 (0.776)
Epoch: [2][210/400] Time 0.310 (0.330) Data 0.000 (0.016) Loss 2.764 (0.790)
Epoch: [2][220/400] Time 0.325 (0.330) Data 0.000 (0.016) Loss 3.958 (0.861)
Epoch: [2][230/400] Time 0.327 (0.330) Data 0.000 (0.015) Loss 2.274 (0.929)
Epoch: [2][240/400] Time 0.325 (0.329) Data 0.000 (0.014) Loss 1.594 (0.996)
Epoch: [2][250/400] Time 0.328 (0.329) Data 0.000 (0.014) Loss 3.023 (1.060)
Epoch: [2][260/400] Time 0.328 (0.329) Data 0.000 (0.013) Loss 3.915 (1.126)
Epoch: [2][270/400] Time 0.324 (0.329) Data 0.000 (0.013) Loss 1.965 (1.170)
Epoch: [2][280/400] Time 0.324 (0.329) Data 0.000 (0.012) Loss 2.712 (1.246)
Epoch: [2][290/400] Time 0.325 (0.329) Data 0.000 (0.012) Loss 2.352 (1.305)
Epoch: [2][300/400] Time 0.324 (0.329) Data 0.000 (0.012) Loss 3.238 (1.353)
Epoch: [2][310/400] Time 0.327 (0.329) Data 0.000 (0.011) Loss 3.769 (1.392)
Epoch: [2][320/400] Time 0.324 (0.329) Data 0.000 (0.011) Loss 2.959 (1.435)
Epoch: [2][330/400] Time 0.336 (0.329) Data 0.000 (0.011) Loss 1.823 (1.467)
Epoch: [2][340/400] Time 0.324 (0.329) Data 0.000 (0.010) Loss 1.722 (1.507)
Epoch: [2][350/400] Time 0.326 (0.328) Data 0.000 (0.010) Loss 3.061 (1.561)
Epoch: [2][360/400] Time 0.322 (0.328) Data 0.000 (0.010) Loss 2.116 (1.602)
Epoch: [2][370/400] Time 0.326 (0.328) Data 0.000 (0.009) Loss 2.611 (1.640)
Epoch: [2][380/400] Time 0.325 (0.328) Data 0.000 (0.009) Loss 3.250 (1.659)
Epoch: [2][390/400] Time 0.324 (0.328) Data 0.000 (0.009) Loss 2.269 (1.680)
Epoch: [2][400/400] Time 0.329 (0.328) Data 0.000 (0.009) Loss 3.291 (1.711)
==> Create pseudo labels for unlabeled data
bruteForceKnn is deprecated; call bfKnn instead
Extract Features: [50/591] Time 0.096 (0.128) Data 0.000 (0.031)
Extract Features: [100/591] Time 0.095 (0.116) Data 0.000 (0.020)
Extract Features: [150/591] Time 0.095 (0.113) Data 0.000 (0.016)
Extract Features: [200/591] Time 0.096 (0.109) Data 0.000 (0.013)
Extract Features: [250/591] Time 0.095 (0.117) Data 0.000 (0.021)
Extract Features: [300/591] Time 0.095 (0.114) Data 0.000 (0.018)
Extract Features: [350/591] Time 0.099 (0.114) Data 0.000 (0.018)
Extract Features: [400/591] Time 0.095 (0.112) Data 0.000 (0.017)
Extract Features: [450/591] Time 0.095 (0.111) Data 0.000 (0.015)
Extract Features: [500/591] Time 0.095 (0.109) Data 0.000 (0.013)
Extract Features: [550/591] Time 0.095 (0.108) Data 0.000 (0.012)
Computing jaccard distance...
Jaccard distance computing time cost: 80.22160363197327
==> Statistics for epoch 3: 918 clusters
Epoch: [3][10/400] Time 0.316 (0.555) Data 0.000 (0.149) Loss 1.602 (1.078)
Epoch: [3][20/400] Time 0.317 (0.433) Data 0.000 (0.075) Loss 1.091 (1.115)
Epoch: [3][30/400] Time 0.313 (0.392) Data 0.000 (0.050) Loss 0.567 (1.073)
Epoch: [3][40/400] Time 0.316 (0.373) Data 0.000 (0.037) Loss 0.692 (0.983)
Epoch: [3][50/400] Time 0.320 (0.361) Data 0.000 (0.030) Loss 1.262 (1.004)
Epoch: [3][60/400] Time 0.316 (0.353) Data 0.000 (0.025) Loss 0.952 (1.047)
Epoch: [3][70/400] Time 0.319 (0.348) Data 0.000 (0.021) Loss 1.061 (1.012)
Epoch: [3][80/400] Time 0.311 (0.343) Data 0.000 (0.019) Loss 0.477 (1.006)
Epoch: [3][90/400] Time 0.311 (0.339) Data 0.000 (0.017) Loss 1.671 (1.012)
Epoch: [3][100/400] Time 0.307 (0.336) Data 0.000 (0.015) Loss 0.437 (0.971)
Epoch: [3][110/400] Time 0.307 (0.334) Data 0.000 (0.014) Loss 0.692 (0.952)
Epoch: [3][120/400] Time 0.308 (0.332) Data 0.000 (0.013) Loss 0.833 (0.932)
Epoch: [3][130/400] Time 0.306 (0.330) Data 0.000 (0.012) Loss 0.702 (0.917)
Epoch: [3][140/400] Time 0.311 (0.328) Data 0.000 (0.011) Loss 0.583 (0.911)
Epoch: [3][150/400] Time 0.309 (0.327) Data 0.000 (0.010) Loss 0.616 (0.889)
Epoch: [3][160/400] Time 0.304 (0.326) Data 0.000 (0.009) Loss 0.753 (0.873)
Epoch: [3][170/400] Time 0.309 (0.325) Data 0.000 (0.009) Loss 0.413 (0.874)
Epoch: [3][180/400] Time 0.316 (0.324) Data 0.000 (0.008) Loss 1.385 (0.873)
Epoch: [3][190/400] Time 0.320 (0.324) Data 0.000 (0.008) Loss 0.507 (0.857)
Epoch: [3][200/400] Time 0.328 (0.324) Data 0.000 (0.008) Loss 0.428 (0.844)
Epoch: [3][210/400] Time 0.327 (0.324) Data 0.000 (0.007) Loss 0.290 (0.827)
Epoch: [3][220/400] Time 0.324 (0.324) Data 0.000 (0.007) Loss 0.578 (0.813)
Epoch: [3][230/400] Time 1.955 (0.331) Data 1.638 (0.014) Loss 1.862 (0.806)
Epoch: [3][240/400] Time 0.326 (0.331) Data 0.000 (0.013) Loss 3.456 (0.906)
Epoch: [3][250/400] Time 0.324 (0.331) Data 0.000 (0.013) Loss 1.491 (0.996)
Epoch: [3][260/400] Time 0.327 (0.331) Data 0.000 (0.012) Loss 2.474 (1.080)
Epoch: [3][270/400] Time 0.329 (0.331) Data 0.000 (0.012) Loss 3.026 (1.141)
Epoch: [3][280/400] Time 0.327 (0.331) Data 0.000 (0.011) Loss 2.114 (1.194)
Epoch: [3][290/400] Time 0.335 (0.330) Data 0.000 (0.011) Loss 4.192 (1.248)
Epoch: [3][300/400] Time 0.326 (0.330) Data 0.000 (0.011) Loss 2.945 (1.303)
Epoch: [3][310/400] Time 0.326 (0.330) Data 0.000 (0.010) Loss 3.788 (1.350)
Epoch: [3][320/400] Time 0.326 (0.330) Data 0.000 (0.010) Loss 2.757 (1.389)
Epoch: [3][330/400] Time 0.325 (0.330) Data 0.000 (0.010) Loss 2.533 (1.431)
Epoch: [3][340/400] Time 0.328 (0.330) Data 0.000 (0.009) Loss 2.604 (1.472)
Epoch: [3][350/400] Time 0.335 (0.330) Data 0.000 (0.009) Loss 2.602 (1.512)
Epoch: [3][360/400] Time 0.326 (0.330) Data 0.000 (0.009) Loss 2.459 (1.537)
Epoch: [3][370/400] Time 0.328 (0.330) Data 0.000 (0.009) Loss 3.923 (1.575)
Epoch: [3][380/400] Time 0.329 (0.330) Data 0.001 (0.008) Loss 2.214 (1.601)
Epoch: [3][390/400] Time 0.326 (0.330) Data 0.000 (0.008) Loss 3.184 (1.631)
Epoch: [3][400/400] Time 0.329 (0.329) Data 0.000 (0.008) Loss 3.025 (1.671)
==> Create pseudo labels for unlabeled data
bruteForceKnn is deprecated; call bfKnn instead
Extract Features: [50/591] Time 0.095 (0.130) Data 0.000 (0.034)
Extract Features: [100/591] Time 0.095 (0.119) Data 0.000 (0.024)
Extract Features: [150/591] Time 0.094 (0.118) Data 0.000 (0.022)
Extract Features: [200/591] Time 0.095 (0.113) Data 0.000 (0.017)
Extract Features: [250/591] Time 0.095 (0.120) Data 0.000 (0.024)
Extract Features: [300/591] Time 0.095 (0.116) Data 0.000 (0.020)
Extract Features: [350/591] Time 0.095 (0.119) Data 0.000 (0.023)
Extract Features: [400/591] Time 0.111 (0.116) Data 0.016 (0.020)
Extract Features: [450/591] Time 0.095 (0.114) Data 0.000 (0.018)
Extract Features: [500/591] Time 0.095 (0.112) Data 0.000 (0.016)
Extract Features: [550/591] Time 0.095 (0.111) Data 0.000 (0.015)
Computing jaccard distance...
Jaccard distance computing time cost: 80.2342221736908
==> Statistics for epoch 4: 955 clusters
Epoch: [4][10/400] Time 0.308 (0.645) Data 0.000 (0.207) Loss 1.111 (0.861)
Epoch: [4][20/400] Time 0.306 (0.477) Data 0.000 (0.104) Loss 1.073 (0.839)
Epoch: [4][30/400] Time 0.311 (0.421) Data 0.000 (0.069) Loss 0.995 (0.920)
Epoch: [4][40/400] Time 0.308 (0.393) Data 0.000 (0.052) Loss 0.785 (0.916)
Epoch: [4][50/400] Time 0.323 (0.376) Data 0.000 (0.041) Loss 0.680 (0.909)
Epoch: [4][60/400] Time 0.304 (0.366) Data 0.000 (0.035) Loss 0.520 (0.856)
Epoch: [4][70/400] Time 0.306 (0.357) Data 0.000 (0.030) Loss 0.747 (0.860)
Epoch: [4][80/400] Time 0.306 (0.351) Data 0.000 (0.026) Loss 1.214 (0.904)
Epoch: [4][90/400] Time 0.314 (0.347) Data 0.000 (0.023) Loss 1.061 (0.905)
Epoch: [4][100/400] Time 0.308 (0.343) Data 0.000 (0.021) Loss 1.168 (0.883)
Epoch: [4][110/400] Time 0.305 (0.340) Data 0.000 (0.019) Loss 1.304 (0.869)
Epoch: [4][120/400] Time 0.306 (0.337) Data 0.000 (0.017) Loss 2.031 (0.863)
Epoch: [4][130/400] Time 0.306 (0.335) Data 0.000 (0.016) Loss 0.845 (0.838)
Epoch: [4][140/400] Time 0.309 (0.333) Data 0.000 (0.015) Loss 0.419 (0.829)
Epoch: [4][150/400] Time 0.307 (0.332) Data 0.000 (0.014) Loss 0.687 (0.827)
Epoch: [4][160/400] Time 0.311 (0.330) Data 0.000 (0.013) Loss 0.680 (0.822)
Epoch: [4][170/400] Time 0.308 (0.329) Data 0.000 (0.012) Loss 1.202 (0.814)
Epoch: [4][180/400] Time 0.319 (0.328) Data 0.000 (0.012) Loss 0.590 (0.798)
Epoch: [4][190/400] Time 0.326 (0.327) Data 0.000 (0.011) Loss 0.313 (0.794)
Epoch: [4][200/400] Time 0.320 (0.327) Data 0.000 (0.010) Loss 0.297 (0.779)
Epoch: [4][210/400] Time 0.324 (0.327) Data 0.000 (0.010) Loss 0.633 (0.761)
Epoch: [4][220/400] Time 0.323 (0.326) Data 0.000 (0.010) Loss 0.367 (0.746)
Epoch: [4][230/400] Time 0.324 (0.326) Data 0.000 (0.009) Loss 0.148 (0.731)
Epoch: [4][240/400] Time 0.314 (0.333) Data 0.000 (0.015) Loss 2.920 (0.741)
Epoch: [4][250/400] Time 0.328 (0.333) Data 0.000 (0.015) Loss 1.443 (0.797)
Epoch: [4][260/400] Time 0.329 (0.332) Data 0.000 (0.014) Loss 2.785 (0.868)
Epoch: [4][270/400] Time 0.325 (0.332) Data 0.000 (0.014) Loss 2.779 (0.941)
Epoch: [4][280/400] Time 0.323 (0.332) Data 0.000 (0.013) Loss 3.587 (0.996)
Epoch: [4][290/400] Time 0.326 (0.332) Data 0.000 (0.013) Loss 2.287 (1.064)
Epoch: [4][300/400] Time 0.322 (0.332) Data 0.000 (0.012) Loss 3.480 (1.114)
Epoch: [4][310/400] Time 0.328 (0.331) Data 0.000 (0.012) Loss 2.585 (1.180)
Epoch: [4][320/400] Time 0.324 (0.331) Data 0.000 (0.011) Loss 2.528 (1.218)
Epoch: [4][330/400] Time 0.325 (0.331) Data 0.000 (0.011) Loss 2.334 (1.259)
Epoch: [4][340/400] Time 0.325 (0.331) Data 0.000 (0.011) Loss 3.087 (1.305)
Epoch: [4][350/400] Time 0.325 (0.331) Data 0.000 (0.010) Loss 3.271 (1.343)
Epoch: [4][360/400] Time 0.324 (0.331) Data 0.000 (0.010) Loss 2.002 (1.381)
Epoch: [4][370/400] Time 0.327 (0.331) Data 0.000 (0.010) Loss 2.175 (1.426)
Epoch: [4][380/400] Time 0.324 (0.330) Data 0.000 (0.010) Loss 1.137 (1.456)
Epoch: [4][390/400] Time 0.330 (0.330) Data 0.000 (0.009) Loss 2.035 (1.484)
Epoch: [4][400/400] Time 0.325 (0.330) Data 0.000 (0.009) Loss 3.066 (1.513)
==> Create pseudo labels for unlabeled data
bruteForceKnn is deprecated; call bfKnn instead
Extract Features: [50/591] Time 0.095 (0.138) Data 0.000 (0.042)
Extract Features: [100/591] Time 0.095 (0.125) Data 0.000 (0.029)
Extract Features: [150/591] Time 0.095 (0.121) Data 0.000 (0.026)
Extract Features: [200/591] Time 0.095 (0.115) Data 0.000 (0.020)
Extract Features: [250/591] Time 0.105 (0.121) Data 0.000 (0.026)
Extract Features: [300/591] Time 0.095 (0.117) Data 0.000 (0.022)
Extract Features: [350/591] Time 0.095 (0.118) Data 0.000 (0.023)
Extract Features: [400/591] Time 0.095 (0.116) Data 0.000 (0.020)
Extract Features: [450/591] Time 0.095 (0.114) Data 0.000 (0.018)
Extract Features: [500/591] Time 0.105 (0.112) Data 0.000 (0.016)
Extract Features: [550/591] Time 0.094 (0.111) Data 0.000 (0.015)
Computing jaccard distance...
Jaccard distance computing time cost: 79.30030059814453
==> Statistics for epoch 5: 970 clusters
Epoch: [5][10/400] Time 0.309 (0.576) Data 0.000 (0.182) Loss 1.060 (0.732)
Epoch: [5][20/400] Time 0.307 (0.443) Data 0.000 (0.091) Loss 0.996 (0.846)
Epoch: [5][30/400] Time 0.311 (0.398) Data 0.000 (0.061) Loss 0.845 (0.824)
Epoch: [5][40/400] Time 0.306 (0.375) Data 0.000 (0.045) Loss 1.105 (0.792)
Epoch: [5][50/400] Time 0.311 (0.362) Data 0.000 (0.036) Loss 0.332 (0.761)
Epoch: [5][60/400] Time 0.307 (0.353) Data 0.000 (0.030) Loss 0.786 (0.739)
Epoch: [5][70/400] Time 0.309 (0.347) Data 0.000 (0.026) Loss 1.172 (0.751)
Epoch: [5][80/400] Time 0.308 (0.342) Data 0.000 (0.023) Loss 0.744 (0.768)
Epoch: [5][90/400] Time 0.308 (0.338) Data 0.000 (0.020) Loss 0.444 (0.770)
Epoch: [5][100/400] Time 0.307 (0.336) Data 0.000 (0.018) Loss 0.451 (0.759)
Epoch: [5][110/400] Time 0.310 (0.333) Data 0.000 (0.017) Loss 0.389 (0.743)
Epoch: [5][120/400] Time 0.316 (0.331) Data 0.000 (0.015) Loss 0.227 (0.745)
Epoch: [5][130/400] Time 0.306 (0.330) Data 0.000 (0.014) Loss 0.697 (0.737)
Epoch: [5][140/400] Time 0.307 (0.328) Data 0.000 (0.013) Loss 0.427 (0.745)
Epoch: [5][150/400] Time 0.306 (0.327) Data 0.000 (0.012) Loss 0.689 (0.733)
Epoch: [5][160/400] Time 0.308 (0.326) Data 0.000 (0.011) Loss 0.206 (0.724)
Epoch: [5][170/400] Time 0.317 (0.325) Data 0.000 (0.011) Loss 0.478 (0.712)
Epoch: [5][180/400] Time 0.325 (0.324) Data 0.000 (0.010) Loss 0.559 (0.714)
Epoch: [5][190/400] Time 0.322 (0.324) Data 0.000 (0.010) Loss 0.236 (0.696)
Epoch: [5][200/400] Time 0.331 (0.325) Data 0.000 (0.009) Loss 0.886 (0.684)
Epoch: [5][210/400] Time 0.331 (0.325) Data 0.000 (0.009) Loss 0.789 (0.684)
Epoch: [5][220/400] Time 0.327 (0.325) Data 0.000 (0.008) Loss 0.667 (0.679)
Epoch: [5][230/400] Time 0.327 (0.325) Data 0.000 (0.008) Loss 0.188 (0.669)
Epoch: [5][240/400] Time 0.321 (0.325) Data 0.000 (0.008) Loss 2.342 (0.666)
Epoch: [5][250/400] Time 0.324 (0.333) Data 0.000 (0.015) Loss 2.278 (0.718)
Epoch: [5][260/400] Time 0.323 (0.333) Data 0.000 (0.015) Loss 2.506 (0.787)
Epoch: [5][270/400] Time 0.327 (0.332) Data 0.000 (0.014) Loss 2.336 (0.847)
Epoch: [5][280/400] Time 0.322 (0.332) Data 0.000 (0.014) Loss 2.349 (0.898)
Epoch: [5][290/400] Time 0.321 (0.332) Data 0.000 (0.013) Loss 1.647 (0.950)
Epoch: [5][300/400] Time 0.328 (0.332) Data 0.000 (0.013) Loss 3.478 (1.008)
Epoch: [5][310/400] Time 0.325 (0.331) Data 0.000 (0.012) Loss 2.518 (1.061)
Epoch: [5][320/400] Time 0.324 (0.331) Data 0.000 (0.012) Loss 1.167 (1.106)
Epoch: [5][330/400] Time 0.324 (0.331) Data 0.000 (0.012) Loss 2.222 (1.146)
Epoch: [5][340/400] Time 0.325 (0.331) Data 0.000 (0.011) Loss 3.402 (1.182)
Epoch: [5][350/400] Time 0.325 (0.331) Data 0.000 (0.011) Loss 4.739 (1.229)
Epoch: [5][360/400] Time 0.326 (0.331) Data 0.000 (0.011) Loss 4.015 (1.259)
Epoch: [5][370/400] Time 0.324 (0.331) Data 0.000 (0.010) Loss 1.788 (1.301)
Epoch: [5][380/400] Time 0.324 (0.330) Data 0.000 (0.010) Loss 2.748 (1.338)
Epoch: [5][390/400] Time 0.325 (0.330) Data 0.000 (0.010) Loss 2.917 (1.375)
Epoch: [5][400/400] Time 0.326 (0.330) Data 0.000 (0.010) Loss 1.367 (1.405)
==> Create pseudo labels for unlabeled data
bruteForceKnn is deprecated; call bfKnn instead
Extract Features: [50/591] Time 0.096 (0.138) Data 0.000 (0.037)
Extract Features: [100/591] Time 0.099 (0.120) Data 0.000 (0.022)
Extract Features: [150/591] Time 0.095 (0.116) Data 0.000 (0.018)
Extract Features: [200/591] Time 0.096 (0.111) Data 0.000 (0.014)
Extract Features: [250/591] Time 0.096 (0.115) Data 0.000 (0.018)
Extract Features: [300/591] Time 0.095 (0.112) Data 0.000 (0.015)
Extract Features: [350/591] Time 0.096 (0.113) Data 0.000 (0.017)
Extract Features: [400/591] Time 0.095 (0.111) Data 0.000 (0.015)
Extract Features: [450/591] Time 0.095 (0.110) Data 0.000 (0.013)
Extract Features: [500/591] Time 0.095 (0.108) Data 0.000 (0.012)
Extract Features: [550/591] Time 0.097 (0.107) Data 0.000 (0.011)
Computing jaccard distance...
Jaccard distance computing time cost: 81.56079530715942
==> Statistics for epoch 6: 1020 clusters
Epoch: [6][10/400] Time 0.312 (0.531) Data 0.000 (0.159) Loss 1.273 (0.809)
Epoch: [6][20/400] Time 0.303 (0.419) Data 0.000 (0.080) Loss 0.737 (0.878)
Epoch: [6][30/400] Time 0.306 (0.381) Data 0.000 (0.053) Loss 0.692 (0.906)
Epoch: [6][40/400] Time 0.303 (0.363) Data 0.000 (0.040) Loss 0.984 (0.892)
Epoch: [6][50/400] Time 0.304 (0.352) Data 0.000 (0.032) Loss 0.339 (0.872)
Epoch: [6][60/400] Time 0.306 (0.344) Data 0.000 (0.027) Loss 0.338 (0.869)
Epoch: [6][70/400] Time 0.319 (0.339) Data 0.000 (0.023) Loss 0.622 (0.833)
Epoch: [6][80/400] Time 0.303 (0.335) Data 0.000 (0.020) Loss 1.244 (0.836)
Epoch: [6][90/400] Time 0.307 (0.332) Data 0.000 (0.018) Loss 0.663 (0.804)
Epoch: [6][100/400] Time 0.310 (0.330) Data 0.000 (0.016) Loss 0.988 (0.787)
Epoch: [6][110/400] Time 0.309 (0.328) Data 0.000 (0.015) Loss 1.156 (0.788)
Epoch: [6][120/400] Time 0.306 (0.326) Data 0.000 (0.013) Loss 0.744 (0.782)
Epoch: [6][130/400] Time 0.306 (0.325) Data 0.000 (0.012) Loss 0.591 (0.759)
Epoch: [6][140/400] Time 0.306 (0.324) Data 0.000 (0.012) Loss 1.088 (0.748)
Epoch: [6][150/400] Time 0.307 (0.323) Data 0.000 (0.011) Loss 0.677 (0.745)
Epoch: [6][160/400] Time 0.306 (0.322) Data 0.000 (0.010) Loss 0.595 (0.738)
Epoch: [6][170/400] Time 0.310 (0.321) Data 0.000 (0.010) Loss 0.525 (0.727)
Epoch: [6][180/400] Time 0.316 (0.320) Data 0.000 (0.009) Loss 1.064 (0.718)
Epoch: [6][190/400] Time 0.327 (0.320) Data 0.000 (0.009) Loss 0.853 (0.718)
Epoch: [6][200/400] Time 0.327 (0.321) Data 0.000 (0.008) Loss 1.243 (0.714)
Epoch: [6][210/400] Time 0.323 (0.321) Data 0.000 (0.008) Loss 0.825 (0.715)
Epoch: [6][220/400] Time 0.324 (0.321) Data 0.000 (0.007) Loss 0.452 (0.702)
Epoch: [6][230/400] Time 0.326 (0.321) Data 0.000 (0.007) Loss 0.169 (0.690)
Epoch: [6][240/400] Time 0.324 (0.321) Data 0.000 (0.007) Loss 0.798 (0.680)
Epoch: [6][250/400] Time 0.324 (0.322) Data 0.000 (0.007) Loss 0.214 (0.672)
Epoch: [6][260/400] Time 0.329 (0.327) Data 0.000 (0.011) Loss 2.267 (0.698)
Epoch: [6][270/400] Time 0.328 (0.327) Data 0.000 (0.011) Loss 2.075 (0.761)
Epoch: [6][280/400] Time 0.331 (0.327) Data 0.000 (0.011) Loss 2.959 (0.826)
Epoch: [6][290/400] Time 0.326 (0.327) Data 0.000 (0.010) Loss 3.220 (0.891)
Epoch: [6][300/400] Time 0.323 (0.327) Data 0.000 (0.010) Loss 3.132 (0.951)
Epoch: [6][310/400] Time 0.325 (0.327) Data 0.000 (0.010) Loss 1.280 (1.005)
Epoch: [6][320/400] Time 0.324 (0.327) Data 0.000 (0.009) Loss 3.639 (1.056)
Epoch: [6][330/400] Time 0.325 (0.327) Data 0.000 (0.009) Loss 2.511 (1.110)
Epoch: [6][340/400] Time 0.325 (0.327) Data 0.000 (0.009) Loss 2.528 (1.164)
Epoch: [6][350/400] Time 0.325 (0.327) Data 0.000 (0.009) Loss 3.477 (1.216)
Epoch: [6][360/400] Time 0.325 (0.327) Data 0.000 (0.008) Loss 1.914 (1.247)
Epoch: [6][370/400] Time 0.331 (0.327) Data 0.000 (0.008) Loss 3.284 (1.289)
Epoch: [6][380/400] Time 0.325 (0.326) Data 0.000 (0.008) Loss 2.513 (1.325)
Epoch: [6][390/400] Time 0.325 (0.326) Data 0.000 (0.008) Loss 2.906 (1.348)
Epoch: [6][400/400] Time 0.331 (0.327) Data 0.000 (0.007) Loss 1.524 (1.381)
==> Create pseudo labels for unlabeled data
bruteForceKnn is deprecated; call bfKnn instead
Extract Features: [50/591] Time 0.105 (0.141) Data 0.000 (0.045)
Extract Features: [100/591] Time 0.096 (0.123) Data 0.000 (0.027)
Extract Features: [150/591] Time 0.095 (0.122) Data 0.000 (0.026)
Extract Features: [200/591] Time 0.095 (0.115) Data 0.000 (0.019)
Extract Features: [250/591] Time 0.095 (0.118) Data 0.000 (0.022)
Extract Features: [300/591] Time 0.095 (0.115) Data 0.000 (0.019)
Extract Features: [350/591] Time 0.097 (0.116) Data 0.000 (0.020)
Extract Features: [400/591] Time 0.096 (0.113) Data 0.000 (0.017)
Extract Features: [450/591] Time 0.100 (0.112) Data 0.000 (0.016)
Extract Features: [500/591] Time 0.095 (0.110) Data 0.000 (0.014)
Extract Features: [550/591] Time 0.096 (0.109) Data 0.000 (0.013)
Computing jaccard distance...
Jaccard distance computing time cost: 78.45359539985657
==> Statistics for epoch 7: 1043 clusters
Epoch: [7][10/400] Time 0.316 (0.543) Data 0.000 (0.144) Loss 0.772 (1.050)
Epoch: [7][20/400] Time 0.306 (0.427) Data 0.000 (0.072) Loss 1.079 (0.910)
Epoch: [7][30/400] Time 0.311 (0.388) Data 0.000 (0.048) Loss 0.827 (0.848)
Epoch: [7][40/400] Time 0.307 (0.368) Data 0.000 (0.036) Loss 0.609 (0.788)
Epoch: [7][50/400] Time 0.305 (0.356) Data 0.000 (0.029) Loss 0.438 (0.836)
Epoch: [7][60/400] Time 0.306 (0.349) Data 0.000 (0.024) Loss 0.258 (0.813)
Epoch: [7][70/400] Time 0.309 (0.343) Data 0.000 (0.021) Loss 0.835 (0.823)
Epoch: [7][80/400] Time 0.306 (0.339) Data 0.000 (0.018) Loss 0.432 (0.825)
Epoch: [7][90/400] Time 0.309 (0.336) Data 0.000 (0.016) Loss 1.806 (0.838)
Epoch: [7][100/400] Time 0.307 (0.333) Data 0.000 (0.015) Loss 0.913 (0.844)
Epoch: [7][110/400] Time 0.303 (0.331) Data 0.000 (0.013) Loss 0.594 (0.826)
Epoch: [7][120/400] Time 0.316 (0.329) Data 0.000 (0.012) Loss 0.646 (0.814)
Epoch: [7][130/400] Time 0.308 (0.328) Data 0.000 (0.011) Loss 1.105 (0.821)
Epoch: [7][140/400] Time 0.306 (0.326) Data 0.000 (0.010) Loss 0.613 (0.806)
Epoch: [7][150/400] Time 0.306 (0.325) Data 0.000 (0.010) Loss 1.000 (0.814)
Epoch: [7][160/400] Time 0.306 (0.324) Data 0.000 (0.009) Loss 0.284 (0.804)
Epoch: [7][170/400] Time 0.311 (0.323) Data 0.000 (0.009) Loss 0.253 (0.794)
Epoch: [7][180/400] Time 0.317 (0.323) Data 0.000 (0.008) Loss 0.336 (0.798)
Epoch: [7][190/400] Time 0.326 (0.323) Data 0.000 (0.008) Loss 0.169 (0.785)
Epoch: [7][200/400] Time 0.323 (0.323) Data 0.000 (0.007) Loss 0.499 (0.768)
Epoch: [7][210/400] Time 0.324 (0.323) Data 0.000 (0.007) Loss 0.561 (0.750)
Epoch: [7][220/400] Time 0.324 (0.323) Data 0.000 (0.007) Loss 0.196 (0.739)
Epoch: [7][230/400] Time 0.326 (0.323) Data 0.000 (0.006) Loss 0.534 (0.750)
Epoch: [7][240/400] Time 0.324 (0.323) Data 0.000 (0.006) Loss 1.187 (0.742)
Epoch: [7][250/400] Time 0.324 (0.324) Data 0.000 (0.006) Loss 0.340 (0.738)
Epoch: [7][260/400] Time 0.324 (0.324) Data 0.000 (0.006) Loss 0.891 (0.731)
Epoch: [7][270/400] Time 0.322 (0.329) Data 0.000 (0.010) Loss 2.816 (0.794)
Epoch: [7][280/400] Time 0.334 (0.329) Data 0.000 (0.010) Loss 3.068 (0.852)
Epoch: [7][290/400] Time 0.328 (0.329) Data 0.000 (0.010) Loss 2.817 (0.937)
Epoch: [7][300/400] Time 0.325 (0.329) Data 0.000 (0.009) Loss 3.563 (1.005)
Epoch: [7][310/400] Time 0.325 (0.328) Data 0.000 (0.009) Loss 3.027 (1.055)
Epoch: [7][320/400] Time 0.324 (0.328) Data 0.000 (0.009) Loss 3.771 (1.100)
Epoch: [7][330/400] Time 0.324 (0.328) Data 0.000 (0.008) Loss 2.072 (1.146)
Epoch: [7][340/400] Time 0.330 (0.328) Data 0.000 (0.008) Loss 2.817 (1.206)
Epoch: [7][350/400] Time 0.326 (0.328) Data 0.000 (0.008) Loss 2.212 (1.245)
Epoch: [7][360/400] Time 0.324 (0.328) Data 0.000 (0.008) Loss 4.032 (1.293)
Epoch: [7][370/400] Time 0.325 (0.328) Data 0.000 (0.008) Loss 1.394 (1.337)
Epoch: [7][380/400] Time 0.322 (0.328) Data 0.000 (0.007) Loss 3.062 (1.374)
Epoch: [7][390/400] Time 0.325 (0.328) Data 0.000 (0.007) Loss 2.750 (1.402)
Epoch: [7][400/400] Time 0.324 (0.328) Data 0.000 (0.007) Loss 3.814 (1.435)
==> Create pseudo labels for unlabeled data
bruteForceKnn is deprecated; call bfKnn instead
Extract Features: [50/591] Time 0.095 (0.140) Data 0.000 (0.043)
Extract Features: [100/591] Time 0.095 (0.122) Data 0.000 (0.026)
Extract Features: [150/591] Time 0.095 (0.116) Data 0.000 (0.020)
Extract Features: [200/591] Time 0.095 (0.111) Data 0.000 (0.015)
Extract Features: [250/591] Time 0.095 (0.115) Data 0.000 (0.019)
Extract Features: [300/591] Time 0.095 (0.112) Data 0.000 (0.015)
Extract Features: [350/591] Time 0.095 (0.113) Data 0.000 (0.017)
Extract Features: [400/591] Time 0.096 (0.111) Data 0.000 (0.015)
Extract Features: [450/591] Time 0.095 (0.109) Data 0.000 (0.013)
Extract Features: [500/591] Time 0.096 (0.108) Data 0.000 (0.012)
Extract Features: [550/591] Time 0.095 (0.107) Data 0.000 (0.011)
Computing jaccard distance...
Jaccard distance computing time cost: 75.05484914779663
==> Statistics for epoch 8: 1007 clusters
Epoch: [8][10/400] Time 0.321 (0.610) Data 0.000 (0.206) Loss 0.475 (1.003)
Epoch: [8][20/400] Time 0.307 (0.459) Data 0.000 (0.103) Loss 0.464 (0.853)
Epoch: [8][30/400] Time 0.306 (0.409) Data 0.000 (0.069) Loss 0.601 (0.885)
Epoch: [8][40/400] Time 0.305 (0.384) Data 0.000 (0.052) Loss 0.556 (0.848)
Epoch: [8][50/400] Time 0.311 (0.369) Data 0.000 (0.041) Loss 0.446 (0.816)
Epoch: [8][60/400] Time 0.306 (0.359) Data 0.000 (0.034) Loss 0.703 (0.819)
Epoch: [8][70/400] Time 0.306 (0.352) Data 0.000 (0.029) Loss 0.385 (0.823)
Epoch: [8][80/400] Time 0.306 (0.347) Data 0.000 (0.026) Loss 1.056 (0.817)
Epoch: [8][90/400] Time 0.320 (0.342) Data 0.000 (0.023) Loss 0.523 (0.818)
Epoch: [8][100/400] Time 0.306 (0.339) Data 0.000 (0.021) Loss 0.763 (0.813)
Epoch: [8][110/400] Time 0.310 (0.336) Data 0.000 (0.019) Loss 0.489 (0.788)
Epoch: [8][120/400] Time 0.307 (0.334) Data 0.000 (0.017) Loss 1.256 (0.792)
Epoch: [8][130/400] Time 0.309 (0.332) Data 0.000 (0.016) Loss 0.587 (0.790)
Epoch: [8][140/400] Time 0.310 (0.330) Data 0.000 (0.015) Loss 0.563 (0.771)
Epoch: [8][150/400] Time 0.305 (0.329) Data 0.000 (0.014) Loss 0.566 (0.772)
Epoch: [8][160/400] Time 0.306 (0.328) Data 0.000 (0.013) Loss 0.898 (0.764)
Epoch: [8][170/400] Time 0.306 (0.326) Data 0.000 (0.012) Loss 1.271 (0.760)
Epoch: [8][180/400] Time 0.318 (0.326) Data 0.000 (0.012) Loss 0.732 (0.753)
Epoch: [8][190/400] Time 0.317 (0.325) Data 0.000 (0.011) Loss 0.160 (0.746)
Epoch: [8][200/400] Time 0.328 (0.325) Data 0.000 (0.010) Loss 0.635 (0.743)
Epoch: [8][210/400] Time 0.325 (0.325) Data 0.000 (0.010) Loss 0.449 (0.735)
Epoch: [8][220/400] Time 0.325 (0.325) Data 0.000 (0.009) Loss 0.453 (0.725)
Epoch: [8][230/400] Time 0.326 (0.325) Data 0.000 (0.009) Loss 0.178 (0.715)
Epoch: [8][240/400] Time 0.331 (0.326) Data 0.000 (0.009) Loss 0.601 (0.701)
Epoch: [8][250/400] Time 0.321 (0.326) Data 0.000 (0.008) Loss 0.193 (0.697)
Epoch: [8][260/400] Time 0.327 (0.332) Data 0.000 (0.014) Loss 1.943 (0.747)
Epoch: [8][270/400] Time 0.327 (0.332) Data 0.000 (0.014) Loss 1.769 (0.806)
Epoch: [8][280/400] Time 0.325 (0.332) Data 0.000 (0.013) Loss 3.078 (0.868)
Epoch: [8][290/400] Time 0.329 (0.331) Data 0.000 (0.013) Loss 1.519 (0.922)
Epoch: [8][300/400] Time 0.325 (0.331) Data 0.000 (0.013) Loss 5.567 (0.978)
Epoch: [8][310/400] Time 0.327 (0.331) Data 0.000 (0.012) Loss 3.098 (1.036)
Epoch: [8][320/400] Time 0.324 (0.331) Data 0.000 (0.012) Loss 3.357 (1.094)
Epoch: [8][330/400] Time 0.327 (0.331) Data 0.000 (0.011) Loss 2.476 (1.142)
Epoch: [8][340/400] Time 0.325 (0.331) Data 0.000 (0.011) Loss 1.725 (1.183)
Epoch: [8][350/400] Time 0.326 (0.330) Data 0.000 (0.011) Loss 3.797 (1.223)
Epoch: [8][360/400] Time 0.324 (0.330) Data 0.000 (0.010) Loss 2.565 (1.268)
Epoch: [8][370/400] Time 0.327 (0.330) Data 0.000 (0.010) Loss 2.462 (1.298)
Epoch: [8][380/400] Time 0.335 (0.330) Data 0.000 (0.010) Loss 2.201 (1.326)
Epoch: [8][390/400] Time 0.328 (0.330) Data 0.000 (0.010) Loss 2.245 (1.367)
Epoch: [8][400/400] Time 0.323 (0.330) Data 0.000 (0.009) Loss 3.165 (1.416)
==> Create pseudo labels for unlabeled data
bruteForceKnn is deprecated; call bfKnn instead
Extract Features: [50/591] Time 0.095 (0.142) Data 0.000 (0.043)
Extract Features: [100/591] Time 0.096 (0.120) Data 0.000 (0.022)
Extract Features: [150/591] Time 0.095 (0.116) Data 0.000 (0.019)
Extract Features: [200/591] Time 0.095 (0.111) Data 0.000 (0.014)
Extract Features: [250/591] Time 0.095 (0.120) Data 0.000 (0.023)
Extract Features: [300/591] Time 0.095 (0.116) Data 0.000 (0.019)
Extract Features: [350/591] Time 0.094 (0.118) Data 0.000 (0.021)
Extract Features: [400/591] Time 0.097 (0.116) Data 0.000 (0.019)
Extract Features: [450/591] Time 0.095 (0.113) Data 0.000 (0.017)
Extract Features: [500/591] Time 0.095 (0.112) Data 0.000 (0.015)
Extract Features: [550/591] Time 0.096 (0.110) Data 0.000 (0.014)
Computing jaccard distance...
Jaccard distance computing time cost: 77.2706127166748
==> Statistics for epoch 9: 986 clusters
Epoch: [9][10/400] Time 0.308 (0.643) Data 0.000 (0.227) Loss 1.186 (0.825)
Epoch: [9][20/400] Time 0.306 (0.476) Data 0.000 (0.114) Loss 0.648 (0.742)
Epoch: [9][30/400] Time 0.306 (0.419) Data 0.000 (0.076) Loss 0.314 (0.817)
Epoch: [9][40/400] Time 0.306 (0.392) Data 0.000 (0.057) Loss 0.753 (0.801)
Epoch: [9][50/400] Time 0.310 (0.375) Data 0.000 (0.046) Loss 0.348 (0.828)
Epoch: [9][60/400] Time 0.309 (0.364) Data 0.000 (0.038) Loss 0.891 (0.838)
Epoch: [9][70/400] Time 0.307 (0.356) Data 0.000 (0.033) Loss 0.395 (0.812)
Epoch: [9][80/400] Time 0.303 (0.350) Data 0.000 (0.028) Loss 1.235 (0.802)
Epoch: [9][90/400] Time 0.309 (0.345) Data 0.000 (0.025) Loss 0.696 (0.781)
Epoch: [9][100/400] Time 0.305 (0.341) Data 0.000 (0.023) Loss 0.225 (0.773)
Epoch: [9][110/400] Time 0.306 (0.338) Data 0.000 (0.021) Loss 0.373 (0.750)
Epoch: [9][120/400] Time 0.306 (0.336) Data 0.000 (0.019) Loss 0.380 (0.737)
Epoch: [9][130/400] Time 0.307 (0.333) Data 0.000 (0.018) Loss 0.561 (0.716)
Epoch: [9][140/400] Time 0.318 (0.332) Data 0.000 (0.016) Loss 1.061 (0.711)
Epoch: [9][150/400] Time 0.306 (0.330) Data 0.000 (0.015) Loss 0.350 (0.714)
Epoch: [9][160/400] Time 0.309 (0.329) Data 0.000 (0.014) Loss 1.136 (0.708)
Epoch: [9][170/400] Time 0.305 (0.327) Data 0.000 (0.013) Loss 0.845 (0.699)
Epoch: [9][180/400] Time 0.318 (0.326) Data 0.000 (0.013) Loss 0.629 (0.692)
Epoch: [9][190/400] Time 0.324 (0.326) Data 0.000 (0.012) Loss 0.950 (0.685)
Epoch: [9][200/400] Time 0.324 (0.326) Data 0.000 (0.011) Loss 0.398 (0.677)
Epoch: [9][210/400] Time 0.325 (0.326) Data 0.000 (0.011) Loss 0.672 (0.675)
Epoch: [9][220/400] Time 0.333 (0.326) Data 0.000 (0.010) Loss 0.439 (0.670)
Epoch: [9][230/400] Time 0.323 (0.326) Data 0.000 (0.010) Loss 0.979 (0.672)
Epoch: [9][240/400] Time 0.322 (0.326) Data 0.000 (0.010) Loss 0.613 (0.669)
Epoch: [9][250/400] Time 0.330 (0.333) Data 0.000 (0.016) Loss 1.372 (0.683)
Epoch: [9][260/400] Time 0.323 (0.333) Data 0.000 (0.016) Loss 2.292 (0.751)
Epoch: [9][270/400] Time 0.327 (0.332) Data 0.000 (0.015) Loss 2.697 (0.820)
Epoch: [9][280/400] Time 0.326 (0.332) Data 0.000 (0.015) Loss 2.454 (0.879)
Epoch: [9][290/400] Time 0.323 (0.332) Data 0.000 (0.014) Loss 2.537 (0.936)
Epoch: [9][300/400] Time 0.324 (0.332) Data 0.000 (0.014) Loss 3.453 (1.009)
Epoch: [9][310/400] Time 0.324 (0.331) Data 0.000 (0.013) Loss 2.497 (1.058)
Epoch: [9][320/400] Time 0.324 (0.331) Data 0.000 (0.013) Loss 2.771 (1.095)
Epoch: [9][330/400] Time 0.324 (0.331) Data 0.000 (0.012) Loss 1.943 (1.133)
Epoch: [9][340/400] Time 0.327 (0.331) Data 0.000 (0.012) Loss 2.769 (1.183)
Epoch: [9][350/400] Time 0.324 (0.331) Data 0.000 (0.012) Loss 2.545 (1.228)
Epoch: [9][360/400] Time 0.324 (0.330) Data 0.000 (0.011) Loss 2.547 (1.266)
Epoch: [9][370/400] Time 0.323 (0.330) Data 0.000 (0.011) Loss 3.456 (1.291)
Epoch: [9][380/400] Time 0.325 (0.330) Data 0.000 (0.011) Loss 3.859 (1.332)
Epoch: [9][390/400] Time 0.328 (0.330) Data 0.000 (0.011) Loss 2.337 (1.373)
Epoch: [9][400/400] Time 0.324 (0.330) Data 0.000 (0.010) Loss 2.996 (1.414)
Extract Features: [50/208] Time 0.090 (0.288) Data 0.000 (0.194)
Extract Features: [100/208] Time 0.090 (0.256) Data 0.000 (0.164)
Extract Features: [150/208] Time 0.090 (0.250) Data 0.000 (0.159)
Extract Features: [200/208] Time 0.090 (0.249) Data 0.000 (0.158)
Mean AP: 17.9%
* Finished epoch 9 model mAP: 17.9% best: 17.9% *
==> Create pseudo labels for unlabeled data
bruteForceKnn is deprecated; call bfKnn instead
Extract Features: [50/591] Time 0.099 (0.239) Data 0.000 (0.147)
Extract Features: [100/591] Time 0.089 (0.170) Data 0.000 (0.077)
Extract Features: [150/591] Time 0.291 (0.154) Data 0.201 (0.062)
Extract Features: [200/591] Time 0.090 (0.139) Data 0.000 (0.047)
Extract Features: [250/591] Time 0.090 (0.143) Data 0.000 (0.052)
Extract Features: [300/591] Time 0.095 (0.135) Data 0.000 (0.043)
Extract Features: [350/591] Time 0.091 (0.138) Data 0.000 (0.046)
Extract Features: [400/591] Time 0.200 (0.136) Data 0.110 (0.044)
Extract Features: [450/591] Time 0.096 (0.132) Data 0.000 (0.039)
Extract Features: [500/591] Time 0.095 (0.129) Data 0.000 (0.036)
Extract Features: [550/591] Time 0.095 (0.126) Data 0.000 (0.033)
Computing jaccard distance...
Jaccard distance computing time cost: 76.44656610488892
==> Statistics for epoch 10: 1032 clusters
Epoch: [10][10/400] Time 0.307 (0.621) Data 0.000 (0.128) Loss 0.428 (0.824)
Epoch: [10][20/400] Time 0.311 (0.464) Data 0.000 (0.064) Loss 0.991 (0.876)
Epoch: [10][30/400] Time 0.307 (0.412) Data 0.000 (0.043) Loss 2.639 (0.936)
Epoch: [10][40/400] Time 0.307 (0.387) Data 0.000 (0.032) Loss 1.441 (0.925)
Epoch: [10][50/400] Time 0.311 (0.371) Data 0.000 (0.026) Loss 0.452 (0.906)
Epoch: [10][60/400] Time 0.306 (0.361) Data 0.000 (0.022) Loss 0.260 (0.883)
Epoch: [10][70/400] Time 0.306 (0.353) Data 0.000 (0.018) Loss 1.225 (0.870)
Epoch: [10][80/400] Time 0.308 (0.348) Data 0.000 (0.016) Loss 0.345 (0.851)
Epoch: [10][90/400] Time 0.307 (0.343) Data 0.000 (0.014) Loss 0.939 (0.850)
Epoch: [10][100/400] Time 0.308 (0.340) Data 0.000 (0.013) Loss 1.056 (0.850)
Epoch: [10][110/400] Time 0.315 (0.337) Data 0.000 (0.012) Loss 0.358 (0.864)
Epoch: [10][120/400] Time 0.305 (0.334) Data 0.000 (0.011) Loss 0.713 (0.859)
Epoch: [10][130/400] Time 0.308 (0.332) Data 0.000 (0.010) Loss 0.449 (0.861)
Epoch: [10][140/400] Time 0.306 (0.330) Data 0.000 (0.009) Loss 0.661 (0.854)
Epoch: [10][150/400] Time 0.306 (0.329) Data 0.000 (0.009) Loss 0.517 (0.850)
Epoch: [10][160/400] Time 0.322 (0.328) Data 0.000 (0.008) Loss 0.139 (0.834)
Epoch: [10][170/400] Time 0.307 (0.327) Data 0.000 (0.008) Loss 0.510 (0.815)
Epoch: [10][180/400] Time 0.307 (0.326) Data 0.000 (0.007) Loss 0.516 (0.798)
Epoch: [10][190/400] Time 0.316 (0.325) Data 0.000 (0.007) Loss 0.823 (0.790)
Epoch: [10][200/400] Time 0.321 (0.325) Data 0.000 (0.007) Loss 0.388 (0.776)
Epoch: [10][210/400] Time 0.328 (0.325) Data 0.000 (0.006) Loss 0.387 (0.766)
Epoch: [10][220/400] Time 0.329 (0.325) Data 0.000 (0.006) Loss 0.266 (0.755)
Epoch: [10][230/400] Time 0.327 (0.325) Data 0.000 (0.006) Loss 1.053 (0.749)
Epoch: [10][240/400] Time 0.327 (0.325) Data 0.000 (0.005) Loss 0.911 (0.749)
Epoch: [10][250/400] Time 0.323 (0.325) Data 0.000 (0.005) Loss 0.562 (0.747)
Epoch: [10][260/400] Time 0.335 (0.332) Data 0.000 (0.011) Loss 1.459 (0.742)
Epoch: [10][270/400] Time 0.323 (0.331) Data 0.000 (0.011) Loss 2.153 (0.812)
Epoch: [10][280/400] Time 0.324 (0.331) Data 0.000 (0.011) Loss 1.939 (0.872)
Epoch: [10][290/400] Time 0.326 (0.331) Data 0.000 (0.010) Loss 1.655 (0.919)
Epoch: [10][300/400] Time 0.325 (0.331) Data 0.000 (0.010) Loss 3.847 (0.970)
Epoch: [10][310/400] Time 0.325 (0.331) Data 0.000 (0.010) Loss 4.099 (1.024)
Epoch: [10][320/400] Time 0.329 (0.331) Data 0.000 (0.009) Loss 3.796 (1.071)
Epoch: [10][330/400] Time 0.328 (0.330) Data 0.000 (0.009) Loss 2.970 (1.125)
Epoch: [10][340/400] Time 0.325 (0.330) Data 0.000 (0.009) Loss 1.813 (1.164)
Epoch: [10][350/400] Time 0.329 (0.330) Data 0.000 (0.008) Loss 3.064 (1.202)
Epoch: [10][360/400] Time 0.327 (0.330) Data 0.000 (0.008) Loss 2.466 (1.246)
Epoch: [10][370/400] Time 0.325 (0.330) Data 0.000 (0.008) Loss 2.198 (1.291)
Epoch: [10][380/400] Time 0.325 (0.330) Data 0.000 (0.008) Loss 2.611 (1.328)
Epoch: [10][390/400] Time 0.327 (0.330) Data 0.000 (0.008) Loss 2.469 (1.367)
Epoch: [10][400/400] Time 0.324 (0.330) Data 0.000 (0.007) Loss 2.353 (1.406)
==> Create pseudo labels for unlabeled data
bruteForceKnn is deprecated; call bfKnn instead
Extract Features: [50/591] Time 0.096 (0.139) Data 0.000 (0.043)
Extract Features: [100/591] Time 0.095 (0.122) Data 0.000 (0.026)
Extract Features: [150/591] Time 0.094 (0.117) Data 0.000 (0.022)
Extract Features: [200/591] Time 0.095 (0.112) Data 0.000 (0.016)
Extract Features: [250/591] Time 0.096 (0.120) Data 0.000 (0.025)
Extract Features: [300/591] Time 0.106 (0.116) Data 0.000 (0.021)
Extract Features: [350/591] Time 0.102 (0.117) Data 0.000 (0.022)
Extract Features: [400/591] Time 0.096 (0.116) Data 0.000 (0.020)
Extract Features: [450/591] Time 0.095 (0.114) Data 0.000 (0.018)
Extract Features: [500/591] Time 0.096 (0.112) Data 0.000 (0.016)
Extract Features: [550/591] Time 0.095 (0.111) Data 0.000 (0.015)
Computing jaccard distance...
Jaccard distance computing time cost: 77.02672600746155
==> Statistics for epoch 11: 996 clusters
Epoch: [11][10/400] Time 0.306 (0.596) Data 0.000 (0.191) Loss 0.723 (0.687)
Epoch: [11][20/400] Time 0.308 (0.454) Data 0.000 (0.095) Loss 1.038 (0.776)
Epoch: [11][30/400] Time 0.305 (0.406) Data 0.000 (0.064) Loss 1.329 (0.777)
Epoch: [11][40/400] Time 0.315 (0.382) Data 0.000 (0.048) Loss 0.821 (0.769)
Epoch: [11][50/400] Time 0.313 (0.367) Data 0.000 (0.038) Loss 0.685 (0.798)
Epoch: [11][60/400] Time 0.306 (0.357) Data 0.000 (0.032) Loss 0.498 (0.759)
Epoch: [11][70/400] Time 0.308 (0.351) Data 0.000 (0.027) Loss 0.607 (0.783)
Epoch: [11][80/400] Time 0.308 (0.345) Data 0.000 (0.024) Loss 1.118 (0.781)
Epoch: [11][90/400] Time 0.314 (0.341) Data 0.000 (0.021) Loss 0.503 (0.772)
Epoch: [11][100/400] Time 0.310 (0.338) Data 0.000 (0.019) Loss 0.603 (0.770)
Epoch: [11][110/400] Time 0.313 (0.336) Data 0.000 (0.017) Loss 0.138 (0.772)
Epoch: [11][120/400] Time 0.315 (0.334) Data 0.000 (0.016) Loss 0.861 (0.761)
Epoch: [11][130/400] Time 0.306 (0.332) Data 0.000 (0.015) Loss 0.557 (0.750)
Epoch: [11][140/400] Time 0.303 (0.330) Data 0.000 (0.014) Loss 0.427 (0.734)
Epoch: [11][150/400] Time 0.304 (0.329) Data 0.000 (0.013) Loss 0.503 (0.746)
Epoch: [11][160/400] Time 0.309 (0.328) Data 0.000 (0.012) Loss 0.562 (0.737)
Epoch: [11][170/400] Time 0.317 (0.327) Data 0.000 (0.011) Loss 0.303 (0.724)
Epoch: [11][180/400] Time 0.317 (0.326) Data 0.000 (0.011) Loss 0.920 (0.714)
Epoch: [11][190/400] Time 0.334 (0.326) Data 0.000 (0.010) Loss 0.647 (0.708)
Epoch: [11][200/400] Time 0.319 (0.326) Data 0.000 (0.010) Loss 0.303 (0.695)
Epoch: [11][210/400] Time 0.329 (0.326) Data 0.000 (0.009) Loss 0.964 (0.689)
Epoch: [11][220/400] Time 0.329 (0.326) Data 0.000 (0.009) Loss 0.417 (0.679)
Epoch: [11][230/400] Time 0.341 (0.326) Data 0.000 (0.008) Loss 0.129 (0.668)
Epoch: [11][240/400] Time 0.325 (0.326) Data 0.000 (0.008) Loss 0.381 (0.656)
Epoch: [11][250/400] Time 1.914 (0.333) Data 1.589 (0.014) Loss 4.432 (0.664)
Epoch: [11][260/400] Time 0.329 (0.332) Data 0.000 (0.014) Loss 2.114 (0.737)
Epoch: [11][270/400] Time 0.324 (0.332) Data 0.000 (0.013) Loss 2.802 (0.808)
Epoch: [11][280/400] Time 0.324 (0.332) Data 0.000 (0.013) Loss 2.699 (0.876)
Epoch: [11][290/400] Time 0.326 (0.332) Data 0.000 (0.012) Loss 3.237 (0.938)
Epoch: [11][300/400] Time 0.327 (0.331) Data 0.000 (0.012) Loss 4.195 (0.988)
Epoch: [11][310/400] Time 0.330 (0.331) Data 0.000 (0.011) Loss 1.865 (1.034)
Epoch: [11][320/400] Time 0.329 (0.331) Data 0.000 (0.011) Loss 3.315 (1.082)
Epoch: [11][330/400] Time 0.328 (0.331) Data 0.000 (0.011) Loss 2.597 (1.129)
Epoch: [11][340/400] Time 0.326 (0.331) Data 0.000 (0.010) Loss 2.316 (1.175)
Epoch: [11][350/400] Time 0.332 (0.331) Data 0.000 (0.010) Loss 2.881 (1.222)
Epoch: [11][360/400] Time 0.327 (0.331) Data 0.000 (0.010) Loss 1.230 (1.266)
Epoch: [11][370/400] Time 0.330 (0.331) Data 0.000 (0.010) Loss 3.832 (1.308)
Epoch: [11][380/400] Time 0.323 (0.331) Data 0.000 (0.009) Loss 3.098 (1.352)
Epoch: [11][390/400] Time 0.325 (0.331) Data 0.000 (0.009) Loss 1.332 (1.378)
Epoch: [11][400/400] Time 0.324 (0.330) Data 0.000 (0.009) Loss 1.343 (1.404)
==> Create pseudo labels for unlabeled data
bruteForceKnn is deprecated; call bfKnn instead
Extract Features: [50/591] Time 0.102 (0.129) Data 0.000 (0.033)
Extract Features: [100/591] Time 0.102 (0.123) Data 0.000 (0.020)
Extract Features: [150/591] Time 0.095 (0.121) Data 0.000 (0.020)
Extract Features: [200/591] Time 0.096 (0.115) Data 0.000 (0.015)
Extract Features: [250/591] Time 0.095 (0.119) Data 0.000 (0.020)
Extract Features: [300/591] Time 0.095 (0.115) Data 0.000 (0.017)
Extract Features: [350/591] Time 0.095 (0.117) Data 0.000 (0.019)
Extract Features: [400/591] Time 0.096 (0.115) Data 0.000 (0.017)
Extract Features: [450/591] Time 0.095 (0.113) Data 0.000 (0.015)
Extract Features: [500/591] Time 0.095 (0.111) Data 0.000 (0.014)
Extract Features: [550/591] Time 0.095 (0.110) Data 0.000 (0.013)
Computing jaccard distance...
Jaccard distance computing time cost: 79.48762369155884
==> Statistics for epoch 12: 1045 clusters
Epoch: [12][10/400] Time 0.306 (0.556) Data 0.000 (0.175) Loss 0.423 (0.824)
Epoch: [12][20/400] Time 0.315 (0.432) Data 0.000 (0.087) Loss 0.613 (0.781)
Epoch: [12][30/400] Time 0.305 (0.390) Data 0.000 (0.058) Loss 1.037 (0.792)
Epoch: [12][40/400] Time 0.311 (0.370) Data 0.000 (0.044) Loss 1.219 (0.792)
Epoch: [12][50/400] Time 0.306 (0.357) Data 0.000 (0.035) Loss 0.553 (0.767)
Epoch: [12][60/400] Time 0.310 (0.349) Data 0.000 (0.029) Loss 1.295 (0.804)
Epoch: [12][70/400] Time 0.306 (0.343) Data 0.000 (0.025) Loss 0.364 (0.774)
Epoch: [12][80/400] Time 0.306 (0.339) Data 0.000 (0.022) Loss 0.824 (0.783)
Epoch: [12][90/400] Time 0.307 (0.335) Data 0.000 (0.019) Loss 0.523 (0.776)
Epoch: [12][100/400] Time 0.309 (0.332) Data 0.000 (0.018) Loss 0.403 (0.752)
Epoch: [12][110/400] Time 0.306 (0.330) Data 0.000 (0.016) Loss 0.474 (0.753)
Epoch: [12][120/400] Time 0.306 (0.328) Data 0.000 (0.015) Loss 0.320 (0.743)
Epoch: [12][130/400] Time 0.306 (0.327) Data 0.000 (0.014) Loss 0.352 (0.731)
Epoch: [12][140/400] Time 0.316 (0.325) Data 0.000 (0.013) Loss 0.510 (0.718)
Epoch: [12][150/400] Time 0.314 (0.324) Data 0.000 (0.012) Loss 0.188 (0.705)
Epoch: [12][160/400] Time 0.305 (0.323) Data 0.000 (0.011) Loss 0.648 (0.704)
Epoch: [12][170/400] Time 0.308 (0.322) Data 0.000 (0.010) Loss 0.237 (0.686)
Epoch: [12][180/400] Time 0.320 (0.322) Data 0.000 (0.010) Loss 0.559 (0.680)
Epoch: [12][190/400] Time 0.331 (0.322) Data 0.000 (0.009) Loss 0.460 (0.676)
Epoch: [12][200/400] Time 0.322 (0.322) Data 0.000 (0.009) Loss 0.483 (0.665)
Epoch: [12][210/400] Time 0.319 (0.322) Data 0.000 (0.008) Loss 0.403 (0.658)
Epoch: [12][220/400] Time 0.334 (0.322) Data 0.000 (0.008) Loss 1.010 (0.649)
Epoch: [12][230/400] Time 0.327 (0.323) Data 0.000 (0.008) Loss 0.217 (0.641)
Epoch: [12][240/400] Time 0.326 (0.323) Data 0.000 (0.007) Loss 0.531 (0.639)
Epoch: [12][250/400] Time 0.328 (0.323) Data 0.000 (0.007) Loss 0.278 (0.630)
Epoch: [12][260/400] Time 0.329 (0.323) Data 0.000 (0.007) Loss 0.396 (0.620)
Epoch: [12][270/400] Time 0.326 (0.330) Data 0.000 (0.014) Loss 2.004 (0.675)
Epoch: [12][280/400] Time 0.326 (0.330) Data 0.000 (0.013) Loss 2.660 (0.735)
Epoch: [12][290/400] Time 0.336 (0.330) Data 0.000 (0.013) Loss 1.527 (0.794)
Epoch: [12][300/400] Time 0.325 (0.330) Data 0.000 (0.012) Loss 3.086 (0.858)
Epoch: [12][310/400] Time 0.321 (0.330) Data 0.000 (0.012) Loss 2.062 (0.913)
Epoch: [12][320/400] Time 0.325 (0.330) Data 0.000 (0.012) Loss 2.735 (0.961)
Epoch: [12][330/400] Time 0.330 (0.330) Data 0.000 (0.011) Loss 1.592 (1.010)
Epoch: [12][340/400] Time 0.324 (0.330) Data 0.000 (0.011) Loss 3.003 (1.056)
Epoch: [12][350/400] Time 0.331 (0.330) Data 0.000 (0.011) Loss 2.037 (1.103)
Epoch: [12][360/400] Time 0.324 (0.330) Data 0.000 (0.010) Loss 2.252 (1.139)
Epoch: [12][370/400] Time 0.322 (0.330) Data 0.000 (0.010) Loss 2.831 (1.191)
Epoch: [12][380/400] Time 0.329 (0.330) Data 0.000 (0.010) Loss 1.552 (1.226)
Epoch: [12][390/400] Time 0.323 (0.330) Data 0.000 (0.009) Loss 2.441 (1.256)
Epoch: [12][400/400] Time 0.326 (0.330) Data 0.000 (0.009) Loss 3.241 (1.298)
==> Create pseudo labels for unlabeled data
bruteForceKnn is deprecated; call bfKnn instead
Extract Features: [50/591] Time 0.097 (0.133) Data 0.000 (0.036)
Extract Features: [100/591] Time 0.095 (0.121) Data 0.000 (0.025)
Extract Features: [150/591] Time 0.097 (0.119) Data 0.000 (0.023)
Extract Features: [200/591] Time 0.095 (0.114) Data 0.000 (0.018)
Extract Features: [250/591] Time 0.095 (0.119) Data 0.000 (0.023)
Extract Features: [300/591] Time 0.095 (0.115) Data 0.000 (0.019)
Extract Features: [350/591] Time 0.096 (0.116) Data 0.000 (0.020)
Extract Features: [400/591] Time 0.095 (0.115) Data 0.000 (0.019)
Extract Features: [450/591] Time 0.097 (0.113) Data 0.000 (0.017)
Extract Features: [500/591] Time 0.095 (0.111) Data 0.000 (0.015)
Extract Features: [550/591] Time 0.095 (0.110) Data 0.000 (0.014)
Computing jaccard distance...
Jaccard distance computing time cost: 77.70485997200012
==> Statistics for epoch 13: 1037 clusters
Epoch: [13][10/400] Time 0.303 (0.568) Data 0.000 (0.188) Loss 0.749 (0.864)
Epoch: [13][20/400] Time 0.313 (0.439) Data 0.000 (0.094) Loss 1.216 (0.836)
Epoch: [13][30/400] Time 0.311 (0.396) Data 0.000 (0.063) Loss 0.830 (0.829)
Epoch: [13][40/400] Time 0.316 (0.375) Data 0.000 (0.047) Loss 0.718 (0.810)
Epoch: [13][50/400] Time 0.305 (0.361) Data 0.000 (0.038) Loss 0.409 (0.813)
Epoch: [13][60/400] Time 0.305 (0.352) Data 0.000 (0.031) Loss 1.045 (0.791)
Epoch: [13][70/400] Time 0.304 (0.346) Data 0.000 (0.027) Loss 0.450 (0.786)
Epoch: [13][80/400] Time 0.305 (0.341) Data 0.000 (0.024) Loss 0.289 (0.775)
Epoch: [13][90/400] Time 0.306 (0.338) Data 0.000 (0.021) Loss 0.453 (0.784)
Epoch: [13][100/400] Time 0.306 (0.335) Data 0.000 (0.019) Loss 0.534 (0.766)
Epoch: [13][110/400] Time 0.310 (0.332) Data 0.000 (0.017) Loss 0.781 (0.762)
Epoch: [13][120/400] Time 0.316 (0.330) Data 0.000 (0.016) Loss 0.345 (0.752)
Epoch: [13][130/400] Time 0.312 (0.328) Data 0.000 (0.015) Loss 0.667 (0.774)
Epoch: [13][140/400] Time 0.315 (0.327) Data 0.000 (0.014) Loss 0.714 (0.763)
Epoch: [13][150/400] Time 0.306 (0.326) Data 0.000 (0.013) Loss 0.323 (0.760)
Epoch: [13][160/400] Time 0.304 (0.325) Data 0.000 (0.012) Loss 0.565 (0.755)
Epoch: [13][170/400] Time 0.310 (0.324) Data 0.000 (0.011) Loss 1.043 (0.752)
Epoch: [13][180/400] Time 0.328 (0.323) Data 0.000 (0.011) Loss 0.660 (0.741)
Epoch: [13][190/400] Time 0.319 (0.323) Data 0.000 (0.010) Loss 1.220 (0.739)
Epoch: [13][200/400] Time 0.322 (0.323) Data 0.000 (0.010) Loss 0.455 (0.731)
Epoch: [13][210/400] Time 0.323 (0.323) Data 0.000 (0.009) Loss 0.707 (0.721)
Epoch: [13][220/400] Time 0.321 (0.324) Data 0.000 (0.009) Loss 0.288 (0.711)
Epoch: [13][230/400] Time 0.324 (0.324) Data 0.000 (0.008) Loss 0.507 (0.701)
Epoch: [13][240/400] Time 0.323 (0.324) Data 0.000 (0.008) Loss 0.428 (0.696)
Epoch: [13][250/400] Time 0.331 (0.324) Data 0.000 (0.008) Loss 0.772 (0.688)
Epoch: [13][260/400] Time 1.781 (0.330) Data 1.449 (0.013) Loss 2.181 (0.692)
Epoch: [13][270/400] Time 0.329 (0.330) Data 0.000 (0.012) Loss 1.114 (0.757)
Epoch: [13][280/400] Time 0.328 (0.330) Data 0.000 (0.012) Loss 3.239 (0.825)
Epoch: [13][290/400] Time 0.324 (0.330) Data 0.000 (0.012) Loss 2.933 (0.898)
Epoch: [13][300/400] Time 0.328 (0.330) Data 0.000 (0.011) Loss 3.617 (0.966)
Epoch: [13][310/400] Time 0.327 (0.329) Data 0.000 (0.011) Loss 2.251 (1.021)
Epoch: [13][320/400] Time 0.325 (0.329) Data 0.000 (0.011) Loss 2.300 (1.081)
Epoch: [13][330/400] Time 0.329 (0.329) Data 0.000 (0.010) Loss 2.395 (1.141)
Epoch: [13][340/400] Time 0.325 (0.329) Data 0.000 (0.010) Loss 2.728 (1.197)
Epoch: [13][350/400] Time 0.325 (0.329) Data 0.000 (0.010) Loss 2.964 (1.239)
Epoch: [13][360/400] Time 0.324 (0.329) Data 0.000 (0.009) Loss 1.909 (1.268)
Epoch: [13][370/400] Time 0.325 (0.329) Data 0.000 (0.009) Loss 2.604 (1.296)
Epoch: [13][380/400] Time 0.334 (0.329) Data 0.000 (0.009) Loss 3.411 (1.331)
Epoch: [13][390/400] Time 0.326 (0.329) Data 0.000 (0.009) Loss 1.892 (1.364)
Epoch: [13][400/400] Time 0.323 (0.329) Data 0.000 (0.008) Loss 2.765 (1.402)
==> Create pseudo labels for unlabeled data
bruteForceKnn is deprecated; call bfKnn instead
Extract Features: [50/591] Time 0.095 (0.132) Data 0.000 (0.037)
Extract Features: [100/591] Time 0.104 (0.117) Data 0.000 (0.021)
Extract Features: [150/591] Time 0.095 (0.115) Data 0.000 (0.019)
Extract Features: [200/591] Time 0.094 (0.111) Data 0.000 (0.014)
Extract Features: [250/591] Time 0.095 (0.114) Data 0.000 (0.018)
Extract Features: [300/591] Time 0.096 (0.111) Data 0.000 (0.015)
Extract Features: [350/591] Time 0.095 (0.113) Data 0.000 (0.017)
Extract Features: [400/591] Time 0.095 (0.111) Data 0.000 (0.015)
Extract Features: [450/591] Time 0.097 (0.109) Data 0.000 (0.013)
Extract Features: [500/591] Time 0.095 (0.108) Data 0.000 (0.012)
Extract Features: [550/591] Time 0.095 (0.107) Data 0.000 (0.011)
Computing jaccard distance...
Jaccard distance computing time cost: 77.19226670265198
==> Statistics for epoch 14: 980 clusters
Epoch: [14][10/400] Time 0.305 (0.535) Data 0.000 (0.157) Loss 0.860 (0.842)
Epoch: [14][20/400] Time 0.325 (0.422) Data 0.000 (0.078) Loss 0.772 (0.859)
Epoch: [14][30/400] Time 0.310 (0.383) Data 0.000 (0.052) Loss 0.457 (0.869)
Epoch: [14][40/400] Time 0.305 (0.364) Data 0.000 (0.039) Loss 0.867 (0.840)
Epoch: [14][50/400] Time 0.306 (0.353) Data 0.000 (0.031) Loss 1.530 (0.838)
Epoch: [14][60/400] Time 0.317 (0.345) Data 0.000 (0.026) Loss 0.606 (0.816)
Epoch: [14][70/400] Time 0.305 (0.340) Data 0.000 (0.022) Loss 0.477 (0.798)
Epoch: [14][80/400] Time 0.305 (0.336) Data 0.000 (0.020) Loss 0.528 (0.786)
Epoch: [14][90/400] Time 0.305 (0.332) Data 0.000 (0.017) Loss 0.501 (0.789)
Epoch: [14][100/400] Time 0.306 (0.330) Data 0.000 (0.016) Loss 0.666 (0.769)
Epoch: [14][110/400] Time 0.309 (0.327) Data 0.000 (0.014) Loss 0.389 (0.752)
Epoch: [14][120/400] Time 0.311 (0.326) Data 0.000 (0.013) Loss 1.188 (0.773)
Epoch: [14][130/400] Time 0.305 (0.324) Data 0.000 (0.012) Loss 0.427 (0.766)
Epoch: [14][140/400] Time 0.321 (0.323) Data 0.000 (0.011) Loss 0.427 (0.756)
Epoch: [14][150/400] Time 0.305 (0.322) Data 0.000 (0.011) Loss 0.552 (0.740)
Epoch: [14][160/400] Time 0.304 (0.321) Data 0.000 (0.010) Loss 0.260 (0.731)
Epoch: [14][170/400] Time 0.306 (0.320) Data 0.000 (0.009) Loss 0.627 (0.717)
Epoch: [14][180/400] Time 0.305 (0.319) Data 0.000 (0.009) Loss 0.350 (0.712)
Epoch: [14][190/400] Time 0.314 (0.319) Data 0.000 (0.008) Loss 0.353 (0.701)
Epoch: [14][200/400] Time 0.321 (0.319) Data 0.000 (0.008) Loss 0.670 (0.689)
Epoch: [14][210/400] Time 0.324 (0.319) Data 0.000 (0.008) Loss 0.621 (0.685)
Epoch: [14][220/400] Time 0.326 (0.319) Data 0.000 (0.007) Loss 0.576 (0.673)
Epoch: [14][230/400] Time 0.336 (0.320) Data 0.000 (0.007) Loss 0.216 (0.662)
Epoch: [14][240/400] Time 0.328 (0.320) Data 0.000 (0.007) Loss 0.501 (0.656)
Epoch: [14][250/400] Time 0.331 (0.327) Data 0.000 (0.013) Loss 2.785 (0.696)
Epoch: [14][260/400] Time 0.324 (0.327) Data 0.000 (0.013) Loss 3.998 (0.765)
Epoch: [14][270/400] Time 0.329 (0.327) Data 0.000 (0.012) Loss 3.469 (0.831)
Epoch: [14][280/400] Time 0.328 (0.327) Data 0.000 (0.012) Loss 2.811 (0.890)
Epoch: [14][290/400] Time 0.333 (0.327) Data 0.000 (0.011) Loss 2.405 (0.940)
Epoch: [14][300/400] Time 0.327 (0.327) Data 0.000 (0.011) Loss 2.850 (1.006)
Epoch: [14][310/400] Time 0.326 (0.327) Data 0.000 (0.011) Loss 2.360 (1.064)
Epoch: [14][320/400] Time 0.323 (0.327) Data 0.000 (0.010) Loss 1.572 (1.117)
Epoch: [14][330/400] Time 0.324 (0.327) Data 0.000 (0.010) Loss 2.024 (1.162)
Epoch: [14][340/400] Time 0.322 (0.327) Data 0.000 (0.010) Loss 2.460 (1.208)
Epoch: [14][350/400] Time 0.325 (0.327) Data 0.000 (0.009) Loss 3.330 (1.246)
Epoch: [14][360/400] Time 0.322 (0.327) Data 0.000 (0.009) Loss 3.123 (1.287)
Epoch: [14][370/400] Time 0.323 (0.327) Data 0.000 (0.009) Loss 1.734 (1.317)
Epoch: [14][380/400] Time 0.327 (0.327) Data 0.000 (0.009) Loss 3.252 (1.352)
Epoch: [14][390/400] Time 0.327 (0.326) Data 0.000 (0.009) Loss 2.937 (1.388)
Epoch: [14][400/400] Time 0.328 (0.326) Data 0.000 (0.008) Loss 2.592 (1.423)
==> Create pseudo labels for unlabeled data
bruteForceKnn is deprecated; call bfKnn instead
Extract Features: [50/591] Time 0.096 (0.151) Data 0.000 (0.052)
Extract Features: [100/591] Time 0.104 (0.125) Data 0.000 (0.027)
Extract Features: [150/591] Time 0.096 (0.119) Data 0.000 (0.022)
Extract Features: [200/591] Time 0.095 (0.113) Data 0.000 (0.017)
Extract Features: [250/591] Time 0.101 (0.123) Data 0.000 (0.026)
Extract Features: [300/591] Time 0.096 (0.118) Data 0.000 (0.022)
Extract Features: [350/591] Time 0.096 (0.119) Data 0.000 (0.023)
Extract Features: [400/591] Time 0.095 (0.116) Data 0.000 (0.020)
Extract Features: [450/591] Time 0.095 (0.114) Data 0.000 (0.018)
Extract Features: [500/591] Time 0.095 (0.113) Data 0.000 (0.016)
Extract Features: [550/591] Time 0.096 (0.112) Data 0.000 (0.015)
Computing jaccard distance...
Jaccard distance computing time cost: 78.13911175727844
==> Statistics for epoch 15: 999 clusters
Epoch: [15][10/400] Time 0.310 (0.546) Data 0.000 (0.181) Loss 0.969 (0.912)
Epoch: [15][20/400] Time 0.306 (0.427) Data 0.000 (0.091) Loss 0.975 (0.951)
Epoch: [15][30/400] Time 0.307 (0.387) Data 0.000 (0.060) Loss 1.656 (0.917)
Epoch: [15][40/400] Time 0.307 (0.368) Data 0.000 (0.045) Loss 1.084 (0.913)
Epoch: [15][50/400] Time 0.308 (0.356) Data 0.000 (0.036) Loss 1.280 (0.899)
Epoch: [15][60/400] Time 0.307 (0.348) Data 0.000 (0.030) Loss 0.431 (0.886)
Epoch: [15][70/400] Time 0.310 (0.342) Data 0.000 (0.026) Loss 0.716 (0.900)
Epoch: [15][80/400] Time 0.303 (0.337) Data 0.000 (0.023) Loss 0.892 (0.888)
Epoch: [15][90/400] Time 0.307 (0.334) Data 0.000 (0.020) Loss 0.719 (0.866)
Epoch: [15][100/400] Time 0.305 (0.332) Data 0.000 (0.018) Loss 0.775 (0.873)
Epoch: [15][110/400] Time 0.305 (0.330) Data 0.000 (0.017) Loss 0.561 (0.865)
Epoch: [15][120/400] Time 0.305 (0.328) Data 0.000 (0.015) Loss 1.598 (0.858)
Epoch: [15][130/400] Time 0.304 (0.326) Data 0.000 (0.014) Loss 0.890 (0.843)
Epoch: [15][140/400] Time 0.306 (0.325) Data 0.000 (0.013) Loss 0.642 (0.844)
Epoch: [15][150/400] Time 0.313 (0.324) Data 0.000 (0.012) Loss 0.621 (0.844)
Epoch: [15][160/400] Time 0.305 (0.323) Data 0.000 (0.011) Loss 0.591 (0.831)
Epoch: [15][170/400] Time 0.304 (0.322) Data 0.000 (0.011) Loss 0.351 (0.816)
Epoch: [15][180/400] Time 0.308 (0.321) Data 0.000 (0.010) Loss 0.211 (0.805)
Epoch: [15][190/400] Time 0.317 (0.321) Data 0.000 (0.010) Loss 1.462 (0.806)
Epoch: [15][200/400] Time 0.321 (0.321) Data 0.000 (0.009) Loss 0.710 (0.800)
Epoch: [15][210/400] Time 0.323 (0.321) Data 0.000 (0.009) Loss 0.609 (0.790)
Epoch: [15][220/400] Time 0.325 (0.321) Data 0.000 (0.008) Loss 0.509 (0.781)
Epoch: [15][230/400] Time 0.324 (0.321) Data 0.000 (0.008) Loss 0.694 (0.787)
Epoch: [15][240/400] Time 0.332 (0.321) Data 0.000 (0.008) Loss 0.263 (0.780)
Epoch: [15][250/400] Time 2.263 (0.329) Data 1.929 (0.015) Loss 3.880 (0.796)
Epoch: [15][260/400] Time 0.325 (0.329) Data 0.000 (0.014) Loss 2.136 (0.858)
Epoch: [15][270/400] Time 0.328 (0.329) Data 0.000 (0.014) Loss 2.138 (0.904)
Epoch: [15][280/400] Time 0.327 (0.329) Data 0.000 (0.013) Loss 1.300 (0.953)
Epoch: [15][290/400] Time 0.328 (0.329) Data 0.000 (0.013) Loss 4.044 (1.023)
Epoch: [15][300/400] Time 0.328 (0.329) Data 0.000 (0.013) Loss 1.594 (1.073)
Epoch: [15][310/400] Time 0.324 (0.329) Data 0.000 (0.012) Loss 4.138 (1.121)
Epoch: [15][320/400] Time 0.327 (0.329) Data 0.000 (0.012) Loss 2.873 (1.159)
Epoch: [15][330/400] Time 0.327 (0.329) Data 0.000 (0.011) Loss 3.776 (1.205)
Epoch: [15][340/400] Time 0.326 (0.328) Data 0.000 (0.011) Loss 1.991 (1.250)
Epoch: [15][350/400] Time 0.327 (0.328) Data 0.000 (0.011) Loss 2.129 (1.279)
Epoch: [15][360/400] Time 0.326 (0.328) Data 0.000 (0.010) Loss 2.569 (1.321)
Epoch: [15][370/400] Time 0.323 (0.328) Data 0.000 (0.010) Loss 3.218 (1.352)
Epoch: [15][380/400] Time 0.327 (0.328) Data 0.000 (0.010) Loss 3.393 (1.383)
Epoch: [15][390/400] Time 0.325 (0.328) Data 0.000 (0.010) Loss 2.497 (1.422)
Epoch: [15][400/400] Time 0.329 (0.328) Data 0.000 (0.009) Loss 1.908 (1.445)
==> Create pseudo labels for unlabeled data
bruteForceKnn is deprecated; call bfKnn instead
Extract Features: [50/591] Time 0.095 (0.139) Data 0.000 (0.039)
Extract Features: [100/591] Time 0.101 (0.123) Data 0.000 (0.024)
Extract Features: [150/591] Time 0.095 (0.115) Data 0.000 (0.017)
Extract Features: [200/591] Time 0.095 (0.111) Data 0.000 (0.013)
Extract Features: [250/591] Time 0.095 (0.117) Data 0.000 (0.020)
Extract Features: [300/591] Time 0.095 (0.114) Data 0.000 (0.016)
Extract Features: [350/591] Time 0.095 (0.115) Data 0.000 (0.018)
Extract Features: [400/591] Time 0.095 (0.114) Data 0.000 (0.017)
Extract Features: [450/591] Time 0.096 (0.112) Data 0.000 (0.015)
Extract Features: [500/591] Time 0.095 (0.110) Data 0.000 (0.014)
Extract Features: [550/591] Time 0.096 (0.109) Data 0.000 (0.012)
Computing jaccard distance...
Jaccard distance computing time cost: 76.17075705528259
==> Statistics for epoch 16: 981 clusters
Epoch: [16][10/400] Time 0.303 (0.564) Data 0.000 (0.186) Loss 0.889 (1.043)
Epoch: [16][20/400] Time 0.309 (0.435) Data 0.000 (0.093) Loss 0.717 (0.995)
Epoch: [16][30/400] Time 0.311 (0.392) Data 0.000 (0.062) Loss 0.745 (0.905)
Epoch: [16][40/400] Time 0.308 (0.372) Data 0.000 (0.047) Loss 1.229 (0.847)
Epoch: [16][50/400] Time 0.305 (0.359) Data 0.000 (0.037) Loss 0.813 (0.806)
Epoch: [16][60/400] Time 0.318 (0.351) Data 0.000 (0.031) Loss 0.542 (0.769)
Epoch: [16][70/400] Time 0.306 (0.345) Data 0.000 (0.027) Loss 0.313 (0.750)
Epoch: [16][80/400] Time 0.305 (0.340) Data 0.000 (0.023) Loss 0.815 (0.737)
Epoch: [16][90/400] Time 0.306 (0.336) Data 0.000 (0.021) Loss 0.574 (0.732)
Epoch: [16][100/400] Time 0.323 (0.334) Data 0.000 (0.019) Loss 0.372 (0.719)
Epoch: [16][110/400] Time 0.309 (0.331) Data 0.000 (0.017) Loss 1.174 (0.712)
Epoch: [16][120/400] Time 0.306 (0.329) Data 0.000 (0.016) Loss 0.448 (0.718)
Epoch: [16][130/400] Time 0.306 (0.328) Data 0.000 (0.014) Loss 0.357 (0.704)
Epoch: [16][140/400] Time 0.329 (0.327) Data 0.000 (0.013) Loss 0.715 (0.710)
Epoch: [16][150/400] Time 0.306 (0.326) Data 0.000 (0.013) Loss 0.545 (0.687)
Epoch: [16][160/400] Time 0.306 (0.325) Data 0.000 (0.012) Loss 0.837 (0.682)
Epoch: [16][170/400] Time 0.309 (0.324) Data 0.000 (0.011) Loss 0.624 (0.694)
Epoch: [16][180/400] Time 0.318 (0.323) Data 0.000 (0.010) Loss 0.628 (0.686)
Epoch: [16][190/400] Time 0.321 (0.323) Data 0.000 (0.010) Loss 0.568 (0.677)
Epoch: [16][200/400] Time 0.327 (0.323) Data 0.000 (0.009) Loss 0.121 (0.663)
Epoch: [16][210/400] Time 0.322 (0.323) Data 0.000 (0.009) Loss 0.579 (0.655)
Epoch: [16][220/400] Time 0.323 (0.323) Data 0.000 (0.009) Loss 0.470 (0.653)
Epoch: [16][230/400] Time 0.327 (0.323) Data 0.000 (0.008) Loss 0.296 (0.640)
Epoch: [16][240/400] Time 0.323 (0.323) Data 0.000 (0.008) Loss 0.371 (0.631)
Epoch: [16][250/400] Time 0.321 (0.331) Data 0.000 (0.016) Loss 2.081 (0.660)
Epoch: [16][260/400] Time 0.324 (0.331) Data 0.000 (0.015) Loss 1.373 (0.720)
Epoch: [16][270/400] Time 0.324 (0.331) Data 0.000 (0.014) Loss 2.600 (0.788)
Epoch: [16][280/400] Time 0.328 (0.331) Data 0.000 (0.014) Loss 3.888 (0.848)
Epoch: [16][290/400] Time 0.327 (0.330) Data 0.000 (0.013) Loss 2.482 (0.910)
Epoch: [16][300/400] Time 0.325 (0.330) Data 0.000 (0.013) Loss 2.155 (0.957)
Epoch: [16][310/400] Time 0.322 (0.330) Data 0.000 (0.013) Loss 2.898 (1.016)
Epoch: [16][320/400] Time 0.324 (0.330) Data 0.000 (0.012) Loss 2.473 (1.060)
Epoch: [16][330/400] Time 0.324 (0.330) Data 0.000 (0.012) Loss 3.973 (1.122)
Epoch: [16][340/400] Time 0.324 (0.330) Data 0.000 (0.012) Loss 2.819 (1.176)
Epoch: [16][350/400] Time 0.323 (0.330) Data 0.000 (0.011) Loss 2.404 (1.217)
Epoch: [16][360/400] Time 0.325 (0.330) Data 0.000 (0.011) Loss 2.704 (1.265)
Epoch: [16][370/400] Time 0.327 (0.329) Data 0.000 (0.011) Loss 1.980 (1.298)
Epoch: [16][380/400] Time 0.324 (0.329) Data 0.000 (0.010) Loss 3.224 (1.335)
Epoch: [16][390/400] Time 0.324 (0.329) Data 0.000 (0.010) Loss 3.390 (1.369)
Epoch: [16][400/400] Time 0.324 (0.329) Data 0.000 (0.010) Loss 2.649 (1.400)
==> Create pseudo labels for unlabeled data
bruteForceKnn is deprecated; call bfKnn instead
Extract Features: [50/591] Time 0.095 (0.143) Data 0.000 (0.047)
Extract Features: [100/591] Time 0.095 (0.126) Data 0.000 (0.030)
Extract Features: [150/591] Time 0.095 (0.122) Data 0.000 (0.024)
Extract Features: [200/591] Time 0.095 (0.116) Data 0.000 (0.018)
Extract Features: [250/591] Time 0.095 (0.119) Data 0.000 (0.022)
Extract Features: [300/591] Time 0.095 (0.116) Data 0.000 (0.020)
Extract Features: [350/591] Time 0.095 (0.120) Data 0.000 (0.023)
Extract Features: [400/591] Time 0.095 (0.117) Data 0.000 (0.021)
Extract Features: [450/591] Time 0.095 (0.115) Data 0.000 (0.018)
Extract Features: [500/591] Time 0.095 (0.113) Data 0.000 (0.016)
Extract Features: [550/591] Time 0.095 (0.111) Data 0.000 (0.015)
Computing jaccard distance...
Jaccard distance computing time cost: 78.84743905067444
==> Statistics for epoch 17: 953 clusters
Epoch: [17][10/400] Time 0.306 (0.576) Data 0.000 (0.216) Loss 0.541 (0.736)
Epoch: [17][20/400] Time 0.305 (0.441) Data 0.000 (0.108) Loss 0.950 (0.758)
Epoch: [17][30/400] Time 0.305 (0.396) Data 0.000 (0.072) Loss 0.616 (0.736)
Epoch: [17][40/400] Time 0.310 (0.374) Data 0.000 (0.054) Loss 1.087 (0.813)
Epoch: [17][50/400] Time 0.305 (0.361) Data 0.000 (0.043) Loss 1.244 (0.763)
Epoch: [17][60/400] Time 0.319 (0.352) Data 0.000 (0.036) Loss 0.589 (0.757)
Epoch: [17][70/400] Time 0.303 (0.345) Data 0.000 (0.031) Loss 0.946 (0.752)
Epoch: [17][80/400] Time 0.308 (0.341) Data 0.000 (0.027) Loss 0.336 (0.759)