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GulpIO Blogpost GulpIO Loader results
(torch) root@7396071ace7e:/home/rgoyal/GulpIO-benchmarks# CUDA_VISIBLE_DEVICES=0,1 python train_gulp.py --config configs/config_gulpio.json
=> active GPUs: 0,1
=> Output folder for this run -- jester_conv_example
> Using 10 processes for data loader.
> Training is getting started...
> Training takes 999999 epochs.
> Current LR : 0.001
Epoch: [0][0/1852] Time 12.410 (12.410) Data 9.079 (9.079) Loss 3.2856 (3.2856) Prec@1 9.375 (9.375) Prec@5 17.188 (17.188)
Epoch: [0][100/1852] Time 0.457 (0.854) Data 0.000 (0.429) Loss 2.5770 (3.0944) Prec@1 26.562 (12.655) Prec@5 68.750 (37.036)
Epoch: [0][200/1852] Time 0.456 (0.818) Data 0.000 (0.432) Loss 2.6250 (2.8789) Prec@1 25.000 (18.043) Prec@5 56.250 (47.528)
Epoch: [0][300/1852] Time 0.458 (0.792) Data 0.000 (0.419) Loss 2.4090 (2.7366) Prec@1 34.375 (21.522) Prec@5 68.750 (53.265)
Epoch: [0][400/1852] Time 0.464 (0.770) Data 0.000 (0.398) Loss 2.0964 (2.6066) Prec@1 28.125 (24.762) Prec@5 76.562 (57.988)
Epoch: [0][500/1852] Time 0.979 (0.751) Data 0.770 (0.377) Loss 2.0077 (2.4919) Prec@1 45.312 (27.561) Prec@5 81.250 (61.642)
Epoch: [0][600/1852] Time 1.648 (0.733) Data 1.448 (0.358) Loss 2.0193 (2.3956) Prec@1 39.062 (30.135) Prec@5 79.688 (64.655)
Epoch: [0][700/1852] Time 0.467 (0.714) Data 0.103 (0.339) Loss 1.8151 (2.3129) Prec@1 39.062 (32.280) Prec@5 85.938 (67.005)
Epoch: [0][800/1852] Time 0.465 (0.697) Data 0.000 (0.319) Loss 1.3762 (2.2394) Prec@1 59.375 (34.131) Prec@5 90.625 (68.959)
Epoch: [0][900/1852] Time 0.464 (0.682) Data 0.000 (0.300) Loss 1.6394 (2.1730) Prec@1 43.750 (35.972) Prec@5 85.938 (70.651)
Epoch: [0][1000/1852] Time 0.463 (0.665) Data 0.000 (0.280) Loss 1.5073 (2.1184) Prec@1 45.312 (37.431) Prec@5 85.938 (72.056)
Epoch: [0][1100/1852] Time 0.464 (0.648) Data 0.017 (0.258) Loss 1.3807 (2.0655) Prec@1 54.688 (38.816) Prec@5 95.312 (73.348)
Epoch: [0][1200/1852] Time 0.461 (0.633) Data 0.000 (0.238) Loss 1.5314 (2.0186) Prec@1 50.000 (40.058) Prec@5 85.938 (74.491)
Epoch: [0][1300/1852] Time 0.490 (0.620) Data 0.000 (0.220) Loss 1.7921 (1.9743) Prec@1 51.562 (41.244) Prec@5 81.250 (75.533)
Epoch: [0][1400/1852] Time 0.463 (0.609) Data 0.000 (0.204) Loss 1.3163 (1.9360) Prec@1 56.250 (42.276) Prec@5 92.188 (76.410)
Epoch: [0][1500/1852] Time 0.463 (0.600) Data 0.000 (0.191) Loss 1.2276 (1.8984) Prec@1 64.062 (43.260) Prec@5 87.500 (77.213)
Epoch: [0][1600/1852] Time 0.464 (0.591) Data 0.000 (0.179) Loss 1.2961 (1.8634) Prec@1 59.375 (44.140) Prec@5 87.500 (77.967)
Epoch: [0][1700/1852] Time 0.464 (0.584) Data 0.000 (0.168) Loss 1.3273 (1.8323) Prec@1 60.938 (45.009) Prec@5 89.062 (78.635)
Epoch: [0][1800/1852] Time 0.469 (0.577) Data 0.000 (0.159) Loss 1.2787 (1.8019) Prec@1 57.812 (45.827) Prec@5 90.625 (79.255)
> Time taken for this 1 train epoch = 1062.678290605545
Test: [0/232] Time 6.247 (6.247) Loss 1.5232 (1.5232) Prec@1 53.125 (53.125) Prec@5 82.812 (82.812)
Test: [100/232] Time 0.181 (0.406) Loss 1.1259 (1.3052) Prec@1 70.312 (59.746) Prec@5 95.312 (89.975)
Test: [200/232] Time 0.182 (0.390) Loss 1.2444 (1.3056) Prec@1 59.375 (59.600) Prec@5 92.188 (90.003)
* Prec@1 59.654 Prec@5 90.011
> Time taken for this 1 validation epoch = 88.27695989608765
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