GulpIO Blogpost GulpIO Loader results

gulpio_results.txt

(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