Open Xiang-Deng-DL opened 4 years ago
I do not know why using ImageFolderLMDB is slower than the original pytorch imagefolder? As seen below, the data loader is still very slow. ==> training... Epoch: [1][0/10010] Time 20.673 (20.673) Data 19.890 (19.890) Loss 18.0700 (18.0700) Acc@1 0.000 (0.000) Acc@5 0.000 (0.000) Epoch: [1][128/10010] Time 5.867 (2.609) Data 5.624 (2.248) Loss 14.5102 (14.8609) Acc@1 0.000 (0.212) Acc@5 2.344 (1.163) Epoch: [1][256/10010] Time 0.427 (2.561) Data 0.000 (2.194) Loss 13.6098 (14.3173) Acc@1 0.781 (0.395) Acc@5 4.688 (2.022) Epoch: [1][384/10010] Time 1.767 (2.547) Data 1.533 (2.185) Loss 13.1298 (14.0282) Acc@1 0.781 (0.611) Acc@5 4.688 (2.713) Epoch: [1][512/10010] Time 0.427 (2.536) Data 0.000 (2.168) Loss 13.2681 (13.7872) Acc@1 1.562 (0.778) Acc@5 7.031 (3.513) Epoch: [1][640/10010] Time 0.428 (2.546) Data 0.000 (2.171) Loss 13.5265 (13.5746) Acc@1 0.781 (0.963) Acc@5 5.469 (4.254) Epoch: [1][768/10010] Time 0.428 (2.528) Data 0.000 (2.148) Loss 13.7340 (13.4076) Acc@1 2.344 (1.185) Acc@5 11.719 (5.024) Epoch: [1][896/10010] Time 0.428 (2.545) Data 0.000 (2.161) Loss 9.5429 (13.2311) Acc@1 2.344 (1.360) Acc@5 14.844 (5.666) Epoch: [1][1024/10010] Time 0.432 (2.552) Data 0.000 (2.167) Loss 13.3585 (13.0788) Acc@1 4.688 (1.572) Acc@5 15.625 (6.357) Epoch: [1][1152/10010] Time 0.427 (2.573) Data 0.000 (2.189) Loss 10.7350 (12.9583) Acc@1 3.125 (1.752) Acc@5 14.844 (6.968) Epoch: [1][1280/10010] Time 0.426 (2.582) Data 0.000 (2.197) Loss 11.1195 (12.8287) Acc@1 1.562 (1.976) Acc@5 10.938 (7.657) Epoch: [1][1408/10010] Time 0.428 (2.596) Data 0.000 (2.210) Loss 11.9660 (12.6995) Acc@1 8.594 (2.179) Acc@5 22.656 (8.289) Epoch: [1][1536/10010] Time 0.428 (2.617) Data 0.000 (2.232) Loss 12.3775 (12.5861) Acc@1 5.469 (2.372) Acc@5 19.531 (8.919) Epoch: [1][1664/10010] Time 0.429 (2.630) Data 0.000 (2.245) Loss 13.2347 (12.4921) Acc@1 3.906 (2.576) Acc@5 15.625 (9.503) Epoch: [1][1792/10010] Time 0.428 (2.644) Data 0.000 (2.260) Loss 13.2709 (12.3985) Acc@1 5.469 (2.781) Acc@5 20.312 (10.083) Epoch: [1][1920/10010] Time 0.429 (2.654) Data 0.000 (2.272) Loss 12.1958 (12.2974) Acc@1 3.906 (2.996) Acc@5 12.500 (10.677) Epoch: [1][2048/10010] Time 0.428 (2.660) Data 0.000 (2.278) Loss 11.1101 (12.1962) Acc@1 8.594 (3.199) Acc@5 19.531 (11.232) Epoch: [1][2176/10010] Time 0.427 (2.668) Data 0.000 (2.287) Loss 10.9185 (12.1079) Acc@1 8.594 (3.425) Acc@5 23.438 (11.803) Epoch: [1][2304/10010] Time 0.427 (2.676) Data 0.000 (2.294) Loss 9.6112 (12.0138) Acc@1 6.250 (3.621) Acc@5 20.312 (12.326) Epoch: [1][2432/10010] Time 0.428 (2.681) Data 0.000 (2.298) Loss 10.1364 (11.9359) Acc@1 5.469 (3.829) Acc@5 17.969 (12.881) Epoch: [1][2560/10010] Time 0.429 (2.690) Data 0.000 (2.307) Loss 11.3065 (11.8573) Acc@1 10.156 (4.054) Acc@5 21.875 (13.425) Epoch: [1][2688/10010] Time 0.427 (2.695) Data 0.000 (2.312) Loss 9.2579 (11.7760) Acc@1 9.375 (4.267) Acc@5 24.219 (13.953) Epoch: [1][2816/10010] Time 0.429 (2.700) Data 0.000 (2.316) Loss 9.7976 (11.7000) Acc@1 7.031 (4.478) Acc@5 24.219 (14.497) Epoch: [1][2944/10010] Time 0.429 (2.703) Data 0.000 (2.319) Loss 10.7845 (11.6275) Acc@1 8.594 (4.686) Acc@5 31.250 (14.988) Epoch: [1][3072/10010] Time 0.429 (2.707) Data 0.000 (2.322) Loss 10.2352 (11.5538) Acc@1 7.812 (4.890) Acc@5 25.781 (15.484) Epoch: [1][3200/10010] Time 0.427 (2.711) Data 0.000 (2.326) Loss 8.9137 (11.4865) Acc@1 7.812 (5.096) Acc@5 28.906 (15.967) Epoch: [1][3328/10010] Time 0.427 (2.716) Data 0.000 (2.333) Loss 9.0424 (11.4188) Acc@1 14.844 (5.327) Acc@5 30.469 (16.466) Epoch: [1][3456/10010] Time 0.425 (2.719) Data 0.000 (2.336) Loss 8.0848 (11.3500) Acc@1 11.719 (5.547) Acc@5 31.250 (16.957) Epoch: [1][3584/10010] Time 0.428 (2.721) Data 0.000 (2.339) Loss 9.6352 (11.2820) Acc@1 8.594 (5.767) Acc@5 31.250 (17.437) Epoch: [1][3712/10010] Time 0.424 (2.737) Data 0.000 (2.355) Loss 9.3809 (11.2152) Acc@1 12.500 (5.973) Acc@5 34.375 (17.908) Epoch: [1][3840/10010] Time 0.428 (2.750) Data 0.000 (2.367) Loss 9.5469 (11.1501) Acc@1 17.188 (6.177) Acc@5 35.156 (18.367) Epoch: [1][3968/10010] Time 14.771 (2.754) Data 14.521 (2.373) Loss 8.2969 (11.0877) Acc@1 21.094 (6.392) Acc@5 40.625 (18.829) Epoch: [1][4096/10010] Time 19.031 (2.757) Data 18.763 (2.375) Loss 10.2194 (11.0260) Acc@1 11.719 (6.595) Acc@5 36.719 (19.264) Epoch: [1][4224/10010] Time 15.243 (2.758) Data 14.990 (2.376) Loss 9.9028 (10.9593) Acc@1 16.406 (6.821) Acc@5 37.500 (19.714) Epoch: [1][4352/10010] Time 0.427 (2.758) Data 0.000 (2.375) Loss 9.0877 (10.8982) Acc@1 10.938 (7.043) Acc@5 35.938 (20.154) Epoch: [1][4480/10010] Time 0.428 (2.759) Data 0.000 (2.376) Loss 8.4131 (10.8390) Acc@1 17.188 (7.257) Acc@5 40.625 (20.591) Epoch: [1][4608/10010] Time 0.428 (2.760) Data 0.000 (2.378) Loss 8.1081 (10.7796) Acc@1 17.188 (7.478) Acc@5 41.406 (21.022) Epoch: [1][4736/10010] Time 0.427 (2.763) Data 0.000 (2.381) Loss 8.6183 (10.7280) Acc@1 16.406 (7.686) Acc@5 37.500 (21.441) Epoch: [1][4864/10010] Time 0.428 (2.763) Data 0.000 (2.381) Loss 8.0330 (10.6725) Acc@1 21.094 (7.886) Acc@5 46.094 (21.837) Epoch: [1][4992/10010] Time 0.432 (2.765) Data 0.000 (2.383) Loss 8.0333 (10.6177) Acc@1 14.062 (8.091) Acc@5 32.031 (22.243) Epoch: [1][5120/10010] Time 0.427 (2.767) Data 0.000 (2.386) Loss 8.0960 (10.5613) Acc@1 17.969 (8.296) Acc@5 41.406 (22.637) Epoch: [1][5248/10010] Time 0.437 (2.768) Data 0.000 (2.387) Loss 10.3152 (10.5071) Acc@1 14.062 (8.490) Acc@5 42.969 (23.038) Epoch: [1][5376/10010] Time 0.427 (2.769) Data 0.000 (2.388) Loss 7.0815 (10.4570) Acc@1 21.094 (8.688) Acc@5 46.875 (23.417) Epoch: [1][5504/10010] Time 0.429 (2.771) Data 0.000 (2.390) Loss 7.7832 (10.4077) Acc@1 18.750 (8.889) Acc@5 37.500 (23.800) Epoch: [1][5632/10010] Time 0.430 (2.772) Data 0.000 (2.392) Loss 7.5529 (10.3525) Acc@1 20.312 (9.094) Acc@5 41.406 (24.189) Epoch: [1][5760/10010] Time 0.427 (2.773) Data 0.000 (2.393) Loss 8.4238 (10.3028) Acc@1 15.625 (9.299) Acc@5 38.281 (24.568) Epoch: [1][5888/10010] Time 0.432 (2.775) Data 0.000 (2.395) Loss 8.2156 (10.2520) Acc@1 18.750 (9.495) Acc@5 45.312 (24.929) Epoch: [1][6016/10010] Time 0.427 (2.775) Data 0.000 (2.395) Loss 6.8980 (10.2046) Acc@1 10.938 (9.690) Acc@5 39.062 (25.283) Epoch: [1][6144/10010] Time 0.427 (2.776) Data 0.000 (2.396) Loss 7.0476 (10.1565) Acc@1 17.969 (9.886) Acc@5 39.844 (25.650) Epoch: [1][6272/10010] Time 0.430 (2.776) Data 0.000 (2.396) Loss 6.9353 (10.1123) Acc@1 21.875 (10.068) Acc@5 47.656 (25.985) Epoch: [1][6400/10010] Time 0.430 (2.777) Data 0.000 (2.397) Loss 7.1662 (10.0699) Acc@1 20.312 (10.249) Acc@5 42.188 (26.319) Epoch: [1][6528/10010] Time 0.428 (2.779) Data 0.000 (2.399) Loss 8.6659 (10.0253) Acc@1 17.969 (10.437) Acc@5 41.406 (26.658) Epoch: [1][6656/10010] Time 0.427 (2.778) Data 0.000 (2.398) Loss 7.8911 (9.9847) Acc@1 24.219 (10.616) Acc@5 42.188 (26.972) Epoch: [1][6784/10010] Time 0.428 (2.778) Data 0.000 (2.398) Loss 7.2867 (9.9397) Acc@1 28.125 (10.805) Acc@5 56.250 (27.306) Epoch: [1][6912/10010] Time 15.802 (2.779) Data 15.550 (2.400) Loss 6.6393 (9.8960) Acc@1 13.281 (10.987) Acc@5 35.938 (27.635) Epoch: [1][7040/10010] Time 17.926 (2.780) Data 17.665 (2.400) Loss 7.5947 (9.8533) Acc@1 21.875 (11.172) Acc@5 46.094 (27.955) Epoch: [1][7168/10010] Time 17.781 (2.780) Data 17.528 (2.400) Loss 6.9177 (9.8136) Acc@1 21.094 (11.351) Acc@5 45.312 (28.273) Epoch: [1][7296/10010] Time 17.527 (2.781) Data 17.265 (2.400) Loss 7.7678 (9.7743) Acc@1 18.750 (11.522) Acc@5 42.969 (28.572) Epoch: [1][7424/10010] Time 19.432 (2.782) Data 19.173 (2.402) Loss 7.9332 (9.7351) Acc@1 23.438 (11.700) Acc@5 50.000 (28.874) Epoch: [1][7552/10010] Time 17.467 (2.782) Data 17.203 (2.401) Loss 7.6893 (9.6940) Acc@1 22.656 (11.887) Acc@5 47.656 (29.188) Epoch: [1][7680/10010] Time 17.817 (2.782) Data 17.555 (2.401) Loss 6.7318 (9.6543) Acc@1 25.781 (12.051) Acc@5 46.875 (29.478) Epoch: [1][7808/10010] Time 17.593 (2.782) Data 17.328 (2.400) Loss 6.4962 (9.6153) Acc@1 21.875 (12.228) Acc@5 44.531 (29.770) Epoch: [1][7936/10010] Time 0.429 (2.782) Data 0.000 (2.400) Loss 6.8113 (9.5767) Acc@1 23.438 (12.404) Acc@5 46.875 (30.066) Epoch: [1][8064/10010] Time 0.427 (2.783) Data 0.000 (2.402) Loss 6.9980 (9.5405) Acc@1 20.312 (12.583) Acc@5 45.312 (30.356) Epoch: [1][8192/10010] Time 0.427 (2.783) Data 0.000 (2.402) Loss 7.9975 (9.5054) Acc@1 24.219 (12.750) Acc@5 49.219 (30.635) Epoch: [1][8320/10010] Time 0.427 (2.783) Data 0.000 (2.402) Loss 7.0064 (9.4687) Acc@1 24.219 (12.919) Acc@5 44.531 (30.910) Epoch: [1][8448/10010] Time 0.427 (2.785) Data 0.000 (2.404) Loss 7.0096 (9.4320) Acc@1 21.094 (13.092) Acc@5 49.219 (31.185) Epoch: [1][8576/10010] Time 0.429 (2.785) Data 0.000 (2.404) Loss 7.2485 (9.3975) Acc@1 22.656 (13.253) Acc@5 51.562 (31.456) Epoch: [1][8704/10010] Time 0.427 (2.786) Data 0.000 (2.405) Loss 8.0318 (9.3637) Acc@1 25.000 (13.416) Acc@5 52.344 (31.717) Epoch: [1][8832/10010] Time 0.426 (2.787) Data 0.000 (2.406) Loss 7.6812 (9.3286) Acc@1 19.531 (13.572) Acc@5 42.188 (31.976) Epoch: [1][8960/10010] Time 0.427 (2.787) Data 0.000 (2.406) Loss 6.5118 (9.2929) Acc@1 26.562 (13.735) Acc@5 51.562 (32.238) Epoch: [1][9088/10010] Time 0.427 (2.788) Data 0.000 (2.406) Loss 7.4556 (9.2585) Acc@1 27.344 (13.901) Acc@5 53.906 (32.497) Epoch: [1][9216/10010] Time 0.428 (2.787) Data 0.000 (2.405) Loss 6.5463 (9.2264) Acc@1 26.562 (14.053) Acc@5 51.562 (32.747) Epoch: [1][9344/10010] Time 0.426 (2.788) Data 0.000 (2.406) Loss 6.0262 (9.1929) Acc@1 29.688 (14.207) Acc@5 57.031 (33.005) Epoch: [1][9472/10010] Time 0.433 (2.788) Data 0.000 (2.406) Loss 7.2222 (9.1606) Acc@1 25.000 (14.363) Acc@5 48.438 (33.257) Epoch: [1][9600/10010] Time 0.433 (2.788) Data 0.000 (2.406) Loss 6.4898 (9.1287) Acc@1 21.875 (14.515) Acc@5 50.781 (33.494) Epoch: [1][9728/10010] Time 0.428 (2.789) Data 0.000 (2.406) Loss 7.7649 (9.0955) Acc@1 26.562 (14.669) Acc@5 60.938 (33.748) Epoch: [1][9856/10010] Time 0.426 (2.790) Data 0.000 (2.407) Loss 5.6638 (9.0646) Acc@1 29.688 (14.819) Acc@5 53.125 (33.993) Epoch: [1][9984/10010] Time 0.427 (2.789) Data 0.000 (2.406) Loss 6.1442 (9.0355) Acc@1 29.688 (14.967) Acc@5 53.906 (34.216)
I do not know why using ImageFolderLMDB is slower than the original pytorch imagefolder? As seen below, the data loader is still very slow. ==> training... Epoch: [1][0/10010] Time 20.673 (20.673) Data 19.890 (19.890) Loss 18.0700 (18.0700) Acc@1 0.000 (0.000) Acc@5 0.000 (0.000) Epoch: [1][128/10010] Time 5.867 (2.609) Data 5.624 (2.248) Loss 14.5102 (14.8609) Acc@1 0.000 (0.212) Acc@5 2.344 (1.163) Epoch: [1][256/10010] Time 0.427 (2.561) Data 0.000 (2.194) Loss 13.6098 (14.3173) Acc@1 0.781 (0.395) Acc@5 4.688 (2.022) Epoch: [1][384/10010] Time 1.767 (2.547) Data 1.533 (2.185) Loss 13.1298 (14.0282) Acc@1 0.781 (0.611) Acc@5 4.688 (2.713) Epoch: [1][512/10010] Time 0.427 (2.536) Data 0.000 (2.168) Loss 13.2681 (13.7872) Acc@1 1.562 (0.778) Acc@5 7.031 (3.513) Epoch: [1][640/10010] Time 0.428 (2.546) Data 0.000 (2.171) Loss 13.5265 (13.5746) Acc@1 0.781 (0.963) Acc@5 5.469 (4.254) Epoch: [1][768/10010] Time 0.428 (2.528) Data 0.000 (2.148) Loss 13.7340 (13.4076) Acc@1 2.344 (1.185) Acc@5 11.719 (5.024) Epoch: [1][896/10010] Time 0.428 (2.545) Data 0.000 (2.161) Loss 9.5429 (13.2311) Acc@1 2.344 (1.360) Acc@5 14.844 (5.666) Epoch: [1][1024/10010] Time 0.432 (2.552) Data 0.000 (2.167) Loss 13.3585 (13.0788) Acc@1 4.688 (1.572) Acc@5 15.625 (6.357) Epoch: [1][1152/10010] Time 0.427 (2.573) Data 0.000 (2.189) Loss 10.7350 (12.9583) Acc@1 3.125 (1.752) Acc@5 14.844 (6.968) Epoch: [1][1280/10010] Time 0.426 (2.582) Data 0.000 (2.197) Loss 11.1195 (12.8287) Acc@1 1.562 (1.976) Acc@5 10.938 (7.657) Epoch: [1][1408/10010] Time 0.428 (2.596) Data 0.000 (2.210) Loss 11.9660 (12.6995) Acc@1 8.594 (2.179) Acc@5 22.656 (8.289) Epoch: [1][1536/10010] Time 0.428 (2.617) Data 0.000 (2.232) Loss 12.3775 (12.5861) Acc@1 5.469 (2.372) Acc@5 19.531 (8.919) Epoch: [1][1664/10010] Time 0.429 (2.630) Data 0.000 (2.245) Loss 13.2347 (12.4921) Acc@1 3.906 (2.576) Acc@5 15.625 (9.503) Epoch: [1][1792/10010] Time 0.428 (2.644) Data 0.000 (2.260) Loss 13.2709 (12.3985) Acc@1 5.469 (2.781) Acc@5 20.312 (10.083) Epoch: [1][1920/10010] Time 0.429 (2.654) Data 0.000 (2.272) Loss 12.1958 (12.2974) Acc@1 3.906 (2.996) Acc@5 12.500 (10.677) Epoch: [1][2048/10010] Time 0.428 (2.660) Data 0.000 (2.278) Loss 11.1101 (12.1962) Acc@1 8.594 (3.199) Acc@5 19.531 (11.232) Epoch: [1][2176/10010] Time 0.427 (2.668) Data 0.000 (2.287) Loss 10.9185 (12.1079) Acc@1 8.594 (3.425) Acc@5 23.438 (11.803) Epoch: [1][2304/10010] Time 0.427 (2.676) Data 0.000 (2.294) Loss 9.6112 (12.0138) Acc@1 6.250 (3.621) Acc@5 20.312 (12.326) Epoch: [1][2432/10010] Time 0.428 (2.681) Data 0.000 (2.298) Loss 10.1364 (11.9359) Acc@1 5.469 (3.829) Acc@5 17.969 (12.881) Epoch: [1][2560/10010] Time 0.429 (2.690) Data 0.000 (2.307) Loss 11.3065 (11.8573) Acc@1 10.156 (4.054) Acc@5 21.875 (13.425) Epoch: [1][2688/10010] Time 0.427 (2.695) Data 0.000 (2.312) Loss 9.2579 (11.7760) Acc@1 9.375 (4.267) Acc@5 24.219 (13.953) Epoch: [1][2816/10010] Time 0.429 (2.700) Data 0.000 (2.316) Loss 9.7976 (11.7000) Acc@1 7.031 (4.478) Acc@5 24.219 (14.497) Epoch: [1][2944/10010] Time 0.429 (2.703) Data 0.000 (2.319) Loss 10.7845 (11.6275) Acc@1 8.594 (4.686) Acc@5 31.250 (14.988) Epoch: [1][3072/10010] Time 0.429 (2.707) Data 0.000 (2.322) Loss 10.2352 (11.5538) Acc@1 7.812 (4.890) Acc@5 25.781 (15.484) Epoch: [1][3200/10010] Time 0.427 (2.711) Data 0.000 (2.326) Loss 8.9137 (11.4865) Acc@1 7.812 (5.096) Acc@5 28.906 (15.967) Epoch: [1][3328/10010] Time 0.427 (2.716) Data 0.000 (2.333) Loss 9.0424 (11.4188) Acc@1 14.844 (5.327) Acc@5 30.469 (16.466) Epoch: [1][3456/10010] Time 0.425 (2.719) Data 0.000 (2.336) Loss 8.0848 (11.3500) Acc@1 11.719 (5.547) Acc@5 31.250 (16.957) Epoch: [1][3584/10010] Time 0.428 (2.721) Data 0.000 (2.339) Loss 9.6352 (11.2820) Acc@1 8.594 (5.767) Acc@5 31.250 (17.437) Epoch: [1][3712/10010] Time 0.424 (2.737) Data 0.000 (2.355) Loss 9.3809 (11.2152) Acc@1 12.500 (5.973) Acc@5 34.375 (17.908) Epoch: [1][3840/10010] Time 0.428 (2.750) Data 0.000 (2.367) Loss 9.5469 (11.1501) Acc@1 17.188 (6.177) Acc@5 35.156 (18.367) Epoch: [1][3968/10010] Time 14.771 (2.754) Data 14.521 (2.373) Loss 8.2969 (11.0877) Acc@1 21.094 (6.392) Acc@5 40.625 (18.829) Epoch: [1][4096/10010] Time 19.031 (2.757) Data 18.763 (2.375) Loss 10.2194 (11.0260) Acc@1 11.719 (6.595) Acc@5 36.719 (19.264) Epoch: [1][4224/10010] Time 15.243 (2.758) Data 14.990 (2.376) Loss 9.9028 (10.9593) Acc@1 16.406 (6.821) Acc@5 37.500 (19.714) Epoch: [1][4352/10010] Time 0.427 (2.758) Data 0.000 (2.375) Loss 9.0877 (10.8982) Acc@1 10.938 (7.043) Acc@5 35.938 (20.154) Epoch: [1][4480/10010] Time 0.428 (2.759) Data 0.000 (2.376) Loss 8.4131 (10.8390) Acc@1 17.188 (7.257) Acc@5 40.625 (20.591) Epoch: [1][4608/10010] Time 0.428 (2.760) Data 0.000 (2.378) Loss 8.1081 (10.7796) Acc@1 17.188 (7.478) Acc@5 41.406 (21.022) Epoch: [1][4736/10010] Time 0.427 (2.763) Data 0.000 (2.381) Loss 8.6183 (10.7280) Acc@1 16.406 (7.686) Acc@5 37.500 (21.441) Epoch: [1][4864/10010] Time 0.428 (2.763) Data 0.000 (2.381) Loss 8.0330 (10.6725) Acc@1 21.094 (7.886) Acc@5 46.094 (21.837) Epoch: [1][4992/10010] Time 0.432 (2.765) Data 0.000 (2.383) Loss 8.0333 (10.6177) Acc@1 14.062 (8.091) Acc@5 32.031 (22.243) Epoch: [1][5120/10010] Time 0.427 (2.767) Data 0.000 (2.386) Loss 8.0960 (10.5613) Acc@1 17.969 (8.296) Acc@5 41.406 (22.637) Epoch: [1][5248/10010] Time 0.437 (2.768) Data 0.000 (2.387) Loss 10.3152 (10.5071) Acc@1 14.062 (8.490) Acc@5 42.969 (23.038) Epoch: [1][5376/10010] Time 0.427 (2.769) Data 0.000 (2.388) Loss 7.0815 (10.4570) Acc@1 21.094 (8.688) Acc@5 46.875 (23.417) Epoch: [1][5504/10010] Time 0.429 (2.771) Data 0.000 (2.390) Loss 7.7832 (10.4077) Acc@1 18.750 (8.889) Acc@5 37.500 (23.800) Epoch: [1][5632/10010] Time 0.430 (2.772) Data 0.000 (2.392) Loss 7.5529 (10.3525) Acc@1 20.312 (9.094) Acc@5 41.406 (24.189) Epoch: [1][5760/10010] Time 0.427 (2.773) Data 0.000 (2.393) Loss 8.4238 (10.3028) Acc@1 15.625 (9.299) Acc@5 38.281 (24.568) Epoch: [1][5888/10010] Time 0.432 (2.775) Data 0.000 (2.395) Loss 8.2156 (10.2520) Acc@1 18.750 (9.495) Acc@5 45.312 (24.929) Epoch: [1][6016/10010] Time 0.427 (2.775) Data 0.000 (2.395) Loss 6.8980 (10.2046) Acc@1 10.938 (9.690) Acc@5 39.062 (25.283) Epoch: [1][6144/10010] Time 0.427 (2.776) Data 0.000 (2.396) Loss 7.0476 (10.1565) Acc@1 17.969 (9.886) Acc@5 39.844 (25.650) Epoch: [1][6272/10010] Time 0.430 (2.776) Data 0.000 (2.396) Loss 6.9353 (10.1123) Acc@1 21.875 (10.068) Acc@5 47.656 (25.985) Epoch: [1][6400/10010] Time 0.430 (2.777) Data 0.000 (2.397) Loss 7.1662 (10.0699) Acc@1 20.312 (10.249) Acc@5 42.188 (26.319) Epoch: [1][6528/10010] Time 0.428 (2.779) Data 0.000 (2.399) Loss 8.6659 (10.0253) Acc@1 17.969 (10.437) Acc@5 41.406 (26.658) Epoch: [1][6656/10010] Time 0.427 (2.778) Data 0.000 (2.398) Loss 7.8911 (9.9847) Acc@1 24.219 (10.616) Acc@5 42.188 (26.972) Epoch: [1][6784/10010] Time 0.428 (2.778) Data 0.000 (2.398) Loss 7.2867 (9.9397) Acc@1 28.125 (10.805) Acc@5 56.250 (27.306) Epoch: [1][6912/10010] Time 15.802 (2.779) Data 15.550 (2.400) Loss 6.6393 (9.8960) Acc@1 13.281 (10.987) Acc@5 35.938 (27.635) Epoch: [1][7040/10010] Time 17.926 (2.780) Data 17.665 (2.400) Loss 7.5947 (9.8533) Acc@1 21.875 (11.172) Acc@5 46.094 (27.955) Epoch: [1][7168/10010] Time 17.781 (2.780) Data 17.528 (2.400) Loss 6.9177 (9.8136) Acc@1 21.094 (11.351) Acc@5 45.312 (28.273) Epoch: [1][7296/10010] Time 17.527 (2.781) Data 17.265 (2.400) Loss 7.7678 (9.7743) Acc@1 18.750 (11.522) Acc@5 42.969 (28.572) Epoch: [1][7424/10010] Time 19.432 (2.782) Data 19.173 (2.402) Loss 7.9332 (9.7351) Acc@1 23.438 (11.700) Acc@5 50.000 (28.874) Epoch: [1][7552/10010] Time 17.467 (2.782) Data 17.203 (2.401) Loss 7.6893 (9.6940) Acc@1 22.656 (11.887) Acc@5 47.656 (29.188) Epoch: [1][7680/10010] Time 17.817 (2.782) Data 17.555 (2.401) Loss 6.7318 (9.6543) Acc@1 25.781 (12.051) Acc@5 46.875 (29.478) Epoch: [1][7808/10010] Time 17.593 (2.782) Data 17.328 (2.400) Loss 6.4962 (9.6153) Acc@1 21.875 (12.228) Acc@5 44.531 (29.770) Epoch: [1][7936/10010] Time 0.429 (2.782) Data 0.000 (2.400) Loss 6.8113 (9.5767) Acc@1 23.438 (12.404) Acc@5 46.875 (30.066) Epoch: [1][8064/10010] Time 0.427 (2.783) Data 0.000 (2.402) Loss 6.9980 (9.5405) Acc@1 20.312 (12.583) Acc@5 45.312 (30.356) Epoch: [1][8192/10010] Time 0.427 (2.783) Data 0.000 (2.402) Loss 7.9975 (9.5054) Acc@1 24.219 (12.750) Acc@5 49.219 (30.635) Epoch: [1][8320/10010] Time 0.427 (2.783) Data 0.000 (2.402) Loss 7.0064 (9.4687) Acc@1 24.219 (12.919) Acc@5 44.531 (30.910) Epoch: [1][8448/10010] Time 0.427 (2.785) Data 0.000 (2.404) Loss 7.0096 (9.4320) Acc@1 21.094 (13.092) Acc@5 49.219 (31.185) Epoch: [1][8576/10010] Time 0.429 (2.785) Data 0.000 (2.404) Loss 7.2485 (9.3975) Acc@1 22.656 (13.253) Acc@5 51.562 (31.456) Epoch: [1][8704/10010] Time 0.427 (2.786) Data 0.000 (2.405) Loss 8.0318 (9.3637) Acc@1 25.000 (13.416) Acc@5 52.344 (31.717) Epoch: [1][8832/10010] Time 0.426 (2.787) Data 0.000 (2.406) Loss 7.6812 (9.3286) Acc@1 19.531 (13.572) Acc@5 42.188 (31.976) Epoch: [1][8960/10010] Time 0.427 (2.787) Data 0.000 (2.406) Loss 6.5118 (9.2929) Acc@1 26.562 (13.735) Acc@5 51.562 (32.238) Epoch: [1][9088/10010] Time 0.427 (2.788) Data 0.000 (2.406) Loss 7.4556 (9.2585) Acc@1 27.344 (13.901) Acc@5 53.906 (32.497) Epoch: [1][9216/10010] Time 0.428 (2.787) Data 0.000 (2.405) Loss 6.5463 (9.2264) Acc@1 26.562 (14.053) Acc@5 51.562 (32.747) Epoch: [1][9344/10010] Time 0.426 (2.788) Data 0.000 (2.406) Loss 6.0262 (9.1929) Acc@1 29.688 (14.207) Acc@5 57.031 (33.005) Epoch: [1][9472/10010] Time 0.433 (2.788) Data 0.000 (2.406) Loss 7.2222 (9.1606) Acc@1 25.000 (14.363) Acc@5 48.438 (33.257) Epoch: [1][9600/10010] Time 0.433 (2.788) Data 0.000 (2.406) Loss 6.4898 (9.1287) Acc@1 21.875 (14.515) Acc@5 50.781 (33.494) Epoch: [1][9728/10010] Time 0.428 (2.789) Data 0.000 (2.406) Loss 7.7649 (9.0955) Acc@1 26.562 (14.669) Acc@5 60.938 (33.748) Epoch: [1][9856/10010] Time 0.426 (2.790) Data 0.000 (2.407) Loss 5.6638 (9.0646) Acc@1 29.688 (14.819) Acc@5 53.125 (33.993) Epoch: [1][9984/10010] Time 0.427 (2.789) Data 0.000 (2.406) Loss 6.1442 (9.0355) Acc@1 29.688 (14.967) Acc@5 53.906 (34.216)