add build_targets_optim

[BBuf]

link Oneflow-Inc/oneflow#9536

[ccssu]

精度验证结果正常 在20个epochs下精度差距 - 0.44599999999999795 ，具体数据如下：

启动指令: python -m oneflow.distributed.launch --nproc_per_node 4 train.py --data data/coco.yaml --weights ' ' --cfg models/yolov5n.yaml --batch 128 --bbox_iou_optim --multi_tensor_optimizer --build_targets_optim --device 4,5,6,7 --epochs 20

	epoch	batch	gpu	bbox_iou_optim	multi_tensor_optimizer	[email protected]	[email protected]:.95
baseline	19	128	4	False	False	31.198999999999998	17.407
本次实验	19	128	4	True	True	30.705	16.961000000000002
本次-baseline	19					-0.4939999999999998	-0.44599999999999795

python -m oneflow.distributed.launch --nproc_per_node 4 train.py --data data/coco.yaml --weights ' ' --cfg models/yolov5n.yaml --batch 128 --bbox_iou_optim --multi_tensor_optimizer --build_targets_optim --device 4,5,6,7 --epochs 20

全部result.csv

               epoch,      train/box_loss,      train/obj_loss,      train/cls_loss,   metrics/precision,      metrics/recall,     metrics/mAP_0.5,metrics/mAP_0.5:0.95,        val/box_loss,        val/obj_loss,        val/cls_loss,               x/lr0,               x/lr1,               x/lr2
                   0,            0.093182,            0.076606,            0.083707,           0.0034488,            0.068622,           0.0041244,             0.00135,            0.082852,            0.057275,            0.079476,            0.070032,           0.0033297,           0.0033297
                   1,            0.076017,            0.077694,            0.076859,             0.61515,             0.02394,            0.016233,           0.0062205,            0.074792,             0.05561,            0.069954,            0.039703,           0.0063332,           0.0063332
                   2,            0.070362,            0.075674,            0.067088,             0.45244,            0.075191,             0.04722,            0.019736,            0.068904,            0.057591,            0.058687,           0.0090428,           0.0090068,           0.0090068
                   3,            0.066933,            0.076041,            0.058288,             0.37677,             0.12383,            0.092613,            0.040804,            0.064703,            0.054779,            0.050293,            0.008515,            0.008515,            0.008515
                   4,            0.064511,            0.074846,            0.052813,             0.39496,             0.17134,             0.13238,            0.063082,            0.062339,            0.054191,            0.044835,             0.00802,             0.00802,             0.00802
                   5,            0.063179,            0.073648,            0.049642,             0.36816,             0.19597,             0.16268,            0.079857,            0.060779,            0.053623,            0.041664,            0.007525,            0.007525,            0.007525
                   6,            0.062163,            0.072918,             0.04724,             0.33008,             0.21392,             0.18523,            0.093926,            0.059704,            0.053372,            0.039742,             0.00703,             0.00703,             0.00703
                   7,            0.061349,            0.073734,            0.045705,             0.38091,             0.23731,             0.20934,             0.10881,            0.058611,            0.053002,            0.037711,            0.006535,            0.006535,            0.006535
                   8,            0.060651,            0.073988,            0.044348,             0.38831,              0.2466,             0.22479,             0.11731,             0.05803,            0.052564,            0.036483,             0.00604,             0.00604,             0.00604
                   9,            0.059962,            0.073595,            0.043254,             0.41124,             0.26339,             0.24331,             0.12977,             0.05743,            0.052763,            0.035348,            0.005545,            0.005545,            0.005545
                  10,            0.059417,            0.072536,            0.042297,             0.41421,             0.27602,             0.25506,             0.13644,            0.057048,            0.052061,            0.034245,             0.00505,             0.00505,             0.00505
                  11,            0.058873,            0.072241,            0.041424,             0.39929,             0.28124,             0.26624,             0.14363,              0.0565,            0.052592,            0.033551,            0.004555,            0.004555,            0.004555
                  12,            0.058494,            0.071352,            0.040809,             0.44046,             0.28721,             0.27507,             0.14928,            0.056286,            0.051716,             0.03283,             0.00406,             0.00406,             0.00406
                  13,            0.057861,            0.072136,            0.039713,             0.42786,             0.29419,             0.28141,              0.1534,            0.056102,            0.051525,             0.03227,            0.003565,            0.003565,            0.003565
                  14,            0.057516,            0.072301,            0.038914,             0.45416,             0.29648,             0.28761,             0.15725,            0.055784,            0.051387,            0.031714,             0.00307,             0.00307,             0.00307
                  15,            0.056954,            0.071751,            0.038346,             0.45428,             0.30127,             0.29314,             0.16077,            0.055604,            0.051567,            0.031298,            0.002575,            0.002575,            0.002575
                  16,             0.05655,            0.071426,             0.03797,              0.4524,             0.30718,             0.29784,             0.16381,            0.055435,            0.051677,            0.030979,             0.00208,             0.00208,             0.00208
                  17,            0.056053,            0.071739,            0.037025,             0.44086,             0.31075,             0.30175,              0.1663,            0.055263,            0.051571,            0.030707,            0.001585,            0.001585,            0.001585
                  18,            0.055577,            0.068904,            0.036529,             0.44778,             0.31155,             0.30443,             0.16825,            0.055255,            0.051014,            0.030517,             0.00109,             0.00109,             0.00109
                  19,            0.054898,             0.07117,            0.035509,             0.45353,             0.31224,             0.30705,             0.16961,            0.055114,            0.051115,            0.030329,            0.000595,            0.000595,            0.000595

[BBuf]

可以再跑一次，我感觉0.5好像差得有点多不知道是不是bug。 @ccssu

[ccssu]

可以再跑一次，我感觉0.5好像差得有点多不知道是不是bug。 @ccssu

精度验证结果大致正常 在 300 个epochs下精度差距 -0.42499999999999716 ，具体数据如下：

启动指令: python -m oneflow.distributed.launch --nproc_per_node 4 train.py --data data/coco.yaml --weights ' ' --cfg models/yolov5n.yaml --batch 128 --bbox_iou_optim --multi_tensor_optimizer --build_targets_optim --device 4,5,6,7 --epochs 300

	epoch	batch	gpu	bbox_iou_optim	multi_tensor_optimizer	[email protected]	[email protected]:.95
baseline	299	128	4	False	False	45.115	27.431
本次实验	299	128	4	True	True	44.373000000000005	27.006000000000004
本次-baseline	299					-0.7419999999999973	-0.42499999999999716

python -m oneflow.distributed.launch --nproc_per_node 4 train.py --data data/coco.yaml --weights ' ' --cfg models/yolov5n.yaml --batch 128 --bbox_iou_optim --multi_tensor_optimizer --build_targets_optim --device 4,5,6,7

[ccssu]

oneflow版本： f59f6dacbe (HEAD -> fused_get_target_offsets,
机器a100
wandb数据：https://wandb.ai/wearmheart/YOLOv5/runs/3l3ku6me?workspace=user-wearmheart
在 300 个epochs下精度差距 -0.42499999999999716

启动指令: python -m oneflow.distributed.launch --nproc_per_node 4 train.py --data data/coco.yaml --weights ' ' --cfg models/yolov5n.yaml --batch 128 --bbox_iou_optim --multi_tensor_optimizer --build_targets_optim --device 4,5,6,7 --epochs 300

	epoch	batch	gpu	bbox_iou_optim	multi_tensor_optimizer	[email protected]	[email protected]:.95
baseline	299	128	4	False	False	45.115	27.431
本次实验	299	128	4	True	True	44.468	26.825
本次-baseline	299					-0.6469999999999985	-0.6060000000000016

python -m oneflow.distributed.launch --nproc_per_node 4 train.py --data data/coco.yaml --weights ' ' --cfg models/yolov5n.yaml --batch 128 --bbox_iou_optim --multi_tensor_optimizer --build_targets_optim --device 4,5,6,7

baseline与current实验对照趋势图