aiprogrammer / advmix Goto Github PK

    for i, (input, target, target_weight, meta) in tqdm(enumerate(train_loader)):

        data_time.update(time.time() - end)

        outputs = model(input)

        target = target[0].cuda(non_blocking=True)
        target_hm = target
        target_weight = target_weight.cuda(non_blocking=True)

        loss = criterion(outputs, target, target_weight)

        # compute gradient and do update step
        optimizer.zero_grad()
        loss.backward()
        optimizer.step()

        if isinstance(input, list):
            input = input[0].cuda(non_blocking=True)
            target = target[0].cuda(non_blocking=True)
            target_hm = target
            target_weight = target_weight[0].cuda(non_blocking=True)
            meta = meta[0]

        outputs = model(input)

        # target = target[0].cuda(non_blocking=True)
        # target_hm = target
        # target_weight = target_weight.cuda(non_blocking=True)

        loss = criterion(outputs, target, target_weight)

        # compute gradient and do update step
        optimizer.zero_grad()
        loss.backward()
        optimizer.step()

TEST:
  BATCH_SIZE_PER_GPU: 32
  COCO_BBOX_FILE: 'data/coco/person_detection_results/COCO_val2017_detections_AP_H_56_person.json'
  BBOX_THRE: 1.0
  IMAGE_THRE: 0.0
  IN_VIS_THRE: 0.2
  MODEL_FILE: ''
  NMS_THRE: 1.0
  OKS_THRE: 0.9
  FLIP_TEST: true
  POST_PROCESS: true
  SHIFT_HEATMAP: true
  USE_GT_BBOX: true