Created by: wchange

环境：hub.baidubce.com/paddlepaddle/paddle:1.8.3-gpu-cuda10.0-cudnn7 GPU：4卡 P40

2020-08-27 06:04:11,335-INFO: If regularizer of a Parameter has been set by 'fluid.ParamAttr' or 'fluid.WeightNormParamAttr' already. The Regularization[L2Decay, regularization_coeff=0.000500] in Optimizer will not take effect, and it will only be applied to other Parameters!
2020-08-27 06:04:17,251-INFO: places would be ommited when DataLoader is not iterable
W0827 06:04:17.349093 18186 device_context.cc:252] Please NOTE: device: 0, CUDA Capability: 61, Driver API Version: 10.2, Runtime API Version: 10.0
W0827 06:04:17.359207 18186 device_context.cc:260] device: 0, cuDNN Version: 7.6.
2020-08-27 06:04:20,479-WARNING: /root/.cache/paddle/weights/ResNet50_vd_ssld_pretrained.pdparams not found, try to load model file saved with [ save_params, save_persistables, save_vars ]
/usr/local/lib/python3.7/site-packages/paddle/fluid/io.py:1998: UserWarning: This list is not set, Because of Paramerter not found in program. There are: fc_0.b_0 fc_0.w_0
  format(" ".join(unused_para_list)))
2020-08-27 06:04:21,235-WARNING: Found an invalid bbox in annotations: im_id: 53, area: 0.0 x1: 0, y1: 195, x2: 0, y2: 360.
2020-08-27 06:04:21,246-INFO: places would be ommited when DataLoader is not iterable
I0827 06:04:30.062239 18186 build_strategy.cc:361] set enable_sequential_execution:1
W0827 06:04:31.650740 18186 fuse_all_reduce_op_pass.cc:74] Find all_reduce operators: 240. To make the speed faster, some all_reduce ops are fused during training, after fusion, the number of all_reduce ops is 171.
2020-08-27 06:05:15,815-INFO: iter: 0, lr: 0.000000, 'loss_xy': '49.234890', 'loss_wh': '151.551819', 'loss_obj': '11701.962891', 'loss_cls': '26.566391', 'loss_iou': '183.181076', 'loss_iou_aware': '487.398712', 'loss': '12599.894531', time: 0.000, eta: 0:00:04
2020-08-27 06:08:34,039-INFO: iter: 100, lr: 0.000125, 'loss_xy': '36.615501', 'loss_wh': '54.858246', 'loss_obj': '268.272827', 'loss_cls': '11.544251', 'loss_iou': '137.742783', 'loss_iou_aware': '23.605310', 'loss': '536.883667', time: 2.518, eta: 1 day, 10:54:26
2020-08-27 06:12:04,756-INFO: iter: 200, lr: 0.000250, 'loss_xy': '35.625519', 'loss_wh': '53.374252', 'loss_obj': '235.519302', 'loss_cls': '3.245854', 'loss_iou': '137.041992', 'loss_iou_aware': '5.009554', 'loss': '474.245117', time: 2.095, eta: 1 day, 4:58:59
2020-08-27 06:15:15,642-INFO: iter: 300, lr: 0.000375, 'loss_xy': '34.211246', 'loss_wh': '50.140015', 'loss_obj': '210.656647', 'loss_cls': '1.414299', 'loss_iou': '130.542068', 'loss_iou_aware': '1.741558', 'loss': '424.454224', time: 1.899, eta: 1 day, 2:13:03
2020-08-27 06:18:34,700-INFO: iter: 400, lr: 0.000500, 'loss_xy': '34.201408', 'loss_wh': '49.520100', 'loss_obj': '205.470718', 'loss_cls': '0.897171', 'loss_iou': '131.437500', 'loss_iou_aware': '0.839541', 'loss': '421.289490', time: 2.013, eta: 1 day, 3:43:53
2020-08-27 06:21:53,043-INFO: iter: 500, lr: 0.000625, 'loss_xy': '33.984039', 'loss_wh': '52.070057', 'loss_obj': '206.255768', 'loss_cls': '0.629900', 'loss_iou': '129.637939', 'loss_iou_aware': '0.666387', 'loss': '420.665039', time: 1.976, eta: 1 day, 3:10:18
2020-08-27 06:25:13,840-INFO: iter: 600, lr: 0.000750, 'loss_xy': '33.495964', 'loss_wh': '48.441265', 'loss_obj': '199.321869', 'loss_cls': '0.532362', 'loss_iou': '128.148361', 'loss_iou_aware': '0.585110', 'loss': '417.723999', time: 1.982, eta: 1 day, 3:11:29
2020-08-27 06:28:34,505-INFO: iter: 700, lr: 0.000875, 'loss_xy': 'nan', 'loss_wh': 'nan', 'loss_obj': 'nan', 'loss_cls': 'nan', 'loss_iou': 'nan', 'loss_iou_aware': 'nan', 'loss': 'nan', time: 2.032, eta: 1 day, 3:49:55

configs/ppyolo/ppyolo_2x_gf2.yml

architecture: YOLOv3
use_gpu: true
max_iters: 50000
log_smooth_window: 100
log_iter: 100
save_dir: output
snapshot_iter: 10000
metric: COCO
pretrain_weights: https://paddle-imagenet-models-name.bj.bcebos.com/ResNet50_vd_ssld_pretrained.tar
weights: output/ppyolo/model_final
num_classes: 1
use_fine_grained_loss: true
use_ema: true
ema_decay: 0.9998

YOLOv3:
  backbone: ResNet
  yolo_head: YOLOv3Head
  use_fine_grained_loss: true

ResNet:
  norm_type: sync_bn
  freeze_at: 0
  freeze_norm: false
  norm_decay: 0.
  depth: 50
  feature_maps: [3, 4, 5]
  variant: d
  dcn_v2_stages: [5]

YOLOv3Head:
  anchor_masks: [[6, 7, 8], [3, 4, 5], [0, 1, 2]]
  anchors: [[10, 13], [16, 30], [33, 23],
            [30, 61], [62, 45], [59, 119],
            [116, 90], [156, 198], [373, 326]]
  norm_decay: 0.
  coord_conv: true
  iou_aware: true
  iou_aware_factor: 0.4
  scale_x_y: 1.05
  spp: true
  yolo_loss: YOLOv3Loss
  nms: MatrixNMS
  drop_block: true

YOLOv3Loss:
  batch_size: 4
  ignore_thresh: 0.7
  scale_x_y: 1.05
  label_smooth: false
  use_fine_grained_loss: true
  iou_loss: IouLoss
  iou_aware_loss: IouAwareLoss

IouLoss:
  loss_weight: 2.5
  max_height: 608
  max_width: 608

IouAwareLoss:
  loss_weight: 1.0
  max_height: 608
  max_width: 608

MatrixNMS:
    background_label: -1
    keep_top_k: 100
    normalized: false
    score_threshold: 0.01
    post_threshold: 0.01

LearningRate:
  base_lr: 0.005
  schedulers:
  - !PiecewiseDecay
    gamma: 0.1
    milestones:
    - 400000
    - 450000
  - !LinearWarmup
    start_factor: 0.
    steps: 4000

OptimizerBuilder:
  optimizer:
    momentum: 0.9
    type: Momentum
  regularizer:
    factor: 0.0005
    type: L2

_READER_: 'ppyolo_reader_gf2.yml'

configs/ppyolo/ppyolo_reader_gf2.yml

TrainReader:
  inputs_def:
    fields: ['image', 'gt_bbox', 'gt_class', 'gt_score']
    num_max_boxes: 100
  dataset:
    !COCODataSet
      image_dir: train
      anno_path: annotations/train.json
      dataset_dir: dataset/gf2
      with_background: false
  sample_transforms:
    - !DecodeImage
      to_rgb: True
      with_mixup: True
    - !MixupImage
      alpha: 1.5
      beta: 1.5
    - !ColorDistort {}
    - !RandomExpand
      fill_value: [123.675, 116.28, 103.53]
    - !RandomCrop {}
    - !RandomFlipImage
      is_normalized: false
    - !NormalizeBox {}
    - !PadBox
      num_max_boxes: 100
    - !BboxXYXY2XYWH {}
  batch_transforms:
  - !RandomShape
    sizes: [320, 352, 384, 416, 448, 480, 512, 544, 576, 608]
    random_inter: True
  - !NormalizeImage
    mean: [0.485, 0.456, 0.406]
    std: [0.229, 0.224, 0.225]
    is_scale: True
    is_channel_first: false
  - !Permute
    to_bgr: false
    channel_first: True
  # Gt2YoloTarget is only used when use_fine_grained_loss set as true,
  # this operator will be deleted automatically if use_fine_grained_loss
  # is set as false
  - !Gt2YoloTarget
    anchor_masks: [[6, 7, 8], [3, 4, 5], [0, 1, 2]]
    anchors: [[10, 13], [16, 30], [33, 23],
              [30, 61], [62, 45], [59, 119],
              [116, 90], [156, 198], [373, 326]]
    downsample_ratios: [32, 16, 8]
  batch_size: 4
  shuffle: true
  mixup_epoch: 25000
  drop_last: true
  worker_num: 8
  bufsize: 1
  use_process: false

EvalReader:
  inputs_def:
    fields: ['image', 'im_size', 'im_id']
    num_max_boxes: 100
  dataset:
    !COCODataSet
      image_dir: val
      anno_path: annotations/val.json
      dataset_dir: dataset/gf2
      with_background: false
  sample_transforms:
    - !DecodeImage
      to_rgb: True
    - !ResizeImage
      target_size: 608
      interp: 2
    - !NormalizeImage
      mean: [0.485, 0.456, 0.406]
      std: [0.229, 0.224, 0.225]
      is_scale: True
      is_channel_first: false
    - !PadBox
      num_max_boxes: 100
    - !Permute
      to_bgr: false
      channel_first: True
  batch_size: 4
  drop_empty: false
  worker_num: 8
  bufsize: 1

TestReader:
  inputs_def:
    image_shape: [3, 608, 608]
    fields: ['image', 'im_size', 'im_id']
  dataset:
    !ImageFolder
      anno_path: annotations/instances_val2017.json
      with_background: false
  sample_transforms:
    - !DecodeImage
      to_rgb: True
    - !ResizeImage
      target_size: 608
      interp: 2
    - !NormalizeImage
      mean: [0.485, 0.456, 0.406]
      std: [0.229, 0.224, 0.225]
      is_scale: True
      is_channel_first: false
    - !Permute
      to_bgr: false
      channel_first: True
  batch_size: 1