cascade_rcnn_r50_fpn_1x_ms_test.yml 3.4 KB
Newer Older
W
wangguanzhong 已提交
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177
architecture: CascadeRCNN
train_feed: FasterRCNNTrainFeed
eval_feed: FasterRCNNEvalFeed
test_feed: FasterRCNNTestFeed
max_iters: 90000
snapshot_iter: 10000
use_gpu: true
log_smooth_window: 20
save_dir: output
pretrain_weights: https://paddle-imagenet-models-name.bj.bcebos.com/ResNet50_cos_pretrained.tar
weights: output/cascade_rcnn_r50_fpn_1x/model_final
metric: COCO
num_classes: 81

CascadeRCNN:
  backbone: ResNet
  fpn: FPN
  rpn_head: FPNRPNHead
  roi_extractor: FPNRoIAlign
  bbox_head: CascadeBBoxHead
  bbox_assigner: CascadeBBoxAssigner

ResNet:
  norm_type: affine_channel
  depth: 50
  feature_maps: [2, 3, 4, 5]
  freeze_at: 2
  variant: b

FPN:
  min_level: 2
  max_level: 6
  num_chan: 256
  spatial_scale: [0.03125, 0.0625, 0.125, 0.25]

FPNRPNHead:
  anchor_generator:
    anchor_sizes: [32, 64, 128, 256, 512]
    aspect_ratios: [0.5, 1.0, 2.0]
    stride: [16.0, 16.0]
    variance: [1.0, 1.0, 1.0, 1.0]
  anchor_start_size: 32
  min_level: 2
  max_level: 6
  num_chan: 256
  rpn_target_assign:
    rpn_batch_size_per_im: 256
    rpn_fg_fraction: 0.5
    rpn_positive_overlap: 0.7
    rpn_negative_overlap: 0.3
    rpn_straddle_thresh: 0.0
  train_proposal:
    min_size: 0.0
    nms_thresh: 0.7
    pre_nms_top_n: 2000
    post_nms_top_n: 2000
  test_proposal:
    min_size: 0.0
    nms_thresh: 0.7
    pre_nms_top_n: 1000
    post_nms_top_n: 1000

FPNRoIAlign:
  canconical_level: 4
  canonical_size: 224
  min_level: 2
  max_level: 5
  box_resolution: 7
  sampling_ratio: 2

CascadeBBoxAssigner:
  batch_size_per_im: 512
  bbox_reg_weights: [10, 20, 30]
  bg_thresh_lo: [0.0, 0.0, 0.0]
  bg_thresh_hi: [0.5, 0.6, 0.7]
  fg_thresh: [0.5, 0.6, 0.7]
  fg_fraction: 0.25

CascadeBBoxHead:
  head: CascadeTwoFCHead
  nms:
    keep_top_k: 100
    nms_threshold: 0.5
    score_threshold: 0.05

CascadeTwoFCHead:
  mlp_dim: 1024

MultiScaleTEST:
  score_thresh: 0.05
  nms_thresh: 0.5
  detections_per_im: 100
  enable_voting: true
  vote_thresh: 0.9

LearningRate:
  base_lr: 0.02
  schedulers:
  - !PiecewiseDecay
    gamma: 0.1
    milestones: [60000, 80000]
  - !LinearWarmup
    start_factor: 0.3333333333333333
    steps: 500

OptimizerBuilder:
  optimizer:
    momentum: 0.9
    type: Momentum
  regularizer:
    factor: 0.0001
    type: L2

FasterRCNNTrainFeed:
  batch_size: 2
  dataset:
    dataset_dir: dataset/coco
    annotation: annotations/instances_train2017.json
    image_dir: train2017
  batch_transforms:
  - !PadBatch
    pad_to_stride: 32
  drop_last: false
  num_workers: 2

FasterRCNNEvalFeed:
  batch_size: 1
  dataset:
    dataset_dir: dataset/coco
    annotation: annotations/instances_val2017.json
    image_dir: val2017
  sample_transforms:
  - !DecodeImage
    to_rgb: true
  - !NormalizeImage
    is_channel_first: false
    is_scale: true
    mean:
    - 0.485
    - 0.456
    - 0.406
    std:
    - 0.229
    - 0.224
    - 0.225
  - !MultiscaleTestResize
    origin_target_size: 800
    origin_max_size: 1333
    target_size:
    - 400
    - 500
    - 600
    - 700
    - 900
    - 1000
    - 1100
    - 1200
    max_size: 2000
    use_flip: true
  - !Permute
    channel_first: true
    to_bgr: false
  batch_transforms:
  - !PadMSTest
    pad_to_stride: 32
  num_scale: 18
  num_workers: 2

FasterRCNNTestFeed:
  batch_size: 1
  dataset:
    annotation: dataset/coco/annotations/instances_val2017.json
  batch_transforms:
  - !PadBatch
    pad_to_stride: 32
  drop_last: false
  num_workers: 2