seed: 123
use_amp: True
warmup_steps: 100
checkpoint: "/workspace/bantam/peopleNet/peoplesegnet_resnet50.step-20000.tlt"
pruned_model_path: "/workspace/bantam/peopleNet/pruned/model.tlt"
learning_rate_steps: "[5000, 10000, 15000]"
learning_rate_decay_levels: "[0.1, 0.02, 0.002]"
total_steps: 20000
train_batch_size: 16
eval_batch_size: 8
num_steps_per_eval: 1000
momentum: 0.9
l2_weight_decay: 0
l1_weight_decay: 0
warmup_learning_rate: 0.0001
init_learning_rate: 0.005

data_config{
    image_size: "(576, 960)"
    augment_input_data: True
    eval_samples: 8
    training_file_pattern: "/workspace/bantam/modifiedData/tf_record/train*"
    validation_file_pattern: "/workspace/bantam/modifiedData/tf_record/val*"
    val_json_file: "/workspace/bantam/modifiedData/cocoLane/val/annotations.json"

    # dataset specific parameters
    num_classes: 2
    skip_crowd_during_training: True
    #prefetch_buffer_size: 8
    n_workers: 32
    shuffle_buffer_size: 4096
}

maskrcnn_config {
  nlayers: 50
  arch: "resnet"
  gt_mask_size: 112
  freeze_blocks: "[0]"
  freeze_bn: True
  # Region Proposal Network
  rpn_positive_overlap: 0.7
  rpn_negative_overlap: 0.3
  rpn_batch_size_per_im: 256
  rpn_fg_fraction: 0.5
  rpn_min_size: 0.

  # Proposal layer.
  batch_size_per_im: 512
  fg_fraction: 0.25
  fg_thresh: 0.5
  bg_thresh_hi: 0.5
  bg_thresh_lo: 0.

  # Faster-RCNN heads.
  fast_rcnn_mlp_head_dim: 1024
  bbox_reg_weights: "(10., 10., 5., 5.)"

  # Mask-RCNN heads.
  include_mask: True
  mrcnn_resolution: 28

  # training
  train_rpn_pre_nms_topn: 2000
  train_rpn_post_nms_topn: 1000
  train_rpn_nms_threshold: 0.7

  # evaluation
  test_detections_per_image: 100
  test_nms: 0.5
  test_rpn_pre_nms_topn: 1000
  test_rpn_post_nms_topn: 1000
  test_rpn_nms_thresh: 0.7

  # model architecture
  min_level: 2
  max_level: 6
  num_scales: 1
  aspect_ratios: "[(1.0, 1.0), (1.4, 0.7), (0.7, 1.4)]"
  anchor_scale: 8

  # localization loss
  rpn_box_loss_weight: 1.0
  fast_rcnn_box_loss_weight: 1.0
  mrcnn_weight_loss_mask: 1.0
}