data:
  root: '/root/DATASET/UAV2MAP/UAV/'
  train_citys:
  - Paris
  - Berlin
  - London
  - Tokyo
  - NewYork
  val_citys:
  - Toronto
  image_size: 256
  train:
    batch_size: 12
    num_workers: 4
  val:
    batch_size: ${..train.batch_size}
    num_workers: ${.batch_size}
  num_classes:
    areas: 7
    ways: 10
    nodes: 33
  pixel_per_meter: 1
  crop_size_meters: 64
  max_init_error: 48
  add_map_mask: true
  resize_image: 512
  pad_to_square: true
  rectify_pitch: true
  augmentation:
    rot90: true
    flip: true
    image:
      apply: true
      brightness: 0.5
      contrast: 0.4
      saturation: 0.4
      hue": 0.5/3.14
model:
  image_size: ${data.image_size}
  latent_dim: 128
  val_citys: ${data.val_citys}
  image_encoder:
    name: feature_extractor_v2
    backbone:
      encoder: resnet50
      pretrained: true
      output_dim: 8
      num_downsample: null
      remove_stride_from_first_conv: false
  name: orienternet
  matching_dim: 8
  z_max: 32
  x_max: 32
  pixel_per_meter: 1
  num_scale_bins: 33
  num_rotations: 64
  map_encoder:
    embedding_dim: 16
    output_dim: 8
    num_classes:
      areas: 7
      ways: 10
      nodes: 33
    backbone:
      encoder: vgg19
      pretrained: false
      output_scales:
      - 0
      num_downsample: 3
      decoder:
      - 128
      - 64
      - 64
      padding: replicate
    unary_prior: false
  bev_net:
    num_blocks: 4
    latent_dim: 128
    output_dim: 8
    confidence: true
experiment:
  name: maplocanet_0906_diffhight
  gpus: 6
  seed: 0
training:
  lr: 0.0001
  lr_scheduler: null
  finetune_from_checkpoint: null
  trainer:
    val_check_interval: 1000
    log_every_n_steps: 100
#    limit_val_batches: 1000
    max_steps: 200000
    devices: ${experiment.gpus}
  checkpointing:
    monitor: "loss/total/val"
    save_top_k: 10
    mode: min

#    filename: '{epoch}-{step}-{loss_SanFrancisco:.2f}'