globals:
  latent_channels: 4
dataset:
  target: echosyn.common.datasets.ContrastivePair
  args:
    root: avae-4f4/ped_a4c
    folder: Latents
    extension: pt
dataloader:
  target: torch.utils.data.DataLoader
  args:
    shuffle: true
    batch_size: 32
    num_workers: 16
    pin_memory: true
    drop_last: true
    persistent_workers: true
backbone:
  target: echosyn.reindentification.model.ResNet18
  args:
    weights: torchvision.models.ResNet18_Weights.IMAGENET1K_V1
    progress: false
model:
  target: echosyn.reindentification.model.ContrastiveModel
  args:
    in_channels: 4
    out_channels: 256
    kl_loss_weight: 0.0
optimizer:
  target: torch.optim.AdamW
  args:
    lr: 0.0001
    betas:
    - 0.9
    - 0.999
    weight_decay: 0.01
    eps: 1.0e-08
scheduler:
  target: torch.optim.lr_scheduler.ConstantLR
  args:
    factor: 1.0
vae:
  target: diffusers.AutoencoderKL
  pretrained: vae/avae-4f4
max_train_steps: 60000
gradient_accumulation_steps: 1
mixed_precision: bf16
max_grad_norm: 10.0
sample_latents: true
validation_steps: 10000
validation_samples: 99999
output_dir: experiments/${wandb_args.group}/${wandb_args.name}
logging_dir: logs
report_to: wandb
wandb_args:
  project: EchoFlow
  name: ped_a4c_4f4
  group: reindentification
checkpointing_steps: 10000
checkpoints_total_limit: 3
resume_from_checkpoint: null
seed: 42
no_wandb: false
num_train_epochs: 750