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Add NVAE configuration and rename MAE notebook
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| # pretrain_tiny_nvae.yaml | ||
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| # general | ||
| experiment: | ||
| name: "pretrain_tiny_nvae" | ||
| project: "sdofm" | ||
| model: "nvae" | ||
| task: "train" # options: train, evaluate (not implemented) | ||
| seed: 0 | ||
| disable_cuda: false | ||
| disable_wandb: false | ||
| wandb: | ||
| entity: "fdlx" | ||
| group: "sdofm-phase1" | ||
| job_type: "pretrain" | ||
| tags: [] | ||
| notes: "" | ||
| fold: null | ||
| evaluate: false # skip training and only evaluate (requires checkpoint to be set) | ||
| checkpoint: null # this is the wandb run_id of the checkpoint to load | ||
| device: null # this is set automatically using the disable_cuda flag and torch.cuda.is_available() | ||
| precision: 64 # 32, 64 | ||
| log_n_batches: 1000 # log every n training batches | ||
| save_results: true # save full results to file and wandb | ||
| accelerator: "auto" # options are "auto", "gpu", "tpu", "ipu", or "cpu" | ||
| distributed: | ||
| enabled: true | ||
| backend: "ddp" | ||
| # nproc_per_node: 1 | ||
| # nnodes: 1 | ||
| world_size: "auto" # The "auto" option recognizes the machine you are on, and selects the appropriate Accelerator. | ||
| # node_rank: 0 | ||
| # local_rank: 0 | ||
| # master_addr: "localhost" | ||
| # master_port: 12345 | ||
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| # dataset configuration | ||
| data: | ||
| min_date: "0000-00-00 00:00:00" # NOT IMPLEMENTED # minimum is '2010-09-09 00:00:11.08' | ||
| max_date: "0000-00-00 00:00:00" # NOT IMPLEMENTED # maximum is '2023-05-26 06:36:08.072' | ||
| month_splits: # non selected months will form training set | ||
| # train: [1,2,3,4,5,6,7,8,9,10] | ||
| val: [11] | ||
| test: [12] | ||
| holdout: [] | ||
| num_workers: 16 # set appropriately for your machine | ||
| output_directory: "output" | ||
| sdoml: | ||
| base_directory: "/mnt/sdoml" | ||
| sub_directory: | ||
| hmi: "HMI.zarr" | ||
| aia: "AIA.zarr" | ||
| eve: EVE_legacy.zarr" | ||
| cache: "cache" | ||
| components: null # null for select all magnetic components ["Bx", "By", "Bz"] | ||
| wavelengths: null # null for select all wavelengths channels ["131A","1600A","1700A","171A","193A","211A","304A","335A","94A"] | ||
| ions: null # null to select all ion channels ["C III", "Fe IX", "Fe VIII", "Fe X", "Fe XI", "Fe XII", "Fe XIII", "Fe XIV", "Fe XIX", "Fe XV", "Fe XVI", "Fe XVIII", "Fe XVI_2", "Fe XX", "Fe XX_2", "Fe XX_3", "H I", "H I_2", "H I_3", "He I", "He II", "He II_2", "He I_2", "Mg IX", "Mg X", "Mg X_2", "Ne VII", "Ne VIII", "O II", "O III", "O III_2", "O II_2", "O IV", "O IV_2", "O V", "O VI", "S XIV", "Si XII", "Si XII_2"] | ||
| frequency: '12min' # smallest is 12min | ||
| mask_with_hmi_threshold: null # None/null for no mask, float for threshold | ||
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| # model configurations | ||
| model: | ||
| # PRETRAINERS | ||
| mae: | ||
| img_size: 224 | ||
| patch_size: 16 | ||
| num_frames: 3 | ||
| tubelet_size: 1 | ||
| in_chans: 3 | ||
| embed_dim: 1024 | ||
| depth: 24 | ||
| num_heads: 16 | ||
| decoder_embed_dim: 512 | ||
| decoder_depth: 8 | ||
| decoder_num_heads: 16 | ||
| mlp_ratio: 4.0 | ||
| # norm_layer: defaults to nn.LayerNorm | ||
| norm_pix_loss: False | ||
| nvae: | ||
| use_se: true | ||
| res_dist: true | ||
| num_x_bits: 8 | ||
| num_latent_scales: 3 # 5 | ||
| num_groups_per_scale: 1 # 16 | ||
| num_latent_per_group: 1 # 10 | ||
| ada_groups: true | ||
| min_groups_per_scale: 1 | ||
| num_channels_enc: 30 | ||
| num_channels_dec: 30 | ||
| num_preprocess_blocks: 2 # 1 | ||
| num_preprocess_cells: 2 | ||
| num_cell_per_cond_enc: 2 | ||
| num_postprocess_blocks: 2 # 1 | ||
| num_postprocess_cells: 2 | ||
| num_cell_per_cond_dec: 2 | ||
| num_mixture_dec: 1 | ||
| num_nf: 2 | ||
| kl_anneal_portion: 0.3 | ||
| kl_const_portion: 0.0001 | ||
| kl_const_coeff: 0.0001 | ||
| # learning_rate: 1e-2 | ||
| # weight_decay: 3e-4 | ||
| weight_decay_norm_anneal: true | ||
| weight_decay_norm_init: 1. | ||
| weight_decay_norm: 1e-2 | ||
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| # FINE-TUNERS | ||
| dimming: | ||
| num_neck_filters: 32 | ||
| output_dim: 1 # not sure why this is implemented for autocorrelation, should be a scalar | ||
| loss: "mse" # options: "mse", "heteroscedastic" | ||
| freeze_encoder: true | ||
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| # ML optimization arguments: | ||
| opt: | ||
| loss: "mse" # options: "mae", "mse", "mape" | ||
| scheduler: "constant" #other options: "cosine", "plateau", "exp" | ||
| scheduler_warmup: 0 | ||
| batch_size: 1 | ||
| learning_rate: 0.0001 | ||
| weight_decay: 3e-4 # 0.0 | ||
| optimiser: "adam" | ||
| epochs: 4 | ||
| patience: 2 | ||
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| # hydra configuration | ||
| hydra: | ||
| mode: MULTIRUN | ||
| run: | ||
| dir: ${data.output_directory}/${now:%Y-%m-%d-%H-%M-%S} | ||
| sweep: | ||
| dir: ${hydra.run.dir} | ||
| subdir: ${hydra.job.num} |
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