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pretrain_32.2M_mae_HP_r512_e128_p16.yaml
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# default.yaml
# MODEL SUMMARY
# | Name | Type | Params
# -------------------------------------------------------
# 0 | autoencoder | MaskedAutoencoderViT3D | 333 M
# -------------------------------------------------------
# 329 M Trainable params
# 4.7 M Non-trainable params
# 333 M Total params
# 1,335.838 Total estimated model params size (MB)
# general
log_level: 'DEBUG'
experiment:
name: "mae-helioprojected-2011subset-r512-e128-p16" # generate random name in wandb
project: "sdofm"
task: "pretrain" # options: train, evaluate (not implemented)
model: "mae"
backbone_checkpoint: null
seed: 0
disable_cuda: false
resuming: false
wandb:
enable: true
entity: "fdlx"
group: "sdofm-phase1"
job_type: "pretrain"
tags: []
notes: ""
output_directory: "wandb_output"
log_model: "all" # can be True (final checkpoint), False (no checkpointing), or "all" (for all epoches)
gcp_storage: # this will checkpoint all epoches, perhaps clean up this config
enabled: true
bucket: "sdofm-checkpoints"
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: '16-mixed' #-true' # (32, 64) for cuda, ('32-true', '16-true', 'bf16-true') for tpu
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"
profiler: null #'XLAProfiler' # 'XLAProfiler' # options are XLAProfiler/PyTorchProfiler Warning: XLA for TPUs only works on single world size
distributed:
enabled: true
world_size: "auto" # The "auto" option recognizes the machine you are on, and selects the appropriate number of accelerators.
log_every_n_steps: 50
# dataset configuration
data:
min_date: '2011-01-01 00:00:00.00' # NOT IMPLEMENTED # minimum is '2010-09-09 00:00:11.08'
max_date: '2011-03-31 23:59:59.99' # 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: [2]
test: [3]
holdout: []
num_workers: 16 # set appropriately for your machine
prefetch_factor: 3
num_frames: 1 # WARNING: This is only read for FINETUNING, model num_frames overrides in BACKBONE
drop_frame_dim: false
# output_directory: "wandb_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
apply_mask: false
feature_engineering:
enabled: true
dclass: 'HelioProjected'
# model configurations
model:
# PRETRAINERS
mae:
img_size: 512
patch_size: 16
num_frames: 1
tubelet_size: 1
in_chans: 9
embed_dim: 128
depth: 24
num_heads: 16
decoder_embed_dim: 512
decoder_depth: 8
decoder_num_heads: 16
mlp_ratio: 4.0
norm_layer: 'LayerNorm'
norm_pix_loss: False
masking_ratio: 0.75
samae:
# uses all parameters as in mae plus these
masking_type: "solar_aware" # 'random' or 'solar_aware'
active_region_mu_degs: 15.73
active_region_std_degs: 6.14
active_region_scale: 1.0
active_region_abs_lon_max_degs: 60
active_region_abs_lat_max_degs: 60
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
# FINE-TUNERS
degragation:
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
# 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: 100
patience: 2
misc:
limb_mask: false
# hydra configuration
# hydra:
# sweeper:
# params:
# model.mae.embed_dim: 256, 512
# model.mae.masking_ratio: 0.5, 0.75
# model.samae.masking_type: "random", "solar_aware"
hydra:
mode: RUN