jasong-ovo
commented
2 years ago base config
full_field: &FULL_FIELD loss: 'l2' lr: 1E-3 scheduler: 'ReduceLROnPlateau' num_data_workers: 4 dt: 1 # how many timesteps ahead the model will predict n_history: 0 #how many previous timesteps to consider prediction_type: 'iterative' prediction_length: 41 #applicable only if prediction_type == 'iterative' n_initial_conditions: 5 #applicable only if prediction_type == 'iterative' ics_type: "default" save_raw_forecasts: !!bool True save_channel: !!bool False masked_acc: !!bool False maskpath: None perturb: !!bool False add_grid: !!bool False N_grid_channels: 0 gridtype: 'sinusoidal' #options 'sinusoidal' or 'linear' roll: !!bool False max_epochs: 50 batch_size: 64
afno hyperparams
num_blocks: 8 nettype: 'afno' patch_size: 8 width: 56 modes: 32 img_size: [720, 1440]
options default, residual
target: 'default' in_channels: [0,1] out_channels: [0,1] #must be same as in_channels if prediction_type == 'iterative' normalization: 'zscore' #options zscore (minmax not supported) train_data_path: '/mnt/petrelfs/gongjunchao/h5s' #'/pscratch/sd/j/jpathak/wind/train' valid_data_path: '/mnt/petrelfs/gongjunchao/h5s' #'/pscratch/sd/j/jpathak/wind/test' inf_data_path: '/pscratch/sd/j/jpathak/wind/out_of_sample' # test set path for inference exp_dir: '/mnt/lustre/gongjunchao/FCN/results' #'/pscratch/sd/j/jpathak/ERA5_expts_gtc/wind' time_means_path: '/pscratch/sd/j/jpathak/wind/time_means.npy' global_means_path: '/pscratch/sd/j/jpathak/wind/global_means.npy' global_stds_path: '/pscratch/sd/j/jpathak/wind/global_stds.npy'
orography: !!bool False orography_path: None
log_to_screen: !!bool True log_to_wandb: !!bool True save_checkpoint: !!bool True
enable_nhwc: !!bool False optimizer_type: 'FusedAdam' crop_size_x: None crop_size_y: None
two_step_training: !!bool False plot_animations: !!bool False
add_noise: !!bool False noise_std: 0
type of dataset
ds_type: 'h5'
afno_backbone: &backbone <<: *FULL_FIELD log_to_wandb: !!bool False lr: 5E-4 batch_size: 64 #64 max_epochs: 150 scheduler: 'CosineAnnealingLR' in_channels: [0, 1 ,2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19] out_channels: [0, 1 ,2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19] orography: !!bool False orography_path: None exp_dir: '/mnt/lustre/gongjunchao/FCN/results' #'/pscratch/sd/j/jpathak/ERA5_expts_gtc/wind' train_data_path: '/mnt/petrelfs/gongjunchao/h5s' #'/pscratch/sd/j/jpathak/wind/train' valid_data_path: '/mnt/petrelfs/gongjunchao/h5s' #'/pscratch/sd/j/jpathak/wind/test' inf_data_path: '/pscratch/sd/s/shas1693/data/era5/out_of_sample' time_means_path: '/pscratch/sd/s/shas1693/data/era5/time_means.npy' global_means_path: '/mnt/lustre/gongjunchao/global_means.npy' #'/pscratch/sd/s/shas1693/data/era5/global_means.npy' global_stds_path: '/mnt/lustre/gongjunchao/global_stds.npy' #'/pscratch/sd/s/shas1693/data/era5/global_stds.npy'
afno_backbone_orography: &backbone_orography <<: *backbone orography: !!bool True orography_path: '/pscratch/sd/s/shas1693/data/era5/static/orography.h5'
afno_backbone_finetune: <<: *backbone lr: 1E-4 batch_size: 64 log_to_wandb: !!bool True max_epochs: 50 pretrained: !!bool True two_step_training: !!bool True pretrained_ckpt_path: '/pscratch/sd/s/shas1693/results/era5_wind/afno_backbone/0/training_checkpoints/best_ckpt.tar'
perturbations: <<: *backbone lr: 1E-4 batch_size: 64 max_epochs: 50 pretrained: !!bool True two_step_training: !!bool True pretrained_ckpt_path: '/pscratch/sd/j/jpathak/ERA5_expts_gtc/wind/afno_20ch_bs_64_lr5em4_blk_8_patch_8_cosine_sched/1/training_checkpoints/best_ckpt.tar' prediction_length: 24 ics_type: "datetime" n_perturbations: 100 save_channel: !bool True save_idx: 4 save_raw_forecasts: !!bool False date_strings: ["2018-01-01 00:00:00"] inference_file_tag: " " valid_data_path: "/pscratch/sd/j/jpathak/ " perturb: !!bool True n_level: 0.3
PRECIP
precip: &precip <<: *backbone in_channels: [0, 1 ,2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19] out_channels: [0] nettype: 'afno' nettype_wind: 'afno' log_to_wandb: !!bool True lr: 2.5E-4 batch_size: 64 max_epochs: 25 precip: '/pscratch/sd/p/pharring/ERA5/precip/total_precipitation' time_means_path_tp: '/pscratch/sd/p/pharring/ERA5/precip/total_precipitation/time_means.npy' model_wind_path: '/pscratch/sd/s/shas1693/results/era5_wind/afno_backbone_finetune/0/training_checkpoints/best_ckpt.tar' precip_eps: !!float 1e-5
Thanks for your amazing work! I want to reproduce your result on lower resolution but I get really bad result on 64*32. How should I change settings?