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408f9e4 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 | ### base config ###
# -*- coding: utf-8 -*-
full_field: &FULL_FIELD
loss: 'l2'
max_epochs: 200
batch_size: 32
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" # default or datetime
date_strings: ["2018-09-14 00:00:00"]
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
enable_nhwc: !!bool False
optimizer_type: 'FusedAdam'
# directory path to store training checkpoints and other output
exp_dir: './exp'
# data
train_data_path: 'data/train'
valid_data_path: 'data/valid'
test_data_path: 'data/test'
# land mask
land_mask: !!bool True
land_mask_path: 'data/land_mask.h5'
# normalization
normalize: !!bool True
normalization: 'zscore' # options zscore, minmax, zscore_lat
global_means_path: 'data/mean_s_t_ssh.npy'
global_stds_path: 'data/std_s_t_ssh.npy'
add_noise: !!bool False
log_to_screen: !!bool True
log_to_wandb: !!bool False
save_checkpoint: !!bool True
plot_animations: !!bool False
#############################################
Triton: &Triton
<<: *FULL_FIELD
nettype: 'Triton'
log_to_wandb: !!bool False
# Train params
lr: 1E-3
batch_size: 32
scheduler: 'CosineAnnealingLR'
loss_channel_wise: True
loss_scale: False
use_loss_scaler_from_metnet3: True
atmos_channels: [93, 94, 95, 96]
ocean_channels: [0, 1 ,2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20 ,21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92]
in_channels: [0, 1 ,2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20 ,21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96]
out_channels: [0, 1 ,2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20 ,21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92]
out_variables: ["S0", "S2", "S5", "S7", "S11", "S15", "S21", "S29", "S40", "S55", "S77", "S92", "S109", "S130", "S155", "S186", "S222", "S266", "S318", "S380", "S453", "S541", "S643", "U0", "U2", "U5", "U7", "U11", "U15", "U21", "U29", "U40", "U55", "U77", "U92", "U109", "U130", "U155", "U186", "U222", "U266", "U318", "U380", "U453", "U541", "U643", "V0", "V2", "V5", "V7", "V11", "V15", "V21", "V29", "V40", "V55", "V77", "V92", "V109", "V130", "V155", "V186", "V222", "V266", "V318", "V380", "V453", "V541", "V643", "T0", "T2", "T5", "T7", "T11", "T15", "T21", "T29", "T40", "T55", "T77", "T92", "T109", "T130", "T155", "T186", "T222", "T266", "T318", "T380", "T453", "T541", "T643", "SSH"]
N_in_channels: 97 # 72
N_out_channels: 93 # 67
#############################################
Fourcastnet: &Fourcastnet
<<: *FULL_FIELD
nettype: 'Fourcastnet'
log_to_wandb: !!bool False
# Train params
lr: 1E-3
batch_size: 32
scheduler: 'CosineAnnealingLR'
loss_channel_wise: True
loss_scale: False
use_loss_scaler_from_metnet3: True
atmos_channels: [93, 94, 95, 96]
ocean_channels: [0, 1 ,2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20 ,21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92]
in_channels: [0, 1 ,2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20 ,21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96]
out_channels: [0, 1 ,2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20 ,21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92]
out_variables: ["S0", "S2", "S5", "S7", "S11", "S15", "S21", "S29", "S40", "S55", "S77", "S92", "S109", "S130", "S155", "S186", "S222", "S266", "S318", "S380", "S453", "S541", "S643", "U0", "U2", "U5", "U7", "U11", "U15", "U21", "U29", "U40", "U55", "U77", "U92", "U109", "U130", "U155", "U186", "U222", "U266", "U318", "U380", "U453", "U541", "U643", "V0", "V2", "V5", "V7", "V11", "V15", "V21", "V29", "V40", "V55", "V77", "V92", "V109", "V130", "V155", "V186", "V222", "V266", "V318", "V380", "V453", "V541", "V643", "T0", "T2", "T5", "T7", "T11", "T15", "T21", "T29", "T40", "T55", "T77", "T92", "T109", "T130", "T155", "T186", "T222", "T266", "T318", "T380", "T453", "T541", "T643", "SSH"]
N_in_channels: 97 # 72
N_out_channels: 93 # 67
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