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Commit 3edb682d authored by xuetaowave's avatar xuetaowave
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data_ljkj_6h_alllevel/AFNO.yaml
+ 1
− 1
View file @ 3edb682d
......@@ -64,7 +64,7 @@ afno_backbone_ljkj: &LJKJ
<<: *FULL_FIELD
log_to_wandb: !!bool False
lr: 5E-4
batch_size: 8
batch_size: 2
patch_size: 2
depth : 6 # default 12
img_size: [192, 288]
......
data_ljkj_6h_alllevel/results/tec_256/afno_backbone_ljkj/d4p4_resabs/hyperparams.yaml 0 → 100644
+ 169
− 0
View file @ 3edb682d
log_to_wandb: 'False'
lr: '0.0005'
batch_size: '2'
patch_size: '2'
depth: '6'
img_size: '[192, 288]'
max_epochs: '1500'
scheduler: CosineAnnealingLR
in_channels_range: '[0, 810, 1]'
out_channels_range: '[0, 810, 1]'
prediction_length: '100'
orography: 'False'
orography_path: None
exp_dir: ./results/tec_256
train_data_path: ./train
valid_data_path: ./test
inf_data_path: ./out_of_sample
time_means_path: ./time_means.npy
global_means_path: ./global_means.npy
global_stds_path: ./global_stds.npy
loss: l2
retrain: 'False'
num_data_workers: '4'
dt: '1'
n_history: '0'
prediction_type: iterative
n_initial_conditions: '5'
ics_type: default
save_raw_forecasts: 'True'
save_channel: 'False'
masked_acc: 'False'
maskpath: None
perturb: 'False'
add_grid: 'False'
N_grid_channels: '0'
gridtype: sinusoidal
roll: 'False'
num_blocks: '8'
nettype: afno
width: '56'
modes: '32'
target: default
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out_channels: '[0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18,
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normalization: zscore
log_to_screen: 'True'
save_checkpoint: 'True'
enable_nhwc: 'False'
optimizer_type: FusedAdam
crop_size_x: None
crop_size_y: None
two_step_training: 'False'
plot_animations: 'False'
add_noise: 'False'
noise_std: '0'
epsilon_factor: '0'
world_size: '1'
experiment_dir:
/home/cxt/work/fourcastnet_TEC/FourCastNetTEC/data_ljkj_6h_alllevel/results/tec_256/afno_backbone_ljkj/d4p4_resabs
checkpoint_path: ./results/tec_256/afno_backbone_ljkj/d4p4_resabs/training_checkpoints/ckpt.tar
best_checkpoint_path:
./results/tec_256/afno_backbone_ljkj/d4p4_resabs/training_checkpoints/best_ckpt.tar
resuming: 'True'
local_rank: '0'
enable_amp: 'True'
name: afno_backbone_ljkj_d4p4_resabs
group: era5_precipafno_backbone_ljkj
project: ERA5_precip
entity: flowgan
N_in_channels: '810'
N_out_channels: '810'
data_ljkj_6h_alllevel/results/tec_256/afno_backbone_ustc/d4p4_resabs/hyperparams.yaml 0 → 100644
+ 169
− 0
View file @ 3edb682d
batch_size: '8'
depth: '6'
patch_size: '2'
log_to_wandb: 'False'
lr: '0.0005'
img_size: '[192, 288]'
max_epochs: '1500'
scheduler: CosineAnnealingLR
in_channels_range: '[0, 810, 1]'
out_channels_range: '[0, 810, 1]'
prediction_length: '100'
orography: 'False'
orography_path: None
exp_dir: ./results/tec_256
train_data_path: ./train
valid_data_path: ./test
inf_data_path: ./out_of_sample
time_means_path: ./time_means.npy
global_means_path: ./global_means.npy
global_stds_path: ./global_stds.npy
loss: l2
retrain: 'False'
num_data_workers: '4'
dt: '1'
n_history: '0'
prediction_type: iterative
n_initial_conditions: '5'
ics_type: default
save_raw_forecasts: 'True'
save_channel: 'False'
masked_acc: 'False'
maskpath: None
perturb: 'False'
add_grid: 'False'
N_grid_channels: '0'
gridtype: sinusoidal
roll: 'False'
num_blocks: '8'
nettype: afno
width: '56'
modes: '32'
target: default
in_channels: '[0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19,
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420, 421, 422, 423, 424, 425, 426, 427, 428, 429, 430, 431, 432, 433, 434, 435,
436, 437, 438, 439, 440, 441, 442, 443, 444, 445, 446, 447, 448, 449, 450, 451,
452, 453, 454, 455, 456, 457, 458, 459, 460, 461, 462, 463, 464, 465, 466, 467,
468, 469, 470, 471, 472, 473, 474, 475, 476, 477, 478, 479, 480, 481, 482, 483,
484, 485, 486, 487, 488, 489, 490, 491, 492, 493, 494, 495, 496, 497, 498, 499,
500, 501, 502, 503, 504, 505, 506, 507, 508, 509, 510, 511, 512, 513, 514, 515,
516, 517, 518, 519, 520, 521, 522, 523, 524, 525, 526, 527, 528, 529, 530, 531,
532, 533, 534, 535, 536, 537, 538, 539, 540, 541, 542, 543, 544, 545, 546, 547,
548, 549, 550, 551, 552, 553, 554, 555, 556, 557, 558, 559, 560, 561, 562, 563,
564, 565, 566, 567, 568, 569, 570, 571, 572, 573, 574, 575, 576, 577, 578, 579,
580, 581, 582, 583, 584, 585, 586, 587, 588, 589, 590, 591, 592, 593, 594, 595,
596, 597, 598, 599, 600, 601, 602, 603, 604, 605, 606, 607, 608, 609, 610, 611,
612, 613, 614, 615, 616, 617, 618, 619, 620, 621, 622, 623, 624, 625, 626, 627,
628, 629, 630, 631, 632, 633, 634, 635, 636, 637, 638, 639, 640, 641, 642, 643,
644, 645, 646, 647, 648, 649, 650, 651, 652, 653, 654, 655, 656, 657, 658, 659,
660, 661, 662, 663, 664, 665, 666, 667, 668, 669, 670, 671, 672, 673, 674, 675,
676, 677, 678, 679, 680, 681, 682, 683, 684, 685, 686, 687, 688, 689, 690, 691,
692, 693, 694, 695, 696, 697, 698, 699, 700, 701, 702, 703, 704, 705, 706, 707,
708, 709, 710, 711, 712, 713, 714, 715, 716, 717, 718, 719, 720, 721, 722, 723,
724, 725, 726, 727, 728, 729, 730, 731, 732, 733, 734, 735, 736, 737, 738, 739,
740, 741, 742, 743, 744, 745, 746, 747, 748, 749, 750, 751, 752, 753, 754, 755,
756, 757, 758, 759, 760, 761, 762, 763, 764, 765, 766, 767, 768, 769, 770, 771,
772, 773, 774, 775, 776, 777, 778, 779, 780, 781, 782, 783, 784, 785, 786, 787,
788, 789, 790, 791, 792, 793, 794, 795, 796, 797, 798, 799, 800, 801, 802, 803,
804, 805, 806, 807, 808, 809]'
normalization: zscore
log_to_screen: 'True'
save_checkpoint: 'True'
enable_nhwc: 'False'
optimizer_type: FusedAdam
crop_size_x: None
crop_size_y: None
two_step_training: 'False'
plot_animations: 'False'
add_noise: 'False'
noise_std: '0'
epsilon_factor: '0'
world_size: '1'
experiment_dir:
/home/cxt/work/fourcastnet_TEC/FourCastNetTEC/data_ljkj_6h_alllevel/results/tec_256/afno_backbone_ustc/d4p4_resabs
checkpoint_path: ./results/tec_256/afno_backbone_ustc/d4p4_resabs/training_checkpoints/ckpt.tar
best_checkpoint_path:
./results/tec_256/afno_backbone_ustc/d4p4_resabs/training_checkpoints/best_ckpt.tar
resuming: 'False'
local_rank: '0'
enable_amp: 'True'
name: afno_backbone_ustc_d4p4_resabs
group: era5_precipafno_backbone_ustc
project: ERA5_precip
entity: flowgan
N_in_channels: '810'
N_out_channels: '810'
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