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cfg.yaml
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dataset:
dataset_name: "sevirlr"
img_height: 128
img_width: 128
in_len: 0
out_len: 1
seq_len: 1
plot_stride: 1
interval_real_time: 10
sample_mode: "sequent"
stride: 1
layout: "NTHWC"
start_date: null
train_test_split_date: [2019, 6, 1]
end_date: null
val_ratio: 0.1
metrics_mode: "0"
metrics_list: ['csi', 'pod', 'sucr', 'bias']
threshold_list: [16, 74, 133, 160, 181, 219]
aug_mode: "1"
layout:
layout: "NHWC"
optim:
total_batch_size: 512
micro_batch_size: 16
float32_matmul_precision: "high"
seed: 0
method: "adam"
lr: 5e-6
betas: [0.5, 0.9]
gradient_clip_val: 1.0
max_epochs: 500
# scheduler
warmup_percentage: 0.1
lr_scheduler_mode: "cosine"
min_lr_ratio: 1.0e-3
warmup_min_lr_ratio: 0.1
# early stopping
monitor: "val/total_loss"
early_stop: false
early_stop_mode: "min"
early_stop_patience: 5
save_top_k: 5
logging:
logging_prefix: "VAE_GAN_SEVIR-LR"
monitor_lr: true
monitor_device: false
track_grad_norm: -1
use_wandb: false
trainer:
check_val_every_n_epoch: 5
log_step_ratio: 0.001
precision: 32
find_unused_parameters: True
num_sanity_val_steps: 2
eval:
train_example_data_idx_list: [0, ]
val_example_data_idx_list: [0, ]
test_example_data_idx_list: [0, 16, 32, 48, 64, 72, 96, 108, 128]
eval_example_only: false
num_vis: 10
model:
data_channels: 1
down_block_types: ['DownEncoderBlock2D', 'DownEncoderBlock2D', 'DownEncoderBlock2D', 'DownEncoderBlock2D']
in_channels: 1
block_out_channels: [128, 256, 512, 512] # downsample `len(block_out_channels) - 1` times
act_fn: 'silu'
latent_channels: 64
up_block_types: ['UpDecoderBlock2D', 'UpDecoderBlock2D', 'UpDecoderBlock2D', 'UpDecoderBlock2D']
norm_num_groups: 32
layers_per_block: 2
out_channels: 1
loss:
disc_start: 50001
kl_weight: 1e-6
disc_weight: 0.5
perceptual_weight: 0.0 # SEVIR does not have RGB channels
disc_in_channels: 1