File size: 2,698 Bytes
d787cb2 | 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 | # FLAME on OxHyperSyntheticCH4 (EMIT) — reference training configuration.
#
# Protocol (aligned to the HyperspectralViTs training regime, arXiv:2410.17248):
# deterministic 64x64 / stride-32 grid windows from
# train_filtered_v2_tiled_64_32.csv, plume windows balanced to ~50% of every
# epoch by a with-replacement weighted sampler, BCE with pos_weight capped at
# pw_max, 50 epochs, bf16 autocast. Validation reproduces the test protocol
# (window pooling, logits >= 0, no morphological filtering).
#
# This configuration trains the self-contained physics variant
# (use_mag_in_seg: false): the segmentation head consumes the model's own
# parameter-free physics score, and mag1c (B_magic30_tile.tif / score_divisor,
# clipped to [0, 2]) acts only as a training-time auxiliary target for the
# score head, co-trained at a weak weight that fades linearly over the whole
# run (aux_weight 0.15, decay_frac 1.0). aux_bg_weight adds mild background
# suppression (score -> 0 where mag1c is silent) and pw_max: 3 provides a mild
# recall bootstrap — both guard against the all-negative collapse.
#
# Alternative (mag-in-seg) variant: set use_mag_in_seg: true so the seg head
# consumes mag1c directly at train and inference time; then set aux_weight: 0,
# aux_bg_weight: 0, pw_max: 1.
dataset: emit
model:
in_channels: 64 # SWIR 2004-2478 nm (EMIT)
width: 32
modes1: 16
modes2: 16
n_fno_layers: 3
n_ufno_layers: 3
dropout_rate: 0.0
seg_channels: [32, 16]
seg_kernel_size: 3
score_divisor: 1750.0 # shared by score normalisation and the mag1c input
rgb_divisor: 20.0
norm_type: group
score_clamp: [-10.0, 10.0]
use_mag_in_seg: false
spectrum_path: resources/emit/ch4_spectrum.npy
centers_path: resources/emit/band_centers.npy
baseline_stats_path: resources/emit/baseline_stats.pt
train:
uid: flame_emit
lr: 5.0e-4
epochs: 50
batch_size: 18 # must be divisible by the number of GPUs
n_workers: 2
log_dir: logs
seed: 42
patience: 25
ckpt_interval: 10
prefetch_factor: 2
vis_interval: 10
pretrain_epochs: 0
aux_weight: 0.15
decay_frac: 1.0
aux_bg_weight: 0.1
grad_clip_norm: 5.0
seg_loss: bce
pw_max: 3
focal_gamma: 0.0
val_threshold: 0.5
amp: bf16
val_window_batch: 256
find_unused_parameters: true
data:
root_dir: datasets/oxhyper_synthetic_ch4
store_dir: datasets/emit_ram64 # flat fp16 memmap store, built automatically
npy_cache_dir: ~ # optional (scripts/build_emit_npy_cache.py)
windows_csv: train_filtered_v2_tiled_64_32.csv
wv_window: [2004.0, 2478.0]
patch_size: 64
stride: 32
augment: true
max_train_windows: ~
max_val_tiles: ~
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