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dist_params = dict(backend='nccl')
log_level = 'INFO'
load_from = None
resume_from = None
cudnn_benchmark = True
custom_imports = dict(imports=['geospatial_fm'])
dataset_type = 'GeospatialDataset'
data_root = '/dccstor/geofm-finetuning/fire-scars/finetune-data/6_bands_no_replant_extended'
num_frames = 1
img_size = 224
num_workers = 4
samples_per_gpu = 4
img_norm_cfg = dict(
means=[
0.033349706741586264, 0.05701185520536176, 0.05889748132001316,
0.2323245113436119, 0.1972854853760658, 0.11944914225186566
],
stds=[
0.02269135568823774, 0.026807560223070237, 0.04004109844362779,
0.07791732423672691, 0.08708738838140137, 0.07241979477437814
])
bands = [0, 1, 2, 3, 4, 5]
tile_size = 224
orig_nsize = 512
crop_size = (224, 224)
img_suffix = '_merged.tif'
seg_map_suffix = '.mask.tif'
ignore_index = -1
image_nodata = -9999
image_nodata_replace = 0
image_to_float32 = True
# pretrained_weights_path = '/dccstor/geofm-finetuning/pretrain_ckpts/mae_weights/2023-04-29_21-50-47/epoch-725-loss-0.0365.pt'
pretrained_weights_path = None
num_layers = 12
patch_size = 16
embed_dim = 768
num_heads = 12
tubelet_size = 1
epochs = 50
eval_epoch_interval = 5
experiment = 'test2'
project_dir = '/dccstor/geofm-finetuning/fire-scars/os'
work_dir = '/dccstor/geofm-finetuning/fire-scars/os/test2'
save_path = '/dccstor/geofm-finetuning/fire-scars/os/test2'
train_pipeline = [
dict(type='LoadGeospatialImageFromFile', to_float32=True),
dict(type='LoadGeospatialAnnotations', reduce_zero_label=False),
dict(type='BandsExtract', bands=[0, 1, 2, 3, 4, 5]),
dict(type='RandomFlip', prob=0.5),
dict(type='ToTensor', keys=['img', 'gt_semantic_seg']),
dict(
type='TorchNormalize',
means=[
0.033349706741586264, 0.05701185520536176, 0.05889748132001316,
0.2323245113436119, 0.1972854853760658, 0.11944914225186566
],
stds=[
0.02269135568823774, 0.026807560223070237, 0.04004109844362779,
0.07791732423672691, 0.08708738838140137, 0.07241979477437814
]),
dict(type='TorchRandomCrop', crop_size=(224, 224)),
dict(type='Reshape', keys=['img'], new_shape=(6, 1, 224, 224)),
dict(type='Reshape', keys=['gt_semantic_seg'], new_shape=(1, 224, 224)),
dict(
type='CastTensor',
keys=['gt_semantic_seg'],
new_type='torch.LongTensor'),
dict(type='Collect', keys=['img', 'gt_semantic_seg'])
]
test_pipeline = [
dict(type='LoadGeospatialImageFromFile', to_float32=True),
dict(type='BandsExtract', bands=[0, 1, 2, 3, 4, 5]),
dict(type='ToTensor', keys=['img']),
dict(
type='TorchNormalize',
means=[
0.033349706741586264, 0.05701185520536176, 0.05889748132001316,
0.2323245113436119, 0.1972854853760658, 0.11944914225186566
],
stds=[
0.02269135568823774, 0.026807560223070237, 0.04004109844362779,
0.07791732423672691, 0.08708738838140137, 0.07241979477437814
]),
dict(
type='Reshape',
keys=['img'],
new_shape=(6, 1, -1, -1),
look_up=dict({
'2': 1,
'3': 2
})),
dict(type='CastTensor', keys=['img'], new_type='torch.FloatTensor'),
dict(
type='CollectTestList',
keys=['img'],
meta_keys=[
'img_info', 'seg_fields', 'img_prefix', 'seg_prefix', 'filename',
'ori_filename', 'img', 'img_shape', 'ori_shape', 'pad_shape',
'scale_factor', 'img_norm_cfg'
])
]
data = dict(
samples_per_gpu=4,
workers_per_gpu=4,
train=dict(
type='FireScars',
data_root=
'/dccstor/geofm-finetuning/fire-scars/finetune-data/6_bands_no_replant_extended',
img_dir='training',
ann_dir='training',
img_suffix='_merged.tif',
seg_map_suffix='.mask.tif',
pipeline=[
dict(type='LoadGeospatialImageFromFile', to_float32=True),
dict(type='LoadGeospatialAnnotations', reduce_zero_label=False),
dict(type='BandsExtract', bands=[0, 1, 2, 3, 4, 5]),
dict(type='RandomFlip', prob=0.5),
dict(type='ToTensor', keys=['img', 'gt_semantic_seg']),
dict(
type='TorchNormalize',
means=[
0.033349706741586264, 0.05701185520536176,
0.05889748132001316, 0.2323245113436119,
0.1972854853760658, 0.11944914225186566
],
stds=[
0.02269135568823774, 0.026807560223070237,
0.04004109844362779, 0.07791732423672691,
0.08708738838140137, 0.07241979477437814
]),
dict(type='TorchRandomCrop', crop_size=(224, 224)),
dict(type='Reshape', keys=['img'], new_shape=(6, 1, 224, 224)),
dict(
type='Reshape',
keys=['gt_semantic_seg'],
new_shape=(1, 224, 224)),
dict(
type='CastTensor',
keys=['gt_semantic_seg'],
new_type='torch.LongTensor'),
dict(type='Collect', keys=['img', 'gt_semantic_seg'])
],
ignore_index=-1),
val=dict(
type='FireScars',
data_root=
'/dccstor/geofm-finetuning/fire-scars/finetune-data/6_bands_no_replant_extended',
img_dir='validation',
ann_dir='validation',
img_suffix='_merged.tif',
seg_map_suffix='.mask.tif',
pipeline=[
dict(type='LoadGeospatialImageFromFile', to_float32=True),
dict(type='BandsExtract', bands=[0, 1, 2, 3, 4, 5]),
dict(type='ToTensor', keys=['img']),
dict(
type='TorchNormalize',
means=[
0.033349706741586264, 0.05701185520536176,
0.05889748132001316, 0.2323245113436119,
0.1972854853760658, 0.11944914225186566
],
stds=[
0.02269135568823774, 0.026807560223070237,
0.04004109844362779, 0.07791732423672691,
0.08708738838140137, 0.07241979477437814
]),
dict(
type='Reshape',
keys=['img'],
new_shape=(6, 1, -1, -1),
look_up=dict({
'2': 1,
'3': 2
})),
dict(
type='CastTensor', keys=['img'], new_type='torch.FloatTensor'),
dict(
type='CollectTestList',
keys=['img'],
meta_keys=[
'img_info', 'seg_fields', 'img_prefix', 'seg_prefix',
'filename', 'ori_filename', 'img', 'img_shape',
'ori_shape', 'pad_shape', 'scale_factor', 'img_norm_cfg'
])
],
ignore_index=-1),
test=dict(
type='FireScars',
data_root=
'/dccstor/geofm-finetuning/fire-scars/finetune-data/6_bands_no_replant_extended',
img_dir='validation',
ann_dir='validation',
img_suffix='_merged.tif',
seg_map_suffix='.mask.tif',
pipeline=[
dict(type='LoadGeospatialImageFromFile', to_float32=True),
dict(type='BandsExtract', bands=[0, 1, 2, 3, 4, 5]),
dict(type='ToTensor', keys=['img']),
dict(
type='TorchNormalize',
means=[
0.033349706741586264, 0.05701185520536176,
0.05889748132001316, 0.2323245113436119,
0.1972854853760658, 0.11944914225186566
],
stds=[
0.02269135568823774, 0.026807560223070237,
0.04004109844362779, 0.07791732423672691,
0.08708738838140137, 0.07241979477437814
]),
dict(
type='Reshape',
keys=['img'],
new_shape=(6, 1, -1, -1),
look_up=dict({
'2': 1,
'3': 2
})),
dict(
type='CastTensor', keys=['img'], new_type='torch.FloatTensor'),
dict(
type='CollectTestList',
keys=['img'],
meta_keys=[
'img_info', 'seg_fields', 'img_prefix', 'seg_prefix',
'filename', 'ori_filename', 'img', 'img_shape',
'ori_shape', 'pad_shape', 'scale_factor', 'img_norm_cfg'
])
],
ignore_index=-1))
optimizer = dict(type='Adam', lr=1.3e-05, betas=(0.9, 0.999))
optimizer_config = dict(grad_clip=None)
lr_config = dict(
policy='poly',
warmup='linear',
warmup_iters=1500,
warmup_ratio=1e-06,
power=1.0,
min_lr=0.0,
by_epoch=False)
log_config = dict(
interval=20,
hooks=[
dict(type='TextLoggerHook', by_epoch=False),
dict(type='TensorboardLoggerHook', by_epoch=False)
])
checkpoint_config = dict(
by_epoch=True,
interval=10,
out_dir=
'/dccstor/geofm-finetuning/carlosgomes/fire_scars/carlos_replicate_experiment_fixed_lr'
)
evaluation = dict(
interval=1180,
metric='mIoU',
pre_eval=True,
save_best='mIoU',
by_epoch=False)
runner = dict(type='IterBasedRunner', max_iters=6300)
workflow = [('train', 1)]
norm_cfg = dict(type='BN', requires_grad=True)
model = dict(
type='TemporalEncoderDecoder',
frozen_backbone=False,
backbone=dict(
type='TemporalViTEncoder',
pretrained=None,
# '/dccstor/geofm-finetuning/pretrain_ckpts/mae_weights/2023-04-29_21-50-47/epoch-725-loss-0.0365.pt',
img_size=224,
patch_size=16,
num_frames=1,
tubelet_size=1,
in_chans=6,
embed_dim=768,
depth=12,
num_heads=12,
mlp_ratio=4.0,
norm_pix_loss=False),
neck=dict(
type='ConvTransformerTokensToEmbeddingNeck',
embed_dim=768,
output_embed_dim=768,
drop_cls_token=True,
Hp=14,
Wp=14),
decode_head=dict(
num_classes=2,
in_channels=768,
type='FCNHead',
in_index=-1,
channels=256,
num_convs=1,
concat_input=False,
dropout_ratio=0.1,
norm_cfg=dict(type='BN', requires_grad=True),
align_corners=False,
loss_decode=dict(
type='DiceLoss', use_sigmoid=False, loss_weight=1,
ignore_index=-1)),
auxiliary_head=dict(
num_classes=2,
in_channels=768,
type='FCNHead',
in_index=-1,
channels=256,
num_convs=2,
concat_input=False,
dropout_ratio=0.1,
norm_cfg=dict(type='BN', requires_grad=True),
align_corners=False,
loss_decode=dict(
type='DiceLoss', use_sigmoid=False, loss_weight=1,
ignore_index=-1)),
train_cfg=dict(),
test_cfg=dict(mode='slide', stride=(112, 112), crop_size=(224, 224)))
gpu_ids = range(0, 1)
auto_resume = False
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