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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'])
num_frames = 3
img_size = 224
num_workers = 2
pretrained_weights_path = '/home/ubuntu/hls-loss-weights/Prithvi_100M.pt'
num_layers = 6
patch_size = 16
embed_dim = 768
num_heads = 8
tubelet_size = 1
epochs = 80
eval_epoch_interval = 2
experiment = 'multiclass_exp_newSplit'
work_dir = '/home/ubuntu/clark_gfm_eval/multiclass_exp_newSplit'
save_path = '/home/ubuntu/clark_gfm_eval/multiclass_exp_newSplit'
gpu_ids = range(0, 1)
dataset_type = 'GeospatialDataset'
data_root = '/home/ubuntu/hls_cdl_reclassed/'
img_norm_cfg = dict(
means=[
494.905781, 815.239594, 924.335066, 2968.881459, 2634.621962,
1739.579917, 494.905781, 815.239594, 924.335066, 2968.881459,
2634.621962, 1739.579917, 494.905781, 815.239594, 924.335066,
2968.881459, 2634.621962, 1739.579917
],
stds=[
284.925432, 357.84876, 575.566823, 896.601013, 951.900334, 921.407808,
284.925432, 357.84876, 575.566823, 896.601013, 951.900334, 921.407808,
284.925432, 357.84876, 575.566823, 896.601013, 951.900334, 921.407808
])
splits = dict(
train=
'/home/ubuntu/hls-foundation-os/fine-tuning-examples/data_splits/crop_classification/training_data.txt',
val=
'/home/ubuntu/hls-foundation-os/fine-tuning-examples/data_splits/crop_classification/validation_data.txt',
test=
'/home/ubuntu/hls-foundation-os/fine-tuning-examples/data_splits/crop_classification/validation_data.txt'
)
bands = [0, 1, 2, 3, 4, 5]
tile_size = 224
orig_nsize = 512
crop_size = (224, 224)
train_pipeline = [
dict(type='LoadGeospatialImageFromFile', to_float32=True),
dict(type='LoadGeospatialAnnotations', reduce_zero_label=True),
dict(type='RandomFlip', prob=0.5),
dict(type='ToTensor', keys=['img', 'gt_semantic_seg']),
dict(
type='TorchNormalize',
means=[
494.905781, 815.239594, 924.335066, 2968.881459, 2634.621962,
1739.579917, 494.905781, 815.239594, 924.335066, 2968.881459,
2634.621962, 1739.579917, 494.905781, 815.239594, 924.335066,
2968.881459, 2634.621962, 1739.579917
],
stds=[
284.925432, 357.84876, 575.566823, 896.601013, 951.900334,
921.407808, 284.925432, 357.84876, 575.566823, 896.601013,
951.900334, 921.407808, 284.925432, 357.84876, 575.566823,
896.601013, 951.900334, 921.407808
]),
dict(type='TorchRandomCrop', crop_size=(224, 224)),
dict(type='Reshape', keys=['img'], new_shape=(6, 3, 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'])
]
val_pipeline = [
dict(type='LoadGeospatialImageFromFile', to_float32=True),
dict(type='LoadGeospatialAnnotations', reduce_zero_label=True),
dict(type='ToTensor', keys=['img', 'gt_semantic_seg']),
dict(
type='TorchNormalize',
means=[
494.905781, 815.239594, 924.335066, 2968.881459, 2634.621962,
1739.579917, 494.905781, 815.239594, 924.335066, 2968.881459,
2634.621962, 1739.579917, 494.905781, 815.239594, 924.335066,
2968.881459, 2634.621962, 1739.579917
],
stds=[
284.925432, 357.84876, 575.566823, 896.601013, 951.900334,
921.407808, 284.925432, 357.84876, 575.566823, 896.601013,
951.900334, 921.407808, 284.925432, 357.84876, 575.566823,
896.601013, 951.900334, 921.407808
]),
dict(type='TorchRandomCrop', crop_size=(224, 224)),
dict(type='Reshape', keys=['img'], new_shape=(6, 3, 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'],
meta_keys=[
'img_info', 'ann_info', 'seg_fields', 'img_prefix', 'seg_prefix',
'filename', 'ori_filename', 'img', 'img_shape', 'ori_shape',
'pad_shape', 'scale_factor', 'img_norm_cfg', 'gt_semantic_seg'
])
]
test_pipeline = [
dict(type='LoadGeospatialImageFromFile', to_float32=True),
dict(type='ToTensor', keys=['img']),
dict(
type='TorchNormalize',
means=[
494.905781, 815.239594, 924.335066, 2968.881459, 2634.621962,
1739.579917, 494.905781, 815.239594, 924.335066, 2968.881459,
2634.621962, 1739.579917, 494.905781, 815.239594, 924.335066,
2968.881459, 2634.621962, 1739.579917
],
stds=[
284.925432, 357.84876, 575.566823, 896.601013, 951.900334,
921.407808, 284.925432, 357.84876, 575.566823, 896.601013,
951.900334, 921.407808, 284.925432, 357.84876, 575.566823,
896.601013, 951.900334, 921.407808
]),
dict(
type='Reshape',
keys=['img'],
new_shape=(6, 3, -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'
])
]
CLASSES = (1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13)
data = dict(
samples_per_gpu=2,
workers_per_gpu=1,
train=dict(
type='GeospatialDataset',
CLASSES=(1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13),
reduce_zero_label=True,
data_root='/home/ubuntu/hls_cdl_reclassed/',
img_dir='/home/ubuntu/hls_cdl_reclassed/training_chips',
ann_dir='/home/ubuntu/hls_cdl_reclassed/training_chips',
pipeline=[
dict(type='LoadGeospatialImageFromFile', to_float32=True),
dict(type='LoadGeospatialAnnotations', reduce_zero_label=True),
dict(type='RandomFlip', prob=0.5),
dict(type='ToTensor', keys=['img', 'gt_semantic_seg']),
dict(
type='TorchNormalize',
means=[
494.905781, 815.239594, 924.335066, 2968.881459,
2634.621962, 1739.579917, 494.905781, 815.239594,
924.335066, 2968.881459, 2634.621962, 1739.579917,
494.905781, 815.239594, 924.335066, 2968.881459,
2634.621962, 1739.579917
],
stds=[
284.925432, 357.84876, 575.566823, 896.601013, 951.900334,
921.407808, 284.925432, 357.84876, 575.566823, 896.601013,
951.900334, 921.407808, 284.925432, 357.84876, 575.566823,
896.601013, 951.900334, 921.407808
]),
dict(type='TorchRandomCrop', crop_size=(224, 224)),
dict(type='Reshape', keys=['img'], new_shape=(6, 3, 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'])
],
img_suffix='_merged.tif',
seg_map_suffix='.mask.tif',
split=
'/home/ubuntu/hls-foundation-os/fine-tuning-examples/data_splits/crop_classification/training_data.txt'
),
val=dict(
type='GeospatialDataset',
CLASSES=(1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13),
reduce_zero_label=True,
data_root='/home/ubuntu/hls_cdl_reclassed/',
img_dir='/home/ubuntu/hls_cdl_reclassed/validation_chips',
ann_dir='/home/ubuntu/hls_cdl_reclassed/validation_chips',
pipeline=[
dict(type='LoadGeospatialImageFromFile', to_float32=True),
dict(type='ToTensor', keys=['img']),
dict(
type='TorchNormalize',
means=[
494.905781, 815.239594, 924.335066, 2968.881459,
2634.621962, 1739.579917, 494.905781, 815.239594,
924.335066, 2968.881459, 2634.621962, 1739.579917,
494.905781, 815.239594, 924.335066, 2968.881459,
2634.621962, 1739.579917
],
stds=[
284.925432, 357.84876, 575.566823, 896.601013, 951.900334,
921.407808, 284.925432, 357.84876, 575.566823, 896.601013,
951.900334, 921.407808, 284.925432, 357.84876, 575.566823,
896.601013, 951.900334, 921.407808
]),
dict(
type='Reshape',
keys=['img'],
new_shape=(6, 3, -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'
])
],
img_suffix='_merged.tif',
seg_map_suffix='.mask.tif',
split=
'/home/ubuntu/hls-foundation-os/fine-tuning-examples/data_splits/crop_classification/validation_data.txt'
),
test=dict(
type='GeospatialDataset',
CLASSES=(1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13),
reduce_zero_label=True,
data_root='/home/ubuntu/hls_cdl_reclassed/',
img_dir='/home/ubuntu/hls_cdl_reclassed/validation_chips',
ann_dir='/home/ubuntu/hls_cdl_reclassed/validation_chips',
pipeline=[
dict(type='LoadGeospatialImageFromFile', to_float32=True),
dict(type='ToTensor', keys=['img']),
dict(
type='TorchNormalize',
means=[
494.905781, 815.239594, 924.335066, 2968.881459,
2634.621962, 1739.579917, 494.905781, 815.239594,
924.335066, 2968.881459, 2634.621962, 1739.579917,
494.905781, 815.239594, 924.335066, 2968.881459,
2634.621962, 1739.579917
],
stds=[
284.925432, 357.84876, 575.566823, 896.601013, 951.900334,
921.407808, 284.925432, 357.84876, 575.566823, 896.601013,
951.900334, 921.407808, 284.925432, 357.84876, 575.566823,
896.601013, 951.900334, 921.407808
]),
dict(
type='Reshape',
keys=['img'],
new_shape=(6, 3, -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'
])
],
img_suffix='_merged.tif',
seg_map_suffix='.mask.tif',
split=
'/home/ubuntu/hls-foundation-os/fine-tuning-examples/data_splits/crop_classification/validation_data.txt'
))
optimizer = dict(
type='Adam', lr=1.5e-05, betas=(0.9, 0.999), weight_decay=0.05)
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=10,
hooks=[dict(type='TextLoggerHook'),
dict(type='TensorboardLoggerHook')])
checkpoint_config = dict(
by_epoch=True,
interval=10,
out_dir='/home/ubuntu/clark_gfm_eval/multiclass_exp_newSplit')
evaluation = dict(interval=2, metric='mIoU', pre_eval=True, save_best='mIoU')
reduce_train_set = dict(reduce_train_set=False)
reduce_factor = dict(reduce_factor=1)
runner = dict(type='EpochBasedRunner', max_epochs=80)
workflow = [('train', 1), ('val', 1)]
norm_cfg = dict(type='BN', requires_grad=True)
loss_weights_multi = [
0.386375, 0.661126, 0.548184, 0.640482, 0.876862, 0.925186, 3.249462,
1.542289, 2.175141, 2.272419, 3.062762, 3.626097, 1.198702
]
loss_func = dict(
type='CrossEntropyLoss',
use_sigmoid=False,
class_weight=[
0.386375, 0.661126, 0.548184, 0.640482, 0.876862, 0.925186, 3.249462,
1.542289, 2.175141, 2.272419, 3.062762, 3.626097, 1.198702
],
avg_non_ignore=True)
output_embed_dim = 2304
model = dict(
type='TemporalEncoderDecoder',
frozen_backbone=False,
backbone=dict(
type='TemporalViTEncoder',
pretrained='/home/ubuntu/hls-loss-weights/Prithvi_100M.pt',
img_size=224,
patch_size=16,
num_frames=3,
tubelet_size=1,
in_chans=6,
embed_dim=768,
depth=6,
num_heads=8,
mlp_ratio=4.0,
norm_pix_loss=False),
neck=dict(
type='ConvTransformerTokensToEmbeddingNeck',
embed_dim=2304,
output_embed_dim=2304,
drop_cls_token=True,
Hp=14,
Wp=14),
decode_head=dict(
num_classes=13,
in_channels=2304,
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='CrossEntropyLoss',
use_sigmoid=False,
class_weight=[
0.386375, 0.661126, 0.548184, 0.640482, 0.876862, 0.925186,
3.249462, 1.542289, 2.175141, 2.272419, 3.062762, 3.626097,
1.198702
],
avg_non_ignore=True)),
auxiliary_head=dict(
num_classes=13,
in_channels=2304,
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='CrossEntropyLoss',
use_sigmoid=False,
class_weight=[
0.386375, 0.661126, 0.548184, 0.640482, 0.876862, 0.925186,
3.249462, 1.542289, 2.175141, 2.272419, 3.062762, 3.626097,
1.198702
],
avg_non_ignore=True)),
train_cfg=dict(),
test_cfg=dict(mode='slide', stride=(112, 112), crop_size=(224, 224)))
auto_resume = False
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