Prithvi-100M-burn-scar / burn_scars_Prithvi_100M.py
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import os
custom_imports = dict(imports=['geospatial_fm'])
dist_params = dict(backend='nccl')
log_level = 'INFO'
load_from = None
resume_from = None
cudnn_benchmark = True
dataset_type = 'GeospatialDataset'
# TO BE DEFINED BY USER: data directory
data_root = '<path to data root>'
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 = (tile_size, tile_size)
img_suffix = '_merged.tif'
seg_map_suffix = '.mask.tif'
ignore_index = -1
image_nodata = -9999
image_nodata_replace = 0
image_to_float32 = True
# model
# TO BE DEFINED BY USER: model path
pretrained_weights_path = '<path to pretrained weights>'
num_layers = 12
patch_size = 16
embed_dim = 768
num_heads = 12
tubelet_size = 1
output_embed_dim = num_frames*embed_dim
max_intervals=10000
evaluation_interval=1000
# TO BE DEFINED BY USER: model path
experiment = '<experiment name>'
project_dir = '<project directory name>'
work_dir = os.path.join(project_dir, experiment)
save_path = work_dir
save_path = work_dir
train_pipeline = [
dict(type='LoadGeospatialImageFromFile', to_float32=image_to_float32, channels_last=True),
dict(type='LoadGeospatialAnnotations', reduce_zero_label=False),
dict(type='BandsExtract', bands=bands),
dict(type='RandomFlip', prob=0.5),
dict(type='ToTensor', keys=['img', 'gt_semantic_seg']),
# to channels first
dict(type="TorchPermute", keys=["img"], order=(2, 0, 1)),
dict(type='TorchNormalize', **img_norm_cfg),
dict(type='TorchRandomCrop', crop_size=(tile_size, tile_size)),
dict(type='Reshape', keys=['img'], new_shape=(len(bands), num_frames, tile_size, tile_size)),
dict(type='Reshape', keys=['gt_semantic_seg'], new_shape=(1, tile_size, tile_size)),
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=image_to_float32, channels_last=True),
dict(type='BandsExtract', bands=bands),
dict(type='ToTensor', keys=['img']),
# to channels first
dict(type="TorchPermute", keys=["img"], order=(2, 0, 1)),
dict(type='TorchNormalize', **img_norm_cfg),
dict(
type='Reshape',
keys=['img'],
new_shape=(len(bands), num_frames, -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 = ('Unburnt land', 'Burn scar')
data = dict(
samples_per_gpu=samples_per_gpu,
workers_per_gpu=num_workers,
train=dict(
type=dataset_type,
CLASSES=CLASSES,
data_root=data_root,
img_dir='training',
ann_dir='training',
img_suffix=img_suffix,
seg_map_suffix=seg_map_suffix,
pipeline=train_pipeline,
ignore_index=-1),
val=dict(
type=dataset_type,
CLASSES=CLASSES,
data_root=data_root,
img_dir='validation',
ann_dir='validation',
img_suffix=img_suffix,
seg_map_suffix=seg_map_suffix,
pipeline=test_pipeline,
ignore_index=-1),
test=dict(
type=dataset_type,
CLASSES=CLASSES,
data_root=data_root,
img_dir='validation',
ann_dir='validation',
img_suffix=img_suffix,
seg_map_suffix=seg_map_suffix,
pipeline=test_pipeline,
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=save_path
)
evaluation = dict(
interval=evaluation_interval,
metric='mIoU',
pre_eval=True,
save_best='mIoU',
by_epoch=False)
loss_func=dict(
type='DiceLoss', use_sigmoid=False, loss_weight=1,
ignore_index=-1)
runner = dict(type='IterBasedRunner', max_iters=max_intervals)
workflow = [('train', 1)]
norm_cfg = dict(type='BN', requires_grad=True)
model = dict(
type='TemporalEncoderDecoder',
frozen_backbone=False,
backbone=dict(
type='TemporalViTEncoder',
pretrained=pretrained_weights_path,
img_size=img_size,
patch_size=patch_size,
num_frames=num_frames,
tubelet_size=tubelet_size,
in_chans=len(bands),
embed_dim=embed_dim,
depth=12,
num_heads=num_heads,
mlp_ratio=4.0,
norm_pix_loss=False),
neck=dict(
type='ConvTransformerTokensToEmbeddingNeck',
embed_dim=embed_dim*num_frames,
output_embed_dim=output_embed_dim,
drop_cls_token=True,
Hp=14,
Wp=14),
decode_head=dict(
num_classes=len(CLASSES),
in_channels=output_embed_dim,
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=loss_func),
auxiliary_head=dict(
num_classes=len(CLASSES),
in_channels=output_embed_dim,
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=loss_func),
train_cfg=dict(),
test_cfg=dict(mode='slide', stride=(int(tile_size/2), int(tile_size/2)), crop_size=(tile_size, tile_size)))
gpu_ids = range(0, 1)
auto_resume = False