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import os |
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custom_imports = dict(imports=['geospatial_fm']) |
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dist_params = dict(backend='nccl') |
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log_level = 'INFO' |
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load_from = None |
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resume_from = None |
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cudnn_benchmark = True |
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dataset_type = 'GeospatialDataset' |
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data_root = '<path to data root>' |
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num_frames = 1 |
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img_size = 224 |
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num_workers = 4 |
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samples_per_gpu = 4 |
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img_norm_cfg = dict( |
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means=[ |
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0.033349706741586264, 0.05701185520536176, 0.05889748132001316, |
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0.2323245113436119, 0.1972854853760658, 0.11944914225186566 |
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], |
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stds=[ |
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0.02269135568823774, 0.026807560223070237, 0.04004109844362779, |
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0.07791732423672691, 0.08708738838140137, 0.07241979477437814 |
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]) |
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bands = [0, 1, 2, 3, 4, 5] |
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tile_size = 224 |
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orig_nsize = 512 |
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crop_size = (tile_size, tile_size) |
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img_suffix = '_merged.tif' |
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seg_map_suffix = '.mask.tif' |
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ignore_index = -1 |
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image_nodata = -9999 |
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image_nodata_replace = 0 |
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image_to_float32 = True |
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pretrained_weights_path = '<path to pretrained weights>' |
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num_layers = 12 |
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patch_size = 16 |
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embed_dim = 768 |
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num_heads = 12 |
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tubelet_size = 1 |
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output_embed_dim = num_frames*embed_dim |
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max_intervals=10000 |
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evaluation_interval=1000 |
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experiment = '<experiment name>' |
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project_dir = '<project directory name>' |
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work_dir = os.path.join(project_dir, experiment) |
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save_path = work_dir |
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save_path = work_dir |
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train_pipeline = [ |
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dict(type='LoadGeospatialImageFromFile', to_float32=image_to_float32), |
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dict(type='LoadGeospatialAnnotations', reduce_zero_label=False), |
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dict(type='BandsExtract', bands=bands), |
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dict(type='RandomFlip', prob=0.5), |
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dict(type='ToTensor', keys=['img', 'gt_semantic_seg']), |
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dict(type="TorchPermute", keys=["img"], order=(2, 0, 1)), |
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dict(type='TorchNormalize', **img_norm_cfg), |
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dict(type='TorchRandomCrop', crop_size=(tile_size, tile_size)), |
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dict(type='Reshape', keys=['img'], new_shape=(len(bands), num_frames, tile_size, tile_size)), |
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dict(type='Reshape', keys=['gt_semantic_seg'], new_shape=(1, tile_size, tile_size)), |
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dict( |
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type='CastTensor', |
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keys=['gt_semantic_seg'], |
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new_type='torch.LongTensor'), |
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dict(type='Collect', keys=['img', 'gt_semantic_seg']) |
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] |
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test_pipeline = [ |
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dict(type='LoadGeospatialImageFromFile', to_float32=image_to_float32), |
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dict(type='BandsExtract', bands=bands), |
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dict(type='ToTensor', keys=['img']), |
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dict(type="TorchPermute", keys=["img"], order=(2, 0, 1)), |
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dict(type='TorchNormalize', **img_norm_cfg), |
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dict( |
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type='Reshape', |
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keys=['img'], |
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new_shape=(len(bands), num_frames, -1, -1), |
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look_up=dict({ |
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'2': 1, |
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'3': 2 |
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})), |
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dict(type='CastTensor', keys=['img'], new_type='torch.FloatTensor'), |
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dict( |
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type='CollectTestList', |
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keys=['img'], |
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meta_keys=[ |
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'img_info', 'seg_fields', 'img_prefix', 'seg_prefix', 'filename', |
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'ori_filename', 'img', 'img_shape', 'ori_shape', 'pad_shape', |
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'scale_factor', 'img_norm_cfg' |
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]) |
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] |
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CLASSES = ('Unburnt land', 'Burn scar') |
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data = dict( |
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samples_per_gpu=samples_per_gpu, |
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workers_per_gpu=num_workers, |
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train=dict( |
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type=dataset_type, |
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CLASSES=CLASSES, |
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data_root=data_root, |
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img_dir='training', |
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ann_dir='training', |
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img_suffix=img_suffix, |
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seg_map_suffix=seg_map_suffix, |
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pipeline=train_pipeline, |
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ignore_index=-1), |
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val=dict( |
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type=dataset_type, |
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CLASSES=CLASSES, |
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data_root=data_root, |
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img_dir='validation', |
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ann_dir='validation', |
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img_suffix=img_suffix, |
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seg_map_suffix=seg_map_suffix, |
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pipeline=test_pipeline, |
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ignore_index=-1), |
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test=dict( |
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type=dataset_type, |
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CLASSES=CLASSES, |
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data_root=data_root, |
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img_dir='validation', |
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ann_dir='validation', |
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img_suffix=img_suffix, |
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seg_map_suffix=seg_map_suffix, |
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pipeline=test_pipeline, |
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ignore_index=-1)) |
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optimizer = dict(type='Adam', lr=1.3e-05, betas=(0.9, 0.999)) |
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optimizer_config = dict(grad_clip=None) |
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lr_config = dict( |
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policy='poly', |
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warmup='linear', |
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warmup_iters=1500, |
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warmup_ratio=1e-06, |
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power=1.0, |
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min_lr=0.0, |
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by_epoch=False) |
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log_config = dict( |
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interval=20, |
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hooks=[ |
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dict(type='TextLoggerHook', by_epoch=False), |
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dict(type='TensorboardLoggerHook', by_epoch=False) |
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]) |
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checkpoint_config = dict( |
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by_epoch=True, |
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interval=10, |
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out_dir=save_path |
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) |
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evaluation = dict( |
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interval=evaluation_interval, |
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metric='mIoU', |
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pre_eval=True, |
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save_best='mIoU', |
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by_epoch=False) |
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loss_func=dict( |
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type='DiceLoss', use_sigmoid=False, loss_weight=1, |
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ignore_index=-1) |
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runner = dict(type='IterBasedRunner', max_iters=max_intervals) |
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workflow = [('train', 1)] |
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norm_cfg = dict(type='BN', requires_grad=True) |
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model = dict( |
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type='TemporalEncoderDecoder', |
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frozen_backbone=False, |
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backbone=dict( |
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type='TemporalViTEncoder', |
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pretrained=pretrained_weights_path, |
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img_size=img_size, |
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patch_size=patch_size, |
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num_frames=num_frames, |
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tubelet_size=tubelet_size, |
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in_chans=len(bands), |
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embed_dim=embed_dim, |
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depth=12, |
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num_heads=num_heads, |
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mlp_ratio=4.0, |
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norm_pix_loss=False), |
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neck=dict( |
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type='ConvTransformerTokensToEmbeddingNeck', |
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embed_dim=embed_dim*num_frames, |
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output_embed_dim=output_embed_dim, |
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drop_cls_token=True, |
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Hp=14, |
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Wp=14), |
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decode_head=dict( |
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num_classes=len(CLASSES), |
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in_channels=output_embed_dim, |
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type='FCNHead', |
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in_index=-1, |
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channels=256, |
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num_convs=1, |
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concat_input=False, |
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dropout_ratio=0.1, |
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norm_cfg=dict(type='BN', requires_grad=True), |
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align_corners=False, |
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loss_decode=loss_func), |
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auxiliary_head=dict( |
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num_classes=len(CLASSES), |
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in_channels=output_embed_dim, |
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type='FCNHead', |
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in_index=-1, |
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channels=256, |
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num_convs=2, |
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concat_input=False, |
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dropout_ratio=0.1, |
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norm_cfg=dict(type='BN', requires_grad=True), |
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align_corners=False, |
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loss_decode=loss_func), |
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train_cfg=dict(), |
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test_cfg=dict(mode='slide', stride=(int(tile_size/2), int(tile_size/2)), crop_size=(tile_size, tile_size))) |
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gpu_ids = range(0, 1) |
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auto_resume = False |
