Hugging Face's logo

ibm-granite
/
granite-geospatial-land-surface-temperature

Model card Files Community
23
granite-geospatial-land-surface-temperature
File size: 4,416 Bytes 
db24bac
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
 
# lightning.pytorch==2.1.1
seed_everything: 0

### Trainer configuration
trainer:
  accelerator: auto
  strategy: auto
  devices: auto
  num_nodes: 1
  # precision: 16-mixed
  logger:
    # You can swtich to TensorBoard for logging by uncommenting the below line and commenting out the procedding line
    #class_path: TensorBoardLogger
    class_path: lightning.pytorch.loggers.csv_logs.CSVLogger
    init_args:
      save_dir: ./experiments
      name: fine_tune_suhi
  callbacks:
    - class_path: RichProgressBar
    - class_path: LearningRateMonitor
      init_args:
        logging_interval: epoch
    - class_path: EarlyStopping
      init_args:
        monitor: val/loss
        patience: 600
  max_epochs: 600
  check_val_every_n_epoch: 1
  log_every_n_steps: 10
  enable_checkpointing: true
  default_root_dir: ./experiments
out_dtype: float32

### Data configuration
data:
  class_path: GenericNonGeoPixelwiseRegressionDataModule
  init_args:
    batch_size: 1
    num_workers: 8
    train_transform:
      - class_path: albumentations.HorizontalFlip
        init_args:
          p: 0.5
      - class_path: albumentations.Rotate
        init_args:
          limit: 30
          border_mode: 0 # cv2.BORDER_CONSTANT
          value: 0
          mask_value: 1
          p: 0.5
      - class_path: ToTensorV2
    # Specify all bands which are in the input data. 
    dataset_bands:
    # 6 HLS bands
      - BLUE
      - GREEN
      - RED
      - NIR_NARROW
      - SWIR_1
      - SWIR_2
    # ERA5-Land t2m_spatial_avg
      - 7
    # ERA5-Land t2m_sunrise_avg
      - 8
    # ERA5-Land t2m_midnight_avg
      - 9
    # ERA5-Land t2m_delta_avg
      - 10
    # cos_tod
      - 11
    # sin_tod
      - 12
    # cos_doy
      - 13
    # sin_doy
      - 14
    # Specify the bands which are used from the input data.
    # Bands 8 - 14 were discarded in the final model
    output_bands:
      - BLUE
      - GREEN
      - RED
      - NIR_NARROW
      - SWIR_1
      - SWIR_2
      - 7
    rgb_indices:
      - 2
      - 1
      - 0
    # Directory roots to training, validation and test datasplits:
    train_data_root: train/inputs
    train_label_data_root: train/targets
    val_data_root: val/inputs
    val_label_data_root: val/targets
    test_data_root: test/inputs
    test_label_data_root: test/targets
    img_grep: "*.inputs.tif"
    label_grep: "*.lst.tif"
    # Nodata value in the input data
    no_data_replace: 0
    # Nodata value in label (target) data 
    no_label_replace: -9999
    # Mean value of the training dataset per band  
    means:
    - 702.4754028320312
    - 1023.23291015625
    - 1118.8924560546875 
    - 2440.750732421875 
    - 2052.705810546875 
    - 1514.15087890625 
    - 21.031919479370117 
    # Standard deviation of the training dataset per band
    stds:
    - 554.8255615234375 
    - 613.5565185546875 
    - 745.929443359375
    - 715.0111083984375 
    - 761.47607421875 
    - 734.991943359375 
    - 8.66781997680664 

### Model configuration
model:
  class_path: terratorch.tasks.PixelwiseRegressionTask
  init_args:
    model_args:
      decoder: UperNetDecoder
      pretrained: false
      backbone: prithvi_swin_L
      img_size: 224
      backbone_drop_path_rate: 0.3
      decoder_channels: 256
      in_channels: 7
      bands:
      - BLUE
      - GREEN
      - RED
      - NIR_NARROW
      - SWIR_1
      - SWIR_2
      - 7
      num_frames: 1
    loss: rmse
    aux_heads:
      - name: aux_head
        decoder: IdentityDecoder
        decoder_args:
          head_dropout: 0.5
          head_channel_list:
          - 1
          head_final_act: torch.nn.LazyLinear
    aux_loss:
      aux_head: 0.4
    ignore_index: -9999
    freeze_backbone: false
    freeze_decoder: false
    model_factory: PrithviModelFactory
    # This block is commented out when inferencing on full tiles.
    # It is possible to inference on full tiles with this paramter on, the benefit is that the compute requirement is smaller.
    # However, using this to inference on a full tile will introduce artefacting/"patchy" predictions.
    # tiled_inference_parameters:
    #    h_crop: 224
    #    h_stride: 224
    #    w_crop: 224
    #    w_stride: 224
    #    average_patches: true
optimizer:
  class_path: torch.optim.AdamW
  init_args:
    lr: 0.0001
    weight_decay: 0.05
lr_scheduler:
  class_path: ReduceLROnPlateau
  init_args:
    monitor: val/loss