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"model_name": "Earthformer",
"model_type": "earthformer",
"architectures": [
"Earthformer"
],
"framework": "PyTorch",
"domain": "climate-and-atmosphere",
"task": "spatiotemporal-forecasting",
"implementation": {
"entry_point": "model/earthformer.py",
"scope": "two-level hierarchical Cuboid Attention space-time Transformer with BTHWC input and output"
},
"architecture": {
"family": "hierarchical space-time Transformer with Cuboid Attention",
"attention_mechanism": "cuboid-window attention with optional global vectors",
"input_format": "BTHWC",
"encoder": {
"levels": 2,
"stem": "Conv2d 3x3 stride 2",
"downsample": "Conv2d 3x3 stride 2",
"cuboid_pattern": [
[
2,
4,
4
]
]
},
"decoder": {
"cuboid_pattern": "axial",
"cross_attention": "CuboidCross(T,1,1): future queries attend to history at each spatial site",
"prediction": "learned future query with per-frame up-projection and encoder skip connection"
},
"activation": "GELU",
"normalization": "LayerNorm",
"repository_default_config": {
"purpose": "CPU smoke verification with synthetic SEVIR data",
"dims": [
4,
8
],
"depths": [
1,
1
],
"heads": 1,
"num_global_vectors": 1,
"ff_ratio": 2.0,
"dropout": 0.0
}
},
"data": {
"dataset": "SEVIR",
"variable": "vertically_integrated_liquid",
"frame_interval_minutes": 5,
"input_length": 13,
"output_length": 12,
"channels": 1,
"official_spatial_size": [
384,
384
],
"official_sampling": "sequent with stride 12",
"default_smoke_spatial_size": [
32,
32
],
"normalization": "unit"
},
"configuration_sources": [
"conf/config.yaml",
"model/earthformer.py",
"script/data_loader.py",
"configuration.json"
]
}
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