67 lines
1.8 KiB
YAML
67 lines
1.8 KiB
YAML
# configs/default.yaml
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# Default Configuration for Geo-Layout Transformer
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# 1. Data Preprocessing
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data:
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patch_size: 10.0 # in microns
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patch_stride: 5.0 # in microns
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layer_mapping:
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"1/0": 0 # Example: GDS Layer 1, Datatype 0 -> Index 0 (e.g., M1)
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"2/0": 1 # Example: GDS Layer 2, Datatype 0 -> Index 1 (e.g., VIA1)
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"3/0": 2 # Example: GDS Layer 3, Datatype 0 -> Index 2 (e.g., M2)
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graph_construction:
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edge_strategy: "knn" # 'knn' or 'radius'
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knn_k: 8 # K for KNN graph construction
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radius_d: 1.0 # Radius for radius graph construction (in microns)
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# 2. Model Architecture
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model:
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# GNN Encoder
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gnn:
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node_input_dim: 5 # Initial node feature dim (e.g., x, y, w, h, area)
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hidden_dim: 128
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output_dim: 256 # Dimension of the patch embedding
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num_layers: 4
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gnn_type: "gat" # 'gat', 'gcn', 'graphsage'
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# Transformer Backbone
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transformer:
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num_layers: 6
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num_heads: 8
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hidden_dim: 256 # Must match gnn.output_dim
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dropout: 0.1
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positional_encoding:
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type: "hybrid" # 'hybrid', 'absolute', 'relative'
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# 3. Training
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training:
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mode: "train" # 'train', 'eval', 'pretrain'
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batch_size: 32
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learning_rate: 0.0001
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epochs: 100
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optimizer: "adamw"
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loss_function: "bce" # 'bce', 'focal_loss'
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weight_decay: 0.01
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scheduler: "cosine" # 'step', 'cosine'
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scheduler_T_0: 10
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scheduler_T_mult: 2
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early_stopping_patience: 10
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save_dir: "checkpoints"
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log_dir: "logs"
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use_amp: false # 是否启用混合精度训练
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gradient_accumulation_steps: 1 # 梯度累积步数
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# 4. Data Splits
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splits:
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train_ratio: 0.8
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val_ratio: 0.1
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test_ratio: 0.1
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random_seed: 42
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# 4. Self-Supervised Pre-training
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pretraining:
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mask_ratio: 0.15
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epochs: 200
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learning_rate: 0.0005
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early_stopping_patience: 10
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