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Oxer11Oxer11
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add pretrain script & config
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‎config/pretrain/angle_gearnet_edge.yaml‎

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output_dir: ~/scratch/protein_outputs
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dataset:
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class: AlphaFoldDB
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path: ~/scratch/protein-datasets/alphafold
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species_start: 0
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species_end: 22
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# species_id: 0
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# split_id: 1
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transform:
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class: Compose
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transforms:
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- class: ProteinView
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view: residue
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- class: TruncateProtein
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max_length: 100
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random: True
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task:
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class: AnglePrediction
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num_sample: 512
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num_class: 8
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num_mlp_layer: 2
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model:
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class: GearNet
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input_dim: 21
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hidden_dims: [512, 512, 512, 512, 512, 512]
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batch_norm: True
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concat_hidden: True
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short_cut: True
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readout: 'sum'
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num_relation: 7
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edge_input_dim: 59
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num_angle_bin: 8
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graph_construction_model:
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class: GraphConstruction
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node_layers:
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- class: AlphaCarbonNode
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edge_layers:
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- class: SequentialEdge
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max_distance: 2
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- class: SpatialEdge
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radius: 10.0
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min_distance: 5
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- class: KNNEdge
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k: 10
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min_distance: 5
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edge_feature: gearnet
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optimizer:
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class: Adam
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lr: 1.0e-3
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engine:
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gpus: {{ gpus }}
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batch_size: 96
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log_interval: 100
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save_interval: 5
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train:
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num_epoch: 50

‎config/pretrain/attr_gearnet_edge.yaml‎

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output_dir: ~/scratch/protein_outputs
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dataset:
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class: AlphaFoldDB
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path: ~/scratch/protein-datasets/alphafold
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species_start: 0
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species_end: 22
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# species_id: 0
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# split_id: 1
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transform:
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class: Compose
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transforms:
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- class: ProteinView
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view: residue
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- class: TruncateProtein
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max_length: 100
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random: True
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task:
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class: AttributeMasking
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mask_rate: 0.15
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num_mlp_layer: 2
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model:
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class: GearNet
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input_dim: 21
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hidden_dims: [512, 512, 512, 512, 512, 512]
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batch_norm: True
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concat_hidden: True
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short_cut: True
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readout: 'sum'
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num_relation: 7
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edge_input_dim: 59
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num_angle_bin: 8
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graph_construction_model:
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class: GraphConstruction
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node_layers:
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- class: AlphaCarbonNode
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edge_layers:
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- class: SequentialEdge
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max_distance: 2
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- class: SpatialEdge
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radius: 10.0
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min_distance: 5
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- class: KNNEdge
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k: 10
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min_distance: 5
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edge_feature: gearnet
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optimizer:
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class: Adam
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lr: 1.0e-3
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engine:
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gpus: {{ gpus }}
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batch_size: 96
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log_interval: 100
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save_interval: 5
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train:
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num_epoch: 50

‎config/pretrain/dihedral_gearnet_edge.yaml‎

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output_dir: ~/scratch/protein_outputs
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dataset:
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class: AlphaFoldDB
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path: ~/scratch/protein-datasets/alphafold
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species_start: 0
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species_end: 22
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# species_id: 0
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# split_id: 1
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transform:
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class: Compose
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transforms:
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- class: ProteinView
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view: residue
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- class: TruncateProtein
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max_length: 100
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random: True
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task:
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class: DihedralPrediction
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num_sample: 512
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num_class: 8
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num_mlp_layer: 2
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model:
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class: GearNet
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input_dim: 21
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hidden_dims: [512, 512, 512, 512, 512, 512]
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batch_norm: True
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concat_hidden: True
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short_cut: True
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readout: 'sum'
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num_relation: 7
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edge_input_dim: 59
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num_angle_bin: 8
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graph_construction_model:
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class: GraphConstruction
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node_layers:
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- class: AlphaCarbonNode
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edge_layers:
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- class: SequentialEdge
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max_distance: 2
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- class: SpatialEdge
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radius: 10.0
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min_distance: 5
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- class: KNNEdge
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k: 10
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min_distance: 5
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edge_feature: gearnet
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optimizer:
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class: Adam
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lr: 1.0e-3
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engine:
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gpus: {{ gpus }}
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batch_size: 96
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log_interval: 100
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save_interval: 5
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train:
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num_epoch: 50

‎config/pretrain/dis_gearnet_edge.yaml‎

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output_dir: ~/scratch/protein_outputs
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dataset:
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class: AlphaFoldDB
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path: ~/scratch/protein-datasets/alphafold
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species_start: 0
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species_end: 22
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# species_id: 0
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# split_id: 1
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transform:
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class: Compose
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transforms:
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- class: ProteinView
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view: residue
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- class: TruncateProtein
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max_length: 100
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random: True
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task:
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class: DistancePrediction
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num_sample: 256
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num_mlp_layer: 2
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model:
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class: GearNet
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input_dim: 21
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hidden_dims: [512, 512, 512, 512, 512, 512]
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batch_norm: True
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concat_hidden: True
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short_cut: True
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readout: 'sum'
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num_relation: 7
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edge_input_dim: 59
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num_angle_bin: 8
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graph_construction_model:
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class: GraphConstruction
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node_layers:
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- class: AlphaCarbonNode
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edge_layers:
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- class: SequentialEdge
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max_distance: 2
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- class: SpatialEdge
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radius: 10.0
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min_distance: 5
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- class: KNNEdge
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k: 10
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min_distance: 5
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edge_feature: gearnet
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optimizer:
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class: Adam
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lr: 1.0e-3
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engine:
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gpus: {{ gpus }}
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batch_size: 128
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log_interval: 100
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save_interval: 5
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train:
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num_epoch: 50

‎config/pretrain/mc_ESM_gearnet.yaml‎

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output_dir: ~/scratch/protein_outputs
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dataset:
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class: AlphaFoldDB
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path: ~/scratch/protein-datasets/alphafold
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species_start: 0
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species_end: 22
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# species_id: 0
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# split_id: 1
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transform:
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class: ProteinView
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view: residue
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task:
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class: Unsupervised
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model:
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class: MultiviewContrast
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crop_funcs:
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- class: SubsequenceNode
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max_length: 50
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- class: SubspaceNode
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entity_level: residue
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min_neighbor: 15
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min_radius: 15.0
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noise_funcs:
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- class: IdentityNode
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- class: RandomEdgeMask
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mask_rate: 0.15
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model:
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class: GearNet
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input_dim: 21
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hidden_dims: [512, 512, 512, 512, 512, 512]
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batch_norm: True
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concat_hidden: True
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short_cut: True
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readout: 'sum'
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num_relation: 7
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edge_input_dim: 59
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num_angle_bin: 8
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graph_construction_model:
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class: GraphConstruction
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node_layers:
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- class: AlphaCarbonNode
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edge_layers:
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- class: SequentialEdge
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max_distance: 2
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- class: SpatialEdge
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radius: 10.0
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min_distance: 5
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- class: KNNEdge
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k: 10
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min_distance: 5
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edge_feature: gearnet
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optimizer:
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class: Adam
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lr: 2.0e-4
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engine:
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gpus: {{ gpus }}
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batch_size: 48
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log_interval: 100
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save_interval: 5
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train:
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num_epoch: 50

‎script/pretrain.py‎

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import os
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import sys
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import math
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import pprint
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import torch
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from torchdrug import core, models, tasks, datasets, utils
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from torchdrug.utils import comm
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sys.path.append(os.path.dirname(os.path.dirname(__file__)))
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import util
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def save(solver, path):
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if isinstance(solver.model, tasks.Unsupervised):
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model = solver.model.model.model
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else:
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model = solver.model.model
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if comm.get_rank() == 0:
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logger.warning("Save checkpoint to %s" % path)
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path = os.path.expanduser(path)
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if comm.get_rank() == 0:
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torch.save(model.state_dict(), path)
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comm.synchronize()
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if __name__ == "__main__":
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args, vars = util.parse_args()
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cfg = util.load_config(args.config, context=vars)
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working_dir = util.create_working_directory(cfg)
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torch.manual_seed(args.seed + comm.get_rank())
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logger = util.get_root_logger()
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if comm.get_rank() == 0:
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logger.warning("Config file: %s" % args.config)
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logger.warning(pprint.pformat(cfg))
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species_start = cfg.dataset.get("species_start", 0)
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species_end = cfg.dataset.get("species_end", 0)
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assert species_end >= species_start
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if species_end > species_start:
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cfg.dataset.species_id = species_start
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cfg.dataset.split_id = 0
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cfg.dataset.pop("species_start")
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cfg.dataset.pop("species_end")
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dataset = core.Configurable.load_config_dict(cfg.dataset)
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solver = util.build_pretrain_solver(cfg, dataset)
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step = cfg.get("save_interval", 1)
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for i in range(0, cfg.train.num_epoch, step):
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kwargs = cfg.train.copy()
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kwargs["num_epoch"] = min(step, cfg.train.num_epoch - i)
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if species_end == species_start:
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solver.train(**kwargs)
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else:
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for species_id in range(species_start, species_end):
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for split_id in range(dataset.species_nsplit[species_id]):
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cfg.dataset.species_id = species_id
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cfg.dataset.split_id = split_id
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dataset = core.Configurable.load_config_dict(cfg.dataset)
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logger.warning('Epoch: {}\tSpecies id: {}\tSplit id: {}\tSplit length: {}'.format(
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i, species_id, split_id, len(dataset)))
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solver.train_set = dataset
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solver.train(**kwargs)
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save(solver, "model_epoch_%d.pth" % (i + kwargs["num_epoch"]))

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