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[Add]Moflow代码paddlescience复现 (#962)
* Update mkdocs.yml * DrugDiscovery Review changed * Fix DrugDiscovery Review changed * Add files via upload * Update moflow_optimize.yaml hidden: '16' -> 16 * Update optimize_moflow.py hidden: '16' ->16, 直接赋值 * Update moflow_basic.py repair create_parameter * Update moflow_dataset.py 特殊依赖包rdkit的处理，在try中导入 * Update moflow_net.py loss return by Dict * Update moflow_net.py * [Delete] ppsci/arch/fpscores.pkl.gz * [Delete] ppsci/arch/moflow_utils.py * [Add] moflow_utils.py * [Fix] moflow_test.yaml Add batch_size * [FIX] optimize_moflow.py 导入moflow_utils修改 * [Fix] test_generate.py 导入moflow_utils修改 * [Fix] train_moflow.py 导入moflow_utils修改 * [FIX] OrderedSet替换成set OrderedSet替换成set，删除对应的包 * 【FIX】删除OrderedSet包 * [FIX] 文档显示问题 * [Fix] Re-upload DrugDiscovery Review changed * [Fix] Repair code-style-check * [Fix] Repair train_moflow & mkdocs code-style-check * [Fix] docs/zh/examples/moflow.md Co-authored-by: HydrogenSulfate <[email protected]> * [Fix] hidden_size error fix 'int' vh = (self.latent_size,) + tuple(hidden_size) + (1,) object is not iterable * [fix] moflow.md view * [FIX] delete space in P moflow.md mat * [fix] repair view Update moflow.md * [fix] change result save path * [fix] change result save path * [Fix] ConstantInitializer -> Constant * [Fix] 增加生成方式说明 * [Fix] optimize model path EVAL->TRAIN Co-authored-by: HydrogenSulfate <[email protected]> * [Fix]pretrained model path Co-authored-by: HydrogenSulfate <[email protected]> * [FIX]添加模型生成模式和优化相关说明 添加模型推理评估命令的四种模式的说明,优化后产生的模型和使用说明 * [FIX]修复运行命令 根据增加的模型修改运行的命令和说明 * [FIX] Repair code-style-check * [Fix] Repair code-style-check --------- Co-authored-by: HydrogenSulfate <[email protected]>
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‎docs/zh/examples/moflow.md‎

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‎examples/moflow/conf/moflow_optimize.yaml‎

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defaults:
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- ppsci_default
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- TRAIN: train_default
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- TRAIN/ema: ema_default
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- TRAIN/swa: swa_default
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- EVAL: eval_default
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- INFER: infer_default
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- hydra/job/config/override_dirname/exclude_keys: exclude_keys_default
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- _self_
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hydra:
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run:
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# dynamic output directory according to running time and override name
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dir: outputs_moflow_optimize/${data_name}
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job:
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name: ${mode} # name of logfile
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chdir: false # keep current working directory unchanged
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sweep:
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# output directory for multirun
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dir: ${hydra.run.dir}
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subdir: ./
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# general settings
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mode: train # running mode: train/eval
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data_name: zinc250k # data select:qm9/zinc250k
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seed: 1
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output_dir: ${hydra:run.dir}
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log_freq: 20
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# set training hyper-parameters
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qm9:
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b_n_flow: 10
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b_n_block: 1
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b_hidden_ch: [128,128]
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a_n_flow: 27
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a_n_block: 1
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a_hidden_gnn: [64]
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a_hidden_lin: [128,64]
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mask_row_size_list: [1]
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mask_row_stride_list: [1]
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learn_dist: True
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noise_scale: 0.6
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b_conv_lu: 1
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atomic_num_list: [6, 7, 8, 9, 0]
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b_n_type: 4
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b_n_squeeze: 3
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a_n_node: 9
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valid_idx: valid_idx_qm9.json
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label_keys: ['A', 'B', 'C', 'mu', 'alpha', 'homo', 'lumo', 'gap', 'r2', 'zpve', 'U0', 'U', 'H', 'G', 'Cv']
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smiles_col: SMILES1
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zinc250k:
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b_n_flow: 10
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b_n_block: 1
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b_hidden_ch: [512,512]
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a_n_flow: 38
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a_n_block: 1
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a_hidden_gnn: [256]
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a_hidden_lin: [512,64]
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mask_row_size_list: [1]
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mask_row_stride_list: [1]
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learn_dist: True
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noise_scale: 0.6
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b_conv_lu: 2
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atomic_num_list: [6, 7, 8, 9, 15, 16, 17, 35, 53, 0]
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b_n_type: 4
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b_n_squeeze: 19
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a_n_node: 38
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valid_idx: valid_idx_zinc.json
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label_keys: ['logP', 'qed', 'SAS']
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smiles_col: smiles
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# set data path
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FILE_PATH: ./datasets/moflow
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# model settings
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MODEL:
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input_keys: ["nodes", "edges"]
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output_keys: ["output", "sum_log_det"]
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hyper_params: null
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MODEL_Prop:
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input_keys: ["nodes", "edges"]
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output_keys: ["output", "sum_log_det"]
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model: null
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hidden_size: null
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# evaluation settings
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EVAL:
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pretrained_model_path: null
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compute_metric_by_batch: false
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eval_with_no_grad: true
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batch_size: 100
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# optimize settings
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OPTIMIZE:
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property_name: plogp # qed/plogp
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batch_size: 256
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topk: 800
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debug: false
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topscore: false
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max_epochs: 3
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learning_rate: 0.001
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weight_decay: 1e-2
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hidden: [16] # Hidden dimension list for output regression
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temperature: 1.0
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consopt: true
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sim_cutoff: 0.0

‎examples/moflow/conf/moflow_test.yaml‎

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defaults:
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- ppsci_default
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- TRAIN: train_default
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- TRAIN/ema: ema_default
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- TRAIN/swa: swa_default
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- EVAL: eval_default
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- INFER: infer_default
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- hydra/job/config/override_dirname/exclude_keys: exclude_keys_default
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- _self_
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hydra:
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run:
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# dynamic output directory according to running time and override name
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dir: outputs_moflow_test/${data_name}
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job:
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name: ${mode} # name of logfile
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chdir: false # keep current working directory unchanged
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sweep:
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# output directory for multirun
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dir: ${hydra.run.dir}
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subdir: ./
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# general settings
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mode: eval # running mode: train/eval
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data_name: zinc250k # data select:qm9/zinc250k
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seed: 1
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output_dir: ${hydra:run.dir}
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save_score: true
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# set testing hyper-parameters
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qm9:
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b_n_flow: 10
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b_n_block: 1
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b_hidden_ch: [128,128]
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a_n_flow: 27
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a_n_block: 1
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a_hidden_gnn: [64]
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a_hidden_lin: [128,64]
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mask_row_size_list: [1]
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mask_row_stride_list: [1]
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learn_dist: True
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noise_scale: 0.6
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b_conv_lu: 1
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atomic_num_list: [6, 7, 8, 9, 0]
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b_n_type: 4
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b_n_squeeze: 3
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a_n_node: 9
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valid_idx: valid_idx_qm9.json
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label_keys: ['A', 'B', 'C', 'mu', 'alpha', 'homo', 'lumo', 'gap', 'r2', 'zpve', 'U0', 'U', 'H', 'G', 'Cv']
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smiles_col: SMILES1
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zinc250k:
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b_n_flow: 10
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b_n_block: 1
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b_hidden_ch: [512,512]
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a_n_flow: 38
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a_n_block: 1
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a_hidden_gnn: [256]
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a_hidden_lin: [512,64]
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mask_row_size_list: [1]
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mask_row_stride_list: [1]
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learn_dist: True
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noise_scale: 0.6
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b_conv_lu: 2
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atomic_num_list: [6, 7, 8, 9, 15, 16, 17, 35, 53, 0]
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b_n_type: 4
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b_n_squeeze: 19
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a_n_node: 38
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valid_idx: valid_idx_zinc.json
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label_keys: ['logP', 'qed', 'SAS']
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smiles_col: smiles
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# set data path
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FILE_PATH: ./datasets/moflow
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# model settings
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MODEL:
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input_keys: ["nodes", "edges"]
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output_keys: ["output", "sum_log_det"]
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hyper_params: null
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# evaluation settings
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EVAL:
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pretrained_model_path: null
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batch_size: 256
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num_workers: 0
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reconstruct: false
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int2point: false
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intgrid: false
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inter_times: 5
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correct_validity: true
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temperature: 1.0
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delta: 0.1
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n_experiments:
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save_fig: true
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EVAL_mode: Intergrid #select EVAL_mode: Reconstruct/Random/Inter2point/Intergrid
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Reconstruct: #重建生成，针对不同数据集的分子进行重建生成
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batch_size: 256
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reconstruct: true
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n_experiments: 0
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Random: #随机生成，针对不同的数据集从潜在空间进行随机生成，10000个样本生成5次
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batch_size: 10000
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temperature: 0.85
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delta: 0.05
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n_experiments: 5
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save_fig: false
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correct_validity: true
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Inter2point: #在潜在空间进行插值，两个分子之间插值可视化生成分子图
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batch_size: 1000
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int2point: true
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temperature: 0.65
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inter_times: 50
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correct_validity: true
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n_experiments: 0
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Intergrid: #在潜在空间进行插值，分子网格进行可视化生成分子图
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batch_size: 1000
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temperature: 0.65
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delta: 5
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intgrid: true
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inter_times: 40
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correct_validity: true
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n_experiments: 0

‎examples/moflow/conf/moflow_train.yaml‎

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defaults:
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- ppsci_default
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- TRAIN: train_default
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- TRAIN/ema: ema_default
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- TRAIN/swa: swa_default
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- EVAL: eval_default
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- INFER: infer_default
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- hydra/job/config/override_dirname/exclude_keys: exclude_keys_default
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- _self_
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hydra:
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run:
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# dynamic output directory according to running time and override name
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dir: outputs_moflow_train/${data_name}
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job:
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name: ${mode} # name of logfile
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chdir: false # keep current working directory unchanged
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sweep:
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# output directory for multirun
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dir: ${hydra.run.dir}
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subdir: ./
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# general settings
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mode: train # running mode: train/eval
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data_name: qm9 # data select:qm9/zinc250k
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seed: 1
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output_dir: ${hydra:run.dir}
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log_freq: 20
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# set training hyper-parameters
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qm9:
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b_n_flow: 10
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b_n_block: 1
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b_hidden_ch: [128,128]
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a_n_flow: 27
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a_n_block: 1
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a_hidden_gnn: [64]
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a_hidden_lin: [128,64]
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mask_row_size_list: [1]
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mask_row_stride_list: [1]
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learn_dist: True
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noise_scale: 0.6
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b_conv_lu: 1
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atomic_num_list: [6, 7, 8, 9, 0]
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b_n_type: 4
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b_n_squeeze: 3
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a_n_node: 9
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valid_idx: valid_idx_qm9.json
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label_keys: ['A', 'B', 'C', 'mu', 'alpha', 'homo', 'lumo', 'gap', 'r2', 'zpve', 'U0', 'U', 'H', 'G', 'Cv']
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smiles_col: SMILES1
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zinc250k:
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b_n_flow: 10
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b_n_block: 1
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b_hidden_ch: [512,512]
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a_n_flow: 38
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a_n_block: 1
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a_hidden_gnn: [256]
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a_hidden_lin: [512,64]
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mask_row_size_list: [1]
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mask_row_stride_list: [1]
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learn_dist: True
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noise_scale: 0.6
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b_conv_lu: 2
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atomic_num_list: [6, 7, 8, 9, 15, 16, 17, 35, 53, 0]
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b_n_type: 4
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b_n_squeeze: 19
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a_n_node: 38
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valid_idx: valid_idx_zinc.json
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label_keys: ['logP', 'qed', 'SAS']
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smiles_col: smiles
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# set data path
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FILE_PATH: ./datasets/moflow
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# model settings
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MODEL:
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input_keys: ["nodes", "edges"]
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output_keys: ["output", "sum_log_det"]
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hyper_params: null
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# training settings
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TRAIN:
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epochs: 200
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save_freq: 50
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eval_during_train: true
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eval_freq: 20
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learning_rate: 0.001
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lr_decay: 0.999995
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batch_size: 256
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num_workers: 8
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pretrained_model_path: null
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checkpoint_path: null
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# evaluation settings
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EVAL:
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pretrained_model_path: null
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compute_metric_by_batch: false
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eval_with_no_grad: true
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batch_size: 100

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