| import torch, json, math, os |
|
|
|
|
| d = { |
| 'debug': True, |
| 'dataset_path': '../data/tongji_data/pos.json', |
| 'train_data_file': '../data/tongji_data/tmp_input_ids_27/', |
| 'train_number_data': 1632069, |
| 'valid_data_file': '../data/tongji_data/test_2016_new_input_id_new.pt', |
| 'fptype': 'morgan', |
| 'valid_ratio': 0.1, |
| 'batch_size': 64, |
| 'lr': 1e-3, |
| 'min_lr': 2e-5, |
| 'weight_decay': 1e-3, |
| 'scheduler_type': 'warmup_cosine', |
| 'warmup_epochs': 1, |
| 'warmup_start_factor': 1.0, |
| 'patience': 2, |
| 'factor': 0.5, |
| 'add_nl': True, |
| 'binary_intn': False, |
| 'max_mz': 2000, |
| 'min_mz': 20, |
| 'energy': 'Energy1', |
| 'epochs': 50, |
| 'bin_size': 0.05, |
| 'ms_embedding_dim': 300, |
| 'ms_feature3_embedding_dim': 135, |
| 'ms3_meta_dim': 50, |
| 'projection_dim': 256, |
| 'ms_projection_layers': 1, |
| 'mol_embedding_dim': 2048, |
| 'mol_projection_layers': 1, |
| 'tsfm_in_ms': True, |
| 'tsfm_in_mol': False, |
| 'tsfm_layers': 6, |
| 'tsfm_heads': 8, |
| 'lstm_layers': 2, |
| 'lstm_in_ms': False, |
| 'lstm_in_mol': False, |
| 'dropout': 0.1, |
| 'nmodels': 1, |
| 'mol_encoder': 'gnn+fp', |
| 'molgnn_n_filters_list': [256, 256, 256], |
| 'molgnn_nhead': 4, |
| 'molgnn_readout_layers': 2, |
| 'seed': 1234, |
| 'dev_name': 'cuda:0', |
| 'keep_best_models_num': 3, |
| 'alpha': 0.5, |
| 'beta': 0.5, |
| 'structure_diag_weight': 0.25, |
| 'fp_dim': 2048, |
| "experiment_name_type": ['mol_gcn_base', 'mol_gat_base', 'mol_gcn_ms1', 'mol_gat_ms1', 'mol_gcn_ms3', 'mol_gat_ms3', |
| 'mol_gcn_ms1_ms3', 'mol_gat_ms1_ms3', |
| |
| 'mol_gcn_base_loss', 'mol_gat_base_loss', 'mol_gcn_ms1_loss', 'mol_gat_ms1_loss', |
| 'mol_gcn_ms3_loss', 'mol_gat_ms3_loss', 'mol_gcn_ms1_ms3_loss', 'mol_gat_ms1_ms3_loss'][14] |
| } |
|
|
|
|
| class ConfigDict(dict): |
| ''' |
| Makes a dictionary behave like an object,with attribute-style access. |
| ''' |
| def __getattr__(self, name): |
| try: |
| return self[name] |
| except: |
| raise AttributeError(name) |
|
|
| def __setattr__(self, name, value): |
| self[name] = value |
|
|
| def save(self, fn, onlyprint=False): |
| if onlyprint: |
| print(self) |
| else: |
| json.dump(self, open(fn, 'w'), indent=2) |
|
|
| def load_dict(self, dic): |
| for k, v in dic.items(): |
| self[k] = v |
| self.calc_ms_embedding_dim() |
|
|
| def load(self, fn): |
| try: |
| if type(fn) is dict: |
| d = fn |
| elif type(fn) is str: |
| if os.path.exists(fn): |
| d = json.load(open(fn, 'r')) |
| else: |
| d = json.loads(fn) |
| self.load_dict(d) |
| except Exception as e: |
| print(e) |
|
|
| def calc_ms_embedding_dim(self): |
| if 'bin_size' in self: |
| self['ms_embedding_dim'] = math.ceil((self['max_mz'] - self['min_mz']) / self['bin_size']) |
| if 'ms_embedding_dim' in self and 'add_nl' in self and self['add_nl']: |
| self['ms_embedding_dim'] += math.ceil((200) / self['bin_size']) |
|
|
| @property |
| def device(self): |
| try: |
| if torch.cuda.is_available(): |
| return torch.device(self['dev_name']) |
| else: |
| return torch.device('cpu') |
| except Exception as e: |
| return torch.device('cpu') |
|
|
|
|
| CFG = ConfigDict() |
| CFG.load_dict(d) |
|
|
