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| # -*- coding: utf-8 -*- | |
| """ | |
| Author: Philipp Seidl | |
| ELLIS Unit Linz, LIT AI Lab, Institute for Machine Learning | |
| Johannes Kepler University Linz | |
| Contact: [email protected] | |
| File contains functions that | |
| """ | |
| from . import model | |
| import torch | |
| import os | |
| MODEL_PATH = 'data/model/' | |
| def smarts2svg(smarts, useSmiles=True, highlightByReactant=True, save_to=''): | |
| """ | |
| draws smiles of smarts to an SVG and displays it in the Notebook, | |
| or optinally can be saved to a file `save_to` | |
| adapted from https://www.kesci.com/mw/project/5c7685191ce0af002b556cc5 | |
| """ | |
| # adapted from https://www.kesci.com/mw/project/5c7685191ce0af002b556cc5 | |
| from rdkit import RDConfig | |
| from rdkit import Chem | |
| from rdkit.Chem import Draw, AllChem | |
| from rdkit.Chem.Draw import rdMolDraw2D | |
| from rdkit import Geometry | |
| import matplotlib.pyplot as plt | |
| import matplotlib.cm as cm | |
| import matplotlib | |
| from IPython.display import SVG, display | |
| rxn = AllChem.ReactionFromSmarts(smarts,useSmiles=useSmiles) | |
| d = Draw.MolDraw2DSVG(900, 100) | |
| # rxn = AllChem.ReactionFromSmarts('[CH3:1][C:2](=[O:3])[OH:4].[CH3:5][NH2:6]>CC(O)C.[Pt]>[CH3:1][C:2](=[O:3])[NH:6][CH3:5].[OH2:4]',useSmiles=True) | |
| colors=[(0.3, 0.7, 0.9),(0.9, 0.7, 0.9),(0.6,0.9,0.3),(0.9,0.9,0.1)] | |
| try: | |
| d.DrawReaction(rxn,highlightByReactant=highlightByReactant) | |
| d.FinishDrawing() | |
| txt = d.GetDrawingText() | |
| # self.assertTrue(txt.find("<svg") != -1) | |
| # self.assertTrue(txt.find("</svg>") != -1) | |
| svg = d.GetDrawingText() | |
| svg2 = svg.replace('svg:','') | |
| svg3 = SVG(svg2) | |
| display(svg3) | |
| if save_to!='': | |
| with open(save_to, 'w') as f_handle: | |
| f_handle.write(svg3.data) | |
| except: | |
| print('Error drawing') | |
| return svg2 | |
| def list_models(model_path=MODEL_PATH): | |
| """returns a list of loadable models""" | |
| return dict(enumerate(list(filter(lambda k: str(k)[-3:]=='.pt', os.listdir(model_path))))) | |
| def load_clf(model_fn='', model_path=MODEL_PATH, device='cpu', model_type='mhn'): | |
| """ returns the model with loaded weights given a filename""" | |
| import json | |
| config_fn = '_'.join(model_fn.split('_')[-2:]).split('.pt')[0] | |
| conf_dict = json.load( open( f"{model_path}{config_fn}_config.json" ) ) | |
| train_conf_dict = json.load( open( f"{model_path}{config_fn}_config.json" ) ) | |
| # specify the config the saved model had | |
| conf = model.ModelConfig(**conf_dict) | |
| conf.device = device | |
| print(conf.__dict__) | |
| if model_type == 'staticQK': | |
| clf = model.StaticQK(conf) | |
| elif model_type == 'mhn': | |
| clf = model.MHN(conf) | |
| elif model_type == 'segler': | |
| clf = model.SeglerBaseline(conf) | |
| elif model_type == 'fortunato': | |
| clf = model.SeglerBaseline(conf) | |
| else: | |
| raise NotImplementedError('model_type',model_type,'not found') | |
| # load the model | |
| PATH = model_path+model_fn | |
| params = torch.load(PATH, map_location=torch.device('cpu')) #!!! | |
| clf.load_state_dict(params, strict=False) | |
| if 'templates+noise' in params.keys(): | |
| print('loading templates+noise') | |
| clf.templates = params['templates+noise'] | |
| #clf.templates.to(clf.config.device) | |
| return clf |