def main(_): config_dic = flags.FLAGS.flag_values_dict() config_dic["out_dir"] = os.path.join(flags.FLAGS.out_base_dir, flags.FLAGS.model_name, str(flags.FLAGS.run_id).zfill(2)) config = AttrDict(config_dic) m(config)
def main(_): config = flags.FLAGS config.out_dir = os.path.join(config.out_base_dir, config.model_name, str(config.run_id).zfill(2)) m(config)
def main(): config = get_args() config.out_dir = os.path.join(config.out_base_dir, config.model_name, str(config.run_id).zfill(2)) print("In main.....") m(config)
def main(_): config = flags.FLAGS if config.nmn_cfg: cfg_path = os.path.join("snmn/cfgs", config.run_id+'.yaml') merge_cfg_from_file(cfg_path) config.data_dir = os.path.join('data', config.dataset) if config.mode == 'test': config.input_keep_prob = 1.0 config.highway_keep_prob = 1.0 config.out_dir = os.path.join(config.out_base_dir, config.dataset, config.model_name, str(config.run_id).zfill(2)) if config.dataset == 'hotpotqa': if config.emb_dim == 300: config.data_dir = join(config.data_dir, '840b300d') elif config.emb_dim == 100: config.data_dir = join(config.data_dir, '6b100d') else: raise NotImplementedError #if config.supervise_bridge_entity: config.data_dir += '-bridge' m(config)
def main(_): config = flags.FLAGS config.out_dir = os.path.join(config.out_base_dir, config.model_name, str(config.run_id).zfill(2)) try: m(config) except Exception as e: exstr = traceback.format_exc() print(repr(e)) with open('error.txt', 'w') as f: f.write(exstr) else: print('c')
def main(_): from basic.main import main as m config = flags.FLAGS config.model_name = "basic-class" config.out_dir = os.path.join(config.out_base_dir, config.model_name, str(config.run_id).zfill(2)) print(config.out_dir) evaluator = m(config) """Generating metrics for the squad model""" metrics = { "metrics": [ { "name": "accuracy-score", "numberValue": str(evaluator.acc), "format": "RAW", }, { "name": "loss", "numberValue": str(evaluator.loss), "format": "RAW", }, ] } import json with open(mlpipeline_metrics_path, "w") as f: json.dump(metrics, f)
def main(_): config = flags.FLAGS if 'train' == config.mode: # get logger logging.basicConfig(level=logging.INFO) logger = logging.getLogger('bidaf') logger.setLevel(logging.INFO) # saving path subfolder_name = strftime("%Y-%m-%d___%H-%M-%S", gmtime()) config.out_base_dir = os.path.join(config.out_base_dir, subfolder_name) if not os.path.exists(config.out_base_dir): os.mkdir(config.out_base_dir) else: raise IOError('%s exist!' % config.out_base_dir) log_file = os.path.join(config.out_base_dir, 'output.log') logger.addHandler(logging.FileHandler(log_file)) logger.info('configurations in file:\n %s \n', vars(config)) config.out_dir = os.path.join(config.out_base_dir, config.model_name, str(config.run_id).zfill(2)) m(config)
def main(_): config = flags.FLAGS config.data_dir = os.path.join('data', config.dataset) if config.mode == 'test': config.input_keep_prob = 1.0 config.highway_keep_prob = 1.0 if config.read_topk_docs > 0: config.use_ranked_docs = True assert config.mac_prediction == 'candidates' or config.mac_prediction == 'span-single' \ or config.mac_prediction == 'span-dual' config.out_dir = os.path.join(config.out_base_dir, config.model_name, config.dataset, str(config.run_id).zfill(2)) if config.hierarchical_attn: config.get_query_subject = True if config.medhop: config.data_dir = join(config.data_dir, "medhop") config.num_steps = 3000 config.save_period = 100 config.log_period = 10 config.eval_period = 6000 config.val_num_batches = 0 if config.oracle == 'extra': assert config.use_assembler if config.split_supports is True: config.data_dir = join(config.data_dir, 'split-supports') if config.select_top_n_doc > 0 or config.use_ranked_docs: if config.filter_by_annotations == 'single': if config.emb_dim == 300: config.data_dir = join( config.data_dir, 'candi-2layer-tfidf-truncated500-300d840b-followsingle' ) else: config.data_dir = join(config.data_dir, 'candi-2layer-tfidf-followsingle') elif config.filter_by_annotations == 'multiple': if config.emb_dim == 300: config.data_dir = join( config.data_dir, 'candi-2layer-tfidf-truncated500-300d840b-followmultiple' ) else: config.data_dir = join( config.data_dir, 'candi-2layer-tfidf-followmultiple') elif config.filter_by_annotations == 'follow': if config.emb_dim == 300: config.data_dir = join( config.data_dir, 'candi-2layer-tfidf-truncated500-300d840b-follow') else: config.data_dir = join(config.data_dir, 'candi-2layer-tfidf-follow') else: if config.emb_dim == 100: config.data_dir = join(config.data_dir, 'candi-2layer-tfidf') elif config.emb_dim == 300: print('300') if config.truncate_at == 500: config.data_dir = join( config.data_dir, 'candi-2layer-tfidf-truncated500-300d840b') elif config.truncate_at == 300: config.data_dir = join( config.data_dir, 'candi-2layer-tfidf-truncated300-300d840b') else: assert False, ("Large model must uses truncated data.") else: raise NotImplementedError else: if config.filter_by_annotations == 'follow': config.data_dir = join(config.data_dir, 'w-candi-follow') elif config.filter_by_annotations == 'single': config.data_dir = join(config.data_dir, 'w-candi-followsingle') elif config.filter_by_annotations == 'multiple': config.data_dir = join(config.data_dir, 'w-candi-followmultiple') else: if config.emb_dim == 100: if config.use_doc_selector: config.data_dir = join(config.data_dir, 'w-candi') else: config.data_dir = join(config.data_dir, 'candi-2layer-tfidf') elif config.emb_dim == 300: print('300') if config.truncate_at == 500: config.data_dir = join( config.data_dir, 'w-candi-truncated500-300d840b') elif config.truncate_at == 300: config.data_dir = join( config.data_dir, 'w-candi-truncated300-300d840b') else: assert False, ("Large model must uses truncated data.") else: raise NotImplementedError else: config.data_dir = join(config.data_dir, 'concat-supports') m(config)
def main(_): config = flags.FLAGS config.out_dir = os.path.join(config.out_base_dir, config.model_name, str(config.run_id).zfill(2)) m(config)