dmlc--dgl
Chao Ma
635dfb4a59
* update metis * update * update dataloader * update dataloader * new script * update * update * update * update * update * update * update * update dataloader * update * update * update * update * update * update * update * Add license to every filer header
218 行
9.7 KiB
Python
218 行
9.7 KiB
Python
# -*- coding: utf-8 -*-
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#
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# setup.py
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#
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# Copyright 2018 Amazon.com, Inc. or its affiliates. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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#
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from dataloader import EvalDataset, TrainDataset
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from dataloader import get_dataset
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import argparse
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import os
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import logging
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import time
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import pickle
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from utils import get_compatible_batch_size
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backend = os.environ.get('DGLBACKEND', 'pytorch')
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if backend.lower() == 'mxnet':
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import multiprocessing as mp
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from train_mxnet import load_model_from_checkpoint
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from train_mxnet import test
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else:
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import torch.multiprocessing as mp
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from train_pytorch import load_model_from_checkpoint
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from train_pytorch import test, test_mp
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class ArgParser(argparse.ArgumentParser):
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def __init__(self):
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super(ArgParser, self).__init__()
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self.add_argument('--model_name', default='TransE',
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choices=['TransE', 'TransE_l1', 'TransE_l2', 'TransR',
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'RESCAL', 'DistMult', 'ComplEx', 'RotatE'],
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help='model to use')
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self.add_argument('--data_path', type=str, default='data',
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help='root path of all dataset')
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self.add_argument('--dataset', type=str, default='FB15k',
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help='dataset name, under data_path')
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self.add_argument('--format', type=str, default='built_in',
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help='the format of the dataset, it can be built_in,'\
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'raw_udd_{htr} and udd_{htr}')
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self.add_argument('--data_files', type=str, default=None, nargs='+',
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help='a list of data files, e.g. entity relation train valid test')
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self.add_argument('--model_path', type=str, default='ckpts',
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help='the place where models are saved')
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self.add_argument('--batch_size_eval', type=int, default=8,
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help='batch size used for eval and test')
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self.add_argument('--neg_sample_size_eval', type=int, default=-1,
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help='negative sampling size for testing')
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self.add_argument('--neg_deg_sample_eval', action='store_true',
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help='negative sampling proportional to vertex degree for testing')
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self.add_argument('--hidden_dim', type=int, default=256,
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help='hidden dim used by relation and entity')
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self.add_argument('-g', '--gamma', type=float, default=12.0,
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help='margin value')
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self.add_argument('--eval_percent', type=float, default=1,
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help='sample some percentage for evaluation.')
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self.add_argument('--no_eval_filter', action='store_true',
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help='do not filter positive edges among negative edges for evaluation')
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self.add_argument('--gpu', type=int, default=[-1], nargs='+',
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help='a list of active gpu ids, e.g. 0')
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self.add_argument('--mix_cpu_gpu', action='store_true',
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help='mix CPU and GPU training')
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self.add_argument('-de', '--double_ent', action='store_true',
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help='double entitiy dim for complex number')
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self.add_argument('-dr', '--double_rel', action='store_true',
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help='double relation dim for complex number')
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self.add_argument('--num_proc', type=int, default=1,
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help='number of process used')
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self.add_argument('--num_thread', type=int, default=1,
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help='number of thread used')
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def parse_args(self):
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args = super().parse_args()
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return args
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def get_logger(args):
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if not os.path.exists(args.model_path):
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raise Exception('No existing model_path: ' + args.model_path)
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log_file = os.path.join(args.model_path, 'eval.log')
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logging.basicConfig(
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format='%(asctime)s %(levelname)-8s %(message)s',
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level=logging.INFO,
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datefmt='%Y-%m-%d %H:%M:%S',
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filename=log_file,
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filemode='w'
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)
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logger = logging.getLogger(__name__)
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print("Logs are being recorded at: {}".format(log_file))
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return logger
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def main(args):
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args.eval_filter = not args.no_eval_filter
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if args.neg_deg_sample_eval:
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assert not args.eval_filter, "if negative sampling based on degree, we can't filter positive edges."
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# load dataset and samplers
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dataset = get_dataset(args.data_path, args.dataset, args.format, args.data_files)
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args.pickle_graph = False
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args.train = False
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args.valid = False
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args.test = True
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args.strict_rel_part = False
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args.soft_rel_part = False
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args.async_update = False
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logger = get_logger(args)
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# Here we want to use the regualr negative sampler because we need to ensure that
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# all positive edges are excluded.
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eval_dataset = EvalDataset(dataset, args)
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if args.neg_sample_size_eval < 0:
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args.neg_sample_size_eval = args.neg_sample_size = eval_dataset.g.number_of_nodes()
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args.batch_size_eval = get_compatible_batch_size(args.batch_size_eval, args.neg_sample_size_eval)
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args.num_workers = 8 # fix num_workers to 8
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if args.num_proc > 1:
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test_sampler_tails = []
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test_sampler_heads = []
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for i in range(args.num_proc):
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test_sampler_head = eval_dataset.create_sampler('test', args.batch_size_eval,
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args.neg_sample_size_eval,
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args.neg_sample_size_eval,
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args.eval_filter,
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mode='chunk-head',
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num_workers=args.num_workers,
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rank=i, ranks=args.num_proc)
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test_sampler_tail = eval_dataset.create_sampler('test', args.batch_size_eval,
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args.neg_sample_size_eval,
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args.neg_sample_size_eval,
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args.eval_filter,
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mode='chunk-tail',
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num_workers=args.num_workers,
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rank=i, ranks=args.num_proc)
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test_sampler_heads.append(test_sampler_head)
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test_sampler_tails.append(test_sampler_tail)
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else:
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test_sampler_head = eval_dataset.create_sampler('test', args.batch_size_eval,
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args.neg_sample_size_eval,
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args.neg_sample_size_eval,
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args.eval_filter,
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mode='chunk-head',
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num_workers=args.num_workers,
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rank=0, ranks=1)
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test_sampler_tail = eval_dataset.create_sampler('test', args.batch_size_eval,
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args.neg_sample_size_eval,
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args.neg_sample_size_eval,
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args.eval_filter,
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mode='chunk-tail',
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num_workers=args.num_workers,
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rank=0, ranks=1)
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# load model
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n_entities = dataset.n_entities
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n_relations = dataset.n_relations
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ckpt_path = args.model_path
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model = load_model_from_checkpoint(logger, args, n_entities, n_relations, ckpt_path)
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if args.num_proc > 1:
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model.share_memory()
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# test
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args.step = 0
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args.max_step = 0
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start = time.time()
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if args.num_proc > 1:
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queue = mp.Queue(args.num_proc)
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procs = []
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for i in range(args.num_proc):
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proc = mp.Process(target=test_mp, args=(args,
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model,
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[test_sampler_heads[i], test_sampler_tails[i]],
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i,
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'Test',
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queue))
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procs.append(proc)
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proc.start()
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total_metrics = {}
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metrics = {}
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logs = []
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for i in range(args.num_proc):
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log = queue.get()
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logs = logs + log
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for metric in logs[0].keys():
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metrics[metric] = sum([log[metric] for log in logs]) / len(logs)
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for k, v in metrics.items():
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print('Test average {} at [{}/{}]: {}'.format(k, args.step, args.max_step, v))
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for proc in procs:
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proc.join()
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else:
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test(args, model, [test_sampler_head, test_sampler_tail])
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print('Test takes {:.3f} seconds'.format(time.time() - start))
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if __name__ == '__main__':
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args = ArgParser().parse_args()
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main(args)
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