1061 lines
46 KiB
Python
1061 lines
46 KiB
Python
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# coding=utf-8
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# Copyright 2018 The Google AI Language Team Authors and The HuggingFace Inc. team.
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# Copyright (c) 2018, NVIDIA CORPORATION. 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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"""BERT finetuning runner."""
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from __future__ import absolute_import, division, print_function
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import argparse
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import csv
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import logging
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import os
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import random
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import sys
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import numpy as np
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import torch
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from torch.utils.data import (DataLoader, RandomSampler, SequentialSampler,
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TensorDataset)
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from torch.utils.data.distributed import DistributedSampler
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from tqdm import tqdm, trange
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from torch.nn import CrossEntropyLoss, MSELoss
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from scipy.stats import pearsonr, spearmanr
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from sklearn.metrics import matthews_corrcoef, f1_score
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from sklearn import metrics
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from pytorch_pretrained_bert.file_utils import PYTORCH_PRETRAINED_BERT_CACHE, WEIGHTS_NAME, CONFIG_NAME
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from pytorch_pretrained_bert.modeling import BertForSequenceClassification, BertConfig
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from pytorch_pretrained_bert.tokenization import BertTokenizer
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from pytorch_pretrained_bert.optimization import BertAdam, WarmupLinearSchedule
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os.environ['CUDA_VISIBLE_DEVICES']= '1'
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#torch.backends.cudnn.deterministic = True
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logger = logging.getLogger(__name__)
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class InputExample(object):
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"""A single training/test example for simple sequence classification."""
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def __init__(self, guid, text_a, text_b=None, text_c=None, label=None):
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"""Constructs a InputExample.
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Args:
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guid: Unique id for the example.
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text_a: string. The untokenized text of the first sequence. For single
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sequence tasks, only this sequence must be specified.
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text_b: (Optional) string. The untokenized text of the second sequence.
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Only must be specified for sequence pair tasks.
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text_c: (Optional) string. The untokenized text of the third sequence.
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Only must be specified for sequence triple tasks.
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label: (Optional) string. The label of the example. This should be
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specified for train and dev examples, but not for test examples.
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"""
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self.guid = guid
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self.text_a = text_a
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self.text_b = text_b
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self.text_c = text_c
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self.label = label
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class InputFeatures(object):
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"""A single set of features of data."""
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def __init__(self, input_ids, input_mask, segment_ids, label_id):
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self.input_ids = input_ids
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self.input_mask = input_mask
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self.segment_ids = segment_ids
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self.label_id = label_id
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class DataProcessor(object):
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"""Base class for data converters for sequence classification data sets."""
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def get_train_examples(self, data_dir):
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"""Gets a collection of `InputExample`s for the train set."""
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raise NotImplementedError()
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def get_dev_examples(self, data_dir):
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"""Gets a collection of `InputExample`s for the dev set."""
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raise NotImplementedError()
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def get_labels(self, data_dir):
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"""Gets the list of labels for this data set."""
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raise NotImplementedError()
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@classmethod
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def _read_tsv(cls, input_file, quotechar=None):
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"""Reads a tab separated value file."""
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with open(input_file, "r", encoding="utf-8") as f:
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reader = csv.reader(f, delimiter="\t", quotechar=quotechar)
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lines = []
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for line in reader:
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if sys.version_info[0] == 2:
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line = list(unicode(cell, 'utf-8') for cell in line)
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lines.append(line)
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return lines
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class KGProcessor(DataProcessor):
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"""Processor for knowledge graph data set."""
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def __init__(self):
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self.labels = set()
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def get_train_examples(self, data_dir):
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"""See base class."""
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return self._create_examples(
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self._read_tsv(os.path.join(data_dir, "train.tsv")), "train", data_dir)
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def get_dev_examples(self, data_dir):
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"""See base class."""
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return self._create_examples(
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self._read_tsv(os.path.join(data_dir, "dev.tsv")), "dev", data_dir)
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def get_test_examples(self, data_dir):
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"""See base class."""
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return self._create_examples(
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self._read_tsv(os.path.join(data_dir, "test.tsv")), "test", data_dir)
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def get_relations(self, data_dir):
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"""Gets all labels (relations) in the knowledge graph."""
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# return list(self.labels)
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with open(os.path.join(data_dir, "relations.txt"), 'r') as f:
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lines = f.readlines()
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relations = []
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for line in lines:
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relations.append(line.strip())
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return relations
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def get_labels(self, data_dir):
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"""Gets all labels (0, 1) for triples in the knowledge graph."""
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return ["0", "1"]
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def get_entities(self, data_dir):
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"""Gets all entities in the knowledge graph."""
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# return list(self.labels)
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with open(os.path.join(data_dir, "entities.txt"), 'r') as f:
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lines = f.readlines()
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entities = []
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for line in lines:
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entities.append(line.strip())
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return entities
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def get_train_triples(self, data_dir):
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"""Gets training triples."""
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return self._read_tsv(os.path.join(data_dir, "train.tsv"))
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def get_dev_triples(self, data_dir):
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"""Gets validation triples."""
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return self._read_tsv(os.path.join(data_dir, "dev.tsv"))
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def get_test_triples(self, data_dir):
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"""Gets test triples."""
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return self._read_tsv(os.path.join(data_dir, "test.tsv"))
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def _create_examples(self, lines, set_type, data_dir):
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"""Creates examples for the training and dev sets."""
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# entity to text
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ent2text = {}
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with open(os.path.join(data_dir, "entity2text.txt"), 'r') as f:
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ent_lines = f.readlines()
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for line in ent_lines:
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temp = line.strip().split('\t')
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if len(temp) == 2:
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end = temp[1]#.find(',')
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ent2text[temp[0]] = temp[1]#[:end]
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if data_dir.find("FB15") != -1:
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with open(os.path.join(data_dir, "entity2textlong.txt"), 'r') as f:
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ent_lines = f.readlines()
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for line in ent_lines:
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temp = line.strip().split('\t')
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#first_sent_end_position = temp[1].find(".")
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ent2text[temp[0]] = temp[1]#[:first_sent_end_position + 1]
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entities = list(ent2text.keys())
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rel2text = {}
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with open(os.path.join(data_dir, "relation2text.txt"), 'r') as f:
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rel_lines = f.readlines()
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for line in rel_lines:
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temp = line.strip().split('\t')
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rel2text[temp[0]] = temp[1]
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lines_str_set = set(['\t'.join(line) for line in lines])
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examples = []
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for (i, line) in enumerate(lines):
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head_ent_text = ent2text[line[0]]
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tail_ent_text = ent2text[line[2]]
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relation_text = rel2text[line[1]]
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if set_type == "dev" or set_type == "test":
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label = "1"
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guid = "%s-%s" % (set_type, i)
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text_a = head_ent_text
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text_b = relation_text
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text_c = tail_ent_text
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self.labels.add(label)
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examples.append(
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InputExample(guid=guid, text_a=text_a, text_b=text_b, text_c = text_c, label=label))
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elif set_type == "train":
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guid = "%s-%s" % (set_type, i)
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text_a = head_ent_text
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text_b = relation_text
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text_c = tail_ent_text
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examples.append(
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InputExample(guid=guid, text_a=text_a, text_b=text_b, text_c = text_c, label="1"))
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rnd = random.random()
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guid = "%s-%s" % (set_type + "_corrupt", i)
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if rnd <= 0.5:
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# corrupting head
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for j in range(5):
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tmp_head = ''
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while True:
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tmp_ent_list = set(entities)
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tmp_ent_list.remove(line[0])
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tmp_ent_list = list(tmp_ent_list)
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tmp_head = random.choice(tmp_ent_list)
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tmp_triple_str = tmp_head + '\t' + line[1] + '\t' + line[2]
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if tmp_triple_str not in lines_str_set:
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break
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tmp_head_text = ent2text[tmp_head]
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examples.append(
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InputExample(guid=guid, text_a=tmp_head_text, text_b=text_b, text_c = text_c, label="0"))
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else:
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# corrupting tail
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tmp_tail = ''
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for j in range(5):
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while True:
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tmp_ent_list = set(entities)
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tmp_ent_list.remove(line[2])
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tmp_ent_list = list(tmp_ent_list)
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tmp_tail = random.choice(tmp_ent_list)
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tmp_triple_str = line[0] + '\t' + line[1] + '\t' + tmp_tail
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if tmp_triple_str not in lines_str_set:
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break
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tmp_tail_text = ent2text[tmp_tail]
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examples.append(
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InputExample(guid=guid, text_a=text_a, text_b=text_b, text_c = tmp_tail_text, label="0"))
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return examples
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def convert_examples_to_features(examples, label_list, max_seq_length, tokenizer, print_info = True):
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"""Loads a data file into a list of `InputBatch`s."""
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label_map = {label : i for i, label in enumerate(label_list)}
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features = []
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for (ex_index, example) in enumerate(examples):
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if ex_index % 10000 == 0 and print_info:
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logger.info("Writing example %d of %d" % (ex_index, len(examples)))
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tokens_a = tokenizer.tokenize(example.text_a)
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tokens_b = None
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tokens_c = None
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if example.text_b and example.text_c:
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tokens_b = tokenizer.tokenize(example.text_b)
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tokens_c = tokenizer.tokenize(example.text_c)
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# Modifies `tokens_a`, `tokens_b` and `tokens_c`in place so that the total
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# length is less than the specified length.
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# Account for [CLS], [SEP], [SEP], [SEP] with "- 4"
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#_truncate_seq_pair(tokens_a, tokens_b, max_seq_length - 3)
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_truncate_seq_triple(tokens_a, tokens_b, tokens_c, max_seq_length - 4)
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else:
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# Account for [CLS] and [SEP] with "- 2"
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if len(tokens_a) > max_seq_length - 2:
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tokens_a = tokens_a[:(max_seq_length - 2)]
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# The convention in BERT is:
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# (a) For sequence pairs:
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# tokens: [CLS] is this jack ##son ##ville ? [SEP] no it is not . [SEP]
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# type_ids: 0 0 0 0 0 0 0 0 1 1 1 1 1 1
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# (b) For single sequences:
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# tokens: [CLS] the dog is hairy . [SEP]
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# type_ids: 0 0 0 0 0 0 0
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# (c) for sequence triples:
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# tokens: [CLS] Steve Jobs [SEP] founded [SEP] Apple Inc .[SEP]
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# type_ids: 0 0 0 0 1 1 0 0 0 0
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#
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# Where "type_ids" are used to indicate whether this is the first
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# sequence or the second sequence or the third sequence. The embedding vectors for `type=0` and
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# `type=1` were learned during pre-training and are added to the wordpiece
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# embedding vector (and position vector). This is not *strictly* necessary
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# since the [SEP] token unambiguously separates the sequences, but it makes
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# it easier for the model to learn the concept of sequences.
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#
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# For classification tasks, the first vector (corresponding to [CLS]) is
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# used as as the "sentence vector". Note that this only makes sense because
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# the entire model is fine-tuned.
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tokens = ["[CLS]"] + tokens_a + ["[SEP]"]
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segment_ids = [0] * len(tokens)
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if tokens_b:
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tokens += tokens_b + ["[SEP]"]
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segment_ids += [1] * (len(tokens_b) + 1)
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if tokens_c:
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tokens += tokens_c + ["[SEP]"]
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segment_ids += [0] * (len(tokens_c) + 1)
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input_ids = tokenizer.convert_tokens_to_ids(tokens)
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# The mask has 1 for real tokens and 0 for padding tokens. Only real
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# tokens are attended to.
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input_mask = [1] * len(input_ids)
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# Zero-pad up to the sequence length.
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padding = [0] * (max_seq_length - len(input_ids))
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input_ids += padding
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input_mask += padding
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segment_ids += padding
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assert len(input_ids) == max_seq_length
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assert len(input_mask) == max_seq_length
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assert len(segment_ids) == max_seq_length
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label_id = label_map[example.label]
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if ex_index < 5 and print_info:
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logger.info("*** Example ***")
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logger.info("guid: %s" % (example.guid))
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logger.info("tokens: %s" % " ".join(
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[str(x) for x in tokens]))
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logger.info("input_ids: %s" % " ".join([str(x) for x in input_ids]))
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logger.info("input_mask: %s" % " ".join([str(x) for x in input_mask]))
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logger.info(
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"segment_ids: %s" % " ".join([str(x) for x in segment_ids]))
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|
logger.info("label: %s (id = %d)" % (example.label, label_id))
|
||
|
|
|
||
|
|
features.append(
|
||
|
|
InputFeatures(input_ids=input_ids,
|
||
|
|
input_mask=input_mask,
|
||
|
|
segment_ids=segment_ids,
|
||
|
|
label_id=label_id))
|
||
|
|
return features
|
||
|
|
|
||
|
|
|
||
|
|
def _truncate_seq_pair(tokens_a, tokens_b, max_length):
|
||
|
|
"""Truncates a sequence pair in place to the maximum length."""
|
||
|
|
|
||
|
|
# This is a simple heuristic which will always truncate the longer sequence
|
||
|
|
# one token at a time. This makes more sense than truncating an equal percent
|
||
|
|
# of tokens from each, since if one sequence is very short then each token
|
||
|
|
# that's truncated likely contains more information than a longer sequence.
|
||
|
|
while True:
|
||
|
|
total_length = len(tokens_a) + len(tokens_b)
|
||
|
|
if total_length <= max_length:
|
||
|
|
break
|
||
|
|
if len(tokens_a) > len(tokens_b):
|
||
|
|
tokens_a.pop()
|
||
|
|
else:
|
||
|
|
tokens_b.pop()
|
||
|
|
|
||
|
|
def _truncate_seq_triple(tokens_a, tokens_b, tokens_c, max_length):
|
||
|
|
"""Truncates a sequence triple in place to the maximum length."""
|
||
|
|
|
||
|
|
# This is a simple heuristic which will always truncate the longer sequence
|
||
|
|
# one token at a time. This makes more sense than truncating an equal percent
|
||
|
|
# of tokens from each, since if one sequence is very short then each token
|
||
|
|
# that's truncated likely contains more information than a longer sequence.
|
||
|
|
while True:
|
||
|
|
total_length = len(tokens_a) + len(tokens_b) + len(tokens_c)
|
||
|
|
if total_length <= max_length:
|
||
|
|
break
|
||
|
|
if len(tokens_a) > len(tokens_b) and len(tokens_a) > len(tokens_c):
|
||
|
|
tokens_a.pop()
|
||
|
|
elif len(tokens_b) > len(tokens_a) and len(tokens_b) > len(tokens_c):
|
||
|
|
tokens_b.pop()
|
||
|
|
elif len(tokens_c) > len(tokens_a) and len(tokens_c) > len(tokens_b):
|
||
|
|
tokens_c.pop()
|
||
|
|
else:
|
||
|
|
tokens_c.pop()
|
||
|
|
|
||
|
|
def simple_accuracy(preds, labels):
|
||
|
|
return (preds == labels).mean()
|
||
|
|
|
||
|
|
def compute_metrics(task_name, preds, labels):
|
||
|
|
assert len(preds) == len(labels)
|
||
|
|
if task_name == "kg":
|
||
|
|
return {"acc": simple_accuracy(preds, labels)}
|
||
|
|
else:
|
||
|
|
raise KeyError(task_name)
|
||
|
|
|
||
|
|
|
||
|
|
def main():
|
||
|
|
parser = argparse.ArgumentParser()
|
||
|
|
|
||
|
|
## Required parameters
|
||
|
|
parser.add_argument("--data_dir",
|
||
|
|
default=None,
|
||
|
|
type=str,
|
||
|
|
required=True,
|
||
|
|
help="The input data dir. Should contain the .tsv files (or other data files) for the task.")
|
||
|
|
parser.add_argument("--bert_model", default=None, type=str, required=True,
|
||
|
|
help="Bert pre-trained model selected in the list: bert-base-uncased, "
|
||
|
|
"bert-large-uncased, bert-base-cased, bert-large-cased, bert-base-multilingual-uncased, "
|
||
|
|
"bert-base-multilingual-cased, bert-base-chinese.")
|
||
|
|
parser.add_argument("--task_name",
|
||
|
|
default=None,
|
||
|
|
type=str,
|
||
|
|
required=True,
|
||
|
|
help="The name of the task to train.")
|
||
|
|
parser.add_argument("--output_dir",
|
||
|
|
default=None,
|
||
|
|
type=str,
|
||
|
|
required=True,
|
||
|
|
help="The output directory where the model predictions and checkpoints will be written.")
|
||
|
|
|
||
|
|
## Other parameters
|
||
|
|
parser.add_argument("--cache_dir",
|
||
|
|
default="",
|
||
|
|
type=str,
|
||
|
|
help="Where do you want to store the pre-trained models downloaded from s3")
|
||
|
|
parser.add_argument("--max_seq_length",
|
||
|
|
default=128,
|
||
|
|
type=int,
|
||
|
|
help="The maximum total input sequence length after WordPiece tokenization. \n"
|
||
|
|
"Sequences longer than this will be truncated, and sequences shorter \n"
|
||
|
|
"than this will be padded.")
|
||
|
|
parser.add_argument("--do_train",
|
||
|
|
action='store_true',
|
||
|
|
help="Whether to run training.")
|
||
|
|
parser.add_argument("--do_eval",
|
||
|
|
action='store_true',
|
||
|
|
help="Whether to run eval on the dev set.")
|
||
|
|
parser.add_argument("--do_predict",
|
||
|
|
action='store_true',
|
||
|
|
help="Whether to run eval on the test set.")
|
||
|
|
parser.add_argument("--do_lower_case",
|
||
|
|
action='store_true',
|
||
|
|
help="Set this flag if you are using an uncased model.")
|
||
|
|
parser.add_argument("--train_batch_size",
|
||
|
|
default=32,
|
||
|
|
type=int,
|
||
|
|
help="Total batch size for training.")
|
||
|
|
parser.add_argument("--eval_batch_size",
|
||
|
|
default=8,
|
||
|
|
type=int,
|
||
|
|
help="Total batch size for eval.")
|
||
|
|
parser.add_argument("--learning_rate",
|
||
|
|
default=5e-5,
|
||
|
|
type=float,
|
||
|
|
help="The initial learning rate for Adam.")
|
||
|
|
parser.add_argument("--num_train_epochs",
|
||
|
|
default=3.0,
|
||
|
|
type=float,
|
||
|
|
help="Total number of training epochs to perform.")
|
||
|
|
parser.add_argument("--warmup_proportion",
|
||
|
|
default=0.1,
|
||
|
|
type=float,
|
||
|
|
help="Proportion of training to perform linear learning rate warmup for. "
|
||
|
|
"E.g., 0.1 = 10%% of training.")
|
||
|
|
parser.add_argument("--no_cuda",
|
||
|
|
action='store_true',
|
||
|
|
help="Whether not to use CUDA when available")
|
||
|
|
parser.add_argument("--local_rank",
|
||
|
|
type=int,
|
||
|
|
default=-1,
|
||
|
|
help="local_rank for distributed training on gpus")
|
||
|
|
parser.add_argument('--seed',
|
||
|
|
type=int,
|
||
|
|
default=42,
|
||
|
|
help="random seed for initialization")
|
||
|
|
parser.add_argument('--gradient_accumulation_steps',
|
||
|
|
type=int,
|
||
|
|
default=1,
|
||
|
|
help="Number of updates steps to accumulate before performing a backward/update pass.")
|
||
|
|
parser.add_argument('--fp16',
|
||
|
|
action='store_true',
|
||
|
|
help="Whether to use 16-bit float precision instead of 32-bit")
|
||
|
|
parser.add_argument('--loss_scale',
|
||
|
|
type=float, default=0,
|
||
|
|
help="Loss scaling to improve fp16 numeric stability. Only used when fp16 set to True.\n"
|
||
|
|
"0 (default value): dynamic loss scaling.\n"
|
||
|
|
"Positive power of 2: static loss scaling value.\n")
|
||
|
|
parser.add_argument('--server_ip', type=str, default='', help="Can be used for distant debugging.")
|
||
|
|
parser.add_argument('--server_port', type=str, default='', help="Can be used for distant debugging.")
|
||
|
|
args = parser.parse_args()
|
||
|
|
|
||
|
|
if args.server_ip and args.server_port:
|
||
|
|
# Distant debugging - see https://code.visualstudio.com/docs/python/debugging#_attach-to-a-local-script
|
||
|
|
import ptvsd
|
||
|
|
print("Waiting for debugger attach")
|
||
|
|
ptvsd.enable_attach(address=(args.server_ip, args.server_port), redirect_output=True)
|
||
|
|
ptvsd.wait_for_attach()
|
||
|
|
|
||
|
|
processors = {
|
||
|
|
"kg": KGProcessor,
|
||
|
|
}
|
||
|
|
|
||
|
|
if args.local_rank == -1 or args.no_cuda:
|
||
|
|
device = torch.device("cuda" if torch.cuda.is_available() and not args.no_cuda else "cpu")
|
||
|
|
n_gpu = torch.cuda.device_count()
|
||
|
|
else:
|
||
|
|
torch.cuda.set_device(args.local_rank)
|
||
|
|
device = torch.device("cuda", args.local_rank)
|
||
|
|
n_gpu = 1
|
||
|
|
# Initializes the distributed backend which will take care of sychronizing nodes/GPUs
|
||
|
|
torch.distributed.init_process_group(backend='nccl')
|
||
|
|
|
||
|
|
logging.basicConfig(format = '%(asctime)s - %(levelname)s - %(name)s - %(message)s',
|
||
|
|
datefmt = '%m/%d/%Y %H:%M:%S',
|
||
|
|
level = logging.INFO if args.local_rank in [-1, 0] else logging.WARN)
|
||
|
|
|
||
|
|
logger.info("device: {} n_gpu: {}, distributed training: {}, 16-bits training: {}".format(
|
||
|
|
device, n_gpu, bool(args.local_rank != -1), args.fp16))
|
||
|
|
|
||
|
|
if args.gradient_accumulation_steps < 1:
|
||
|
|
raise ValueError("Invalid gradient_accumulation_steps parameter: {}, should be >= 1".format(
|
||
|
|
args.gradient_accumulation_steps))
|
||
|
|
|
||
|
|
args.train_batch_size = args.train_batch_size // args.gradient_accumulation_steps
|
||
|
|
args.seed = random.randint(1, 200)
|
||
|
|
random.seed(args.seed)
|
||
|
|
np.random.seed(args.seed)
|
||
|
|
torch.manual_seed(args.seed)
|
||
|
|
|
||
|
|
if n_gpu > 0:
|
||
|
|
torch.cuda.manual_seed_all(args.seed)
|
||
|
|
|
||
|
|
if not args.do_train and not args.do_eval:
|
||
|
|
raise ValueError("At least one of `do_train` or `do_eval` must be True.")
|
||
|
|
|
||
|
|
if os.path.exists(args.output_dir) and os.listdir(args.output_dir) and args.do_train:
|
||
|
|
raise ValueError("Output directory ({}) already exists and is not empty.".format(args.output_dir))
|
||
|
|
if not os.path.exists(args.output_dir):
|
||
|
|
os.makedirs(args.output_dir)
|
||
|
|
|
||
|
|
task_name = args.task_name.lower()
|
||
|
|
|
||
|
|
if task_name not in processors:
|
||
|
|
raise ValueError("Task not found: %s" % (task_name))
|
||
|
|
|
||
|
|
processor = processors[task_name]()
|
||
|
|
|
||
|
|
label_list = processor.get_labels(args.data_dir)
|
||
|
|
num_labels = len(label_list)
|
||
|
|
|
||
|
|
entity_list = processor.get_entities(args.data_dir)
|
||
|
|
#print(entity_list)
|
||
|
|
|
||
|
|
tokenizer = BertTokenizer.from_pretrained(args.bert_model, do_lower_case=args.do_lower_case)
|
||
|
|
|
||
|
|
train_examples = None
|
||
|
|
num_train_optimization_steps = 0
|
||
|
|
if args.do_train:
|
||
|
|
train_examples = processor.get_train_examples(args.data_dir)
|
||
|
|
num_train_optimization_steps = int(
|
||
|
|
len(train_examples) / args.train_batch_size / args.gradient_accumulation_steps) * args.num_train_epochs
|
||
|
|
if args.local_rank != -1:
|
||
|
|
num_train_optimization_steps = num_train_optimization_steps // torch.distributed.get_world_size()
|
||
|
|
|
||
|
|
# Prepare model
|
||
|
|
cache_dir = args.cache_dir if args.cache_dir else os.path.join(str(PYTORCH_PRETRAINED_BERT_CACHE), 'distributed_{}'.format(args.local_rank))
|
||
|
|
model = BertForSequenceClassification.from_pretrained(args.bert_model,
|
||
|
|
cache_dir=cache_dir,
|
||
|
|
num_labels=num_labels)
|
||
|
|
if args.fp16:
|
||
|
|
model.half()
|
||
|
|
model.to(device)
|
||
|
|
if args.local_rank != -1:
|
||
|
|
try:
|
||
|
|
from apex.parallel import DistributedDataParallel as DDP
|
||
|
|
except ImportError:
|
||
|
|
raise ImportError("Please install apex from https://www.github.com/nvidia/apex to use distributed and fp16 training.")
|
||
|
|
|
||
|
|
model = DDP(model)
|
||
|
|
elif n_gpu > 1:
|
||
|
|
model = torch.nn.DataParallel(model)
|
||
|
|
#model = torch.nn.parallel.data_parallel(model)
|
||
|
|
# Prepare optimizer
|
||
|
|
param_optimizer = list(model.named_parameters())
|
||
|
|
no_decay = ['bias', 'LayerNorm.bias', 'LayerNorm.weight']
|
||
|
|
optimizer_grouped_parameters = [
|
||
|
|
{'params': [p for n, p in param_optimizer if not any(nd in n for nd in no_decay)], 'weight_decay': 0.01},
|
||
|
|
{'params': [p for n, p in param_optimizer if any(nd in n for nd in no_decay)], 'weight_decay': 0.0}
|
||
|
|
]
|
||
|
|
if args.fp16:
|
||
|
|
try:
|
||
|
|
from apex.optimizers import FP16_Optimizer
|
||
|
|
from apex.optimizers import FusedAdam
|
||
|
|
except ImportError:
|
||
|
|
raise ImportError("Please install apex from https://www.github.com/nvidia/apex to use distributed and fp16 training.")
|
||
|
|
|
||
|
|
optimizer = FusedAdam(optimizer_grouped_parameters,
|
||
|
|
lr=args.learning_rate,
|
||
|
|
bias_correction=False,
|
||
|
|
max_grad_norm=1.0)
|
||
|
|
if args.loss_scale == 0:
|
||
|
|
optimizer = FP16_Optimizer(optimizer, dynamic_loss_scale=True)
|
||
|
|
else:
|
||
|
|
optimizer = FP16_Optimizer(optimizer, static_loss_scale=args.loss_scale)
|
||
|
|
warmup_linear = WarmupLinearSchedule(warmup=args.warmup_proportion,
|
||
|
|
t_total=num_train_optimization_steps)
|
||
|
|
|
||
|
|
else:
|
||
|
|
optimizer = BertAdam(optimizer_grouped_parameters,
|
||
|
|
lr=args.learning_rate,
|
||
|
|
warmup=args.warmup_proportion,
|
||
|
|
t_total=num_train_optimization_steps)
|
||
|
|
|
||
|
|
global_step = 0
|
||
|
|
nb_tr_steps = 0
|
||
|
|
tr_loss = 0
|
||
|
|
if args.do_train:
|
||
|
|
|
||
|
|
train_features = convert_examples_to_features(
|
||
|
|
train_examples, label_list, args.max_seq_length, tokenizer)
|
||
|
|
logger.info("***** Running training *****")
|
||
|
|
logger.info(" Num examples = %d", len(train_examples))
|
||
|
|
logger.info(" Batch size = %d", args.train_batch_size)
|
||
|
|
logger.info(" Num steps = %d", num_train_optimization_steps)
|
||
|
|
all_input_ids = torch.tensor([f.input_ids for f in train_features], dtype=torch.long)
|
||
|
|
all_input_mask = torch.tensor([f.input_mask for f in train_features], dtype=torch.long)
|
||
|
|
all_segment_ids = torch.tensor([f.segment_ids for f in train_features], dtype=torch.long)
|
||
|
|
|
||
|
|
all_label_ids = torch.tensor([f.label_id for f in train_features], dtype=torch.long)
|
||
|
|
|
||
|
|
train_data = TensorDataset(all_input_ids, all_input_mask, all_segment_ids, all_label_ids)
|
||
|
|
if args.local_rank == -1:
|
||
|
|
train_sampler = RandomSampler(train_data)
|
||
|
|
else:
|
||
|
|
train_sampler = DistributedSampler(train_data)
|
||
|
|
train_dataloader = DataLoader(train_data, sampler=train_sampler, batch_size=args.train_batch_size)
|
||
|
|
|
||
|
|
model.train()
|
||
|
|
#print(model)
|
||
|
|
for _ in trange(int(args.num_train_epochs), desc="Epoch"):
|
||
|
|
tr_loss = 0
|
||
|
|
nb_tr_examples, nb_tr_steps = 0, 0
|
||
|
|
for step, batch in enumerate(tqdm(train_dataloader, desc="Iteration")):
|
||
|
|
batch = tuple(t.to(device) for t in batch)
|
||
|
|
input_ids, input_mask, segment_ids, label_ids = batch
|
||
|
|
|
||
|
|
# define a new function to compute loss values for both output_modes
|
||
|
|
logits = model(input_ids, segment_ids, input_mask, labels=None)
|
||
|
|
#print(logits, logits.shape)
|
||
|
|
|
||
|
|
loss_fct = CrossEntropyLoss()
|
||
|
|
loss = loss_fct(logits.view(-1, num_labels), label_ids.view(-1))
|
||
|
|
|
||
|
|
if n_gpu > 1:
|
||
|
|
loss = loss.mean() # mean() to average on multi-gpu.
|
||
|
|
if args.gradient_accumulation_steps > 1:
|
||
|
|
loss = loss / args.gradient_accumulation_steps
|
||
|
|
|
||
|
|
if args.fp16:
|
||
|
|
optimizer.backward(loss)
|
||
|
|
else:
|
||
|
|
loss.backward()
|
||
|
|
|
||
|
|
tr_loss += loss.item()
|
||
|
|
nb_tr_examples += input_ids.size(0)
|
||
|
|
nb_tr_steps += 1
|
||
|
|
if (step + 1) % args.gradient_accumulation_steps == 0:
|
||
|
|
if args.fp16:
|
||
|
|
# modify learning rate with special warm up BERT uses
|
||
|
|
# if args.fp16 is False, BertAdam is used that handles this automatically
|
||
|
|
lr_this_step = args.learning_rate * warmup_linear.get_lr(global_step/num_train_optimization_steps,
|
||
|
|
args.warmup_proportion)
|
||
|
|
for param_group in optimizer.param_groups:
|
||
|
|
param_group['lr'] = lr_this_step
|
||
|
|
optimizer.step()
|
||
|
|
optimizer.zero_grad()
|
||
|
|
global_step += 1
|
||
|
|
print("Training loss: ", tr_loss, nb_tr_examples)
|
||
|
|
|
||
|
|
if args.do_train and (args.local_rank == -1 or torch.distributed.get_rank() == 0):
|
||
|
|
# Save a trained model, configuration and tokenizer
|
||
|
|
model_to_save = model.module if hasattr(model, 'module') else model # Only save the model it-self
|
||
|
|
|
||
|
|
# If we save using the predefined names, we can load using `from_pretrained`
|
||
|
|
output_model_file = os.path.join(args.output_dir, WEIGHTS_NAME)
|
||
|
|
output_config_file = os.path.join(args.output_dir, CONFIG_NAME)
|
||
|
|
|
||
|
|
torch.save(model_to_save.state_dict(), output_model_file)
|
||
|
|
model_to_save.config.to_json_file(output_config_file)
|
||
|
|
tokenizer.save_vocabulary(args.output_dir)
|
||
|
|
|
||
|
|
# Load a trained model and vocabulary that you have fine-tuned
|
||
|
|
model = BertForSequenceClassification.from_pretrained(args.output_dir, num_labels=num_labels)
|
||
|
|
tokenizer = BertTokenizer.from_pretrained(args.output_dir, do_lower_case=args.do_lower_case)
|
||
|
|
else:
|
||
|
|
model = BertForSequenceClassification.from_pretrained(args.bert_model, num_labels=num_labels)
|
||
|
|
model.to(device)
|
||
|
|
|
||
|
|
if args.do_eval and (args.local_rank == -1 or torch.distributed.get_rank() == 0):
|
||
|
|
|
||
|
|
eval_examples = processor.get_dev_examples(args.data_dir)
|
||
|
|
eval_features = convert_examples_to_features(
|
||
|
|
eval_examples, label_list, args.max_seq_length, tokenizer)
|
||
|
|
logger.info("***** Running evaluation *****")
|
||
|
|
logger.info(" Num examples = %d", len(eval_examples))
|
||
|
|
logger.info(" Batch size = %d", args.eval_batch_size)
|
||
|
|
all_input_ids = torch.tensor([f.input_ids for f in eval_features], dtype=torch.long)
|
||
|
|
all_input_mask = torch.tensor([f.input_mask for f in eval_features], dtype=torch.long)
|
||
|
|
all_segment_ids = torch.tensor([f.segment_ids for f in eval_features], dtype=torch.long)
|
||
|
|
|
||
|
|
all_label_ids = torch.tensor([f.label_id for f in eval_features], dtype=torch.long)
|
||
|
|
|
||
|
|
eval_data = TensorDataset(all_input_ids, all_input_mask, all_segment_ids, all_label_ids)
|
||
|
|
# Run prediction for full data
|
||
|
|
eval_sampler = SequentialSampler(eval_data)
|
||
|
|
eval_dataloader = DataLoader(eval_data, sampler=eval_sampler, batch_size=args.eval_batch_size)
|
||
|
|
|
||
|
|
# Load a trained model and vocabulary that you have fine-tuned
|
||
|
|
model = BertForSequenceClassification.from_pretrained(args.output_dir, num_labels=num_labels)
|
||
|
|
tokenizer = BertTokenizer.from_pretrained(args.output_dir, do_lower_case=args.do_lower_case)
|
||
|
|
model.to(device)
|
||
|
|
|
||
|
|
model.eval()
|
||
|
|
eval_loss = 0
|
||
|
|
nb_eval_steps = 0
|
||
|
|
preds = []
|
||
|
|
|
||
|
|
for input_ids, input_mask, segment_ids, label_ids in tqdm(eval_dataloader, desc="Evaluating"):
|
||
|
|
input_ids = input_ids.to(device)
|
||
|
|
input_mask = input_mask.to(device)
|
||
|
|
segment_ids = segment_ids.to(device)
|
||
|
|
label_ids = label_ids.to(device)
|
||
|
|
|
||
|
|
with torch.no_grad():
|
||
|
|
logits = model(input_ids, segment_ids, input_mask, labels=None)
|
||
|
|
|
||
|
|
# create eval loss and other metric required by the task
|
||
|
|
loss_fct = CrossEntropyLoss()
|
||
|
|
tmp_eval_loss = loss_fct(logits.view(-1, num_labels), label_ids.view(-1))
|
||
|
|
print(label_ids.view(-1))
|
||
|
|
|
||
|
|
eval_loss += tmp_eval_loss.mean().item()
|
||
|
|
nb_eval_steps += 1
|
||
|
|
if len(preds) == 0:
|
||
|
|
preds.append(logits.detach().cpu().numpy())
|
||
|
|
else:
|
||
|
|
preds[0] = np.append(
|
||
|
|
preds[0], logits.detach().cpu().numpy(), axis=0)
|
||
|
|
|
||
|
|
eval_loss = eval_loss / nb_eval_steps
|
||
|
|
preds = preds[0]
|
||
|
|
|
||
|
|
preds = np.argmax(preds, axis=1)
|
||
|
|
result = compute_metrics(task_name, preds, all_label_ids.numpy())
|
||
|
|
loss = tr_loss/nb_tr_steps if args.do_train else None
|
||
|
|
|
||
|
|
result['eval_loss'] = eval_loss
|
||
|
|
result['global_step'] = global_step
|
||
|
|
result['loss'] = loss
|
||
|
|
|
||
|
|
output_eval_file = os.path.join(args.output_dir, "eval_results.txt")
|
||
|
|
with open(output_eval_file, "w") as writer:
|
||
|
|
logger.info("***** Eval results *****")
|
||
|
|
for key in sorted(result.keys()):
|
||
|
|
logger.info(" %s = %s", key, str(result[key]))
|
||
|
|
writer.write("%s = %s\n" % (key, str(result[key])))
|
||
|
|
|
||
|
|
if args.do_predict and (args.local_rank == -1 or torch.distributed.get_rank() == 0):
|
||
|
|
|
||
|
|
train_triples = processor.get_train_triples(args.data_dir)
|
||
|
|
dev_triples = processor.get_dev_triples(args.data_dir)
|
||
|
|
test_triples = processor.get_test_triples(args.data_dir)
|
||
|
|
all_triples = train_triples + dev_triples + test_triples
|
||
|
|
|
||
|
|
all_triples_str_set = set()
|
||
|
|
for triple in all_triples:
|
||
|
|
triple_str = '\t'.join(triple)
|
||
|
|
all_triples_str_set.add(triple_str)
|
||
|
|
|
||
|
|
eval_examples = processor.get_test_examples(args.data_dir)
|
||
|
|
eval_features = convert_examples_to_features(
|
||
|
|
eval_examples, label_list, args.max_seq_length, tokenizer)
|
||
|
|
logger.info("***** Running Prediction *****")
|
||
|
|
logger.info(" Num examples = %d", len(eval_examples))
|
||
|
|
logger.info(" Batch size = %d", args.eval_batch_size)
|
||
|
|
all_input_ids = torch.tensor([f.input_ids for f in eval_features], dtype=torch.long)
|
||
|
|
all_input_mask = torch.tensor([f.input_mask for f in eval_features], dtype=torch.long)
|
||
|
|
all_segment_ids = torch.tensor([f.segment_ids for f in eval_features], dtype=torch.long)
|
||
|
|
|
||
|
|
all_label_ids = torch.tensor([f.label_id for f in eval_features], dtype=torch.long)
|
||
|
|
|
||
|
|
eval_data = TensorDataset(all_input_ids, all_input_mask, all_segment_ids, all_label_ids)
|
||
|
|
# Run prediction for full data
|
||
|
|
eval_sampler = SequentialSampler(eval_data)
|
||
|
|
eval_dataloader = DataLoader(eval_data, sampler=eval_sampler, batch_size=args.eval_batch_size)
|
||
|
|
# Load a trained model and vocabulary that you have fine-tuned
|
||
|
|
model = BertForSequenceClassification.from_pretrained(args.output_dir, num_labels=num_labels)
|
||
|
|
tokenizer = BertTokenizer.from_pretrained(args.output_dir, do_lower_case=args.do_lower_case)
|
||
|
|
model.to(device)
|
||
|
|
model.eval()
|
||
|
|
eval_loss = 0
|
||
|
|
nb_eval_steps = 0
|
||
|
|
preds = []
|
||
|
|
|
||
|
|
for input_ids, input_mask, segment_ids, label_ids in tqdm(eval_dataloader, desc="Testing"):
|
||
|
|
input_ids = input_ids.to(device)
|
||
|
|
input_mask = input_mask.to(device)
|
||
|
|
segment_ids = segment_ids.to(device)
|
||
|
|
label_ids = label_ids.to(device)
|
||
|
|
|
||
|
|
with torch.no_grad():
|
||
|
|
logits = model(input_ids, segment_ids, input_mask, labels=None)
|
||
|
|
|
||
|
|
loss_fct = CrossEntropyLoss()
|
||
|
|
tmp_eval_loss = loss_fct(logits.view(-1, num_labels), label_ids.view(-1))
|
||
|
|
|
||
|
|
eval_loss += tmp_eval_loss.mean().item()
|
||
|
|
nb_eval_steps += 1
|
||
|
|
if len(preds) == 0:
|
||
|
|
preds.append(logits.detach().cpu().numpy())
|
||
|
|
else:
|
||
|
|
preds[0] = np.append(
|
||
|
|
preds[0], logits.detach().cpu().numpy(), axis=0)
|
||
|
|
|
||
|
|
eval_loss = eval_loss / nb_eval_steps
|
||
|
|
preds = preds[0]
|
||
|
|
print(preds, preds.shape)
|
||
|
|
|
||
|
|
all_label_ids = all_label_ids.numpy()
|
||
|
|
|
||
|
|
preds = np.argmax(preds, axis=1)
|
||
|
|
|
||
|
|
result = compute_metrics(task_name, preds, all_label_ids)
|
||
|
|
loss = tr_loss/nb_tr_steps if args.do_train else None
|
||
|
|
|
||
|
|
result['eval_loss'] = eval_loss
|
||
|
|
result['global_step'] = global_step
|
||
|
|
result['loss'] = loss
|
||
|
|
|
||
|
|
output_eval_file = os.path.join(args.output_dir, "test_results.txt")
|
||
|
|
with open(output_eval_file, "w") as writer:
|
||
|
|
logger.info("***** Test results *****")
|
||
|
|
for key in sorted(result.keys()):
|
||
|
|
logger.info(" %s = %s", key, str(result[key]))
|
||
|
|
writer.write("%s = %s\n" % (key, str(result[key])))
|
||
|
|
print("Triple classification acc is : ")
|
||
|
|
print(metrics.accuracy_score(all_label_ids, preds))
|
||
|
|
|
||
|
|
# run link prediction
|
||
|
|
ranks = []
|
||
|
|
ranks_left = []
|
||
|
|
ranks_right = []
|
||
|
|
|
||
|
|
hits_left = []
|
||
|
|
hits_right = []
|
||
|
|
hits = []
|
||
|
|
|
||
|
|
top_ten_hit_count = 0
|
||
|
|
|
||
|
|
for i in range(10):
|
||
|
|
hits_left.append([])
|
||
|
|
hits_right.append([])
|
||
|
|
hits.append([])
|
||
|
|
'''
|
||
|
|
file_prefix = str(args.data_dir[7:])
|
||
|
|
f = open(file_prefix + '_ranks.txt','r')
|
||
|
|
lines = f.readlines()
|
||
|
|
for line in lines:
|
||
|
|
temp = line.strip().split()
|
||
|
|
rank1 = int(temp[0])
|
||
|
|
ranks_left.append(rank1+1)
|
||
|
|
print('left: ', rank1)
|
||
|
|
ranks.append(rank1+1)
|
||
|
|
if rank1 < 10:
|
||
|
|
top_ten_hit_count += 1
|
||
|
|
rank2 = int(temp[1])
|
||
|
|
ranks.append(rank2+1)
|
||
|
|
ranks_right.append(rank2+1)
|
||
|
|
print('right: ', rank2)
|
||
|
|
print('mean rank until now: ', np.mean(ranks))
|
||
|
|
if rank2 < 10:
|
||
|
|
top_ten_hit_count += 1
|
||
|
|
print("hit@10 until now: ", top_ten_hit_count * 1.0 / len(ranks))
|
||
|
|
for hits_level in range(10):
|
||
|
|
if rank1 <= hits_level:
|
||
|
|
hits[hits_level].append(1.0)
|
||
|
|
hits_left[hits_level].append(1.0)
|
||
|
|
else:
|
||
|
|
hits[hits_level].append(0.0)
|
||
|
|
hits_left[hits_level].append(0.0)
|
||
|
|
|
||
|
|
if rank2 <= hits_level:
|
||
|
|
hits[hits_level].append(1.0)
|
||
|
|
hits_right[hits_level].append(1.0)
|
||
|
|
else:
|
||
|
|
hits[hits_level].append(0.0)
|
||
|
|
hits_right[hits_level].append(0.0)
|
||
|
|
|
||
|
|
'''
|
||
|
|
for test_triple in test_triples:
|
||
|
|
head = test_triple[0]
|
||
|
|
relation = test_triple[1]
|
||
|
|
tail = test_triple[2]
|
||
|
|
#print(test_triple, head, relation, tail)
|
||
|
|
|
||
|
|
head_corrupt_list = [test_triple]
|
||
|
|
for corrupt_ent in entity_list:
|
||
|
|
if corrupt_ent != head:
|
||
|
|
tmp_triple = [corrupt_ent, relation, tail]
|
||
|
|
tmp_triple_str = '\t'.join(tmp_triple)
|
||
|
|
if tmp_triple_str not in all_triples_str_set:
|
||
|
|
# may be slow
|
||
|
|
head_corrupt_list.append(tmp_triple)
|
||
|
|
|
||
|
|
tmp_examples = processor._create_examples(head_corrupt_list, "test", args.data_dir)
|
||
|
|
print(len(tmp_examples))
|
||
|
|
tmp_features = convert_examples_to_features(tmp_examples, label_list, args.max_seq_length, tokenizer, print_info = False)
|
||
|
|
all_input_ids = torch.tensor([f.input_ids for f in tmp_features], dtype=torch.long)
|
||
|
|
all_input_mask = torch.tensor([f.input_mask for f in tmp_features], dtype=torch.long)
|
||
|
|
all_segment_ids = torch.tensor([f.segment_ids for f in tmp_features], dtype=torch.long)
|
||
|
|
all_label_ids = torch.tensor([f.label_id for f in tmp_features], dtype=torch.long)
|
||
|
|
|
||
|
|
eval_data = TensorDataset(all_input_ids, all_input_mask, all_segment_ids, all_label_ids)
|
||
|
|
# Run prediction for temp data
|
||
|
|
eval_sampler = SequentialSampler(eval_data)
|
||
|
|
eval_dataloader = DataLoader(eval_data, sampler=eval_sampler, batch_size=args.eval_batch_size)
|
||
|
|
model.eval()
|
||
|
|
|
||
|
|
preds = []
|
||
|
|
|
||
|
|
for input_ids, input_mask, segment_ids, label_ids in tqdm(eval_dataloader, desc="Testing"):
|
||
|
|
|
||
|
|
input_ids = input_ids.to(device)
|
||
|
|
input_mask = input_mask.to(device)
|
||
|
|
segment_ids = segment_ids.to(device)
|
||
|
|
label_ids = label_ids.to(device)
|
||
|
|
|
||
|
|
with torch.no_grad():
|
||
|
|
logits = model(input_ids, segment_ids, input_mask, labels=None)
|
||
|
|
if len(preds) == 0:
|
||
|
|
batch_logits = logits.detach().cpu().numpy()
|
||
|
|
preds.append(batch_logits)
|
||
|
|
|
||
|
|
else:
|
||
|
|
batch_logits = logits.detach().cpu().numpy()
|
||
|
|
preds[0] = np.append(preds[0], batch_logits, axis=0)
|
||
|
|
|
||
|
|
preds = preds[0]
|
||
|
|
# get the dimension corresponding to current label 1
|
||
|
|
#print(preds, preds.shape)
|
||
|
|
rel_values = preds[:, all_label_ids[0]]
|
||
|
|
rel_values = torch.tensor(rel_values)
|
||
|
|
#print(rel_values, rel_values.shape)
|
||
|
|
_, argsort1 = torch.sort(rel_values, descending=True)
|
||
|
|
#print(max_values)
|
||
|
|
#print(argsort1)
|
||
|
|
argsort1 = argsort1.cpu().numpy()
|
||
|
|
rank1 = np.where(argsort1 == 0)[0][0]
|
||
|
|
print('left: ', rank1)
|
||
|
|
ranks.append(rank1+1)
|
||
|
|
ranks_left.append(rank1+1)
|
||
|
|
if rank1 < 10:
|
||
|
|
top_ten_hit_count += 1
|
||
|
|
|
||
|
|
tail_corrupt_list = [test_triple]
|
||
|
|
for corrupt_ent in entity_list:
|
||
|
|
if corrupt_ent != tail:
|
||
|
|
tmp_triple = [head, relation, corrupt_ent]
|
||
|
|
tmp_triple_str = '\t'.join(tmp_triple)
|
||
|
|
if tmp_triple_str not in all_triples_str_set:
|
||
|
|
# may be slow
|
||
|
|
tail_corrupt_list.append(tmp_triple)
|
||
|
|
|
||
|
|
tmp_examples = processor._create_examples(tail_corrupt_list, "test", args.data_dir)
|
||
|
|
#print(len(tmp_examples))
|
||
|
|
tmp_features = convert_examples_to_features(tmp_examples, label_list, args.max_seq_length, tokenizer, print_info = False)
|
||
|
|
all_input_ids = torch.tensor([f.input_ids for f in tmp_features], dtype=torch.long)
|
||
|
|
all_input_mask = torch.tensor([f.input_mask for f in tmp_features], dtype=torch.long)
|
||
|
|
all_segment_ids = torch.tensor([f.segment_ids for f in tmp_features], dtype=torch.long)
|
||
|
|
all_label_ids = torch.tensor([f.label_id for f in tmp_features], dtype=torch.long)
|
||
|
|
|
||
|
|
eval_data = TensorDataset(all_input_ids, all_input_mask, all_segment_ids, all_label_ids)
|
||
|
|
# Run prediction for temp data
|
||
|
|
eval_sampler = SequentialSampler(eval_data)
|
||
|
|
eval_dataloader = DataLoader(eval_data, sampler=eval_sampler, batch_size=args.eval_batch_size)
|
||
|
|
model.eval()
|
||
|
|
preds = []
|
||
|
|
|
||
|
|
for input_ids, input_mask, segment_ids, label_ids in tqdm(eval_dataloader, desc="Testing"):
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input_ids = input_ids.to(device)
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input_mask = input_mask.to(device)
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segment_ids = segment_ids.to(device)
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label_ids = label_ids.to(device)
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with torch.no_grad():
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logits = model(input_ids, segment_ids, input_mask, labels=None)
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if len(preds) == 0:
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batch_logits = logits.detach().cpu().numpy()
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preds.append(batch_logits)
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else:
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batch_logits = logits.detach().cpu().numpy()
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preds[0] = np.append(preds[0], batch_logits, axis=0)
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preds = preds[0]
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# get the dimension corresponding to current label 1
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rel_values = preds[:, all_label_ids[0]]
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rel_values = torch.tensor(rel_values)
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_, argsort1 = torch.sort(rel_values, descending=True)
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argsort1 = argsort1.cpu().numpy()
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rank2 = np.where(argsort1 == 0)[0][0]
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ranks.append(rank2+1)
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ranks_right.append(rank2+1)
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print('right: ', rank2)
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print('mean rank until now: ', np.mean(ranks))
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||
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if rank2 < 10:
|
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top_ten_hit_count += 1
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print("hit@10 until now: ", top_ten_hit_count * 1.0 / len(ranks))
|
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|
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file_prefix = str(args.data_dir[7:]) + "_" + str(args.train_batch_size) + "_" + str(args.learning_rate) + "_" + str(args.max_seq_length) + "_" + str(args.num_train_epochs)
|
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|
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#file_prefix = str(args.data_dir[7:])
|
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|
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f = open(file_prefix + '_ranks.txt','a')
|
||
|
|
f.write(str(rank1) + '\t' + str(rank2) + '\n')
|
||
|
|
f.close()
|
||
|
|
# this could be done more elegantly, but here you go
|
||
|
|
for hits_level in range(10):
|
||
|
|
if rank1 <= hits_level:
|
||
|
|
hits[hits_level].append(1.0)
|
||
|
|
hits_left[hits_level].append(1.0)
|
||
|
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else:
|
||
|
|
hits[hits_level].append(0.0)
|
||
|
|
hits_left[hits_level].append(0.0)
|
||
|
|
|
||
|
|
if rank2 <= hits_level:
|
||
|
|
hits[hits_level].append(1.0)
|
||
|
|
hits_right[hits_level].append(1.0)
|
||
|
|
else:
|
||
|
|
hits[hits_level].append(0.0)
|
||
|
|
hits_right[hits_level].append(0.0)
|
||
|
|
|
||
|
|
|
||
|
|
for i in [0,2,9]:
|
||
|
|
logger.info('Hits left @{0}: {1}'.format(i+1, np.mean(hits_left[i])))
|
||
|
|
logger.info('Hits right @{0}: {1}'.format(i+1, np.mean(hits_right[i])))
|
||
|
|
logger.info('Hits @{0}: {1}'.format(i+1, np.mean(hits[i])))
|
||
|
|
logger.info('Mean rank left: {0}'.format(np.mean(ranks_left)))
|
||
|
|
logger.info('Mean rank right: {0}'.format(np.mean(ranks_right)))
|
||
|
|
logger.info('Mean rank: {0}'.format(np.mean(ranks)))
|
||
|
|
logger.info('Mean reciprocal rank left: {0}'.format(np.mean(1./np.array(ranks_left))))
|
||
|
|
logger.info('Mean reciprocal rank right: {0}'.format(np.mean(1./np.array(ranks_right))))
|
||
|
|
logger.info('Mean reciprocal rank: {0}'.format(np.mean(1./np.array(ranks))))
|
||
|
|
|
||
|
|
if __name__ == "__main__":
|
||
|
|
main()
|