train_predictor.py

# coding=utf-8
# Copyright 2018 The Google AI Language Team Authors and The HuggingFace Inc. team.
# Copyright (c) 2018, NVIDIA CORPORATION.  All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""BERT finetuning runner."""

from __future__ import absolute_import, division, print_function
from collections import OrderedDict
import argparse
import csv
import logging
import os
import random
import sys
import io
import json

import numpy as np
import torch
from torch.utils.data import (DataLoader, RandomSampler, SequentialSampler,
                              TensorDataset)
from torch.utils.data.distributed import DistributedSampler

from torch.nn import CrossEntropyLoss, MSELoss, BCEWithLogitsLoss
from scipy.stats import pearsonr, spearmanr
from sklearn.metrics import matthews_corrcoef, f1_score

from pytorch_pretrained_bert.file_utils import PYTORCH_PRETRAINED_BERT_CACHE, WEIGHTS_NAME, CONFIG_NAME
from pytorch_pretrained_bert.modeling import BertForSequenceClassification, BertConfig
from pytorch_pretrained_bert.tokenization import BertTokenizer
from pytorch_pretrained_bert.optimization import BertAdam, WarmupLinearSchedule
from pprint import pprint
from tools import obtain_TP_TN_FN_FP

logger = logging.getLogger(__name__)


class InputExample(object):
    """A single training/test example for simple sequence classification."""

    def __init__(self, file, turn, guid, text_m, text_a, text_b=None, label=None):
        """Constructs a InputExample.

        Args:
            guid: Unique id for the example.
            text_a: string. The untokenized text of the first sequence. For single
            sequence tasks, only this sequence must be specified.
            text_b: (Optional) string. The untokenized text of the second sequence.
            Only must be specified for sequence pair tasks.
            label: (Optional) string. The label of the example. This should be
            specified for train and dev examples, but not for test examples.
        """
        self.file = file
        self.turn = turn
        self.guid = guid
        self.text_m = text_m
        self.text_a = text_a
        self.text_b = text_b
        self.label = label


class InputFeatures(object):
    """A single set of features of data."""

    def __init__(self, file, turn, input_ids, input_mask, segment_ids, label_id):
        self.file = file
        self.turn = turn
        self.input_ids = input_ids
        self.input_mask = input_mask
        self.segment_ids = segment_ids
        self.label_id = label_id


class DataProcessor(object):
    """Base class for data converters for sequence classification data sets."""

    def get_train_examples(self, data_dir):
        """Gets a collection of `InputExample`s for the train set."""
        raise NotImplementedError()

    def get_dev_examples(self, data_dir):
        """Gets a collection of `InputExample`s for the dev set."""
        raise NotImplementedError()

    def get_labels(self):
        """Gets the list of labels for this data set."""
        raise NotImplementedError()

    @classmethod
    def _read_tsv(cls, input_file, quotechar=None):
        """Reads a tab separated value file."""
        with open(input_file, "r", encoding="utf-8") as f:
            reader = csv.reader(f, delimiter="\t", quotechar=quotechar)
            lines = []
            idx = 0
            for line in reader:
                idx += 1
                # if idx > 100: break
                if sys.version_info[0] == 2:
                    line = list(unicode(cell, 'utf-8') for cell in line)
                lines.append(line)
            return lines


class QqpProcessor(DataProcessor):
    """Processor for the QQP data set (GLUE version)."""

    def get_train_examples(self, data_dir):
        """See base class."""
        return self._create_examples(
            self._read_tsv(os.path.join(data_dir, "train.tsv")), "train")

    def get_dev_examples(self, data_dir, dataset="dev"):
        """See base class."""
        return self._create_examples(
            self._read_tsv(os.path.join(data_dir, "{}.tsv".format(dataset))), dataset)

    def get_labels(self):
        """See base class."""
        return [str(i) for i in range(44)]

    def _create_examples(self, lines, set_type):
        """Creates examples for the training and dev sets."""
        examples = []
        for (i, line) in enumerate(lines):
            guid = "%s-%s" % (set_type, line[0])
            # column_types = [int(x) for x in line[2].split()]
            file_name = line[0]
            turn_num = line[1]
            text_m = line[2]
            text_a = line[3]
            text_b = line[4]
            label = json.loads(line[5])
            examples.append(InputExample(file=file_name, turn=turn_num, guid=guid, \
                                          text_m=text_m, text_a=text_a, text_b=text_b, label=label))
        return examples


def convert_examples_to_features(examples, label_list, max_seq_length,
                                 tokenizer, output_mode, verbose=False):
    """Loads a data file into a list of `InputBatch`s."""
    label_map = {label: i for i, label in enumerate(label_list)}

    features = []
    pos_buf = []
    neg_buf = []
    logger.info("convert_examples_to_features ...")
    for (ex_index, example) in enumerate(examples):
        if ex_index % 10000 == 0:
            logger.info("Writing example %d of %d" % (ex_index, len(examples)))

        tokens_a = tokenizer.tokenize(example.text_a)

        tokens_b = tokenizer.tokenize(example.text_b)

        tokens_m = tokenizer.tokenize(example.text_m)
        # Modifies `tokens_a` and `tokens_b` in place so that the total
        # length is less than the specified length.
        # Account for [CLS], [SEP], [SEP] with "- 3"
        _truncate_seq_pair(tokens_a, tokens_b, max_seq_length - 3)

        # The convention in BERT is:
        # (a) For sequence pairs:
        #  tokens:   [CLS] is this jack ##son ##ville ? [SEP] no it is not . [SEP]
        #  type_ids: 0   0  0    0    0     0       0 0    1  1  1  1   1 1
        # (b) For single sequences:
        #  tokens:   [CLS] the dog is hairy . [SEP]
        #  type_ids: 0   0   0   0  0     0 0
        #
        # Where "type_ids" are used to indicate whether this is the first
        # sequence or the second sequence. The embedding vectors for `type=0` and
        # `type=1` were learned during pre-training and are added to the wordpiece
        # embedding vector (and position vector). This is not *strictly* necessary
        # since the [SEP] token unambiguously separates the sequences, but it makes
        # it easier for the model to learn the concept of sequences.
        #
        # For classification tasks, the first vector (corresponding to [CLS]) is
        # used as as the "sentence vector". Note that this only makes sense because
        # the entire model is fine-tuned.

        # NOTE: fact is tokens_b and is now in front
        tokens = ["[CLS]"] + tokens_a + ["[SEP]"]
        segment_ids = [0] * (len(tokens_a) + 2)

        assert len(tokens) == len(segment_ids)

        tokens += tokens_b + ["[SEP]"]
        segment_ids += [1] * (len(tokens_b) + 1)

        if len(tokens) < max_seq_length:
            if len(tokens_m) > max_seq_length - len(tokens) - 1:
                tokens_m = tokens_m[:max_seq_length - len(tokens) - 1]

            tokens += tokens_m + ['[SEP]']
            segment_ids += [0] * (len(tokens_m) + 1)

        input_ids = tokenizer.convert_tokens_to_ids(tokens)

        # The mask has 1 for real tokens and 0 for padding tokens. Only real
        # tokens are attended to.
        input_mask = [1] * len(input_ids)

        # Zero-pad up to the sequence length.
        padding = [0] * (max_seq_length - len(input_ids))
        input_ids += padding
        input_mask += padding
        segment_ids += padding

        assert len(input_ids) == max_seq_length
        assert len(input_mask) == max_seq_length
        assert len(segment_ids) == max_seq_length

        label_id = example.label

        if verbose and ex_index < 5:
            logger.info("*** Example ***")
            logger.info("guid: %s" % (example.guid))
            logger.info("tokens: %s" % " ".join([str(x) for x in tokens]))
            logger.info("input_ids: %s" % " ".join([str(x) for x in input_ids]))
            logger.info("input_mask: %s" % " ".join([str(x) for x in input_mask]))
            logger.info("segment_ids: %s" % " ".join([str(x) for x in segment_ids]))
            logger.info("label: %s (id = %d)" % (example.label, label_id))

        features.append(
            InputFeatures(file=example.file,
                          turn=example.turn,
                          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 simple_accuracy(preds, labels):
    return (preds == labels).mean()


def acc_and_f1(preds, labels):
    acc = simple_accuracy(preds, labels)
    f1 = f1_score(y_true=labels, y_pred=preds)
    return {
        "acc": acc,
        "f1": f1,
        "acc_and_f1": (acc + f1) / 2,
    }


def pearson_and_spearman(preds, labels):
    pearson_corr = pearsonr(preds, labels)[0]
    spearman_corr = spearmanr(preds, labels)[0]
    return {
        "pearson": pearson_corr,
        "spearmanr": spearman_corr,
        "corr": (pearson_corr + spearman_corr) / 2,
    }


def compute_metrics(task_name, preds, labels):
    assert len(preds) == len(labels)
    if task_name == "qqp":
        return acc_and_f1(preds, labels)
    else:
        raise KeyError(task_name)


def main():
    logger.info("Running %s" % ' '.join(sys.argv))

    parser = argparse.ArgumentParser()
    ## Required parameters
    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("--data_dir",
                        default="data/",
                        type=str,
                        help="The input data dir. Should contain the .tsv files (or other data files) for the task.")
    parser.add_argument("--output_dir",
                        default="checkpoints/predictor/",
                        type=str,
                        help="The output directory where the model predictions and checkpoints will be written.")
    parser.add_argument("--load_dir",
                        type=str,
                        help="The output directory where the model checkpoints will be loaded during evaluation")
    parser.add_argument('--load_step',
                        type=int,
                        default=0,
                        help="The checkpoint step to be loaded")
    parser.add_argument("--fact",
                        default="first",
                        choices=["first", "second"],
                        type=str,
                        help="Whether to put fact in front.")
    parser.add_argument("--test_set",
                        default="dev",
                        choices=["dev", "test", "simple_test", "complex_test", "small_test"],
                        help="Which test set is used for evaluation",
                        type=str)
    parser.add_argument("--train_batch_size",
                    default=18,
                    type=int,
                    help="Total batch size for training.")
    parser.add_argument("--eval_batch_size",
                        default=18,
                        type=int,
                        help="Total batch size for eval.")
    ## Other parameters
    parser.add_argument("--bert_model",
                        default="bert-base-uncased",
                        type=str,
                        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="QQP",
                        type=str,
                        help="The name of the task to train.")
    parser.add_argument('--period',
                        type=int,
                        default=500)
    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=256,
                        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_lower_case",
                        action='store_true',
                        help="Set this flag if you are using an uncased model.")
    parser.add_argument("--learning_rate",
                        default=5e-5,
                        type=float,
                        help="The initial learning rate for Adam.")
    parser.add_argument("--num_train_epochs",
                        default=20.0,
                        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()
    pprint(vars(args))
    sys.stdout.flush()

    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 = {
        "qqp": QqpProcessor,
    }

    output_modes = {
        "qqp": "classification",
    }

    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

    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.")

    logger.info("Datasets are loaded from {}\n Outputs will be saved to {}".format(args.data_dir, args.output_dir))
    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]()
    output_mode = output_modes[task_name]

    label_list = processor.get_labels()
    num_labels = len(label_list)

    tokenizer = BertTokenizer.from_pretrained(args.bert_model, do_lower_case=args.do_lower_case)

    train_examples = None
    num_train_optimization_steps = None
    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()

    cache_dir = args.cache_dir if args.cache_dir else os.path.join(str(PYTORCH_PRETRAINED_BERT_CACHE), 'distributed_{}'.format(args.local_rank))
    if args.load_dir:
        load_dir = args.load_dir
    else:
        load_dir = args.bert_model

    model = BertForSequenceClassification.from_pretrained(load_dir, 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)

    # Prepare optimizer
    if args.do_train:
        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
    tr_loss = 0
    best_F1 = 0
    if args.do_train:
        train_features = convert_examples_to_features(
            train_examples, label_list, args.max_seq_length, tokenizer, output_mode)
        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.float)

        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()
        for epoch in range(int(args.num_train_epochs)):
            logger.info("Training epoch {} ...".format(epoch))
            nb_tr_examples, nb_tr_steps = 0, 0
            for step, batch in enumerate(train_dataloader):
                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)

                loss_fct = BCEWithLogitsLoss()
                
                loss = loss_fct(logits.view(-1, 1), label_ids.view(-1, 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, args.warmup_proportion)
                        for param_group in optimizer.param_groups:
                            param_group['lr'] = lr_this_step
                    optimizer.step()
                    optimizer.zero_grad()
                    model.zero_grad()
                    global_step += 1

                if (step + 1) % args.period == 0:
                    # Save a trained model, configuration and tokenizer
                    model_to_save = model.module if hasattr(model, 'module') else model 

                    # If we save using the predefined names, we can load using `from_pretrained`
                    model.eval()
                    torch.set_grad_enabled(False)  # turn off gradient tracking
                    F1 = evaluate(args, model, device, processor, label_list, num_labels, tokenizer, output_mode)
                    
                    if F1 > best_F1:
                        output_dir = os.path.join(args.output_dir, 'save_step_{}'.format(global_step))
                        if not os.path.exists(output_dir):
                            os.makedirs(output_dir)

                        output_model_file = os.path.join(output_dir, WEIGHTS_NAME)
                        output_config_file = os.path.join(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(output_dir)

                        best_F1 = F1

                    model.train()  # turn on train mode
                    torch.set_grad_enabled(True)  # start gradient tracking
                    tr_loss = 0

    # do eval before exit
    if args.do_eval:
        if not args.do_train:
            global_step = 0
            output_dir = None
        save_dir = output_dir if output_dir is not None else args.load_dir
        load_step = args.load_step
        
        if args.load_dir is not None:
            load_step = int(os.path.split(args.load_dir)[1].replace('save_step_', ''))
            print("load_step = {}".format(load_step))
        
        F1 = evaluate(args, model, device, processor, label_list, num_labels, tokenizer, output_mode)
        
        with open("test_result.txt", 'a') as f:
            print("load step: {} F1: {}".format(str(load_step), str(F1)), file=f)


def evaluate(args, model, device, processor, label_list, num_labels, tokenizer, output_mode):
    if args.do_eval and (args.local_rank == -1 or torch.distributed.get_rank() == 0):
        eval_examples = processor.get_dev_examples(args.data_dir, dataset=args.test_set)
        eval_features = convert_examples_to_features(
            eval_examples, label_list, args.max_seq_length, tokenizer, output_mode)
        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.float)

        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)
        
        idx = 0
        TP, TN, FN, FP = 0, 0, 0, 0
        output = {}
        for input_ids, input_mask, segment_ids, label_ids in eval_dataloader:
            input_ids = input_ids.to(device)
            input_mask = input_mask.to(device)
            segment_ids = segment_ids.to(device)
            label_ids = label_ids.to(device)
            batch_size = input_ids.size(0)
            with torch.no_grad():
                logits = model(input_ids, segment_ids, input_mask, labels=None)
                logits = torch.sigmoid(logits)
            preds = (logits > 0.4).float()
            preds_numpy = preds.cpu().long().data.numpy()
            for i in range(idx, idx + batch_size):
                if eval_features[i].file not in output:
                    output[eval_features[i].file] = {}
                output[eval_features[i].file][eval_features[i].turn] = preds_numpy[i - idx].tolist()
            TP, TN, FN, FP = obtain_TP_TN_FN_FP(preds, label_ids, TP, TN, FN, FP)
            idx += batch_size

        with open("data/BERT_{}_prediction.json".format(args.test_set), 'w') as f:
            json.dump(output, f)

        precision = TP / (TP + FP + 0.001)
        recall = TP / (TP + FN + 0.001)
        F1 = 2 * precision * recall / (precision + recall + 0.001)
        logger.info("precision is {} recall is {} F1 is {}".format(precision, recall, F1))
        return F1

if __name__ == "__main__":
    main()