run_multimodal_classifier.py

# coding=utf-8
# Copyright 2018 The Google AI Language Team Authors and The HugginFace 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
from __future__ import division
from __future__ import print_function

import csv
import os
import logging
import argparse
import random
from tqdm import tqdm, trange

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

from my_bert.tokenization import BertTokenizer
from my_bert.mm_modeling import (ResBertForMMSequenceClassification, MBertForMMSequenceClassification,
                          MBertNoPoolingForMMSequenceClassification, TomBertForMMSequenceClassification,
                          TomBertNoPoolingForMMSequenceClassification)
from my_bert.optimization import BertAdam
from my_bert.file_utils import PYTORCH_PRETRAINED_BERT_CACHE

import resnet.resnet as resnet
from resnet.resnet_utils import myResnet

from torchvision import transforms
from PIL import Image

from sklearn.metrics import precision_recall_fscore_support

__author__ = "Jianfei"

def image_process(image_path, transform):
    image = Image.open(image_path).convert('RGB')
    image = transform(image)
    return image

def macro_f1(y_true, y_pred):
    preds = np.argmax(y_pred, axis=-1)
    true = y_true
    p_macro, r_macro, f_macro, support_macro \
      = precision_recall_fscore_support(true, preds, average='macro')
    #f_macro = 2*p_macro*r_macro/(p_macro+r_macro)
    return p_macro, r_macro, f_macro

logging.basicConfig(format = '%(asctime)s - %(levelname)s - %(name)s -   %(message)s',
                    datefmt = '%m/%d/%Y %H:%M:%S',
                    level = logging.INFO)
logger = logging.getLogger(__name__)


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

    def __init__(self, guid, 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.guid = guid
        self.text_a = text_a
        self.text_b = text_b
        self.label = label

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

    def __init__(self, guid, text_a, text_b, img_id, 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.guid = guid
        self.text_a = text_a
        self.text_b = text_b
        self.img_id = img_id
        self.label = label


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

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

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

    def __init__(self, input_ids, input_mask, added_input_mask, segment_ids, s2_input_ids, s2_input_mask, \
                 s2_segment_ids, img_feat, label_id):
        self.input_ids = input_ids
        self.input_mask = input_mask
        self.added_input_mask = added_input_mask
        self.segment_ids = segment_ids
        self.s2_input_ids = s2_input_ids
        self.s2_input_mask = s2_input_mask
        self.s2_segment_ids = s2_segment_ids
        self.img_feat = img_feat
        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_test_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 = []
            for line in reader:
                lines.append(line)
            return lines


class AbmsaProcessor(DataProcessor):
    """Processor for the MRPC data set (GLUE version)."""

    def get_train_examples(self, data_dir):
        """See base class."""
        logger.info("LOOKING AT {}".format(os.path.join(data_dir, "train.tsv")))
        return self._create_examples(
            self._read_tsv(os.path.join(data_dir, "train.tsv")), "train")

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

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

    def get_labels(self):
        """See base class."""
        return ["0", "1", "2"]

    def _create_examples(self, lines, set_type):
        """Creates examples for the training and dev sets."""
        examples = []
        for (i, line) in enumerate(lines):
            if i == 0:
                continue
            guid = "%s-%s" % (set_type, i)
            text_a = line[3].lower()
            text_b = line[4].lower()
            img_id = line[2]
            label = line[1]
            examples.append(
                MMInputExample(guid=guid, text_a=text_a, text_b=text_b, img_id=img_id, label=label))
        return examples

def convert_examples_to_features(examples, label_list, max_seq_length, tokenizer):
    """Loads a data file into a list of `InputBatch`s."""

    label_map = {label : i for i, label in enumerate(label_list)}

    features = []
    for (ex_index, example) in enumerate(examples):
        tokens_a = tokenizer.tokenize(example.text_a)

        tokens_b = None
        if example.text_b:
            tokens_b = tokenizer.tokenize(example.text_b)
            # 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)
        else:
            # Account for [CLS] and [SEP] with "- 2"
            if len(tokens_a) > max_seq_length - 2:
                tokens_a = tokens_a[:(max_seq_length - 2)]

        # 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 unambigiously 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.
        tokens = ["[CLS]"] + tokens_a + ["[SEP]"]
        segment_ids = [0] * len(tokens)

        if tokens_b:
            tokens += tokens_b + ["[SEP]"]
            segment_ids += [1] * (len(tokens_b) + 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 = label_map[example.label]
        if ex_index < 1:
            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(input_ids=input_ids,
                              input_mask=input_mask,
                              segment_ids=segment_ids,
                              label_id=label_id))
    return features

def convert_mm_examples_to_features(examples, label_list, max_seq_length, max_entity_length, tokenizer, crop_size, path_img):
    """Loads a data file into a list of `InputBatch`s."""

    label_map = {label : i for i, label in enumerate(label_list)}

    features = []
    count = 0

    transform = transforms.Compose([
        transforms.RandomCrop(crop_size),  # args.crop_size, by default it is set to be 224
        transforms.RandomHorizontalFlip(),
        transforms.ToTensor(),
        transforms.Normalize((0.485, 0.456, 0.406),
                             (0.229, 0.224, 0.225))])

    sent_length_a = 0
    entity_length_b = 0
    total_length = 0
    for (ex_index, example) in enumerate(examples):
        tokens_a = tokenizer.tokenize(example.text_a)

        tokens_b = tokenizer.tokenize(example.text_b)
        if len(tokens_b) >= entity_length_b:
            entity_length_b = len(tokens_b)
        if len(tokens_a) >= sent_length_a:
            sent_length_a = len(tokens_a)

        if len(tokens_b) > max_entity_length - 2:
            s2_tokens = tokens_b[:(max_entity_length - 2)]
        else:
            s2_tokens = tokens_b
        s2_tokens = ["[CLS]"] + s2_tokens + ["[SEP]"]
        s2_segment_ids = [0] * len(s2_tokens)
        s2_input_ids = tokenizer.convert_tokens_to_ids(s2_tokens)
        s2_input_mask = [1] * len(s2_input_ids)

        # Zero-pad up to the sequence length.
        s2_padding = [0] * (max_entity_length - len(s2_input_ids))
        s2_input_ids += s2_padding
        s2_input_mask += s2_padding
        s2_segment_ids += s2_padding

        # 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 unambigiously 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.
        tokens = ["[CLS]"] + tokens_a + ["[SEP]"]
        segment_ids = [0] * len(tokens)

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

        input_ids = tokenizer.convert_tokens_to_ids(tokens)

        if len(tokens) >= total_length:
            total_length = len(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)
        added_input_mask = [1] * (len(input_ids)+49) #1 or 49 is for encoding regional image representations

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

        image_name = example.img_id
        image_path = os.path.join(path_img, image_name)

        if not os.path.exists(image_path):
            print(image_path)
        try:
            image = image_process(image_path, transform)
        except:
            count += 1
            # print('image has problem!')
            image_path_fail = os.path.join(path_img, '17_06_4705.jpg')
            image = image_process(image_path_fail, transform)

        if ex_index < 1:
            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(
                MMInputFeatures(input_ids=input_ids, input_mask=input_mask, added_input_mask=added_input_mask,
                              segment_ids=segment_ids,
                              s2_input_ids=s2_input_ids, s2_input_mask=s2_input_mask, s2_segment_ids=s2_segment_ids,
                              img_feat = image,
                              label_id=label_id))

    print('the number of problematic samples: ' + str(count))
    print('the max length of sentence a: '+str(sent_length_a+2) + ' entity b: '+str(entity_length_b+2) + \
          ' total length: '+str(total_length+3))
    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 accuracy(out, labels):
    outputs = np.argmax(out, axis=1)
    return np.sum(outputs == labels)

def warmup_linear(x, warmup=0.002):
    if x < warmup:
        return x/warmup
    return 1.0 - x

def main():
    parser = argparse.ArgumentParser()

    ## Required parameters
    parser.add_argument("--data_dir",
                        default='../absa_data/twitter',
                        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='twitter',
                        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("--max_seq_length",
                        default=64,
                        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("--max_entity_length",
                        default=16,
                        type=int,
                        help="The maximum entity 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_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=16,
                        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=8.0,
                        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('--fine_tune_cnn', action='store_true', help='fine tune pre-trained CNN if True')
    parser.add_argument('--resnet_root', default='./resnet', help='path the pre-trained cnn models')
    parser.add_argument('--crop_size', type=int, default=224, help='crop size of image')
    parser.add_argument('--path_image', default='../pytorch-pretrained-BERT/twitter_subimages/', help='path to images')
    parser.add_argument('--mm_model', default='TomBert', help='model name') # TomBert, TomBertNoPooling, MBert, MBertNoPooling, ResBert
    parser.add_argument('--pooling', default='first', help='pooling method') # first, cls, concat
    parser.add_argument('--bertlayer', action='store_true', help='whether to add another bert layer')
    parser.add_argument('--tfn', action='store_true', help='whether to use TFN')

    args = parser.parse_args()

    print("**************current model: "+args.mm_model+"******************")
    if args.mm_model == "ResBert" and args.bertlayer:
        print("add another bert layer")
        if args.mm_model == "ResBert" and args.tfn:
            print("add another tfn layer")
    elif args.mm_model == "TomBert" or args.mm_model == "MBert":
        print("pooling method: " + args.pooling)
    print("*"*50)

    if args.task_name == "twitter":        # this refers to twitter-2017 dataset
        args.path_image = "../pytorch-pretrained-BERT/twitter_subimages/"
    elif args.task_name == "twitter2015":  # this refers to twitter-2015 dataset
        args.path_image = "../pytorch-pretrained-BERT/twitter15_images/"
    else:
        print("The task name is not right!")

    processors = {
        "twitter2015": AbmsaProcessor,    # our twitter-2015 dataset
        "twitter": AbmsaProcessor         # our twitter-2017 dataset
    }

    num_labels_task = {
        "twitter2015": 3,                # our twitter-2015 dataset
        "twitter": 3                     # our twitter-2017 dataset
    }

    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')
    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 = int(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.")

    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))
    os.makedirs(args.output_dir, exist_ok=True)

    task_name = args.task_name.lower()

    if task_name not in processors:
        raise ValueError("Task not found: %s" % (task_name))

    processor = processors[task_name]()
    num_labels = num_labels_task[task_name]
    label_list = processor.get_labels()

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

    train_examples = None
    num_train_steps = None
    if args.do_train:
        train_examples = processor.get_train_examples(args.data_dir)
        num_train_steps = int(
            len(train_examples) / args.train_batch_size / args.gradient_accumulation_steps * args.num_train_epochs)

    # Prepare model
    if args.mm_model == 'ResBert':
        model = ResBertForMMSequenceClassification.from_pretrained(args.bert_model,
                                                                   cache_dir=PYTORCH_PRETRAINED_BERT_CACHE / 'distributed_{}'.format(
                                                                       args.local_rank),
                                                                   num_labels=num_labels,
                                                                   bert_layer=args.bertlayer,
                                                                   tfn=args.tfn)
    elif args.mm_model == 'MBert':
        model = MBertForMMSequenceClassification.from_pretrained(args.bert_model,
                                                                cache_dir=PYTORCH_PRETRAINED_BERT_CACHE / 'distributed_{}'.format(
                                                                    args.local_rank),
                                                                num_labels=num_labels,
                                                                pooling=args.pooling)
    elif args.mm_model == 'MBertNoPooling':
        model = MBertNoPoolingForMMSequenceClassification.from_pretrained(args.bert_model,
                                                                cache_dir=PYTORCH_PRETRAINED_BERT_CACHE / 'distributed_{}'.format(
                                                                    args.local_rank),
                                                                num_labels=num_labels)
    elif args.mm_model == 'TomBertNoPooling':
        model = TomBertNoPoolingForMMSequenceClassification.from_pretrained(args.bert_model,
                                                                cache_dir=PYTORCH_PRETRAINED_BERT_CACHE / 'distributed_{}'.format(
                                                                    args.local_rank),
                                                                num_labels=num_labels)
    else: # TomBert by default
        model = TomBertForMMSequenceClassification.from_pretrained(args.bert_model,
                                                                cache_dir=PYTORCH_PRETRAINED_BERT_CACHE / 'distributed_{}'.format(
                                                                    args.local_rank),
                                                                num_labels=num_labels,
                                                                pooling=args.pooling)
    net = getattr(resnet, 'resnet152')()
    net.load_state_dict(torch.load(os.path.join(args.resnet_root, 'resnet152.pth')))
    encoder = myResnet(net, args.fine_tune_cnn, device)
    if args.fp16:
        model.half()
        encoder.half()
    model.to(device)
    encoder.to(device)
    if args.local_rank != -1:
        try:
            from apex.parallel import DistributedDataParallel as DDP
        except ImportError:
            raise ImportError("Please install apex from https://github.com/nvidia/apex to use distributed and fp16 training.")

        model = DDP(model)
        encoder = DDP(encoder)
    elif n_gpu > 1:
        model = torch.nn.DataParallel(model)
        encoder = torch.nn.DataParallel(encoder)

    # 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}
        ]
    t_total = num_train_steps
    if args.local_rank != -1:
        t_total = t_total // torch.distributed.get_world_size()
    if args.fp16:
        try:
            from apex.optimizers import FP16_Optimizer
            from apex.optimizers import FusedAdam
        except ImportError:
            raise ImportError("Please install apex from https://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)

    else:
        optimizer = BertAdam(optimizer_grouped_parameters,
                             lr=args.learning_rate,
                             warmup=args.warmup_proportion,
                             t_total=t_total)

    global_step = 0
    nb_tr_steps = 0
    tr_loss = 0
    output_model_file = os.path.join(args.output_dir, "pytorch_model.bin")
    output_encoder_file = os.path.join(args.output_dir, "pytorch_encoder.bin")
    if args.do_train:
        train_features = convert_mm_examples_to_features(
            train_examples, label_list, args.max_seq_length, args.max_entity_length, tokenizer, args.crop_size, args.path_image)

        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_added_input_mask = torch.tensor([f.added_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_s2_input_ids = torch.tensor([f.s2_input_ids for f in train_features], dtype=torch.long)
        all_s2_input_mask = torch.tensor([f.s2_input_mask for f in train_features], dtype=torch.long)
        all_s2_segment_ids = torch.tensor([f.s2_segment_ids for f in train_features], dtype=torch.long)
        all_img_feats = torch.stack([f.img_feat for f in train_features])
        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_added_input_mask, all_segment_ids,\
                                   all_s2_input_ids, all_s2_input_mask, all_s2_segment_ids,
                                   all_img_feats, 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)
        #'''
        eval_examples = processor.get_dev_examples(args.data_dir)
        eval_features = convert_mm_examples_to_features(
            eval_examples, label_list, args.max_seq_length, args.max_entity_length, tokenizer, args.crop_size, args.path_image)

        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_added_input_mask = torch.tensor([f.added_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_s2_input_ids = torch.tensor([f.s2_input_ids for f in eval_features], dtype=torch.long)
        all_s2_input_mask = torch.tensor([f.s2_input_mask for f in eval_features], dtype=torch.long)
        all_s2_segment_ids = torch.tensor([f.s2_segment_ids for f in eval_features], dtype=torch.long)
        all_img_feats = torch.stack([f.img_feat for f in eval_features])
        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_added_input_mask, all_segment_ids, \
                                  all_s2_input_ids, all_s2_input_mask, all_s2_segment_ids,\
                                  all_img_feats, 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)

        max_acc = 0.0
        #'''
        logger.info("*************** Running training ***************")
        for train_idx in trange(int(args.num_train_epochs), desc="Epoch"):

            logger.info("********** Epoch: "+ str(train_idx) + " **********")
            logger.info("  Num examples = %d", len(train_examples))
            logger.info("  Batch size = %d", args.train_batch_size)
            logger.info("  Num steps = %d", num_train_steps)
            model.train()
            encoder.train()
            encoder.zero_grad()
            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, added_input_mask, segment_ids, s2_input_ids, s2_input_mask, s2_segment_ids, \
                img_feats, label_ids = batch
                with torch.no_grad():
                    imgs_f, img_mean, img_att = encoder(img_feats)
                if train_idx == 0 and step == 0:
                    loss = model(input_ids, s2_input_ids, img_att, segment_ids, s2_segment_ids, input_mask, s2_input_mask, \
                             added_input_mask, label_ids, True)
                else:
                    loss = model(input_ids, s2_input_ids, img_att, segment_ids, s2_segment_ids, input_mask, s2_input_mask, \
                             added_input_mask, label_ids)
                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:
                    # modify learning rate with special warm up BERT uses
                    lr_this_step = args.learning_rate * warmup_linear(global_step/t_total, args.warmup_proportion)
                    for param_group in optimizer.param_groups:
                        param_group['lr'] = lr_this_step
                    optimizer.step()
                    optimizer.zero_grad()
                    global_step += 1

            logger.info("***** Running evaluation on Dev Set*****")
            logger.info("  Num examples = %d", len(eval_examples))
            logger.info("  Batch size = %d", args.eval_batch_size)
            model.eval()
            encoder.eval()
            eval_loss, eval_accuracy = 0, 0
            nb_eval_steps, nb_eval_examples = 0, 0

            true_label_list = []
            pred_label_list = []

            for input_ids, input_mask, added_input_mask, segment_ids,  s2_input_ids, s2_input_mask, s2_segment_ids, \
                img_feats, label_ids in tqdm(eval_dataloader, desc="Evaluating"):
                input_ids = input_ids.to(device)
                input_mask = input_mask.to(device)
                added_input_mask = added_input_mask.to(device)
                segment_ids = segment_ids.to(device)
                s2_input_ids = s2_input_ids.to(device)
                s2_input_mask = s2_input_mask.to(device)
                s2_segment_ids = s2_segment_ids.to(device)
                img_feats = img_feats.to(device)
                label_ids = label_ids.to(device)

                with torch.no_grad():
                    imgs_f, img_mean, img_att = encoder(img_feats)
                    tmp_eval_loss = model(input_ids, s2_input_ids, img_att, segment_ids, s2_segment_ids,
                                          input_mask, s2_input_mask, added_input_mask, label_ids)
                    logits = model(input_ids, s2_input_ids, img_att, segment_ids, s2_segment_ids, input_mask,
                                   s2_input_mask, added_input_mask)

                logits = logits.detach().cpu().numpy()
                label_ids = label_ids.to('cpu').numpy()
                true_label_list.append(label_ids)
                pred_label_list.append(logits)
                tmp_eval_accuracy = accuracy(logits, label_ids)

                eval_loss += tmp_eval_loss.mean().item()
                eval_accuracy += tmp_eval_accuracy

                nb_eval_examples += input_ids.size(0)
                nb_eval_steps += 1

            eval_loss = eval_loss / nb_eval_steps
            eval_accuracy = eval_accuracy / nb_eval_examples
            loss = tr_loss / nb_tr_steps if args.do_train else None
            true_label = np.concatenate(true_label_list)
            pred_outputs = np.concatenate(pred_label_list)
            precision, recall, F_score = macro_f1(true_label, pred_outputs)
            result = {'eval_loss': eval_loss,
                      'eval_accuracy': eval_accuracy,
                      'f_score': F_score,
                      'global_step': global_step,
                      'loss': loss}

            logger.info("***** Dev Eval results *****")
            for key in sorted(result.keys()):
                logger.info("  %s = %s", key, str(result[key]))

            if eval_accuracy >= max_acc:
                # Save a trained model
                model_to_save = model.module if hasattr(model, 'module') else model  # Only save the model it-self
                encoder_to_save = encoder.module if hasattr(encoder, 'module') else encoder  # Only save the model it-self
                if args.do_train:
                    torch.save(model_to_save.state_dict(), output_model_file)
                    torch.save(encoder_to_save.state_dict(), output_encoder_file)
                max_acc = eval_accuracy

    # Load a trained model that you have fine-tuned
    model_state_dict = torch.load(output_model_file)
    if args.mm_model == 'ResBert':
        model = ResBertForMMSequenceClassification.from_pretrained(args.bert_model,
                                                                   state_dict=model_state_dict,
                                                                   num_labels=num_labels,
                                                                   bert_layer=args.bertlayer,
                                                                   tfn=args.tfn)
    elif args.mm_model == 'MBert':
        model = MBertForMMSequenceClassification.from_pretrained(args.bert_model,
                                                                state_dict=model_state_dict,
                                                                num_labels=num_labels,
                                                                pooling=args.pooling)
    elif args.mm_model == 'MBertNoPooling':
        model = MBertNoPoolingForMMSequenceClassification.from_pretrained(args.bert_model,
                                                                state_dict=model_state_dict,
                                                                num_labels=num_labels)
    elif args.mm_model == 'TomBertNoPooling':
        model = TomBertNoPoolingForMMSequenceClassification.from_pretrained(args.bert_model,
                                                                state_dict=model_state_dict,
                                                                num_labels=num_labels)
    else: # TomBert by default
        model = TomBertForMMSequenceClassification.from_pretrained(args.bert_model,
                                                                state_dict=model_state_dict,
                                                                num_labels=num_labels,
                                                                pooling=args.pooling)
    model.to(device)
    encoder_state_dict = torch.load(output_encoder_file)
    encoder.load_state_dict(encoder_state_dict)
    encoder.to(device)

    if args.do_eval and (args.local_rank == -1 or torch.distributed.get_rank() == 0):
        eval_examples = processor.get_test_examples(args.data_dir)
        eval_features = convert_mm_examples_to_features(
            eval_examples, label_list, args.max_seq_length, args.max_entity_length, tokenizer, args.crop_size, args.path_image)
        logger.info("***** Running evaluation on Test Set*****")
        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_added_input_mask = torch.tensor([f.added_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_s2_input_ids = torch.tensor([f.s2_input_ids for f in eval_features], dtype=torch.long)
        all_s2_input_mask = torch.tensor([f.s2_input_mask for f in eval_features], dtype=torch.long)
        all_s2_segment_ids = torch.tensor([f.s2_segment_ids for f in eval_features], dtype=torch.long)
        all_img_feats = torch.stack([f.img_feat for f in eval_features])
        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_added_input_mask, all_segment_ids, \
                                  all_s2_input_ids, all_s2_input_mask, all_s2_segment_ids,
                                  all_img_feats, 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)

        model.eval()
        encoder.eval()
        eval_loss, eval_accuracy = 0, 0
        nb_eval_steps, nb_eval_examples = 0, 0

        true_label_list = []
        pred_label_list = []
 
        for input_ids, input_mask, added_input_mask, segment_ids,  s2_input_ids, s2_input_mask, s2_segment_ids, \
            img_feats, label_ids in tqdm(eval_dataloader, desc="Evaluating"):
            input_ids = input_ids.to(device)
            input_mask = input_mask.to(device)
            added_input_mask = added_input_mask.to(device)
            segment_ids = segment_ids.to(device)
            s2_input_ids = s2_input_ids.to(device)
            s2_input_mask = s2_input_mask.to(device)
            s2_segment_ids = s2_segment_ids.to(device)
            img_feats = img_feats.to(device)
            label_ids = label_ids.to(device)

            with torch.no_grad():
                imgs_f, img_mean, img_att = encoder(img_feats)
                tmp_eval_loss = model(input_ids, s2_input_ids, img_att, segment_ids, s2_segment_ids,
                                      input_mask, s2_input_mask, added_input_mask, label_ids)
                logits = model(input_ids, s2_input_ids, img_att, segment_ids, s2_segment_ids, input_mask,
                               s2_input_mask, added_input_mask)

            logits = logits.detach().cpu().numpy()
            label_ids = label_ids.to('cpu').numpy()
            true_label_list.append(label_ids)
            pred_label_list.append(logits)
            tmp_eval_accuracy = accuracy(logits, label_ids)

            eval_loss += tmp_eval_loss.mean().item()
            eval_accuracy += tmp_eval_accuracy

            nb_eval_examples += input_ids.size(0)
            nb_eval_steps += 1

        eval_loss = eval_loss / nb_eval_steps
        eval_accuracy = eval_accuracy / nb_eval_examples
        loss = tr_loss/nb_tr_steps if args.do_train else None
        true_label = np.concatenate(true_label_list)
        pred_outputs = np.concatenate(pred_label_list)
        precision, recall, F_score = macro_f1(true_label, pred_outputs)
        result = {'eval_loss': eval_loss,
                  'eval_accuracy': eval_accuracy,
                  'precision': precision,
                  'recall': recall,
                  'f_score': F_score,
                  'global_step': global_step,
                  'loss': loss}

        pred_label = np.argmax(pred_outputs, axis=-1)
        fout_p = open(os.path.join(args.output_dir, "pred.txt"), 'w')
        fout_t = open(os.path.join(args.output_dir, "true.txt"), 'w')

        for i in range(len(pred_label)):
            attstr = str(pred_label[i])
            fout_p.write(attstr + '\n')
        for i in range(len(true_label)):
            attstr = str(true_label[i])
            fout_t.write(attstr + '\n')

        fout_p.close()
        fout_t.close()

        output_eval_file = os.path.join(args.output_dir, "eval_results.txt")
        with open(output_eval_file, "w") as writer:
            logger.info("***** Test 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 __name__ == "__main__":
    main()