Full Code of Paranioar/SGRAF for AI

Repository: Paranioar/SGRAF
Branch: main
Commit: 50d0c6f9caf7
Files: 13
Total size: 735.6 KB

Directory structure:
gitextract_96luedpf/

├── README.md
├── data/
│   └── data.md
├── data.py
├── evaluation.py
├── model.py
├── opts.py
├── requirements.txt
├── runs/
│   └── runs.md
├── train.py
├── visualize.py
├── vocab/
│   ├── coco_precomp_vocab.json
│   └── f30k_precomp_vocab.json
└── vocab.py

================================================
FILE CONTENTS
================================================

================================================
FILE: README.md
================================================
# SGRAF
*PyTorch implementation for AAAI2021 paper of [**“Similarity Reasoning and Filtration for Image-Text Matching”**](https://drive.google.com/file/d/1tAE_qkAxiw1CajjHix9EXoI7xu2t66iQ/view?usp=sharing).* 

*It is built on top of the [SCAN](https://github.com/kuanghuei/SCAN) and [Awesome_Matching](https://github.com/Paranioar/Awesome_Matching_Pretraining_Transfering).* 

*We have released two versions of SGRAF: **Branch `main` for python2.7**; **Branch `python3.6` for python3.6**.*  

*If any problems, please contact me at r1228240468@gmail.com. (r1228240468@mail.dlut.edu.cn is deprecated)*

## Introduction

**The framework of SGRAF:**

<img src="./fig/model.png" width = "100%" height="50%">

**The updated results (Better than the original paper)**
<table>
   <tr> <td rowspan="2">Dataset</td> <td rowspan="2", align="center">Module</td> 
        <td colspan="3", align="center">Sentence retrieval</td> <td colspan="3", align="center">Image retrieval</td> </tr>
   <tr> <td>R@1</td><td>R@5</td><td>R@10</td> <td>R@1</td><td>R@5</td><td>R@10</td> </tr>
   <tr> <td rowspan="3">Flick30k</td>
        <td>SAF</td> <td>75.6</td><td>92.7</td><td>96.9</td> <td>56.5</td><td>82.0</td><td>88.4</td> </tr>
   <tr> <td>SGR</td> <td>76.6</td><td>93.7</td><td>96.6</td> <td>56.1</td><td>80.9</td><td>87.0</td> </tr>
   <tr> <td>SGRAF</td> <td>78.4</td><td>94.6</td><td>97.5</td> <td>58.2</td><td>83.0</td><td>89.1</td> </tr>
   <tr> <td rowspan="3">MSCOCO1k</td>
        <td>SAF</td> <td>78.0</td><td>95.9</td><td>98.5</td> <td>62.2</td><td>89.5</td><td>95.4</td> </tr>
   <tr> <td>SGR</td> <td>77.3</td><td>96.0</td><td>98.6</td> <td>62.1</td><td>89.6</td><td>95.3</td> </tr>
   <tr> <td>SGRAF</td> <td>79.2</td><td>96.5</td><td>98.6</td> <td>63.5</td><td>90.2</td><td>95.8</td> </tr>
   <tr> <td rowspan="3">MSCOCO5k</td>
        <td>SAF</td> <td>55.5</td><td>83.8</td><td>91.8</td> <td>40.1</td><td>69.7</td><td>80.4</td> </tr>
   <tr> <td>SGR</td> <td>57.3</td><td>83.2</td><td>90.6</td> <td>40.5</td><td>69.6</td><td>80.3</td> </tr>
   <tr> <td>SGRAF</td> <td>58.8</td><td>84.8</td><td>92.1</td> <td>41.6</td><td>70.9</td><td>81.5</td> </tr>
  

   
</table> 

## Requirements 
We recommended the following dependencies for ***Branch `main`***.

*  Python 2.7  
*  [PyTorch (>=0.4.1)](http://pytorch.org/)    
*  [NumPy (>=1.12.1)](http://www.numpy.org/)   
*  [TensorBoard](https://github.com/TeamHG-Memex/tensorboard_logger)  
*  Punkt Sentence Tokenizer:
```python
import nltk
nltk.download()
> d punkt
```

## Download data and vocab
We follow [SCAN](https://github.com/kuanghuei/SCAN) to obtain image features and vocabularies, which can be downloaded by using:

```bash
https://www.kaggle.com/datasets/kuanghueilee/scan-features
```
Another download link is available below：

```bash
https://drive.google.com/drive/u/0/folders/1os1Kr7HeTbh8FajBNegW8rjJf6GIhFqC
```

## Pre-trained models and evaluation
The pretrained models are only for **Branch `python3.6`(python3.6)**, not for **Branch `main`(python2.7)**.  
Modify the **model_path**, **data_path**, **vocab_path** in the `evaluation.py` file. Then run `evaluation.py`:

```bash
python evaluation.py
```

Note that `fold5=True` is only for evaluation on mscoco1K (5 folders average) while `fold5=False` for mscoco5K and flickr30K. Pretrained models and Log files can be downloaded from [Flickr30K_SGRAF](https://drive.google.com/file/d/1OBRIn1-Et49TDu8rk0wgP0wKXlYRk4Uj/view?usp=sharing) and [MSCOCO_SGRAF](https://drive.google.com/file/d/1SpuORBkTte_LqOboTgbYRN5zXhn4M7ag/view?usp=sharing).

## Training new models from scratch
Modify the **data_path**, **vocab_path**, **model_name**, **logger_name** in the `opts.py` file. Then run `train.py`:

For MSCOCO:

```bash
(For SGR) python train.py --data_name coco_precomp --num_epochs 20 --lr_update 10 --module_name SGR
(For SAF) python train.py --data_name coco_precomp --num_epochs 20 --lr_update 10 --module_name SAF
```

For Flickr30K:

```bash
(For SGR) python train.py --data_name f30k_precomp --num_epochs 40 --lr_update 30 --module_name SGR
(For SAF) python train.py --data_name f30k_precomp --num_epochs 30 --lr_update 20 --module_name SAF
```

## Reference

If SGRAF is useful for your research, please cite the following paper:

      @inproceedings{Diao2021SGRAF,
         title={Similarity reasoning and filtration for image-text matching},
         author={Diao, Haiwen and Zhang, Ying and Ma, Lin and Lu, Huchuan},
         booktitle={Proceedings of the AAAI conference on artificial intelligence},
         volume={35},
         number={2},
         pages={1218--1226},
         year={2021}
      }

## License

[Apache License 2.0](http://www.apache.org/licenses/LICENSE-2.0).  




================================================
FILE: data/data.md
================================================
The folder used to store the data:

**File Structure**  
```
├── data/
|   ├── f30k_precomp/
|   ├── coco_precomp/
```


================================================
FILE: data.py
================================================
"""Data provider"""

import torch
import torch.utils.data as data

import os
import nltk
import numpy as np


class PrecompDataset(data.Dataset):
    """
    Load precomputed captions and image features
    Possible options: f30k_precomp, coco_precomp
    """

    def __init__(self, data_path, data_split, vocab):
        self.vocab = vocab
        loc = data_path + '/'

        # load the raw captions
        self.captions = []
        with open(loc+'%s_caps.txt' % data_split, 'rb') as f:
            for line in f:
                self.captions.append(line.strip())

        # load the image features
        self.images = np.load(loc+'%s_ims.npy' % data_split)
        self.length = len(self.captions)

        # rkiros data has redundancy in images, we divide by 5
        if self.images.shape[0] != self.length:
            self.im_div = 5
        else:
            self.im_div = 1

        # the development set for coco is large and so validation would be slow
        if data_split == 'dev':
            self.length = 5000

    def __getitem__(self, index):
        # handle the image redundancy
        img_id = index/self.im_div
        image = torch.Tensor(self.images[img_id])
        caption = self.captions[index]
        vocab = self.vocab

        # convert caption (string) to word ids.
        tokens = nltk.tokenize.word_tokenize(
            str(caption).lower().decode('utf-8'))
        caption = []
        caption.append(vocab('<start>'))
        caption.extend([vocab(token) for token in tokens])
        caption.append(vocab('<end>'))
        target = torch.Tensor(caption)

        return image, target, index, img_id

    def __len__(self):
        return self.length


def collate_fn(data):
    """
    Build mini-batch tensors from a list of (image, caption, index, img_id) tuples.
    Args:
        data: list of (image, target, index, img_id) tuple.
            - image: torch tensor of shape (36, 2048).
            - target: torch tensor of shape (?) variable length.
    Returns:
        - images: torch tensor of shape (batch_size, 36, 2048).
        - targets: torch tensor of shape (batch_size, padded_length).
        - lengths: list; valid length for each padded caption.
    """
    # Sort a data list by caption length
    data.sort(key=lambda x: len(x[1]), reverse=True)
    images, captions, ids, img_ids = zip(*data)

    # Merge images (convert tuple of 2D tensor to 3D tensor)
    images = torch.stack(images, 0)

    # Merget captions (convert tuple of 1D tensor to 2D tensor)
    lengths = [len(cap) for cap in captions]
    targets = torch.zeros(len(captions), max(lengths)).long()
    for i, cap in enumerate(captions):
        end = lengths[i]
        targets[i, :end] = cap[:end]

    return images, targets, lengths, ids


def get_precomp_loader(data_path, data_split, vocab, opt, batch_size=100,
                       shuffle=True, num_workers=2):
    dset = PrecompDataset(data_path, data_split, vocab)

    data_loader = torch.utils.data.DataLoader(dataset=dset,
                                              batch_size=batch_size,
                                              shuffle=shuffle,
                                              pin_memory=True,
                                              collate_fn=collate_fn)
    return data_loader


def get_loaders(data_name, vocab, batch_size, workers, opt):
    # get the data path
    dpath = os.path.join(opt.data_path, data_name)

    # get the train_loader
    train_loader = get_precomp_loader(dpath, 'train', vocab, opt,
                                      batch_size, True, workers)
    # get the val_loader
    val_loader = get_precomp_loader(dpath, 'dev', vocab, opt,
                                    100, False, workers)
    return train_loader, val_loader


def get_test_loader(split_name, data_name, vocab, batch_size, workers, opt):
    # get the data path
    dpath = os.path.join(opt.data_path, data_name)

    # get the test_loader
    test_loader = get_precomp_loader(dpath, split_name, vocab, opt,
                                     100, False, workers)
    return test_loader


================================================
FILE: evaluation.py
================================================
"""Evaluation"""

from __future__ import print_function
import os
import sys
import time

import torch
import numpy as np

from data import get_test_loader
from vocab import Vocabulary, deserialize_vocab
from model import SGRAF
from collections import OrderedDict


class AverageMeter(object):
    """Computes and stores the average and current value"""

    def __init__(self):
        self.reset()

    def reset(self):
        self.val = 0
        self.avg = 0
        self.sum = 0
        self.count = 0

    def update(self, val, n=0):
        self.val = val
        self.sum += val * n
        self.count += n
        self.avg = self.sum / (.0001 + self.count)

    def __str__(self):
        """String representation for logging
        """
        # for values that should be recorded exactly e.g. iteration number
        if self.count == 0:
            return str(self.val)
        # for stats
        return '%.4f (%.4f)' % (self.val, self.avg)


class LogCollector(object):
    """A collection of logging objects that can change from train to val"""

    def __init__(self):
        # to keep the order of logged variables deterministic
        self.meters = OrderedDict()

    def update(self, k, v, n=0):
        # create a new meter if previously not recorded
        if k not in self.meters:
            self.meters[k] = AverageMeter()
        self.meters[k].update(v, n)

    def __str__(self):
        """Concatenate the meters in one log line
        """
        s = ''
        for i, (k, v) in enumerate(self.meters.iteritems()):
            if i > 0:
                s += '  '
            s += k + ' ' + str(v)
        return s

    def tb_log(self, tb_logger, prefix='', step=None):
        """Log using tensorboard
        """
        for k, v in self.meters.iteritems():
            tb_logger.log_value(prefix + k, v.val, step=step)


def encode_data(model, data_loader, log_step=10, logging=print):
    """Encode all images and captions loadable by `data_loader`
    """
    val_logger = LogCollector()

    # switch to evaluate mode
    model.val_start()

    # np array to keep all the embeddings
    img_embs = None
    cap_embs = None

    max_n_word = 0
    for i, (images, captions, lengths, ids) in enumerate(data_loader):
        max_n_word = max(max_n_word, max(lengths))

    for i, (images, captions, lengths, ids) in enumerate(data_loader):
        # make sure val logger is used
        model.logger = val_logger

        # compute the embeddings
        with torch.no_grad():
            img_emb, cap_emb, cap_len = model.forward_emb(images, captions, lengths)
        if img_embs is None:
            img_embs = np.zeros((len(data_loader.dataset), img_emb.size(1), img_emb.size(2)))
            cap_embs = np.zeros((len(data_loader.dataset), max_n_word, cap_emb.size(2)))
            cap_lens = [0] * len(data_loader.dataset)
        # cache embeddings
        img_embs[ids] = img_emb.data.cpu().numpy().copy()
        cap_embs[ids, :max(lengths), :] = cap_emb.data.cpu().numpy().copy()

        for j, nid in enumerate(ids):
            cap_lens[nid] = cap_len[j]

        del images, captions
    return img_embs, cap_embs, cap_lens


def evalrank(model_path, data_path=None, split='dev', fold5=False):
    """
    Evaluate a trained model on either dev or test. If `fold5=True`, 5 fold
    cross-validation is done (only for MSCOCO). Otherwise, the full data is
    used for evaluation.
    """
    # load model and options
    checkpoint = torch.load(model_path)
    opt = checkpoint['opt']
    save_epoch = checkpoint['epoch']
    print(opt)
    if data_path is not None:
        opt.data_path = data_path

    # load vocabulary used by the model
    vocab = deserialize_vocab(os.path.join(opt.vocab_path, '%s_vocab.json' % opt.data_name))
    opt.vocab_size = len(vocab)

    # construct model
    model = SGRAF(opt)

    # load model state
    model.load_state_dict(checkpoint['model'])

    print('Loading dataset')
    data_loader = get_test_loader(split, opt.data_name, vocab,
                                  opt.batch_size, opt.workers, opt)
    print("=> loaded checkpoint_epoch {}".format(save_epoch))

    print('Computing results...')
    img_embs, cap_embs, cap_lens = encode_data(model, data_loader)
    print('Images: %d, Captions: %d' %
          (img_embs.shape[0] / 5, cap_embs.shape[0]))

    if not fold5:
        # no cross-validation, full evaluation
        img_embs = np.array([img_embs[i] for i in range(0, len(img_embs), 5)])

        # record computation time of validation
        start = time.time()
        sims = shard_attn_scores(model, img_embs, cap_embs, cap_lens, opt, shard_size=100)
        end = time.time()
        print("calculate similarity time:", end-start)

        # bi-directional retrieval
        r, rt = i2t(img_embs, cap_embs, cap_lens, sims, return_ranks=True)
        ri, rti = t2i(img_embs, cap_embs, cap_lens, sims, return_ranks=True)
        ar = (r[0] + r[1] + r[2]) / 3
        ari = (ri[0] + ri[1] + ri[2]) / 3
        rsum = r[0] + r[1] + r[2] + ri[0] + ri[1] + ri[2]
        print("rsum: %.1f" % rsum)
        print("Average i2t Recall: %.1f" % ar)
        print("Image to text: %.1f %.1f %.1f %.1f %.1f" % r)
        print("Average t2i Recall: %.1f" % ari)
        print("Text to image: %.1f %.1f %.1f %.1f %.1f" % ri)
    else:
        # 5fold cross-validation, only for MSCOCO
        results = []
        for i in range(5):
            img_embs_shard = img_embs[i * 5000:(i + 1) * 5000:5]
            cap_embs_shard = cap_embs[i * 5000:(i + 1) * 5000]
            cap_lens_shard = cap_lens[i * 5000:(i + 1) * 5000]

            start = time.time()
            sims = shard_attn_scores(model, img_embs_shard, cap_embs_shard, cap_lens_shard, opt, shard_size=100)
            end = time.time()
            print("calculate similarity time:", end-start)

            r, rt0 = i2t(img_embs_shard, cap_embs_shard, cap_lens_shard, sims, return_ranks=True)
            print("Image to text: %.1f, %.1f, %.1f, %.1f, %.1f" % r)
            ri, rti0 = t2i(img_embs_shard, cap_embs_shard, cap_lens_shard, sims, return_ranks=True)
            print("Text to image: %.1f, %.1f, %.1f, %.1f, %.1f" % ri)

            if i == 0:
                rt, rti = rt0, rti0
            ar = (r[0] + r[1] + r[2]) / 3
            ari = (ri[0] + ri[1] + ri[2]) / 3
            rsum = r[0] + r[1] + r[2] + ri[0] + ri[1] + ri[2]
            print("rsum: %.1f ar: %.1f ari: %.1f" % (rsum, ar, ari))
            results += [list(r) + list(ri) + [ar, ari, rsum]]

        print("-----------------------------------")
        print("Mean metrics: ")
        mean_metrics = tuple(np.array(results).mean(axis=0).flatten())
        print("rsum: %.1f" % (mean_metrics[10] * 6))
        print("Average i2t Recall: %.1f" % mean_metrics[11])
        print("Image to text: %.1f %.1f %.1f %.1f %.1f" %
              mean_metrics[:5])
        print("Average t2i Recall: %.1f" % mean_metrics[12])
        print("Text to image: %.1f %.1f %.1f %.1f %.1f" %
              mean_metrics[5:10])


def shard_attn_scores(model, img_embs, cap_embs, cap_lens, opt, shard_size=100):
    n_im_shard = (len(img_embs) - 1) // shard_size + 1
    n_cap_shard = (len(cap_embs) - 1) // shard_size + 1

    sims = np.zeros((len(img_embs), len(cap_embs)))
    for i in range(n_im_shard):
        im_start, im_end = shard_size * i, min(shard_size * (i + 1), len(img_embs))
        for j in range(n_cap_shard):
            sys.stdout.write('\r>> shard_attn_scores batch (%d,%d)' % (i, j))
            ca_start, ca_end = shard_size * j, min(shard_size * (j + 1), len(cap_embs))

            with torch.no_grad():
                im = torch.from_numpy(img_embs[im_start:im_end]).float().cuda()
                ca = torch.from_numpy(cap_embs[ca_start:ca_end]).float().cuda()
                l = cap_lens[ca_start:ca_end]
                sim = model.forward_sim(im, ca, l)

            sims[im_start:im_end, ca_start:ca_end] = sim.data.cpu().numpy()
    sys.stdout.write('\n')
    return sims


def i2t(images, captions, caplens, sims, npts=None, return_ranks=False):
    """
    Images->Text (Image Annotation)
    Images: (N, n_region, d) matrix of images
    Captions: (5N, max_n_word, d) matrix of captions
    CapLens: (5N) array of caption lengths
    sims: (N, 5N) matrix of similarity im-cap
    """
    npts = images.shape[0]
    ranks = np.zeros(npts)
    top1 = np.zeros(npts)

    for index in range(npts):
        inds = np.argsort(sims[index])[::-1]

        # Score
        rank = 1e20
        for i in range(5 * index, 5 * index + 5, 1):
            tmp = np.where(inds == i)[0][0]
            if tmp < rank:
                rank = tmp
        ranks[index] = rank
        top1[index] = inds[0]

    # Compute metrics
    r1 = 100.0 * len(np.where(ranks < 1)[0]) / len(ranks)
    r5 = 100.0 * len(np.where(ranks < 5)[0]) / len(ranks)
    r10 = 100.0 * len(np.where(ranks < 10)[0]) / len(ranks)
    medr = np.floor(np.median(ranks)) + 1
    meanr = ranks.mean() + 1
    if return_ranks:
        return (r1, r5, r10, medr, meanr), (ranks, top1)
    else:
        return (r1, r5, r10, medr, meanr)


def t2i(images, captions, caplens, sims, npts=None, return_ranks=False):
    """
    Text->Images (Image Search)
    Images: (N, n_region, d) matrix of images
    Captions: (5N, max_n_word, d) matrix of captions
    CapLens: (5N) array of caption lengths
    sims: (N, 5N) matrix of similarity im-cap
    """
    npts = images.shape[0]
    ranks = np.zeros(5 * npts)
    top1 = np.zeros(5 * npts)

    # --> (5N(caption), N(image))
    sims = sims.T

    for index in range(npts):
        for i in range(5):
            inds = np.argsort(sims[5 * index + i])[::-1]
            ranks[5 * index + i] = np.where(inds == index)[0][0]
            top1[5 * index + i] = inds[0]

    # Compute metrics
    r1 = 100.0 * len(np.where(ranks < 1)[0]) / len(ranks)
    r5 = 100.0 * len(np.where(ranks < 5)[0]) / len(ranks)
    r10 = 100.0 * len(np.where(ranks < 10)[0]) / len(ranks)
    medr = np.floor(np.median(ranks)) + 1
    meanr = ranks.mean() + 1
    if return_ranks:
        return (r1, r5, r10, medr, meanr), (ranks, top1)
    else:
        return (r1, r5, r10, medr, meanr)


if __name__ == '__main__':
    evalrank("/apdcephfs/share_1313228/home/haiwendiao/SGRAF-master/runs/SAF_module/checkpoint/model_best.pth.tar",
             data_path="/apdcephfs/share_1313228/home/haiwendiao", split="test", fold5=False)


================================================
FILE: model.py
================================================
"""SGRAF model"""

import torch
import torch.nn as nn

import torch.nn.functional as F

import torch.backends.cudnn as cudnn
from torch.nn.utils.rnn import pack_padded_sequence, pad_packed_sequence
from torch.nn.utils.clip_grad import clip_grad_norm_

import numpy as np
from collections import OrderedDict


def l1norm(X, dim, eps=1e-8):
    """L1-normalize columns of X"""
    norm = torch.abs(X).sum(dim=dim, keepdim=True) + eps
    X = torch.div(X, norm)
    return X


def l2norm(X, dim=-1, eps=1e-8):
    """L2-normalize columns of X"""
    norm = torch.pow(X, 2).sum(dim=dim, keepdim=True).sqrt() + eps
    X = torch.div(X, norm)
    return X


def cosine_sim(x1, x2, dim=-1, eps=1e-8):
    """Returns cosine similarity between x1 and x2, computed along dim."""
    w12 = torch.sum(x1 * x2, dim)
    w1 = torch.norm(x1, 2, dim)
    w2 = torch.norm(x2, 2, dim)
    return (w12 / (w1 * w2).clamp(min=eps)).squeeze()


class EncoderImage(nn.Module):
    """
    Build local region representations by common-used FC-layer.
    Args: - images: raw local detected regions, shape: (batch_size, 36, 2048).
    Returns: - img_emb: finial local region embeddings, shape:  (batch_size, 36, 1024).
    """
    def __init__(self, img_dim, embed_size, no_imgnorm=False):
        super(EncoderImage, self).__init__()
        self.embed_size = embed_size
        self.no_imgnorm = no_imgnorm
        self.fc = nn.Linear(img_dim, embed_size)

        self.init_weights()

    def init_weights(self):
        """Xavier initialization for the fully connected layer"""
        r = np.sqrt(6.) / np.sqrt(self.fc.in_features +
                                  self.fc.out_features)
        self.fc.weight.data.uniform_(-r, r)
        self.fc.bias.data.fill_(0)

    def forward(self, images):
        """Extract image feature vectors."""
        # assuming that the precomputed features are already l2-normalized
        img_emb = self.fc(images)

        # normalize in the joint embedding space
        if not self.no_imgnorm:
            img_emb = l2norm(img_emb, dim=-1)

        return img_emb

    def load_state_dict(self, state_dict):
        """Overwrite the default one to accept state_dict from Full model"""
        own_state = self.state_dict()
        new_state = OrderedDict()
        for name, param in state_dict.items():
            if name in own_state:
                new_state[name] = param

        super(EncoderImage, self).load_state_dict(new_state)


class EncoderText(nn.Module):
    """
    Build local word representations by common-used Bi-GRU or GRU.
    Args: - images: raw local word ids, shape: (batch_size, L).
    Returns: - img_emb: final local word embeddings, shape: (batch_size, L, 1024).
    """
    def __init__(self, vocab_size, word_dim, embed_size, num_layers,
                 use_bi_gru=False, no_txtnorm=False):
        super(EncoderText, self).__init__()
        self.embed_size = embed_size
        self.no_txtnorm = no_txtnorm

        # word embedding
        self.embed = nn.Embedding(vocab_size, word_dim)
        self.dropout = nn.Dropout(0.4)

        # caption embedding
        self.use_bi_gru = use_bi_gru
        self.cap_rnn = nn.GRU(word_dim, embed_size, num_layers, batch_first=True, bidirectional=use_bi_gru)

        self.init_weights()

    def init_weights(self):
        self.embed.weight.data.uniform_(-0.1, 0.1)

    def forward(self, captions, lengths):
        """Handles variable size captions"""
        # embed word ids to vectors
        cap_emb = self.embed(captions)
        cap_emb = self.dropout(cap_emb)

        # pack the caption
        packed = pack_padded_sequence(cap_emb, lengths, batch_first=True)

        # forward propagate RNN
        out, _ = self.cap_rnn(packed)

        # reshape output to (batch_size, hidden_size)
        cap_emb, _ = pad_packed_sequence(out, batch_first=True)

        if self.use_bi_gru:
            cap_emb = (cap_emb[:, :, :cap_emb.size(2)/2] + cap_emb[:, :, cap_emb.size(2)/2:])/2

        # normalization in the joint embedding space
        if not self.no_txtnorm:
            cap_emb = l2norm(cap_emb, dim=-1)

        return cap_emb


class VisualSA(nn.Module):
    """
    Build global image representations by self-attention.
    Args: - local: local region embeddings, shape: (batch_size, 36, 1024)
          - raw_global: raw image by averaging regions, shape: (batch_size, 1024)
    Returns: - new_global: final image by self-attention, shape: (batch_size, 1024).
    """
    def __init__(self, embed_dim, dropout_rate, num_region):
        super(VisualSA, self).__init__()

        self.embedding_local = nn.Sequential(nn.Linear(embed_dim, embed_dim),
                                             nn.BatchNorm1d(num_region),
                                             nn.Tanh(), nn.Dropout(dropout_rate))
        self.embedding_global = nn.Sequential(nn.Linear(embed_dim, embed_dim),
                                              nn.BatchNorm1d(embed_dim),
                                              nn.Tanh(), nn.Dropout(dropout_rate))
        self.embedding_common = nn.Sequential(nn.Linear(embed_dim, 1))

        self.init_weights()
        self.softmax = nn.Softmax(dim=1)

    def init_weights(self):
        for embeddings in self.children():
            for m in embeddings:
                if isinstance(m, nn.Linear):
                    r = np.sqrt(6.) / np.sqrt(m.in_features + m.out_features)
                    m.weight.data.uniform_(-r, r)
                    m.bias.data.fill_(0)
                elif isinstance(m, nn.BatchNorm1d):
                    m.weight.data.fill_(1)
                    m.bias.data.zero_()

    def forward(self, local, raw_global):
        # compute embedding of local regions and raw global image
        l_emb = self.embedding_local(local)
        g_emb = self.embedding_global(raw_global)

        # compute the normalized weights, shape: (batch_size, 36)
        g_emb = g_emb.unsqueeze(1).repeat(1, l_emb.size(1), 1)
        common = l_emb.mul(g_emb)
        weights = self.embedding_common(common).squeeze(2)
        weights = self.softmax(weights)

        # compute final image, shape: (batch_size, 1024)
        new_global = (weights.unsqueeze(2) * local).sum(dim=1)
        new_global = l2norm(new_global, dim=-1)

        return new_global


class TextSA(nn.Module):
    """
    Build global text representations by self-attention.
    Args: - local: local word embeddings, shape: (batch_size, L, 1024)
          - raw_global: raw text by averaging words, shape: (batch_size, 1024)
    Returns: - new_global: final text by self-attention, shape: (batch_size, 1024).
    """

    def __init__(self, embed_dim, dropout_rate):
        super(TextSA, self).__init__()

        self.embedding_local = nn.Sequential(nn.Linear(embed_dim, embed_dim),
                                             nn.Tanh(), nn.Dropout(dropout_rate))
        self.embedding_global = nn.Sequential(nn.Linear(embed_dim, embed_dim),
                                              nn.Tanh(), nn.Dropout(dropout_rate))
        self.embedding_common = nn.Sequential(nn.Linear(embed_dim, 1))

        self.init_weights()
        self.softmax = nn.Softmax(dim=1)

    def init_weights(self):
        for embeddings in self.children():
            for m in embeddings:
                if isinstance(m, nn.Linear):
                    r = np.sqrt(6.) / np.sqrt(m.in_features + m.out_features)
                    m.weight.data.uniform_(-r, r)
                    m.bias.data.fill_(0)
                elif isinstance(m, nn.BatchNorm1d):
                    m.weight.data.fill_(1)
                    m.bias.data.zero_()

    def forward(self, local, raw_global):
        # compute embedding of local words and raw global text
        l_emb = self.embedding_local(local)
        g_emb = self.embedding_global(raw_global)

        # compute the normalized weights, shape: (batch_size, L)
        g_emb = g_emb.unsqueeze(1).repeat(1, l_emb.size(1), 1)
        common = l_emb.mul(g_emb)
        weights = self.embedding_common(common).squeeze(2)
        weights = self.softmax(weights)

        # compute final text, shape: (batch_size, 1024)
        new_global = (weights.unsqueeze(2) * local).sum(dim=1)
        new_global = l2norm(new_global, dim=-1)

        return new_global


class GraphReasoning(nn.Module):
    """
    Perform the similarity graph reasoning with a full-connected graph
    Args: - sim_emb: global and local alignments, shape: (batch_size, L+1, 256)
    Returns; - sim_sgr: reasoned graph nodes after several steps, shape: (batch_size, L+1, 256)
    """
    def __init__(self, sim_dim):
        super(GraphReasoning, self).__init__()

        self.graph_query_w = nn.Linear(sim_dim, sim_dim)
        self.graph_key_w = nn.Linear(sim_dim, sim_dim)
        self.sim_graph_w = nn.Linear(sim_dim, sim_dim)
        self.relu = nn.ReLU()

        self.init_weights()

    def forward(self, sim_emb):
        sim_query = self.graph_query_w(sim_emb)
        sim_key = self.graph_key_w(sim_emb)
        sim_edge = torch.softmax(torch.bmm(sim_query, sim_key.permute(0, 2, 1)), dim=-1)
        sim_sgr = torch.bmm(sim_edge, sim_emb)
        sim_sgr = self.relu(self.sim_graph_w(sim_sgr))
        return sim_sgr

    def init_weights(self):
        for m in self.children():
            if isinstance(m, nn.Linear):
                r = np.sqrt(6.) / np.sqrt(m.in_features + m.out_features)
                m.weight.data.uniform_(-r, r)
                m.bias.data.fill_(0)
            elif isinstance(m, nn.BatchNorm1d):
                m.weight.data.fill_(1)
                m.bias.data.zero_()


class AttentionFiltration(nn.Module):
    """
    Perform the similarity Attention Filtration with a gate-based attention
    Args: - sim_emb: global and local alignments, shape: (batch_size, L+1, 256)
    Returns; - sim_saf: aggregated alignment after attention filtration, shape: (batch_size, 256)
    """
    def __init__(self, sim_dim):
        super(AttentionFiltration, self).__init__()

        self.attn_sim_w = nn.Linear(sim_dim, 1)
        self.bn = nn.BatchNorm1d(1)

        self.init_weights()

    def forward(self, sim_emb):
        sim_attn = l1norm(torch.sigmoid(self.bn(self.attn_sim_w(sim_emb).permute(0, 2, 1))), dim=-1)
        sim_saf = torch.matmul(sim_attn, sim_emb)
        sim_saf = l2norm(sim_saf.squeeze(1), dim=-1)
        return sim_saf

    def init_weights(self):
        for m in self.children():
            if isinstance(m, nn.Linear):
                r = np.sqrt(6.) / np.sqrt(m.in_features + m.out_features)
                m.weight.data.uniform_(-r, r)
                m.bias.data.fill_(0)
            elif isinstance(m, nn.BatchNorm1d):
                m.weight.data.fill_(1)
                m.bias.data.zero_()


class EncoderSimilarity(nn.Module):
    """
    Compute the image-text similarity by SGR, SAF, AVE
    Args: - img_emb: local region embeddings, shape: (batch_size, 36, 1024)
          - cap_emb: local word embeddings, shape: (batch_size, L, 1024)
    Returns:
        - sim_all: final image-text similarities, shape: (batch_size, batch_size).
    """
    def __init__(self, embed_size, sim_dim, module_name='AVE', sgr_step=3):
        super(EncoderSimilarity, self).__init__()
        self.module_name = module_name

        self.v_global_w = VisualSA(embed_size, 0.4, 36)
        self.t_global_w = TextSA(embed_size, 0.4)

        self.sim_tranloc_w = nn.Linear(embed_size, sim_dim)
        self.sim_tranglo_w = nn.Linear(embed_size, sim_dim)

        self.sim_eval_w = nn.Linear(sim_dim, 1)
        self.sigmoid = nn.Sigmoid()

        if module_name == 'SGR':
            self.SGR_module = nn.ModuleList([GraphReasoning(sim_dim) for i in range(sgr_step)])
        elif module_name == 'SAF':
            self.SAF_module = AttentionFiltration(sim_dim)
        else:
            raise ValueError('Invalid input of opt.module_name in opts.py')

        self.init_weights()

    def forward(self, img_emb, cap_emb, cap_lens):
        sim_all = []
        n_image = img_emb.size(0)
        n_caption = cap_emb.size(0)

        # get enhanced global images by self-attention
        img_ave = torch.mean(img_emb, 1)
        img_glo = self.v_global_w(img_emb, img_ave)

        for i in range(n_caption):
            # get the i-th sentence
            n_word = cap_lens[i]
            cap_i = cap_emb[i, :n_word, :].unsqueeze(0)
            cap_i_expand = cap_i.repeat(n_image, 1, 1)

            # get enhanced global i-th text by self-attention
            cap_ave_i = torch.mean(cap_i, 1)
            cap_glo_i = self.t_global_w(cap_i, cap_ave_i)

            # local-global alignment construction
            Context_img = SCAN_attention(cap_i_expand, img_emb, smooth=9.0)
            sim_loc = torch.pow(torch.sub(Context_img, cap_i_expand), 2)
            sim_loc = l2norm(self.sim_tranloc_w(sim_loc), dim=-1)

            sim_glo = torch.pow(torch.sub(img_glo, cap_glo_i), 2)
            sim_glo = l2norm(self.sim_tranglo_w(sim_glo), dim=-1)

            # concat the global and local alignments
            sim_emb = torch.cat([sim_glo.unsqueeze(1), sim_loc], 1)

            # compute the final similarity vector
            if self.module_name == 'SGR':
                for module in self.SGR_module:
                    sim_emb = module(sim_emb)
                sim_vec = sim_emb[:, 0, :]
            else:
                sim_vec = self.SAF_module(sim_emb)

            # compute the final similarity score
            sim_i = self.sigmoid(self.sim_eval_w(sim_vec))
            sim_all.append(sim_i)

        # (n_image, n_caption)
        sim_all = torch.cat(sim_all, 1)

        return sim_all

    def init_weights(self):
        for m in self.children():
            if isinstance(m, nn.Linear):
                r = np.sqrt(6.) / np.sqrt(m.in_features + m.out_features)
                m.weight.data.uniform_(-r, r)
                m.bias.data.fill_(0)
            elif isinstance(m, nn.BatchNorm1d):
                m.weight.data.fill_(1)
                m.bias.data.zero_()


def SCAN_attention(query, context, smooth, eps=1e-8):
    """
    query: (n_context, queryL, d)
    context: (n_context, sourceL, d)
    """
    # --> (batch, d, queryL)
    queryT = torch.transpose(query, 1, 2)

    # (batch, sourceL, d)(batch, d, queryL)
    # --> (batch, sourceL, queryL)
    attn = torch.bmm(context, queryT)

    attn = nn.LeakyReLU(0.1)(attn)
    attn = l2norm(attn, 2)

    # --> (batch, queryL, sourceL)
    attn = torch.transpose(attn, 1, 2).contiguous()
    # --> (batch, queryL, sourceL
    attn = F.softmax(attn*smooth, dim=2)

    # --> (batch, sourceL, queryL)
    attnT = torch.transpose(attn, 1, 2).contiguous()

    # --> (batch, d, sourceL)
    contextT = torch.transpose(context, 1, 2)
    # (batch x d x sourceL)(batch x sourceL x queryL)
    # --> (batch, d, queryL)
    weightedContext = torch.bmm(contextT, attnT)
    # --> (batch, queryL, d)
    weightedContext = torch.transpose(weightedContext, 1, 2)
    weightedContext = l2norm(weightedContext, dim=-1)

    return weightedContext


class ContrastiveLoss(nn.Module):
    """
    Compute contrastive loss
    """
    def __init__(self, margin=0, max_violation=False):
        super(ContrastiveLoss, self).__init__()
        self.margin = margin
        self.max_violation = max_violation

    def forward(self, scores):
        # compute image-sentence score matrix
        diagonal = scores.diag().view(scores.size(0), 1)
        d1 = diagonal.expand_as(scores)
        d2 = diagonal.t().expand_as(scores)

        # compare every diagonal score to scores in its column
        # caption retrieval
        cost_s = (self.margin + scores - d1).clamp(min=0)
        # compare every diagonal score to scores in its row
        # image retrieval
        cost_im = (self.margin + scores - d2).clamp(min=0)

        # clear diagonals
        mask = torch.eye(scores.size(0)) > .5
        if torch.cuda.is_available():
            I = mask.cuda()
        cost_s = cost_s.masked_fill_(I, 0)
        cost_im = cost_im.masked_fill_(I, 0)

        # keep the maximum violating negative for each query
        if self.max_violation:
            cost_s = cost_s.max(1)[0]
            cost_im = cost_im.max(0)[0]
        return cost_s.sum() + cost_im.sum()


class SGRAF(object):
    """
    Similarity Reasoning and Filtration (SGRAF) Network
    """
    def __init__(self, opt):
        # Build Models
        self.grad_clip = opt.grad_clip
        self.img_enc = EncoderImage(opt.img_dim, opt.embed_size,
                                    no_imgnorm=opt.no_imgnorm)
        self.txt_enc = EncoderText(opt.vocab_size, opt.word_dim,
                                   opt.embed_size, opt.num_layers, 
                                   use_bi_gru=opt.bi_gru,  
                                   no_txtnorm=opt.no_txtnorm)
        self.sim_enc = EncoderSimilarity(opt.embed_size, opt.sim_dim,
                                         opt.module_name, opt.sgr_step)

        if torch.cuda.is_available():
            self.img_enc.cuda()
            self.txt_enc.cuda()
            self.sim_enc.cuda()
            cudnn.benchmark = True

        # Loss and Optimizer
        self.criterion = ContrastiveLoss(margin=opt.margin,
                                         max_violation=opt.max_violation)
        params = list(self.txt_enc.parameters())
        params += list(self.img_enc.parameters())
        params += list(self.sim_enc.parameters())
        self.params = params

        self.optimizer = torch.optim.Adam(params, lr=opt.learning_rate)
        self.Eiters = 0

    def state_dict(self):
        state_dict = [self.img_enc.state_dict(), self.txt_enc.state_dict(), self.sim_enc.state_dict()]
        return state_dict

    def load_state_dict(self, state_dict):
        self.img_enc.load_state_dict(state_dict[0])
        self.txt_enc.load_state_dict(state_dict[1])
        self.sim_enc.load_state_dict(state_dict[2])

    def train_start(self):
        """switch to train mode"""
        self.img_enc.train()
        self.txt_enc.train()
        self.sim_enc.train()

    def val_start(self):
        """switch to evaluate mode"""
        self.img_enc.eval()
        self.txt_enc.eval()
        self.sim_enc.eval()

    def forward_emb(self, images, captions, lengths):
        """Compute the image and caption embeddings"""
        if torch.cuda.is_available():
            images = images.cuda()
            captions = captions.cuda()

        # Forward feature encoding
        img_embs = self.img_enc(images)
        cap_embs = self.txt_enc(captions, lengths)
        return img_embs, cap_embs, lengths

    def forward_sim(self, img_embs, cap_embs, cap_lens):
        # Forward similarity encoding
        sims = self.sim_enc(img_embs, cap_embs, cap_lens)
        return sims

    def forward_loss(self, sims, **kwargs):
        """Compute the loss given pairs of image and caption embeddings
        """
        loss = self.criterion(sims)
        self.logger.update('Loss', loss.item(), sims.size(0))
        return loss

    def train_emb(self, images, captions, lengths, ids=None, *args):
        """One training step given images and captions.
        """
        self.Eiters += 1
        self.logger.update('Eit', self.Eiters)
        self.logger.update('lr', self.optimizer.param_groups[0]['lr'])

        # compute the embeddings
        img_embs, cap_embs, cap_lens = self.forward_emb(images, captions, lengths)
        sims = self.forward_sim(img_embs, cap_embs, cap_lens)

        # measure accuracy and record loss
        self.optimizer.zero_grad()
        loss = self.forward_loss(sims)

        # compute gradient and do SGD step
        loss.backward()
        if self.grad_clip > 0:
            clip_grad_norm_(self.params, self.grad_clip)
        self.optimizer.step()


================================================
FILE: opts.py
================================================
"""Argument parser"""

import argparse


def parse_opt():
    # Hyper Parameters
    parser = argparse.ArgumentParser()
    # --------------------------- data path -------------------------#
    parser.add_argument('--data_path', default='/apdcephfs/share_1313228/home/haiwendiao',
                        help='path to datasets')
    parser.add_argument('--data_name', default='f30k_precomp',
                        help='{coco,f30k}_precomp')
    parser.add_argument('--vocab_path', default='/apdcephfs/share_1313228/home/haiwendiao/SGRAF-master/vocab/',
                        help='Path to saved vocabulary json files.')
    parser.add_argument('--model_name', default='/apdcephfs/share_1313228/home/haiwendiao/SGRAF-master/runs/f30k_SGR/checkpoint',
                        help='Path to save the model.')
    parser.add_argument('--logger_name', default='/apdcephfs/share_1313228/home/haiwendiao/SGRAF-master/runs/f30k_SGR/log',
                        help='Path to save Tensorboard log.')

    # ----------------------- training setting ----------------------#
    parser.add_argument('--batch_size', default=128, type=int,
                        help='Size of a training mini-batch.')
    parser.add_argument('--num_epochs', default=40, type=int,
                        help='Number of training epochs.')
    parser.add_argument('--lr_update', default=30, type=int,
                        help='Number of epochs to update the learning rate.')
    parser.add_argument('--learning_rate', default=.0002, type=float,
                        help='Initial learning rate.')
    parser.add_argument('--workers', default=10, type=int,
                        help='Number of data loader workers.')
    parser.add_argument('--log_step', default=10, type=int,
                        help='Number of steps to print and record the log.')
    parser.add_argument('--val_step', default=1000, type=int,
                        help='Number of steps to run validation.')
    parser.add_argument('--grad_clip', default=2., type=float,
                        help='Gradient clipping threshold.')
    parser.add_argument('--margin', default=0.2, type=float,
                        help='Rank loss margin.')
    parser.add_argument('--max_violation', action='store_false',
                        help='Use max instead of sum in the rank loss.')

    # ------------------------- model setting -----------------------#
    parser.add_argument('--img_dim', default=2048, type=int,
                        help='Dimensionality of the image embedding.')
    parser.add_argument('--word_dim', default=300, type=int,
                        help='Dimensionality of the word embedding.')
    parser.add_argument('--embed_size', default=1024, type=int,
                        help='Dimensionality of the joint embedding.')
    parser.add_argument('--sim_dim', default=256, type=int,
                        help='Dimensionality of the sim embedding.')
    parser.add_argument('--num_layers', default=1, type=int,
                        help='Number of GRU layers.')
    parser.add_argument('--bi_gru', action='store_false',
                        help='Use bidirectional GRU.')
    parser.add_argument('--no_imgnorm', action='store_true',
                        help='Do not normalize the image embeddings.')
    parser.add_argument('--no_txtnorm', action='store_true',
                        help='Do not normalize the text embeddings.')
    parser.add_argument('--module_name', default='SGR', type=str,
                        help='SGR, SAF')
    parser.add_argument('--sgr_step', default=3, type=int,
                        help='Step of the SGR.')

    opt = parser.parse_args()
    print(opt)
    return opt


================================================
FILE: requirements.txt
================================================
backports.functools-lru-cache==1.6.1
backports.weakref==1.0.post1
bleach==1.5.0
boto3==1.17.8
botocore==1.20.8
certifi==2019.11.28
cffi==1.14.0
chardet==4.0.0
click==7.1.2
cloudpickle==1.3.0
cycler==0.10.0
Cython==0.29.13
decorator==4.4.2
enum34==1.1.10
funcsigs==1.0.2
futures==3.3.0
html5lib==0.9999999
idna==2.10
jmespath==0.10.0
joblib==0.14.1
kiwisolver==1.1.0
Markdown==3.1.1
matplotlib==2.2.4
mock==3.0.5
networkx==2.2
nltk==3.4.5
numpy==1.16.5
olefile==0.46
opencv-python==4.2.0.32
pandas==0.24.2
Pillow==6.2.1
protobuf==3.12.2
ptflops==0.6.4
pycocotools==2.0
pycparser==2.20
pyparsing==2.4.7
python-dateutil==2.8.1
pytz==2020.1
PyWavelets==1.0.3
regex==2020.11.13
requests==2.25.1
s3transfer==0.3.4
sacremoses==0.0.43
scikit-image==0.14.5
scipy==1.2.3
singledispatch==3.4.0.3
six==1.15.0
subprocess32==3.5.4
tensorboard-logger==0.1.0
tensorflow==1.4.0
tensorflow-tensorboard==0.4.0
torch==0.4.1.post2
torchvision==0.2.0
tqdm==4.56.2
urllib3==1.26.3
Werkzeug==1.0.1


================================================
FILE: runs/runs.md
================================================
The folder used to store the model:

**File Structure**
```
├── runs/
|   ├── f30k_SAF/
|   |   ├── checkpoint/
|   |   ├── log/
|   ├── f30k_SGR/
|   |   ├── checkpoint/
|   |   ├── log/
```


================================================
FILE: train.py
================================================
"""
# Pytorch implementation for AAAI2021 paper from
# https://arxiv.org/pdf/2101.01368.
# "Similarity Reasoning and Filtration for Image-Text Matching"
# Haiwen Diao, Ying Zhang, Lin Ma, Huchuan Lu
#
# Writen by Haiwen Diao, 2020
"""

import os
import time
import shutil

import torch
import numpy

import data
import opts
from vocab import Vocabulary, deserialize_vocab
from model import SGRAF
from evaluation import i2t, t2i, AverageMeter, LogCollector, encode_data, shard_attn_scores

import logging
import tensorboard_logger as tb_logger

os.environ["CUDA_VISIBLE_DEVICES"] = "0"


def main():
    opt = opts.parse_opt()
    logging.basicConfig(format='%(asctime)s %(message)s', level=logging.INFO)
    tb_logger.configure(opt.logger_name, flush_secs=5)

    # Load Vocabulary Wrapper
    vocab = deserialize_vocab(os.path.join(opt.vocab_path, '%s_vocab.json' % opt.data_name))
    opt.vocab_size = len(vocab)

    # Load data loaders
    train_loader, val_loader = data.get_loaders(opt.data_name, vocab, opt.batch_size, opt.workers, opt)

    # Construct the model
    model = SGRAF(opt)

    # Train the Model
    best_rsum = 0

    for epoch in range(opt.num_epochs):
        print(opt.logger_name)
        print(opt.model_name)

        adjust_learning_rate(opt, model.optimizer, epoch)

        # train for one epoch
        train(opt, train_loader, model, epoch, val_loader)

        # evaluate on validation set
        r_sum = validate(opt, val_loader, model)

        # remember best R@ sum and save checkpoint
        is_best = r_sum > best_rsum
        best_rsum = max(r_sum, best_rsum)

        if not os.path.exists(opt.model_name):
            os.mkdir(opt.model_name)
        save_checkpoint({
            'epoch': epoch + 1,
            'model': model.state_dict(),
            'best_rsum': best_rsum,
            'opt': opt,
            'Eiters': model.Eiters,
        }, is_best, filename='checkpoint_{}.pth.tar'.format(epoch), prefix=opt.model_name + '/')


def train(opt, train_loader, model, epoch, val_loader):
    # average meters to record the training statistics
    batch_time = AverageMeter()
    data_time = AverageMeter()
    train_logger = LogCollector()

    end = time.time()
    for i, train_data in enumerate(train_loader):
        # switch to train mode
        model.train_start()

        # measure data loading time
        data_time.update(time.time() - end)

        # make sure train logger is used
        model.logger = train_logger

        # Update the model
        model.train_emb(*train_data)

        # measure elapsed time
        batch_time.update(time.time() - end)
        end = time.time()

        # Print log info
        if model.Eiters % opt.log_step == 0:
            logging.info(
                'Epoch: [{0}][{1}/{2}]\t'
                '{e_log}\t'
                'Time {batch_time.val:.3f} ({batch_time.avg:.3f})\t'
                'Data {data_time.val:.3f} ({data_time.avg:.3f})\t'
                .format(
                    epoch, i, len(train_loader), batch_time=batch_time,
                    data_time=data_time, e_log=str(model.logger)))

        # Record logs in tensorboard
        tb_logger.log_value('epoch', epoch, step=model.Eiters)
        tb_logger.log_value('step', i, step=model.Eiters)
        tb_logger.log_value('batch_time', batch_time.val, step=model.Eiters)
        tb_logger.log_value('data_time', data_time.val, step=model.Eiters)
        model.logger.tb_log(tb_logger, step=model.Eiters)

        # validate at every val_step
        if model.Eiters % opt.val_step == 0:
            validate(opt, val_loader, model)


def validate(opt, val_loader, model):
    # compute the encoding for all the validation images and captions
    img_embs, cap_embs, cap_lens = encode_data(model, val_loader, opt.log_step, logging.info)

    # clear duplicate 5*images and keep 1*images
    img_embs = numpy.array([img_embs[i] for i in range(0, len(img_embs), 5)])

    # record computation time of validation
    start = time.time()
    sims = shard_attn_scores(model, img_embs, cap_embs, cap_lens, opt, shard_size=100)
    end = time.time()
    print("calculate similarity time:", end-start)

    # caption retrieval
    (r1, r5, r10, medr, meanr) = i2t(img_embs, cap_embs, cap_lens, sims)
    logging.info("Image to text: %.1f, %.1f, %.1f, %.1f, %.1f" % (r1, r5, r10, medr, meanr))

    # image retrieval
    (r1i, r5i, r10i, medri, meanr) = t2i(img_embs, cap_embs, cap_lens, sims)
    logging.info("Text to image: %.1f, %.1f, %.1f, %.1f, %.1f" % (r1i, r5i, r10i, medri, meanr))

    # sum of recalls to be used for early stopping
    r_sum = r1 + r5 + r10 + r1i + r5i + r10i

    # record metrics in tensorboard
    tb_logger.log_value('r1', r1, step=model.Eiters)
    tb_logger.log_value('r5', r5, step=model.Eiters)
    tb_logger.log_value('r10', r10, step=model.Eiters)
    tb_logger.log_value('medr', medr, step=model.Eiters)
    tb_logger.log_value('meanr', meanr, step=model.Eiters)
    tb_logger.log_value('r1i', r1i, step=model.Eiters)
    tb_logger.log_value('r5i', r5i, step=model.Eiters)
    tb_logger.log_value('r10i', r10i, step=model.Eiters)
    tb_logger.log_value('medri', medri, step=model.Eiters)
    tb_logger.log_value('meanr', meanr, step=model.Eiters)
    tb_logger.log_value('r_sum', r_sum, step=model.Eiters)

    return r_sum


def save_checkpoint(state, is_best, filename='checkpoint.pth.tar', prefix=''):
    tries = 15
    error = None

    # deal with unstable I/O. Usually not necessary.
    while tries:
        try:
            torch.save(state, prefix + filename)
            if is_best:
                shutil.copyfile(prefix + filename, prefix + 'model_best.pth.tar')
        except IOError as e:
            error = e
            tries -= 1
        else:
            break
        print('model save {} failed, remaining {} trials'.format(filename, tries))
        if not tries:
            raise error


def adjust_learning_rate(opt, optimizer, epoch):
    """
    Sets the learning rate to the initial LR
    decayed by 10 after opt.lr_update epoch
    """
    lr = opt.learning_rate * (0.1 ** (epoch // opt.lr_update))
    for param_group in optimizer.param_groups:
        param_group['lr'] = lr


if __name__ == '__main__':
    main()


================================================
FILE: visualize.py
================================================
"""
# Please refer to https://github.com/Paranioar/RCAR for related visualization code.
# It now includes visualize_attention_mechanism, visualize_similarity_distribution, visualize_rank_result, and etc.

# I will continue to update more related visualization codes when I am free.
# If you find these codes are useful, please cite our papers and star our projects. (We do need it! HaHaHaHa.)
# Thanks for the interest in our projects.
"""










================================================
FILE: vocab/coco_precomp_vocab.json
================================================
{"idx2word": {"0": "<pad>", "1": "<start>", "2": "<end>", "3": "<unk>", "4": "wrought-iron", "5": "woods", "6": "hanging", "7": "woody", "8": "comically", "9": "canes", "10": "bringing", "11": "wooded", "12": "broiler", "13": "wooden", "14": "broiled", "15": "crotch", "16": "snuggles", "17": "scraper", "18": "snuggled", "19": "cooking", "20": "numeral", "21": "crouch", "22": "chins", "23": "china", "24": "kids", "25": "climbed", "26": "talbe", "27": "spotty", "28": "climber", "29": "golden", "30": "projection", "31": "stern", "32": "music", "33": "yahoo", "34": "schoolboys", "35": "copse", "36": "locked", "37": "locker", "38": "wand", "39": "pints", "40": "want", "41": "cookers", "42": "pinto", "43": "travel", "44": "barstools", "45": "over-sized", "46": "dinosaurs", "47": "wrong", "48": "domed", "49": "colorfully", "50": "tulip", "51": "fir", "52": "fit", "53": "screaming", "54": "fix", "55": "bulding", "56": "fin", "57": "effects", "58": "sixteen", "59": "undeveloped", "60": "arrow", "61": "windmill", "62": "telescope", "63": "grapefruits", "64": "smirk", "65": "mason", "66": "pumpkins", "67": "semi-trailer", "68": "bannanas", "69": "kfc", "70": "windmills", "71": "renovated", "72": "service", "73": "reuben", "74": "needed", "75": "master", "76": "bannister", "77": "handcuffs", "78": "idly", "79": "idle", "80": "feeling", "81": "dozen", "82": "mostly", "83": "racers", "84": "mouth", "85": "singer", "86": "tech", "87": "scream", "88": "saying", "89": "teresa", "90": "padded", "91": "pontoons", "92": "rich", "93": "rice", "94": "plate", "95": "umbilical", "96": "altogether", "97": "nicely", "98": "boarder", "99": "pretzel", "100": "patch", "101": "boarded", "102": "alternative", "103": "suticase", "104": "lots", "105": "extend", "106": "nature", "107": "wheelbarrow", "108": "heating", "109": "levitating", "110": "lookin", "111": "humming", "112": "union", "113": "fro", "114": ".", "115": "much", "116": "fry", "117": "tallest", "118": "obese", "119": "spin", "120": "professionally", "121": "paraglider", "122": "k", "123": "canoeing", "124": "eighteen", "125": "conditioner", "126": "hone", "127": "memorial", "128": "honk", "129": "split", "130": "boiled", "131": "marches", "132": "boiler", "133": "stillness", "134": "goofing", "135": "corporate", "136": "plaque", "137": "bellow", "138": "waterboard", "139": "lasso", "140": "ham", "141": "had", "142": "hay", "143": "duffel", "144": "has", "145": "hat", "146": "municipal", "147": "elders", "148": "confection", "149": "clustered", "150": "shadow", "151": "crowd", "152": "crown", "153": "topping", "154": "crows", "155": "billboard", "156": "bottom", "157": "treadmill", "158": "slivers", "159": "binder", "160": "starring", "161": "benches", "162": "benched", "163": "maxwell", "164": "mangos", "165": "shoots", "166": "fabric", "167": "altitude", "168": "tame", "169": "grasping", "170": "avocados", "171": "grooms", "172": "denoting", "173": "peperoni", "174": "duel", "175": "rippling", "176": "peson", "177": "open-air", "178": "servings", "179": "smashed", "180": "passenger", "181": "ornaments", "182": "calzones", "183": "paperwork", "184": "triangles", "185": "crowns", "186": "pasadena", "187": "role", "188": "roll", "189": "palms", "190": "ointment", "191": "transported", "192": "intent", "193": "smelling", "194": "windsocks", "195": "overturned", "196": "gown", "197": "childs", "198": "chain", "199": "whoever", "200": "chair", "201": "ballet", "202": "crates", "203": "crater", "204": "mache", "205": "balled", "206": "lit-up", "207": "choice", "208": "gloomy", "209": "stays", "210": "exact", "211": "minute", "212": "talbot", "213": "cooks", "214": "minnie", "215": "skewer", "216": "meadow", "217": "trails", "218": "chopping", "219": "shirts", "220": "adorns", "221": "headset", "222": "celebrates", "223": "climbs", "224": "address", "225": "dusty", "226": "queue", "227": "inquisitively", "228": "para-sailing", "229": "working", "230": "tundra", "231": "consoles", "232": "riders", "233": "following", "234": "zippers", "235": "admired", "236": "mirrors", "237": "mailboxes", "238": "parachute", "239": "locks", "240": "admires", "241": "listens", "242": "fueled", "243": "surfing", "244": "mango", "245": "pulled", "246": "blowup", "247": "webpage", "248": "years", "249": "milked", "250": "suspension", "251": "apron", "252": "drilling", "253": "sorted", "254": "leather", "255": "fisherman", "256": "quartet", "257": "retrieve", "258": "receipt", "259": "sponsor", "260": "entering", "261": "salads", "262": "troll", "263": "seriously", "264": "internet", "265": "complicated", "266": "grandma", "267": "modest", "268": "pouches", "269": "saving", "270": "ona", "271": "one", "272": "veldt", "273": "ont", "274": "shawl", "275": "herds", "276": "crossroads", "277": "wandering", "278": "turned", "279": "locations", "280": "jewels", "281": "fashionable", "282": "washbasin", "283": "zoo", "284": "sprawls", "285": "printer", "286": "opposite", "287": "spewing", "288": "buffet", "289": "printed", "290": "bi-plane", "291": "aggressive", "292": "guarded", "293": "suitcases", "294": "tilting", "295": "balconies", "296": "awaiting", "297": "tiki", "298": "tortilla", "299": "vision", "300": "enjoys", "301": "majestically", "302": "flipped", "303": "s", "304": "workplace", "305": "concentrates", "306": "grooming", "307": "west", "308": "wants", "309": "formed", "310": "dark-colored", "311": "photos", "312": "former", "313": "newspaper", "314": "situation", "315": "necks", "316": "engaged", "317": "engages", "318": "demolition", "319": "wires", "320": "edged", "321": "singapore", "322": "edges", "323": "wired", "324": "advertisement", "325": "masks", "326": "pecks", "327": "steamed", "328": "being", "329": "recycled", "330": "steamer", "331": "rover", "332": "grounded", "333": "plumes", "334": "sumo", "335": "traffic", "336": "world", "337": "postal", "338": "shutter", "339": "seating", "340": "tvs", "341": "diving", "342": "stagecoach", "343": "thailand", "344": "luxurious", "345": "pelicans", "346": "lively", "347": "bubbly", "348": "lounging", "349": "rumbles", "350": "sealed", "351": "bubble", "352": "wits", "353": "foreheads", "354": "with", "355": "pull", "356": "rush", "357": "rags", "358": "dirty", "359": "rust", "360": "watches", "361": "watched", "362": "cream", "363": "puppy", "364": "passerby", "365": "waving", "366": "tricky", "367": "tricks", "368": "dyed", "369": "beware", "370": "ceramic", "371": "roadway", "372": "sand", "373": "small", "374": "past", "375": "displays", "376": "pass", "377": "crosswalks", "378": "clock", "379": "para-sail", "380": "full", "381": "hash", "382": "diapers", "383": "portrays", "384": "frumpled", "385": "civilians", "386": "experience", "387": "prior", "388": "followed", "389": "adidas", "390": "unrecognizable", "391": "more", "392": "door", "393": "company", "394": "corrected", "395": "exposing", "396": "installing", "397": "learn", "398": "knocked", "399": "seagull", "400": "scramble", "401": "huge", "402": "respective", "403": "speedboat", "404": "hugs", "405": "sprinkle", "406": "intended", "407": "fluffed", "408": "resemble", "409": "twisting", "410": "overcooked", "411": "trellis", "412": "wooly", "413": "installed", "414": "stylus", "415": "paper", "416": "scott", "417": "signs", "418": "smiling", "419": "schoolhouse", "420": "roots", "421": "hounds", "422": "sauce", "423": "colleague", "424": "gadget", "425": "frisbe", "426": "frisby", "427": "frizzy", "428": "weeds", "429": "weedy", "430": "courses", "431": "piping", "432": "chipping", "433": "brunette", "434": "operation", "435": "lipstick", "436": "scoops", "437": "denotes", "438": "terra", "439": "calzone", "440": "outfitted", "441": "airway", "442": "plowing", "443": "pairs", "444": "carry-on", "445": "benedict", "446": "moderately", "447": "saint", "448": "easel", "449": "interviewed", "450": "burrito", "451": "ave.", "452": "tummy", "453": "emirates", "454": "guarding", "455": "graffiti", "456": "blond", "457": "burried", "458": "sell", "459": "self", "460": "also", "461": "butterflies", "462": "singles", "463": "dirtbike", "464": "sometimes", "465": "barred", "466": "barren", "467": "barrel", "468": "bulletin", "469": "blended", "470": "blender", "471": "access", "472": "wraps", "473": "streetlights", "474": "gooey", "475": "cassette", "476": "removable", "477": "columns", "478": "sunny", "479": "compass", "480": "restrooms", "481": "seperated", "482": "caramel", "483": "tanker", "484": "delicately", "485": "touch", "486": "sips", "487": "analog", "488": "project", "489": "sparsely", "490": "untouched", "491": "last", "492": "connection", "493": "objects", "494": "bell", "495": "iphones", "496": "desktop", "497": "belt", "498": "suburbs", "499": "extravagant", "500": "formations", "501": "treatment", "502": "frito", "503": "awake", "504": "presses", "505": "caged", "506": "admire", "507": "pressed", "508": "cages", "509": "30", "510": "motors", "511": "cuisine", "512": "flooded", "513": "infamous", "514": "clasped", "515": "cheddar", "516": "goblet", "517": "parents", "518": "emergency", "519": "couple", "520": "marquee", "521": "individuals", "522": "spins", "523": "sundaes", "524": "bouncy", "525": "underbelly", "526": "and/or", "527": "scrubs", "528": "measurements", "529": "novelty", "530": "cupped", "531": "inserting", "532": "concealed", "533": "obscured", "534": "wrinkles", "535": "melbourne", "536": "frisbie", "537": "wrinkled", "538": "canning", "539": "chirping", "540": "sightseeing", "541": "span", "542": "harnessed", "543": "tasting", "544": "sock", "545": "harnesses", "546": "opens", "547": "hawaiian", "548": "atlantic", "549": "peepee", "550": "paired", "551": "faded", "552": "chat", "553": "surveying", "554": "lufthansa", "555": "lane", "556": "land", "557": "wafer", "558": "beverage", "559": "amish", "560": "splashing", "561": "cobalt", "562": "totes", "563": "windowsill", "564": "glows", "565": "carelessly", "566": "decorating", "567": "forks", "568": "harbor", "569": "disheveled", "570": "crook", "571": "video", "572": "boutonniere", "573": "sweaty", "574": "flowing", "575": "fifteen", "576": "survey", "577": "derby", "578": "makes", "579": "maker", "580": "thats", "581": "nibbling", "582": "strainer", "583": "next", "584": "eleven", "585": "pencil", "586": "baby", "587": "placard", "588": "customer", "589": "scatter", "590": "wedge", "591": "process", "592": "lock", "593": "billboards", "594": "rode", "595": "promotional", "596": "nears", "597": "rods", "598": "tightrope", "599": "bilingual", "600": "clasping", "601": "chow", "602": "curving", "603": "robot", "604": "houston", "605": "thigh", "606": "spatula", "607": "directs", "608": "perfect", "609": "forest", "610": "glassed", "611": "scenic", "612": "glasses", "613": "bump", "614": "books", "615": "'", "616": "boutique", "617": "frowns", "618": "stab", "619": "chilli", "620": "backless", "621": "gallons", "622": "could", "623": "chilly", "624": "length", "625": "receding", "626": "motorbikes", "627": "scene", "628": "festival", "629": "enforcement", "630": "vigorous", "631": "quarry", "632": "false", "633": "tonight", "634": "mountainous", "635": "dishes", "636": "dished", "637": "petals", "638": "romaine", "639": "bred", "640": "thanksgiving", "641": "woolly", "642": "greenhouse", "643": "taps", "644": "jay", "645": "jar", "646": "terminal", "647": "jal", "648": "jam", "649": "tape", "650": "riding", "651": "matress", "652": "styrofoam", "653": "aer", "654": "vying", "655": "stuff", "656": "rapped", "657": "frame", "658": "skateboarding", "659": "tented", "660": "staring", "661": "doorway", "662": "potties", "663": "feather", "664": ">", "665": "describe", "666": "commuter", "667": "stuffing", "668": "mover", "669": "tattoo", "670": "moves", "671": "innings", "672": "colts", "673": "ofa", "674": "off", "675": "shotgun", "676": "patterns", "677": "audio", "678": "newest", "679": "clocks", "680": "web", "681": "wee", "682": "wed", "683": "wet", "684": "villagers", "685": "stooped", "686": "tick", "687": "pier", "688": "pies", "689": "become", "690": "choosing", "691": "flush", "692": "hissing", "693": "blonde", "694": "hipsters", "695": "mementos", "696": "saucepan", "697": "pomeranian", "698": "pressure", "699": "swimming", "700": "letters", "701": "brownies", "702": "bagged", "703": "tossed", "704": "places", "705": "excitement", "706": "placed", "707": "tosses", "708": "problem", "709": "nurses", "710": "compared", "711": "tubular", "712": "details", "713": "ponytail", "714": "outlets", "715": "exposure", "716": "searches", "717": "compete", "718": "gardens", "719": "magnetic", "720": "nursery", "721": "worth", "722": "progression", "723": "ocean-side", "724": "notifying", "725": "jamaica", "726": "panda", "727": "machines", "728": "croutons", "729": "protection", "730": "watermelons", "731": "wii-mote", "732": "otters", "733": "lavatory", "734": "cities", "735": "bra", "736": "plot", "737": "plow", "738": "sweater", "739": "coins", "740": "bundles", "741": "bundled", "742": "separated", "743": "separates", "744": "blocking", "745": "cuddle", "746": "era", "747": "elbow", "748": "indicated", "749": "indicates", "750": "yarn", "751": "basebal", "752": "recovery", "753": "carriers", "754": "provide", "755": "nuts", "756": "ladder", "757": "hong", "758": "bared", "759": "arch", "760": "popsicle", "761": "production", "762": "coffee", "763": "safe", "764": "collide", "765": "reaches", "766": "l", "767": "dingy", "768": "flyers", "769": "feeds", "770": "dumping", "771": "two-tiered", "772": "sled", "773": "slew", "774": "trainers", "775": "barrier", "776": "colorless", "777": "fastening", "778": "lollipop", "779": "shrubbery", "780": "baseman", "781": "alcohol", "782": "sprig", "783": "another", "784": "disembark", "785": "electronic", "786": "doge", "787": "elevator", "788": "john", "789": "dogs", "790": "emblem", "791": "cereal", "792": "historical", "793": "giraffes", "794": "portraits", "795": "giraffee", 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