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:**
**The updated results (Better than the original paper)**
| Dataset | Module |
Sentence retrieval | Image retrieval |
| R@1 | R@5 | R@10 | R@1 | R@5 | R@10 |
| Flick30k |
SAF | 75.6 | 92.7 | 96.9 | 56.5 | 82.0 | 88.4 |
| SGR | 76.6 | 93.7 | 96.6 | 56.1 | 80.9 | 87.0 |
| SGRAF | 78.4 | 94.6 | 97.5 | 58.2 | 83.0 | 89.1 |
| MSCOCO1k |
SAF | 78.0 | 95.9 | 98.5 | 62.2 | 89.5 | 95.4 |
| SGR | 77.3 | 96.0 | 98.6 | 62.1 | 89.6 | 95.3 |
| SGRAF | 79.2 | 96.5 | 98.6 | 63.5 | 90.2 | 95.8 |
| MSCOCO5k |
SAF | 55.5 | 83.8 | 91.8 | 40.1 | 69.7 | 80.4 |
| SGR | 57.3 | 83.2 | 90.6 | 40.5 | 69.6 | 80.3 |
| SGRAF | 58.8 | 84.8 | 92.1 | 41.6 | 70.9 | 81.5 |
## 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(''))
caption.extend([vocab(token) for token in tokens])
caption.append(vocab(''))
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": "", "1": "", "2": "", "3": "", "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", "796": "contents", "797": "convenient", "798": "gravy", "799": "disembarking", "800": "germany", "801": "grave", "802": "swamp", "803": "bracket", "804": "reserve", "805": "firehose", "806": "lense", "807": "panned", "808": "mid-flight", "809": "cashews", "810": "rectangle", "811": "cappuccino", "812": "hangings", "813": "skiiing", "814": "roundabout", "815": "runs", "816": "scrubber", "817": "emo", "818": "emu", "819": "gears", "820": "ems", "821": "dustbin", "822": "zookeeper", "823": "hairbrush", "824": "techniques", "825": "pastel", "826": "draws", "827": "smoggy", "828": "pasted", "829": "away", "830": "drawn", "831": "shields", "832": "handful", "833": "visited", "834": "kitchen", "835": "climate", "836": "tone", "837": "sissors", "838": "tons", "839": "tony", "840": "4-way", "841": "attacked", "842": "cylinder", "843": "tissue", "844": "cone", "845": "warthog", "846": "wheel", "847": "crockery", "848": "hang", "849": "hand", "850": "traffice", "851": "on", "852": "musical", "853": "trainer", "854": "heart-shaped", "855": "yamaha", "856": "amoco", "857": "lcd", "858": "hairless", "859": "cooler", "860": "sparse", "861": "night", "862": "born", "863": "confusing", "864": "congratulate", "865": "asking", "866": "adorable", "867": "peek", "868": "peel", "869": "architectural", "870": "peer", "871": "pees", "872": "peep", "873": "breaded", "874": "candlesticks", "875": "rendering", "876": "needles", "877": "sailboats", "878": "captive", "879": "different", "880": "coverings", "881": "o'clock", "882": "enthused", "883": "skinny", "884": "alertly", "885": "beds", "886": "gutting", "887": "concept", "888": "silverware", "889": "horseback", "890": "battle", "891": "layers", "892": "automobiles", "893": "dark-haired", "894": "turns", "895": "gun", "896": "gum", "897": "guy", "898": "cost", "899": "barbie", "900": "shares", "901": "peels", "902": "shared", "903": "teaches", "904": "teacher", "905": "sending", "906": "bangs", "907": "franklin", "908": "ancient", "909": "pillow", "910": "macintosh", "911": "extra", "912": "uphill", "913": "puffed", "914": "or", "915": "intently", "916": "countertops", "917": "skaters", "918": "ibm", "919": "decked", "920": "decker", "921": "parasailor", "922": "chip", "923": "sake", "924": "chin", "925": "chic", "926": "discussion", "927": "spreads", "928": "varnished", "929": "product", "930": "disgusted", "931": "produce", "932": "vases", "933": "noses", "934": "grandson", "935": "brussel", "936": "corona", "937": "serving", "938": "barnyard", "939": "ended", "940": "tablets", "941": "still", "942": "spacious", "943": "tiara", "944": "non", "945": "slacks", "946": "not", "947": "now", "948": "galloping", "949": "drop", "950": "unloaded", "951": "furnishing", "952": "stooping", "953": "soy", "954": "year", "955": "monitors", "956": "tangled", "957": "fe", "958": "blind", "959": "rink", "960": "rind", "961": "ring", "962": "caught", "963": "junkyard", "964": "kabob", "965": "professionals", "966": "launching", 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================================================
FILE: vocab/f30k_precomp_vocab.json
================================================
{"idx2word": {"0": "", "1": "", "2": "", "3": "", "4": "raining", "5": "writings", "6": "white-walled", "7": "yellow", "8": "four", "9": "prices", "10": "woods", "11": "hanging", "12": "marching", "13": "canes", "14": "advantage", "15": "snowing", "16": "powdery", "17": "sunlit", "18": "fingernails", "19": "shaving", "20": "shielding", "21": "button-down", "22": "propane", "23": "dell", "24": "leisurely", "25": "politician", "26": "screaming", "27": "revelers", "28": "wooded", "29": "prize", "30": "wooden", "31": "satchel", "32": "piling", "33": "stonehurst", "34": "crotch", "35": "ornamental", "36": "tired", "37": "miller", "38": "railing", "39": "tires", "40": "elegant", "41": "second", "42": "available", "43": "tether", "44": "blouse", "45": "admire", "46": "cooking", "47": "sooners", "48": "fingers", "49": "designing", "50": "crouch", "51": "reporter", "52": "jasmine", "53": "here", "54": "herd", "55": "china", "56": "cart", "57": "dorm", "58": "confronts", "59": "kids", "60": "elaborate", "61": "climbed", "62": "electricity", "63": "military", "64": "climber", "65": "missing", "66": "water-skiing", "67": "cheerfully", "68": "golden", "69": "projection", "70": "vaults", "71": "off-white", "72": "brought", "73": "pallet", "74": "unit", "75": "opponents", "76": "painters", "77": "browse", "78": "symphony", "79": "music", "80": "strike", "81": "playboy", "82": "females", "83": "relay", "84": "relax", "85": "brings", "86": "hurt", "87": "glass", "88": "tying", "89": "90", "90": "hole", "91": "hold", "92": "94", "93": "96", "94": "locked", "95": "40", "96": "blade", "97": "locker", "98": "leaped", "99": "cubicle", "100": "wheelbarrows", "101": "melons", "102": "wand", "103": "household", "104": "organized", "105": "leotards", "106": "caution", "107": "reviewing", "108": "hoe", "109": "travel", "110": "drying", "111": "damage", "112": "machine", "113": "how", "114": "hot", "115": "hop", "116": "gaming", "117": "over-sized", "118": "beauty", "119": "sippy", "120": "shores", "121": "wrong", "122": "types", "123": "colorfully", "124": "youths", "125": "cigarettes", "126": "crocks", "127": "keeps", "128": "wind", "129": "wine", "130": "blind", "131": "murals", "132": "playhouse", "133": "fir", "134": "lovingly", "135": "fit", "136": "polka-dotted", "137": "bringing", "138": "backpack", "139": "chiseling", "140": "hidden", "141": "copying", "142": "doghouse", "143": "colonial", "144": "slate", "145": "hula-hoop", "146": "slicing", "147": "effects", "148": "man-made", "149": "silver", "150": "blazer", "151": "multi-color", "152": "dumps", "153": "skills", "154": "arrow", "155": "volcano", "156": "development", "157": "financial", "158": "telescope", "159": "garment", "160": "spider", "161": "bowls", "162": "laboratory", "163": "message", "164": "whip", "165": "rv", "166": "ha", "167": "carying", "168": "re", "169": "guitarists", "170": "foundation", "171": "surveys", "172": "headlights", "173": "pumpkins", "174": "grapes", "175": "mallets", "176": "sheet", "177": "knit", "178": "speedo", "179": "exposing", "180": "shelves", "181": "atm", "182": "ups", "183": "atv", "184": "musicians", "185": "coeds", "186": "kingdom", "187": "bratwurst", "188": "speeds", "189": "shopper", "190": "elmo", "191": "playfully", "192": "wash", "193": "spinning", "194": "chevron", "195": "bitten", "196": "basketball", "197": "service", "198": "similarly", "199": "returns", "200": "needed", "201": "master", "202": "blossoms", "203": "legs", "204": "listen", "205": "cushions", "206": "lunges", "207": "prosthetic", "208": "lavender", "209": "tugs", "210": "crawl", "211": "plushie", "212": "trek", "213": "icicle", "214": "half-pipe", "215": "tree", "216": "likely", "217": "idly", "218": "nations", "219": "project", "220": "idle", "221": "feeling", "222": "dozes", "223": "girder", "224": "runner", "225": "boston", "226": "paddles", "227": "shrubs", "228": "paddled", "229": "potty", "230": "dozen", "231": "escalator", "232": "person", "233": "paramedics", 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"opening": 8265, "seesaws": 8266, "joy": 8267, "beverages": 8268, "huts": 8269, "uncompleted": 8270, "cake": 8448, "job": 8272, "joe": 8273, "jog": 8274, "stucco": 8275, "": 2, "goal": 7562, "grain": 8276, "tactical": 8277, "canopy": 8278, "safely": 8279, "grounds": 8280, "wall": 8282, "shoving": 8283, "walk": 8284, "packaging": 8285, "confronts": 58, "trays": 8287, "motorbike": 8288, "hindu": 450, "reindeer": 8290, "offers": 7061, "butchers": 8291, "mike": 8292, "penny": 2125, "verizon": 8294, "painted": 8295, "gnawing": 8454, "graveled": 8297, "walkways": 8298, "painter": 7465, "kerchief": 8300, "eyelashes": 8301, "abandoned": 8302, "corset": 8303, "vanilla": 8304, "choices": 8305, "will": 8306, "hovering": 3265, "wild": 8308, "wile": 8309, "happened": 8310, "attracts": 8311, "layer": 8312, "ringing": 8313, "rock-climbing": 7530, "pews": 7547, "bystanders": 8317, "lonely": 7065, "gothic": 8318, "frying": 6672, "perhaps": 8319, "vintage": 8320, "headlight": 1752, "cross": 8321, "member": 7592, "gets": 8322, "tomatoes": 8323, "difficult": 7479, "cellphones": 8325, "elbows": 8326, "coached": 8327, "recline": 8328, "diploma": 8329, "coaches": 8330, "adirondack": 8331, "student": 8332, "pedal": 8333, "whale": 8334, "collar": 7676, "warming": 8336, "celtic": 8337, "gutter": 2925, "wheelbarrows": 100, "banging": 8339, "fighting": 8340, "spectator": 8341, "english": 7717, "bandanna": 7730, "rocker": 7734, "rocket": 8344, "camel": 7864, "identically": 3373, "cakes": 7231, "cries": 8347, "batteries": 8348, "fishing": 8349, "waterskiing": 8463, "happiness": 8352, "toilets": 8353, "rapid": 8354, "console": 8355, "bell": 8356, "smith": 5135, "discuss": 8358, "arresting": 8359, "concourse": 8360, "book": 8465, "attractive": 8362, "jars": 8363, "ski": 8364, "identical": 8365, "talkie": 8367, "masquerade": 8368, "protesters": 8369, "know": 8370, "facial": 8371, "raincoats": 5442, "press": 8372, "name": 7072, "check-out": 8373, "pamphlet": 8374, "miami": 8375, "loses": 8376, "clutch": 8377, "wonders": 8378, "because": 8379, "shabby": 8380, "elevators": 8381, "mattresses": 8382, "scared": 8383, "church": 8384, "searching": 8385, "platforms": 8386, "twirling": 8387, "racking": 8388, "leaf": 8390, "lead": 8391, "leak": 8392, "lean": 8393, "sandpit": 4783, "detergent": 8395, "leap": 8396, "campfire": 8397, "odeon": 8398, "earrings": 8399, "leader": 8400, "ripped": 8401, "dangling": 8402, "mitt": 8403, "benefit": 8404, "pasta": 8406, "fluorescent": 8407, "paste": 8409, "throne": 8410, "throng": 8411, "incline": 8074, "getting": 8077, "column": 8079, "haired": 6546, "biscuit": 8082, "shipping": 8415, "carries": 8416, "carrier": 8098, "sweat": 8418, "owl": 8419, "own": 8420, "polished": 8421, "gymnastic": 8123, "travelers": 8423, "possessions": 7230, "headphone": 8425, "soaks": 7615, "polishes": 8145, "brush": 8428, "yankee": 8429, "rowboat": 8430, "van": 8431, "artifacts": 8432, "spiral": 8433, "smoothing": 8435, "powdered": 8436, "upwards": 8476, "vat": 8438, "sweatband": 8440, "bratwurst": 187, "squeeze": 8442, "made": 8443, "hitter": 8444, "protesting": 8445, "record": 8446, "below": 8447, "infants": 8271, "demonstrate": 8449, "broadly": 6938, "stirring": 8451, "skydiver": 8452, "maroon": 8296, "boardwalk": 8455, "trotting": 8456, "plaques": 6385, "mutual": 8458, "'ll": 8459, "incredible": 8460, "guitarist": 8461, "boot": 8462, "cave": 3515, "illinois": 8351, "curtained": 8464, "other": 8361, "boom": 8466, "sick": 3589, "wheelers": 2800, "sloping": 8468, "album": 8469, "junk": 8470, "kinds": 8471, "mulch": 8472, "casually-dressed": 8473, "pumps": 8475, "cliff": 8437, "dominoes": 8477, "laundromat": 8479, "sash": 8480}, "idx": 8481}
================================================
FILE: vocab.py
================================================
"""Vocabulary wrapper"""
import nltk
from collections import Counter
import argparse
import os
import json
annotations = {
'coco_precomp': ['train_caps.txt', 'dev_caps.txt'],
'f30k_precomp': ['train_caps.txt', 'dev_caps.txt'],
}
class Vocabulary(object):
"""Simple vocabulary wrapper."""
def __init__(self):
self.word2idx = {}
self.idx2word = {}
self.idx = 0
def add_word(self, word):
if word not in self.word2idx:
self.word2idx[word] = self.idx
self.idx2word[self.idx] = word
self.idx += 1
def __call__(self, word):
if word not in self.word2idx:
return self.word2idx['']
return self.word2idx[word]
def __len__(self):
return len(self.word2idx)
def serialize_vocab(vocab, dest):
d = {}
d['word2idx'] = vocab.word2idx
d['idx2word'] = vocab.idx2word
d['idx'] = vocab.idx
with open(dest, "w") as f:
json.dump(d, f)
def deserialize_vocab(src):
with open(src) as f:
d = json.load(f)
vocab = Vocabulary()
vocab.word2idx = d['word2idx']
vocab.idx2word = d['idx2word']
vocab.idx = d['idx']
return vocab
def from_txt(txt):
captions = []
with open(txt, 'rb') as f:
for line in f:
captions.append(line.strip())
return captions
def build_vocab(data_path, data_name, caption_file, threshold):
"""Build a simple vocabulary wrapper."""
counter = Counter()
for path in caption_file[data_name]:
full_path = os.path.join(os.path.join(data_path, data_name), path)
captions = from_txt(full_path)
for i, caption in enumerate(captions):
tokens = nltk.tokenize.word_tokenize(
caption.lower().decode('utf-8'))
counter.update(tokens)
if i % 1000 == 0:
print("[%d/%d] tokenized the captions." % (i, len(captions)))
# Discard if the occurrence of the word is less than min_word_cnt.
words = [word for word, cnt in counter.items() if cnt >= threshold]
# Create a vocab wrapper and add some special tokens.
vocab = Vocabulary()
vocab.add_word('')
vocab.add_word('')
vocab.add_word('')
vocab.add_word('')
# Add words to the vocabulary.
for i, word in enumerate(words):
vocab.add_word(word)
return vocab
def main(data_path, data_name):
vocab = build_vocab(data_path, data_name, caption_file=annotations, threshold=4)
serialize_vocab(vocab, './vocab/%s_vocab.json' % data_name)
print("Saved vocabulary file to ", './vocab/%s_vocab.json' % data_name)
if __name__ == '__main__':
parser = argparse.ArgumentParser()
parser.add_argument('--data_path', default='data')
parser.add_argument('--data_name', default='f30k_precomp',
help='{coco,f30k}_precomp')
opt = parser.parse_args()
main(opt.data_path, opt.data_name)