# Image Captioning¶

## Encoder Decoder Network¶

Last tutorial we introduced the encoder-decoder structure for translation. Rather than one model, we have two:

• Encoder that maps the input to some conceptual representation rather than individual words.
• Decoder that maps the representation into output words.

## Image Encoder, Text Decoder¶

Since we have a separate encoder and decoder, we could also have an encoder that encodes images, and a decoder that decodes text, giving us an image captioning model.

## Encoder: Convolutional Neural Network¶

Wheras recurrent neural networks' repeating structure makes it a natural fit for sequential sentence data, convolutional neural networks are a natural fit for images.

Convolutions look for visual traits in local patches in images, such as shapes and colors.

# Exercise: Image Captioning¶

Today we'll implement an image captioning model to describe images.

## Requirements¶

In [1]:
from collections import Counter, defaultdict
from gensim.models import Word2Vec
from IPython import display
from nltk import word_tokenize
from nltk.translate.bleu_score import sentence_bleu
from PIL import Image
from torch import nn
from torch.autograd import Variable
from torchvision import models, transforms

import json
import matplotlib
import numpy as np
import matplotlib.pyplot as plt
import random
import torch
import torch.nn.functional as F


Here we will also define a constant to decide whether to use the GPU (with CUDA specifically) or the CPU. If you don't have a GPU, set this to False. Later when we create tensors, this variable will be used to decide whether we keep them on CPU or move them to GPU.

In [27]:
use_cuda = True


The dataset, MSCOCO, contains 5 English captions per image.

We will be representing each word in a language as a one-hot vector, or giant vector of zeros except for a single one (at the index of the word). Compared to the dozens of characters that might exist in a language, there are many many more words, so the encoding vector is much larger.

There's a bit of pre-processing code below, that loads the data and converts it into one-hot vectors.

In [37]:
# Load annotations file for the training images.
train_ids = [entry['id'] for entry in mscoco_train['images']][:1000]
train_id_to_file = {entry['id']: 'data/train2014/' + entry['file_name'] for entry in mscoco_train['images']}

# Extract out the captions for the training images
train_id_set = set(train_ids)
train_id_to_captions = defaultdict(list)
for entry in mscoco_train['annotations']:
if entry['image_id'] in train_id_set:
train_id_to_captions[entry['image_id']].append(entry['caption'])

# Load annotations file for the validation images.
val_ids = [entry['id'] for entry in mscoco_val['images']]
val_id_to_file = {entry['id']: 'data/val2014/' + entry['file_name'] for entry in mscoco_val['images']}

# Extract out the captions for the validation images
val_id_set = set(val_ids)
val_id_to_captions = defaultdict(list)
for entry in mscoco_val['annotations']:
if entry['image_id'] in val_id_set:
val_id_to_captions[entry['image_id']].append(entry['caption'])

# Load annotations file for the testing images
test_ids = [entry['id'] for entry in mscoco_test['images']]
test_id_to_file = {entry['id']: 'data/val2014/' + entry['file_name'] for entry in mscoco_test['images']}

In [38]:
sentences = [sentence for caption_set in train_id_to_captions.values() for sentence in caption_set]

# Lower-case the sentence, tokenize them and add <SOS> and <EOS> tokens
sentences = [["<SOS>"] + word_tokenize(sentence.lower()) + ["<EOS>"] for sentence in sentences]

# Create the vocabulary. Note that we add an <UNK> token to represent words not in our vocabulary.
vocabularySize = 1000
word_counts = Counter([word for sentence in sentences for word in sentence])
vocabulary = ["<UNK>"] + [e[0] for e in word_counts.most_common(vocabularySize-1)]
word2index = {word:index for index,word in enumerate(vocabulary)}
one_hot_embeddings = np.eye(vocabularySize)

# Define the max sequence length to be the longest sentence in the training data.
maxSequenceLength = max([len(sentence) for sentence in sentences])

def preprocess_numberize(sentence):
"""
Given a sentence, in the form of a string, this function will preprocess it
into list of numbers (denoting the index into the vocabulary).
"""
tokenized = word_tokenize(sentence.lower())

# Add the <SOS>/<EOS> tokens and numberize (all unknown words are represented as <UNK>).
tokenized = ["<SOS>"] + tokenized + ["<EOS>"]
numberized = [word2index.get(word, 0) for word in tokenized]

return numberized

def preprocess_one_hot(sentence):
"""
Given a sentence, in the form of a string, this function will preprocess it
into a numpy array of one-hot vectors.
"""
numberized = preprocess_numberize(sentence)

# Represent each word as it's one-hot embedding
one_hot_embedded = one_hot_embeddings[numberized]

return one_hot_embedded

In [15]:
# Define a global transformer to appropriately scale images and subsequently convert them to a Tensor.
img_size = 224
transforms.Resize(img_size),
transforms.CenterCrop(img_size),
transforms.ToTensor(),
])
"""
Simple function to load and preprocess the image.

1. Open the image.
2. Scale/crop it and convert it to a float tensor.
3. Convert it to a variable (all inputs to PyTorch models must be variables).
4. Add another dimension to the start of the Tensor (b/c VGG expects a batch).
5. Move the variable onto the GPU.
"""
image = Image.open(filename).convert('RGB')
image_var = Variable(image_tensor, volatile=volatile).unsqueeze(0)
return image_var.cuda()


### Exploring Data¶

We can explore the data a bit, to get a sense of what we're working with.

In [39]:
display.display(Image.open(train_id_to_file[train_ids[0]]))

for caption in train_id_to_captions[train_ids[0]]:
print(caption)