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import torch
import pandas as pd
from torch.nn.utils.rnn import pad_sequence
from PIL import Image
import spacy
import os
from torch.utils.data import Dataset
spacy_eng = spacy.load('en_core_web_sm')
class Vocabulary:
def __init__(self, freq_threshold=5):
self.freq_threshold = freq_threshold
self.itos = {0: "<PAD>", 1: "<SOS>", 2: "<EOS>", 3: "<UNK>"}
self.stoi = {v: k for k, v in self.itos.items()}
def __len__(self):
return len(self.itos)
@staticmethod
def tokenize(text):
return [token.text.lower() for token in spacy_eng.tokenizer(text)]
def build_vocab(self, sent_list):
freqs = {}
idx = 4
for sent in sent_list:
for word in self.tokenize(sent):
if word not in freqs:
freqs[word] = 1
else:
freqs[word] += 1
if freqs[word] == self.freq_threshold:
self.itos[idx] = word
self.stoi[word] = idx
idx += 1
def numericalize(self, sents):
tokens = self.tokenize(sents)
return [self.stoi[token] if token in self.stoi else self.stoi["<UNK>"]
for token in tokens]
class FlickrDataset(Dataset):
def __init__(self, root_dir, csv_file, transforms=None, freq_threshold=5):
self.root_dir = root_dir
self.df = pd.read_csv(csv_file)
self.transforms = transforms
self.img_pts = self.df.iloc[:, 0]
self.caps = self.df.iloc[:, 1]
self.vocab = Vocabulary(freq_threshold)
self.vocab.build_vocab(self.caps.tolist())
def __len__(self):
return len(self.df)
def __getitem__(self, idx):
captions = self.caps[idx]
img_pt = self.img_pts[idx]
img = Image.open(os.path.join(self.root_dir, img_pt)).convert('RGB')
if self.transforms is not None:
img = self.transforms(img)
encoded_cap = []
encoded_cap += [self.vocab.stoi["<SOS>"]] # stoi string to index
encoded_cap += self.vocab.numericalize(captions)
encoded_cap += [self.vocab.stoi["<EOS>"]]
encoded_cap = torch.LongTensor(encoded_cap)
return img, encoded_cap
class CapsCollate:
def __init__(self, pad_idx, batch_first=False):
self.pad_idx = pad_idx
self.batch_first = batch_first
def __call__(self, batch):
imgs = [item[0].unsqueeze(0) for item in batch]
imgs = torch.cat(imgs, dim=0)
targets = [item[1] for item in batch]
targets = pad_sequence(targets, batch_first=self.batch_first, padding_value=self.pad_idx)
return imgs, targets
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