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import os # when loading file paths
import pandas as pd # for lookup in annotation file
import spacy # for tokenizer
import torch
from torch.nn.utils.rnn import pad_sequence # pad batch
from torch.utils.data import DataLoader, Dataset
from PIL import Image # Load img
import torchvision.transforms as transforms
import json
# Download with: python -m spacy download en
spacy_eng = spacy.load("en_core_web_sm")
class Vocabulary:
def __init__(self, freq_threshold):
self.itos = {0: "<PAD>", 1: "<SOS>", 2: "<EOS>", 3: "<UNK>"}
self.stoi = {"<PAD>": 0, "<SOS>": 1, "<EOS>": 2, "<UNK>": 3}
self.freq_threshold = freq_threshold
def __len__(self):
return len(self.stoi)
@staticmethod
def tokenizer_eng(text):
return [tok.text.lower() for tok in spacy_eng.tokenizer(text)]
def build_vocabulary(self, sentence_list):
frequencies = {}
idx = 4
for sentence in sentence_list:
for word in self.tokenizer_eng(sentence):
if word not in frequencies:
frequencies[word] = 1
else:
frequencies[word] += 1
if frequencies[word] == self.freq_threshold:
self.stoi[word] = idx
self.itos[idx] = word
idx += 1
def numericalize(self, text):
tokenized_text = self.tokenizer_eng(text)
return [
self.stoi[token] if token in self.stoi else self.stoi["<UNK>"]
for token in tokenized_text
]
class FlickrDataset(Dataset):
def __init__(self, root_dir, captions_file, transform=None, freq_threshold=5):
self.root_dir = root_dir
self.df = pd.read_csv(captions_file)
self.transform = transform
# Get img, caption columns
self.imgs = self.df["image_name"]
self.captions = self.df["comment"]
# Initialize vocabulary and build vocab
self.vocab = Vocabulary(freq_threshold)
self.vocab.build_vocabulary(self.captions.tolist())
def __len__(self):
return len(self.df)
def __getitem__(self, index):
caption = self.captions[index]
img_id = self.imgs[index]
img = Image.open(os.path.join(self.root_dir, img_id)).convert("RGB")
if self.transform is not None:
img = self.transform(img)
numericalized_caption = [self.vocab.stoi["<SOS>"]]
numericalized_caption += self.vocab.numericalize(caption)
numericalized_caption.append(self.vocab.stoi["<EOS>"])
return img, torch.tensor(numericalized_caption)
class MyCollate:
def __init__(self, pad_idx):
self.pad_idx = pad_idx
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=False, padding_value=self.pad_idx)
return imgs, targets
def get_loader(
root_folder,
annotation_file,
transform,
batch_size=64,
num_workers=2,
shuffle=True,
pin_memory=True,
):
dataset = FlickrDataset(root_folder, annotation_file, transform=transform)
pad_idx = dataset.vocab.stoi["<PAD>"]
loader = DataLoader(
dataset=dataset,
batch_size=batch_size,
num_workers=num_workers,
shuffle=shuffle,
pin_memory=pin_memory,
collate_fn=MyCollate(pad_idx=pad_idx),
)
return loader, dataset
if __name__ == "__main__":
transform = transforms.Compose(
[transforms.Resize((224, 224)), transforms.ToTensor(),]
)
loader, dataset = get_loader(
"/home/koushik/vscode/Projects/pytorch/img2text_v1/flickr30k/flickr30k_images/", "/home/koushik/vscode/Projects/pytorch/img2text_v1/flickr30k/results.csv", transform=transform
)
for idx, (imgs, captions) in enumerate(loader):
print(imgs.shape)
print(captions.shape)
print(len(dataset.vocab))
test = {"itos":dataset.vocab.itos, "stoi": dataset.vocab.stoi}
json.dump(test, open('test.json', 'w'))
break
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