import nltk import os import torch import torch.utils.data as data from vocabulary import Vocabulary from PIL import Image from pycocotools.coco import COCO import numpy as np from tqdm import tqdm import random import json def get_loader(transform, mode='train', batch_size=1, vocab_threshold=None, vocab_file='/models/vocab.pkl', start_word="", end_word="", unk_word="", vocab_from_file=True, num_workers=0, cocoapi_loc='/opt'): """Returns the data loader. Args: transform: Image transform. mode: One of 'train' or 'test'. batch_size: Batch size (if in testing mode, must have batch_size=1). vocab_threshold: Minimum word count threshold. vocab_file: File containing the vocabulary. start_word: Special word denoting sentence start. end_word: Special word denoting sentence end. unk_word: Special word denoting unknown words. vocab_from_file: If False, create vocab from scratch & override any existing vocab_file. If True, load vocab from from existing vocab_file, if it exists. num_workers: Number of subprocesses to use for data loading cocoapi_loc: The location of the folder containing the COCO API: https://github.com/cocodataset/cocoapi """ assert mode in ['train', 'test'], "mode must be one of 'train' or 'test'." if vocab_from_file==False: assert mode=='train', "To generate vocab from captions file, must be in training mode (mode='train')." # Based on mode (train, val, test), obtain img_folder and annotations_file. if mode == 'train': if vocab_from_file==True: assert os.path.exists(vocab_file), "vocab_file does not exist. Change vocab_from_file to False to create vocab_file." img_folder = os.path.join(cocoapi_loc, 'cocoapi/images/train2014/') annotations_file = os.path.join(cocoapi_loc, 'cocoapi/annotations/captions_train2014.json') if mode == 'test': assert batch_size==1, "Please change batch_size to 1 if testing your model." assert os.path.exists(vocab_file), "Must first generate vocab.pkl from training data." assert vocab_from_file==True, "Change vocab_from_file to True." img_folder = '/content/opt/cocoapi/images/test2014' annotations_file = '/content/gdrive/MyDrive/image_info_test2014.json' # COCO caption dataset. dataset = CoCoDataset(transform=transform, mode=mode, batch_size=batch_size, vocab_threshold=vocab_threshold, vocab_file=vocab_file, start_word=start_word, end_word=end_word, unk_word=unk_word, annotations_file=annotations_file, vocab_from_file=vocab_from_file, img_folder=img_folder) if mode == 'train': # Randomly sample a caption length, and sample indices with that length. indices = dataset.get_train_indices() # Create and assign a batch sampler to retrieve a batch with the sampled indices. initial_sampler = data.sampler.SubsetRandomSampler(indices=indices) # data loader for COCO dataset. data_loader = data.DataLoader(dataset=dataset, num_workers=num_workers, batch_sampler=data.sampler.BatchSampler(sampler=initial_sampler, batch_size=dataset.batch_size, drop_last=False)) else: data_loader = data.DataLoader(dataset=dataset, batch_size=dataset.batch_size, shuffle=True, num_workers=num_workers) return data_loader class CoCoDataset(data.Dataset): def __init__(self, transform, mode, batch_size, vocab_threshold, vocab_file, start_word, end_word, unk_word, annotations_file, vocab_from_file, img_folder): self.transform = transform self.mode = mode self.batch_size = batch_size self.vocab = Vocabulary(vocab_threshold, vocab_file, start_word, end_word, unk_word, annotations_file, vocab_from_file) self.img_folder = img_folder if self.mode == 'train': self.coco = COCO(annotations_file) self.ids = list(self.coco.anns.keys()) print('Obtaining caption lengths...') all_tokens = [nltk.tokenize.word_tokenize(str(self.coco.anns[self.ids[index]]['caption']).lower()) for index in tqdm(np.arange(len(self.ids)))] self.caption_lengths = [len(token) for token in all_tokens] else: test_info = json.loads(open(annotations_file).read()) self.paths = [item['file_name'] for item in test_info['images']] def __getitem__(self, index): # obtain image and caption if in training mode if self.mode == 'train': ann_id = self.ids[index] caption = self.coco.anns[ann_id]['caption'] img_id = self.coco.anns[ann_id]['image_id'] path = self.coco.loadImgs(img_id)[0]['file_name'] # Convert image to tensor and pre-process using transform image = Image.open(os.path.join(self.img_folder, path)).convert('RGB') image = self.transform(image) # Convert caption to tensor of word ids. tokens = nltk.tokenize.word_tokenize(str(caption).lower()) caption = [] caption.append(self.vocab(self.vocab.start_word)) caption.extend([self.vocab(token) for token in tokens]) caption.append(self.vocab(self.vocab.end_word)) caption = torch.Tensor(caption).long() # return pre-processed image and caption tensors return image, caption # obtain image if in test mode else: path = self.paths[index] # Convert image to tensor and pre-process using transform PIL_image = Image.open(os.path.join(self.img_folder, path)).convert('RGB') orig_image = np.array(PIL_image) image = self.transform(PIL_image) # return original image and pre-processed image tensor return orig_image, image def get_train_indices(self): sel_length = np.random.choice(self.caption_lengths) all_indices = np.where([self.caption_lengths[i] == sel_length for i in np.arange(len(self.caption_lengths))])[0] indices = list(np.random.choice(all_indices, size=self.batch_size)) return indices def __len__(self): if self.mode == 'train': return len(self.ids) else: return len(self.paths)