import torch
import torch.utils.data as data
import torchvision.transforms as transforms
import os
import nltk
from PIL import Image
from pycocotools.coco import COCO
import numpy as np
import json as jsonmod
def get_paths(path, name='coco', use_restval=False):
"""
Returns paths to images and annotations for the given datasets. For MSCOCO
indices are also returned to control the data split being used.
The indices are extracted from the Karpathy et al. splits using this
snippet:
>>> import json
>>> dataset=json.load(open('dataset_coco.json','r'))
>>> A=[]
>>> for i in range(len(D['images'])):
... if D['images'][i]['split'] == 'val':
... A+=D['images'][i]['sentids'][:5]
...
:param name: Dataset names
:param use_restval: If True, the the `restval` data is included in train.
"""
roots = {}
ids = {}
if 'coco' == name:
imgdir = os.path.join(path, 'images')
capdir = os.path.join(path, 'annotations')
roots['train'] = {
'img': os.path.join(imgdir, 'train2014'),
'cap': os.path.join(capdir, 'captions_train2014.json')
}
roots['val'] = {
'img': os.path.join(imgdir, 'val2014'),
'cap': os.path.join(capdir, 'captions_val2014.json')
}
roots['test'] = {
'img': os.path.join(imgdir, 'val2014'),
'cap': os.path.join(capdir, 'captions_val2014.json')
}
roots['trainrestval'] = {
'img': (roots['train']['img'], roots['val']['img']),
'cap': (roots['train']['cap'], roots['val']['cap'])
}
ids['train'] = np.load(os.path.join(capdir, 'coco_train_ids.npy'))
ids['val'] = np.load(os.path.join(capdir, 'coco_dev_ids.npy'))[:5000]
ids['test'] = np.load(os.path.join(capdir, 'coco_test_ids.npy'))
ids['trainrestval'] = (
ids['train'],
np.load(os.path.join(capdir, 'coco_restval_ids.npy')))
if use_restval:
roots['train'] = roots['trainrestval']
ids['train'] = ids['trainrestval']
elif 'f8k' == name:
imgdir = os.path.join(path, 'images')
cap = os.path.join(path, 'dataset_flickr8k.json')
roots['train'] = {'img': imgdir, 'cap': cap}
roots['val'] = {'img': imgdir, 'cap': cap}
roots['test'] = {'img': imgdir, 'cap': cap}
ids = {'train': None, 'val': None, 'test': None}
elif 'f30k' == name:
imgdir = os.path.join(path, 'images')
cap = os.path.join(path, 'dataset_flickr30k.json')
roots['train'] = {'img': imgdir, 'cap': cap}
roots['val'] = {'img': imgdir, 'cap': cap}
roots['test'] = {'img': imgdir, 'cap': cap}
ids = {'train': None, 'val': None, 'test': None}
return roots, ids
class CocoDataset(data.Dataset):
"""COCO Custom Dataset compatible with torch.utils.data.DataLoader."""
def __init__(self, root, json, vocab, transform=None, ids=None):
"""
Args:
root: image directory.
json: coco annotation file path.
vocab: vocabulary wrapper.
transform: transformer for image.
"""
self.root = root
# when using `restval`, two json files are needed
if isinstance(json, tuple):
self.coco = (COCO(json[0]), COCO(json[1]))
else:
self.coco = (COCO(json),)
self.root = (root,)
# if ids provided by get_paths, use split-specific ids
if ids is None:
self.ids = list(self.coco.anns.keys())
else:
self.ids = ids
# if `restval` data is to be used, record the break point for ids
if isinstance(self.ids, tuple):
self.bp = len(self.ids[0])
self.ids = list(self.ids[0]) + list(self.ids[1])
else:
self.bp = len(self.ids)
self.vocab = vocab
self.transform = transform
def __getitem__(self, index):
"""This function returns a tuple that is further passed to collate_fn
"""
vocab = self.vocab
root, caption, img_id, path, image = self.get_raw_item(index)
if self.transform is not None:
image = self.transform(image)
# Convert caption (string) to word ids.
tokens = nltk.tokenize.word_tokenize(
str(caption).lower().decode('utf-8'))
caption = []
caption.append(vocab('<start>'))
caption.extend([vocab(token) for token in tokens])
caption.append(vocab('<end>'))
target = torch.Tensor(caption)
return image, target, index, img_id
def get_raw_item(self, index):
if index < self.bp:
coco = self.coco[0]
root = self.root[0]
else:
coco = self.coco[1]
root = self.root[1]
ann_id = self.ids[index]
caption = coco.anns[ann_id]['caption']
img_id = coco.anns[ann_id]['image_id']
path = coco.loadImgs(img_id)[0]['file_name']
image = Image.open(os.path.join(root, path)).convert('RGB')
return root, caption, img_id, path, image
def __len__(self):
return len(self.ids)
class FlickrDataset(data.Dataset):
"""
Dataset loader for Flickr30k and Flickr8k full datasets.
"""
def __init__(self, root, json, split, vocab, transform=None):
self.root = root
self.vocab = vocab
self.split = split
self.transform = transform
self.dataset = jsonmod.load(open(json, 'r'))['images']
self.ids = []
for i, d in enumerate(self.dataset):
if d['split'] == split:
self.ids += [(i, x) for x in range(len(d['sentences']))]
def __getitem__(self, index):
"""This function returns a tuple that is further passed to collate_fn
"""
vocab = self.vocab
root = self.root
ann_id = self.ids[index]
img_id = ann_id[0]
caption = self.dataset[img_id]['sentences'][ann_id[1]]['raw']
path = self.dataset[img_id]['filename']
image = Image.open(os.path.join(root, path)).convert('RGB')
if self.transform is not None:
image = self.transform(image)
# Convert caption (string) to word ids.
tokens = nltk.tokenize.word_tokenize(
str(caption).lower().decode('utf-8'))
caption = []
caption.append(vocab('<start>'))
caption.extend([vocab(token) for token in tokens])
caption.append(vocab('<end>'))
target = torch.Tensor(caption)
return image, target, index, img_id
def __len__(self):
return len(self.ids)
class PrecompDataset(data.Dataset):
"""
Load precomputed captions and image features
Possible options: f8k, f30k, coco, 10crop
"""
def __init__(self, data_path, data_split, vocab):
self.vocab = vocab
loc = data_path + '/'
# Captions
self.captions = []
with open(loc+'%s_caps.txt' % data_split, 'rb') as f:
for line in f:
self.captions.append(line.strip())
# 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, 10crop doesn't
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('<start>'))
caption.extend([vocab(token) for token in tokens])
caption.append(vocab('<end>'))
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) tuples.
Args:
data: list of (image, caption) tuple.
- image: torch tensor of shape (3, 256, 256).
- caption: torch tensor of shape (?); variable length.
Returns:
images: torch tensor of shape (batch_size, 3, 256, 256).
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 3D tensor to 4D 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_loader_single(data_name, split, root, json, vocab, transform,
batch_size=100, shuffle=True,
num_workers=2, ids=None, collate_fn=collate_fn):
"""Returns torch.utils.data.DataLoader for custom coco dataset."""
if 'coco' in data_name:
# COCO custom dataset
dataset = CocoDataset(root=root,
json=json,
vocab=vocab,
transform=transform, ids=ids)
elif 'f8k' in data_name or 'f30k' in data_name:
dataset = FlickrDataset(root=root,
split=split,
json=json,
vocab=vocab,
transform=transform)
# Data loader
data_loader = torch.utils.data.DataLoader(dataset=dataset,
batch_size=batch_size,
shuffle=shuffle,
pin_memory=True,
num_workers=num_workers,
collate_fn=collate_fn)
return data_loader
def get_precomp_loader(data_path, data_split, vocab, opt, batch_size=100,
shuffle=True, num_workers=2):
"""Returns torch.utils.data.DataLoader for custom coco dataset."""
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_transform(data_name, split_name, opt):
normalizer = transforms.Normalize(mean=[0.485, 0.456, 0.406],
std=[0.229, 0.224, 0.225])
t_list = []
if split_name == 'train':
t_list = [transforms.RandomResizedCrop(opt.crop_size),
transforms.RandomHorizontalFlip()]
elif split_name == 'val':
t_list = [transforms.Resize(256), transforms.CenterCrop(224)]
elif split_name == 'test':
t_list = [transforms.Resize(256), transforms.CenterCrop(224)]
t_end = [transforms.ToTensor(), normalizer]
transform = transforms.Compose(t_list + t_end)
return transform
def get_loaders(data_name, vocab, crop_size, batch_size, workers, opt):
dpath = os.path.join(opt.data_path, data_name)
if opt.data_name.endswith('_precomp'):
train_loader = get_precomp_loader(dpath, 'train', vocab, opt,
batch_size, True, workers)
val_loader = get_precomp_loader(dpath, 'dev', vocab, opt,
batch_size, False, workers)
else:
# Build Dataset Loader
roots, ids = get_paths(dpath, data_name, opt.use_restval)
transform = get_transform(data_name, 'train', opt)
train_loader = get_loader_single(opt.data_name, 'train',
roots['train']['img'],
roots['train']['cap'],
vocab, transform, ids=ids['train'],
batch_size=batch_size, shuffle=True,
num_workers=workers,
collate_fn=collate_fn)
transform = get_transform(data_name, 'val', opt)
val_loader = get_loader_single(opt.data_name, 'val',
roots['val']['img'],
roots['val']['cap'],
vocab, transform, ids=ids['val'],
batch_size=batch_size, shuffle=False,
num_workers=workers,
collate_fn=collate_fn)
return train_loader, val_loader
def get_test_loader(split_name, data_name, vocab, crop_size, batch_size,
workers, opt):
dpath = os.path.join(opt.data_path, data_name)
if opt.data_name.endswith('_precomp'):
test_loader = get_precomp_loader(dpath, split_name, vocab, opt,
batch_size, False, workers)
else:
# Build Dataset Loader
roots, ids = get_paths(dpath, data_name, opt.use_restval)
transform = get_transform(data_name, split_name, opt)
test_loader = get_loader_single(opt.data_name, split_name,
roots[split_name]['img'],
roots[split_name]['cap'],
vocab, transform, ids=ids[split_name],
batch_size=batch_size, shuffle=False,
num_workers=workers,
collate_fn=collate_fn)
return test_loader
