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data.py
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from faulthandler import is_enabled
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
from transformers import BertTokenizer
from pytorch_lightning import LightningDataModule
from torch.utils.data.distributed import DistributedSampler
import six
def get_pretrained_tokenizer(from_pretrained):
if torch.distributed.is_initialized():
if torch.distributed.get_rank() == 0:
BertTokenizer.from_pretrained(
from_pretrained, do_lower_case="uncased" in from_pretrained
)
torch.distributed.barrier()
return BertTokenizer.from_pretrained(
from_pretrained, do_lower_case="uncased" in from_pretrained
)
def convert_to_unicode(text):
"""Converts `text` to Unicode (if it's not already), assuming utf-8 input."""
if six.PY3:
if isinstance(text, str):
return text
elif isinstance(text, bytes):
return text.decode("utf-8", "ignore")
else:
raise ValueError("Unsupported string type: %s" % (type(text)))
elif six.PY2:
if isinstance(text, str):
return text.decode("utf-8", "ignore")
elif isinstance(text, unicode):
return text
else:
raise ValueError("Unsupported string type: %s" % (type(text)))
else:
raise ValueError("Not running on Python2 or Python 3?")
def convert_to_feature(raw, seq_length, tokenizer):
line = convert_to_unicode(raw)
tokens_a = tokenizer.tokenize(line)
#print(tokens_a)
#assert 1==0
# Modifies `tokens_a` in place so that the total
# length is less than the specified length.
# Account for [CLS] and [SEP] with "- 2"
if len(tokens_a) > seq_length - 2:
tokens_a = tokens_a[0:(seq_length - 2)]
tokens = []
input_type_ids = []
tokens.append("[CLS]")
input_type_ids.append(0)
for token in tokens_a:
tokens.append(token)
input_type_ids.append(0)
tokens.append("[SEP]")
input_type_ids.append(0)
input_ids = tokenizer.convert_tokens_to_ids(tokens)
# The mask has 1 for real tokens and 0 for padding tokens. Only real
# tokens are attended to.
input_mask = [1] * len(input_ids)
# Zero-pad up to the sequence length.
while len(input_ids) < seq_length:
input_ids.append(0)
input_mask.append(0)
input_type_ids.append(0)
assert len(input_ids) == seq_length
assert len(input_mask) == seq_length
assert len(input_type_ids) == seq_length
return tokens, input_ids, input_mask, input_type_ids
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'))[:25000]
#ids['test'] = np.load(os.path.join(capdir, 'coco_test_ids.npy'))[20000:25000]
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, max_len, tokenizer, transform=None, ids=None):
"""
Args:
root: image directory.
json: coco annotation file path.
vocab: vocabulary wrapper.
transform: transformer for image.
"""
#
self.max_len = max_len
self.tokenizer = get_pretrained_tokenizer(tokenizer)
#
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.
is_bert = True
if is_bert:
tokens, input_ids, input_mask, input_type_ids = convert_to_feature(caption, self.max_len, self.tokenizer)
input_ids = torch.tensor(input_ids, dtype=torch.long)
input_mask = torch.tensor(input_mask, dtype=torch.long)
input_type_ids = torch.tensor(input_type_ids, dtype=torch.long)
return image, input_ids, index, img_id, input_mask, input_type_ids
else:
tokens = nltk.tokenize.word_tokenize(
str(caption).lower())
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
# 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, max_len, tokenizer, transform=None):
#
self.max_len = max_len
self.tokenizer = get_pretrained_tokenizer(tokenizer)
#
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.
is_bert = True
if is_bert:
tokens, input_ids, input_mask, input_type_ids = convert_to_feature(caption, self.max_len, self.tokenizer)
input_ids = torch.tensor(input_ids, dtype=torch.long)
input_mask = torch.tensor(input_mask, dtype=torch.long)
input_type_ids = torch.tensor(input_type_ids, dtype=torch.long)
return image, input_ids, index, img_id, input_mask, input_type_ids
else:
tokens = nltk.tokenize.word_tokenize(
str(caption).lower())
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)
def collate_fn_bert(data):
# Sort a data list by caption length
data.sort(key=lambda x: torch.sum(x[-2]), reverse=True)
images, input_ids, ids, img_ids, input_mask, input_type_ids = zip(*data)
# Merge images (convert tuple of 2D tensor to DD tensor)
images = torch.stack(images, 0)
# Merget captions (convert tuple of 1D tensor to 2D tensor)
lengths = [torch.sum(cap) for cap in input_mask]
input_ids = torch.stack(input_ids, 0)
input_mask = torch.stack(input_mask, 0)
input_type_ids = torch.stack(input_type_ids, 0)
ids = np.array(ids)
return images, input_ids, lengths, ids, input_mask, input_type_ids
def get_transform(data_name, split_name, crop_size):
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(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
class F30kDataModule(LightningDataModule):
def __init__(self, _config, dist=False):
super().__init__()
#_config['data_root'], _config['datasets'], vocab, _config['max_text_len'], _config['tokenizer'], _config['image_size'], _config['per_gpu_batchsize'], _config['num_workers'])
self.data_path = os.path.join(_config["data_root"], _config['datasets'])
self.datasets = _config['datasets']
self.vocab = None
self.num_workers = _config["num_workers"]
self.batch_size = _config["per_gpu_batchsize"]
self.eval_batch_size = self.batch_size
self.image_size = _config["image_size"]
self.max_text_len = _config["max_text_len"]
self.tokenizer = _config["tokenizer"]
self.setup_flag = False
self.dist = dist
self.roots, self.ids = get_paths(self.data_path, self.datasets)
def set_train_dataset(self):
transform = get_transform(self.datasets, 'train', self.image_size)
self.train_dataset = FlickrDataset(root=self.roots['train']['img'],
split='train',
json=self.roots['train']['cap'],
vocab=self.vocab,
max_len=self.max_text_len,
tokenizer=self.tokenizer,
transform=transform)
def set_val_dataset(self):
transform = get_transform(self.datasets, 'val', self.image_size)
self.val_dataset = FlickrDataset(root=self.roots['val']['img'],
split='val',
json=self.roots['val']['cap'],
vocab=self.vocab,
max_len=self.max_text_len,
tokenizer=self.tokenizer,
transform=transform)
def set_test_dataset(self):
transform = get_transform(self.datasets, 'test', self.image_size)
self.test_dataset = FlickrDataset(root=self.roots['test']['img'],
split='test',
json=self.roots['test']['cap'],
vocab=self.vocab,
max_len=self.max_text_len,
tokenizer=self.tokenizer,
transform=transform)
def setup(self, stage):
if not self.setup_flag:
self.set_train_dataset()
self.set_val_dataset()
self.set_test_dataset()
self.setup_flag = True
'''if self.dist:
self.train_sampler = DistributedSampler(self.train_dataset, shuffle=True)
self.val_sampler = DistributedSampler(self.val_dataset, shuffle=False)
else:
self.train_sampler = None
self.val_sampler = None'''
def train_dataloader(self):
loader = torch.utils.data.DataLoader(dataset=self.train_dataset,
batch_size=self.batch_size,
#sampler=self.train_sampler,
shuffle=True,
pin_memory=True,
num_workers=self.num_workers,
collate_fn=collate_fn_bert)
return loader
def val_dataloader(self):
loader = torch.utils.data.DataLoader(dataset=self.val_dataset,
batch_size=self.batch_size,
#sampler=self.val_sampler,
shuffle=False,
pin_memory=True,
num_workers=self.num_workers,
collate_fn=collate_fn_bert)
return loader
def test_dataloader(self):
loader = torch.utils.data.DataLoader(dataset=self.test_dataset,
batch_size=self.batch_size,
#sampler=self.val_sampler,
shuffle=False,
pin_memory=True,
num_workers=self.num_workers,
collate_fn=collate_fn_bert)
return loader
class MscocoDataModule(LightningDataModule):
def __init__(self, _config, dist=False):
super().__init__()
#_config['data_root'], _config['datasets'], vocab, _config['max_text_len'], _config['tokenizer'], _config['image_size'], _config['per_gpu_batchsize'], _config['num_workers'])
self.data_path = os.path.join(_config["data_root"], _config['datasets'])
self.datasets = _config['datasets']
self.vocab = None
self.num_workers = _config["num_workers"]
self.batch_size = _config["per_gpu_batchsize"]
self.eval_batch_size = self.batch_size
self.image_size = _config["image_size"]
self.max_text_len = _config["max_text_len"]
self.tokenizer = _config["tokenizer"]
self.setup_flag = False
self.dist = dist
self.roots, self.ids = get_paths(self.data_path, self.datasets)
def set_train_dataset(self):
transform = get_transform(self.datasets, 'train', self.image_size)
self.train_dataset = CocoDataset(root=self.roots['train']['img'],
json=self.roots['train']['cap'],
vocab=self.vocab,
max_len=self.max_text_len,
tokenizer=self.tokenizer,
transform=transform, ids=self.ids['train'])
def set_val_dataset(self):
transform = get_transform(self.datasets, 'val', self.image_size)
self.val_dataset = CocoDataset(root=self.roots['val']['img'],
json=self.roots['val']['cap'],
vocab=self.vocab,
max_len=self.max_text_len,
tokenizer=self.tokenizer,
transform=transform, ids=self.ids['val'])
def set_test_dataset(self):
transform = get_transform(self.datasets, 'test', self.image_size)
self.test_dataset = CocoDataset(root=self.roots['test']['img'],
json=self.roots['test']['cap'],
vocab=self.vocab,
max_len=self.max_text_len,
tokenizer=self.tokenizer,
transform=transform, ids=self.ids['test'])
def setup(self, stage):
if not self.setup_flag:
self.set_train_dataset()
self.set_val_dataset()
self.set_test_dataset()
self.setup_flag = True
'''if self.dist:
self.train_sampler = DistributedSampler(self.train_dataset, shuffle=True)
self.val_sampler = DistributedSampler(self.val_dataset, shuffle=False)
else:
self.train_sampler = None
self.val_sampler = None'''
def train_dataloader(self):
loader = torch.utils.data.DataLoader(dataset=self.train_dataset,
batch_size=self.batch_size,
#sampler=self.train_sampler,
shuffle=True,
pin_memory=True,
num_workers=self.num_workers,
collate_fn=collate_fn_bert)
return loader
def val_dataloader(self):
loader = torch.utils.data.DataLoader(dataset=self.val_dataset,
batch_size=self.batch_size,
#sampler=self.val_sampler,
shuffle=False,
pin_memory=True,
num_workers=self.num_workers,
collate_fn=collate_fn_bert)
return loader
def test_dataloader(self):
loader = torch.utils.data.DataLoader(dataset=self.test_dataset,
batch_size=self.batch_size,
#sampler=self.val_sampler,
shuffle=False,
pin_memory=True,
num_workers=self.num_workers,
collate_fn=collate_fn_bert)
return loader