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import os
from typing import List, Union
import cv2
from PIL import Image
import lmdb
import numpy as np
import pyarrow as pa
import torch
from torch.utils.data import Dataset
from torchvision.transforms import functional as F
from bert.tokenization_bert import BertTokenizer
import random
from .simple_tokenizer import SimpleTokenizer as _Tokenizer
# from nltk import word_tokenize, pos_tag
info = {
'refcoco': {
'train_seen': 35473,
'val_seen': 3175,
'val_unseen': 445,
'test_seen':3200,
'test_unseen':394
},
'refcoco+': {
'train_seen': 35375,
'val_seen': 3171,
'val_unseen': 444,
'test_seen':3189,
'test_unseen':394
},
'refcoco_u': {
'train_seen': 33093,
'val_seen': 2000,
'val_unseen': 386,
'test_seen': 3935,
'test_unseen': 759,
},
'refcoco_g': {
'train_seen': 35105,
'val_seen': 3923,
'val_unseen': 760
}
}
_tokenizer = _Tokenizer()
def tokenize(texts: Union[str, List[str]],
context_length: int = 77,
truncate: bool = False) -> torch.LongTensor:
"""
Returns the tokenized representation of given input string(s)
Parameters
----------
texts : Union[str, List[str]]
An input string or a list of input strings to tokenize
context_length : int
The context length to use; all CLIP models use 77 as the context length
truncate: bool
Whether to truncate the text in case its encoding is longer than the context length
Returns
-------
A two-dimensional tensor containing the resulting tokens, shape = [number of input strings, context_length]
"""
if isinstance(texts, str):
texts = [texts]
sot_token = _tokenizer.encoder["<|startoftext|>"]
eot_token = _tokenizer.encoder["<|endoftext|>"]
all_tokens = [[sot_token] + _tokenizer.encode(text) + [eot_token]
for text in texts]
result = torch.zeros(len(all_tokens), context_length, dtype=torch.long)
for i, tokens in enumerate(all_tokens):
if len(tokens) > context_length:
if truncate:
tokens = tokens[:context_length]
tokens[-1] = eot_token
else:
raise RuntimeError(
f"Input {texts[i]} is too long for context length {context_length}"
)
result[i, :len(tokens)] = torch.tensor(tokens)
return result
def loads_pyarrow(buf):
"""
Args:
buf: the output of `dumps`.
"""
return pa.deserialize(buf)
class RefDataset(Dataset):
def __init__(self, lmdb_dir, mask_dir, dataset, split, mode, input_size,
word_length):
super(RefDataset, self).__init__()
self.lmdb_dir = lmdb_dir
self.mask_dir = mask_dir
self.dataset = dataset
self.split = split
self.mode = mode
self.input_size = (input_size, input_size)
#self.mask_size = [13, 26, 52]
self.word_length = word_length
self.mean = torch.tensor([0.485, 0.456, 0.406]).reshape(3, 1, 1)
self.std = torch.tensor([0.229, 0.224, 0.225]).reshape(3, 1, 1)
self.length = info[dataset][split]
self.env = None
def _init_db(self):
self.env = lmdb.open(self.lmdb_dir,
subdir=os.path.isdir(self.lmdb_dir),
readonly=True,
lock=False,
readahead=False,
meminit=False)
with self.env.begin(write=False) as txn:
self.length = loads_pyarrow(txn.get(b'__len__'))
self.keys = loads_pyarrow(txn.get(b'__keys__'))
def __len__(self):
return self.length
def __getitem__(self, index):
# Delay loading LMDB data until after initialization: https://github.com/chainer/chainermn/issues/129
if self.env is None:
self._init_db()
env = self.env
with env.begin(write=False) as txn:
byteflow = txn.get(self.keys[index])
ref = loads_pyarrow(byteflow)
# img
ori_img = cv2.imdecode(np.frombuffer(ref['img'], np.uint8),
cv2.IMREAD_COLOR)
img = cv2.cvtColor(ori_img, cv2.COLOR_BGR2RGB)
img_size = img.shape[:2]
# mask
seg_id = ref['seg_id']
mask_dir = os.path.join(self.mask_dir, str(seg_id) + '.png')
# sentences
idx = np.random.choice(ref['num_sents'])
sents = ref['sents']
# transform
# mask transform
mask = cv2.imdecode(np.frombuffer(ref['mask'], np.uint8),
cv2.IMREAD_GRAYSCALE)
mask = mask / 255.
if self.mode == 'train':
sent = sents[idx]
# sentence -> vector
img, mask, sent = self.convert(img, mask, sent, inference=False)
word_vec = tokenize(sent, self.word_length, True).squeeze(0)
pad_mask = (word_vec != 0).float()
return img, word_vec, mask, pad_mask
elif self.mode == 'val':
# sentence -> vector
sent = sents[-1]
word_vec = tokenize(sent, self.word_length, True).squeeze(0)
pad_mask = (word_vec != 0).float()
img, mask, sent = self.convert(img, mask, sent, inference=False)
return img, word_vec, mask, pad_mask
else:
# sentence -> vector
word_vecs = []
pad_masks = []
for sent in sents:
word_vec = tokenize(sent, self.word_length, True).squeeze(0)
word_vecs.append(word_vec)
pad_mask = (word_vec != 0).float()
pad_masks.append(pad_mask)
img, mask, sent = self.convert(img, mask, sent, inference=True)
return ori_img, img, word_vecs, mask, pad_masks, seg_id, sents
def convert(self, img, mask, sent, inference=False):
img = Image.fromarray(np.uint8(img))
mask = Image.fromarray(np.uint8(mask), mode="P")
img = F.resize(img, self.input_size)
if not inference:
mask = F.resize(mask, self.input_size, interpolation=Image.NEAREST)
img = F.to_tensor(img)
mask = torch.as_tensor(np.asarray(mask).copy(), dtype=torch.int64)
img = F.normalize(img, mean=self.mean, std=self.std)
return img, mask, sent
def __repr__(self):
return self.__class__.__name__ + "(" + \
f"db_path={self.lmdb_dir}, " + \
f"dataset={self.dataset}, " + \
f"split={self.split}, " + \
f"mode={self.mode}, " + \
f"input_size={self.input_size}, " + \
f"word_length={self.word_length}"
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