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import os
import json
import torch
import numpy as np
from torch.utils.data import Dataset
from PIL import Image
from tqdm import tqdm
import faiss
import torch.nn.functional as F
from sentence_transformers import SentenceTransformer
import torchvision.transforms as transforms
from random import choice
class CCDataset(Dataset):
def __init__(self, json_file, root_dir, vocab, transform, split, max_length, s_pretrained, device):
super(CCDataset, self).__init__()
self.vocab = vocab
self.split = split
self.max_length = max_length
self.device = device
self.transform = transform
assert self.split in {'train', 'val', 'test'}
s_model = SentenceTransformer(s_pretrained)
self.s_model = s_model.to(device)
self.root_dir = root_dir
self.convert = transforms.ToTensor()
with open(json_file) as f:
data = json.load(f)['images']
self.raw_dataset = [entry for entry in data if entry['split'] == split]
self.sentences = []
self.embeddings = []
self.images = []
self.captions = []
for record in tqdm(self.raw_dataset, desc='Tokenize ' + self.split):
self.sentences.extend(self.tokenize(record['sentences']))
for record in tqdm(self.raw_dataset, desc='Embeddings ' + self.split):
self.embeddings.extend(self.compute_embeddings(record['sentences']))
self.preprocess()
del self.raw_dataset
del self.sentences
del self.embeddings
del self.s_model
def tokenize(self, batch):
for elem in batch:
tokens = [self.vocab[x] if x in self.vocab.keys() else self.vocab['UNK'] for x in elem['tokens']]
if len(tokens) > self.max_length - 2:
continue
tokens = [self.vocab['START']] + tokens + [self.vocab['END']]
mask = [False] * len(tokens)
diff = self.max_length - len(tokens)
tokens += [self.vocab['PAD']] * diff
mask += [True] * diff # True = pad
elem['input_ids'] = tokens
elem['mask'] = mask
if len(batch) < 5:
diff = 5 - len(batch)
batch += [choice(batch) for _ in range(diff)]
assert len(batch) == 5
return batch
def compute_embeddings(self, batch):
sents = [x['raw'].strip() for x in batch]
embs = self.s_model.encode(sents)
return embs
def __len__(self):
return len(self.captions)
def __getitem__(self, idx):
img_idx = idx // 5 if self.split == 'train' else idx
elem = self.captions[idx]
for k, v in self.images[img_idx].items():
elem[k] = v
return elem
def preprocess(self):
idx = 0
prev_idx = -1
pbar = tqdm(total=len(self.sentences), desc='Preprocessing ' + self.split)
while idx < len(self.sentences):
img_idx = idx // 5
assert (self.sentences[idx]['imgid'] == self.raw_dataset[img_idx]['imgid'])
input_ids = torch.tensor(self.sentences[idx]['input_ids'], dtype=torch.long)
mask = torch.tensor(self.sentences[idx]['mask'], dtype=torch.bool)
raws = [x['raw'] for x in self.raw_dataset[img_idx]['sentences']]
flag = -1 if self.raw_dataset[img_idx]['changeflag'] == 0 else self.raw_dataset[img_idx]['imgid']
flag = torch.tensor(flag, dtype=torch.long)
embs = torch.tensor(self.embeddings[idx]) if len(self.embeddings) > 0 else None
self.captions.append({'input_ids': input_ids, 'pad_masks': mask, 'raws': raws, 'flags': flag, 'embs': embs})
if img_idx != prev_idx:
before_img_path = os.path.join(self.root_dir, self.raw_dataset[img_idx]['filepath'], 'A',
self.raw_dataset[img_idx]['filename'])
image_before = Image.open(before_img_path)
after_img_path = os.path.join(self.root_dir, self.raw_dataset[img_idx]['filepath'], 'B',
self.raw_dataset[img_idx]['filename'])
image_after = Image.open(after_img_path)
image_before = self.transform(image_before).unsqueeze(0)
image_after = self.transform(image_after).unsqueeze(0)
self.images.append({'image_before': image_before, 'image_after': image_after, 'flags': flag})
prev_idx = img_idx
inc = 1 if self.split == 'train' else 5
idx += inc
pbar.update(inc)
pbar.close()
class Batcher:
def __init__(self, dataset, batch_size, max_len, device, hd=0, model=None, shuffle=False):
self.dataset = dataset
self.batch_size = batch_size
self.hd = hd
self.max_len = max_len
self.device = device
self.model = model
self.index = None
self.visual = None
self.textual = None
self.ptr = 0
self.indices = np.arange(len(self.dataset))
self.shuffle = shuffle
if shuffle:
np.random.shuffle(self.indices)
if model and hd > 0 and self.dataset.split == 'train':
self.create_index()
def __iter__(self):
return self
def __len__(self):
return len(self.dataset) // self.batch_size
def __next__(self):
if self.ptr >= len(self.dataset):
self.ptr = 0
self.index = None
self.visual = None
self.textual = None
if self.shuffle:
np.random.shuffle(self.indices)
if self.model and self.hd > 0 and self.dataset.split == 'train':
self.create_index()
raise StopIteration
batched = 0
samples = []
hard_negatives = []
while self.ptr < len(self.dataset) and batched < self.batch_size:
sample = self.dataset[self.indices[self.ptr]]
samples.append(sample)
if self.hd > 0 and self.dataset.split == 'train':
hard_neg = self.mine_negatives(self.indices[self.ptr], self.hd)
hard_negatives.extend(hard_neg)
self.ptr += 1
batched += 1
return self.create_batch(samples + hard_negatives)
def get_elem(self, idx):
return self.dataset[idx]
@torch.no_grad()
def create_index(self):
is_training = self.model.training
self.model.eval()
self.index = faiss.IndexFlatIP(self.model.feature_dim)
prev_img = None
for idx in tqdm(range(len(self.dataset)), desc='Creating index'):
sample = self.dataset[idx]
imgs1, imgs2, = sample['image_before'], sample['image_after']
input_ids, mask = sample['input_ids'], sample['pad_masks']
if idx // 5 != prev_img:
imgs1 = imgs1.to(self.device)
imgs2 = imgs2.to(self.device)
vis_emb, _, = self.model.encoder(imgs1, imgs2)
self.visual = torch.cat([self.visual, vis_emb.cpu()]) if self.visual is not None else vis_emb.cpu()
prev_img = prev_img + 1 if prev_img is not None else 0
input_ids = input_ids.unsqueeze(0).to(self.device)
mask = mask.unsqueeze(0).to(self.device)
_, text_emb, _, _ = self.model.decoder(input_ids, None, mask, None)
self.textual = torch.cat([self.textual, text_emb.cpu()]) if self.textual is not None else text_emb.cpu()
if torch.cuda.is_available():
torch.cuda.empty_cache()
self.visual = F.normalize(self.visual, p=2, dim=1)
self.textual = F.normalize(self.textual, p=2, dim=1)
self.index.add(self.visual)
if is_training:
self.model.train()
def mine_negatives(self, idx, n):
negatives = []
m = 4
label = self.dataset[idx]['flags'].item()
while len(negatives) < n and (n * m) < self.index.ntotal:
k = n * m
indeces = self.index.search(self.textual[idx].unsqueeze(0), k)[1][0]
indeces = [x * 5 for x in indeces]
negatives = [self.dataset[x] for x in indeces if self.dataset[x]['flags'].item() != label][:n]
m *= 2
return negatives
def create_batch(self, samples):
images_before = images_after = input_ids = pad_mask = labels = flags = embs = None
raws = []
for sample in samples:
img1 = sample['image_before']
img2 = sample['image_after']
tokens = sample['input_ids']
mask = sample['pad_masks']
flag = sample['flags']
emb = sample['embs']
tokens = tokens.unsqueeze(0)
mask = mask.unsqueeze(0)
flag = flag.unsqueeze(0)
lab = tokens.clone() * ~mask
lab += torch.tensor([[-100]], dtype=torch.long).repeat(1, self.max_len) * mask
if emb is not None:
emb = emb.unsqueeze(0)
images_before = torch.cat([images_before, img1]) if images_before is not None else img1
images_after = torch.cat([images_after, img2]) if images_after is not None else img2
input_ids = torch.cat([input_ids, tokens]) if input_ids is not None else tokens
labels = torch.cat([labels, lab]) if labels is not None else lab
pad_mask = torch.cat([pad_mask, mask]) if pad_mask is not None else mask
flags = torch.cat([flags, flag]) if flags is not None else flag
if emb is not None:
embs = torch.cat([embs, emb]) if embs is not None else emb
raws.append(sample['raws'])
return {'images_before': images_before, 'images_after': images_after, 'input_ids': input_ids,
'pad_mask': pad_mask, 'labels': labels, 'flags': flags, 'raws': raws, 'embs': embs}
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