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import os
import sys
import json
import torch.utils.data as data
import torch
import itertools
from torchvision import transforms
from torch.autograd import Variable
import numpy as np
from PIL import Image
import torchvision.transforms.functional as TF
import random
from bert.tokenization_bert import BertTokenizer
import h5py
from refer.refer import REFER
from args import get_parser
# Dataset configuration initialization
# parser = get_parser()
# args = parser.parse_args()
class ReferDataset(data.Dataset):
def __init__(self,
args,
image_transforms=None,
target_transforms=None,
split='train',
eval_mode=False):
self.classes = []
self.image_transforms = image_transforms
self.target_transform = target_transforms
self.split = split
self.refer = REFER(args.refer_data_root, args.dataset, args.splitBy)
self.max_tokens = 20
ref_ids = self.refer.getRefIds(split=self.split)
img_ids = self.refer.getImgIds(ref_ids)
all_imgs = self.refer.Imgs
self.imgs = list(all_imgs[i] for i in img_ids)
self.ref_ids = ref_ids
self.input_ids = []
self.attention_masks = []
self.tokenizer = BertTokenizer.from_pretrained(args.bert_tokenizer)
# for metric learning
self.ROOT = '/data2/projects/seunghoon/VerbRIS/VerbCentric_CY/datasets/VRIS'
self.metric_learning = args.metric_learning
self.exclude_multiobj = args.exclude_multiobj
self.metric_mode = args.metric_mode
self.exclude_position = False
self.hp_selection = args.hp_selection
if self.metric_learning and eval_mode == False:
self.hardneg_prob = args.hn_prob
self.multi_obj_ref_ids = self._load_multi_obj_ref_ids()
self.hardpos_meta, self.hardneg_meta = self._load_metadata()
else:
self.hardneg_prob = 0.0
self.multi_obj_ref_ids = None
self.hardpos_meta, self.hardneg_meta = None, None
self.eval_mode = eval_mode
# if we are testing on a dataset, test all sentences of an object;
# o/w, we are validating during training, randomly sample one sentence for efficiency
for r in ref_ids:
ref = self.refer.Refs[r]
sentences_for_ref = []
attentions_for_ref = []
for i, (el, sent_id) in enumerate(zip(ref['sentences'], ref['sent_ids'])):
sentence_raw = el['raw']
attention_mask = [0] * self.max_tokens
padded_input_ids = [0] * self.max_tokens
input_ids = self.tokenizer.encode(text=sentence_raw, add_special_tokens=True)
# truncation of tokens
input_ids = input_ids[:self.max_tokens]
padded_input_ids[:len(input_ids)] = input_ids
attention_mask[:len(input_ids)] = [1]*len(input_ids)
sentences_for_ref.append(torch.tensor(padded_input_ids).unsqueeze(0))
attentions_for_ref.append(torch.tensor(attention_mask).unsqueeze(0))
self.input_ids.append(sentences_for_ref)
self.attention_masks.append(attentions_for_ref)
def _tokenize(self, sentence):
attention_mask = [0] * self.max_tokens
padded_input_ids = [0] * self.max_tokens
input_ids = self.tokenizer.encode(text=sentence, add_special_tokens=True)
# truncation of tokens
input_ids = input_ids[:self.max_tokens]
padded_input_ids[:len(input_ids)] = input_ids
attention_mask[:len(input_ids)] = [1]*len(input_ids)
# match shape as (1, max_tokens)
return torch.tensor(padded_input_ids).unsqueeze(0), torch.tensor(attention_mask).unsqueeze(0)
def _load_multi_obj_ref_ids(self):
# Load multi-object reference IDs based on configurations
if not self.exclude_multiobj and not self.exclude_position :
return None
elif self.exclude_position:
multiobj_path = os.path.join(self.ROOT, 'multiobj_ov2_nopos.txt')
elif self.exclude_multiobj :
multiobj_path = os.path.join(self.ROOT, 'multiobj_ov3.txt')
with open(multiobj_path, 'r') as f:
return [int(line.strip()) for line in f.readlines()]
def _load_metadata(self):
# Load metadata for hard positive verb phrases, hard negative queries
if 'refined' in self.metric_mode or 'hardneg' in self.metric_mode :
hardpos_path = os.path.join(self.ROOT, 'hardpos_verdict_gref_v4.json')
else :
hardpos_path = os.path.join(self.ROOT, 'hardpos_verbphrase_0906upd.json')
# do not use hardneg_path
hardneg_path = os.path.join(self.ROOT, 'hardneg_verb.json')
with open(hardpos_path, 'r', encoding='utf-8') as f:
hardpos_json = json.load(f)
if "hardpos_only" in self.metric_mode :
hardneg_json = None
else :
with open(hardneg_path, 'r', encoding='utf-8') as q:
hardneg_json = json.load(q)
return hardpos_json, hardneg_json
def _get_hardpos_verb(self, ref, seg_id, sent_idx) :
if seg_id in self.multi_obj_ref_ids:
return ''
# Extract metadata for hard positives if present
hardpos_dict = self.hardpos_meta.get(str(seg_id), {})
if self.hp_selection == 'strict' :
sent_id_list = list(hardpos_dict.keys())
cur_hardpos = hardpos_dict.get(sent_id_list[sent_idx], {}).get('phrases', [])
else :
cur_hardpos = list(itertools.chain.from_iterable(hardpos_dict[sid]['phrases'] for sid in hardpos_dict))
if cur_hardpos:
# Assign a hard positive verb phrase if available
raw_verb = random.choice(cur_hardpos)
return raw_verb
return ''
def get_classes(self):
return self.classes
def __len__(self):
return len(self.ref_ids)
def __getitem__(self, index):
this_ref_id = self.ref_ids[index]
this_img_id = self.refer.getImgIds(this_ref_id)
this_img = self.refer.Imgs[this_img_id[0]]
IMAGE_DIR = '/data2/dataset/COCO2014/trainval2014/'
img = Image.open(os.path.join(IMAGE_DIR, this_img['file_name'])).convert("RGB")
ref = self.refer.loadRefs(this_ref_id)
ref_mask = np.array(self.refer.getMask(ref[0])['mask'])
annot = np.zeros(ref_mask.shape)
annot[ref_mask == 1] = 1
annot = Image.fromarray(annot.astype(np.uint8), mode="P")
if self.image_transforms is not None:
# resize, from PIL to tensor, and mean and std normalization
img, target = self.image_transforms(img, annot)
if self.eval_mode:
embedding = []
att = []
for s in range(len(self.input_ids[index])):
e = self.input_ids[index][s]
a = self.attention_masks[index][s]
embedding.append(e.unsqueeze(-1))
att.append(a.unsqueeze(-1))
tensor_embeddings = torch.cat(embedding, dim=-1)
attention_mask = torch.cat(att, dim=-1)
return img, target, tensor_embeddings, attention_mask
else: # train phase
choice_sent = np.random.choice(len(self.input_ids[index]))
tensor_embeddings = self.input_ids[index][choice_sent]
attention_mask = self.attention_masks[index][choice_sent]
if self.metric_learning:
pos_sent = torch.zeros_like(tensor_embeddings)
pos_attn_mask = torch.zeros_like(attention_mask)
if 'hardpos_' in self.metric_mode or self.hardneg_prob == 0.0:
if 'refined' in self.metric_mode :
pos_sent_picked = self._get_hardpos_verb(ref, this_ref_id, choice_sent)
else :
pos_sents = self.hardpos_meta[str(this_ref_id)].values()
# drop elements with none
pos_sents = [s for s in pos_sents if s is not None]
pos_sent_picked = random.choice(list(pos_sents))
if pos_sent_picked:
pos_sent, pos_attn_mask = self._tokenize(pos_sent_picked)
return img, target, tensor_embeddings, attention_mask, pos_sent, pos_attn_mask
else:
neg_sent = torch.zeros_like(tensor_embeddings)
neg_attn_mask = torch.zeros_like(attention_mask)
pos_sents = self.hardpos_meta[str(this_ref_id)].values()
# drop elements with none
pos_sents = [s for s in pos_sents if s is not None]
pos_sent_picked = random.choice(list(pos_sents))
if pos_sent_picked:
pos_sent, pos_attn_mask = self._tokenize(pos_sent_picked)
if random.random() < self.hardneg_prob:
neg_sents = self.hardneg_meta[str(this_ref_id)].values()
neg_sents = [s for s in neg_sents if s is not None]
neg_sent_picked = random.choice(list(neg_sents))
#print("neg_sent: ", neg_sent)
if neg_sent_picked:
neg_sent, neg_attn_mask = self._tokenize(neg_sent_picked)
return img, target, tensor_embeddings, attention_mask, pos_sent, pos_attn_mask, neg_sent, neg_attn_mask
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