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import os
import sys
import torch.utils.data as data
import torch
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.dataset = args.dataset
self.args = args
if args.dataset == 'refcocog' and args.split in ['testA', 'testB']:
print(f"Easy & Hard Example Experiments - dataset : {args.dataset}, split : {args.split}")
from refer.refer_test import REFER
self.refer = REFER(args.refer_data_root, args.dataset, args.splitBy)
else :
from refer.refer import REFER
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)
# n_obj binning
import json
nobj_fpath = '/data/projects/chaeyun/Co-DETR/SotaFiltered_thres035_binned_nobj.json'
with open(nobj_fpath, 'r') as f:
nobj_dict = json.load(f)
ref_ids = nobj_dict[args.n_obj_bin]
# self.refer.Refs = {k : self.refer.Refs[k] for k in 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.sent_lens = []
self.tokenizer = BertTokenizer.from_pretrained(args.bert_tokenizer)
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 = []
sent_lens_for_ref = []
for i, (el, sent_id) in enumerate(zip(ref['sentences'], ref['sent_ids'])):
sentence_raw = el['raw']
# sent_len = len(sentence_raw.split())
# if sent_len <= self.args.seq_len.min or sent_len >= self.args.seq_len.max :
# continue
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))
# sent_lens_for_ref.append(torch.tensor(sent_len))
self.input_ids.append(sentences_for_ref)
self.attention_masks.append(attentions_for_ref)
self.sent_lens.append(sent_lens_for_ref)
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]]
img = Image.open(os.path.join(self.refer.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 = []
# sent_lens = []
for s in range(len(self.input_ids[index])):
e = self.input_ids[index][s]
a = self.attention_masks[index][s]
# ss = self.sent_lens[index][s]
embedding.append(e.unsqueeze(-1))
att.append(a.unsqueeze(-1))
# sent_lens.append(ss)
tensor_embeddings = torch.cat(embedding, dim=-1)
attention_mask = torch.cat(att, dim=-1)
# sent_lens = torch.tensor(sent_lens)
else:
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]
# sent_lens = self.sent_lens[index][choice_sent]
return img, target, tensor_embeddings, attention_mask #, sent_lens
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