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# -*- coding: utf-8 -*-
"""
refcoco, refcoco+ and refcocog referring image detection and segmentation PyTorch dataset.
"""
import sys
import cv2
import os
import torch
import json
import random
import numpy as np
import os.path as osp
import torch.utils.data as data
sys.path.append('.')
import utils
import re
# from pytorch_pretrained_bert.tokenization import BertTokenizer
from utils.transforms import letterbox, random_affine, random_copy, random_crop, random_erase
import copy
import clip
sys.modules['utils'] = utils
cv2.setNumThreads(0)
class ReferDataset(data.Dataset):
SUPPORTED_DATASETS = {
'refcoco': {
'splits': ('train', 'val', 'testA', 'testB'),
'params': {'dataset': 'refcoco', 'split_by': 'unc'}
},
'refcoco+': {
'splits': ('train', 'val', 'testA', 'testB'),
'params': {'dataset': 'refcoco+', 'split_by': 'unc'}
},
'refcocog': {
'splits': ('train', 'val', 'test'),
'params': {'dataset': 'refcocog', 'split_by': 'unc'}
},
'refcocog_g': {
'splits': ('train', 'val'),
'params': {'dataset': 'refcocog', 'split_by': 'google'}
},
'refcocog_u': {
'splits': ('train', 'val', 'test'),
'params': {'dataset': 'refcocog', 'split_by': 'unc'}
},
'grefcoco': {
'splits': ('train', 'val', 'testA', 'testB'),
'params': {'dataset': 'grefcoco', 'split_by': 'unc'}
}
}
def __init__(self, data_root, split_root='data', dataset='refcoco', imsize=256, splitby='umd',
transform=None, augment=False, split='train', max_query_len=128, metric_learning=None):
images_tmp = []
self.data_root = data_root
self.split_root = split_root
self.dataset = dataset
self.imsize = imsize
self.query_len = max_query_len
self.transform = transform
self.word_len = 17
self.emb_size = 384
self.split = split
self.augment=augment
valid_splits = self.SUPPORTED_DATASETS[self.dataset]['splits']
if split not in valid_splits:
raise ValueError(
'Dataset {0} does not have split {1}'.format(
self.dataset, split))
self.anns_root = osp.join(self.data_root, 'anns', self.dataset, self.split+'.txt')
if self.dataset == 'refcocog' :
mask_anno_str = '{0}_{1}'.format(self.dataset, splitby)
self.mask_root = osp.join(self.data_root, 'masks', mask_anno_str)
else :
self.mask_root = osp.join(self.data_root, 'masks', self.dataset)
self.im_dir = osp.join(self.data_root, 'images', 'train2014')
# if self.dataset in ['refcoco', 'refcoco+']
dataset_path = osp.join(self.split_root, self.dataset)
splits = [split]
for split in splits:
imgset_file = '{0}_{1}.pth'.format(self.dataset, split)
imgset_path = osp.join(dataset_path, imgset_file)
images_tmp += torch.load(imgset_path)
# hardpos related
self.ROOT = '/data2/dataset/RefCOCO/VRIS'
if self.dataset == 'refcoco' :
self.all_hp_root = '/data2/dataset/RefCOCO/refcoco/SBERT_rcc_unc'
elif self.dataset == 'refcoco+' :
self.all_hp_root = '/data2/dataset/RefCOCO/refcoco+/SBERT_rccp_unc'
self.metric_learning = metric_learning
if self.metric_learning :
self.exclude_position = True
self.exclude_multiobj = True
self.hp_selection = 'strict'
self.multi_obj_ref_ids = None
self.hardpos_meta = None
# make new self.images file with sentence idx and total sent num (per ref_id)
from collections import defaultdict
ref_sentence_counts = defaultdict(int)
for item in images_tmp:
ref_sentence_counts[item[1]] += 1
if self.split == 'train' :
images = []
ref_sentence_indices = defaultdict(int)
for item in images_tmp:
img_name, seg_id, box, sentence = item
sent_index = ref_sentence_indices[seg_id]
total_sentences = ref_sentence_counts[seg_id]
images.append((img_name, seg_id, box, sentence, sent_index, total_sentences))
ref_sentence_indices[seg_id] += 1
self.images = images
else :
self.images = images_tmp
else :
self.images = images_tmp
def exists_dataset(self):
return osp.exists(osp.join(self.split_root, self.dataset))
def _get_hardpos_verb_rcc(self, seg_id, sent_idx):
emb_folder = os.path.join(self.all_hp_root, str(seg_id))
emb_files = sorted([f for f in os.listdir(emb_folder) if f.startswith(f"hp_") and f.endswith(".npy")])
if self.hp_selection == 'strict' :
# choose only corresponding (selected) sentence embedding
emb_file = emb_files[sent_idx]
else :
# choose any sentence embedding
emb_files = sorted([f for f in os.listdir(emb_folder) if f.startswith(f"hp_") and f.endswith(".npy")])
emb_file = random.choice(emb_files)
selected_emb = np.load(os.path.join(emb_folder, emb_file))
verb_embed = torch.from_numpy(selected_emb)
return verb_embed
def pull_item(self, idx):
# if metric learning and in train mode
if self.metric_learning and self.augment :
# sent_idx refers to index of sent among sent_num-1
img_file, seg_id, bbox, phrase, sent_idx, sent_num = self.images[idx]
else :
img_file, seg_id, bbox, phrase = self.images[idx]
bbox = np.array(bbox, dtype=int) # x1y1x2y2
img_path = osp.join(self.im_dir, img_file)
img = cv2.imread(img_path) # BGR [512, 640, 3]
## duplicate channel if gray image
if img.shape[-1] > 1:
img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB) #RGB
else:
img = np.stack([img] * 3)
## seg map
seg_map = np.load(osp.join(self.mask_root, str(seg_id)+'.npy')) # [512, 640]
seg_map = np.array(seg_map).astype(np.float32)
if self.metric_learning and self.split == 'train' :
return img, phrase, bbox, seg_map, seg_id, sent_idx
else :
return img, phrase, bbox, seg_map, seg_id
def __len__(self):
return len(self.images)
def __getitem__(self, idx):
if self.metric_learning and self.augment :
img, phrase, bbox, seg_map, seg_id, sent_idx = self.pull_item(idx)
else :
img, phrase, bbox, seg_map, seg_id = self.pull_item(idx)
phrase = phrase.lower()
if self.augment:
augment_flip, augment_hsv, augment_affine, augment_crop, augment_copy, augment_erase = \
True, True, True, False, False, False
## seems a bug in torch transformation resize, so separate in advance
h,w = img.shape[0], img.shape[1]
# print("img.shape", img.shape)
if self.augment:
## random horizontal flip
if augment_flip and random.random() > 0.5:
img = cv2.flip(img, 1)
seg_map = cv2.flip(seg_map, 1)
bbox[0], bbox[2] = w-bbox[2]-1, w-bbox[0]-1
phrase = phrase.replace('right','*&^special^&*').replace('left','right').replace('*&^special^&*','left')
## random copy and add left or right
if augment_copy:
img, seg_map, phrase, bbox = random_copy(img, seg_map, phrase, bbox)
## random erase for occluded
if augment_erase:
img, seg_map = random_erase(img, seg_map)
## random padding and crop
if augment_crop:
img, seg_map = random_crop(img, seg_map, 40, h, w)
## random intensity, saturation change
if augment_hsv:
fraction = 0.50
img_hsv = cv2.cvtColor(cv2.cvtColor(img, cv2.COLOR_RGB2BGR), cv2.COLOR_BGR2HSV)
S = img_hsv[:, :, 1].astype(np.float32)
V = img_hsv[:, :, 2].astype(np.float32)
a = (random.random() * 2 - 1) * fraction + 1
if a > 1:
np.clip(S, a_min=0, a_max=255, out=S)
a = (random.random() * 2 - 1) * fraction + 1
V *= a
if a > 1:
np.clip(V, a_min=0, a_max=255, out=V)
img_hsv[:, :, 1] = S.astype(np.uint8)
img_hsv[:, :, 2] = V.astype(np.uint8)
img = cv2.cvtColor(cv2.cvtColor(img_hsv, cv2.COLOR_HSV2BGR), cv2.COLOR_BGR2RGB)
img, seg_map, ratio, dw, dh = letterbox(img, seg_map, self.imsize)
bbox[0], bbox[2] = bbox[0]*ratio+dw, bbox[2]*ratio+dw
bbox[1], bbox[3] = bbox[1]*ratio+dh, bbox[3]*ratio+dh
## random affine transformation
if augment_affine:
img, seg_map, bbox, M = random_affine(img, seg_map, bbox, \
degrees=(-5, 5), translate=(0.10, 0.10), scale=(0.90, 1.10)) # 255 white fill
else: ## should be inference, or specified training
img, _, ratio, dw, dh = letterbox(img, None, self.imsize)
bbox[0], bbox[2] = bbox[0]*ratio+dw, bbox[2]*ratio+dw
bbox[1], bbox[3] = bbox[1]*ratio+dh, bbox[3]*ratio+dh
draw_img = copy.deepcopy(img)
# Norm, to tensor
if self.transform is not None:
img = self.transform(img)
## encode phrase to clip input
word_id = clip.tokenize(phrase, 17, truncate=True)
word_mask = ~ (word_id == 0)
orig_word_id = np.array(word_id, dtype=int)
orig_word_mask = np.array(word_mask, dtype=int)
# Get hardpos verb phrase
if self.metric_learning and self.augment:
original_emb = self._get_hardpos_verb_rcc(seg_id, sent_idx)
if self.augment: # train
seg_map = cv2.resize(seg_map, (self.imsize // 2, self.imsize // 2),interpolation=cv2.INTER_NEAREST) # (208, 208)
seg_map = np.reshape(seg_map, [1, np.shape(seg_map)[0], np.shape(seg_map)[1]])
if self.metric_learning :
params = {
'seg_id' : seg_id,
'sent' : phrase,
'hardpos_emb' : original_emb.unsqueeze(0)
}
return img, orig_word_id, orig_word_mask, np.array(bbox, dtype=np.float32), \
np.array(seg_map, dtype=np.float32), params
else :
return img, orig_word_id, orig_word_mask, \
np.array(bbox, dtype=np.float32), np.array(seg_map, dtype=np.float32)
else:
seg_map = np.reshape(seg_map, [1, np.shape(seg_map)[0], np.shape(seg_map)[1]])
return img, orig_word_id, orig_word_mask, \
np.array(bbox, dtype=np.float32), np.array(seg_map, dtype=np.float32), np.array(ratio, dtype=np.float32), \
np.array(dw, dtype=np.float32), np.array(dh, dtype=np.float32), self.images[idx][0], self.images[idx][3], np.array(draw_img, dtype=np.uint8)
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