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# -*- coding: utf-8 -*-
"""
refcoco, refcoco+ and refcocog referring image detection and segmentation PyTorch dataset.
"""
import sys
import cv2
import torch
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)
def read_examples(input_line, unique_id):
"""Read a list of `InputExample`s from an input file."""
examples = []
# unique_id = 0
line = input_line #reader.readline()
# if not line:
# break
line = line.strip()
text_a = None
text_b = None
m = re.match(r"^(.*) \|\|\| (.*)$", line)
if m is None:
text_a = line
else:
text_a = m.group(1) #'man in black'
text_b = m.group(2)
examples.append(
InputExample(unique_id=unique_id, text_a=text_a, text_b=text_b))
# unique_id += 1
return examples
def _truncate_seq_pair(tokens_a, tokens_b, max_length):
while True:
total_length = len(tokens_a) + len(tokens_b)
if total_length <= max_length:
break
if len(tokens_a) > len(tokens_b):
tokens_a.pop()
else:
tokens_b.pop()
## Bert text encoding
class InputExample(object):
def __init__(self, unique_id, text_a, text_b):
self.unique_id = unique_id
self.text_a = text_a
self.text_b = text_b
class InputFeatures(object):
"""A single set of features of data."""
def __init__(self, unique_id, tokens, input_ids, input_mask, input_type_ids):
self.unique_id = unique_id
self.tokens = tokens
self.input_ids = input_ids
self.input_mask = input_mask
self.input_type_ids = input_type_ids
def convert_examples_to_features(examples, seq_length, tokenizer):
"""Loads a data file into a list of `InputBatch`s."""
features = []
for (ex_index, example) in enumerate(examples):
tokens_a = tokenizer.tokenize(example.text_a) # ['far', 'left', 'vase']
tokens_b = None
if example.text_b:
tokens_b = tokenizer.tokenize(example.text_b)
if tokens_b:
# Modifies `tokens_a` and `tokens_b` in place so that the total
# length is less than the specified length.
# Account for [CLS], [SEP], [SEP] with "- 3"
_truncate_seq_pair(tokens_a, tokens_b, seq_length - 3)
else:
# Account for [CLS] and [SEP] with "- 2"
if len(tokens_a) > seq_length - 2:
tokens_a = tokens_a[0:(seq_length - 2)]
tokens = []
input_type_ids = []
tokens.append("[CLS]")
input_type_ids.append(0)
for token in tokens_a:
tokens.append(token)
input_type_ids.append(0)
tokens.append("[SEP]")
input_type_ids.append(0)
if tokens_b:
for token in tokens_b:
tokens.append(token)
input_type_ids.append(1)
tokens.append("[SEP]")
input_type_ids.append(1)
input_ids = tokenizer.convert_tokens_to_ids(tokens)
# The mask has 1 for real tokens and 0 for padding tokens. Only real
# tokens are attended to.
input_mask = [1] * len(input_ids)
# Zero-pad up to the sequence length.
while len(input_ids) < seq_length:
input_ids.append(0)
input_mask.append(0)
input_type_ids.append(0)
assert len(input_ids) == seq_length
assert len(input_mask) == seq_length
assert len(input_type_ids) == seq_length
features.append(
InputFeatures(
unique_id=example.unique_id,
tokens=tokens,
input_ids=input_ids,
input_mask=input_mask,
input_type_ids=input_type_ids))
return features
class DatasetNotFoundError(Exception):
pass
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': 'umd'}
},
'refcocog_g': {
'splits': ('train', 'val'),
'params': {'dataset': 'refcocog', 'split_by': 'google'}
},
'refcocog_u': {
'splits': ('train', 'val', 'test'),
'params': {'dataset': 'refcocog', 'split_by': 'umd'}
},
'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,
bert_model='bert-base-uncased'):
self.images = []
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.split = split
self.tokenizer = BertTokenizer.from_pretrained(bert_model, do_lower_case=True) # should be true for English
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_u' :
dataset = 'refcocog'
mask_anno_str = '{0}_{1}'.format(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 == 'refcocog_u' :
dataset = 'refcocog'
dataset_path = osp.join(self.split_root, dataset + '_' + splitby)
splits = [split]
for split in splits:
imgset_file = '{0}_{1}_{2}.pth'.format(dataset, splitby, split)
imgset_path = osp.join(dataset_path, imgset_file)
self.images += torch.load(imgset_path)
else :
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)
self.images += torch.load(imgset_path)
# def exists_dataset(self):
# return osp.exists(osp.join(self.split_root, self.dataset))
def pull_item(self, idx):
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)
return img, phrase, bbox, seg_map
def __len__(self):
return len(self.images)
def __getitem__(self, idx):
img, phrase, bbox, seg_map = 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)
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]])
return img, np.array(word_id, dtype=int), np.array(word_mask, dtype=int), \
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, np.array(word_id, dtype=int), np.array(word_mask, dtype=int), \
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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