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BERT_token_process.py
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BERT_token_process.py
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import time
import torch.utils.data as data
import numpy as np
import os
import pickle
from PIL import Image
# from skimage.transform import resize
from imageio import imread
# from scipy.misc import imread, imresize
import torch
#判断路径是否存在
def check_exists(root):
if os.path.exists(root):
return True
return False
class CUHKPEDES_BERT_token(data.Dataset):
'''
Args:
root (string): Base root directory of dataset where [split].pkl and [split].h5 exists
split (string): 'train', 'val' or 'test'
transform (callable, optional): A function/transform that takes in an PIL image
and returns a transformed vector. E.g, ''transform.RandomCrop'
target_transform (callable, optional): A funciton/transform that tkes in the
targt and transfomrs it.
'''
#BERT
pklname_list = ['BERT_encode/BERT_id_train_64_new.npz',
'BERT_encode/BERT_id_val_64_new.npz',
'BERT_encode/BERT_id_test_64_new.npz']
def __init__(self, root, split, max_length, transform=None, target_transform=None,
cap_transform=None):
self.root = root
self.max_length = max_length
self.transform = transform
self.target_transform = target_transform
self.cap_transform = cap_transform
self.split = split.lower() # 返回将字符串中所有大写字符转换为小写后生成的字符串。
if not check_exists(self.root):
print(self.root)
raise RuntimeError('Dataset not found or corrupted.' +
'Please follow the directions to generate datasets')
if self.split == 'train':
self.pklname = self.pklname_list[0]
with open(os.path.join("./data", self.pklname), 'rb') as f_pkl:
data = pickle.load(f_pkl)
self.train_labels = [int(i)-1 for i in data['labels']]
self.train_captions = data['caption_id']
self.train_images = data['images_path']
self.train_attention_mask = data['attention_mask']
# data_h5py = h5py.File(os.path.join(self.root, self.h5name), 'r')
# self.train_images = data_h5py['images']
elif self.split == 'val':
self.pklname = self.pklname_list[1]
with open(os.path.join("./data", self.pklname), 'rb') as f_pkl:
data = pickle.load(f_pkl)
self.val_labels = [int(i) - 11004 for i in data['labels']]
self.val_captions = data['caption_id']
self.val_images = data['images_path']
self.val_attention_mask = data['attention_mask']
# data_h5py = h5py.File(os.path.join(self.root, self.h5name), 'r')
# self.val_images = data_h5py['images']
elif self.split == 'test':
self.pklname = self.pklname_list[2]
with open(os.path.join("./data", self.pklname), 'rb') as f_pkl:
data = pickle.load(f_pkl)
self.test_labels = [int(i) -12004 for i in data['labels']]
self.test_captions = data['caption_id']
self.test_images = data['images_path']
self.test_attention_mask = data['attention_mask']
# data_h5py = h5py.File(os.path.join(self.root, self.h5name), 'r')
# self.test_images = data_h5py['images']
else:
raise RuntimeError('Wrong split which should be one of "train","val" or "test"')
def __getitem__(self, index):
"""
Args:
index(int): Index
Returns:
tuple: (images, labels, captions)
"""
if self.split == 'train':
img_path, caption, attention_mask, label = 'CUHK-PEDES/imgs/'+self.train_images[4], self.train_captions[1], \
self.train_attention_mask[1],self.train_labels[1]
elif self.split == 'val':
img_path, caption, attention_mask, label = 'CUHK-PEDES/imgs/'+self.val_images[5], self.val_captions[5],\
self.val_attention_mask[5],self.val_labels[5]
else:
img_path, caption, attention_mask, label = 'CUHK-PEDES/imgs/'+self.test_images[4], self.test_captions[4],\
self.test_attention_mask[4], self.test_labels[4]
img_path = os.path.join(self.root, img_path)
img = imread(img_path)
if len(img.shape) == 2:
img = np.dstack((img, img, img))
img = Image.fromarray(img)
if self.transform is not None:
img = self.transform(img)
if self.target_transform is not None:
label = self.target_transform(label)
label=torch.tensor(label)
if self.cap_transform is not None:
caption = self.cap_transform(caption)
# caption = caption[1:-1]
caption = np.array(caption)
attention_mask = np.array(attention_mask)
if len(caption) >= self.max_length:
caption = caption[:self.max_length]
attention_mask = attention_mask[:self.max_length]
else:
pad = np.zeros((self.max_length - len(caption), 1), dtype=np.int64)
caption = np.append(caption, pad)
attention_mask = np.append(attention_mask, pad)
caption = torch.tensor(caption).long()
attention_mask = torch.tensor(attention_mask).long()
return img, caption, label, attention_mask
def __len__(self):
if self.split == 'train':
return len(self.train_labels)
elif self.split == 'val':
return len(self.val_labels)
else:
return len(self.test_labels)
if __name__ == '__main__':
import torchvision.transforms as transforms
from train_config import parse_args
args=parse_args()
args.embedding_type='BERT'
args.max_length = 60
args.batch_size=77
transform_val_list = [
transforms.Resize((384, 128)),
transforms.ToTensor(),
transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
]
split = 'train'
transform=transforms.Compose(transform_val_list)
data_split = CUHKPEDES_BERT_token(args.dir, split, args.max_length,transform=transform)
loader = data.DataLoader(data_split, args.batch_size, shuffle=False, num_workers=0)
sample=next(iter(loader))
img, caption, label, mask=sample
# print(img.shape)
# print(caption.shape)
print(label)
print(label[-1])
print(caption[-1])
print(mask[-1])
print(caption[-1].shape)
print(mask[-1].shape)
# print()
# print(mask.shape)