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import os
import pickle
import h5py
import torch
from torch.utils.data import Dataset
import torchvision.transforms as transforms
from PIL import Image
class DenseCapDataset(Dataset):
@staticmethod
def collate_fn(batch):
"""Use in torch.utils.data.DataLoader
"""
return tuple(zip(*batch)) # as tuples instead of stacked tensors
@staticmethod
def get_transform():
"""More complicated transform utils in torchvison/references/detection/transforms.py
"""
transform = transforms.Compose([
transforms.ToTensor(),
transforms.Normalize((0.485, 0.456, 0.406), (0.229, 0.224, 0.225)),
])
return transform
def __init__(self, img_dir_root, vg_data_path, look_up_tables_path, dataset_type=None, transform=None):
assert dataset_type in {None, 'train', 'test', 'val'}
super(DenseCapDataset, self).__init__()
self.img_dir_root = img_dir_root
self.vg_data_path = vg_data_path
self.look_up_tables_path = look_up_tables_path
self.dataset_type = dataset_type # if dataset_type is None, all data will be use
self.transform = transform
# === load data here ====
self.vg_data = h5py.File(vg_data_path, 'r')
self.look_up_tables = pickle.load(open(look_up_tables_path, 'rb'))
def set_dataset_type(self, dataset_type, verbose=True):
assert dataset_type in {None, 'train', 'test', 'val'}
if verbose:
print('[DenseCapDataset]: {} switch to {}'.format(self.dataset_type, dataset_type))
self.dataset_type = dataset_type
def __getitem__(self, idx):
vg_idx = self.look_up_tables['split'][self.dataset_type][idx] if self.dataset_type else idx
img_path = os.path.join(self.img_dir_root, self.look_up_tables['idx_to_directory'][vg_idx],
self.look_up_tables['idx_to_filename'][vg_idx])
img = Image.open(img_path).convert("RGB")
if self.transform is not None:
img = self.transform(img)
else:
img = transforms.ToTensor()(img)
first_box_idx = self.vg_data['img_to_first_box'][vg_idx]
last_box_idx = self.vg_data['img_to_last_box'][vg_idx]
boxes = torch.as_tensor(self.vg_data['boxes'][first_box_idx: last_box_idx+1], dtype=torch.float32)
caps = torch.as_tensor(self.vg_data['captions'][first_box_idx: last_box_idx+1], dtype=torch.long)
caps_len = torch.as_tensor(self.vg_data['lengths'][first_box_idx: last_box_idx+1], dtype=torch.long)
targets = {
'boxes': boxes,
'caps': caps,
'caps_len': caps_len,
}
info = {
'idx': vg_idx,
'dir': self.look_up_tables['idx_to_directory'][vg_idx],
'file_name': self.look_up_tables['idx_to_filename'][vg_idx]
}
return img, targets, info
def __len__(self):
if self.dataset_type:
return len(self.look_up_tables['split'][self.dataset_type])
else:
return len(self.look_up_tables['filename_to_idx'])
if __name__ == '__main__':
IMG_DIR_ROOT = './data/visual-genome'
VG_DATA_PATH = './data/VG-regions.h5'
LOOK_UP_TABLES_PATH = './data/VG-regions-dicts.pkl'
dcd = DenseCapDataset(IMG_DIR_ROOT, VG_DATA_PATH, LOOK_UP_TABLES_PATH)
print('all', len(dcd))
print(dcd[0])
for data_type in {'train', 'test', 'val'}:
dcd.set_dataset_type(data_type)
print(data_type, len(dcd))
print(dcd[0])
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