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