96 lines
3.5 KiB
Python
96 lines
3.5 KiB
Python
import os
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import json
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import torch
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from torchvision import datasets
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from torchvision import transforms
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from torch.utils.data import DataLoader
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from functools import partial
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class_to_idx = {
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'aeroplane':0, 'bicycle':1, 'bird':2, 'boat':3, 'bottle':4,
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'bus':5, 'car':6, 'cat':7, 'chair':8, 'cow':9, 'diningtable':10,
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'dog':11, 'horse':12, 'motorbike':13, 'person':14, 'pottedplant':15,
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'sheep':16, 'sofa':17, 'train':18, 'tvmonitor':19
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}
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idx_to_class = {i:c for c, i in class_to_idx.items()}
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def get_pascal_voc2007_data(image_root, split='train'):
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"""
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Use torchvision.datasets
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https://pytorch.org/docs/stable/torchvision/datasets.html#torchvision.datasets.VOCDetection
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"""
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train_dataset = datasets.VOCDetection(image_root, year='2007', image_set=split,
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download=False)
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return train_dataset
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def pascal_voc2007_loader(dataset, batch_size, num_workers=0, shuffle=False, proposal_path=None):
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"""
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Data loader for Pascal VOC 2007.
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https://pytorch.org/docs/stable/data.html#torch.utils.data.DataLoader
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"""
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collate_fn = partial(voc_collate_fn, proposal_path=proposal_path)
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train_loader = DataLoader(dataset,
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batch_size=batch_size,
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shuffle=shuffle, pin_memory=True,
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num_workers=num_workers,
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collate_fn=collate_fn)
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return train_loader
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def voc_collate_fn(batch_lst, reshape_size=224, proposal_path=None):
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preprocess = transforms.Compose([
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transforms.Resize((reshape_size, reshape_size)),
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transforms.ToTensor(),
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transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]),
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])
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batch_size = len(batch_lst)
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img_batch = torch.zeros(batch_size, 3, reshape_size, reshape_size)
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box_list = []
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box_batch_idx = []
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w_list = []
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h_list = []
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img_id_list = []
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proposal_list = []
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proposal_batch_idx = []
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for i in range(batch_size):
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img, ann = batch_lst[i]
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w_list.append(img.size[0]) # image width
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h_list.append(img.size[1]) # image height
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img_id_list.append(ann['annotation']['filename'])
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img_batch[i] = preprocess(img)
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all_bbox = ann['annotation']['object']
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if type(all_bbox) == dict: # inconsistency in the annotation file
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all_bbox = [all_bbox]
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for bbox_idx, one_bbox in enumerate(all_bbox):
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bbox = one_bbox['bndbox']
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obj_cls = one_bbox['name']
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box_list.append(torch.Tensor([float(bbox['xmin']), float(bbox['ymin']),
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float(bbox['xmax']), float(bbox['ymax']), class_to_idx[obj_cls]]))
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box_batch_idx.append(i)
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if proposal_path is not None:
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proposal_fn = ann['annotation']['filename'].replace('.jpg', '.json')
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with open(os.path.join(proposal_path, proposal_fn), 'r') as f:
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proposal = json.load(f)
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for p in proposal:
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proposal_list.append([p['x_min'], p['y_min'], p['x_max'], p['y_max']])
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proposal_batch_idx.append(i)
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h_batch = torch.tensor(h_list)
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w_batch = torch.tensor(w_list)
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box_batch = torch.stack(box_list)
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box_batch_ids = torch.tensor(box_batch_idx, dtype=torch.long)
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proposals = torch.tensor(proposal_list, dtype=box_batch.dtype)
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proposal_batch_ids = torch.tensor(proposal_batch_idx, dtype=torch.long)
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assert len(box_batch) == len(box_batch_ids)
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assert len(proposals) == len(proposal_batch_ids)
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return img_batch, box_batch, box_batch_ids, proposals, proposal_batch_ids, w_batch, h_batch, img_id_list |