WZMIAOMIAO/deep-learning-for-image-processing · error · FileNotFoundError

VOCdevkit dose not in path:'{}'.

Error message

VOCdevkit dose not in path:'{}'.

What it means

validation.py checks that `<parser_data.data_path>/VOCdevkit` exists before building the validation DataLoader; otherwise it raises FileNotFoundError. It prevents evaluation against a missing dataset.

Source

Thrown at pytorch_object_detection/faster_rcnn/validation.py:111

    device = torch.device(parser_data.device if torch.cuda.is_available() else "cpu")
    print("Using {} device training.".format(device.type))

    data_transform = {
        "val": transforms.Compose([transforms.ToTensor()])
    }

    # read class_indict
    label_json_path = './pascal_voc_classes.json'
    assert os.path.exists(label_json_path), "json file {} dose not exist.".format(label_json_path)
    with open(label_json_path, 'r') as f:
        class_dict = json.load(f)

    category_index = {v: k for k, v in class_dict.items()}

    VOC_root = parser_data.data_path
    # check voc root
    if os.path.exists(os.path.join(VOC_root, "VOCdevkit")) is False:
        raise FileNotFoundError("VOCdevkit dose not in path:'{}'.".format(VOC_root))

    # 注意这里的collate_fn是自定义的，因为读取的数据包括image和targets，不能直接使用默认的方法合成batch
    batch_size = parser_data.batch_size
    nw = min([os.cpu_count(), batch_size if batch_size > 1 else 0, 8])  # number of workers
    print('Using %g dataloader workers' % nw)

    # load validation data set
    val_dataset = VOCDataSet(VOC_root, "2012", data_transform["val"], "val.txt")
    val_dataset_loader = torch.utils.data.DataLoader(val_dataset,
                                                     batch_size=1,
                                                     shuffle=False,
                                                     num_workers=nw,
                                                     pin_memory=True,
                                                     collate_fn=val_dataset.collate_fn)

    # create model num_classes equal background + 20 classes
    # 注意，这里的norm_layer要和训练脚本中保持一致
    backbone = resnet50_fpn_backbone(norm_layer=torch.nn.BatchNorm2d)

View on GitHub (pinned to 1ec3fe6f37)

Solutions

1. Extract the VOC2012 dataset so `<data_path>/VOCdevkit` exists
2. Pass the parent directory containing VOCdevkit as --data-path
3. Verify VOCdevkit/VOC2012/ImageSets/Main (and Annotations) exist under the given path

Example fix

# before
python validation.py --data-path ./data/VOCdevkit
# after
python validation.py --data-path ./data  # ./data/VOCdevkit exists

import os
data_path = parser_data.data_path
if not os.path.exists(os.path.join(data_path, "VOCdevkit")):
    raise SystemExit(f"VOCdevkit not found under {data_path}")

try:
    run_validation(parser_data)
except FileNotFoundError as e:
    if "VOCdevkit" in str(e):
        print(f"Dataset missing at {parser_data.data_path}; extract VOC2012 first.")
        sys.exit(1)

When it happens

Trigger: Running validation.py with --data-path pointing to a directory without the VOCdevkit subfolder, or pointing at VOCdevkit itself instead of its parent.

Common situations: Dataset not extracted on the eval machine; wrong --data-path value copied from another script; relative path resolved from unexpected working directory.

Related errors

• VOCdevkit dose not in path:'{}'.
• VOCdevkit dose not in path:'{}'.
• VOCdevkit dose not in path:'{}'.
• image: {} isn't RGB mode.
• dataset have {} classes, but input {}

AI-assisted analysis of WZMIAOMIAO/deep-learning-for-image-processing@1ec3fe6f37 (2026-08-30). Data as JSON: /api/errors/e638a4134bc37975. Report an issue: GitHub.