# 比较与torchvision.datasets.VOCDetection的差异
[](https://gitee.com/mindspore/docs/blob/r2.3.1/docs/mindspore/source_zh_cn/note/api_mapping/pytorch_diff/VOCDetection.md)
## torchvision.datasets.VOCDetection
```python
class torchvision.datasets.VOCDetection(
root: str,
year: str='2012',
image_set: str='train',
download: bool=False,
transform: Optional[Callable]=None,
target_transform: Optional[Callable]=None,
transforms: Optional[Callable]=None
)
```
更多内容详见[torchvision.datasets.VOCDetection](https://pytorch.org/vision/0.9/datasets.html#torchvision.datasets.VOCDetection)。
## mindspore.dataset.VOCDataset
```python
class mindspore.dataset.VOCDataset(
dataset_dir,
task="Segmentation",
usage="train",
class_indexing=None,
num_samples=None,
num_parallel_workers=None,
shuffle=None,
decode=False,
sampler=None,
num_shards=None,
shard_id=None,
cache=None,
extra_metadata=False,
decrypt=None
)
```
更多内容详见[mindspore.dataset.VOCDataset](https://mindspore.cn/docs/zh-CN/r2.3.1/api_python/dataset/mindspore.dataset.VOCDataset.html#mindspore.dataset.VOCDataset)。
## 差异对比
PyTorch：生成PASCAL VOC 目标检测格式数据集。
MindSpore：用于读取和分析VOC数据集的源数据集。
| 分类 | 子类 |PyTorch | MindSpore | 差异 |
| --- | --- | --- | --- |--- |
|参数 | 参数1 | root | dataset_dir | - |
| | 参数2 | year | - | MindSpore不支持 |
| | 参数3 | image_set | usage |- |
| | 参数4 | download | - | MindSpore不支持 |
| | 参数5 | transform | - | MindSpore通过 `mindspore.dataset.map` 操作支持 |
| | 参数6 | target_transform | - | MindSpore通过 `mindspore.dataset.map` 操作支持 |
| | 参数7 | transforms | - | MindSpore通过 `mindspore.dataset.map` 操作支持 |
| | 参数8 | - | task | 指定读取VOC数据的任务类型 |
| | 参数9 | - | class_indexing | 指定一个从label名称到label索引的映射 |
| | 参数10 | - | num_samples | 指定从数据集中读取的样本数 |
| | 参数11 | - | num_parallel_workers | 指定读取数据的工作线程数 |
| | 参数12 | - | shuffle | 指定是否混洗数据集 |
| | 参数13 | - | decode | 指定是否对图像进行解码 |
| | 参数14 | - | sampler | 指定采样器 |
| | 参数15 | - | num_shards | 指定分布式训练时将数据集进行划分的分片数 |
| | 参数16 | - | shard_id | 指定分布式训练时使用的分片ID号 |
| | 参数17 | - | cache | 指定单节点数据缓存服务 |
| | 参数18 | - | extra_metadata | 用于指定是否额外输出一个数据列用于表示图片元信息 |
| | 参数19 | - | decrypt | 图像解密函数 |
## 代码示例
```python
import mindspore.dataset as ds
import torchvision.transforms as T
import torchvision.datasets as datasets
from torch.utils.data import DataLoader
# In MindSpore, the generated dataset with different task setting has different output columns.
voc_dataset_dir = "/path/to/voc_dataset_directory/"
# task = Detection, output columns: [image, dtype=uint8], [bbox, dtype=float32], [label, dtype=uint32], [difficult, dtype=uint32], [truncate, dtype=uint32].
dataset = ds.VOCDataset(dataset_dir=voc_dataset_dir, task="Detection", usage="train")
for item in dataset:
print("item:", item[0])
print(len(item[0]))
break
# out:
# item: [255 216 255 ... 3 255 217]
# 147025
# In torch, the output will be result of transform, eg. RandomCrop
root = "/path/to/voc_dataset_directory2/"
dataset = datasets.VOCDetection(root, image_set='train', year='2012', transform=T.ToTensor())
dataloader = DataLoader(dataset=dataset, num_workers=8, batch_size=1, shuffle=True)
for epoch in range(1):
for i, (data, label) in enumerate(dataloader):
print((data, label)[0])
# out:
# tensor([[[[0.7176, 0.7176, 0.7216, ..., 0.7843, 0.7843, 0.7843],
# [0.7216, 0.7216, 0.7216, ..., 0.7882, 0.7882, 0.7882],
# [0.7216, 0.7255, 0.7255, ..., 0.7882, 0.7882, 0.7882],
# ...,
# ...
# ...,
# [0.6667, 0.6667, 0.6667, ..., 0.8118, 0.8118, 0.8078],
# [0.6627, 0.6627, 0.6588, ..., 0.8078, 0.8039, 0.8000],
# [0.6627, 0.6627, 0.6588, ..., 0.8078, 0.8039, 0.8000]]]])
# {'annotation': {'folder': ['VOC2012'], 'filename': ['61.jpg'], 'source': {'database': ['simulate VOC2007 Database'],
# 'annotation': ['simulate VOC2007'], 'image': ['flickr']}, 'size': {'width': ['500'], 'height': ['333'], 'depth': ['3']}, 'segmented': ['1'],
# 'object': [{'name': ['train'], 'pose': ['Unspecified'], 'truncated': ['0'], 'difficult': ['0'], 'bndbox': {'xmin': ['252'], 'ymin': ['42'],
# 'xmax': ['445'], 'ymax': ['282']}}, {'name': ['person'], 'pose': ['Frontal'], 'truncated': ['0'], 'difficult': ['0'], 'bndbox': {'xmin': ['204'],
# 'ymin': ['198'], 'xmax': ['271'], 'ymax': ['293']}}]}}
```