# 比较与torchvision.datasets.VOCDetection的差异 [![查看源文件](https://mindspore-website.obs.cn-north-4.myhuaweicloud.com/website-images/master/resource/_static/logo_source.svg)](https://gitee.com/mindspore/docs/blob/master/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/master/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']}}]}} ```