比较与torchvision.datasets.VOCDetection的差异

查看源文件

torchvision.datasets.VOCDetection

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

mindspore.dataset.VOCDataset

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

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

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']}}]}}