Differences with torchvision.datasets.CIFAR10
torchvision.datasets.CIFAR10
class torchvision.datasets.CIFAR10(
root: str,
train: bool = True,
transform: Optional[Callable] = None,
target_transform: Optional[Callable] = None,
download: bool = False)
For more information, see torchvision.datasets.CIFAR10.
mindspore.dataset.Cifar10Dataset
class mindspore.dataset.Cifar10Dataset(
dataset_dir,
usage=None,
num_samples=None,
num_parallel_workers=None,
shuffle=None,
sampler=None,
num_shards=None,
shard_id=None,
cache=None)
For more information, see mindspore.dataset.Cifar10Dataset.
Differences
PyTorch: Read the CIFAR-10 dataset(only support CIFAR-10 python version). API integrates the transformation operations for image and label.
MindSpore: Read the CIFAR-10 dataset(only support CIFAR-10 binary version). Downloading dataset from web is not supported. Transforms for image and label depends on mindshare.dataset.map
operation.
Categories |
Subcategories |
PyTorch |
MindSpore |
Difference |
---|---|---|---|---|
Parameter |
Parameter1 |
root |
dataset_dir |
- |
Parameter2 |
train |
- |
Specify whether it is the training set,supported by |
|
Parameter3 |
transform |
- |
Supported by |
|
Parameter4 |
target_transform |
- |
Supported by |
|
Parameter5 |
download |
- |
Not supported by MindSpore |
|
Parameter6 |
- |
usage |
Specify a subset of the dataset |
|
Parameter7 |
- |
num_samples |
The number of images to be included in the dataset. |
|
Parameter8 |
- |
num_parallel_workers |
Number of worker threads to read the data |
|
Parameter9 |
- |
shuffle |
Whether to perform shuffle on the dataset |
|
Parameter10 |
- |
sampler |
Object used to choose samples from the dataset |
|
Parameter11 |
- |
num_shards |
Number of shards that the dataset will be divided into |
|
Parameter12 |
- |
shard_id |
The shard ID used in distributed training |
|
Parameter13 |
- |
cache |
Specify single-node data caching service |
Code Example
# PyTorch
import torchvision.transforms as T
import torchvision.datasets as datasets
from torch.utils.data import DataLoader
root = "/path/to/dataset_directory/"
dataset = datasets.CIFAR10(root, train=True, transform=T.RandomCrop((28, 28)), download=True)
dataloader = DataLoader(dataset)
# MindSpore
import mindspore.dataset as ds
import mindspore.dataset.vision as vision
# Download the dataset files, unzip into the following structure
# .
# └── /path/to/dataset_directory/
# ├── data_batch_1.bin
# ├── data_batch_2.bin
# ├── data_batch_3.bin
# ├── data_batch_4.bin
# ├── data_batch_5.bin
# ├── test_batch.bin
# ├── readme.html
# └── batches.meta.txt
root = "/path/to/dataset_directory/"
ms_dataloader = ds.Cifar10Dataset(root, usage='train')
ms_dataloader = ms_dataloader.map(vision.RandomCrop((28, 28)), ["image"])