mindspore.dataset.STL10Dataset
- class mindspore.dataset.STL10Dataset(dataset_dir, usage=None, num_samples=None, num_parallel_workers=None, shuffle=None, sampler=None, num_shards=None, shard_id=None, cache=None)[source]
STL-10 dataset.
The generated dataset has two columns:
[image, label]
. The tensor of columnimage
is of the uint8 type. The tensor of columnlabel
is of a scalar of int32 type.- Parameters
dataset_dir (str) – Path to the root directory that contains the dataset.
usage (str, optional) – Usage of this dataset, can be
'train'
,'test'
,'unlabeled'
,'train+unlabeled'
or'all'
.'train'
will read from 5,000 train samples,'test'
will read from 8,000 test samples,'unlabeled'
will read from all 100,000 samples, and'train+unlabeled'
will read from 105000 samples,'all'
will read all the samples Default:None
, all samples.num_samples (int, optional) – The number of images to be included in the dataset. Default:
None
, all images.num_parallel_workers (int, optional) – Number of worker threads to read the data. Default:
None
, will use global default workers(8), it can be set bymindspore.dataset.config.set_num_parallel_workers()
.shuffle (bool, optional) – Whether to perform shuffle on the dataset. Default:
None
, expected order behavior shown in the table below.sampler (Sampler, optional) – Object used to choose samples from the dataset. Default:
None
, expected order behavior shown in the table below.num_shards (int, optional) – Number of shards that the dataset will be divided into. Default:
None
. When this argument is specified, num_samples reflects the max sample number of per shard.shard_id (int, optional) – The shard ID within num_shards . Default:
None
. This argument can only be specified when num_shards is also specified.cache (DatasetCache, optional) – Use tensor caching service to speed up dataset processing. More details: Single-Node Data Cache . Default:
None
, which means no cache is used.
- Raises
RuntimeError – If dataset_dir is not valid or does not exist or does not contain data files.
RuntimeError – If sampler and shuffle are specified at the same time.
RuntimeError – If sampler and num_shards/shard_id are specified at the same time.
RuntimeError – If num_shards is specified but shard_id is None.
RuntimeError – If shard_id is specified but num_shards is None.
ValueError – If usage is invalid.
ValueError – If num_parallel_workers exceeds the max thread numbers.
ValueError – If shard_id is not in range of [0, num_shards ).
- Tutorial Examples:
Note
This dataset can take in a sampler. ‘sampler’ and ‘shuffle’ are mutually exclusive. The table below shows what input arguments are allowed and their expected behavior.
Parameter ‘sampler’
Parameter ‘shuffle’
Expected Order Behavior
None
None
random order
None
True
random order
None
False
sequential order
Sampler object
None
order defined by sampler
Sampler object
True
not allowed
Sampler object
False
not allowed
Examples
>>> import mindspore.dataset as ds >>> stl10_dataset_dir = "/path/to/stl10_dataset_directory" >>> >>> # 1) Get all samples from STL10 dataset in sequence >>> dataset = ds.STL10Dataset(dataset_dir=stl10_dataset_dir, shuffle=False) >>> >>> # 2) Randomly select 350 samples from STL10 dataset >>> dataset = ds.STL10Dataset(dataset_dir=stl10_dataset_dir, num_samples=350, shuffle=True) >>> >>> # 3) Get samples from STL10 dataset for shard 0 in a 2-way distributed training >>> dataset = ds.STL10Dataset(dataset_dir=stl10_dataset_dir, num_shards=2, shard_id=0)
About STL10 dataset:
STL10 dataset consists of 10 classes: airplane, bird, car, cat, deer, dog, horse, monkey, ship, truck. Images are 96x96 pixels, color. 500 training images, 800 test images per class and 100000 unlabeled images. Labels are 0-indexed, and unlabeled images have -1 as their labels.
Here is the original STL10 dataset structure. You can unzip the dataset files into this directory structure and read by MindSpore’s API.
. └── stl10_dataset_dir ├── train_X.bin ├── train_y.bin ├── test_X.bin ├── test_y.bin └── unlabeled_X.bin
Citation of STL10 dataset:
@techreport{Coates10, author = {Adam Coates}, title = {Learning multiple layers of features from tiny images}, year = {20010}, howpublished = {https://cs.stanford.edu/~acoates/stl10/}, description = {The STL-10 dataset consists of 96x96 RGB images in 10 classes, with 500 training images and 800 testing images per class. There are 5000 training images and 8000 test images. It also has 100000 unlabeled images for unsupervised learning. These examples are extracted from a similar but broader distribution of images. } }
Pre-processing Operation
Apply a function in this dataset. |
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Concatenate the dataset objects in the input list. |
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Filter dataset by prediction. |
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Map func to each row in dataset and flatten the result. |
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Apply each operation in operations to this dataset. |
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The specified columns will be selected from the dataset and passed into the pipeline with the order specified. |
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Rename the columns in input datasets. |
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Repeat this dataset count times. |
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Reset the dataset for next epoch. |
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Save the dynamic data processed by the dataset pipeline in common dataset format. |
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Shuffle the dataset by creating a cache with the size of buffer_size . |
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Skip the first N elements of this dataset. |
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Split the dataset into smaller, non-overlapping datasets. |
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Takes at most given numbers of elements from the dataset. |
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Zip the datasets in the sense of input tuple of datasets. |
Batch
Combine batch_size number of consecutive rows into batch which apply per_batch_map to the samples first. |
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Bucket elements according to their lengths. |
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Combine batch_size number of consecutive rows into batch which apply pad_info to the samples first. |
Iterator
Create an iterator over the dataset. |
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Create an iterator over the dataset. |
Attribute
Return the size of batch. |
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Return the class index. |
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Return the names of the columns in dataset. |
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Return the number of batches in an epoch. |
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Get the replication times in RepeatDataset. |
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Get the column index, which represents the corresponding relationship between the data column order and the network when using the sink mode. |
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Get the number of classes in a dataset. |
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Get the shapes of output data. |
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Get the types of output data. |
Apply Sampler
Add a child sampler for the current dataset. |
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Replace the last child sampler of the current dataset, remaining the parent sampler unchanged. |
Others
Return a transferred Dataset that transfers data through a device. |
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Release a blocking condition and trigger callback with given data. |
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Add a blocking condition to the input Dataset and a synchronize action will be applied. |
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Serialize a pipeline into JSON string and dump into file if filename is provided. |