mindspore.dataset.SVHNDataset

class mindspore.dataset.SVHNDataset(dataset_dir, usage=None, num_samples=None, num_parallel_workers=1, shuffle=None, sampler=None, num_shards=None, shard_id=None)[source]

A source dataset that reads and parses SVHN dataset.

The generated dataset has two columns: [image, label] . The tensor of column image is of the uint8 type. The tensor of column label is of a scalar of uint32 type.

Parameters

dataset_dir (str) – Path to the root directory that contains the dataset.
usage (str, optional) – Specify the ‘train’, ‘test’, ‘extra’ or ‘all’ parts of dataset. Default: None, will read all samples.
num_samples (int, optional) – The number of samples to be included in the dataset. Default: None, all images.
num_parallel_workers (int, optional) – Number of subprocesses used to fetch the dataset in parallel. Default: 1.
shuffle (bool, optional) – Whether or not 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. Random accessible input is required. 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 must be specified only when num_shards is also specified.

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 invalid (< 0 or >= num_shards).

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.

Expected Order Behavior of Using ‘sampler’ and ‘shuffle’
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

>>> svhn_dataset_dir = "/path/to/svhn_dataset_directory"
>>> dataset = ds.SVHNDataset(dataset_dir=svhn_dataset_dir, usage="train")

About SVHN dataset:

SVHN dataset consists of 10 digit classes and is obtained from house numbers in Google Street View images.

Here is the original SVHN dataset structure. You can unzip the dataset files into this directory structure and read by MindSpore’s API.

.
└── svhn_dataset_dir
     ├── train_32x32.mat
     ├── test_32x32.mat
     └── extra_32x32.mat

Citation:

@article{
  title={Reading Digits in Natural Images with Unsupervised Feature Learning},
  author={Yuval Netzer, Tao Wang, Adam Coates, Alessandro Bissacco, Bo Wu, Andrew Y. Ng},
  conference={NIPS Workshop on Deep Learning and Unsupervised Feature Learning 2011.},
  year={2011},
  publisher={NIPS}
  url={http://ufldl.stanford.edu/housenumbers}
}

Pre-processing Operation

`mindspore.dataset.Dataset.apply`	Apply a function in this dataset.
`mindspore.dataset.Dataset.concat`	Concatenate the dataset objects in the input list.
`mindspore.dataset.Dataset.filter`	Filter dataset by prediction.
`mindspore.dataset.Dataset.flat_map`	Map func to each row in dataset and flatten the result.
`mindspore.dataset.Dataset.map`	Apply each operation in operations to this dataset.
`mindspore.dataset.Dataset.project`	The specified columns will be selected from the dataset and passed into the pipeline with the order specified.
`mindspore.dataset.Dataset.rename`	Rename the columns in input datasets.
`mindspore.dataset.Dataset.repeat`	Repeat this dataset count times.
`mindspore.dataset.Dataset.reset`	Reset the dataset for next epoch.
`mindspore.dataset.Dataset.save`	Save the dynamic data processed by the dataset pipeline in common dataset format.
`mindspore.dataset.Dataset.shuffle`	Shuffle the dataset by creating a cache with the size of buffer_size .
`mindspore.dataset.Dataset.skip`	Skip the first N elements of this dataset.
`mindspore.dataset.Dataset.split`	Split the dataset into smaller, non-overlapping datasets.
`mindspore.dataset.Dataset.take`	Takes at most given numbers of elements from the dataset.
`mindspore.dataset.Dataset.zip`	Zip the datasets in the sense of input tuple of datasets.

Batch

`mindspore.dataset.Dataset.batch`	Combine batch_size number of consecutive rows into batch which apply per_batch_map to the samples first.
`mindspore.dataset.Dataset.bucket_batch_by_length`	Bucket elements according to their lengths.
`mindspore.dataset.Dataset.padded_batch`	Combine batch_size number of consecutive rows into batch which apply pad_info to the samples first.

Iterator

`mindspore.dataset.Dataset.create_dict_iterator`	Create an iterator over the dataset.
`mindspore.dataset.Dataset.create_tuple_iterator`	Create an iterator over the dataset.

Attribute

`mindspore.dataset.Dataset.get_batch_size`	Return the size of batch.
`mindspore.dataset.Dataset.get_class_indexing`	Return the class index.
`mindspore.dataset.Dataset.get_col_names`	Return the names of the columns in dataset.
`mindspore.dataset.Dataset.get_dataset_size`	Return the number of batches in an epoch.
`mindspore.dataset.Dataset.get_repeat_count`	Get the replication times in RepeatDataset.
`mindspore.dataset.Dataset.input_indexs`	Get the column index, which represents the corresponding relationship between the data column order and the network when using the sink mode.
`mindspore.dataset.Dataset.num_classes`	Get the number of classes in a dataset.
`mindspore.dataset.Dataset.output_shapes`	Get the shapes of output data.
`mindspore.dataset.Dataset.output_types`	Get the types of output data.

Apply Sampler

`mindspore.dataset.MappableDataset.add_sampler`	Add a child sampler for the current dataset.
`mindspore.dataset.MappableDataset.use_sampler`	Replace the last child sampler of the current dataset, remaining the parent sampler unchanged.

Others

`mindspore.dataset.Dataset.device_que`	Return a transferred Dataset that transfers data through a device.
`mindspore.dataset.Dataset.sync_update`	Release a blocking condition and trigger callback with given data.
`mindspore.dataset.Dataset.sync_wait`	Add a blocking condition to the input Dataset and a synchronize action will be applied.
`mindspore.dataset.Dataset.to_json`	Serialize a pipeline into JSON string and dump into file if filename is provided.