mindspore.dataset.USPSDataset
- class mindspore.dataset.USPSDataset(dataset_dir, usage=None, num_samples=None, num_parallel_workers=None, shuffle=Shuffle.GLOBAL, num_shards=None, shard_id=None, cache=None)[source]
USPS(U.S. Postal Service) dataset.
The generated dataset has two columns:
[image, label]. The tensor of columnimageis of the uint8 type. The tensor of columnlabelis of the uint32 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'or'all'.'train'will read from 7,291 train samples,'test'will read from 2,007 test samples,'all'will read from all 9,298 samples. Default:None, will read all samples.num_samples (int, optional) – The number of images to be included in the dataset. Default:
None, will read 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 (Union[bool, Shuffle], optional) –
Perform reshuffling of the data every epoch. Bool type and Shuffle enum are both supported to pass in. Default:
Shuffle.GLOBAL. If shuffle isFalse, no shuffling will be performed. If shuffle isTrue, it is equivalent to setting shuffle tomindspore.dataset.Shuffle.GLOBAL. Set the mode of data shuffling by passing in enumeration variables:Shuffle.GLOBAL: Shuffle both the files and samples.Shuffle.FILES: Shuffle files only.
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. Used in data parallel training .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 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:
Examples
>>> import mindspore.dataset as ds >>> usps_dataset_dir = "/path/to/usps_dataset_directory" >>> >>> # Read 3 samples from USPS dataset >>> dataset = ds.USPSDataset(dataset_dir=usps_dataset_dir, num_samples=3)
About USPS dataset:
USPS is a digit dataset automatically scanned from envelopes by the U.S. Postal Service containing a total of 9,298 16×16 pixel grayscale samples. The images are centered, normalized and show a broad range of font styles.
Here is the original USPS dataset structure. You can download and unzip the dataset files into this directory structure and read by MindSpore's API.
. └── usps_dataset_dir ├── usps ├── usps.tCitation:
@article{hull1994database, title={A database for handwritten text recognition research}, author={Hull, Jonathan J.}, journal={IEEE Transactions on pattern analysis and machine intelligence}, volume={16}, number={5}, pages={550--554}, year={1994}, publisher={IEEE} }
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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Take the first specified number of samples 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 that yields samples of type dict, while the key is the column name and the value is the data. |
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Create an iterator over the dataset that yields samples of type list, whose elements are the data for each column. |
Attribute
Return the size of batch. |
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Get the mapping dictionary from category names to category indexes. |
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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
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. |