mindspore.dataset.Caltech101Dataset

class mindspore.dataset.Caltech101Dataset(dataset_dir, target_type=None, num_samples=None, num_parallel_workers=1, shuffle=None, decode=False, sampler=None, num_shards=None, shard_id=None)

Caltech 101 dataset.

The columns of the generated dataset depend on the value of target_type .

• When target_type is 'category', the columns are [image, category] .

• When target_type is 'annotation', the columns are [image, annotation] .

• When target_type is 'all', the columns are [image, category, annotation] .

The tensor of column image is of the uint8 type. The tensor of column category is of the uint32 type. The tensor of column annotation is a 2-dimensional ndarray that stores the contour of the image and consists of a series of points.

Parameters

• dataset_dir (str) – Path to the root directory that contains the dataset. This root directory contains two subdirectories, one is called 101_ObjectCategories, which stores images, and the other is called Annotations, which stores annotations.

• target_type (str, optional) – Target of the image. If target_type is 'category', return category represents the target class. If target_type is 'annotation', return annotation. If target_type is 'all', return category and annotation. Default: None , means 'category'.

• 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 subprocesses to read the data. Default: 1.

• shuffle (bool, optional) – Whether or not to perform shuffle on the dataset. Default: None , expected order behavior shown in the table below.

• decode (bool, optional) – Whether or not to decode the images after reading. Default: False.

• 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 maximum 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.

Raises

• RuntimeError – If dataset_dir 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 shard_id is not in range of [0, num_shards ).

• ValueError – If target_type is not 'category', 'annotation' or 'all' .

• ValueError – If num_parallel_workers exceeds the max thread numbers.

Tutorial Examples:

• Load & Process Data With Dataset Pipeline

Note

• The parameters num_samples , shuffle , num_shards , shard_id can be used to control the sampler used in the dataset, and their effects when combined with parameter sampler are as follows.

Sampler obtained by different combinations of parameters sampler and num_samples , shuffle , num_shards , shard_id

Parameter sampler	Parameter num_shards / shard_id	Parameter shuffle	Parameter num_samples	Sampler Used
mindspore.dataset.Sampler type	None	None	None	sampler
numpy.ndarray,list,tuple,int type	/	/	num_samples	SubsetSampler(indices = sampler , num_samples = num_samples )
iterable type	/	/	num_samples	IterSampler(sampler = sampler , num_samples = num_samples )
None	num_shards / shard_id	None / True	num_samples	DistributedSampler(num_shards = num_shards , shard_id = shard_id , shuffle = True , num_samples = num_samples )
None	num_shards / shard_id	False	num_samples	DistributedSampler(num_shards = num_shards , shard_id = shard_id , shuffle = False , num_samples = num_samples )
None	None	None / True	None	RandomSampler(num_samples = num_samples )
None	None	None / True	num_samples	RandomSampler(replacement = True , num_samples = num_samples )
None	None	False	num_samples	SequentialSampler(num_samples = num_samples )

Examples

>>> import mindspore.dataset as ds
>>> caltech101_dataset_directory = "/path/to/caltech101_dataset_directory"
>>>
>>> # 1) Read all samples (image files) in caltech101_dataset_directory with 8 threads
>>> dataset = ds.Caltech101Dataset(dataset_dir=caltech101_dataset_directory, num_parallel_workers=8)
>>>
>>> # 2) Read all samples (image files) with the target_type "annotation"
>>> dataset = ds.Caltech101Dataset(dataset_dir=caltech101_dataset_directory, target_type="annotation")

About Caltech101Dataset:

Pictures of objects belonging to 101 categories, about 40 to 800 images per category. Most categories have about 50 images. The size of each image is roughly 300 x 200 pixels. The official provides the contour data of each object in each picture, which is the annotation.

Here is the original Caltech101 dataset structure, and you can unzip the dataset files into the following directory structure, which are read by MindSpore API.

.
└── caltech101_dataset_directory
    ├── 101_ObjectCategories
    │    ├── Faces
    │    │    ├── image_0001.jpg
    │    │    ├── image_0002.jpg
    │    │    ...
    │    ├── Faces_easy
    │    │    ├── image_0001.jpg
    │    │    ├── image_0002.jpg
    │    │    ...
    │    ├── ...
    └── Annotations
         ├── Airplanes_Side_2
         │    ├── annotation_0001.mat
         │    ├── annotation_0002.mat
         │    ...
         ├── Faces_2
         │    ├── annotation_0001.mat
         │    ├── annotation_0002.mat
         │    ...
         ├── ...

Citation:

@article{FeiFei2004LearningGV,
author    = {Li Fei-Fei and Rob Fergus and Pietro Perona},
title     = {Learning Generative Visual Models from Few Training Examples:
            An Incremental Bayesian Approach Tested on 101 Object Categories},
journal   = {Computer Vision and Pattern Recognition Workshop},
year      = {2004},
url       = {https://data.caltech.edu/records/mzrjq-6wc02},
}

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	Take the first specified number of samples 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 that yields samples of type dict, while the key is the column name and the value is the data.
mindspore.dataset.Dataset.create_tuple_iterator	Create an iterator over the dataset that yields samples of type list, whose elements are the data for each column.

Attribute

mindspore.dataset.Dataset.get_batch_size	Return the size of batch.
mindspore.dataset.Dataset.get_class_indexing	Get the mapping dictionary from category names to category indexes.
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.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.