mindspore.dataset.VOCDataset

class mindspore.dataset.VOCDataset(dataset_dir, task='Segmentation', usage='train', class_indexing=None, num_samples=None, num_parallel_workers=None, shuffle=None, decode=False, sampler=None, num_shards=None, shard_id=None, cache=None, extra_metadata=False, decrypt=None)[source]

A source dataset that reads and parses VOC dataset.

The generated dataset with different task setting has different output columns:

  • task = Detection , output columns: [image, dtype=uint8] , [bbox, dtype=float32] , [label, dtype=uint32] , [difficult, dtype=uint32] , [truncate, dtype=uint32] .

  • task = Segmentation , output columns: [image, dtype=uint8] , [target,dtype=uint8] .

Parameters
  • dataset_dir (str) – Path to the root directory that contains the dataset.

  • task (str, optional) – Set the task type of reading voc data, now only support ‘Segmentation’ or ‘Detection’. Default: ‘Segmentation’.

  • usage (str, optional) – Set the task type of ImageSets. Default: ‘train’. If task is ‘Segmentation’, image and annotation list will be loaded in ./ImageSets/Segmentation/usage + “.txt”; If task is ‘Detection’, image and annotation list will be loaded in ./ImageSets/Main/usage + “.txt”; if task and usage are not set, image and annotation list will be loaded in ./ImageSets/Segmentation/train.txt as default.

  • class_indexing (dict, optional) – A str-to-int mapping from label name to index, only valid in ‘Detection’ task. Default: None, the folder names will be sorted alphabetically and each class will be given a unique index starting from 0.

  • 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 workers to read the data. Default: None, number set in the config.

  • shuffle (bool, optional) – Whether to perform shuffle on the dataset. Default: None, expected order behavior shown in the table below.

  • decode (bool, optional) – 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.

  • 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.

  • extra_metadata (bool, optional) – Flag to add extra meta-data to row. If True, an additional column named [_meta-filename, dtype=string] will be output at the end. Default: False.

  • decrypt (callable, optional) – Image decryption function, which accepts the path of the encrypted image file and returns the decrypted bytes data. Default: None, no decryption.

Raises
  • RuntimeError – If dataset_dir does not contain data files.

  • RuntimeError – If xml of Annotations is an invalid format.

  • RuntimeError – If xml of Annotations loss attribution of object .

  • RuntimeError – If xml of Annotations loss attribution of bndbox .

  • 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 num_parallel_workers exceeds the max thread numbers.

  • ValueError – If task is not equal ‘Segmentation’ or ‘Detection’.

  • ValueError – If task equal ‘Segmentation’ but class_indexing is not None.

  • ValueError – If txt related to mode is not exist.

  • ValueError – If shard_id is invalid (< 0 or >= num_shards).

Note

  • Column ‘[_meta-filename, dtype=string]’ won’t be output unless an explicit rename dataset op is added to remove the prefix(‘_meta-‘).

  • 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

>>> voc_dataset_dir = "/path/to/voc_dataset_directory"
>>>
>>> # 1) Read VOC data for segmentation training
>>> dataset = ds.VOCDataset(dataset_dir=voc_dataset_dir, task="Segmentation", usage="train")
>>>
>>> # 2) Read VOC data for detection training
>>> dataset = ds.VOCDataset(dataset_dir=voc_dataset_dir, task="Detection", usage="train")
>>>
>>> # 3) Read all VOC dataset samples in voc_dataset_dir with 8 threads in random order
>>> dataset = ds.VOCDataset(dataset_dir=voc_dataset_dir, task="Detection", usage="train",
...                         num_parallel_workers=8)
>>>
>>> # 4) Read then decode all VOC dataset samples in voc_dataset_dir in sequence
>>> dataset = ds.VOCDataset(dataset_dir=voc_dataset_dir, task="Detection", usage="train",
...                         decode=True, shuffle=False)
>>>
>>> # In VOC dataset, if task='Segmentation', each dictionary has keys "image" and "target"
>>> # In VOC dataset, if task='Detection', each dictionary has keys "image" and "annotation"

About VOC dataset:

The PASCAL Visual Object Classes (VOC) challenge is a benchmark in visual object category recognition and detection, providing the vision and machine learning communities with a standard dataset of images and annotation, and standard evaluation procedures.

You can unzip the original VOC-2012 dataset files into this directory structure and read by MindSpore’s API.

.
└── voc2012_dataset_dir
    ├── Annotations
    │    ├── 2007_000027.xml
    │    ├── 2007_000032.xml
    │    ├── ...
    ├── ImageSets
    │    ├── Action
    │    ├── Layout
    │    ├── Main
    │    └── Segmentation
    ├── JPEGImages
    │    ├── 2007_000027.jpg
    │    ├── 2007_000032.jpg
    │    ├── ...
    ├── SegmentationClass
    │    ├── 2007_000032.png
    │    ├── 2007_000033.png
    │    ├── ...
    └── SegmentationObject
         ├── 2007_000032.png
         ├── 2007_000033.png
         ├── ...

Citation:

@article{Everingham10,
author       = {Everingham, M. and Van~Gool, L. and Williams, C. K. I. and Winn, J. and Zisserman, A.},
title        = {The Pascal Visual Object Classes (VOC) Challenge},
journal      = {International Journal of Computer Vision},
volume       = {88},
year         = {2012},
number       = {2},
month        = {jun},
pages        = {303--338},
biburl       = {http://host.robots.ox.ac.uk/pascal/VOC/pubs/everingham10.html#bibtex},
howpublished = {http://host.robots.ox.ac.uk/pascal/VOC/voc2012/index.html}
}

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.