mindspore.dataset.KITTIDataset

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class mindspore.dataset.KITTIDataset(dataset_dir, usage=None, num_samples=None, num_parallel_workers=None, shuffle=None, decode=False, sampler=None, num_shards=None, shard_id=None, cache=None)[source]

KITTI dataset.

When usage is "train", the generated dataset has multiple columns: [image, label, truncated, occluded, alpha, bbox, dimensions, location, rotation_y] ; When usage is “test”, the generated dataset has only one column: [image] . The tensor of column image is of the uint8 type. The tensor of column label is of the uint32 type. The tensor of column truncated is of the float32 type. The tensor of column occluded is of the uint32 type. The tensor of column alpha is of the float32 type. The tensor of column bbox is of the float32 type. The tensor of column dimensions is of the float32 type. The tensor of column location is of the float32 type. The tensor of column rotation_y is of the float32 type.

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

  • usage (str, optional) – Usage of this dataset, can be "train" or "test" . "train" will read 7481 train samples, "test" will read from 7518 test samples without label. Default: None , will use "train" .

  • num_samples (int, optional) – The number of images to be included in the dataset. Default: None , will include 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 by mindspore.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.

  • 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 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 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 dataset_dir is not exist.

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

Tutorial Examples:

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
>>> kitti_dataset_dir = "/path/to/kitti_dataset_directory"
>>>
>>> # 1) Read all KITTI train dataset samples in kitti_dataset_dir in sequence
>>> dataset = ds.KITTIDataset(dataset_dir=kitti_dataset_dir, usage="train")
>>>
>>> # 2) Read then decode all KITTI test dataset samples in kitti_dataset_dir in sequence
>>> dataset = ds.KITTIDataset(dataset_dir=kitti_dataset_dir, usage="test",
...                           decode=True, shuffle=False)

About KITTI dataset:

KITTI (Karlsruhe Institute of Technology and Toyota Technological Institute) is one of the most popular datasets for use in mobile robotics and autonomous driving. It consists of hours of traffic scenarios recorded with a variety of sensor modalities, including high-resolution RGB, grayscale stereo cameras, and a 3D laser scanner. Despite its popularity, the dataset itself does not contain ground truth for semantic segmentation. However, various researchers have manually annotated parts of the dataset to fit their necessities. Álvarez et al. generated ground truth for 323 images from the road detection challenge with three classes: road, vehicles and sky. Zhang et al. annotated 252 (140 for training and 112 for testing) acquisitions – RGB and Velodyne scans – from the tracking challenge for ten object categories: building, sky, road, vegetation, sidewalk, car, pedestrian, cyclist, sign/pole, and fence.

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

.
└── kitti_dataset_directory
    ├── data_object_image_2
    │    ├──training
    │    │    ├──image_2
    │    │    │    ├── 000000000001.jpg
    │    │    │    ├── 000000000002.jpg
    │    │    │    ├── ...
    │    ├──testing
    │    │    ├── image_2
    │    │    │    ├── 000000000001.jpg
    │    │    │    ├── 000000000002.jpg
    │    │    │    ├── ...
    ├── data_object_label_2
    │    ├──training
    │    │    ├──label_2
    │    │    │    ├── 000000000001.jpg
    │    │    │    ├── 000000000002.jpg
    │    │    │    ├── ...

Citation:

@INPROCEEDINGS{Geiger2012CVPR,
author={Andreas Geiger and Philip Lenz and Raquel Urtasun},
title={Are we ready for Autonomous Driving? The KITTI Vision Benchmark Suite},
booktitle={Conference on Computer Vision and Pattern Recognition (CVPR)},
year={2012}
}

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.

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

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.