mindspore.dataset.ArgoverseDataset

class mindspore.dataset.ArgoverseDataset(data_dir, column_names='graph', num_parallel_workers=1, shuffle=None, python_multiprocessing=True, perf_mode=True)[source]

Load argoverse dataset and create graph.

Here argoverse dataset is public dataset for autonomous driving, current implement ArgoverseDataset is mainly for loading Motion Forecasting Dataset in argoverse dataset, recommend to visit official website for more detail: https://www.argoverse.org/av1.html#download-link.

Parameters

data_dir (str) – directory for loading dataset, here contains origin format data and will be loaded in process method.
column_names (Union[str, list[str]], optional) – single column name or list of column names of the dataset, num of column name should be equal to num of item in return data when implement method like __getitem__ , recommend to specify it with column_names=[“edge_index”, “x”, “y”, “cluster”, “valid_len”, “time_step_len”] like the following example.
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.
python_multiprocessing (bool, optional) – Parallelize Python operations with multiple worker process. This option could be beneficial if the Python operation is computational heavy. Default: True.
perf_mode (bool, optional) – mode for obtaining higher performance when iterate created dataset(will call __getitem__ method in this process). Default True, will save all the data in graph (like edge index, node feature and graph feature) into graph feature.

Examples

>>> from mindspore.dataset import ArgoverseDataset
>>>
>>> argoverse_dataset_dir = "/path/to/argoverse_dataset_directory"
>>> graph_dataset = ArgoverseDataset(data_dir=argoverse_dataset_dir,
...                                  column_names=["edge_index", "x", "y", "cluster", "valid_len",
...                                                "time_step_len"])
>>> for item in graph_dataset.create_dict_iterator(output_numpy=True, num_epochs=1):
...     pass

load(): Load data from given(processed) path, you can also override this method in your dataset class.

process()[source]: Process method for argoverse dataset, here we load original dataset and create a lot of graphs based on it. Pre-processed method mainly refers to: https://github.com/xk-huang/yet-another-vectornet/blob/master/dataset.py.

save(): Save processed data into disk in numpy.npz format, you can also override this method in your dataset class.

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.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.