mindspore.dataset.InMemoryGraphDataset

class mindspore.dataset.InMemoryGraphDataset(data_dir, save_dir='./processed', column_names='graph', num_samples=None, num_parallel_workers=1, shuffle=None, num_shards=None, shard_id=None, python_multiprocessing=True, max_rowsize=6)

Basic Dataset for loading graph into memory.

Recommended to Implement your own dataset with inheriting this class, and implement your own method like process , save and load , refer source code of ArgoverseDataset for how to implement your own dataset. When init your own dataset like ArgoverseDataset, The executed process like follows. Check if there are already processed data under given data_dir , if so will call load method to load it directly, otherwise it will call process method to create graphs and call save method to save the graphs into save_dir .

You can access graph in created dataset using graphs = my_dataset.graphs and also you can iterate dataset and get data using my_dataset.create_tuple_iterator() (in this way you need to implement methods like __getitem__ and __len__), referring to the following example for detail. Note: we have overwritten the __new__ method to reinitialize __init__ internally, which means the user-defined __new__ method won’t work.

Parameters

• data_dir (str) – directory for loading dataset, here contains origin format data and will be loaded in process method.

• save_dir (str) – relative directory for saving processed dataset, this directory is under data_dir . Default: ‘./processed’.

• 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__ . Default: ‘graph’.

• num_samples (int, optional) – The number of samples to be included in the dataset. Default: None, all samples.

• 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. This parameter can only be specified when the implemented dataset has a random access attribute ( __getitem__ ). Default: None.

• 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 must be specified only when num_shards is also specified.

• 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.

• max_rowsize (int, optional) – Maximum size of row in MB that is used for shared memory allocation to copy data between processes. This is only used if python_multiprocessing is set to True. Default: 6 MB.

Raises

• TypeError – If data_dir is not of type str.

• TypeError – If save_dir is not of type str.

• TypeError – If num_parallel_workers is not of type int.

• TypeError – If shuffle is not of type bool.

• TypeError – If python_multiprocessing is not of type bool.

• TypeError – If perf_mode is not of type bool.

• RuntimeError – If data_dir is not valid or does not exit.

• 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.

Examples

>>> from mindspore.dataset import InMemoryGraphDataset, Graph
>>>
>>> class MyDataset(InMemoryGraphDataset):
...     def __init__(self, data_dir):
...         super().__init__(data_dir)
...
...     def process(self):
...         # create graph with loading data in given data_dir
...         # here create graph with numpy array directly instead
...         edges = np.array([[0, 1], [1, 2]])
...         graph = Graph(edges=edges)
...         self.graphs.append(graph)
...
...     def __getitem__(self, index):
...         # this method and '__len__' method are required when iterating created dataset
...         graph = self.graphs[index]
...         return graph.get_all_edges('0')
...
...     def __len__(self):
...         return len(self.graphs)

load()

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

process()

Process method based on origin dataset, override this method in your our dataset class.

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.