tfg.datasets.modelnet40.ModelNet40

2024-05-19 11:10| 来源: 网络整理| 查看: 265

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ModelNet40.

tfg.datasets.modelnet40.ModelNet40( *, file_format: Union[None, str, file_adapters.FileFormat] = None, **kwargs )

Args

file_format EXPERIMENTAL, may change at any time; Format of the record files in which dataset will be read/written to. If None, defaults to tfrecord. **kwargs Arguments passed to DatasetBuilder.

Attributes

builder_config tfds.core.BuilderConfig for this builder. canonical_version

data_dir

data_path

info tfds.core.DatasetInfo for this builder. release_notes

supported_versions

version

versions Versions (canonical + availables), in preference order. Methods as_dataset as_dataset( split: Optional[Tree[splits_lib.SplitArg]] = None, *, batch_size: Optional[int] = None, shuffle_files: bool = False, decoders: Optional[TreeDict[decode.partial_decode.DecoderArg]] = None, read_config: Optional[read_config_lib.ReadConfig] = None, as_supervised: bool = False )

Constructs a tf.data.Dataset.

Callers must pass arguments as keyword arguments.

The output types vary depending on the parameters. Examples:

builder = tfds.builder('imdb_reviews') builder.download_and_prepare() # Default parameters: Returns the dict of tf.data.Dataset ds_all_dict = builder.as_dataset() assert isinstance(ds_all_dict, dict) print(ds_all_dict.keys()) # ==> ['test', 'train', 'unsupervised'] assert isinstance(ds_all_dict['test'], tf.data.Dataset) # Each dataset (test, train, unsup.) consists of dictionaries # {'label': , # 'text': } # {'label': , # 'text': } # With as_supervised: tf.data.Dataset only contains (feature, label) tuples ds_all_supervised = builder.as_dataset(as_supervised=True) assert isinstance(ds_all_supervised, dict) print(ds_all_supervised.keys()) # ==> ['test', 'train', 'unsupervised'] assert isinstance(ds_all_supervised['test'], tf.data.Dataset) # Each dataset (test, train, unsup.) consists of tuples (text, label) # (, # ) # (, # ) # Same as above plus requesting a particular split ds_test_supervised = builder.as_dataset(as_supervised=True, split='test') assert isinstance(ds_test_supervised, tf.data.Dataset) # The dataset consists of tuples (text, label) # (, # ) # (, # )

Args

split Which split of the data to load (e.g. 'train', 'test', ['train', 'test'], 'train[80%:]',...). See our split API guide. If None, will return all splits in a Dict[Split, tf.data.Dataset]. batch_size int, batch size. Note that variable-length features will be 0-padded if batch_size is set. Users that want more custom behavior should use batch_size=None and use the tf.data API to construct a custom pipeline. If batch_size == -1, will return feature dictionaries of the whole dataset with tf.Tensors instead of a tf.data.Dataset. shuffle_files bool, whether to shuffle the input files. Defaults to False. decoders Nested dict of Decoder objects which allow to customize the decoding. The structure should match the feature structure, but only customized feature keys need to be present. See the guide for more info. read_config tfds.ReadConfig, Additional options to configure the input pipeline (e.g. seed, num parallel reads,...). as_supervised bool, if True, the returned tf.data.Dataset will have a 2-tuple structure (input, label) according to builder.info.supervised_keys. If False, the default, the returned tf.data.Dataset will have a dictionary with all the features.

Returns tf.data.Dataset, or if split=None, dict.

If batch_size is -1, will return feature dictionaries containing the entire dataset in tf.Tensors instead of a tf.data.Dataset.

dataset_info_from_configs dataset_info_from_configs( **kwargs )

Returns the DatasetInfo using given kwargs and config files.

Sub-class should call this and add information not present in config files using kwargs directly passed to tfds.core.DatasetInfo object.

If information is present both in passed arguments and config files, config files will prevail.

Args

**kwargs kw args to pass to DatasetInfo directly. download_and_prepare download_and_prepare( *, download_dir: Optional[epath.PathLike] = None, download_config: Optional[download.DownloadConfig] = None, file_format: Optional[Union[str, file_adapters.FileFormat]] = None ) -> None

Downloads and prepares dataset for reading.

Args

download_dir directory where downloaded files are stored. Defaults to "~/tensorflow-datasets/downloads". download_config tfds.download.DownloadConfig, further configuration for downloading and preparing dataset. file_format optional str or file_adapters.FileFormat, format of the record files in which the dataset will be written.

Raises

IOError if there is not enough disk space available. RuntimeError when the config cannot be found. get_default_builder_config get_default_builder_config() -> Optional[BuilderConfig]

Returns the default builder config if there is one.

Note that for dataset builders that cannot use the cls.BUILDER_CONFIGS, we need a method that uses the instance to get BUILDER_CONFIGS and DEFAULT_BUILDER_CONFIG_NAME.

Returns the default builder config if there is one

get_metadata @classmethod get_metadata() -> dataset_metadata.DatasetMetadata

Returns metadata (README, CITATIONS, ...) specified in config files.

The config files are read from the same package where the DatasetBuilder has been defined, so those metadata might be wrong for legacy builders.

get_reference get_reference( namespace: Optional[str] = None ) -> naming.DatasetReference

Returns a reference to the dataset produced by this dataset builder.

Includes the config if specified, the version, and the data_dir that should contain this dataset.

Arguments

namespace if this dataset is a community dataset, and therefore has a namespace, then the namespace must be provided such that it can be set in the reference. Note that a dataset builder is not aware that it is part of a namespace.

Returns a reference to this instantiated builder.

load

View source

@staticmethod load( *args, **kwargs )

Class Variables

BUILDER_CONFIGS [] DEFAULT_BUILDER_CONFIG_NAME None MANUAL_DOWNLOAD_INSTRUCTIONS None MAX_SIMULTANEOUS_DOWNLOADS None RELEASE_NOTES { }

SUPPORTED_VERSIONS [] VERSION Instance of tensorflow_datasets.core.utils.version.Version builder_config_cls None builder_configs { }

code_path Instance of etils.epath.gpath.PosixGPath default_builder_config None name 'model_net40' pkg_dir_path None url_infos None

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