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Copy file name to clipboardexpand all lines: docs/reference/attention-neural-bag-of-feature-learner.md
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It can also be used to train a time-series classifier.
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The [AttentionNeuralBagOfFeatureLearner](#opendr.perception.heart_anomaly_detection.attention_neural_bag_of_feature.attention_neural_bag_of_feature_learner.py) class has the following public methods:
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The [AttentionNeuralBagOfFeatureLearner](/src/opendr/perception/heart_anomaly_detection/attention_neural_bag_of_feature/attention_neural_bag_of_feature_learner.py) class has the following public methods:
Copy file name to clipboardexpand all lines: docs/reference/detr.md
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Inside this folder, the model is saved as *"detr_[backbone_model].pth"* and the metadata file as *"detr_[backbone].json"*.
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If the directory already exists, the *"detr_[backbone_model].pth"* and *"detr_[backbone].json"* files are overwritten.
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If [`self.optimize`](#DetrLearner.optimize) was run previously, it saves the optimized ONNX model in a similar fashion with an
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If [`self.optimize`](/src/opendr/perception/object_detection_2d/detr/detr_learner.py#L599) was run previously, it saves the optimized ONNX model in a similar fashion with an
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*".onnx"* extension, by copying it from the *self.temp_path* it was saved previously during conversion.
Copy file name to clipboardexpand all lines: docs/reference/engine-data.md
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### Class engine.data.Data
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Bases: `abc.ABC`
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Data abstract class allows for representing different types of data.
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[Data](/src/opendr/engine/data.py#L24) abstract class allows for representing different types of data.
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This class serves as the basis for more complicated data types.
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For data classes, conversion from (using the constructor) and to [NumPy](https://numpy.org) arrays (using the `numpy()` method) will be supported to make the library compliant with the standard pipelines used by the computer vision and robotics communities.
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This class provides abstract methods for returning a [NumPy](https://numpy.org) compatible representation of data `numpy()`.
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The [Data](#class-engine.data.data) class has the following public methods:
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The *Data* class has the following public methods:
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#### data()
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Return the data argument.
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This method returns the internal representation of the data, which might not be a [NumPy](https://numpy.org) array.
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A class used for representing multidimensional timeseries data.
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The [Timeseries](#class_engine.data.Timeseries) class has the following public methods:
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The [Timeseries](/src/opendr/engine/data.py#L145) class has the following public methods:
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#### Timeseries(data=None)
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Construct a new [Timeseries](#class_engine.data.Timeseries) object based from *data*.
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Construct a new *Timeseries* object based from *data*.
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*data* is expected to be a 2-D array that can be casted into a 2-D [NumPy](https://numpy.org) array, where the first dimension corresponds to time and the second to the features.
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#### data()
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A class used for representing multidimensional vector data.
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The [Vector](#class_engine.data.Vector) class has the following public methods:
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The [Vector](/src/opendr/engine/data.py#L79) class has the following public methods:
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#### Vector(data=None)
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Construct a new [Vector](#class_engine.data.Vector) object based from *data*.
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Construct a new *Vector* object based from *data*.
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*data* is expected to be a 1-D array that can be casted into a 1-D [NumPy](https://numpy.org) array.
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#### data()
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A class used for representing image data.
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The [Image](#class_engine.data.Image) class has the following public methods:
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The [Image](/src/opendr/engine/data.py#L211) class has the following public methods:
Construct a new [Image](#class_engine.data.Image) object based on *data*.
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Construct a new *Image* object based on *data*.
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*data* is expected to be a 3-D array that can be casted into a 3-D [NumPy](https://numpy.org) array.
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*dtype* is expected to be a [NumPy](https://numpy.org) data type.
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*guess_format* if set to True, then tries to automatically infer whether an [OpenCV](https://opencv.org) image was supplied and then automatically converts it into OpenDR format.
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This method transforms the internal CHW/RGB representation into HWC/BGR used by OpenCV.
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#### open(filename)
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Construct a new [Image](#class-engine.data.Image) object from the given image file.
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Construct a new *Image* object from the given image file.
Construct a new [PointCloudWithCalibration](#class_engine.data.PointCloud) object based on *data*.
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Construct a new *PointCloudWithCalibration* object based on *data*.
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*data* is expected to be a 2-D array that can be casted into a 2-D [NumPy](https://numpy.org) array.
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*calib* is expected to be a dictionary with `P`, `R0_rect`, `Tr_velo_to_cam` and `Tr_imu_to_velo` matrices in [NumPy](https://numpy.org)-compatible format.
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-`P[x]` matrices project a point in the rectified referenced camera coordinate to the `camera[x]` image.
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A class used for representing a sequence of body skeletons in a video.
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The [SkeletonSequence](#class_engine.data.SkeletonSequence) class has the following public methods:
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The [SkeletonSequence](/src/opendr/engine/data.py#L631) class has the following public methods:
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#### SkeletonSequence(data=None)
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Construct a new [SkeletonSequence](#class_engine.data.SkeletonSequence) object based on *data*.
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Construct a new *SkeletonSequence* object based on *data*.
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*data* is expected to be a 5-D array that can be casted into a 5-D [NumPy](https://numpy.org) array.
Copy file name to clipboardexpand all lines: docs/reference/engine-datasets.md
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### Class engine.datasets.Dataset
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Bases: `abc.ABC`
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Dataset abstract class for representing different types of datasets.
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[Dataset](/src/opendr/engine/datasets.py#L21) abstract class for representing different types of datasets.
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This class serves as the basis for more complicated datasets.
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The [Dataset](#class-engine.datasets.Dataset) class has the following public methods:
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The *Dataset* class has the following public methods:
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#### Dataset()
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### Class engine.datasets.DatasetIterator
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Bases: `engine.datasets.Dataset`
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DatasetIterator serves as an abstraction layer over the different types of datasets.
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[DatasetIterator](/src/opendr/engine/datasets.py#L31) serves as an abstraction layer over the different types of datasets.
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In this way it provides the opportunity to users to implement different kinds of datasets, while providing a uniform interface.
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DatasetIterator should return a tuple of ([engine.data.Data](#class_engine.data.Data), [engine.target.Target](#class_engine.target.Target)) using the index operator `dataset_iterator[idx]`
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DatasetIterator should return a tuple of (`engine.data.Data`, [`engine.target.Target`](/src/opendr/engine/target.py)) using the index operator `dataset_iterator[idx]`
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The [DatasetIterator](#class-engine.datasets.DatasetIterator) class has the following public methods:
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The *DatasetIterator* class has the following public methods:
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#### DatasetIterator()
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Construct a new [DatasetIterator](#class-engine.datasets.DatasetIterator) object.
ExternalDataset provides a way for handling well-known external dataset formats (e.g., COCO, PascalVOC, Imagenet, etc.) directly by OpenDR, without requiring any special effort from the users for writing a specific loader.
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The [ExternalDataset](#class-engine.datasets.ExternalDataset) class has the following public methods:
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The [ExternalDataset](/src/opendr/engine/datasets.py#L101) class has the following public methods:
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#### ExternalDataset(path, dataset_type)
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Construct a new [ExternalDataset](#class-engine.datasets.ExternalDataset) object based on *dataset_type*.
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Construct a new *ExternalDataset* object based on *dataset_type*.
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*dataset_type* is expected to be a string with a name of the dataset type like "voc", "coco", "imagenet", "kitti", etc.
Construct a new [MappedDatasetIterator](#class-engine.datasets.MappedDatasetIterator) object based on existing *original*[DatasetIterator](#class-engine.datasets.DatasetIterator) and the *map_function*.
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Construct a new *MappedDatasetIterator* object based on existing *original*[DatasetIterator](/src/opendr/engine/datasets.py#L31) and the *map_function*.
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### Examples
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***Generation of a MappedDatasetIterator from an existing DatasetIterator**.
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