Wrapper for Classification Models¶
Provides class to wrap existing models in different frameworks so that they provide a unified API to the benchmarks.
KerasModel |
Create a Model instance from a Keras model. |
PyTorchModel |
Creates a Model instance from a PyTorch module. |
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class
perceptron.models.classification.KerasModel(model, bounds, channel_axis=3, preprocessing=(0, 1), predicts='probabilities')[source]¶ Create a
Modelinstance from a Keras model.Parameters: - model : keras.model.Model
The Keras model that are loaded.
- bounds : tuple
Tuple of lower and upper bound for the pixel values, usually (0, 1) or (0, 255).
- channel_axis : int
The index of the axis that represents color channels.
- preprocessing: 2-element tuple with floats or numpy arrays
Elementwises preprocessing of input; we first substract the first element of preprocessing from the input and then divide the input by the second element.
- predicts : str
Specifies whether the Keras model predicts logits or probabilities. Logits are preferred, but probabilities are the default.
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class
perceptron.models.classification.PyTorchModel(model, bounds, num_classes, channel_axis=1, device=None, preprocessing=(0, 1))[source]¶ Creates a
Modelinstance from a PyTorch module.Parameters: - model : torch.nn.Module
The PyTorch model that are loaded.
- bounds : tuple
Tuple of lower and upper bound for the pixel values, usually (0, 1) or (0, 255).
- num_classes : int
Number of classes for which the model will output predictions.
- channel_axis : int
The index of the axis that represents color channels.
- device : string
A string specifying the device to do computation on. If None, will default to “cuda:0” if torch.cuda.is_available() or “cpu” if not.
- preprocessing: 2-element tuple with floats or numpy arrays
Elementwises preprocessing of input; we first subtract the first element of preprocessing from the input and then divide the input by the second element.