tlt.models.pytorch_model.PyTorchModel

class tlt.models.pytorch_model.PyTorchModel(model_name: str, framework: FrameworkType, use_case: UseCaseType)

Base class to represent a PyTorch model

__init__(model_name: str, framework: FrameworkType, use_case: UseCaseType)

Class constructor

Methods

__init__(model_name, framework, use_case)	Class constructor
benchmark(dataset[, saved_model_dir, ...])	Use Intel Neural Compressor to benchmark the model with the dataset argument.
evaluate(dataset)	Evaluate the model using the specified dataset.
export(output_dir)	Export the serialized model to an output directory
freeze_layer(layer_name)	Freezes the model's layer using a layer name :param layer_name: The layer name that will be frozen in the model :type layer_name: string
list_layers([verbose])	Lists all of the named modules (e.g.
load_from_directory(model_dir)	Load a saved model from the model_dir path
optimize_graph(output_dir[, overwrite_model])	Performs FP32 graph optimization using the Intel Neural Compressor on the model and writes the inference-optimized model to the output_dir. Graph optimization includes converting variables to constants, removing training-only operations like checkpoint saving, stripping out parts of the graph that are never reached, removing debug operations like CheckNumerics, folding batch normalization ops into the pre-calculated weights, and fusing common operations into unified versions. :param output_dir: Writable output directory to save the optimized model :type output_dir: str :param overwrite_model: Specify whether or not to overwrite the output_dir, if it already exists (default: False) :type overwrite_model: bool.
quantize(output_dir, dataset[, config, ...])	Performs post training quantization using the Intel Neural Compressor on the model using the dataset.
train(dataset, output_dir[, epochs, ...])	Train the model using the specified dataset
unfreeze_layer(layer_name)	Unfreezes the model's layer using a layer name :param layer_name: The layer name that will be frozen in the model :type layer_name: string

Attributes

framework	Framework with which the model is compatible
learning_rate	Learning rate for the model
model_name	Name of the model
preprocessor	Preprocessor for the model
use_case	Use case (or category) to which the model belongs