:py:mod:`python.dgbpy.dgbtorch`
===============================

.. py:module:: python.dgbpy.dgbtorch


Module Contents
---------------


Functions
~~~~~~~~~

.. autoapisummary::

   python.dgbpy.dgbtorch.getMLPlatform
   python.dgbpy.dgbtorch.getParams
   python.dgbpy.dgbtorch.getDefaultModel
   python.dgbpy.dgbtorch.getModelsByType
   python.dgbpy.dgbtorch.getModelsByInfo
   python.dgbpy.dgbtorch.get_model_shape
   python.dgbpy.dgbtorch.getModelDims
   python.dgbpy.dgbtorch.load
   python.dgbpy.dgbtorch.onnx_from_torch
   python.dgbpy.dgbtorch.save
   python.dgbpy.dgbtorch.train
   python.dgbpy.dgbtorch.apply
   python.dgbpy.dgbtorch.getTrainTestDataLoaders
   python.dgbpy.dgbtorch.getDataLoader
   python.dgbpy.dgbtorch.getDataLoaders
   python.dgbpy.dgbtorch.DataGenerator



Attributes
~~~~~~~~~~

.. autoapisummary::

   python.dgbpy.dgbtorch.device
   python.dgbpy.dgbtorch.torch_dict
   python.dgbpy.dgbtorch.platform
   python.dgbpy.dgbtorch.cudacores
   python.dgbpy.dgbtorch.savetypes
   python.dgbpy.dgbtorch.defsavetype


.. py:data:: device
   

   

.. py:data:: torch_dict
   

   

.. py:data:: platform
   

   

.. py:data:: cudacores
   :annotation: = ['1', '2', '4', '8', '16', '32', '48', '64', '96', '128', '144', '192', '256', '288', '384',...

   

.. py:function:: getMLPlatform()


.. py:function:: getParams(nntype=torch_dict['type'], learnrate=torch_dict['learnrate'], epochs=torch_dict['epochs'], epochdrop=torch_dict['epochdrop'], batch=torch_dict['batch_size'])


.. py:function:: getDefaultModel(setup, type=torch_dict['type'])


.. py:function:: getModelsByType(learntype, classification, ndim)


.. py:function:: getModelsByInfo(infos)


.. py:function:: get_model_shape(shape, nrattribs, attribfirst=True)


.. py:function:: getModelDims(model_shape, data_format)


.. py:data:: savetypes
   :annotation: = ['onnx', 'joblib', 'pickle']

   

.. py:data:: defsavetype
   

   

.. py:function:: load(modelfnm)


.. py:function:: onnx_from_torch(model, infos)


.. py:function:: save(model, outfnm, infos, save_type=defsavetype)


.. py:function:: train(model, imgdp, params)


.. py:function:: apply(model, info, samples, scaler, isclassification, withpred, withprobs, withconfidence, doprobabilities)


.. py:function:: getTrainTestDataLoaders(traindataset, testdataset, batchsize=torch_dict['batch_size'])


.. py:function:: getDataLoader(dataset, batch_size=torch_dict['batch_size'], drop_last=False)


.. py:function:: getDataLoaders(traindataset, testdataset, batchsize=torch_dict['batch_size'])


.. py:function:: DataGenerator(imgdp, batchsize)