python.dgbpy.dgbtorch

Module Contents

Functions

getMLPlatform()
getParams(nntype=torch_dict['type'], learnrate=torch_dict['learnrate'], epochs=torch_dict['epochs'], epochdrop=torch_dict['epochdrop'], batch=torch_dict['batch_size'])
getDefaultModel(setup, type=torch_dict['type'])
getModelsByType(learntype, classification, ndim)
getModelsByInfo(infos)
get_model_shape(shape, nrattribs, attribfirst=True)
getModelDims(model_shape, data_format)
load(modelfnm)
onnx_from_torch(model, infos)
save(model, outfnm, infos, save_type=defsavetype)
train(model, imgdp, params)
apply(model, info, samples, scaler, isclassification, withpred, withprobs, withconfidence, doprobabilities)
getTrainTestDataLoaders(traindataset, testdataset, batchsize=torch_dict['batch_size'])
getDataLoader(dataset, batch_size=torch_dict['batch_size'], drop_last=False)
getDataLoaders(traindataset, testdataset, batchsize=torch_dict['batch_size'])
DataGenerator(imgdp, batchsize)

Attributes

device
torch_dict
platform
cudacores
savetypes
defsavetype

python.dgbpy.dgbtorch.device

python.dgbpy.dgbtorch.torch_dict

python.dgbpy.dgbtorch.platform

python.dgbpy.dgbtorch.cudacores = ['1', '2', '4', '8', '16', '32', '48', '64', '96', '128', '144', '192', '256', '288', '384',...

python.dgbpy.dgbtorch.getMLPlatform()

python.dgbpy.dgbtorch.getParams(nntype=torch_dict['type'], learnrate=torch_dict['learnrate'], epochs=torch_dict['epochs'], epochdrop=torch_dict['epochdrop'], batch=torch_dict['batch_size'])

python.dgbpy.dgbtorch.getDefaultModel(setup, type=torch_dict['type'])

python.dgbpy.dgbtorch.getModelsByType(learntype, classification, ndim)

python.dgbpy.dgbtorch.getModelsByInfo(infos)

python.dgbpy.dgbtorch.get_model_shape(shape, nrattribs, attribfirst=True)

python.dgbpy.dgbtorch.getModelDims(model_shape, data_format)

python.dgbpy.dgbtorch.savetypes = ['onnx', 'joblib', 'pickle']

python.dgbpy.dgbtorch.defsavetype

python.dgbpy.dgbtorch.load(modelfnm)

python.dgbpy.dgbtorch.onnx_from_torch(model, infos)

python.dgbpy.dgbtorch.save(model, outfnm, infos, save_type=defsavetype)

python.dgbpy.dgbtorch.train(model, imgdp, params)

python.dgbpy.dgbtorch.apply(model, info, samples, scaler, isclassification, withpred, withprobs, withconfidence, doprobabilities)

python.dgbpy.dgbtorch.getTrainTestDataLoaders(traindataset, testdataset, batchsize=torch_dict['batch_size'])

python.dgbpy.dgbtorch.getDataLoader(dataset, batch_size=torch_dict['batch_size'], drop_last=False)

python.dgbpy.dgbtorch.getDataLoaders(traindataset, testdataset, batchsize=torch_dict['batch_size'])

python.dgbpy.dgbtorch.DataGenerator(imgdp, batchsize)