Source code for deepfinder.protocols.protocol_load_training_model

from pwem.protocols import EMProtocol, FileParam
from pyworkflow import BETA
from pyworkflow.protocol import IntParam, GT
from pyworkflow.utils import Message

from deepfinder.objects import DeepFinderNet

[docs]class ProtDeepFinderLoadTrainingModel(EMProtocol): """Use two data-independent reconstructed tomograms to train a 3D cryo-CARE network.""" _label = 'Load Training Model' _devStatus = BETA # -------------------------- DEFINE param functions ---------------------- def _defineParams(self, form): """ Define the input parameters that will be used. Params: form: this is the form to be populated with sections and params. """ # You need a params to belong to a section: form.addSection(label=Message.LABEL_INPUT) form.addParam('netWeightsFile', FileParam, label='Model weights file', important=True, allowsNull=False, help='File which contains the weights for the neural network (.h5 file).') form.addParam('numClasses', IntParam, label='Number of classes', important=True, allowsNull=False, validators=[GT(0)], help='Number of classes corresponding to this model (background included).') def _insertAllSteps(self): self._insertFunctionStep('createOutputStep')
[docs] def createOutputStep(self): netWeights = DeepFinderNet() netWeights.setPath(self.netWeightsFile.get()) netWeights.setNbOfClasses(self.numClasses.get()) self._defineOutputs(netWeights=netWeights)
# --------------------------- INFO functions ----------------------------------- def _summary(self): summary = [] if self.isFinished(): summary.append("Loaded training model info:\n" "Net weights file = *{}*\n" "Number of classes = *{}*\n".format( self.netWeightsFile.get(), self.numClasses.get())) return summary