Source code for cryocare.protocols.protocol_training

import json
import operator

from pwem.protocols import EMProtocol
from pyworkflow.protocol import IntParam, PointerParam, FloatParam, params, GT, LEVEL_ADVANCED, GE
from pyworkflow.utils import Message
from scipion.constants import PYTHON

from cryocare import Plugin
from cryocare.constants import CRYOCARE_MODEL
from cryocare.objects import CryocareModel


[docs]class ProtCryoCARETraining(EMProtocol): """Use two data-independent reconstructed tomograms to train a 3D cryo-CARE network.""" _label = 'CryoCARE Training' _configPath = None # -------------------------- 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('train_data', PointerParam, pointerClass='CryocareTrainData', label='Training data', important=True, allowsNull=False, help='Training data extracted from even and odd tomograms.') form.addSection(label='Training Parameters') form.addParam('epochs', IntParam, default=200, label='Training epochs', validators=[GT(0)], help='Number of epochs for which the network is trained. ' 'An epoch refers to one cycle through the full training dataset. ' 'It gives the network a chance to see the previous data to readjust ' 'the model parameters so that the model is not biased towards the ' 'last few data points during training.') form.addParam('batch_size', IntParam, default=16, label='Batch size', validators=[GT(0)], help='Size of the training batch. ' 'An entire big dataset cannot be passed into the neural net at once, ' 'so it is divided into batches. The batch size is the total number of ' 'training examples present in a single batch.') form.addParam('learning_rate', FloatParam, default=0.0004, label='Learning rate', validators=[GT(0)], expertLevel=LEVEL_ADVANCED, help='Training learning rate. ' 'In machine learning and statistics, the learning rate is a tuning ' 'parameter in an optimization algorithm that determines the step size ' 'at each iteration while moving toward a minimum of a loss function. ' 'Large learning rates result in unstable training and tiny rates ' 'result in a failure to train.') form.addSection(label='U-Net Parameters') form.addParam('unet_kern_size', IntParam, default=3, label='Convolution kernel size', help='Size of the convolution kernels used in the U-Net. ' 'Convolutional neural networks are basically a stack of layers ' 'which are defined by the action of a number of filters on the input. ' 'Those filters are usually called kernels. They can be conceptually ' 'interpreted as feature extractors.') form.addParam('unet_n_depth', IntParam, default=0, label='U-Net depth', validators=[GE(0)], help='Depth of the U-Net.') form.addParam('unet_n_first', IntParam, default=16, label='Number of initial feature channels', validators=[GT(0)], expertLevel=LEVEL_ADVANCED, help='Number of initial feature channels.') form.addHidden(params.GPU_LIST, params.StringParam, default='0', expertLevel=params.LEVEL_ADVANCED, label="Choose GPU IDs", help="GPU ID, normally it is 0.") def _insertAllSteps(self): self._insertFunctionStep('prepareTrainingStep') self._insertFunctionStep('trainingStep') self._insertFunctionStep('createOutputStep')
[docs] def prepareTrainingStep(self): config = { 'train_data': self.train_data.get().getTrainData(), 'epochs': self.epochs.get(), 'batch_size': self.batch_size.get(), 'unet_kern_size': self.unet_kern_size.get(), 'unet_n_depth': self._getUNetDepth(), 'unet_n_first': self.unet_n_first.get(), 'learning_rate': self.learning_rate.get(), 'model_name': CRYOCARE_MODEL, 'path': self._getExtraPath() } self._configPath = self._getExtraPath('train_config.json') with open(self._configPath, 'w+') as f: json.dump(config, f, indent=2)
[docs] def trainingStep(self): Plugin.runCryocare(self, PYTHON, '$(which cryoCARE_train.py) --conf {}'.format(self._configPath), gpuId=getattr(self, params.GPU_LIST).get())
[docs] def createOutputStep(self): model = CryocareModel(basedir=self._getExtraPath(), mean_std=self.train_data.get().getMeanStd()) self._defineOutputs(model=model)
# --------------------------- INFO functions ----------------------------------- def _summary(self): summary = [] if self.isFinished(): summary.append("Generated training model info:\n" "model_dir = *{}*\n" "normalization_file = *{}*".format (self._getExtraPath(CRYOCARE_MODEL), self.train_data.get().getMeanStd())) return summary def _validate(self): validateMsgs = [] if self.unet_kern_size.get() % 2 != 1: validateMsgs.append('Convolution kernel size has to be an odd number.') return validateMsgs # --------------------------- UTIL functions ----------------------------------- def _getUNetDepth(self): # Estimate the best net depth value according to the patch size if the user left this field empty if self.unet_n_depth.get() == 0: refValues = [72, 96, 128] # Corresponds to a net depth of 2, 3 and 4, respectively netDepth = [2, 3, 4] diff = [abs(i - self.train_data.get().getPatchSize()) for i in refValues] ind, _ = min(enumerate(diff), key=operator.itemgetter(0)) return netDepth[ind] else: return self.unet_n_depth.get()