idtrackerai

Source code for epoch_runner

# This file is part of idtracker.ai a multiple animals tracking system
# described in [1].
# Copyright (C) 2017- Francisco Romero Ferrero, Mattia G. Bergomi,
# Francisco J.H. Heras, Robert Hinz, Gonzalo G. de Polavieja and the
# Champalimaud Foundation.
#
# idtracker.ai is free software: you can redistribute it and/or modify
# it under the terms of the GNU General Public License as published by
# the Free Software Foundation, either version 3 of the License, or
# (at your option) any later version.
#
# This program is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
# GNU General Public License for more details. In addition, we require
# derivatives or applications to acknowledge the authors by citing [1].
#
# You should have received a copy of the GNU General Public License
# along with this program.  If not, see <https://www.gnu.org/licenses/>.
#
# For more information please send an email (idtrackerai@gmail.com) or
# use the tools available at https://gitlab.com/polavieja_lab/idtrackerai.git.
#
# [1] Romero-Ferrero, F., Bergomi, M.G., Hinz, R.C., Heras, F.J.H., de Polavieja, G.G., Nature Methods, 2019.
# idtracker.ai: tracking all individuals in small or large collectives of unmarked animals.
# (F.R.-F. and M.G.B. contributed equally to this work.
# Correspondence should be addressed to G.G.d.P: gonzalo.polavieja@neuro.fchampalimaud.org)

import sys

import numpy as np

if sys.argv[0] == 'idtrackeraiApp.py' or 'idtrackeraiGUI' in sys.argv[0]:
    from kivy.logger import Logger
    logger = Logger
else:
    import logging
    logger = logging.getLogger("__main__.epoch_runner")

[docs]class EpochRunner(object): """ Runs an epoch divided in batches for a given operation and a given data set (:class:`~get_data.DataSet`). Attributes ---------- epochs_completed : int Number of epochs completed in the current step of the training starting_epoch : int Epoch at which the training step started print_flag : bool If `True` prints the values of the loss, the accucacy, and the individual accuracy at the end of the epoch data_set : <DataSet object> Object containing the images and labels to be passed through the network in the current epoch (see :class:`~get_data.DataSet`) batch_size : int Number of images to be passed through the network in each bach. """ def __init__(self, data_set, starting_epoch = 0, print_flag = False, batch_size = None): self._epochs_completed = 0 self.starting_epoch = starting_epoch self.print_flag = print_flag self.data_set = data_set self.batch_size = batch_size @property def epochs_completed(self): return self._epochs_completed
[docs] def next_batch(self, batch_size): """Returns the images and labels for the next batch to be computed. Images and labels are extracted from a :class:`get_data.DataSet` object Parameters ---------- batch_size : int Number of examples to be passed through the network in this batch Returns ------- images : ndarray Array of shape [batch_size, height, width, channels] containing the images to be used in this batch labels : ndarray Array of shape [batch_size, number_of_classes] containing the labels corresponding to the images to be used in this batch See Also -------- :class:`get_data.DataSet` """ start = self._index_in_epoch self._index_in_epoch += batch_size end = self._index_in_epoch return (self.data_set.images[start:end], self.data_set.labels[start:end])
[docs] def run_epoch(self, name, store_loss_and_accuracy, batch_operation): """Performs a given `batch_operation` for an entire epoch and stores the values of the loss and the accurcaies in a :class:`~store_accuracy_and_loss.Store_Accuracy_and_Loss` object for visualization Parameters ---------- name : string A string to be printed in the epoch information. Typically 'Training' or 'Validation'. store_loss_and_accuracy : <Store_Accuracy_and_Loss object> Object collecting the values of the loss, accurcay and individual accuracies (see :class:`store_accuracy_and_loss.Store_Accuracy_and_Loss`) batch_operation : func Function to be run in the epoch Returns ------- feed_dict : dict Dictionary with the parameters and variables needed to run the `batch_operation`. It is used to save the Tensorflow summaries if needed See Also -------- :class:`get_data.DataSet` :class:`store_accuracy_and_loss.Store_Accuracy_and_Loss` :meth:`next_batch` """ loss_epoch = [] accuracy_epoch = [] individual_accuracy_epoch = [] self._index_in_epoch = 0 while self._index_in_epoch < self.data_set._num_images: loss_acc_batch, feed_dict = batch_operation(self.next_batch(self.batch_size)) loss_epoch.append(loss_acc_batch[0]) accuracy_epoch.append(loss_acc_batch[1]) individual_accuracy_epoch.append(loss_acc_batch[2]) loss_epoch = np.mean(np.vstack(loss_epoch)) accuracy_epoch = np.mean(np.vstack(accuracy_epoch)) individual_accuracy_epoch = np.nanmean(np.vstack(individual_accuracy_epoch),axis=0) if self.print_flag: logger.info('%s (epoch %i). Loss: %f, accuracy %f, individual accuracy: %s' %(name, self.starting_epoch + self._epochs_completed, loss_epoch, accuracy_epoch , individual_accuracy_epoch)) # self._index_in_epoch_train = 0 store_loss_and_accuracy.append_data(loss_epoch, accuracy_epoch, individual_accuracy_epoch) return feed_dict