PyQt5 GUI с моделью Keras, которая работает в отдельном потоке, застревает при повторном нажатии кнопки «Run»

Question

У меня есть приложение, которое получает файл .pickle с помеченными данными поезда и должно создать нейронную сеть (с помощью Keras).Он должен тренироваться на данных и показывать ошибки обучения / проверки в реальном времени на холсте с помощью matplotlib и показывать прогресс с QprogressBar.

У меня есть собственный обратный вызов, который отправляет pyqtSignal в основной графический интерфейс пользователя в каждую эпохуконец, отправка текущей эпохи и накопленного поезда и ошибок проверки.Затем в основной программе есть функция, которая получает сигнал и запускает метод обновления.

Все работает нормально, пока я не нажимаю в окне графического интерфейса - тогда приложение зависает (но сеть все еще работает в оболочке),Я полагаю, что прерывание по клику запускает какой-то цикл, который останавливает всю программу, но я не могу понять, какие именно.

Я искал другие вопросы, касающиеся зависания графического интерфейса PyQt5 при использовании потоков, но это не такнайти ответ - здесь

Я пытался использовать Qthread.start () вместо Qthread.run () для Qthread - но в этом случае графики не обновляются вall.

Я написал полный пример, чтобы продемонстрировать проблему (файл данных должен быть в формате .pickle и содержать список [X, y] из X - samples как numpy ndarray, а y -соответствующие метки как numpy ndarray и могут быть найдены в https://www.kaggle.com/luciferadmin/heart-disease-uci-in-pickle-format):

import sys
import os
import pickle as pkl
from keras.layers import Input, Dense
from keras.callbacks import Callback
from keras.models import Model
from PyQt5.QtWidgets import QApplication, QProgressBar, QWidget, QVBoxLayout, QPushButton, QLineEdit, QFileDialog
from PyQt5.QtCore import QThread, pyqtSignal
import matplotlib
from matplotlib.backends.backend_qt5agg import FigureCanvasQTAgg as FigureCanvas
from matplotlib.figure import Figure
from PyQt5.QtWidgets import (QSizePolicy)
matplotlib.use('Qt5Agg')


class Plot(FigureCanvas):
    def __init__(self, x_label, y_label, parent=None, width=5, height=4, dpi=100):
        fig = Figure(figsize=(width, height), dpi=dpi)

        self.axes = fig.add_subplot(111)

        self.compute_initial_figure()
        self.axes.set_xlabel(x_label)
        self.axes.set_ylabel(y_label)

        FigureCanvas.__init__(self, fig)
        self.setParent(parent)

        FigureCanvas.setSizePolicy(self, QSizePolicy.Expanding, QSizePolicy.Expanding)

        FigureCanvas.updateGeometry(self)

    def compute_initial_figure(self):
        self.axes.set_xticks(range(1, 100, 10))


class MultiPlot(Plot):
    def __init__(self, parent=None, x_axis_name='X', y_axis_name='Y', width=5, height=4, dpi=100):
        super().__init__(x_axis_name, y_axis_name, parent, width, height, dpi)
        self.compute_initial_figure()

    def compute_initial_figure(self):
        self.axes.set_xticks(range(0, 100, 10))

    def plot_multi_data(self, x_axis_name='X', y_axis_name='Y', plot_labels=None, y_list=None):
        if y_list is not None:
            self.axes.clear()

            graph_handles = []

            markers = ['b:', 'r']
            y_index = 0

            for y in y_list:

                x = range(1, len(y) + 1)
                label = plot_labels[y_index]

                new_plot, = self.axes.plot(x, y, markers[y_index], markersize=2, label=label)
                graph_handles.append(new_plot)
                y_index += 1

                self.axes.set_xticks(x, int(len(list(x))/10))
            self.axes.legend(handles=graph_handles, loc=0, fontsize=8, shadow=True)

        self.axes.set_xlabel(x_axis_name)
        self.axes.set_ylabel(y_axis_name)

        self.draw()


class TrainPlotCallback(Callback):
    def __init__(self, signal):
        Callback.__init__(self)
        self.train_err = []
        self.val_err = []
        self.signal = signal

    def on_epoch_end(self, epoch, logs={}):
        self.train_err.append(1 - logs.get('acc'))
        self.val_err.append(1 - logs.get('val_acc'))

        self.signal.emit(epoch, [self.train_err, self.val_err])


def classification_model(data_input_path, on_epoch_end_signal):

    # ///////////////////// TEST /////////////////////
    if os.path.exists(data_input_path):
        plot_losses = TrainPlotCallback(on_epoch_end_signal)
        with open(data_input_path, 'rb') as pickle_in:
            data = pkl.load(pickle_in)
            X = data[0]
            y = data[1]

        input_size = X.shape[1]

        # MODEL CREATION
        # ///////////////////// INPUT LAYER /////////////////////
        inputs = Input(shape=(input_size,))
        # ///////////////////// INPUT LAYER /////////////////////
        # ///////////////////// HIDDEN LAYER /////////////////////
        x = Dense(10, activation='relu', kernel_initializer='normal')(inputs)   # THE FIRST LAYER
        # ///////////////////// HIDDEN LAYER /////////////////////
        # ///////////////////// OUTPUT LAYERS /////////////////////
        predictions = Dense(len(y[0]), activation='softmax')(x)  # the length of the output layer is as the length of the classes being predicted.
        # ///////////////////// OUTPUT LAYERS /////////////////////
        # MODEL CREATION

        # ///////////////////// MODEL DEFINITION /////////////////////
        model = Model(inputs=inputs, outputs=predictions)
        model.compile(optimizer='Adam',
                      loss='categorical_crossentropy',
                      metrics=['acc'])
        # ///////////////////// MODEL DEFINITION /////////////////////

        # ///////////////////// MODEL TRAINING /////////////////////
        model.fit(X, y, validation_split=0.2, batch_size=100, epochs=100, callbacks=[plot_losses])
        # ///////////////////// MODEL TRAINING /////////////////////


class ModelThread(QThread):
    epoch_end_signal = pyqtSignal(int, list)  # signal that has epoch # as the first parameter, and a list that contains the error values for the train and validation.

    def __init__(self, data_input_path):
        QThread.__init__(self)
        self.data_input_path = data_input_path

    def __del__(self):
        self.wait()

    def run(self):
        classification_model(data_input_path=self.data_input_path,
                             on_epoch_end_signal=self.epoch_end_signal
                             )


class DashBoard(QWidget):
    def __init__(self):
        super().__init__()
        self.main_v_box = QVBoxLayout(self)

        #  <<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<< STRINGS >>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>
        self.input_data_path_str = ''
        #  <<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<< STRINGS >>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>
        #  <<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<< PROGRESS BAR >>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>
        self.progress_bar = QProgressBar()
        #  <<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<< PROGRESS BAR >>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>
        #  <<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<< BUTTONS >>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>
        self.run_model_btn = QPushButton('Run')
        self.browse_train_data_file_path_btn = QPushButton('Browse')
        #  <<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<< BUTTONS >>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>
        #  <<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<< MULTI PLOTS >>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>
        self.in_training_plot = MultiPlot(x_axis_name='Epoch Number', y_axis_name='Error')
        #  <<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<< MULTI PLOTS >>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>
        #  <<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<< LINE EDITS >>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>
        self.train_data_file_path_le = QLineEdit()
        #  <<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<< LINE EDITS >>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>
        self.init()
        self.pack()
        self.showMaximized()

    def init(self):
        self.progress_bar.hide()
        self.browse_train_data_file_path_btn.clicked.connect(self.on_btn_click)
        self.run_model_btn.clicked.connect(self.on_btn_click)

    def pack(self):
        self.main_v_box.addWidget(self.train_data_file_path_le)
        self.main_v_box.addWidget(self.browse_train_data_file_path_btn)
        self.main_v_box.addWidget(self.in_training_plot)
        self.main_v_box.addWidget(self.run_model_btn)
        self.main_v_box.addWidget(self.progress_bar)

    def on_btn_click(self):
        btn_index = self.sender()

        if btn_index == self.browse_train_data_file_path_btn:
            self.input_data_path_str = QFileDialog.getOpenFileName(self, '.pickle files', os.getenv('HOME'), '*.pickle')[0]
            self.train_data_file_path_le.setText(self.input_data_path_str)
        elif btn_index == self.run_model_btn:
            model_thread = ModelThread(data_input_path=self.input_data_path_str)
            model_thread.epoch_end_signal.connect(self.update_ui_on_epoch_end)
            self.progress_bar.show()
            model_thread.run()
            self.progress_bar.hide()

    def update_ui_on_epoch_end(self, current_epoch_num, error_lists):
        if current_epoch_num < 100:
            self.progress_bar.setValue(current_epoch_num)
        else:
            self.progress_bar.setValue(100)
        self.in_training_plot.plot_multi_data(x_axis_name='Epoch', y_axis_name='Error', plot_labels=['Train Accuracy', 'Validation Accuracy'], y_list=[error_lists[0], error_lists[1]])

    def run_model(self):
        if os.path.exists(self.train_data_file_path_str) and os.path.exists(self.output_data_path_str):
            train_thread = ModelThread(data_input_path='')
            train_thread.epoch_end_signal.connect(self.update_ui_on_epoch_end)
            # train_thread.start()
            self.progress_bar.show()
            train_thread.run()
            self.progress_bar.hide()


if __name__ == '__main__':
    app = QApplication(sys.argv)
    main_menu = DashBoard()
    sys.exit(app.exec_())

Answer 1

Ваша первоначальная ошибка заключается в том, что вы не должны вызывать run напрямую, а запускаете, но ваш поток является локальной переменной, которая будет удалена через мгновение.

Вместо создания пользовательского QThread лучшим решением является создание объекта QObject, который находится в другом потоке, и вызов функции с использованием QTimer.singleShot.

import os
import sys
from functools import partial
import pickle as pkl
from keras.layers import Input, Dense
from keras.callbacks import Callback
from keras.models import Model

from PyQt5 import QtCore, QtWidgets

import matplotlib
matplotlib.use('Qt5Agg')

from matplotlib.backends.backend_qt5agg import FigureCanvasQTAgg as FigureCanvas
from matplotlib.figure import Figure

class Plot(FigureCanvas):
    def __init__(self, x_label, y_label, parent=None, width=5, height=4, dpi=100):
        fig = Figure(figsize=(width, height), dpi=dpi)

        self.axes = fig.add_subplot(111)

        self.compute_initial_figure()
        self.axes.set_xlabel(x_label)
        self.axes.set_ylabel(y_label)

        FigureCanvas.__init__(self, fig)
        self.setParent(parent)

        FigureCanvas.setSizePolicy(self, QtWidgets.QSizePolicy.Expanding, QtWidgets.QSizePolicy.Expanding)

        FigureCanvas.updateGeometry(self)

    def compute_initial_figure(self):
        self.axes.set_xticks(range(1, 100, 10))


class MultiPlot(Plot):
    def __init__(self, parent=None, x_axis_name='X', y_axis_name='Y', width=5, height=4, dpi=100):
        super().__init__(x_axis_name, y_axis_name, parent, width, height, dpi)
        self.compute_initial_figure()

    def compute_initial_figure(self):
        self.axes.set_xticks(range(0, 100, 10))

    def plot_multi_data(self, x_axis_name='X', y_axis_name='Y', plot_labels=None, y_list=None):
        if y_list is not None:
            self.axes.clear()

            graph_handles = []

            markers = ['b:', 'r']
            y_index = 0

            for y in y_list:

                x = range(1, len(y) + 1)
                label = plot_labels[y_index]

                new_plot, = self.axes.plot(x, y, markers[y_index], markersize=2, label=label)
                graph_handles.append(new_plot)
                y_index += 1

                self.axes.set_xticks(x, int(len(list(x))/10))
            self.axes.legend(handles=graph_handles, loc=0, fontsize=8, shadow=True)

        self.axes.set_xlabel(x_axis_name)
        self.axes.set_ylabel(y_axis_name)

        self.draw()


class TrainPlotCallback(Callback):
    def __init__(self, signal):
        Callback.__init__(self)
        self.train_err = []
        self.val_err = []
        self.signal = signal

    def on_epoch_end(self, epoch, logs={}):
        self.train_err.append(1 - logs.get('acc'))
        self.val_err.append(1 - logs.get('val_acc'))
        self.signal.emit(epoch, [self.train_err, self.val_err])

def classification_model(data_input_path, on_epoch_end_signal):

    # ///////////////////// TEST /////////////////////
    if os.path.exists(data_input_path):
        plot_losses = TrainPlotCallback(on_epoch_end_signal)
        with open(data_input_path, 'rb') as pickle_in:
            data = pkl.load(pickle_in)
            X = data[0]
            y = data[1]

        input_size = X.shape[1]

        # MODEL CREATION
        # ///////////////////// INPUT LAYER /////////////////////
        inputs = Input(shape=(input_size,))
        # ///////////////////// INPUT LAYER /////////////////////
        # ///////////////////// HIDDEN LAYER /////////////////////
        x = Dense(10, activation='relu', kernel_initializer='normal')(inputs)   # THE FIRST LAYER
        # ///////////////////// HIDDEN LAYER /////////////////////
        # ///////////////////// OUTPUT LAYERS /////////////////////
        predictions = Dense(len(y[0]), activation='softmax')(x)  # the length of the output layer is as the length of the classes being predicted.
        # ///////////////////// OUTPUT LAYERS /////////////////////
        # MODEL CREATION

        # ///////////////////// MODEL DEFINITION /////////////////////
        model = Model(inputs=inputs, outputs=predictions)
        model.compile(optimizer='Adam',
                      loss='categorical_crossentropy',
                      metrics=['acc'])
        # ///////////////////// MODEL DEFINITION /////////////////////

        # ///////////////////// MODEL TRAINING /////////////////////
        model.fit(X, y, validation_split=0.2, batch_size=100, epochs=100, callbacks=[plot_losses])
        # ///////////////////// MODEL TRAINING /////////////////////


class Worker(QtCore.QObject):
    started = QtCore.pyqtSignal()
    finished = QtCore.pyqtSignal()
    epoch_end_signal = QtCore.pyqtSignal(int, list)  # signal that has epoch # as the first parameter, and a list that contains the error values for the train and validation.

    @QtCore.pyqtSlot(str)
    def start_task(self, input_path):
        self.started.emit()
        classification_model(data_input_path=input_path,
                             on_epoch_end_signal=self.epoch_end_signal)
        self.finished.emit()


class DashBoard(QtWidgets.QWidget):
    def __init__(self):
        super().__init__()
        self.main_v_box = QtWidgets.QVBoxLayout(self)
        self.input_data_path_str = ''
        self.progress_bar = QtWidgets.QProgressBar()
        self.run_model_btn = QtWidgets.QPushButton('Run')
        self.browse_train_data_file_path_btn = QtWidgets.QPushButton('Browse')
        self.in_training_plot = MultiPlot(x_axis_name='Epoch Number', y_axis_name='Error')
        self.train_data_file_path_le = QtWidgets.QLineEdit()
        self.init()
        self.pack()
        self.showMaximized()

    def init(self):
        self.worker = Worker()
        thread = QtCore.QThread(self)
        thread.start()
        self.worker.moveToThread(thread)
        self.progress_bar.hide()
        self.browse_train_data_file_path_btn.clicked.connect(self.on_btn_click)
        self.run_model_btn.clicked.connect(self.on_btn_click)
        self.worker.epoch_end_signal.connect(self.update_ui_on_epoch_end)
        self.worker.started.connect(self.progress_bar.show)
        self.worker.finished.connect(self.progress_bar.hide)
        self.worker.started.connect(partial(self.run_model_btn.setEnabled, False))
        self.worker.finished.connect(partial(self.run_model_btn.setEnabled, True))

    def pack(self):
        self.main_v_box.addWidget(self.train_data_file_path_le)
        self.main_v_box.addWidget(self.browse_train_data_file_path_btn)
        self.main_v_box.addWidget(self.in_training_plot)
        self.main_v_box.addWidget(self.run_model_btn)
        self.main_v_box.addWidget(self.progress_bar)

    def on_btn_click(self):
        btn_index = self.sender()

        if btn_index == self.browse_train_data_file_path_btn:
            self.input_data_path_str, _ = QtWidgets.QFileDialog.getOpenFileName(self, '.pickle files', os.getenv('HOME'), '*.pickle')
            self.train_data_file_path_le.setText(self.input_data_path_str)
        elif btn_index == self.run_model_btn:
            QtCore.QTimer.singleShot(0, partial(self.worker.start_task, self.input_data_path_str))

    def update_ui_on_epoch_end(self, current_epoch_num, error_lists):
        if current_epoch_num < 100:
            self.progress_bar.setValue(current_epoch_num)
        else:
            self.progress_bar.setValue(100)
        self.in_training_plot.plot_multi_data(x_axis_name='Epoch', y_axis_name='Error', plot_labels=['Train Accuracy', 'Validation Accuracy'], y_list=[error_lists[0], error_lists[1]])


if __name__ == '__main__':
    app = QtWidgets.QApplication(sys.argv)
    main_menu = DashBoard()
    sys.exit(app.exec_())