带有在单独线程中运行的 Keras 模型的 PyQt5 GUI，当再次按下 "run" 按钮时卡住
PyQt5 GUI with Keras model that runs in a separate thread, stucks when "run" button is pressed again
我有一个应用程序可以接收带有标记火车数据的 .pickle 文件，并且应该构建一个神经网络（使用 Keras）。它应该对数据进行训练，并使用 matplotlib 在 canvas 上实时显示 train/validation 错误，并使用 QprogressBar 显示进度。
我有一个自定义回调，它在每个纪元结束时将 pyqtSignal 发送到主 GUI，发送当前纪元以及累积的训练和验证错误。然后在主程序中有一个接收信号并触发更新方法的函数。
一切正常，直到我按下 GUI window - 然后应用程序卡住（但网络仍在 shell 中运行）。我想点击中断会触发一些循环，使整个程序冻结，但我不知道是哪个循环。
我"ve searched other questions regarding the PyQt5 GUI being stuck when using threads, but did"找不到答案 - here
我"ve tried to use Qthread.start() instead of Qthread.run() for the Qthread - but in this case the plots don"根本没有更新。
我已经写了一个完整的例子来演示这个问题（数据文件应该是 .pickle 格式并且包含一个列表 [X, y] X - 样本作为一个 numpy ndarray，和 y - 相应的标签作为 numpy ndarray，可以在 https://www.kaggle.com/luciferadmin/heart-disease-uci-in-pickle-format) 找到：
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_())
你最初的错误是你不应该直接调用运行而是开始，但是你的线程是一个局部变量，稍后将被删除。
与其创建自定义 QThread，更好的解决方案是创建一个存在于另一个线程中的 QObject，并使用 QTimer.singleShot.
调用该函数
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_())
我有一个应用程序可以接收带有标记火车数据的 .pickle 文件，并且应该构建一个神经网络（使用 Keras）。它应该对数据进行训练，并使用 matplotlib 在 canvas 上实时显示 train/validation 错误，并使用 QprogressBar 显示进度。
我有一个自定义回调，它在每个纪元结束时将 pyqtSignal 发送到主 GUI，发送当前纪元以及累积的训练和验证错误。然后在主程序中有一个接收信号并触发更新方法的函数。
一切正常，直到我按下 GUI window - 然后应用程序卡住（但网络仍在 shell 中运行）。我想点击中断会触发一些循环，使整个程序冻结，但我不知道是哪个循环。
我"ve searched other questions regarding the PyQt5 GUI being stuck when using threads, but did"找不到答案 - here
我"ve tried to use Qthread.start() instead of Qthread.run() for the Qthread - but in this case the plots don"根本没有更新。
我已经写了一个完整的例子来演示这个问题（数据文件应该是 .pickle 格式并且包含一个列表 [X, y] X - 样本作为一个 numpy ndarray，和 y - 相应的标签作为 numpy ndarray，可以在 https://www.kaggle.com/luciferadmin/heart-disease-uci-in-pickle-format) 找到：
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_())
你最初的错误是你不应该直接调用运行而是开始，但是你的线程是一个局部变量，稍后将被删除。
与其创建自定义 QThread，更好的解决方案是创建一个存在于另一个线程中的 QObject，并使用 QTimer.singleShot.
调用该函数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_())