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main.py
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main.py
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from PyQt5.QtWidgets import QApplication, QMainWindow, QFileDialog, \
QMessageBox, QHBoxLayout, QColorDialog, QShortcut, \
QVBoxLayout
from PyQt5 import QtWidgets
import MainWindow_3D_PP
from utils.coords2labels import Coord_to_Label, Coord_to_Label_v1, label_gen_show
from utils.normalization import InputNorm, norm_show
from utils.coord_gen import coords_gen, coords_gen_show
import json
# from pyqtgraph.graphicsItems.ViewBox import axisCtrlTemplate_pyqt5
# from pyqtgraph.graphicsItems.PlotItem import plotConfigTemplate_pyqt5
# from pyqtgraph.imageview import ImageViewTemplate_pyqt5
import pyqtgraph as pg
import mrcfile
import numpy as np
import cv2
from PyQt5.QtGui import QKeySequence
from PyQt5.QtCore import pyqtSlot
from skimage import filters
import os
import threading, inspect, time
from train import *
from test import *
from utils.coordFormatConvert import *
from utils.utils import *
"""Particle color for different categories."""
colors = [(244, 67, 54), # 1
(255, 235, 59), # 2
(156, 39, 176), # 3
(33, 150, 243), # 4
(0, 188, 212), # 5
(139, 195, 74), # 6
(255, 152, 0), # 7
(63, 81, 181), # 8
(255, 193, 7), # 9
(255, 0, 0), # 10
(0, 255, 0), # 11
(0, 0, 255), # 12
(255, 255, 0), # 13
(255, 0, 255), # 14
(0, 255, 255), # 15
]
class Stats(QtWidgets.QMainWindow):
def __init__(self):
QtWidgets.QMainWindow.__init__(self)
self.ui = MainWindow_3D_PP.Ui_DeepETPicker()
self.ui.setupUi(self)
self.ui.retranslateUi(self)
self.ui.edit_dset_name.textChanged.connect(self.DsetNameChanged)
self.ui.pBut_add_coord.clicked.connect(self.openCoordPath)
self.ui.pBut_add_base.clicked.connect(self.openBasePath)
self.ui.pBut_sel_tomo.clicked.connect(self.openTomoFile)
self.ui.bb_c2l_ok.clicked.connect(self.c2l_ok)
self.ui.cbox_coord_format.currentIndexChanged.connect(self.coord_format_change)
self.ui.cbox_tomo_format.currentIndexChanged.connect(self.lable_format_change)
self.ui.cbox_label_type.currentIndexChanged.connect(self.lable_type_change)
self.label_type = self.ui.cbox_label_type.currentText()
self.ui.sb_label_diameter.valueChanged.connect(self.label_diameter_change)
self.label_diameter = self.ui.sb_label_diameter.value()
self.ui.cbox_ocp_type.currentIndexChanged.connect(self.ocp_type_change)
self.ocp_type = self.ui.cbox_ocp_type.currentText()
self.ui.edit_ocp_diameter.textChanged.connect(self.ocp_diameter_change)
self.ocp_diameter = self.ui.edit_ocp_diameter.text()
self.ui.sb_cls_num.valueChanged.connect(self.cls_num_change)
self.cls_num = self.ui.sb_cls_num.value()
self.input_norm = self.ui.butG_pre_norm.checkedButton().text()
self.ui.butG_pre_norm.buttonClicked.connect(self.RadButtonClicked)
self.ui.pBut_save_configs.clicked.connect(self.saveConfigs)
self.ui.pBut_load_configs.clicked.connect(self.loadConfigs)
# Show results
self.sectionView("")
# self.vbox = QVBoxLayout()
self.hbox = QHBoxLayout()
self.hbox.addWidget(self.win, stretch=3)
self.hbox.addWidget(self.ui.gBox_res_load, stretch=2)
# self.hbox.setStretch(5, 1)
# self.vbox.addLayout(self.hbox)
# set global layout
self.ui.tab_res_show.setLayout(self.hbox)
self.sub1.scene().sigMouseClicked.connect(self.mouseClicked)
pg.SignalProxy(self.sub1.scene().sigMouseClicked, rateLimit=60, slot=self.mouseClicked)
self.ui.pBut_show_tomo.clicked.connect(self.showTomo)
self.ui.pBut_show_label.clicked.connect(self.showLabel)
self.ui.pBut_show_res.clicked.connect(self.showTomoLabel)
self.res_label = []
self.res_label_type = self.ui.butG_res_label_type.checkedButton().text()
if self.res_label_type == 'Mask':
self.ui.hSlider_circle_diameter.setEnabled(False)
self.ui.cbox_circle.setEnabled(False)
self.ui.sb_circle_width.setEnabled(False)
else:
self.ui.hSlider_circle_diameter.setEnabled(True)
self.ui.cbox_circle.setEnabled(True)
self.ui.sb_circle_width.setEnabled(True)
self.ui.butG_res_label_type.buttonClicked.connect(self.RBut_LabelClicked)
self.circle_width = self.ui.sb_circle_width.value()
self.ui.sb_circle_width.valueChanged.connect(self.circle_width_change)
self.ui.hSlider_circle_diameter.valueChanged.connect(self.diameterChange)
self.ui.hSlider_circle_diameter.setValue(7)
self.circle_diameter = self.ui.hSlider_circle_diameter.value()
self.old_diameter = self.circle_diameter
self.ui.edit_circle_diameter.setText(str(self.circle_diameter))
self.ui.hSlider_mask_alpha.valueChanged.connect(self.maskAlphaChange)
self.ui.hSlider_mask_alpha.setValue(20)
self.mask_alpha = self.ui.hSlider_mask_alpha.value()
self.old_alpha = self.mask_alpha
self.ui.edit_mask_alpha.setText(f"{float(self.mask_alpha) / 100.:0.2f}")
self.ui.cbox_circle.stateChanged.connect(self.isPlotCircle)
if self.ui.cbox_circle.isChecked():
self.flag_show_circle = True
self.ui.hSlider_circle_diameter.setEnabled(True)
else:
self.flag_show_circle = False
self.ui.hSlider_circle_diameter.setEnabled(False)
self.ui.cbox_mask_show.stateChanged.connect(self.isShowMask)
self.flag_show_mask = True
self.ui.pBut_setColor.clicked.connect(self.setColor)
self.color = (255, 0, 0)
# Res: params adjust
self.ui.cbox_gaus_filter.stateChanged.connect(self.isGauFilter)
self.ui.edit_gaussian_kernel.textChanged.connect(self.isGauFilter)
self.ui.edit_gaussian_sigma.textChanged.connect(self.isGauFilter)
if self.ui.cbox_gaus_filter.isChecked():
self.ui.edit_gaussian_kernel.setEnabled(True)
self.ui.edit_gaussian_sigma.setEnabled(True)
self.ui.label_29.setEnabled(True)
self.ui.label_30.setEnabled(True)
self.gau_kernel = int(self.ui.edit_gaussian_kernel.text())
self.gau_sigma = float(self.ui.edit_gaussian_sigma.text())
else:
self.ui.edit_gaussian_kernel.setEnabled(False)
self.ui.edit_gaussian_sigma.setEnabled(False)
self.ui.label_29.setEnabled(False)
self.ui.label_30.setEnabled(False)
self.ui.pBut_paramAdjust_ok.clicked.connect(self.paramAdjust_ok)
self.ui.hSlider_x.valueChanged.connect(self.xyzChange)
self.ui.hSlider_y.valueChanged.connect(self.xyzChange)
self.ui.hSlider_z.valueChanged.connect(self.xyzChange)
# QShortcut(QKeySequence(self.tr("Command+")))
QShortcut(QKeySequence(self.tr("Escape")), self, self.close)
QShortcut(QKeySequence(self.tr("Ctrl+D")), self, self.changeShowMask)
QShortcut(QKeySequence(self.tr("Up")), self, self.changeZ_up)
QShortcut(QKeySequence(self.tr("Down")), self, self.changeZ_down)
self.ui.pBut_SAV_save.clicked.connect(self.saveVideo)
# Training
self.train_dsetName = self.ui.train_edit_dsetName.text()
self.ui.train_edit_dsetName.textChanged.connect(self.train_dsetName_change)
self.train_modelName = self.ui.train_cbox_modelName.currentText()
self.ui.train_cbox_modelName.currentIndexChanged.connect(self.train_modelName_change)
self.train_cls_num = self.ui.train_sb_clsNum.value()
self.ui.train_sb_clsNum.valueChanged.connect(self.train_cls_num_change)
self.train_batchSize = self.ui.train_sb_batchSize.value()
self.ui.train_sb_batchSize.valueChanged.connect(self.train_batchSize_change)
self.train_patchSize = self.ui.train_sb_patchSize.value()
self.ui.train_sb_patchSize.valueChanged.connect(self.train_patchSize_change)
self.train_paddingSize = self.ui.train_sb_paddingSize.value()
self.ui.train_sb_paddingSize.valueChanged.connect(self.train_paddingSize_change)
self.train_maxEpochs = self.ui.train_sb_maxEpochs.value()
self.ui.train_sb_maxEpochs.valueChanged.connect(self.train_maxEpochs_change)
self.ui.train_edit_segThresh.textChanged.connect(self.train_segThresh_change)
self.train_segThresh = float(self.ui.train_edit_segThresh.text())
self.ui.train_edit_lr.textChanged.connect(self.train_lr_change)
self.train_lr = float(self.ui.train_edit_lr.text())
self.ui.train_edit_gpuIds.textChanged.connect(self.train_gpuIds_change)
self.train_gpuIds = self.ui.train_edit_gpuIds.text()
self.ui.train_pBut_saveConfigs.clicked.connect(self.train_saveConfigs)
self.ui.train_pBut_loadConfigs.clicked.connect(self.train_loadConfigs)
self.ui.train_pBut_clear.clicked.connect(self.train_show_clear)
self.ui.train_pBut_ok.clicked.connect(self.train_ok)
self.ui.train_pBut_stop.clicked.connect(self.train_stop)
self.ui.edit_train_set_ids.textChanged.connect(self.train_set_ids_change)
self.train_set_ids = self.ui.edit_train_set_ids.text()
self.ui.edit_val_set_ids.textChanged.connect(self.val_set_ids_change)
self.val_set_ids = self.ui.edit_val_set_ids.text()
self.ui.train_pBut_dsetList.clicked.connect(self.train_dsetList)
# Hiding
self.ui.train_edit_segThresh.setVisible(False) # train_seg_edit
self.ui.label_89.setVisible(False) # train_seg label
self.ui.radB_norm.setVisible(False) # normalization
self.ui.test_edit_segThresh.setVisible(False)
self.ui.label_15.setVisible(False)
self.ui.page.setVisible(False)
self.ui.tab_show_video.removeItem(5)
self.ui.edit_test_set_ids.setVisible(False)
self.ui.cbox_ocp_type.setVisible(False)
self.ui.label_22.setVisible(False)
# test
self.ui.test_pBut_trainConfigs.clicked.connect(self.test_load_trainConfigs)
self.ui.test_pBut_weightPath.clicked.connect(self.test_load_weightPath)
self.test_patchSize = self.ui.test_sb_patchSize.value()
self.ui.test_sb_patchSize.valueChanged.connect(self.test_patchSize_change)
self.test_paddingSize = self.ui.test_sb_paddingSize.value()
self.ui.test_sb_paddingSize.valueChanged.connect(self.test_paddingSize_change)
self.ui.test_edit_segThresh.textChanged.connect(self.test_segThresh_change)
self.test_segThresh = float(self.ui.test_edit_segThresh.text())
self.ui.test_edit_gpuIds.textChanged.connect(self.test_gpuIds_change)
self.test_gpuIds = self.ui.test_edit_gpuIds.text()
self.ui.test_pBut_ok.clicked.connect(self.test_ok)
self.ui.test_pBut_stop.clicked.connect(self.test_stop)
self.ui.edit_test_set_ids.textChanged.connect(self.test_set_ids_change)
self.test_set_ids = self.ui.edit_test_set_ids.text()
self.ui.test_cbox_format.currentIndexChanged.connect(self.test_format_change)
# format conversion
self.ui.test_pBut_coordPath.clicked.connect(self.test_select_CoordPath)
self.ui.test_edit_clsId.textChanged.connect(self.test_clsId_change)
self.test_clsId = self.ui.test_edit_clsId.text()
self.ui.test_pBut_convertOk.clicked.connect(self.convert_ok)
# manual picking
self.ui.mpick_ckb_enable.stateChanged.connect(self.mpick_enable_stateChanged)
if self.ui.mpick_ckb_enable.isChecked():
self.ui.mpick_slider_labelDiameter.setEnabled(True)
self.ui.mpick_edit_labelDameter.setEnabled(True)
self.ui.mpick_sb_classId.setEnabled(True)
self.ui.mpick_sb_labelWidth.setEnabled(True)
self.ui.mpick_pBut_setColor.setEnabled(True)
else:
self.ui.mpick_slider_labelDiameter.setEnabled(False)
self.ui.mpick_edit_labelDameter.setEnabled(False)
self.ui.mpick_sb_classId.setEnabled(False)
self.ui.mpick_sb_labelWidth.setEnabled(False)
self.ui.mpick_pBut_setColor.setEnabled(False)
self.ui.mpick_slider_labelDiameter.valueChanged.connect(self.mpick_labelDiameterChange)
self.ui.mpick_slider_labelDiameter.setValue(7)
self.mpick_circle_diameter = self.ui.mpick_slider_labelDiameter.value()
self.mpick_old_diameter = self.mpick_circle_diameter
self.ui.mpick_edit_labelDameter.setText(str(self.mpick_circle_diameter))
self.mpick_circle_width = self.ui.mpick_sb_labelWidth.value()
self.ui.mpick_sb_labelWidth.valueChanged.connect(self.mpick_labelWidth_change)
self.mpick_clsId = self.ui.mpick_sb_classId.value()
self.ui.mpick_sb_classId.valueChanged.connect(self.mpick_classId_change)
self.ui.mpick_pBut_setColor.clicked.connect(self.mpick_setColor)
self.color = (255, 0, 0)
self.mouse_double = False
self.ui.mpick_pBut_savePath.clicked.connect(self.mpick_savePath)
self.ui.mpick_edit_savePath.textChanged.connect(self.mpick_savePathChange)
self.mpick_save_path = self.ui.mpick_edit_savePath.text()
self.ui.mpick_edit_saveName.textChanged.connect(self.mpick_saveNameChange)
self.mpick_save_name = self.ui.mpick_edit_saveName.text()
self.ui.mpick_pBut_save.clicked.connect(self.mpick_save)
self.ui.mpick_ckb_clear.stateChanged.connect(self.mpick_clear_stateChanged)
if self.ui.mpick_ckb_clear.isChecked():
self.self.mpick_coords = []
self.mpick_coords_np = np.array(self.mpick_coords)
# manual picking
def mpick_enable_stateChanged(self):
if self.ui.mpick_ckb_enable.isChecked():
self.ui.mpick_slider_labelDiameter.setEnabled(True)
self.ui.mpick_edit_labelDameter.setEnabled(True)
self.ui.mpick_sb_classId.setEnabled(True)
self.ui.mpick_sb_labelWidth.setEnabled(True)
self.ui.mpick_pBut_setColor.setEnabled(True)
self.mpick_coords = []
self.mpick_coords_np = np.array(self.mpick_coords)
if self.res_label_type == 'Coords' and self.res_label != []:
self.mpick_coords = self.res_label.tolist()
self.mpick_coords_np = np.array(self.res_label)
for idx, xyz in enumerate(self.mpick_coords_np):
self.res_show_info(f"{idx}:{xyz}")
self.mpick_circle_diameter = self.circle_diameter
self.ui.mpick_slider_labelDiameter.setValue(self.circle_diameter)
self.mpick_circle_width = self.circle_width
self.ui.mpick_sb_labelWidth.setValue(self.circle_width)
self.ui.cbox_circle.setChecked(False)
else:
self.ui.mpick_slider_labelDiameter.setEnabled(False)
self.ui.mpick_edit_labelDameter.setEnabled(False)
self.ui.mpick_sb_classId.setEnabled(False)
self.ui.mpick_sb_labelWidth.setEnabled(False)
self.ui.mpick_pBut_setColor.setEnabled(False)
def mpick_clear_stateChanged(self):
if self.ui.mpick_ckb_clear.isChecked():
self.mpick_coords = []
self.mpick_coords_np = np.array(self.mpick_coords)
self.MC_updata()
def mpick_labelDiameterChange(self):
self.mpick_circle_diameter = self.ui.mpick_slider_labelDiameter.value()
self.ui.mpick_edit_labelDameter.setText(str(self.mpick_circle_diameter))
self.MC_updata()
def mpick_labelWidth_change(self):
self.mpick_circle_width = self.ui.mpick_sb_labelWidth.value()
self.MC_updata()
def mpick_classId_change(self):
self.mpick_clsId = self.ui.mpick_sb_classId.value()
self.MC_updata()
def mpick_setColor(self):
Qcolor = QColorDialog.getColor()
self.color = (Qcolor.red(), Qcolor.green(), Qcolor.blue())
if self.ui.mpick_ckb_enable:
colors[self.mpick_clsId - 1] = self.color
self.MC_updata()
def res_show_info(self, info):
self.ui.res_txtB.insertPlainText(f"{info}\n")
self.ui.res_txtB.ensureCursorVisible()
def mpick_savePath(self):
self.mpick_save_path = QFileDialog.getExistingDirectory(self, 'Select the path')
if self.mpick_save_path != "":
self.ui.mpick_edit_savePath.setText(self.mpick_save_path)
self.res_show_info(f"Save path of manual picking: {self.mpick_save_path}")
def mpick_savePathChange(self):
self.mpick_save_path = self.ui.mpick_edit_savePath.text()
def mpick_saveNameChange(self):
self.mpick_save_name = self.ui.mpick_edit_saveName.text()
def mpick_save(self):
if self.mpick_save_path == "" \
or self.mpick_save_name == "" \
or self.mpick_coords_np.shape[0] < 1:
QMessageBox.critical(self, 'Error', 'Incomplete information')
else:
os.makedirs(self.mpick_save_path, exist_ok=True)
np.savetxt(os.path.join(self.mpick_save_path, self.mpick_save_name),
self.mpick_coords_np,
fmt='%s', delimiter='\t', newline='\n')
# Train components
def train_show_info(self, info):
self.ui.train_txtB.insertPlainText(f"{info}\n")
self.ui.train_txtB.ensureCursorVisible()
def train_dsetName_change(self):
self.train_dsetName = self.ui.train_edit_dsetName.text()
self.train_show_info(f"Training dataset Name: {self.train_dsetName}")
def train_modelName_change(self):
if self.ui.train_cbox_modelName.currentIndex != 0:
self.train_modelName = self.ui.train_cbox_modelName.currentText()
self.train_show_info(f"Training model name: {self.train_modelName}")
def train_cls_num_change(self):
self.train_cls_num = self.ui.train_sb_clsNum.value()
self.train_show_info(f"Number of training classes: {self.train_cls_num}")
def train_batchSize_change(self):
self.train_batchSize = self.ui.train_sb_batchSize.value()
self.train_show_info(f"Training - batch size: {self.train_batchSize}")
def train_patchSize_change(self):
self.train_patchSize = self.ui.train_sb_patchSize.value()
self.train_show_info(f"Training - patch size: {self.train_patchSize}")
def train_paddingSize_change(self):
self.train_paddingSize = self.ui.train_sb_paddingSize.value()
self.train_show_info(f"Training - padding size: {self.train_paddingSize}")
def train_maxEpochs_change(self):
self.train_maxEpochs = self.ui.train_sb_maxEpochs.value()
self.train_show_info(f"Training - max epochs: {self.train_maxEpochs}")
def train_segThresh_change(self):
self.train_segThresh = float(self.ui.train_edit_segThresh.text())
self.train_show_info(f"Training - segmentation threshold: {self.train_segThresh}")
def train_lr_change(self):
self.train_lr = float(self.ui.train_edit_lr.text())
self.train_show_info(f"Training - learning rate: {self.train_lr}")
def train_gpuIds_change(self):
self.train_gpuIds = self.ui.train_edit_gpuIds.text()
self.train_show_info(f"Training - gpu ids: {self.train_gpuIds}")
def train_set_ids_change(self):
self.train_set_ids = self.ui.edit_train_set_ids.text()
self.train_show_info(f"Train dataset ids: {self.train_set_ids}")
def val_set_ids_change(self):
self.val_set_ids = self.ui.edit_val_set_ids.text()
self.train_show_info(f"Val dataset ids: {self.val_set_ids}")
def train_saveConfigs(self):
if self.ui.train_edit_segThresh.text() == "" \
or self.ui.edit_train_set_ids.text() == "" \
or self.ui.edit_val_set_ids.text() == "" \
or self.ui.train_edit_gpuIds.text() == "" \
or self.ui.train_edit_dsetName.text() == "" \
or (self.ui.edit_load_configs.text() == "" and self.ui.train_edit_loadConfigs == ""):
QMessageBox.critical(self, 'Error', 'Incomplete information')
else:
base_path = self.c2l_basePath if isinstance(self.c2l_basePath, str) else self.c2l_basePath[0]
input_norm = self.input_norm if isinstance(self.input_norm, str) else self.input_norm[0]
tomo_name = 'data_std' if 'standardization' in input_norm else 'data_norm'
label_name = (self.label_type if isinstance(self.label_type, str) else self.label_type[0]) + \
str(self.label_diameter if isinstance(self.label_diameter, int) else self.label_diameter[0])
# ocp_name = (self.ocp_type if isinstance(self.ocp_type, str) else self.ocp_type[0]) + \
# str(self.ocp_diameter if isinstance(self.ocp_diameter, int) else self.ocp_diameter[0])
ocp_name = 'data_ocp' # + str(self.ocp_diameter if isinstance(self.ocp_diameter, int) else self.ocp_diameter[0])
self.train_configs = dict(
dset_name=(self.train_dsetName if isinstance(self.train_dsetName, str) else self.train_dsetName[
0]),
base_path=self.c2l_basePath if isinstance(self.c2l_basePath, str) else self.c2l_basePath[0],
coord_path=os.path.join(base_path, 'coords'),
coord_format=self.coord_format if isinstance(self.coord_format, str) else self.coord_format[0],
tomo_path=os.path.join(base_path, tomo_name),
tomo_format=self.tomo_format if isinstance(self.tomo_format, str) else self.tomo_format[0],
num_cls=self.train_cls_num if isinstance(self.train_cls_num, int) else self.train_cls_num[0],
label_name=(self.label_type if isinstance(self.label_type, str) else self.label_type[0]) + \
str(self.label_diameter if isinstance(self.label_diameter, int) else self.label_diameter[0]),
label_path=os.path.join(base_path, label_name),
label_type=self.label_type if isinstance(self.label_type, str) else self.label_type[0],
label_diameter=self.label_diameter if isinstance(self.label_diameter, int) else self.label_diameter[0],
ocp_type=self.ocp_type if isinstance(self.ocp_type, str) else self.ocp_type[0],
ocp_diameter=self.ocp_diameter if isinstance(self.ocp_diameter, str) else self.ocp_diameter[0],
ocp_name=ocp_name,
ocp_path=os.path.join(base_path, ocp_name),
norm_type=self.input_norm if isinstance(self.input_norm, str) else self.input_norm[0],
model_name=self.train_modelName if isinstance(self.train_modelName, str) else self.train_modelName[0],
train_set_ids=self.train_set_ids if isinstance(self.train_set_ids, str) else self.train_set_ids[0],
val_set_ids=self.val_set_ids if isinstance(self.val_set_ids, str) else self.val_set_ids[0],
batch_size=self.train_batchSize if isinstance(self.train_batchSize, int) else self.train_batchSize[0],
patch_size=self.train_patchSize if isinstance(self.train_patchSize, int) else self.train_patchSize[0],
padding_size=self.train_paddingSize if isinstance(self.train_paddingSize, int) else
self.train_paddingSize[0],
lr=self.train_lr if isinstance(self.train_lr, float) else self.train_lr[0],
max_epochs=self.train_maxEpochs if isinstance(self.train_maxEpochs, int) else self.train_maxEpochs[0],
seg_thresh=self.train_segThresh if isinstance(self.train_segThresh, float) else self.train_segThresh[0],
gpu_ids=self.train_gpuIds if isinstance(self.train_gpuIds, str) else self.train_gpuIds[0],
)
config_save_path = os.path.join(base_path, 'configs')
os.makedirs(config_save_path, exist_ok=True)
self.train_config_save_path = f"{config_save_path}/{self.train_dsetName}.py"
with open(self.train_config_save_path, 'w') as f:
f.write("train_configs=")
json.dump(self.train_configs, f, separators=(',\n'+' '*len('train_configs={'), ': '))
self.train_show_info(f"save train configs to '{config_save_path}/{self.train_dsetName}.py'")
def train_loadConfigs(self):
self.train_config_file, _ = QFileDialog.getOpenFileName(self, 'Select the training configs')
if self.train_config_file != "":
self.ui.train_edit_loadConfigs.setText(self.train_config_file)
self.train_show_info(f"Load training configs: {self.train_config_file}")
train_config_name = self.train_config_file.split('/')[-1][:-3]
base_config = '.'.join(self.train_config_file.split('/')[:-1])
# print(sys.modules.keys())
# # delete the package in the sys.modules
# if f"{base_config}.{train_config_name}" in list(sys.modules.keys()):
# del sys.modules[f"{base_config}.{train_config_name}"]
# config = importlib.import_module(f"{base_config}.{train_config_name}")
# self.train_configs = config.train_configs
with open(self.train_config_file, 'r') as f:
self.train_configs = json.loads(''.join(f.readlines()).lstrip('train_configs='))
self.train_dsetName = self.train_configs['dset_name']
self.base_path = self.train_configs['base_path']
self.coord_path = self.train_configs['coord_path']
self.coord_format = self.train_configs['coord_format']
self.tomo_path = self.train_configs['tomo_path']
self.tomo_format = self.train_configs['tomo_format']
self.train_numCls = self.train_configs['num_cls']
self.label_name = self.train_configs['label_name']
self.label_path = self.train_configs['label_path']
self.label_type = self.train_configs['label_type']
self.label_diameter = self.train_configs['label_diameter']
self.ocp_type = self.train_configs['ocp_type']
self.ocp_diameter = self.train_configs['ocp_diameter']
self.ocp_name = self.train_configs['ocp_name']
self.ocp_path = self.train_configs['ocp_path']
self.norm_type = self.train_configs['norm_type']
self.train_modelName = self.train_configs['model_name']
self.train_set_ids = self.train_configs['train_set_ids']
self.val_set_ids = self.train_configs['val_set_ids']
self.train_batchSize = self.train_configs['batch_size']
self.train_patchSize = self.train_configs['patch_size']
self.train_paddingSize = self.train_configs['padding_size']
self.train_lr = self.train_configs['lr']
self.train_maxEpochs = self.train_configs['max_epochs']
self.train_segThresh = self.train_configs['seg_thresh']
self.train_gpuIds = self.train_configs['gpu_ids']
self.train_dsetName = self.train_dsetName if isinstance(self.train_dsetName, str) else self.train_dsetName[0]
self.base_path = self.base_path if isinstance(self.base_path, str) else self.base_path[0]
self.coord_path = self.coord_path if isinstance(self.coord_path, str) else self.coord_path[0]
self.coord_format = self.coord_format if isinstance(self.coord_format, str) else self.coord_format[0]
self.tomo_path = self.tomo_path if isinstance(self.tomo_path, str) else self.tomo_path[0]
self.tomo_format = self.tomo_format if isinstance(self.tomo_format, str) else self.tomo_format[0]
self.train_numCls = self.train_numCls if isinstance(self.train_numCls, int) else self.train_numCls[0]
self.label_name = self.label_name if isinstance(self.label_name, str) else self.label_name[0]
self.label_path = self.label_path if isinstance(self.label_path, str) else self.label_path[0]
self.label_type = self.label_type if isinstance(self.label_type, str) else self.label_type[0]
self.label_diameter = self.label_diameter if isinstance(self.label_diameter, int) else self.label_diameter[0]
self.ocp_type = self.ocp_type if isinstance(self.ocp_type, str) else self.ocp_type[0]
self.ocp_diameter = self.ocp_diameter if isinstance(self.ocp_diameter, str) else self.ocp_diameter[0]
self.ocp_name = self.ocp_name if isinstance(self.ocp_name, str) else self.ocp_name[0]
self.ocp_path = self.ocp_path if isinstance(self.ocp_path, str) else self.ocp_path[0]
self.norm_type = self.norm_type if isinstance(self.norm_type, str) else self.norm_type[0]
self.val_set_ids = self.val_set_ids if isinstance(self.val_set_ids, str) else self.val_set_ids[
0]
self.train_set_ids = self.train_set_ids if isinstance(self.train_set_ids, str) else self.train_set_ids[
0]
self.train_modelName = self.train_modelName if isinstance(self.train_modelName, str) else self.train_modelName[
0]
self.train_batchSize = self.train_batchSize if isinstance(self.train_batchSize, int) else self.train_batchSize[
0]
self.train_patchSize = self.train_patchSize if isinstance(self.train_patchSize, int) else self.train_patchSize[
0]
self.train_paddingSize = self.train_paddingSize if isinstance(self.train_paddingSize, int) else \
self.train_paddingSize[0]
self.train_lr = self.train_lr if isinstance(self.train_lr, float) else \
self.train_lr[0]
self.train_maxEpochs = self.train_maxEpochs if isinstance(self.train_maxEpochs, int) else self.train_maxEpochs[
0]
self.train_segThresh = self.train_segThresh if isinstance(self.train_segThresh, float) else \
self.train_segThresh[0]
self.train_gpuIds = self.train_gpuIds if isinstance(self.train_gpuIds, str) else self.train_gpuIds[0]
self.c2l_basePath = self.base_path
self.ui.edit_base_path.setText(self.base_path)
self.ui.cbox_coord_format.setCurrentText(self.coord_format)
self.ui.cbox_tomo_format.setCurrentText(self.tomo_format)
self.ui.cbox_label_type.setCurrentText(self.label_type)
self.ui.cbox_ocp_type.setCurrentText(self.ocp_type)
self.ui.sb_cls_num.setValue(self.train_numCls)
self.ui.sb_label_diameter.setValue(self.label_diameter)
self.ui.edit_ocp_diameter.setText(self.ocp_diameter)
self.ui.train_edit_dsetName.setText(self.train_dsetName.split('.')[0])
self.ui.train_cbox_modelName.setCurrentText(self.train_modelName)
self.ui.train_sb_clsNum.setValue(self.train_numCls)
self.ui.train_sb_batchSize.setValue(self.train_batchSize)
self.ui.train_sb_patchSize.setValue(self.train_patchSize)
self.ui.train_sb_paddingSize.setValue(self.train_paddingSize)
self.ui.train_edit_lr.setText(str(self.train_lr))
self.ui.train_sb_maxEpochs.setValue(self.train_maxEpochs)
self.ui.train_edit_segThresh.setText(str(self.train_segThresh))
self.ui.train_edit_gpuIds.setText(self.train_gpuIds)
self.ui.edit_train_set_ids.setText(self.train_set_ids)
self.ui.edit_val_set_ids.setText(self.val_set_ids)
if self.input_norm == "standardization":
self.ui.radB_norm.setChecked(False)
self.ui.radB_std.setChecked(True)
elif self.input_norm == "normalization":
self.ui.radB_std.setChecked(False)
self.ui.radB_norm.setChecked(True)
self.train_show_info('*' * 100)
for i in self.train_configs.keys():
self.train_show_info(f'{i}: {self.train_configs[i]}')
self.train_show_info('*' * 100)
def train_loadConfigs_v1(self):
self.train_config_file = self.train_config_save_path
if self.train_config_file != "":
self.ui.train_edit_loadConfigs.setText(self.train_config_file)
self.train_show_info(f"Load training configs: {self.train_config_file}")
train_config_name = self.train_config_file.split('/')[-1][:-3]
base_config = '.'.join(self.train_config_file.split('/')[:-1])
# print(sys.modules.keys())
# # delete the package in the sys.modules
# if f"{base_config}.{train_config_name}" in list(sys.modules.keys()):
# del sys.modules[f"{base_config}.{train_config_name}"]
# config = importlib.import_module(f"{base_config}.{train_config_name}")
# self.train_configs = config.train_configs
with open(self.train_config_file, 'r') as f:
self.train_configs = json.loads(''.join(f.readlines()).lstrip('train_configs='))
self.train_dsetName = self.train_configs['dset_name']
self.base_path = self.train_configs['base_path']
self.coord_path = self.train_configs['coord_path']
self.coord_format = self.train_configs['coord_format']
self.tomo_path = self.train_configs['tomo_path']
self.tomo_format = self.train_configs['tomo_format']
self.train_numCls = self.train_configs['num_cls']
self.label_name = self.train_configs['label_name']
self.label_path = self.train_configs['label_path']
self.label_type = self.train_configs['label_type']
self.label_diameter = self.train_configs['label_diameter']
self.ocp_type = self.train_configs['ocp_type']
self.ocp_diameter = self.train_configs['ocp_diameter']
self.ocp_name = self.train_configs['ocp_name']
self.ocp_path = self.train_configs['ocp_path']
self.norm_type = self.train_configs['norm_type']
self.train_set_ids = self.train_configs['train_set_ids']
self.val_set_ids = self.train_configs['val_set_ids']
self.train_modelName = self.train_configs['model_name']
self.train_batchSize = self.train_configs['batch_size']
self.train_patchSize = self.train_configs['patch_size']
self.train_paddingSize = self.train_configs['padding_size']
self.train_lr = self.train_configs['lr']
self.train_maxEpochs = self.train_configs['max_epochs']
self.train_segThresh = self.train_configs['seg_thresh']
self.train_gpuIds = self.train_configs['gpu_ids']
self.train_dsetName = self.train_dsetName if isinstance(self.train_dsetName, str) else self.train_dsetName[0]
self.base_path = self.base_path if isinstance(self.base_path, str) else self.base_path[0]
self.coord_path = self.coord_path if isinstance(self.coord_path, str) else self.coord_path[0]
self.coord_format = self.coord_format if isinstance(self.coord_format, str) else self.coord_format[0]
self.tomo_path = self.tomo_path if isinstance(self.tomo_path, str) else self.tomo_path[0]
self.tomo_format = self.tomo_format if isinstance(self.tomo_format, str) else self.tomo_format[0]
self.train_numCls = self.train_numCls if isinstance(self.train_numCls, int) else self.train_numCls[0]
self.label_name = self.label_name if isinstance(self.label_name, str) else self.label_name[0]
self.label_path = self.label_path if isinstance(self.label_path, str) else self.label_path[0]
self.label_type = self.label_type if isinstance(self.label_type, str) else self.label_type[0]
self.label_diameter = self.label_diameter if isinstance(self.label_diameter, int) else self.label_diameter[0]
self.ocp_type = self.ocp_type if isinstance(self.ocp_type, str) else self.ocp_type[0]
self.ocp_diameter = self.ocp_diameter if isinstance(self.ocp_diameter, str) else self.ocp_diameter[0]
self.ocp_name = self.ocp_name if isinstance(self.ocp_name, str) else self.ocp_name[0]
self.ocp_path = self.ocp_path if isinstance(self.ocp_path, str) else self.ocp_path[0]
self.norm_type = self.norm_type if isinstance(self.norm_type, str) else self.norm_type[0]
self.val_set_ids = self.val_set_ids if isinstance(self.val_set_ids, str) else self.val_set_ids[
0]
self.train_set_ids = self.train_set_ids if isinstance(self.train_set_ids, str) else self.train_set_ids[
0]
self.train_modelName = self.train_modelName if isinstance(self.train_modelName, str) else self.train_modelName[
0]
self.train_batchSize = self.train_batchSize if isinstance(self.train_batchSize, int) else self.train_batchSize[
0]
self.train_patchSize = self.train_patchSize if isinstance(self.train_patchSize, int) else self.train_patchSize[
0]
self.train_paddingSize = self.train_paddingSize if isinstance(self.train_paddingSize, int) else \
self.train_paddingSize[0]
self.train_lr = self.train_lr if isinstance(self.train_lr, float) else \
self.train_lr[0]
self.train_maxEpochs = self.train_maxEpochs if isinstance(self.train_maxEpochs, int) else self.train_maxEpochs[
0]
self.train_segThresh = self.train_segThresh if isinstance(self.train_segThresh, float) else \
self.train_segThresh[0]
self.train_gpuIds = self.train_gpuIds if isinstance(self.train_gpuIds, str) else self.train_gpuIds[0]
self.c2l_basePath = self.base_path
self.ui.edit_base_path.setText(self.base_path)
self.ui.cbox_coord_format.setCurrentText(self.coord_format)
self.ui.cbox_tomo_format.setCurrentText(self.tomo_format)
self.ui.cbox_label_type.setCurrentText(self.label_type)
self.ui.cbox_ocp_type.setCurrentText(self.ocp_type)
self.ui.sb_cls_num.setValue(self.train_numCls)
self.ui.sb_label_diameter.setValue(self.label_diameter)
self.ui.edit_ocp_diameter.setText(self.ocp_diameter)
self.ui.train_edit_dsetName.setText(self.train_dsetName.split('.')[0])
self.ui.train_cbox_modelName.setCurrentText(self.train_modelName)
self.ui.train_sb_clsNum.setValue(self.train_numCls)
self.ui.train_sb_batchSize.setValue(self.train_batchSize)
self.ui.train_sb_patchSize.setValue(self.train_patchSize)
self.ui.train_sb_paddingSize.setValue(self.train_paddingSize)
self.ui.train_edit_lr.setText(str(self.train_lr))
self.ui.train_sb_maxEpochs.setValue(self.train_maxEpochs)
self.ui.train_edit_segThresh.setText(str(self.train_segThresh))
self.ui.train_edit_gpuIds.setText(self.train_gpuIds)
self.ui.edit_train_set_ids.setText(self.train_set_ids)
self.ui.edit_val_set_ids.setText(self.val_set_ids)
if self.input_norm == "standardization":
self.ui.radB_norm.setChecked(False)
self.ui.radB_std.setChecked(True)
elif self.input_norm == "normalization":
self.ui.radB_std.setChecked(False)
self.ui.radB_norm.setChecked(True)
self.train_show_info('*' * 100)
for i in self.train_configs.keys():
self.train_show_info(f'{i}: {self.train_configs[i]}')
self.train_show_info('*' * 100)
def train_dsetList(self):
coord_path = f"{self.c2l_basePath}/coords/num_name.csv"
coord_data = np.loadtxt(coord_path, delimiter='\t', dtype=str).reshape(-1, 3)
self.train_show_info(f'*' * 100)
self.train_show_info(f"Dataset list:")
self.train_show_info(f"Number\t Name \t Cls_id")
for item in coord_data:
self.train_show_info(f"{item[0].split('.')[0]}\t{item[1]}\t{item[2]}")
self.train_show_info(f'*' * 100)
def train_show_clear(self):
self.ui.train_txtB.clear()
def train_stop(self):
try:
# self.train_thread.n = 0
# self.train_thread.join()
# os.system(f"kill -9 {self.train_thread.pid_num}")
stop_thread(self.train_thread)
# self.train_t.pause()
# os.system(f"kill {self.train_pid}")
# self.train_thread.terminate()
except:
pass
self.train_show_info('*' * 100)
self.train_show_info('Training Stopped')
self.train_show_info('*' * 100)
def train_ok(self):
self.train_saveConfigs()
self.train_loadConfigs_v1()
if self.ui.train_edit_segThresh.text() == "" \
or self.ui.train_edit_gpuIds.text() == "" \
or self.ui.train_edit_dsetName.text() == "" \
or (self.ui.edit_load_configs.text() == "" and self.ui.train_edit_loadConfigs == ""):
QMessageBox.critical(self, 'Error', 'Incomplete information')
else:
self.train_show_info('*' * 100)
self.train_show_info('Final training configuration parameters')
self.train_show_info('*' * 100)
self.train_show_info(f"Training dataset Name: {self.train_dsetName}")
self.train_show_info(f"Training model name: {self.train_modelName}")
self.train_show_info(f"Number of training classes: {self.train_cls_num}")
self.train_show_info(f"Training - batch size: {self.train_batchSize}")
self.train_show_info(f"Training - patch size: {self.train_patchSize}")
self.train_show_info(f"Training - padding size: {self.train_paddingSize}")
self.train_show_info(f"Training - max epochs: {self.train_maxEpochs}")
self.train_show_info(f"Training - segmentation threshold: {self.train_segThresh}")
self.train_show_info(f"Training - gpu ids: {self.train_gpuIds}")
self.train_show_info('*' * 100)
self.csv_path = f"{self.base_path}/coords/num_name.csv"
self.csv_data = np.loadtxt(self.csv_path, delimiter='\t', dtype=str).reshape(-1, 3)
train_list = []
for item in self.train_set_ids.split(','):
if '-' in item:
tmp = [int(i) for i in item.split('-')]
train_list.extend(np.arange(tmp[0], tmp[1] + 1).tolist())
else:
train_list.append(int(item))
val_list = []
for item in self.val_set_ids.split(','):
if '-' in item:
tmp = [int(i) for i in item.split('-')]
val_list.extend(np.arange(tmp[0], tmp[1] + 1).tolist())
else:
val_list.append(int(item))
csv_data_new = []
others = []
for item in self.csv_data:
if (int(item[2]) in train_list) and int(item[2]) not in val_list:
csv_data_new.insert(0, item.tolist())
elif int(item[2]) not in val_list:
others.append(item.tolist())
for item in self.csv_data:
if int(item[2]) in val_list:
csv_data_new.append(item.tolist())
csv_data_new.extend(others)
np.savetxt(self.csv_path,
np.array(csv_data_new),
delimiter='\t',
newline='\n',
fmt='%s')
if self.train_cls_num == 1:
use_sigmoid = True
use_softmax = False
train_cls_num = self.train_cls_num
elif self.train_cls_num > 1:
train_cls_num = self.train_cls_num + 1
use_sigmoid = False
use_softmax = True
options = BaseOptions()
args = options.gather_options()
args.block_size = self.train_patchSize
args.num_classes = train_cls_num
args.loss_func_seg = 'Dice'
args.optim = 'AdamW'
args.weight_decay = 0.01
args.learning_rate = self.train_lr
args.batch_size = self.train_batchSize
args.max_epoch = self.train_maxEpochs
args.network = self.train_modelName
args.use_bg = True
args.use_IP = True
args.use_coord = True
args.use_sigmoid = use_sigmoid
args.use_softmax = use_softmax
args.threshold = self.train_segThresh
# args.data_split = [0, 1, 0, 1, 0, 1]
# print(train_list, val_list)
val_first = len(train_list) if val_list[0] not in train_list else len(train_list) - 1
self.train_show_info(f"val_first:{val_first:.0f}")
args.data_split = [0, len(train_list), # train
val_first, val_first + 1, # val
val_first, val_first + 1] # test_val
self.train_show_info(f"data_split:{args.data_split}")
args.f_maps = [24, 48, 72, 108]
args.random_num = 0
args.configs = os.path.join(self.base_path, 'configs', f'{self.train_dsetName}.py')
args.loader_type = 'dataloader_DynamicLoad'
args.test_use_pad = True
args.pad_size = [self.train_paddingSize]
args.test_mode = 'val'
args.val_batch_size = self.train_batchSize
args.val_block_size = self.train_patchSize
args.scheduler = 'OneCycleLR'
args.gpu_id = [int(i) for i in self.train_gpuIds.split(',')]
args.meanPool_NMS = True
"""
threading.Thread
"""
self.train_emit = EmittingStr()
self.train_emit.textWritten.connect(self.train_show_info)
self.train_thread = threading.Thread(target=train_func, args=(args, self.train_emit))
# self.train_thread = myThread(1, train, args, self.train_emit)
self.train_thread.start()
# self.train_thread.join()
# print(self.train_pid)
"""
Class threading
"""
# self.train_t = Concur(train, args, self.train_emit)
# self.train_t.start()
# self.train_t.resume()
"""
QThread
"""
# class Qthread_job(QThread):
# def __init__(self, job, args, stdout):
# super(Qthread_job, self).__init__()
# self.job = job
# self.args = args
# self.stdout = stdout
# def run(self):
# self.job(self.args, self.stdout)
#
# self.train_thread = Qthread_job(train, args, self.train_emit)
# self.train_thread.start()
"""
test
"""
def test_set_ids_change(self):
self.test_set_ids = self.ui.edit_test_set_ids.text()
self.test_show_info(f"Test dataset ids: {self.test_set_ids}")
def test_load_trainConfigs(self):
self.train_config_file, _ = QFileDialog.getOpenFileName(self, 'Select the training configs')
if self.train_config_file != "":
self.ui.train_edit_loadConfigs.setText(self.train_config_file)
self.test_show_info(f"Load training configs: {self.train_config_file}")
with open(self.train_config_file, 'r') as f:
self.train_configs = json.loads(''.join(f.readlines()).lstrip('train_configs='))
self.train_dsetName = self.train_configs['dset_name']
self.base_path = self.train_configs['base_path']
self.coord_path = self.train_configs['coord_path']
self.coord_format = self.train_configs['coord_format']
self.tomo_path = self.train_configs['tomo_path']
self.tomo_format = self.train_configs['tomo_format']
self.train_numCls = self.train_configs['num_cls']
self.label_name = self.train_configs['label_name']
self.label_path = self.train_configs['label_path']
self.label_type = self.train_configs['label_type']
self.label_diameter = self.train_configs['label_diameter']
self.ocp_type = self.train_configs['ocp_type']
self.ocp_diameter = self.train_configs['ocp_diameter']
self.ocp_name = self.train_configs['ocp_name']
self.ocp_path = self.train_configs['ocp_path']
self.norm_type = self.train_configs['norm_type']
self.train_modelName = self.train_configs['model_name']
self.train_batchSize = self.train_configs['batch_size']
self.train_patchSize = self.train_configs['patch_size']
self.train_paddingSize = self.train_configs['padding_size']
self.train_maxEpochs = self.train_configs['max_epochs']
self.train_segThresh = self.train_configs['seg_thresh']
self.train_gpuIds = self.train_configs['gpu_ids']
self.test_gpuIds = self.train_configs['gpu_ids']
self.train_dsetName = self.train_dsetName if isinstance(self.train_dsetName, str) else self.train_dsetName[0]
self.base_path = self.base_path if isinstance(self.base_path, str) else self.base_path[0]
self.coord_path = self.coord_path if isinstance(self.coord_path, str) else self.coord_path[0]
self.coord_format = self.coord_format if isinstance(self.coord_format, str) else self.coord_format[0]
self.tomo_path = self.tomo_path if isinstance(self.tomo_path, str) else self.tomo_path[0]
self.tomo_format = self.tomo_format if isinstance(self.tomo_format, str) else self.tomo_format[0]
self.train_numCls = self.train_numCls if isinstance(self.train_numCls, int) else self.train_numCls[0]
self.label_name = self.label_name if isinstance(self.label_name, str) else self.label_name[0]
self.label_path = self.label_path if isinstance(self.label_path, str) else self.label_path[0]
self.label_type = self.label_type if isinstance(self.label_type, str) else self.label_type[0]
self.label_diameter = self.label_diameter if isinstance(self.label_diameter, int) else self.label_diameter[0]
self.ocp_type = self.ocp_type if isinstance(self.ocp_type, str) else self.ocp_type[0]
self.ocp_diameter = self.ocp_diameter if isinstance(self.ocp_diameter, str) else self.ocp_diameter[0]
self.ocp_name = self.ocp_name if isinstance(self.ocp_name, str) else self.ocp_name[0]
self.ocp_path = self.ocp_path if isinstance(self.ocp_path, str) else self.ocp_path[0]
self.norm_type = self.norm_type if isinstance(self.norm_type, str) else self.norm_type[0]
self.train_modelName = self.train_modelName if isinstance(self.train_modelName, str) else self.train_modelName[
0]
self.train_batchSize = self.train_batchSize if isinstance(self.train_batchSize, int) else self.train_batchSize[
0]
self.train_patchSize = self.train_patchSize if isinstance(self.train_patchSize, int) else self.train_patchSize[
0]
self.train_paddingSize = self.train_paddingSize if isinstance(self.train_paddingSize, int) else \
self.train_paddingSize[0]
self.train_maxEpochs = self.train_maxEpochs if isinstance(self.train_maxEpochs, int) else self.train_maxEpochs[
0]
self.train_segThresh = self.train_segThresh if isinstance(self.train_segThresh, float) else \
self.train_segThresh[0]
self.train_gpuIds = self.train_gpuIds if isinstance(self.train_gpuIds, str) else self.train_gpuIds[0]
self.c2l_basePath = self.base_path
self.ui.edit_base_path.setText(self.base_path)
self.ui.cbox_coord_format.setCurrentText(self.coord_format)
self.ui.cbox_tomo_format.setCurrentText(self.tomo_format)
self.ui.cbox_label_type.setCurrentText(self.label_type)
self.label_type = self.ui.cbox_label_type.currentText()
self.ui.cbox_ocp_type.setCurrentText(self.ocp_type)
self.ui.sb_cls_num.setValue(self.train_numCls)
self.ui.sb_label_diameter.setValue(self.label_diameter)
self.ui.edit_ocp_diameter.setText(self.ocp_diameter)
self.ui.train_edit_dsetName.setText(self.train_dsetName.split('.')[0])
self.ui.train_cbox_modelName.setCurrentText(self.train_modelName)
self.ui.train_sb_clsNum.setValue(self.train_numCls)
self.ui.train_sb_batchSize.setValue(self.train_batchSize)
self.ui.train_sb_patchSize.setValue(self.train_patchSize)
self.ui.train_sb_paddingSize.setValue(self.train_paddingSize)
self.ui.train_sb_maxEpochs.setValue(self.train_maxEpochs)
self.ui.train_edit_segThresh.setText(str(self.train_segThresh))
self.ui.train_edit_gpuIds.setText(self.train_gpuIds)
self.ui.test_sb_patchSize.setValue(self.train_patchSize)
self.ui.test_sb_paddingSize.setValue(self.train_paddingSize)
self.ui.test_edit_segThresh.setText(str(self.train_segThresh))
self.ui.test_edit_trainConfigs.setText(self.train_config_file)
self.ui.test_edit_gpuIds.setText(self.train_configs['gpu_ids'])
if self.input_norm == "standardization":
self.ui.radB_norm.setChecked(False)
self.ui.radB_std.setChecked(True)
elif self.input_norm == "normalization":
self.ui.radB_std.setChecked(False)
self.ui.radB_norm.setChecked(True)
self.test_show_info('*' * 100)
for i in self.train_configs.keys():
self.test_show_info(f'{i}: {self.train_configs[i]}')
self.test_show_info('*' * 100)
def test_stop(self):
try:
# self.test_thread.quit()
stop_thread(self.test_thread)
except:
pass
self.test_show_info('*' * 100)
self.test_show_info('Testing stopped!')
self.test_show_info('*' * 100)
def test_ok(self):
if self.ui.test_sb_patchSize.text() == "" \