def save_selected_activations(self):
if not self.input:
self.log_window(title='Error!',
Msg='Please indicate a input image')
else:
save_act_path = file_dialog(
title='choose a folder to save the images',
type='/').openFolderDialog()
selected_idx = self.actList.selectionModel().selectedIndexes()
for idx in selected_idx:
layer_name = self.actList.item(idx.row()).text()
rlt = np.squeeze(self.activations[layer_name])
if rlt.ndim == 3:
for i in range(rlt.shape[-1]):
check_N_mkdir(save_act_path +
layer_name.replace('/', '_'))
Image.fromarray(
rlt[:, :, i]).save(save_act_path +
layer_name.replace('/', '_') +
'/{}.tif'.format(i))
elif rlt.ndim == 2:
check_N_mkdir(save_act_path + layer_name.replace('/', '_'))
Image.fromarray(
rlt[:, :]).save(save_act_path +
layer_name.replace('/', '_') +
'/act.tif')
else:
logger.debug(
'got an unexpected ndim of the activations: {}'.format(
rlt.ndim))
def ckptFileDialog(self):
tmp = file_dialog(title='choose .meta file').openFileNameDialog()
if tmp:
self.ckptPathLine.setText(tmp)
self.set_ckpt()
def set_log_dir(self):
log_dir_dial = file_dialog(title='select a folder to save the model',
type='/')
self.log_dir = log_dir_dial.openFolderDialog()
self.log_dir_line.setText(self.log_dir)
def activation_plugin(self):
# os.environ["CUDA_VISIBLE_DEVICES"] = "-1" #note: here might have conflict if there's an ongoing training with GPU
import tensorflow as tf
# get ckpt file
dialog = file_dialog(
title='select ckpts (*.meta) to retrieve activations',
type='.meta')
ckpt_paths = dialog.openFileNamesDialog()
logger.debug('ckpt_paths: {}'.format(ckpt_paths))
if ckpt_paths is not None and (not not ckpt_paths):
# get input path
dialog = file_dialog(
title='select folder which contains datas for analyses')
data_folder = dialog.openFolderDialog()
logger.debug('data_folder: {}'.format(data_folder))
if data_folder is not None and (not not data_folder):
if len(ckpt_paths) != 0:
# restore from ckpt the nodes
tf.reset_default_graph()
logger.debug(ckpt_paths[0])
_ = tf.train.import_meta_graph(
ckpt_paths[0],
clear_devices=True,
)
# get arguments
graph = tf.get_default_graph().as_graph_def()
nodes = print_nodes_name(graph)
steps = [
re.search('step(\d+)', ck_pth).group(1)
for ck_pth in ckpt_paths
]
# retrive nodes of activations
options = []
for node in nodes:
tmp = re.search(
'(^[a-zA-Z]+\d*\/).*(leaky|relu|sigmoid|tanh|logits\/identity|up\d+\/Reshape\_4|concat)$',
node)
if tmp is not None:
tmp = tmp.string
if 'identity' in tmp:
iden = tmp
else:
options.append(tmp)
# open nodes list dialog
nodes_list = node_list_logic(options=options)
nodes_list.exec()
if nodes_list.result() == 1:
acts = nodes_list.return_nodes()
if iden:
acts.append(iden)
types = nodes_list.return_analysis_types()
if len(types) == 0:
types = ['activation']
terminal = [
'python',
os.path.join(parentDir, 'main_analytic.py'),
'-ckpt',
*ckpt_paths,
'-step',
*steps,
'-type',
*types,
'-node',
*acts,
'-dir',
data_folder,
]
logger.debug(terminal)
proc = subprocess.Popen(terminal)
proc.wait()
def selectckpt(self):
ckpt_dialog = file_dialog(title='select a checkpoint file .meta',
type='.meta')
ckpt_path = ckpt_dialog.openFileNameDialog()
self.ckpt_path = ckpt_path
self.ckptLine.setText(self.ckpt_path)
def set_tst_dir(self):
tst_dir_dial = file_dialog(
title='select a folder where includes the testing dataset',
type='/')
self.tst_dir = tst_dir_dial.openFolderDialog()
self.test_dir_line.setText(self.tst_dir)
def chooseFolder(self):
self.dir = file_dialog(title='choose a folder to save the plot/legend/data', type='/').openFolderDialog()
if not os.path.isdir(self.dir):
logger.error('cannot find training files in this folder')
def selectRaw(self):
self.raw_folder = file_dialog(
title='choose a folder where contains the raw tomogram .tif'
).openFolderDialog()
self.rawLine.setText(self.raw_folder)
def save_diff(self):
if self.diff[self.current_page] is not None:
save_path = file_dialog(title='select a place to save the difference image', type='/').openFolderDialog()
if save_path is not None:
Image.fromarray(self.diff[self.current_page]).save(save_path+'/diff.tif')
def saveResult(self):
save_path = file_dialog(title='choose a folder to save the plot|legend|data', type='/').openFolderDialog()
self.pd_df.to_csv(os.path.join(save_path, 'data.csv'), index=False)
self.MPLwidget.canvas1.figure.savefig(os.path.join(save_path, 'plot.png'))
Image.fromarray(np.asarray(self.fig2)).save(os.path.join(save_path, 'legend.png'))
def choose_save_path(self):
folder_path = file_dialog(title='select folder to save curves .csv',
type='/').openFolderDialog()
self.save_path_box.setText(folder_path)
def selectTstPath(self):
tstPath_dialog = file_dialog(
title='select testing data included directory', type='.meta')
tstPath = tstPath_dialog.openFolderDialog()
self.tstPath = tstPath
self.tstPathLine.setText(self.tstPath)
def selectValPath(self):
valPath_dialog = file_dialog(
title='select validation data included directory', type='.meta')
valPath = valPath_dialog.openFolderDialog()
self.valPath = valPath
self.valPathLine.setText(self.valPath)
def inputFileDialog(self):
tmp = file_dialog(title='choose .tif for input',
type='.tif').openFileNameDialog()
if tmp:
self.inputPathLine.setText(tmp)
self.set_input()
def set_grad_path(self):
grad_dial = file_dialog(title='select a training that you want to view its gradients', type='/')
self.path = grad_dial.openFolderDialog()
if self.path:
self.lineEdit.setText(self.path)
self.extract_gradient()
def selectMeta(self):
self.meta_path = file_dialog(title='choose .meta file',
type='.meta').openFileNameDialog()
self.metaLine.setText(self.meta_path)
def set_val_dir(self):
val_dir_dial = file_dialog(
title='select a folder where includes the validation dataset',
type='/')
self.val_dir = val_dir_dial.openFolderDialog()
self.val_dir_line.setText(self.val_dir)
def selectPred(self):
self.pred_folder = file_dialog(
title='choose a folder to put the segmentation').openFolderDialog(
)
self.predLine.setText(self.pred_folder)
def selectWherePath(self):
wherePath_dialog = file_dialog(
title='select a directory to save your models', type='.meta')
wherePath = wherePath_dialog.openFolderDialog()
self.wherePath = wherePath
self.whereToSave.setText(self.wherePath)
