def main(batch_size=64,
max_epochs=100,
validation_split=0.2,
early_stop=EarlyStopping()):
model_hdf5_path = "./hdf5s/"
if args.dataset == 'taxi':
sampler = file_loader.file_loader()
elif args.dataset == 'bike':
sampler = file_loader.file_loader(config_path="data_bike.json")
else:
raise Exception("Can not recognize dataset, please enter taxi or bike")
modeler = models.models()
if args.model_name == "stdn":
# training
att_cnnx, att_flow, att_x, cnnx, flow, x, y = sampler.sample_stdn(datatype="train",
att_lstm_num=args.att_lstm_num, \
long_term_lstm_seq_len=args.long_term_lstm_seq_len,
short_term_lstm_seq_len=args.short_term_lstm_seq_len, \
nbhd_size=args.nbhd_size,
cnn_nbhd_size=args.cnn_nbhd_size)
print(("Start training {0} with input shape {2} / {1}".format(
args.model_name, x.shape, cnnx[0].shape)))
model = modeler.stdn(att_lstm_num=args.att_lstm_num,
att_lstm_seq_len=args.long_term_lstm_seq_len, \
lstm_seq_len=len(cnnx), feature_vec_len=x.shape[-1], \
cnn_flat_size=args.cnn_flat_size, nbhd_size=cnnx[0].shape[1],
nbhd_type=cnnx[0].shape[-1])
model.fit( \
x=att_cnnx + att_flow + att_x + cnnx + flow + [x, ], \
y=y, \
batch_size=batch_size, validation_split=validation_split, epochs=max_epochs,
callbacks=[early_stop])
att_cnnx, att_flow, att_x, cnnx, flow, x, y = sampler.sample_stdn(
datatype="test",
nbhd_size=args.nbhd_size,
cnn_nbhd_size=args.cnn_nbhd_size)
y_pred = model.predict( \
x=att_cnnx + att_flow + att_x + cnnx + flow + [x, ], )
threshold = float(
sampler.threshold) / sampler.config["volume_train_max"]
print(("Evaluating threshold: {0}.".format(threshold)))
(prmse, pmape), (drmse, dmape) = eval_lstm(y, y_pred, threshold)
print((
"Test on model {0}:\npickup rmse = {1}, pickup mape = {2}%\ndropoff rmse = {3}, dropoff mape = {4}%"
.format(args.model_name, prmse, pmape * 100, drmse, dmape * 100)))
currTime = datetime.datetime.now().strftime("%Y%m%d%H%M%S")
model.save(model_hdf5_path + args.model_name + currTime + ".hdf5")
return
else:
print("Cannot recognize parameter...")
return
def main(att_lstm_num = 3, long_term_lstm_seq_len = 3, short_term_lstm_seq_len = 7, cnn_nbhd_size = 3, nbhd_size = 2, cnn_flat_size = 128,\
batch_size = 64, max_epochs = 100, validation_split = 0.2, early_stop = EarlyStopping()):
model_hdf5_path = "./hdf5s/"
model_name = ""
if len(sys.argv) == 1:
print("no parameters")
return
else:
model_name = sys.argv[1]
sampler = file_loader.file_loader()
modeler = models.models()
if model_name[2:] == "stdn":
#training
att_cnnx, att_flow, att_x, cnnx, flow, x, y = sampler.sample_stdn(datatype = "train", att_lstm_num = att_lstm_num,\
long_term_lstm_seq_len = long_term_lstm_seq_len, short_term_lstm_seq_len = short_term_lstm_seq_len,\
nbhd_size = nbhd_size, cnn_nbhd_size = cnn_nbhd_size)
print("Start training {0} with input shape {2} / {1}".format(
model_name[2:], x.shape, cnnx[0].shape))
model = modeler.stdn(att_lstm_num = att_lstm_num, att_lstm_seq_len = long_term_lstm_seq_len,\
lstm_seq_len = len(cnnx), feature_vec_len = x.shape[-1],\
cnn_flat_size = cnn_flat_size, nbhd_size = cnnx[0].shape[1], nbhd_type = cnnx[0].shape[-1])
model.fit(\
x = att_cnnx + att_flow + att_x + cnnx + flow + [x,],\
y = y,\
batch_size=batch_size, validation_split = validation_split, epochs=max_epochs, callbacks=[early_stop])
att_cnnx, att_flow, att_x, cnnx, flow, x, y = sampler.sample_stdn(
datatype="test", nbhd_size=nbhd_size, cnn_nbhd_size=cnn_nbhd_size)
y_pred = model.predict(\
x = att_cnnx + att_flow + att_x + cnnx + flow + [x,],)
threshold = float(
sampler.threshold) / sampler.config["volume_train_max"]
print("Evaluating threshold: {0}.".format(threshold))
(prmse, pmape), (drmse, dmape) = eval_lstm(y, y_pred, threshold)
print(
"Test on model {0}:\npickup rmse = {1}, pickup mape = {2}%\ndropoff rmse = {3}, dropoff mape = {4}%"
.format(model_name[2:], prmse, pmape * 100, drmse, dmape * 100))
currTime = datetime.datetime.now().strftime("%Y%m%d%H%M%S")
model.save(model_hdf5_path + model_name[2:] + currTime + ".hdf5")
return
print("Cannot recognize parameter...")
return
def klikniecie(self):
otworz_plik = QFileDialog.getOpenFileName(self, 'Open file', '',
"Object files (*.obj)")
self.widget_modelu.wstaw_obiekt(file_loader(otworz_plik[0]))
self.widget_modelu.update()
def button_powierzchnia(self):
self.widget_modelu.rysuj_powierzchnie = self.eliminacja_powierzchni_CB.isChecked(
)
self.widget_modelu.update()
def retranslateUi(self):
_translate = QtCore.QCoreApplication.translate
self.setWindowTitle(_translate("MainWindow", "Prezentacja Modelu 3D"))
self.label_pozycja_kamery.setText(
_translate("MainWindow", "Przesunięcie"))
self.label_kierunek_patrzenia.setText(_translate(
"MainWindow", "Obrót"))
self.eliminacja_powierzchni_CB.setText(
_translate("MainWindow", "Eliminacja Powierzchni \n"
"zasłoniętych"))
self.label_rotate.setText(_translate("MainWindow", "rotate"))
self.button_wczytaj_obiekt.setText(
_translate("MainWindow", "Wczytaj Obiekt"))
if __name__ == '__main__':
import sys
app = QApplication(sys.argv)
okno = Ui_MainWindow()
okno.setupUi()
okno.widget_modelu.wstaw_obiekt(
file_loader("low-poly-fox-by-pixelmannen.obj"))
okno.widget_modelu.przesun(0, 0, 0)
okno.widget_modelu.obroc(0, 0, 0)
sys.exit(app.exec_())