def train():
KITTI_train_gen = KITTILoader(subset='training')
dim_avg, dim_cnt = KITTI_train_gen.get_average_dimension()
new_data = orientation_confidence_flip(KITTI_train_gen.image_data, dim_avg)
model = nn.network()
# model.load_weights('3dbox_weights_mob.hdf5')
early_stop = callbacks.EarlyStopping(monitor='val_loss',
min_delta=0.001,
patience=10,
mode='min',
verbose=1)
checkpoint = callbacks.ModelCheckpoint('3dbox_weights_mob.hdf5',
monitor='val_loss',
verbose=1,
save_best_only=True,
mode='min',
period=1)
tensorboard = callbacks.TensorBoard(log_dir='logs/',
histogram_freq=0,
write_graph=True,
write_images=False)
all_examples = len(new_data)
trv_split = int(cfg().split * all_examples) # train val split
train_gen = data_gen(new_data[:trv_split])
valid_gen = data_gen(new_data[trv_split:all_examples])
train_num = int(np.ceil(trv_split / cfg().batch_size))
valid_num = int(np.ceil((all_examples - trv_split) / cfg().batch_size))
# choose the minimizer to be sgd
minimizer = optimizer.SGD(lr=0.0001, momentum=0.9)
# multi task learning
model.compile(
optimizer=minimizer, #minimizer,
loss={
'dimensions': 'mean_squared_error',
'orientation': orientation_loss,
'confidence': 'binary_crossentropy'
},
loss_weights={
'dimensions': 1.,
'orientation': 10.,
'confidence': 5.
})
# d:0.0088 o:0.0042, c:0.0098
model.fit_generator(generator=train_gen,
steps_per_epoch=train_num,
epochs=500,
verbose=1,
validation_data=valid_gen,
validation_steps=valid_num,
shuffle=True,
callbacks=[early_stop, checkpoint, tensorboard],
max_queue_size=3)
def train():
KITTI_train_gen = KITTILoader(subset='training')
dim_avg, dim_cnt = KITTI_train_gen.get_average_dimension()
new_data = orientation_confidence_flip(KITTI_train_gen.image_data, dim_avg)
model = nn.network()
#model.load_weights('model00000296.hdf5')
early_stop = callbacks.EarlyStopping(monitor='val_loss', min_delta=0.001, patience=10, mode='min', verbose=1)
checkpoint = callbacks.ModelCheckpoint('model{epoch:08d}.hdf5', monitor='val_loss', verbose=1, save_best_only=False, mode='min', period=1)
tensorboard = callbacks.TensorBoard(log_dir='logs/', histogram_freq=0, write_graph=True, write_images=False)
all_examples = len(new_data)
trv_split = int(cfg().split * all_examples) # train val split
train_gen = data_gen(new_data[: trv_split])
valid_gen = data_gen(new_data[trv_split : all_examples])
print("READY FOR TRAINING")
train_num = int(np.ceil(trv_split / cfg().batch_size))
valid_num = int(np.ceil((all_examples - trv_split) / cfg().batch_size))
#gen_flow = gen_flow_for_two_inputs(X_train, X_angle_train, y_train)
# choose the minimizer to be sgd
# minimizer = optimizer.SGD(lr=0.0001, momentum = 0.9)
minimizer = optimizer.Adam(lr=0.0001)
# multi task learning
model.compile(optimizer=minimizer, #minimizer,
loss={'dimensions': 'mean_squared_error', 'orientation': orientation_loss, 'confidence': 'categorical_crossentropy'},
loss_weights={'dimensions': 1., 'orientation': 10., 'confidence': 5.})
print("####################################################")
print(K.get_value(model.optimizer.lr))
# Tambahan aing
def scheduler(epoch):
if epoch%10==0 and epoch!=0:
lr = K.get_value(model.optimizer.lr)
K.set_value(model.optimizer.lr, lr*.8)
print("lr changed to {}".format(lr*.8))
print("lr = ", K.get_value(model.optimizer.lr))
return K.get_value(model.optimizer.lr)
lr_sched = callbacks.LearningRateScheduler(scheduler)
# d:0.0088 o:0.0042, c:0.0098
# steps_per_epoch=train_num,
# validation_steps=valid_num,
# callbacks=[early_stop, checkpoint, tensorboard],
model.fit_generator(generator=train_gen,
steps_per_epoch=train_num,
epochs=500,
verbose=1,
validation_data=valid_gen,
validation_steps=valid_num,
shuffle=True,
callbacks=[checkpoint, tensorboard, lr_sched],
max_queue_size=3)
def train():
KITTI_train_gen = KITTILoader(subset='training')
dim_avg, dim_cnt = KITTI_train_gen.get_average_dimension()
new_data = orientation_confidence_flip(KITTI_train_gen.image_data, dim_avg)
model = nn.network()
# model.load_weights('3dbox_weights_mob.hdf5')
early_stop = callbacks.EarlyStopping(monitor='val_loss',
min_delta=0.001,
patience=10,
mode='min',
verbose=1)
checkpoint = callbacks.ModelCheckpoint(
'3dbox_mbnv2_{}x{}_float32.hdf5'.format(cfg().norm_h,
cfg().norm_w),
monitor='val_loss',
verbose=1,
save_best_only=True,
mode='min',
period=1)
tensorboard = callbacks.TensorBoard(log_dir='logs/',
histogram_freq=0,
write_graph=True,
write_images=False)
all_examples = len(new_data)
trv_split = int(cfg().split * all_examples) # train val split
train_gen = data_gen(new_data[:trv_split])
valid_gen = data_gen(new_data[trv_split:all_examples])
train_num = int(np.ceil(trv_split / cfg().batch_size))
valid_num = int(np.ceil((all_examples - trv_split) / cfg().batch_size))
# choose the minimizer to be sgd
minimizer = optimizer.SGD(lr=0.0001, momentum=0.9)
# multi task learning
model.compile(
optimizer=minimizer, #minimizer,
loss={
'dimensions': 'mean_squared_error',
'orientation': orientation_loss,
'confidence': 'binary_crossentropy'
},
loss_weights={
'dimensions': 1.,
'orientation': 10.,
'confidence': 5.
})
# d:0.0088 o:0.0042, c:0.0098
model.fit_generator(generator=train_gen,
steps_per_epoch=train_num,
epochs=500,
verbose=1,
validation_data=valid_gen,
validation_steps=valid_num,
shuffle=True,
callbacks=[early_stop, checkpoint, tensorboard],
max_queue_size=3)
tf.saved_model.save(model,
'saved_model_{}x{}'.format(cfg().norm_h,
cfg().norm_w))
model.save('3dbox_mbnv2_{}x{}_float32.h5'.format(cfg().norm_h,
cfg().norm_w))
full_model = tf.function(lambda inputs: model(inputs))
full_model = full_model.get_concrete_function(
inputs=(tf.TensorSpec(model.inputs[0].shape, model.inputs[0].dtype)))
frozen_func = convert_variables_to_constants_v2(full_model,
lower_control_flow=False)
frozen_func.graph.as_graph_def()
tf.io.write_graph(graph_or_graph_def=frozen_func.graph,
logdir=".",
name="3dbox_mbnv2_{}x{}_float32.pb".format(
cfg().norm_h,
cfg().norm_w),
as_text=False)