def __init__(self,
city_name,
avoid_stopping,
memory_fraction=0.25,
image_cut=[115, 510]):
Agent.__init__(self)
scopeName = 'NET'
scopeName1 = 'First'
scopeName2 = 'Second'
self.dropout_vec = [1.0] * 8 + [0.7] * 2 + [0.5] * 2 + [0.5] * 1 + [
0.5, 1.
] * 2
config_gpu = tf.ConfigProto()
# GPU to be selected, just take zero , select GPU with CUDA_VISIBLE_DEVICES
config_gpu.gpu_options.visible_device_list = '0'
config_gpu.gpu_options.per_process_gpu_memory_fraction = memory_fraction
self._image_size = (88, 200, 3)
self._avoid_stopping = avoid_stopping
self._sess = tf.Session(config=config_gpu)
with tf.device('/gpu:0'):
self._input_images = tf.placeholder("float",
shape=[
None, self._image_size[0],
self._image_size[1],
self._image_size[2]
],
name="input_image")
self._input_data = tf.placeholder(tf.float32,
shape=[None, 1],
name="input_speed")
self._dout = tf.placeholder("float", shape=[len(self.dropout_vec)])
with tf.variable_scope(scopeName) as scope:
self._network_tensor = load_imitation_learning_network(
self._input_images, self._input_data, self._image_size,
self._dout, scopeName1, scopeName2)
import os
dir_path = os.path.dirname(__file__)
self._models_path = dir_path + '/model/test/'
# tf.reset_default_graph()
self._sess.run(tf.global_variables_initializer())
self.load_model()
self._image_cut = image_cut
def __init__(self, checkpoint):
Agent.__init__(self)
self.checkpoint = checkpoint # We save the checkpoint for some interesting future use.
self.model = CoILModel(g_conf.MODEL_NAME)
self.model.load_state_dict(checkpoint['state_dict'])
self.model.cuda()
def __init__(self, city_name, avoid_stopping, memory_fraction=0.25, image_cut=[115, 510]):
Agent.__init__(self)
self._image_size = (88, 200, 3)
self._avoid_stopping = avoid_stopping
# Reading models and weights
dir_path = 'E:\GP\org data less'
self._model_path = dir_path + '/BH1_Nvidia.h5'
self._weights_path = dir_path + '/BH1_Nividia_at_epoch_40.h5'
self._image_cut = image_cut
self.model = keras.models.load_model(self._model_path, custom_objects={'masked_loss_function': masked_loss_function})
self.model.load_weights(self._weights_path)
def __init__(self,
city_name,
mode,
num_control,
path=None,
image_cut=[115, 510],
gpu_fraction=0.75):
Agent.__init__(self)
# set keras session
config_gpu = tf.ConfigProto()
config_gpu.gpu_options.allow_growth = True
config_gpu.gpu_options.per_process_gpu_memory_fraction = gpu_fraction
KTF.set_session(tf.Session(config=config_gpu))
self.model = None
self.mode = mode
self.num_control = num_control
self.path = path
self._image_cut = image_cut
self.init()
def __init__(self,
city_name,
avoid_stopping,
memory_fraction=0.25,
image_cut=[115, 510]):
Agent.__init__(self)
self.images = list([])
self.imagenum = 0
self._image_size = (88, 200, 3)
self._avoid_stopping = avoid_stopping
self.network = make_network()
self._sess = tf.Session(config=tf.ConfigProto(
log_device_placement=False))
self._sess.run(tf.global_variables_initializer())
saver = tf.train.Saver(write_version=saver_pb2.SaverDef.V2)
saver.restore(self._sess, './agents/imitation/mymodel/epoch-149.ckpt')
print('hellohellohellohellohellohello')
self._image_cut = image_cut
