Exception in thread Thread-1: bootstrab inner

Question

I am following reinforcement learning carla simulation and i got Exception in thread Thread-1: error and i do not know what is the actual problem of this code ? In video tutorial, it does not seem for the following error to present

This is my code :##############################################################################################################################################################################################################

import glob
import os
import sys
import random
import time
import numpy as np
import cv2
import math
from collections import deque
from keras.applications.xception import Xception
from keras.layers import Dense, GlobalAveragePooling2D
from keras.optimizers import Adam
from keras.models import Model
from keras.callbacks import TensorBoard

import tensorflow as tf
from tensorflow.python.keras.backend import set_session

from threading import Thread

from tqdm import tqdm

try:
    sys.path.append(glob.glob('../carla/dist/carla-*%d.%d-%s.egg' % (
        sys.version_info.major,
        sys.version_info.minor,
        'win-amd64' if os.name == 'nt' else 'linux-x86_64'))[0])
except IndexError:
    pass
import carla

tf.compat.v1.disable_eager_execution()
SHOW_PREVIEW = False
IM_WIDTH = 640
IM_HEIGHT = 480
SECONDS_PER_EPISODE = 10
REPLAY_MEMORY_SIZE = 5_000
MIN_REPLAY_MEMORY_SIZE = 1_000
MINIBATCH_SIZE = 16
PREDICTION_BATCH_SIZE = 1
TRAINING_BATCH_SIZE = MINIBATCH_SIZE // 4
UPDATE_TARGET_EVERY = 5
MODEL_NAME = "Xception"

MEMORY_FRACTION = 0.8
MIN_REWARD = -200

EPISODES = 100

DISCOUNT = 0.99
epsilon = 1
EPSILON_DECAY = 0.9975 ## 0.9975 99975 0.95
MIN_EPSILON = 0.001

AGGREGATE_STATS_EVERY = 10


# Own Tensorboard class
class ModifiedTensorBoard(TensorBoard):

    def __init__(self, **kwargs):
        super().__init__(**kwargs)
        self.step = 1
        self.writer = tf.summary.create_file_writer(self.log_dir)
        self._log_write_dir = self.log_dir

    def set_model(self, model):
        self.model = model

        self._train_dir = os.path.join(self._log_write_dir, 'train')
        self._train_step = self.model._train_counter

        self._val_dir = os.path.join(self._log_write_dir, 'validation')
        self._val_step = self.model._test_counter

        self._should_write_train_graph = False

    def on_epoch_end(self, epoch, logs=None):
        self.update_stats(**logs)

    def on_batch_end(self, batch, logs=None):
        pass

    def on_train_end(self, _):
        pass

    def update_stats(self, **stats):
        with self.writer.as_default():
          for key, value in stats.items():
            tf.summary.scalar(key, value, step = self.step)
            self.writer.flush()



class CarEnv:
    SHOW_CAM = SHOW_PREVIEW
    STEER_AMT = 1.0
    im_width = IM_WIDTH
    im_height = IM_HEIGHT
    front_camera = None

    def __init__(self):
        self.client = carla.Client("localhost", 2000)
        self.client.set_timeout(2.0)
        self.world = self.client.get_world()
        self.blueprint_library = self.world.get_blueprint_library()
        self.model_3 = self.blueprint_library.filter("model3")[0]

    def reset(self):
        self.collision_hist = []
        self.actor_list = []

        self.transform = random.choice(self.world.get_map().get_spawn_points())
        self.vehicle = self.world.spawn_actor(self.model_3, self.transform)
        self.actor_list.append(self.vehicle)

        self.rgb_cam = self.blueprint_library.find('sensor.camera.rgb')
        self.rgb_cam.set_attribute("image_size_x", f"{self.im_width}")
        self.rgb_cam.set_attribute("image_size_y", f"{self.im_height}")
        self.rgb_cam.set_attribute("fov", f"110")

        transform = carla.Transform(carla.Location(x=2.5, z=0.7))
        self.sensor = self.world.spawn_actor(self.rgb_cam, transform, attach_to=self.vehicle)
        self.actor_list.append(self.sensor)
        self.sensor.listen(lambda data: self.process_img(data))

        self.vehicle.apply_control(carla.VehicleControl(throttle=0.0, brake=0.0))
        time.sleep(4)

        colsensor = self.blueprint_library.find("sensor.other.collision")
        self.colsensor = self.world.spawn_actor(colsensor, transform, attach_to=self.vehicle)
        self.actor_list.append(self.colsensor)
        self.colsensor.listen(lambda event: self.collision_data(event))

        while self.front_camera is None:
            time.sleep(0.01)

        self.episode_start = time.time()
        self.vehicle.apply_control(carla.VehicleControl(throttle=0.0, brake=0.0))

        return self.front_camera

    def collision_data(self, event):
        self.collision_hist.append(event)

    def process_img(self, image):
        i = np.array(image.raw_data)
        #print(i.shape)
        i2 = i.reshape((self.im_height, self.im_width, 4))
        i3 = i2[:, :, :3]
        if self.SHOW_CAM:
            cv2.imshow("", i3)
            cv2.waitKey(1)
        self.front_camera = i3

    def step(self, action):
        if action == 0:
            self.vehicle.apply_control(carla.VehicleControl(throttle=1.0, steer=-1*self.STEER_AMT))
        elif action == 1:
            self.vehicle.apply_control(carla.VehicleControl(throttle=1.0, steer= 0))
        elif action == 2:
            self.vehicle.apply_control(carla.VehicleControl(throttle=1.0, steer=1*self.STEER_AMT))

        v = self.vehicle.get_velocity()
        kmh = int(3.6 * math.sqrt(v.x**2 + v.y**2 + v.z**2))

        if len(self.collision_hist) != 0:
            done = True
            reward = -200
        elif kmh < 50:
            done = False
            reward = -1
        else:
            done = False
            reward = 1

        if self.episode_start + SECONDS_PER_EPISODE < time.time():
            done = True

        return self.front_camera, reward, done, None


class DQNAgent:
    def __init__(self):
        self.model = self.create_model()
        self.target_model = self.create_model()
        self.target_model.set_weights(self.model.get_weights())

        self.replay_memory = deque(maxlen=REPLAY_MEMORY_SIZE)

        self.tensorboard = ModifiedTensorBoard(log_dir=f"logs/{MODEL_NAME}-{int(time.time())}")
        self.target_update_counter = 0
        self.graph = tf.compat.v1.get_default_graph()

        self.terminate = False
        self.last_logged_episode = 0
        self.training_initialized = False

    def create_model(self):
        base_model = Xception(weights=None, include_top=False, input_shape=(IM_HEIGHT, IM_WIDTH,3))

        x = base_model.output
        x = GlobalAveragePooling2D()(x)

        predictions = Dense(3, activation="linear")(x)
        model = Model(inputs=base_model.input, outputs=predictions)
        model.compile(loss="mse", optimizer=Adam(learning_rate=0.001), metrics=["accuracy"])
        return model

    def update_replay_memory(self, transition):
        # transition = (current_state, action, reward, new_state, done)
        self.replay_memory.append(transition)

    def train(self):
        if len(self.replay_memory) < MIN_REPLAY_MEMORY_SIZE:
            return

        minibatch = random.sample(self.replay_memory, MINIBATCH_SIZE)

        current_states = np.array([transition[0] for transition in minibatch])/255
        with self.graph.as_default():
            current_qs_list = self.model.predict(current_states, PREDICTION_BATCH_SIZE)

        new_current_states = np.array([transition[3] for transition in minibatch])/255
        with self.graph.as_default():
            future_qs_list = self.target_model.predict(new_current_states, PREDICTION_BATCH_SIZE)

        X = []
        y = []

        for index, (current_state, action, reward, new_state, done) in enumerate(minibatch):
            if not done:
                max_future_q = np.max(future_qs_list[index])
                new_q = reward + DISCOUNT * max_future_q
                print(1)
            else:
                new_q = reward

            current_qs = current_qs_list[index]
            current_qs[action] = new_q

            X.append(current_state)
            y.append(current_qs)

        log_this_step = False
        if self.tensorboard.step > self.last_logged_episode:
            log_this_step = True
            self.last_log_episode = self.tensorboard.step

        with self.graph.as_default():
            self.model.fit(np.array(X)/255, np.array(y), batch_size=TRAINING_BATCH_SIZE, verbose=0, shuffle=False, callbacks=[self.tensorboard] if log_this_step else None)


        if log_this_step:
            self.target_update_counter += 1
            print(target_update_counter)

        if self.target_update_counter > UPDATE_TARGET_EVERY:
            self.target_model.set_weights(self.model.get_weights())
            self.target_update_counter = 0

    def get_qs(self, state):
        return self.model.predict(np.array(state).reshape(-1, *state.shape)/255)[0]

    def train_in_loop(self):
        X = np.random.uniform(size=(1, IM_HEIGHT, IM_WIDTH, 3)).astype(np.float32)
        y = np.random.uniform(size=(1, 3)).astype(np.float32)
        with self.graph.as_default():
            self.model.fit(X,y, verbose=False, batch_size=32)

        self.training_initialized = True

        while True:
            if self.terminate:
                return
            self.train()
            time.sleep(0.01)



if __name__ == '__main__':
    FPS = 60
    # For stats
    ep_rewards = [-200]

    # For more repetitive results
    random.seed(1)
    np.random.seed(1)
    tf.random.set_seed(1)

    # Memory fraction, used mostly when trai8ning multiple agents
    gpu_options = tf.compat.v1.GPUOptions(per_process_gpu_memory_fraction=MEMORY_FRACTION)
    set_session(tf.compat.v1.Session(config=tf.compat.v1.ConfigProto(gpu_options=gpu_options)))


    # Create models folder
    if not os.path.isdir('models'):
        os.makedirs('models')

    # Create agent and environment
    agent = DQNAgent()
    env = CarEnv()


    # Start training thread and wait for training to be initialized
    trainer_thread = Thread(target=agent.train_in_loop, daemon=True)
    trainer_thread.start()
   # while not agent.training_initialized:
   #     time.sleep(0.01)

    # Initialize predictions - forst prediction takes longer as of initialization that has to be done
    # It's better to do a first prediction then before we start iterating over episode steps
    agent.get_qs(np.ones((env.im_height, env.im_width, 3)))

    # Iterate over episodes
    for episode in tqdm(range(1, EPISODES + 1), ascii=True, unit='episodes'):
        #try:

            env.collision_hist = []

            # Update tensorboard step every episode
            agent.tensorboard.step = episode

            # Restarting episode - reset episode reward and step number
            episode_reward = 0
            step = 1

            # Reset environment and get initial state
            current_state = env.reset()

            # Reset flag and start iterating until episode ends
            done = False
            episode_start = time.time()

            # Play for given number of seconds only
            while True:

                # This part stays mostly the same, the change is to query a model for Q values
                if np.random.random() > epsilon:
                    # Get action from Q table
                    action = np.argmax(agent.get_qs(current_state))
                else:
                    # Get random action
                    action = np.random.randint(0, 3)
                    # This takes no time, so we add a delay matching 60 FPS (prediction above takes longer)
                    time.sleep(1/FPS)

                new_state, reward, done, _ = env.step(action)

                # Transform new continous state to new discrete state and count reward
                episode_reward += reward

                # Every step we update replay memory
                agent.update_replay_memory((current_state, action, reward, new_state, done))

                current_state = new_state
                step += 1

                if done:
                    break

            # End of episode - destroy agents
            for actor in env.actor_list:
                actor.destroy()

            # Append episode reward to a list and log stats (every given number of episodes)
            ep_rewards.append(episode_reward)
            if not episode % AGGREGATE_STATS_EVERY or episode == 1:
                average_reward = sum(ep_rewards[-AGGREGATE_STATS_EVERY:])/len(ep_rewards[-AGGREGATE_STATS_EVERY:])
                min_reward = min(ep_rewards[-AGGREGATE_STATS_EVERY:])
                max_reward = max(ep_rewards[-AGGREGATE_STATS_EVERY:])
                agent.tensorboard.update_stats(reward_avg=average_reward, reward_min=min_reward, reward_max=max_reward, epsilon=epsilon)

                # Save model, but only when min reward is greater or equal a set value
                if min_reward >= MIN_REWARD:
                    agent.model.save(f'models/{MODEL_NAME}__{max_reward:_>7.2f}max_{average_reward:_>7.2f}avg_{min_reward:_>7.2f}min__{int(time.time())}.model')

            # Decay epsilon
            if epsilon > MIN_EPSILON:
                epsilon *= EPSILON_DECAY
                epsilon = max(MIN_EPSILON, epsilon)


    # Set termination flag for training thread and wait for it to finish
    agent.terminate = True
    trainer_thread.join()
    agent.model.save(f'models/{MODEL_NAME}__{max_reward:_>7.2f}max_{average_reward:_>7.2f}avg_{min_reward:_>7.2f}min__{int(time.time())}.model')

this is the error

Exception in thread Thread-1:
Traceback (most recent call last):
  File "/usr/lib/python3.8/threading.py", line 932, in _bootstrap_inner
    self.run()
  File "/usr/lib/python3.8/threading.py", line 870, in run
    self._target(*self._args, **self._kwargs)
  File "RL_trial_one.py", line 269, in train_in_loop
    self.model.fit(X,y, verbose=False, batch_size=32)
  File "/home/server/.local/lib/python3.8/site-packages/keras/engine/training_v1.py", line 776, in fit
    return func.fit(
  File "/home/server/.local/lib/python3.8/site-packages/keras/engine/training_arrays_v1.py", line 641, in fit
    return fit_loop(
  File "/home/server/.local/lib/python3.8/site-packages/keras/engine/training_arrays_v1.py", line 377, in model_iteration
    batch_outs = f(ins_batch)
  File "/home/server/.local/lib/python3.8/site-packages/keras/backend.py", line 4284, in __call__
    fetched = self._callable_fn(*array_vals,
  File "/home/server/.local/lib/python3.8/site-packages/tensorflow/python/client/session.py", line 1480, in __call__
    ret = tf_session.TF_SessionRunCallable(self._session._session,
tensorflow.python.framework.errors_impl.FailedPreconditionError: 2 root error(s) found.
  (0) FAILED_PRECONDITION: Could not find variable block14_sepconv1_bn/beta. This could mean that the variable has been deleted. In TF1, it can also mean the variable is uninitialized. Debug info: container=localhost, status error message=Resource localhost/block14_sepconv1_bn/beta/N10tensorflow3VarE does not exist.
     [[{{function_node block14_sepconv1_bn_cond_true_4975}}{{node ReadVariableOp_1}}]]
     [[block2_sepconv2_bn/cond/else/_113/ReadVariableOp/_713]]
  (1) FAILED_PRECONDITION: Could not find variable block14_sepconv1_bn/beta. This could mean that the variable has been deleted. In TF1, it can also mean the variable is uninitialized. Debug info: container=localhost, status error message=Resource localhost/block14_sepconv1_bn/beta/N10tensorflow3VarE does not exist.
     [[{{function_node block14_sepconv1_bn_cond_true_4975}}{{node ReadVariableOp_1}}]]
0 successful operations.
0 derived errors ignored.
 81%|################################################################################################################################7                              | 81/100 [12:00<02:49,  8.90s/episodes]