def plot_predictions(self, cfg, tub_paths, model_path):
"""
Plot model predictions for angle and throttle against data from tubs.
"""
from donkeycar.parts.datastore import TubGroup
from donkeycar.parts.keras import KerasLinear #KerasCategorical
import pandas as pd
import matplotlib.pyplot as plt
tg = TubGroup(tub_paths)
model_path = os.path.expanduser(model_path)
# model = KerasCategorical()
model = KerasLinear()
model.load(model_path)
# gen = tg.get_batch_gen(None, batch_size=len(tg.df),shuffle=False, df=tg.df)
# gen = tg.get_batch_gen(None, None, batch_size=len(tg.df),shuffle=False, df=tg.df)
# arr = next(gen)
user_angles = []
user_throttles = []
pilot_angles = []
pilot_throttles = []
for tub in tg.tubs:
num_records = tub.get_num_records()
for iRec in tub.get_index(shuffled=False):
record = tub.get_record(iRec)
img = record["cam/image_array"]
user_angle = float(record["user/angle"])
user_throttle = float(record["user/throttle"])
pilot_angle, pilot_throttle = model.run(img)
user_angles.append(user_angle)
user_throttles.append(user_throttle)
pilot_angles.append(pilot_angle)
pilot_throttles.append(pilot_throttle)
angles_df = pd.DataFrame({'user_angle': user_angles, 'pilot_angle': pilot_angles})
throttles_df = pd.DataFrame({'user_throttle': user_throttles, 'pilot_throttle': pilot_throttles})
fig = plt.figure()
title = "Model Predictions\nTubs: {}\nModel: {}".format(tub_paths, model_path)
fig.suptitle(title)
ax1 = fig.add_subplot(211)
ax2 = fig.add_subplot(212)
angles_df.plot(ax=ax1)
throttles_df.plot(ax=ax2)
ax1.legend(loc=4)
ax2.legend(loc=4)
plt.show()
from donkeycar.parts.keras import KerasLinear
from PIL import Image
import numpy as np
input_shape = (120, 160, 3)
roi_crop = (0, 0)
kl = KerasLinear(input_shape=input_shape, roi_crop=roi_crop)
#kl.load('/home/pi/mycar/models/mypilot20.h5')
img = Image.open('./528.jpg')
img = np.array(img)
#print(img.shape)
out = kl.run(img)
print(out)
def learn(self,
total_timesteps,
callback=None,
log_interval=1,
tb_log_name="SAC",
print_freq=100,
save_path=None):
with TensorboardWriter(self.graph, self.tensorboard_log,
tb_log_name) as writer:
self._setup_learn()
# Transform to callable if needed
self.learning_rate = get_schedule_fn(self.learning_rate)
start_time = time.time()
episode_rewards = [0.0]
is_teleop_env = hasattr(self.env, "wait_for_teleop_reset")
# TeleopEnv
if is_teleop_env:
print("Waiting for teleop")
obs = self.env.wait_for_teleop_reset()
else:
obs = self.env.reset()
self.episode_reward = np.zeros((1, ))
ep_info_buf = deque(maxlen=100)
ep_len = 0
self.n_updates = 0
infos_values = []
mb_infos_vals = []
model_path = "--Path to model--/myNewModdel.h5"
# model_path= None
if model_path is not None:
cfg = dk.load_config(
config_path='--Path to config file inside mycar/config.py')
kl = KerasLinear()
kl.load(model_path)
# vae = self.env.get_vae()
self.training_started = False
self.start_training = False
for step in range(total_timesteps):
# Compute current learning_rate
frac = 1.0 - step / total_timesteps
current_lr = self.learning_rate(frac)
if callback is not None:
# Only stop training if return value is False, not when it is None. This is for backwards
# compatibility with callbacks that have no return statement.
if callback(locals(), globals()) is False:
break
# Before training starts, randomly sample actions
# from a uniform distribution for better exploration.
# Afterwards, use the learned policy.
if step < self.learning_starts and not self.training_started:
if model_path is not None:
try:
img_arr = self.env.get_images()
# print(img_arr[0].shape)
img_arr = np.asarray(img_arr[0])
img_arr = normalize_and_crop(img_arr, cfg)
croppedImgH = img_arr.shape[0]
croppedImgW = img_arr.shape[1]
if img_arr.shape[2] == 3 and cfg.IMAGE_DEPTH == 1:
img_arr = dk.utils.rgb2gray(img_arr).reshape(
croppedImgH, croppedImgW, 1)
steering, throttle = kl.run(img_arr)
action = [steering, throttle / 6.0]
action = np.asarray(action)
# rescaled_action = action * np.abs(self.action_space.low)
rescaled_action = action
print('Predicted action :', action)
except Exception as e:
print(e)
action = self.env.action_space.sample()
rescaled_action = action
else:
action = self.env.action_space.sample()
rescaled_action = action
print(action)
# No need to rescale when sampling random action
elif not self.training_started:
self.start_training = True
obs = self.env.reset()
else:
action = self.policy_tf.step(
obs[None], deterministic=False).flatten()
# Rescale from [-1, 1] to the correct bounds
rescaled_action = action * np.abs(self.action_space.low)
assert action.shape == self.env.action_space.shape
new_obs, reward, done, info = self.env.step(rescaled_action)
ep_len += 1
if print_freq > 0 and ep_len % print_freq == 0 and ep_len > 0:
print("{} steps".format(ep_len))
# Store transition in the replay buffer.
self.replay_buffer.add(obs, action, reward, new_obs,
float(done))
obs = new_obs
# Retrieve reward and episode length if using Monitor wrapper
maybe_ep_info = info.get('episode')
if maybe_ep_info is not None:
ep_info_buf.extend([maybe_ep_info])
if writer is not None:
# Write reward per episode to tensorboard
ep_reward = np.array([reward]).reshape((1, -1))
ep_done = np.array([done]).reshape((1, -1))
self.episode_reward = total_episode_reward_logger(
self.episode_reward, ep_reward, ep_done, writer, step)
if ep_len > self.train_freq:
print("Additional training")
self.env.reset()
mb_infos_vals = self.optimize(step, writer, current_lr)
done = True
episode_rewards[-1] += reward
if done or self.start_training:
self.start_training = False
if not (isinstance(self.env, VecEnv) or is_teleop_env):
obs = self.env.reset()
print("Episode finished. Reward: {:.2f} {} Steps".format(
episode_rewards[-1], ep_len))
episode_rewards.append(0.0)
ep_len = 0
mb_infos_vals = self.optimize(step, writer, current_lr)
# Refresh obs when using TeleopEnv
if is_teleop_env:
print("Waiting for teleop")
obs = self.env.wait_for_teleop_reset()
# Log losses and entropy, useful for monitor training
if len(mb_infos_vals) > 0:
infos_values = np.mean(mb_infos_vals, axis=0)
if len(episode_rewards[-101:-1]) == 0:
mean_reward = -np.inf
else:
mean_reward = round(
float(np.mean(episode_rewards[-101:-1])), 1)
num_episodes = len(episode_rewards)
if self.verbose >= 1 and done and log_interval is not None and len(
episode_rewards) % log_interval == 0:
fps = int(step / (time.time() - start_time))
logger.logkv("episodes", num_episodes)
logger.logkv("mean 100 episode reward", mean_reward)
logger.logkv(
'ep_rewmean',
safe_mean([ep_info['r'] for ep_info in ep_info_buf]))
logger.logkv(
'eplenmean',
safe_mean([ep_info['l'] for ep_info in ep_info_buf]))
logger.logkv("n_updates", self.n_updates)
logger.logkv("current_lr", current_lr)
logger.logkv("fps", fps)
logger.logkv('time_elapsed',
"{:.2f}".format(time.time() - start_time))
if len(infos_values) > 0:
for (name, val) in zip(self.infos_names, infos_values):
logger.logkv(name, val)
logger.logkv("total timesteps", step)
logger.dumpkvs()
# Reset infos:
infos_values = []
if is_teleop_env:
self.env.is_training = False
# Use last batch
print("Final optimization before saving")
self.env.reset()
mb_infos_vals = self.optimize(step, writer, current_lr)
plt.figure(1)
plt.plot(episode_rewards)
plt.title('Episode Rewards')
plt.ylabel("Reward")
plt.xlabel("Epoch")
filename = "training" + str(random.random()) + ".png"
plt.savefig(filename)
plt.show()
return self
