class MyEnv:
def __init__(self, env_):
self.env = env_
self.dev = torch.device('cuda:0' if torch.cuda.is_available() else 'cpu')
self.action_space = env_.action_space
self._state_steps = 3
self.observation_space = (80, 160, 3*self._state_steps)
print("obs shape {}".format(self.observation_space))
self.state_shape = 32*self._state_steps
# vae
self.vae = VAE()
model_path = "./vae/vae_ikeda_ver1_32.pth"
self.vae.load_state_dict(torch.load(model_path))
self.vae.to(self.dev)
self._gen_id = 0 # 何回目のgenerateかを保持
self._frames = [] # mp4生成用にframeを保存
# pre processing
self._state_frames = deque(maxlen=self._state_steps) # 変換前のframe
self._gen_id = 0 # 何回目のgenerateかを保持
self._frames = [] # mp4生成用にframeを保存
self._step_repeat_times = 2
# self.num_envs = 1
def close(self):
self.env.close()
def step(self, action, show=False):
rews = 0.0
for i in range(self._step_repeat_times):
n_state, rew, done, info = self.env.step(action)
rews += rew
if i == 0:
self._state_frames.append(self.adjust_picture(n_state))
if show:
self._frames.append(np.array(n_state))
if done:
break
n_state_return = self.convert_state() # state 生成
rew = self.change_rew(rews/self._step_repeat_times, info)
if info["cte"] > 2.5:
done = True
rew = -1.0
elif info["cte"] < -5.0:
done = True
rew = -1.0
return n_state_return, rew, done, info
def change_rew(self, rew, info):
if info["speed"] < 0.0:
return -0.6
if abs(info["cte"]) < 1.0:
rew = info["speed"] / 100.0
else:
rew = -0.6
return rew
def reset(self):
rand_step = random.randrange(10)
self.env.reset()
for _ in range(rand_step + self._state_steps):
action = self.env.action_space.sample()
for i in range(self._step_repeat_times):
n_state, _, _, _ = self.env.step(action)
if i == 0:
self._state_frames.append(self.adjust_picture(n_state))
state = self.convert_state()
return state
def seed(self, seed_):
self.env.seed(seed_)
def adjust_picture(self, pict):
vae_state = self.convert_state_vae(pict)
return vae_state
def convert_state(self):
state_pre = []
for state in self._state_frames:
state_pre.append(state)
state = np.concatenate(state_pre, 0)
return state
def convert_state_to_tensor(self, state): # state(array) -> np.array -> convert some -> tensor
state_ = np.array(state).reshape((160, 120, 3))
state_ = state_[0:160, 40:120, :].reshape((1, 80, 160, 3))
state_ = torch.from_numpy(state_).permute(0, 3, 1, 2).float().to(self.dev)
return state_
def convert_state_vae(self, state):
state_ = self.convert_state_to_tensor(state)
state_, _, _ = self.vae.encode(state_)
state_ = state_.clone().detach().cpu().numpy()[0]
return state_
def generate_mp4(self):
# for mp4
fourcc = cv2.VideoWriter_fourcc('m', 'p', '4', 'v')
fps = 10
video = cv2.VideoWriter('./mp4/output' + str(self._gen_id) + ".mp4", fourcc, fps, (120, 160))
if not video.isOpened():
print("can't be opened")
# for path
os.mkdir("./tmp")
current_path = os.getcwd() # 現在のディレクトリ
# main procedure
for idx, frame in enumerate(self._frames):
fram = Image.fromarray(frame, "RGB")
path = current_path + "/tmp/frame" + str(idx) + ".png"
fram.save(path, 'PNG')
img = cv2.imread(path)
img = cv2.resize(img, (120, 160))
if img is None:
print("can't read")
break
video.write(img)
video.release()
shutil.rmtree("./tmp")
self._frames.clear()
self._gen_id += 1
class MyEnv:
def __init__(self, env_):
self.env = env_
self.dev = torch.device(
'cuda:0' if torch.cuda.is_available() else 'cpu')
self.action_space = env_.action_space
# self.observation_space = (1, 32)
# vae
self.vae = VAE()
model_path = "./vae/vae.pth"
self.vae.load_state_dict(torch.load(model_path))
self.vae.to(self.dev)
self._gen_id = 0 # 何回目のgenerateかを保持
self._frames = [] # mp4生成用にframeを保存
def step(self, action, show=False):
n_state, rew, done, info = self.env.step(action)
if show:
self._frames.append(np.array(n_state))
n_state = self.convert_state_vae(n_state)
rew = self.change_rew(rew, info)
if info["cte"] > 3.5:
done = True
rew = -1.0
elif info["cte"] < -5.0:
done = True
rew = -1.0
return n_state, rew, done, info
def change_rew(self, rew, info):
if info["speed"] < 0.0:
return -0.6
elif abs(info["cte"]) >= 2.0:
return -1.0
if rew > 0.0:
rew /= 20.0
if info["speed"] > 3.0:
rew += info["speed"] / 30.0
return rew
def reset(self):
state = self.env.reset()
state = self.convert_state_vae(state)
return state
def seed(self, seed_):
self.env.seed(seed_)
def convert_state_to_tensor(
self, state): # state(array) -> np.array -> convert some -> tensor
state_ = np.array(state).reshape((160, 120, 3))
# print("state_ shape {}".format(state1.shape))
state_ = state_[0:160, 40:120, :].reshape((1, 80, 160, 3))
# print("state shape {}".format(state_.shape))
# state_ = state_.reshape((1, 80, 160, 3))
state_ = torch.from_numpy(state_).permute(0, 3, 1,
2).float().to(self.dev)
state_ /= 255.0
return state_
def convert_state_vae(self, state):
state_ = self.convert_state_to_tensor(state)
state_, _, _ = self.vae.encode(state_)
state_ = state_.clone().detach().cpu().numpy()[0]
return state_
def generate_mp4(self):
# for mp4
fourcc = cv2.VideoWriter_fourcc('m', 'p', '4', 'v')
fps = 10
current_path = os.getcwd() # 現在のディレクトリ
video = cv2.VideoWriter(
current_path + '/mp4/output' + str(self._gen_id) + ".mp4", fourcc,
fps, (120, 160))
if not video.isOpened():
print("can't be opened")
# for path
os.mkdir("./tmp")
current_path = os.getcwd() # 現在のディレクトリ
# main procedure
for idx, frame in enumerate(self._frames):
fram = Image.fromarray(frame, "RGB")
path = current_path + "/tmp/frame" + str(idx) + ".png"
fram.save(path, 'PNG')
img = cv2.imread(path)
img = cv2.resize(img, (120, 160))
if img is None:
print("can't read")
break
video.write(img)
video.release()
shutil.rmtree("./tmp")
self._frames.clear()
self._gen_id += 1
