-
Notifications
You must be signed in to change notification settings - Fork 0
/
code_pretrain.py
246 lines (216 loc) · 9.69 KB
/
code_pretrain.py
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
import os
os.environ["CUDA_DEVICE_ORDER"]="PCI_BUS_ID"
os.environ["CUDA_VISIBLE_DEVICES"]='0' # '1', '2', '3', '0,1'
os.environ['TF_FORCE_GPU_ALLOW_GROWTH'] = 'true'
import csv
import time
import math
import random
import argparse
import numpy as np
# import gym
import cv2
import tensorflow as tf
from env.navigate import NaviEnv
from models.code_vae import Actor, DiscretePosterior
from models.VAE_Network import VAE_Encoder
from utils.memory import HorizonMemory, ReplayMemory
from utils.util import *
from utils.config import *
class CodeVAE:
def __init__(self, global_norm, lr, actor_units, code_units,
epochs, batch_size, data_dir, demo_list):
# build network
self.actor = Actor(lr=lr, hidden_units=actor_units)
self.prior = DiscretePosterior(lr=lr, hidden_units=code_units)
self.encoder = VAE_Encoder(latent_num=64)
self.opt = tf.keras.optimizers.Adam(learning_rate=lr)
# set hyperparameters
self.epochs = epochs
self.batch_size = batch_size
self.grad_global_norm = global_norm
self.init_temperature = 2.0
self.temperature = self.init_temperature
self.min_temperature = 0.5
self.temp_decay = 1e-3
self.beta = 1e-4
# build expert demonstration Pipeline
self.data_dir = data_dir
self.demo_list = os.listdir(data_dir)
self.demo_group_num = 500
self.demo_rotate = 3
assert len(demo_list) >= self.demo_group_num
self.set_demo()
self.total = 0
# ready
self.dummy_forward()
self.vars = self.actor.trainable_variables + self.prior.trainable_variables
def dummy_forward(self):
# connect networks
dummy_state = np.zeros([1] + STATE_SHAPE, dtype=np.float32)
dummy_action = np.zeros([1] + ACTION_SHAPE, dtype=np.float32)
dummy_code = np.zeros([1] + [DISC_CODE_NUM], dtype=np.float32)
self.encoder(dummy_state)
self.prior(self.encoder, dummy_state, dummy_action, dummy_code)
self.actor(self.encoder, dummy_state, dummy_code)
def set_demo(self):
self.demo_list = os.listdir(data_dir)
selected_demos = random.sample(self.demo_list, self.demo_group_num)
self.expert_states = []
self.expert_actions = []
for demo_name in selected_demos:
demo = np.load(self.data_dir + demo_name)
states = demo['state']
actions = demo['action']
self.expert_states.append(states)
self.expert_actions.append(actions)
# self.expert_states = np.concatenate(expert_states, axis=0)
# self.expert_actions = np.concatenate(expert_actions, axis=0)
del demo
def update_temperature(self, epoch):
self.temperature = \
max(self.min_temperature, self.init_temperature * math.exp(-self.temp_decay * epoch))
def label_prev_code(self, s, a):
# sequential labeling
prev_codes = []
running_code = np.eye(DISC_CODE_NUM, dtype=np.float32)[
[np.random.randint(0, DISC_CODE_NUM)]
] # initial code
# c_0 ~ c_t-1 [N-1, C]
for t in range(1, len(s)):
# s_t a_t-1 c_t-1 -> c_t
prev_codes.append(running_code)
running_code = self.prior(self.encoder, s[t:t+1], a[t-1:t], running_code).numpy()
running_code = np.eye(DISC_CODE_NUM, dtype=np.float32)[
[np.random.choice(DISC_CODE_NUM, p=running_code[0])]
]
return np.concatenate(prev_codes, axis=0)
def update(self):
# load expert demonstration
# states, prev_actions = self.get_demonstration()
# about 20000 samples
states = []
actions = []
prev_actions = []
prev_codes = []
for s, a in zip(self.expert_states, self.expert_actions):
states.append(s[1:]) # s_1: s_t
prev_actions.append(a[:-1]) # a_0 : a_t-1
actions.append(a[1:]) # a_1 : a_t
prev_code = self.label_prev_code(s, a) # c_0 : c_t-1
prev_codes.append(prev_code)
states = np.concatenate(states, axis=0)
actions = np.concatenate(actions, axis=0)
prev_actions = np.concatenate(prev_actions, axis=0)
prev_codes = np.concatenate(prev_codes, axis=0)
# print(prev_codes)
batch_num = len(states) // self.batch_size
index = np.arange(len(states))
loss = 0
for epoch in range(self.epochs):
np.random.shuffle(index)
for i in range(batch_num):
idx = index[i*self.batch_size : (i+1)*self.batch_size]
state = states[idx] # (N, S) s_t
action = actions[idx] # (N, A) a_t
prev_action = prev_actions[idx] # (N, A) a_t-1
prev_code = prev_codes[idx] # (N, C) c_t-1
# update vae
with tf.GradientTape() as tape:
code = self.prior(self.encoder, state, prev_action, prev_code) # (N, C) c_t
sampled_code = tf_reparameterize(code, self.temperature)
policy = self.actor(self.encoder, state, sampled_code) # (N, A) a_t
log_probs = tf.math.log(sampled_code + 1e-8)
log_prior_probs = tf.math.log(1 / DISC_CODE_NUM)
kld_loss = tf.reduce_mean(tf.reduce_sum(sampled_code * (log_probs - log_prior_probs), axis=1))
actor_loss = -tf.reduce_mean(tf.reduce_sum(action * tf.math.log(policy + 1e-8), axis=1)) # (N-1, )
vae_loss = self.beta * kld_loss + actor_loss
print(('{:.2f} '*4).format(*policy.numpy()[100]) +' / '+ ('{:.2f} ' * 4).format(*code.numpy()[100])+' / ' +
('{:.2f} '*4).format(*sampled_code.numpy()[100]), '%.2f %.2f %.2f %.2f' %(vae_loss.numpy(), kld_loss.numpy(), actor_loss.numpy(), self.temperature), end='\r')
grads = tape.gradient(vae_loss, self.vars)
if self.grad_global_norm > 0:
grads, _ = tf.clip_by_global_norm(grads, self.grad_global_norm)
self.opt.apply_gradients(zip(grads, self.vars))
loss += vae_loss.numpy()
self.total += 1
self.update_temperature(self.total)
loss /= self.epochs * batch_num
return loss
def save_model(self, dir, tag=''):
self.actor.save_weights(dir + tag + 'actor.h5')
self.prior.save_weights(dir + tag + 'prior.h5')
def load_model(self, dir, tag=''):
if os.path.exists(dir + tag + 'actor.h5'):
self.actor.load_weights(dir + tag + 'actor.h5')
print('Actor loaded... %s%sactor.h5' % (dir, tag))
if os.path.exists(dir + tag + 'prior.h5'):
self.prior.load_weights(dir + tag + 'prior.h5')
print('prior loaded... %s%sprior.h5' % (dir, tag))
def load_encoder(self, dir, tag=''):
if os.path.exists(dir + tag + 'encoder.h5'):
self.encoder.load_weights(dir + tag + 'encoder.h5')
print('Encoder loaded... %s%sencoder.h5' % (dir, tag))
if __name__ == '__main__':
parser = argparse.ArgumentParser()
parser.add_argument('--load_model', action='store_true')
parser.add_argument('--init_enc', action='store_true')
parser.add_argument('--test', action='store_true')
parser.add_argument('--render', action='store_true')
parser.add_argument('--verbose', action='store_true')
parser.add_argument('--global_norm',type=float, default=0.0)
parser.add_argument('--lr', type=float, default=1e-4)
parser.add_argument('--epochs', type=int, default=1)
parser.add_argument('--batch_size', type=int, default=256)
parser.add_argument('--save_rate', type=int, default=20)
parser.add_argument('--env', type=str, default='Navi-v1')
parser.add_argument('--log_name', type=str, default='code')
parser.add_argument('--actor_units',type=int, nargs='*', default=[256, 128])
parser.add_argument('--code_units', type=int, nargs='*', default=[64, 64])
args = parser.parse_args()
env_name = args.env
data_dir = 'data/%s/' % env_name
model_dir = 'weights/%s/' % args.log_name
bc_dir = 'weights/bc/'
demo_list = os.listdir(data_dir)
if not os.path.exists(data_dir):
os.makedirs(data_dir)
if not os.path.exists(model_dir):
os.makedirs(model_dir)
agent = CodeVAE(
global_norm=args.global_norm,
lr=args.lr,
actor_units=args.actor_units,
code_units=args.code_units,
epochs=args.epochs,
batch_size=args.batch_size,
data_dir=data_dir,
demo_list=demo_list
)
if args.load_model:
agent.load_model(model_dir)
if not args.init_enc:
agent.load_encoder(bc_dir)
epoch = 0
stats = []
while True:
# reset demo
if epoch % agent.demo_rotate == 0:
# start = time.time()
agent.set_demo()
# print('load demo', time.time()-start)
# start=time.time()
loss = agent.update()
# print('update', time.time() - start)
if args.verbose:
print('E:%d... loss: %.4f\t\t\t' % (epoch, loss), end='\r')
# done
stats.append([loss, agent.temperature])
if not args.test and epoch % args.save_rate == 0:
m_loss, m_temp = np.mean(stats, axis=0)
agent.save_model(model_dir)
with open('%s.csv' % args.log_name, 'a', newline='') as f:
wrt = csv.writer(f)
for row in stats:
wrt.writerow(row)
stats.clear()
epoch += 1