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train_refcoco_global.py
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train_refcoco_global.py
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#!/usr/bin/env python
# -*- coding: utf-8 -*-
# File: train_refcoco_global.py
# Author: Fan Wu <jxwufan@gmail.com>
from abc import ABCMeta
import numpy as np
import tensorflow as tf
import os, sys, re, time
import random
import uuid
import argparse
import multiprocessing, threading
from collections import deque
import six
from six.moves import queue
from tensorpack import *
from tensorpack.RL.simulator import SimulatorProcess, SimulatorProcessBase
from tensorpack.utils.concurrency import *
from tensorpack.utils.serialize import *
from tensorpack.utils.timer import *
from tensorpack.utils.stat import *
from tensorpack.tfutils import symbolic_functions as symbf
from tensorpack.RL import *
import utils.common as common
from utils.common import (play_model, Evaluator, eval_model_multithread)
from agent.refcocoenv_global import RefcocoEnv
from config.config import cfg
import tensorflow.contrib.slim as slim
from tensorpack.callbacks.base import Callback
from tensorpack.tfutils.common import get_global_step
from tensorpack.utils.serialize import *
import multiprocessing as mp
import zmq
from utils.image import image_utils
import os
from tensorflow.contrib.rnn.python.ops.rnn_cell import LayerNormBasicLSTMCell
GAMMA = cfg.GAMMA
LOCAL_TIME_MAX = 5
STEP_PER_EPOCH = 20000
EVAL_EPISODE = 50
BATCH_SIZE = 128
SIMULATOR_PROC = 50
PREDICTOR_THREAD_PER_GPU = 2
PREDICTOR_THREAD = None
EVALUATE_PROC = min(multiprocessing.cpu_count() // 2, 20)
NUM_ACTIONS = cfg.NUM_ACTIONS
HISTORY_LENGTH = cfg.HISTORY_LENGTH
RNN_SIZE = 1024
ENV_NAME = None
global_step = None
APPRENTICESHIP_LR = False
name_base = str(uuid.uuid1())[:6]
PIPE_DIR = os.environ.get('TENSORPACK_PIPEDIR', '.').rstrip('/')
namec2s = 'ipc://{}/sim-c2s-{}'.format(PIPE_DIR, name_base)
names2c = 'ipc://{}/sim-s2c-{}'.format(PIPE_DIR, name_base)
VISUAL_LEN = 4096
SPATIAL_LEN = 5
HISTORY_LEN = 450
LANG_LEN = 4800
def softmax(logit):
exp = np.exp(logit)
return exp / np.sum(exp)
def get_player(viz=False, train=False, dumpdir=None):
pl = RefcocoEnv(ENV_NAME, "train")
global NUM_ACTIONS
NUM_ACTIONS = pl.get_action_space().num_actions()
return pl
class MySimulatorWorker(SimulatorProcessBase):
def __init__(self, idx, pipe_c2s, pipe_s2c):
super(MySimulatorWorker, self).__init__(idx)
self.idx = idx
self.pipe_c2s = pipe_c2s
self.pipe_s2c = pipe_s2c
def connect(self):
#Set pipe to master
context = zmq.Context()
self.pipe_c2s_socket = context.socket(zmq.PUSH)
self.pipe_c2s_socket.setsockopt(zmq.IDENTITY, self.identity)
self.pipe_c2s_socket.set_hwm(2)
self.pipe_c2s_socket.connect(self.pipe_c2s)
self.pipe_s2c_socket = context.socket(zmq.DEALER)
self.pipe_s2c_socket.setsockopt(zmq.IDENTITY, self.identity)
#self.pipe_s2c_socket.set_hwm(5)
self.pipe_s2c_socket.connect(self.pipe_s2c)
def run(self):
os.environ['CUDA_VISIBLE_DEVICES'] = ""
np.random.seed(int(self.idx))
self.connect()
#Build player after connected
player = self._build_player()
state = player.current_state()
reward, isOver = 0, False
while True:
self.pipe_c2s_socket.send(dumps(
(self.identity, state, reward, isOver)),
copy=False)
action, rnn_state = loads(self.pipe_s2c_socket.recv(copy=False).bytes)
player.rnn_state = rnn_state
reward, isOver = player.action(action)
state = player.current_state()
def _build_player(self):
return get_player(train=True)
class Model(ModelDesc):
def _get_input_vars(self):
assert NUM_ACTIONS is not None
return [InputVar(tf.float32, (None, SPATIAL_LEN + VISUAL_LEN + LANG_LEN + HISTORY_LENGTH*NUM_ACTIONS + RNN_SIZE*2), 'state'),
InputVar(tf.int64, (None,), 'action'),
InputVar(tf.float32, (None,), 'futurereward') ]
def _get_NN_prediction(self, state):
visual = state[:,:VISUAL_LEN]
lang = state[:,VISUAL_LEN:VISUAL_LEN+LANG_LEN]
lang = slim.fully_connected(lang, VISUAL_LEN, scope='fc/lang')
other = state[:,VISUAL_LEN+LANG_LEN: SPATIAL_LEN+VISUAL_LEN+LANG_LEN+HISTORY_LENGTH*NUM_ACTIONS]
rnn_state = state[:, SPATIAL_LEN+VISUAL_LEN+LANG_LEN+HISTORY_LENGTH*NUM_ACTIONS:]
c = rnn_state[:, :RNN_SIZE]
h = rnn_state[:, RNN_SIZE:]
rnn_state = tf.nn.rnn_cell.LSTMStateTuple(c, h)
context = tf.mul(visual, lang)
context = tf.nn.l2_normalize(context, 1)
l = tf.concat(1, [context, other])
l = slim.fully_connected(l, 1024, scope='fc/fc1')
l = slim.fully_connected(l, 1024, scope='fc/fc2')
rnn_cell = LayerNormBasicLSTMCell(RNN_SIZE)
l, rnn_state = rnn_cell(l, rnn_state)
c, h = rnn_state
rnn_state = tf.concat(1, [c, h], name='rnn_state')
policy = slim.fully_connected(l, 9, activation_fn=None, scope='fc/fc-pi')
value = slim.fully_connected(l, 1, activation_fn=None, scope='fc/fc-v')
return policy, value
def _build_graph(self, inputs):
state, action, futurereward = inputs
policy, self.value = self._get_NN_prediction(state)
self.value = tf.squeeze(self.value, [1], name='pred_value') # (B,)
self.logits = tf.nn.softmax(policy, name='logits')
expf = tf.get_variable('explore_factor', shape=[],
initializer=tf.constant_initializer(1), trainable=False)
logitsT = tf.nn.softmax(policy * expf, name='logitsT')
is_training = get_current_tower_context().is_training
if not is_training:
return
log_probs = tf.log(self.logits + 1e-6)
log_pi_a_given_s = tf.reduce_sum(
log_probs * tf.one_hot(action, NUM_ACTIONS), 1)
advantage = tf.sub(tf.stop_gradient(self.value), futurereward, name='advantage')
policy_loss = tf.reduce_sum(log_pi_a_given_s * advantage, name='policy_loss')
xentropy_loss = tf.reduce_sum(
self.logits * log_probs, name='xentropy_loss')
value_loss = tf.nn.l2_loss(self.value - futurereward, name='value_loss')
pred_reward = tf.reduce_mean(self.value, name='predict_reward')
advantage = symbf.rms(advantage, name='rms_advantage')
summary.add_moving_summary(policy_loss, xentropy_loss, value_loss, pred_reward, advantage)
entropy_beta = tf.get_variable('entropy_beta', shape=[],
initializer=tf.constant_initializer(0.01), trainable=False)
self.cost = tf.add_n([policy_loss, xentropy_loss * entropy_beta, value_loss])
self.cost = tf.truediv(self.cost,
tf.cast(tf.shape(futurereward)[0], tf.float32),
name='cost')
def get_gradient_processor(self):
return [MapGradient(lambda grad: tf.clip_by_average_norm(grad, 0.1)),
SummaryGradient()]
class MySimulatorMaster(SimulatorMaster, Callback):
def __init__(self, pipe_c2s, pipe_s2c, model):
super(MySimulatorMaster, self).__init__(pipe_c2s, pipe_s2c)
self.M = model
self.queue = queue.Queue(maxsize=BATCH_SIZE*8*2)
def _setup_graph(self):
self.sess = self.trainer.sess
self.async_predictor = MultiThreadAsyncPredictor(
self.trainer.get_predict_funcs(['state'], ['logitsT', 'pred_value', 'rnn_state'],
PREDICTOR_THREAD), batch_size=15)
self.async_predictor.run()
def _on_state(self, state, ident):
def cb(outputs):
distrib, value, rnn_state = outputs.result()
action = np.random.choice(len(distrib), p=distrib)
client = self.clients[ident]
client.memory.append(TransitionExperience(state, action, None, value=value))
self.send_queue.put([ident, dumps([action, rnn_state,])])
self.async_predictor.put_task([state], cb)
def _on_episode_over(self, ident):
self._parse_memory(0, ident, True)
def _on_datapoint(self, ident):
client = self.clients[ident]
if len(client.memory) == LOCAL_TIME_MAX + 1:
R = client.memory[-1].value
self._parse_memory(R, ident, False)
def _parse_memory(self, init_r, ident, isOver):
client = self.clients[ident]
mem = client.memory
if not isOver:
last = mem[-1]
mem = mem[:-1]
mem.reverse()
R = float(init_r)
for idx, k in enumerate(mem):
R = np.clip(k.reward, -5, 5) + GAMMA * R
self.queue.put([k.state, k.action, R])
if not isOver:
client.memory = [last]
else:
client.memory = []
class GlobalStepSetter(Callback):
def trigger_step(self):
global global_step
global_step = get_global_step()
def get_config():
logger.auto_set_dir()
M = Model()
master = MySimulatorMaster(namec2s, names2c, M)
dataflow = BatchData(DataFromQueue(master.queue), BATCH_SIZE)
lr = symbf.get_scalar_var('learning_rate', 0.0001, summary=True)
return TrainConfig(
dataset=dataflow,
optimizer=tf.train.AdamOptimizer(lr, epsilon=1e-3),
callbacks=Callbacks([
StatPrinter(), ModelSaver(),
HumanHyperParamSetter('learning_rate', 'hyper.txt'),
HumanHyperParamSetter('entropy_beta', 'hyper.txt'),
HumanHyperParamSetter('explore_factor', 'hyper.txt'),
master,
StartProcOrThread(master),
# PeriodicCallback(Evaluator(EVAL_EPISODE, ['state'], ['logits', 'rnn_state']), 1),
GlobalStepSetter(),
]),
session_config=get_default_sess_config(0.5),
model=M,
step_per_epoch=STEP_PER_EPOCH,
max_epoch=1000,
)
if __name__ == '__main__':
global global_step
parser = argparse.ArgumentParser()
parser.add_argument('--gpu', help='comma separated list of GPU(s) to use.')
parser.add_argument('--load', help='load model')
parser.add_argument('--env', help='env', required=True)
parser.add_argument('--task', help='task to perform',
choices=['play', 'eval', 'train'], default='train')
args = parser.parse_args()
ENV_NAME = args.env
assert ENV_NAME
procs = [MySimulatorWorker(k, namec2s, names2c) for k in range(SIMULATOR_PROC)]
ensure_proc_terminate(procs)
start_proc_mask_signal(procs)
#p = get_player(); del p # set NUM_ACTIONS
if args.gpu:
os.environ['CUDA_VISIBLE_DEVICES'] = args.gpu
if args.task != 'train':
assert args.load is not None
if args.task != 'train':
cfg = PredictConfig(
model=Model(),
session_init=SaverRestore(args.load),
input_names=['state'],
output_names=['logits'])
if args.task == 'play':
play_model(cfg)
elif args.task == 'eval':
eval_model_multithread(cfg, EVAL_EPISODE)
else:
if args.gpu:
nr_gpu = get_nr_gpu()
if nr_gpu > 1:
predict_tower = range(nr_gpu)[-nr_gpu/2:]
else:
predict_tower = [0]
PREDICTOR_THREAD = len(predict_tower) * PREDICTOR_THREAD_PER_GPU
train_tower = range(nr_gpu)[:-nr_gpu/2] or [0]
logger.info("[BA3C] Train on gpu {} and infer on gpu {}".format(
','.join(map(str, train_tower)), ','.join(map(str, predict_tower))))
else:
nr_gpu = 0
PREDICTOR_THREAD = 1
predict_tower = [0]
train_tower = [0]
config = get_config()
if args.load:
config.session_init = SaverRestore(args.load)
global_step = int(args.load.split('-')[-1])
else:
#config.session_init = SaverRestore(cfg.CKPT_PATH)
global_step = 0
config.tower = train_tower
AsyncMultiGPUTrainer(config, predict_tower=predict_tower).train()