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5960497 | 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 | import threading
import numpy as np
from mpi4py import MPI
import tensorflow as tf
from baselines.her.util import reshape_for_broadcasting
class Normalizer:
def __init__(self, size, eps=1e-2, default_clip_range=np.inf, sess=None):
"""A normalizer that ensures that observations are approximately distributed according to
a standard Normal distribution (i.e. have mean zero and variance one).
Args:
size (int): the size of the observation to be normalized
eps (float): a small constant that avoids underflows
default_clip_range (float): normalized observations are clipped to be in
[-default_clip_range, default_clip_range]
sess (object): the TensorFlow session to be used
"""
self.size = size
self.eps = eps
self.default_clip_range = default_clip_range
self.sess = sess if sess is not None else tf.compat.v1.get_default_session()
self.local_sum = np.zeros(self.size, np.float32)
self.local_sumsq = np.zeros(self.size, np.float32)
self.local_count = np.zeros(1, np.float32)
self.sum_tf = tf.compat.v1.get_variable(
initializer=tf.compat.v1.zeros_initializer(), shape=self.local_sum.shape, name='sum',
trainable=False, dtype=tf.float32)
self.sumsq_tf = tf.compat.v1.get_variable(
initializer=tf.compat.v1.zeros_initializer(), shape=self.local_sumsq.shape, name='sumsq',
trainable=False, dtype=tf.float32)
self.count_tf = tf.compat.v1.get_variable(
initializer=tf.compat.v1.ones_initializer(), shape=self.local_count.shape, name='count',
trainable=False, dtype=tf.float32)
self.mean = tf.compat.v1.get_variable(
initializer=tf.compat.v1.zeros_initializer(), shape=(self.size,), name='mean',
trainable=False, dtype=tf.float32)
self.std = tf.compat.v1.get_variable(
initializer=tf.compat.v1.ones_initializer(), shape=(self.size,), name='std',
trainable=False, dtype=tf.float32)
self.count_pl = tf.compat.v1.placeholder(name='count_pl', shape=(1,), dtype=tf.float32)
self.sum_pl = tf.compat.v1.placeholder(name='sum_pl', shape=(self.size,), dtype=tf.float32)
self.sumsq_pl = tf.compat.v1.placeholder(name='sumsq_pl', shape=(self.size,), dtype=tf.float32)
self.update_op = tf.group(
self.count_tf.assign_add(self.count_pl),
self.sum_tf.assign_add(self.sum_pl),
self.sumsq_tf.assign_add(self.sumsq_pl)
)
self.recompute_op = tf.group(
tf.compat.v1.assign(self.mean, self.sum_tf / self.count_tf),
tf.compat.v1.assign(self.std, tf.sqrt(tf.maximum(
tf.square(self.eps),
self.sumsq_tf / self.count_tf - tf.square(self.sum_tf / self.count_tf)
))),
)
self.lock = threading.Lock()
def update(self, v):
v = v.reshape(-1, self.size)
with self.lock:
self.local_sum += v.sum(axis=0)
self.local_sumsq += (np.square(v)).sum(axis=0)
self.local_count[0] += v.shape[0]
def normalize(self, v, clip_range=None):
if clip_range is None:
clip_range = self.default_clip_range
mean = reshape_for_broadcasting(self.mean, v)
std = reshape_for_broadcasting(self.std, v)
return tf.clip_by_value((v - mean) / std, -clip_range, clip_range)
def denormalize(self, v):
mean = reshape_for_broadcasting(self.mean, v)
std = reshape_for_broadcasting(self.std, v)
return mean + v * std
def _mpi_average(self, x):
buf = np.zeros_like(x)
MPI.COMM_WORLD.Allreduce(x, buf, op=MPI.SUM)
buf /= MPI.COMM_WORLD.Get_size()
return buf
def synchronize(self, local_sum, local_sumsq, local_count, root=None):
local_sum[...] = self._mpi_average(local_sum)
local_sumsq[...] = self._mpi_average(local_sumsq)
local_count[...] = self._mpi_average(local_count)
return local_sum, local_sumsq, local_count
def recompute_stats(self):
with self.lock:
# Copy over results.
local_count = self.local_count.copy()
local_sum = self.local_sum.copy()
local_sumsq = self.local_sumsq.copy()
# Reset.
self.local_count[...] = 0
self.local_sum[...] = 0
self.local_sumsq[...] = 0
# We perform the synchronization outside of the lock to keep the critical section as short
# as possible.
synced_sum, synced_sumsq, synced_count = self.synchronize(
local_sum=local_sum, local_sumsq=local_sumsq, local_count=local_count)
self.sess.run(self.update_op, feed_dict={
self.count_pl: synced_count,
self.sum_pl: synced_sum,
self.sumsq_pl: synced_sumsq,
})
self.sess.run(self.recompute_op)
class IdentityNormalizer:
def __init__(self, size, std=1.):
self.size = size
self.mean = tf.zeros(self.size, tf.float32)
self.std = std * tf.ones(self.size, tf.float32)
def update(self, x):
pass
def normalize(self, x, clip_range=None):
return x / self.std
def denormalize(self, x):
return self.std * x
def synchronize(self):
pass
def recompute_stats(self):
pass
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