ray/rllib/utils/tf_ops.py

217 lines
7.5 KiB
Python

import gym
from gym.spaces import Discrete, MultiDiscrete
import numpy as np
import tree
from ray.rllib.utils.framework import try_import_tf
tf1, tf, tfv = try_import_tf()
def convert_to_non_tf_type(stats):
"""Converts values in `stats` to non-Tensor numpy or python types.
Args:
stats (any): Any (possibly nested) struct, the values in which will be
converted and returned as a new struct with all tf (eager) tensors
being converted to numpy types.
Returns:
Any: A new struct with the same structure as `stats`, but with all
values converted to non-tf Tensor types.
"""
# The mapping function used to numpyize torch Tensors.
def mapping(item):
if isinstance(item, (tf.Tensor, tf.Variable)):
return item.numpy()
else:
return item
return tree.map_structure(mapping, stats)
def explained_variance(y, pred):
_, y_var = tf.nn.moments(y, axes=[0])
_, diff_var = tf.nn.moments(y - pred, axes=[0])
return tf.maximum(-1.0, 1 - (diff_var / y_var))
def get_placeholder(*, space=None, value=None, name=None, time_axis=False):
from ray.rllib.models.catalog import ModelCatalog
if space is not None:
if isinstance(space, (gym.spaces.Dict, gym.spaces.Tuple)):
return ModelCatalog.get_action_placeholder(space, None)
return tf1.placeholder(
shape=(None, ) + ((None, ) if time_axis else ()) + space.shape,
dtype=tf.float32 if space.dtype == np.float64 else space.dtype,
name=name,
)
else:
assert value is not None
shape = value.shape[1:]
return tf1.placeholder(
shape=(None, ) + ((None, )
if time_axis else ()) + (shape if isinstance(
shape, tuple) else tuple(shape.as_list())),
dtype=tf.float32 if value.dtype == np.float64 else value.dtype,
name=name,
)
def huber_loss(x, delta=1.0):
"""Reference: https://en.wikipedia.org/wiki/Huber_loss"""
return tf.where(
tf.abs(x) < delta,
tf.math.square(x) * 0.5, delta * (tf.abs(x) - 0.5 * delta))
def one_hot(x, space):
if isinstance(space, Discrete):
return tf.one_hot(x, space.n)
elif isinstance(space, MultiDiscrete):
return tf.concat(
[tf.one_hot(x[:, i], n) for i, n in enumerate(space.nvec)],
axis=-1)
else:
raise ValueError("Unsupported space for `one_hot`: {}".format(space))
def reduce_mean_ignore_inf(x, axis):
"""Same as tf.reduce_mean() but ignores -inf values."""
mask = tf.not_equal(x, tf.float32.min)
x_zeroed = tf.where(mask, x, tf.zeros_like(x))
return (tf.reduce_sum(x_zeroed, axis) / tf.reduce_sum(
tf.cast(mask, tf.float32), axis))
def minimize_and_clip(optimizer, objective, var_list, clip_val=10.0):
"""Minimized `objective` using `optimizer` w.r.t. variables in
`var_list` while ensure the norm of the gradients for each
variable is clipped to `clip_val`
"""
# Accidentally passing values < 0.0 will break all gradients.
assert clip_val > 0.0, clip_val
if tf.executing_eagerly():
tape = optimizer.tape
grads_and_vars = list(
zip(list(tape.gradient(objective, var_list)), var_list))
else:
grads_and_vars = optimizer.compute_gradients(
objective, var_list=var_list)
for i, (grad, var) in enumerate(grads_and_vars):
if grad is not None:
grads_and_vars[i] = (tf.clip_by_norm(grad, clip_val), var)
return grads_and_vars
def make_tf_callable(session_or_none, dynamic_shape=False):
"""Returns a function that can be executed in either graph or eager mode.
The function must take only positional args.
If eager is enabled, this will act as just a function. Otherwise, it
will build a function that executes a session run with placeholders
internally.
Args:
session_or_none (tf.Session): tf.Session if in graph mode, else None.
dynamic_shape (bool): True if the placeholders should have a dynamic
batch dimension. Otherwise they will be fixed shape.
Returns:
a Python function that can be called in either mode.
"""
if tf.executing_eagerly():
assert session_or_none is None
else:
assert session_or_none is not None
def make_wrapper(fn):
# Static-graph mode: Create placeholders and make a session call each
# time the wrapped function is called. Return this session call's
# outputs.
if session_or_none is not None:
args_placeholders = []
kwargs_placeholders = {}
symbolic_out = [None]
def call(*args, **kwargs):
args_flat = []
for a in args:
if type(a) is list:
args_flat.extend(a)
else:
args_flat.append(a)
args = args_flat
if symbolic_out[0] is None:
with session_or_none.graph.as_default():
for i, v in enumerate(args):
if dynamic_shape:
if len(v.shape) > 0:
shape = (None, ) + v.shape[1:]
else:
shape = ()
else:
shape = v.shape
args_placeholders.append(
tf1.placeholder(
dtype=v.dtype,
shape=shape,
name="arg_{}".format(i)))
for k, v in kwargs.items():
if dynamic_shape:
if len(v.shape) > 0:
shape = (None, ) + v.shape[1:]
else:
shape = ()
else:
shape = v.shape
kwargs_placeholders[k] = \
tf1.placeholder(
dtype=v.dtype,
shape=shape,
name="kwarg_{}".format(k))
symbolic_out[0] = fn(*args_placeholders,
**kwargs_placeholders)
feed_dict = dict(zip(args_placeholders, args))
feed_dict.update(
{kwargs_placeholders[k]: kwargs[k]
for k in kwargs.keys()})
ret = session_or_none.run(symbolic_out[0], feed_dict)
return ret
return call
# Eager mode (call function as is).
else:
return fn
return make_wrapper
def scope_vars(scope, trainable_only=False):
"""
Get variables inside a scope
The scope can be specified as a string
Parameters
----------
scope: str or VariableScope
scope in which the variables reside.
trainable_only: bool
whether or not to return only the variables that were marked as
trainable.
Returns
-------
vars: [tf.Variable]
list of variables in `scope`.
"""
return tf1.get_collection(
tf1.GraphKeys.TRAINABLE_VARIABLES
if trainable_only else tf1.GraphKeys.VARIABLES,
scope=scope if isinstance(scope, str) else scope.name)