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import numpy as np
import tensorflow as tf # pylint: ignore-module
import copy
import os
import functools
import collections
import multiprocessing
def switch(condition, then_expression, else_expression):
"""Switches between two operations depending on a scalar value (int or bool).
Note that both `then_expression` and `else_expression`
should be symbolic tensors of the *same shape*.
# Arguments
condition: scalar tensor.
then_expression: TensorFlow operation.
else_expression: TensorFlow operation.
"""
x_shape = copy.copy(then_expression.get_shape())
x = tf.cond(tf.cast(condition, 'bool'),
lambda: then_expression,
lambda: else_expression)
x.set_shape(x_shape)
return x
# ================================================================
# Extras
# ================================================================
def lrelu(x, leak=0.2):
f1 = 0.5 * (1 + leak)
f2 = 0.5 * (1 - leak)
return f1 * x + f2 * abs(x)
# ================================================================
# Mathematical utils
# ================================================================
def huber_loss(x, delta=1.0):
"""Reference: https://en.wikipedia.org/wiki/Huber_loss"""
return tf.where(
tf.abs(x) < delta,
tf.square(x) * 0.5,
delta * (tf.abs(x) - 0.5 * delta)
)
# ================================================================
# Global session
# ================================================================
def make_session(num_cpu=None, make_default=False, graph=None):
"""Returns a session that will use <num_cpu> CPU's only"""
if num_cpu is None:
num_cpu = int(os.getenv('RCALL_NUM_CPU', multiprocessing.cpu_count()))
tf_config = tf.ConfigProto(
inter_op_parallelism_threads=num_cpu,
intra_op_parallelism_threads=num_cpu,
allow_soft_placement=True
)
tf_config.gpu_options.allow_growth = True
if make_default:
return tf.InteractiveSession(config=tf_config, graph=graph)
else:
return tf.Session(config=tf_config, graph=graph)
def single_threaded_session():
"""Returns a session which will only use a single CPU"""
return make_session(num_cpu=1)
def in_session(f):
@functools.wraps(f)
def newfunc(*args, **kwargs):
with tf.Session():
f(*args, **kwargs)
return newfunc
ALREADY_INITIALIZED = set()
def initialize():
"""Initialize all the uninitialized variables in the global scope."""
new_variables = set(tf.global_variables()) - ALREADY_INITIALIZED
tf.get_default_session().run(tf.variables_initializer(new_variables))
ALREADY_INITIALIZED.update(new_variables)
# ================================================================
# Model components
# ================================================================
def normc_initializer(std=1.0, axis=0):
def _initializer(shape, dtype=None, partition_info=None): # pylint: disable=W0613
out = np.random.randn(*shape).astype(np.float32)
out *= std / np.sqrt(np.square(out).sum(axis=axis, keepdims=True))
return tf.constant(out)
return _initializer
def conv2d(x, num_filters, name, filter_size=(3, 3), stride=(1, 1), pad="SAME", dtype=tf.float32, collections=None,
summary_tag=None):
with tf.variable_scope(name):
stride_shape = [1, stride[0], stride[1], 1]
filter_shape = [filter_size[0], filter_size[1], int(x.get_shape()[3]), num_filters]
# there are "num input feature maps * filter height * filter width"
# inputs to each hidden unit
fan_in = intprod(filter_shape[:3])
# each unit in the lower layer receives a gradient from:
# "num output feature maps * filter height * filter width" /
# pooling size
fan_out = intprod(filter_shape[:2]) * num_filters
# initialize weights with random weights
w_bound = np.sqrt(6. / (fan_in + fan_out))
w = tf.get_variable("W", filter_shape, dtype, tf.random_uniform_initializer(-w_bound, w_bound),
collections=collections)
b = tf.get_variable("b", [1, 1, 1, num_filters], initializer=tf.zeros_initializer(),
collections=collections)
if summary_tag is not None:
tf.summary.image(summary_tag,
tf.transpose(tf.reshape(w, [filter_size[0], filter_size[1], -1, 1]),
[2, 0, 1, 3]),
max_images=10)
return tf.nn.conv2d(x, w, stride_shape, pad) + b
# ================================================================
# Theano-like Function
# ================================================================
def function(inputs, outputs, updates=None, givens=None):
"""Just like Theano function. Take a bunch of tensorflow placeholders and expressions
computed based on those placeholders and produces f(inputs) -> outputs. Function f takes
values to be fed to the input's placeholders and produces the values of the expressions
in outputs.
Input values can be passed in the same order as inputs or can be provided as kwargs based
on placeholder name (passed to constructor or accessible via placeholder.op.name).
Example:
x = tf.placeholder(tf.int32, (), name="x")
y = tf.placeholder(tf.int32, (), name="y")
z = 3 * x + 2 * y
lin = function([x, y], z, givens={y: 0})
with single_threaded_session():
initialize()
assert lin(2) == 6
assert lin(x=3) == 9
assert lin(2, 2) == 10
assert lin(x=2, y=3) == 12
Parameters
----------
inputs: [tf.placeholder, tf.constant, or object with make_feed_dict method]
list of input arguments
outputs: [tf.Variable] or tf.Variable
list of outputs or a single output to be returned from function. Returned
value will also have the same shape.
"""
if isinstance(outputs, list):
return _Function(inputs, outputs, updates, givens=givens)
elif isinstance(outputs, (dict, collections.OrderedDict)):
f = _Function(inputs, outputs.values(), updates, givens=givens)
return lambda *args, **kwargs: type(outputs)(zip(outputs.keys(), f(*args, **kwargs)))
else:
f = _Function(inputs, [outputs], updates, givens=givens)
return lambda *args, **kwargs: f(*args, **kwargs)[0]
class _Function(object):
def __init__(self, inputs, outputs, updates, givens):
for inpt in inputs:
if not hasattr(inpt, 'make_feed_dict') and not (type(inpt) is tf.Tensor and len(inpt.op.inputs) == 0):
assert False, "inputs should all be placeholders, constants, or have a make_feed_dict method"
self.inputs = inputs
updates = updates or []
self.update_group = tf.group(*updates)
self.outputs_update = list(outputs) + [self.update_group]
self.givens = {} if givens is None else givens
def _feed_input(self, feed_dict, inpt, value):
if hasattr(inpt, 'make_feed_dict'):
feed_dict.update(inpt.make_feed_dict(value))
else:
feed_dict[inpt] = value
def __call__(self, *args):
assert len(args) <= len(self.inputs), "Too many arguments provided"
feed_dict = {}
# Update the args
for inpt, value in zip(self.inputs, args):
self._feed_input(feed_dict, inpt, value)
# Update feed dict with givens.
for inpt in self.givens:
feed_dict[inpt] = feed_dict.get(inpt, self.givens[inpt])
results = tf.get_default_session().run(self.outputs_update, feed_dict=feed_dict)[:-1]
return results
# ================================================================
# Flat vectors
# ================================================================
def var_shape(x):
out = x.get_shape().as_list()
assert all(isinstance(a, int) for a in out), \
"shape function assumes that shape is fully known"
return out
def numel(x):
return intprod(var_shape(x))
def intprod(x):
return int(np.prod(x))
def flatgrad(loss, var_list, clip_norm=None):
grads = tf.gradients(loss, var_list)
if clip_norm is not None:
grads = [tf.clip_by_norm(grad, clip_norm=clip_norm) for grad in grads]
return tf.concat(axis=0, values=[
tf.reshape(grad if grad is not None else tf.zeros_like(v), [numel(v)])
for (v, grad) in zip(var_list, grads)
])
class SetFromFlat(object):
def __init__(self, var_list, dtype=tf.float32):
assigns = []
shapes = list(map(var_shape, var_list))
total_size = np.sum([intprod(shape) for shape in shapes])
self.theta = theta = tf.placeholder(dtype, [total_size])
start = 0
assigns = []
for (shape, v) in zip(shapes, var_list):
size = intprod(shape)
assigns.append(tf.assign(v, tf.reshape(theta[start:start + size], shape)))
start += size
self.op = tf.group(*assigns)
def __call__(self, theta):
tf.get_default_session().run(self.op, feed_dict={self.theta: theta})
class GetFlat(object):
def __init__(self, var_list):
self.op = tf.concat(axis=0, values=[tf.reshape(v, [numel(v)]) for v in var_list])
def __call__(self):
return tf.get_default_session().run(self.op)
_PLACEHOLDER_CACHE = {} # name -> (placeholder, dtype, shape)
def get_placeholder(name, dtype, shape):
if name in _PLACEHOLDER_CACHE:
out, dtype1, shape1 = _PLACEHOLDER_CACHE[name]
assert dtype1 == dtype and shape1 == shape
return out
else:
out = tf.placeholder(dtype=dtype, shape=shape, name=name)
_PLACEHOLDER_CACHE[name] = (out, dtype, shape)
return out
def get_placeholder_cached(name):
return _PLACEHOLDER_CACHE[name][0]
def flattenallbut0(x):
return tf.reshape(x, [-1, intprod(x.get_shape().as_list()[1:])])
# ================================================================
# Diagnostics
# ================================================================
def display_var_info(vars):
from baselines import logger
count_params = 0
for v in vars:
name = v.name
if "/Adam" in name or "beta1_power" in name or "beta2_power" in name: continue
v_params = np.prod(v.shape.as_list())
count_params += v_params
if "/b:" in name or "/biases" in name: continue # Wx+b, bias is not interesting to look at => count params, but not print
logger.info(" %s%s %i params %s" % (name, " "*(55-len(name)), v_params, str(v.shape)))
logger.info("Total model parameters: %0.2f million" % (count_params*1e-6))
def get_available_gpus():
# recipe from here:
# https://stackoverflow.com/questions/38559755/how-to-get-current-available-gpus-in-tensorflow?utm_medium=organic&utm_source=google_rich_qa&utm_campaign=google_rich_qa
from tensorflow.python.client import device_lib
local_device_protos = device_lib.list_local_devices()
return [x.name for x in local_device_protos if x.device_type == 'GPU']
# ================================================================
# Saving variables
# ================================================================
def load_state(fname):
saver = tf.train.Saver()
saver.restore(tf.get_default_session(), fname)
def save_state(fname):
os.makedirs(os.path.dirname(fname), exist_ok=True)
saver = tf.train.Saver()
saver.save(tf.get_default_session(), fname)
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