159 lines
4.9 KiB
Python
159 lines
4.9 KiB
Python
# Copyright 2016 The TensorFlow Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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# ==============================================================================
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"""Builder for TensorFlow models specified using specs_ops.
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"""
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from __future__ import absolute_import
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from __future__ import division
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from __future__ import print_function
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from six import exec_
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from tensorflow.contrib.specs.python import params_ops
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from tensorflow.contrib.specs.python import specs_lib
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from tensorflow.contrib.specs.python import specs_ops
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from tensorflow.python.util import tf_inspect
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def eval_params(params, environment=None):
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"""Evaluates a parameter specification and returns the environment.
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Args:
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params: parameter assignments as a string
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environment: a dictionary of input bindings
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Returns:
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Environment with additional bindings created by
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executing `params`
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Raises:
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Exception: other exceptions raised during execution of `params`
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"""
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specs_lib.check_keywords(params)
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bindings = {}
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if environment:
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bindings.update(environment)
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exec_(params, vars(params_ops), bindings) # pylint: disable=exec-used
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return bindings
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def eval_spec(spec, environment=None):
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"""Evaluates a spec and returns the environment.
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This function allows you to use a spec to obtain multiple bindings
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in an environment. That is useful if you use the spec language to
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specify multiple components of a larger network, for example: "left
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= Cr(64, [5,5]); right = Fc(64)" Usually, you will want to use
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`create_net` or `create_net_fun` below.
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Args:
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spec: specification as a string
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environment: a dictionary of input bindings
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Returns:
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Environment with additional bindings created by spec.
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Raises:
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Exception: other exceptions raised during execution of `spec`
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"""
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specs_lib.check_keywords(spec)
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bindings = {}
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if environment:
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bindings.update(environment)
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exec_(spec, vars(specs_ops), bindings) # pylint: disable=exec-used
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return bindings
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def create_net_fun(spec, environment=None):
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"""Evaluates a spec and returns the binding of `net`.
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Specs are written in a DSL based on function composition. A spec
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like `net = Cr(64, [3, 3])` assigns an object that represents a
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single argument function capable of creating a network to
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the variable `net`.
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Args:
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spec: specification as a string, ending with a `net = ...` statement
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environment: a dictionary of input bindings
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Returns:
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A callable that instantiates the `net` binding.
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Raises:
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ValueError: spec failed to create a `net`
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Exception: other exceptions raised during execution of `spec`
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"""
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bindings = eval_spec(spec, environment)
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net = bindings.get("net", None)
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if net is None:
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raise ValueError("spec failed to create 'net': %s" % (spec,))
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return net.funcall
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def create_net(spec, inputs, environment=None):
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"""Evaluates a spec and creates a network instance given the inputs.
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Args:
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spec: specification as a string, ending with a `net = ...` statement
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inputs: input that `net` is applied to
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environment: a dictionary of input bindings
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Returns:
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A callable that instantiates the `net` binding.
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Raises:
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ValueError: spec failed to create a `net`
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Exception: other exceptions raised during execution of `spec`
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"""
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return create_net_fun(spec, environment)(inputs)
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class LocalImport(object):
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"""A class that allows us to temporarily import something.
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Attributes:
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frame: the frame in which the context manager was invocked
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names: a dictionary containing the new bindings
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old: variable bindings that have been shadowed by the import
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"""
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def __init__(self, names):
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"""Create a context manager that binds the names in values.
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Args:
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names: A dictionary or module containing the bindings.
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"""
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if not isinstance(names, dict):
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names = vars(names)
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self.names = names
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def __enter__(self):
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self.frame = tf_inspect.currentframe()
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bindings = self.frame.f_back.f_globals
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self.old = {k: bindings.get(k, None) for k in self.names.keys()}
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bindings.update(self.names)
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def __exit__(self, some_type, value, traceback):
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del some_type, value, traceback
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bindings = self.frame.f_back.f_globals
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bindings.update(self.old)
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for k, v in self.old.items():
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if v is None:
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del bindings[k]
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del self.frame
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ops = LocalImport(specs_ops)
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