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
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
|
# Copyright 2017 The TensorFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ==============================================================================
"""Some useful session run hooks."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import os
from tensorflow.python.estimator import estimator as estimator_lib
from tensorflow.python.framework import ops
from tensorflow.python.ops import array_ops
from tensorflow.python.ops import control_flow_ops
from tensorflow.python.ops import state_ops
from tensorflow.python.training import training
# pylint: disable=protected-access
class InMemoryEvaluatorHook(training.SessionRunHook):
"""Hook to run evaluation in training without a checkpoint.
Example:
```python
def train_input_fn():
...
return train_dataset
def eval_input_fn():
...
return eval_dataset
estimator = tf.estimator.DNNClassifier(...)
evaluator = tf.contrib.estimator.InMemoryEvaluatorHook(
estimator, eval_input_fn)
estimator.train(train_input_fn, hooks=[evaluator])
```
Current limitations of this approach are:
* It doesn't support multi-node distributed mode.
* It doesn't support saveable objects other than variables (such as boosted
tree support)
* It doesn't support custom saver logic (such as ExponentialMovingAverage
support)
"""
def __init__(self,
estimator,
input_fn,
steps=None,
hooks=None,
name=None,
every_n_iter=100):
"""Initializes a `InMemoryEvaluatorHook`.
Args:
estimator: A `tf.estimator.Estimator` instance to call evaluate.
input_fn: Equivalent to the `input_fn` arg to `estimator.evaluate`. A
function that constructs the input data for evaluation.
See @{$premade_estimators#create_input_functions} for more
information. The function should construct and return one of
the following:
* A 'tf.data.Dataset' object: Outputs of `Dataset` object must be a
tuple (features, labels) with same constraints as below.
* A tuple (features, labels): Where `features` is a `Tensor` or a
dictionary of string feature name to `Tensor` and `labels` is a
`Tensor` or a dictionary of string label name to `Tensor`. Both
`features` and `labels` are consumed by `model_fn`. They should
satisfy the expectation of `model_fn` from inputs.
steps: Equivalent to the `steps` arg to `estimator.evaluate`. Number of
steps for which to evaluate model. If `None`, evaluates until `input_fn`
raises an end-of-input exception.
hooks: Equivalent to the `hooks` arg to `estimator.evaluate`. List of
`SessionRunHook` subclass instances. Used for callbacks inside the
evaluation call.
name: Equivalent to the `name` arg to `estimator.evaluate`. Name of the
evaluation if user needs to run multiple evaluations on different data
sets, such as on training data vs test data. Metrics for different
evaluations are saved in separate folders, and appear separately in
tensorboard.
every_n_iter: `int`, runs the evaluator once every N training iteration.
Raises:
ValueError: if `every_n_iter` is non-positive or it's not a single machine
training
"""
if every_n_iter is None or every_n_iter <= 0:
raise ValueError('invalid every_n_iter=%s.' % every_n_iter)
if (estimator.config.num_ps_replicas > 0 or
estimator.config.num_worker_replicas > 1):
raise ValueError(
'InMemoryEvaluator supports only single machine (aka Local) setting.')
self._estimator = estimator
self._input_fn = input_fn
self._steps = steps
self._name = name
self._every_n_iter = every_n_iter
self._eval_dir = os.path.join(self._estimator.model_dir, 'eval'
if not name else 'eval_' + name)
self._graph = None
self._hooks = estimator_lib._check_hooks_type(hooks)
self._hooks.extend(self._estimator._convert_eval_steps_to_hooks(steps))
self._timer = training.SecondOrStepTimer(every_steps=every_n_iter)
def begin(self):
"""Build eval graph and restoring op."""
self._timer.reset()
self._iter_count = 0
self._graph = ops.Graph()
with self._graph.as_default():
(self._scaffold, self._update_op, self._eval_dict,
self._all_hooks) = self._estimator._evaluate_build_graph(
self._input_fn, self._hooks, checkpoint_path=None)
if self._scaffold.saver is not None:
raise ValueError('InMemoryEvaluator does not support custom saver')
if self._scaffold.init_fn is not None:
raise ValueError('InMemoryEvaluator does not support custom init_fn')
self._var_name_to_eval_var = {
v.name: v for v in ops.get_collection(ops.GraphKeys.GLOBAL_VARIABLES)
}
self._var_name_to_placeholder = {
v.name: array_ops.placeholder(v.dtype)
for v in ops.get_collection(ops.GraphKeys.GLOBAL_VARIABLES)
}
def after_create_session(self, session, coord): # pylint: disable=unused-argument
"""Does first run which shows the eval metrics before training."""
if ops.get_collection(ops.GraphKeys.SAVEABLE_OBJECTS):
raise ValueError(
'InMemoryEvaluator does not support saveables other than global '
'variables.')
self._var_name_to_train_var = {
v.name: v for v in ops.get_collection(ops.GraphKeys.GLOBAL_VARIABLES)
}
var_names_to_transfer = set(self._var_name_to_placeholder.keys()) & set(
self._var_name_to_train_var.keys())
# Filter training var names that are not exist in evaluation
self._var_name_to_train_var = {
v_name: self._var_name_to_train_var[v_name]
for v_name in var_names_to_transfer
}
# Filter eval var names that are not exist in training
self._var_name_to_eval_var = {
v_name: self._var_name_to_eval_var[v_name]
for v_name in var_names_to_transfer
}
with self._graph.as_default():
self._var_feed_op = control_flow_ops.group([
state_ops.assign(self._var_name_to_eval_var[v_name],
self._var_name_to_placeholder[v_name])
for v_name in var_names_to_transfer
])
self._evaluate(session)
def _evaluate(self, train_session):
var_name_to_value = train_session.run(self._var_name_to_train_var)
placeholder_to_value = {
self._var_name_to_placeholder[v_name]: var_name_to_value[v_name]
for v_name in var_name_to_value
}
def feed_variables(scaffold, session):
del scaffold
session.run(self._var_feed_op, feed_dict=placeholder_to_value)
scaffold = training.Scaffold(
init_fn=feed_variables, copy_from_scaffold=self._scaffold)
with self._graph.as_default():
return self._estimator._evaluate_run(
checkpoint_path=None,
scaffold=scaffold,
update_op=self._update_op,
eval_dict=self._eval_dict,
all_hooks=self._all_hooks,
output_dir=self._eval_dir)
self._timer.update_last_triggered_step(self._iter_count)
def after_run(self, run_context, run_values): # pylint: disable=unused-argument
"""Runs evaluator."""
self._iter_count += 1
if self._timer.should_trigger_for_step(self._iter_count):
self._evaluate(run_context.session)
def end(self, session): # pylint: disable=unused-argument
"""Runs evaluator for final model."""
self._evaluate(session)
# pylint: enable=protected-access
|
