diff options
Diffstat (limited to 'tensorflow/contrib/slim/python/slim/evaluation_test.py')
| -rw-r--r-- | tensorflow/contrib/slim/python/slim/evaluation_test.py | 46 |
1 files changed, 5 insertions, 41 deletions
diff --git a/tensorflow/contrib/slim/python/slim/evaluation_test.py b/tensorflow/contrib/slim/python/slim/evaluation_test.py index 870f504d10..d9e0f54b72 100644 --- a/tensorflow/contrib/slim/python/slim/evaluation_test.py +++ b/tensorflow/contrib/slim/python/slim/evaluation_test.py @@ -20,7 +20,6 @@ from __future__ import print_function import glob import os -import shutil import time import numpy as np @@ -30,8 +29,6 @@ from tensorflow.contrib.metrics.python.ops import metric_ops from tensorflow.contrib.slim.python.slim import evaluation from tensorflow.contrib.training.python.training import evaluation as evaluation_lib from tensorflow.core.protobuf import saver_pb2 -from tensorflow.python.debug.lib import debug_data -from tensorflow.python.debug.wrappers import hooks from tensorflow.python.framework import constant_op from tensorflow.python.framework import dtypes from tensorflow.python.framework import errors @@ -233,7 +230,11 @@ class SingleEvaluationTest(test.TestCase): with self.assertRaises(errors.NotFoundError): evaluation.evaluate_once('', checkpoint_path, log_dir) - def _prepareCheckpoint(self, checkpoint_path): + def testRestoredModelPerformance(self): + checkpoint_path = os.path.join(self.get_temp_dir(), 'model.ckpt') + log_dir = os.path.join(self.get_temp_dir(), 'log_dir1/') + + # First, save out the current model to a checkpoint: init_op = control_flow_ops.group(variables.global_variables_initializer(), variables.local_variables_initializer()) saver = saver_lib.Saver(write_version=saver_pb2.SaverDef.V1) @@ -241,13 +242,6 @@ class SingleEvaluationTest(test.TestCase): sess.run(init_op) saver.save(sess, checkpoint_path) - def testRestoredModelPerformance(self): - checkpoint_path = os.path.join(self.get_temp_dir(), 'model.ckpt') - log_dir = os.path.join(self.get_temp_dir(), 'log_dir1/') - - # First, save out the current model to a checkpoint: - self._prepareCheckpoint(checkpoint_path) - # Next, determine the metric to evaluate: value_op, update_op = metric_ops.streaming_accuracy(self._predictions, self._labels) @@ -257,36 +251,6 @@ class SingleEvaluationTest(test.TestCase): '', checkpoint_path, log_dir, eval_op=update_op, final_op=value_op) self.assertAlmostEqual(accuracy_value, self._expected_accuracy) - def testAdditionalHooks(self): - checkpoint_path = os.path.join(self.get_temp_dir(), 'model.ckpt') - log_dir = os.path.join(self.get_temp_dir(), 'log_dir1/') - - # First, save out the current model to a checkpoint: - self._prepareCheckpoint(checkpoint_path) - - # Next, determine the metric to evaluate: - value_op, update_op = metric_ops.streaming_accuracy(self._predictions, - self._labels) - - dumping_root = os.path.join(self.get_temp_dir(), 'tfdbg_dump_dir') - dumping_hook = hooks.DumpingDebugHook(dumping_root, log_usage=False) - try: - # Run the evaluation and verify the results: - accuracy_value = evaluation.evaluate_once( - '', checkpoint_path, log_dir, eval_op=update_op, final_op=value_op, - hooks=[dumping_hook]) - self.assertAlmostEqual(accuracy_value, self._expected_accuracy) - - dump = debug_data.DebugDumpDir( - glob.glob(os.path.join(dumping_root, 'run_*'))[0]) - # Here we simply assert that the dumped data has been loaded and is - # non-empty. We do not care about the detailed model-internal tensors or - # their values. - self.assertTrue(dump.dumped_tensor_data) - finally: - if os.path.isdir(dumping_root): - shutil.rmtree(dumping_root) - if __name__ == '__main__': test.main() |