Update the export_output_test to not rely on Keras metrics.
The API interface of export_outputs_for_mode made it clear that the input metric need to be a tuple of metric value and update_op, rather than metric instance itself. PiperOrigin-RevId: 315932763 Change-Id: Id5c032e90c678313e9b42933dbbaada82b7f7d08
This commit is contained in:
Scott Zhu
TensorFlower Gardener
parent
6bee0d45f8
commit
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@ -62,7 +62,6 @@ py_test(
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"//tensorflow/python:dtypes",
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"//tensorflow/python:framework_ops",
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"//tensorflow/python:sparse_tensor",
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"//tensorflow/python/keras",
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"//tensorflow/python/saved_model:signature_constants",
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],
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)
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@ -97,7 +96,6 @@ py_test(
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"//tensorflow/python:framework_ops",
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"//tensorflow/python:framework_test_lib",
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"//tensorflow/python:sparse_tensor",
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"//tensorflow/python/keras",
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"//tensorflow/python/saved_model:signature_constants",
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"//tensorflow/python/saved_model:signature_def_utils",
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],
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@ -26,9 +26,9 @@ from tensorflow.python.framework import constant_op
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from tensorflow.python.framework import dtypes
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from tensorflow.python.framework import ops
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from tensorflow.python.framework import sparse_tensor
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from tensorflow.python.keras import metrics as metrics_module
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from tensorflow.python.ops import array_ops
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from tensorflow.python.ops import control_flow_ops
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from tensorflow.python.ops import metrics as metrics_module
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from tensorflow.python.platform import test
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from tensorflow.python.saved_model import signature_constants
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from tensorflow.python.saved_model.model_utils import export_output as export_output_lib
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@ -244,10 +244,9 @@ class SupervisedOutputTest(test.TestCase):
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with context.graph_mode():
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loss = {'my_loss': constant_op.constant([0])}
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predictions = {u'output1': constant_op.constant(['foo'])}
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metric_obj = metrics_module.Mean()
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metric_obj.update_state(constant_op.constant([0]))
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mean, update_op = metrics_module.mean_tensor(constant_op.constant([0]))
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metrics = {
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'metrics': metric_obj,
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'metrics': (mean, update_op),
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'metrics2': (constant_op.constant([0]), constant_op.constant([10]))
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}
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@ -256,7 +255,7 @@ class SupervisedOutputTest(test.TestCase):
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self.assertEqual(
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outputter.predictions['predictions/output1'], predictions['output1'])
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self.assertEqual(outputter.metrics['metrics/update_op'].name,
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'metric_op_wrapper:0')
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'mean/update_op:0')
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self.assertEqual(
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outputter.metrics['metrics2/update_op'], metrics['metrics2'][1])
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@ -267,14 +266,14 @@ class SupervisedOutputTest(test.TestCase):
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self.assertEqual(
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outputter.predictions, {'predictions': predictions['output1']})
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self.assertEqual(outputter.metrics['metrics/update_op'].name,
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'metric_op_wrapper_1:0')
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'mean/update_op:0')
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def test_supervised_outputs_none(self):
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outputter = MockSupervisedOutput(
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constant_op.constant([0]), None, None)
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self.assertEqual(len(outputter.loss), 1)
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self.assertEqual(outputter.predictions, None)
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self.assertEqual(outputter.metrics, None)
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self.assertLen(outputter.loss, 1)
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self.assertIsNone(outputter.predictions)
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self.assertIsNone(outputter.metrics)
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def test_supervised_outputs_invalid(self):
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with self.assertRaisesRegexp(ValueError, 'predictions output value must'):
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@ -291,11 +290,9 @@ class SupervisedOutputTest(test.TestCase):
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with context.graph_mode():
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loss = {('my', 'loss'): constant_op.constant([0])}
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predictions = {(u'output1', '2'): constant_op.constant(['foo'])}
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metric_obj = metrics_module.Mean()
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metric_obj.update_state(constant_op.constant([0]))
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mean, update_op = metrics_module.mean_tensor(constant_op.constant([0]))
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metrics = {
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('metrics', '1'):
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metric_obj,
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('metrics', '1'): (mean, update_op),
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('metrics', '2'): (constant_op.constant([0]),
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constant_op.constant([10]))
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}
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@ -316,10 +313,9 @@ class SupervisedOutputTest(test.TestCase):
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with context.graph_mode():
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loss = {'loss': constant_op.constant([0])}
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predictions = {u'predictions': constant_op.constant(['foo'])}
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metric_obj = metrics_module.Mean()
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metric_obj.update_state(constant_op.constant([0]))
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mean, update_op = metrics_module.mean_tensor(constant_op.constant([0]))
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metrics = {
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'metrics_1': metric_obj,
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'metrics_1': (mean, update_op),
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'metrics_2': (constant_op.constant([0]), constant_op.constant([10]))
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}
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@ -337,10 +333,9 @@ class SupervisedOutputTest(test.TestCase):
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with context.graph_mode():
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loss = {'my_loss': constant_op.constant([0])}
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predictions = {u'output1': constant_op.constant(['foo'])}
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metric_obj = metrics_module.Mean()
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metric_obj.update_state(constant_op.constant([0]))
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mean, update_op = metrics_module.mean_tensor(constant_op.constant([0]))
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metrics = {
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'metrics_1': metric_obj,
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'metrics_1': (mean, update_op),
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'metrics_2': (constant_op.constant([0]), constant_op.constant([10]))
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}
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@ -350,11 +345,11 @@ class SupervisedOutputTest(test.TestCase):
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'labels': constant_op.constant(100, shape=(100, 1))}
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sig_def = outputter.as_signature_def(receiver)
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self.assertTrue('loss/my_loss' in sig_def.outputs)
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self.assertTrue('metrics_1/value' in sig_def.outputs)
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self.assertTrue('metrics_2/value' in sig_def.outputs)
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self.assertTrue('predictions/output1' in sig_def.outputs)
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self.assertTrue('features' in sig_def.inputs)
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self.assertIn('loss/my_loss', sig_def.outputs)
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self.assertIn('metrics_1/value', sig_def.outputs)
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self.assertIn('metrics_2/value', sig_def.outputs)
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self.assertIn('predictions/output1', sig_def.outputs)
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self.assertIn('features', sig_def.inputs)
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def test_eval_signature_def(self):
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with context.graph_mode():
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@ -367,38 +362,36 @@ class SupervisedOutputTest(test.TestCase):
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'labels': constant_op.constant(100, shape=(100, 1))}
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sig_def = outputter.as_signature_def(receiver)
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self.assertTrue('loss/my_loss' in sig_def.outputs)
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self.assertFalse('metrics/value' in sig_def.outputs)
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self.assertTrue('predictions/output1' in sig_def.outputs)
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self.assertTrue('features' in sig_def.inputs)
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self.assertIn('loss/my_loss', sig_def.outputs)
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self.assertNotIn('metrics/value', sig_def.outputs)
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self.assertIn('predictions/output1', sig_def.outputs)
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self.assertIn('features', sig_def.inputs)
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def test_metric_op_is_tensor(self):
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"""Tests that ops.Operation is wrapped by a tensor for metric_ops."""
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with context.graph_mode():
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loss = {'my_loss': constant_op.constant([0])}
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predictions = {u'output1': constant_op.constant(['foo'])}
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metric_obj = metrics_module.Mean()
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metric_obj.update_state(constant_op.constant([0]))
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mean, update_op = metrics_module.mean_tensor(constant_op.constant([0]))
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metrics = {
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'metrics_1': metric_obj,
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'metrics_1': (mean, update_op),
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'metrics_2': (constant_op.constant([0]), control_flow_ops.no_op())
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}
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outputter = MockSupervisedOutput(loss, predictions, metrics)
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self.assertTrue(outputter.metrics['metrics_1/update_op'].name.startswith(
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'metric_op_wrapper'))
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self.assertTrue(
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isinstance(outputter.metrics['metrics_1/update_op'], ops.Tensor))
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self.assertTrue(
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isinstance(outputter.metrics['metrics_1/value'], ops.Tensor))
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'mean/update_op'))
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self.assertIsInstance(
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outputter.metrics['metrics_1/update_op'], ops.Tensor)
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self.assertIsInstance(outputter.metrics['metrics_1/value'], ops.Tensor)
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self.assertEqual(outputter.metrics['metrics_2/value'],
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metrics['metrics_2'][0])
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self.assertTrue(outputter.metrics['metrics_2/update_op'].name.startswith(
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'metric_op_wrapper'))
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self.assertTrue(
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isinstance(outputter.metrics['metrics_2/update_op'], ops.Tensor))
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self.assertIsInstance(
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outputter.metrics['metrics_2/update_op'], ops.Tensor)
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if __name__ == '__main__':
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