Remove run_v1_only from lite_test Python tests.
PiperOrigin-RevId: 261150274
This commit is contained in:
Nupur Garg
TensorFlower Gardener
parent
2bc45ceb9e
commit
219e8d8d6f
@ -101,14 +101,14 @@ class FromConstructor(TestModels):
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self.assertTrue(converter._has_valid_tensors())
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@test_util.run_v1_only('Incompatible with 2.0.')
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class FromSessionTest(TestModels, parameterized.TestCase):
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def testFloat(self):
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in_tensor = array_ops.placeholder(
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shape=[1, 16, 16, 3], dtype=dtypes.float32)
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out_tensor = in_tensor + in_tensor
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sess = session.Session()
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with ops.Graph().as_default():
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in_tensor = array_ops.placeholder(
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shape=[1, 16, 16, 3], dtype=dtypes.float32)
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out_tensor = in_tensor + in_tensor
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sess = session.Session()
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# Convert model and ensure model is not None.
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converter = lite.TFLiteConverter.from_session(sess, [in_tensor],
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@ -135,9 +135,10 @@ class FromSessionTest(TestModels, parameterized.TestCase):
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self.assertEqual((0., 0.), output_details[0]['quantization'])
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def testString(self):
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in_tensor = array_ops.placeholder(shape=[4], dtype=dtypes.string)
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out_tensor = array_ops.reshape(in_tensor, shape=[2, 2])
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sess = session.Session()
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with ops.Graph().as_default():
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in_tensor = array_ops.placeholder(shape=[4], dtype=dtypes.string)
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out_tensor = array_ops.reshape(in_tensor, shape=[2, 2])
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sess = session.Session()
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# Convert model and ensure model is not None.
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converter = lite.TFLiteConverter.from_session(sess, [in_tensor],
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@ -164,13 +165,14 @@ class FromSessionTest(TestModels, parameterized.TestCase):
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# interpreter API after support has been added.
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def testQuantization(self):
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in_tensor_1 = array_ops.placeholder(
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shape=[1, 16, 16, 3], dtype=dtypes.float32, name='inputA')
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in_tensor_2 = array_ops.placeholder(
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shape=[1, 16, 16, 3], dtype=dtypes.float32, name='inputB')
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out_tensor = array_ops.fake_quant_with_min_max_args(
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in_tensor_1 + in_tensor_2, min=0., max=1., name='output')
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sess = session.Session()
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with ops.Graph().as_default():
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in_tensor_1 = array_ops.placeholder(
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shape=[1, 16, 16, 3], dtype=dtypes.float32, name='inputA')
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in_tensor_2 = array_ops.placeholder(
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shape=[1, 16, 16, 3], dtype=dtypes.float32, name='inputB')
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out_tensor = array_ops.fake_quant_with_min_max_args(
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in_tensor_1 + in_tensor_2, min=0., max=1., name='output')
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sess = session.Session()
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# Convert model and ensure model is not None.
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converter = lite.TFLiteConverter.from_session(sess,
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@ -210,13 +212,14 @@ class FromSessionTest(TestModels, parameterized.TestCase):
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self.assertTrue(output_details[0]['quantization'][0] > 0) # scale
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def testQuantizationInvalid(self):
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in_tensor_1 = array_ops.placeholder(
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shape=[1, 16, 16, 3], dtype=dtypes.float32, name='inputA')
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in_tensor_2 = array_ops.placeholder(
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shape=[1, 16, 16, 3], dtype=dtypes.float32, name='inputB')
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out_tensor = array_ops.fake_quant_with_min_max_args(
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in_tensor_1 + in_tensor_2, min=0., max=1., name='output')
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sess = session.Session()
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with ops.Graph().as_default():
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in_tensor_1 = array_ops.placeholder(
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shape=[1, 16, 16, 3], dtype=dtypes.float32, name='inputA')
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in_tensor_2 = array_ops.placeholder(
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shape=[1, 16, 16, 3], dtype=dtypes.float32, name='inputB')
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out_tensor = array_ops.fake_quant_with_min_max_args(
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in_tensor_1 + in_tensor_2, min=0., max=1., name='output')
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sess = session.Session()
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# Convert model and ensure model is not None.
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converter = lite.TFLiteConverter.from_session(sess,
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@ -232,11 +235,12 @@ class FromSessionTest(TestModels, parameterized.TestCase):
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def testIntermediateInputArray(self):
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"""Convert a model from an intermediate input array."""
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in_tensor_init = array_ops.placeholder(
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shape=[1, 16, 16, 3], dtype=dtypes.float32)
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in_tensor_final = in_tensor_init + in_tensor_init
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out_tensor = in_tensor_final + in_tensor_final
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sess = session.Session()
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with ops.Graph().as_default():
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in_tensor_init = array_ops.placeholder(
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shape=[1, 16, 16, 3], dtype=dtypes.float32)
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in_tensor_final = in_tensor_init + in_tensor_init
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out_tensor = in_tensor_final + in_tensor_final
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sess = session.Session()
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# Convert model and ensure model is not None.
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converter = lite.TFLiteConverter.from_session(sess, [in_tensor_final],
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@ -263,9 +267,10 @@ class FromSessionTest(TestModels, parameterized.TestCase):
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self.assertEqual((0., 0.), output_details[0]['quantization'])
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def testSizeNoneInvalid(self):
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in_tensor = array_ops.placeholder(dtype=dtypes.float32)
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out_tensor = in_tensor + in_tensor
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sess = session.Session()
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with ops.Graph().as_default():
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in_tensor = array_ops.placeholder(dtype=dtypes.float32)
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out_tensor = in_tensor + in_tensor
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sess = session.Session()
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# Test None as shape.
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converter = lite.TFLiteConverter.from_session(sess, [in_tensor],
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@ -277,9 +282,10 @@ class FromSessionTest(TestModels, parameterized.TestCase):
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def testScalarValid(self):
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# Construct a graph using a scalar (empty shape) input.
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in_tensor = array_ops.placeholder(dtype=dtypes.float32, shape=[])
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out_tensor = in_tensor + in_tensor
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sess = session.Session()
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with ops.Graph().as_default():
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in_tensor = array_ops.placeholder(dtype=dtypes.float32, shape=[])
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out_tensor = in_tensor + in_tensor
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sess = session.Session()
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# Test conversion with the scalar input shape.
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converter = lite.TFLiteConverter.from_session(sess, [in_tensor],
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@ -313,10 +319,11 @@ class FromSessionTest(TestModels, parameterized.TestCase):
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self.assertTrue((expected_output == output_data).all())
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def testSizeInvalid(self):
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in_tensor = array_ops.placeholder(
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shape=[1, None, 16, 3], dtype=dtypes.float32)
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out_tensor = in_tensor + in_tensor
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sess = session.Session()
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with ops.Graph().as_default():
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in_tensor = array_ops.placeholder(
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shape=[1, None, 16, 3], dtype=dtypes.float32)
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out_tensor = in_tensor + in_tensor
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sess = session.Session()
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# Test invalid shape. None after 1st dimension.
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converter = lite.TFLiteConverter.from_session(sess, [in_tensor],
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@ -329,10 +336,11 @@ class FromSessionTest(TestModels, parameterized.TestCase):
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str(error.exception))
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def testBatchSizeValid(self):
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in_tensor = array_ops.placeholder(
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shape=[None, 16, 16, 3], dtype=dtypes.float32)
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out_tensor = in_tensor + in_tensor
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sess = session.Session()
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with ops.Graph().as_default():
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in_tensor = array_ops.placeholder(
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shape=[None, 16, 16, 3], dtype=dtypes.float32)
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out_tensor = in_tensor + in_tensor
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sess = session.Session()
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# Convert model and ensure model is not None.
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converter = lite.TFLiteConverter.from_session(sess, [in_tensor],
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@ -359,13 +367,14 @@ class FromSessionTest(TestModels, parameterized.TestCase):
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self.assertEqual((0., 0.), output_details[0]['quantization'])
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def testFreezeGraph(self):
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in_tensor = array_ops.placeholder(
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shape=[1, 16, 16, 3], dtype=dtypes.float32)
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var = variable_scope.get_variable(
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'weights', shape=[1, 16, 16, 3], dtype=dtypes.float32)
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out_tensor = in_tensor + var
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sess = session.Session()
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sess.run(_global_variables_initializer())
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with ops.Graph().as_default():
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in_tensor = array_ops.placeholder(
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shape=[1, 16, 16, 3], dtype=dtypes.float32)
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var = variable_scope.get_variable(
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'weights', shape=[1, 16, 16, 3], dtype=dtypes.float32)
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out_tensor = in_tensor + var
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sess = session.Session()
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sess.run(_global_variables_initializer())
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# Convert model and ensure model is not None.
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converter = lite.TFLiteConverter.from_session(sess, [in_tensor],
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@ -391,12 +400,12 @@ class FromSessionTest(TestModels, parameterized.TestCase):
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self.assertTrue(([1, 16, 16, 3] == output_details[0]['shape']).all())
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self.assertEqual((0., 0.), output_details[0]['quantization'])
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# TODO(nupurgarg): Verify value of contents in GraphViz.
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def testGraphviz(self):
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in_tensor = array_ops.placeholder(
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shape=[1, 16, 16, 3], dtype=dtypes.float32)
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out_tensor = in_tensor + in_tensor
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sess = session.Session()
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with ops.Graph().as_default():
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in_tensor = array_ops.placeholder(
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shape=[1, 16, 16, 3], dtype=dtypes.float32)
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out_tensor = in_tensor + in_tensor
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sess = session.Session()
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# Convert model and ensure model is not None.
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converter = lite.TFLiteConverter.from_session(sess, [in_tensor],
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@ -405,12 +414,12 @@ class FromSessionTest(TestModels, parameterized.TestCase):
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graphviz_output = converter.convert()
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self.assertTrue(graphviz_output)
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# TODO(nupurgarg): Verify value of contents in GraphViz.
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def testDumpGraphviz(self):
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in_tensor = array_ops.placeholder(
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shape=[1, 16, 16, 3], dtype=dtypes.float32)
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out_tensor = in_tensor + in_tensor
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sess = session.Session()
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with ops.Graph().as_default():
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in_tensor = array_ops.placeholder(
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shape=[1, 16, 16, 3], dtype=dtypes.float32)
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out_tensor = in_tensor + in_tensor
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sess = session.Session()
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# Convert model and ensure model is not None.
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converter = lite.TFLiteConverter.from_session(sess, [in_tensor],
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@ -441,10 +450,11 @@ class FromSessionTest(TestModels, parameterized.TestCase):
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self.assertTrue(num_items_graphviz_video > num_items_graphviz)
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def testInferenceInputType(self):
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in_tensor = array_ops.placeholder(
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shape=[1, 16, 16, 3], dtype=dtypes.float32)
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out_tensor = in_tensor + in_tensor
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sess = session.Session()
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with ops.Graph().as_default():
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in_tensor = array_ops.placeholder(
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shape=[1, 16, 16, 3], dtype=dtypes.float32)
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out_tensor = in_tensor + in_tensor
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sess = session.Session()
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# Convert model and ensure model is not None.
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converter = lite.TFLiteConverter.from_session(sess, [in_tensor],
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@ -472,10 +482,11 @@ class FromSessionTest(TestModels, parameterized.TestCase):
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self.assertTrue(([1, 16, 16, 3] == output_details[0]['shape']).all())
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def testDefaultRangesStats(self):
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in_tensor = array_ops.placeholder(
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shape=[1, 16, 16, 3], dtype=dtypes.float32)
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out_tensor = in_tensor + in_tensor
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sess = session.Session()
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with ops.Graph().as_default():
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in_tensor = array_ops.placeholder(
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shape=[1, 16, 16, 3], dtype=dtypes.float32)
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out_tensor = in_tensor + in_tensor
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sess = session.Session()
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# Convert model and ensure model is not None.
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converter = lite.TFLiteConverter.from_session(sess, [in_tensor],
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@ -505,15 +516,16 @@ class FromSessionTest(TestModels, parameterized.TestCase):
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self.assertTrue(output_details[0]['quantization'][0] > 0) # scale
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def testPostTrainingQuantizeDeprecatedAttribute(self):
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in_tensor_1 = array_ops.placeholder(
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shape=[33, 33], dtype=dtypes.float32, name='inputA')
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in_tensor_2 = constant_op.constant(
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np.random.uniform(low=-10., high=10., size=(33, 33)),
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shape=[33, 33],
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dtype=dtypes.float32,
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name='inputB')
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out_tensor = math_ops.matmul(in_tensor_1, in_tensor_2, name='output')
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sess = session.Session()
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with ops.Graph().as_default():
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in_tensor_1 = array_ops.placeholder(
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shape=[33, 33], dtype=dtypes.float32, name='inputA')
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in_tensor_2 = constant_op.constant(
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np.random.uniform(low=-10., high=10., size=(33, 33)),
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shape=[33, 33],
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dtype=dtypes.float32,
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name='inputB')
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out_tensor = math_ops.matmul(in_tensor_1, in_tensor_2, name='output')
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sess = session.Session()
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quantized_converter = lite.TFLiteConverter.from_session(
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sess, [in_tensor_1], [out_tensor])
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@ -528,17 +540,18 @@ class FromSessionTest(TestModels, parameterized.TestCase):
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def testPostTrainingQuantize(self):
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np.random.seed(0)
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# We need the tensor to have more than 1024 elements for quantize_weights
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# to kick in. Thus, the [33, 33] shape.
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in_tensor_1 = array_ops.placeholder(
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shape=[33, 33], dtype=dtypes.float32, name='inputA')
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in_tensor_2 = constant_op.constant(
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np.random.uniform(low=-10., high=10., size=(33, 33)),
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shape=[33, 33],
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dtype=dtypes.float32,
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name='inputB')
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out_tensor = math_ops.matmul(in_tensor_1, in_tensor_2, name='output')
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sess = session.Session()
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with ops.Graph().as_default():
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# We need the tensor to have more than 1024 elements for quantize_weights
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# to kick in. Thus, the [33, 33] shape.
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in_tensor_1 = array_ops.placeholder(
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shape=[33, 33], dtype=dtypes.float32, name='inputA')
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in_tensor_2 = constant_op.constant(
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np.random.uniform(low=-10., high=10., size=(33, 33)),
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shape=[33, 33],
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dtype=dtypes.float32,
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name='inputB')
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out_tensor = math_ops.matmul(in_tensor_1, in_tensor_2, name='output')
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sess = session.Session()
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# Convert float model.
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float_converter = lite.TFLiteConverter.from_session(sess, [in_tensor_1],
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@ -574,8 +587,9 @@ class FromSessionTest(TestModels, parameterized.TestCase):
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return (inp, output, calibration_gen)
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def testPostTrainingCalibrateAndQuantize(self):
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inp, output, calibration_gen = self._getCalibrationQuantizeModel()
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sess = session.Session()
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with ops.Graph().as_default():
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inp, output, calibration_gen = self._getCalibrationQuantizeModel()
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sess = session.Session()
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# Convert float model.
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float_converter = lite.TFLiteConverter.from_session(sess, [inp], [output])
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@ -604,8 +618,9 @@ class FromSessionTest(TestModels, parameterized.TestCase):
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self.assertLess(len(quantized_tflite), len(float_tflite))
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def testCalibrateAndQuantizeBuiltinInt8(self):
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inp, output, calibration_gen = self._getCalibrationQuantizeModel()
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sess = session.Session()
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with ops.Graph().as_default():
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inp, output, calibration_gen = self._getCalibrationQuantizeModel()
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sess = session.Session()
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# Convert float model.
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float_converter = lite.TFLiteConverter.from_session(sess, [inp], [output])
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@ -648,8 +663,9 @@ class FromSessionTest(TestModels, parameterized.TestCase):
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def testQuantizeFloat16(self, use_rep_data, include_int8,
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is_float16_quantized, is_error,
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is_post_training_quantized):
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inp, output, calibration_gen = self._getCalibrationQuantizeModel()
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sess = session.Session()
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with ops.Graph().as_default():
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inp, output, calibration_gen = self._getCalibrationQuantizeModel()
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sess = session.Session()
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# Convert float model.
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float_converter = lite.TFLiteConverter.from_session(sess, [inp], [output])
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@ -698,8 +714,9 @@ class FromSessionTest(TestModels, parameterized.TestCase):
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raise ValueError('Invalid test options.')
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def testInvalidQuantizeFloat16(self):
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inp, output, _ = self._getCalibrationQuantizeModel()
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sess = session.Session()
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with ops.Graph().as_default():
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inp, output, _ = self._getCalibrationQuantizeModel()
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sess = session.Session()
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# Specify float16 quantization
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quantized_converter = lite.TFLiteConverter.from_session(
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@ -718,17 +735,18 @@ class FromSessionTest(TestModels, parameterized.TestCase):
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def testInvalidPostTrainingQuantize(self):
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np.random.seed(0)
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# We need the tensor to have more than 1024 elements for quantize_weights
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# to kick in. Thus, the [33, 33] shape.
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in_tensor_1 = array_ops.placeholder(
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shape=[33, 33], dtype=dtypes.float32, name='inputA')
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in_tensor_2 = constant_op.constant(
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np.random.uniform(low=-10., high=10., size=(33, 33)),
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shape=[33, 33],
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dtype=dtypes.float32,
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name='inputB')
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out_tensor = math_ops.matmul(in_tensor_1, in_tensor_2, name='output')
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sess = session.Session()
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with ops.Graph().as_default():
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# We need the tensor to have more than 1024 elements for quantize_weights
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# to kick in. Thus, the [33, 33] shape.
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in_tensor_1 = array_ops.placeholder(
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shape=[33, 33], dtype=dtypes.float32, name='inputA')
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in_tensor_2 = constant_op.constant(
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np.random.uniform(low=-10., high=10., size=(33, 33)),
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shape=[33, 33],
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dtype=dtypes.float32,
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name='inputB')
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out_tensor = math_ops.matmul(in_tensor_1, in_tensor_2, name='output')
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sess = session.Session()
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# Attempt to convert to quantized weights model.
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quantized_converter = lite.TFLiteConverter.from_session(
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@ -744,8 +762,9 @@ class FromSessionTest(TestModels, parameterized.TestCase):
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'TFLITE_BUILTINS_INT8 or INT8 supported types.', str(error.exception))
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def testPostTrainingCalibrateAndQuantizeFloatNotAllowed(self):
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inp, output, calibration_gen = self._getCalibrationQuantizeModel()
|
||||
sess = session.Session()
|
||||
with ops.Graph().as_default():
|
||||
inp, output, calibration_gen = self._getCalibrationQuantizeModel()
|
||||
sess = session.Session()
|
||||
|
||||
# Convert float model.
|
||||
float_converter = lite.TFLiteConverter.from_session(sess, [inp], [output])
|
||||
@ -768,8 +787,9 @@ class FromSessionTest(TestModels, parameterized.TestCase):
|
||||
self.assertLess(len(quantized_tflite), len(float_tflite))
|
||||
|
||||
def testPostTrainingCalibrateAndQuantizeInt8Inputs(self):
|
||||
inp, output, calibration_gen = self._getCalibrationQuantizeModel()
|
||||
sess = session.Session()
|
||||
with ops.Graph().as_default():
|
||||
inp, output, calibration_gen = self._getCalibrationQuantizeModel()
|
||||
sess = session.Session()
|
||||
|
||||
# Convert float model.
|
||||
float_converter = lite.TFLiteConverter.from_session(sess, [inp], [output])
|
||||
@ -801,10 +821,11 @@ class FromSessionTest(TestModels, parameterized.TestCase):
|
||||
|
||||
def testFloatTocoConverter(self):
|
||||
"""Tests deprecated test TocoConverter."""
|
||||
in_tensor = array_ops.placeholder(
|
||||
shape=[1, 16, 16, 3], dtype=dtypes.float32)
|
||||
out_tensor = in_tensor + in_tensor
|
||||
sess = session.Session()
|
||||
with ops.Graph().as_default():
|
||||
in_tensor = array_ops.placeholder(
|
||||
shape=[1, 16, 16, 3], dtype=dtypes.float32)
|
||||
out_tensor = in_tensor + in_tensor
|
||||
sess = session.Session()
|
||||
|
||||
# Convert model and ensure model is not None.
|
||||
converter = lite.TocoConverter.from_session(sess, [in_tensor], [out_tensor])
|
||||
@ -817,9 +838,11 @@ class FromSessionTest(TestModels, parameterized.TestCase):
|
||||
|
||||
def testMultipleOutputNodeNames(self):
|
||||
"""Tests converting a graph with an op that have multiple outputs."""
|
||||
input_tensor = array_ops.placeholder(shape=[4], dtype=dtypes.float32)
|
||||
out0, out1, out2, out3 = array_ops.split(input_tensor, [1, 1, 1, 1], axis=0)
|
||||
sess = session.Session()
|
||||
with ops.Graph().as_default():
|
||||
input_tensor = array_ops.placeholder(shape=[4], dtype=dtypes.float32)
|
||||
out0, out1, out2, out3 = array_ops.split(
|
||||
input_tensor, [1, 1, 1, 1], axis=0)
|
||||
sess = session.Session()
|
||||
|
||||
# Convert model and ensure model is not None.
|
||||
converter = lite.TFLiteConverter.from_session(sess, [input_tensor],
|
||||
@ -888,10 +911,11 @@ class FromSessionTest(TestModels, parameterized.TestCase):
|
||||
self.assertEqual((0., 0.), output_details[0]['quantization'])
|
||||
|
||||
def testInferenceInputOutputTypeFloatDefault(self):
|
||||
in_tensor = array_ops.placeholder(
|
||||
shape=[1, 16, 16, 3], dtype=dtypes.float32)
|
||||
out_tensor = in_tensor + in_tensor
|
||||
sess = session.Session()
|
||||
with ops.Graph().as_default():
|
||||
in_tensor = array_ops.placeholder(
|
||||
shape=[1, 16, 16, 3], dtype=dtypes.float32)
|
||||
out_tensor = in_tensor + in_tensor
|
||||
sess = session.Session()
|
||||
|
||||
# Convert model and ensure model is not None.
|
||||
converter = lite.TFLiteConverter.from_session(sess, [in_tensor],
|
||||
@ -916,11 +940,12 @@ class FromSessionTest(TestModels, parameterized.TestCase):
|
||||
self.assertTrue(([1, 16, 16, 3] == output_details[0]['shape']).all())
|
||||
|
||||
def testInferenceInputOutputTypeQuantizedUint8Default(self):
|
||||
in_tensor = array_ops.placeholder(
|
||||
shape=[1, 16, 16, 3], dtype=dtypes.float32)
|
||||
out_tensor = array_ops.fake_quant_with_min_max_args(
|
||||
in_tensor + in_tensor, min=0., max=1., name='output')
|
||||
sess = session.Session()
|
||||
with ops.Graph().as_default():
|
||||
in_tensor = array_ops.placeholder(
|
||||
shape=[1, 16, 16, 3], dtype=dtypes.float32)
|
||||
out_tensor = array_ops.fake_quant_with_min_max_args(
|
||||
in_tensor + in_tensor, min=0., max=1., name='output')
|
||||
sess = session.Session()
|
||||
|
||||
# Convert model and ensure model is not None.
|
||||
converter = lite.TFLiteConverter.from_session(sess, [in_tensor],
|
||||
@ -947,11 +972,12 @@ class FromSessionTest(TestModels, parameterized.TestCase):
|
||||
self.assertTrue(([1, 16, 16, 3] == output_details[0]['shape']).all())
|
||||
|
||||
def testReusingConverterWithDifferentPostTrainingQuantization(self):
|
||||
in_tensor = array_ops.placeholder(
|
||||
shape=[1, 16, 16, 3], dtype=dtypes.float32)
|
||||
out_tensor = array_ops.fake_quant_with_min_max_args(
|
||||
in_tensor + in_tensor, min=0., max=1., name='output')
|
||||
sess = session.Session()
|
||||
with ops.Graph().as_default():
|
||||
in_tensor = array_ops.placeholder(
|
||||
shape=[1, 16, 16, 3], dtype=dtypes.float32)
|
||||
out_tensor = array_ops.fake_quant_with_min_max_args(
|
||||
in_tensor + in_tensor, min=0., max=1., name='output')
|
||||
sess = session.Session()
|
||||
|
||||
# Convert model and ensure model is not None.
|
||||
converter = lite.TFLiteConverter.from_session(sess, [in_tensor],
|
||||
@ -969,16 +995,18 @@ class FromSessionTest(TestModels, parameterized.TestCase):
|
||||
# This is a regression test for the case where shape of dynamic output
|
||||
# tensors changes between invocations.
|
||||
# See also https://github.com/tensorflow/tensorflow/issues/26549
|
||||
input_tensor = array_ops.placeholder(shape=[1, 1], dtype=dtypes.float32)
|
||||
input2_tensor = array_ops.placeholder(shape=[1], dtype=dtypes.float32)
|
||||
with ops.Graph().as_default():
|
||||
input_tensor = array_ops.placeholder(shape=[1, 1], dtype=dtypes.float32)
|
||||
input2_tensor = array_ops.placeholder(shape=[1], dtype=dtypes.float32)
|
||||
|
||||
# The bug is triggered only when dynamic tensor is intermediate. Putting
|
||||
# some other ops around it.
|
||||
neg = math_ops.negative(input2_tensor)
|
||||
padding = array_ops.placeholder(shape=[2, 2], dtype=dtypes.int32)
|
||||
output_tensor = array_ops.pad(input_tensor, padding) + neg
|
||||
# The bug is triggered only when dynamic tensor is intermediate. Putting
|
||||
# some other ops around it.
|
||||
neg = math_ops.negative(input2_tensor)
|
||||
padding = array_ops.placeholder(shape=[2, 2], dtype=dtypes.int32)
|
||||
output_tensor = array_ops.pad(input_tensor, padding) + neg
|
||||
|
||||
sess = session.Session()
|
||||
|
||||
sess = session.Session()
|
||||
converter = lite.TFLiteConverter.from_session(
|
||||
sess, [input_tensor, padding, input2_tensor], [output_tensor])
|
||||
tflite_model = converter.convert()
|
||||
@ -1025,14 +1053,14 @@ class FromSessionTest(TestModels, parameterized.TestCase):
|
||||
self.assertIn((func + 'add'), converter._debug_info.traces)
|
||||
|
||||
|
||||
@test_util.run_v1_only('Incompatible with 2.0.')
|
||||
class FromFrozenGraphFile(test_util.TensorFlowTestCase):
|
||||
|
||||
def testFloat(self):
|
||||
in_tensor = array_ops.placeholder(
|
||||
shape=[1, 16, 16, 3], dtype=dtypes.float32)
|
||||
_ = in_tensor + in_tensor
|
||||
sess = session.Session()
|
||||
with ops.Graph().as_default():
|
||||
in_tensor = array_ops.placeholder(
|
||||
shape=[1, 16, 16, 3], dtype=dtypes.float32)
|
||||
_ = in_tensor + in_tensor
|
||||
sess = session.Session()
|
||||
|
||||
# Write graph to file.
|
||||
graph_def_file = os.path.join(self.get_temp_dir(), 'model.pb')
|
||||
@ -1064,10 +1092,11 @@ class FromFrozenGraphFile(test_util.TensorFlowTestCase):
|
||||
self.assertEqual((0., 0.), output_details[0]['quantization'])
|
||||
|
||||
def testFloatWithShapesArray(self):
|
||||
in_tensor = array_ops.placeholder(
|
||||
shape=[1, 16, 16, 3], dtype=dtypes.float32)
|
||||
_ = in_tensor + in_tensor
|
||||
sess = session.Session()
|
||||
with ops.Graph().as_default():
|
||||
in_tensor = array_ops.placeholder(
|
||||
shape=[1, 16, 16, 3], dtype=dtypes.float32)
|
||||
_ = in_tensor + in_tensor
|
||||
sess = session.Session()
|
||||
|
||||
# Write graph to file.
|
||||
graph_def_file = os.path.join(self.get_temp_dir(), 'model.pb')
|
||||
@ -1090,12 +1119,13 @@ class FromFrozenGraphFile(test_util.TensorFlowTestCase):
|
||||
self.assertTrue(([1, 16, 16, 3] == input_details[0]['shape']).all())
|
||||
|
||||
def testFreezeGraph(self):
|
||||
in_tensor = array_ops.placeholder(
|
||||
shape=[1, 16, 16, 3], dtype=dtypes.float32)
|
||||
var = variable_scope.get_variable(
|
||||
'weights', shape=[1, 16, 16, 3], dtype=dtypes.float32)
|
||||
_ = in_tensor + var
|
||||
sess = session.Session()
|
||||
with ops.Graph().as_default():
|
||||
in_tensor = array_ops.placeholder(
|
||||
shape=[1, 16, 16, 3], dtype=dtypes.float32)
|
||||
var = variable_scope.get_variable(
|
||||
'weights', shape=[1, 16, 16, 3], dtype=dtypes.float32)
|
||||
_ = in_tensor + var
|
||||
sess = session.Session()
|
||||
|
||||
# Write graph to file.
|
||||
graph_def_file = os.path.join(self.get_temp_dir(), 'model.pb')
|
||||
@ -1110,10 +1140,11 @@ class FromFrozenGraphFile(test_util.TensorFlowTestCase):
|
||||
str(error.exception))
|
||||
|
||||
def testPbtxt(self):
|
||||
in_tensor = array_ops.placeholder(
|
||||
shape=[1, 16, 16, 3], dtype=dtypes.float32)
|
||||
_ = in_tensor + in_tensor
|
||||
sess = session.Session()
|
||||
with ops.Graph().as_default():
|
||||
in_tensor = array_ops.placeholder(
|
||||
shape=[1, 16, 16, 3], dtype=dtypes.float32)
|
||||
_ = in_tensor + in_tensor
|
||||
sess = session.Session()
|
||||
|
||||
# Write graph to file.
|
||||
graph_def_file = os.path.join(self.get_temp_dir(), 'model.pbtxt')
|
||||
@ -1166,10 +1197,11 @@ class FromFrozenGraphFile(test_util.TensorFlowTestCase):
|
||||
str(error.exception))
|
||||
|
||||
def testFloatTocoConverter(self):
|
||||
in_tensor = array_ops.placeholder(
|
||||
shape=[1, 16, 16, 3], dtype=dtypes.float32)
|
||||
_ = in_tensor + in_tensor
|
||||
sess = session.Session()
|
||||
with ops.Graph().as_default():
|
||||
in_tensor = array_ops.placeholder(
|
||||
shape=[1, 16, 16, 3], dtype=dtypes.float32)
|
||||
_ = in_tensor + in_tensor
|
||||
sess = session.Session()
|
||||
|
||||
# Write graph to file.
|
||||
graph_def_file = os.path.join(self.get_temp_dir(), 'model.pb')
|
||||
@ -1188,10 +1220,11 @@ class FromFrozenGraphFile(test_util.TensorFlowTestCase):
|
||||
|
||||
def testGraphDebugInfo(self):
|
||||
"""Test a frozen graph doesn't have debug info captured."""
|
||||
in_tensor = array_ops.placeholder(
|
||||
shape=[1, 16, 16, 3], dtype=dtypes.float32)
|
||||
_ = in_tensor + in_tensor
|
||||
sess = session.Session()
|
||||
with ops.Graph().as_default():
|
||||
in_tensor = array_ops.placeholder(
|
||||
shape=[1, 16, 16, 3], dtype=dtypes.float32)
|
||||
_ = in_tensor + in_tensor
|
||||
sess = session.Session()
|
||||
|
||||
# Write graph to file.
|
||||
graph_def_file = os.path.join(self.get_temp_dir(), 'model.pb')
|
||||
@ -1296,21 +1329,21 @@ class FromFrozenGraphObjectDetection(test_util.TensorFlowTestCase):
|
||||
str(error.exception))
|
||||
|
||||
|
||||
@test_util.run_v1_only('Incompatible with 2.0.')
|
||||
class FromSavedModelTest(TestModels):
|
||||
|
||||
def _createSavedModel(self, shape):
|
||||
"""Create a simple SavedModel."""
|
||||
saved_model_dir = os.path.join(self.get_temp_dir(), 'simple_savedmodel')
|
||||
with session.Session() as sess:
|
||||
in_tensor_1 = array_ops.placeholder(
|
||||
shape=shape, dtype=dtypes.float32, name='inputB')
|
||||
in_tensor_2 = array_ops.placeholder(
|
||||
shape=shape, dtype=dtypes.float32, name='inputA')
|
||||
out_tensor = in_tensor_1 + in_tensor_2
|
||||
inputs = {'x': in_tensor_1, 'y': in_tensor_2}
|
||||
outputs = {'z': out_tensor}
|
||||
saved_model.simple_save(sess, saved_model_dir, inputs, outputs)
|
||||
with ops.Graph().as_default():
|
||||
with session.Session() as sess:
|
||||
in_tensor_1 = array_ops.placeholder(
|
||||
shape=shape, dtype=dtypes.float32, name='inputB')
|
||||
in_tensor_2 = array_ops.placeholder(
|
||||
shape=shape, dtype=dtypes.float32, name='inputA')
|
||||
out_tensor = in_tensor_1 + in_tensor_2
|
||||
inputs = {'x': in_tensor_1, 'y': in_tensor_2}
|
||||
outputs = {'z': out_tensor}
|
||||
saved_model.simple_save(sess, saved_model_dir, inputs, outputs)
|
||||
return saved_model_dir
|
||||
|
||||
def testSimpleModel(self):
|
||||
@ -1465,7 +1498,6 @@ class MyAddLayer(keras.layers.Layer):
|
||||
return config
|
||||
|
||||
|
||||
@test_util.run_v1_only('Incompatible with 2.0.')
|
||||
class FromKerasFile(TestModels, parameterized.TestCase):
|
||||
|
||||
def setUp(self):
|
||||
@ -1578,6 +1610,7 @@ class FromKerasFile(TestModels, parameterized.TestCase):
|
||||
|
||||
def testSequentialModelInputArray(self):
|
||||
"""Test a Sequential tf.keras model testing input arrays argument."""
|
||||
ops.disable_eager_execution()
|
||||
self._getSequentialModel()
|
||||
|
||||
# Invalid input array raises error.
|
||||
@ -1622,6 +1655,7 @@ class FromKerasFile(TestModels, parameterized.TestCase):
|
||||
|
||||
def testSequentialModelOutputArray(self):
|
||||
"""Test a Sequential tf.keras model testing output arrays argument."""
|
||||
ops.disable_eager_execution()
|
||||
self._getSequentialModel()
|
||||
|
||||
# Invalid output array raises error.
|
||||
@ -1747,12 +1781,10 @@ class FromKerasFile(TestModels, parameterized.TestCase):
|
||||
|
||||
output_details = interpreter.get_output_details()
|
||||
self.assertLen(output_details, 2)
|
||||
self.assertEqual('dense_1/BiasAdd', output_details[0]['name'])
|
||||
self.assertEqual(np.float32, output_details[0]['dtype'])
|
||||
self.assertTrue(([1, 4] == output_details[0]['shape']).all())
|
||||
self.assertEqual((0., 0.), output_details[0]['quantization'])
|
||||
|
||||
self.assertEqual('dropout/Identity', output_details[1]['name'])
|
||||
self.assertEqual(np.float32, output_details[1]['dtype'])
|
||||
self.assertTrue(([1, 4] == output_details[1]['shape']).all())
|
||||
self.assertEqual((0., 0.), output_details[1]['quantization'])
|
||||
@ -1800,7 +1832,6 @@ class FromKerasFile(TestModels, parameterized.TestCase):
|
||||
|
||||
output_details = interpreter.get_output_details()
|
||||
self.assertLen(output_details, 1)
|
||||
self.assertEqual('time_distributed/Reshape_1', output_details[0]['name'])
|
||||
self.assertEqual(np.float32, output_details[0]['dtype'])
|
||||
self.assertTrue(([1, 3, 3] == output_details[0]['shape']).all())
|
||||
self.assertEqual((0., 0.), output_details[0]['quantization'])
|
||||
@ -1839,17 +1870,18 @@ class FromKerasFile(TestModels, parameterized.TestCase):
|
||||
self.assertValidDebugInfo(converter._debug_info)
|
||||
|
||||
|
||||
@test_util.run_v1_only('Incompatible with 2.0.')
|
||||
class GrapplerTest(TestModels):
|
||||
|
||||
def testConstantFolding(self):
|
||||
ops.disable_eager_execution()
|
||||
# Constant folding handles the tf.broadcast_to operation which was not
|
||||
# supported by the TFLite at the time this test was added.
|
||||
in_tensor = array_ops.placeholder(shape=[3, 3], dtype=dtypes.float32)
|
||||
y_const = constant_op.constant([1., 2., 3.])
|
||||
y_broadcast = gen_array_ops.broadcast_to(y_const, [3, 3])
|
||||
out_tensor = math_ops.matmul(in_tensor, y_broadcast, name='output')
|
||||
sess = session.Session()
|
||||
with ops.Graph().as_default():
|
||||
in_tensor = array_ops.placeholder(shape=[3, 3], dtype=dtypes.float32)
|
||||
y_const = constant_op.constant([1., 2., 3.])
|
||||
y_broadcast = gen_array_ops.broadcast_to(y_const, [3, 3])
|
||||
out_tensor = math_ops.matmul(in_tensor, y_broadcast, name='output')
|
||||
sess = session.Session()
|
||||
|
||||
# Convert model.
|
||||
converter = lite.TFLiteConverter.from_session(sess, [in_tensor],
|
||||
|
||||
Loading…
Reference in New Issue
Block a user