Snap for 11041982 from 2d7e052d3203129b682419883495a6739bfadac6 to mainline-cellbroadcast-release
Change-Id: I712740e315af415de6c3317309059b707f573e7e
diff --git a/tensorflow/lite/delegates/nnapi/nnapi_delegate_test.cc b/tensorflow/lite/delegates/nnapi/nnapi_delegate_test.cc
index beccac3..448e253 100644
--- a/tensorflow/lite/delegates/nnapi/nnapi_delegate_test.cc
+++ b/tensorflow/lite/delegates/nnapi/nnapi_delegate_test.cc
@@ -37,6 +37,8 @@
using ::testing::ElementsAre;
using ::testing::ElementsAreArray;
+using ::testing::FloatNear;
+using ::testing::Matcher;
// TODO(b/110368244): figure out how to share the existing tests in kernels/ but
// with the delegation on. Also, add more unit tests to improve code coverage.
@@ -51,6 +53,21 @@
return true;
}
+auto NnapiArrayFloatNear(const std::vector<float>& values,
+ bool relaxed = false) {
+ // Uses the same tolerance as NNAPI generated tests.
+ const float atol = relaxed ? 5 * 0.0009765625f : 1e-5f;
+ const float rtol = relaxed ? 5 * 0.0009765625f : 5 * 1.1920928955078125e-7f;
+
+ std::vector<Matcher<float>> matchers;
+ matchers.reserve(values.size());
+ for (const float& v : values) {
+ const float tolerance = atol + rtol * std::abs(v);
+ matchers.emplace_back(FloatNear(v, tolerance));
+ }
+ return ElementsAreArray(matchers);
+}
+
class SingleOpModelWithNNAPI : public SingleOpModel {
public:
SingleOpModelWithNNAPI() { options_.disallow_nnapi_cpu = false; }
@@ -196,7 +213,7 @@
m.PopulateTensor<float>(m.input1(), {-2.0, 0.2, 0.7, 0.8});
m.PopulateTensor<float>(m.input2(), {0.1, 0.2, 0.3, 0.5});
ASSERT_EQ(m.Invoke(), kTfLiteOk);
- EXPECT_THAT(m.GetOutput(), ElementsAreArray({-1.9, 0.4, 1.0, 1.3}));
+ EXPECT_THAT(m.GetOutput(), NnapiArrayFloatNear({-1.9, 0.4, 1.0, 1.3}));
}
// Do a test with scalar input using no activation.
@@ -207,7 +224,7 @@
m.PopulateTensor<float>(m.input1(), {-2.0, 0.2, 0.7, 0.7});
m.PopulateTensor<float>(m.input2(), {0.1});
ASSERT_EQ(m.Invoke(), kTfLiteOk);
- EXPECT_THAT(m.GetOutput(), ElementsAreArray({-1.9, 0.3, 0.8, 0.8}));
+ EXPECT_THAT(m.GetOutput(), NnapiArrayFloatNear({-1.9, 0.3, 0.8, 0.8}));
}
// Do a test with the NN API using no activation.
@@ -220,7 +237,8 @@
m.PopulateTensor<float>(m.input1(), {-2.0, -1.0, 1.0, 2.0});
m.PopulateTensor<float>(m.input2(), {1.0, 2.0, 3.0, 4.0});
ASSERT_EQ(m.Invoke(), kTfLiteOk);
- EXPECT_THAT(m.GetOutput(), ElementsAreArray({-1.0, 1.0, 4.0, 6.0}));
+ EXPECT_THAT(m.GetOutput(),
+ NnapiArrayFloatNear({-1.0, 1.0, 4.0, 6.0}, /*relaxed=*/true));
}
// Do a test with the NN api with relu.
@@ -231,7 +249,7 @@
m.PopulateTensor<float>(m.input1(), {-2.0, 0.2, 0.7, 0.8});
m.PopulateTensor<float>(m.input2(), {0.1, 0.2, 0.3, 0.5});
ASSERT_EQ(m.Invoke(), kTfLiteOk);
- EXPECT_THAT(m.GetOutput(), ElementsAreArray({0.0, 0.4, 1.0, 1.3}));
+ EXPECT_THAT(m.GetOutput(), NnapiArrayFloatNear({0.0, 0.4, 1.0, 1.3}));
}
// Verify that resize attempts succeed.
@@ -246,7 +264,8 @@
m.PopulateTensor<float>(m.input1(), {-2.0, 0.2, 0.7, 0.8, 0.9, 0.7});
m.PopulateTensor<float>(m.input2(), {0.1, 0.2, 0.3, 0.5, 0.2, 0.8});
ASSERT_EQ(m.Invoke(), kTfLiteOk);
- EXPECT_THAT(m.GetOutput(), ElementsAreArray({-1.9, 0.4, 1.0, 1.3, 1.1, 1.5}));
+ EXPECT_THAT(m.GetOutput(),
+ NnapiArrayFloatNear({-1.9, 0.4, 1.0, 1.3, 1.1, 1.5}));
EXPECT_EQ(m.ResizeInputTensor(m.input1(), {1, 2, 2, 1}), kTfLiteOk);
EXPECT_EQ(m.ResizeInputTensor(m.input2(), {1, 2, 2, 1}), kTfLiteOk);
@@ -254,7 +273,7 @@
m.PopulateTensor<float>(m.input1(), {0.7, 0.8, 0.9, 0.7});
m.PopulateTensor<float>(m.input2(), {0.3, 0.5, 0.2, 0.8});
ASSERT_EQ(m.Invoke(), kTfLiteOk);
- EXPECT_THAT(m.GetOutput(), ElementsAreArray({1.0, 1.3, 1.1, 1.5}));
+ EXPECT_THAT(m.GetOutput(), NnapiArrayFloatNear({1.0, 1.3, 1.1, 1.5}));
}
TEST(NNAPIDelegate, ResizeDynamicBatchInputTensorsWorks) {
@@ -337,7 +356,7 @@
m.PopulateTensor<float>(m.input1(), {-2.0, 0.2, 0.7, 0.8});
m.PopulateTensor<float>(m.input2(), {0.1, 0.2, 0.3, 0.5});
ASSERT_EQ(m.Invoke(), kTfLiteOk);
- EXPECT_THAT(m.GetOutput(), ElementsAreArray({-1.9, 0.4, 1.0, 1.3}));
+ EXPECT_THAT(m.GetOutput(), NnapiArrayFloatNear({-1.9, 0.4, 1.0, 1.3}));
}
// Sanity check for the state-ful NNAPI delegate with accelerator_name
@@ -354,7 +373,7 @@
m.PopulateTensor<float>(m.input1(), {-2.0, 0.2, 0.7, 0.8});
m.PopulateTensor<float>(m.input2(), {0.1, 0.2, 0.3, 0.5});
ASSERT_EQ(m.Invoke(), kTfLiteOk);
- EXPECT_THAT(m.GetOutput(), ElementsAreArray({-1.9, 0.4, 1.0, 1.3}));
+ EXPECT_THAT(m.GetOutput(), NnapiArrayFloatNear({-1.9, 0.4, 1.0, 1.3}));
}
// Sanity check for the state-ful NNAPI delegate with invalid accelerator_name
@@ -380,7 +399,7 @@
m.PopulateTensor<float>(m.input1(), {-2.0, 0.2, 0.7, 0.8});
m.PopulateTensor<float>(m.input2(), {0.1, 0.2, 0.3, 0.5});
ASSERT_EQ(m.Invoke(), kTfLiteOk);
- EXPECT_THAT(m.GetOutput(), ElementsAreArray({-1.9, 0.4, 1.0, 1.3}));
+ EXPECT_THAT(m.GetOutput(), NnapiArrayFloatNear({-1.9, 0.4, 1.0, 1.3}));
}
// Sanity check for the state-ful NNAPI delegate with compilation caching
@@ -398,7 +417,7 @@
m.PopulateTensor<float>(m.input1(), {-2.0, 0.2, 0.7, 0.8});
m.PopulateTensor<float>(m.input2(), {0.1, 0.2, 0.3, 0.5});
ASSERT_EQ(m.Invoke(), kTfLiteOk);
- EXPECT_THAT(m.GetOutput(), ElementsAreArray({-1.9, 0.4, 1.0, 1.3}));
+ EXPECT_THAT(m.GetOutput(), NnapiArrayFloatNear({-1.9, 0.4, 1.0, 1.3}));
}
// Sanity check for the state-ful NNAPI delegate with QoS hints.
@@ -416,7 +435,7 @@
m.PopulateTensor<float>(m.input1(), {-2.0, 0.2, 0.7, 0.8});
m.PopulateTensor<float>(m.input2(), {0.1, 0.2, 0.3, 0.5});
ASSERT_EQ(m.Invoke(), kTfLiteOk);
- EXPECT_THAT(m.GetOutput(), ElementsAreArray({-1.9, 0.4, 1.0, 1.3}));
+ EXPECT_THAT(m.GetOutput(), NnapiArrayFloatNear({-1.9, 0.4, 1.0, 1.3}));
}
// Sanity check for the state-ful NNAPI delegate using TfLiteBufferHandle.
@@ -480,7 +499,7 @@
m.MarkInputTensorDataStale(m.input1());
m.PopulateTensor<float>(m.input2(), {0.1, 0.2, 0.3, 0.5});
ASSERT_EQ(m.Invoke(), kTfLiteOk);
- EXPECT_THAT(m.GetOutput(), ElementsAreArray({-1.9, 0.4, 1.0, 1.3}));
+ EXPECT_THAT(m.GetOutput(), NnapiArrayFloatNear({-1.9, 0.4, 1.0, 1.3}));
// Run the inference multiple times with the same buffer so that the execution
// can be reused.
@@ -490,7 +509,7 @@
memcpy(input1_memory_data, input1_data, kInput1ByteSize);
m.MarkInputTensorDataStale(m.input1());
ASSERT_EQ(m.Invoke(), kTfLiteOk);
- EXPECT_THAT(m.GetOutput(), ElementsAreArray({-1.9 + i, 0.4, 1.0, 1.3}));
+ EXPECT_THAT(m.GetOutput(), NnapiArrayFloatNear({-1.9f + i, 0.4f, 1.0f, 1.3f}));
}
// Run the inference multiple times and each time register a buffer.
@@ -505,7 +524,8 @@
m.MarkInputTensorDataStale(m.input1());
m.PopulateTensor<float>(m.input2(), {0.1, 0.2, 0.3, 0.5});
ASSERT_EQ(m.Invoke(), kTfLiteOk);
- EXPECT_THAT(m.GetOutput(), ElementsAreArray({-1.9 + i, 0.4, 1.0, 1.3}));
+ EXPECT_THAT(m.GetOutput(),
+ NnapiArrayFloatNear({-1.9f + i, 0.4f, 1.0f, 1.3f}));
}
}
@@ -540,8 +560,7 @@
m.PopulateTensor<float>(m.input1(), {-2.0, 0.2, 0.7, 0.8});
m.PopulateTensor<float>(m.input2(), {0.1, 0.2, 0.3, 0.5});
ASSERT_EQ(m.Invoke(), kTfLiteOk);
- EXPECT_THAT(m.GetOutput(),
- ElementsAreArray(ArrayFloatNear({-0.2, 0.04, 0.21, 0.4})));
+ EXPECT_THAT(m.GetOutput(), NnapiArrayFloatNear({-0.2, 0.04, 0.21, 0.4}));
}
class FloatPoolingOpModel : public SingleOpModelWithNNAPI {
@@ -582,7 +601,7 @@
3, 2, 10, 7, //
});
ASSERT_EQ(m.Invoke(), kTfLiteOk);
- EXPECT_THAT(m.GetOutput(), ElementsAreArray({2.75, 5.75}));
+ EXPECT_THAT(m.GetOutput(), NnapiArrayFloatNear({2.75, 5.75}));
}
TEST(NNAPIDelegate, MaxPoolWithNoActivation) {
@@ -595,7 +614,7 @@
3, 2, 10, 7, //
});
ASSERT_EQ(m.Invoke(), kTfLiteOk);
- EXPECT_THAT(m.GetOutput(), ElementsAreArray({6, 10}));
+ EXPECT_THAT(m.GetOutput(), NnapiArrayFloatNear({6, 10}));
}
TEST(NNAPIDelegate, L2PoolWithNoActivation) {
@@ -608,7 +627,7 @@
3, 2, 10, 7, //
});
ASSERT_EQ(m.Invoke(), kTfLiteOk);
- EXPECT_THAT(m.GetOutput(), ElementsAreArray({3.5, 6.5}));
+ EXPECT_THAT(m.GetOutput(), NnapiArrayFloatNear({3.5, 6.5}));
}
class ConvolutionOpModel : public SingleOpModelWithNNAPI {
@@ -814,7 +833,7 @@
ASSERT_EQ(m.Invoke(), kTfLiteOk);
- EXPECT_THAT(m.GetOutput(), ElementsAreArray({
+ EXPECT_THAT(m.GetOutput(), NnapiArrayFloatNear({
18, 2, 5, // first batch, left
18, 2, 5, // first batch, right
17, 4, 3, // second batch, left
@@ -914,7 +933,7 @@
// | 5 | 5 | 5 |
// | 5 | 5 | 5 |
// | 5 | 5 | 5 |
- EXPECT_THAT(m.GetOutput(), ElementsAreArray({5, 5, 5, 5, 5, 5, 5, 5, 5}));
+ EXPECT_THAT(m.GetOutput(), NnapiArrayFloatNear({5, 5, 5, 5, 5, 5, 5, 5, 5}));
}
class QuantizedConvolutionOpModel : public ConvolutionOpModel {
@@ -1200,7 +1219,7 @@
ASSERT_EQ(m.Invoke(), kTfLiteOk);
- EXPECT_THAT(m.GetOutput(), ElementsAreArray({
+ EXPECT_THAT(m.GetOutput(), NnapiArrayFloatNear({
71, -34, 99, -20, //
91, -26, 127, -4, //
}));
@@ -1412,10 +1431,9 @@
EXPECT_THAT(
m.GetOutput(),
- ElementsAreArray(ArrayFloatNear(
- {0.011656231, 0.031684921, 0.086128544, 0.234121657, 0.636408647,
- 0.636408647, 0.234121657, 0.086128544, 0.031684921, 0.011656231},
- 1e-6)));
+ NnapiArrayFloatNear({0.011656231, 0.031684921, 0.086128544, 0.234121657,
+ 0.636408647, 0.636408647, 0.234121657, 0.086128544,
+ 0.031684921, 0.011656231}));
}
TEST(SoftmaxOpTest, Beta2) {
@@ -1426,11 +1444,9 @@
ASSERT_EQ(m.Invoke(), kTfLiteOk);
- EXPECT_THAT(
- m.GetOutput(),
- ElementsAreArray(ArrayFloatNear(
- {0.000290076, 0.002143387, 0.015837606, 0.117024957, 0.864703974},
- 1e-6)));
+ EXPECT_THAT(m.GetOutput(),
+ NnapiArrayFloatNear({0.000290076, 0.002143387, 0.015837606,
+ 0.117024957, 0.864703974}));
}
TEST(SoftmaxOpTest, 3dInput) {
@@ -1446,12 +1462,11 @@
EXPECT_THAT(
m.GetOutput(),
- ElementsAreArray(ArrayFloatNear(
+ NnapiArrayFloatNear(
{0.011656231, 0.031684921, 0.086128544, 0.234121657, 0.636408647,
0.636408647, 0.234121657, 0.086128544, 0.031684921, 0.011656231,
0.636408647, 0.011656231, 0.031684921, 0.086128544, 0.234121657,
- 0.011656231, 0.636408647, 0.234121657, 0.086128544, 0.031684921},
- 1e-6)));
+ 0.011656231, 0.636408647, 0.234121657, 0.086128544, 0.031684921}));
}
TEST(SoftmaxOpTest, 4dInput) {
@@ -1467,12 +1482,11 @@
EXPECT_THAT(
m.GetOutput(),
- ElementsAreArray(ArrayFloatNear(
+ NnapiArrayFloatNear(
{0.011656231, 0.031684921, 0.086128544, 0.234121657, 0.636408647,
0.636408647, 0.234121657, 0.086128544, 0.031684921, 0.011656231,
0.636408647, 0.011656231, 0.031684921, 0.086128544, 0.234121657,
- 0.011656231, 0.636408647, 0.234121657, 0.086128544, 0.031684921},
- 1e-6)));
+ 0.011656231, 0.636408647, 0.234121657, 0.086128544, 0.031684921}));
}
class ReshapeOpModel : public SingleOpModelWithNNAPI {
@@ -1507,7 +1521,7 @@
ReshapeOpModel m({1, 2, 4, 1}, {2, 2, 2});
m.SetInput({1, 2, 3, 4, 5, 6, 7, 8});
ASSERT_EQ(m.Invoke(), kTfLiteOk);
- EXPECT_THAT(m.GetOutput(), ElementsAreArray({1, 2, 3, 4, 5, 6, 7, 8}));
+ EXPECT_THAT(m.GetOutput(), NnapiArrayFloatNear({1, 2, 3, 4, 5, 6, 7, 8}));
EXPECT_THAT(m.GetOutputShape(), ElementsAreArray({2, 2, 2}));
}
@@ -1548,9 +1562,9 @@
EXPECT_THAT(m.GetOutputShape(), ElementsAreArray({24}));
EXPECT_THAT(
m.GetOutput(),
- ElementsAreArray({1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0,
- 9.0, 10.0, 11.0, 12.0, 13.0, 14.0, 15.0, 16.0,
- 17.0, 18.0, 19.0, 20.0, 21.0, 22.0, 23.0, 24.0}));
+ NnapiArrayFloatNear({1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0,
+ 9.0, 10.0, 11.0, 12.0, 13.0, 14.0, 15.0, 16.0,
+ 17.0, 18.0, 19.0, 20.0, 21.0, 22.0, 23.0, 24.0}));
}
TEST(NNAPIDelegate, SqueezeWithAxisTest) {
@@ -1564,9 +1578,9 @@
EXPECT_THAT(m.GetOutputShape(), ElementsAreArray({1, 24}));
EXPECT_THAT(
m.GetOutput(),
- ElementsAreArray({1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0,
- 9.0, 10.0, 11.0, 12.0, 13.0, 14.0, 15.0, 16.0,
- 17.0, 18.0, 19.0, 20.0, 21.0, 22.0, 23.0, 24.0}));
+ NnapiArrayFloatNear({1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0,
+ 9.0, 10.0, 11.0, 12.0, 13.0, 14.0, 15.0, 16.0,
+ 17.0, 18.0, 19.0, 20.0, 21.0, 22.0, 23.0, 24.0}));
}
class L2NormOpModel : public SingleOpModelWithNNAPI {
@@ -1601,7 +1615,7 @@
ASSERT_EQ(m.Invoke(), kTfLiteOk);
EXPECT_THAT(m.GetOutputShape(), ElementsAreArray({1, 1, 1, 6}));
EXPECT_THAT(m.GetOutput(),
- ElementsAreArray({-0.55, 0.3, 0.35, 0.6, -0.35, 0.05}));
+ NnapiArrayFloatNear({-0.55, 0.3, 0.35, 0.6, -0.35, 0.05}));
}
class TransposeSimpleModel : public SingleOpModelWithNNAPI {
@@ -1636,9 +1650,9 @@
12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23});
ASSERT_EQ(m.Invoke(), kTfLiteOk);
EXPECT_THAT(m.GetOutputShape(), ElementsAreArray({4, 2, 3}));
- EXPECT_THAT(m.GetOutput(),
- ElementsAreArray({0, 4, 8, 12, 16, 20, 1, 5, 9, 13, 17, 21,
- 2, 6, 10, 14, 18, 22, 3, 7, 11, 15, 19, 23}));
+ EXPECT_THAT(m.GetOutput(), NnapiArrayFloatNear(
+ {0, 4, 8, 12, 16, 20, 1, 5, 9, 13, 17, 21,
+ 2, 6, 10, 14, 18, 22, 3, 7, 11, 15, 19, 23}));
}
class ElementwiseOpBaseModel : public SingleOpModelWithNNAPI {
@@ -1669,7 +1683,7 @@
3.f, -2.f, 10.f, 1.f, //
});
ASSERT_EQ(m.Invoke(), kTfLiteOk);
- EXPECT_THAT(m.ExtractVector<float>(m.output()), ElementsAreArray({
+ EXPECT_THAT(m.ExtractVector<float>(m.output()), NnapiArrayFloatNear({
0.f, 6.2f, 2.f, 4.f, //
3.f, 2.f, 10.f, 1.f, //
}));
@@ -1680,9 +1694,9 @@
ElementwiseOpFloatModel m(BuiltinOperator_EXP, {3, 1, 2});
m.PopulateTensor<float>(m.input(), {1.0, 0.0, -1.0, 1.0, 1.0, -1.0});
ASSERT_EQ(m.Invoke(), kTfLiteOk);
- EXPECT_THAT(m.ExtractVector<float>(m.output()),
- ElementsAreArray(ArrayFloatNear(
- {2.71828, 1, 0.367879, 2.71828, 2.71828, 0.367879})));
+ EXPECT_THAT(
+ m.ExtractVector<float>(m.output()),
+ NnapiArrayFloatNear({2.71828, 1, 0.367879, 2.71828, 2.71828, 0.367879}));
EXPECT_THAT(m.GetTensorShape(m.output()), ElementsAreArray({3, 1, 2}));
}
@@ -1691,7 +1705,7 @@
m.PopulateTensor<float>(m.input(), {1, 3.1415926, 1, 1});
ASSERT_EQ(m.Invoke(), kTfLiteOk);
EXPECT_THAT(m.ExtractVector<float>(m.output()),
- ElementsAreArray(ArrayFloatNear({0, 1.14473, 0, 0})));
+ NnapiArrayFloatNear({0, 1.14473, 0, 0}));
EXPECT_THAT(m.GetTensorShape(m.output()), ElementsAreArray({1, 1, 4, 1}));
}
@@ -1700,7 +1714,7 @@
m.PopulateTensor<float>(m.input(), {1, 2, 4, 9});
ASSERT_EQ(m.Invoke(), kTfLiteOk);
EXPECT_THAT(m.ExtractVector<float>(m.output()),
- ElementsAreArray(ArrayFloatNear({1, 0.7071, 0.5, 0.33333})));
+ NnapiArrayFloatNear({1, 0.7071, 0.5, 0.33333}));
EXPECT_THAT(m.GetTensorShape(m.output()), ElementsAreArray({1, 1, 4, 1}));
}
@@ -1709,7 +1723,7 @@
m.PopulateTensor<float>(m.input(), {0, 3.1415926, -3.1415926, 1});
ASSERT_EQ(m.Invoke(), kTfLiteOk);
EXPECT_THAT(m.ExtractVector<float>(m.output()),
- ElementsAreArray(ArrayFloatNear({0, 0, 0, 0.84147})));
+ NnapiArrayFloatNear({0, 0, 0, 0.84147}));
EXPECT_THAT(m.GetTensorShape(m.output()), ElementsAreArray({1, 1, 4, 1}));
}
@@ -1718,7 +1732,7 @@
m.PopulateTensor<float>(m.input(), {0, 1, 2, 4});
ASSERT_EQ(m.Invoke(), kTfLiteOk);
EXPECT_THAT(m.ExtractVector<float>(m.output()),
- ElementsAreArray(ArrayFloatNear({0, 1, 1.41421, 2})));
+ NnapiArrayFloatNear({0, 1, 1.41421, 2}));
EXPECT_THAT(m.GetTensorShape(m.output()), ElementsAreArray({1, 1, 4, 1}));
}
@@ -1753,8 +1767,7 @@
m.PopulateTensor<float>(m.input1(), {-2.0, 0.2, 0.7, 0.8});
m.PopulateTensor<float>(m.input2(), {0.1, 0.2, 0.3, 0.5});
ASSERT_EQ(m.Invoke(), kTfLiteOk);
- EXPECT_THAT(m.GetOutput(),
- ElementsAreArray(ArrayFloatNear({-2.1, 0.0, 0.4, 0.3})));
+ EXPECT_THAT(m.GetOutput(), NnapiArrayFloatNear({-2.1, 0.0, 0.4, 0.3}));
}
class FloatDivOpModel : public SingleOpModelWithNNAPI {
@@ -1788,7 +1801,7 @@
m.PopulateTensor<float>(m.input1(), {-2.0, 0.2, 0.8, 0.8});
m.PopulateTensor<float>(m.input2(), {0.1, 0.2, 0.4, 0.2});
ASSERT_EQ(m.Invoke(), kTfLiteOk);
- EXPECT_THAT(m.GetOutput(), ElementsAreArray(ArrayFloatNear({-20, 1, 2, 4})));
+ EXPECT_THAT(m.GetOutput(), NnapiArrayFloatNear({-20, 1, 2, 4}));
}
class BaseConcatenationOpModel : public SingleOpModelWithNNAPI {
@@ -1828,7 +1841,7 @@
/*num_inputs=*/1);
m0.SetInput(0, {1.0f, 3.0f, 4.0f, 7.0f});
ASSERT_EQ(m0.Invoke(), kTfLiteOk);
- EXPECT_THAT(m0.GetOutput(), ElementsAreArray({1, 3, 4, 7}));
+ EXPECT_THAT(m0.GetOutput(), NnapiArrayFloatNear({1, 3, 4, 7}));
}
TEST(NNAPIDelegate, ConcatenationFourInputs) {
@@ -1840,7 +1853,7 @@
m0.SetInput(3, {1.3f, 3.3f, 4.3f, 7.3f});
ASSERT_EQ(m0.Invoke(), kTfLiteOk);
EXPECT_THAT(m0.GetOutput(),
- ElementsAreArray({
+ NnapiArrayFloatNear({
1.0f, 3.0f, 1.1f, 3.1f, 1.2f, 3.2f, 1.3f, 3.3f, //
4.0f, 7.0f, 4.1f, 7.1f, 4.2f, 7.2f, 4.3f, 7.3f, //
}));
@@ -1991,7 +2004,7 @@
FloorOpModel model({2}, TensorType_FLOAT32);
model.PopulateTensor<float>(model.input(), {8.5, 0.0});
ASSERT_EQ(model.Invoke(), kTfLiteOk);
- EXPECT_THAT(model.GetOutput(), ElementsAreArray({8, 0}));
+ EXPECT_THAT(model.GetOutput(), NnapiArrayFloatNear({8, 0}));
EXPECT_THAT(model.GetOutputShape(), ElementsAreArray({2}));
}
@@ -2011,7 +2024,7 @@
});
ASSERT_EQ(model.Invoke(), kTfLiteOk);
EXPECT_THAT(model.GetOutput(),
- ElementsAreArray({0, 8, 0, 9, 0, -1, -9, -1, -10, -1}));
+ NnapiArrayFloatNear({0, 8, 0, 9, 0, -1, -9, -1, -10, -1}));
EXPECT_THAT(model.GetOutputShape(), ElementsAreArray({2, 1, 1, 5}));
}
@@ -2046,9 +2059,8 @@
m.SetInput({-1.1, 0.6, 0.7, 1.2, -0.7, 0.1});
ASSERT_EQ(m.Invoke(), kTfLiteOk);
// The result is every input divided by 2.
- EXPECT_THAT(
- m.GetOutput(),
- ElementsAreArray(ArrayFloatNear({-0.55, 0.3, 0.35, 0.6, -0.35, 0.05})));
+ EXPECT_THAT(m.GetOutput(),
+ NnapiArrayFloatNear({-0.55, 0.3, 0.35, 0.6, -0.35, 0.05}));
}
TEST(NNAPIDelegate, LocalResponseNormWithAlpha) {
@@ -2057,8 +2069,8 @@
m.SetInput({-1.1, 0.6, 0.7, 1.2, -0.7, 0.1});
ASSERT_EQ(m.Invoke(), kTfLiteOk);
// The result is every input divided by 3.
- EXPECT_THAT(m.GetOutput(), ElementsAreArray(ArrayFloatNear(
- {-0.275, 0.15, 0.175, 0.3, -0.175, 0.025})));
+ EXPECT_THAT(m.GetOutput(),
+ NnapiArrayFloatNear({-0.275, 0.15, 0.175, 0.3, -0.175, 0.025}));
}
TEST(NNAPIDelegate, LocalResponseNormWithBias) {
@@ -2067,9 +2079,8 @@
m.SetInput({-1.1, 0.6, 0.7, 1.2, -0.7, 0.1});
ASSERT_EQ(m.Invoke(), kTfLiteOk);
// The result is every input divided by 5.
- EXPECT_THAT(
- m.GetOutput(),
- ElementsAreArray(ArrayFloatNear({-0.22, 0.12, 0.14, 0.24, -0.14, 0.02})));
+ EXPECT_THAT(m.GetOutput(),
+ NnapiArrayFloatNear({-0.22, 0.12, 0.14, 0.24, -0.14, 0.02}));
}
TEST(NNAPIDelegate, LocalResponseNormSmallRadius) {
@@ -2077,10 +2088,9 @@
/*alpha=*/4.0, /*beta=*/0.5);
m.SetInput({-1.1, 0.6, 0.7, 1.2, -0.7, 0.1});
ASSERT_EQ(m.Invoke(), kTfLiteOk);
- EXPECT_THAT(
- m.GetOutput(),
- ElementsAreArray(ArrayFloatNear(
- {-0.264926, 0.125109, 0.140112, 0.267261, -0.161788, 0.0244266})));
+ EXPECT_THAT(m.GetOutput(),
+ NnapiArrayFloatNear({-0.264926, 0.125109, 0.140112, 0.267261,
+ -0.161788, 0.0244266}));
}
class LSHProjectionOpModel : public SingleOpModelWithNNAPI {
@@ -2256,7 +2266,7 @@
3, -2, 10, 1, //
});
ASSERT_EQ(m.Invoke(), kTfLiteOk);
- EXPECT_THAT(m.GetOutput(), ElementsAreArray({
+ EXPECT_THAT(m.GetOutput(), NnapiArrayFloatNear({
0, 0, 2, 4, //
3, 0, 10, 1, //
}));
@@ -2270,7 +2280,7 @@
0.3, -2.0, 1.1, -0.1, //
});
ASSERT_EQ(m.Invoke(), kTfLiteOk);
- EXPECT_THAT(m.GetOutput(), ElementsAreArray({
+ EXPECT_THAT(m.GetOutput(), NnapiArrayFloatNear({
0.0, -0.6, 0.2, -0.4, //
0.3, -1.0, 1.0, -0.1, //
}));
@@ -2284,7 +2294,7 @@
3, -2, 10, 1, //
});
ASSERT_EQ(m.Invoke(), kTfLiteOk);
- EXPECT_THAT(m.GetOutput(), ElementsAreArray({
+ EXPECT_THAT(m.GetOutput(), NnapiArrayFloatNear({
0, 0, 2, 4, //
3, 0, 6, 1, //
}));
@@ -2298,10 +2308,10 @@
3, -2, 10, 1, //
});
ASSERT_EQ(m.Invoke(), kTfLiteOk);
- EXPECT_THAT(m.GetOutput(), ElementsAreArray(ArrayFloatNear({
+ EXPECT_THAT(m.GetOutput(), NnapiArrayFloatNear({
0.5, 0.002473, 0.880797, 0.982014, //
0.952574, 0.119203, 0.999955, 0.731059, //
- })));
+ }));
}
TEST(NNAPIDelegate, LogisticQuantized) {
@@ -2369,16 +2379,14 @@
m.SetInput<float>({3, 6});
m.SetSize({1, 3});
ASSERT_EQ(m.Invoke(), kTfLiteOk);
- EXPECT_THAT(m.GetOutput<float>(),
- ElementsAreArray(ArrayFloatNear({3, 5, 6})));
+ EXPECT_THAT(m.GetOutput<float>(), NnapiArrayFloatNear({3, 5, 6}));
}
TEST(ResizeBilinear, HorizontalConstant) {
ResizeBilinearOpModel const_m({TensorType_FLOAT32, {1, 1, 2, 1}}, {1, 3});
const_m.SetInput<float>({3, 6});
ASSERT_EQ(const_m.Invoke(), kTfLiteOk);
- EXPECT_THAT(const_m.GetOutput<float>(),
- ElementsAreArray(ArrayFloatNear({3, 5, 6})));
+ EXPECT_THAT(const_m.GetOutput<float>(), NnapiArrayFloatNear({3, 5, 6}));
}
TEST(ResizeBilinear, Vertical) {
@@ -2386,16 +2394,14 @@
m.SetInput<float>({3, 9});
m.SetSize({3, 1});
ASSERT_EQ(m.Invoke(), kTfLiteOk);
- EXPECT_THAT(m.GetOutput<float>(),
- ElementsAreArray(ArrayFloatNear({3, 7, 9})));
+ EXPECT_THAT(m.GetOutput<float>(), NnapiArrayFloatNear({3, 7, 9}));
}
TEST(ResizeBilinear, VerticalConstant) {
ResizeBilinearOpModel const_m({TensorType_FLOAT32, {1, 2, 1, 1}}, {3, 1});
const_m.SetInput<float>({3, 9});
ASSERT_EQ(const_m.Invoke(), kTfLiteOk);
- EXPECT_THAT(const_m.GetOutput<float>(),
- ElementsAreArray(ArrayFloatNear({3, 7, 9})));
+ EXPECT_THAT(const_m.GetOutput<float>(), NnapiArrayFloatNear({3, 7, 9}));
}
TEST(ResizeBilinear, TwoDimensional) {
@@ -2406,11 +2412,11 @@
});
m.SetSize({3, 3});
ASSERT_EQ(m.Invoke(), kTfLiteOk);
- EXPECT_THAT(m.GetOutput<float>(), ElementsAreArray(ArrayFloatNear({
+ EXPECT_THAT(m.GetOutput<float>(), NnapiArrayFloatNear({
3, 5, 6, //
7, 9, 10, //
9, 11, 12, //
- })));
+ }));
}
TEST(ResizeBilinear, TwoDimensionalConstant) {
@@ -2420,11 +2426,11 @@
9, 12 //
});
ASSERT_EQ(const_m.Invoke(), kTfLiteOk);
- EXPECT_THAT(const_m.GetOutput<float>(), ElementsAreArray(ArrayFloatNear({
+ EXPECT_THAT(const_m.GetOutput<float>(), NnapiArrayFloatNear({
3, 5, 6, //
7, 9, 10, //
9, 11, 12, //
- })));
+ }));
}
template <typename T>
@@ -2487,8 +2493,8 @@
m.SetInput({1, 2, 3, 4, 5, 6});
ASSERT_EQ(m.Invoke(), kTfLiteOk);
EXPECT_THAT(m.GetOutput(),
- ElementsAreArray({0, 1, 2, 3, 0, 0, 0, 0, 4, 5, 6, 0, 0, 0,
- 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}));
+ NnapiArrayFloatNear({0, 1, 2, 3, 0, 0, 0, 0, 4, 5, 6, 0, 0, 0,
+ 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}));
EXPECT_THAT(m.GetOutputShape(), ElementsAreArray({1, 4, 7, 1}));
}
@@ -2538,8 +2544,8 @@
m.SetInput({1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16});
ASSERT_EQ(m.Invoke(), kTfLiteOk);
EXPECT_THAT(m.GetOutputShape(), ElementsAreArray({4, 2, 2, 1}));
- EXPECT_THAT(m.GetOutput(), ElementsAreArray({1, 3, 9, 11, 2, 4, 10, 12, 5, 7,
- 13, 15, 6, 8, 14, 16}));
+ EXPECT_THAT(m.GetOutput(), NnapiArrayFloatNear({1, 3, 9, 11, 2, 4, 10, 12, 5,
+ 7, 13, 15, 6, 8, 14, 16}));
}
TEST(NNAPIDelegate, SpaceToBatchNDMultipleInputBatchesConstTest) {
@@ -2547,8 +2553,8 @@
m.SetInput({1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16});
ASSERT_EQ(m.Invoke(), kTfLiteOk);
EXPECT_THAT(m.GetOutputShape(), ElementsAreArray({8, 1, 2, 1}));
- EXPECT_THAT(m.GetOutput(), ElementsAreArray({1, 3, 9, 11, 2, 4, 10, 12, 5, 7,
- 13, 15, 6, 8, 14, 16}));
+ EXPECT_THAT(m.GetOutput(), NnapiArrayFloatNear({1, 3, 9, 11, 2, 4, 10, 12, 5,
+ 7, 13, 15, 6, 8, 14, 16}));
}
TEST(NNAPIDelegate, SpaceToBatchNDSimplePaddingConstTest) {
@@ -2556,7 +2562,7 @@
m.SetInput({1, 2, 3, 4, 5, 6, 7, 8, 9, 10});
ASSERT_EQ(m.Invoke(), kTfLiteOk);
EXPECT_THAT(m.GetOutputShape(), ElementsAreArray({6, 2, 2, 1}));
- EXPECT_THAT(m.GetOutput(), ElementsAreArray({
+ EXPECT_THAT(m.GetOutput(), NnapiArrayFloatNear({
0, 0, 0, 5, 0, 0, 0, 6, 0, 1, 0, 7,
0, 2, 0, 8, 0, 3, 0, 9, 0, 4, 0, 10,
}));
@@ -2567,7 +2573,7 @@
m.SetInput({1, 2, 3, 4, 5, 6, 7, 8});
ASSERT_EQ(m.Invoke(), kTfLiteOk);
EXPECT_THAT(m.GetOutputShape(), ElementsAreArray({6, 2, 4, 1}));
- EXPECT_THAT(m.GetOutput(), ElementsAreArray({
+ EXPECT_THAT(m.GetOutput(), NnapiArrayFloatNear({
0, 0, 0, 0, 0, 5, 0, 0, 0, 0, 0, 0, 0, 6, 0, 0,
0, 1, 0, 0, 0, 7, 0, 0, 0, 2, 0, 0, 0, 8, 0, 0,
0, 3, 0, 0, 0, 0, 0, 0, 0, 4, 0, 0, 0, 0, 0, 0,
@@ -2623,7 +2629,7 @@
m.SetInput({1, 2, 3, 4});
ASSERT_EQ(m.Invoke(), kTfLiteOk);
EXPECT_THAT(m.GetOutputShape(), ElementsAreArray({2}));
- EXPECT_THAT(m.GetOutput(), ElementsAreArray({2, 3}));
+ EXPECT_THAT(m.GetOutput(), NnapiArrayFloatNear({2, 3}));
}
TEST(StridedSliceOpTest, In1D_BeginMask) {
@@ -2631,7 +2637,7 @@
m.SetInput({1, 2, 3, 4});
ASSERT_EQ(m.Invoke(), kTfLiteOk);
EXPECT_THAT(m.GetOutputShape(), ElementsAreArray({3}));
- EXPECT_THAT(m.GetOutput(), ElementsAreArray({1, 2, 3}));
+ EXPECT_THAT(m.GetOutput(), NnapiArrayFloatNear({1, 2, 3}));
}
TEST(StridedSliceOpTest, In2D_Stride2) {
@@ -2640,7 +2646,7 @@
m.SetInput({1, 2, 3, 4, 5, 6});
ASSERT_EQ(m.Invoke(), kTfLiteOk);
EXPECT_THAT(m.GetOutputShape(), ElementsAreArray({1, 2}));
- EXPECT_THAT(m.GetOutput(), ElementsAreArray({1, 3}));
+ EXPECT_THAT(m.GetOutput(), NnapiArrayFloatNear({1, 3}));
}
TEST(StridedSliceOpTest, In2D_EndMask) {
@@ -2649,7 +2655,7 @@
m.SetInput({1, 2, 3, 4, 5, 6});
ASSERT_EQ(m.Invoke(), kTfLiteOk);
EXPECT_THAT(m.GetOutputShape(), ElementsAreArray({1, 3}));
- EXPECT_THAT(m.GetOutput(), ElementsAreArray({4, 5, 6}));
+ EXPECT_THAT(m.GetOutput(), NnapiArrayFloatNear({4, 5, 6}));
}
TEST(StridedSliceOpTest, In3D_IdentityShrinkAxis4) {
@@ -2658,7 +2664,7 @@
m.SetInput({1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12});
ASSERT_EQ(m.Invoke(), kTfLiteOk);
EXPECT_THAT(m.GetOutputShape(), ElementsAreArray({2, 3}));
- EXPECT_THAT(m.GetOutput(), ElementsAreArray({1, 3, 5, 7, 9, 11}));
+ EXPECT_THAT(m.GetOutput(), NnapiArrayFloatNear({1, 3, 5, 7, 9, 11}));
}
static float rnn_input[] = {
@@ -2885,7 +2891,7 @@
expected.insert(expected.end(), golden_start, golden_end);
expected.insert(expected.end(), golden_start, golden_end);
- EXPECT_THAT(rnn.GetOutput(), ElementsAreArray(ArrayFloatNear(expected)));
+ EXPECT_THAT(rnn.GetOutput(), NnapiArrayFloatNear(expected));
}
}
@@ -4803,7 +4809,7 @@
m.SetInput(data);
ASSERT_EQ(m.Invoke(), kTfLiteOk);
EXPECT_THAT(m.GetOutputShape(), ElementsAreArray({2}));
- EXPECT_THAT(m.GetOutput<float>(), ElementsAreArray(ArrayFloatNear({12, 13})));
+ EXPECT_THAT(m.GetOutput<float>(), NnapiArrayFloatNear({12, 13}));
}
TEST(NNAPIDelegate, MeanFloatKeepDims) {
@@ -4815,8 +4821,7 @@
m.SetInput(data);
ASSERT_EQ(m.Invoke(), kTfLiteOk);
EXPECT_THAT(m.GetOutputShape(), ElementsAreArray({1, 3, 1}));
- EXPECT_THAT(m.GetOutput<float>(),
- ElementsAreArray(ArrayFloatNear({10.5, 12.5, 14.5})));
+ EXPECT_THAT(m.GetOutput<float>(), NnapiArrayFloatNear({10.5, 12.5, 14.5}));
}
class BaseEmbeddingLookupOpModel : public SingleOpModelWithNNAPI {
@@ -4871,11 +4876,11 @@
ASSERT_EQ(m.Invoke(), kTfLiteOk);
EXPECT_THAT(m.GetOutput(),
- ElementsAreArray(ArrayFloatNear({
+ NnapiArrayFloatNear({
1.00, 1.01, 1.02, 1.03, 1.10, 1.11, 1.12, 1.13, // Row 1
0.00, 0.01, 0.02, 0.03, 0.10, 0.11, 0.12, 0.13, // Row 0
2.00, 2.01, 2.02, 2.03, 2.10, 2.11, 2.12, 2.13, // Row 2
- })));
+ }));
}
class HashtableLookupOpModel : public SingleOpModelWithNNAPI {
@@ -4955,12 +4960,12 @@
ASSERT_EQ(m.Invoke(), kTfLiteOk);
- EXPECT_THAT(m.GetOutput(), ElementsAreArray(ArrayFloatNear({
+ EXPECT_THAT(m.GetOutput(), NnapiArrayFloatNear({
2.0, 2.1, // 2-nd item
0, 0, // Not found
0.0, 0.1, // 0-th item
1.0, 1.1, // 1-st item
- })));
+ }));
EXPECT_THAT(m.GetHit(), ElementsAreArray({
1,
0,
@@ -4978,12 +4983,12 @@
ASSERT_EQ(m.Invoke(), kTfLiteOk);
- EXPECT_THAT(m.GetOutput(), ElementsAreArray(ArrayFloatNear({
+ EXPECT_THAT(m.GetOutput(), NnapiArrayFloatNear({
0.4, // 2-nd item
0, // Not found
0.0, // 0-th item
0.1, // 1-st item
- })));
+ }));
EXPECT_THAT(m.GetHit(), ElementsAreArray({
1,
0,
@@ -5039,7 +5044,7 @@
});
m.SetAlpha({0.0f, 1.0f, 2.0f});
ASSERT_EQ(m.Invoke(), kTfLiteOk);
- EXPECT_THAT(m.GetOutput(), ElementsAreArray({
+ EXPECT_THAT(m.GetOutput(), NnapiArrayFloatNear({
0.0f, 0.0f, 0.0f, // Row 1, Column 1
1.0f, 1.0f, 1.0f, // Row 1, Column 2
0.0f, -1.0f, -2.0f, // Row 2, Column 1
@@ -5150,8 +5155,8 @@
{TensorType_FLOAT32});
m.SetInput({1, 2, 3, 4});
ASSERT_EQ(m.Invoke(), kTfLiteOk);
- EXPECT_THAT(m.GetOutput(), ElementsAreArray({0, 0, 0, 0, 0, 1, 2, 0, 0, 3, 4,
- 0, 0, 0, 0, 0}));
+ EXPECT_THAT(m.GetOutput(), NnapiArrayFloatNear({0, 0, 0, 0, 0, 1, 2, 0, 0, 3,
+ 4, 0, 0, 0, 0, 0}));
EXPECT_THAT(m.GetOutputShape(), ElementsAreArray({1, 4, 4, 1}));
}
@@ -5162,8 +5167,8 @@
{0, 0, 1, 1, 1, 1, 0, 0}, 5, {TensorType_FLOAT32});
m.SetInput({1, 2, 3, 4});
ASSERT_EQ(m.Invoke(), kTfLiteOk);
- EXPECT_THAT(m.GetOutput(), ElementsAreArray({5, 5, 5, 5, 5, 1, 2, 5, 5, 3, 4,
- 5, 5, 5, 5, 5}));
+ EXPECT_THAT(m.GetOutput(), NnapiArrayFloatNear({5, 5, 5, 5, 5, 1, 2, 5, 5, 3,
+ 4, 5, 5, 5, 5, 5}));
EXPECT_THAT(m.GetOutputShape(), ElementsAreArray({1, 4, 4, 1}));
}
@@ -5174,7 +5179,7 @@
{0, 1, 0, 0, 0, 0, 0, 1}, 5, {TensorType_FLOAT32});
m.SetInput({3, 3});
ASSERT_EQ(m.Invoke(), kTfLiteOk);
- EXPECT_THAT(m.GetOutput(), ElementsAreArray({3, 5, 3, 5, 5, 5, 5, 5}));
+ EXPECT_THAT(m.GetOutput(), NnapiArrayFloatNear({3, 5, 3, 5, 5, 5, 5, 5}));
EXPECT_THAT(m.GetOutputShape(), ElementsAreArray({2, 1, 2, 2}));
}
@@ -5184,8 +5189,8 @@
m.SetInput({1, 2, 3, 4});
m.SetPaddings({0, 0, 1, 1, 1, 1, 0, 0});
ASSERT_EQ(m.Invoke(), kTfLiteOk);
- EXPECT_THAT(m.GetOutput(), ElementsAreArray({0, 0, 0, 0, 0, 1, 2, 0, 0, 3, 4,
- 0, 0, 0, 0, 0}));
+ EXPECT_THAT(m.GetOutput(), NnapiArrayFloatNear({0, 0, 0, 0, 0, 1, 2, 0, 0, 3,
+ 4, 0, 0, 0, 0, 0}));
EXPECT_THAT(m.GetOutputShape(), ElementsAreArray({1, 4, 4, 1}));
}
@@ -5195,8 +5200,8 @@
m.SetInput({1, 2, 3, 4});
m.SetPaddings({0, 0, 1, 1, 1, 1, 0, 0});
ASSERT_EQ(m.Invoke(), kTfLiteOk);
- EXPECT_THAT(m.GetOutput(), ElementsAreArray({5, 5, 5, 5, 5, 1, 2, 5, 5, 3, 4,
- 5, 5, 5, 5, 5}));
+ EXPECT_THAT(m.GetOutput(), NnapiArrayFloatNear({5, 5, 5, 5, 5, 1, 2, 5, 5, 3,
+ 4, 5, 5, 5, 5, 5}));
EXPECT_THAT(m.GetOutputShape(), ElementsAreArray({1, 4, 4, 1}));
}
@@ -5206,8 +5211,8 @@
m.SetInput({1, 2, 3, 4, 5, 6});
ASSERT_EQ(m.Invoke(), kTfLiteOk);
EXPECT_THAT(m.GetOutput(),
- ElementsAreArray({0, 1, 2, 3, 0, 0, 0, 0, 4, 5, 6, 0, 0, 0,
- 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}));
+ NnapiArrayFloatNear({0, 1, 2, 3, 0, 0, 0, 0, 4, 5, 6, 0, 0, 0,
+ 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}));
EXPECT_THAT(m.GetOutputShape(), ElementsAreArray({1, 4, 7, 1}));
}
@@ -5218,8 +5223,8 @@
m.SetPaddings({0, 0, 0, 2, 1, 3, 0, 0});
ASSERT_EQ(m.Invoke(), kTfLiteOk);
EXPECT_THAT(m.GetOutput(),
- ElementsAreArray({0, 1, 2, 3, 0, 0, 0, 0, 4, 5, 6, 0, 0, 0,
- 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}));
+ NnapiArrayFloatNear({0, 1, 2, 3, 0, 0, 0, 0, 4, 5, 6, 0, 0, 0,
+ 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0}));
EXPECT_THAT(m.GetOutputShape(), ElementsAreArray({1, 4, 7, 1}));
}