experiments/STT-tensorflow
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Clarify tests and remove tests for unsupported types.

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PiperOrigin-RevId: 265178265
This commit is contained in:
Nat Jeffries 2019-08-23 18:46:22 -07:00 committed by TensorFlower Gardener
parent dac733509f
commit ff72e812c4
2 changed files with 18 additions and 135 deletions
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125
tensorflow/lite/experimental/micro/kernels/arg_min_max_test.cc
View File

@ -24,7 +24,6 @@ namespace tflite {
namespace testing {
namespace {
// If expected output is empty, the test is expected to fail.
void TestArgMinMax(TfLiteTensor* input_tensor, TfLiteTensor* axis_tensor,
TfLiteTensor* output_tensor,
std::initializer_list<int> expected_output_data,
@ -72,13 +71,6 @@ void TestArgMinMax(TfLiteTensor* input_tensor, TfLiteTensor* axis_tensor,
TF_LITE_MICRO_EXPECT_EQ(kTfLiteOk, registration->prepare(&context, &node));
}
TF_LITE_MICRO_EXPECT_NE(nullptr, registration->invoke);
// TODO(139763483): this is misleading. We should be more explicit about
// expecting failure conditions.
if (!expected_output_data.size()) {
TF_LITE_MICRO_EXPECT_EQ(kTfLiteError,
registration->invoke(&context, &node));
return;
}
TF_LITE_MICRO_EXPECT_EQ(kTfLiteOk, registration->invoke(&context, &node));
if (registration->free) {
registration->free(&context, user_data);
@ -88,6 +80,7 @@ void TestArgMinMax(TfLiteTensor* input_tensor, TfLiteTensor* axis_tensor,
output_tensor->data.i32[i], 1e-5f);
}
}
} // namespace
} // namespace testing
} // namespace tflite
@ -149,26 +142,6 @@ TF_LITE_MICRO_TEST(GetMaxArgInt8) {
{1});
}
TF_LITE_MICRO_TEST(GetMaxArgInt32) {
using tflite::testing::F2Q32;
int32_t output_data[1];
float input_scale = 7.436e-9;
TfLiteIntArray* input_dims =
tflite::testing::IntArrayFromInitializer({4, 1, 1, 1, 4});
auto input_data = {F2Q32(1, input_scale), F2Q32(9, input_scale),
F2Q32(7, input_scale), F2Q32(3, input_scale)};
auto input_tensor = tflite::testing::CreateQuantized32Tensor(
input_data, input_dims, "input_tensor", input_scale);
auto axis_tensor = tflite::testing::CreateTensor<int32_t, kTfLiteInt32>(
{3}, tflite::testing::IntArrayFromInitializer({3, 1, 1, 1}),
"axis_tensor");
auto output_tensor = tflite::testing::CreateTensor<int32_t, kTfLiteInt32>(
output_data, tflite::testing::IntArrayFromInitializer({3, 1, 1, 1}),
"output_tensor");
tflite::testing::TestArgMinMax(&input_tensor, &axis_tensor, &output_tensor,
{}); // Expects {1} if supported.
}
TF_LITE_MICRO_TEST(GetMaxArgMulDimensions) {
using tflite::testing::F2Q;
int32_t output_data[2];
@ -217,54 +190,6 @@ TF_LITE_MICRO_TEST(GetMaxArgNegativeAxis) {
{0, 1, 0, 0});
}
TF_LITE_MICRO_TEST(GetMaxArgOutput64) {
using tflite::testing::F2Q;
int64_t output_data[2];
float input_min = 0;
float input_max = 15.9375;
TfLiteIntArray* input_dims =
tflite::testing::IntArrayFromInitializer({4, 1, 1, 2, 4});
auto input_data = {
F2Q(10, input_min, input_max), F2Q(2, input_min, input_max),
F2Q(7, input_min, input_max), F2Q(8, input_min, input_max),
F2Q(1, input_min, input_max), F2Q(9, input_min, input_max),
F2Q(7, input_min, input_max), F2Q(3, input_min, input_max)};
auto input_tensor = tflite::testing::CreateQuantizedTensor(
input_data, input_dims, "input_tensor", input_min, input_max);
auto axis_tensor = tflite::testing::CreateTensor<int32_t, kTfLiteInt32>(
{3}, tflite::testing::IntArrayFromInitializer({3, 1, 1, 1}),
"axis_tensor");
auto output_tensor = tflite::testing::CreateTensor<int64_t, kTfLiteInt64>(
output_data, tflite::testing::IntArrayFromInitializer({3, 1, 1, 2}),
"output_tensor");
tflite::testing::TestArgMinMax(&input_tensor, &axis_tensor, &output_tensor,
{}); // Expects {0, 1} if supported.
}
TF_LITE_MICRO_TEST(GetMaxArgAxis64) {
using tflite::testing::F2Q;
int32_t output_data[2];
float input_min = 0;
float input_max = 15.9375;
TfLiteIntArray* input_dims =
tflite::testing::IntArrayFromInitializer({4, 1, 1, 2, 4});
auto input_data = {
F2Q(10, input_min, input_max), F2Q(2, input_min, input_max),
F2Q(7, input_min, input_max), F2Q(8, input_min, input_max),
F2Q(1, input_min, input_max), F2Q(9, input_min, input_max),
F2Q(7, input_min, input_max), F2Q(3, input_min, input_max)};
auto input_tensor = tflite::testing::CreateQuantizedTensor(
input_data, input_dims, "input_tensor", input_min, input_max);
auto axis_tensor = tflite::testing::CreateTensor<int64_t, kTfLiteInt64>(
{3}, tflite::testing::IntArrayFromInitializer({3, 1, 1, 1}),
"axis_tensor");
auto output_tensor = tflite::testing::CreateTensor<int32_t, kTfLiteInt32>(
output_data, tflite::testing::IntArrayFromInitializer({3, 1, 1, 2}),
"output_tensor");
tflite::testing::TestArgMinMax(&input_tensor, &axis_tensor, &output_tensor,
{}); // Expects {0, 1} if supported.
}
TF_LITE_MICRO_TEST(GetMinArgFloat) {
int32_t output_data[1];
TfLiteIntArray* input_dims =
@ -346,52 +271,4 @@ TF_LITE_MICRO_TEST(GetMinArgMulDimensions) {
{0, 0}, true);
}
TF_LITE_MICRO_TEST(GetMinArgOutput64) {
using tflite::testing::F2Q;
float input_min = 0;
float input_max = 15.9375;
int64_t output_data[2];
TfLiteIntArray* input_dims =
tflite::testing::IntArrayFromInitializer({4, 1, 1, 2, 4});
auto input_data = {
F2Q(10, input_min, input_max), F2Q(2, input_min, input_max),
F2Q(7, input_min, input_max), F2Q(8, input_min, input_max),
F2Q(1, input_min, input_max), F2Q(9, input_min, input_max),
F2Q(7, input_min, input_max), F2Q(3, input_min, input_max)};
auto input_tensor = tflite::testing::CreateQuantizedTensor(
input_data, input_dims, "input_tensor", input_min, input_max);
auto axis_tensor = tflite::testing::CreateTensor<int32_t, kTfLiteInt32>(
{3}, tflite::testing::IntArrayFromInitializer({3, 1, 1, 1}),
"axis_tensor");
auto output_tensor = tflite::testing::CreateTensor<int64_t, kTfLiteInt64>(
output_data, tflite::testing::IntArrayFromInitializer({3, 1, 1, 2}),
"output_tensor");
tflite::testing::TestArgMinMax(&input_tensor, &axis_tensor, &output_tensor,
{}, true); // Expects {1, 0} if supported.
}
TF_LITE_MICRO_TEST(GetMinArgAxis64) {
using tflite::testing::F2Q;
float input_min = 0;
float input_max = 15.9375;
int32_t output_data[2];
TfLiteIntArray* input_dims =
tflite::testing::IntArrayFromInitializer({4, 1, 1, 2, 4});
auto input_data = {
F2Q(10, input_min, input_max), F2Q(2, input_min, input_max),
F2Q(7, input_min, input_max), F2Q(8, input_min, input_max),
F2Q(1, input_min, input_max), F2Q(9, input_min, input_max),
F2Q(7, input_min, input_max), F2Q(3, input_min, input_max)};
auto input_tensor = tflite::testing::CreateQuantizedTensor(
input_data, input_dims, "input_tensor", input_min, input_max);
auto axis_tensor = tflite::testing::CreateTensor<int64_t, kTfLiteInt64>(
{3}, tflite::testing::IntArrayFromInitializer({3, 1, 1, 1}),
"axis_tensor");
auto output_tensor = tflite::testing::CreateTensor<int32_t, kTfLiteInt32>(
output_data, tflite::testing::IntArrayFromInitializer({3, 1, 1, 2}),
"output_tensor");
tflite::testing::TestArgMinMax(&input_tensor, &axis_tensor, &output_tensor,
{}, true); // Expects {1, 0} if supported
}
TF_LITE_MICRO_TESTS_END

28
tensorflow/lite/experimental/micro/kernels/reshape_test.cc
View File

@ -38,7 +38,8 @@ template <typename T>
void TestReshapeImpl(TfLiteTensor* input_tensor, TfLiteTensor* shape_tensor,
TfLiteTensor* output_tensor,
std::initializer_list<T> expected_output,
std::initializer_list<int> expected_dims) {
std::initializer_list<int> expected_dims,
bool expect_failure) {
TfLiteContext context;
TfLiteTensor tensors[3];
TfLiteNode node;
@ -85,8 +86,7 @@ void TestReshapeImpl(TfLiteTensor* input_tensor, TfLiteTensor* shape_tensor,
if (registration->prepare) {
TF_LITE_MICRO_EXPECT_EQ(kTfLiteOk, registration->prepare(&context, &node));
}
TF_LITE_MICRO_EXPECT_NE(nullptr, registration->invoke);
if (expected_output.size() == 0) {
if (expect_failure) {
TF_LITE_MICRO_EXPECT_EQ(kTfLiteError,
registration->invoke(&context, &node));
return;
@ -116,7 +116,8 @@ void TestReshape(std::initializer_list<int> input_dims_data,
std::initializer_list<int32_t> shape_data,
int* output_dims_data, uint8_t* output_data_raw,
std::initializer_list<T> expected_output,
std::initializer_list<int> expected_dims) {
std::initializer_list<int> expected_dims,
bool expect_failure = false) {
TfLiteIntArray* input_dims = IntArrayFromInitializer(input_dims_data);
TfLiteIntArray* output_dims = IntArrayFromInts(output_dims_data);
TfLiteTensor input_tensor = CreateTensor<T, tensor_input_type>(
@ -126,13 +127,13 @@ void TestReshape(std::initializer_list<int> input_dims_data,
output_data, output_dims, "input_tensor");
// Reshape param is passed as op's param.
TestReshapeImpl<T>(&input_tensor, nullptr, &output_tensor, expected_output,
expected_dims);
expected_dims, expect_failure);
// Reshape param is passed as a tensor.
TfLiteIntArray* shape_dims = IntArrayFromInitializer(shape_dims_data);
auto shape_tensor = CreateTensor<int32_t, kTfLiteInt32>(
shape_data, shape_dims, "shape_tensor");
TestReshapeImpl<T>(&input_tensor, &shape_tensor, &output_tensor,
expected_output, expected_dims);
expected_output, expected_dims, expect_failure);
}
} // namespace
} // namespace testing
@ -155,7 +156,8 @@ TF_LITE_MICRO_TEST(MismatchedDimensions) {
{2, 1}, // shape_data
output_dims, // output_dims
output_data, {}, // expected_output
{} // expected_dims
{}, // expected_dims
true // expect failure
);
}
@ -183,7 +185,8 @@ TF_LITE_MICRO_TEST(TooManySpecialDimensions) {
{-1, -1, 2, 4}, // shape_data
output_dims, // output_dims
output_data, {}, // expected_output
{} // expected_dims
{}, // expected_dims
true // expect failure
);
}
@ -205,7 +208,8 @@ TF_LITE_MICRO_TEST(InvalidShape) {
nullptr, // shape_tensor
&output_tensor, // output_tensor
{}, // expected_output
{} // expected_dims
{}, // expected_dims
true // expect failure
);
}
@ -271,13 +275,15 @@ TF_LITE_MICRO_TEST(LegacyScalarOutput) {
&shape_tensor, // shape_tensor
&output_tensor, // output_tensor
{}, // expected_output
{} // expected_dims
{}, // expected_dims
true // expect failure
);
tflite::testing::TestReshapeImpl<float>(&input_tensor, // input_tensor
nullptr, // shape_tensor
&output_tensor, // output_tensor
{3}, // expected_output
{} // expected_dims
{}, // expected_dims
false // expect failure
);
}