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Added the Padding and Activation scenarios #26062

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merged 1 commit into from Mar 18, 2019
Merged

Added the Padding and Activation scenarios #26062

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112 changes: 110 additions & 2 deletions tensorflow/lite/kernels/pooling_test.cc
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Expand Up @@ -26,8 +26,23 @@ using ::testing::ElementsAreArray;

class BasePoolingOpModel : public SingleOpModel {
public:
// TODO(ahentz): Also test different activation types, bias, padding types,
// stride values.
BasePoolingOpModel(const BuiltinOperator& type, const TensorData& input,
int stride_width, int stride_height, int filter_width,
int filter_height, const Padding& padding,
const ActivationFunctionType& fused_activation_function,
const TensorData& output) {
input_ = AddInput(input);
output_ = AddOutput(output);

SetBuiltinOp(type, BuiltinOptions_Pool2DOptions,
CreatePool2DOptions(builder_, padding, stride_width,
stride_height, filter_width, filter_height,
fused_activation_function)
.Union());

BuildInterpreter({GetShape(input_)});
}

BasePoolingOpModel(BuiltinOperator type, const TensorData& input,
int filter_width, int filter_height,
const TensorData& output) {
Expand Down Expand Up @@ -115,6 +130,99 @@ std::vector<float> ReplicateDepthRamp(const std::vector<float>& image_plane,
return ramped_data;
}

TEST(FloatPoolingOpTest, AveragePoolWithPad) {
FloatPoolingOpModel m(BuiltinOperator_AVERAGE_POOL_2D,
/*input=*/{TensorType_FLOAT32, {1, 3, 4, 1}},
/*stride_width=*/2, /*stride_height=*/2,
/*filter_width=*/2, /*filter_height=*/2, Padding_SAME,
ActivationFunctionType_RELU,
/*output=*/{TensorType_FLOAT32, {}});
m.SetInput({
0, -6, 2, 4, //
3, 2, -10, 7, //
4, 8, 1, 2, //
});
m.Invoke();
EXPECT_THAT(m.GetOutput(), ElementsAreArray({-0.0, 0.75, 6.0, 1.5}));
}

TEST(FloatPoolingOpTest, AveragePoolWithRELU6) {
FloatPoolingOpModel m(BuiltinOperator_AVERAGE_POOL_2D,
/*input=*/{TensorType_FLOAT32, {1, 2, 4, 1}},
/*stride_width=*/2, /*stride_height=*/2,
/*filter_width=*/2, /*filter_height=*/2, Padding_SAME,
ActivationFunctionType_RELU6,
/*output=*/{TensorType_FLOAT32, {}});
m.SetInput({
0, -6, 2, 4, //
3, 2, 10, 7, //
});
m.Invoke();
EXPECT_THAT(m.GetOutput(), ElementsAreArray({0.0, 5.75}));
}

TEST(FloatPoolingOpTest, AveragePoolWithTanh) {
FloatPoolingOpModel m(BuiltinOperator_AVERAGE_POOL_2D,
/*input=*/{TensorType_FLOAT32, {1, 2, 4, 1}},
/*stride_width=*/2, /*stride_height=*/2,
/*filter_width=*/2, /*filter_height=*/2, Padding_SAME,
ActivationFunctionType_TANH,
/*output=*/{TensorType_FLOAT32, {}});
m.SetInput({
0, -6, 2, 4, //
3, 2, 10, 7, //
});
m.Invoke();
EXPECT_THAT(m.GetOutput(), ElementsAreArray({-0.25, 5.75}));
}

TEST(FloatPoolingOpTest, AveragePoolWithSign) {
FloatPoolingOpModel m(BuiltinOperator_AVERAGE_POOL_2D,
/*input=*/{TensorType_FLOAT32, {1, 2, 4, 1}},
/*stride_width=*/2, /*stride_height=*/2,
/*filter_width=*/2, /*filter_height=*/2, Padding_SAME,
ActivationFunctionType_SIGN_BIT,
/*output=*/{TensorType_FLOAT32, {}});
m.SetInput({
0, -6, 2, 4, //
3, 2, -10, 7, //
});
m.Invoke();
EXPECT_THAT(m.GetOutput(), ElementsAreArray({-0.25, 0.75}));
}

TEST(FloatPoolingOpTest, AveragePoolWithRelu_N1_TO_1) {
FloatPoolingOpModel m(BuiltinOperator_AVERAGE_POOL_2D,
/*input=*/{TensorType_FLOAT32, {1, 2, 4, 1}},
/*stride_width=*/2, /*stride_height=*/2,
/*filter_width=*/2, /*filter_height=*/2, Padding_SAME,
ActivationFunctionType_RELU_N1_TO_1,
/*output=*/{TensorType_FLOAT32, {}});
m.SetInput({
0, -6, 2, 4, //
3, 2, -10, -7, //
});
m.Invoke();
EXPECT_THAT(m.GetOutput(), ElementsAreArray({-0.25, -1.00}));
}

TEST(FloatPoolingOpTest, AveragePoolWithStride) {
FloatPoolingOpModel m(BuiltinOperator_AVERAGE_POOL_2D,
/*input=*/{TensorType_FLOAT32, {1, 3, 4, 1}},
/*stride_width=*/1, /*stride_height=*/1,
/*filter_width=*/2, /*filter_height=*/2, Padding_SAME,
ActivationFunctionType_RELU,
/*output=*/{TensorType_FLOAT32, {}});
m.SetInput({
0, -6, 2, 4, //
3, 2, -10, 7, //
4, 8, 1, 2, //
});
m.Invoke();
EXPECT_THAT(m.GetOutput(), ElementsAreArray({0.0, 0.0, 0.75, 5.5, 4.25, 0.25,
0.0, 4.5, 6.0, 4.5, 1.5, 2.0}));
}

TEST(FloatPoolingOpTest, AveragePool) {
FloatPoolingOpModel m(BuiltinOperator_AVERAGE_POOL_2D,
/*input=*/{TensorType_FLOAT32, {1, 2, 4, 1}},
Expand Down