Merge pull request #26815 from amitsrivastava78:depthwise_conv_test
PiperOrigin-RevId: 245848981
This commit is contained in:
TensorFlower Gardener
commit
3e2f6fc75b
@ -39,8 +39,73 @@ using ::testing::ElementsAreArray;
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class BaseDepthwiseConvolutionOpModel : public SingleOpModel {
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public:
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// TODO(ahentz): Also test different activation types, bias, padding types,
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// stride values.
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BaseDepthwiseConvolutionOpModel(
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TfLiteRegistration* registration, const TensorData& input,
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const TensorData& filter, const TensorData& output, Padding padding_type,
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int stride_width, int stride_height,
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const ActivationFunctionType& fused_activation_function,
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int dilation_factor = 1) {
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input_ = AddInput(input);
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filter_ = AddInput(filter);
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int bias_size = GetShape(filter_)[3];
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if (input.type == TensorType_FLOAT32) {
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bias_ = AddInput({TensorType_FLOAT32, {bias_size}});
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} else {
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// This is a quantized version. The scale of 'bias' depends on the scales
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// of input and filter. Supposedly this is correctly set during quantized
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// training.
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if (filter.per_channel_quantization) {
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// per channel quantization.
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std::vector<float> bias_scale(
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filter.per_channel_quantization_scales.size());
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std::vector<int64_t> bias_zero_points(
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filter.per_channel_quantization_scales.size());
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for (int i = 0; i < filter.per_channel_quantization_scales.size();
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++i) {
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bias_scale[i] =
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input.scale * filter.per_channel_quantization_scales[i];
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bias_zero_points[i] = 0;
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}
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TensorData bias{TensorType_INT32,
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{bias_size},
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/*min=*/0,
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/*max=*/0,
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/*scale=*/0,
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/*zero_point=*/0,
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true,
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/*per_channel_scale=*/bias_scale,
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/*per_channel_zero_point=*/bias_zero_points,
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/*channel_index==*/0};
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bias_ = AddInput(bias);
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} else {
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// per tensor quantization.
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auto bias_scale = GetScale(input_) * GetScale(filter_);
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TensorData bias{TensorType_INT32, {bias_size}, 0, 0, bias_scale};
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bias_ = AddInput(bias);
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}
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}
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output_ = AddOutput(output);
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int input_depth = GetShape(input_)[3];
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int output_depth = GetShape(filter_)[3];
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int depth_mul = output_depth / input_depth;
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SetBuiltinOp(
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BuiltinOperator_DEPTHWISE_CONV_2D,
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BuiltinOptions_DepthwiseConv2DOptions,
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CreateDepthwiseConv2DOptions(
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builder_, padding_type, stride_width, stride_height, depth_mul,
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fused_activation_function, dilation_factor, dilation_factor)
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.Union());
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resolver_ = absl::make_unique<SingleOpResolver>(
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BuiltinOperator_DEPTHWISE_CONV_2D, registration);
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BuildInterpreter({GetShape(input_), GetShape(filter_), GetShape(bias_)});
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}
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BaseDepthwiseConvolutionOpModel(TfLiteRegistration* registration,
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const TensorData& input,
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const TensorData& filter,
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@ -144,6 +209,215 @@ class DepthwiseConvolutionOpTest : public SingleOpTest {
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}
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};
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TEST_P(DepthwiseConvolutionOpTest, ActivationReluTest) {
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DepthwiseConvolutionOpModel m(
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GetRegistration(), {TensorType_FLOAT32, {1, 3, 2, 2}},
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{TensorType_FLOAT32, {1, 2, 2, 4}}, {TensorType_FLOAT32, {}},
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Padding_VALID,
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/*stride_width*/ 1,
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/*stride_height*/ 1,
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/*ActivationFunctionType*/ ActivationFunctionType_RELU);
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m.SetInput({
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1, 2, 7, 8, // column 1
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3, 4, 9, 10, // column 2
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5, 6, 11, 12, // column 3
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});
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m.SetFilter({
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1, 2, 3, 4, //
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-9, 10, -11, 12, //
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5, 6, 7, 8, //
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13, -14, 15, -16, //
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});
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m.SetBias({1, 2, 3, 4});
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m.Invoke();
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EXPECT_THAT(m.GetOutput(), ElementsAreArray({
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71, 0, 99, 0, //
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91, 0, 127, 0, //
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}));
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}
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TEST_P(DepthwiseConvolutionOpTest, ActivationReluN1Test) {
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DepthwiseConvolutionOpModel m(
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GetRegistration(), {TensorType_FLOAT32, {1, 3, 2, 2}},
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{TensorType_FLOAT32, {1, 2, 2, 4}}, {TensorType_FLOAT32, {}},
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Padding_VALID,
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/*stride_width*/ 1,
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/*stride_height*/ 1,
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/*ActivationFunctionType*/ ActivationFunctionType_RELU_N1_TO_1);
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m.SetInput({
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1, 2, 7, 8, // column 1
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3, 4, 9, 10, // column 2
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5, 6, 11, 12, // column 3
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});
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m.SetFilter({
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1, 2, 3, 4, //
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-9, 10, -11, 12, //
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5, 6, 7, 8, //
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13, -14, 15, -16, //
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});
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m.SetBias({1, 2, 3, 4});
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m.Invoke();
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EXPECT_THAT(m.GetOutput(), ElementsAreArray({
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1, -1, 1, -1, //
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1, -1, 1, -1, //
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}));
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}
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TEST_P(DepthwiseConvolutionOpTest, ActivationRelu6Test) {
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DepthwiseConvolutionOpModel m(
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GetRegistration(), {TensorType_FLOAT32, {1, 3, 2, 2}},
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{TensorType_FLOAT32, {1, 2, 2, 4}}, {TensorType_FLOAT32, {}},
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Padding_VALID,
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/*stride_width*/ 1,
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/*stride_height*/ 1,
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/*ActivationFunctionType*/ ActivationFunctionType_RELU6);
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m.SetInput({
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1, 2, 7, 8, // column 1
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3, 4, 9, 10, // column 2
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5, 6, 11, 12, // column 3
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});
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m.SetFilter({
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1, 2, 3, 4, //
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-9, 10, -11, 12, //
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5, 6, 7, 8, //
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13, -14, 15, -16, //
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});
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m.SetBias({1, 2, 3, 4});
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m.Invoke();
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EXPECT_THAT(m.GetOutput(), ElementsAreArray({
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6, 0, 6, 0, //
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6, 0, 6, 0, //
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}));
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}
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TEST_P(DepthwiseConvolutionOpTest, ActivationTanhTest) {
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DepthwiseConvolutionOpModel m(
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GetRegistration(), {TensorType_FLOAT32, {1, 3, 2, 2}},
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{TensorType_FLOAT32, {1, 2, 2, 4}}, {TensorType_FLOAT32, {}},
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Padding_VALID,
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/*stride_width*/ 1,
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/*stride_height*/ 1,
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/*ActivationFunctionType*/ ActivationFunctionType_TANH);
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m.SetInput({
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1, 2, 7, 8, // column 1
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3, 4, 9, 10, // column 2
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5, 6, 11, 12, // column 3
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});
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m.SetFilter({
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1, 2, 3, 4, //
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-9, 10, -11, 12, //
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5, 6, 7, 8, //
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13, -14, 15, -16, //
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});
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m.SetBias({1, 2, 3, 4});
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m.Invoke();
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EXPECT_THAT(m.GetOutput(), ElementsAreArray({
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71, -34, 99, -20, //
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91, -26, 127, -4, //
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}));
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}
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TEST_P(DepthwiseConvolutionOpTest, ActivationSignTest) {
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DepthwiseConvolutionOpModel m(
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GetRegistration(), {TensorType_FLOAT32, {1, 3, 2, 2}},
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{TensorType_FLOAT32, {1, 2, 2, 4}}, {TensorType_FLOAT32, {}},
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Padding_VALID,
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/*stride_width*/ 1,
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/*stride_height*/ 1,
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/*ActivationFunctionType*/ ActivationFunctionType_SIGN_BIT);
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m.SetInput({
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1, 2, 7, 8, // column 1
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3, 4, 9, 10, // column 2
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5, 6, 10, 11, // column 3
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});
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m.SetFilter({
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1, 2, 3, 4, //
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-9, 10, -11, 12, //
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5, 6, 7, 8, //
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13, -14, 15, -16, //
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});
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m.SetBias({1, 2, 3, 4});
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m.Invoke();
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EXPECT_THAT(m.GetOutput(), ElementsAreArray({
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71, -34, 99, -20, //
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78, -12, 112, 12, //
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}));
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}
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TEST_P(DepthwiseConvolutionOpTest, StrideTest) {
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DepthwiseConvolutionOpModel m(
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GetRegistration(), {TensorType_FLOAT32, {1, 3, 2, 2}},
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{TensorType_FLOAT32, {1, 2, 2, 4}}, {TensorType_FLOAT32, {}},
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Padding_VALID,
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/*stride_width*/ 2,
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/*stride_height*/ 2,
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/*ActivationFunctionType*/ ActivationFunctionType_NONE);
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m.SetInput({
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1, 2, 7, 8, // column 1
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3, 4, 9, 10, // column 2
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5, 6, 11, 12, // column 3
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});
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m.SetFilter({
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1, 2, 3, 4, //
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-9, 10, -11, 12, //
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5, 6, 7, 8, //
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13, -14, 15, -16, //
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});
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m.SetBias({1, 2, 3, 4});
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m.Invoke();
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EXPECT_THAT(m.GetOutput(), ElementsAreArray({
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71, -34, 99, -20, //
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}));
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}
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TEST_P(DepthwiseConvolutionOpTest, PaddingTest) {
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DepthwiseConvolutionOpModel m(
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GetRegistration(), {TensorType_FLOAT32, {1, 3, 2, 2}},
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{TensorType_FLOAT32, {1, 2, 2, 4}}, {TensorType_FLOAT32, {}},
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Padding_SAME,
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/*stride_width*/ 2,
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/*stride_height*/ 2,
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/*ActivationFunctionType*/ ActivationFunctionType_NONE);
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m.SetInput({
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1, 2, 7, 8, // column 1
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3, 4, 9, 10, // column 2
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5, 6, 11, 12, // column 3
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});
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m.SetFilter({
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1, 2, 3, 4, //
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-9, 10, -11, 12, //
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5, 6, 7, 8, //
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13, -14, 15, -16, //
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});
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m.SetBias({1, 2, 3, 4});
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m.Invoke();
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EXPECT_THAT(m.GetOutput(), ElementsAreArray({
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71, -34, 99, -20, //
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-93, 122, -111, 172, //
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}));
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}
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TEST_P(DepthwiseConvolutionOpTest, SimpleTest) {
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DepthwiseConvolutionOpModel m(GetRegistration(),
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{TensorType_FLOAT32, {1, 3, 2, 2}},
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