IVGCVSW-5422 Add per-channel quantization tests to depthwise
Signed-off-by: Jan Eilers <jan.eilers@arm.com>
Change-Id: Ie51ce20540e5e7a704ce2b4be4e8cf64f91ea990
diff --git a/src/armnnTfLiteParser/test/DepthwiseConvolution2D.cpp b/src/armnnTfLiteParser/test/DepthwiseConvolution2D.cpp
index 5d220eb..7380d88 100644
--- a/src/armnnTfLiteParser/test/DepthwiseConvolution2D.cpp
+++ b/src/armnnTfLiteParser/test/DepthwiseConvolution2D.cpp
@@ -222,4 +222,364 @@
true);
}
+struct DepthwiseConvolution2dFixture2 : public ParserFlatbuffersFixture
+{
+ explicit DepthwiseConvolution2dFixture2(const std::string& inputShape,
+ const std::string& outputShape,
+ const std::string& filterShape,
+ const std::string& filterData,
+ const std::string& strides,
+ const std::string& paddingType,
+ const std::string biasShape = "",
+ const std::string biasData = "",
+ const std::string filter_quant_min = "[ 0.0 ]",
+ const std::string filter_quant_max = "[ 255.0 ]",
+ const std::string filter_quant_scale = "[ 1.0 ]",
+ const std::string filter_quant_zero_point = "[ 0 ]",
+ const std::string filter_quant_axis = ""
+ )
+ {
+ std::string inputTensors = "[ 0, 2 ]";
+ std::string biasTensor = "";
+ std::string biasBuffer = "";
+ if (biasShape.size() > 0 && biasData.size() > 0)
+ {
+ inputTensors = "[ 0, 2, 3 ]";
+ biasTensor = R"(
+ {
+ "shape": )" + biasShape + R"( ,
+ "type": "INT32",
+ "buffer": 3,
+ "name": "biasTensor",
+ "quantization": {
+ "min": [ 0.0 ],
+ "max": [ 255.0 ],
+ "scale": [ 1.0 ],
+ "zero_point": [ 0 ],
+ }
+ } )";
+ biasBuffer = R"(
+ { "data": )" + biasData + R"(, }, )";
+ }
+
+ std::string filter_qantization =
+ R"(
+ "min": )" + filter_quant_min + R"(,
+ "max": )" + filter_quant_max + R"(,
+ "scale": )" + filter_quant_scale + R"(,
+ "zero_point": )" + filter_quant_zero_point;
+ // A given quantization axis indicates if per channel quantization is used for filters
+ if (filter_quant_axis.size() > 0)
+ {
+ filter_qantization +=
+ R"(,
+ "quantized_dimension": )" + filter_quant_axis;
+ }
+ m_JsonString = R"(
+ {
+ "version": 3,
+ "operator_codes": [ { "builtin_code": "DEPTHWISE_CONV_2D" } ],
+ "subgraphs": [ {
+ "tensors": [
+ {
+ "shape": )" + inputShape + R"(,
+ "type": "INT8",
+ "buffer": 0,
+ "name": "inputTensor",
+ "quantization": {
+ "min": [ 0.0 ],
+ "max": [ 255.0 ],
+ "scale": [ 1.0 ],
+ "zero_point": [ 0 ],
+ }
+ },
+ {
+ "shape": )" + outputShape + R"(,
+ "type": "INT8",
+ "buffer": 1,
+ "name": "outputTensor",
+ "quantization": {
+ "min": [ 0.0 ],
+ "max": [ 511.0 ],
+ "scale": [ 1.0 ],
+ "zero_point": [ 0 ],
+ }
+ },
+ {
+ "shape": )" + filterShape + R"(,
+ "type": "INT8",
+ "buffer": 2,
+ "name": "filterTensor",
+ "quantization": {)" + filter_qantization + R"(
+ }
+ }, )" + biasTensor + R"(
+ ],
+ "inputs": [ 0 ],
+ "outputs": [ 1 ],
+ "operators": [
+ {
+ "opcode_index": 0,
+ "inputs": )" + inputTensors + R"(,
+ "outputs": [ 1 ],
+ "builtin_options_type": "DepthwiseConv2DOptions",
+ "builtin_options": {
+ "padding": ")" + paddingType + R"(",
+ "stride_w": )" + strides+ R"(,
+ "stride_h": )" + strides+ R"(,
+ "depth_multiplier": 1,
+ "fused_activation_function": "NONE"
+ },
+ "custom_options_format": "FLEXBUFFERS"
+ }
+ ],
+ } ],
+ "buffers" : [
+ { },
+ { },
+ { "data": )" + filterData + R"(, }, )"
+ + biasBuffer + R"(
+ ]
+ }
+ )";
+ SetupSingleInputSingleOutput("inputTensor", "outputTensor");
+ }
+};
+
+
+// No quantization meaning scale=1.0 and offset=0.0 and tensor quantization
+struct DepthwiseConvolution2dNoQuantFixture : DepthwiseConvolution2dFixture2
+{
+ DepthwiseConvolution2dNoQuantFixture()
+ : DepthwiseConvolution2dFixture2("[ 1, 3, 3, 3 ]", // inputShape
+ "[ 1, 3, 3, 3 ]", // outputShape
+ "[ 1, 3, 3, 3 ]", // filterShape
+ "[ 9,8,7, 6,5,4, 3,2,1, "
+ "9,8,7, 6,5,4, 3,2,1, "
+ "9,8,7, 6,5,4, 3,2,1 ]", // filterData
+ "1", // stride w and h
+ "SAME", // padding type
+ "", // bias shape
+ "" // bias data
+ )
+ {}
+};
+
+// No quantization meaning scale=1.0 and offset=0.0 and tensor quantization
+BOOST_FIXTURE_TEST_CASE(ParseDepthwiseConv2DNoQuant, DepthwiseConvolution2dNoQuantFixture)
+{
+ RunTest<4, armnn::DataType::QAsymmS8>(
+ 0,
+ { 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1},
+ { 18, 14, 10, 36, 30, 24, 30, 26, 22, 27, 21, 15, 54, 45,
+ 36, 45, 39, 33, 18, 14, 10, 36, 30, 24, 30, 26, 22});
+}
+
+// Uses per channel quantization on weights but with scales = 1.0 and offsets = 0.0
+struct DepthwiseConvolution2dNoChannelQuantFixture : DepthwiseConvolution2dFixture2
+{
+ DepthwiseConvolution2dNoChannelQuantFixture()
+ : DepthwiseConvolution2dFixture2("[ 1, 3, 3, 3 ]", // inputShape
+ "[ 1, 3, 3, 3 ]", // outputShape
+ "[ 1, 3, 3, 3 ]", // filterShape
+ "[ 9,8,7, 6,5,4, 3,2,1, 9,8,7, 6,5,4, 3,2,1, 9,8,7, 6,5,4, 3,2,1 ]", // filterData
+ "1", // stride w and h
+ "SAME", // padding type
+ "", // bias shape
+ "", // bias data
+ "[ 0.0 ]", // filter quantization min values
+ "[ 255.0 ]", // filter quantization max values
+ "[ 1.0, 1.0, 1.0]", // filter quantization scales
+ "[ 0, 0, 0]", // filter quantization zero-points
+ "3" // filter quantized axis
+ // (in case of per channel quantization)
+ )
+ {}
+};
+
+// Uses per channel quantization on weights but with scales = 1.0 and offsets = 0.0
+BOOST_FIXTURE_TEST_CASE(ParseDepthwiseConv2DFilterNoChannelQuant, DepthwiseConvolution2dNoChannelQuantFixture)
+{
+ RunTest<4, armnn::DataType::QAsymmS8>(
+ 0,
+ { 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1},
+ { 18, 14, 10, 36, 30, 24, 30, 26, 22, 27, 21, 15, 54, 45,
+ 36, 45, 39, 33, 18, 14, 10, 36, 30, 24, 30, 26, 22});
+}
+
+// Uses per channel quantization on weights but all scales are set to the same value
+struct DepthwiseConvolution2dWeightsPerChannelQuantFixture : DepthwiseConvolution2dFixture2
+{
+ DepthwiseConvolution2dWeightsPerChannelQuantFixture()
+ : DepthwiseConvolution2dFixture2("[ 1, 3, 3, 3 ]", // inputShape
+ "[ 1, 3, 3, 3 ]", // outputShape
+ "[ 1, 3, 3, 3 ]", // filterShape
+ // filterData is [ 9,8,7, 6,5,4, 3,2,1, 9,8,7, 6,5,4, 3,2,1, 9,8,7, 6,5,4, 3,2,1 ]
+ // quantized per channel with q_dim=3
+ "[36, 32, 28, 24, 20, 16, 12, 8, 4, 36, 32, 28, 24, "
+ "20, 16, 12, 8, 4, 36, 32, 28, 24, 20, 16, 12, 8, 4]",
+ "1", // stride w and h
+ "SAME", // padding type
+ "", // bias shape
+ "", // bias data
+ "[ 0.0 ]", // filter quantization min values
+ "[ 255.0 ]", // filter quantization max values
+ "[ 0.25, 0.25, 0.25]", // filter quantization scales
+ "[ 0, 0, 0]", // filter quantization zero-points
+ "3" // filter quantized axis
+ // (in case of per channel quantization)
+ )
+ {}
+};
+
+// Weights are per channel quantized but all scales are set to the same value
+BOOST_FIXTURE_TEST_CASE(ParseDepthwiseConv2DFilterWeightsPerChannelQuant,
+ DepthwiseConvolution2dWeightsPerChannelQuantFixture)
+{
+ RunTest<4, armnn::DataType::QAsymmS8>(
+ 0,
+ { 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1},
+ { 18, 14, 10, 36, 30, 24, 30, 26, 22, 27, 21, 15, 54, 45,
+ 36, 45, 39, 33, 18, 14, 10, 36, 30, 24, 30, 26, 22});
+}
+
+// Uses per channel quantization on weights all scales are different in this test
+struct DepthwiseConvolution2dWeightsPerChannelQuant1Fixture : DepthwiseConvolution2dFixture2
+{
+ DepthwiseConvolution2dWeightsPerChannelQuant1Fixture()
+ : DepthwiseConvolution2dFixture2("[ 1, 3, 3, 3 ]", // inputShape
+ "[ 1, 3, 3, 3 ]", // outputShape
+ "[ 1, 3, 3, 3 ]", // filterShape
+ // filterData is [ 9,8,7, 6,5,4, 3,2,1, 9,8,7, 6,5,4, 3,2,1, 9,8,7, 6,5,4, 3,2,1 ]
+ // quantized per channel with q_dim=3
+ "[36, 40, 70, 24, 25, 40, 12, 10, 10, 36, 40, 70, 24, "
+ "25, 40, 12, 10, 10, 36, 40, 70, 24, 25, 40, 12, 10, 10]",
+ "1", // stride w and h
+ "SAME", // padding type
+ "", // bias shape
+ "", // bias data
+ "[ 0.0 ]", // filter quantization min values
+ "[ 255.0 ]", // filter quantization max values
+ "[ 0.25, 0.2, 0.1]", // filter quantization scales
+ "[ 0, 0, 0]", // filter quantization zero-points
+ "3" // filter quantized axis
+ // (in case of per channel quantization)
+ )
+ {}
+};
+
+// Uses per channel quantization on weights all scales are different in this test
+BOOST_FIXTURE_TEST_CASE(ParseDepthwiseConv2DFilterWeightsPerChannelQuant1,
+ DepthwiseConvolution2dWeightsPerChannelQuant1Fixture)
+{
+ RunTest<4, armnn::DataType::QAsymmS8>(
+ 0,
+ { 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1},
+ { 18, 14, 10, 36, 30, 24, 30, 26, 22, 27, 21, 15, 54, 45,
+ 36, 45, 39, 33, 18, 14, 10, 36, 30, 24, 30, 26, 22});
+}
+
+
+// Uses per channel quantization on weights all scales are different in this test
+// Uses different shape for weights and input compared to the other tests above
+struct DepthwiseConvolution2dWeightsPerChannelQuant2Fixture : DepthwiseConvolution2dFixture2
+{
+ DepthwiseConvolution2dWeightsPerChannelQuant2Fixture()
+ : DepthwiseConvolution2dFixture2("[ 1, 4, 4, 4 ]", // inputShape
+ "[ 1, 4, 4, 4 ]", // outputShape
+ "[ 1, 2, 2, 4 ]", // filterShape
+ // filterData is [ 9,8,7,6, 5,4,3,2, 1,9,8,7, 6,5,4,3 ]
+ // quantized per channel with q_dim=3
+ "[36, 40, 70, 20, 20, 20, 30, 6, 4, 45, 80, 23, 24, 25, 40, 10]",
+ "1", // stride w and h
+ "SAME", // padding type
+ "", // bias shape
+ "", // bias data
+ "[ 0.0 ]", // filter quantization min values
+ "[ 255.0 ]", // filter quantization max values
+ "[ 0.25, 0.2, 0.1, 0.3]", // filter quantization scales
+ "[ 0, 0, 0, 0]", // filter quantization zero-points
+ "3" // filter quantized axis
+ // (in case of per channel quantization)
+ )
+ {}
+};
+
+// Uses per channel quantization on weights all scales are different in this test
+// Uses different shape for weights and input compared to the other tests above
+BOOST_FIXTURE_TEST_CASE(ParseDepthwiseConv2DFilterWeightsPerChannelQuant2,
+ DepthwiseConvolution2dWeightsPerChannelQuant2Fixture)
+{
+ RunTest<4, armnn::DataType::QAsymmS8>(
+ 0,
+ { 1,1,1,1, 1,1,1,1, 1,1,1,1, 1,1,1,1,
+ 1,1,1,1, 1,1,1,1, 1,1,1,1, 1,1,1,1,
+ 1,1,1,1, 1,1,1,1, 1,1,1,1, 1,1,1,1,
+ 1,1,1,1, 1,1,1,1, 1,1,1,1, 1,1,1,1},
+ { 21, 26, 22, 18, 21, 26, 22, 18, 21, 26, 22, 18, 10, 17, 15, 13,
+ 21, 26, 22, 18, 21, 26, 22, 18, 21, 26, 22, 18, 10, 17, 15, 13,
+ 21, 26, 22, 18, 21, 26, 22, 18, 21, 26, 22, 18, 10, 17, 15, 13,
+ 14, 12, 10, 8, 14, 12, 10, 8, 14, 12, 10, 8, 9, 8, 7, 6});
+}
+
+// Test for depthwise_multiplier different to one (M > 1)
+struct DepthwiseConvolution2dWeightsPerChannelQuant4Fixture : DepthwiseConvolution2dFixture2
+{
+ DepthwiseConvolution2dWeightsPerChannelQuant4Fixture()
+ : DepthwiseConvolution2dFixture2("[ 1, 4, 4, 4 ]", // inputShape
+ "[ 1, 4, 4, 16 ]", // outputShape
+ "[ 1, 2, 2, 16 ]", // filterShape
+ // filter data is [ 9,8,7,6, 5,4,3,2, 1,9,8,7, 6,5,4,3,
+ // 9,8,7,6, 5,4,3,2, 1,9,8,7, 6,5,4,3,
+ // 9,8,7,6, 5,4,3,2, 1,9,8,7, 6,5,4,3,
+ // 9,8,7,6, 5,4,3,2, 1,9,8,7, 6,5,4,3 ]
+ // quantized per channel with q_dim=3
+ "[36, 40, 70, 20, 20, 20, 30, 6, 4, 45, 80, 23, 24, 25, 40, 10, "
+ "36, 40, 70, 20, 20, 20, 30, 6, 4, 45, 80, 23, 24, 25, 40, 10, "
+ "36, 40, 70, 20, 20, 20, 30, 6, 4, 45, 80, 23, 24, 25, 40, 10, "
+ "36, 40, 70, 20, 20, 20, 30, 6, 4, 45, 80, 23, 24, 25, 40, 10]",
+ "1", // stride w and h
+ "SAME", // padding type
+ "", // bias shape
+ "", // bias data
+ "[ 0.0 ]", // filter quantization min values
+ "[ 255.0 ]", // filter quantization max values
+ "[ 0.25, 0.2, 0.1, 0.3,"
+ "0.25, 0.2, 0.1, 0.3,"
+ "0.25, 0.2, 0.1, 0.3,"
+ "0.25, 0.2, 0.1, 0.3]", // filter quantization scales
+ "[ 0, 0, 0, 0]", // filter quantization zero-points
+ "3" // filter quantized axis
+ // (in case of per channel quantization)
+ )
+ {}
+};
+
+// Test for depthwise_multiplier different to one (M > 1)
+BOOST_FIXTURE_TEST_CASE(ParseDepthwiseConv2DFilterWeightsPerChannelQuant4,
+ DepthwiseConvolution2dWeightsPerChannelQuant4Fixture)
+{
+ RunTest<4, armnn::DataType::QAsymmS8>(
+ 0,
+ { 1,1,1,1, 1,1,1,1, 1,1,1,1, 1,1,1,1,
+ 1,1,1,1, 1,1,1,1, 1,1,1,1, 1,1,1,1,
+ 1,1,1,1, 1,1,1,1, 1,1,1,1, 1,1,1,1,
+ 1,1,1,1, 1,1,1,1, 1,1,1,1, 1,1,1,1},
+ { 36, 32, 28, 24, 20, 16, 12, 8, 4, 36, 32, 28, 24, 20, 16, 12,
+ 36, 32, 28, 24, 20, 16, 12, 8, 4, 36, 32, 28, 24, 20, 16, 12,
+ 36, 32, 28, 24, 20, 16, 12, 8, 4, 36, 32, 28, 24, 20, 16, 12,
+ 18, 16, 14, 12, 10, 8, 6, 4, 2, 18, 16, 14, 12, 10, 8, 6,
+ 36, 32, 28, 24, 20, 16, 12, 8, 4, 36, 32, 28, 24, 20, 16, 12,
+ 36, 32, 28, 24, 20, 16, 12, 8, 4, 36, 32, 28, 24, 20, 16, 12,
+ 36, 32, 28, 24, 20, 16, 12, 8, 4, 36, 32, 28, 24, 20, 16, 12,
+ 18, 16, 14, 12, 10, 8, 6, 4, 2, 18, 16, 14, 12, 10, 8, 6,
+ 36, 32, 28, 24, 20, 16, 12, 8, 4, 36, 32, 28, 24, 20, 16, 12,
+ 36, 32, 28, 24, 20, 16, 12, 8, 4, 36, 32, 28, 24, 20, 16, 12,
+ 36, 32, 28, 24, 20, 16, 12, 8, 4, 36, 32, 28, 24, 20, 16, 12,
+ 18, 16, 14, 12, 10, 8, 6, 4, 2, 18, 16, 14, 12, 10, 8, 6,
+ 18, 16, 14, 12, 10, 8, 6, 4, 2, 18, 16, 14, 12, 10, 8, 6,
+ 18, 16, 14, 12, 10, 8, 6, 4, 2, 18, 16, 14, 12, 10, 8, 6,
+ 18, 16, 14, 12, 10, 8, 6, 4, 2, 18, 16, 14, 12, 10, 8, 6,
+ 9, 8, 7, 6, 5, 4, 3, 2, 1, 9, 8, 7, 6, 5, 4, 3});
+}
+
BOOST_AUTO_TEST_SUITE_END()