add int8 quantized unit_test for conv2d
Signed-off-by: Jing.Deng <Jing.Deng@verisilicon.com>
This commit is contained in:
Jing.Deng
Kainan Cha
parent
a7d962ac5c
commit
e2c52d2d8a
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@ -1320,3 +1320,210 @@ TEST(Conv2d, shape_9_9_1_1_uint8_DilationQuantizedTest) {
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EXPECT_TRUE(output_tensor->CopyDataFromTensor(output.data()));
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EXPECT_EQ(golden, output);
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}
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TEST(Conv2d, shape_3_2_2_1_int8_QuantizedPerTensorTest) {
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auto ctx = tim::vx::Context::Create();
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auto graph = ctx->CreateGraph();
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tim::vx::ShapeType input_shape({3, 2, 2, 1}); //whcn
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tim::vx::ShapeType weight_shape({2, 2, 2, 2}); //whio
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tim::vx::ShapeType bias_shape({weight_shape[3]});
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tim::vx::ShapeType output_shape(
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{2, 1, weight_shape[3], input_shape[3]}); //whcn
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float InputMin = -63.5, InputMax = 64, WeightMin = -63.5, WeightMax = 64,
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OutputMin = -63.5, OutputMax = 64;
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std::pair<float, int32_t> scalesAndZp;
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scalesAndZp = QuantizationParams<int8_t>(InputMin, InputMax);
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std::vector<float> scalesInput = {scalesAndZp.first};
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std::vector<int32_t> zeroPointsInput = {scalesAndZp.second};
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scalesAndZp = QuantizationParams<int8_t>(WeightMin, WeightMax);
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std::vector<float> scalesWeight = {1};
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std::vector<int32_t> zeroPointsWeight = {0};
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std::vector<float> scalesBias = {scalesInput[0] * scalesWeight[0]};
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std::vector<int32_t> zeroPointsBias = {0};
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scalesAndZp = QuantizationParams<int8_t>(OutputMin, OutputMax);
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std::vector<float> scalesOutput = {scalesAndZp.first};
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std::vector<int32_t> zeroPointsOutput = {scalesAndZp.second};
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tim::vx::Quantization quantInput(tim::vx::QuantType::ASYMMETRIC, 2,
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scalesInput, zeroPointsInput);
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tim::vx::Quantization quantWeight(tim::vx::QuantType::ASYMMETRIC, 2,
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scalesWeight, zeroPointsWeight);
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tim::vx::Quantization quantBias(tim::vx::QuantType::ASYMMETRIC, 2, scalesBias,
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zeroPointsBias);
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tim::vx::Quantization quantOutput(tim::vx::QuantType::ASYMMETRIC, 2,
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scalesOutput, zeroPointsOutput);
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tim::vx::TensorSpec input_spec(tim::vx::DataType::INT8, input_shape,
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tim::vx::TensorAttribute::INPUT, quantInput);
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tim::vx::TensorSpec weight_spec(tim::vx::DataType::INT8, weight_shape,
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tim::vx::TensorAttribute::CONSTANT,
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quantWeight);
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tim::vx::TensorSpec bias_spec(tim::vx::DataType::INT32, bias_shape,
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tim::vx::TensorAttribute::CONSTANT, quantBias);
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tim::vx::TensorSpec output_spec(tim::vx::DataType::INT8, output_shape,
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tim::vx::TensorAttribute::OUTPUT,
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quantOutput);
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// Input data nchw
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// min:-63.5 max:64 scale:0.5 Zp:-1
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std::vector<float> input_data_float = {3, 1, -2, 4, 2, -3,
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2, -1, -3, 3, -2, -4};
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std::vector<int8_t> input_data =
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Quantize<int8_t>(input_data_float, scalesInput[0], zeroPointsInput[0]);
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// weight_float_data = {1, 3, 3, 5, 2, 4, 4, 6, 7, 5, 3, 1, 8, 6, 4, 2};
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std::vector<int8_t> weight_data = {1, 3, 3, 5, 2, 4, 4, 6,
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7, 5, 3, 1, 8, 6, 4, 2};
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// bias data
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std::vector<float> bias_data_float = {3, -2};
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std::vector<int32_t> bias_data =
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Quantize<int32_t>(bias_data_float, scalesBias[0], zeroPointsBias[0]);
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// golden_int8_data = {61, -115, 111, -89}
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// min:-63.5 max:64 scale:0.5 Zp:-1
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std::vector<float> golden_float = {31, -57, 56, -44};
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std::vector<int8_t> golden =
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Quantize<int8_t>(golden_float, scalesOutput[0], zeroPointsOutput[0]);
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auto input_tensor = graph->CreateTensor(input_spec);
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auto weight_tensor = graph->CreateTensor(weight_spec, weight_data.data());
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auto bias_tensor = graph->CreateTensor(bias_spec, bias_data.data());
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auto output_tensor = graph->CreateTensor(output_spec);
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std::array<uint32_t, 2> ksize({weight_shape[1], weight_shape[2]});
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std::array<uint32_t, 2> stride({1, 1});
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std::array<uint32_t, 2> dilation({1, 1});
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auto padding = tim::vx::PadType::VALID;
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auto conv2d = graph->CreateOperation<tim::vx::ops::Conv2d>(
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weight_shape[3], padding, ksize, stride, dilation);
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(*conv2d)
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.BindInput(input_tensor)
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.BindInput(weight_tensor)
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.BindInput(bias_tensor)
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.BindOutput(output_tensor);
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EXPECT_TRUE(graph->Compile());
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input_tensor->CopyDataToTensor(input_data.data());
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EXPECT_TRUE(graph->Run());
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uint32_t output_size = 1;
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for (auto i : output_tensor->GetShape()) {
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output_size *= i;
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}
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std::vector<int8_t> output(output_size);
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EXPECT_TRUE(output_tensor->CopyDataFromTensor(output.data()));
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EXPECT_EQ(golden, output);
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}
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TEST(Conv2d, shape_3_2_2_1_int8_QuantizedPerChannelTest) {
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auto ctx = tim::vx::Context::Create();
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auto graph = ctx->CreateGraph();
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tim::vx::ShapeType input_shape({3, 2, 2, 1}); //whcn
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tim::vx::ShapeType weight_shape({2, 2, 2, 2}); //whio
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tim::vx::ShapeType bias_shape({weight_shape[3]});
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tim::vx::ShapeType output_shape(
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{2, 1, weight_shape[3], input_shape[3]}); //whcn
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float InputMin = -63.5, InputMax = 64, WeightMin = 0, WeightMax = 0,
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OutputMin = -63.5, OutputMax = 64;
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std::pair<float, int32_t> scalesAndZp;
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scalesAndZp = QuantizationParams<int8_t>(InputMin, InputMax);
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std::vector<float> scalesInput = {scalesAndZp.first};
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std::vector<int32_t> zeroPointsInput = {scalesAndZp.second};
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scalesAndZp = QuantizationParams<int8_t>(WeightMin, WeightMax);
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std::vector<float> scalesWeight = {1, 2};
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std::vector<int32_t> zeroPointsWeight = {0, 0};
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std::vector<float> scalesBias = {scalesInput[0] * scalesWeight[0],
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scalesInput[0] * scalesWeight[1]};
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std::vector<int32_t> zeroPointsBias = {0, 0};
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scalesAndZp = QuantizationParams<int8_t>(OutputMin, OutputMax);
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std::vector<float> scalesOutput = {scalesAndZp.first};
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std::vector<int32_t> zeroPointsOutput = {scalesAndZp.second};
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tim::vx::Quantization quantInput(tim::vx::QuantType::ASYMMETRIC, 2,
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scalesInput, zeroPointsInput);
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tim::vx::Quantization quantWeight(tim::vx::QuantType::SYMMETRIC_PER_CHANNEL,
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2, scalesWeight, zeroPointsWeight);
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tim::vx::Quantization quantBias(tim::vx::QuantType::SYMMETRIC_PER_CHANNEL, 0,
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scalesBias, zeroPointsBias);
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tim::vx::Quantization quantOutput(tim::vx::QuantType::ASYMMETRIC, 2,
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scalesOutput, zeroPointsOutput);
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tim::vx::TensorSpec input_spec(tim::vx::DataType::INT8, input_shape,
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tim::vx::TensorAttribute::INPUT, quantInput);
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tim::vx::TensorSpec weight_spec(tim::vx::DataType::INT8, weight_shape,
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tim::vx::TensorAttribute::CONSTANT,
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quantWeight);
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tim::vx::TensorSpec bias_spec(tim::vx::DataType::INT32, bias_shape,
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tim::vx::TensorAttribute::CONSTANT, quantBias);
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tim::vx::TensorSpec output_spec(tim::vx::DataType::INT8, output_shape,
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tim::vx::TensorAttribute::OUTPUT,
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quantOutput);
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// Input data nchw
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// min:-63.5 max:64 scale:0.5 Zp:-1
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std::vector<float> input_data_float = {3, 1, -2, 4, 2, -3,
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2, -1, -3, 3, -2, -4};
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std::vector<int8_t> input_data =
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Quantize<int8_t>(input_data_float, scalesInput[0], zeroPointsInput[0]);
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// weight_data_float = {1, 3, 3, 5, 2, 4, 4, 6, 7, 5, 3, 1, 8, 6, 4, 2};
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std::vector<int8_t> weight_data = {1, 3, 3, 5, 2, 4, 4, 6,
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4, 3, 2, 1, 4, 3, 2, 1};
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// bias_data_float ={3, -2};
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std::vector<int32_t> bias_data = {6, -2};
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// golden data
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// min:-63.5 max:64 scale:0.5 Zp:-1
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std::vector<float> golden_float = {31, -57, 64, -46};
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std::vector<int8_t> golden =
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Quantize<int8_t>(golden_float, scalesOutput[0], zeroPointsOutput[0]);
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auto input_tensor = graph->CreateTensor(input_spec);
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auto weight_tensor = graph->CreateTensor(weight_spec, weight_data.data());
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auto bias_tensor = graph->CreateTensor(bias_spec, bias_data.data());
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auto output_tensor = graph->CreateTensor(output_spec);
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std::array<uint32_t, 2> ksize({weight_shape[1], weight_shape[2]});
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std::array<uint32_t, 2> stride({1, 1});
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std::array<uint32_t, 2> dilation({1, 1});
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auto padding = tim::vx::PadType::VALID;
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auto conv2d = graph->CreateOperation<tim::vx::ops::Conv2d>(
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weight_shape[3], padding, ksize, stride, dilation);
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(*conv2d)
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.BindInput(input_tensor)
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.BindInput(weight_tensor)
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.BindInput(bias_tensor)
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.BindOutput(output_tensor);
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EXPECT_TRUE(graph->Compile());
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input_tensor->CopyDataToTensor(input_data.data());
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EXPECT_TRUE(graph->Run());
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uint32_t output_size = 1;
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for (auto i : output_tensor->GetShape()) {
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output_size *= i;
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}
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std::vector<int8_t> output(output_size);
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EXPECT_TRUE(output_tensor->CopyDataFromTensor(output.data()));
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EXPECT_EQ(golden, output);
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}
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