add uint8 and int8 unit_test for depthwise convolution. modify the api of 'conv2d' constructor
Signed-off-by: Jing.Deng <Jing.Deng@verisilicon.com>
This commit is contained in:
Jing.Deng
Kainan Cha
parent
574c036a69
commit
1672ef99ed
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@ -2,7 +2,6 @@
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#include "gtest/gtest.h"
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#include "src/tim/vx/test_utils.h"
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#include "tim/transform/layout_inference.h"
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#include "tim/vx/context.h"
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#include "tim/vx/graph.h"
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#include "tim/vx/types.h"
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@ -54,13 +53,12 @@ TEST(Conv2d, shape_4_2_1_1_float32_PaddingTest) {
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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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auto padding = tim::vx::PadType::SAME;
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std::array<uint32_t, 2> stride({1, 1});
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std::array<uint32_t, 2> dilation({0, 0});
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auto padding = tim::vx::PadType::SAME;
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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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padding, 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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@ -124,13 +122,12 @@ TEST(Conv2d, shape_4_2_2_2_float32_PointwiseTest) {
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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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auto padding = tim::vx::PadType::SAME;
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std::array<uint32_t, 2> stride({1, 1});
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std::array<uint32_t, 2> dilation({0, 0});
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auto padding = tim::vx::PadType::SAME;
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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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padding, 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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@ -196,13 +193,12 @@ TEST(Conv2d, shape_4_2_1_2_float32_SimpleTest) {
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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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auto padding = tim::vx::PadType::SAME;
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std::array<uint32_t, 2> stride({2, 2});
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std::array<uint32_t, 2> dilation({0, 0});
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auto padding = tim::vx::PadType::SAME;
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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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padding, 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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@ -263,13 +259,12 @@ TEST(Conv2d, shape_4_2_2_2_float32_SimpleChannelsTest) {
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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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auto padding = tim::vx::PadType::SAME;
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std::array<uint32_t, 2> stride({2, 2});
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std::array<uint32_t, 2> dilation({0, 0});
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auto padding = tim::vx::PadType::SAME;
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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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padding, 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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@ -335,13 +330,12 @@ TEST(Conv2d, shape_6_3_1_1_float32_SimpleAnisotropicStridesTest) {
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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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auto padding = tim::vx::PadType::VALID;
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std::array<uint32_t, 2> stride({3, 1});
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std::array<uint32_t, 2> dilation({0, 0});
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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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padding, 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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@ -401,13 +395,12 @@ TEST(Conv2d, shape_4_3_1_1_float32_HandCalculatedTest) {
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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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auto padding = tim::vx::PadType::SAME;
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std::array<uint32_t, 2> stride({1, 1});
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std::array<uint32_t, 2> dilation({0, 0});
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auto padding = tim::vx::PadType::SAME;
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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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padding, 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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@ -467,13 +460,12 @@ TEST(Conv2d, shape_4_3_1_1_float32_HandCalculatedConstFilterTest) {
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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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auto padding = tim::vx::PadType::SAME;
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std::array<uint32_t, 2> stride({1, 1});
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std::array<uint32_t, 2> dilation({0, 0});
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auto padding = tim::vx::PadType::SAME;
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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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padding, 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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@ -533,13 +525,12 @@ TEST(Conv2d, shape_4_3_1_1_float32_HandCalculatedBiasTest) {
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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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auto padding = tim::vx::PadType::SAME;
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std::array<uint32_t, 2> stride({1, 1});
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std::array<uint32_t, 2> dilation({0, 0});
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auto padding = tim::vx::PadType::SAME;
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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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padding, 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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@ -598,13 +589,12 @@ TEST(Conv2d, shape_4_3_1_1_float32_HandCalculatedValidTest) {
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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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auto padding = tim::vx::PadType::VALID;
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std::array<uint32_t, 2> stride({1, 1});
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std::array<uint32_t, 2> dilation({0, 0});
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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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padding, 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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@ -667,13 +657,12 @@ TEST(Conv2d, shape_4_2_2_2_float32_DisabledPointwiseMultifilterTest) {
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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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auto padding = tim::vx::PadType::VALID;
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std::array<uint32_t, 2> stride({1, 1});
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std::array<uint32_t, 2> dilation({0, 0});
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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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padding, 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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@ -736,13 +725,12 @@ TEST(Conv2d, shape_9_9_1_1_float32_SimpleDilationTest) {
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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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auto padding = tim::vx::PadType::VALID;
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std::array<uint32_t, 2> stride({1, 1});
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std::array<uint32_t, 2> dilation({3, 3});
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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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padding, 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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@ -803,13 +791,12 @@ TEST(Conv2d, shape_4_2_1_2_float32_StrideTest) {
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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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auto padding = tim::vx::PadType::VALID;
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std::array<uint32_t, 2> stride({1, 1});
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std::array<uint32_t, 2> dilation({0, 0});
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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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padding, 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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@ -869,13 +856,12 @@ TEST(Conv2d, shape_4_2_1_2_float32_InputAndFilterSameWidthHeightTest) {
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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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auto padding = tim::vx::PadType::VALID;
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std::array<uint32_t, 2> stride({1, 1});
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std::array<uint32_t, 2> dilation({0, 0});
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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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padding, 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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@ -974,13 +960,12 @@ TEST(Conv2d, shape_4_2_1_2_uint8_QuantizedTest1) {
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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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auto padding = tim::vx::PadType::VALID;
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std::array<uint32_t, 2> stride({2, 2});
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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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padding, 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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@ -1080,13 +1065,12 @@ TEST(Conv2d, shape_4_2_1_2_uint8_QuantizedTest2) {
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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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auto padding = tim::vx::PadType::VALID;
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std::array<uint32_t, 2> stride({2, 2});
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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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padding, 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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@ -1185,13 +1169,12 @@ TEST(Conv2d, shape_6_3_1_1_uint8_AnisotropicStridesQuantizedTest) {
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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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auto padding = tim::vx::PadType::VALID;
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std::array<uint32_t, 2> stride({3, 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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padding, 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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@ -1293,13 +1276,12 @@ TEST(Conv2d, shape_9_9_1_1_uint8_DilationQuantizedTest) {
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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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auto padding = tim::vx::PadType::VALID;
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std::array<uint32_t, 2> stride({1, 1});
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std::array<uint32_t, 2> dilation({3, 3});
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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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padding, 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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@ -1397,13 +1379,12 @@ TEST(Conv2d, shape_3_2_2_1_int8_QuantizedPerTensorTest) {
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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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auto padding = tim::vx::PadType::VALID;
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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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padding, 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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@ -1500,13 +1481,12 @@ TEST(Conv2d, shape_3_2_2_1_int8_QuantizedPerChannelTest) {
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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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auto padding = tim::vx::PadType::VALID;
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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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padding, 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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@ -1,4 +1,5 @@
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#include "gtest/gtest.h"
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#include "src/tim/vx/test_utils.h"
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#include "tim/vx/context.h"
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#include "tim/vx/graph.h"
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#include "tim/vx/ops/conv2d.h"
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@ -70,7 +71,7 @@ TEST(DepthwiseConv, shape_2_3_2_1_float32_SimpleTest) {
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EXPECT_EQ(golden, output);
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}
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TEST(DepthwiseConv, shape_2_3_2_1_float32_StrideTest) {
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TEST(DepthwiseConv, shape_2_3_2_1_float32_StrideValidTest) {
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auto ctx = tim::vx::Context::Create();
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auto graph = ctx->CreateGraph();
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@ -136,6 +137,138 @@ TEST(DepthwiseConv, shape_2_3_2_1_float32_StrideTest) {
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EXPECT_EQ(golden, output);
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}
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TEST(DepthwiseConv, shape_2_3_2_1_float32_StrideSameTest) {
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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({2, 3, 2, 1}); //whcn
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tim::vx::ShapeType weight_shape({2, 2, 4, 1}); //whoi
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tim::vx::ShapeType bias_shape({weight_shape[2]});
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tim::vx::ShapeType output_shape(
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{1, 2, weight_shape[2], input_shape[3]}); //whcn
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tim::vx::TensorSpec input_spec(tim::vx::DataType::FLOAT32, input_shape,
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tim::vx::TensorAttribute::INPUT);
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tim::vx::TensorSpec weight_spec(tim::vx::DataType::FLOAT32, weight_shape,
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tim::vx::TensorAttribute::CONSTANT);
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tim::vx::TensorSpec bias_spec(tim::vx::DataType::FLOAT32, bias_shape,
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tim::vx::TensorAttribute::CONSTANT);
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tim::vx::TensorSpec output_spec(tim::vx::DataType::FLOAT32, output_shape,
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tim::vx::TensorAttribute::OUTPUT);
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// Input data nchw
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std::vector<float> input_data = {1, 7, 3, 9, 5, 11, 2, 8, 4, 10, 6, 12};
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// weight data iohw
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std::vector<float> weight_data = {1, -9, 5, 13, 2, 10, 6, -14,
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3, -11, 7, 15, 4, 12, 8, -16};
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// bias data
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std::vector<float> bias_data = {1, 2, 3, 4};
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// nchw
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std::vector<float> golden = {71, -93, -34, 122, 99, -111, -20, 172};
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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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auto padding = tim::vx::PadType::SAME;
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std::array<uint32_t, 2> stride({2, 2});
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||||
std::array<uint32_t, 2> dilation({1, 1});
|
||||
int32_t multiplier = weight_shape[2] / input_shape[2];
|
||||
|
||||
auto conv2d = graph->CreateOperation<tim::vx::ops::Conv2d>(
|
||||
padding, stride, dilation, multiplier);
|
||||
(*conv2d)
|
||||
.BindInput(input_tensor)
|
||||
.BindInput(weight_tensor)
|
||||
.BindInput(bias_tensor)
|
||||
.BindOutput(output_tensor);
|
||||
|
||||
EXPECT_TRUE(graph->Compile());
|
||||
|
||||
input_tensor->CopyDataToTensor(input_data.data());
|
||||
|
||||
EXPECT_TRUE(graph->Run());
|
||||
|
||||
uint32_t output_size = 1;
|
||||
for (auto i : output_tensor->GetShape()) {
|
||||
output_size *= i;
|
||||
}
|
||||
std::vector<float> output(output_size);
|
||||
EXPECT_TRUE(output_tensor->CopyDataFromTensor(output.data()));
|
||||
EXPECT_EQ(golden, output);
|
||||
}
|
||||
|
||||
TEST(DepthwiseConv, shape_2_3_2_1_float32_StrideSameDilationTest) {
|
||||
auto ctx = tim::vx::Context::Create();
|
||||
auto graph = ctx->CreateGraph();
|
||||
|
||||
tim::vx::ShapeType input_shape({2, 3, 2, 1}); //whcn
|
||||
tim::vx::ShapeType weight_shape({2, 2, 4, 1}); //whoi
|
||||
tim::vx::ShapeType bias_shape({weight_shape[2]});
|
||||
tim::vx::ShapeType output_shape(
|
||||
{1, 2, weight_shape[2], input_shape[3]}); //whcn
|
||||
|
||||
tim::vx::TensorSpec input_spec(tim::vx::DataType::FLOAT32, input_shape,
|
||||
tim::vx::TensorAttribute::INPUT);
|
||||
tim::vx::TensorSpec weight_spec(tim::vx::DataType::FLOAT32, weight_shape,
|
||||
tim::vx::TensorAttribute::CONSTANT);
|
||||
tim::vx::TensorSpec bias_spec(tim::vx::DataType::FLOAT32, bias_shape,
|
||||
tim::vx::TensorAttribute::CONSTANT);
|
||||
tim::vx::TensorSpec output_spec(tim::vx::DataType::FLOAT32, output_shape,
|
||||
tim::vx::TensorAttribute::OUTPUT);
|
||||
|
||||
// Input data nchw
|
||||
std::vector<float> input_data = {1, 7, 3, 9, 5, 11, 2, 8, 4, 10, 6, 12};
|
||||
|
||||
// weight data iohw
|
||||
std::vector<float> weight_data = {1, -9, 5, 13, 2, 10, 6, -14,
|
||||
3, -11, 7, 15, 4, 12, 8, -16};
|
||||
|
||||
// bias data
|
||||
std::vector<float> bias_data = {1, 2, 3, 4};
|
||||
|
||||
// nchw
|
||||
std::vector<float> golden = {1, 1, 2, 2, 3, 3, 4, 4};
|
||||
|
||||
auto input_tensor = graph->CreateTensor(input_spec);
|
||||
auto weight_tensor = graph->CreateTensor(weight_spec, weight_data.data());
|
||||
auto bias_tensor = graph->CreateTensor(bias_spec, bias_data.data());
|
||||
|
||||
auto output_tensor = graph->CreateTensor(output_spec);
|
||||
|
||||
auto padding = tim::vx::PadType::SAME;
|
||||
std::array<uint32_t, 2> stride({2, 2});
|
||||
std::array<uint32_t, 2> dilation({10, 10});
|
||||
int32_t multiplier = weight_shape[2] / input_shape[2];
|
||||
|
||||
auto conv2d = graph->CreateOperation<tim::vx::ops::Conv2d>(
|
||||
padding, stride, dilation, multiplier);
|
||||
(*conv2d)
|
||||
.BindInput(input_tensor)
|
||||
.BindInput(weight_tensor)
|
||||
.BindInput(bias_tensor)
|
||||
.BindOutput(output_tensor);
|
||||
|
||||
EXPECT_TRUE(graph->Compile());
|
||||
|
||||
input_tensor->CopyDataToTensor(input_data.data());
|
||||
|
||||
EXPECT_TRUE(graph->Run());
|
||||
|
||||
uint32_t output_size = 1;
|
||||
for (auto i : output_tensor->GetShape()) {
|
||||
output_size *= i;
|
||||
}
|
||||
std::vector<float> output(output_size);
|
||||
EXPECT_TRUE(output_tensor->CopyDataFromTensor(output.data()));
|
||||
EXPECT_EQ(golden, output);
|
||||
}
|
||||
|
||||
TEST(DepthwiseConv, shape_2_3_2_1_float32_PaddingTest) {
|
||||
auto ctx = tim::vx::Context::Create();
|
||||
auto graph = ctx->CreateGraph();
|
||||
|
|
@ -471,3 +604,772 @@ TEST(DepthwiseConv, shape_2_2_1_4_float32_BatchSameTest) {
|
|||
EXPECT_TRUE(output_tensor->CopyDataFromTensor(output.data()));
|
||||
EXPECT_EQ(golden, output);
|
||||
}
|
||||
|
||||
TEST(DepthwiseConv, shape_2_3_2_1_uint8_QuantizedTest) {
|
||||
auto ctx = tim::vx::Context::Create();
|
||||
auto graph = ctx->CreateGraph();
|
||||
|
||||
tim::vx::ShapeType input_shape({2, 3, 2, 1}); //whcn
|
||||
tim::vx::ShapeType weight_shape({2, 2, 4, 1}); //whoi
|
||||
tim::vx::ShapeType bias_shape({weight_shape[2]});
|
||||
tim::vx::ShapeType output_shape(
|
||||
{1, 2, weight_shape[2], input_shape[3]}); //whcn
|
||||
|
||||
float InputMin = -63.5, InputMax = 64, WeightMin = -63.5, WeightMax = 64,
|
||||
OutputMin = -127, OutputMax = 128;
|
||||
|
||||
std::pair<float, int32_t> scalesAndZp;
|
||||
|
||||
scalesAndZp = QuantizationParams<u_int8_t>(InputMin, InputMax);
|
||||
std::vector<float> scalesInput = {scalesAndZp.first};
|
||||
std::vector<int32_t> zeroPointsInput = {scalesAndZp.second};
|
||||
|
||||
scalesAndZp = QuantizationParams<u_int8_t>(WeightMin, WeightMax);
|
||||
std::vector<float> scalesWeight = {scalesAndZp.first};
|
||||
std::vector<int32_t> zeroPointsWeight = {scalesAndZp.second};
|
||||
|
||||
std::vector<float> scalesBias = {scalesInput[0] * scalesWeight[0]};
|
||||
std::vector<int32_t> zeroPointsBias = {0};
|
||||
|
||||
scalesAndZp = QuantizationParams<u_int8_t>(OutputMin, OutputMax);
|
||||
std::vector<float> scalesOutput = {scalesAndZp.first};
|
||||
std::vector<int32_t> zeroPointsOutput = {scalesAndZp.second};
|
||||
tim::vx::Quantization quantInput(tim::vx::QuantType::ASYMMETRIC, 2,
|
||||
scalesInput, zeroPointsInput);
|
||||
tim::vx::Quantization quantWeight(tim::vx::QuantType::ASYMMETRIC, 2,
|
||||
scalesWeight, zeroPointsWeight);
|
||||
tim::vx::Quantization quantBias(tim::vx::QuantType::ASYMMETRIC, 2, scalesBias,
|
||||
zeroPointsBias);
|
||||
tim::vx::Quantization quantOutput(tim::vx::QuantType::ASYMMETRIC, 2,
|
||||
scalesOutput, zeroPointsOutput);
|
||||
|
||||
tim::vx::TensorSpec input_spec(tim::vx::DataType::UINT8, input_shape,
|
||||
tim::vx::TensorAttribute::INPUT, quantInput);
|
||||
tim::vx::TensorSpec weight_spec(tim::vx::DataType::UINT8, weight_shape,
|
||||
tim::vx::TensorAttribute::CONSTANT,
|
||||
quantWeight);
|
||||
tim::vx::TensorSpec bias_spec(tim::vx::DataType::INT32, bias_shape,
|
||||
tim::vx::TensorAttribute::CONSTANT, quantBias);
|
||||
tim::vx::TensorSpec output_spec(tim::vx::DataType::UINT8, output_shape,
|
||||
tim::vx::TensorAttribute::OUTPUT,
|
||||
quantOutput);
|
||||
|
||||
// Input data nchw
|
||||
// min:-63.5 max:64 scale:0.5 Zp:-1
|
||||
std::vector<float> input_data_float = {1, 7, 3, 9, 5, 11, 2, 8, 4, 10, 6, 12};
|
||||
std::vector<uint8_t> input_data =
|
||||
Quantize<uint8_t>(input_data_float, scalesInput[0], zeroPointsInput[0]);
|
||||
|
||||
// weight data iohw
|
||||
// min:-63.5 max:64 scale:0.5 Zp:-1
|
||||
std::vector<float> weight_data_float = {1, -9, 5, 13, 2, 10, 6, -14,
|
||||
3, -11, 7, 15, 4, 12, 8, -16};
|
||||
std::vector<uint8_t> weight_data =
|
||||
Quantize<uint8_t>(weight_data_float, scalesWeight[0], zeroPointsInput[0]);
|
||||
|
||||
// bias data
|
||||
// scale:0.25 Zp:0
|
||||
std::vector<float> bias_data_float = {1, 2, 3, 4};
|
||||
std::vector<int32_t> bias_data =
|
||||
Quantize<int32_t>(bias_data_float, scalesBias[0], zeroPointsBias[0]);
|
||||
|
||||
// golden
|
||||
// min:-127 max:128 scale:1 Zp:-1
|
||||
std::vector<float> golden_float = {71, 91, -34, -26, 99, 127, -20, -4};
|
||||
std::vector<u_int8_t> golden =
|
||||
Quantize<uint8_t>(golden_float, scalesOutput[0], zeroPointsOutput[0]);
|
||||
|
||||
auto input_tensor = graph->CreateTensor(input_spec);
|
||||
auto weight_tensor = graph->CreateTensor(weight_spec, weight_data.data());
|
||||
auto bias_tensor = graph->CreateTensor(bias_spec, bias_data.data());
|
||||
|
||||
auto output_tensor = graph->CreateTensor(output_spec);
|
||||
|
||||
auto padding = tim::vx::PadType::VALID;
|
||||
std::array<uint32_t, 2> stride({1, 1});
|
||||
std::array<uint32_t, 2> dilation({1, 1});
|
||||
int32_t multiplier = weight_shape[2] / input_shape[2];
|
||||
|
||||
auto conv2d = graph->CreateOperation<tim::vx::ops::Conv2d>(
|
||||
padding, stride, dilation, multiplier);
|
||||
(*conv2d)
|
||||
.BindInput(input_tensor)
|
||||
.BindInput(weight_tensor)
|
||||
.BindInput(bias_tensor)
|
||||
.BindOutput(output_tensor);
|
||||
|
||||
EXPECT_TRUE(graph->Compile());
|
||||
|
||||
input_tensor->CopyDataToTensor(input_data.data());
|
||||
|
||||
EXPECT_TRUE(graph->Run());
|
||||
|
||||
uint32_t output_size = 1;
|
||||
for (auto i : output_tensor->GetShape()) {
|
||||
output_size *= i;
|
||||
}
|
||||
std::vector<uint8_t> output(output_size);
|
||||
EXPECT_TRUE(output_tensor->CopyDataFromTensor(output.data()));
|
||||
EXPECT_EQ(golden, output);
|
||||
}
|
||||
|
||||
TEST(DepthwiseConv, shape_9_9_1_1_uint8_QuantizedDilationdValidTest) {
|
||||
auto ctx = tim::vx::Context::Create();
|
||||
auto graph = ctx->CreateGraph();
|
||||
|
||||
tim::vx::ShapeType input_shape({9, 9, 1, 1}); //whcn
|
||||
tim::vx::ShapeType weight_shape({3, 3, 1, 1}); //whoi
|
||||
tim::vx::ShapeType bias_shape({weight_shape[2]});
|
||||
tim::vx::ShapeType output_shape(
|
||||
{3, 3, weight_shape[2], input_shape[3]}); //whcn
|
||||
|
||||
float InputMin = 0, InputMax = 255, WeightMin = 0, WeightMax = 255,
|
||||
OutputMin = 0, OutputMax = 255;
|
||||
|
||||
std::pair<float, int32_t> scalesAndZp;
|
||||
|
||||
scalesAndZp = QuantizationParams<u_int8_t>(InputMin, InputMax);
|
||||
std::vector<float> scalesInput = {scalesAndZp.first};
|
||||
std::vector<int32_t> zeroPointsInput = {scalesAndZp.second};
|
||||
|
||||
scalesAndZp = QuantizationParams<u_int8_t>(WeightMin, WeightMax);
|
||||
std::vector<float> scalesWeight = {scalesAndZp.first};
|
||||
std::vector<int32_t> zeroPointsWeight = {scalesAndZp.second};
|
||||
|
||||
std::vector<float> scalesBias = {scalesInput[0] * scalesWeight[0]};
|
||||
std::vector<int32_t> zeroPointsBias = {0};
|
||||
|
||||
scalesAndZp = QuantizationParams<u_int8_t>(OutputMin, OutputMax);
|
||||
std::vector<float> scalesOutput = {scalesAndZp.first};
|
||||
std::vector<int32_t> zeroPointsOutput = {scalesAndZp.second};
|
||||
tim::vx::Quantization quantInput(tim::vx::QuantType::ASYMMETRIC, 2,
|
||||
scalesInput, zeroPointsInput);
|
||||
tim::vx::Quantization quantWeight(tim::vx::QuantType::ASYMMETRIC, 2,
|
||||
scalesWeight, zeroPointsWeight);
|
||||
tim::vx::Quantization quantBias(tim::vx::QuantType::ASYMMETRIC, 2, scalesBias,
|
||||
zeroPointsBias);
|
||||
tim::vx::Quantization quantOutput(tim::vx::QuantType::ASYMMETRIC, 2,
|
||||
scalesOutput, zeroPointsOutput);
|
||||
|
||||
tim::vx::TensorSpec input_spec(tim::vx::DataType::UINT8, input_shape,
|
||||
tim::vx::TensorAttribute::INPUT, quantInput);
|
||||
tim::vx::TensorSpec weight_spec(tim::vx::DataType::UINT8, weight_shape,
|
||||
tim::vx::TensorAttribute::CONSTANT,
|
||||
quantWeight);
|
||||
tim::vx::TensorSpec bias_spec(tim::vx::DataType::INT32, bias_shape,
|
||||
tim::vx::TensorAttribute::CONSTANT, quantBias);
|
||||
tim::vx::TensorSpec output_spec(tim::vx::DataType::UINT8, output_shape,
|
||||
tim::vx::TensorAttribute::OUTPUT,
|
||||
quantOutput);
|
||||
|
||||
// Input data nchw
|
||||
// min:0 max:255 scale:1 Zp:-128
|
||||
std::vector<float> input_data_float = {
|
||||
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
|
||||
0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 0, 0, 0, 0, 0, 1, 1, 1,
|
||||
0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
|
||||
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0};
|
||||
std::vector<uint8_t> input_data =
|
||||
Quantize<uint8_t>(input_data_float, scalesInput[0], zeroPointsInput[0]);
|
||||
|
||||
// weight data iohw
|
||||
// min:0 max:255 scale:1 Zp:-128
|
||||
std::vector<float> weight_data_float = {1, 2, 3, 4, 5, 6, 7, 8, 9};
|
||||
std::vector<uint8_t> weight_data =
|
||||
Quantize<uint8_t>(weight_data_float, scalesWeight[0], zeroPointsInput[0]);
|
||||
|
||||
// bias data
|
||||
// scale:1 Zp:0
|
||||
std::vector<float> bias_data_float = {0};
|
||||
std::vector<int32_t> bias_data =
|
||||
Quantize<int32_t>(bias_data_float, scalesBias[0], zeroPointsBias[0]);
|
||||
|
||||
// golden
|
||||
// min:0 max:255 scale:1 Zp:-128
|
||||
std::vector<float> golden_float = {5, 5, 5, 5, 5, 5, 5, 5, 5};
|
||||
std::vector<u_int8_t> golden =
|
||||
Quantize<uint8_t>(golden_float, scalesOutput[0], zeroPointsOutput[0]);
|
||||
|
||||
auto input_tensor = graph->CreateTensor(input_spec);
|
||||
auto weight_tensor = graph->CreateTensor(weight_spec, weight_data.data());
|
||||
auto bias_tensor = graph->CreateTensor(bias_spec, bias_data.data());
|
||||
|
||||
auto output_tensor = graph->CreateTensor(output_spec);
|
||||
|
||||
auto padding = tim::vx::PadType::VALID;
|
||||
std::array<uint32_t, 2> stride({1, 1});
|
||||
std::array<uint32_t, 2> dilation({3, 3});
|
||||
int32_t multiplier = weight_shape[2] / input_shape[2];
|
||||
|
||||
auto conv2d = graph->CreateOperation<tim::vx::ops::Conv2d>(
|
||||
padding, stride, dilation, multiplier);
|
||||
(*conv2d)
|
||||
.BindInput(input_tensor)
|
||||
.BindInput(weight_tensor)
|
||||
.BindInput(bias_tensor)
|
||||
.BindOutput(output_tensor);
|
||||
|
||||
EXPECT_TRUE(graph->Compile());
|
||||
|
||||
input_tensor->CopyDataToTensor(input_data.data());
|
||||
|
||||
EXPECT_TRUE(graph->Run());
|
||||
|
||||
uint32_t output_size = 1;
|
||||
for (auto i : output_tensor->GetShape()) {
|
||||
output_size *= i;
|
||||
}
|
||||
std::vector<uint8_t> output(output_size);
|
||||
EXPECT_TRUE(output_tensor->CopyDataFromTensor(output.data()));
|
||||
EXPECT_EQ(golden, output);
|
||||
}
|
||||
|
||||
TEST(DepthwiseConv, shape_3_3_1_1_uint8_QuantizedDilationdSameTest) {
|
||||
auto ctx = tim::vx::Context::Create();
|
||||
auto graph = ctx->CreateGraph();
|
||||
|
||||
tim::vx::ShapeType input_shape({3, 3, 1, 1}); //whcn
|
||||
tim::vx::ShapeType weight_shape({2, 2, 1, 1}); //whoi
|
||||
tim::vx::ShapeType bias_shape({weight_shape[2]});
|
||||
tim::vx::ShapeType output_shape(
|
||||
{3, 3, weight_shape[2], input_shape[3]}); //whcn
|
||||
|
||||
float InputMin = 0, InputMax = 255, WeightMin = 0, WeightMax = 255,
|
||||
OutputMin = 0, OutputMax = 255;
|
||||
|
||||
std::pair<float, int32_t> scalesAndZp;
|
||||
|
||||
scalesAndZp = QuantizationParams<u_int8_t>(InputMin, InputMax);
|
||||
std::vector<float> scalesInput = {scalesAndZp.first};
|
||||
std::vector<int32_t> zeroPointsInput = {scalesAndZp.second};
|
||||
|
||||
scalesAndZp = QuantizationParams<u_int8_t>(WeightMin, WeightMax);
|
||||
std::vector<float> scalesWeight = {scalesAndZp.first};
|
||||
std::vector<int32_t> zeroPointsWeight = {scalesAndZp.second};
|
||||
|
||||
std::vector<float> scalesBias = {scalesInput[0] * scalesWeight[0]};
|
||||
std::vector<int32_t> zeroPointsBias = {0};
|
||||
|
||||
scalesAndZp = QuantizationParams<u_int8_t>(OutputMin, OutputMax);
|
||||
std::vector<float> scalesOutput = {scalesAndZp.first};
|
||||
std::vector<int32_t> zeroPointsOutput = {scalesAndZp.second};
|
||||
tim::vx::Quantization quantInput(tim::vx::QuantType::ASYMMETRIC, 2,
|
||||
scalesInput, zeroPointsInput);
|
||||
tim::vx::Quantization quantWeight(tim::vx::QuantType::ASYMMETRIC, 2,
|
||||
scalesWeight, zeroPointsWeight);
|
||||
tim::vx::Quantization quantBias(tim::vx::QuantType::ASYMMETRIC, 2, scalesBias,
|
||||
zeroPointsBias);
|
||||
tim::vx::Quantization quantOutput(tim::vx::QuantType::ASYMMETRIC, 2,
|
||||
scalesOutput, zeroPointsOutput);
|
||||
|
||||
tim::vx::TensorSpec input_spec(tim::vx::DataType::UINT8, input_shape,
|
||||
tim::vx::TensorAttribute::INPUT, quantInput);
|
||||
tim::vx::TensorSpec weight_spec(tim::vx::DataType::UINT8, weight_shape,
|
||||
tim::vx::TensorAttribute::CONSTANT,
|
||||
quantWeight);
|
||||
tim::vx::TensorSpec bias_spec(tim::vx::DataType::INT32, bias_shape,
|
||||
tim::vx::TensorAttribute::CONSTANT, quantBias);
|
||||
tim::vx::TensorSpec output_spec(tim::vx::DataType::UINT8, output_shape,
|
||||
tim::vx::TensorAttribute::OUTPUT,
|
||||
quantOutput);
|
||||
|
||||
// Input data nchw
|
||||
// min:0 max:255 scale:1 Zp:-128
|
||||
std::vector<float> input_data_float = {1, 1, 1, 1, 1, 1, 1, 1, 1};
|
||||
std::vector<uint8_t> input_data =
|
||||
Quantize<uint8_t>(input_data_float, scalesInput[0], zeroPointsInput[0]);
|
||||
|
||||
// weight data iohw
|
||||
// min:0 max:255 scale:1 Zp:-128
|
||||
std::vector<float> weight_data_float = {1, 2, 3, 4};
|
||||
std::vector<uint8_t> weight_data =
|
||||
Quantize<uint8_t>(weight_data_float, scalesWeight[0], zeroPointsInput[0]);
|
||||
|
||||
// bias data
|
||||
// scale:1 Zp:0
|
||||
std::vector<float> bias_data_float = {0};
|
||||
std::vector<int32_t> bias_data =
|
||||
Quantize<int32_t>(bias_data_float, scalesBias[0], zeroPointsBias[0]);
|
||||
|
||||
// golden
|
||||
// min:0 max:255 scale:1 Zp:-128
|
||||
std::vector<float> golden_float = {4, 7, 3, 6, 10, 4, 2, 3, 1};
|
||||
std::vector<u_int8_t> golden =
|
||||
Quantize<uint8_t>(golden_float, scalesOutput[0], zeroPointsOutput[0]);
|
||||
|
||||
auto input_tensor = graph->CreateTensor(input_spec);
|
||||
auto weight_tensor = graph->CreateTensor(weight_spec, weight_data.data());
|
||||
auto bias_tensor = graph->CreateTensor(bias_spec, bias_data.data());
|
||||
|
||||
auto output_tensor = graph->CreateTensor(output_spec);
|
||||
|
||||
auto padding = tim::vx::PadType::SAME;
|
||||
std::array<uint32_t, 2> stride({1, 1});
|
||||
std::array<uint32_t, 2> dilation({2, 2});
|
||||
int32_t multiplier = weight_shape[2] / input_shape[2];
|
||||
|
||||
auto conv2d = graph->CreateOperation<tim::vx::ops::Conv2d>(
|
||||
padding, stride, dilation, multiplier);
|
||||
(*conv2d)
|
||||
.BindInput(input_tensor)
|
||||
.BindInput(weight_tensor)
|
||||
.BindInput(bias_tensor)
|
||||
.BindOutput(output_tensor);
|
||||
|
||||
EXPECT_TRUE(graph->Compile());
|
||||
|
||||
input_tensor->CopyDataToTensor(input_data.data());
|
||||
|
||||
EXPECT_TRUE(graph->Run());
|
||||
|
||||
uint32_t output_size = 1;
|
||||
for (auto i : output_tensor->GetShape()) {
|
||||
output_size *= i;
|
||||
}
|
||||
std::vector<uint8_t> output(output_size);
|
||||
EXPECT_TRUE(output_tensor->CopyDataFromTensor(output.data()));
|
||||
EXPECT_EQ(golden, output);
|
||||
}
|
||||
|
||||
TEST(DepthwiseConv, shape_3_2_2_1_int8_PerTensorTest) {
|
||||
auto ctx = tim::vx::Context::Create();
|
||||
auto graph = ctx->CreateGraph();
|
||||
|
||||
tim::vx::ShapeType input_shape({3, 2, 2, 1}); //whcn
|
||||
tim::vx::ShapeType weight_shape({2, 2, 4, 1}); //whoi
|
||||
tim::vx::ShapeType bias_shape({weight_shape[2]});
|
||||
tim::vx::ShapeType output_shape(
|
||||
{2, 1, weight_shape[2], input_shape[3]}); //whcn
|
||||
|
||||
float InputMin = -63.5, InputMax = 64, OutputMin = -63.5, OutputMax = 64;
|
||||
|
||||
std::pair<float, int32_t> scalesAndZp;
|
||||
|
||||
scalesAndZp = QuantizationParams<int8_t>(InputMin, InputMax);
|
||||
std::vector<float> scalesInput = {scalesAndZp.first};
|
||||
std::vector<int32_t> zeroPointsInput = {scalesAndZp.second};
|
||||
|
||||
std::vector<float> scalesWeight = {1};
|
||||
std::vector<int32_t> zeroPointsWeight = {0};
|
||||
|
||||
int32_t sizeofweight = scalesWeight.size();
|
||||
std::vector<float> scalesBias(sizeofweight);
|
||||
std::vector<int32_t> zeroPointsBias(sizeofweight);
|
||||
for (int i = 0; i < sizeofweight; i++) {
|
||||
scalesBias[i] = scalesInput[0] * scalesWeight[i];
|
||||
zeroPointsBias[i] = 0;
|
||||
}
|
||||
|
||||
scalesAndZp = QuantizationParams<int8_t>(OutputMin, OutputMax);
|
||||
std::vector<float> scalesOutput = {scalesAndZp.first};
|
||||
std::vector<int32_t> zeroPointsOutput = {scalesAndZp.second};
|
||||
tim::vx::Quantization quantInput(tim::vx::QuantType::ASYMMETRIC, 2,
|
||||
scalesInput, zeroPointsInput);
|
||||
tim::vx::Quantization quantWeight(tim::vx::QuantType::ASYMMETRIC, 2,
|
||||
scalesWeight, zeroPointsWeight);
|
||||
tim::vx::Quantization quantBias(tim::vx::QuantType::ASYMMETRIC, 2, scalesBias,
|
||||
zeroPointsBias);
|
||||
tim::vx::Quantization quantOutput(tim::vx::QuantType::ASYMMETRIC, 2,
|
||||
scalesOutput, zeroPointsOutput);
|
||||
|
||||
tim::vx::TensorSpec input_spec(tim::vx::DataType::INT8, input_shape,
|
||||
tim::vx::TensorAttribute::INPUT, quantInput);
|
||||
tim::vx::TensorSpec weight_spec(tim::vx::DataType::INT8, weight_shape,
|
||||
tim::vx::TensorAttribute::CONSTANT,
|
||||
quantWeight);
|
||||
tim::vx::TensorSpec bias_spec(tim::vx::DataType::INT32, bias_shape,
|
||||
tim::vx::TensorAttribute::CONSTANT, quantBias);
|
||||
tim::vx::TensorSpec output_spec(tim::vx::DataType::INT8, output_shape,
|
||||
tim::vx::TensorAttribute::OUTPUT,
|
||||
quantOutput);
|
||||
|
||||
// Input data nchw
|
||||
// min:-63.5 max:64 scale:0.5 Zp:-1
|
||||
std::vector<float> input_data_float = {3, 1, -2, 4, 2, -3,
|
||||
2, -1, -3, 3, -2, -4};
|
||||
std::vector<int8_t> input_data =
|
||||
Quantize<int8_t>(input_data_float, scalesInput[0], zeroPointsInput[0]);
|
||||
|
||||
// weight data iohw
|
||||
std::vector<float> weight_data_float = {1, 3, 7, 3, 2, 4, 8, 4,
|
||||
3, 5, 5, 1, 4, 6, 6, 2};
|
||||
std::vector<int8_t> weight_data =
|
||||
Quantize<int8_t>(weight_data_float, scalesWeight[0], zeroPointsWeight[0]);
|
||||
|
||||
// bias data
|
||||
std::vector<int32_t> bias_data = {6, -4, 8, 12};
|
||||
|
||||
// golden
|
||||
// min:-63.5 max:64 scale:0.5 Zp:-1
|
||||
std::vector<float> golden_float = {43, 3, 48, -4, 18, -28, 22, -36};
|
||||
std::vector<int8_t> golden =
|
||||
Quantize<int8_t>(golden_float, scalesOutput[0], zeroPointsOutput[0]);
|
||||
|
||||
auto input_tensor = graph->CreateTensor(input_spec);
|
||||
auto weight_tensor = graph->CreateTensor(weight_spec, weight_data.data());
|
||||
auto bias_tensor = graph->CreateTensor(bias_spec, bias_data.data());
|
||||
|
||||
auto output_tensor = graph->CreateTensor(output_spec);
|
||||
|
||||
auto padding = tim::vx::PadType::VALID;
|
||||
std::array<uint32_t, 2> stride({1, 1});
|
||||
std::array<uint32_t, 2> dilation({1, 1});
|
||||
int32_t multiplier = weight_shape[2] / input_shape[2];
|
||||
|
||||
auto conv2d = graph->CreateOperation<tim::vx::ops::Conv2d>(
|
||||
padding, stride, dilation, multiplier);
|
||||
(*conv2d)
|
||||
.BindInput(input_tensor)
|
||||
.BindInput(weight_tensor)
|
||||
.BindInput(bias_tensor)
|
||||
.BindOutput(output_tensor);
|
||||
|
||||
EXPECT_TRUE(graph->Compile());
|
||||
|
||||
input_tensor->CopyDataToTensor(input_data.data());
|
||||
|
||||
EXPECT_TRUE(graph->Run());
|
||||
|
||||
uint32_t output_size = 1;
|
||||
for (auto i : output_tensor->GetShape()) {
|
||||
output_size *= i;
|
||||
}
|
||||
std::vector<int8_t> output(output_size);
|
||||
EXPECT_TRUE(output_tensor->CopyDataFromTensor(output.data()));
|
||||
EXPECT_EQ(golden, output);
|
||||
}
|
||||
|
||||
TEST(DepthwiseConv, shape_3_2_2_1_int8_PerAxisTest) {
|
||||
auto ctx = tim::vx::Context::Create();
|
||||
auto graph = ctx->CreateGraph();
|
||||
|
||||
tim::vx::ShapeType input_shape({3, 2, 2, 1}); //whcn
|
||||
tim::vx::ShapeType weight_shape({2, 2, 4, 1}); //whoi
|
||||
tim::vx::ShapeType bias_shape({weight_shape[2]});
|
||||
tim::vx::ShapeType output_shape(
|
||||
{2, 1, weight_shape[2], input_shape[3]}); //whcn
|
||||
|
||||
float InputMin = -63.5, InputMax = 64, OutputMin = -63.5, OutputMax = 64;
|
||||
|
||||
std::pair<float, int32_t> scalesAndZp;
|
||||
|
||||
scalesAndZp = QuantizationParams<int8_t>(InputMin, InputMax);
|
||||
std::vector<float> scalesInput = {scalesAndZp.first};
|
||||
std::vector<int32_t> zeroPointsInput = {scalesAndZp.second};
|
||||
|
||||
std::vector<float> scalesWeight = {1, 2, 3, 4};
|
||||
std::vector<int32_t> zeroPointsWeight = {0, 0, 0, 0};
|
||||
|
||||
int32_t sizeofweight = scalesWeight.size();
|
||||
std::vector<float> scalesBias(sizeofweight);
|
||||
std::vector<int32_t> zeroPointsBias(sizeofweight);
|
||||
for (int i = 0; i < sizeofweight; i++) {
|
||||
scalesBias[i] = scalesInput[0] * scalesWeight[i];
|
||||
zeroPointsBias[i] = 0;
|
||||
}
|
||||
|
||||
scalesAndZp = QuantizationParams<int8_t>(OutputMin, OutputMax);
|
||||
std::vector<float> scalesOutput = {scalesAndZp.first};
|
||||
std::vector<int32_t> zeroPointsOutput = {scalesAndZp.second};
|
||||
tim::vx::Quantization quantInput(tim::vx::QuantType::ASYMMETRIC, 2,
|
||||
scalesInput, zeroPointsInput);
|
||||
tim::vx::Quantization quantWeight(tim::vx::QuantType::SYMMETRIC_PER_CHANNEL,
|
||||
2, scalesWeight, zeroPointsWeight);
|
||||
tim::vx::Quantization quantBias(tim::vx::QuantType::SYMMETRIC_PER_CHANNEL, 0,
|
||||
scalesBias, zeroPointsBias);
|
||||
tim::vx::Quantization quantOutput(tim::vx::QuantType::ASYMMETRIC, 2,
|
||||
scalesOutput, zeroPointsOutput);
|
||||
|
||||
tim::vx::TensorSpec input_spec(tim::vx::DataType::INT8, input_shape,
|
||||
tim::vx::TensorAttribute::INPUT, quantInput);
|
||||
tim::vx::TensorSpec weight_spec(tim::vx::DataType::INT8, weight_shape,
|
||||
tim::vx::TensorAttribute::CONSTANT,
|
||||
quantWeight);
|
||||
tim::vx::TensorSpec bias_spec(tim::vx::DataType::INT32, bias_shape,
|
||||
tim::vx::TensorAttribute::CONSTANT, quantBias);
|
||||
tim::vx::TensorSpec output_spec(tim::vx::DataType::INT8, output_shape,
|
||||
tim::vx::TensorAttribute::OUTPUT,
|
||||
quantOutput);
|
||||
|
||||
// Input data nchw
|
||||
// min:-63.5 max:64 scale:0.5 Zp:-1
|
||||
std::vector<float> input_data_float = {3, 1, -2, 4, 2, -3,
|
||||
2, -1, -3, 3, -2, -4};
|
||||
|
||||
std::vector<int8_t> input_data =
|
||||
Quantize<int8_t>(input_data_float, scalesInput[0], zeroPointsInput[0]);
|
||||
|
||||
// weight data iohw
|
||||
std::vector<int8_t> weight_data = {1, 3, 7, 3, 1, 2, 4, 2,
|
||||
1, 2, 2, 0, 1, 2, 2, 1};
|
||||
|
||||
// bias data
|
||||
std::vector<int32_t> bias_data = {6, -2, 2, 3};
|
||||
|
||||
// golden
|
||||
// min:-63.5 max:64 scale:0.5 Zp:-1
|
||||
std::vector<float> golden_float = {43, 3, 48, -4, 21, -30, 22, -54};
|
||||
std::vector<int8_t> golden =
|
||||
Quantize<int8_t>(golden_float, scalesOutput[0], zeroPointsOutput[0]);
|
||||
|
||||
auto input_tensor = graph->CreateTensor(input_spec);
|
||||
auto weight_tensor = graph->CreateTensor(weight_spec, weight_data.data());
|
||||
auto bias_tensor = graph->CreateTensor(bias_spec, bias_data.data());
|
||||
|
||||
auto output_tensor = graph->CreateTensor(output_spec);
|
||||
|
||||
auto padding = tim::vx::PadType::VALID;
|
||||
std::array<uint32_t, 2> stride({1, 1});
|
||||
std::array<uint32_t, 2> dilation({1, 1});
|
||||
int32_t multiplier = weight_shape[2] / input_shape[2];
|
||||
|
||||
auto conv2d = graph->CreateOperation<tim::vx::ops::Conv2d>(
|
||||
padding, stride, dilation, multiplier);
|
||||
(*conv2d)
|
||||
.BindInput(input_tensor)
|
||||
.BindInput(weight_tensor)
|
||||
.BindInput(bias_tensor)
|
||||
.BindOutput(output_tensor);
|
||||
|
||||
EXPECT_TRUE(graph->Compile());
|
||||
|
||||
input_tensor->CopyDataToTensor(input_data.data());
|
||||
|
||||
EXPECT_TRUE(graph->Run());
|
||||
|
||||
uint32_t output_size = 1;
|
||||
for (auto i : output_tensor->GetShape()) {
|
||||
output_size *= i;
|
||||
}
|
||||
std::vector<int8_t> output(output_size);
|
||||
EXPECT_TRUE(output_tensor->CopyDataFromTensor(output.data()));
|
||||
EXPECT_EQ(golden, output);
|
||||
}
|
||||
|
||||
TEST(DepthwiseConv, shape_3_3_8_1_int8_PerChannelValidTest) {
|
||||
auto ctx = tim::vx::Context::Create();
|
||||
auto graph = ctx->CreateGraph();
|
||||
|
||||
tim::vx::ShapeType input_shape({3, 3, 8, 1}); //whcn
|
||||
tim::vx::ShapeType weight_shape({3, 3, 8, 1}); //whoi
|
||||
tim::vx::ShapeType bias_shape({weight_shape[2]});
|
||||
tim::vx::ShapeType output_shape(
|
||||
{1, 1, weight_shape[2], input_shape[3]}); //whcn
|
||||
|
||||
float InputMin = -63.5, InputMax = 64, OutputMin = -63.5, OutputMax = 64;
|
||||
|
||||
std::pair<float, int32_t> scalesAndZp;
|
||||
|
||||
scalesAndZp = QuantizationParams<int8_t>(InputMin, InputMax);
|
||||
std::vector<float> scalesInput = {scalesAndZp.first};
|
||||
std::vector<int32_t> zeroPointsInput = {scalesAndZp.second};
|
||||
|
||||
std::vector<float> scalesWeight = {0.1, 0.2, 0.3, 0.4, 0.4, 0.3, 0.2, 0.1};
|
||||
std::vector<int32_t> zeroPointsWeight = {0, 0, 0, 0, 0, 0, 0, 0};
|
||||
|
||||
int32_t sizeofweight = scalesWeight.size();
|
||||
std::vector<float> scalesBias(sizeofweight);
|
||||
std::vector<int32_t> zeroPointsBias(sizeofweight);
|
||||
for (int i = 0; i < sizeofweight; i++) {
|
||||
scalesBias[i] = scalesInput[0] * scalesWeight[i];
|
||||
zeroPointsBias[i] = 0;
|
||||
}
|
||||
|
||||
scalesAndZp = QuantizationParams<int8_t>(OutputMin, OutputMax);
|
||||
std::vector<float> scalesOutput = {scalesAndZp.first};
|
||||
std::vector<int32_t> zeroPointsOutput = {scalesAndZp.second};
|
||||
tim::vx::Quantization quantInput(tim::vx::QuantType::ASYMMETRIC, 2,
|
||||
scalesInput, zeroPointsInput);
|
||||
tim::vx::Quantization quantWeight(tim::vx::QuantType::SYMMETRIC_PER_CHANNEL,
|
||||
2, scalesWeight, zeroPointsWeight);
|
||||
tim::vx::Quantization quantBias(tim::vx::QuantType::SYMMETRIC_PER_CHANNEL, 0,
|
||||
scalesBias, zeroPointsBias);
|
||||
tim::vx::Quantization quantOutput(tim::vx::QuantType::ASYMMETRIC, 2,
|
||||
scalesOutput, zeroPointsOutput);
|
||||
|
||||
tim::vx::TensorSpec input_spec(tim::vx::DataType::INT8, input_shape,
|
||||
tim::vx::TensorAttribute::INPUT, quantInput);
|
||||
tim::vx::TensorSpec weight_spec(tim::vx::DataType::INT8, weight_shape,
|
||||
tim::vx::TensorAttribute::CONSTANT,
|
||||
quantWeight);
|
||||
tim::vx::TensorSpec bias_spec(tim::vx::DataType::INT32, bias_shape,
|
||||
tim::vx::TensorAttribute::CONSTANT, quantBias);
|
||||
tim::vx::TensorSpec output_spec(tim::vx::DataType::INT8, output_shape,
|
||||
tim::vx::TensorAttribute::OUTPUT,
|
||||
quantOutput);
|
||||
|
||||
// Input data nchw
|
||||
// min:-63.5 max:64 scale:0.5 Zp:-1
|
||||
std::vector<float> input_data_float = {
|
||||
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0,
|
||||
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
|
||||
1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0};
|
||||
|
||||
std::vector<int8_t> input_data =
|
||||
Quantize<int8_t>(input_data_float, scalesInput[0], zeroPointsInput[0]);
|
||||
|
||||
// weight data iohw
|
||||
std::vector<int8_t> weight_data = {
|
||||
10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10,
|
||||
10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10,
|
||||
13, 13, 13, 13, 13, 13, 13, 13, 13, 20, 20, 20, 20, 20, 20, 20, 20, 20,
|
||||
35, 35, 35, 35, 35, 35, 35, 35, 35, 80, 80, 80, 80, 80, 80, 80, 80, 80};
|
||||
|
||||
// bias data
|
||||
std::vector<int32_t> bias_data = {0, 0, 0, 0, 0, 0, 0, 0};
|
||||
|
||||
// golden
|
||||
// min:-63.5 max:64 scale:0.5 Zp:-1
|
||||
std::vector<float> golden_float = {9, 18, 0, 0, 47, 54, 0, 0};
|
||||
std::vector<int8_t> golden =
|
||||
Quantize<int8_t>(golden_float, scalesOutput[0], zeroPointsOutput[0]);
|
||||
|
||||
auto input_tensor = graph->CreateTensor(input_spec);
|
||||
auto weight_tensor = graph->CreateTensor(weight_spec, weight_data.data());
|
||||
auto bias_tensor = graph->CreateTensor(bias_spec, bias_data.data());
|
||||
|
||||
auto output_tensor = graph->CreateTensor(output_spec);
|
||||
|
||||
auto padding = tim::vx::PadType::VALID;
|
||||
std::array<uint32_t, 2> stride({1, 1});
|
||||
std::array<uint32_t, 2> dilation({1, 1});
|
||||
int32_t multiplier = weight_shape[2] / input_shape[2];
|
||||
|
||||
auto conv2d = graph->CreateOperation<tim::vx::ops::Conv2d>(
|
||||
padding, stride, dilation, multiplier);
|
||||
(*conv2d)
|
||||
.BindInput(input_tensor)
|
||||
.BindInput(weight_tensor)
|
||||
.BindInput(bias_tensor)
|
||||
.BindOutput(output_tensor);
|
||||
|
||||
EXPECT_TRUE(graph->Compile());
|
||||
|
||||
input_tensor->CopyDataToTensor(input_data.data());
|
||||
|
||||
EXPECT_TRUE(graph->Run());
|
||||
|
||||
uint32_t output_size = 1;
|
||||
for (auto i : output_tensor->GetShape()) {
|
||||
output_size *= i;
|
||||
}
|
||||
std::vector<int8_t> output(output_size);
|
||||
EXPECT_TRUE(output_tensor->CopyDataFromTensor(output.data()));
|
||||
EXPECT_EQ(golden, output);
|
||||
}
|
||||
|
||||
TEST(DepthwiseConv, shape_3_3_8_1_int8_PerChannelSameTest) {
|
||||
auto ctx = tim::vx::Context::Create();
|
||||
auto graph = ctx->CreateGraph();
|
||||
|
||||
tim::vx::ShapeType input_shape({3, 3, 8, 1}); //whcn
|
||||
tim::vx::ShapeType weight_shape({3, 3, 8, 1}); //whoi
|
||||
tim::vx::ShapeType bias_shape({weight_shape[2]});
|
||||
tim::vx::ShapeType output_shape(
|
||||
{3, 3, weight_shape[2], input_shape[3]}); //whcn
|
||||
|
||||
float InputMin = -63.5, InputMax = 64, OutputMin = -63.5, OutputMax = 64;
|
||||
|
||||
std::pair<float, int32_t> scalesAndZp;
|
||||
|
||||
scalesAndZp = QuantizationParams<int8_t>(InputMin, InputMax);
|
||||
std::vector<float> scalesInput = {scalesAndZp.first};
|
||||
std::vector<int32_t> zeroPointsInput = {scalesAndZp.second};
|
||||
|
||||
std::vector<float> scalesWeight = {0.1, 0.2, 0.3, 0.4, 0.4, 0.3, 0.2, 0.1};
|
||||
std::vector<int32_t> zeroPointsWeight = {0, 0, 0, 0, 0, 0, 0, 0};
|
||||
|
||||
int32_t sizeofweight = scalesWeight.size();
|
||||
std::vector<float> scalesBias(sizeofweight);
|
||||
std::vector<int32_t> zeroPointsBias(sizeofweight);
|
||||
for (int i = 0; i < sizeofweight; i++) {
|
||||
scalesBias[i] = scalesInput[0] * scalesWeight[i];
|
||||
zeroPointsBias[i] = 0;
|
||||
}
|
||||
|
||||
scalesAndZp = QuantizationParams<int8_t>(OutputMin, OutputMax);
|
||||
std::vector<float> scalesOutput = {scalesAndZp.first};
|
||||
std::vector<int32_t> zeroPointsOutput = {scalesAndZp.second};
|
||||
tim::vx::Quantization quantInput(tim::vx::QuantType::ASYMMETRIC, 2,
|
||||
scalesInput, zeroPointsInput);
|
||||
tim::vx::Quantization quantWeight(tim::vx::QuantType::SYMMETRIC_PER_CHANNEL,
|
||||
2, scalesWeight, zeroPointsWeight);
|
||||
tim::vx::Quantization quantBias(tim::vx::QuantType::SYMMETRIC_PER_CHANNEL, 0,
|
||||
scalesBias, zeroPointsBias);
|
||||
tim::vx::Quantization quantOutput(tim::vx::QuantType::ASYMMETRIC, 2,
|
||||
scalesOutput, zeroPointsOutput);
|
||||
|
||||
tim::vx::TensorSpec input_spec(tim::vx::DataType::INT8, input_shape,
|
||||
tim::vx::TensorAttribute::INPUT, quantInput);
|
||||
tim::vx::TensorSpec weight_spec(tim::vx::DataType::INT8, weight_shape,
|
||||
tim::vx::TensorAttribute::CONSTANT,
|
||||
quantWeight);
|
||||
tim::vx::TensorSpec bias_spec(tim::vx::DataType::INT32, bias_shape,
|
||||
tim::vx::TensorAttribute::CONSTANT, quantBias);
|
||||
tim::vx::TensorSpec output_spec(tim::vx::DataType::INT8, output_shape,
|
||||
tim::vx::TensorAttribute::OUTPUT,
|
||||
quantOutput);
|
||||
|
||||
// Input data nchw
|
||||
// min:-63.5 max:64 scale:0.5 Zp:-1
|
||||
std::vector<float> input_data_float = {
|
||||
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0,
|
||||
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
|
||||
1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0};
|
||||
|
||||
std::vector<int8_t> input_data =
|
||||
Quantize<int8_t>(input_data_float, scalesInput[0], zeroPointsInput[0]);
|
||||
|
||||
// weight data iohw
|
||||
std::vector<int8_t> weight_data = {
|
||||
10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10,
|
||||
10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10,
|
||||
13, 13, 13, 13, 13, 13, 13, 13, 13, 20, 20, 20, 20, 20, 20, 20, 20, 20,
|
||||
35, 35, 35, 35, 35, 35, 35, 35, 35, 80, 80, 80, 80, 80, 80, 80, 80, 80};
|
||||
|
||||
// bias data
|
||||
std::vector<int32_t> bias_data = {0, 0, 0, 0, 0, 0, 0, 0};
|
||||
|
||||
// golden
|
||||
// min:-63.5 max:64 scale:0.5 Zp:-1
|
||||
std::vector<float> golden_float = {
|
||||
4, 6, 4, 6, 9, 6, 4, 6, 4, 8, 12, 8, 12, 18, 12, 8, 12, 8,
|
||||
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
|
||||
21, 31, 21, 31, 47, 31, 21, 31, 21, 24, 36, 24, 36, 54, 36, 24, 36, 24,
|
||||
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0};
|
||||
std::vector<int8_t> golden =
|
||||
Quantize<int8_t>(golden_float, scalesOutput[0], zeroPointsOutput[0]);
|
||||
|
||||
auto input_tensor = graph->CreateTensor(input_spec);
|
||||
auto weight_tensor = graph->CreateTensor(weight_spec, weight_data.data());
|
||||
auto bias_tensor = graph->CreateTensor(bias_spec, bias_data.data());
|
||||
|
||||
auto output_tensor = graph->CreateTensor(output_spec);
|
||||
|
||||
auto padding = tim::vx::PadType::SAME;
|
||||
std::array<uint32_t, 2> stride({1, 1});
|
||||
std::array<uint32_t, 2> dilation({1, 1});
|
||||
int32_t multiplier = weight_shape[2] / input_shape[2];
|
||||
|
||||
auto conv2d = graph->CreateOperation<tim::vx::ops::Conv2d>(
|
||||
padding, stride, dilation, multiplier);
|
||||
(*conv2d)
|
||||
.BindInput(input_tensor)
|
||||
.BindInput(weight_tensor)
|
||||
.BindInput(bias_tensor)
|
||||
.BindOutput(output_tensor);
|
||||
|
||||
EXPECT_TRUE(graph->Compile());
|
||||
|
||||
input_tensor->CopyDataToTensor(input_data.data());
|
||||
|
||||
EXPECT_TRUE(graph->Run());
|
||||
|
||||
uint32_t output_size = 1;
|
||||
for (auto i : output_tensor->GetShape()) {
|
||||
output_size *= i;
|
||||
}
|
||||
std::vector<int8_t> output(output_size);
|
||||
EXPECT_TRUE(output_tensor->CopyDataFromTensor(output.data()));
|
||||
EXPECT_EQ(golden, output);
|
||||
}
|
||||
|
|
|
|||
Loading…
Reference in New Issue