IVGCVSW-5842 Remove cross-wiring in depthwise
* Reading tensor infos won't allow a permutation vector anymore.
The permutation only changed the quantization dimension not the
shape and was therefore misleading
* The permutation of the full tensor info is now performed in
armnnUtils::Permuted
* Changed TfLite Parser depthwise parsing function
* Added unit tests to TfLite Parser with more random data
* Changed TfLite Delegate depthwise parsing function
* Added unit test to the delegate with per channel quantization
!android-nn-driver:5412
Signed-off-by: Jan Eilers <jan.eilers@arm.com>
Change-Id: I1f985ee69547bcaf16a72201e00a6b6fe1ef9a97
diff --git a/delegate/src/test/Convolution2dTest.cpp b/delegate/src/test/Convolution2dTest.cpp
index 2ce2944..6f498ce 100644
--- a/delegate/src/test/Convolution2dTest.cpp
+++ b/delegate/src/test/Convolution2dTest.cpp
@@ -166,8 +166,10 @@
expectedOutputValues,
biasShape,
biasValues,
- 1, // filter scale
- 4, // filter offset
+ {1.0f}, // biasScale
+ {0}, // biasOffset
+ {1.0f}, // filterScale
+ {4}, // filterOffsets
2, // output scale
20); // output offset
}
diff --git a/delegate/src/test/ConvolutionTestHelper.hpp b/delegate/src/test/ConvolutionTestHelper.hpp
index b2a3c88..1b33c1d 100644
--- a/delegate/src/test/ConvolutionTestHelper.hpp
+++ b/delegate/src/test/ConvolutionTestHelper.hpp
@@ -34,13 +34,16 @@
const std::vector <int32_t>& outputTensorShape,
const std::vector <T>& filterData,
const std::vector <B>& biasData,
- float filterScale = 1.0f,
- int filterOffset = 0,
+ const std::vector<float> biasScales = {1.0f},
+ const std::vector<int64_t> biasOffsets = {0},
+ const std::vector<float> filterScales = {1.0f},
+ const std::vector<int64_t> filterOffsets = {0},
float outputQuantScale = 2.0f,
int outputQuantOffset = 0,
float quantScale = 1.0f,
int quantOffset = 0,
- int32_t depth_multiplier = 1)
+ int32_t depth_multiplier = 1,
+ int32_t filterQuantizationDim = 0)
{
using namespace tflite;
flatbuffers::FlatBufferBuilder flatBufferBuilder;
@@ -67,12 +70,23 @@
0,
flatBufferBuilder.CreateVector<float>({ outputQuantScale }),
flatBufferBuilder.CreateVector<int64_t>({ outputQuantOffset }));
+
auto filterQuantizationParameters =
- CreateQuantizationParameters(flatBufferBuilder,
- 0,
- 0,
- flatBufferBuilder.CreateVector<float>({ filterScale }),
- flatBufferBuilder.CreateVector<int64_t>({ filterOffset }));
+ CreateQuantizationParameters(flatBufferBuilder,
+ 0,
+ 0,
+ flatBufferBuilder.CreateVector<float>(filterScales),
+ flatBufferBuilder.CreateVector<int64_t>(filterOffsets),
+ tflite::QuantizationDetails_NONE,
+ 0,
+ filterQuantizationDim);
+
+ auto biasQuantizationParameters =
+ CreateQuantizationParameters(flatBufferBuilder,
+ 0,
+ 0,
+ flatBufferBuilder.CreateVector<float>(biasScales),
+ flatBufferBuilder.CreateVector<int64_t>(biasOffsets));
std::array<flatbuffers::Offset<Tensor>, 4> tensors;
tensors[0] = CreateTensor(flatBufferBuilder,
@@ -100,7 +114,7 @@
biasTensorType,
2,
flatBufferBuilder.CreateString("bias"),
- quantizationParameters);
+ biasQuantizationParameters);
tensors[3] = CreateTensor(flatBufferBuilder,
flatBufferBuilder.CreateVector<int32_t>(outputTensorShape.data(),
outputTensorShape.size()),
@@ -192,13 +206,16 @@
std::vector<T>& expectedOutputValues,
const std::vector<int32_t>& biasShape = {},
const std::vector<B>& biasValues = {},
- float filterScale = 1.0f,
- int filterOffset = 0,
+ const std::vector<float> biasScales = {1.0f},
+ const std::vector<int64_t> biasOffsets = {0},
+ const std::vector<float> filterScales = {1.0f},
+ const std::vector<int64_t> filterOffsets = {0},
float outputQuantScale = 2.0f,
int outputQuantOffset = 0,
float quantScale = 1.0f,
int quantOffset = 0,
- int32_t depth_multiplier = 1)
+ int32_t depth_multiplier = 1,
+ int32_t filterQuantizationDim = 3)
{
using namespace tflite;
@@ -218,13 +235,16 @@
outputShape,
filterValues,
biasValues,
- filterScale,
- filterOffset,
+ biasScales,
+ biasOffsets,
+ filterScales,
+ filterOffsets,
outputQuantScale,
outputQuantOffset,
quantScale,
quantOffset,
- depth_multiplier);
+ depth_multiplier,
+ filterQuantizationDim);
const Model* tfLiteModel = GetModel(modelBuffer.data());
diff --git a/delegate/src/test/DepthwiseConvolution2dTest.cpp b/delegate/src/test/DepthwiseConvolution2dTest.cpp
index 6ca4569..ca10f2c 100644
--- a/delegate/src/test/DepthwiseConvolution2dTest.cpp
+++ b/delegate/src/test/DepthwiseConvolution2dTest.cpp
@@ -70,12 +70,14 @@
expectedOutputValues,
biasShape,
biasValues,
- 1.0f, // filterScale
- 0, // filterOffset
- 2.0f, // outputQuantScale
- 0, // outputQuantOffset
- 1.0f, // quantScale
- 0, // quantOffset
+ {1.0f}, // biasScale
+ {0}, // biasOffset
+ {1.0f}, // filterScale
+ {0}, // filterOffsets
+ 2.0f, // outputQuantScale
+ 0, // outputQuantOffset
+ 1.0f, // quantScale
+ 0, // quantOffset
depth_multiplier);
}
@@ -126,6 +128,100 @@
biasValues);
}
+void DepthwiseConv2dSameInt8PerChannelTest(std::vector<armnn::BackendId>& backends)
+{
+ // Set input data
+ std::vector<int32_t> inputShape { 1, 4, 4, 4 };
+ std::vector<int32_t> filterShape { 1, 2, 2, 16 };
+ std::vector<int32_t> biasShape {16} ;
+ std::vector<int32_t> outputShape { 1, 4, 4, 16 };
+
+ static std::vector<int8_t> inputValues =
+ {
+ 3,3,3,4, 4,4,0,0, 0,3,4,3, 0,2,2,3,
+ 3,0,3,0, 0,3,2,1, 4,1,2,2, 0,0,0,4,
+ 3,2,2,2, 2,1,0,4, 4,3,2,4, 3,2,0,0,
+ 4,1,4,4, 1,0,4,3, 3,2,0,3, 1,1,0,2
+ };
+
+ std::vector<int8_t> filterValues = { 12,20,10, 3, 2,24, 9,10, 5,16,30,12, 3,10, 4,32,
+ 8, 0,30, 3, 0,16,12,15,20,12, 0, 3, 9,20, 8, 8,
+ 12,15,20, 0, 0, 0, 3,15,15, 8,40,12, 9, 5, 2,24,
+ 4, 0, 0, 6, 6, 0, 3, 5,20, 8,20, 3, 6,15, 4, 0 };
+ std::vector<float> filterScales = { 0.25, 0.2, 0.1, 0.3333333333,
+ 0.5, 0.125, 0.33333333, 0.2,
+ 0.2, 0.25, 0.1, 0.333333333,
+ 0.3333333333, 0.2, 0.5, 0.125 };
+
+ int32_t filterQuantizationDim = 3;
+
+ int32_t depth_multiplier = 4;
+
+ std::vector<int32_t> biasValues = { 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0 };
+
+ float inputScale = 1.0f;
+ std::vector<float> biasScales {};
+ std::vector<int64_t> biasOffsets {};
+ std::vector<int64_t> filterOffsets {};
+ for (const auto& filterScale: filterScales)
+ {
+ biasScales.push_back(inputScale * filterScale);
+ // filter and bias offset always needs to be zero for per channel. We don't support anything else
+ biasOffsets.push_back(0);
+ filterOffsets.push_back(0);
+ }
+
+ std::vector<int8_t> expectedOutputValues =
+ {
+ 26,21,21, 7,12,17,28,21,20,22,25,26, 6,11,10,16,
+ 16,16, 4,12, 7,18,28,27,30,20,12,14,16,19,17, 6,
+ 12,12, 8, 0, 3,13,18,15,18,26,20,26,26,32,28,21,
+ 0, 0, 0, 0, 2, 6, 6, 4, 2, 8, 6, 8,15,10,10,24,
+ 20,21, 9, 7, 3, 6,15,16,17,22,17,22,17,18,14, 7,
+ 18, 6,16,12,12,11,17,15,18,18,10,12,27,26,22,18,
+ 27,28,12,10, 7, 3, 8,13, 8,12,14,16,26,24,24,24,
+ 9, 9, 6, 0, 0, 0, 2, 6, 0, 0, 0, 0, 4, 8, 8,16,
+ 26,24,17, 7, 2, 8,11,10,30,24,30,28,32,33,30,24,
+ 20,11,16,12, 7, 9,17,13,20,14,16,18,31,36,33,29,
+ 28,25,19, 9, 6,13,20,19, 2, 8, 6, 8,17,17,15,25,
+ 12,15, 5, 3, 2, 6, 7, 7, 0, 0, 0, 0, 6, 2, 2, 6,
+ 14,16, 7, 5, 1, 3, 3, 2,20,28,12,20,13,20,20,19,
+ 9, 4,10, 4, 0, 4, 8, 6, 4,16,12,16,12,18,18,15,
+ 11,12, 6, 4, 2, 8,10, 7, 0, 0, 0, 0, 9,14,14,14,
+ 3, 4, 1, 1, 1, 3, 3, 2, 0, 0, 0, 0, 2, 4, 4, 8
+ };
+
+ tflite::Padding padding = tflite::Padding_SAME;
+
+ ConvolutionTest<int8_t, int32_t>(tflite::BuiltinOperator_DEPTHWISE_CONV_2D,
+ ::tflite::TensorType_INT8,
+ 1, // strideX
+ 1, // strideY
+ 1, // dilationX
+ 1, // dilationY
+ padding,
+ tflite::ActivationFunctionType_NONE,
+ backends,
+ inputShape,
+ filterShape,
+ outputShape,
+ inputValues,
+ filterValues,
+ expectedOutputValues,
+ biasShape,
+ biasValues,
+ biasScales,
+ biasOffsets,
+ filterScales,
+ filterOffsets,
+ 1.0f,
+ 0,
+ inputScale,
+ 0,
+ depth_multiplier,
+ filterQuantizationDim);
+}
+
TEST_SUITE("DepthwiseConv2d_CpuRef_Tests")
{
@@ -141,6 +237,12 @@
DepthwiseConv2dSameUint8Test(backends);
}
+TEST_CASE ("DepthwiseConv2d_Same_Int8_PerChannelQuantization_CpuRef_Test")
+{
+ std::vector<armnn::BackendId> backends = {armnn::Compute::CpuRef};
+ DepthwiseConv2dSameInt8PerChannelTest(backends);
+}
+
}//End of TEST_SUITE("DepthwiseConv2d_CpuRef_Tests")
TEST_SUITE("DepthwiseConv2d_CpuAcc_Tests")