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Add group parameter for deconv API 

Limitation: only support depthwise deconvolution

Signed-off-by: xiang.zhang <xiang.zhang@verisilicon.com>
This commit is contained in:
xiang.zhang 2021-05-19 15:41:29 +08:00 committed by Kainan Cha
parent 8ab7759e3c
commit b1b7eadefc
4 changed files with 107 additions and 16 deletions
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10
include/tim/vx/ops/deconv.h
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@ -34,23 +34,25 @@ namespace ops {
class DeConv2d : public Operation {
public:
DeConv2d(Graph* graph, int32_t weights, PadType pad_type,
DeConv2d(Graph* graph, int32_t oc_count_, PadType pad_type,
const std::array<uint32_t, 2>& ksize,
const std::array<uint32_t, 2>& stride,
const std::array<uint32_t, 2>& output_padding);
DeConv2d(Graph* graph, int32_t weights, PadType pad_type,
DeConv2d(Graph* graph, int32_t oc_count_, PadType pad_type,
const std::array<uint32_t, 2>& ksize,
const std::array<uint32_t, 2>& stride,
const std::array<uint32_t, 2>& output_padding,
const std::array<uint32_t, 4>& pad);
const std::array<uint32_t, 4>& pad,
const uint32_t group = 1);
protected:
const uint32_t weights_;
const uint32_t oc_count_; // output channel count
const PadType pad_type_;
const std::array<uint32_t, 2> ksize_;
const std::array<uint32_t, 2> stride_;
const std::array<uint32_t, 2> output_padding_;
const std::array<uint32_t, 4> pad_;
const uint32_t group_;
};
} // namespace ops

3
src/tim/CMakeLists.txt
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@ -30,9 +30,6 @@ if(TIM_VX_ENABLE_LAYOUT_INFER)
)
endif()
set(UT_SRC)
aux_source_directory(./vx/ut VX_UT_SRC)
list(APPEND UT_SRC ${VX_UT_SRC})
foreach(src_file ${SRC})
if(${src_file} MATCHES ".*_test\.cc")
list(REMOVE_ITEM SRC ${src_file})

24
src/tim/vx/ops/deconv.cc
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@ -21,9 +21,10 @@
* DEALINGS IN THE SOFTWARE.
*
*****************************************************************************/
#include "tim/vx/ops/deconv.h"
#include <cassert>
#include "operation_private.h"
#include "type_utils.h"
#include "vsi_nn_pub.h"
@ -32,33 +33,38 @@ namespace tim {
namespace vx {
namespace ops {
DeConv2d::DeConv2d(Graph* graph, int32_t weights, PadType pad_type,
DeConv2d::DeConv2d(Graph* graph, int32_t oc_count, PadType pad_type,
const std::array<uint32_t, 2>& ksize,
const std::array<uint32_t, 2>& stride,
const std::array<uint32_t, 2>& output_padding)
: DeConv2d(graph, weights, pad_type, ksize, stride, output_padding,
: DeConv2d(graph, oc_count, pad_type, ksize, stride, output_padding,
{0, 0, 0, 0}) {
}
DeConv2d::DeConv2d(Graph* graph, int32_t weights, PadType pad_type,
DeConv2d::DeConv2d(Graph* graph, int32_t oc_count, PadType pad_type,
const std::array<uint32_t, 2>& ksize,
const std::array<uint32_t, 2>& stride,
const std::array<uint32_t, 2>& output_padding,
const std::array<uint32_t, 4>& pad)
const std::array<uint32_t, 4>& pad,
const uint32_t group)
: Operation(graph, VSI_NN_OP_DECONVOLUTION),
weights_(weights),
oc_count_(oc_count),
pad_type_(pad_type),
ksize_(ksize),
stride_(stride),
output_padding_(output_padding),
pad_(pad) {
pad_(pad),
group_(group) {
// TODO(Sven): only support depthwise usage
assert(group != 1 && group == oc_count);
this->impl()->node()->nn_param.deconv.ksize[0] = ksize_[0];
this->impl()->node()->nn_param.deconv.ksize[1] = ksize_[1];
this->impl()->node()->nn_param.deconv.stride[0] = stride_[0];
this->impl()->node()->nn_param.deconv.stride[1] = stride_[1];
this->impl()->node()->nn_param.deconv.pad_type = TranslatePadType(pad_type_);
this->impl()->node()->nn_param.deconv.weights = weights_;
this->impl()->node()->nn_param.deconv.group = 1;
this->impl()->node()->nn_param.deconv.weights = oc_count_;
this->impl()->node()->nn_param.deconv.group = group_;
this->impl()->node()->nn_param.deconv.output_padding[0] = output_padding_[0];
this->impl()->node()->nn_param.deconv.output_padding[1] = output_padding_[1];
this->impl()->node()->nn_param.deconv.pad[0] = pad_[0];

86
src/tim/vx/ops/deconv2d_test.cc Normal file
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@ -0,0 +1,86 @@
#include "tim/vx/context.h"
#include "tim/vx/graph.h"
#include "tim/vx/ops/deconv.h"
#include "gtest/gtest.h"
namespace {
size_t element_count(const tim::vx::ShapeType& shape) {
size_t sz = 1;
for (auto d : shape) {
sz *= d;
}
return sz;
}
} // namespace
TEST(OP, deconv_group) {
auto ctx = tim::vx::Context::Create();
auto graph = ctx->CreateGraph();
tim::vx::ShapeType input_shape ({3, 3, 2, 1}); //whcn
tim::vx::ShapeType kernel_shape({3, 3, 2, 1}); //whc1 same as depthwise convolution
tim::vx::ShapeType output_shape({5, 5, 2, 1}); //whcn
tim::vx::TensorSpec input_spec (tim::vx::DataType::FLOAT32, input_shape, tim::vx::TensorAttribute::INPUT);
tim::vx::TensorSpec kernel_spec (tim::vx::DataType::FLOAT32, kernel_shape, tim::vx::TensorAttribute::CONSTANT);
tim::vx::TensorSpec output_spec (tim::vx::DataType::FLOAT32, output_shape, tim::vx::TensorAttribute::OUTPUT);
auto input_tensor = graph->CreateTensor(input_spec);
auto output_tensor = graph->CreateTensor(output_spec);
auto kernel_tensor = graph->CreateTensor(kernel_spec);
std::vector<float> input_data = {3.0f, 8.0f, 1.0f,
9.0f, 5.0f, 7.0f,
3.0f, 2.0f, 3.0f,
7.0f, 9.0f, 1.0f,
5.0f, 2.0f, 3.0f,
9.0f, 0.0f, 2.0f};
std::vector<float> kernel_data =
{9.0f, 0.0f, 3.0f,
0.0f, 0.0f, 0.0f,
1.0f, 0.0f, 2.0f,
3.0f, 0.0f, 7.0f,
0.0f, 0.0f, 0.0f,
0.0f, 0.0f, 8.0f,
};
std::vector<float> output_data(element_count(output_shape));
EXPECT_TRUE(input_tensor->CopyDataToTensor(input_data.data(), input_data.size()*4));
EXPECT_TRUE(kernel_tensor->CopyDataToTensor(kernel_data.data(), kernel_data.size()*4));
auto add = graph->CreateOperation<tim::vx::ops::DeConv2d>(
2,
tim::vx::PadType::SAME,
std::array<uint32_t, 2>({3, 3}), /*ksize*/
std::array<uint32_t, 2>({1, 1}), /*stride*/
std::array<uint32_t, 2>({1, 1}), /*dilation*/
std::array<uint32_t, 4>({0, 0, 0, 0}), /*pad*/
2/*group*/);
(*add).BindInputs({input_tensor, kernel_tensor}).BindOutputs({output_tensor});
EXPECT_TRUE(graph->Compile());
EXPECT_TRUE(graph->Run());
EXPECT_TRUE(output_tensor->CopyDataFromTensor(output_data.data()));
std::vector<float> golden = {
27.0f, 72.0f, 18.0f, 24.0f, 3.0f,
81.0f, 45.0f, 90.0f, 15.0f, 21.0f,
30.0f, 26.0f, 43.0f, 22.0f, 11.0f,
9.0f, 5.0f, 25.0f, 10.0f, 14.0f,
3.0f, 2.0f, 9.0f, 4.0f, 6.0f,
21.0f, 27.0f, 52.0f, 63.0f, 7.0f,
15.0f, 6.0f, 44.0f, 14.0f, 21.0f,
27.0f, 0.0f, 125.0f, 72.0f, 22.0f,
0.0f, 0.0f, 40.0f, 16.0f, 24.0f,
0.0f, 0.0f, 72.0f, 0.0f, 16.0f};
EXPECT_EQ(golden, output_data) << "Result mismatch";
}