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Add group parameter for deconv API (#51) 

* Add group parameter for deconv API

Limitation: only support depthwise deconvolution

Signed-off-by: xiang.zhang <xiang.zhang@verisilicon.com>

* Add single channel case and fix build warning

Signed-off-by: xiang.zhang <xiang.zhang@verisilicon.com>
This commit is contained in:
Sven 2021-05-20 10:56:52 +08:00 committed by GitHub
parent 7c0d2f59bb
commit e3b127df50
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GPG Key ID: 4AEE18F83AFDEB23
2 changed files with 54 additions and 2 deletions
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2
src/tim/vx/ops/deconv.cc
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@ -57,7 +57,7 @@ DeConv2d::DeConv2d(Graph* graph, int32_t oc_count, PadType pad_type,
group_(group) {
// TODO(Sven): only support depthwise usage
assert(group != 1 && group == oc_count);
assert((group == 1U) || 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];

54
src/tim/vx/ops/deconv2d_test.cc
View File

@ -17,7 +17,7 @@ size_t element_count(const tim::vx::ShapeType& shape) {
} // namespace
TEST(OP, deconv_group) {
TEST(OP, deconv_depthwise_two_channel) {
auto ctx = tim::vx::Context::Create();
auto graph = ctx->CreateGraph();
@ -84,3 +84,55 @@ TEST(OP, deconv_group) {
EXPECT_EQ(golden, output_data) << "Result mismatch";
}
TEST(OP, deconv_single_channel) {
auto ctx = tim::vx::Context::Create();
auto graph = ctx->CreateGraph();
tim::vx::ShapeType input_shape ({3, 3, 1, 1}); //whcn
tim::vx::ShapeType kernel_shape({3, 3, 1, 1}); //whc1 same as depthwise convolution
tim::vx::ShapeType output_shape({5, 5, 1, 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,
};
std::vector<float> kernel_data = {
9.0f, 0.0f, 3.0f, 0.0f, 0.0f, 0.0f, 1.0f, 0.0f, 2.0f,
};
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>(
1,
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*/
1/*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,
};
EXPECT_EQ(golden, output_data) << "Result mismatch";
}