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Add pad value for grouped_conv1d (#292) 

https://github.com/VeriSilicon/TIM-VX/issues/284

Signed-off-by: Chen Xin <jack.chen@verisilicon.com>

Co-authored-by: Chen Xin <jack.chen@verisilicon.com>
This commit is contained in:
chxin66 2022-02-21 19:11:36 +08:00 committed by GitHub
parent fe31a47bf9
commit 242a6bd05a
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GPG Key ID: 4AEE18F83AFDEB23
3 changed files with 79 additions and 20 deletions
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19
include/tim/vx/ops/groupedconv1d.h
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@ -55,22 +55,25 @@ namespace ops {
class GroupedConv1d : public DirectMapOp {
public:
GroupedConv1d(Graph* graph, PadType padding,
uint32_t stride,
uint32_t dilation,
uint32_t group,
DataLayout input_layout = DataLayout::WCN,
DataLayout kernel_layout = DataLayout::WIcOc);
GroupedConv1d(Graph* graph, PadType padding, std::array<uint32_t, 2> pad,
uint32_t stride, uint32_t dilation, uint32_t group,
DataLayout input_layout = DataLayout::WCN,
DataLayout kernel_layout = DataLayout::WIcOc);
GroupedConv1d(Graph* graph, PadType padding, const uint32_t stride,
const uint32_t dilation, uint32_t group,
DataLayout input_layout = DataLayout::WCN,
DataLayout kernel_layout = DataLayout::WIcOc);
DataLayout KernelDataLayout() { return kernel_layout_; }
std::shared_ptr<Operation> Clone(std::shared_ptr<Graph>& graph) const override;
std::shared_ptr<Operation> Clone(
std::shared_ptr<Graph>& graph) const override;
protected:
const PadType padding_;
const std::array<uint32_t, 2> pad_;
const uint32_t stride_;
const uint32_t dilation_;
const std::array<uint32_t, 2> pad_;
const uint32_t group_;
const DataLayout kernel_layout_;
};

34
src/tim/vx/ops/groupedconv1d.cc
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@ -31,27 +31,37 @@ namespace tim {
namespace vx {
namespace ops {
GroupedConv1d::GroupedConv1d(Graph* graph,
PadType padding,
const uint32_t stride,
const uint32_t dilation,
uint32_t group,
DataLayout input_layout, DataLayout kernel_layout)
GroupedConv1d::GroupedConv1d(Graph* graph, PadType padding,
const uint32_t stride, const uint32_t dilation,
uint32_t group, DataLayout input_layout,
DataLayout kernel_layout)
: GroupedConv1d(graph, padding, {0, 0}, stride, dilation, group, input_layout, kernel_layout) {}
GroupedConv1d::GroupedConv1d(Graph* graph, PadType padding,
std::array<uint32_t, 2> pad, const uint32_t stride,
const uint32_t dilation, uint32_t group,
DataLayout input_layout, DataLayout kernel_layout)
: DirectMapOp(graph, VSI_NN_OP_GROUPED_CONV1D, 3, 1, input_layout),
padding_(padding), stride_(stride), dilation_(dilation),
pad_({0,0}), group_(group),
padding_(padding),
pad_(pad),
stride_(stride),
dilation_(dilation),
group_(group),
kernel_layout_(kernel_layout) {
this->impl()->node()->nn_param.grouped_conv1d.pad_type = TranslatePadType(padding_);
this->impl()->node()->nn_param.grouped_conv1d.pad_type =
TranslatePadType(padding_);
this->impl()->node()->nn_param.grouped_conv1d.pad[0] = pad_[0];
this->impl()->node()->nn_param.grouped_conv1d.pad[1] = pad_[1];
this->impl()->node()->nn_param.grouped_conv1d.stride = stride_;
this->impl()->node()->nn_param.grouped_conv1d.group = group_;
this->impl()->node()->nn_param.grouped_conv1d.dilation = dilation_;
}
}
std::shared_ptr<Operation> GroupedConv1d::Clone(
std::shared_ptr<Graph>& graph) const {
return graph->CreateOperation<GroupedConv1d>(
this->padding_, this->stride_, this->dilation_, this->group_, this->impl_->layout_,
this->kernel_layout_);
this->padding_, this->pad_, this->stride_, this->dilation_, this->group_,
this->impl_->layout_, this->kernel_layout_);
}
} // namespace ops

46
src/tim/vx/ops/groupedconv1d_test.cc
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@ -70,3 +70,49 @@ TEST(GroupedConv1d, shape_6_2_1_float_ksize_6_stride_1_group_2_no_bias_wcn) {
EXPECT_TRUE(output_tensor->CopyDataFromTensor(output.data()));
EXPECT_TRUE(ArraysMatch(golden, output, 1e-5f));
}
TEST(GroupedConv1d, shape_6_2_1_float_ksize_6_stride_1_group_2_no_bias_wcn_PaddingTest) {
auto ctx = tim::vx::Context::Create();
auto graph = ctx->CreateGraph();
tim::vx::ShapeType in_shape({2, 4, 1});
tim::vx::ShapeType param_shape({3, 2, 4});
tim::vx::ShapeType out_shape({2, 4, 1});
tim::vx::TensorSpec input_spec(tim::vx::DataType::FLOAT32,
in_shape, tim::vx::TensorAttribute::INPUT);
tim::vx::TensorSpec param_spec(tim::vx::DataType::FLOAT32,
param_shape, tim::vx::TensorAttribute::INPUT);
tim::vx::TensorSpec output_spec(tim::vx::DataType::FLOAT32,
out_shape, tim::vx::TensorAttribute::OUTPUT);
auto input_tensor = graph->CreateTensor(input_spec);
auto weight_tensor = graph->CreateTensor(param_spec);
auto output_tensor = graph->CreateTensor(output_spec);
std::vector<float> in_data = {
-1, 0, 1, -1.5, 0.5, 1.5, 1, 1
};
std::vector<float> weight = {
-3, -2, -1.5, 1.5, 2, 3,
-2.5, -2, -1.5, 1.5, 2, 2.5,
-1, 0, 1, -1.5, 0.5, 1.5,
-1.5, 1.5, 2, -1, 0, 1,
};
std::vector<float> golden = {
1.5, -2.25, 1, -2.25, -1.5, -3, 0.5, -3.25
};
EXPECT_TRUE(input_tensor->CopyDataToTensor(in_data.data(), in_data.size() * sizeof(float)));
EXPECT_TRUE(weight_tensor->CopyDataToTensor(weight.data(), weight.size() * sizeof(float)));
auto op = graph->CreateOperation<tim::vx::ops::GroupedConv1d>(tim::vx::PadType::VALID, 1, 1, 2);
(*op).BindInputs({input_tensor, weight_tensor}).BindOutputs({output_tensor});
EXPECT_TRUE(graph->Compile());
EXPECT_TRUE(graph->Run());
std::vector<float> output(golden.size());
EXPECT_TRUE(output_tensor->CopyDataFromTensor(output.data()));
// EXPECT_TRUE(ArraysMatch(golden, output, 1e-5f));
EXPECT_EQ(golden, output);
}