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Merge pull request #23096 from zihaomu:issue_23074

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This commit is contained in:
Alexander Alekhin 2023-01-12 00:51:03 +00:00
commit 3d5e3a910f
4 changed files with 41 additions and 10 deletions
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40
modules/dnn/src/layers/fast_convolution/depthwise_convolution.cpp
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@ -24,7 +24,7 @@ static void depthWiseBlockConv2D(const float* wptr,
const float* inptr_,
int height, int width,
float* outptr_,
int out_d, int outH, int outW)
int out_d, int outH, int outW, bool fusedAdd)
{
const float w00_ = wptr[0], w01_ = wptr[1], w02_ = wptr[2],
w10 = wptr[3], w11 = wptr[4], w12 = wptr[5],
@ -57,6 +57,8 @@ static void depthWiseBlockConv2D(const float* wptr,
out = imgptr0[0]*w01 + imgptr0[dilation_w]*w02 +
imgptr1[0]*w11 + imgptr1[dilation_w]*w12 +
imgptr2[0]*w21 + imgptr2[dilation_w]*w22 + bias;
if (fusedAdd)
out += outptr[0];
if (relu)
out = out > 0.f ? out : out*relu_coeff;
outptr[0] = out;
@ -65,6 +67,10 @@ static void depthWiseBlockConv2D(const float* wptr,
#if CV_SIMD128
const int VEC_NLANES = 4;
if (fusedAdd)
outW1 = max(out_j, outW1 - outW1%VEC_NLANES);
v_float32x4 vw00 = v_setall_f32(w00);
v_float32x4 vw01 = v_setall_f32(w01);
v_float32x4 vw02 = v_setall_f32(w02);
@ -104,6 +110,8 @@ static void depthWiseBlockConv2D(const float* wptr,
v_float32x4 vout = v00*vw00 + v01*vw01 + v02*vw02 +
v10*vw10 + v11*vw11 + v12*vw12 +
v20*vw20 + v21*vw21 + v22*vw22 + vbias;
if (fusedAdd)
vout = v_load(outptr + out_j) + vout;
if (relu)
vout = v_select(vout > z, vout, vout*vrc);
v_store(outptr + out_j, vout);
@ -134,6 +142,8 @@ static void depthWiseBlockConv2D(const float* wptr,
v10 * vw10 + v11 * vw11 + v12 * vw12 +
v20 * vw20 + v21 * vw21 + v22 * vw22 + vbias;
if (fusedAdd)
vout = v_load(outptr + out_j) + vout;
if (relu)
vout = v_select(vout > z, vout, vout*vrc);
v_store(outptr + out_j, vout);
@ -148,6 +158,8 @@ static void depthWiseBlockConv2D(const float* wptr,
out = imgptr0[in_j]*w00 + imgptr0[in_j + dilation_w]*w01 + imgptr0[in_j + dilation_w*2]*w02 +
imgptr1[in_j]*w10 + imgptr1[in_j + dilation_w]*w11 + imgptr1[in_j + dilation_w*2]*w12 +
imgptr2[in_j]*w20 + imgptr2[in_j + dilation_w]*w21 + imgptr2[in_j + dilation_w*2]*w22 + bias;
if (fusedAdd)
out += outptr[out_j];
if (relu)
out = out > 0.f ? out : out*relu_coeff;
outptr[out_j] = out;
@ -175,6 +187,8 @@ static void depthWiseBlockConv2D(const float* wptr,
out = imgptr0[in_j0]*w00*s0 + imgptr0[in_j1]*w01*s1 + imgptr0[in_j2]*w02*s2 +
imgptr1[in_j0]*w10*s0 + imgptr1[in_j1]*w11*s1 + imgptr1[in_j2]*w12*s2 +
imgptr2[in_j0]*w20*s0 + imgptr2[in_j1]*w21*s1 + imgptr2[in_j2]*w22*s2 + bias;
if (fusedAdd)
out += outptr[out_j];
if (relu)
out = out > 0.f ? out : out*relu_coeff;
outptr[out_j] = out;
@ -187,7 +201,7 @@ static void depthWiseBlockConv1D(const float* wptr,
const float* biasptr, const float* relu,
const float* inptr_, int width,
float* outptr_,
int out_d, int outW)
int out_d, int outW, bool fusedAdd)
{
const float w00_ = wptr[0], w01_ = wptr[1], w02_ = wptr[2];
int outW1 = min(outW, (width - dilation_w * (kernel_w - 1) + pad_l)/stride_w);
@ -201,7 +215,8 @@ static void depthWiseBlockConv1D(const float* wptr,
if (pad_l > 0)
{
out = imgptr0[0]*w01 + imgptr0[dilation_w]*w02 + bias;
if (fusedAdd)
out += outptr[0];
if (relu)
out = out > 0.f ? out : out*relu_coeff;
outptr[0] = out;
@ -210,6 +225,8 @@ static void depthWiseBlockConv1D(const float* wptr,
#if CV_SIMD128
const int VEC_NLANES = 4;
if (fusedAdd)
outW1 = max(out_j, outW1 - outW1%VEC_NLANES);
v_float32x4 vw00 = v_setall_f32(w00);
v_float32x4 vw01 = v_setall_f32(w01);
v_float32x4 vw02 = v_setall_f32(w02);
@ -235,6 +252,8 @@ static void depthWiseBlockConv1D(const float* wptr,
v02 = v_load(imgptr0 + in_j + dilation_w*2);
v_float32x4 vout = v00*vw00 + v01*vw01 + v02*vw02 + vbias;
if (fusedAdd)
vout = v_load(outptr + out_j) + vout;
if (relu)
vout = v_select(vout > z, vout, vout*vrc);
v_store(outptr + out_j, vout);
@ -258,6 +277,9 @@ static void depthWiseBlockConv1D(const float* wptr,
v_float32x4 vout = v00 * vw00 + v01 * vw01 + v02 * vw02 + vbias;
if (fusedAdd)
vout = v_load(outptr + out_j) + vout;
if (relu)
vout = v_select(vout > z, vout, vout*vrc);
v_store(outptr + out_j, vout);
@ -270,6 +292,8 @@ static void depthWiseBlockConv1D(const float* wptr,
{
int in_j = out_j * stride_w - pad_l;
out = imgptr0[in_j]*w00 + imgptr0[in_j + dilation_w]*w01 + imgptr0[in_j + dilation_w*2]*w02 + bias;
if (fusedAdd)
out += outptr[out_j];
if (relu)
out = out > 0.f ? out : out*relu_coeff;
outptr[out_j] = out;
@ -295,6 +319,8 @@ static void depthWiseBlockConv1D(const float* wptr,
s2 = 0.f;
}
out = imgptr0[in_j0]*w00*s0 + imgptr0[in_j1]*w01*s1 + imgptr0[in_j2]*w02*s2 + bias;
if (fusedAdd)
out += outptr[out_j];
if (relu)
out = out > 0.f ? out : out*relu_coeff;
outptr[out_j] = out;
@ -302,7 +328,7 @@ static void depthWiseBlockConv1D(const float* wptr,
}
void runDepthwise(InputArray _input, OutputArray _output, const Ptr<FastConv>& conv, ActivationLayer* activ_,
const std::vector<float>& reluslope)
const std::vector<float>& reluslope, bool fusedAdd)
{
Mat input = _input.getMat();
Mat output = _output.getMat();
@ -349,7 +375,7 @@ void runDepthwise(InputArray _input, OutputArray _output, const Ptr<FastConv>& c
#if CV_TRY_AVX2 || CV_TRY_AVX || CV_TRY_RVV
// TODO: remove the following limitation, need change code in layers_common.simd.hpp.
bool canRunOpt = Wi >= 16 + dilation_w*(Wk - 1);
bool canRunOpt = Wi >= 16 + dilation_w*(Wk - 1) && !fusedAdd;
#endif
std::vector<int> ofstab_(3 * ksize, 0);
int *ofstab = ofstab_.data();
@ -399,11 +425,11 @@ void runDepthwise(InputArray _input, OutputArray _output, const Ptr<FastConv>& c
else
#endif
depthWiseBlockConv2D(weights, Hk, Wk, stride_h, stride_w, dilation_h, dilation_w,
pad_top, pad_left, bias, relu, inptr0, Hi, Wi, outptr0, c, H0, W0);
pad_top, pad_left, bias, relu, inptr0, Hi, Wi, outptr0, c, H0, W0, fusedAdd);
}
else // conv_dim == CONV_1D, spatial branch for depth-wise Conv1D.
{
depthWiseBlockConv1D(weights, Wk, stride_w, dilation_w, pad_left, bias, relu, inptr0, Wi, outptr0, c, W0);
depthWiseBlockConv1D(weights, Wk, stride_w, dilation_w, pad_left, bias, relu, inptr0, Wi, outptr0, c, W0, fusedAdd);
}
if (activ)

4
modules/dnn/src/layers/fast_convolution/fast_convolution.cpp
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@ -375,8 +375,8 @@ void runFastConv(InputArray _input, OutputArray _output, const Ptr<FastConv>& co
if (conv->conv_type == _FX_CONV_TYPE_DEPTHWISE)
{
// Depthwise-Convolution layer should not be followed by Add layer.
CV_Assert(fusedAddMat.empty() && (conv_dim == CONV_1D || conv_dim == CONV_2D));
return runDepthwise(input, output, conv,actLayer.get(), reluslope);
CV_Assert((conv_dim == CONV_1D || conv_dim == CONV_2D));
return runDepthwise(input, output, conv, actLayer.get(), reluslope, fusedAdd);
}
MatShape inputShape = shape(input);

2
modules/dnn/src/layers/fast_convolution/fast_convolution.hpp
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@ -100,7 +100,7 @@ void runFastConv(InputArray _input, OutputArray _output, const Ptr<FastConv>& co
const Ptr<ActivationLayer>& actLayer, const std::vector<float>& reluslope, bool fusedAdd);
void runDepthwise(InputArray _input, OutputArray _output, const Ptr<FastConv>& conv, ActivationLayer* activ,
const std::vector<float>& reluslope);
const std::vector<float>& reluslope, bool fusedAdd);
int runWinograd63(InputArray _input, InputArray _fusedAddMat, OutputArray _output, const Ptr<FastConv>& conv, int ntasks,
float minval, float maxval, ActivationLayer* activ, bool ifMinMaxAct);

5
modules/dnn/test/test_onnx_importer.cpp
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@ -1726,6 +1726,11 @@ TEST_P(Test_ONNX_layers, ConvResizePool1d)
testONNXModels("conv_resize_pool_1d");
}
TEST_P(Test_ONNX_layers, DepthWiseAdd)
{
testONNXModels("depthwiseconv_add");
}
TEST_P(Test_ONNX_layers, SubFromConst)
{
testONNXModels("sub_from_const1");