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fix depth wise issue.

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This commit is contained in:
zihaomu 2023-01-28 11:41:00 +08:00
parent 840b1d5c94
commit f45a12439a
2 changed files with 49 additions and 39 deletions
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83
modules/dnn/src/layers/fast_convolution/depthwise_convolution.cpp
View File

@ -29,7 +29,7 @@ static void depthWiseBlockConv2D(const float* wptr,
const float w00_ = wptr[0], w01_ = wptr[1], w02_ = wptr[2],
w10 = wptr[3], w11 = wptr[4], w12 = wptr[5],
w20_ = wptr[6], w21_ = wptr[7], w22_ = wptr[8];
int outW1 = min(outW, (width - dilation_w*(kernel_w - 1) + pad_l)/stride_w);
const int outW1 = min(outW, (width - dilation_w*(kernel_w - 1) + pad_l)/stride_w);
float relu_coeff = relu ? relu[out_d] : 1.f, bias = biasptr[out_d];
for (int out_i = 0; out_i < outH; out_i++)
@ -67,35 +67,37 @@ 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);
v_float32x4 vw10 = v_setall_f32(w10);
v_float32x4 vw11 = v_setall_f32(w11);
v_float32x4 vw12 = v_setall_f32(w12);
v_float32x4 vw20 = v_setall_f32(w20);
v_float32x4 vw21 = v_setall_f32(w21);
v_float32x4 vw22 = v_setall_f32(w22);
v_float32x4 z = v_setzero_f32();
v_float32x4 vbias = v_setall_f32(bias);
v_float32x4 vrc = v_setall_f32(relu_coeff);
if (stride_w == 1 || (stride_w == 2 && dilation_w == 1))
if ((stride_w == 1 || (stride_w == 2 && dilation_w == 1)) && (outW1 - out_j) >= VEC_NLANES)
{
if( stride_w == 1 )
v_float32x4 vw00 = v_setall_f32(w00);
v_float32x4 vw01 = v_setall_f32(w01);
v_float32x4 vw02 = v_setall_f32(w02);
v_float32x4 vw10 = v_setall_f32(w10);
v_float32x4 vw11 = v_setall_f32(w11);
v_float32x4 vw12 = v_setall_f32(w12);
v_float32x4 vw20 = v_setall_f32(w20);
v_float32x4 vw21 = v_setall_f32(w21);
v_float32x4 vw22 = v_setall_f32(w22);
v_float32x4 z = v_setzero_f32();
v_float32x4 vbias = v_setall_f32(bias);
v_float32x4 vrc = v_setall_f32(relu_coeff);
if (stride_w == 1)
{
for( ; out_j < outW1; out_j += VEC_NLANES )
for (; out_j < outW1; out_j += VEC_NLANES)
{
if (out_j + VEC_NLANES > outW1)
// Tail processing.
if (out_j > outW1 - VEC_NLANES)
{
if (out_j <= pad_l || outW1 - VEC_NLANES < 0)
// If fusedAdd is true, what is stored in outptr is not a meaningless value,
// but the number being added. And we should avoid use tail processing in this case.
// Because the tail process will make some elements compute twice,
// which will lead to result errors.
if (fusedAdd)
break;
out_j = outW1 - VEC_NLANES;
}
int in_j = out_j * stride_w - pad_l;
v_float32x4 v00 = v_load(imgptr0 + in_j),
v01 = v_load(imgptr0 + in_j + dilation_w),
@ -119,11 +121,12 @@ static void depthWiseBlockConv2D(const float* wptr,
}
else // (stride_w == 2 && dilation_w == 1)
{
for( ; out_j < outW1; out_j += VEC_NLANES )
for (; out_j < outW1; out_j += VEC_NLANES)
{
if (out_j + VEC_NLANES > outW1 && out_j > pad_l)
// Tail processing.
if (out_j > outW1 - VEC_NLANES)
{
if (outW1 - VEC_NLANES < 0)
if (fusedAdd)
break;
out_j = outW1 - VEC_NLANES;
}
@ -204,7 +207,7 @@ static void depthWiseBlockConv1D(const float* wptr,
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);
const int outW1 = min(outW, (width - dilation_w * (kernel_w - 1) + pad_l)/stride_w);
float relu_coeff = relu ? relu[out_d] : 1.f, bias = biasptr[out_d];
int out_j = 0;
@ -225,27 +228,27 @@ 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);
v_float32x4 z = v_setzero_f32();
v_float32x4 vbias = v_setall_f32(bias);
v_float32x4 vrc = v_setall_f32(relu_coeff);
if (stride_w == 1 || (stride_w == 2 && dilation_w == 1))
if ((stride_w == 1 || (stride_w == 2 && dilation_w == 1)) && (outW1 - out_j) >= VEC_NLANES)
{
v_float32x4 vw00 = v_setall_f32(w00);
v_float32x4 vw01 = v_setall_f32(w01);
v_float32x4 vw02 = v_setall_f32(w02);
v_float32x4 z = v_setzero_f32();
v_float32x4 vbias = v_setall_f32(bias);
v_float32x4 vrc = v_setall_f32(relu_coeff);
if( stride_w == 1 )
{
for( ; out_j < outW1; out_j += VEC_NLANES )
{
// Tail processing.
if (out_j + VEC_NLANES > outW1)
{
if (out_j <= pad_l || outW1 - VEC_NLANES < 0)
if (fusedAdd)
break;
out_j = outW1 - VEC_NLANES;
}
int in_j = out_j * stride_w - pad_l;
v_float32x4 v00 = v_load(imgptr0 + in_j),
v01 = v_load(imgptr0 + in_j + dilation_w),
@ -263,12 +266,14 @@ static void depthWiseBlockConv1D(const float* wptr,
{
for( ; out_j < outW1; out_j += VEC_NLANES )
{
// Tail processing.
if (out_j + VEC_NLANES > outW1)
{
if (out_j <= pad_l || outW1 - VEC_NLANES < 0)
if (fusedAdd)
break;
out_j = outW1 - VEC_NLANES;
}
int in_j = out_j * stride_w - pad_l;
v_float32x4 v00, v01, v02, unused;

5
modules/dnn/test/test_onnx_importer.cpp
View File

@ -1731,6 +1731,11 @@ TEST_P(Test_ONNX_layers, DepthWiseAdd)
testONNXModels("depthwiseconv_add");
}
TEST_P(Test_ONNX_layers, DepthStride2)
{
testONNXModels("depthwise_stride2");
}
TEST_P(Test_ONNX_layers, SubFromConst)
{
testONNXModels("sub_from_const1");