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提交 1e151706 编写于 作者: H hjchen2
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Optimize pooling which efficiency has increased by 30% for googlenet, Fix pooling3x3 for stride 2

上级 e7201cf6
展开全部 隐藏空白更改
内联 并排
Showing with 726 addition and 403 deletion
src/operators/kernel/central-arm-func/elementwise_add_arm_func.h
浏览文件 @ 1e151706
......@@ -110,7 +110,7 @@ inline void ElementwiseAddCompute(const ElementwiseAddParam<CPU> &param) {
break;
case 3:
vst1_f32(output, vget_low_f32(r0));
vst1_lane_f32(output, vget_high_f32(r0), 0);
vst1q_lane_f32(output, r0, 2);
break;
}
}
......
src/operators/math/pooling.h
浏览文件 @ 1e151706
......@@ -53,7 +53,7 @@ struct PoolingVal<AVG> {
++count;
return *this;
}
inline float Value() { return (count > 0) ? val / count : 0.f; }
inline float Value() { return (count > 0) ? val * (1.f / count) : 0.f; }
};
#if defined(__ARM_NEON) || defined(__ARM_NEON__)
......@@ -67,6 +67,16 @@ inline float32x4_t vPoolInitq_f32<AVG>() {
return vdupq_n_f32(0.f);
}
template <PoolingType P = MAX>
inline float32x2_t vPoolInit_f32() {
return vdup_n_f32(-std::numeric_limits<float>::max());
}
template <>
inline float32x2_t vPoolInit_f32<AVG>() {
return vdup_n_f32(0.f);
}
template <PoolingType P = MAX>
inline float32x4_t vPoolPreq_f32(const float32x4_t &x1, const float32x4_t &x2) {
return vmaxq_f32(x1, x2);
......@@ -78,6 +88,28 @@ inline float32x4_t vPoolPreq_f32<AVG>(const float32x4_t &x1,
return vaddq_f32(x1, x2);
}
template <PoolingType P = MAX>
inline float32x2_t vPoolPre_f32(const float32x2_t &x1, const float32x2_t &x2) {
return vmax_f32(x1, x2);
}
template <>
inline float32x2_t vPoolPre_f32<AVG>(const float32x2_t &x1,
const float32x2_t &x2) {
return vadd_f32(x1, x2);
}
template <PoolingType P = MAX>
inline float32x2_t vpPoolPre_f32(const float32x2_t &x1, const float32x2_t &x2) {
return vpmax_f32(x1, x2);
}
template <>
inline float32x2_t vpPoolPre_f32<AVG>(const float32x2_t &x1,
const float32x2_t &x2) {
return vpadd_f32(x1, x2);
}
template <PoolingType P = MAX>
inline float32x4_t vPoolPostq_f32(const float32x4_t &x,
const float32x4_t &post) {
......@@ -89,6 +121,18 @@ inline float32x4_t vPoolPostq_f32<AVG>(const float32x4_t &x,
const float32x4_t &post) {
return vmulq_f32(x, post);
}
template <PoolingType P = MAX>
inline float32x2_t vPoolPost_f32(const float32x2_t &x,
const float32x2_t &post) {
return x;
}
template <>
inline float32x2_t vPoolPost_f32<AVG>(const float32x2_t &x,
const float32x2_t &post) {
return vmul_f32(x, post);
}
#endif // __ARM_NEON__
template <PoolingType P = MAX>
......
src/operators/math/pooling2x2.cpp
浏览文件 @ 1e151706
......@@ -40,7 +40,7 @@ namespace math {
template <PoolingType P, int Stride = 1>
struct Pooling2x2NormalRowLoadInput {
inline void operator()(const float *input, float32x4_t *x0, float32x4_t *x1) {
void operator()(const float *input, float32x4_t *x0, float32x4_t *x1) {
x0[0] = vld1q_f32(input);
x0[1] = vld1q_f32(input + 4);
x1[0] = vextq_f32(x0[0], x0[1], 1);
......@@ -50,7 +50,7 @@ struct Pooling2x2NormalRowLoadInput {
template <PoolingType P>
struct Pooling2x2NormalRowLoadInput<P, 2> {
inline void operator()(const float *input, float32x4_t *x0, float32x4_t *x1) {
void operator()(const float *input, float32x4_t *x0, float32x4_t *x1) {
float32x4x2_t t0 = vld2q_f32(input);
float32x4x2_t t1 = vld2q_f32(input + 8);
x0[0] = t0.val[0];
......
src/operators/math/pooling3x3.cpp
浏览文件 @ 1e151706
此差异已折叠。
test/operators/test_pool_op.cpp
浏览文件 @ 1e151706
......@@ -169,55 +169,55 @@ int main(int argc, char *argv[]) {
<< "float, pooling_type=avg, kernel=3, pad=5, stride=2";
paddle_mobile::TestPoolOp<1, 3, 5, 2>(in_channels, in_height, in_width);
LOG(paddle_mobile::kLOG_INFO)
<< "float, pooling_type=max, kernel=2, pad=0, stride=1";
paddle_mobile::TestPoolOp<0, 2, 0, 1>(in_channels, in_height, in_width);
LOG(paddle_mobile::kLOG_INFO)
<< "float, pooling_type=max, kernel=2, pad=1, stride=1";
paddle_mobile::TestPoolOp<0, 2, 1, 1>(in_channels, in_height, in_width);
LOG(paddle_mobile::kLOG_INFO)
<< "float, pooling_type=max, kernel=2, pad=2, stride=1";
paddle_mobile::TestPoolOp<0, 2, 2, 1>(in_channels, in_height, in_width);
LOG(paddle_mobile::kLOG_INFO)
<< "float, pooling_type=max, kernel=2, pad=5, stride=1";
paddle_mobile::TestPoolOp<0, 2, 5, 1>(in_channels, in_height, in_width);
LOG(paddle_mobile::kLOG_INFO)
<< "float, pooling_type=avg, kernel=2, pad=0, stride=1";
paddle_mobile::TestPoolOp<1, 2, 0, 1>(in_channels, in_height, in_width);
LOG(paddle_mobile::kLOG_INFO)
<< "float, pooling_type=avg, kernel=2, pad=1, stride=1";
paddle_mobile::TestPoolOp<1, 2, 1, 1>(in_channels, in_height, in_width);
LOG(paddle_mobile::kLOG_INFO)
<< "float, pooling_type=avg, kernel=2, pad=2, stride=1";
paddle_mobile::TestPoolOp<1, 2, 2, 1>(in_channels, in_height, in_width);
LOG(paddle_mobile::kLOG_INFO)
<< "float, pooling_type=avg, kernel=2, pad=5, stride=1";
paddle_mobile::TestPoolOp<1, 2, 5, 1>(in_channels, in_height, in_width);
LOG(paddle_mobile::kLOG_INFO)
<< "float, pooling_type=max, kernel=2, pad=0, stride=2";
paddle_mobile::TestPoolOp<0, 2, 0, 2>(in_channels, in_height, in_width);
LOG(paddle_mobile::kLOG_INFO)
<< "float, pooling_type=max, kernel=2, pad=1, stride=2";
paddle_mobile::TestPoolOp<0, 2, 1, 2>(in_channels, in_height, in_width);
LOG(paddle_mobile::kLOG_INFO)
<< "float, pooling_type=max, kernel=2, pad=2, stride=2";
paddle_mobile::TestPoolOp<0, 2, 2, 2>(in_channels, in_height, in_width);
LOG(paddle_mobile::kLOG_INFO)
<< "float, pooling_type=max, kernel=2, pad=5, stride=2";
paddle_mobile::TestPoolOp<0, 2, 5, 2>(in_channels, in_height, in_width);
LOG(paddle_mobile::kLOG_INFO)
<< "float, pooling_type=avg, kernel=2, pad=0, stride=2";
paddle_mobile::TestPoolOp<1, 2, 0, 2>(in_channels, in_height, in_width);
LOG(paddle_mobile::kLOG_INFO)
<< "float, pooling_type=avg, kernel=2, pad=1, stride=2";
paddle_mobile::TestPoolOp<1, 2, 1, 2>(in_channels, in_height, in_width);
LOG(paddle_mobile::kLOG_INFO)
<< "float, pooling_type=avg, kernel=2, pad=2, stride=2";
paddle_mobile::TestPoolOp<1, 2, 2, 2>(in_channels, in_height, in_width);
LOG(paddle_mobile::kLOG_INFO)
<< "float, pooling_type=avg, kernel=2, pad=5, stride=2";
paddle_mobile::TestPoolOp<1, 2, 5, 2>(in_channels, in_height, in_width);
// LOG(paddle_mobile::kLOG_INFO)
// << "float, pooling_type=max, kernel=2, pad=0, stride=1";
// paddle_mobile::TestPoolOp<0, 2, 0, 1>(in_channels, in_height, in_width);
// LOG(paddle_mobile::kLOG_INFO)
// << "float, pooling_type=max, kernel=2, pad=1, stride=1";
// paddle_mobile::TestPoolOp<0, 2, 1, 1>(in_channels, in_height, in_width);
// LOG(paddle_mobile::kLOG_INFO)
// << "float, pooling_type=max, kernel=2, pad=2, stride=1";
// paddle_mobile::TestPoolOp<0, 2, 2, 1>(in_channels, in_height, in_width);
// LOG(paddle_mobile::kLOG_INFO)
// << "float, pooling_type=max, kernel=2, pad=5, stride=1";
// paddle_mobile::TestPoolOp<0, 2, 5, 1>(in_channels, in_height, in_width);
//
// LOG(paddle_mobile::kLOG_INFO)
// << "float, pooling_type=avg, kernel=2, pad=0, stride=1";
// paddle_mobile::TestPoolOp<1, 2, 0, 1>(in_channels, in_height, in_width);
// LOG(paddle_mobile::kLOG_INFO)
// << "float, pooling_type=avg, kernel=2, pad=1, stride=1";
// paddle_mobile::TestPoolOp<1, 2, 1, 1>(in_channels, in_height, in_width);
// LOG(paddle_mobile::kLOG_INFO)
// << "float, pooling_type=avg, kernel=2, pad=2, stride=1";
// paddle_mobile::TestPoolOp<1, 2, 2, 1>(in_channels, in_height, in_width);
// LOG(paddle_mobile::kLOG_INFO)
// << "float, pooling_type=avg, kernel=2, pad=5, stride=1";
// paddle_mobile::TestPoolOp<1, 2, 5, 1>(in_channels, in_height, in_width);
//
// LOG(paddle_mobile::kLOG_INFO)
// << "float, pooling_type=max, kernel=2, pad=0, stride=2";
// paddle_mobile::TestPoolOp<0, 2, 0, 2>(in_channels, in_height, in_width);
// LOG(paddle_mobile::kLOG_INFO)
// << "float, pooling_type=max, kernel=2, pad=1, stride=2";
// paddle_mobile::TestPoolOp<0, 2, 1, 2>(in_channels, in_height, in_width);
// LOG(paddle_mobile::kLOG_INFO)
// << "float, pooling_type=max, kernel=2, pad=2, stride=2";
// paddle_mobile::TestPoolOp<0, 2, 2, 2>(in_channels, in_height, in_width);
// LOG(paddle_mobile::kLOG_INFO)
// << "float, pooling_type=max, kernel=2, pad=5, stride=2";
// paddle_mobile::TestPoolOp<0, 2, 5, 2>(in_channels, in_height, in_width);
//
// LOG(paddle_mobile::kLOG_INFO)
// << "float, pooling_type=avg, kernel=2, pad=0, stride=2";
// paddle_mobile::TestPoolOp<1, 2, 0, 2>(in_channels, in_height, in_width);
// LOG(paddle_mobile::kLOG_INFO)
// << "float, pooling_type=avg, kernel=2, pad=1, stride=2";
// paddle_mobile::TestPoolOp<1, 2, 1, 2>(in_channels, in_height, in_width);
// LOG(paddle_mobile::kLOG_INFO)
// << "float, pooling_type=avg, kernel=2, pad=2, stride=2";
// paddle_mobile::TestPoolOp<1, 2, 2, 2>(in_channels, in_height, in_width);
// LOG(paddle_mobile::kLOG_INFO)
// << "float, pooling_type=avg, kernel=2, pad=5, stride=2";
// paddle_mobile::TestPoolOp<1, 2, 5, 2>(in_channels, in_height, in_width);
}
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