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未验证 提交 292e69a8 编写于 作者: Z zhangyang0701 提交者： GitHub
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Merge branch 'develop' into develop

上级 79196cbf 63ee0b8f
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Showing with 199 addition and 112 deletion
src/operators/kernel/central-arm-func/pool_arm_func.h
浏览文件 @ 292e69a8
......@@ -76,15 +76,17 @@ void PoolCompute(const PoolParam<CPU> &param) {
}
}
} else if (ksize[0] == 2 && ksize[0] == ksize[1]) {
} else if (ksize[0] == 2 && ksize[0] == ksize[1] && strides[0] == 2 &&
strides[0] == strides[1] && paddings[0] == paddings[1] &&
paddings[1] == 0) {
#if __ARM_NEON
#if __aarch64__
PoolBasic(pooling_type, ksize, strides, paddings, in_x, out);
#else
if (pooling_type == "max") {
math::Pool2x2Max(strides, paddings, in_x, out);
math::Pool2x2Maxs2p0(strides, paddings, in_x, out);
} else if (pooling_type == "avg") {
math::Pool2x2Avg(strides, paddings, in_x, out);
math::Pool2x2Avgs2p0(strides, paddings, in_x, out);
}
#endif
#else
......
src/operators/math/pool_2x2.cpp
浏览文件 @ 292e69a8
......@@ -20,21 +20,15 @@ limitations under the License. */
namespace paddle_mobile {
namespace operators {
namespace math {
#define FLT_MAX __FLT_MAX__
void Pool2x2Max(vector<int> strides, vector<int> paddings, const Tensor *input,
Tensor *output) {
#if __ARM_NEON
#if __aarch64__
#else
void Pool2x2Maxs2p0(vector<int> strides, vector<int> paddings,
const Tensor *input, Tensor *output) {
const int batch_size = input->dims()[0];
const int input_height = input->dims()[2];
const int input_width = input->dims()[3];
const int output_channels = output->dims()[1];
int output_height = output->dims()[2];
const int output_width = output->dims()[3];
const int ksize_height = 2;
......@@ -47,72 +41,110 @@ void Pool2x2Max(vector<int> strides, vector<int> paddings, const Tensor *input,
const int input_channel_stride = input_height * input_width;
const int output_channel_stride = output_height * output_width;
const int input_batch_stride = output_channels * input_channel_stride;
const int output_batch_stride = output_channels * output_channel_stride;
const float *input_data = input->data<float>();
float *output_data = output->mutable_data<float>();
int out_w_num = output_width >> 2;
const int in_h_num = output_height >> 1;
const int input_batch_stride = output_channels * input_channel_stride;
const int output_batch_stride = output_channels * output_channel_stride;
int remain = output_width - out_w_num << 2;
int w1 = input_width / 16;
int _w1 = input_width % 16;
int w2 = _w1 / 4;
int _w2 = _w1 % 4;
for (int i = 0; i < batch_size; ++i) {
for (int c = 0; c < output_channels; ++c) {
const float *input_data_chanel_row_next = input_data + input_width;
for (; output_height > 0; output_height--) {
if (out_w_num > 0) {
for (int ph = 0; ph < input_height; ph += 2) {
const float *in_ptr1 = input_data + i * input_batch_stride +
c * input_channel_stride + ph * input_width;
const float *in_ptr2 = in_ptr1 + input_width;
if (ph + 1 >= input_height) {
in_ptr2 = static_cast<float *>(
paddle_mobile::memory::Alloc(sizeof(float) * input_width));
memset(static_cast<void *>(const_cast<float *>(in_ptr2)), -FLT_MAX,
sizeof(float) * input_width);
}
float *out_ptr = output_data + i * output_batch_stride +
c * output_channel_stride + ph / 2 * output_width;
asm volatile(
"max_loop: \n\t"
"vld1.f32 {q0,q1}, [%[in_ptr1]]! \n\t"
"vld1.f32 {q2,q3}, [%[in_ptr2]]! \n\t"
"subs %[w1], %[w1], #1 \n\t"
"blt end_w1_%= \n\t"
"loop_w1_%=: \n\t"
"pld [%[in_ptr1], #64] \n\t"
"pld [%[in_ptr2], #64] \n\t"
"vld1.f32 {q0, q1}, [%[in_ptr1]]! \n\t"
"vld1.f32 {q2, q3}, [%[in_ptr2]]! \n\t"
"vld1.f32 {q6, q7}, [%[in_ptr1]]! \n\t"
"vld1.f32 {q8, q9}, [%[in_ptr2]]! \n\t"
"vmax.f32 q0, q0, q2 \n\t"
"vmax.f32 q1, q1, q3 \n\t"
"vmax.f32 q6, q6, q8 \n\t"
"vmax.f32 q7, q7, q9 \n\t"
"vpmax.f32 d8, d0, d1 \n\t"
"vpmax.f32 d9, d2, d3 \n\t"
"vpmax.f32 d10, d12, d13 \n\t"
"vpmax.f32 d11, d14, d15 \n\t"
"vst1.32 {q4, q5}, [%[out_ptr]]! \n\t"
"subs %[w1], %[w1], #1 \n\t"
"bge loop_w1_%= \n\t"
"end_w1_%=: \n\t"
"subs %[w2], %[w2], #1 \n\t"
"blt end_w2_%= \n\t"
"loop_w2_%=: \n\t"
"vld1.f32 {q0}, [%[in_ptr1]]! \n\t"
"vld1.f32 {q1}, [%[in_ptr2]]! \n\t"
"vmax.f32 q0, q0, q1 \n\t"
"vpmax.f32 d4, d0, d1 \n\t"
"vpmax.f32 d5, d2, d3 \n\t"
"subs %[out_w_num], #1 \n\t"
"vst1.32 {q2}, [%[out_ptr]]! \n\t"
"bne max_loop \n\t"
: [in_ptr1] "+r"(input_data),
[in_ptr2] "+r"(input_data_chanel_row_next),
[out_ptr] "+r"(output_data), [out_w_num] "+r"(out_w_num)
"vst1.32 {d4}, [%[out_ptr]]! \n\t"
"subs %[w2], %[w2], #1 \n\t"
"bge loop_w2_%= \n\t"
"end_w2_%=: \n\t"
:
: "memory", "q0", "q1", "q2", "q3");
}
: [w1] "r"(w1), [w2] "r"(w2), [in_ptr1] "r"(in_ptr1),
[in_ptr2] "r"(in_ptr2), [out_ptr] "r"(out_ptr)
: "memory", "q0", "q1", "q2", "q3", "q4", "q5", "q6", "q7", "q8",
"q9");
for (; remain > 0; remain--) {
float max_row1 = std::max(input_data[0], input_data[1]);
float max_row2 = std::max(input_data_chanel_row_next[0],
input_data_chanel_row_next[1]);
*output_data = std::max(max_row1, max_row2);
input_data += 2;
input_data_chanel_row_next += 2;
output_data++;
if (_w2 != 0) {
in_ptr1 += 16 * w1 + 4 * w2;
in_ptr2 += 16 * w1 + 4 * w2;
out_ptr += 8 * w1 + 2 * w2;
if (_w2 == 1) {
*out_ptr = (*in_ptr1 > *in_ptr2) ? *in_ptr1 : *in_ptr2;
} else if (_w2 == 2) {
float temp = (*in_ptr1++ > *in_ptr2++) ? *in_ptr1++ : *in_ptr2++;
float temp1 = (*in_ptr1 > *in_ptr2) ? *in_ptr1 : *in_ptr2;
*out_ptr = (temp > temp1) ? temp : temp1;
} else if (_w2 == 3) {
float temp = (*in_ptr1++ > *in_ptr2++) ? *in_ptr1++ : *in_ptr2++;
float temp1 = (*in_ptr1++ > *in_ptr2++) ? *in_ptr1++ : *in_ptr2++;
*out_ptr++ = (temp > temp1) ? temp : temp1;
*out_ptr = (*in_ptr1 > *in_ptr2) ? *in_ptr1 : *in_ptr2;
}
}
}
input_data += input_channel_stride;
output_data += output_channel_stride;
}
input_data += input_batch_stride;
output_data += output_batch_stride;
}
#endif
#else
#endif
}
void Pool2x2Avg(vector<int> strides, vector<int> paddings, const Tensor *input,
Tensor *output) {
#if __ARM_NEON
#if __aarch64__
#else
void Pool2x2Avgs2p0(vector<int> strides, vector<int> paddings,
const Tensor *input, Tensor *output) {
const int batch_size = input->dims()[0];
const int input_height = input->dims()[2];
const int input_width = input->dims()[3];
const int output_channels = output->dims()[1];
int output_height = output->dims()[2];
const int output_width = output->dims()[3];
const int ksize_height = 2;
......@@ -125,59 +157,114 @@ void Pool2x2Avg(vector<int> strides, vector<int> paddings, const Tensor *input,
const int input_channel_stride = input_height * input_width;
const int output_channel_stride = output_height * output_width;
const int input_batch_stride = output_channels * input_channel_stride;
const int output_batch_stride = output_channels * output_channel_stride;
const float *input_data = input->data<float>();
float *output_data = output->mutable_data<float>();
int out_w_num = output_width >> 2;
const int input_batch_stride = output_channels * input_channel_stride;
const int output_batch_stride = output_channels * output_channel_stride;
float vqua[] = {0.25f, 0.25f, 0.25f, 0.25f};
int remain = output_width - out_w_num << 2;
int w1 = input_width / 16;
int _w1 = input_width % 16;
int w2 = _w1 / 4;
int _w2 = _w1 % 4;
float quarter = 1 / 4;
for (int i = 0; i < batch_size; ++i) {
for (int c = 0; c < output_channels; ++c) {
const float *input_data_chanel_row_next = input_data + input_width;
for (; output_height > 0; output_height--) {
if (out_w_num > 0) {
for (int ph = 0; ph < input_height; ph += 2) {
const float *in_ptr1 = input_data + i * input_batch_stride +
c * input_channel_stride + ph * input_width;
const float *in_ptr2 = in_ptr1 + input_width;
if (ph + 1 >= input_height) {
in_ptr2 = static_cast<float *>(
paddle_mobile::memory::Alloc(sizeof(float) * input_width));
memset(static_cast<void *>(const_cast<float *>(in_ptr2)), 0,
sizeof(float) * input_width);
}
float *out_ptr = output_data + i * output_batch_stride +
c * output_channel_stride + ph / 2 * output_width;
asm volatile(
"avg_loop: \n\t"
"vld1.32 {q0,q1}, [%[in_ptr1]]! \n\t"
"vld1.32 {q2,q3}, [%[in_ptr2]]! \n\t"
"vadd.f32 q0, q0, q2 \n\t"
"subs %[w1], %[w1], #1 \n\t"
"blt end_w1_%= \n\t"
"loop_w1_%=: \n\t"
"pld [%[in_ptr1], #64] \n\t"
"pld [%[in_ptr2], #64] \n\t"
"vmov.f32 d0[0], %[quarter] \n\t"
"vld1.f32 {q1, q2}, [%[in_ptr1]]! \n\t"
"vld1.f32 {q3, q4}, [%[in_ptr2]]! \n\t"
"vld1.f32 {q7, q8}, [%[in_ptr1]]! \n\t"
"vld1.f32 {q9, q10}, [%[in_ptr2]]! \n\t"
"vadd.f32 q1, q1, q3 \n\t"
"vpadd.f32 d4, d0, d1 \n\t"
"vpadd.f32 d5, d2, d3 \n\t"
"vld1.32 {q4}, [%[vqua]]! \n\t"
"vmul.f32 q2, q2, q4 \n\t"
"subs %[out_w_num], #1 \n\t"
"vst1.32 {q2}, [%[out_ptr]]! \n\t"
"bne avg_loop \n\t"
: [in_ptr1] "+r"(input_data),
[in_ptr2] "+r"(input_data_chanel_row_next),
[out_ptr] "+r"(output_data), [out_w_num] "+r"(out_w_num)
: [vqua] "r"(vqua)
: "memory", "q0", "q1", "q2", "q3", "q4");
}
"vadd.f32 q2, q2, q4 \n\t"
"vadd.f32 q7, q7, q9 \n\t"
"vadd.f32 q8, q8, q10 \n\t"
for (; remain > 0; remain--) {
float max_row1 = std::max(input_data[0], input_data[1]);
float max_row2 = std::max(input_data_chanel_row_next[0],
input_data_chanel_row_next[1]);
*output_data = std::max(max_row1, max_row2);
input_data += 2;
input_data_chanel_row_next += 2;
output_data++;
"vpadd.f32 d10, d2, d3 \n\t"
"vpadd.f32 d11, d4, d5 \n\t"
"vpadd.f32 d12, d14, d15 \n\t"
"vpadd.f32 d13, d16, d17 \n\t"
"vmul.f32 q5, q5, d0[0] \n\t"
"vmul.f32 q6, q6, d0[0] \n\t"
"vst1.32 {q5, q6}, [%[out_ptr]]! \n\t"
"subs %[w1], %[w1], #1 \n\t"
"bge loop_w1_%= \n\t"
"end_w1_%=: \n\t"
"subs %[w2], %[w2], #1 \n\t"
"blt end_w2_%= \n\t"
"loop_w2_%=: \n\t"
"vld1.f32 {q1}, [%[in_ptr1]]! \n\t"
"vld1.f32 {q2}, [%[in_ptr2]]! \n\t"
"vadd.f32 q1, q1, q2 \n\t"
"vpadd.f32 d4, d2, d3 \n\t"
"vmul.f32 d4, d4, d0[0] \n\t"
"vst1.32 {d4}, [%[out_ptr]]! \n\t"
"subs %[w2], %[w2], #1 \n\t"
"bge loop_w2_%= \n\t"
"end_w2_%=: \n\t"
:
: [w1] "r"(w1), [w2] "r"(w2), [in_ptr1] "r"(in_ptr1),
[in_ptr2] "r"(in_ptr2), [out_ptr] "r"(out_ptr),
[quarter] "r"(quarter)
: "memory", "q0", "q1", "q2", "q3", "q4", "q5", "q6", "q7", "q8",
"q9", "q10");
if (_w2 != 0) {
in_ptr1 += 16 * w1 + 4 * w2;
in_ptr2 += 16 * w1 + 4 * w2;
out_ptr += 8 * w1 + 2 * w2;
if (_w2 == 1) {
*out_ptr = 0.5 * (*in_ptr1 + *in_ptr2);
} else if (_w2 == 2) {
float temp = 0;
temp += *in_ptr1++;
temp += *in_ptr2++;
temp += *in_ptr1;
temp += *in_ptr2;
*out_ptr = 0.5 * temp;
} else if (_w2 == 3) {
float temp = 0;
temp += *in_ptr1++;
temp += *in_ptr2++;
temp += *in_ptr1++;
temp += *in_ptr2++;
*out_ptr++ = 0.5 * temp;
*out_ptr = 0.5 * (*in_ptr1 + *in_ptr2);
}
}
}
input_data += input_channel_stride;
output_data += output_channel_stride;
}
input_data += input_batch_stride;
output_data += output_batch_stride;
}
#endif
#else
#endif
}
//}
......
src/operators/math/pool_2x2.h
浏览文件 @ 292e69a8
......@@ -26,11 +26,11 @@ namespace math {
using framework::Tensor;
using std::vector;
void Pool2x2Max(vector<int> strides, vector<int> paddings, const Tensor *input,
Tensor *output);
void Pool2x2Maxs2p0(vector<int> strides, vector<int> paddings,
const Tensor *input, Tensor *output);
void Pool2x2Avg(vector<int> strides, vector<int> paddings, const Tensor *in_x,
Tensor *out);
void Pool2x2Avgs2p0(vector<int> strides, vector<int> paddings,
const Tensor *in_x, Tensor *out);
} // namespace math
} // namespace operators
} // namespace paddle_mobile
......
src/operators/math/pool_3x3.cpp
浏览文件 @ 292e69a8
......@@ -558,15 +558,13 @@ void Pool3x3Max(vector<int> strides, vector<int> paddings, const Tensor *input,
const float *input_seg = input_data + c * input_channel_stride;
float *output_seg = output_data + c * output_channel_stride;
for (int ph = 0; ph < output_height; ph++) {
for (int pw = 0; pw < output_width; pw++) {
int hstart = ph * stride - padding;
int wstart = pw * stride - padding;
int hend = min(hstart + 3, input_height + padding);
int wend = min(wstart + 3, input_width + padding);
int hend = min(hstart + 3, input_height);
hstart = max(hstart, 0);
for (int pw = 0; pw < output_width; pw++) {
int wstart = pw * stride - padding;
int wend = min(wstart + 3, input_width);
wstart = max(wstart, 0);
hend = min(hend, input_height);
wend = min(wend, input_width);
const float *pos1 = input_seg + hstart * input_width + wstart;
const float *pos2 = input_seg + (hstart + 1) * input_width + wstart;
const float *pos3 = input_seg + (hstart + 2) * input_width + wstart;
......
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