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PaddleDetection
提交 3a631fbb 编写于 作者: C Chunwei
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paddle/fluid/lite/arm/math/activation.cc 已删除 100644 → 0
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paddle/fluid/lite/arm/math/activation.h 已删除 100644 → 0
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// Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserved.
//
// Licensed under the Apache License, Version 2.0 (the "License");
// you may not use this file except in compliance with the License.
// You may obtain a copy of the License at
//
// http://www.apache.org/licenses/LICENSE-2.0
//
// Unless required by applicable law or agreed to in writing, software
// distributed under the License is distributed on an "AS IS" BASIS,
// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
// See the License for the specific language governing permissions and
// limitations under the License.
#pragma once
namespace paddle {
namespace lite {
namespace arm {
namespace math {
template <typename T>
void act_relu(const T* din, T* dout, int size, int threads);
template <typename T>
void act_relu_neg(const T* din, T* dout, int size, const float negative_slope,
int threads);
template <typename T>
void act_clipped_relu(const T* din, T* dout, int size, const float coef,
int threads);
template <typename T>
void act_prelu(const T* din, T* dout, int outer_size, int channel_size,
int inner_size, bool channel_shared, float* channel_slope,
int threads);
template <typename T>
void act_sigmoid(const T* din, T* dout, int size, int threads);
template <typename T>
void act_tanh(const T* din, T* dout, int size, int threads);
template <typename T>
void act_swish(const T* din, T* dout, int size, const float coef, int threads);
} // namespace math
} // namespace arm
} // namespace lite
} // namespace paddle
paddle/fluid/lite/arm/math/concat.cc 已删除 100644 → 0
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// Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserved.
//
// Licensed under the Apache License, Version 2.0 (the "License");
// you may not use this file except in compliance with the License.
// You may obtain a copy of the License at
//
// http://www.apache.org/licenses/LICENSE-2.0
//
// Unless required by applicable law or agreed to in writing, software
// distributed under the License is distributed on an "AS IS" BASIS,
// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
// See the License for the specific language governing permissions and
// limitations under the License.
#include "paddle/fluid/lite/arm/math/concat.h"
#include <algorithm>
#include <limits>
#include <memory>
#include "paddle/fluid/lite/arm/math/funcs.h"
namespace paddle {
namespace lite {
namespace arm {
namespace math {
void concat_func(const std::vector<lite::Tensor *> &input, const int axis,
lite::Tensor *output) {
size_t num = input.size();
int rows = 1;
auto dim_0 = input[0]->dims();
for (int i = 0; i < axis; ++i) {
rows *= dim_0[i];
}
int out_rows = rows, out_cols = 0;
std::vector<int64_t> input_cols(input.size());
for (int i = 0; i < num; ++i) {
int t_cols = input[i]->numel() / rows;
out_cols += t_cols;
input_cols[i] = t_cols;
}
// computation
for (int k = 0; k < out_rows; ++k) {
float *dst_ptr = output->mutable_data<float>() + k * out_cols;
int col_idx = 0;
for (int j = 0; j < num; ++j) {
int col_len = input_cols[j];
const float *src_prt = input[j]->data<float>() + k * col_len;
std::memcpy(dst_ptr + col_idx, src_prt, sizeof(float) * col_len);
col_idx += col_len;
}
}
}
} // namespace math
} // namespace arm
} // namespace lite
} // namespace paddle
paddle/fluid/lite/arm/math/concat.h 已删除 100644 → 0
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// Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserved.
//
// Licensed under the Apache License, Version 2.0 (the "License");
// you may not use this file except in compliance with the License.
// You may obtain a copy of the License at
//
// http://www.apache.org/licenses/LICENSE-2.0
//
// Unless required by applicable law or agreed to in writing, software
// distributed under the License is distributed on an "AS IS" BASIS,
// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
// See the License for the specific language governing permissions and
// limitations under the License.
#pragma once
#include <algorithm>
#include <string>
#include <vector>
#include "paddle/fluid/lite/operators/op_params.h"
#include "paddle/fluid/lite/utils/cp_logging.h"
namespace paddle {
namespace lite {
namespace arm {
namespace math {
void concat_func(const std::vector<lite::Tensor *> &input, const int axis,
lite::Tensor *output);
} // namespace math
} // namespace arm
} // namespace lite
} // namespace paddle
paddle/fluid/lite/arm/math/dropout.cc 已删除 100644 → 0
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// Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserved.
//
// Licensed under the Apache License, Version 2.0 (the "License");
// you may not use this file except in compliance with the License.
// You may obtain a copy of the License at
//
// http://www.apache.org/licenses/LICENSE-2.0
//
// Unless required by applicable law or agreed to in writing, software
// distributed under the License is distributed on an "AS IS" BASIS,
// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
// See the License for the specific language governing permissions and
// limitations under the License.
#include "paddle/fluid/lite/arm/math/dropout.h"
#include "paddle/fluid/lite/arm/math/funcs.h"
namespace paddle {
namespace lite {
namespace arm {
namespace math {
template <>
void dropout_down<float>(const float* din, float* dout, int num, float prob) {
const float scale = 1.0f - prob;
int cnt = num >> 4;
int remain = num % 16;
float32x4_t vscale = vdupq_n_f32(scale);
#pragma omp parallel for
for (int i = 0; i < cnt; i++) {
const float* din_ptr = din + (i << 4);
float* dout_ptr = dout + (i << 4);
float32x4_t din0 = vld1q_f32(din_ptr);
float32x4_t din1 = vld1q_f32(din_ptr + 4);
float32x4_t din2 = vld1q_f32(din_ptr + 8);
float32x4_t din3 = vld1q_f32(din_ptr + 12);
float32x4_t vmul0 = vmulq_f32(din0, vscale);
float32x4_t vmul1 = vmulq_f32(din1, vscale);
float32x4_t vmul2 = vmulq_f32(din2, vscale);
float32x4_t vmul3 = vmulq_f32(din3, vscale);
vst1q_f32(dout_ptr, vmul0);
vst1q_f32(dout_ptr + 4, vmul1);
vst1q_f32(dout_ptr + 8, vmul2);
vst1q_f32(dout_ptr + 12, vmul3);
}
if (remain > 0) {
const float* din_ptr = din + (cnt << 4);
float* dout_ptr = dout + (cnt << 4);
for (int i = 0; i < remain; i++) {
*dout_ptr = *din_ptr * scale;
dout_ptr++;
din_ptr++;
}
}
}
template <>
void dropout_up<float>(const float* din, float* dout, int num) {
int cnt = num >> 4;
int remain = num % 16;
#pragma omp parallel for
for (int i = 0; i < cnt; i++) {
const float* din_ptr = din + (i << 4);
float* dout_ptr = dout + (i << 4);
float32x4_t din0 = vld1q_f32(din_ptr);
float32x4_t din1 = vld1q_f32(din_ptr + 4);
float32x4_t din2 = vld1q_f32(din_ptr + 8);
float32x4_t din3 = vld1q_f32(din_ptr + 12);
vst1q_f32(dout_ptr, din0);
vst1q_f32(dout_ptr + 4, din1);
vst1q_f32(dout_ptr + 8, din2);
vst1q_f32(dout_ptr + 12, din3);
}
if (remain > 0) {
const float* din_ptr = din + (cnt << 4);
float* dout_ptr = dout + (cnt << 4);
for (int i = 0; i < remain; i++) {
*dout_ptr = *din_ptr;
dout_ptr++;
din_ptr++;
}
}
}
} // namespace math
} // namespace arm
} // namespace lite
} // namespace paddle
paddle/fluid/lite/arm/math/dropout.h 已删除 100644 → 0
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// Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserved.
//
// Licensed under the Apache License, Version 2.0 (the "License");
// you may not use this file except in compliance with the License.
// You may obtain a copy of the License at
//
// http://www.apache.org/licenses/LICENSE-2.0
//
// Unless required by applicable law or agreed to in writing, software
// distributed under the License is distributed on an "AS IS" BASIS,
// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
// See the License for the specific language governing permissions and
// limitations under the License.
#pragma once
#include <string>
namespace paddle {
namespace lite {
namespace arm {
namespace math {
template <typename T>
void dropout_down(const T* din, T* dout, int num, float prob);
template <typename T>
void dropout_up(const T* din, T* dout, int num);
} // namespace math
} // namespace arm
} // namespace lite
} // namespace paddle
paddle/fluid/lite/arm/math/elementwise.cc 已删除 100644 → 0
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// Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserved.
//
// Licensed under the Apache License, Version 2.0 (the "License");
// you may not use this file except in compliance with the License.
// You may obtain a copy of the License at
//
// http://www.apache.org/licenses/LICENSE-2.0
//
// Unless required by applicable law or agreed to in writing, software
// distributed under the License is distributed on an "AS IS" BASIS,
// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
// See the License for the specific language governing permissions and
// limitations under the License.
#include "paddle/fluid/lite/arm/math/elementwise.h"
#include "paddle/fluid/lite/arm/math/funcs.h"
namespace paddle {
namespace lite {
namespace arm {
namespace math {
template <>
void elementwise_add<float>(const float* dinx, const float* diny, float* dout,
int num) {
int cnt = num >> 4;
int remain = num % 16;
#pragma omp parallel for
for (int i = 0; i < cnt; i++) {
const float* dinx_ptr = dinx + (i << 4);
const float* diny_ptr = diny + (i << 4);
float* dout_ptr = dout + (i << 4);
float32x4_t dinx0 = vld1q_f32(dinx_ptr);
float32x4_t dinx1 = vld1q_f32(dinx_ptr + 4);
float32x4_t dinx2 = vld1q_f32(dinx_ptr + 8);
float32x4_t dinx3 = vld1q_f32(dinx_ptr + 12);
float32x4_t diny0 = vld1q_f32(diny_ptr);
float32x4_t diny1 = vld1q_f32(diny_ptr + 4);
float32x4_t diny2 = vld1q_f32(diny_ptr + 8);
float32x4_t diny3 = vld1q_f32(diny_ptr + 12);
dinx0 = vaddq_f32(dinx0, diny0);
dinx1 = vaddq_f32(dinx1, diny1);
dinx2 = vaddq_f32(dinx2, diny2);
dinx3 = vaddq_f32(dinx3, diny3);
vst1q_f32(dout_ptr, dinx0);
vst1q_f32(dout_ptr + 4, dinx1);
vst1q_f32(dout_ptr + 8, dinx2);
vst1q_f32(dout_ptr + 12, dinx3);
}
if (remain > 0) {
const float* dinx_ptr = dinx + (cnt << 4);
const float* diny_ptr = diny + (cnt << 4);
float* dout_ptr = dout + (cnt << 4);
for (int i = 0; i < remain; i++) {
*dout_ptr = *dinx_ptr + *diny_ptr;
dout_ptr++;
dinx_ptr++;
diny_ptr++;
}
}
}
template <>
void elementwise_add_relu<float>(const float* dinx, const float* diny,
float* dout, int num) {
int cnt = num >> 4;
int remain = num % 16;
float32x4_t vzero = vdupq_n_f32(0.f);
#pragma omp parallel for
for (int i = 0; i < cnt; i++) {
const float* dinx_ptr = dinx + (i << 4);
const float* diny_ptr = diny + (i << 4);
float* dout_ptr = dout + (i << 4);
float32x4_t dinx0 = vld1q_f32(dinx_ptr);
float32x4_t dinx1 = vld1q_f32(dinx_ptr + 4);
float32x4_t dinx2 = vld1q_f32(dinx_ptr + 8);
float32x4_t dinx3 = vld1q_f32(dinx_ptr + 12);
float32x4_t diny0 = vld1q_f32(diny_ptr);
float32x4_t diny1 = vld1q_f32(diny_ptr + 4);
float32x4_t diny2 = vld1q_f32(diny_ptr + 8);
float32x4_t diny3 = vld1q_f32(diny_ptr + 12);
dinx0 = vaddq_f32(dinx0, diny0);
dinx1 = vaddq_f32(dinx1, diny1);
dinx2 = vaddq_f32(dinx2, diny2);
dinx3 = vaddq_f32(dinx3, diny3);
// relu
dinx0 = vmaxq_f32(dinx0, vzero);
dinx1 = vmaxq_f32(dinx1, vzero);
dinx2 = vmaxq_f32(dinx2, vzero);
dinx3 = vmaxq_f32(dinx3, vzero);
vst1q_f32(dout_ptr, dinx0);
vst1q_f32(dout_ptr + 4, dinx1);
vst1q_f32(dout_ptr + 8, dinx2);
vst1q_f32(dout_ptr + 12, dinx3);
}
if (remain > 0) {
const float* dinx_ptr = dinx + (cnt << 4);
const float* diny_ptr = diny + (cnt << 4);
float* dout_ptr = dout + (cnt << 4);
for (int i = 0; i < remain; i++) {
float tmp = *dinx_ptr + *diny_ptr;
*dout_ptr = tmp > 0.f ? tmp : 0.f;
dout_ptr++;
dinx_ptr++;
diny_ptr++;
}
}
}
template <>
void elementwise_add_broadcast<float>(const float* dinx, const float* diny,
float* dout, int batch, int channels,
int num) {
#pragma omp parallel for collapse(2)
for (int i = 0; i < batch; ++i) {
for (int j = 0; j < channels; ++j) {
int offset = (i * channels + j) * num;
const float* din_ptr = dinx + offset;
const float diny_data = diny[j];
float* dout_ptr = dout + offset;
int cnt = num >> 4;
int remain = num % 16;
float32x4_t rb = vdupq_n_f32(diny_data);
for (int k = 0; k < cnt; ++k) {
float32x4_t din0 = vld1q_f32(din_ptr);
float32x4_t din1 = vld1q_f32(din_ptr + 4);
float32x4_t din2 = vld1q_f32(din_ptr + 8);
float32x4_t din3 = vld1q_f32(din_ptr + 12);
din0 = vaddq_f32(din0, rb);
din1 = vaddq_f32(din1, rb);
din2 = vaddq_f32(din2, rb);
din3 = vaddq_f32(din3, rb);
vst1q_f32(dout_ptr, din0);
vst1q_f32(dout_ptr + 4, din1);
vst1q_f32(dout_ptr + 8, din2);
vst1q_f32(dout_ptr + 12, din3);
din_ptr += 16;
dout_ptr += 16;
}
if (remain >= 8) {
float32x4_t din0 = vld1q_f32(din_ptr);
float32x4_t din1 = vld1q_f32(din_ptr + 4);
din0 = vaddq_f32(din0, rb);
din1 = vaddq_f32(din1, rb);
vst1q_f32(dout_ptr, din0);
vst1q_f32(dout_ptr + 4, din1);
din_ptr += 8;
dout_ptr += 8;
remain -= 8;
}
if (remain >= 4) {
float32x4_t din0 = vld1q_f32(din_ptr);
din0 = vaddq_f32(din0, rb);
vst1q_f32(dout_ptr, din0);
din_ptr += 4;
dout_ptr += 4;
remain -= 4;
}
if (remain > 0) {
for (int p = 0; p < remain; p++) {
*dout_ptr = *din_ptr + diny_data;
dout_ptr++;
din_ptr++;
}
}
}
}
}
template <>
void elementwise_add_relu_broadcast<float>(const float* dinx, const float* diny,
float* dout, int batch, int channels,
int num) {
float32x4_t vzero = vdupq_n_f32(0.f);
#pragma omp parallel for collapse(2)
for (int i = 0; i < batch; ++i) {
for (int j = 0; j < channels; ++j) {
int offset = (i * channels + j) * num;
const float* din_ptr = dinx + offset;
const float diny_data = diny[j];
float* dout_ptr = dout + offset;
int cnt = num >> 4;
int remain = num % 16;
float32x4_t rb = vdupq_n_f32(diny_data);
for (int k = 0; k < cnt; ++k) {
float32x4_t din0 = vld1q_f32(din_ptr);
float32x4_t din1 = vld1q_f32(din_ptr + 4);
float32x4_t din2 = vld1q_f32(din_ptr + 8);
float32x4_t din3 = vld1q_f32(din_ptr + 12);
din0 = vaddq_f32(din0, rb);
din1 = vaddq_f32(din1, rb);
din2 = vaddq_f32(din2, rb);
din3 = vaddq_f32(din3, rb);
// relu
din0 = vmaxq_f32(din0, vzero);
din1 = vmaxq_f32(din1, vzero);
din2 = vmaxq_f32(din2, vzero);
din3 = vmaxq_f32(din3, vzero);
vst1q_f32(dout_ptr, din0);
vst1q_f32(dout_ptr + 4, din1);
vst1q_f32(dout_ptr + 8, din2);
vst1q_f32(dout_ptr + 12, din3);
din_ptr += 16;
dout_ptr += 16;
}
if (remain >= 8) {
float32x4_t din0 = vld1q_f32(din_ptr);
float32x4_t din1 = vld1q_f32(din_ptr + 4);
din0 = vaddq_f32(din0, rb);
din1 = vaddq_f32(din1, rb);
// relu
din0 = vmaxq_f32(din0, vzero);
din1 = vmaxq_f32(din1, vzero);
vst1q_f32(dout_ptr, din0);
vst1q_f32(dout_ptr + 4, din1);
din_ptr += 8;
dout_ptr += 8;
remain -= 8;
}
if (remain >= 4) {
float32x4_t din0 = vld1q_f32(din_ptr);
din0 = vaddq_f32(din0, rb);
// relu
din0 = vmaxq_f32(din0, vzero);
vst1q_f32(dout_ptr, din0);
din_ptr += 4;
dout_ptr += 4;
remain -= 4;
}
if (remain > 0) {
for (int p = 0; p < remain; p++) {
float tmp = *din_ptr + diny_data;
*dout_ptr = tmp > 0.f ? tmp : 0.f;
dout_ptr++;
din_ptr++;
}
}
}
}
}
} // namespace math
} // namespace arm
} // namespace lite
} // namespace paddle
paddle/fluid/lite/arm/math/elementwise.h 已删除 100644 → 0
浏览文件 @ 0f9e7057
// Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserved.
//
// Licensed under the Apache License, Version 2.0 (the "License");
// you may not use this file except in compliance with the License.
// You may obtain a copy of the License at
//
// http://www.apache.org/licenses/LICENSE-2.0
//
// Unless required by applicable law or agreed to in writing, software
// distributed under the License is distributed on an "AS IS" BASIS,
// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
// See the License for the specific language governing permissions and
// limitations under the License.
#pragma once
namespace paddle {
namespace lite {
namespace arm {
namespace math {
template <typename T>
void elementwise_add(const T* dinx, const T* diny, T* dout, int num);
template <typename T>
void elementwise_add_relu(const T* dinx, const T* diny, T* dout, int num);
template <typename T>
void elementwise_add_broadcast(const T* dinx, const T* diny, T* dout, int batch,
int channels, int num);
template <typename T>
void elementwise_add_relu_broadcast(const T* dinx, const T* diny, T* dout,
int batch, int channels, int num);
} // namespace math
} // namespace arm
} // namespace lite
} // namespace paddle
paddle/fluid/lite/arm/math/pooling.cc 已删除 100644 → 0
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此差异已折叠。
paddle/fluid/lite/arm/math/pooling.h 已删除 100644 → 0
浏览文件 @ 0f9e7057
// Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserved.
//
// Licensed under the Apache License, Version 2.0 (the "License");
// you may not use this file except in compliance with the License.
// You may obtain a copy of the License at
//
// http://www.apache.org/licenses/LICENSE-2.0
//
// Unless required by applicable law or agreed to in writing, software
// distributed under the License is distributed on an "AS IS" BASIS,
// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
// See the License for the specific language governing permissions and
// limitations under the License.
#pragma once
#include <algorithm>
#include <string>
#include <vector>
#include "paddle/fluid/lite/utils/cp_logging.h"
namespace paddle {
namespace lite {
namespace arm {
namespace math {
// !pooling fp32 Op
void pooling_basic(const float* din, float* dout, int num, int chout, int hout,
int wout, int chin, int hin, int win,
const std::vector<int>& ksize,
const std::vector<int>& strides,
const std::vector<int>& paddings, bool global_pooling,
bool exclusive, bool adaptive, bool ceil_mode,
bool use_quantizer, const std::string& pooling_type);
void pooling_global_max(const float* din, float* dout, int num, int chout,
int hout, int wout, int chin, int hin, int win);
void pooling_global_avg(const float* din, float* dout, int num, int chout,
int hout, int wout, int chin, int hin, int win);
void pooling2x2s2_max(const float* din, float* dout, int num, int chout,
int hout, int wout, int chin, int hin, int win);
void pooling2x2s2_avg(const float* din, float* dout, int num, int chout,
int hout, int wout, int chin, int hin, int win,
bool exclusive);
void pooling3x3s1p1_max(const float* din, float* dout, int num, int chout,
int hout, int wout, int chin, int hin, int win);
void pooling3x3s1p1_avg(const float* din, float* dout, int num, int chout,
int hout, int wout, int chin, int hin, int win,
bool exclusive);
void pooling3x3s2p1_max(const float* din, float* dout, int num, int chout,
int hout, int wout, int chin, int hin, int win);
void pooling3x3s2p1_avg(const float* din, float* dout, int num, int chout,
int hout, int wout, int chin, int hin, int win,
bool exclusive);
void pooling3x3s2p0_max(const float* din, float* dout, int num, int chout,
int hout, int wout, int chin, int hin, int win);
void pooling3x3s2p0_avg(const float* din, float* dout, int num, int chout,
int hout, int wout, int chin, int hin, int win,
bool exclusive);
} // namespace math
} // namespace arm
} // namespace lite
} // namespace paddle
paddle/fluid/lite/arm/math/scale.cc 已删除 100644 → 0
浏览文件 @ 0f9e7057
// Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserved.
//
// Licensed under the Apache License, Version 2.0 (the "License");
// you may not use this file except in compliance with the License.
// You may obtain a copy of the License at
//
// http://www.apache.org/licenses/LICENSE-2.0
//
// Unless required by applicable law or agreed to in writing, software
// distributed under the License is distributed on an "AS IS" BASIS,
// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
// See the License for the specific language governing permissions and
// limitations under the License.
#include "paddle/fluid/lite/arm/math/scale.h"
#include "paddle/fluid/lite/arm/math/funcs.h"
namespace paddle {
namespace lite {
namespace arm {
namespace math {
template <>
void scale<float>(const float* din, float* dout, int num, float scale,
float bias) {
int cnt = num >> 4;
int remain = num % 16;
float32x4_t vscale = vdupq_n_f32(scale);
float32x4_t vbias = vdupq_n_f32(bias);
#pragma omp parallel for
for (int i = 0; i < cnt; i++) {
const float* din_ptr = din + (i << 4);
float* dout_ptr = dout + (i << 4);
float32x4_t din0 = vld1q_f32(din_ptr);
float32x4_t din1 = vld1q_f32(din_ptr + 4);
float32x4_t din2 = vld1q_f32(din_ptr + 8);
float32x4_t din3 = vld1q_f32(din_ptr + 12);
float32x4_t vsum1 = vmlaq_f32(vbias, din0, vscale);
float32x4_t vsum2 = vmlaq_f32(vbias, din1, vscale);
float32x4_t vsum3 = vmlaq_f32(vbias, din2, vscale);
float32x4_t vsum4 = vmlaq_f32(vbias, din3, vscale);
vst1q_f32(dout_ptr, vsum1);
vst1q_f32(dout_ptr + 4, vsum2);
vst1q_f32(dout_ptr + 8, vsum3);
vst1q_f32(dout_ptr + 12, vsum4);
}
if (remain > 0) {
const float* din_ptr = din + (cnt << 4);
float* dout_ptr = dout + (cnt << 4);
for (int i = 0; i < remain; i++) {
*dout_ptr = *din_ptr * scale + bias;
dout_ptr++;
din_ptr++;
}
}
}
template <>
void scale<float>(const float* din, float* dout, int outer_dim, int scale_dim,
int inner_dim, const float* scale_data,
const float* bias_data) {
int cnt = inner_dim >> 4;
int remain = inner_dim % 16;
int size = inner_dim * scale_dim;
for (int n = 0; n < outer_dim; n++) {
const float* din_ptr_n = din + n * size;
float* dout_ptr_n = dout + n * size;
#pragma omp parallel for
for (int i = 0; i < scale_dim; i++) {
const float* din_ptr = din_ptr_n + i * inner_dim;
float* dout_ptr = dout_ptr_n + i * inner_dim;
float scale = scale_data[i];
float32x4_t vscale = vdupq_n_f32(scale);
float bias = bias_data[i];
float32x4_t vbias = vdupq_n_f32(bias);
for (int j = 0; j < cnt; j++) {
float32x4_t din0 = vld1q_f32(din_ptr);
float32x4_t din1 = vld1q_f32(din_ptr + 4);
float32x4_t din2 = vld1q_f32(din_ptr + 8);
float32x4_t din3 = vld1q_f32(din_ptr + 12);
float32x4_t vsum1 = vmlaq_f32(vbias, din0, vscale);
float32x4_t vsum2 = vmlaq_f32(vbias, din1, vscale);
float32x4_t vsum3 = vmlaq_f32(vbias, din2, vscale);
float32x4_t vsum4 = vmlaq_f32(vbias, din3, vscale);
din_ptr += 16;
vst1q_f32(dout_ptr, vsum1);
vst1q_f32(dout_ptr + 4, vsum2);
vst1q_f32(dout_ptr + 8, vsum3);
vst1q_f32(dout_ptr + 12, vsum4);
dout_ptr += 16;
}
for (int j = 0; j < remain; j++) {
*dout_ptr = *din_ptr * scale + bias;
dout_ptr++;
din_ptr++;
}
}
}
}
template <>
void scale<float>(const float* din, float* dout, int outer_dim, int scale_dim,
const float* scale_data, const float* bias_data) {
int cnt = scale_dim >> 4;
int remain = scale_dim % 16;
for (int n = 0; n < outer_dim; n++) {
const float* din_ptr_n = din + n * scale_dim;
float* dout_ptr_n = dout + n * scale_dim;
#pragma omp parallel for
for (int i = 0; i < cnt; i++) {
int idx = i << 4;
const float* din_ptr = din_ptr_n + idx;
const float* scale_ptr = scale_data + idx;
const float* bias_ptr = bias_data + idx;
float* dout_ptr = dout_ptr_n + idx;
float32x4_t din0 = vld1q_f32(din_ptr);
float32x4_t vscale0 = vld1q_f32(scale_ptr);
float32x4_t vbias0 = vld1q_f32(bias_ptr);
float32x4_t din1 = vld1q_f32(din_ptr + 4);
float32x4_t vscale1 = vld1q_f32(scale_ptr + 4);
float32x4_t vbias1 = vld1q_f32(bias_ptr + 4);
float32x4_t din2 = vld1q_f32(din_ptr + 8);
float32x4_t vscale2 = vld1q_f32(scale_ptr + 8);
float32x4_t vbias2 = vld1q_f32(bias_ptr + 8);
float32x4_t vsum1 = vmlaq_f32(vbias0, din0, vscale0);
float32x4_t vsum2 = vmlaq_f32(vbias1, din1, vscale1);
float32x4_t din3 = vld1q_f32(din_ptr + 12);
float32x4_t vscale3 = vld1q_f32(scale_ptr + 12);
float32x4_t vbias3 = vld1q_f32(bias_ptr + 12);
vst1q_f32(dout_ptr, vsum1);
vst1q_f32(dout_ptr + 4, vsum2);
float32x4_t vsum3 = vmlaq_f32(vbias2, din2, vscale2);
float32x4_t vsum4 = vmlaq_f32(vbias3, din3, vscale3);
vst1q_f32(dout_ptr + 8, vsum3);
vst1q_f32(dout_ptr + 12, vsum4);
}
int idx = cnt << 4;
const float* din_ptr = din_ptr_n + idx;
float* dout_ptr = dout_ptr_n + idx;
const float* scale_ptr = scale_data + idx;
const float* bias_ptr = bias_data + idx;
for (int j = 0; j < remain; j++) {
*dout_ptr = *din_ptr * (*scale_ptr) + (*bias_ptr);
dout_ptr++;
din_ptr++;
scale_ptr++;
bias_ptr++;
}
}
}
} // namespace math
} // namespace arm
} // namespace lite
} // namespace paddle
paddle/fluid/lite/arm/math/scale.h 已删除 100644 → 0
浏览文件 @ 0f9e7057
// Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserved.
//
// Licensed under the Apache License, Version 2.0 (the "License");
// you may not use this file except in compliance with the License.
// You may obtain a copy of the License at
//
// http://www.apache.org/licenses/LICENSE-2.0
//
// Unless required by applicable law or agreed to in writing, software
// distributed under the License is distributed on an "AS IS" BASIS,
// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
// See the License for the specific language governing permissions and
// limitations under the License.
#pragma once
namespace paddle {
namespace lite {
namespace arm {
namespace math {
template <typename T>
void scale(const T* din, T* dout, int num, float scale, float bias);
template <typename T>
void scale(const T* din, T* dout, int outer_dim, int scale_dim, int inner_dim,
const float* scale_data, const float* bias_data);
template <typename T>
void scale(const T* din, T* dout, int outer_dim, int scale_dim,
const float* scale_data, const float* bias_data);
} // namespace math
} // namespace arm
} // namespace lite
} // namespace paddle
paddle/fluid/lite/arm/math/softmax.cc 已删除 100644 → 0
浏览文件 @ 0f9e7057
此差异已折叠。
paddle/fluid/lite/arm/math/softmax.h 已删除 100644 → 0
浏览文件 @ 0f9e7057
// Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserved.
//
// Licensed under the Apache License, Version 2.0 (the "License");
// you may not use this file except in compliance with the License.
// You may obtain a copy of the License at
//
// http://www.apache.org/licenses/LICENSE-2.0
//
// Unless required by applicable law or agreed to in writing, software
// distributed under the License is distributed on an "AS IS" BASIS,
// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
// See the License for the specific language governing permissions and
// limitations under the License.
#pragma once
namespace paddle {
namespace lite {
namespace arm {
namespace math {
template <typename T>
void softmax_basic(const T* din, T* dout, const int axis_size,
const int inner_num, const int outer_num);
template <typename T>
void softmax_inner8_axis4(const T* din, T* dout, const int axis_size,
const int inner_num, const int outer_num);
template <typename T>
void softmax_inner4_axis4(const T* din, T* dout, const int axis_size,
const int inner_num, const int outer_num);
template <typename T>
void softmax_inner8(const T* din, T* dout, const int axis_size,
const int inner_num, const int outer_num);
template <typename T>
void softmax_inner4(const T* din, T* dout, const int axis_size,
const int inner_num, const int outer_num);
template <typename T>
void softmax_inner1_large_axis(const T* din, T* dout, const int outer_size,
const int axis_size);
template <typename T>
void softmax_inner1_small_axis(const T* din, T* dout, const int outer_size,
const int axis_size);
} // namespace math
} // namespace arm
} // namespace lite
} // namespace paddle
paddle/fluid/lite/arm/math/split.cc 已删除 100644 → 0
浏览文件 @ 0f9e7057
// Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserved.
//
// Licensed under the Apache License, Version 2.0 (the "License");
// you may not use this file except in compliance with the License.
// You may obtain a copy of the License at
//
// http://www.apache.org/licenses/LICENSE-2.0
//
// Unless required by applicable law or agreed to in writing, software
// distributed under the License is distributed on an "AS IS" BASIS,
// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
// See the License for the specific language governing permissions and
// limitations under the License.
#include "paddle/fluid/lite/arm/math/split.h"
#include <algorithm>
#include "paddle/fluid/lite/arm/math/funcs.h"
namespace paddle {
namespace lite {
namespace arm {
namespace math {
template <>
void split_cpy<float>(const float* din, float* dout, int num) {
int cnt = num >> 4;
int remain = num % 16;
#pragma omp parallel for
for (int i = 0; i < cnt; i++) {
const float* din_ptr = din + (i << 4);
float* dout_ptr = dout + (i << 4);
float32x4_t din0 = vld1q_f32(din_ptr);
float32x4_t din1 = vld1q_f32(din_ptr + 4);
float32x4_t din2 = vld1q_f32(din_ptr + 8);
float32x4_t din3 = vld1q_f32(din_ptr + 12);
vst1q_f32(dout_ptr, din0);
vst1q_f32(dout_ptr + 4, din1);
vst1q_f32(dout_ptr + 8, din2);
vst1q_f32(dout_ptr + 12, din3);
}
if (remain > 0) {
const float* din_ptr = din + (cnt << 4);
float* dout_ptr = dout + (cnt << 4);
for (int i = 0; i < remain; i++) {
*dout_ptr = *din_ptr;
dout_ptr++;
din_ptr++;
}
}
}
template <>
void split<float>(const float* din, const std::vector<lite::Tensor*>& dout,
const int axis, const std::vector<int>& in_strides) {
int input_offset = 0;
for (auto out : dout) {
auto out_dim = out->dims();
std::vector<int> out_strides(out_dim.size());
out_strides[out_dim.size() - 1] = out_dim[out_dim.size() - 1];
for (int i = out_dim.size() - 2; i >= 0; --i) {
out_strides[i] = out_strides[i + 1] * out_dim[i];
}
float* out_data = out->mutable_data<float>();
int before = out_strides[0] / out_strides[axis];
int in_after = in_strides[axis];
int out_after = out_strides[axis];
for (int i = 0; i < before; ++i) {
split_cpy(din + input_offset + i * in_after, out_data + i * out_after,
out_after);
}
input_offset += out_strides[axis];
}
}
} // namespace math
} // namespace arm
} // namespace lite
} // namespace paddle
paddle/fluid/lite/arm/math/split.h 已删除 100644 → 0
浏览文件 @ 0f9e7057
// Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserved.
//
// Licensed under the Apache License, Version 2.0 (the "License");
// you may not use this file except in compliance with the License.
// You may obtain a copy of the License at
//
// http://www.apache.org/licenses/LICENSE-2.0
//
// Unless required by applicable law or agreed to in writing, software
// distributed under the License is distributed on an "AS IS" BASIS,
// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
// See the License for the specific language governing permissions and
// limitations under the License.
#pragma once
#include <vector>
#include "paddle/fluid/lite/core/op_lite.h"
namespace paddle {
namespace lite {
namespace arm {
namespace math {
template <typename T>
void split_cpy(const T* din, T* dout, int num);
template <typename T>
void split(const T* din, const std::vector<lite::Tensor*>& dout, const int axis,
const std::vector<int>& in_strides);
} // namespace math
} // namespace arm
} // namespace lite
} // namespace paddle
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