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提交 73bac0f2 编写于 作者: C chenjiaoAngel
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fix conflict, test=develop

上级 40482334
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Showing with 254 addition and 572 deletion
lite/backends/arm/math/pooling.cc
浏览文件 @ 73bac0f2
此差异已折叠。
lite/backends/arm/math/pooling.h
浏览文件 @ 73bac0f2
......@@ -77,54 +77,54 @@ void pooling1x1s2p0_max(const float* din,
int pad_right);
void pooling2x2s2p0_max(const float* din,
float* dout,
int num,
int chout,
int hout,
int wout,
int chin,
int hin,
int win,
int pad_bottom,
int pad_right);
float* dout,
int num,
int chout,
int hout,
int wout,
int chin,
int hin,
int win,
int pad_bottom,
int pad_right);
void pooling2x2s2p0_avg(const float* din,
float* dout,
int num,
int chout,
int hout,
int wout,
int chin,
int hin,
int win,
bool exclusive,
int pad_bottom,
int pad_right);
float* dout,
int num,
int chout,
int hout,
int wout,
int chin,
int hin,
int win,
bool exclusive,
int pad_bottom,
int pad_right);
void pooling2x2s2p1_max(const float* din,
float* dout,
int num,
int chout,
int hout,
int wout,
int chin,
int hin,
int win,
int pad_bottom,
int pad_right);
float* dout,
int num,
int chout,
int hout,
int wout,
int chin,
int hin,
int win,
int pad_bottom,
int pad_right);
void pooling2x2s2p1_avg(const float* din,
float* dout,
int num,
int chout,
int hout,
int wout,
int chin,
int hin,
int win,
bool exclusive,
int pad_bottom,
int pad_right);
float* dout,
int num,
int chout,
int hout,
int wout,
int chin,
int hin,
int win,
bool exclusive,
int pad_bottom,
int pad_right);
void pooling3x3s1p1_max(const float* din,
float* dout,
......
lite/backends/fpga/KD/pes/pooling_pe.hpp
浏览文件 @ 73bac0f2
......@@ -70,8 +70,7 @@ class PoolingPE : public PE {
param_.poolingArgs = args;
// use_cpu_ = output->shape().width() == 1 && output->shape().height() == 1
// &&
// (k_width > 7 || k_height > 7);
// && (k_width > 7 || k_height > 7);
use_cpu_ = output->shape().width() == 1 && output->shape().height() == 1 &&
(k_width > 255 || k_height > 255);
// use_cpu_ = param_.type == AVERAGE;
......
lite/kernels/arm/pool_compute.cc
浏览文件 @ 73bac0f2
......@@ -109,60 +109,60 @@ void PoolCompute::Run() {
kps_equal) {
if (pooling_type == "max") {
lite::arm::math::pooling2x2s2p0_max(din,
dout,
out_dims[0],
out_dims[1],
out_dims[2],
out_dims[3],
in_dims[1],
in_dims[2],
in_dims[3],
paddings[1],
paddings[3]);
dout,
out_dims[0],
out_dims[1],
out_dims[2],
out_dims[3],
in_dims[1],
in_dims[2],
in_dims[3],
paddings[1],
paddings[3]);
return;
} else if (pooling_type == "avg") {
lite::arm::math::pooling2x2s2p0_avg(din,
dout,
out_dims[0],
out_dims[1],
out_dims[2],
out_dims[3],
in_dims[1],
in_dims[2],
in_dims[3],
exclusive,
paddings[1],
paddings[3]);
dout,
out_dims[0],
out_dims[1],
out_dims[2],
out_dims[3],
in_dims[1],
in_dims[2],
in_dims[3],
exclusive,
paddings[1],
paddings[3]);
return;
}
} else if (ksize[0] == 2 && strides[0] == 2 && paddings[0] == 1 &&
kps_equal) {
if (pooling_type == "max") {
lite::arm::math::pooling2x2s2p1_max(din,
dout,
out_dims[0],
out_dims[1],
out_dims[2],
out_dims[3],
in_dims[1],
in_dims[2],
in_dims[3],
paddings[1],
paddings[3]);
dout,
out_dims[0],
out_dims[1],
out_dims[2],
out_dims[3],
in_dims[1],
in_dims[2],
in_dims[3],
paddings[1],
paddings[3]);
return;
} else if (pooling_type == "avg") {
lite::arm::math::pooling2x2s2p1_avg(din,
dout,
out_dims[0],
out_dims[1],
out_dims[2],
out_dims[3],
in_dims[1],
in_dims[2],
in_dims[3],
exclusive,
paddings[1],
paddings[3]);
dout,
out_dims[0],
out_dims[1],
out_dims[2],
out_dims[3],
in_dims[1],
in_dims[2],
in_dims[3],
exclusive,
paddings[1],
paddings[3]);
return;
}
} else if (ksize[0] == 3 && strides[0] == 1 && paddings[0] == 1 &&
......
lite/kernels/npu/bridges/pool_op.cc
浏览文件 @ 73bac0f2
......@@ -64,20 +64,6 @@ int PoolConverter(void* ctx, OpLite* op, KernelBase* kernel) {
return FAILED;
}
auto padding = op_info->GetAttr<std::vector<int>>("paddings");
bool pads_equal = (padding[0] == padding[1]) && (padding[2] == padding[3]);
if (!pads_equal) {
LOG(FATAL)
<< "padding requires pad_left == pad_right, pad_top == pad_bottom";
}
auto npu_pad =
ge::AttrValue::LIST_INT{padding[0], padding[1], padding[2], padding[3]};
auto strides = op_info->GetAttr<std::vector<int>>("strides");
auto npu_stride = ge::AttrValue::LIST_INT(strides.begin(), strides.end());
int npu_ceil_mode = 0;
if (op_info->HasAttr("ceil_mode")) {
npu_ceil_mode = op_info->GetAttr<bool>("ceil_mode") ? 1 : 0;
// pad mode
int pad_mode = 0;
std::string padding_algorithm("");
......
lite/kernels/xpu/bridges/pool_op_test.cc 已删除 100644 → 0
浏览文件 @ 40482334
// 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 "lite/operators/pool_op.h"
#include <gtest/gtest.h>
#include "lite/core/op_registry.h"
#include "lite/kernels/xpu/bridges/registry.h"
#include "lite/kernels/xpu/bridges/test_helper.h"
namespace paddle {
namespace lite {
namespace kernels {
namespace xpu {
namespace bridges {
void pool_ref(const std::shared_ptr<operators::PoolOpLite> op) {
Scope* scope = op->scope();
const OpInfo* op_info = op->op_info();
auto x = scope->FindVar(op_info->Input("X").front())->GetMutable<Tensor>();
auto out =
scope->FindVar(op_info->Output("Out").front())->GetMutable<Tensor>();
auto& in_dims = x->dims();
auto& out_dims = out->dims();
const float* src_ptr = x->data<const float>();
float* dst_ptr = out->mutable_data<float>();
std::vector<int> ksize = op_info->GetAttr<std::vector<int>>("ksize");
std::vector<int> strides = op_info->GetAttr<std::vector<int>>("strides");
std::vector<int> paddings = op_info->GetAttr<std::vector<int>>("paddings");
bool exclusive = op_info->GetAttr<bool>("exclusive");
std::string pooling_type = op_info->GetAttr<std::string>("pooling_type");
bool global_pooling = op_info->GetAttr<bool>("global_pooling");
int in_n = in_dims[0];
int in_c = in_dims[1];
int in_h = in_dims[2];
int in_w = in_dims[3];
int size_in_n = in_c * in_h * in_w;
int size_in_c = in_h * in_w;
int out_h = out_dims[2];
int out_w = out_dims[3];
int size_out_n = in_c * out_h * out_w;
int size_out_c = out_h * out_w;
int window_h = ksize[0];
int window_w = ksize[1];
int stride_h = strides[0];
int stride_w = strides[1];
int pad_h = paddings[0];
int pad_w = paddings[2];
if (global_pooling == true) {
for (int n = 0; n < in_n; ++n) {
for (int c = 0; c < in_c; ++c) {
const float* src = src_ptr + n * size_in_n + c * size_in_c;
float res = src[0];
if (pooling_type == "max") {
for (int i = 1; i < size_in_c; ++i) {
float cur_val = src[i];
res = cur_val > res ? cur_val : res;
}
} else if (pooling_type == "avg") {
for (int i = 1; i < size_in_c; ++i) {
float cur_val = src[i];
res += cur_val;
}
res /= size_in_c;
}
dst_ptr[n * size_out_n + c] = res;
}
}
} else {
for (int n = 0; n < in_n; ++n) {
for (int c = 0; c < in_c; ++c) {
for (int h = 0; h < out_h; ++h) {
int sh = h * stride_h;
int eh = sh + window_h;
sh = (sh - pad_h) < 0 ? 0 : sh - pad_h;
eh = (eh - pad_h) > in_h ? in_h : eh - pad_h;
for (int w = 0; w < out_w; ++w) {
int sw = w * stride_w;
int ew = sw + window_w;
sw = (sw - pad_w) < 0 ? 0 : sw - pad_w;
ew = (ew - pad_w) > in_w ? in_w : ew - pad_w;
int pooling_size = (ew - sw) * (eh - sh);
if (pooling_size == 0) continue;
float res = 0.f;
for (int kh = sh; kh < eh; ++kh) {
for (int kw = sw; kw < ew; ++kw) {
int src_idx = n * size_in_n + c * size_in_c + kh * in_w + kw;
if (kh == sh && kw == sw) {
res = src_ptr[src_idx];
} else {
if (pooling_type == "max") {
res = res >= src_ptr[src_idx] ? res : src_ptr[src_idx];
}
if (pooling_type == "avg") {
res += src_ptr[src_idx];
}
}
}
}
if (pooling_type == "avg") {
if (exclusive) {
res /= pooling_size;
} else {
res /= window_h * window_w;
}
}
dst_ptr[n * size_out_n + c * size_out_c + h * out_w + w] = res;
}
}
}
}
}
}
void test_pool(int bs,
int ic,
int ih,
int iw,
std::string pooling_type,
bool ceil_mode,
bool global_pooling,
bool exclusive,
int ksize,
int stride,
int padding) {
// prepare input&output variables
Scope scope;
std::string x_var_name = "x";
std::string out_var_name = "out";
std::string out_ref_var_name = "out_ref";
auto* x = scope.Var(x_var_name)->GetMutable<Tensor>();
auto* out = scope.Var(out_var_name)->GetMutable<Tensor>();
auto* out_ref = scope.Var(out_ref_var_name)->GetMutable<Tensor>();
x->Resize({bs, ic, ih, iw});
// initialize input&output data
FillTensor<float>(x);
// initialize op desc
cpp::OpDesc opdesc;
opdesc.SetType("pool2d");
opdesc.SetInput("X", {x_var_name});
opdesc.SetOutput("Out", {out_var_name});
opdesc.SetAttr("pooling_type", pooling_type);
opdesc.SetAttr("ksize", std::vector<int>({ksize, ksize}));
opdesc.SetAttr("global_pooling", global_pooling);
opdesc.SetAttr("exclusive", exclusive);
opdesc.SetAttr("strides", std::vector<int>({stride, stride}));
opdesc.SetAttr("paddings",
std::vector<int>({padding, padding, padding, padding}));
opdesc.SetAttr("ceil_mode", ceil_mode);
// create and convert op to XPU model, then run it on XPU
auto op = CreateOp<operators::PoolOpLite>(opdesc, &scope);
LauchOp(op, {x_var_name}, {out_var_name});
out_ref->CopyDataFrom(*out);
// execute reference implementation and save to output tensor
pool_ref(op);
// compare results
auto* out_data = out->mutable_data<float>();
auto* out_ref_data = out_ref->mutable_data<float>();
for (int i = 0; i < out->dims().production(); i++) {
EXPECT_NEAR(out_data[i], out_ref_data[i], 1e-5);
}
}
TEST(XPUBridges, pool) {
for (auto pooling_type : {"max", "avg"}) {
for (auto bs : {1, 3}) {
for (auto ic : {2}) {
for (auto ih : {3}) {
for (auto iw : {4}) {
test_pool(bs, ic, ih, iw, pooling_type, true, true, true, 0, 1, 0);
}
}
}
}
}
for (auto pooling_type : {"max"}) {
for (auto ceil_mode : {true, false}) {
for (auto ksize : {2, 3}) {
for (auto stride : {1, 2}) {
for (auto padding : {0, 1}) {
for (auto bs : {1, 3}) {
for (auto ic : {2}) {
for (auto ih : {3}) {
for (auto iw : {4}) {
test_pool(bs,
ic,
ih,
iw,
pooling_type,
ceil_mode,
false,
true,
ksize,
stride,
padding);
}
}
}
}
}
}
}
}
}
for (auto pooling_type : {"avg"}) {
for (auto ceil_mode : {true, false}) {
for (auto exclusive : {true, false}) {
for (auto ksize : {2, 3}) {
for (auto stride : {1, 2}) {
for (auto padding : {0, 1}) {
for (auto bs : {1, 3}) {
for (auto ic : {2}) {
for (auto ih : {3}) {
for (auto iw : {4}) {
test_pool(bs,
ic,
ih,
iw,
pooling_type,
ceil_mode,
false,
exclusive,
ksize,
stride,
padding);
}
}
}
}
}
}
}
}
}
}
}
} // namespace bridges
} // namespace xpu
} // namespace kernels
} // namespace lite
} // namespace paddle
USE_LITE_OP(pool2d);
USE_XPU_BRIDGE(pool2d);
lite/operators/pool_op.cc
浏览文件 @ 73bac0f2
......@@ -41,39 +41,6 @@ bool PoolOpLite::CheckShape() const {
return true;
}
inline void UpdatePadding(std::vector<int>* paddings,
const bool global_pooling,
const bool adaptive,
const std::string padding_algorithm,
const lite::DDim data_dims,
const std::vector<int>& strides,
const std::vector<int>& ksize) {
// when padding_algorithm is "VALID" or "SAME"
if (padding_algorithm == "SAME") {
for (int i = 0; i < strides.size(); ++i) {
int out_size = (data_dims[i + 2] + strides[i] - 1) / strides[i];
int pad_sum =
std::max((out_size - 1) * strides[i] + ksize[i] - data_dims[i + 2],
(int64_t)0);
int pad_0 = pad_sum / 2;
int pad_1 = pad_sum - pad_0;
*(paddings->begin() + i * 2) = pad_0;
*(paddings->begin() + i * 2 + 1) = pad_1;
}
} else if (padding_algorithm == "VALID") {
for (auto it = paddings->begin(); it != paddings->end(); it++) {
*it = 0;
}
}
// if global_pooling == true or adaptive == true, padding will be ignore
if (global_pooling || adaptive) {
for (auto it = paddings->begin(); it != paddings->end(); it++) {
*it = 0;
}
}
}
int PoolOutputSize(int input_size,
int filter_size,
int pad_left,
......
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