提交 75659275 编写于 作者: R Ruilong Liu 提交者: GitHub

Merge pull request #668 from codeWorm2015/develop

fix  #667 format files
......@@ -420,6 +420,6 @@ std::vector<typename Executor<Dtype, P>::Ptype> Executor<Dtype, P>::Predict(
template class Executor<CPU, Precision::FP32>;
template class Executor<GPU_MALI, Precision::FP32>;
template class Executor<FPGA, Precision::FP16>;
template class Executor<FPGA, Precision::FP32>;
} // namespace paddle_mobile
......@@ -29,7 +29,7 @@ class FeedOp : public framework::OperatorBase<DeviceType> {
std::shared_ptr<framework::Scope> scope)
: framework::OperatorBase<DeviceType>(type, inputs, outputs, attrs,
scope),
param_(inputs, outputs, attrs, *scope) {}
param_(inputs, outputs, attrs, scope.get()) {}
void RunImpl() const { param_.Out()->ShareDataWith(*param_.InputX()); }
void Init() {}
......
......@@ -14,8 +14,6 @@ limitations under the License. */
#ifdef DROPOUT_OP
#pragma once
#include "operators/kernel/dropout_kernel.h"
#include <operators/math/transform.h>
......
......@@ -17,7 +17,7 @@ limitations under the License. */
#include "framework/operator.h"
#include "operators/op_param.h"
#pragma once;
#pragma once
namespace paddle_mobile {
namespace operators {
......
......@@ -20,13 +20,11 @@ limitations under the License. */
#include "operators/math/vol2col.h"
#include "operators/op_param.h"
#pragma once;
#pragma once
namespace paddle_mobile {
namespace operators {
using namespace framework;
template <typename DeviceType, typename T>
class Im2SequenceKernel
: public framework::OpKernelBase<DeviceType, Im2SequenceParam> {
......
......@@ -14,8 +14,6 @@ limitations under the License. */
#ifdef FUSION_FC_OP
#pragma once
#include "operators/kernel/fusion_fc_kernel.h"
namespace paddle_mobile {
......
......@@ -15,7 +15,7 @@ limitations under the License. */
#include "framework/operator.h"
#include "operators/op_param.h"
#pragma once;
#pragma once
namespace paddle_mobile {
namespace operators {
......
......@@ -15,7 +15,7 @@ limitations under the License. */
#include "framework/operator.h"
#include "operators/op_param.h"
#pragma once;
#pragma once
namespace paddle_mobile {
namespace operators {
......
......@@ -15,7 +15,7 @@ limitations under the License. */
#include "framework/operator.h"
#include "operators/op_param.h"
#pragma once;
#pragma once
namespace paddle_mobile {
namespace operators {
......
......@@ -651,10 +651,10 @@ class MultiClassNMSParam : public OpParam {
class FeedParam : public OpParam {
public:
FeedParam(const VariableNameMap &inputs, const VariableNameMap &outputs,
const AttributeMap &attrs, Scope const &scope) {
input_x_ = InputXFrom<LoDTensor>(inputs, scope);
out_ = OutFrom<LoDTensor>(outputs, scope);
auto var = scope.Var("batch_size");
const AttributeMap &attrs, Scope *scope) {
input_x_ = InputXFrom<LoDTensor>(inputs, *scope);
out_ = OutFrom<LoDTensor>(outputs, *scope);
auto var = scope->Var("batch_size");
batch_size = var->GetValue<int>();
}
const Tensor *InputX() const { return input_x_; }
......
......@@ -12,6 +12,8 @@ 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 <string>
#include "../test_helper.h"
#include "io/loader.h"
......@@ -20,12 +22,10 @@ int main() {
// ../../../test/models/googlenet
// ../../../test/models/mobilenet
// auto program = loader.Load(g_googlenet, true);
// auto program = loader.Load(g_mobilenet_ssd, true);
auto program = loader.Load(g_mobilenet_ssd, true);
// auto program = loader.Load(g_googlenet_combine + "/model",
// g_googlenet_combine +
// "/params", true);
auto program = loader.Load(std::string(g_ocr) + "/model",
std::string(g_ocr) + "/params", false);
// program.originProgram->Description("program desc: ");
return 0;
}
......@@ -24,6 +24,7 @@ limitations under the License. */
#include "framework/ddim.h"
#include "framework/tensor.h"
static const char *g_ocr = "../models/ocr";
static const char *g_mobilenet_ssd = "../models/mobilenet+ssd";
static const char *g_mobilenet_ssd_gesture = "../models/mobilenet+ssd_gesture";
static const char *g_squeezenet = "../models/squeezenet";
......
/* Copyright (c) 2018 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 <iostream>
#include <cstdlib>
#include <ctime>
#include "../test_helper.h"
#include "common/log.h"
#include "memory/t_malloc.h"
#include "operators/math/gemm.h"
#define a(i, j) a[(i)*lda + (j)]
#define b(i, j) b[(i)*ldb + (j)]
#define c(i, j) c[(i)*ldc + (j)]
#define c1(i, j) c1[(i)*ldc + (j)]
void print_matirx(int m, int n, int ldc, float *c) {
for (int i = 0; i < m; ++i) {
std::cout << c(i, 0);
for (int j = 1; j < n; ++j) {
std::cout << " | " << c(i, j);
}
std::cout << std::endl;
}
std::cout << std::endl;
}
int do_sgemm(int m, int n, int k, bool relu, int t1, int t2, int pr) {
int lda = k;
int ldb = n;
int ldc = n;
float *a = static_cast<float *>(paddle_mobile::memory::Alloc(sizeof(float) * m * k));
float *b = static_cast<float *>(paddle_mobile::memory::Alloc(sizeof(float) * k * n));
float *c = static_cast<float *>(paddle_mobile::memory::Alloc(sizeof(float) * m * n));
float *c1 = static_cast<float *>(paddle_mobile::memory::Alloc(sizeof(float) * m * n));
float* scale = static_cast<float *>(paddle_mobile::memory::Alloc(sizeof(float) * m));
float* bias = static_cast<float *>(paddle_mobile::memory::Alloc(sizeof(float) * m));
srand(unsigned(time(0)));
for (int i = 0; i < m * k; ++i) {
a[i] = t1 + rand() % t2;
}
for (int i = 0; i < k * n; ++i) {
b[i] = t1 + rand() % t2;
}
for (int i = 0; i < m; ++i) {
scale[i] = t1 + rand() % t2;
}
for (int i = 0; i < m; ++i) {
bias[i] = t1 + rand() % t2;
}
for (int i = 0; i < m; ++i) {
for (int j = 0; j < n; ++j) {
float r = 0;
for (int p = 0; p < k; p++) {
r += a(i, p) * b(p, j);
}
r *= scale[i];
r += bias[i];
if (relu && (r < 0)) {
r = 0;
}
c1(i, j) = r;
}
}
paddle_mobile::operators::math::SgemmWithBn(m, n, k, 0.9, a, lda,
b, ldb, 0.3, c, ldc, relu, scale, bias);
int eq = 0;
int neq = 0;
for (int i = 0; i < m * n; ++i) {
if (static_cast<int>(c[i]) == static_cast<int>(c1[i])) {
++eq;
} else {
++neq;
}
}
if (pr > 0) {
std::cout << "A:" << std::endl;
print_matirx(m, k, lda, a);
std::cout << "B:" << std::endl;
print_matirx(k, n, ldb, b);
std::cout << "C:" << std::endl;
print_matirx(m, n, ldc, c);
std::cout << "C1:" << std::endl;
print_matirx(m, n, ldc, c1);
}
std::cout << "mnk=" << m << " " << n << " " << k <<
" relu=" << relu <<
" eq=" << eq << " neq=" << neq << std::endl;
paddle_mobile::memory::Free(a);
paddle_mobile::memory::Free(b);
paddle_mobile::memory::Free(c);
paddle_mobile::memory::Free(c1);
paddle_mobile::memory::Free(scale);
paddle_mobile::memory::Free(bias);
return 0;
}
int main() {
do_sgemm(9, 9, 9, true, 10, 10, 10);
do_sgemm(10, 6, 12, false, 10, 10, 0);
do_sgemm(512, 256, 384, false, 10, 10, 0);
do_sgemm(1366, 768, 256, false, 10, 10, 0);
do_sgemm(1255, 755, 333, false, 10, 10, 0);
do_sgemm(555, 777, 999, false, 10, 10, 0);
do_sgemm(10, 6, 12, true, -4, 10, 0);
do_sgemm(512, 256, 384, true, -4, 10, 0);
do_sgemm(1366, 768, 256, true, -4, 10, 0);
do_sgemm(1255, 755, 333, true, -4, 10, 0);
do_sgemm(555, 777, 999, true, -4, 10, 0);
return 0;
}
......@@ -40,8 +40,8 @@ build_for_android() {
fi
if [ -z "$PLATFORM" ]; then
# PLATFORM="arm-v7a" # Users could choose "arm-v8a" platform.
PLATFORM="arm-v8a"
PLATFORM="arm-v7a" # Users could choose "arm-v8a" platform.
# PLATFORM="arm-v8a"
fi
if [ "${PLATFORM}" = "arm-v7a" ]; then
......
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