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提交 23fc896b 编写于 作者: T tensor-tang
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Merge remote-tracking branch 'ups/develop' into fea/fusion_seqconv_add 

test=develop
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README.md
浏览文件 @ 23fc896b
......@@ -2,8 +2,8 @@
[![Build Status](https://travis-ci.org/PaddlePaddle/Paddle.svg?branch=develop)](https://travis-ci.org/PaddlePaddle/Paddle)
[![Documentation Status](https://img.shields.io/badge/docs-latest-brightgreen.svg?style=flat)](http://www.paddlepaddle.org/docs/develop/documentation/en/getstarted/index_en.html)
[![Documentation Status](https://img.shields.io/badge/中文文档-最新-brightgreen.svg)](http://www.paddlepaddle.org/docs/develop/documentation/zh/getstarted/index_cn.html)
[![Documentation Status](https://img.shields.io/badge/docs-latest-brightgreen.svg?style=flat)](http://paddlepaddle.org/documentation/docs/en/1.0/getstarted/index_en.html)
[![Documentation Status](https://img.shields.io/badge/中文文档-最新-brightgreen.svg)](http://paddlepaddle.org/documentation/docs/zh/1.0/beginners_guide/index.html)
[![Release](https://img.shields.io/github/release/PaddlePaddle/Paddle.svg)](https://github.com/PaddlePaddle/Paddle/releases)
[![License](https://img.shields.io/badge/license-Apache%202-blue.svg)](LICENSE)
......@@ -19,7 +19,7 @@ Our vision is to enable deep learning for everyone via PaddlePaddle.
Please refer to our [release announcement](https://github.com/PaddlePaddle/Paddle/releases) to track the latest feature of PaddlePaddle.
### Latest PaddlePaddle Release: [Fluid 1.0.0](https://github.com/PaddlePaddle/Paddle/tree/release/1.0.0)
### Latest PaddlePaddle Release: [Fluid 1.0.1](https://github.com/PaddlePaddle/Paddle/tree/release/1.0.0)
### Install Latest Stable Release:
```
# Linux CPU
......@@ -27,9 +27,9 @@ pip install paddlepaddle
# Linux GPU cuda9cudnn7
pip install paddlepaddle-gpu
# Linux GPU cuda8cudnn7
pip install paddlepaddle-gpu==0.15.0.post87
pip install paddlepaddle-gpu==1.0.1.post87
# Linux GPU cuda8cudnn5
pip install paddlepaddle-gpu==0.15.0.post85
pip install paddlepaddle-gpu==1.0.1.post85
# For installation on other platform, refer to http://paddlepaddle.org/
```
......
cmake/generic.cmake
浏览文件 @ 23fc896b
......@@ -311,6 +311,8 @@ function(cc_test TARGET_NAME)
set_property(TEST ${TARGET_NAME} PROPERTY ENVIRONMENT FLAGS_cpu_deterministic=true)
set_property(TEST ${TARGET_NAME} PROPERTY ENVIRONMENT FLAGS_init_allocated_mem=true)
set_property(TEST ${TARGET_NAME} PROPERTY ENVIRONMENT FLAGS_cudnn_deterministic=true)
# No unit test should exceed 10 minutes.
set_tests_properties(${TARGET_NAME} PROPERTIES TIMEOUT 600)
endif()
endfunction(cc_test)
......@@ -629,6 +631,8 @@ function(py_test TARGET_NAME)
PYTHONPATH=${PADDLE_BINARY_DIR}/python ${py_test_ENVS}
${PYTHON_EXECUTABLE} -u ${py_test_SRCS} ${py_test_ARGS}
WORKING_DIRECTORY ${CMAKE_CURRENT_BINARY_DIR})
# No unit test should exceed 10 minutes.
set_tests_properties(${TARGET_NAME} PROPERTIES TIMEOUT 600)
endif()
endfunction()
......
paddle/fluid/API.spec
浏览文件 @ 23fc896b
......@@ -61,12 +61,12 @@ paddle.fluid.layers.cos_sim ArgSpec(args=['X', 'Y'], varargs=None, keywords=None
paddle.fluid.layers.cross_entropy ArgSpec(args=['input', 'label', 'soft_label', 'ignore_index'], varargs=None, keywords=None, defaults=(False, -100))
paddle.fluid.layers.square_error_cost ArgSpec(args=['input', 'label'], varargs=None, keywords=None, defaults=None)
paddle.fluid.layers.chunk_eval ArgSpec(args=['input', 'label', 'chunk_scheme', 'num_chunk_types', 'excluded_chunk_types'], varargs=None, keywords=None, defaults=(None,))
paddle.fluid.layers.sequence_conv ArgSpec(args=['input', 'num_filters', 'filter_size', 'filter_stride', 'padding', 'bias_attr', 'param_attr', 'act'], varargs=None, keywords=None, defaults=(3, 1, None, None, None, None))
paddle.fluid.layers.sequence_conv ArgSpec(args=['input', 'num_filters', 'filter_size', 'filter_stride', 'padding', 'bias_attr', 'param_attr', 'act', 'name'], varargs=None, keywords=None, defaults=(3, 1, None, None, None, None, None))
paddle.fluid.layers.conv2d ArgSpec(args=['input', 'num_filters', 'filter_size', 'stride', 'padding', 'dilation', 'groups', 'param_attr', 'bias_attr', 'use_cudnn', 'act', 'name'], varargs=None, keywords=None, defaults=(1, 0, 1, None, None, None, True, None, None))
paddle.fluid.layers.conv3d ArgSpec(args=['input', 'num_filters', 'filter_size', 'stride', 'padding', 'dilation', 'groups', 'param_attr', 'bias_attr', 'use_cudnn', 'act', 'name'], varargs=None, keywords=None, defaults=(1, 0, 1, None, None, None, True, None, None))
paddle.fluid.layers.sequence_pool ArgSpec(args=['input', 'pool_type'], varargs=None, keywords=None, defaults=None)
paddle.fluid.layers.sequence_softmax ArgSpec(args=['input', 'param_attr', 'bias_attr', 'use_cudnn'], varargs=None, keywords=None, defaults=(None, None, False))
paddle.fluid.layers.softmax ArgSpec(args=['input', 'param_attr', 'bias_attr', 'use_cudnn', 'name'], varargs=None, keywords=None, defaults=(None, None, True, None))
paddle.fluid.layers.sequence_softmax ArgSpec(args=['input', 'use_cudnn', 'name'], varargs=None, keywords=None, defaults=(False, None))
paddle.fluid.layers.softmax ArgSpec(args=['input', 'use_cudnn', 'name'], varargs=None, keywords=None, defaults=(True, None))
paddle.fluid.layers.pool2d ArgSpec(args=['input', 'pool_size', 'pool_type', 'pool_stride', 'pool_padding', 'global_pooling', 'use_cudnn', 'ceil_mode', 'name'], varargs=None, keywords=None, defaults=(-1, 'max', 1, 0, False, True, False, None))
paddle.fluid.layers.pool3d ArgSpec(args=['input', 'pool_size', 'pool_type', 'pool_stride', 'pool_padding', 'global_pooling', 'use_cudnn', 'ceil_mode', 'name'], varargs=None, keywords=None, defaults=(-1, 'max', 1, 0, False, True, False, None))
paddle.fluid.layers.batch_norm ArgSpec(args=['input', 'act', 'is_test', 'momentum', 'epsilon', 'param_attr', 'bias_attr', 'data_layout', 'in_place', 'name', 'moving_mean_name', 'moving_variance_name', 'do_model_average_for_mean_and_var', 'fuse_with_relu'], varargs=None, keywords=None, defaults=(None, False, 0.9, 1e-05, None, None, 'NCHW', False, None, None, None, False, False))
......@@ -97,8 +97,8 @@ paddle.fluid.layers.warpctc ArgSpec(args=['input', 'label', 'blank', 'norm_by_ti
paddle.fluid.layers.sequence_reshape ArgSpec(args=['input', 'new_dim'], varargs=None, keywords=None, defaults=None)
paddle.fluid.layers.transpose ArgSpec(args=['x', 'perm', 'name'], varargs=None, keywords=None, defaults=(None,))
paddle.fluid.layers.im2sequence ArgSpec(args=['input', 'filter_size', 'stride', 'padding', 'input_image_size', 'out_stride', 'name'], varargs=None, keywords=None, defaults=(1, 1, 0, None, 1, None))
paddle.fluid.layers.nce ArgSpec(args=['input', 'label', 'num_total_classes', 'sample_weight', 'param_attr', 'bias_attr', 'num_neg_samples'], varargs=None, keywords=None, defaults=(None, None, None, None))
paddle.fluid.layers.hsigmoid ArgSpec(args=['input', 'label', 'num_classes', 'param_attr', 'bias_attr'], varargs=None, keywords=None, defaults=(None, None))
paddle.fluid.layers.nce ArgSpec(args=['input', 'label', 'num_total_classes', 'sample_weight', 'param_attr', 'bias_attr', 'num_neg_samples', 'name'], varargs=None, keywords=None, defaults=(None, None, None, None, None))
paddle.fluid.layers.hsigmoid ArgSpec(args=['input', 'label', 'num_classes', 'param_attr', 'bias_attr', 'name'], varargs=None, keywords=None, defaults=(None, None, None))
paddle.fluid.layers.beam_search ArgSpec(args=['pre_ids', 'pre_scores', 'ids', 'scores', 'beam_size', 'end_id', 'level', 'name'], varargs=None, keywords=None, defaults=(0, None))
paddle.fluid.layers.row_conv ArgSpec(args=['input', 'future_context_size', 'param_attr', 'act'], varargs=None, keywords=None, defaults=(None, None))
paddle.fluid.layers.multiplex ArgSpec(args=['inputs', 'index'], varargs=None, keywords=None, defaults=None)
......
paddle/fluid/framework/ir/CMakeLists.txt
浏览文件 @ 23fc896b
......@@ -10,7 +10,7 @@ function(pass_library TARGET DEST)
set(oneValueArgs "")
set(multiValueArgs SRCS DEPS)
cmake_parse_arguments(op_library "${options}" "${oneValueArgs}" "${multiValueArgs}" ${ARGN})
cc_library(${TARGET} SRCS ${TARGET}.cc DEPS graph_pattern_detector pass ${op_library_DEPS})
cc_library(${TARGET} SRCS ${TARGET}.cc DEPS graph_pattern_detector pass fuse_pass_base ${op_library_DEPS})
# add more DEST here, such as train, dist and collect USE_PASS into a file automatically.
if (${DEST} STREQUAL "base" OR ${DEST} STREQUAL "inference")
message(STATUS "add pass ${TARGET} ${DEST}")
......@@ -25,13 +25,11 @@ cc_library(graph_helper SRCS graph_helper.cc DEPS graph)
cc_library(pass SRCS pass.cc DEPS graph node graph_helper)
cc_library(graph_traits SRCS graph_traits.cc DEPS graph)
cc_library(graph_pattern_detector SRCS graph_pattern_detector.cc DEPS graph graph_helper graph_traits)
cc_library(fuse_pass_base SRCS fuse_pass_base.cc DEPS pass)
pass_library(graph_to_program_pass base)
pass_library(graph_viz_pass base)
pass_library(fc_fuse_pass inference)
if (WITH_MKLDNN)
pass_library(conv_relu_mkldnn_fuse_pass inference)
endif ()
pass_library(attention_lstm_fuse_pass inference)
pass_library(infer_clean_graph_pass inference)
pass_library(fc_lstm_fuse_pass inference)
......@@ -39,6 +37,10 @@ pass_library(embedding_fc_lstm_fuse_pass inference)
pass_library(fc_gru_fuse_pass inference)
pass_library(seq_concat_fc_fuse_pass inference)
pass_library(conv_bn_fuse_pass inference)
if(WITH_MKLDNN)
pass_library(mkldnn_placement_pass base)
pass_library(conv_relu_mkldnn_fuse_pass inference)
endif()
cc_library(fuse_elewise_add_act_pass SRCS fuse_elewise_add_act_pass.cc DEPS pass graph_pattern_detector )
......
paddle/fluid/framework/ir/conv_bn_fuse_pass.cc
浏览文件 @ 23fc896b
......@@ -126,12 +126,21 @@ std::unique_ptr<ir::Graph> ConvBNFusePass::ApplyImpl(
// conv, batch_norm,
// conv_weight, conv_out,
// bn_scale, bn_bias, bn_mean, bn_variance,
// bn_out, bn_mean_out, bn_variance_out, bn_saved_mean, bn_saved_variance
// bn_out, bn_mean_out, bn_variance_out, bn_saved_mean,
// bn_saved_variance
GET_CONV_BN_NODES(conv_bn_pattern);
// check if fuse can be done and if MKL-DNN should be used
FuseOptions fuse_option = FindFuseOption(*conv, *batch_norm);
if (fuse_option == DO_NOT_FUSE) {
VLOG(3) << "do not perform conv+bn fuse";
return;
}
// Create eltwise_y (conv bias) variable
VarDesc eltwise_y_in_desc(
patterns::PDNodeName(name_scope_, "eltwise_y_in"));
eltwise_y_in_desc.SetPersistable(true);
auto* eltwise_y_in_node = g->CreateVarNode(&eltwise_y_in_desc);
auto* eltwise_y_in_tensor =
scope->Var(eltwise_y_in_node->Name())->GetMutable<LoDTensor>();
......@@ -151,27 +160,59 @@ std::unique_ptr<ir::Graph> ConvBNFusePass::ApplyImpl(
*bn_mean, *bn_variance, eltwise_y_in_tensor,
epsilon);
// Create an elementwise add node
OpDesc desc;
desc.SetInput("X", std::vector<std::string>({conv_out->Name()}));
desc.SetInput("Y", std::vector<std::string>({eltwise_y_in_node->Name()}));
desc.SetOutput("Out", std::vector<std::string>({bn_out->Name()}));
desc.SetType("elementwise_add");
desc.SetAttr("axis", 1);
bool a = boost::get<bool>(conv->Op()->GetAttr("use_mkldnn"));
desc.SetAttr("use_mkldnn", a);
auto eltwise_op = g->CreateOpNode(&desc); // OpDesc will be copied.
GraphSafeRemoveNodes(graph.get(), {bn_scale, bn_bias, bn_mean, bn_variance,
batch_norm, bn_mean_out, bn_variance_out,
bn_saved_mean, bn_saved_variance});
PADDLE_ENFORCE(subgraph.count(conv_input));
IR_NODE_LINK_TO(conv_out, eltwise_op);
IR_NODE_LINK_TO(eltwise_y_in_node, eltwise_op);
IR_NODE_LINK_TO(eltwise_op, bn_out);
found_conv_bn_count++;
// with MKL-DNN fuse conv+bn into conv with bias
// without MKL-DNN fuse conv+bn into conv+elementwise_add
if (fuse_option == FUSE_MKLDNN) {
auto input_names = conv->Op()->InputNames();
bool has_bias = std::find(input_names.begin(), input_names.end(),
"Bias") != input_names.end();
if (has_bias && conv->Op()->Input("Bias").size() > 0) {
// reuse existing conv bias node
auto conv_bias_names = conv->Op()->Input("Bias");
PADDLE_ENFORCE_EQ(conv_bias_names.size(), 1);
auto* conv_bias_var = scope->FindVar(conv_bias_names[0]);
auto* conv_bias_tensor = conv_bias_var->GetMutable<LoDTensor>();
PADDLE_ENFORCE_EQ(conv_bias_tensor->dims(),
eltwise_y_in_tensor->dims());
auto eigen_conv_bias = EigenVector<float>::From(*conv_bias_tensor);
eigen_conv_bias += EigenVector<float>::From(*eltwise_y_in_tensor);
} else {
// add new conv_bias node
conv->Op()->SetInput(
"Bias", std::vector<std::string>({eltwise_y_in_node->Name()}));
IR_NODE_LINK_TO(eltwise_y_in_node, conv);
}
conv->Op()->SetOutput("Output",
std::vector<std::string>({bn_out->Name()}));
GraphSafeRemoveNodes(
graph.get(),
{conv_out, bn_scale, bn_bias, bn_mean, bn_variance, batch_norm,
bn_mean_out, bn_variance_out, bn_saved_mean, bn_saved_variance});
IR_NODE_LINK_TO(conv, bn_out);
found_conv_bn_count++;
} else { // fuse_option == FUSE_NATIVE
// create an elementwise add node.
OpDesc desc;
desc.SetInput("X", std::vector<std::string>({conv_out->Name()}));
desc.SetInput("Y", std::vector<std::string>({eltwise_y_in_node->Name()}));
desc.SetOutput("Out", std::vector<std::string>({bn_out->Name()}));
desc.SetType("elementwise_add");
desc.SetAttr("axis", 1);
auto eltwise_op = g->CreateOpNode(&desc); // OpDesc will be copied.
GraphSafeRemoveNodes(
graph.get(),
{bn_scale, bn_bias, bn_mean, bn_variance, batch_norm, bn_mean_out,
bn_variance_out, bn_saved_mean, bn_saved_variance});
IR_NODE_LINK_TO(conv_out, eltwise_op);
IR_NODE_LINK_TO(eltwise_y_in_node, eltwise_op);
IR_NODE_LINK_TO(eltwise_op, bn_out);
found_conv_bn_count++;
}
};
gpd(graph.get(), handler);
......@@ -237,7 +278,6 @@ std::unique_ptr<ir::Graph> ConvEltwiseAddBNFusePass::ApplyImpl(
{bn_scale, bn_bias, bn_mean, bn_variance, batch_norm, bn_mean_out,
bn_variance_out, bn_saved_mean, bn_saved_variance, eltwise_out});
PADDLE_ENFORCE(subgraph.count(conv_input));
IR_NODE_LINK_TO(eltwise, bn_out);
found_conv_bn_count++;
......
paddle/fluid/framework/ir/conv_relu_mkldnn_fuse_pass.cc
浏览文件 @ 23fc896b
......@@ -46,6 +46,12 @@ std::unique_ptr<ir::Graph> ConvReLUFusePass::ApplyImpl(
GET_IR_NODE_FROM_SUBGRAPH(relu_out, relu_out, conv_relu_pattern); // Out
GET_IR_NODE_FROM_SUBGRAPH(relu, relu, conv_relu_pattern); // ReLU op
FuseOptions fuse_option = FindFuseOption(*conv, *relu);
if (fuse_option == DO_NOT_FUSE) {
VLOG(3) << "do not perform conv+relu fuse";
return;
}
// Transform Conv node into ConvReLU node.
OpDesc* desc = conv->Op();
desc->SetOutput("Output", std::vector<std::string>({relu_out->Name()}));
......
paddle/fluid/framework/ir/conv_relu_mkldnn_fuse_pass_tester.cc
浏览文件 @ 23fc896b
......@@ -20,17 +20,19 @@ namespace paddle {
namespace framework {
namespace ir {
void SetOp(ProgramDesc* prog, const std::string& type,
void SetOp(ProgramDesc* prog, const std::string& type, const std::string& name,
const std::vector<std::string>& inputs,
const std::vector<std::string>& outputs) {
const std::vector<std::string>& outputs, bool use_mkldnn = false) {
auto* op = prog->MutableBlock(0)->AppendOp();
op->SetType(type);
if (type == "conv2d") {
op->SetAttr("use_mkldnn", true);
op->SetAttr("use_mkldnn", use_mkldnn);
op->SetAttr("name", name);
op->SetInput("Input", {inputs[0]});
op->SetInput("Filter", {inputs[1]});
op->SetInput("Bias", {inputs[2]});
} else if (type == "relu") {
op->SetAttr("use_mkldnn", use_mkldnn);
op->SetInput("X", inputs);
}
op->SetOutput("Out", outputs);
......@@ -43,7 +45,8 @@ void SetOp(ProgramDesc* prog, const std::string& type,
ProgramDesc BuildProgramDesc() {
ProgramDesc prog;
for (auto& v :
std::vector<std::string>({"a", "b", "c", "weights", "bias", "f", "g"})) {
std::vector<std::string>({"a", "b", "c", "weights", "bias", "f", "g",
"h", "weights2", "bias2", "k", "l"})) {
auto* var = prog.MutableBlock(0)->Var(v);
var->SetType(proto::VarType::SELECTED_ROWS);
if (v == "weights" || v == "bias") {
......@@ -51,14 +54,24 @@ ProgramDesc BuildProgramDesc() {
}
}
SetOp(&prog, "OP0", std::vector<std::string>({"a"}),
SetOp(&prog, "OP0", "op0", std::vector<std::string>({"a"}),
std::vector<std::string>({"b"}));
SetOp(&prog, "OP1", std::vector<std::string>({"b"}),
SetOp(&prog, "OP1", "op1", std::vector<std::string>({"b"}),
std::vector<std::string>({"c"}));
SetOp(&prog, "conv2d", std::vector<std::string>({"c", "weights", "bias"}),
std::vector<std::string>({"f"}));
SetOp(&prog, "relu", std::vector<std::string>({"f"}),
std::vector<std::string>({"g"}));
// conv+relu, both with MKL-DNN
SetOp(&prog, "conv2d", "conv1",
std::vector<std::string>({"c", "weights", "bias"}),
std::vector<std::string>({"f"}), true);
SetOp(&prog, "relu", "relu1", std::vector<std::string>({"f"}),
std::vector<std::string>({"g"}), true);
SetOp(&prog, "OP3", "op3", std::vector<std::string>({"g"}),
std::vector<std::string>({"h"}));
// conv+relu, only one with MKL-DNN
SetOp(&prog, "conv2d", "conv2",
std::vector<std::string>({"h", "weights2", "bias2"}),
std::vector<std::string>({"k"}), true);
SetOp(&prog, "relu", "relu2", std::vector<std::string>({"k"}),
std::vector<std::string>({"l"}));
return prog;
}
......@@ -88,10 +101,16 @@ TEST(ConvReLUFusePass, basic) {
auto* op = node->Op();
ASSERT_TRUE(op->HasAttr("use_mkldnn"));
EXPECT_TRUE(boost::get<bool>(op->GetAttr("use_mkldnn")));
ASSERT_TRUE(op->HasAttr("fuse_relu"));
bool fuse_relu = boost::get<bool>(op->GetAttr("fuse_relu"));
if (fuse_relu) {
++conv_relu_count;
// check if only "conv1" convolution is fused
auto op_name = boost::get<std::string>(op->GetAttr("name"));
if (op_name == "conv1") {
ASSERT_TRUE(op->HasAttr("fuse_relu"));
bool fuse_relu = boost::get<bool>(op->GetAttr("fuse_relu"));
if (fuse_relu) {
++conv_relu_count;
}
} else if (op_name == "conv2") {
ASSERT_FALSE(op->HasAttr("fuse_relu"));
}
}
}
......
paddle/fluid/framework/ir/fuse_pass_base.cc 0 → 100644
浏览文件 @ 23fc896b
// 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 "paddle/fluid/framework/ir/fuse_pass_base.h"
namespace paddle {
namespace framework {
namespace ir {
void FusePassBase::Init(const std::string& repr, Graph* graph) const {
repr_ = repr;
graph_ = graph;
}
Scope* FusePassBase::param_scope() const {
PADDLE_ENFORCE(graph_->Has(kParamScopeAttr));
return graph_->Get<framework::Scope*>(kParamScopeAttr);
}
void FusePassBase::AddStatis(int count_of_fused) const {
PADDLE_ENFORCE(graph_);
PADDLE_ENFORCE(!repr_.empty());
if (!graph_->Has(kFuseStatisAttr)) {
graph_->Set(kFuseStatisAttr, new std::unordered_map<std::string, int>);
}
auto& info =
graph_->Get<std::unordered_map<std::string, int>>(kFuseStatisAttr);
info[repr_] = count_of_fused;
}
FuseOptions FusePassBase::FindFuseOption(const Node& node1,
const Node& node2) const {
#ifdef PADDLE_WITH_MKLDNN
bool node1_mkldnn = node1.Op()->HasAttr("use_mkldnn") &&
boost::get<bool>(node1.Op()->GetAttr("use_mkldnn"));
bool node2_mkldnn = node2.Op()->HasAttr("use_mkldnn") &&
boost::get<bool>(node2.Op()->GetAttr("use_mkldnn"));
if (node1_mkldnn && node2_mkldnn)
return FUSE_MKLDNN;
else if (!node1_mkldnn && !node2_mkldnn)
return FUSE_NATIVE;
else
return DO_NOT_FUSE;
#else
return FUSE_NATIVE;
#endif
};
} // namespace ir
} // namespace framework
} // namespace paddle
paddle/fluid/framework/ir/fuse_pass_base.h
浏览文件 @ 23fc896b
......@@ -25,32 +25,24 @@ namespace ir {
static const char kParamScopeAttr[] = "__param_scope__";
static const char kFuseStatisAttr[] = "__fuse_statis__";
enum FuseOptions {
DO_NOT_FUSE, // fusing will not be done
FUSE_NATIVE, // fusing will be done without MKL-DNN
FUSE_MKLDNN // fusing will be done with MKL-DNN
};
class FusePassBase : public Pass {
public:
void Init(const std::string& repr, Graph* graph) const {
repr_ = repr;
graph_ = graph;
}
Scope* param_scope() const {
PADDLE_ENFORCE(graph_->Has(kParamScopeAttr));
return graph_->Get<framework::Scope*>(kParamScopeAttr);
}
void AddStatis(int count_of_fused) const {
PADDLE_ENFORCE(graph_);
PADDLE_ENFORCE(!repr_.empty());
if (!graph_->Has(kFuseStatisAttr)) {
graph_->Set(kFuseStatisAttr, new std::unordered_map<std::string, int>);
}
auto& info =
graph_->Get<std::unordered_map<std::string, int>>(kFuseStatisAttr);
info[repr_] = count_of_fused;
}
void Init(const std::string& repr, Graph* graph) const;
Scope* param_scope() const;
void AddStatis(int count_of_fused) const;
virtual ~FusePassBase() {}
protected:
virtual FuseOptions FindFuseOption(const Node& node1,
const Node& node2) const;
mutable Graph* graph_;
mutable std::string repr_;
};
......
paddle/fluid/framework/ir/graph_pattern_detector.cc
浏览文件 @ 23fc896b
......@@ -259,6 +259,8 @@ GraphPatternDetector::DetectPatterns() {
return result;
}
// TODO(Superjomn) enhance the function as it marks unique unique as duplicates
// see https://github.com/PaddlePaddle/Paddle/issues/13550
void GraphPatternDetector::UniquePatterns(
std::vector<GraphPatternDetector::subgraph_t> *subgraphs) {
if (subgraphs->empty()) return;
......
paddle/fluid/framework/ir/mkldnn_placement_pass.cc 0 → 100644
浏览文件 @ 23fc896b
/* 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 "paddle/fluid/framework/ir/mkldnn_placement_pass.h"
namespace paddle {
namespace framework {
namespace ir {
std::unique_ptr<ir::Graph> MKLDNNPlacementPass::ApplyImpl(
std::unique_ptr<ir::Graph> graph) const {
VLOG(3) << "Aplies MKL-DNN placement strategy.";
for (const Node* n : graph->Nodes()) {
if (n->IsOp() && n->Op()->HasAttr("use_mkldnn")) {
n->Op()->SetAttr("use_mkldnn", true);
}
}
return graph;
}
} // namespace ir
} // namespace framework
} // namespace paddle
REGISTER_PASS(mkldnn_placement_pass,
paddle::framework::ir::MKLDNNPlacementPass);
paddle/fluid/framework/ir/mkldnn_placement_pass.h 0 → 100644
浏览文件 @ 23fc896b
/* 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. */
#pragma once
#include "paddle/fluid/framework/ir/pass.h"
namespace paddle {
namespace framework {
namespace ir {
class MKLDNNPlacementPass : public Pass {
protected:
std::unique_ptr<ir::Graph> ApplyImpl(
std::unique_ptr<ir::Graph> graph) const override;
};
} // namespace ir
} // namespace framework
} // namespace paddle
paddle/fluid/framework/op_desc.cc
浏览文件 @ 23fc896b
......@@ -85,10 +85,6 @@ class CompileTimeInferShapeContext : public InferShapeContext {
VLOG(3) << "input " << in << " is not LodTensor";
return;
}
PADDLE_ENFORCE_EQ(in_var->GetType(), proto::VarType::LOD_TENSOR,
"The %d-th output of Output(%s) must be LoDTensor.", j,
out);
out_var->SetLoDLevel(in_var->GetLoDLevel());
}
......
paddle/fluid/inference/analysis/analyzer.cc
浏览文件 @ 23fc896b
......@@ -101,7 +101,11 @@ Analyzer::Analyzer() { Register("manager1", new DfgPassManagerImpl); }
void Analyzer::Run(Argument* argument) {
std::vector<std::string> passes;
for (auto& pass : all_ir_passes_) {
if (use_mkldnn_) {
VLOG(3) << "Adding MKL-DNN placement pass";
passes.push_back("mkldnn_placement_pass");
}
for (auto& pass : ir_passes_) {
if (!disabled_ir_passes_.count(pass)) {
passes.push_back(pass);
passes.push_back("graph_viz_pass"); // add graphviz for debug.
......@@ -117,11 +121,26 @@ void Analyzer::Run(Argument* argument) {
}
}
Analyzer& Analyzer::IncludeAllIrPasses() {
ir_passes_ = all_ir_passes_;
return *this;
}
Analyzer& Analyzer::DisableIrPasses(const std::vector<std::string>& passes) {
disabled_ir_passes_.insert(passes.begin(), passes.end());
return *this;
}
Analyzer& Analyzer::IncludeIrPasses(const std::vector<std::string>& passes) {
ir_passes_ = passes;
return *this;
}
Analyzer& Analyzer::SetUseMkldnn(bool use_mkldnn) {
use_mkldnn_ = use_mkldnn;
return *this;
}
} // namespace analysis
} // namespace inference
} // namespace paddle
paddle/fluid/inference/analysis/analyzer.h
浏览文件 @ 23fc896b
......@@ -54,6 +54,9 @@ class Analyzer : public OrderedRegistry<PassManager> {
void Run(Argument* argument);
Analyzer& DisableIrPasses(const std::vector<std::string>& passes);
Analyzer& IncludeIrPasses(const std::vector<std::string>& passes);
Analyzer& IncludeAllIrPasses();
Analyzer& SetUseMkldnn(bool use_mkldnn);
DISABLE_COPY_AND_ASSIGN(Analyzer);
......@@ -81,6 +84,9 @@ class Analyzer : public OrderedRegistry<PassManager> {
}};
std::unordered_set<std::string> disabled_ir_passes_;
// Ir passes to run
std::vector<std::string> ir_passes_;
bool use_mkldnn_;
};
} // namespace analysis
......
paddle/fluid/inference/api/analysis_predictor.cc
浏览文件 @ 23fc896b
......@@ -225,10 +225,24 @@ void AnalysisPredictor::OptimizeInferenceProgram() {
argument_.origin_program_desc.reset(
new ProgramDesc(*inference_program_->Proto()));
PADDLE_ENFORCE(
config_.ir_mode == contrib::AnalysisConfig::IrPassMode::kExclude,
"Only kExclude is supported yet.");
Analyzer().DisableIrPasses(config_.ir_passes).Run(&argument_);
switch (config_.ir_mode) {
case contrib::AnalysisConfig::IrPassMode::kExclude:
Analyzer()
.IncludeAllIrPasses()
.SetUseMkldnn(config_._use_mkldnn)
.DisableIrPasses(config_.ir_passes)
.Run(&argument_);
break;
case contrib::AnalysisConfig::IrPassMode::kInclude:
Analyzer()
.SetUseMkldnn(config_._use_mkldnn)
.IncludeIrPasses(config_.ir_passes)
.Run(&argument_);
break;
default:
LOG(ERROR) << "Only kExclude and kInclude modes are supoorted yet.";
}
CHECK(argument_.transformed_program_desc);
VLOG(5) << "to prepare executor";
......
paddle/fluid/inference/api/paddle_inference_api.h
浏览文件 @ 23fc896b
......@@ -259,10 +259,17 @@ struct AnalysisConfig : public NativeConfig {
kExclude // Specify the disabled passes in `ir_passes`.
};
void SetIncludeMode() {
ir_mode = IrPassMode::kInclude;
// this pass has to be run at the beginning of all fuse passes
ir_passes = {"infer_clean_graph_pass"};
}
// Determine whether to perform graph optimization.
bool enable_ir_optim = true;
// Manually determine the IR passes to run.
IrPassMode ir_mode{IrPassMode::kExclude};
// passes to be excluded/included
std::vector<std::string> ir_passes{"embedding_fc_lstm_fuse_pass"};
// NOT stable yet.
......
paddle/fluid/inference/tests/api/analyzer_rnn2_tester.cc
浏览文件 @ 23fc896b
......@@ -18,12 +18,12 @@ namespace paddle {
namespace inference {
using namespace framework; // NOLINT
static std::vector<float> result_data;
struct DataRecord {
std::vector<std::vector<std::vector<float>>> link_step_data_all;
std::vector<size_t> lod;
std::vector<std::vector<float>> rnn_link_data;
std::vector<float> result_data;
size_t num_samples; // total number of samples
size_t batch_iter{0};
size_t batch_size{1};
......@@ -57,6 +57,7 @@ struct DataRecord {
std::ifstream file(path);
std::string line;
int num_lines = 0;
result_data.clear();
while (std::getline(file, line)) {
num_lines++;
std::vector<std::string> data;
......@@ -135,13 +136,12 @@ TEST(Analyzer_rnn2, profile) {
if (FLAGS_num_threads == 1 && !FLAGS_test_all_data) {
// the first inference result
DataRecord data(FLAGS_infer_data, FLAGS_batch_size);
PADDLE_ENFORCE_GT(outputs.size(), 0);
size_t size = GetSize(outputs[0]);
PADDLE_ENFORCE_GT(size, 0);
float *result = static_cast<float *>(outputs[0].data.data());
for (size_t i = 0; i < size; i++) {
EXPECT_NEAR(result[i], data.result_data[i], 1e-3);
EXPECT_NEAR(result[i], result_data[i], 1e-3);
}
}
}
......
paddle/fluid/operators/detection/CMakeLists.txt
浏览文件 @ 23fc896b
......@@ -20,7 +20,7 @@ detection_library(box_coder_op SRCS box_coder_op.cc box_coder_op.cu)
detection_library(iou_similarity_op SRCS iou_similarity_op.cc
iou_similarity_op.cu)
detection_library(mine_hard_examples_op SRCS mine_hard_examples_op.cc)
detection_library(multiclass_nms_op SRCS multiclass_nms_op.cc)
detection_library(multiclass_nms_op SRCS multiclass_nms_op.cc poly_util.cc gpc.cc)
detection_library(prior_box_op SRCS prior_box_op.cc prior_box_op.cu)
detection_library(anchor_generator_op SRCS anchor_generator_op.cc
anchor_generator_op.cu)
......
paddle/fluid/operators/detection/gpc.cc 0 → 100644
浏览文件 @ 23fc896b
此差异已折叠。
paddle/fluid/operators/detection/gpc.h 0 → 100644
浏览文件 @ 23fc896b
// 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.
/***************************************************************************
*
* Copyright (c) 2015 Baidu.com, Inc. All Rights Reserved
*
**************************************************************************/
/**
* @file include/gpc.h
* @author huhan02(com@baidu.com)
* @date 2015/12/18 13:52:10
* @brief
*
* @modified by sunyipeng
* @email sunyipeng@baidu.com
* @date 2018/6/12
**/
#ifndef PADDLE_FLUID_OPERATORS_DETECTION_GPC_H_ // GPC_H_
#define PADDLE_FLUID_OPERATORS_DETECTION_GPC_H_ // GPC_H_
#include <float.h>
#include <math.h>
#include <stdio.h>
#include <stdlib.h>
namespace gpc {
typedef enum { // Set operation type
GPC_DIFF, // Difference
GPC_INT, // Intersection
GPC_XOR, // Exclusive or
GPC_UNION // Union
} gpc_op;
typedef struct { // Polygon vertex structure
double x; // Vertex x component
double y; // vertex y component
} gpc_vertex;
typedef struct { // Vertex list structure
int num_vertices; // Number of vertices in list
gpc_vertex *vertex; // Vertex array pointer
} gpc_vertex_list;
typedef struct { // Polygon set structure
int num_contours; // Number of contours in polygon
int *hole; // Hole external contour flags
gpc_vertex_list *contour; // Contour array pointer
} gpc_polygon;
typedef struct { // Tristrip set structure
int num_strips; // Number of tristrips
gpc_vertex_list *strip; // Tristrip array pointer
} gpc_tristrip;
typedef enum { LEFT, RIGHT } gpc_left_right;
typedef enum { ABOVE, BELOW } gpc_above_below;
typedef enum { CLIP, SUBJ } gpc_clip_subj;
typedef enum { /* Edge intersection classes */
NUL, /* Empty non-intersection */
EMX, /* External maximum */
ELI, /* External left intermediate */
TED, /* Top edge */
ERI, /* External right intermediate */
RED, /* Right edge */
IMM, /* Internal maximum and minimum */
IMN, /* Internal minimum */
EMN, /* External minimum */
EMM, /* External maximum and minimum */
LED, /* Left edge */
ILI, /* Internal left intermediate */
BED, /* Bottom edge */
IRI, /* Internal right intermediate */
IMX, /* Internal maximum */
FUL /* Full non-intersection */
} vertex_type;
typedef enum { /* Horizontal edge states */
NH, /* No horizontal edge */
BH, /* Bottom horizontal edge */
TH /* Top horizontal edge */
} h_state;
typedef enum { /* Edge bundle state */
UNBUNDLED, /* Isolated edge not within a bundle */
BUNDLE_HEAD, /* Bundle head node */
BUNDLE_TAIL /* Passive bundle tail node */
} bundle_state;
typedef struct v_shape { /* Internal vertex list datatype */
double x; /* X coordinate component */
double y; /* Y coordinate component */
struct v_shape *next; /* Pointer to next vertex in list */
} vertex_node;
typedef struct p_shape { /* Internal contour / tristrip type */
int active; /* Active flag / vertex count */
int hole; /* Hole / external contour flag */
vertex_node *v[2]; /* Left and right vertex list ptrs */
struct p_shape *next; /* Pointer to next polygon contour */
struct p_shape *proxy; /* Pointer to actual structure used */
} polygon_node;
typedef struct edge_shape {
gpc_vertex vertex; /* Piggy-backed contour vertex data */
gpc_vertex bot; /* Edge lower (x, y) coordinate */
gpc_vertex top; /* Edge upper (x, y) coordinate */
double xb; /* Scanbeam bottom x coordinate */
double xt; /* Scanbeam top x coordinate */
double dx; /* Change in x for a unit y increase */
int type; /* Clip / subject edge flag */
int bundle[2][2]; /* Bundle edge flags */
int bside[2]; /* Bundle left / right indicators */
bundle_state bstate[2]; /* Edge bundle state */
polygon_node *outp[2]; /* Output polygon / tristrip pointer */
struct edge_shape *prev; /* Previous edge in the AET */
struct edge_shape *next; /* Next edge in the AET */
struct edge_shape *pred; /* Edge connected at the lower end */
struct edge_shape *succ; /* Edge connected at the upper end */
struct edge_shape *next_bound; /* Pointer to next bound in LMT */
} edge_node;
inline bool gpc_eq(float a, float b) { return (fabs(a - b) <= 1e-6); }
inline bool gpc_prev_index(float a, float b) { return (fabs(a - b) <= 1e-6); }
inline int gpc_prev_index(int i, int n) { return ((i - 1 + n) % n); }
inline int gpc_next_index(int i, int n) { return ((i + 1) % n); }
inline int gpc_optimal(gpc_vertex *v, int i, int n) {
return (v[(i + 1) % n].y != v[i].y || v[(i - 1 + n) % n].y != v[i].y);
}
inline int gpc_fwd_min(edge_node *v, int i, int n) {
return (v[(i + 1) % n].vertex.y > v[i].vertex.y &&
v[(i - 1 + n) % n].vertex.y >= v[i].vertex.y);
}
inline int gpc_not_fmax(edge_node *v, int i, int n) {
return (v[(i + 1) % n].vertex.y > v[i].vertex.y);
}
inline int gpc_rev_min(edge_node *v, int i, int n) {
return (v[(i + 1) % n].vertex.y >= v[i].vertex.y &&
v[(i - 1 + n) % n].vertex.y > v[i].vertex.y);
}
inline int gpc_not_rmax(edge_node *v, int i, int n) {
return (v[(i - 1 + n) % n].vertex.y > v[i].vertex.y);
}
// inline void gpc_p_edge(edge_node *d, edge_node *e, int p, double i, double j)
// {
inline void gpc_p_edge(edge_node *d, edge_node *e, int p) {
d = e;
do {
d = d->prev;
} while (!d->outp[p]);
// i = d->bot.x + d->dx * (j - d->bot.y);
}
// inline void gpc_n_edge(edge_node *d, edge_node *e, int p, double i, double j)
// {
inline void gpc_n_edge(edge_node *d, edge_node *e, int p) {
d = e;
do {
d = d->next;
} while (!d->outp[p]);
// i = d->bot.x + d->dx * (j - d->bot.y);
}
template <typename T>
void gpc_malloc(T *&p, int b, char *s) {
if (b > 0) {
p = (T *)malloc(b);
if (!p) {
fprintf(stderr, "gpc malloc failure: %s\n", s);
exit(0);
}
} else {
p = NULL;
}
}
template <typename T>
void gpc_free(T *&p) {
if (p) {
free(p);
p = NULL;
}
}
/*
===========================================================================
Public Function Prototypes
===========================================================================
*/
void add_vertex(vertex_node **t, double x, double y);
void gpc_vertex_create(edge_node *e, int p, int s, double x, double y);
/*
void gpc_read_polygon(FILE *infile_ptr, int read_hole_flags,
gpc_polygon *polygon);
void gpc_write_polygon(FILE *outfile_ptr, int write_hole_flags,
gpc_polygon *polygon);
*/
void gpc_add_contour(gpc_polygon *polygon, gpc_vertex_list *contour, int hole);
void gpc_polygon_clip(gpc_op set_operation, gpc_polygon *subject_polygon,
gpc_polygon *clip_polygon, gpc_polygon *result_polygon);
void gpc_tristrip_clip(gpc_op set_operation, gpc_polygon *subject_polygon,
gpc_polygon *clip_polygon,
gpc_tristrip *result_tristrip);
void gpc_polygon_to_tristrip(gpc_polygon *polygon, gpc_tristrip *tristrip);
void gpc_free_polygon(gpc_polygon *polygon);
void gpc_free_tristrip(gpc_tristrip *tristrip);
} // namespace gpc
#endif // PADDLE_FLUID_OPERATORS_DETECTION_GPC_H_
/* vim: set expandtab ts=4 sw=4 sts=4 tw=100: */
paddle/fluid/operators/detection/multiclass_nms_op.cc
浏览文件 @ 23fc896b
......@@ -9,10 +9,11 @@ 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/framework/op_registry.h"
#include "paddle/fluid/operators/detection/poly_util.h"
namespace paddle {
namespace operators {
......@@ -20,9 +21,6 @@ namespace operators {
using Tensor = framework::Tensor;
using LoDTensor = framework::LoDTensor;
constexpr int64_t kOutputDim = 6;
constexpr int64_t kBBoxSize = 4;
class MultiClassNMSOp : public framework::OperatorWithKernel {
public:
using framework::OperatorWithKernel::OperatorWithKernel;
......@@ -42,10 +40,15 @@ class MultiClassNMSOp : public framework::OperatorWithKernel {
"The rank of Input(BBoxes) must be 3.");
PADDLE_ENFORCE_EQ(score_dims.size(), 3,
"The rank of Input(Scores) must be 3.");
PADDLE_ENFORCE_EQ(box_dims[2], 4,
"The 2nd dimension of Input(BBoxes) must be 4, "
"represents the layout of coordinate "
"[xmin, ymin, xmax, ymax]");
PADDLE_ENFORCE(box_dims[2] == 4 || box_dims[2] == 8 || box_dims[2] == 16 ||
box_dims[2] == 24 || box_dims[2] == 32,
"The 2nd dimension of Input(BBoxes) must be 4 or 8, "
"represents the layout of coordinate "
"[xmin, ymin, xmax, ymax] or "
"4 points: [x1, y1, x2, y2, x3, y3, x4, y4] or "
"8 points: [xi, yi] i= 1,2,...,8 or "
"12 points: [xi, yi] i= 1,2,...,12 or "
"16 points: [xi, yi] i= 1,2,...,16");
PADDLE_ENFORCE_EQ(box_dims[1], score_dims[2],
"The 1st dimensiong of Input(BBoxes) must be equal to "
"3rd dimension of Input(Scores), which represents the "
......@@ -53,7 +56,7 @@ class MultiClassNMSOp : public framework::OperatorWithKernel {
// Here the box_dims[0] is not the real dimension of output.
// It will be rewritten in the computing kernel.
ctx->SetOutputDim("Out", {box_dims[1], 6});
ctx->SetOutputDim("Out", {box_dims[1], box_dims[2] + 2});
}
protected:
......@@ -128,6 +131,21 @@ static inline T JaccardOverlap(const T* box1, const T* box2,
}
}
template <class T>
T PolyIoU(const T* box1, const T* box2, const size_t box_size,
const bool normalized) {
T bbox1_area = PolyArea<T>(box1, box_size, normalized);
T bbox2_area = PolyArea<T>(box2, box_size, normalized);
T inter_area = PolyOverlapArea<T>(box1, box2, box_size, normalized);
if (bbox1_area == 0 || bbox2_area == 0 || inter_area == 0) {
// If coordinate values are is invalid
// if area size <= 0, return 0.
return T(0.);
} else {
return inter_area / (bbox1_area + bbox2_area - inter_area);
}
}
template <typename T>
class MultiClassNMSKernel : public framework::OpKernel<T> {
public:
......@@ -137,6 +155,8 @@ class MultiClassNMSKernel : public framework::OpKernel<T> {
// The total boxes for each instance.
int64_t num_boxes = bbox.dims()[0];
// 4: [xmin ymin xmax ymax]
// 8: [x1 y1 x2 y2 x3 y3 x4 y4]
// 16, 24, or 32: [x1 y1 x2 y2 ... xn yn], n = 8, 12 or 16
int64_t box_size = bbox.dims()[1];
std::vector<T> scores_data(num_boxes);
......@@ -154,8 +174,19 @@ class MultiClassNMSKernel : public framework::OpKernel<T> {
for (size_t k = 0; k < selected_indices->size(); ++k) {
if (keep) {
const int kept_idx = (*selected_indices)[k];
T overlap = JaccardOverlap<T>(bbox_data + idx * box_size,
T overlap = T(0.);
// 4: [xmin ymin xmax ymax]
if (box_size == 4) {
overlap = JaccardOverlap<T>(bbox_data + idx * box_size,
bbox_data + kept_idx * box_size, true);
}
// 8: [x1 y1 x2 y2 x3 y3 x4 y4] or 16, 24, 32
if (box_size == 8 || box_size == 16 || box_size == 24 ||
box_size == 32) {
overlap =
PolyIoU<T>(bbox_data + idx * box_size,
bbox_data + kept_idx * box_size, box_size, true);
}
keep = overlap <= adaptive_threshold;
} else {
break;
......@@ -228,7 +259,9 @@ class MultiClassNMSKernel : public framework::OpKernel<T> {
void MultiClassOutput(const Tensor& scores, const Tensor& bboxes,
const std::map<int, std::vector<int>>& selected_indices,
Tensor* outs) const {
int predict_dim = scores.dims()[1];
int64_t predict_dim = scores.dims()[1];
int64_t box_size = bboxes.dims()[1];
int64_t out_dim = bboxes.dims()[1] + 2;
auto* scores_data = scores.data<T>();
auto* bboxes_data = bboxes.data<T>();
auto* odata = outs->data<T>();
......@@ -240,11 +273,11 @@ class MultiClassNMSKernel : public framework::OpKernel<T> {
const std::vector<int>& indices = it.second;
for (size_t j = 0; j < indices.size(); ++j) {
int idx = indices[j];
const T* bdata = bboxes_data + idx * kBBoxSize;
odata[count * kOutputDim] = label; // label
odata[count * kOutputDim + 1] = sdata[idx]; // score
// xmin, ymin, xmax, ymax
std::memcpy(odata + count * kOutputDim + 2, bdata, 4 * sizeof(T));
const T* bdata = bboxes_data + idx * box_size;
odata[count * out_dim] = label; // label
odata[count * out_dim + 1] = sdata[idx]; // score
// xmin, ymin, xmax, ymax or multi-points coordinates
std::memcpy(odata + count * out_dim + 2, bdata, box_size * sizeof(T));
count++;
}
}
......@@ -261,6 +294,7 @@ class MultiClassNMSKernel : public framework::OpKernel<T> {
int64_t class_num = score_dims[1];
int64_t predict_dim = score_dims[2];
int64_t box_dim = boxes->dims()[2];
int64_t out_dim = boxes->dims()[2] + 2;
std::vector<std::map<int, std::vector<int>>> all_indices;
std::vector<size_t> batch_starts = {0};
......@@ -283,7 +317,7 @@ class MultiClassNMSKernel : public framework::OpKernel<T> {
T* od = outs->mutable_data<T>({1}, ctx.GetPlace());
od[0] = -1;
} else {
outs->mutable_data<T>({num_kept, kOutputDim}, ctx.GetPlace());
outs->mutable_data<T>({num_kept, out_dim}, ctx.GetPlace());
for (int64_t i = 0; i < batch_size; ++i) {
Tensor ins_score = scores->Slice(i, i + 1);
ins_score.Resize({class_num, predict_dim});
......@@ -311,10 +345,11 @@ class MultiClassNMSOpMaker : public framework::OpProtoAndCheckerMaker {
public:
void Make() override {
AddInput("BBoxes",
"(Tensor) A 3-D Tensor with shape [N, M, 4] represents the "
"(Tensor) A 3-D Tensor with shape "
"[N, M, 4 or 8 16 24 32] represents the "
"predicted locations of M bounding bboxes, N is the batch size. "
"Each bounding box has four coordinate values and the layout is "
"[xmin, ymin, xmax, ymax].");
"[xmin, ymin, xmax, ymax], when box size equals to 4.");
AddInput("Scores",
"(Tensor) A 3-D Tensor with shape [N, C, M] represents the "
"predicted confidence predictions. N is the batch size, C is the "
......@@ -351,8 +386,12 @@ class MultiClassNMSOpMaker : public framework::OpProtoAndCheckerMaker {
AddOutput("Out",
"(LoDTensor) A 2-D LoDTensor with shape [No, 6] represents the "
"detections. Each row has 6 values: "
"[label, confidence, xmin, ymin, xmax, ymax], No is the total "
"number of detections in this mini-batch. For each instance, "
"[label, confidence, xmin, ymin, xmax, ymax] or "
"(LoDTensor) A 2-D LoDTensor with shape [No, 10] represents the "
"detections. Each row has 10 values: "
"[label, confidence, x1, y1, x2, y2, x3, y3, x4, y4]. No is the "
"total number of detections in this mini-batch."
"For each instance, "
"the offsets in first dimension are called LoD, the number of "
"offset is N + 1, if LoD[i + 1] - LoD[i] == 0, means there is "
"no detected bbox.");
......
paddle/fluid/operators/detection/poly_util.cc 0 → 100644
浏览文件 @ 23fc896b
/* 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. */
#ifndef POLY_UTIL_CC_
#define POLY_UTIL_CC_
#include "paddle/fluid/operators/detection/poly_util.h"
#include "paddle/fluid/framework/op_registry.h"
namespace paddle {
namespace operators {
using gpc::gpc_polygon_clip;
using gpc::gpc_free_polygon;
template <class T>
void Array2PointVec(const T*& box, const size_t box_size,
std::vector<Point_<T>>& vec) {
size_t pts_num = box_size / 2;
vec.resize(pts_num);
for (size_t i = 0; i < pts_num; i++) {
vec.at(i).x = box[2 * i];
vec.at(i).y = box[2 * i + 1];
}
}
template <class T>
void Array2Poly(const T*& box, const size_t box_size, gpc::gpc_polygon& poly) {
size_t pts_num = box_size / 2;
poly.num_contours = 1;
poly.hole = (int*)malloc(sizeof(int));
poly.hole[0] = 0;
poly.contour = (gpc::gpc_vertex_list*)malloc(sizeof(gpc::gpc_vertex_list));
poly.contour->num_vertices = pts_num;
poly.contour->vertex =
(gpc::gpc_vertex*)malloc(sizeof(gpc::gpc_vertex) * pts_num);
for (size_t i = 0; i < pts_num; ++i) {
poly.contour->vertex[i].x = box[2 * i];
poly.contour->vertex[i].y = box[2 * i + 1];
}
}
template <class T>
void PointVec2Poly(const std::vector<Point_<T>>& vec, gpc::gpc_polygon& poly) {
int pts_num = vec.size();
poly.num_contours = 1;
poly.hole = (int*)malloc(sizeof(int));
poly.hole[0] = 0;
poly.contour = (gpc::gpc_vertex_list*)malloc(sizeof(gpc::gpc_vertex_list));
poly.contour->num_vertices = pts_num;
poly.contour->vertex =
(gpc::gpc_vertex*)malloc(sizeof(gpc::gpc_vertex) * pts_num);
for (size_t i = 0; i < pts_num; ++i) {
poly.contour->vertex[i].x = vec[i].x;
poly.contour->vertex[i].y = vec[i].y;
}
}
template <class T>
void Poly2PointVec(const gpc::gpc_vertex_list& contour,
std::vector<Point_<T>>& vec) {
int pts_num = contour.num_vertices;
vec.resize(pts_num);
for (int i = 0; i < pts_num; i++) {
vec.at(i).x = contour.vertex[i].x;
vec.at(i).y = contour.vertex[i].y;
}
}
template <class T>
T GetContourArea(std::vector<Point_<T>>& vec) {
size_t pts_num = vec.size();
if (pts_num < 3) return T(0.);
T area = T(0.);
for (size_t i = 0; i < pts_num; ++i) {
area += vec[i].x * vec[(i + 1) % pts_num].y -
vec[i].y * vec[(i + 1) % pts_num].x;
}
return std::fabs(area / 2.0);
}
template <class T>
T PolyArea(const T* box, const size_t box_size, const bool normalized) {
// If coordinate values are is invalid
// if area size <= 0, return 0.
std::vector<Point_<T>> vec;
Array2PointVec<T>(box, box_size, vec);
return GetContourArea<T>(vec);
}
template <class T>
T PolyOverlapArea(const T* box1, const T* box2, const size_t box_size,
const bool normalized) {
gpc::gpc_polygon poly1;
gpc::gpc_polygon poly2;
Array2Poly<T>(box1, box_size, poly1);
Array2Poly<T>(box2, box_size, poly2);
gpc::gpc_polygon respoly;
gpc::gpc_op op = gpc::GPC_INT;
gpc::gpc_polygon_clip(op, &poly2, &poly1, &respoly);
T inter_area = T(0.);
int contour_num = respoly.num_contours;
for (int i = 0; i < contour_num; ++i) {
std::vector<Point_<T>> resvec;
Poly2PointVec<T>(respoly.contour[i], resvec);
// inter_area += std::fabs(cv::contourArea(resvec)) + 0.5f *
// (cv::arcLength(resvec, true));
inter_area += GetContourArea<T>(resvec);
}
gpc::gpc_free_polygon(&poly1);
gpc::gpc_free_polygon(&poly2);
gpc::gpc_free_polygon(&respoly);
return inter_area;
}
} // namespace operators
} // namespace paddle
#endif
paddle/fluid/operators/detection/poly_util.h 0 → 100644
浏览文件 @ 23fc896b
/* 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. */
#ifndef POLY_UTIL_H_
#define POLY_UTIL_H_
#include <vector>
#include "paddle/fluid/framework/op_registry.h"
#include "paddle/fluid/operators/detection/gpc.h"
namespace paddle {
namespace operators {
template <class T>
class Point_ {
public:
// default constructor
Point_() {}
Point_(T _x, T _y) {}
Point_(const Point_& pt) {}
Point_& operator=(const Point_& pt);
// conversion to another data type
// template<typename _T> operator Point_<_T>() const;
// conversion to the old-style C structures
// operator Vec<T, 2>() const;
// checks whether the point is inside the specified rectangle
// bool inside(const Rect_<T>& r) const;
T x; //!< x coordinate of the point
T y; //!< y coordinate of the point
};
template <class T>
void Array2PointVec(const T*& box, const size_t box_size,
std::vector<Point_<T>>& vec);
template <class T>
void Array2Poly(const T*& box, const size_t box_size, gpc::gpc_polygon& poly);
template <class T>
void PointVec2Poly(const std::vector<Point_<T>>& vec, gpc::gpc_polygon& poly);
template <class T>
void Poly2PointVec(const gpc::gpc_vertex_list& contour,
std::vector<Point_<T>>& vec);
template <class T>
T GetContourArea(std::vector<Point_<T>>& vec);
template <class T>
T PolyArea(const T* box, const size_t box_size, const bool normalized);
template <class T>
T PolyOverlapArea(const T* box1, const T* box2, const size_t box_size,
const bool normalized);
} // namespace operators
} // namespace paddle
#include "paddle/fluid/operators/detection/poly_util.cc"
#endif // POLY_UTIL_H_
paddle/fluid/operators/detection/polygon_box_transform_op.cc
浏览文件 @ 23fc896b
......@@ -41,9 +41,9 @@ class PolygonBoxTransformCPUKernel : public framework::OpKernel<T> {
for (int id_w = 0; id_w < width; ++id_w) {
id = id_n * height * width + width * id_h + id_w;
if (id_n % 2 == 0) {
out_data[id] = id_w - in_data[id];
out_data[id] = id_w * 4 - in_data[id];
} else {
out_data[id] = id_h - in_data[id];
out_data[id] = id_h * 4 - in_data[id];
}
}
}
......
paddle/fluid/operators/detection/polygon_box_transform_op.cu
浏览文件 @ 23fc896b
......@@ -32,9 +32,9 @@ __global__ void PolygonBoxTransformKernel(const int n, const int h, const int w,
if (id_n < n && id_h < h && id_w < w) {
int id = id_n * h * w + w * id_h + id_w;
if (id_n % 2 == 0) {
output[id] = id_w - input[id];
output[id] = id_w * 4 - input[id];
} else {
output[id] = id_h - input[id];
output[id] = id_h * 4 - input[id];
}
}
}
......
paddle/fluid/operators/math/CMakeLists.txt
浏览文件 @ 23fc896b
......@@ -76,5 +76,5 @@ cc_test(concat_test SRCS concat_test.cc DEPS concat)
cc_test(cpu_vec_test SRCS cpu_vec_test.cc DEPS blas cpu_info)
cc_library(jit_kernel
SRCS jit_kernel.cc jit_kernel_blas.cc jit_kernel_exp.cc jit_kernel_lstm.cc
DEPS cpu_info cblas activation_functions)
DEPS cpu_info cblas)
cc_test(jit_kernel_test SRCS jit_kernel_test.cc DEPS jit_kernel)
paddle/fluid/operators/math/jit_kernel_lstm.cc
浏览文件 @ 23fc896b
......@@ -25,13 +25,18 @@ limitations under the License. */
namespace paddle {
namespace operators {
namespace math {
#ifdef __AVX__
namespace jitkernel {
namespace detail {
__m256 Exp(__m256 a);
} // namespace detail
#ifdef __AVX__
__m256 ExpAVX(__m256 x);
#endif
namespace jitkernel {
#ifdef __AVX2__
__m256 ExpAVX2(__m256 x);
#endif
} // namespace detail
namespace jit = platform::jit;
#ifdef __AVX__
......@@ -43,43 +48,72 @@ class AVXAct {
virtual __m256 Compute(__m256 x) const = 0;
};
template <act_type type>
template <act_type type, jit::cpu_isa_t isa>
class AVXActImpl : public AVXAct {
public:
__m256 Compute(__m256 x) const override { PADDLE_THROW("Unkown type!"); }
};
template <>
__m256 AVXActImpl<kSigmoid>::Compute(__m256 x) const {
__m256 ones = _mm256_set1_ps(1.0f);
x = _mm256_max_ps(x, _mm256_set1_ps(SIGMOID_THRESHOLD_MIN));
x = _mm256_min_ps(x, _mm256_set1_ps(SIGMOID_THRESHOLD_MAX));
x = _mm256_sub_ps(_mm256_set1_ps(0.0f), x);
x = detail::Exp(x);
x = _mm256_add_ps(ones, x);
return _mm256_div_ps(ones, x);
}
#define AVX_SIGMOID(isa, expisa) \
template <> \
__m256 AVXActImpl<kSigmoid, isa>::Compute(__m256 x) const { \
__m256 ones = _mm256_set1_ps(1.0f); \
x = _mm256_max_ps(x, _mm256_set1_ps(SIGMOID_THRESHOLD_MIN)); \
x = _mm256_min_ps(x, _mm256_set1_ps(SIGMOID_THRESHOLD_MAX)); \
x = _mm256_sub_ps(_mm256_set1_ps(0.0f), x); \
x = expisa(x); \
x = _mm256_add_ps(ones, x); \
return _mm256_div_ps(ones, x); \
}
template <>
__m256 AVXActImpl<kTanh>::Compute(__m256 x) const {
__m256 ones = _mm256_set1_ps(1.0f);
x = _mm256_mul_ps(_mm256_set1_ps(-2.0f), x);
x = _mm256_min_ps(x, _mm256_set1_ps(EXP_MAX_INPUT));
x = detail::Exp(x);
x = _mm256_add_ps(ones, x);
x = _mm256_div_ps(_mm256_set1_ps(2.0f), x);
return _mm256_sub_ps(x, ones);
}
#define AVX_TANH(isa, expisa) \
template <> \
__m256 AVXActImpl<kTanh, isa>::Compute(__m256 x) const { \
__m256 ones = _mm256_set1_ps(1.0f); \
x = _mm256_mul_ps(_mm256_set1_ps(-2.0f), x); \
x = _mm256_min_ps(x, _mm256_set1_ps(EXP_MAX_INPUT)); \
x = expisa(x); \
x = _mm256_add_ps(ones, x); \
x = _mm256_div_ps(_mm256_set1_ps(2.0f), x); \
return _mm256_sub_ps(x, ones); \
}
template <>
__m256 AVXActImpl<kRelu>::Compute(__m256 x) const {
return _mm256_max_ps(x, _mm256_setzero_ps());
}
#define AVX_RELU(isa) \
template <> \
__m256 AVXActImpl<kRelu, isa>::Compute(__m256 x) const { \
return _mm256_max_ps(x, _mm256_setzero_ps()); \
}
#define AVX_IDENTITY(isa) \
template <> \
__m256 AVXActImpl<kIdentity, isa>::Compute(__m256 x) const { \
return x; \
}
#define FOR_EACH_AVX_ISA(macro_) \
macro_(jit::avx); \
macro_(jit::avx2); \
macro_(jit::avx512f)
FOR_EACH_AVX_ISA(AVX_RELU);
FOR_EACH_AVX_ISA(AVX_IDENTITY);
AVX_SIGMOID(jit::avx, detail::ExpAVX);
AVX_TANH(jit::avx, detail::ExpAVX);
#ifdef __AVX2__
AVX_SIGMOID(jit::avx2, detail::ExpAVX2);
AVX_SIGMOID(jit::avx512f, detail::ExpAVX2);
AVX_TANH(jit::avx2, detail::ExpAVX2);
AVX_TANH(jit::avx512f, detail::ExpAVX2);
#endif
#undef FOR_EACH_AVX_ISA
#undef AVX_IDENTITY
#undef AVX_RELU
#undef AVX_TANH
#undef AVX_SIGMOID
template <>
__m256 AVXActImpl<kIdentity>::Compute(__m256 x) const {
return x;
}
#endif
template <typename T>
......@@ -119,23 +153,6 @@ class LSTMKernelImpl : public LSTMKernel<T> {
act_cell_d_ = GetActKernel<T>(act_cell, d);
vmul_d_ = KernelPool::Instance().template Get<VMulKernel<T>>(d);
vadd_d_ = KernelPool::Instance().template Get<VAddKernel<T>>(d);
#ifdef __AVX__
auto GetAVXAct = [&](const std::string& type) -> std::unique_ptr<AVXAct> {
if (type == "sigmoid") {
return std::unique_ptr<AVXAct>(new AVXActImpl<kSigmoid>());
} else if (type == "relu") {
return std::unique_ptr<AVXAct>(new AVXActImpl<kRelu>());
} else if (type == "tanh") {
return std::unique_ptr<AVXAct>(new AVXActImpl<kTanh>());
} else if (type == "identity" || type == "") {
return std::unique_ptr<AVXAct>(new AVXActImpl<kIdentity>());
}
PADDLE_THROW("Not support type: %s", type);
};
avx_act_gate_ = GetAVXAct(act_gate);
avx_act_cand_ = GetAVXAct(act_cand);
avx_act_cell_ = GetAVXAct(act_cell);
#endif
}
void ComputeCtHt(T* gates, const T* ct_1, T* ct, T* ht, const T* wp_data,
......@@ -175,26 +192,61 @@ class LSTMKernelImpl : public LSTMKernel<T> {
#endif
};
#define INTRI8_FLOAT(isa) \
template <> \
void LSTMKernelImpl<float, isa, kEQ8>::ComputeCtHt( \
float* gates, const float* ct_1, float* ct, float* ht, \
const float* wp_data, float* checked) const { \
/* gates: W_ch, W_ih, W_fh, W_oh */ \
__m256 c, i, f, o; \
c = _mm256_loadu_ps(gates); \
i = _mm256_loadu_ps(gates + 8); \
f = _mm256_loadu_ps(gates + 16); \
o = _mm256_loadu_ps(gates + 24); \
/* C_t = C_t-1 * fgated + cand_gated * igated*/ \
c = _mm256_mul_ps(avx_act_cand_->Compute(c), avx_act_gate_->Compute(i)); \
i = _mm256_loadu_ps(ct_1); \
f = _mm256_mul_ps(i, avx_act_gate_->Compute(f)); \
f = _mm256_add_ps(c, f); \
_mm256_storeu_ps(ct, f); \
/* H_t = act_cell(C_t) * ogated */ \
o = _mm256_mul_ps(avx_act_cell_->Compute(f), avx_act_gate_->Compute(o)); \
_mm256_storeu_ps(ht, o); \
#define INTRI8_FLOAT(isa) \
template <> \
LSTMKernelImpl<float, isa, kEQ8>::LSTMKernelImpl( \
const std::string& act_gate, const std::string& act_cand, \
const std::string& act_cell, int d) \
: LSTMKernel<float>() { \
auto GetAVXAct = [&](const std::string& type) -> std::unique_ptr<AVXAct> { \
if (type == "sigmoid") { \
return std::unique_ptr<AVXAct>(new AVXActImpl<kSigmoid, isa>()); \
} else if (type == "relu") { \
return std::unique_ptr<AVXAct>(new AVXActImpl<kRelu, isa>()); \
} else if (type == "tanh") { \
return std::unique_ptr<AVXAct>(new AVXActImpl<kTanh, isa>()); \
} else if (type == "identity" || type == "") { \
return std::unique_ptr<AVXAct>(new AVXActImpl<kIdentity, isa>()); \
} \
PADDLE_THROW("Not support type: %s", type); \
}; \
avx_act_gate_ = GetAVXAct(act_gate); \
avx_act_cand_ = GetAVXAct(act_cand); \
avx_act_cell_ = GetAVXAct(act_cell); \
} \
template <> \
void LSTMKernelImpl<float, isa, kEQ8>::ComputeCtHt( \
float* gates, const float* ct_1, float* ct, float* ht, \
const float* wp_data, float* checked) const { \
/* gates: W_ch, W_ih, W_fh, W_oh */ \
__m256 c, i, f, o; \
c = _mm256_loadu_ps(gates); \
i = _mm256_loadu_ps(gates + 8); \
f = _mm256_loadu_ps(gates + 16); \
o = _mm256_loadu_ps(gates + 24); \
/* C_t = C_t-1 * fgated + cand_gated * igated*/ \
c = _mm256_mul_ps(avx_act_cand_->Compute(c), avx_act_gate_->Compute(i)); \
i = _mm256_loadu_ps(ct_1); \
f = _mm256_mul_ps(i, avx_act_gate_->Compute(f)); \
f = _mm256_add_ps(c, f); \
_mm256_storeu_ps(ct, f); \
/* H_t = act_cell(C_t) * ogated */ \
o = _mm256_mul_ps(avx_act_cell_->Compute(f), avx_act_gate_->Compute(o)); \
_mm256_storeu_ps(ht, o); \
} \
template <> \
void LSTMKernelImpl<float, isa, kEQ8>::ComputeC1H1( \
float* gates, float* ct, float* ht, const float* wp_data) const { \
__m256 c, i, o; \
c = _mm256_loadu_ps(gates); \
i = _mm256_loadu_ps(gates + 8); \
o = _mm256_loadu_ps(gates + 24); \
/* C_t = igated * cgated*/ \
c = _mm256_mul_ps(avx_act_gate_->Compute(i), avx_act_cand_->Compute(c)); \
_mm256_storeu_ps(ct, c); \
/* H_t = act_cell(C_t) * ogated */ \
o = _mm256_mul_ps(avx_act_cell_->Compute(c), avx_act_gate_->Compute(o)); \
_mm256_storeu_ps(ht, o); \
}
// TODO(TJ): optimize keq16
......
paddle/fluid/operators/roi_pool_op.cc
浏览文件 @ 23fc896b
......@@ -174,4 +174,4 @@ REGISTER_OP_CPU_KERNEL(
REGISTER_OP_CPU_KERNEL(
roi_pool_grad,
ops::CPUROIPoolGradOpKernel<paddle::platform::CPUDeviceContext, float>,
ops::CPUROIPoolOpKernel<paddle::platform::CPUDeviceContext, double>);
ops::CPUROIPoolGradOpKernel<paddle::platform::CPUDeviceContext, double>);
paddle/fluid/operators/roi_pool_op.cu
浏览文件 @ 23fc896b
......@@ -249,4 +249,4 @@ REGISTER_OP_CUDA_KERNEL(
REGISTER_OP_CUDA_KERNEL(
roi_pool_grad,
ops::GPUROIPoolGradOpKernel<paddle::platform::CUDADeviceContext, float>,
ops::GPUROIPoolOpKernel<paddle::platform::CUDADeviceContext, double>);
ops::GPUROIPoolGradOpKernel<paddle::platform::CUDADeviceContext, double>);
python/paddle/fluid/layers/nn.py
浏览文件 @ 23fc896b
此差异已折叠。
python/paddle/fluid/nets.py
浏览文件 @ 23fc896b
......@@ -64,23 +64,33 @@ def simple_img_conv_pool(input,
average-pooling. Default :math:`max`.
global_pooling (bool): Whether to use the global pooling. If global_pooling = true,
pool_size and pool_padding while be ignored. Default False
conv_stride (int|list|tuple): The stride size of the Conv2d Layer. If stride is a
conv_stride (int|list|tuple): The stride size of the conv2d Layer. If stride is a
list or tuple, it must contain two integers, (conv_stride_H, conv_stride_W). Otherwise,
the conv_stride_H = conv_stride_W = conv_stride. Default: conv_stride = 1.
conv_padding (int|list|tuple): The padding size of the Conv2d Layer. If padding is
conv_padding (int|list|tuple): The padding size of the conv2d Layer. If padding is
a list or tuple, it must contain two integers, (conv_padding_H, conv_padding_W).
Otherwise, the conv_padding_H = conv_padding_W = conv_padding. Default: conv_padding = 0.
conv_dilation (int|list|tuple): The dilation size of the Conv2d Layer. If dilation is
conv_dilation (int|list|tuple): The dilation size of the conv2d Layer. If dilation is
a list or tuple, it must contain two integers, (conv_dilation_H, conv_dilation_W).
Otherwise, the conv_dilation_H = conv_dilation_W = conv_dilation. Default: conv_dilation = 1.
conv_groups (int): The groups number of the Conv2d Layer. According to grouped
conv_groups (int): The groups number of the conv2d Layer. According to grouped
convolution in Alex Krizhevsky's Deep CNN paper: when group=2,
the first half of the filters is only connected to the first half
of the input channels, while the second half of the filters is only
connected to the second half of the input channels. Default: groups=1
param_attr (ParamAttr): The parameters to the Conv2d Layer. Default: None
bias_attr (ParamAttr): Bias parameter for the Conv2d layer. Default: None
act (str): Activation type for Conv2d. Default: None
connected to the second half of the input channels. Default: groups=1.
param_attr (ParamAttr|None): The parameter attribute for learnable parameters/weights
of conv2d. If it is set to None or one attribute of ParamAttr, conv2d
will create ParamAttr as param_attr. If the Initializer of the param_attr
is not set, the parameter is initialized with :math:`Normal(0.0, std)`,
and the :math:`std` is :math:`(\\frac{2.0 }{filter\_elem\_num})^{0.5}`.
Default: None.
bias_attr (ParamAttr|bool|None): The parameter attribute for the bias of conv2d.
If it is set to False, no bias will be added to the output units.
If it is set to None or one attribute of ParamAttr, conv2d
will create ParamAttr as bias_attr. If the Initializer of the bias_attr
is not set, the bias is initialized zero. Default: None.
act (str): Activation type for conv2d, if it is set to None, activation is not
appended. Default: None.
use_cudnn (bool): Use cudnn kernel or not, it is valid only when the cudnn
library is installed. Default: True
......
python/paddle/fluid/regularizer.py
浏览文件 @ 23fc896b
......@@ -237,6 +237,7 @@ class L1DecayRegularizer(WeightDecayRegularizer):
'Ids': idx},
outputs={'Out': decay},
attrs={'is_sparse': True})
param = decay
# Append sign op
block.append_op(
......
python/paddle/fluid/tests/unittests/test_polygon_box_transform.py
浏览文件 @ 23fc896b
......@@ -37,7 +37,7 @@ def PolygonBoxRestore(input):
indexes = indexes.repeat(
[batch_size], axis=0) # [batch_size, geo_channels/2, 2, h, w]
return indexes.reshape(
input.shape) - input # [batch_size, geo_channels, h, w]
input.shape) * 4 - input # [batch_size, geo_channels, h, w]
class TestPolygonBoxRestoreOp(OpTest):
......
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