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PaddleDetection
提交 db5e3dd7 编写于 作者: F fengjiayi
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Merge branch 'develop' of https://github.com/PaddlePaddle/Paddle into fix_CudnnHolder_bug

上级 7b577b92 90b5be85
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Dockerfile
浏览文件 @ db5e3dd7
......@@ -53,7 +53,7 @@ RUN curl -s -q https://glide.sh/get | sh
# and its size is only one-third of the official one.
# 2. Manually add ~IPluginFactory() in IPluginFactory class of NvInfer.h, otherwise, it couldn't work in paddle.
# See https://github.com/PaddlePaddle/Paddle/issues/10129 for details.
RUN wget -qO- http://paddlepaddledeps.bj.bcebos.com/TensorRT-4.0.0.3.Ubuntu-16.04.4.x86_64-gnu.cuda-8.0.cudnn7.0.tar.gz | \
RUN wget -qO- http://paddlepaddledeps.cdn.bcebos.com/TensorRT-4.0.0.3.Ubuntu-16.04.4.x86_64-gnu.cuda-8.0.cudnn7.0.tar.gz | \
tar -xz -C /usr/local && \
cp -rf /usr/local/TensorRT/include /usr && \
cp -rf /usr/local/TensorRT/lib /usr
......
cmake/inference_lib.cmake
浏览文件 @ db5e3dd7
......@@ -128,16 +128,13 @@ set(src_dir "${PADDLE_SOURCE_DIR}/paddle/fluid")
set(dst_dir "${FLUID_INSTALL_DIR}/paddle/fluid")
set(module "framework")
if (NOT WIN32)
copy(framework_lib DEPS framework_py_proto
SRCS ${src_dir}/${module}/*.h ${src_dir}/${module}/details/*.h ${PADDLE_BINARY_DIR}/paddle/fluid/framework/framework.pb.h
DSTS ${dst_dir}/${module} ${dst_dir}/${module}/details ${dst_dir}/${module}
)
else()
copy(framework_lib
set(framework_lib_deps framework_py_proto)
endif(NOT WIN32)
copy(framework_lib DEPS ${framework_lib_deps}
SRCS ${src_dir}/${module}/*.h ${src_dir}/${module}/details/*.h ${PADDLE_BINARY_DIR}/paddle/fluid/framework/framework.pb.h
DSTS ${dst_dir}/${module} ${dst_dir}/${module}/details ${dst_dir}/${module}
${src_dir}/${module}/ir/*.h
DSTS ${dst_dir}/${module} ${dst_dir}/${module}/details ${dst_dir}/${module} ${dst_dir}/${module}/ir
)
endif(NOT WIN32)
set(module "memory")
copy(memory_lib
......@@ -161,7 +158,8 @@ set(module "inference")
copy(inference_lib DEPS ${inference_deps}
SRCS ${src_dir}/${module}/*.h ${PADDLE_BINARY_DIR}/paddle/fluid/inference/libpaddle_fluid.*
${src_dir}/${module}/api/paddle_inference_api.h ${src_dir}/${module}/api/demo_ci
DSTS ${dst_dir}/${module} ${dst_dir}/${module} ${dst_dir}/${module} ${dst_dir}/${module}
${PADDLE_BINARY_DIR}/paddle/fluid/inference/api/paddle_inference_pass.h
DSTS ${dst_dir}/${module} ${dst_dir}/${module} ${dst_dir}/${module} ${dst_dir}/${module} ${dst_dir}/${module}
)
set(module "platform")
......
doc/fluid/new_docs/beginners_guide/basics/machine_translation/README.cn.md
浏览文件 @ db5e3dd7
......@@ -60,6 +60,7 @@
图3. 编码器-解码器框架
</div>
<a name="编码器"></a>
#### 编码器
编码阶段分为三步：
......@@ -81,7 +82,7 @@
机器翻译任务的训练过程中，解码阶段的目标是最大化下一个正确的目标语言词的概率。思路是：
1. 每一个时刻，根据源语言句子的编码信息（又叫上下文向量，context vector）`$c$`、真实目标语言序列的第`$i$`个词`$u_i$`和`$i$`时刻RNN的隐层状态`$z_i$`，计算出下一个隐层状态`$z_{i+1}$`。计算公式如下：
$$z_{i+1}=\phi_{\theta '} \left ( c,u_i,z_i \right )$$
其中`$\phi _{\theta '}$`是一个非线性激活函数；`$c=q\mathbf{h}$`是源语言句子的上下文向量，在不使用[注意力机制](#注意力机制)时，如果[编码器](#编码器)的输出是源语言句子编码后的最后一个元素，则可以定义`$c=h_T$`；`$u_i$`是目标语言序列的第`$i$`个单词，`$u_0$`是目标语言序列的开始标记`<s>`，表示解码开始；`$z_i$`是`$i$`时刻解码RNN的隐层状态，`$z_0$`是一个全零的向量。
其中`$\phi _{\theta '}$`是一个非线性激活函数；`$c=q\mathbf{h}$`是源语言句子的上下文向量，在不使用注意力机制时，如果[编码器](#编码器)的输出是源语言句子编码后的最后一个元素，则可以定义`$c=h_T$`；`$u_i$`是目标语言序列的第`$i$`个单词，`$u_0$`是目标语言序列的开始标记`<s>`，表示解码开始；`$z_i$`是`$i$`时刻解码RNN的隐层状态，`$z_0$`是一个全零的向量。
2. 将`$z_{i+1}$`通过`softmax`归一化，得到目标语言序列的第`$i+1$`个单词的概率分布`$p_{i+1}$`。概率分布公式如下：
$$p\left ( u_{i+1}|u_{&lt;i+1},\mathbf{x} \right )=softmax(W_sz_{i+1}+b_z)$$
......@@ -93,6 +94,7 @@ $$p\left ( u_{i+1}|u_{&lt;i+1},\mathbf{x} \right )=softmax(W_sz_{i+1}+b_z)$$
机器翻译任务的生成过程，通俗来讲就是根据预先训练的模型来翻译源语言句子。生成过程中的解码阶段和上述训练过程的有所差异，具体介绍请见[柱搜索算法](#柱搜索算法)。
<a name="柱搜索算法"></a>
### 柱搜索算法
柱搜索（[beam search](http://en.wikipedia.org/wiki/Beam_search)）是一种启发式图搜索算法，用于在图或树中搜索有限集合中的最优扩展节点，通常用在解空间非常大的系统（如机器翻译、语音识别）中，原因是内存无法装下图或树中所有展开的解。如在机器翻译任务中希望翻译“`<s>你好<e>`”，就算目标语言字典中只有3个词（`<s>`, `<e>`, `hello`），也可能生成无限句话（`hello`循环出现的次数不定），为了找到其中较好的翻译结果，我们可采用柱搜索算法。
......
doc/fluid/new_docs/beginners_guide/basics/understand_sentiment/README.cn.md
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......@@ -149,6 +149,8 @@ def convolution_net(data, input_dim, class_dim, emb_dim, hid_dim):
网络的输入`input_dim`表示的是词典的大小，`class_dim`表示类别数。这里，我们使用[`sequence_conv_pool`](https://github.com/PaddlePaddle/Paddle/blob/develop/python/paddle/trainer_config_helpers/networks.py) API实现了卷积和池化操作。
<a name="栈值双向LSTM"></a>
### 栈式双向LSTM
栈式双向神经网络`stacked_lstm_net`的代码片段如下：
......
doc/fluid/new_docs/beginners_guide/basics/word2vec/README.cn.md
浏览文件 @ db5e3dd7
......@@ -50,7 +50,7 @@ similarity: -0.0997506977351
```
以上结果可以通过运行`calculate_dis.py`, 加载字典里的单词和对应训练特征结果得到，我们将在[应用模型](#应用模型)中详细描述用法。
以上结果可以通过运行`calculate_dis.py`, 加载字典里的单词和对应训练特征结果得到，我们将在[模型应用](#模型应用)中详细描述用法。
## 模型概览
......@@ -189,6 +189,7 @@ dream that one day <e>
最后，每个输入会按其单词次在字典里的位置，转化成整数的索引序列，作为PaddlePaddle的输入。
<a name="训练模型"></a>
## 编程实现
本配置的模型结构如下图所示：
......@@ -349,6 +350,7 @@ Step 20: Average Cost 5.766995
...
```
<a name="模型应用"></a>
## 模型应用
在模型训练后，我们可以用它做一些预测。
......
doc/fluid/new_docs/beginners_guide/quick_start/recognize_digits/README.cn.md
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......@@ -102,7 +102,7 @@ Softmax回归模型采用了最简单的两层神经网络，即只有输入层
池化是非线性下采样的一种形式，主要作用是通过减少网络的参数来减小计算量，并且能够在一定程度上控制过拟合。通常在卷积层的后面会加上一个池化层。池化包括最大池化、平均池化等。其中最大池化是用不重叠的矩形框将输入层分成不同的区域，对于每个矩形框的数取最大值作为输出层，如图6所示。
更详细的关于卷积神经网络的具体知识可以参考[斯坦福大学公开课]( http://cs231n.github.io/convolutional-networks/ )和[图像分类](https://github.com/PaddlePaddle/book/blob/develop/image_classification/README.md)教程。
更详细的关于卷积神经网络的具体知识可以参考[斯坦福大学公开课]( http://cs231n.github.io/convolutional-networks/ )和[图像分类]( https://github.com/PaddlePaddle/book/tree/develop/03.image_classification )教程。
### 常见激活函数介绍
- sigmoid激活函数： $ f(x) = sigmoid(x) = \frac{1}{1+e^{-x}} $
......
doc/fluid/new_docs/user_guides/howto/debug/visualdl.md
浏览文件 @ db5e3dd7
......@@ -149,7 +149,7 @@ python setup.py bdist_wheel
pip install --upgrade dist/visualdl-*.whl
```
如果打包和安装遇到其他问题，不安装只想运行Visual DL可以看[这里](https://github.com/PaddlePaddle/VisualDL/blob/develop/docs/how_to_dev_frontend_en.md)
如果打包和安装遇到其他问题，不安装只想运行Visual DL可以看[这里](https://github.com/PaddlePaddle/VisualDL/blob/develop/docs/develop/how_to_dev_frontend_cn.md)
## SDK
......
paddle/fluid/framework/ir/CMakeLists.txt
浏览文件 @ db5e3dd7
set(pass_file ${PADDLE_BINARY_DIR}/paddle/fluid/inference/api/paddle_inference_pass.h)
file(WRITE ${pass_file} "// Generated by the paddle/fluid/framework/ir/CMakeLists.txt. DO NOT EDIT!\n\n")
file(APPEND ${pass_file} "\#include \"paddle/fluid/framework/ir/pass.h\"\n")
function(pass_library TARGET)
set(options "")
set(oneValueArgs "")
set(multiValueArgs SRCS DEPS)
cmake_parse_arguments(op_library "${options}" "${oneValueArgs}" "${multiValueArgs}" ${ARGN})
cc_library(${TARGET} SRCS ${TARGET}.cc DEPS graph_pattern_detector pass)
file(APPEND ${pass_file} "USE_PASS(${TARGET});\n")
set(PASS_LIBRARY ${TARGET} ${PASS_LIBRARY} PARENT_SCOPE)
endfunction()
cc_library(node SRCS node.cc DEPS proto_desc)
cc_library(graph SRCS graph.cc DEPS node)
cc_library(graph_helper SRCS graph_helper.cc DEPS graph)
cc_library(pass SRCS pass.cc DEPS graph node graph_helper)
cc_library(graph_viz_pass SRCS graph_viz_pass.cc DEPS graph pass graph_helper)
cc_library(graph_to_program_pass SRCS graph_to_program_pass.cc DEPS graph pass 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(fc_fuse_pass SRCS fc_fuse_pass.cc DEPS graph graph_pattern_detector)
cc_library(attention_lstm_fuse_pass SRCS attention_lstm_fuse_pass.cc DEPS graph graph_pattern_detector)
cc_library(infer_clean_graph_pass SRCS infer_clean_graph_pass.cc DEPS graph pass)
cc_library(fc_lstm_fuse_pass SRCS fc_lstm_fuse_pass.cc DEPS graph graph_pattern_detector)
cc_library(seq_concat_fc_fuse_pass SRCS seq_concat_fc_fuse_pass.cc DEPS graph graph_pattern_detector)
pass_library(graph_to_program_pass)
pass_library(graph_viz_pass)
pass_library(fc_fuse_pass)
pass_library(attention_lstm_fuse_pass)
pass_library(infer_clean_graph_pass)
pass_library(fc_lstm_fuse_pass)
pass_library(seq_concat_fc_fuse_pass)
set(GLOB_PASS_LIB ${PASS_LIBRARY} CACHE INTERNAL "Global PASS library")
cc_test(pass_test SRCS pass_test.cc DEPS graph pass graph_helper)
cc_test(graph_test SRCS graph_test.cc DEPS graph graph_helper op_registry)
cc_test(graph_helper_test SRCS graph_helper_test.cc DEPS graph graph_helper op_registry)
cc_test(graph_to_program_pass_test SRCS graph_to_program_pass_test.cc DEPS graph_to_program_pass)
cc_test(test_graph_pattern_detector SRCS graph_pattern_detector_tester.cc DEPS graph_pattern_detector)
cc_test(test_fc_fuse_pass SRCS fc_fuse_pass_tester.cc DEPS fc_fuse_pass graph_pattern_detector graph pass graph_traits framework_proto)
cc_test(test_fc_fuse_pass SRCS fc_fuse_pass_tester.cc DEPS fc_fuse_pass framework_proto)
paddle/fluid/framework/ir/attention_lstm_fuse_pass.cc
浏览文件 @ db5e3dd7
......@@ -99,17 +99,13 @@ void FindWhileOp(Graph* graph) {
auto* cell_init = graph->RetriveNode(6);
auto* hidden_init = graph->RetriveNode(8);
#define LINK_TO(node0, node1) \
node0->outputs.push_back(node1); \
node1->inputs.push_back(node0);
auto* lstm_op = graph->CreateOpNode(&op_desc);
PrepareParameters(graph, param);
LINK_TO(X, lstm_op);
LINK_TO(cell_init, lstm_op);
LINK_TO(hidden_init, lstm_op);
LINK_TO(lstm_op, LSTMOUT);
IR_NODE_LINK_TO(X, lstm_op);
IR_NODE_LINK_TO(cell_init, lstm_op);
IR_NODE_LINK_TO(hidden_init, lstm_op);
IR_NODE_LINK_TO(lstm_op, LSTMOUT);
GraphSafeRemoveNodes(graph, marked_nodes);
}
......
paddle/fluid/framework/ir/fc_fuse_pass.cc
浏览文件 @ db5e3dd7
......@@ -21,74 +21,26 @@ namespace paddle {
namespace framework {
namespace ir {
bool VarOutLinksToOp(Node* node, const std::string& op_type) {
for (auto* out : node->outputs) {
if (out->IsOp() && out->Op()->Type() == op_type) {
return true;
}
}
return false;
}
void BuildFCPattern(PDPattern* pattern) {
// Create Operators
auto* mul_op = pattern->NewNode("mul")->assert_is_op("mul");
auto* elementwise_add_op =
pattern->NewNode("elementwise_add")->assert_is_op("elementwise_add");
// Create variables
// w
auto* mul_weight_var = pattern->NewNode("mul_weight")
->AsInput()
->assert_is_op_nth_input("mul", "Y", 0);
// x
auto* mul_tmp_var = pattern->NewNode("mul_tmp_var")
->AsInput()
->assert_is_op_nth_input("mul", "X", 0);
// intermediate variable, will be removed in the IR after fuse.
auto* mul_out_var = pattern->NewNode("mul_out")
->AsIntermediate()
->assert_is_only_output_of_op("mul")
->assert_is_op_input("elementwise_add");
// bias
auto* elementwise_add_tmp_var = pattern->NewNode("elementwise_add_tmpvar")
->assert_is_op_input("elementwise_add")
->AsInput();
// output
auto* elementwise_add_out_var = pattern->NewNode("elementwise_add_out")
->AsOutput()
->assert_is_op_output("elementwise_add");
mul_op->LinksFrom({mul_weight_var, mul_tmp_var}).LinksTo({mul_out_var});
elementwise_add_op->LinksFrom({mul_out_var, elementwise_add_tmp_var})
.LinksTo({elementwise_add_out_var});
}
// Replace the node `from` in the links to `to`
bool LinksReplace(std::vector<Node*>* links, Node* from, Node* to) {
for (auto*& n : *links) {
if (n == from) {
n = to;
return true;
}
}
return false;
}
std::unique_ptr<ir::Graph> FCFusePass::ApplyImpl(
std::unique_ptr<ir::Graph> graph) const {
PADDLE_ENFORCE(graph.get());
FusePassBase::Init("fc", graph.get());
FusePassBase::Init("fc_fuse", graph.get());
std::unordered_set<Node*> nodes2delete;
GraphPatternDetector gpd;
BuildFCPattern(gpd.mutable_pattern());
// BuildFCPattern(gpd.mutable_pattern());
auto* x = gpd.mutable_pattern()
->NewNode("fc_fuse/x")
->AsInput()
->assert_is_op_input("mul", "X");
patterns::FC(gpd.mutable_pattern(), "fc_fuse", x, true /*with bias*/);
#define GET_NODE(id) \
PADDLE_ENFORCE(subgraph.count(gpd.pattern().RetrieveNode(#id)), \
PADDLE_ENFORCE(subgraph.count(gpd.pattern().RetrieveNode("fc_fuse/" #id)), \
"pattern has no Node called %s", #id); \
auto* id = subgraph.at(gpd.pattern().RetrieveNode(#id)); \
PADDLE_ENFORCE_NOT_NULL(id, "subgraph has no node %s", #id);
auto* id = subgraph.at(gpd.pattern().RetrieveNode("fc_fuse/" #id)); \
PADDLE_ENFORCE_NOT_NULL(id, "subgraph has no node %s", "fc_fuse/" #id);
int found_fc_count = 0;
auto handler = [&](const GraphPatternDetector::subgraph_t& subgraph,
......@@ -98,10 +50,10 @@ std::unique_ptr<ir::Graph> FCFusePass::ApplyImpl(
// scenerio.
// FC's fusion is simple, just op fuse, no need to process the
// parameters.
GET_NODE(mul_tmp_var); // x
GET_NODE(mul_weight); // Y
GET_NODE(elementwise_add_tmpvar); // bias
GET_NODE(elementwise_add_out); // Out
GET_NODE(x); // x
GET_NODE(w); // Y
GET_NODE(fc_bias); // bias
GET_NODE(fc_out); // Out
GET_NODE(mul); // MUL op
GET_NODE(elementwise_add); // ELEMENT_ADD op
GET_NODE(mul_out); // tmp
......@@ -109,32 +61,22 @@ std::unique_ptr<ir::Graph> FCFusePass::ApplyImpl(
// Create an FC Node.
OpDesc desc;
std::string fc_x_in = mul_tmp_var->Name();
std::string fc_Y_in = mul_weight->Name();
std::string fc_bias_in = elementwise_add_tmpvar->Name();
std::string fc_out = elementwise_add_out->Name();
std::string fc_x_in = x->Name();
std::string fc_Y_in = w->Name();
std::string fc_bias_in = fc_bias->Name();
std::string fc_out_out = fc_out->Name();
desc.SetInput("Input", std::vector<std::string>({fc_x_in}));
desc.SetInput("W", std::vector<std::string>({fc_Y_in}));
desc.SetInput("Bias", std::vector<std::string>({fc_bias_in}));
desc.SetOutput("Out", std::vector<std::string>({fc_out}));
desc.SetOutput("Out", std::vector<std::string>({fc_out_out}));
desc.SetType("fc");
auto fc_node = g->CreateOpNode(&desc); // OpDesc will be copied.
fc_node->inputs =
std::vector<Node*>({mul_tmp_var, mul_weight, elementwise_add_tmpvar});
fc_node->outputs.push_back(elementwise_add_out);
// Update link relatons
PADDLE_ENFORCE(LinksReplace(&mul_tmp_var->outputs, mul, fc_node));
PADDLE_ENFORCE(LinksReplace(&mul_weight->outputs, mul, fc_node));
PADDLE_ENFORCE(LinksReplace(&elementwise_add_tmpvar->outputs,
elementwise_add, fc_node));
PADDLE_ENFORCE(
LinksReplace(&elementwise_add_out->inputs, elementwise_add, fc_node));
GraphSafeRemoveNodes(graph.get(), {mul, elementwise_add, mul_out});
// Drop old nodes
graph->RemoveNode(mul);
graph->RemoveNode(elementwise_add);
graph->RemoveNode(mul_out); // tmp variable
IR_NODE_LINK_TO(x, fc_node);
IR_NODE_LINK_TO(w, fc_node);
IR_NODE_LINK_TO(fc_bias, fc_node);
IR_NODE_LINK_TO(fc_node, fc_out);
found_fc_count++;
};
......
paddle/fluid/framework/ir/fc_lstm_fuse_pass.cc
浏览文件 @ db5e3dd7
......@@ -121,15 +121,11 @@ int BuildFusion(Graph* graph, const std::string& name_scope, Scope* scope,
#undef TMP_NEW
#undef TMP_NAME
#define LINK_TO(a, b) \
a->outputs.push_back(b); \
b->inputs.push_back(a);
LINK_TO(input_n, op);
LINK_TO(weight_x_n, op);
LINK_TO(weight_h_n, op);
LINK_TO(bias_n, op);
LINK_TO(op, hidden_n);
#undef LINK_TO
IR_NODE_LINK_TO(input_n, op);
IR_NODE_LINK_TO(weight_x_n, op);
IR_NODE_LINK_TO(weight_h_n, op);
IR_NODE_LINK_TO(bias_n, op);
IR_NODE_LINK_TO(op, hidden_n);
return op;
};
......
paddle/fluid/framework/ir/graph_pattern_detector.cc
浏览文件 @ db5e3dd7
......@@ -111,6 +111,11 @@ bool GraphPatternDetector::MarkPDNodesInGraph(const ir::Graph& graph) {
return false;
}
}
for (auto& item : pdnodes2nodes_) {
for (auto& n : item.second) {
GetMarkedNodes(const_cast<Graph*>(&graph)).insert(n);
}
}
VLOG(3) << pdnodes2nodes_.size() << " nodes marked";
return !pdnodes2nodes_.empty();
......@@ -278,7 +283,7 @@ void GraphPatternDetector::RemoveOverlappedMatch(
for (const auto& subgraph : *subgraphs) {
bool valid = true;
for (auto& item : subgraph) {
if (node_set.count(item.second)) {
if (item.first->IsIntermediate() && node_set.count(item.second)) {
valid = false;
break;
}
......@@ -334,22 +339,22 @@ PDNode& PDNode::LinksFrom(const std::vector<PDNode*>& others) {
}
PDNode* PDNode::assert_is_op() {
asserts_.emplace_back([this](Node* x) { return x && x->IsOp(); });
asserts_.emplace_back([](Node* x) { return x && x->IsOp(); });
return this;
}
PDNode* PDNode::assert_is_op(const std::string& op_type) {
asserts_.emplace_back([this, op_type](Node* x) {
asserts_.emplace_back([op_type](Node* x) {
return x && x->IsOp() && x->Op()->Type() == op_type;
});
return this;
}
PDNode* PDNode::assert_is_var() {
asserts_.emplace_back([this](Node* x) { return x && x->IsVar(); });
asserts_.emplace_back([](Node* x) { return x && x->IsVar(); });
return this;
}
PDNode* PDNode::assert_var_not_persistable() {
assert_is_var();
asserts_.emplace_back([this](Node* x) { return !x->Var()->Persistable(); });
asserts_.emplace_back([](Node* x) { return !x->Var()->Persistable(); });
return this;
}
PDNode* PDNode::assert_is_persistable_var() {
......@@ -491,16 +496,18 @@ void GraphSafeRemoveNodes(Graph* graph,
for (auto it = node->inputs.begin(); it != node->inputs.end();) {
if (nodes.count(*it)) {
it = const_cast<Node*>(node)->inputs.erase(it);
} else
} else {
it++;
}
}
for (auto it = node->outputs.begin(); it != node->outputs.end();) {
if (nodes.count(*it)) {
it = const_cast<Node*>(node)->outputs.erase(it);
} else
} else {
it++;
}
}
}
}
bool VarLinksFromOp(Node* node, const std::string& op_type) {
for (auto* out : node->inputs) {
......
paddle/fluid/framework/ir/graph_pattern_detector.h
浏览文件 @ db5e3dd7
......@@ -245,6 +245,8 @@ class GraphPatternDetector {
void UniquePatterns(std::vector<subgraph_t>* subgraphs);
// Remove overlapped match subgraphs, when overlapped, keep the previous one.
// The intermediate PDNodes will be removed, so can't shared by multiple
// patterns.
void RemoveOverlappedMatch(std::vector<subgraph_t>* subgraphs);
// Validate whether the intermediate nodes are linked by external nodes.
......@@ -295,6 +297,10 @@ PDNode* LSTM(PDPattern* pattern, const std::string& name_scope, PDNode* x);
} // namespace patterns
#define IR_NODE_LINK_TO(a, b) \
a->outputs.push_back(b); \
b->inputs.push_back(a);
} // namespace ir
} // namespace framework
} // namespace paddle
paddle/fluid/framework/ir/graph_pattern_detector_tester.cc
浏览文件 @ db5e3dd7
......@@ -140,8 +140,9 @@ TEST(GraphPatternDetecter, MultiSubgraph) {
return node->IsOp() && (node->Name() == "op2" || node->Name() == "op3");
},
"OP0");
auto* any_var = x.mutable_pattern()->NewNode(
[](Node* node) { return node->IsVar(); }, "VAR");
auto* any_var = x.mutable_pattern()
->NewNode([](Node* node) { return node->IsVar(); }, "VAR")
->AsIntermediate();
auto* any_op1 = x.mutable_pattern()->NewNode(
[](Node* node) { return node->IsOp(); }, "OP1");
......
paddle/fluid/framework/ir/infer_clean_graph_pass.cc
浏览文件 @ db5e3dd7
......@@ -13,42 +13,41 @@
// limitations under the License.
#include <algorithm>
#include "paddle/fluid/framework/ir/fuse_pass_base.h"
#include "paddle/fluid/framework/ir/graph.h"
#include "paddle/fluid/framework/ir/pass.h"
#include "paddle/fluid/framework/ir/graph_pattern_detector.h"
namespace paddle {
namespace framework {
namespace ir {
class InferCleanGraphPass : public Pass {
class InferCleanGraphPass : public FusePassBase {
public:
virtual ~InferCleanGraphPass() {}
protected:
std::unique_ptr<ir::Graph> ApplyImpl(std::unique_ptr<ir::Graph> graph) const {
FusePassBase::Init("original_graph", graph.get());
PADDLE_ENFORCE(graph.get());
auto is_valid_node = [](Node* x) {
return x && IsControlDepVar(*x) && x->IsVar() && !x->Var();
};
std::unordered_set<Node*> invalid_nodes;
std::unordered_set<const Node*> invalid_nodes;
int valid_op = 0;
for (auto* node : graph->Nodes()) {
if (is_valid_node(node)) {
invalid_nodes.insert(node);
} else if (node->IsOp()) {
// Collect all the operators to help tracking number of operators.
++valid_op;
}
}
// remove nodes from the graph.
for (auto* node : invalid_nodes) {
graph->RemoveNode(node);
}
GraphSafeRemoveNodes(graph.get(), invalid_nodes);
// clean edges.
for (auto* node : graph->Nodes()) {
CleanEdges(&node->inputs, invalid_nodes);
CleanEdges(&node->outputs, invalid_nodes);
}
AddStatis(valid_op);
return graph;
}
......
paddle/fluid/framework/ir/seq_concat_fc_fuse_pass.cc
浏览文件 @ db5e3dd7
......@@ -219,16 +219,13 @@ std::unique_ptr<ir::Graph> SeqConcatFcFusePass::ApplyImpl(
op_desc.SetAttr("fc_activation", act->Op()->Type());
auto* op_node = graph->CreateOpNode(&op_desc);
// Add links
#define NODE_LINKS(a, b) \
a->outputs.push_back(b); \
b->inputs.push_back(a);
NODE_LINKS(fc_w, op_node);
NODE_LINKS(fc_bias, op_node);
NODE_LINKS(concat_in0, op_node);
NODE_LINKS(sequence_expand0_in, op_node);
NODE_LINKS(sequence_expand1_in, op_node);
NODE_LINKS(op_node, fc_out);
// Add links
IR_NODE_LINK_TO(fc_w, op_node);
IR_NODE_LINK_TO(fc_bias, op_node);
IR_NODE_LINK_TO(concat_in0, op_node);
IR_NODE_LINK_TO(sequence_expand0_in, op_node);
IR_NODE_LINK_TO(sequence_expand1_in, op_node);
IR_NODE_LINK_TO(op_node, fc_out);
// Clean nodes.
std::unordered_set<const Node*> marked_nodes;
......@@ -241,7 +238,6 @@ std::unique_ptr<ir::Graph> SeqConcatFcFusePass::ApplyImpl(
marked_nodes.erase(sequence_expand0_in);
marked_nodes.erase(sequence_expand1_in);
marked_nodes.erase(fc_out);
GraphSafeRemoveNodes(graph, marked_nodes);
});
......
paddle/fluid/inference/CMakeLists.txt
浏览文件 @ db5e3dd7
......@@ -10,7 +10,7 @@ set(FLUID_CORE_MODULES proto_desc memory lod_tensor executor)
# TODO(panyx0718): Should this be called paddle_fluid_inference_api_internal?
cc_library(paddle_fluid_api
SRCS io.cc
DEPS ${FLUID_CORE_MODULES} ${GLOB_OP_LIB} graph_to_program_pass)
DEPS ${FLUID_CORE_MODULES} ${GLOB_OP_LIB})
get_property(fluid_modules GLOBAL PROPERTY FLUID_MODULES)
......@@ -22,7 +22,7 @@ cc_library(paddle_fluid_origin DEPS ${fluid_modules} paddle_fluid_api)
#endif()
# Create static library
cc_library(paddle_fluid DEPS ${fluid_modules} paddle_fluid_api paddle_inference_api)
cc_library(paddle_fluid DEPS ${fluid_modules} paddle_fluid_api paddle_inference_api analysis_predictor)
if(NOT APPLE)
# TODO(liuyiqu: Temporarily disable the link flag because it is not support on Mac.
set(LINK_FLAGS "-Wl,--retain-symbols-file ${CMAKE_CURRENT_SOURCE_DIR}/paddle_fluid.sym")
......@@ -32,6 +32,7 @@ endif()
# Create shared library
cc_library(paddle_fluid_shared SHARED
SRCS io.cc ${CMAKE_CURRENT_SOURCE_DIR}/api/api.cc ${CMAKE_CURRENT_SOURCE_DIR}/api/api_impl.cc
${CMAKE_CURRENT_SOURCE_DIR}/api/analysis_predictor.cc
DEPS ${fluid_modules} paddle_fluid_api)
set_target_properties(paddle_fluid_shared PROPERTIES OUTPUT_NAME paddle_fluid)
......
paddle/fluid/inference/analysis/CMakeLists.txt
浏览文件 @ db5e3dd7
......@@ -33,7 +33,7 @@ function (inference_analysis_test TARGET)
endif()
cc_test(${TARGET}
SRCS "${analysis_test_SRCS}"
DEPS analysis graph fc_fuse_pass graph_viz_pass infer_clean_graph_pass graph_pattern_detector pass ${analysis_test_EXTRA_DEPS}
DEPS analysis pass ${GLOB_PASS_LIB} ${analysis_test_EXTRA_DEPS}
ARGS --inference_model_dir=${PYTHON_TESTS_DIR}/book/word2vec.inference.model ${mem_opt} ${analysis_test_ARGS})
set_tests_properties(${TARGET} PROPERTIES DEPENDS test_word2vec)
endif(WITH_TESTING)
......@@ -56,25 +56,13 @@ if (NOT EXISTS ${DITU_INSTALL_DIR} AND WITH_TESTING)
endif()
inference_analysis_test(test_analyzer SRCS analyzer_tester.cc
EXTRA_DEPS paddle_inference_api paddle_fluid_api ir_pass_manager analysis
analysis_predictor
# ir
fc_fuse_pass
fc_lstm_fuse_pass
seq_concat_fc_fuse_pass
graph_viz_pass
infer_clean_graph_pass
graph_pattern_detector
infer_clean_graph_pass
attention_lstm_fuse_pass
paddle_inference_api
pass
EXTRA_DEPS paddle_inference_api paddle_fluid_api ir_pass_manager analysis_predictor
ARGS --infer_ditu_rnn_model=${DITU_INSTALL_DIR}/model
--infer_ditu_rnn_data=${DITU_INSTALL_DIR}/data.txt)
inference_analysis_test(test_data_flow_graph SRCS data_flow_graph_tester.cc)
inference_analysis_test(test_data_flow_graph_to_fluid_pass SRCS data_flow_graph_to_fluid_pass_tester.cc EXTRA_DEPS paddle_inference_api)
inference_analysis_test(test_fluid_to_ir_pass SRCS fluid_to_ir_pass_tester.cc EXTRA_DEPS paddle_fluid)
inference_analysis_test(test_data_flow_graph_to_fluid_pass SRCS data_flow_graph_to_fluid_pass_tester.cc)
inference_analysis_test(test_fluid_to_ir_pass SRCS fluid_to_ir_pass_tester.cc)
inference_analysis_test(test_fluid_to_data_flow_graph_pass SRCS fluid_to_data_flow_graph_pass_tester.cc)
inference_analysis_test(test_subgraph_splitter SRCS subgraph_splitter_tester.cc)
inference_analysis_test(test_dfg_graphviz_draw_pass SRCS dfg_graphviz_draw_pass_tester.cc)
......
paddle/fluid/inference/analysis/analyzer_tester.cc
浏览文件 @ db5e3dd7
......@@ -22,6 +22,7 @@
#include "paddle/fluid/inference/api/analysis_predictor.h"
#include "paddle/fluid/inference/api/helper.h"
#include "paddle/fluid/inference/api/paddle_inference_api.h"
#include "paddle/fluid/inference/api/paddle_inference_pass.h"
#include "paddle/fluid/inference/utils/singleton.h"
#include "paddle/fluid/platform/profiler.h"
......@@ -327,9 +328,20 @@ void TestDituRNNPrediction(const std::string &model_path,
LOG(INFO) << "fused " << item.first << " " << item.second;
}
ASSERT_TRUE(fuse_statis.count("fc"));
EXPECT_EQ(fuse_statis.at("fc"), 1);
EXPECT_EQ(fuse_statis.at("fc_nobias_lstm_fuse"), 1);
int num_ops = 0;
for (auto &node :
analysis_predictor->analysis_argument().main_dfg->nodes.nodes()) {
if (node->IsFunction()) {
++num_ops;
}
}
LOG(INFO) << "has num ops: " << num_ops;
ASSERT_TRUE(fuse_statis.count("fc_fuse"));
EXPECT_EQ(fuse_statis.at("fc_fuse"), 1);
EXPECT_EQ(fuse_statis.at("fc_nobias_lstm_fuse"), 2); // bi-directional LSTM
EXPECT_EQ(num_ops,
13); // After graph optimization, only 13 operators exists.
}
}
......@@ -357,10 +369,3 @@ TEST(Analyzer, DituRNN_with_analysis_with_IR) {
} // namespace analysis
} // namespace inference
} // namespace paddle
USE_PASS(fc_fuse_pass);
USE_PASS(seq_concat_fc_fuse_pass);
USE_PASS(fc_lstm_fuse_pass);
USE_PASS(graph_viz_pass);
USE_PASS(infer_clean_graph_pass);
USE_PASS(attention_lstm_fuse_pass);
paddle/fluid/inference/analysis/fluid_to_ir_pass_tester.cc
浏览文件 @ db5e3dd7
......@@ -16,6 +16,7 @@
#include <gtest/gtest.h>
#include "paddle/fluid/inference/analysis/ut_helper.h"
#include "paddle/fluid/inference/api/paddle_inference_pass.h"
namespace paddle {
namespace inference {
......@@ -33,10 +34,3 @@ TEST(FluidToIrPass, Test) {
} // namespace analysis
} // namespace inference
} // namespace paddle
USE_PASS(graph_viz_pass);
USE_PASS(infer_clean_graph_pass);
USE_PASS(attention_lstm_fuse_pass);
USE_PASS(fc_lstm_fuse_pass);
USE_PASS(seq_concat_fc_fuse_pass);
USE_PASS(fc_fuse_pass);
paddle/fluid/inference/api/CMakeLists.txt
浏览文件 @ db5e3dd7
......@@ -18,10 +18,7 @@ if(APPLE)
endif(APPLE)
set(inference_deps paddle_inference_api paddle_fluid_api analysis pass ir_pass_manager
graph_viz_pass fc_fuse_pass
infer_clean_graph_pass
)
set(inference_deps paddle_inference_api paddle_fluid_api analysis pass ir_pass_manager ${GLOB_PASS_LIB})
if(WITH_GPU AND TENSORRT_FOUND)
set(inference_deps ${inference_deps} paddle_inference_tensorrt_subgraph_engine)
......
paddle/fluid/inference/api/analysis_predictor.cc
浏览文件 @ db5e3dd7
......@@ -18,6 +18,7 @@
#include "paddle/fluid/framework/ir/pass.h"
#include "paddle/fluid/framework/scope.h"
#include "paddle/fluid/inference/api/paddle_inference_api.h"
#include "paddle/fluid/inference/api/paddle_inference_pass.h"
#include "paddle/fluid/inference/utils/singleton.h"
namespace paddle {
......@@ -133,7 +134,3 @@ std::unique_ptr<PaddlePredictor> CreatePaddlePredictor<
}
} // namespace paddle
USE_PASS(fc_fuse_pass);
USE_PASS(graph_viz_pass);
USE_PASS(infer_clean_graph_pass);
paddle/fluid/inference/api/helper.h
浏览文件 @ db5e3dd7
......@@ -16,6 +16,7 @@
#include <sys/time.h>
#include <algorithm>
#include <numeric>
#include <sstream>
#include <string>
#include <vector>
......
paddle/fluid/operators/detection/bbox_util.h 0 → 100644
浏览文件 @ db5e3dd7
/* 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/eigen.h"
#include "paddle/fluid/framework/tensor.h"
namespace paddle {
namespace operators {
/*
* transform that computes target bounding-box regression deltas
* given proposal boxes and ground-truth boxes.
*/
template <typename T>
inline void BoxToDelta(const int box_num, const framework::Tensor& ex_boxes,
const framework::Tensor& gt_boxes, const T* weights,
const bool normalized, framework::Tensor* box_delta) {
auto ex_boxes_et = framework::EigenTensor<T, 2>::From(ex_boxes);
auto gt_boxes_et = framework::EigenTensor<T, 2>::From(gt_boxes);
auto trg = framework::EigenTensor<T, 2>::From(*box_delta);
T ex_w, ex_h, ex_ctr_x, ex_ctr_y, gt_w, gt_h, gt_ctr_x, gt_ctr_y;
for (int64_t i = 0; i < box_num; ++i) {
ex_w = ex_boxes_et(i, 2) - ex_boxes_et(i, 0) + (normalized == false);
ex_h = ex_boxes_et(i, 3) - ex_boxes_et(i, 1) + (normalized == false);
ex_ctr_x = ex_boxes_et(i, 0) + 0.5 * ex_w;
ex_ctr_y = ex_boxes_et(i, 1) + 0.5 * ex_h;
gt_w = gt_boxes_et(i, 2) - gt_boxes_et(i, 0) + (normalized == false);
gt_h = gt_boxes_et(i, 3) - gt_boxes_et(i, 1) + (normalized == false);
gt_ctr_x = gt_boxes_et(i, 0) + 0.5 * gt_w;
gt_ctr_y = gt_boxes_et(i, 1) + 0.5 * gt_h;
trg(i, 0) = (gt_ctr_x - ex_ctr_x) / ex_w;
trg(i, 1) = (gt_ctr_y - ex_ctr_y) / ex_h;
trg(i, 2) = std::log(gt_w / ex_w);
trg(i, 3) = std::log(gt_h / ex_h);
if (weights) {
trg(i, 0) = trg(i, 0) / weights[0];
trg(i, 1) = trg(i, 1) / weights[1];
trg(i, 2) = trg(i, 2) / weights[2];
trg(i, 3) = trg(i, 3) / weights[3];
}
}
}
template <typename T>
void Gather(const T* in, const int in_stride, const int* index, const int num,
T* out) {
const int stride_bytes = in_stride * sizeof(T);
for (int i = 0; i < num; ++i) {
int id = index[i];
memcpy(out + i * in_stride, in + id * in_stride, stride_bytes);
}
}
} // namespace operators
} // namespace paddle
paddle/fluid/operators/detection/generate_proposal_labels_op.cc
浏览文件 @ db5e3dd7
......@@ -14,6 +14,7 @@ limitations under the License. */
#include <string>
#include <vector>
#include "paddle/fluid/framework/op_registry.h"
#include "paddle/fluid/operators/detection/bbox_util.h"
#include "paddle/fluid/operators/gather.h"
#include "paddle/fluid/operators/math/concat.h"
#include "paddle/fluid/operators/math/math_function.h"
......@@ -133,31 +134,6 @@ void BboxOverlaps(const Tensor& r_boxes, const Tensor& c_boxes,
}
}
template <typename T>
void BoxToDelta(int box_num, const Tensor& ex_boxes, const Tensor& gt_boxes,
const std::vector<float>& weights, Tensor* box_delta) {
auto ex_boxes_et = framework::EigenTensor<T, 2>::From(ex_boxes);
auto gt_boxes_et = framework::EigenTensor<T, 2>::From(gt_boxes);
auto box_delta_et = framework::EigenTensor<T, 2>::From(*box_delta);
T ex_w, ex_h, ex_ctr_x, ex_ctr_y, gt_w, gt_h, gt_ctr_x, gt_ctr_y;
for (int64_t i = 0; i < box_num; ++i) {
ex_w = ex_boxes_et(i, 2) - ex_boxes_et(i, 0) + 1;
ex_h = ex_boxes_et(i, 3) - ex_boxes_et(i, 1) + 1;
ex_ctr_x = ex_boxes_et(i, 0) + 0.5 * ex_w;
ex_ctr_y = ex_boxes_et(i, 1) + 0.5 * ex_h;
gt_w = gt_boxes_et(i, 2) - gt_boxes_et(i, 0) + 1;
gt_h = gt_boxes_et(i, 3) - gt_boxes_et(i, 1) + 1;
gt_ctr_x = gt_boxes_et(i, 0) + 0.5 * gt_w;
gt_ctr_y = gt_boxes_et(i, 1) + 0.5 * gt_h;
box_delta_et(i, 0) = (gt_ctr_x - ex_ctr_x) / ex_w / weights[0];
box_delta_et(i, 1) = (gt_ctr_y - ex_ctr_y) / ex_h / weights[1];
box_delta_et(i, 2) = log(gt_w / ex_w) / ex_w / weights[2];
box_delta_et(i, 3) = log(gt_h / ex_h) / ex_h / weights[3];
}
}
template <typename T>
std::vector<std::vector<int>> SampleFgBgGt(
const platform::CPUDeviceContext& context, Tensor* iou,
......@@ -243,12 +219,11 @@ void GatherBoxesLabels(const platform::CPUDeviceContext& context,
Tensor* sampled_labels, Tensor* sampled_gts) {
int fg_num = fg_inds.size();
int bg_num = bg_inds.size();
int gt_num = fg_num + bg_num;
Tensor fg_inds_t, bg_inds_t, gt_box_inds_t, gt_label_inds_t;
int* fg_inds_data = fg_inds_t.mutable_data<int>({fg_num}, context.GetPlace());
int* bg_inds_data = bg_inds_t.mutable_data<int>({bg_num}, context.GetPlace());
int* gt_box_inds_data =
gt_box_inds_t.mutable_data<int>({gt_num}, context.GetPlace());
gt_box_inds_t.mutable_data<int>({fg_num}, context.GetPlace());
int* gt_label_inds_data =
gt_label_inds_t.mutable_data<int>({fg_num}, context.GetPlace());
std::copy(fg_inds.begin(), fg_inds.end(), fg_inds_data);
......@@ -303,18 +278,20 @@ std::vector<Tensor> SampleRoisForOneImage(
// Gather boxes and labels
Tensor sampled_boxes, sampled_labels, sampled_gts;
int boxes_num = fg_inds.size() + bg_inds.size();
int fg_num = fg_inds.size();
int bg_num = bg_inds.size();
int boxes_num = fg_num + bg_num;
framework::DDim bbox_dim({boxes_num, kBoxDim});
sampled_boxes.mutable_data<T>(bbox_dim, context.GetPlace());
sampled_labels.mutable_data<int>({boxes_num}, context.GetPlace());
sampled_gts.mutable_data<T>(bbox_dim, context.GetPlace());
sampled_gts.mutable_data<T>({fg_num, kBoxDim}, context.GetPlace());
GatherBoxesLabels<T>(context, boxes, *gt_boxes, *gt_classes, fg_inds, bg_inds,
gt_inds, &sampled_boxes, &sampled_labels, &sampled_gts);
// Compute targets
Tensor bbox_targets_single;
bbox_targets_single.mutable_data<T>(bbox_dim, context.GetPlace());
BoxToDelta<T>(boxes_num, sampled_boxes, sampled_gts, bbox_reg_weights,
BoxToDelta<T>(fg_num, sampled_boxes, sampled_gts, nullptr, false,
&bbox_targets_single);
// Scale rois
......@@ -427,7 +404,7 @@ class GenerateProposalLabelsKernel : public framework::OpKernel<T> {
auto rpn_rois_lod = rpn_rois->lod().back();
auto gt_classes_lod = gt_classes->lod().back();
auto gt_boxes_lod = gt_boxes->lod().back();
for (size_t i = 0; i < n; ++i) {
for (int i = 0; i < n; ++i) {
Tensor rpn_rois_slice =
rpn_rois->Slice(rpn_rois_lod[i], rpn_rois_lod[i + 1]);
Tensor gt_classes_slice =
......
paddle/fluid/operators/detection/generate_proposals_op.cc
浏览文件 @ db5e3dd7
......@@ -311,8 +311,7 @@ class GenerateProposalsKernel : public framework::OpKernel<T> {
rpn_rois->mutable_data<T>({bbox_deltas->numel() / 4, 4},
context.GetPlace());
rpn_roi_probs->mutable_data<T>({scores->numel() / 4, 1},
context.GetPlace());
rpn_roi_probs->mutable_data<T>({scores->numel(), 1}, context.GetPlace());
Tensor bbox_deltas_swap, scores_swap;
bbox_deltas_swap.mutable_data<T>({num, h_bbox, w_bbox, c_bbox},
......@@ -421,7 +420,7 @@ class GenerateProposalsKernel : public framework::OpKernel<T> {
CPUGather<T>(ctx, proposals, keep, &bbox_sel);
CPUGather<T>(ctx, scores_sel, keep, &scores_filter);
if (nms_thresh <= 0) {
return std::make_pair(bbox_sel, scores_sel);
return std::make_pair(bbox_sel, scores_filter);
}
Tensor keep_nms = NMS<T>(ctx, &bbox_sel, &scores_filter, nms_thresh, eta);
......
paddle/fluid/operators/detection/rpn_target_assign_op.cc
浏览文件 @ db5e3dd7
......@@ -14,6 +14,7 @@ limitations under the License. */
#include <random>
#include "paddle/fluid/framework/op_registry.h"
#include "paddle/fluid/operators/detection/bbox_util.h"
#include "paddle/fluid/operators/math/math_function.h"
namespace paddle {
......@@ -46,156 +47,219 @@ class RpnTargetAssignOp : public framework::OperatorWithKernel {
auto in_dims = ctx->GetInputDim("DistMat");
PADDLE_ENFORCE_EQ(in_dims.size(), 2,
"The rank of Input(DistMat) must be 2.");
ctx->SetOutputDim("LocationIndex", {-1});
ctx->SetOutputDim("ScoreIndex", {-1});
ctx->SetOutputDim("TargetLabel", {-1, 1});
ctx->SetOutputDim("TargetBBox", {-1, 4});
}
protected:
framework::OpKernelType GetExpectedKernelType(
const framework::ExecutionContext& ctx) const override {
return framework::OpKernelType(
framework::ToDataType(
ctx.Input<framework::LoDTensor>("DistMat")->type()),
platform::CPUPlace());
}
};
template <typename T>
class RpnTargetAssignKernel : public framework::OpKernel<T> {
public:
void Compute(const framework::ExecutionContext& context) const override {
auto* anchor_t = context.Input<Tensor>("Anchor"); // (H*W*A) * 4
auto* gt_bbox_t = context.Input<Tensor>("GtBox");
auto* dist_t = context.Input<LoDTensor>("DistMat");
auto* loc_index_t = context.Output<Tensor>("LocationIndex");
auto* score_index_t = context.Output<Tensor>("ScoreIndex");
auto* tgt_bbox_t = context.Output<Tensor>("TargetBBox");
auto* tgt_lbl_t = context.Output<Tensor>("TargetLabel");
auto lod = dist_t->lod().back();
int64_t batch_num = static_cast<int64_t>(lod.size() - 1);
int64_t anchor_num = dist_t->dims()[1];
PADDLE_ENFORCE_EQ(anchor_num, anchor_t->dims()[0]);
int rpn_batch_size = context.Attr<int>("rpn_batch_size_per_im");
float pos_threshold = context.Attr<float>("rpn_positive_overlap");
float neg_threshold = context.Attr<float>("rpn_negative_overlap");
float fg_fraction = context.Attr<float>("fg_fraction");
int fg_num_per_batch = static_cast<int>(rpn_batch_size * fg_fraction);
int64_t max_num = batch_num * anchor_num;
auto place = context.GetPlace();
tgt_bbox_t->mutable_data<T>({max_num, 4}, place);
auto* loc_index = loc_index_t->mutable_data<int>({max_num}, place);
auto* score_index = score_index_t->mutable_data<int>({max_num}, place);
Tensor tmp_tgt_lbl;
auto* tmp_lbl_data = tmp_tgt_lbl.mutable_data<int64_t>({max_num}, place);
auto& dev_ctx = context.device_context<platform::CPUDeviceContext>();
math::SetConstant<platform::CPUDeviceContext, int64_t> iset;
iset(dev_ctx, &tmp_tgt_lbl, static_cast<int64_t>(-1));
std::random_device rnd;
std::minstd_rand engine;
int seed =
context.Attr<bool>("fix_seed") ? context.Attr<int>("seed") : rnd();
engine.seed(seed);
int fg_num = 0;
int bg_num = 0;
for (int i = 0; i < batch_num; ++i) {
Tensor dist = dist_t->Slice(lod[i], lod[i + 1]);
Tensor gt_bbox = gt_bbox_t->Slice(lod[i], lod[i + 1]);
auto fg_bg_gt = SampleFgBgGt(dev_ctx, dist, pos_threshold, neg_threshold,
rpn_batch_size, fg_num_per_batch, engine,
tmp_lbl_data + i * anchor_num);
int cur_fg_num = fg_bg_gt[0].size();
int cur_bg_num = fg_bg_gt[1].size();
std::transform(fg_bg_gt[0].begin(), fg_bg_gt[0].end(), loc_index,
[i, anchor_num](int d) { return d + i * anchor_num; });
memcpy(score_index, loc_index, cur_fg_num * sizeof(int));
std::transform(fg_bg_gt[1].begin(), fg_bg_gt[1].end(),
score_index + cur_fg_num,
[i, anchor_num](int d) { return d + i * anchor_num; });
// get target bbox deltas
if (cur_fg_num) {
Tensor fg_gt;
T* gt_data = fg_gt.mutable_data<T>({cur_fg_num, 4}, place);
Tensor tgt_bbox = tgt_bbox_t->Slice(fg_num, fg_num + cur_fg_num);
T* tgt_data = tgt_bbox.data<T>();
Gather<T>(anchor_t->data<T>(), 4,
reinterpret_cast<int*>(&fg_bg_gt[0][0]), cur_fg_num,
tgt_data);
Gather<T>(gt_bbox.data<T>(), 4, reinterpret_cast<int*>(&fg_bg_gt[2][0]),
cur_fg_num, gt_data);
BoxToDelta<T>(cur_fg_num, tgt_bbox, fg_gt, nullptr, false, &tgt_bbox);
}
loc_index += cur_fg_num;
score_index += cur_fg_num + cur_bg_num;
fg_num += cur_fg_num;
bg_num += cur_bg_num;
}
int lbl_num = fg_num + bg_num;
PADDLE_ENFORCE_LE(fg_num, max_num);
PADDLE_ENFORCE_LE(lbl_num, max_num);
tgt_bbox_t->Resize({fg_num, 4});
loc_index_t->Resize({fg_num});
score_index_t->Resize({lbl_num});
auto* lbl_data = tgt_lbl_t->mutable_data<int64_t>({lbl_num, 1}, place);
Gather<int64_t>(tmp_lbl_data, 1, score_index_t->data<int>(), lbl_num,
lbl_data);
}
private:
void ScoreAssign(const T* dist_data, const Tensor& anchor_to_gt_max,
const int row, const int col, const float pos_threshold,
const float neg_threshold, int64_t* target_label_data,
const float neg_threshold, int64_t* target_label,
std::vector<int>* fg_inds, std::vector<int>* bg_inds) const {
int fg_offset = fg_inds->size();
int bg_offset = bg_inds->size();
float epsilon = 0.0001;
for (int64_t i = 0; i < row; ++i) {
const T* v = dist_data + i * col;
T max_dist = *std::max_element(v, v + col);
T max = *std::max_element(v, v + col);
for (int64_t j = 0; j < col; ++j) {
T val = dist_data[i * col + j];
if (val == max_dist) target_label_data[j] = 1;
if (std::abs(max - v[j]) < epsilon) {
target_label[j] = 1;
}
}
}
// Pick the fg/bg and count the number
// Pick the fg/bg
const T* anchor_to_gt_max_data = anchor_to_gt_max.data<T>();
for (int64_t j = 0; j < col; ++j) {
if (anchor_to_gt_max.data<T>()[j] > pos_threshold) {
target_label_data[j] = 1;
} else if (anchor_to_gt_max.data<T>()[j] < neg_threshold) {
target_label_data[j] = 0;
if (anchor_to_gt_max_data[j] >= pos_threshold) {
target_label[j] = 1;
} else if (anchor_to_gt_max_data[j] < neg_threshold) {
target_label[j] = 0;
}
if (target_label_data[j] == 1) {
fg_inds->push_back(fg_offset + j);
} else if (target_label_data[j] == 0) {
bg_inds->push_back(bg_offset + j);
if (target_label[j] == 1) {
fg_inds->push_back(j);
} else if (target_label[j] == 0) {
bg_inds->push_back(j);
}
}
}
void ReservoirSampling(const int num, const int offset,
std::minstd_rand engine,
void ReservoirSampling(const int num, std::minstd_rand engine,
std::vector<int>* inds) const {
std::uniform_real_distribution<float> uniform(0, 1);
const int64_t size = static_cast<int64_t>(inds->size() - offset);
if (size > num) {
for (int64_t i = num; i < size; ++i) {
size_t len = inds->size();
if (len > static_cast<size_t>(num)) {
for (size_t i = num; i < len; ++i) {
int rng_ind = std::floor(uniform(engine) * i);
if (rng_ind < num)
std::iter_swap(inds->begin() + rng_ind + offset,
inds->begin() + i + offset);
std::iter_swap(inds->begin() + rng_ind, inds->begin() + i);
}
inds->resize(num);
}
}
void RpnTargetAssign(const framework::ExecutionContext& ctx,
const Tensor& dist, const float pos_threshold,
const float neg_threshold, const int rpn_batch_size,
const int fg_num, std::minstd_rand engine,
std::vector<int>* fg_inds, std::vector<int>* bg_inds,
int64_t* target_label_data) const {
// std::vector<std::vector<int>> RpnTargetAssign(
std::vector<std::vector<int>> SampleFgBgGt(
const platform::CPUDeviceContext& ctx, const Tensor& dist,
const float pos_threshold, const float neg_threshold,
const int rpn_batch_size, const int fg_num, std::minstd_rand engine,
int64_t* target_label) const {
auto* dist_data = dist.data<T>();
int64_t row = dist.dims()[0];
int64_t col = dist.dims()[1];
int fg_offset = fg_inds->size();
int bg_offset = bg_inds->size();
int row = dist.dims()[0];
int col = dist.dims()[1];
std::vector<int> fg_inds;
std::vector<int> bg_inds;
std::vector<int> gt_inds;
// Calculate the max IoU between anchors and gt boxes
Tensor anchor_to_gt_max;
anchor_to_gt_max.mutable_data<T>(
framework::make_ddim({static_cast<int64_t>(col), 1}),
platform::CPUPlace());
auto& place = *ctx.template device_context<platform::CPUDeviceContext>()
.eigen_device();
auto x = EigenMatrix<T>::From(dist);
auto x_col_max = EigenMatrix<T>::From(anchor_to_gt_max);
x_col_max.device(place) =
x.maximum(Eigen::DSizes<int, 1>(0))
.reshape(Eigen::DSizes<int, 2>(static_cast<int64_t>(col), 1));
// Map from anchor to gt box that has highest overlap
auto place = ctx.GetPlace();
Tensor anchor_to_gt_max, anchor_to_gt_argmax;
anchor_to_gt_max.mutable_data<T>({col}, place);
int* argmax = anchor_to_gt_argmax.mutable_data<int>({col}, place);
auto x = framework::EigenMatrix<T>::From(dist);
auto x_col_max = framework::EigenVector<T>::Flatten(anchor_to_gt_max);
auto x_col_argmax =
framework::EigenVector<int>::Flatten(anchor_to_gt_argmax);
x_col_max = x.maximum(Eigen::DSizes<int, 1>(0));
x_col_argmax = x.argmax(0).template cast<int>();
// Follow the Faster RCNN's implementation
ScoreAssign(dist_data, anchor_to_gt_max, row, col, pos_threshold,
neg_threshold, target_label_data, fg_inds, bg_inds);
neg_threshold, target_label, &fg_inds, &bg_inds);
// Reservoir Sampling
ReservoirSampling(fg_num, fg_offset, engine, fg_inds);
int bg_num = rpn_batch_size - (fg_inds->size() - fg_offset);
ReservoirSampling(bg_num, bg_offset, engine, bg_inds);
}
ReservoirSampling(fg_num, engine, &fg_inds);
int fg_num2 = static_cast<int>(fg_inds.size());
int bg_num = rpn_batch_size - fg_num2;
ReservoirSampling(bg_num, engine, &bg_inds);
void Compute(const framework::ExecutionContext& context) const override {
auto* dist = context.Input<LoDTensor>("DistMat");
auto* loc_index = context.Output<Tensor>("LocationIndex");
auto* score_index = context.Output<Tensor>("ScoreIndex");
auto* tgt_lbl = context.Output<Tensor>("TargetLabel");
auto col = dist->dims()[1];
int64_t n = dist->lod().size() == 0UL
? 1
: static_cast<int64_t>(dist->lod().back().size() - 1);
if (dist->lod().size()) {
PADDLE_ENFORCE_EQ(dist->lod().size(), 1UL,
"Only support 1 level of LoD.");
gt_inds.reserve(fg_num2);
for (int i = 0; i < fg_num2; ++i) {
gt_inds.emplace_back(argmax[fg_inds[i]]);
}
int rpn_batch_size = context.Attr<int>("rpn_batch_size_per_im");
float pos_threshold = context.Attr<float>("rpn_positive_overlap");
float neg_threshold = context.Attr<float>("rpn_negative_overlap");
float fg_fraction = context.Attr<float>("fg_fraction");
int fg_num = static_cast<int>(rpn_batch_size * fg_fraction);
std::vector<std::vector<int>> fg_bg_gt;
fg_bg_gt.emplace_back(fg_inds);
fg_bg_gt.emplace_back(bg_inds);
fg_bg_gt.emplace_back(gt_inds);
int64_t* target_label_data =
tgt_lbl->mutable_data<int64_t>({n * col, 1}, context.GetPlace());
auto& dev_ctx = context.device_context<platform::CPUDeviceContext>();
math::SetConstant<platform::CPUDeviceContext, int64_t> iset;
iset(dev_ctx, tgt_lbl, static_cast<int>(-1));
std::vector<int> fg_inds;
std::vector<int> bg_inds;
std::random_device rnd;
std::minstd_rand engine;
int seed =
context.Attr<bool>("fix_seed") ? context.Attr<int>("seed") : rnd();
engine.seed(seed);
if (n == 1) {
RpnTargetAssign(context, *dist, pos_threshold, neg_threshold,
rpn_batch_size, fg_num, engine, &fg_inds, &bg_inds,
target_label_data);
} else {
auto lod = dist->lod().back();
for (size_t i = 0; i < lod.size() - 1; ++i) {
Tensor one_ins = dist->Slice(lod[i], lod[i + 1]);
RpnTargetAssign(context, one_ins, pos_threshold, neg_threshold,
rpn_batch_size, fg_num, engine, &fg_inds, &bg_inds,
target_label_data + i * col);
}
}
int* loc_index_data = loc_index->mutable_data<int>(
{static_cast<int>(fg_inds.size())}, context.GetPlace());
int* score_index_data = score_index->mutable_data<int>(
{static_cast<int>(fg_inds.size() + bg_inds.size())},
context.GetPlace());
memcpy(loc_index_data, reinterpret_cast<int*>(&fg_inds[0]),
fg_inds.size() * sizeof(int));
memcpy(score_index_data, reinterpret_cast<int*>(&fg_inds[0]),
fg_inds.size() * sizeof(int));
memcpy(score_index_data + fg_inds.size(),
reinterpret_cast<int*>(&bg_inds[0]), bg_inds.size() * sizeof(int));
return fg_bg_gt;
}
};
class RpnTargetAssignOpMaker : public framework::OpProtoAndCheckerMaker {
public:
void Make() override {
AddInput("Anchor",
"(Tensor) input anchor is a 2-D Tensor with shape [H*W*A, 4].");
AddInput("GtBox", "(LoDTensor) input groud-truth bbox with shape [K, 4].");
AddInput(
"DistMat",
"(LoDTensor or Tensor) this input is a 2-D LoDTensor with shape "
......@@ -241,12 +305,15 @@ class RpnTargetAssignOpMaker : public framework::OpProtoAndCheckerMaker {
"ScoreIndex",
"(Tensor), The indexes of foreground and background anchors in all "
"RPN anchors(The rest anchors are ignored). The shape of the "
"ScoreIndex is [F + B], F and B depend on the value of input "
"tensor and attributes.");
AddOutput("TargetLabel",
"ScoreIndex is [F + B], F and B are sampled foreground and backgroud "
" number.");
AddOutput("TargetBBox",
"(Tensor<int64_t>), The target bbox deltas with shape "
"[F, 4], F is the sampled foreground number.");
AddOutput(
"TargetLabel",
"(Tensor<int64_t>), The target labels of each anchor with shape "
"[K * M, 1], "
"K and M is the same as they are in DistMat.");
"[F + B, 1], F and B are sampled foreground and backgroud number.");
AddComment(R"DOC(
This operator can be, for given the IoU between the ground truth bboxes and the
anchors, to assign classification and regression targets to each prediction.
......
paddle/fluid/operators/gru_unit_op.h
浏览文件 @ db5e3dd7
......@@ -92,12 +92,12 @@ class GRUUnitKernel : public framework::OpKernel<T> {
gate_data, frame_size * 3);
// calculate activited gate
Eigen::array<int, 2> extents = {batch_size, frame_size};
Eigen::array<int, 2> u_offsets = {0, 0};
Eigen::array<int, 2> extents{{batch_size, frame_size}};
Eigen::array<int, 2> u_offsets{{0, 0}};
ActCompute(context.Attr<int>("gate_activation"), place,
g.slice(u_offsets, extents), g.slice(u_offsets, extents));
auto u = g.slice(u_offsets, extents); // update gate
Eigen::array<int, 2> r_offsets = {0, frame_size};
Eigen::array<int, 2> r_offsets{{0, frame_size}};
ActCompute(context.Attr<int>("gate_activation"), place,
g.slice(r_offsets, extents), g.slice(r_offsets, extents));
auto r = g.slice(r_offsets, extents); // reset gate
......@@ -107,7 +107,7 @@ class GRUUnitKernel : public framework::OpKernel<T> {
weight_data + frame_size * frame_size * 2, frame_size, 1,
gate_data + frame_size * 2, frame_size * 3);
Eigen::array<int, 2> c_offsets = {0, frame_size * 2};
Eigen::array<int, 2> c_offsets{{0, frame_size * 2}};
ActCompute(context.Attr<int>("activation"), place,
g.slice(c_offsets, extents), g.slice(c_offsets, extents));
auto c = g.slice(c_offsets, extents); // output candidate
......@@ -171,12 +171,12 @@ class GRUUnitGradKernel : public framework::OpKernel<T> {
int batch_size = input->dims()[0];
int frame_size = hidden_prev->dims()[1];
Eigen::array<int, 2> extents = {batch_size, frame_size};
Eigen::array<int, 2> u_offsets = {0, 0};
Eigen::array<int, 2> extents{{batch_size, frame_size}};
Eigen::array<int, 2> u_offsets{{0, 0}};
auto u = g.slice(u_offsets, extents); // update gate
Eigen::array<int, 2> r_offsets = {0, frame_size};
Eigen::array<int, 2> r_offsets{{0, frame_size}};
auto r = g.slice(r_offsets, extents); // reset gate
Eigen::array<int, 2> c_offsets = {0, frame_size * 2};
Eigen::array<int, 2> c_offsets{{0, frame_size * 2}};
auto c = g.slice(c_offsets, extents); // output candidate
// backward for unactivated update gate
......
paddle/fluid/operators/roi_pool_op.cu
浏览文件 @ db5e3dd7
......@@ -31,7 +31,7 @@ static inline int NumBlocks(const int N) {
template <typename T>
__global__ void GPUROIPoolForward(
const int nthreads, const T* input_data, const int64_t* input_rois,
const int nthreads, const T* input_data, const T* input_rois,
const float spatial_scale, const int channels, const int height,
const int width, const int pooled_height, const int pooled_width,
int* roi_batch_id_data, T* output_data, int64_t* argmax_data) {
......@@ -43,7 +43,7 @@ __global__ void GPUROIPoolForward(
int c = (i / pooled_width / pooled_height) % channels;
int n = i / pooled_width / pooled_height / channels;
const int64_t* offset_input_rois = input_rois + n * kROISize;
const T* offset_input_rois = input_rois + n * kROISize;
int roi_batch_ind = roi_batch_id_data[n];
int roi_start_w = round(offset_input_rois[0] * spatial_scale);
int roi_start_h = round(offset_input_rois[1] * spatial_scale);
......@@ -93,7 +93,7 @@ __global__ void GPUROIPoolForward(
template <typename T>
__global__ void GPUROIPoolBackward(
const int nthreads, const int64_t* input_rois, const T* output_grad,
const int nthreads, const T* input_rois, const T* output_grad,
const int64_t* argmax_data, const int num_rois, const float spatial_scale,
const int channels, const int height, const int width,
const int pooled_height, const int pooled_width, int* roi_batch_id_data,
......@@ -174,8 +174,8 @@ class GPUROIPoolOpKernel : public framework::OpKernel<T> {
GPUROIPoolForward<
T><<<blocks, threads, 0, ctx.cuda_device_context().stream()>>>(
output_size, in->data<T>(), rois->data<int64_t>(), spatial_scale,
channels, height, width, pooled_height, pooled_width,
output_size, in->data<T>(), rois->data<T>(), spatial_scale, channels,
height, width, pooled_height, pooled_width,
roi_batch_id_list_gpu.data<int>(), out->mutable_data<T>(ctx.GetPlace()),
argmax->mutable_data<int64_t>(ctx.GetPlace()));
}
......@@ -228,7 +228,7 @@ class GPUROIPoolGradOpKernel : public framework::OpKernel<T> {
if (output_grad_size > 0) {
GPUROIPoolBackward<
T><<<blocks, threads, 0, ctx.cuda_device_context().stream()>>>(
output_grad_size, rois->data<int64_t>(), out_grad->data<T>(),
output_grad_size, rois->data<T>(), out_grad->data<T>(),
argmax->data<int64_t>(), rois_num, spatial_scale, channels, height,
width, pooled_height, pooled_width,
roi_batch_id_list_gpu.data<int>(),
......
paddle/fluid/operators/roi_pool_op.h
浏览文件 @ db5e3dd7
......@@ -72,7 +72,7 @@ class CPUROIPoolOpKernel : public framework::OpKernel<T> {
T* output_data = out->mutable_data<T>(ctx.GetPlace());
int64_t* argmax_data = argmax->mutable_data<int64_t>(ctx.GetPlace());
const int64_t* rois_data = rois->data<int64_t>();
const T* rois_data = rois->data<T>();
for (int n = 0; n < rois_num; ++n) {
int roi_batch_id = roi_batch_id_data[n];
int roi_start_w = round(rois_data[0] * spatial_scale);
......@@ -171,7 +171,7 @@ class CPUROIPoolGradOpKernel : public framework::OpKernel<T> {
}
}
const int64_t* rois_data = rois->data<int64_t>();
const T* rois_data = rois->data<T>();
const T* out_grad_data = out_grad->data<T>();
const int64_t* argmax_data = argmax->data<int64_t>();
T* in_grad_data = in_grad->mutable_data<T>(ctx.GetPlace());
......
python/paddle/fluid/layers/detection.py
浏览文件 @ db5e3dd7
......@@ -145,26 +145,23 @@ def rpn_target_assign(loc,
"""
helper = LayerHelper('rpn_target_assign', **locals())
# 1. Compute the regression target bboxes
target_bbox = box_coder(
prior_box=anchor_box,
prior_box_var=anchor_var,
target_box=gt_box,
code_type='encode_center_size',
box_normalized=False)
# 2. Compute overlaps between the prior boxes and the gt boxes overlaps
# Compute overlaps between the prior boxes and the gt boxes overlaps
iou = iou_similarity(x=gt_box, y=anchor_box)
# 3. Assign target label to anchors
loc_index = helper.create_tmp_variable(dtype=anchor_box.dtype)
score_index = helper.create_tmp_variable(dtype=anchor_box.dtype)
target_label = helper.create_tmp_variable(dtype=anchor_box.dtype)
# Assign target label to anchors
loc_index = helper.create_tmp_variable(dtype='int32')
score_index = helper.create_tmp_variable(dtype='int32')
target_label = helper.create_tmp_variable(dtype='int64')
target_bbox = helper.create_tmp_variable(dtype=anchor_box.dtype)
helper.append_op(
type="rpn_target_assign",
inputs={'DistMat': iou},
inputs={'Anchor': anchor_box,
'GtBox': gt_box,
'DistMat': iou},
outputs={
'LocationIndex': loc_index,
'ScoreIndex': score_index,
'TargetLabel': target_label
'TargetLabel': target_label,
'TargetBBox': target_bbox,
},
attrs={
'rpn_batch_size_per_im': rpn_batch_size_per_im,
......@@ -173,16 +170,16 @@ def rpn_target_assign(loc,
'fg_fraction': fg_fraction
})
# 4. Reshape and gather the target entry
scores = nn.reshape(x=scores, shape=(-1, 2))
loc = nn.reshape(x=loc, shape=(-1, 4))
target_label = nn.reshape(x=target_label, shape=(-1, 1))
target_bbox = nn.reshape(x=target_bbox, shape=(-1, 4))
loc_index.stop_gradient = True
score_index.stop_gradient = True
target_label.stop_gradient = True
target_bbox.stop_gradient = True
scores = nn.reshape(x=scores, shape=(-1, 1))
loc = nn.reshape(x=loc, shape=(-1, 4))
predicted_scores = nn.gather(scores, score_index)
predicted_location = nn.gather(loc, loc_index)
target_label = nn.gather(target_label, score_index)
target_bbox = nn.gather(target_bbox, loc_index)
return predicted_scores, predicted_location, target_label, target_bbox
......
python/paddle/fluid/tests/test_detection.py
浏览文件 @ db5e3dd7
......@@ -281,7 +281,7 @@ class TestRpnTargetAssign(unittest.TestCase):
gt_box = layers.data(
name='gt_box', shape=[4], lod_level=1, dtype='float32')
predicted_scores, predicted_location, target_label, target_bbox = layers.rpn_target_assign(
pred_scores, pred_loc, tgt_lbl, tgt_bbox = layers.rpn_target_assign(
loc=loc,
scores=scores,
anchor_box=anchor_box,
......@@ -292,15 +292,13 @@ class TestRpnTargetAssign(unittest.TestCase):
rpn_positive_overlap=0.7,
rpn_negative_overlap=0.3)
self.assertIsNotNone(predicted_scores)
self.assertIsNotNone(predicted_location)
self.assertIsNotNone(target_label)
self.assertIsNotNone(target_bbox)
assert predicted_scores.shape[1] == 2
assert predicted_location.shape[1] == 4
assert predicted_location.shape[1] == target_bbox.shape[1]
print(str(program))
self.assertIsNotNone(pred_scores)
self.assertIsNotNone(pred_loc)
self.assertIsNotNone(tgt_lbl)
self.assertIsNotNone(tgt_bbox)
assert pred_scores.shape[1] == 1
assert pred_loc.shape[1] == 4
assert pred_loc.shape[1] == tgt_bbox.shape[1]
class TestGenerateProposals(unittest.TestCase):
......
python/paddle/fluid/tests/unittests/test_fusion_gru_op.py
浏览文件 @ db5e3dd7
......@@ -37,7 +37,7 @@ def fusion_gru(
h0,
wh,
np.zeros(
(1, wh.shape[1]), dtype='float64'),
(1, wh.shape[1]), dtype='float32'),
is_reverse,
act_state,
act_gate)
......@@ -62,15 +62,15 @@ class TestFusionGRUOp(OpTest):
T = sum(self.lod[0])
N = len(self.lod[0])
x = np.random.rand(T, self.M).astype('float64')
wx = np.random.rand(self.M, 3 * self.D).astype('float64')
wh = np.random.rand(self.D, 3 * self.D).astype('float64')
x = np.random.rand(T, self.M).astype('float32')
wx = np.random.rand(self.M, 3 * self.D).astype('float32')
wh = np.random.rand(self.D, 3 * self.D).astype('float32')
bias = np.random.rand(
1, 3 * self.D).astype('float64') if self.with_bias else np.zeros(
(1, 3 * self.D), dtype='float64')
1, 3 * self.D).astype('float32') if self.with_bias else np.zeros(
(1, 3 * self.D), dtype='float32')
h0 = np.random.rand(
N, self.D).astype('float64') if self.with_h0 else np.zeros(
(N, self.D), dtype='float64')
N, self.D).astype('float32') if self.with_h0 else np.zeros(
(N, self.D), dtype='float32')
_, _, _, hidden = fusion_gru(
x, self.lod, h0, wx, wh, bias, self.is_reverse,
......@@ -93,7 +93,9 @@ class TestFusionGRUOp(OpTest):
}
def test_check_output(self):
self.check_output(atol=1e-8)
for use_seq in {True, False}:
self.attrs['use_seq'] = use_seq
self.check_output()
class TestFusionGRUOpNoInitial(TestFusionGRUOp):
......
python/paddle/fluid/tests/unittests/test_fusion_lstm_op.py
浏览文件 @ db5e3dd7
......@@ -114,6 +114,8 @@ class TestFusionLSTMOp(OpTest):
}
def test_check_output(self):
for use_seq in {True, False}:
self.attrs['use_seq'] = use_seq
self.check_output()
......
python/paddle/fluid/tests/unittests/test_generate_proposal_labels.py
浏览文件 @ db5e3dd7
......@@ -177,8 +177,8 @@ def _box_to_delta(ex_boxes, gt_boxes, weights):
dx = (gt_ctr_x - ex_ctr_x) / ex_w / weights[0]
dy = (gt_ctr_y - ex_ctr_y) / ex_h / weights[1]
dw = (np.log(gt_w / ex_w)) / ex_w / weights[2]
dh = (np.log(gt_h / ex_h)) / ex_h / weights[3]
dw = (np.log(gt_w / ex_w)) / weights[2]
dh = (np.log(gt_h / ex_h)) / weights[3]
targets = np.vstack([dx, dy, dw, dh]).transpose()
return targets
......
python/paddle/fluid/tests/unittests/test_roi_pool_op.py
浏览文件 @ db5e3dd7
......@@ -61,7 +61,7 @@ class TestROIPoolOp(OpTest):
for i in range(self.rois_num):
roi = self.rois[i]
roi_batch_id = roi[0]
roi_batch_id = int(roi[0])
roi_start_w = int(cpt.round(roi[1] * self.spatial_scale))
roi_start_h = int(cpt.round(roi[2] * self.spatial_scale))
roi_end_w = int(cpt.round(roi[3] * self.spatial_scale))
......@@ -125,7 +125,7 @@ class TestROIPoolOp(OpTest):
roi = [bno, x1, y1, x2, y2]
rois.append(roi)
self.rois_num = len(rois)
self.rois = np.array(rois).astype("int64")
self.rois = np.array(rois).astype("float32")
def setUp(self):
self.op_type = "roi_pool"
......
python/paddle/fluid/tests/unittests/test_rpn_target_assign_op.py
浏览文件 @ db5e3dd7
......@@ -18,12 +18,17 @@ import unittest
import numpy as np
import paddle.fluid.core as core
from op_test import OpTest
from test_anchor_generator_op import anchor_generator_in_python
from test_generate_proposal_labels import _generate_groundtruth
from test_generate_proposal_labels import _bbox_overlaps, _box_to_delta
def rpn_target_assign(iou, rpn_batch_size_per_im, rpn_positive_overlap,
rpn_negative_overlap, fg_fraction):
iou = np.transpose(iou)
def rpn_target_assign(gt_anchor_iou, rpn_batch_size_per_im,
rpn_positive_overlap, rpn_negative_overlap, fg_fraction):
iou = np.transpose(gt_anchor_iou)
anchor_to_gt_max = iou.max(axis=1)
anchor_to_gt_argmax = iou.argmax(axis=1)
gt_to_anchor_argmax = iou.argmax(axis=0)
gt_to_anchor_max = iou[gt_to_anchor_argmax, np.arange(iou.shape[1])]
anchors_with_max_overlap = np.where(iou == gt_to_anchor_max)[0]
......@@ -42,59 +47,113 @@ def rpn_target_assign(iou, rpn_batch_size_per_im, rpn_positive_overlap,
num_bg = rpn_batch_size_per_im - np.sum(tgt_lbl == 1)
bg_inds = np.where(anchor_to_gt_max < rpn_negative_overlap)[0]
tgt_lbl[bg_inds] = 0
if len(bg_inds) > num_bg:
enable_inds = bg_inds[np.random.randint(len(bg_inds), size=num_bg)]
tgt_lbl[enable_inds] = 0
bg_inds = np.where(tgt_lbl == 0)[0]
tgt_lbl[bg_inds] = 0
loc_index = fg_inds
score_index = np.hstack((fg_inds, bg_inds))
tgt_lbl = np.expand_dims(tgt_lbl, axis=1)
return loc_index, score_index, tgt_lbl
gt_inds = anchor_to_gt_argmax[fg_inds]
return loc_index, score_index, tgt_lbl, gt_inds
def get_anchor(n, c, h, w):
input_feat = np.random.random((n, c, h, w)).astype('float32')
anchors, _ = anchor_generator_in_python(
input_feat=input_feat,
anchor_sizes=[32., 64.],
aspect_ratios=[0.5, 1.0],
variances=[1.0, 1.0, 1.0, 1.0],
stride=[16.0, 16.0],
offset=0.5)
return anchors
def rpn_blob(anchor, gt_boxes, iou, lod, rpn_batch_size_per_im,
rpn_positive_overlap, rpn_negative_overlap, fg_fraction):
loc_indexes = []
score_indexes = []
tmp_tgt_labels = []
tgt_bboxes = []
anchor_num = anchor.shape[0]
batch_size = len(lod) - 1
for i in range(batch_size):
b, e = lod[i], lod[i + 1]
iou_slice = iou[b:e, :]
bboxes_slice = gt_boxes[b:e, :]
loc_idx, score_idx, tgt_lbl, gt_inds = rpn_target_assign(
iou_slice, rpn_batch_size_per_im, rpn_positive_overlap,
rpn_negative_overlap, fg_fraction)
fg_bboxes = bboxes_slice[gt_inds]
fg_anchors = anchor[loc_idx]
box_deltas = _box_to_delta(fg_anchors, fg_bboxes, [1., 1., 1., 1.])
if i == 0:
loc_indexes = loc_idx
score_indexes = score_idx
tmp_tgt_labels = tgt_lbl
tgt_bboxes = box_deltas
else:
loc_indexes = np.concatenate(
[loc_indexes, loc_idx + i * anchor_num])
score_indexes = np.concatenate(
[score_indexes, score_idx + i * anchor_num])
tmp_tgt_labels = np.concatenate([tmp_tgt_labels, tgt_lbl])
tgt_bboxes = np.vstack([tgt_bboxes, box_deltas])
tgt_labels = tmp_tgt_labels[score_indexes]
return loc_indexes, score_indexes, tgt_bboxes, tgt_labels
class TestRpnTargetAssignOp(OpTest):
def setUp(self):
iou = np.random.random((10, 8)).astype("float32")
self.op_type = "rpn_target_assign"
self.inputs = {'DistMat': iou}
self.attrs = {
'rpn_batch_size_per_im': 256,
'rpn_positive_overlap': 0.95,
'rpn_negative_overlap': 0.3,
'fg_fraction': 0.25,
'fix_seed': True
}
loc_index, score_index, tgt_lbl = rpn_target_assign(iou, 256, 0.95, 0.3,
0.25)
self.outputs = {
'LocationIndex': loc_index,
'ScoreIndex': score_index,
'TargetLabel': tgt_lbl,
}
n, c, h, w = 2, 4, 14, 14
anchor = get_anchor(n, c, h, w)
gt_num = 10
anchor = anchor.reshape(-1, 4)
anchor_num = anchor.shape[0]
def test_check_output(self):
self.check_output()
im_shapes = [[64, 64], [64, 64]]
gt_box, lod = _generate_groundtruth(im_shapes, 3, 4)
bbox = np.vstack([v['boxes'] for v in gt_box])
iou = _bbox_overlaps(bbox, anchor)
anchor = anchor.astype('float32')
bbox = bbox.astype('float32')
iou = iou.astype('float32')
loc_index, score_index, tgt_bbox, tgt_lbl = rpn_blob(
anchor, bbox, iou, [0, 4, 8], 25600, 0.95, 0.03, 0.25)
class TestRpnTargetAssignOp2(OpTest):
def setUp(self):
iou = np.random.random((10, 20)).astype("float32")
self.op_type = "rpn_target_assign"
self.inputs = {'DistMat': iou}
self.inputs = {
'Anchor': anchor,
'GtBox': (bbox, [[4, 4]]),
'DistMat': (iou, [[4, 4]]),
}
self.attrs = {
'rpn_batch_size_per_im': 128,
'rpn_positive_overlap': 0.5,
'rpn_negative_overlap': 0.5,
'fg_fraction': 0.5,
'rpn_batch_size_per_im': 25600,
'rpn_positive_overlap': 0.95,
'rpn_negative_overlap': 0.03,
'fg_fraction': 0.25,
'fix_seed': True
}
loc_index, score_index, tgt_lbl = rpn_target_assign(iou, 128, 0.5, 0.5,
0.5)
self.outputs = {
'LocationIndex': loc_index,
'ScoreIndex': score_index,
'TargetLabel': tgt_lbl,
'LocationIndex': loc_index.astype('int32'),
'ScoreIndex': score_index.astype('int32'),
'TargetBBox': tgt_bbox.astype('float32'),
'TargetLabel': tgt_lbl.astype('int64'),
}
def test_check_output(self):
......
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