Merge remote-tracking branch 'upstream/develop' into develop

.travis.yml

@@ -2,7 +2,6 @@ group: deprecated-2017Q2
 language: cpp
 cache:
   directories:
-    - $HOME/third_party
     - $HOME/.ccache
     - $HOME/.cache/pip
 sudo: required
@@ -10,15 +9,13 @@ dist: trusty
 os:
   - linux
 env:
-  - JOB=DOCS
-  - JOB=BUILD_AND_TEST
-  - JOB=PRE_COMMIT
+  - JOB=build_doc
+  - JOB=check_style
 addons:
   apt:
     packages:
       - gcc-4.8
       - g++-4.8
-      - gfortran-4.8
       - git
       - build-essential
       - python
@@ -35,18 +32,7 @@ addons:
       - libtool
       - ccache
 before_install:
-  - |
-    if [ ${JOB} == "BUILD_AND_TEST" ]; then
-      local change_list=`git diff --name-only $TRAVIS_COMMIT_RANGE`
-      if [ $? -eq 0 ]; then  # if git diff return no zero, then rerun unit test.
-        if ! echo ${change_list} | grep -qvE '(\.md$)|(\.rst$)|(\.jpg$)|(\.png$)'
-        then
-          echo "Only markdown docs were updated, stopping build process."
-          exit
-        fi
-      fi
-    fi
-  - if [[ "$JOB" == "PRE_COMMIT" ]]; then sudo ln -s /usr/bin/clang-format-3.8 /usr/bin/clang-format; fi
+  - if [[ "$JOB" == "check_style" ]]; then sudo ln -s /usr/bin/clang-format-3.8 /usr/bin/clang-format; fi
   # Paddle is using protobuf 3.1 currently. Protobuf 3.2 breaks the compatibility. So we specify the python 
   # protobuf version.
   - pip install numpy wheel 'protobuf==3.1' sphinx==1.5.6 recommonmark sphinx-rtd-theme==0.1.9 virtualenv pre-commit requests==2.9.2 LinkChecker
@@ -55,9 +41,7 @@ before_install:
   - |
     function timeout() { perl -e 'alarm shift; exec @ARGV' "$@"; }
 script:
-  - | 
-    timeout 2580 paddle/scripts/travis/main.sh  # 43min timeout
-    RESULT=$?; if [ $RESULT -eq 0 ] || [ $RESULT -eq 142 ]; then true; else false; fi;
+  - paddle/scripts/travis/$JOB.sh
 notifications:
   email:
     on_success: change

Dockerfile

@@ -25,7 +25,7 @@ COPY ./paddle/scripts/docker/root/ /root/
 RUN apt-get update && \
     apt-get install -y \
     git python-pip python-dev openssh-server bison  \
-    wget unzip tar xz-utils bzip2 gzip coreutils  \
+    wget unzip tar xz-utils bzip2 gzip coreutils ntp \
     curl sed grep graphviz libjpeg-dev zlib1g-dev  \
     python-numpy python-matplotlib gcc g++ \
     automake locales clang-format-3.8 swig doxygen cmake  \

doc/api/v2/config/evaluators.rst

@@ -99,3 +99,12 @@ value_printer
 ..  automodule:: paddle.v2.evaluator
     :members:  value_printer
     :noindex:
+
+Detection
+=====
+
+detection_map
+-------------
+..  automodule:: paddle.v2.evaluator
+    :members:  detection_map
+    :noindex:

go/cmd/pserver/pserver.go

@@ -5,18 +5,35 @@ import (
 	"net/http"
 	"net/rpc"
 	"strconv"
+	"time"
 
 	"github.com/namsral/flag"
 
 	"github.com/PaddlePaddle/Paddle/go/pserver"
+	log "github.com/sirupsen/logrus"
 )
 
 func main() {
 	port := flag.Int("port", 0, "port of the pserver")
+	etcdEndpoint := flag.String("etcd-endpoint", "http://127.0.0.1:2379",
+		"comma separated endpoint string for pserver to connect to etcd")
+	etcdTimeout := flag.Int("etcd-timeout", 5, "timeout for etcd calls")
+	logLevel := flag.String("log-level", "info",
+		"log level, possible values: debug, info, warning, error, fatal, panic")
 	flag.Parse()
 
-	s := pserver.NewService()
-	err := rpc.Register(s)
+	level, err := log.ParseLevel(*logLevel)
+	if err != nil {
+		panic(err)
+	}
+	log.SetLevel(level)
+
+	timeout := time.Second * time.Duration((*etcdTimeout))
+	s, err := pserver.NewService(*etcdEndpoint, timeout)
+	if err != nil {
+		panic(err)
+	}
+	err = rpc.Register(s)
 	if err != nil {
 		panic(err)
 	}
@@ -27,7 +44,9 @@ func main() {
 		panic(err)
 	}
 
+	log.Infof("start pserver at port %d", *port)
 	err = http.Serve(l, nil)
+
 	if err != nil {
 		panic(err)
 	}

go/pserver/client_test.go

@@ -7,6 +7,7 @@ import (
 	"strconv"
 	"strings"
 	"testing"
+	"time"
 
 	"github.com/PaddlePaddle/Paddle/go/pserver"
 )
@@ -30,9 +31,12 @@ func init() {
 		port[i] = p
 
 		go func(l net.Listener) {
-			s := pserver.NewService()
+			s, err := pserver.NewService("", time.Second*5)
+			if err != nil {
+				panic(err)
+			}
 			server := rpc.NewServer()
-			err := server.Register(s)
+			err = server.Register(s)
 			if err != nil {
 				panic(err)
 			}

go/pserver/service.go

 package pserver
 
 import (
+	"context"
 	"errors"
 	"fmt"
+	"strconv"
+	"strings"
 	"sync"
+	"time"
+
+	"github.com/PaddlePaddle/Paddle/go/utils/networkhelper"
+	"github.com/coreos/etcd/clientv3"
+	"github.com/coreos/etcd/clientv3/concurrency"
+	log "github.com/sirupsen/logrus"
 )
 
 // ElementType is the type of elements of a Parameter.
@@ -24,6 +33,9 @@ const (
 	Float64
 )
 
+// PsDesired is etcd path for store desired pserver count
+const PsDesired = "/ps_desired"
+
 // Parameter is a piece of data to sync with the parameter server.
 type Parameter struct {
 	Name        string
@@ -47,14 +59,121 @@ type Service struct {
 	mu       sync.Mutex
 	opt      *optimizer
 	paramMap map[string]Parameter
+
+	etcdEndpoints string
+	etcdClient    *clientv3.Client
+	// etcdTimeout is also used as retry intervals.
+	etcdTimeout time.Duration
+	// desired number of pservers in the job.
+	// assume desired will not change during one training job.
+	desired int
+	// FIXME: ensure GetExternalIP gets the correct ip for trainers to connect.
+	externalIP string
 }
 
-// NewService creates a new service.
-func NewService() *Service {
+// NewService creates a new service, will bypass etcd registration if no
+// endpoints specified.
+func NewService(endpoints string, timeout time.Duration) (*Service, error) {
 	s := &Service{opt: newOptimizer(sgd, 0.005)}
 	s.paramMap = make(map[string]Parameter)
 	s.initialized = make(chan struct{})
-	return s
+	s.etcdEndpoints = endpoints
+	s.etcdTimeout = timeout
+
+	var err error
+	s.externalIP, err = networkhelper.GetExternalIP()
+	if err != nil {
+		return nil, err
+	}
+
+	if endpoints != "" {
+		// initialize connection to etcd, try
+		ep := strings.Split(s.etcdEndpoints, ",")
+		for {
+			cli, err := clientv3.New(clientv3.Config{
+				Endpoints:   ep,
+				DialTimeout: s.etcdTimeout,
+			})
+			if err != nil {
+				log.Errorf("connect to etcd error: %v", err)
+				time.Sleep(s.etcdTimeout)
+				continue
+			}
+			s.etcdClient = cli
+			log.Debugf("inited client to %s", s.etcdEndpoints)
+			break
+		}
+		// wait and set s.desired init value
+		for {
+			ctx, cancel := context.WithTimeout(context.Background(), time.Second)
+			resp, err := s.etcdClient.Get(ctx, PsDesired)
+			cancel()
+			if err != nil {
+				log.Errorf("getting %s error: %v", PsDesired, err)
+				time.Sleep(s.etcdTimeout)
+				continue
+			}
+			if len(resp.Kvs) != 0 {
+				s.desired, err = strconv.Atoi(string(resp.Kvs[0].Value))
+				if err != nil {
+					log.Errorf("value of %s invalid %v\n", PsDesired, err)
+					time.Sleep(s.etcdTimeout)
+					// NOTE: wait util ps_desired value change
+					continue
+				}
+				break
+			}
+		}
+		// try register pserver node on etcd
+		for {
+			ctx, cancel := context.WithTimeout(context.Background(), time.Second)
+			_, err := s.registerPserverEtcd(ctx)
+			cancel()
+			if err != nil {
+				log.Warn(err)
+				time.Sleep(s.etcdTimeout)
+				continue
+			}
+			break
+		}
+	} // if endpoints != ""
+	// Bypass etcd registration if no endpoints specified
+	return s, nil
+}
+
+// registerPserverEtcd registers pserver node on etcd using transaction.
+func (s *Service) registerPserverEtcd(ctx context.Context) (*clientv3.TxnResponse, error) {
+	return concurrency.NewSTM(s.etcdClient, func(c concurrency.STM) error {
+		registered := false
+		for i := 0; i < s.desired; i++ {
+			psKey := "/ps/" + strconv.Itoa(i)
+			log.Debugf("checking %s", psKey)
+			ps := c.Get(psKey)
+			log.Debugf("got value (%s) for key: %s", ps, psKey)
+
+			if ps == "" {
+				resp, err := s.etcdClient.Grant(context.TODO(), 5)
+				if err != nil {
+					log.Fatal(err)
+				}
+				// find the first id and write info
+				c.Put(psKey, s.externalIP, clientv3.WithLease(resp.ID))
+				log.Debugf("set pserver node %s with value %s", psKey, s.externalIP)
+				_, kaerr := s.etcdClient.KeepAlive(context.TODO(), resp.ID)
+				if kaerr != nil {
+					log.Errorf("keepalive etcd node error: %v", kaerr)
+					return kaerr
+				}
+				log.Debug("register finished")
+				registered = true
+				break
+			}
+		}
+		if registered == true {
+			return nil
+		}
+		return errors.New("not registerd, may due to already have enough pservers")
+	}, concurrency.WithAbortContext(ctx), concurrency.WithIsolation(concurrency.RepeatableReads))
 }
 
 // InitParam initializes a parameter.

go/pserver/service_test.go

@@ -10,12 +10,15 @@ import (
 )
 
 func TestFull(t *testing.T) {
-	s := pserver.NewService()
+	s, err := pserver.NewService("", time.Second*5)
+	if err != nil {
+		t.Error(err)
+	}
 	var p pserver.Parameter
 	p.Name = "param_a"
 	p.Content = []byte{1, 0, 0, 0, 2, 0, 0, 0, 3, 0, 0, 0}
 	p.ElementType = pserver.Int32
-	err := s.InitParam(pserver.ParameterWithConfig{Param: p, Config: nil}, nil)
+	err = s.InitParam(pserver.ParameterWithConfig{Param: p, Config: nil}, nil)
 	if err != nil {
 		t.FailNow()
 	}
@@ -72,8 +75,11 @@ func TestFull(t *testing.T) {
 }
 
 func TestMultipleInit(t *testing.T) {
-	s := pserver.NewService()
-	err := s.FinishInitParams(0, nil)
+	s, err := pserver.NewService("", time.Second*5)
+	if err != nil {
+		t.Error(err)
+	}
+	err = s.FinishInitParams(0, nil)
 	if err != nil {
 		t.FailNow()
 	}
@@ -85,15 +91,18 @@ func TestMultipleInit(t *testing.T) {
 }
 
 func TestUninitialized(t *testing.T) {
-	s := pserver.NewService()
-	err := s.SendGrad(pserver.Gradient{}, nil)
+	s, err := pserver.NewService("", time.Second*5)
+	err = s.SendGrad(pserver.Gradient{}, nil)
 	if err.Error() != pserver.Uninitialized {
 		t.FailNow()
 	}
 }
 
 func TestBlockUntilInitialized(t *testing.T) {
-	s := pserver.NewService()
+	s, err := pserver.NewService("", time.Second*5)
+	if err != nil {
+		t.Error(err)
+	}
 	ch := make(chan struct{}, 2)
 	errCh := make(chan error, 2)
 	var wg sync.WaitGroup
@@ -133,7 +142,7 @@ func TestBlockUntilInitialized(t *testing.T) {
 	p.Name = "param_a"
 	p.Content = []byte{1, 0, 0, 0, 2, 0, 0, 0, 3, 0, 0, 0}
 	p.ElementType = pserver.Int32
-	err := s.InitParam(pserver.ParameterWithConfig{Param: p, Config: nil}, nil)
+	err = s.InitParam(pserver.ParameterWithConfig{Param: p, Config: nil}, nil)
 	if err != nil {
 		t.FailNow()
 	}

go/utils/networkhelper/helper.go

+package networkhelper
+
+import (
+	"errors"
+	"net"
+)
+
+// GetExternalIP returns the ip address of local network interface, not the
+// loopback device.
+func GetExternalIP() (string, error) {
+	ifaces, err := net.Interfaces()
+	if err != nil {
+		return "", err
+	}
+	for _, iface := range ifaces {
+		if iface.Flags&net.FlagUp == 0 {
+			continue // interface down
+		}
+		if iface.Flags&net.FlagLoopback != 0 {
+			continue // loopback interface
+		}
+		addrs, err := iface.Addrs()
+		if err != nil {
+			return "", err
+		}
+		for _, addr := range addrs {
+			var ip net.IP
+			switch v := addr.(type) {
+			case *net.IPNet:
+				ip = v.IP
+			case *net.IPAddr:
+				ip = v.IP
+			}
+			if ip == nil || ip.IsLoopback() {
+				continue
+			}
+			ip = ip.To4()
+			if ip == nil {
+				continue // not an ipv4 address
+			}
+			return ip.String(), nil
+		}
+	}
+	return "", errors.New("are you connected to the network?")
+}

go/utils/networkhelper/helper_test.go

+package networkhelper
+
+import "testing"
+
+func TestGetIP(t *testing.T) {
+	_, err := GetExternalIP()
+	if err != nil {
+		t.Errorf("GetExternalIP returns error : %v\n", err)
+	}
+}

paddle/gserver/evaluators/DetectionMAPEvaluator.cpp

+/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve.
+
+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 "Evaluator.h"
+#include "paddle/gserver/layers/DetectionUtil.h"
+
+using std::map;
+using std::vector;
+using std::pair;
+using std::make_pair;
+
+namespace paddle {
+
+/**
+ * @brief detection map Evaluator
+ *
+ * The config file api is detection_map_evaluator.
+ */
+class DetectionMAPEvaluator : public Evaluator {
+public:
+  DetectionMAPEvaluator()
+      : evaluateDifficult_(false), cpuOutput_(nullptr), cpuLabel_(nullptr) {}
+
+  virtual void start() {
+    Evaluator::start();
+    allTruePos_.clear();
+    allFalsePos_.clear();
+    numPos_.clear();
+  }
+
+  virtual real evalImp(std::vector<Argument>& arguments) {
+    overlapThreshold_ = config_.overlap_threshold();
+    backgroundId_ = config_.background_id();
+    evaluateDifficult_ = config_.evaluate_difficult();
+    apType_ = config_.ap_type();
+
+    MatrixPtr detectTmpValue = arguments[0].value;
+    Matrix::resizeOrCreate(cpuOutput_,
+                           detectTmpValue->getHeight(),
+                           detectTmpValue->getWidth(),
+                           false,
+                           false);
+
+    MatrixPtr labelTmpValue = arguments[1].value;
+    Matrix::resizeOrCreate(cpuLabel_,
+                           labelTmpValue->getHeight(),
+                           labelTmpValue->getWidth(),
+                           false,
+                           false);
+
+    cpuOutput_->copyFrom(*detectTmpValue);
+    cpuLabel_->copyFrom(*labelTmpValue);
+
+    Argument label = arguments[1];
+    const int* labelIndex = label.sequenceStartPositions->getData(false);
+    size_t batchSize = label.getNumSequences();
+
+    vector<map<size_t, vector<NormalizedBBox>>> allGTBBoxes;
+    vector<map<size_t, vector<pair<real, NormalizedBBox>>>> allDetectBBoxes;
+
+    for (size_t n = 0; n < batchSize; ++n) {
+      map<size_t, vector<NormalizedBBox>> bboxes;
+      for (int i = labelIndex[n]; i < labelIndex[n + 1]; ++i) {
+        vector<NormalizedBBox> bbox;
+        getBBoxFromLabelData(cpuLabel_->getData() + i * 6, 1, bbox);
+        int c = cpuLabel_->getData()[i * 6];
+        bboxes[c].push_back(bbox[0]);
+      }
+      allGTBBoxes.push_back(bboxes);
+    }
+
+    size_t n = 0;
+    const real* cpuOutputData = cpuOutput_->getData();
+    for (size_t imgId = 0; imgId < batchSize; ++imgId) {
+      map<size_t, vector<pair<real, NormalizedBBox>>> bboxes;
+      size_t curImgId = static_cast<size_t>((cpuOutputData + n * 7)[0]);
+      while (curImgId == imgId && n < cpuOutput_->getHeight()) {
+        vector<real> label;
+        vector<real> score;
+        vector<NormalizedBBox> bbox;
+        getBBoxFromDetectData(cpuOutputData + n * 7, 1, label, score, bbox);
+        bboxes[label[0]].push_back(make_pair(score[0], bbox[0]));
+        ++n;
+        curImgId = static_cast<size_t>((cpuOutputData + n * 7)[0]);
+      }
+      allDetectBBoxes.push_back(bboxes);
+    }
+
+    for (size_t n = 0; n < batchSize; ++n) {
+      for (map<size_t, vector<NormalizedBBox>>::iterator it =
+               allGTBBoxes[n].begin();
+           it != allGTBBoxes[n].end();
+           ++it) {
+        size_t count = 0;
+        if (evaluateDifficult_) {
+          count = it->second.size();
+        } else {
+          for (size_t i = 0; i < it->second.size(); ++i)
+            if (!(it->second[i].isDifficult)) ++count;
+        }
+        if (numPos_.find(it->first) == numPos_.end() && count != 0) {
+          numPos_[it->first] = count;
+        } else {
+          numPos_[it->first] += count;
+        }
+      }
+    }
+
+    // calcTFPos
+    calcTFPos(batchSize, allGTBBoxes, allDetectBBoxes);
+
+    return 0;
+  }
+
+  virtual void printStats(std::ostream& os) const {
+    real mAP = calcMAP();
+    os << "Detection mAP=" << mAP;
+  }
+
+  virtual void distributeEval(ParameterClient2* client) {
+    LOG(FATAL) << "Distribute detection evaluation not implemented.";
+  }
+
+protected:
+  void calcTFPos(const size_t batchSize,
+                 const vector<map<size_t, vector<NormalizedBBox>>>& allGTBBoxes,
+                 const vector<map<size_t, vector<pair<real, NormalizedBBox>>>>&
+                     allDetectBBoxes) {
+    for (size_t n = 0; n < allDetectBBoxes.size(); ++n) {
+      if (allGTBBoxes[n].size() == 0) {
+        for (map<size_t, vector<pair<real, NormalizedBBox>>>::const_iterator
+                 it = allDetectBBoxes[n].begin();
+             it != allDetectBBoxes[n].end();
+             ++it) {
+          size_t label = it->first;
+          for (size_t i = 0; i < it->second.size(); ++i) {
+            allTruePos_[label].push_back(make_pair(it->second[i].first, 0));
+            allFalsePos_[label].push_back(make_pair(it->second[i].first, 1));
+          }
+        }
+      } else {
+        for (map<size_t, vector<pair<real, NormalizedBBox>>>::const_iterator
+                 it = allDetectBBoxes[n].begin();
+             it != allDetectBBoxes[n].end();
+             ++it) {
+          size_t label = it->first;
+          vector<pair<real, NormalizedBBox>> predBBoxes = it->second;
+          if (allGTBBoxes[n].find(label) == allGTBBoxes[n].end()) {
+            for (size_t i = 0; i < predBBoxes.size(); ++i) {
+              allTruePos_[label].push_back(make_pair(predBBoxes[i].first, 0));
+              allFalsePos_[label].push_back(make_pair(predBBoxes[i].first, 1));
+            }
+          } else {
+            vector<NormalizedBBox> gtBBoxes =
+                allGTBBoxes[n].find(label)->second;
+            vector<bool> visited(gtBBoxes.size(), false);
+            // Sort detections in descend order based on scores
+            std::sort(predBBoxes.begin(),
+                      predBBoxes.end(),
+                      sortScorePairDescend<NormalizedBBox>);
+            for (size_t i = 0; i < predBBoxes.size(); ++i) {
+              real maxOverlap = -1.0;
+              size_t maxIdx = 0;
+              for (size_t j = 0; j < gtBBoxes.size(); ++j) {
+                real overlap =
+                    jaccardOverlap(predBBoxes[i].second, gtBBoxes[j]);
+                if (overlap > maxOverlap) {
+                  maxOverlap = overlap;
+                  maxIdx = j;
+                }
+              }
+              if (maxOverlap > overlapThreshold_) {
+                if (evaluateDifficult_ ||
+                    (!evaluateDifficult_ && !gtBBoxes[maxIdx].isDifficult)) {
+                  if (!visited[maxIdx]) {
+                    allTruePos_[label].push_back(
+                        make_pair(predBBoxes[i].first, 1));
+                    allFalsePos_[label].push_back(
+                        make_pair(predBBoxes[i].first, 0));
+                    visited[maxIdx] = true;
+                  } else {
+                    allTruePos_[label].push_back(
+                        make_pair(predBBoxes[i].first, 0));
+                    allFalsePos_[label].push_back(
+                        make_pair(predBBoxes[i].first, 1));
+                  }
+                }
+              } else {
+                allTruePos_[label].push_back(make_pair(predBBoxes[i].first, 0));
+                allFalsePos_[label].push_back(
+                    make_pair(predBBoxes[i].first, 1));
+              }
+            }
+          }
+        }
+      }
+    }
+  }
+
+  real calcMAP() const {
+    real mAP = 0.0;
+    size_t count = 0;
+    for (map<size_t, size_t>::const_iterator it = numPos_.begin();
+         it != numPos_.end();
+         ++it) {
+      size_t label = it->first;
+      size_t labelNumPos = it->second;
+      if (labelNumPos == 0 || allTruePos_.find(label) == allTruePos_.end())
+        continue;
+      vector<pair<real, size_t>> labelTruePos = allTruePos_.find(label)->second;
+      vector<pair<real, size_t>> labelFalsePos =
+          allFalsePos_.find(label)->second;
+      // Compute average precision.
+      vector<size_t> tpCumSum;
+      getAccumulation(labelTruePos, &tpCumSum);
+      vector<size_t> fpCumSum;
+      getAccumulation(labelFalsePos, &fpCumSum);
+      std::vector<real> precision, recall;
+      size_t num = tpCumSum.size();
+      // Compute Precision.
+      for (size_t i = 0; i < num; ++i) {
+        CHECK_LE(tpCumSum[i], labelNumPos);
+        precision.push_back(static_cast<real>(tpCumSum[i]) /
+                            static_cast<real>(tpCumSum[i] + fpCumSum[i]));
+        recall.push_back(static_cast<real>(tpCumSum[i]) / labelNumPos);
+      }
+      // VOC2007 style
+      if (apType_ == "11point") {
+        vector<real> maxPrecisions(11, 0.0);
+        int startIdx = num - 1;
+        for (int j = 10; j >= 0; --j)
+          for (int i = startIdx; i >= 0; --i) {
+            if (recall[i] < j / 10.) {
+              startIdx = i;
+              if (j > 0) maxPrecisions[j - 1] = maxPrecisions[j];
+              break;
+            } else {
+              if (maxPrecisions[j] < precision[i])
+                maxPrecisions[j] = precision[i];
+            }
+          }
+        for (int j = 10; j >= 0; --j) mAP += maxPrecisions[j] / 11;
+        ++count;
+      } else if (apType_ == "Integral") {
+        // Nature integral
+        real averagePrecisions = 0.;
+        real prevRecall = 0.;
+        for (size_t i = 0; i < num; ++i) {
+          if (fabs(recall[i] - prevRecall) > 1e-6)
+            averagePrecisions += precision[i] * fabs(recall[i] - prevRecall);
+          prevRecall = recall[i];
+        }
+        mAP += averagePrecisions;
+        ++count;
+      } else {
+        LOG(FATAL) << "Unkown ap version: " << apType_;
+      }
+    }
+    if (count != 0) mAP /= count;
+    return mAP * 100;
+  }
+
+  void getAccumulation(vector<pair<real, size_t>> inPairs,
+                       vector<size_t>* accuVec) const {
+    std::stable_sort(
+        inPairs.begin(), inPairs.end(), sortScorePairDescend<size_t>);
+    accuVec->clear();
+    size_t sum = 0;
+    for (size_t i = 0; i < inPairs.size(); ++i) {
+      sum += inPairs[i].second;
+      accuVec->push_back(sum);
+    }
+  }
+
+  std::string getTypeImpl() const { return "detection_map"; }
+
+  real getValueImpl() const { return calcMAP(); }
+
+private:
+  real overlapThreshold_;  // overlap threshold when determining whether matched
+  bool evaluateDifficult_;  // whether evaluate difficult ground truth
+  size_t backgroundId_;     // class index of background
+  std::string apType_;      // how to calculate mAP (Integral or 11point)
+
+  MatrixPtr cpuOutput_;
+  MatrixPtr cpuLabel_;
+
+  map<size_t, size_t> numPos_;  // counts of true objects each classification
+  map<size_t, vector<pair<real, size_t>>>
+      allTruePos_;  // true positive prediction
+  map<size_t, vector<pair<real, size_t>>>
+      allFalsePos_;  // false positive prediction
+};
+
+REGISTER_EVALUATOR(detection_map, DetectionMAPEvaluator);
+
+}  // namespace paddle

paddle/gserver/tests/test_Evaluator.cpp

@@ -138,6 +138,23 @@ void testEvaluatorAll(TestConfig testConf,
   testEvaluator(testConf, testEvaluatorName, batchSize, false);
 }
 
+TEST(Evaluator, detection_map) {
+  TestConfig config;
+  config.evaluatorConfig.set_type("detection_map");
+  config.evaluatorConfig.set_overlap_threshold(0.5);
+  config.evaluatorConfig.set_background_id(0);
+  config.evaluatorConfig.set_ap_type("Integral");
+  config.evaluatorConfig.set_evaluate_difficult(0);
+
+  config.inputDefs.push_back({INPUT_DATA, "output", 7});
+  config.inputDefs.push_back({INPUT_SEQUENCE_DATA, "label", 6});
+  config.evaluatorConfig.set_evaluate_difficult(false);
+  testEvaluatorAll(config, "detection_map", 100);
+
+  config.evaluatorConfig.set_evaluate_difficult(true);
+  testEvaluatorAll(config, "detection_map", 100);
+}
+
 TEST(Evaluator, classification_error) {
   TestConfig config;
   config.evaluatorConfig.set_type("classification_error");

paddle/parameter/ParameterUpdaterHook.cpp

@@ -14,11 +14,13 @@ limitations under the License. */
 
 #include "ParameterUpdaterHook.h"
 
+#include <algorithm>
 #include <atomic>
 #include <fstream>
 #include <mutex>
 #include <thread>
 #include <unordered_map>
+#include <vector>
 
 #include "paddle/math/Vector.h"
 #include "paddle/parameter/Parameter.h"
@@ -29,106 +31,76 @@ namespace paddle {
 
 /**
  * The static pruning hook
- *
- * Static means user load a mask map before training started. This map will
- * define which link/weight between neural is disabled.
+ * Static means user specify a sparsity_ratio before training started, and the
+ * network will prune the parameters based on the sparsity_ratio. More details
+ * can be found https://arxiv.org/pdf/1506.02626.pdf.
  */
+
 class StaticPruningHook : public IParameterUpdaterHook {
 public:
-  /**
-   * The Mask Map Header.
-   * The map file started with this header.
-   *
-   * In Version 0, reset file will be:
-   *  contains header.size bit, each bit means such weight is enabled or not.
-   *    if bit is 1, then such weight is enabled.
-   *  at end, the file will round to byte, and the low bits of end byte will be
-   *  filled by zero.
-   *
-   */
-  struct StaticMaskHeader {
-    uint32_t version;
-    size_t size;
-  } __attribute__((__packed__));
-
-  explicit StaticPruningHook(const std::string& mask_filename) : initCount_(0) {
-    bool ok = this->loadMaskFile(mask_filename);
-    if (!ok) {
-      LOG(WARNING) << "Fail to load mask file " << mask_filename
-                   << " in current directory, searching in init_model_path";
-      std::string combineMaskFilename =
-          path::join(FLAGS_init_model_path, mask_filename);
-      CHECK(this->loadMaskFile(combineMaskFilename))
-          << "Cannot load " << mask_filename << " in ./" << mask_filename
-          << " and " << combineMaskFilename;
-    }
-    VLOG(3) << mask_filename << " mask size = " << this->mask_.size();
+  explicit StaticPruningHook(const ParameterUpdaterHookConfig &hookConfig)
+      : initCount_(0) {
+    sparsityRatio_ = hookConfig.sparsity_ratio();
   }
 
-  void update(Parameter* para) {
+  static bool sortPairAscend(const std::pair<real, size_t> &pair1,
+                             const std::pair<real, size_t> &pair2) {
+    return pair1.first > pair2.first;
+  }
+
+  void update(Parameter *para) {
     updateThreadChecker_.check();
-    auto& vec = para->getBuf(PARAMETER_GRADIENT);
+    auto &vec = para->getBuf(PARAMETER_GRADIENT);
     if (vec) {
       vec->dotMul(*maskVec_);
     }
   }
 
-  void init(Parameter* para) {
-    size_t initCount = this->initCount_.fetch_add(1);
-    CHECK_EQ(initCount, 0UL) << "Currently the StaticPruningHook must invoke "
-                                "in same ParamterUpdater";
-    VLOG(3) << "Initialize Parameter " << para;
-    SetDevice device(para->getDeviceId());
+  void generateMask(Parameter *para) {
+    VectorPtr maskTemp = Vector::create(para->getSize(), false);
+    maskTemp->zeroMem();
+    real *maskTempData = maskTemp->getData();
+    size_t nonZeroNum = para->getSize() * (1 - sparsityRatio_);
 
-    auto maskVec = Vector::create(this->mask_.size(), false);
-    {  // Initialize maskVec with float mask vector
-      real* dataPtr = maskVec->getData();
-      size_t i = 0;
-      for (bool m : mask_) {
-        dataPtr[i++] = m ? 1.0 : 0.0;
-      }
-    }
+    VectorPtr paraVec = para->getBuf(PARAMETER_VALUE);
+    VectorPtr paraCpuCopy = Vector::create(para->getSize(), false);
+
+    paraCpuCopy->copyFrom(*paraVec);
+    std::vector<std::pair<real, size_t>> param;
+
+    for (size_t i = 0; i < para->getSize(); i++)
+      param.push_back(std::make_pair(fabs(paraCpuCopy->getData()[i]), i));
+
+    std::partial_sort(
+        param.begin(), param.begin() + nonZeroNum, param.end(), sortPairAscend);
+    for (size_t i = 0; i < nonZeroNum; i++) maskTempData[param[i].second] = 1.0;
 
     // Currently just use a mask vector for hack.
-    // @TODO(yuyang18): Implemented the mask operation in vector.
     if (para->useGpu()) {
-      maskVec_ = Vector::create(this->mask_.size(), para->useGpu());
-      maskVec_->copyFrom(*maskVec);
+      maskVec_ = Vector::create(para->getSize(), para->useGpu());
+      maskVec_->copyFrom(*maskTemp);
     } else {
-      maskVec_ = maskVec;
+      maskVec_ = maskTemp;
     }
-
-    auto& vec = para->getBuf(PARAMETER_VALUE);
-    vec->dotMul(*maskVec_);
   }
 
-private:
-  bool loadMaskFile(const std::string& mask_filename) {
-    std::ifstream fin;
-    fin.open(mask_filename);
-    if (fin.is_open()) {
-      StaticMaskHeader header;
-      fin.read(reinterpret_cast<char*>(&header), sizeof(StaticMaskHeader));
-      CHECK_EQ(header.version, 0UL);
-      mask_.resize(header.size);
-      uint8_t buf;
-      for (size_t i = 0; i < header.size; ++i, buf <<= 1) {
-        if (i % 8 == 0) {
-          fin.read(reinterpret_cast<char*>(&buf), sizeof(uint8_t));
-        }
-        mask_[i] = buf & 0x80;
-      }
-      fin.close();
-      return true;
-    } else {
-      return false;
-    }
+  void init(Parameter *para) {
+    generateMask(para);
+    size_t initCount = this->initCount_.fetch_add(1);
+    CHECK_EQ(initCount, 0UL) << "Currently the StaticPruningHook must invoke "
+                                "in same ParamterUpdater";
+    VLOG(3) << "Initialize Parameter " << para;
+    SetDevice device(para->getDeviceId());
+
+    auto &paraVec = para->getBuf(PARAMETER_VALUE);
+    paraVec->dotMul(*maskVec_);
   }
 
+private:
   SameThreadChecker updateThreadChecker_;
   std::atomic<size_t> initCount_;
   VectorPtr maskVec_;
-  std::vector<bool> mask_;
+  real sparsityRatio_;
 };
 
 IParameterUpdaterHook::IParameterUpdaterHook() {}
@@ -145,7 +117,7 @@ IParameterUpdaterHook::~IParameterUpdaterHook() {}
  */
 class StringIntPairHasher {
 public:
-  size_t operator()(const std::pair<std::string, int>& k) const {
+  size_t operator()(const std::pair<std::string, int> &k) const {
     return intHasher_(strHasher_(k.first) + k.second);
   }
 
@@ -162,19 +134,19 @@ static WeakKVCache<std::pair<std::string, int>,
 /**
  * ParameterUpdaterHook actually factory method.
  */
-static IParameterUpdaterHook* createImpl(
-    const ParameterUpdaterHookConfig& config) {
-  auto& type = config.type();
+static IParameterUpdaterHook *createImpl(
+    const ParameterUpdaterHookConfig &config) {
+  auto &type = config.type();
   if (type == "pruning") {
-    if (config.has_purning_mask_filename()) {
-      return new StaticPruningHook(config.purning_mask_filename());
-    }
+    return new StaticPruningHook(config);
   }
+
+  LOG(FATAL) << "Unknown Hook type:  " << type;
   return nullptr;
 }
 
 std::shared_ptr<IParameterUpdaterHook> IParameterUpdaterHook::create(
-    const ParameterConfig& paramConfig, int idx) {
+    const ParameterConfig &paramConfig, int idx) {
   std::pair<std::string, int> key = {paramConfig.name(), idx};
   return g_hookCache_.get(
       key, [&] { return createImpl(paramConfig.update_hooks(idx)); });

paddle/scripts/travis/build_and_test.sh

-#!/bin/bash
-source ./common.sh
-
-NPROC=1
-export PYTHONPATH=/opt/python/2.7.12/lib/python2.7/site-packages
-export PYTHONHOME=/opt/python/2.7.12
-export PATH=/opt/python/2.7.12/bin:${PATH}
-cmake .. -DCMAKE_Fortran_COMPILER=/usr/bin/gfortran-4.8 -DON_TRAVIS=ON -DWITH_COVERAGE=ON -DCOVERALLS_UPLOAD=ON ${EXTRA_CMAKE_OPTS}
-NRPOC=`nproc`
-make -j $NPROC
-make coveralls
-sudo make install

paddle/scripts/travis/docs.sh → paddle/scripts/travis/build_doc.sh

 #!/bin/bash
+set -e
+
+# Create the build directory for CMake.
+mkdir -p $TRAVIS_BUILD_DIR/build
+cd $TRAVIS_BUILD_DIR/build
 
-# Add set -e, cd to directory.
-source ./common.sh
 # Compile Documentation only.
-cmake .. -DCMAKE_BUILD_TYPE=Debug -DCMAKE_Fortran_COMPILER=/usr/bin/gfortran-4.8 -DWITH_GPU=OFF -DWITH_DOC=OFF -DWITH_STYLE_CHECK=OFF ${EXTRA_CMAKE_OPTS}
+cmake .. -DCMAKE_BUILD_TYPE=Debug -DWITH_GPU=OFF -DWITH_DOC=OFF -DWITH_STYLE_CHECK=OFF
 mkdir output
 make -j `nproc`
 find .. -name '*whl' | xargs pip install  # install all wheels.
 rm -rf *
-cmake .. -DCMAKE_BUILD_TYPE=Debug -DCMAKE_Fortran_COMPILER=/usr/bin/gfortran-4.8 -DWITH_GPU=OFF -DWITH_DOC=ON ${EXTRA_CMAKE_OPTS}
-make paddle_docs paddle_docs_cn
+cmake .. -DCMAKE_BUILD_TYPE=Debug -DWITH_GPU=OFF -DWITH_DOC=ON
+make -j `nproc` paddle_docs paddle_docs_cn
 
 # check websites for broken links
 linkchecker doc/en/html/index.html

paddle/scripts/travis/precommit.sh → paddle/scripts/travis/check_style.sh

 #!/bin/bash
 function abort(){
-    echo "Your commit not fit PaddlePaddle code style" 1>&2
-    echo "Please use pre-commit scripts to auto-format your code" 1>&2
+    echo "Your change doesn't follow PaddlePaddle's code style." 1>&2
+    echo "Please use pre-commit to reformat your code and git push again." 1>&2
     exit 1
 }
 
 trap 'abort' 0
 set -e
-source common.sh
-cd ..
+
+cd $TRAVIS_BUILD_DIR
 export PATH=/usr/bin:$PATH
 pre-commit install
 clang-format --version

paddle/scripts/travis/common.sh

-#!/bin/bash
-set -e
-mkdir -p ../../../build
-cd ../../../build
-mkdir -p $HOME/third_party
-EXTRA_CMAKE_OPTS="-DTHIRD_PARTY_PATH=${HOME}/third_party"

paddle/scripts/travis/main.sh

-#!/bin/bash
-cd `dirname $0`
-
-if [ ${JOB} == "BUILD_AND_TEST" ]; then
-  ./build_and_test.sh
-elif [ ${JOB} == "DOCS" ]; then
-  ./docs.sh
-elif [ ${JOB} == "PRE_COMMIT" ]; then
-  ./precommit.sh
-else
-  echo Unknown job ${JOB}
-  exit 1
-fi

proto/ModelConfig.proto

@@ -489,6 +489,15 @@ message EvaluatorConfig {
   // Used by ClassificationErrorEvaluator
   // top # classification error
   optional int32 top_k = 13 [default = 1];
+
+  // Used by DetectionMAPEvaluator
+  optional double overlap_threshold = 14 [default = 0.5];
+
+  optional int32 background_id = 15 [default = 0];
+
+  optional bool evaluate_difficult = 16 [default = false];
+
+  optional string ap_type = 17 [default = "11point"];
 }
 
 message LinkConfig {

proto/ParameterConfig.proto

@@ -25,8 +25,10 @@ enum ParameterInitStrategy {
 }
 
 message ParameterUpdaterHookConfig {
+  // hook type such as  'pruning'
   required string type = 1;
-  optional string purning_mask_filename = 2;
+  // this represents the ratio of zero element to be set by the Parameter 
+  optional double sparsity_ratio = 2 [default = 0.6];
 }
 
 message ParameterConfig {

python/paddle/trainer/config_parser.py

@@ -1280,20 +1280,23 @@ def parse_maxout(maxout, input_layer_name, maxout_conf):
 
 # Define an evaluator
 @config_func
-def Evaluator(
-        name,
-        type,
-        inputs,
-        chunk_scheme=None,
-        num_chunk_types=None,
-        classification_threshold=None,
-        positive_label=None,
-        dict_file=None,
-        result_file=None,
-        num_results=None,
-        top_k=None,
-        delimited=None,
-        excluded_chunk_types=None, ):
+def Evaluator(name,
+              type,
+              inputs,
+              chunk_scheme=None,
+              num_chunk_types=None,
+              classification_threshold=None,
+              positive_label=None,
+              dict_file=None,
+              result_file=None,
+              num_results=None,
+              top_k=None,
+              delimited=None,
+              excluded_chunk_types=None,
+              overlap_threshold=None,
+              background_id=None,
+              evaluate_difficult=None,
+              ap_type=None):
     evaluator = g_config.model_config.evaluators.add()
     evaluator.type = type
     evaluator.name = MakeLayerNameInSubmodel(name)
@@ -1327,6 +1330,18 @@ def Evaluator(
     if excluded_chunk_types:
         evaluator.excluded_chunk_types.extend(excluded_chunk_types)
 
+    if overlap_threshold is not None:
+        evaluator.overlap_threshold = overlap_threshold
+
+    if background_id is not None:
+        evaluator.background_id = background_id
+
+    if evaluate_difficult is not None:
+        evaluator.evaluate_difficult = evaluate_difficult
+
+    if ap_type is not None:
+        evaluator.ap_type = ap_type
+
 
 class LayerBase(object):
     def __init__(
@@ -3124,11 +3139,11 @@ def Layer(name, type, **xargs):
 @config_func
 def ParameterHook(type, **kwargs):
     if type == 'pruning':
-        mask_filename = kwargs.get('mask_filename', None)
-        assert mask_filename is not None
         hook = ParameterUpdaterHookConfig()
         hook.type = type
-        hook.purning_mask_filename = mask_filename
+        sparsity_ratio = kwargs.get('sparsity_ratio', None)
+        if sparsity_ratio is not None:
+            hook.sparsity_ratio = sparsity_ratio
         return hook
     else:
         return None
@@ -3236,13 +3251,13 @@ def Parameter(name,
 
     if update_hooks is not None:
         if hasattr(update_hooks, '__call__'):
-            update_hooks = update_hooks(para.name)
+            update_hooks = update_hooks()
 
         if isinstance(update_hooks, list):
             for hook in update_hooks:
                 para.update_hooks.extend([hook])
         else:
-            para.update_hooks.extend(update_hooks)
+            para.update_hooks.extend([update_hooks])
 
     g_parameter_map[name] = para
     if initializer is not None:

python/paddle/trainer_config_helpers/attrs.py

@@ -14,7 +14,8 @@
 
 from paddle.trainer.config_parser import *
 __all__ = [
-    'ParamAttr', 'ExtraAttr', 'ParameterAttribute', 'ExtraLayerAttribute'
+    'HookAttr', 'ParamAttr', 'ExtraAttr', 'ParameterAttribute',
+    'ExtraLayerAttribute'
 ]
 
 
@@ -55,6 +56,40 @@ def is_compatible_with(x, Type):
         return False
 
 
+class HookAttribute(object):
+    """
+    Hook Attribute object. As a member of ParameterAttribute class, the hook is an auxiliary operation that occurs 
+    during training process of a layer with parameters, such as img_conv layer, fc layer.
+
+    :param  type: Hook type, currently supported types: 
+                        'pruning' :  user specify a sparsity_ratio before training started, and the
+                            network will prune the parameters based on the sparsity_ratio. 
+                            eg: The definition of Hook object can be hk = HookAttribute('pruning', 0.6)
+                            The specific usage can be paddle.layer.img_conv(input=img, filter_size=3,
+                                                                       num_channels=3, num_filters=64,
+                                                                       param_attr=ParameterAttribute(update_hooks=hk) )
+                            The pruning details can be found https://arxiv.org/pdf/1506.02626.pdf
+    :type type: string
+
+    :param sparsity_ratio: Must be specified if hook type is 'pruning', 
+                        it represents the ratio of the zero elements to be set by the Parameter.
+    :type sparsity_ratio: float or None
+	
+    """
+
+    def __init__(self, type, sparsity_ratio=None):
+        self.type = type
+        self.sparsity_ratio = sparsity_ratio
+        if self.sparsity_ratio is not None:
+            assert is_compatible_with(
+                self.sparsity_ratio,
+                float), 'sparisity_ratio must be float type'
+            assert self.sparsity_ratio <= 1 and self.sparsity_ratio >= 0, 'sparsity_ratio must be a float between [0, 1] '
+
+    def __call__(self):
+        return ParameterHook(self.type, sparsity_ratio=self.sparsity_ratio)
+
+
 class ParameterAttribute(object):
     """
     Parameter Attributes object. To fine-tuning network training process, user
@@ -114,6 +149,7 @@ class ParameterAttribute(object):
                  momentum=None,
                  gradient_clipping_threshold=None,
                  sparse_update=False,
+                 update_hooks=None,
                  initializer=None):
         self.attr = {}
 
@@ -169,6 +205,9 @@ class ParameterAttribute(object):
         if initializer is not None:
             self.attr['initializer'] = initializer
 
+        if update_hooks:
+            self.attr['update_hooks'] = update_hooks
+
     def set_default_parameter_name(self, name):
         """
         Set default parameter name. If parameter not set, then will use default
@@ -244,5 +283,6 @@ class ExtraLayerAttribute(object):
             return attr.attr
 
 
+HookAttr = HookAttribute
 ParamAttr = ParameterAttribute
 ExtraAttr = ExtraLayerAttribute

python/paddle/trainer_config_helpers/evaluators.py

@@ -21,7 +21,8 @@ __all__ = [
     "chunk_evaluator", "sum_evaluator", "column_sum_evaluator",
     "value_printer_evaluator", "gradient_printer_evaluator",
     "maxid_printer_evaluator", "maxframe_printer_evaluator",
-    "seqtext_printer_evaluator", "classification_error_printer_evaluator"
+    "seqtext_printer_evaluator", "classification_error_printer_evaluator",
+    "detection_map_evaluator"
 ]
 
 
@@ -31,10 +32,11 @@ class EvaluatorAttribute(object):
     FOR_RANK = 1 << 2
     FOR_PRINT = 1 << 3
     FOR_UTILS = 1 << 4
+    FOR_DETECTION = 1 << 5
 
     KEYS = [
         "for_classification", "for_regression", "for_rank", "for_print",
-        "for_utils"
+        "for_utils", "for_detection"
     ]
 
     @staticmethod
@@ -57,22 +59,25 @@ def evaluator(*attrs):
     return impl
 
 
-def evaluator_base(
-        input,
-        type,
-        label=None,
-        weight=None,
-        name=None,
-        chunk_scheme=None,
-        num_chunk_types=None,
-        classification_threshold=None,
-        positive_label=None,
-        dict_file=None,
-        result_file=None,
-        num_results=None,
-        delimited=None,
-        top_k=None,
-        excluded_chunk_types=None, ):
+def evaluator_base(input,
+                   type,
+                   label=None,
+                   weight=None,
+                   name=None,
+                   chunk_scheme=None,
+                   num_chunk_types=None,
+                   classification_threshold=None,
+                   positive_label=None,
+                   dict_file=None,
+                   result_file=None,
+                   num_results=None,
+                   delimited=None,
+                   top_k=None,
+                   excluded_chunk_types=None,
+                   overlap_threshold=None,
+                   background_id=None,
+                   evaluate_difficult=None,
+                   ap_type=None):
     """
     Evaluator will evaluate the network status while training/testing.
 
@@ -107,6 +112,14 @@ def evaluator_base(
     :type weight: LayerOutput.
     :param top_k: number k in top-k error rate
     :type top_k: int
+    :param overlap_threshold: In detection tasks to filter detection results
+    :type overlap_threshold: float
+    :param background_id: Identifier of background class
+    :type background_id: int
+    :param evaluate_difficult: Whether to evaluate difficult objects
+    :type evaluate_difficult: bool
+    :param ap_type: How to calculate average persicion
+    :type ap_type: str
     """
     # inputs type assertions.
     assert classification_threshold is None or isinstance(
@@ -136,7 +149,61 @@ def evaluator_base(
         delimited=delimited,
         num_results=num_results,
         top_k=top_k,
-        excluded_chunk_types=excluded_chunk_types, )
+        excluded_chunk_types=excluded_chunk_types,
+        overlap_threshold=overlap_threshold,
+        background_id=background_id,
+        evaluate_difficult=evaluate_difficult,
+        ap_type=ap_type)
+
+
+@evaluator(EvaluatorAttribute.FOR_DETECTION)
+@wrap_name_default()
+def detection_map_evaluator(input,
+                            label,
+                            overlap_threshold=0.5,
+                            background_id=0,
+                            evaluate_difficult=False,
+                            ap_type="11point",
+                            name=None):
+    """
+    Detection mAP Evaluator. It will print mean Average Precision (mAP) for detection.
+
+    The detection mAP Evaluator based on the output of detection_output layer counts
+    the true positive and the false positive bbox and integral them to get the
+    mAP.
+
+    The simple usage is:
+
+    .. code-block:: python
+
+       eval =  detection_map_evaluator(input=det_output,label=lbl)
+
+    :param input: Input layer.
+    :type input: LayerOutput
+    :param label: Label layer.
+    :type label: LayerOutput
+    :param overlap_threshold: The bbox overlap threshold of a true positive.
+    :type overlap_threshold: float
+    :param background_id: The background class index.
+    :type background_id: int
+    :param evaluate_difficult: Whether evaluate a difficult ground truth.
+    :type evaluate_difficult: bool
+    """
+    if not isinstance(input, list):
+        input = [input]
+
+    if label:
+        input.append(label)
+
+    evaluator_base(
+        name=name,
+        type="detection_map",
+        input=input,
+        label=label,
+        overlap_threshold=overlap_threshold,
+        background_id=background_id,
+        evaluate_difficult=evaluate_difficult,
+        ap_type=ap_type)
 
 
 @evaluator(EvaluatorAttribute.FOR_CLASSIFICATION)

python/paddle/trainer_config_helpers/layers.py

@@ -3839,7 +3839,8 @@ def classification_cost(input,
                         weight=None,
                         name=None,
                         evaluator=classification_error_evaluator,
-                        layer_attr=None):
+                        layer_attr=None,
+                        coeff=1.):
     """
     classification cost Layer.
 
@@ -3855,6 +3856,8 @@ def classification_cost(input,
     :param evaluator: Evaluator method.
     :param layer_attr: layer's extra attribute.
     :type layer_attr: ExtraLayerAttribute
+    :param coeff: The coefficient affects the gradient in the backward.
+    :type coeff: float
     :return: LayerOutput object.
     :rtype: LayerOutput
     """
@@ -3868,6 +3871,7 @@ def classification_cost(input,
         name=name,
         type="multi-class-cross-entropy",
         inputs=ipts,
+        coeff=coeff,
         **ExtraLayerAttribute.to_kwargs(layer_attr))
 
     def __add_evaluator__(e):

python/paddle/v2/attr.py

@@ -17,10 +17,12 @@ import paddle.trainer_config_helpers.attrs
 __all__ = [
     "Param",
     "Extra",
+    "Hook",
 ]
 
 Param = paddle.trainer_config_helpers.attrs.ParameterAttribute
 Extra = paddle.trainer_config_helpers.attrs.ExtraLayerAttribute
+Hook = paddle.trainer_config_helpers.attrs.HookAttribute
 
 for each in paddle.trainer_config_helpers.attrs.__all__:
     globals()[each] = getattr(paddle.trainer_config_helpers.attrs, each)