Merge pull request #2467 from pkuyym/ssd_map

Add DetectionMAPEvaluator

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:

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");

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 {

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__(

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)