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doc/api/v2/config/layer.rst

@@ -478,6 +478,6 @@ Detection output Layer
 ======================
 
 detection_output
----
+----------------
 ..  autoclass:: paddle.v2.layer.detection_output
     :noindex:

paddle/gserver/layers/DetectionOutputLayer.h

@@ -22,14 +22,14 @@ limitations under the License. */
 namespace paddle {
 
 /**
- * The detection output layer for a SSD detection task. This layer apply the
- * Non-maximum suppression to the all predicted bounding box and keep the
+ * The detection output layer for a SSD detection task. This layer applies the
+ * Non-maximum suppression to the all predicted bounding box and keeps the
  * Top-K bounding boxes.
- * - Input: This layer needs three input layers: This first input layer
+ * - Input: This layer needs three input layers: The first input layer
  *          is the priorbox layer. The rest two input layers are convolution
  *          layers for generating bbox location offset and the classification
  *          confidence.
- * - Output: The predict bounding box location.
+ * - Output: The predict bounding box locations.
  */
 
 class DetectionOutputLayer : public Layer {

paddle/gserver/layers/MultiBoxLossLayer.cpp

@@ -258,8 +258,7 @@ void MultiBoxLossLayer::forward(PassType passType) {
   }
   real loss = locLoss_ + confLoss_;
   MatrixPtr outV = getOutputValue();
-  std::vector<real> tmp(batchSize, loss);
-  outV->copyFrom(&tmp[0], batchSize);
+  outV->assign(loss);
 }
 
 void MultiBoxLossLayer::backward(const UpdateCallback& callback) {
@@ -336,6 +335,9 @@ void MultiBoxLossLayer::backward(const UpdateCallback& callback) {
     const MatrixPtr inLocG = getInputGrad(*getLocInputLayer(n));
     const MatrixPtr inConfG = getInputGrad(*getConfInputLayer(n));
     size_t height = getInput(*getLocInputLayer(n)).getFrameHeight();
+    // only for unittest, there are no width and height information
+    // when constructing matrix in unittest, so we should
+    // set the shape in configuration
     if (!height) height = layerConf.height();
     size_t width = getInput(*getLocInputLayer(n)).getFrameWidth();
     if (!width) width = layerConf.width();

paddle/gserver/layers/MultiBoxLossLayer.h

@@ -30,7 +30,7 @@ namespace paddle {
  * The loss is composed by the location loss and the confidence loss.
  * The location loss is a smooth L1 loss and the confidence loss is
  * a softmax loss.
- * - Input: This layer need four input layers: This first input layer
+ * - Input: This layer needs four input layers: The first input layer
  *          is the priorbox layer and the second layer is a label layer.
  *          The rest two input layers are convolution layers for generating
  *          bbox location offset and the classification confidence.

python/paddle/trainer_config_helpers/layers.py

@@ -1072,10 +1072,10 @@ def multibox_loss_layer(input_loc,
 
     :param name: The Layer Name.
     :type name: basestring
-    :param input_loc: The input predict location.
-    :type input_loc: LayerOutput
+    :param input_loc: The input predict locations.
+    :type input_loc: LayerOutput | List of LayerOutput
     :param input_conf: The input priorbox confidence.
-    :type input_conf: LayerOutput
+    :type input_conf: LayerOutput | List of LayerOutput
     :param priorbox: The input priorbox location and the variance.
     :type priorbox: LayerOutput
     :param label: The input label.
@@ -1146,10 +1146,10 @@ def detection_output_layer(input_loc,
 
     :param name: The Layer Name.
     :type name: basestring
-    :param input_loc: The input predict location.
-    :type input_loc: LayerOutput
+    :param input_loc: The input predict locations.
+    :type input_loc: LayerOutput | List of LayerOutput.
     :param input_conf: The input priorbox confidence.
-    :type input_conf: LayerOutput
+    :type input_conf: LayerOutput | List of LayerOutput.
     :param priorbox: The input priorbox location and the variance.
     :type priorbox: LayerOutput
     :param num_classes: The number of the classification.
@@ -1166,22 +1166,20 @@ def detection_output_layer(input_loc,
     :type background_id: int
     :return: LayerOutput
     """
-    input_loc_num = 0
-    input_conf_num = 0
-
     if isinstance(input_loc, LayerOutput):
         input_loc = [input_loc]
     assert isinstance(input_loc, collections.Sequence)  # list or tuple
     for each in input_loc:
         assert isinstance(each, LayerOutput)
-        input_loc_num += 1
+    input_loc_num = len(input_loc)
 
     if isinstance(input_conf, LayerOutput):
         input_conf = [input_conf]
     assert isinstance(input_conf, collections.Sequence)  # list or tuple
     for each in input_conf:
         assert isinstance(each, LayerOutput)
-        input_conf_num += 1
+    input_conf_num = len(input_conf)
+
     # Check the input layer number.
     assert input_loc_num == input_conf_num
 

python/paddle/trainer_config_helpers/tests/configs/protostr/test_detection_output_layer.protostr

+type: "nn"
+layers {
+  name: "input_loc"
+  type: "data"
+  size: 16
+  active_type: ""
+  height: 16
+  width: 1
+}
+layers {
+  name: "input_conf"
+  type: "data"
+  size: 8
+  active_type: ""
+  height: 1
+  width: 8
+}
+layers {
+  name: "priorbox"
+  type: "data"
+  size: 32
+  active_type: ""
+  height: 4
+  width: 8
+}
+layers {
+  name: "test_detection_output"
+  type: "detection_output"
+  size: 1400
+  active_type: ""
+  inputs {
+    input_layer_name: "priorbox"
+    detection_output_conf {
+      num_classes: 21
+      nms_threshold: 0.45
+      nms_top_k: 400
+      background_id: 0
+      input_num: 1
+      keep_top_k: 200
+      confidence_threshold: 0.01
+    }
+  }
+  inputs {
+    input_layer_name: "input_loc"
+  }
+  inputs {
+    input_layer_name: "input_conf"
+  }
+}
+input_layer_names: "priorbox"
+input_layer_names: "input_loc"
+input_layer_names: "input_conf"
+output_layer_names: "test_detection_output"
+sub_models {
+  name: "root"
+  layer_names: "input_loc"
+  layer_names: "input_conf"
+  layer_names: "priorbox"
+  layer_names: "test_detection_output"
+  input_layer_names: "priorbox"
+  input_layer_names: "input_loc"
+  input_layer_names: "input_conf"
+  output_layer_names: "test_detection_output"
+  is_recurrent_layer_group: false
+}
+

python/paddle/trainer_config_helpers/tests/configs/protostr/test_multibox_loss_layer.protostr

+type: "nn"
+layers {
+  name: "input_loc"
+  type: "data"
+  size: 16
+  active_type: ""
+  height: 16
+  width: 1
+}
+layers {
+  name: "input_conf"
+  type: "data"
+  size: 8
+  active_type: ""
+  height: 1
+  width: 8
+}
+layers {
+  name: "priorbox"
+  type: "data"
+  size: 32
+  active_type: ""
+  height: 4
+  width: 8
+}
+layers {
+  name: "label"
+  type: "data"
+  size: 24
+  active_type: ""
+  height: 4
+  width: 6
+}
+layers {
+  name: "test_multibox_loss"
+  type: "multibox_loss"
+  size: 1
+  active_type: ""
+  inputs {
+    input_layer_name: "priorbox"
+    multibox_loss_conf {
+      num_classes: 21
+      overlap_threshold: 0.5
+      neg_pos_ratio: 3.0
+      neg_overlap: 0.5
+      background_id: 0
+      input_num: 1
+    }
+  }
+  inputs {
+    input_layer_name: "label"
+  }
+  inputs {
+    input_layer_name: "input_loc"
+  }
+  inputs {
+    input_layer_name: "input_conf"
+  }
+}
+input_layer_names: "priorbox"
+input_layer_names: "label"
+input_layer_names: "input_loc"
+input_layer_names: "input_conf"
+output_layer_names: "test_multibox_loss"
+sub_models {
+  name: "root"
+  layer_names: "input_loc"
+  layer_names: "input_conf"
+  layer_names: "priorbox"
+  layer_names: "label"
+  layer_names: "test_multibox_loss"
+  input_layer_names: "priorbox"
+  input_layer_names: "label"
+  input_layer_names: "input_loc"
+  input_layer_names: "input_conf"
+  output_layer_names: "test_multibox_loss"
+  is_recurrent_layer_group: false
+}
+