add detection output python api (#8389)

python/paddle/v2/fluid/layers/__init__.py

@@ -16,6 +16,8 @@ import ops
 from ops import *
 import nn
 from nn import *
+import detection
+from detection import *
 import io
 from io import *
 import tensor
@@ -28,6 +30,7 @@ import math_op_patch
 from math_op_patch import *
 
 __all__ = []
+__all__ += detection.__all__
 __all__ += nn.__all__
 __all__ += io.__all__
 __all__ += tensor.__all__

python/paddle/v2/fluid/layers/detection.py

@@ -0,0 +1,116 @@
+#   Copyright (c) 2018 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.
+"""
+All layers just related to the detection neural network.
+"""
+
+from ..layer_helper import LayerHelper
+
+__all__ = ['detection_output', ]
+
+
+def detection_output(scores,
+                     loc,
+                     prior_box,
+                     prior_box_var,
+                     background_label=0,
+                     nms_threshold=0.3,
+                     nms_top_k=400,
+                     keep_top_k=200,
+                     score_threshold=0.01,
+                     nms_eta=1.0):
+    """
+    **Detection Output Layer**
+
+    This layer applies the NMS to the output of network and computes the 
+    predict bounding box location. The output's shape of this layer could
+    be zero if there is no valid bounding box.
+
+    Args:
+        scores(Variable): A 3-D Tensor with shape [N, C, M] represents the
+            predicted confidence predictions. N is the batch size, C is the
+            class number, M is number of bounding boxes. For each category
+            there are total M scores which corresponding M bounding boxes.
+        loc(Variable): A 3-D Tensor with shape [N, M, 4] represents the
+            predicted locations of M bounding bboxes. N is the batch size,
+            and each bounding box has four coordinate values and the layout
+            is [xmin, ymin, xmax, ymax].
+        prior_box(Variable): A 2-D Tensor with shape [M, 4] holds M boxes,
+            each box is represented as [xmin, ymin, xmax, ymax],
+            [xmin, ymin] is the left top coordinate of the anchor box,
+            if the input is image feature map, they are close to the origin
+            of the coordinate system. [xmax, ymax] is the right bottom
+            coordinate of the anchor box.
+        prior_box_var(Variable): A 2-D Tensor with shape [M, 4] holds M group
+            of variance.
+        background_label(float): The index of background label,
+            the background label will be ignored. If set to -1, then all
+            categories will be considered.
+        nms_threshold(float): The threshold to be used in NMS.
+        nms_top_k(int): Maximum number of detections to be kept according
+            to the confidences aftern the filtering detections based on
+            score_threshold.
+        keep_top_k(int): Number of total bboxes to be kept per image after
+            NMS step. -1 means keeping all bboxes after NMS step.
+        score_threshold(float): Threshold to filter out bounding boxes with
+            low confidence score. If not provided, consider all boxes.
+        nms_eta(float): The parameter for adaptive NMS.
+
+    Returns:
+        The detected bounding boxes which are a Tensor.
+
+    Examples:
+        .. code-block:: python
+
+        pb = layers.data(name='prior_box', shape=[10, 4],
+                         append_batch_size=False, dtype='float32')
+        pbv = layers.data(name='prior_box_var', shape=[10, 4],
+                          append_batch_size=False, dtype='float32')
+        loc = layers.data(name='target_box', shape=[21, 4],
+                          append_batch_size=False, dtype='float32')
+        scores = layers.data(name='scores', shape=[2, 21, 10],
+                          append_batch_size=False, dtype='float32')
+        nmsed_outs = fluid.layers.detection_output(scores=scores,
+                                       loc=loc,
+                                       prior_box=pb,
+                                       prior_box_var=pbv)
+    """
+
+    helper = LayerHelper("detection_output", **locals())
+    decoded_box = helper.create_tmp_variable(dtype=loc.dtype)
+    helper.append_op(
+        type="box_coder",
+        inputs={
+            'PriorBox': prior_box,
+            'PriorBoxVar': prior_box_var,
+            'TargetBox': loc
+        },
+        outputs={'OutputBox': decoded_box},
+        attrs={'code_type': 'decode_center_size'})
+    nmsed_outs = helper.create_tmp_variable(dtype=decoded_box.dtype)
+
+    helper.append_op(
+        type="multiclass_nms",
+        inputs={'Scores': scores,
+                'BBoxes': decoded_box},
+        outputs={'Out': nmsed_outs},
+        attrs={
+            'background_label': 0,
+            'nms_threshold': nms_threshold,
+            'nms_top_k': nms_top_k,
+            'keep_top_k': keep_top_k,
+            'score_threshold': score_threshold,
+            'nms_eta': 1.0
+        })
+    return nmsed_outs

python/paddle/v2/fluid/tests/test_detection.py

@@ -0,0 +1,53 @@
+#   Copyright (c) 2018 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.
+
+from __future__ import print_function
+import unittest
+
+import paddle.v2.fluid.layers as layers
+from paddle.v2.fluid.framework import Program, program_guard
+
+
+class TestBook(unittest.TestCase):
+    def test_detection_output(self):
+        program = Program()
+        with program_guard(program):
+            pb = layers.data(
+                name='prior_box',
+                shape=[10, 4],
+                append_batch_size=False,
+                dtype='float32')
+            pbv = layers.data(
+                name='prior_box_var',
+                shape=[10, 4],
+                append_batch_size=False,
+                dtype='float32')
+            loc = layers.data(
+                name='target_box',
+                shape=[20, 4],
+                append_batch_size=False,
+                dtype='float32')
+            scores = layers.data(
+                name='scores',
+                shape=[2, 20, 10],
+                append_batch_size=False,
+                dtype='float32')
+            out = layers.detection_output(
+                scores=scores, loc=loc, prior_box=pb, prior_box_var=pbv)
+            self.assertIsNotNone(out)
+        print(str(program))
+
+
+if __name__ == '__main__':
+    unittest.main()