Add multiclass_nms,anchor_generator,prior_box in dygraph (#1627)

ppdet/modeling/layers.py

@@ -45,7 +45,7 @@ class AnchorGeneratorRPN(object):
         stride = self.stride if (
             level is None or self.anchor_start_size is None) else (
                 self.stride[0] * (2.**level), self.stride[1] * (2.**level))
-        anchor, var = fluid.layers.anchor_generator(
+        anchor, var = ops.anchor_generator(
             input=input,
             anchor_sizes=anchor_sizes,
             aspect_ratios=self.aspect_ratios,
@@ -367,7 +367,7 @@ class DecodeClipNms(object):
 @register
 @serializable
 class MultiClassNMS(object):
-    __op__ = fluid.layers.multiclass_nms
+    __op__ = ops.multiclass_nms
     __append_doc__ = True
 
     def __init__(self,

ppdet/modeling/ops.py

@@ -27,13 +27,13 @@ from functools import reduce
 __all__ = [
     'roi_pool',
     'roi_align',
-    #'prior_box',
-    #'anchor_generator',
+    'prior_box',
+    'anchor_generator',
     #'generate_proposals',
     'iou_similarity',
     #'box_coder',
     'yolo_box',
-    #'multiclass_nms',
+    'multiclass_nms',
     'distribute_fpn_proposals',
     'collect_fpn_proposals',
     'matrix_nms',
@@ -84,6 +84,7 @@ def roi_pool(input,
     ..  code-block:: python
 
         import paddle
+        from ppdet.modeling import ops
         paddle.enable_static()
 
         x = paddle.static.data(
@@ -187,6 +188,7 @@ def roi_align(input,
         .. code-block:: python
 
             import paddle
+            from ppdet.modeling import ops
             paddle.enable_static()
 
             x = paddle.static.data(
@@ -278,12 +280,12 @@ def iou_similarity(x, y, box_normalized=True, name=None):
     Examples:
         .. code-block:: python
 
-            import numpy as np
             import paddle
+            from ppdet.modeling import ops
             paddle.enable_static()
 
-            x = paddle.data(name='x', shape=[None, 4], dtype='float32')
-            y = paddle.data(name='y', shape=[None, 4], dtype='float32')
+            x = paddle.static.data(name='x', shape=[None, 4], dtype='float32')
+            y = paddle.static.data(name='y', shape=[None, 4], dtype='float32')
             iou = ops.iou_similarity(x=x, y=y)
     """
 
@@ -355,8 +357,8 @@ def collect_fpn_proposals(multi_rois,
     Examples:
         .. code-block:: python
            
-            import paddle.fluid as fluid
             import paddle
+            from ppdet.modeling import ops
             paddle.enable_static()
             multi_rois = []
             multi_scores = []
@@ -367,7 +369,7 @@ def collect_fpn_proposals(multi_rois,
                 multi_scores.append(paddle.static.data(
                     name='score_'+str(i), shape=[None, 1], dtype='float32', lod_level=1))
 
-            fpn_rois = fluid.layers.collect_fpn_proposals(
+            fpn_rois = ops.collect_fpn_proposals(
                 multi_rois=multi_rois, 
                 multi_scores=multi_scores,
                 min_level=2, 
@@ -475,12 +477,12 @@ def distribute_fpn_proposals(fpn_rois,
     Examples:
         .. code-block:: python
 
-            import paddle.fluid as fluid
             import paddle
+            from ppdet.modeling import ops
             paddle.enable_static()
             fpn_rois = paddle.static.data(
                 name='data', shape=[None, 4], dtype='float32', lod_level=1)
-            multi_rois, restore_ind = fluid.layers.distribute_fpn_proposals(
+            multi_rois, restore_ind = ops.distribute_fpn_proposals(
                 fpn_rois=fpn_rois,
                 min_level=2,
                 max_level=5,
@@ -621,6 +623,7 @@ def yolo_box(
     .. code-block:: python
 
         import paddle
+        from ppdet.modeling import ops
         
         paddle.enable_static()
         x = paddle.static.data(name='x', shape=[None, 255, 13, 13], dtype='float32')
@@ -671,6 +674,411 @@ def yolo_box(
     return boxes, scores
 
 
+def prior_box(input,
+              image,
+              min_sizes,
+              max_sizes=None,
+              aspect_ratios=[1.],
+              variance=[0.1, 0.1, 0.2, 0.2],
+              flip=False,
+              clip=False,
+              steps=[0.0, 0.0],
+              offset=0.5,
+              min_max_aspect_ratios_order=False,
+              name=None):
+    """
+
+    This op generates prior boxes for SSD(Single Shot MultiBox Detector) algorithm.
+    Each position of the input produce N prior boxes, N is determined by
+    the count of min_sizes, max_sizes and aspect_ratios, The size of the
+    box is in range(min_size, max_size) interval, which is generated in
+    sequence according to the aspect_ratios.
+
+    Parameters:
+       input(Tensor): 4-D tensor(NCHW), the data type should be float32 or float64.
+       image(Tensor): 4-D tensor(NCHW), the input image data of PriorBoxOp,
+            the data type should be float32 or float64.
+       min_sizes(list|tuple|float): the min sizes of generated prior boxes.
+       max_sizes(list|tuple|None): the max sizes of generated prior boxes.
+            Default: None.
+       aspect_ratios(list|tuple|float): the aspect ratios of generated
+            prior boxes. Default: [1.].
+       variance(list|tuple): the variances to be encoded in prior boxes.
+            Default:[0.1, 0.1, 0.2, 0.2].
+       flip(bool): Whether to flip aspect ratios. Default:False.
+       clip(bool): Whether to clip out-of-boundary boxes. Default: False.
+       step(list|tuple): Prior boxes step across width and height, If
+            step[0] equals to 0.0 or step[1] equals to 0.0, the prior boxes step across
+            height or weight of the input will be automatically calculated.
+            Default: [0., 0.]
+       offset(float): Prior boxes center offset. Default: 0.5
+       min_max_aspect_ratios_order(bool): If set True, the output prior box is
+            in order of [min, max, aspect_ratios], which is consistent with
+            Caffe. Please note, this order affects the weights order of
+            convolution layer followed by and does not affect the final
+            detection results. Default: False.
+       name(str, optional): The default value is None.  Normally there is no need for 
+            user to set this property. For more information, please refer to :ref:`api_guide_Name`
+
+    Returns:
+        Tuple: A tuple with two Variable (boxes, variances)
+
+        boxes(Tensor): the output prior boxes of PriorBox.
+        4-D tensor, the layout is [H, W, num_priors, 4].
+        H is the height of input, W is the width of input,
+        num_priors is the total box count of each position of input.
+
+        variances(Tensor): the expanded variances of PriorBox.
+        4-D tensor, the layput is [H, W, num_priors, 4].
+        H is the height of input, W is the width of input
+        num_priors is the total box count of each position of input
+
+    Examples:
+        .. code-block:: python
+
+        import paddle
+        from ppdet.modeling import ops
+
+        paddle.enable_static()
+        input = paddle.static.data(name="input", shape=[None,3,6,9])
+        image = paddle.static.data(name="image", shape=[None,3,9,12])
+        box, var = ops.prior_box(
+                    input=input,
+                    image=image,
+                    min_sizes=[100.],
+                    clip=True,
+                    flip=True)
+    """
+    helper = LayerHelper("prior_box", **locals())
+    dtype = helper.input_dtype()
+    check_variable_and_dtype(
+        input, 'input', ['uint8', 'int8', 'float32', 'float64'], 'prior_box')
+
+    def _is_list_or_tuple_(data):
+        return (isinstance(data, list) or isinstance(data, tuple))
+
+    if not _is_list_or_tuple_(min_sizes):
+        min_sizes = [min_sizes]
+    if not _is_list_or_tuple_(aspect_ratios):
+        aspect_ratios = [aspect_ratios]
+    if not (_is_list_or_tuple_(steps) and len(steps) == 2):
+        raise ValueError('steps should be a list or tuple ',
+                         'with length 2, (step_width, step_height).')
+
+    min_sizes = list(map(float, min_sizes))
+    aspect_ratios = list(map(float, aspect_ratios))
+    steps = list(map(float, steps))
+
+    cur_max_sizes = None
+    if max_sizes is not None and len(max_sizes) > 0 and max_sizes[0] > 0:
+        if not _is_list_or_tuple_(max_sizes):
+            max_sizes = [max_sizes]
+        cur_max_sizes = max_sizes
+
+    if in_dygraph_mode():
+        attrs = [
+            'min_sizes', min_sizes, 'aspect_ratios', aspect_ratios, 'variances',
+            variance, 'flip', flip, 'clip', clip, 'step_w', steps[0], 'step_h',
+            steps[1], 'offset', offset, 'min_max_aspect_ratios_order',
+            min_max_aspect_ratios_order
+        ]
+        if cur_max_sizes is not None:
+            attrs.extend('max_sizes', max_sizes)
+        attrs = tuple(attrs)
+        box, var = core.ops.prior_box(input, image, *attrs)
+        return box, var
+
+    attrs = {
+        'min_sizes': min_sizes,
+        'aspect_ratios': aspect_ratios,
+        'variances': variance,
+        'flip': flip,
+        'clip': clip,
+        'step_w': steps[0],
+        'step_h': steps[1],
+        'offset': offset,
+        'min_max_aspect_ratios_order': min_max_aspect_ratios_order
+    }
+
+    if cur_max_sizes is not None:
+        attrs['max_sizes'] = cur_max_sizes
+
+    box = helper.create_variable_for_type_inference(dtype)
+    var = helper.create_variable_for_type_inference(dtype)
+    helper.append_op(
+        type="prior_box",
+        inputs={"Input": input,
+                "Image": image},
+        outputs={"Boxes": box,
+                 "Variances": var},
+        attrs=attrs, )
+    box.stop_gradient = True
+    var.stop_gradient = True
+    return box, var
+
+
+def anchor_generator(input,
+                     anchor_sizes=None,
+                     aspect_ratios=None,
+                     variance=[0.1, 0.1, 0.2, 0.2],
+                     stride=None,
+                     offset=0.5,
+                     name=None):
+    """
+
+    This op generate anchors for Faster RCNN algorithm.
+    Each position of the input produce N anchors, N =
+    size(anchor_sizes) * size(aspect_ratios). The order of generated anchors
+    is firstly aspect_ratios loop then anchor_sizes loop.
+
+    Args:
+       input(Tensor): 4-D Tensor with shape [N,C,H,W]. The input feature map.
+       anchor_sizes(float32|list|tuple, optional): The anchor sizes of generated
+          anchors, given in absolute pixels e.g. [64., 128., 256., 512.].
+          For instance, the anchor size of 64 means the area of this anchor 
+          equals to 64**2. None by default.
+       aspect_ratios(float32|list|tuple, optional): The height / width ratios 
+           of generated anchors, e.g. [0.5, 1.0, 2.0]. None by default.
+       variance(list|tuple, optional): The variances to be used in box 
+           regression deltas. The data type is float32, [0.1, 0.1, 0.2, 0.2] by 
+           default.
+       stride(list|tuple, optional): The anchors stride across width and height.
+           The data type is float32. e.g. [16.0, 16.0]. None by default.
+       offset(float32, optional): Prior boxes center offset. 0.5 by default.
+       name(str, optional): For detailed information, please refer 
+           to :ref:`api_guide_Name`. Usually name is no need to set and None 
+           by default. 
+
+    Returns:
+        Tuple:
+
+        Anchors(Tensor): The output anchors with a layout of [H, W, num_anchors, 4].
+        H is the height of input, W is the width of input,
+        num_anchors is the box count of each position. 
+        Each anchor is in (xmin, ymin, xmax, ymax) format an unnormalized.
+
+        Variances(Tensor): The expanded variances of anchors
+        with a layout of [H, W, num_priors, 4].
+        H is the height of input, W is the width of input
+        num_anchors is the box count of each position.
+        Each variance is in (xcenter, ycenter, w, h) format.
+
+
+    Examples:
+
+        .. code-block:: python
+
+            import paddle
+            from ppdet.modeling import ops
+
+            paddle.enable_static()
+            conv1 = paddle.static.data(name='input', shape=[None, 48, 16, 16], dtype='float32')
+            anchor, var = ops.anchor_generator(
+                input=conv1,
+                anchor_sizes=[64, 128, 256, 512],
+                aspect_ratios=[0.5, 1.0, 2.0],
+                variance=[0.1, 0.1, 0.2, 0.2],
+                stride=[16.0, 16.0],
+                offset=0.5)
+    """
+    helper = LayerHelper("anchor_generator", **locals())
+    dtype = helper.input_dtype()
+
+    def _is_list_or_tuple_(data):
+        return (isinstance(data, list) or isinstance(data, tuple))
+
+    if not _is_list_or_tuple_(anchor_sizes):
+        anchor_sizes = [anchor_sizes]
+    if not _is_list_or_tuple_(aspect_ratios):
+        aspect_ratios = [aspect_ratios]
+    if not (_is_list_or_tuple_(stride) and len(stride) == 2):
+        raise ValueError('stride should be a list or tuple ',
+                         'with length 2, (stride_width, stride_height).')
+
+    anchor_sizes = list(map(float, anchor_sizes))
+    aspect_ratios = list(map(float, aspect_ratios))
+    stride = list(map(float, stride))
+
+    if in_dygraph_mode():
+        attrs = ('anchor_sizes', anchor_sizes, 'aspect_ratios', aspect_ratios,
+                 'variances', variance, 'stride', stride, 'offset', offset)
+        anchor, var = core.ops.anchor_generator(input, *attrs)
+        return anchor, var
+
+    attrs = {
+        'anchor_sizes': anchor_sizes,
+        'aspect_ratios': aspect_ratios,
+        'variances': variance,
+        'stride': stride,
+        'offset': offset
+    }
+
+    anchor = helper.create_variable_for_type_inference(dtype)
+    var = helper.create_variable_for_type_inference(dtype)
+    helper.append_op(
+        type="anchor_generator",
+        inputs={"Input": input},
+        outputs={"Anchors": anchor,
+                 "Variances": var},
+        attrs=attrs, )
+    anchor.stop_gradient = True
+    var.stop_gradient = True
+    return anchor, var
+
+
+def multiclass_nms(bboxes,
+                   scores,
+                   score_threshold,
+                   nms_top_k,
+                   keep_top_k,
+                   nms_threshold=0.3,
+                   normalized=True,
+                   nms_eta=1.,
+                   background_label=0,
+                   return_index=False,
+                   rois_num=None,
+                   name=None):
+    """
+    This operator is to do multi-class non maximum suppression (NMS) on
+    boxes and scores.
+    In the NMS step, this operator greedily selects a subset of detection bounding
+    boxes that have high scores larger than score_threshold, if providing this
+    threshold, then selects the largest nms_top_k confidences scores if nms_top_k
+    is larger than -1. Then this operator pruns away boxes that have high IOU
+    (intersection over union) overlap with already selected boxes by adaptive
+    threshold NMS based on parameters of nms_threshold and nms_eta.
+    Aftern NMS step, at most keep_top_k number of total bboxes are to be kept
+    per image if keep_top_k is larger than -1.
+    Args:
+        bboxes (Tensor): Two types of bboxes are supported:
+                           1. (Tensor) A 3-D Tensor with shape
+                           [N, M, 4 or 8 16 24 32] represents the
+                           predicted locations of M bounding bboxes,
+                           N is the batch size. Each bounding box has four
+                           coordinate values and the layout is
+                           [xmin, ymin, xmax, ymax], when box size equals to 4.
+                           2. (LoDTensor) A 3-D Tensor with shape [M, C, 4]
+                           M is the number of bounding boxes, C is the
+                           class number
+        scores (Tensor): Two types of scores are supported:
+                           1. (Tensor) 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. Please note, M is equal to the 2nd dimension
+                           of BBoxes.
+                           2. (LoDTensor) A 2-D LoDTensor with shape [M, C].
+                           M is the number of bbox, C is the class number.
+                           In this case, input BBoxes should be the second
+                           case with shape [M, C, 4].
+        background_label (int): The index of background label, the background
+                                label will be ignored. If set to -1, then all
+                                categories will be considered. Default: 0
+        score_threshold (float): Threshold to filter out bounding boxes with
+                                 low confidence score. If not provided,
+                                 consider all boxes.
+        nms_top_k (int): Maximum number of detections to be kept according to
+                         the confidences after the filtering detections based
+                         on score_threshold.
+        nms_threshold (float): The threshold to be used in NMS. Default: 0.3
+        nms_eta (float): The threshold to be used in NMS. Default: 1.0
+        keep_top_k (int): Number of total bboxes to be kept per image after NMS
+                          step. -1 means keeping all bboxes after NMS step.
+        normalized (bool): Whether detections are normalized. Default: True
+        return_index(bool): Whether return selected index. Default: False
+        rois_num(Tensor): 1-D Tensor contains the number of RoIs in each image. 
+            The shape is [B] and data type is int32. B is the number of images.
+            If it is not None then return a list of 1-D Tensor. Each element 
+            is the output RoIs' number of each image on the corresponding level
+            and the shape is [B]. None by default.
+        name(str): Name of the multiclass nms op. Default: None.
+    Returns:
+        A tuple with two Variables: (Out, Index) if return_index is True,
+        otherwise, a tuple with one Variable(Out) is returned.
+        Out: A 2-D LoDTensor with shape [No, 6] represents the detections.
+        Each row has 6 values: [label, confidence, xmin, ymin, xmax, ymax]
+        or A 2-D LoDTensor with shape [No, 10] represents the detections.
+        Each row has 10 values: [label, confidence, x1, y1, x2, y2, x3, y3,
+        x4, y4]. No is the total number of detections.
+        If all images have not detected results, all elements in LoD will be
+        0, and output tensor is empty (None).
+        Index: Only return when return_index is True. A 2-D LoDTensor with
+        shape [No, 1] represents the selected index which type is Integer.
+        The index is the absolute value cross batches. No is the same number
+        as Out. If the index is used to gather other attribute such as age,
+        one needs to reshape the input(N, M, 1) to (N * M, 1) as first, where
+        N is the batch size and M is the number of boxes.
+    Examples:
+        .. code-block:: python
+
+            import paddle
+            from ppdet.modeling import ops
+            boxes = paddle.static.data(name='bboxes', shape=[81, 4],
+                                      dtype='float32', lod_level=1)
+            scores = paddle.static.data(name='scores', shape=[81],
+                                      dtype='float32', lod_level=1)
+            out, index = ops.multiclass_nms(bboxes=boxes,
+                                            scores=scores,
+                                            background_label=0,
+                                            score_threshold=0.5,
+                                            nms_top_k=400,
+                                            nms_threshold=0.3,
+                                            keep_top_k=200,
+                                            normalized=False,
+                                            return_index=True)
+    """
+    helper = LayerHelper('multiclass_nms3', **locals())
+
+    if in_dygraph_mode():
+        assert rois_num is not None, "rois_num should not be None in dygraph mode."
+        attrs = ('background_label', background_label, 'score_threshold',
+                 score_threshold, 'nms_top_k', nms_top_k, 'nms_threshold',
+                 nms_threshold, 'keep_top_k', keep_top_k, 'nms_eta', nms_eta,
+                 'normalized', normalized)
+        output, index, nms_rois_num = core.ops.multiclass_nms3(bboxes, scores,
+                                                               rois_num, *attrs)
+        if return_index:
+            return output, index, nms_rois_num
+        else:
+            return output, nms_rois_num
+
+    output = helper.create_variable_for_type_inference(dtype=bboxes.dtype)
+    index = helper.create_variable_for_type_inference(dtype='int')
+
+    inputs = {'BBoxes': bboxes, 'Scores': scores}
+    outputs = {'Out': output, 'Index': index}
+
+    if rois_num is not None:
+        inputs['RoisNum'] = rois_num
+        nms_rois_num = helper.create_variable_for_type_inference(dtype='int32')
+        outputs['NmsRoisNum'] = nms_rois_num
+
+    helper.append_op(
+        type="multiclass_nms3",
+        inputs=inputs,
+        attrs={
+            'background_label': background_label,
+            'score_threshold': score_threshold,
+            'nms_top_k': nms_top_k,
+            'nms_threshold': nms_threshold,
+            'keep_top_k': keep_top_k,
+            'nms_eta': nms_eta,
+            'normalized': normalized
+        },
+        outputs=outputs)
+    output.stop_gradient = True
+    index.stop_gradient = True
+
+    if return_index and rois_num is not None:
+        return output, index, nms_rois_num
+    elif return_index and rois_num is None:
+        return output, index
+    elif not return_index and rois_num is not None:
+        return output, nms_rois_num
+    return output
+
+
 def matrix_nms(bboxes,
                scores,
                score_threshold,
@@ -686,16 +1094,13 @@ def matrix_nms(bboxes,
                name=None):
     """
     **Matrix NMS**
-
     This operator does matrix non maximum suppression (NMS).
-
     First selects a subset of candidate bounding boxes that have higher scores
     than score_threshold (if provided), then the top k candidate is selected if
     nms_top_k is larger than -1. Score of the remaining candidate are then
     decayed according to the Matrix NMS scheme.
     Aftern NMS step, at most keep_top_k number of total bboxes are to be kept
     per image if keep_top_k is larger than -1.
-
     Args:
         bboxes (Tensor): A 3-D Tensor with shape [N, M, 4] represents the
                            predicted locations of M bounding bboxes,
@@ -728,30 +1133,22 @@ def matrix_nms(bboxes,
         return_index(bool): Whether return selected index. Default: False
         return_rois_num(bool): whether return rois_num. Default: True
         name(str): Name of the matrix nms op. Default: None.
-
     Returns:
         A tuple with three Tensor: (Out, Index, RoisNum) if return_index is True,
         otherwise, a tuple with two Tensor (Out, RoisNum) is returned.
-
         Out (Tensor): A 2-D Tensor with shape [No, 6] containing the
              detection results.
              Each row has 6 values: [label, confidence, xmin, ymin, xmax, ymax]
              (After version 1.3, when no boxes detected, the lod is changed
              from {0} to {1})
-
         Index (Tensor): A 2-D Tensor with shape [No, 1] containing the
             selected indices, which are absolute values cross batches.
-
         rois_num (Tensor): A 1-D Tensor with shape [N] containing 
             the number of detected boxes in each image.
-
     Examples:
         .. code-block:: python
-
-
             import paddle
             from ppdet.modeling import ops
-
             boxes = paddle.static.data(name='bboxes', shape=[None,81, 4],
                                       dtype='float32', lod_level=1)
             scores = paddle.static.data(name='scores', shape=[None,81],
@@ -759,7 +1156,6 @@ def matrix_nms(bboxes,
             out = ops.matrix_nms(bboxes=boxes, scores=scores, background_label=0,
                                  score_threshold=0.5, post_threshold=0.1,
                                  nms_top_k=400, keep_top_k=200, normalized=False)
-
     """
     check_variable_and_dtype(bboxes, 'BBoxes', ['float32', 'float64'],
                              'matrix_nms')

ppdet/modeling/tests/test_ops.py

@@ -383,7 +383,7 @@ class TestIoUSimilarity(LayerTest):
         self.assertTrue(np.array_equal(iou_np, iou_dy_np))
 
 
-class TestYOLO_Box(LayerTest):
+class TestYOLOBox(LayerTest):
     def test_yolo_box(self):
 
         # x shape [N C H W], C=K * (5 + class_num), class_num=10, K=2
@@ -414,11 +414,6 @@ class TestYOLO_Box(LayerTest):
                 feed={
                     'x': np_x,
                     'origin_shape': np_origin_shape,
-                    'anchors': [10, 13, 30, 13],
-                    'class_num': 10,
-                    'conf_thresh': 0.01,
-                    'downsample_ratio': 32,
-                    'scale_x_y': 1.0,
                 },
                 fetch_list=[boxes, scores],
                 with_lod=False)
@@ -463,6 +458,185 @@ class TestYOLO_Box(LayerTest):
                 scale_x_y=1.2)
 
 
+class TestPriorBox(LayerTest):
+    def test_prior_box(self):
+        input_np = np.random.rand(2, 10, 32, 32).astype('float32')
+        image_np = np.random.rand(2, 10, 40, 40).astype('float32')
+        min_sizes = [2, 4]
+        with self.static_graph():
+            input = paddle.static.data(
+                name='input', shape=[2, 10, 32, 32], dtype='float32')
+            image = paddle.static.data(
+                name='image', shape=[2, 10, 40, 40], dtype='float32')
+
+            box, var = ops.prior_box(
+                input=input,
+                image=image,
+                min_sizes=min_sizes,
+                clip=True,
+                flip=True)
+            box_np, var_np = self.get_static_graph_result(
+                feed={
+                    'input': input_np,
+                    'image': image_np,
+                },
+                fetch_list=[box, var],
+                with_lod=False)
+
+        with self.dynamic_graph():
+            inputs_dy = base.to_variable(input_np)
+            image_dy = base.to_variable(image_np)
+
+            box_dy, var_dy = ops.prior_box(
+                input=inputs_dy,
+                image=image_dy,
+                min_sizes=min_sizes,
+                clip=True,
+                flip=True)
+            box_dy_np = box_dy.numpy()
+            var_dy_np = var_dy.numpy()
+
+        self.assertTrue(np.array_equal(box_np, box_dy_np))
+        self.assertTrue(np.array_equal(var_np, var_dy_np))
+
+    def test_prior_box_error(self):
+        program = Program()
+        paddle.enable_static()
+        with program_guard(program):
+            input = paddle.static.data(
+                name='input', shape=[2, 10, 32, 32], dtype='int32')
+            image = paddle.static.data(
+                name='image', shape=[2, 10, 40, 40], dtype='int32')
+            self.assertRaises(
+                TypeError,
+                ops.prior_box,
+                input=input,
+                image=image,
+                min_sizes=[2, 4],
+                clip=True,
+                flip=True)
+
+
+class TestAnchorGenerator(LayerTest):
+    def test_anchor_generator(self):
+        b, c, h, w = 2, 48, 16, 16
+        input_np = np.random.rand(2, 48, 16, 16).astype('float32')
+        paddle.enable_static()
+        with self.static_graph():
+            input = paddle.static.data(
+                name='input', shape=[b, c, h, w], dtype='float32')
+
+            anchor, var = ops.anchor_generator(
+                input=input,
+                anchor_sizes=[64, 128, 256, 512],
+                aspect_ratios=[0.5, 1.0, 2.0],
+                variance=[0.1, 0.1, 0.2, 0.2],
+                stride=[16.0, 16.0],
+                offset=0.5)
+            anchor_np, var_np = self.get_static_graph_result(
+                feed={'input': input_np, },
+                fetch_list=[anchor, var],
+                with_lod=False)
+
+        with self.dynamic_graph():
+            inputs_dy = base.to_variable(input_np)
+
+            anchor_dy, var_dy = ops.anchor_generator(
+                input=inputs_dy,
+                anchor_sizes=[64, 128, 256, 512],
+                aspect_ratios=[0.5, 1.0, 2.0],
+                variance=[0.1, 0.1, 0.2, 0.2],
+                stride=[16.0, 16.0],
+                offset=0.5)
+            anchor_dy_np = anchor_dy.numpy()
+            var_dy_np = var_dy.numpy()
+
+        self.assertTrue(np.array_equal(anchor_np, anchor_dy_np))
+        self.assertTrue(np.array_equal(var_np, var_dy_np))
+
+
+class TestMulticlassNms(LayerTest):
+    def test_multiclass_nms(self):
+        boxes_np = np.random.rand(81, 4).astype('float32')
+        scores_np = np.random.rand(81).astype('float32')
+        rois_num_np = np.array([40, 41]).astype('int32')
+        with self.static_graph():
+            boxes = paddle.static.data(
+                name='bboxes', shape=[81, 4], dtype='float32', lod_level=1)
+            scores = paddle.static.data(
+                name='scores', shape=[81], dtype='float32', lod_level=1)
+            rois_num = paddle.static.data(
+                name='rois_num', shape=[40, 41], dtype='int32')
+
+            output = ops.multiclass_nms(
+                bboxes=boxes,
+                scores=scores,
+                background_label=0,
+                score_threshold=0.5,
+                nms_top_k=400,
+                nms_threshold=0.3,
+                keep_top_k=200,
+                normalized=False,
+                return_index=True,
+                rois_num=rois_num)
+            out_np, index_np, nms_rois_num_np = self.get_static_graph_result(
+                feed={
+                    'bboxes': boxes_np,
+                    'scores': scores_np,
+                    'rois_num': rois_num_np
+                },
+                fetch_list=output,
+                with_lod=False)
+
+        with self.dynamic_graph():
+            boxes_dy = base.to_variable(boxes_np)
+            scores_dy = base.to_variable(scores_np)
+            rois_num_dy = base.to_variable(rois_num_np)
+
+            out_dy, index_dy, nms_rois_num_dy = ops.multiclass_nms(
+                bboxes=boxes_dy,
+                scores=scores_dy,
+                background_label=0,
+                score_threshold=0.5,
+                nms_top_k=400,
+                nms_threshold=0.3,
+                keep_top_k=200,
+                normalized=False,
+                return_index=True,
+                rois_num=rois_num_dy)
+            out_dy_np = out_dy.numpy()
+            index_dy_np = index_dy.numpy()
+            nms_rois_num_dy_np = nms_rois_num_dy.numpy()
+
+        self.assertTrue(np.array_equal(out_np, out_dy_np))
+        self.assertTrue(np.array_equal(index_np, index_dy_np))
+        self.assertTrue(np.array_equal(nms_rois_num_np, nms_rois_num_dy_np))
+
+    def test_multiclass_nms_error(self):
+        program = Program()
+        paddle.enable_static()
+        with program_guard(program):
+            boxes = paddle.static.data(
+                name='bboxes', shape=[81, 4], dtype='float32', lod_level=1)
+            scores = paddle.static.data(
+                name='scores', shape=[81], dtype='float32', lod_level=1)
+            rois_num = paddle.static.data(
+                name='rois_num', shape=[40, 41], dtype='int32')
+            self.assertRaises(
+                TypeError,
+                ops.multiclass_nms,
+                boxes=boxes,
+                scores=scores,
+                background_label=0,
+                score_threshold=0.5,
+                nms_top_k=400,
+                nms_threshold=0.3,
+                keep_top_k=200,
+                normalized=False,
+                return_index=True,
+                rois_num=rois_num)
+
+
 class TestMatrixNMS(LayerTest):
     def test_matrix_nms(self):
         N, M, C = 7, 1200, 21