FasterRCNN Anchor Generator Op (#11218)

* Add anchor generator operator for Faster-RCNN.
* Add unittest testing.
* Add Python API.

paddle/fluid/operators/detection/CMakeLists.txt

@@ -22,6 +22,8 @@ iou_similarity_op.cu)
 detection_library(mine_hard_examples_op SRCS mine_hard_examples_op.cc)
 detection_library(multiclass_nms_op SRCS multiclass_nms_op.cc)
 detection_library(prior_box_op SRCS prior_box_op.cc prior_box_op.cu)
+detection_library(anchor_generator_op SRCS anchor_generator_op.cc
+anchor_generator_op.cu)
 detection_library(target_assign_op SRCS target_assign_op.cc
 target_assign_op.cu)
 detection_library(polygon_box_transform_op SRCS polygon_box_transform_op.cc

paddle/fluid/operators/detection/anchor_generator_op.cc

+/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserved.
+
+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 "paddle/fluid/operators/detection/anchor_generator_op.h"
+
+namespace paddle {
+namespace operators {
+
+class AnchorGeneratorOp : public framework::OperatorWithKernel {
+ public:
+  using framework::OperatorWithKernel::OperatorWithKernel;
+
+  void InferShape(framework::InferShapeContext* ctx) const override {
+    PADDLE_ENFORCE(ctx->HasInput("Input"),
+                   "Input(Input) of AnchorGeneratorOp should not be null.");
+    PADDLE_ENFORCE(ctx->HasOutput("Anchors"),
+                   "Output(Anchors) of AnchorGeneratorOp should not be null.");
+    PADDLE_ENFORCE(
+        ctx->HasOutput("Variances"),
+        "Output(Variances) of AnchorGeneratorOp should not be null.");
+
+    auto input_dims = ctx->GetInputDim("Input");
+    PADDLE_ENFORCE(input_dims.size() == 4, "The layout of input is NCHW.");
+
+    auto anchor_sizes = ctx->Attrs().Get<std::vector<float>>("anchor_sizes");
+    auto aspect_ratios = ctx->Attrs().Get<std::vector<float>>("aspect_ratios");
+    auto stride = ctx->Attrs().Get<std::vector<float>>("stride");
+    auto variances = ctx->Attrs().Get<std::vector<float>>("variances");
+
+    size_t num_anchors = aspect_ratios.size() * anchor_sizes.size();
+
+    std::vector<int64_t> dim_vec(4);
+    dim_vec[0] = input_dims[2];
+    dim_vec[1] = input_dims[3];
+    dim_vec[2] = num_anchors;
+    dim_vec[3] = 4;
+    ctx->SetOutputDim("Anchors", framework::make_ddim(dim_vec));
+    ctx->SetOutputDim("Variances", framework::make_ddim(dim_vec));
+  }
+
+ protected:
+  framework::OpKernelType GetExpectedKernelType(
+      const framework::ExecutionContext& ctx) const override {
+    return framework::OpKernelType(
+        framework::ToDataType(ctx.Input<framework::Tensor>("Input")->type()),
+        ctx.device_context());
+  }
+};
+
+class AnchorGeneratorOpMaker : public framework::OpProtoAndCheckerMaker {
+ public:
+  void Make() override {
+    AddInput("Input",
+             "(Tensor, default Tensor<float>), "
+             "the input feature is a tensor with a rank of 4. "
+             "The layout is NCHW.");
+    AddOutput("Anchors",
+              "(Tensor, default Tensor<float>), the output is a "
+              "tensor with a rank of 4. The layout is [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");
+    AddOutput("Variances",
+              "(Tensor, default Tensor<float>), the expanded variances for "
+              "normalizing bbox regression targets. The layout is [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 variance is in (xcenter, ycenter, w, h) format");
+
+    AddAttr<std::vector<float>>(
+        "anchor_sizes",
+        "(vector<float>) List of Region Proposal Network(RPN) anchor sizes "
+        " 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.")
+        .AddCustomChecker([](const std::vector<float>& anchor_sizes) {
+          PADDLE_ENFORCE_GT(anchor_sizes.size(), 0,
+                            "Size of anchor_sizes must be at least 1.");
+          for (size_t i = 0; i < anchor_sizes.size(); ++i) {
+            PADDLE_ENFORCE_GT(anchor_sizes[i], 0.0,
+                              "anchor_sizes[%d] must be positive.", i);
+          }
+        });
+    AddAttr<std::vector<float>>(
+        "aspect_ratios",
+        "(vector<float>) List of Region Proposal Network(RPN) anchor aspect "
+        "ratios, e.g. (0.5, 1, 2)."
+        "For instacne, the aspect ratio of 0.5 means the height / width of "
+        "this anchor equals 0.5.");
+
+    AddAttr<std::vector<float>>("variances",
+                                "(vector<float>) List of variances to be used "
+                                "in box regression deltas")
+        .AddCustomChecker([](const std::vector<float>& variances) {
+          PADDLE_ENFORCE_EQ(variances.size(), 4,
+                            "Must and only provide 4 variance.");
+          for (size_t i = 0; i < variances.size(); ++i) {
+            PADDLE_ENFORCE_GT(variances[i], 0.0,
+                              "variance[%d] must be greater than 0.", i);
+          }
+        });
+
+    AddAttr<std::vector<float>>("stride",
+                                "Anchors stride across width and height, "
+                                "with a default of (16, 16)")
+        .SetDefault(std::vector<float>(2, 16.0))
+        .AddCustomChecker([](const std::vector<float>& stride) {
+          PADDLE_ENFORCE_EQ(
+              stride.size(), 2,
+              "Must and only provide 2 stride for width and height.");
+          for (size_t i = 0; i < stride.size(); ++i) {
+            PADDLE_ENFORCE_GT(stride[i], 0.0,
+                              "stride[%d] should be larger than 0.", i);
+          }
+        });
+
+    AddAttr<float>("offset",
+                   "(float) "
+                   "Anchor center offset, with a default of 0.5")
+        .SetDefault(0.5);
+    AddComment(R"DOC(
+AnchorGenerator operator
+Generates anchors for Faster RCNN, FPN etc. algorithm.
+Each position of the input produce N anchors, N =
+ size(anchor_sizes) * size(aspect_ratios).
+
+Please get more information from the following papers:
+https://arxiv.org/abs/1506.01497.
+)DOC");
+  }
+};
+
+}  // namespace operators
+}  // namespace paddle
+
+namespace ops = paddle::operators;
+REGISTER_OPERATOR(anchor_generator, ops::AnchorGeneratorOp,
+                  ops::AnchorGeneratorOpMaker,
+                  paddle::framework::EmptyGradOpMaker);
+
+REGISTER_OP_CPU_KERNEL(anchor_generator, ops::AnchorGeneratorOpKernel<float>,
+                       ops::AnchorGeneratorOpKernel<double>);

paddle/fluid/operators/detection/anchor_generator_op.cu

+/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserved.
+
+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 "paddle/fluid/operators/detection/anchor_generator_op.h"
+
+namespace paddle {
+namespace operators {
+
+template <typename T>
+__global__ void GenAnchors(T* out, const T* aspect_ratios, const int ar_num,
+                           const T* anchor_sizes, const int as_num,
+                           const T* stride, const int sd_num, const int height,
+                           const int width, const T offset) {
+  int num_anchors = as_num * ar_num;
+  int box_num = height * width * num_anchors;
+  for (int i = blockIdx.x * blockDim.x + threadIdx.x; i < box_num;
+       i += blockDim.x * gridDim.x) {
+    int h_idx = i / (num_anchors * width);
+    int w_idx = (i / num_anchors) % width;
+    T stride_width = stride[0];
+    T stride_height = stride[1];
+    T x_ctr = (w_idx * stride_width) + offset * (stride_width - 1);
+    T y_ctr = (h_idx * stride_height) + offset * (stride_height - 1);
+    T area, area_ratios;
+    T base_w, base_h;
+    T scale_w, scale_h;
+    T anchor_width, anchor_height;
+    int anch_idx = i % num_anchors;
+    int ar_idx = anch_idx / as_num;
+    int as_idx = anch_idx % as_num;
+    T aspect_ratio = aspect_ratios[ar_idx];
+    T anchor_size = anchor_sizes[as_idx];
+    area = stride_width * stride_height;
+    area_ratios = area / aspect_ratio;
+    base_w = round(sqrt(area_ratios));
+    base_h = round(base_w * aspect_ratio);
+    scale_w = anchor_size / stride_width;
+    scale_h = anchor_size / stride_height;
+    anchor_width = scale_w * base_w;
+    anchor_height = scale_h * base_h;
+
+    T xmin = (x_ctr - 0.5 * (anchor_width - 1));
+    T ymin = (y_ctr - 0.5 * (anchor_height - 1));
+    T xmax = (x_ctr + 0.5 * (anchor_width - 1));
+    T ymax = (y_ctr + 0.5 * (anchor_height - 1));
+    out[i * 4] = xmin;
+    out[i * 4 + 1] = ymin;
+    out[i * 4 + 2] = xmax;
+    out[i * 4 + 3] = ymax;
+  }
+}
+
+template <typename T>
+__global__ void SetVariance(T* out, const T* var, const int vnum,
+                            const int num) {
+  for (int i = blockIdx.x * blockDim.x + threadIdx.x; i < num;
+       i += blockDim.x * gridDim.x) {
+    out[i] = var[i % vnum];
+  }
+}
+
+template <typename T>
+class AnchorGeneratorOpCUDAKernel : public framework::OpKernel<T> {
+ public:
+  void Compute(const framework::ExecutionContext& ctx) const override {
+    auto* input = ctx.Input<paddle::framework::Tensor>("Input");
+    auto* anchors = ctx.Output<paddle::framework::Tensor>("Anchors");
+    auto* vars = ctx.Output<paddle::framework::Tensor>("Variances");
+
+    auto anchor_sizes = ctx.Attr<std::vector<float>>("anchor_sizes");
+    auto aspect_ratios = ctx.Attr<std::vector<float>>("aspect_ratios");
+    auto stride = ctx.Attr<std::vector<float>>("stride");
+    auto variances = ctx.Attr<std::vector<float>>("variances");
+
+    T offset = static_cast<T>(ctx.Attr<float>("offset"));
+
+    auto width = input->dims()[3];
+    auto height = input->dims()[2];
+
+    int num_anchors = aspect_ratios.size() * anchor_sizes.size();
+
+    int box_num = width * height * num_anchors;
+
+    int block = 512;
+    int grid = (box_num + block - 1) / block;
+
+    auto stream =
+        ctx.template device_context<platform::CUDADeviceContext>().stream();
+
+    anchors->mutable_data<T>(ctx.GetPlace());
+    vars->mutable_data<T>(ctx.GetPlace());
+
+    framework::Tensor ar;
+    framework::TensorFromVector(aspect_ratios, ctx.device_context(), &ar);
+
+    framework::Tensor as;
+    framework::TensorFromVector(anchor_sizes, ctx.device_context(), &as);
+
+    framework::Tensor sd;
+    framework::TensorFromVector(stride, ctx.device_context(), &sd);
+
+    GenAnchors<T><<<grid, block, 0, stream>>>(
+        anchors->data<T>(), ar.data<T>(), aspect_ratios.size(), as.data<T>(),
+        anchor_sizes.size(), sd.data<T>(), stride.size(), height, width,
+        offset);
+
+    framework::Tensor v;
+    framework::TensorFromVector(variances, ctx.device_context(), &v);
+    grid = (box_num * 4 + block - 1) / block;
+    SetVariance<T><<<grid, block, 0, stream>>>(vars->data<T>(), v.data<T>(),
+                                               variances.size(), box_num * 4);
+  }
+};  // namespace operators
+
+}  // namespace operators
+}  // namespace paddle
+
+namespace ops = paddle::operators;
+REGISTER_OP_CUDA_KERNEL(anchor_generator,
+                        ops::AnchorGeneratorOpCUDAKernel<float>,
+                        ops::AnchorGeneratorOpCUDAKernel<double>);

paddle/fluid/operators/detection/anchor_generator_op.h

+/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserved.
+
+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. */
+
+#pragma once
+#include <algorithm>
+#include <vector>
+#include "paddle/fluid/framework/op_registry.h"
+#include "paddle/fluid/operators/math/math_function.h"
+#include "paddle/fluid/platform/transform.h"
+
+namespace paddle {
+namespace operators {
+
+template <typename T>
+class AnchorGeneratorOpKernel : public framework::OpKernel<T> {
+ public:
+  void Compute(const framework::ExecutionContext& ctx) const override {
+    auto* input = ctx.Input<paddle::framework::Tensor>("Input");
+    auto* anchors = ctx.Output<paddle::framework::Tensor>("Anchors");
+    auto* vars = ctx.Output<paddle::framework::Tensor>("Variances");
+
+    auto anchor_sizes = ctx.Attr<std::vector<float>>("anchor_sizes");
+    auto aspect_ratios = ctx.Attr<std::vector<float>>("aspect_ratios");
+    auto stride = ctx.Attr<std::vector<float>>("stride");
+    auto variances = ctx.Attr<std::vector<float>>("variances");
+
+    T offset = static_cast<T>(ctx.Attr<float>("offset"));
+
+    auto feature_width = input->dims()[3];
+    auto feature_height = input->dims()[2];
+
+    T stride_width, stride_height;
+    stride_width = stride[0];
+    stride_height = stride[1];
+
+    int num_anchors = aspect_ratios.size() * anchor_sizes.size();
+
+    anchors->mutable_data<T>(ctx.GetPlace());
+    vars->mutable_data<T>(ctx.GetPlace());
+
+    auto e_anchors = framework::EigenTensor<T, 4>::From(*anchors);
+    for (int h_idx = 0; h_idx < feature_height; ++h_idx) {
+      for (int w_idx = 0; w_idx < feature_width; ++w_idx) {
+        T x_ctr = (w_idx * stride_width) + offset * (stride_width - 1);
+        T y_ctr = (h_idx * stride_height) + offset * (stride_height - 1);
+        T area, area_ratios;
+        T base_w, base_h;
+        T scale_w, scale_h;
+        T anchor_width, anchor_height;
+        int idx = 0;
+        for (size_t r = 0; r < aspect_ratios.size(); ++r) {
+          auto ar = aspect_ratios[r];
+          for (size_t s = 0; s < anchor_sizes.size(); ++s) {
+            auto anchor_size = anchor_sizes[s];
+            area = stride_width * stride_height;
+            area_ratios = area / ar;
+            base_w = round(sqrt(area_ratios));
+            base_h = round(base_w * ar);
+            scale_w = anchor_size / stride_width;
+            scale_h = anchor_size / stride_height;
+            anchor_width = scale_w * base_w;
+            anchor_height = scale_h * base_h;
+            e_anchors(h_idx, w_idx, idx, 0) =
+                (x_ctr - 0.5 * (anchor_width - 1));
+            e_anchors(h_idx, w_idx, idx, 1) =
+                (y_ctr - 0.5 * (anchor_height - 1));
+            e_anchors(h_idx, w_idx, idx, 2) =
+                (x_ctr + 0.5 * (anchor_width - 1));
+            e_anchors(h_idx, w_idx, idx, 3) =
+                (y_ctr + 0.5 * (anchor_height - 1));
+            idx++;
+          }
+        }
+      }
+    }
+
+    framework::Tensor var_t;
+    var_t.mutable_data<T>(
+        framework::make_ddim({1, static_cast<int>(variances.size())}),
+        ctx.GetPlace());
+    auto var_et = framework::EigenTensor<T, 2>::From(var_t);
+    for (size_t i = 0; i < variances.size(); ++i) {
+      var_et(0, i) = variances[i];
+    }
+
+    int anchor_num = feature_height * feature_width * num_anchors;
+    auto var_dim = vars->dims();
+    vars->Resize({anchor_num, static_cast<int>(variances.size())});
+
+    auto e_vars = framework::EigenMatrix<T, Eigen::RowMajor>::From(*vars);
+    e_vars = var_et.broadcast(Eigen::DSizes<int, 2>(anchor_num, 1));
+
+    vars->Resize(var_dim);
+  }
+};  // namespace operators
+
+}  // namespace operators
+}  // namespace paddle

python/paddle/fluid/layers/detection.py

@@ -30,6 +30,7 @@ __all__ = [
     'detection_output',
     'ssd_loss',
     'detection_map',
+    'anchor_generator',
 ]
 
 __auto__ = [
@@ -998,3 +999,95 @@ def multi_box_head(inputs,
     box.stop_gradient = True
     var.stop_gradient = True
     return mbox_locs_concat, mbox_confs_concat, 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):
+    """
+    **Anchor generator operator**
+
+    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(Variable): The input feature map, the format is NCHW.
+       anchor_sizes(list|tuple|float): 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.
+       aspect_ratios(list|tuple|float): The height / width ratios of generated
+            anchors, e.g. [0.5, 1.0, 2.0].
+       variance(list|tuple): The variances to be used in box regression deltas.
+            Default:[0.1, 0.1, 0.2, 0.2].
+       stride(list|turple): The anchors stride across width and height,
+            e.g. [16.0, 16.0]
+       offset(float): Prior boxes center offset. Default: 0.5
+       name(str): Name of the prior box op. Default: None.
+
+    Returns:
+        Anchors(Variable):  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(Variable): 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
+
+            anchor, var = 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))
+
+    attrs = {
+        'anchor_sizes': anchor_sizes,
+        'aspect_ratios': aspect_ratios,
+        'variances': variance,
+        'stride': stride,
+        'offset': offset
+    }
+
+    anchor = helper.create_tmp_variable(dtype)
+    var = helper.create_tmp_variable(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

python/paddle/fluid/tests/test_detection.py

@@ -127,6 +127,24 @@ class TestPriorBox(unittest.TestCase):
         assert box.shape[3] == 4
 
 
+class TestAnchorGenerator(unittest.TestCase):
+    def test_anchor_generator(self):
+        data_shape = [3, 224, 224]
+        images = fluid.layers.data(
+            name='pixel', shape=data_shape, dtype='float32')
+        conv1 = fluid.layers.conv2d(images, 3, 3, 2)
+        anchor, var = fluid.layers.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)
+        assert len(anchor.shape) == 4
+        assert anchor.shape == var.shape
+        assert anchor.shape[3] == 4
+
+
 class TestMultiBoxHead(unittest.TestCase):
     def test_multi_box_head(self):
         data_shape = [3, 224, 224]

python/paddle/fluid/tests/unittests/test_anchor_generator_op.py

+#   Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
+#
+# 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://w_idxw.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.
+
+import unittest
+import numpy as np
+import sys
+import math
+from op_test import OpTest
+
+
+def anchor_generator_in_python(input_feat, anchor_sizes, aspect_ratios,
+                               variances, stride, offset):
+    num_anchors = len(aspect_ratios) * len(anchor_sizes)
+    layer_h = input_feat.shape[2]
+    layer_w = input_feat.shape[3]
+    out_dim = (layer_h, layer_w, num_anchors, 4)
+    out_anchors = np.zeros(out_dim).astype('float32')
+
+    for h_idx in range(layer_h):
+        for w_idx in range(layer_w):
+            x_ctr = (w_idx * stride[0]) + offset * (stride[0] - 1)
+            y_ctr = (h_idx * stride[1]) + offset * (stride[1] - 1)
+            idx = 0
+            for r in range(len(aspect_ratios)):
+                ar = aspect_ratios[r]
+                for s in range(len(anchor_sizes)):
+                    anchor_size = anchor_sizes[s]
+                    area = stride[0] * stride[1]
+                    area_ratios = area / ar
+                    base_w = np.round(np.sqrt(area_ratios))
+                    base_h = np.round(base_w * ar)
+                    scale_w = anchor_size / stride[0]
+                    scale_h = anchor_size / stride[1]
+                    w = scale_w * base_w
+                    h = scale_h * base_h
+                    out_anchors[h_idx, w_idx, idx, :] = [
+                        (x_ctr - 0.5 * (w - 1)), (y_ctr - 0.5 * (h - 1)),
+                        (x_ctr + 0.5 * (w - 1)), (y_ctr + 0.5 * (h - 1))
+                    ]
+                    idx += 1
+
+    # set the variance.
+    out_var = np.tile(variances, (layer_h, layer_w, num_anchors, 1))
+    out_anchors = out_anchors.astype('float32')
+    out_var = out_var.astype('float32')
+    return out_anchors, out_var
+
+
+class TestAnchorGeneratorOp(OpTest):
+    def set_data(self):
+        self.init_test_params()
+        self.init_test_input()
+        self.init_test_output()
+        self.inputs = {'Input': self.input}
+
+        self.attrs = {
+            'anchor_sizes': self.anchor_sizes,
+            'aspect_ratios': self.aspect_ratios,
+            'stride': self.stride,
+            'offset': self.offset,
+            'variances': self.variances,
+        }
+
+        self.outputs = {'Anchors': self.out_anchors, 'Variances': self.out_var}
+
+    def test_check_output(self):
+        self.check_output()
+
+    def setUp(self):
+        self.op_type = "anchor_generator"
+        self.set_data()
+
+    def init_test_params(self):
+        self.batch_size = 1
+        self.input_channels = 2
+        self.layer_h = 2
+        self.layer_w = 2
+
+        self.anchor_sizes = [64., 128., 256., 512.]
+        self.aspect_ratios = [0.5, 1., 2.]
+        self.stride = [16., 16.]
+
+        self.offset = 0.5
+
+        self.variances = [0.1, 0.1, 0.2, 0.2]
+
+    def init_test_input(self):
+        self.input = np.random.random(
+            (self.batch_size, self.input_channels, self.layer_h,
+             self.layer_w)).astype('float32')
+
+    def init_test_output(self):
+        self.out_anchors, self.out_var = anchor_generator_in_python(
+            self.input, self.anchor_sizes, self.aspect_ratios, self.variances,
+            self.stride, self.offset)
+
+
+if __name__ == '__main__':
+    unittest.main()