test_detection.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://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.

import contextlib
import unittest

import numpy as np
from unittests.test_imperative_base import new_program_scope

import paddle
import paddle.fluid as fluid
import paddle.fluid.layers as layers
from paddle.fluid import core
from paddle.fluid.dygraph import base
from paddle.fluid.framework import Program, program_guard
from paddle.fluid.layers import detection

paddle.enable_static()


class LayerTest(unittest.TestCase):
    @classmethod
    def setUpClass(cls):
        cls.seed = 111

    @classmethod
    def tearDownClass(cls):
        pass

    def _get_place(self, force_to_use_cpu=False):
        # this option for ops that only have cpu kernel
        if force_to_use_cpu:
            return core.CPUPlace()
        else:
            if core.is_compiled_with_cuda():
                return core.CUDAPlace(0)
            return core.CPUPlace()

    @contextlib.contextmanager
    def static_graph(self):
        with new_program_scope():
            fluid.default_startup_program().random_seed = self.seed
            fluid.default_main_program().random_seed = self.seed
            yield

    def get_static_graph_result(
        self, feed, fetch_list, with_lod=False, force_to_use_cpu=False
    ):
        exe = fluid.Executor(self._get_place(force_to_use_cpu))
        exe.run(fluid.default_startup_program())
        return exe.run(
            fluid.default_main_program(),
            feed=feed,
            fetch_list=fetch_list,
            return_numpy=(not with_lod),
        )

    @contextlib.contextmanager
    def dynamic_graph(self, force_to_use_cpu=False):
        with fluid.dygraph.guard(
            self._get_place(force_to_use_cpu=force_to_use_cpu)
        ):
            fluid.default_startup_program().random_seed = self.seed
            fluid.default_main_program().random_seed = self.seed
            yield


class TestDetection(unittest.TestCase):
    def test_box_coder_api(self):
        program = Program()
        with program_guard(program):
            x = layers.data(name='x', shape=[4], dtype='float32')
            y = layers.data(name='z', shape=[4], dtype='float32', lod_level=1)
            bcoder = layers.box_coder(
                prior_box=x,
                prior_box_var=[0.1, 0.2, 0.1, 0.2],
                target_box=y,
                code_type='encode_center_size',
            )
            self.assertIsNotNone(bcoder)
        print(str(program))

    def test_box_coder_error(self):
        program = Program()
        with program_guard(program):
            x1 = fluid.data(name='x1', shape=[10, 4], dtype='int32')
            y1 = fluid.data(
                name='y1', shape=[10, 4], dtype='float32', lod_level=1
            )
            x2 = fluid.data(name='x2', shape=[10, 4], dtype='float32')
            y2 = fluid.data(
                name='y2', shape=[10, 4], dtype='int32', lod_level=1
            )

            self.assertRaises(
                TypeError,
                layers.box_coder,
                prior_box=x1,
                prior_box_var=[0.1, 0.2, 0.1, 0.2],
                target_box=y1,
                code_type='encode_center_size',
            )
            self.assertRaises(
                TypeError,
                layers.box_coder,
                prior_box=x2,
                prior_box_var=[0.1, 0.2, 0.1, 0.2],
                target_box=y2,
                code_type='encode_center_size',
            )


class TestPriorBox(unittest.TestCase):
    def test_prior_box(self):
        program = Program()
        with program_guard(program):
            data_shape = [3, 224, 224]
            images = fluid.layers.data(
                name='pixel', shape=data_shape, dtype='float32'
            )
            conv1 = fluid.layers.conv2d(images, 3, 3, 2)
            box, var = layers.prior_box(
                input=conv1,
                image=images,
                min_sizes=[100.0],
                aspect_ratios=[1.0],
                flip=True,
                clip=True,
            )
            assert len(box.shape) == 4
            assert box.shape == var.shape
            assert box.shape[3] == 4


class TestPriorBox2(unittest.TestCase):
    def test_prior_box(self):
        program = Program()
        with program_guard(program):
            data_shape = [None, 3, None, None]
            images = fluid.data(name='pixel', shape=data_shape, dtype='float32')
            conv1 = fluid.layers.conv2d(images, 3, 3, 2)
            box, var = layers.prior_box(
                input=conv1,
                image=images,
                min_sizes=[100.0],
                aspect_ratios=[1.0],
                flip=True,
                clip=True,
            )
            assert len(box.shape) == 4
            assert box.shape == var.shape
            assert box.shape[3] == 4


class TestDensityPriorBox(unittest.TestCase):
    def test_density_prior_box(self):
        program = Program()
        with program_guard(program):
            data_shape = [3, 224, 224]
            images = fluid.layers.data(
                name='pixel', shape=data_shape, dtype='float32'
            )
            conv1 = fluid.layers.conv2d(images, 3, 3, 2)
            box, var = layers.density_prior_box(
                input=conv1,
                image=images,
                densities=[3, 4],
                fixed_sizes=[50.0, 60.0],
                fixed_ratios=[1.0],
                clip=True,
            )
            assert len(box.shape) == 4
            assert box.shape == var.shape
            assert box.shape[-1] == 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 TestGenerateProposalLabels(unittest.TestCase):
    def check_out(self, outs):
        rois = outs[0]
        labels_int32 = outs[1]
        bbox_targets = outs[2]
        bbox_inside_weights = outs[3]
        bbox_outside_weights = outs[4]
        assert rois.shape[1] == 4
        assert rois.shape[0] == labels_int32.shape[0]
        assert rois.shape[0] == bbox_targets.shape[0]
        assert rois.shape[0] == bbox_inside_weights.shape[0]
        assert rois.shape[0] == bbox_outside_weights.shape[0]
        assert bbox_targets.shape[1] == 4 * self.class_nums
        assert bbox_inside_weights.shape[1] == 4 * self.class_nums
        assert bbox_outside_weights.shape[1] == 4 * self.class_nums
        if len(outs) == 6:
            max_overlap_with_gt = outs[5]
            assert max_overlap_with_gt.shape[0] == rois.shape[0]

    def test_generate_proposal_labels(self):
        program = Program()
        with program_guard(program):
            rpn_rois = fluid.data(
                name='rpn_rois', shape=[4, 4], dtype='float32', lod_level=1
            )
            gt_classes = fluid.data(
                name='gt_classes', shape=[6], dtype='int32', lod_level=1
            )
            is_crowd = fluid.data(
                name='is_crowd', shape=[6], dtype='int32', lod_level=1
            )
            gt_boxes = fluid.data(
                name='gt_boxes', shape=[6, 4], dtype='float32', lod_level=1
            )
            im_info = fluid.data(name='im_info', shape=[1, 3], dtype='float32')
            max_overlap = fluid.data(
                name='max_overlap', shape=[4], dtype='float32', lod_level=1
            )
            self.class_nums = 5
            outs = fluid.layers.generate_proposal_labels(
                rpn_rois=rpn_rois,
                gt_classes=gt_classes,
                is_crowd=is_crowd,
                gt_boxes=gt_boxes,
                im_info=im_info,
                batch_size_per_im=2,
                fg_fraction=0.5,
                fg_thresh=0.5,
                bg_thresh_hi=0.5,
                bg_thresh_lo=0.0,
                bbox_reg_weights=[0.1, 0.1, 0.2, 0.2],
                class_nums=self.class_nums,
            )
            outs_1 = fluid.layers.generate_proposal_labels(
                rpn_rois=rpn_rois,
                gt_classes=gt_classes,
                is_crowd=is_crowd,
                gt_boxes=gt_boxes,
                im_info=im_info,
                batch_size_per_im=2,
                fg_fraction=0.5,
                fg_thresh=0.5,
                bg_thresh_hi=0.5,
                bg_thresh_lo=0.0,
                bbox_reg_weights=[0.1, 0.1, 0.2, 0.2],
                class_nums=self.class_nums,
                is_cascade_rcnn=True,
                max_overlap=max_overlap,
                return_max_overlap=True,
            )

            self.check_out(outs)
            self.check_out(outs_1)
            rois = outs[0]


class TestGenerateMaskLabels(unittest.TestCase):
    def test_generate_mask_labels(self):
        program = Program()
        with program_guard(program):
            im_info = layers.data(
                name='im_info',
                shape=[1, 3],
                dtype='float32',
                lod_level=1,
                append_batch_size=False,
            )
            gt_classes = layers.data(
                name='gt_classes',
                shape=[2, 1],
                dtype='int32',
                lod_level=1,
                append_batch_size=False,
            )
            is_crowd = layers.data(
                name='is_crowd',
                shape=[2, 1],
                dtype='int32',
                lod_level=1,
                append_batch_size=False,
            )
            gt_segms = layers.data(
                name='gt_segms',
                shape=[20, 2],
                dtype='float32',
                lod_level=3,
                append_batch_size=False,
            )
            rois = layers.data(
                name='rois',
                shape=[4, 4],
                dtype='float32',
                lod_level=1,
                append_batch_size=False,
            )
            labels_int32 = layers.data(
                name='labels_int32',
                shape=[4, 1],
                dtype='int32',
                lod_level=1,
                append_batch_size=False,
            )
            num_classes = 5
            resolution = 14
            outs = fluid.layers.generate_mask_labels(
                im_info=im_info,
                gt_classes=gt_classes,
                is_crowd=is_crowd,
                gt_segms=gt_segms,
                rois=rois,
                labels_int32=labels_int32,
                num_classes=num_classes,
                resolution=resolution,
            )
            mask_rois, roi_has_mask_int32, mask_int32 = outs
            assert mask_rois.shape[1] == 4
            assert mask_int32.shape[1] == num_classes * resolution * resolution


class TestMultiBoxHead(unittest.TestCase):
    def test_multi_box_head(self):
        data_shape = [3, 224, 224]
        mbox_locs, mbox_confs, box, var = self.multi_box_head_output(data_shape)

        assert len(box.shape) == 2
        assert box.shape == var.shape
        assert box.shape[1] == 4
        assert mbox_locs.shape[1] == mbox_confs.shape[1]

    def multi_box_head_output(self, data_shape):
        images = fluid.layers.data(
            name='pixel', shape=data_shape, dtype='float32'
        )
        conv1 = fluid.layers.conv2d(images, 3, 3, 2)
        conv2 = fluid.layers.conv2d(conv1, 3, 3, 2)
        conv3 = fluid.layers.conv2d(conv2, 3, 3, 2)
        conv4 = fluid.layers.conv2d(conv3, 3, 3, 2)
        conv5 = fluid.layers.conv2d(conv4, 3, 3, 2)

        mbox_locs, mbox_confs, box, var = layers.multi_box_head(
            inputs=[conv1, conv2, conv3, conv4, conv5, conv5],
            image=images,
            num_classes=21,
            min_ratio=20,
            max_ratio=90,
            aspect_ratios=[
                [2.0],
                [2.0, 3.0],
                [2.0, 3.0],
                [2.0, 3.0],
                [2.0],
                [2.0],
            ],
            base_size=300,
            offset=0.5,
            flip=True,
            clip=True,
        )

        return mbox_locs, mbox_confs, box, var


class TestDetectionMAP(unittest.TestCase):
    def test_detection_map(self):
        program = Program()
        with program_guard(program):
            detect_res = layers.data(
                name='detect_res',
                shape=[10, 6],
                append_batch_size=False,
                dtype='float32',
            )
            label = layers.data(
                name='label',
                shape=[10, 6],
                append_batch_size=False,
                dtype='float32',
            )

            map_out = detection.detection_map(detect_res, label, 21)
            self.assertIsNotNone(map_out)
            self.assertEqual(map_out.shape, (1,))
        print(str(program))


class TestGenerateProposals(LayerTest):
    def test_generate_proposals(self):
        scores_np = np.random.rand(2, 3, 4, 4).astype('float32')
        bbox_deltas_np = np.random.rand(2, 12, 4, 4).astype('float32')
        im_info_np = np.array([[8, 8, 0.5], [6, 6, 0.5]]).astype('float32')
        anchors_np = np.reshape(np.arange(4 * 4 * 3 * 4), [4, 4, 3, 4]).astype(
            'float32'
        )
        variances_np = np.ones((4, 4, 3, 4)).astype('float32')

        with self.static_graph():
            scores = fluid.data(
                name='scores', shape=[2, 3, 4, 4], dtype='float32'
            )
            bbox_deltas = fluid.data(
                name='bbox_deltas', shape=[2, 12, 4, 4], dtype='float32'
            )
            im_info = fluid.data(name='im_info', shape=[2, 3], dtype='float32')
            anchors = fluid.data(
                name='anchors', shape=[4, 4, 3, 4], dtype='float32'
            )
            variances = fluid.data(
                name='var', shape=[4, 4, 3, 4], dtype='float32'
            )
            rois, roi_probs, rois_num = fluid.layers.generate_proposals(
                scores,
                bbox_deltas,
                im_info,
                anchors,
                variances,
                pre_nms_top_n=10,
                post_nms_top_n=5,
                return_rois_num=True,
            )
            (
                rois_stat,
                roi_probs_stat,
                rois_num_stat,
            ) = self.get_static_graph_result(
                feed={
                    'scores': scores_np,
                    'bbox_deltas': bbox_deltas_np,
                    'im_info': im_info_np,
                    'anchors': anchors_np,
                    'var': variances_np,
                },
                fetch_list=[rois, roi_probs, rois_num],
                with_lod=False,
            )

        with self.dynamic_graph():
            scores_dy = base.to_variable(scores_np)
            bbox_deltas_dy = base.to_variable(bbox_deltas_np)
            im_info_dy = base.to_variable(im_info_np)
            anchors_dy = base.to_variable(anchors_np)
            variances_dy = base.to_variable(variances_np)
            rois, roi_probs, rois_num = fluid.layers.generate_proposals(
                scores_dy,
                bbox_deltas_dy,
                im_info_dy,
                anchors_dy,
                variances_dy,
                pre_nms_top_n=10,
                post_nms_top_n=5,
                return_rois_num=True,
            )
            rois_dy = rois.numpy()
            roi_probs_dy = roi_probs.numpy()
            rois_num_dy = rois_num.numpy()

        np.testing.assert_array_equal(np.array(rois_stat), rois_dy)
        np.testing.assert_array_equal(np.array(roi_probs_stat), roi_probs_dy)
        np.testing.assert_array_equal(np.array(rois_num_stat), rois_num_dy)


class TestBoxClip(unittest.TestCase):
    def test_box_clip(self):
        program = Program()
        with program_guard(program):
            input_box = layers.data(
                name='input_box', shape=[7, 4], dtype='float32', lod_level=1
            )
            im_info = layers.data(name='im_info', shape=[3], dtype='float32')
            out = layers.box_clip(input_box, im_info)
            self.assertIsNotNone(out)


class TestMulticlassNMS(unittest.TestCase):
    def test_multiclass_nms(self):
        program = Program()
        with program_guard(program):
            bboxes = layers.data(
                name='bboxes', shape=[-1, 10, 4], dtype='float32'
            )
            scores = layers.data(name='scores', shape=[-1, 10], dtype='float32')
            output = layers.multiclass_nms(bboxes, scores, 0.3, 400, 200, 0.7)
            self.assertIsNotNone(output)

    def test_multiclass_nms_error(self):
        program = Program()
        with program_guard(program):
            bboxes1 = fluid.data(
                name='bboxes1', shape=[10, 10, 4], dtype='int32'
            )
            scores1 = fluid.data(
                name='scores1', shape=[10, 10], dtype='float32'
            )
            bboxes2 = fluid.data(
                name='bboxes2', shape=[10, 10, 4], dtype='float32'
            )
            scores2 = fluid.data(name='scores2', shape=[10, 10], dtype='int32')
            self.assertRaises(
                TypeError,
                layers.multiclass_nms,
                bboxes=bboxes1,
                scores=scores1,
                score_threshold=0.5,
                nms_top_k=400,
                keep_top_k=200,
            )
            self.assertRaises(
                TypeError,
                layers.multiclass_nms,
                bboxes=bboxes2,
                scores=scores2,
                score_threshold=0.5,
                nms_top_k=400,
                keep_top_k=200,
            )


class TestMulticlassNMS2(unittest.TestCase):
    def test_multiclass_nms2(self):
        program = Program()
        with program_guard(program):
            bboxes = layers.data(
                name='bboxes', shape=[-1, 10, 4], dtype='float32'
            )
            scores = layers.data(name='scores', shape=[-1, 10], dtype='float32')
            output = fluid.contrib.multiclass_nms2(
                bboxes, scores, 0.3, 400, 200, 0.7
            )
            output2, index = fluid.contrib.multiclass_nms2(
                bboxes, scores, 0.3, 400, 200, 0.7, return_index=True
            )
            self.assertIsNotNone(output)
            self.assertIsNotNone(output2)
            self.assertIsNotNone(index)


class TestCollectFpnPropsals(LayerTest):
    def test_collect_fpn_proposals(self):
        multi_bboxes_np = []
        multi_scores_np = []
        rois_num_per_level_np = []
        for i in range(4):
            bboxes_np = np.random.rand(5, 4).astype('float32')
            scores_np = np.random.rand(5, 1).astype('float32')
            rois_num = np.array([2, 3]).astype('int32')
            multi_bboxes_np.append(bboxes_np)
            multi_scores_np.append(scores_np)
            rois_num_per_level_np.append(rois_num)

        with self.static_graph():
            multi_bboxes = []
            multi_scores = []
            rois_num_per_level = []
            for i in range(4):
                bboxes = fluid.data(
                    name='rois' + str(i),
                    shape=[5, 4],
                    dtype='float32',
                    lod_level=1,
                )
                scores = fluid.data(
                    name='scores' + str(i),
                    shape=[5, 1],
                    dtype='float32',
                    lod_level=1,
                )
                rois_num = fluid.data(
                    name='rois_num' + str(i), shape=[None], dtype='int32'
                )

                multi_bboxes.append(bboxes)
                multi_scores.append(scores)
                rois_num_per_level.append(rois_num)

            fpn_rois, rois_num = layers.collect_fpn_proposals(
                multi_bboxes,
                multi_scores,
                2,
                5,
                10,
                rois_num_per_level=rois_num_per_level,
            )
            feed = {}
            for i in range(4):
                feed['rois' + str(i)] = multi_bboxes_np[i]
                feed['scores' + str(i)] = multi_scores_np[i]
                feed['rois_num' + str(i)] = rois_num_per_level_np[i]
            fpn_rois_stat, rois_num_stat = self.get_static_graph_result(
                feed=feed, fetch_list=[fpn_rois, rois_num], with_lod=True
            )
            fpn_rois_stat = np.array(fpn_rois_stat)
            rois_num_stat = np.array(rois_num_stat)

        with self.dynamic_graph():
            multi_bboxes_dy = []
            multi_scores_dy = []
            rois_num_per_level_dy = []
            for i in range(4):
                bboxes_dy = base.to_variable(multi_bboxes_np[i])
                scores_dy = base.to_variable(multi_scores_np[i])
                rois_num_dy = base.to_variable(rois_num_per_level_np[i])
                multi_bboxes_dy.append(bboxes_dy)
                multi_scores_dy.append(scores_dy)
                rois_num_per_level_dy.append(rois_num_dy)
            fpn_rois_dy, rois_num_dy = fluid.layers.collect_fpn_proposals(
                multi_bboxes_dy,
                multi_scores_dy,
                2,
                5,
                10,
                rois_num_per_level=rois_num_per_level_dy,
            )
            fpn_rois_dy = fpn_rois_dy.numpy()
            rois_num_dy = rois_num_dy.numpy()

        np.testing.assert_array_equal(fpn_rois_stat, fpn_rois_dy)
        np.testing.assert_array_equal(rois_num_stat, rois_num_dy)

    def test_collect_fpn_proposals_error(self):
        def generate_input(bbox_type, score_type, name):
            multi_bboxes = []
            multi_scores = []
            for i in range(4):
                bboxes = fluid.data(
                    name='rois' + name + str(i),
                    shape=[10, 4],
                    dtype=bbox_type,
                    lod_level=1,
                )
                scores = fluid.data(
                    name='scores' + name + str(i),
                    shape=[10, 1],
                    dtype=score_type,
                    lod_level=1,
                )
                multi_bboxes.append(bboxes)
                multi_scores.append(scores)
            return multi_bboxes, multi_scores

        program = Program()
        with program_guard(program):
            bbox1 = fluid.data(
                name='rois', shape=[5, 10, 4], dtype='float32', lod_level=1
            )
            score1 = fluid.data(
                name='scores', shape=[5, 10, 1], dtype='float32', lod_level=1
            )
            bbox2, score2 = generate_input('int32', 'float32', '2')
            self.assertRaises(
                TypeError,
                layers.collect_fpn_proposals,
                multi_rois=bbox1,
                multi_scores=score1,
                min_level=2,
                max_level=5,
                post_nms_top_n=2000,
            )
            self.assertRaises(
                TypeError,
                layers.collect_fpn_proposals,
                multi_rois=bbox2,
                multi_scores=score2,
                min_level=2,
                max_level=5,
                post_nms_top_n=2000,
            )


class TestDistributeFpnProposals(LayerTest):
    def test_distribute_fpn_proposals(self):
        rois_np = np.random.rand(10, 4).astype('float32')
        rois_num_np = np.array([4, 6]).astype('int32')
        with self.static_graph():
            rois = fluid.data(name='rois', shape=[10, 4], dtype='float32')
            rois_num = fluid.data(name='rois_num', shape=[None], dtype='int32')
            (
                multi_rois,
                restore_ind,
                rois_num_per_level,
            ) = layers.distribute_fpn_proposals(
                fpn_rois=rois,
                min_level=2,
                max_level=5,
                refer_level=4,
                refer_scale=224,
                rois_num=rois_num,
            )
            fetch_list = multi_rois + [restore_ind] + rois_num_per_level
            output_stat = self.get_static_graph_result(
                feed={'rois': rois_np, 'rois_num': rois_num_np},
                fetch_list=fetch_list,
                with_lod=True,
            )
            output_stat_np = []
            for output in output_stat:
                output_np = np.array(output)
                if len(output_np) > 0:
                    output_stat_np.append(output_np)

        with self.dynamic_graph():
            rois_dy = base.to_variable(rois_np)
            rois_num_dy = base.to_variable(rois_num_np)
            (
                multi_rois_dy,
                restore_ind_dy,
                rois_num_per_level_dy,
            ) = layers.distribute_fpn_proposals(
                fpn_rois=rois_dy,
                min_level=2,
                max_level=5,
                refer_level=4,
                refer_scale=224,
                rois_num=rois_num_dy,
            )
            print(type(multi_rois_dy))
            output_dy = multi_rois_dy + [restore_ind_dy] + rois_num_per_level_dy
            output_dy_np = []
            for output in output_dy:
                output_np = output.numpy()
                if len(output_np) > 0:
                    output_dy_np.append(output_np)

        for res_stat, res_dy in zip(output_stat_np, output_dy_np):
            np.testing.assert_array_equal(res_stat, res_dy)

    def test_distribute_fpn_proposals_error(self):
        program = Program()
        with program_guard(program):
            fpn_rois = fluid.data(
                name='data_error', shape=[10, 4], dtype='int32', lod_level=1
            )
            self.assertRaises(
                TypeError,
                layers.distribute_fpn_proposals,
                fpn_rois=fpn_rois,
                min_level=2,
                max_level=5,
                refer_level=4,
                refer_scale=224,
            )


class TestBoxDecoderAndAssign(unittest.TestCase):
    def test_box_decoder_and_assign(self):
        program = Program()
        with program_guard(program):
            pb = fluid.data(name='prior_box', shape=[None, 4], dtype='float32')
            pbv = fluid.data(name='prior_box_var', shape=[4], dtype='float32')
            loc = fluid.data(
                name='target_box', shape=[None, 4 * 81], dtype='float32'
            )
            scores = fluid.data(
                name='scores', shape=[None, 81], dtype='float32'
            )
            (
                decoded_box,
                output_assign_box,
            ) = fluid.layers.box_decoder_and_assign(pb, pbv, loc, scores, 4.135)
            self.assertIsNotNone(decoded_box)
            self.assertIsNotNone(output_assign_box)

    def test_box_decoder_and_assign_error(self):
        def generate_input(pb_type, pbv_type, loc_type, score_type, name):
            pb = fluid.data(
                name='prior_box' + name, shape=[None, 4], dtype=pb_type
            )
            pbv = fluid.data(
                name='prior_box_var' + name, shape=[4], dtype=pbv_type
            )
            loc = fluid.data(
                name='target_box' + name, shape=[None, 4 * 81], dtype=loc_type
            )
            scores = fluid.data(
                name='scores' + name, shape=[None, 81], dtype=score_type
            )
            return pb, pbv, loc, scores

        program = Program()
        with program_guard(program):
            pb1, pbv1, loc1, scores1 = generate_input(
                'int32', 'float32', 'float32', 'float32', '1'
            )
            pb2, pbv2, loc2, scores2 = generate_input(
                'float32', 'float32', 'int32', 'float32', '2'
            )
            pb3, pbv3, loc3, scores3 = generate_input(
                'float32', 'float32', 'float32', 'int32', '3'
            )
            self.assertRaises(
                TypeError,
                layers.box_decoder_and_assign,
                prior_box=pb1,
                prior_box_var=pbv1,
                target_box=loc1,
                box_score=scores1,
                box_clip=4.0,
            )
            self.assertRaises(
                TypeError,
                layers.box_decoder_and_assign,
                prior_box=pb2,
                prior_box_var=pbv2,
                target_box=loc2,
                box_score=scores2,
                box_clip=4.0,
            )
            self.assertRaises(
                TypeError,
                layers.box_decoder_and_assign,
                prior_box=pb3,
                prior_box_var=pbv3,
                target_box=loc3,
                box_score=scores3,
                box_clip=4.0,
            )


if __name__ == '__main__':
    paddle.enable_static()
    unittest.main()