test_ops.py

#   Copyright (c) 2020 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.

from __future__ import print_function
import os, sys
# add python path of PadleDetection to sys.path
parent_path = os.path.abspath(os.path.join(__file__, *(['..'] * 4)))
if parent_path not in sys.path:
    sys.path.append(parent_path)

import unittest
import numpy as np

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

import ppdet.modeling.ops as ops
from ppdet.modeling.tests.test_base import LayerTest


def make_rois(h, w, rois_num, output_size):
    rois = np.zeros((0, 4)).astype('float32')
    for roi_num in rois_num:
        roi = np.zeros((roi_num, 4)).astype('float32')
        roi[:, 0] = np.random.randint(0, h - output_size[0], size=roi_num)
        roi[:, 1] = np.random.randint(0, w - output_size[1], size=roi_num)
        roi[:, 2] = np.random.randint(roi[:, 0] + output_size[0], h)
        roi[:, 3] = np.random.randint(roi[:, 1] + output_size[1], w)
        rois = np.vstack((rois, roi))
    return rois


class TestCollectFpnProposals(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)

        paddle.enable_static()
        with self.static_graph():
            multi_bboxes = []
            multi_scores = []
            rois_num_per_level = []
            for i in range(4):
                bboxes = paddle.static.data(
                    name='rois' + str(i),
                    shape=[5, 4],
                    dtype='float32',
                    lod_level=1)
                scores = paddle.static.data(
                    name='scores' + str(i),
                    shape=[5, 1],
                    dtype='float32',
                    lod_level=1)
                rois_num = paddle.static.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 = ops.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)

        paddle.disable_static()
        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 = ops.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()

        self.assertTrue(np.array_equal(fpn_rois_stat, fpn_rois_dy))
        self.assertTrue(np.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 = paddle.static.data(
                    name='rois' + name + str(i),
                    shape=[10, 4],
                    dtype=bbox_type,
                    lod_level=1)
                scores = paddle.static.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

        paddle.enable_static()
        program = Program()
        with program_guard(program):
            bbox1 = paddle.static.data(
                name='rois', shape=[5, 10, 4], dtype='float32', lod_level=1)
            score1 = paddle.static.data(
                name='scores', shape=[5, 10, 1], dtype='float32', lod_level=1)
            bbox2, score2 = generate_input('int32', 'float32', '2')
            self.assertRaises(
                TypeError,
                ops.collect_fpn_proposals,
                multi_rois=bbox1,
                multi_scores=score1,
                min_level=2,
                max_level=5,
                post_nms_top_n=2000)
            self.assertRaises(
                TypeError,
                ops.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 = paddle.static.data(
                name='rois', shape=[10, 4], dtype='float32')
            rois_num = paddle.static.data(
                name='rois_num', shape=[None], dtype='int32')
            multi_rois, restore_ind, rois_num_per_level = ops.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 = ops.distribute_fpn_proposals(
                fpn_rois=rois_dy,
                min_level=2,
                max_level=5,
                refer_level=4,
                refer_scale=224,
                rois_num=rois_num_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):
            self.assertTrue(np.array_equal(res_stat, res_dy))

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


class TestROIAlign(LayerTest):
    def test_roi_align(self):
        b, c, h, w = 2, 12, 20, 20
        inputs_np = np.random.rand(b, c, h, w).astype('float32')
        rois_num = [4, 6]
        output_size = (7, 7)
        rois_np = make_rois(h, w, rois_num, output_size)
        rois_num_np = np.array(rois_num).astype('int32')
        with self.static_graph():
            inputs = paddle.static.data(
                name='inputs', shape=[b, c, h, w], dtype='float32')
            rois = paddle.static.data(
                name='rois', shape=[10, 4], dtype='float32')
            rois_num = paddle.static.data(
                name='rois_num', shape=[None], dtype='int32')

            output = ops.roi_align(
                input=inputs,
                rois=rois,
                output_size=output_size,
                rois_num=rois_num)
            output_np, = self.get_static_graph_result(
                feed={
                    'inputs': inputs_np,
                    'rois': rois_np,
                    'rois_num': rois_num_np
                },
                fetch_list=output,
                with_lod=False)

        with self.dynamic_graph():
            inputs_dy = base.to_variable(inputs_np)
            rois_dy = base.to_variable(rois_np)
            rois_num_dy = base.to_variable(rois_num_np)

            output_dy = ops.roi_align(
                input=inputs_dy,
                rois=rois_dy,
                output_size=output_size,
                rois_num=rois_num_dy)
            output_dy_np = output_dy.numpy()

        self.assertTrue(np.array_equal(output_np, output_dy_np))

    def test_roi_align_error(self):
        program = Program()
        with program_guard(program):
            inputs = paddle.static.data(
                name='inputs', shape=[2, 12, 20, 20], dtype='float32')
            rois = paddle.static.data(
                name='data_error', shape=[10, 4], dtype='int32', lod_level=1)
            self.assertRaises(
                TypeError,
                ops.roi_align,
                input=inputs,
                rois=rois,
                output_size=(7, 7))


class TestROIPool(LayerTest):
    def test_roi_pool(self):
        b, c, h, w = 2, 12, 20, 20
        inputs_np = np.random.rand(b, c, h, w).astype('float32')
        rois_num = [4, 6]
        output_size = (7, 7)
        rois_np = make_rois(h, w, rois_num, output_size)
        rois_num_np = np.array(rois_num).astype('int32')
        with self.static_graph():
            inputs = paddle.static.data(
                name='inputs', shape=[b, c, h, w], dtype='float32')
            rois = paddle.static.data(
                name='rois', shape=[10, 4], dtype='float32')
            rois_num = paddle.static.data(
                name='rois_num', shape=[None], dtype='int32')

            output, _ = ops.roi_pool(
                input=inputs,
                rois=rois,
                output_size=output_size,
                rois_num=rois_num)
            output_np, = self.get_static_graph_result(
                feed={
                    'inputs': inputs_np,
                    'rois': rois_np,
                    'rois_num': rois_num_np
                },
                fetch_list=[output],
                with_lod=False)

        with self.dynamic_graph():
            inputs_dy = base.to_variable(inputs_np)
            rois_dy = base.to_variable(rois_np)
            rois_num_dy = base.to_variable(rois_num_np)

            output_dy, _ = ops.roi_pool(
                input=inputs_dy,
                rois=rois_dy,
                output_size=output_size,
                rois_num=rois_num_dy)
            output_dy_np = output_dy.numpy()

        self.assertTrue(np.array_equal(output_np, output_dy_np))

    def test_roi_pool_error(self):
        program = Program()
        with program_guard(program):
            inputs = paddle.static.data(
                name='inputs', shape=[2, 12, 20, 20], dtype='float32')
            rois = paddle.static.data(
                name='data_error', shape=[10, 4], dtype='int32', lod_level=1)
            self.assertRaises(
                TypeError,
                ops.roi_pool,
                input=inputs,
                rois=rois,
                output_size=(7, 7))


class TestIoUSimilarity(LayerTest):
    def test_iou_similarity(self):
        b, c, h, w = 2, 12, 20, 20
        inputs_np = np.random.rand(b, c, h, w).astype('float32')
        output_size = (7, 7)
        x_np = make_rois(h, w, [20], output_size)
        y_np = make_rois(h, w, [10], output_size)
        with self.static_graph():
            program = Program()
            with program_guard(program):
                x = paddle.static.data(name='x', shape=[20, 4], dtype='float32')
                y = paddle.static.data(name='y', shape=[10, 4], dtype='float32')

                iou = ops.iou_similarity(x=x, y=y)
                iou_np, = self.get_static_graph_result(
                    feed={
                        'x': x_np,
                        'y': y_np,
                    },
                    fetch_list=[iou],
                    with_lod=False)

        with self.dynamic_graph():
            x_dy = base.to_variable(x_np)
            y_dy = base.to_variable(y_np)

            iou_dy = ops.iou_similarity(x=x_dy, y=y_dy)
            iou_dy_np = iou_dy.numpy()

        self.assertTrue(np.array_equal(iou_np, iou_dy_np))


class TestYOLO_Box(LayerTest):
    def test_yolo_box(self):

        # x shape [N C H W], C=K * (5 + class_num), class_num=10, K=2
        np_x = np.random.random([1, 30, 7, 7]).astype('float32')
        np_origin_shape = np.array([[608, 608]], dtype='int32')
        class_num = 10
        conf_thresh = 0.01
        downsample_ratio = 32
        scale_x_y = 1.2

        # static
        with self.static_graph():
            # x shape [N C H W], C=K * (5 + class_num), class_num=10, K=2
            x = paddle.static.data(
                name='x', shape=[1, 30, 7, 7], dtype='float32')
            origin_shape = paddle.static.data(
                name='origin_shape', shape=[1, 2], dtype='int32')

            boxes, scores = ops.yolo_box(
                x,
                origin_shape, [10, 13, 30, 13],
                class_num,
                conf_thresh,
                downsample_ratio,
                scale_x_y=scale_x_y)

            boxes_np, scores_np = self.get_static_graph_result(
                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)

        # dygraph
        with self.dynamic_graph():
            x_dy = fluid.layers.assign(np_x)
            origin_shape_dy = fluid.layers.assign(np_origin_shape)

            boxes_dy, scores_dy = ops.yolo_box(
                x_dy,
                origin_shape_dy, [10, 13, 30, 13],
                10,
                0.01,
                32,
                scale_x_y=scale_x_y)

            boxes_dy_np = boxes_dy.numpy()
            scores_dy_np = scores_dy.numpy()

            self.assertTrue(np.array_equal(boxes_np, boxes_dy_np))
            self.assertTrue(np.array_equal(scores_np, scores_dy_np))

    def test_yolo_box_error(self):
        paddle.enable_static()
        program = Program()
        with program_guard(program):
            # x shape [N C H W], C=K * (5 + class_num), class_num=10, K=2
            x = paddle.static.data(
                name='x', shape=[1, 30, 7, 7], dtype='float32')
            origin_shape = paddle.static.data(
                name='origin_shape', shape=[1, 2], dtype='int32')

            self.assertRaises(
                TypeError,
                ops.yolo_box,
                x,
                origin_shape, [10, 13, 30, 13],
                10.123,
                0.01,
                32,
                scale_x_y=1.2)


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