paddlepaddle--paddle/test/legacy_test/test_multiclass_nms_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://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 copy
import unittest

import numpy as np
from op_test import OpTest

import paddle
from paddle import _C_ops
from paddle.base.layer_helper import LayerHelper


def multiclass_nms3(
    bboxes,
    scores,
    rois_num=None,
    score_threshold=0.3,
    nms_top_k=1000,
    keep_top_k=100,
    nms_threshold=0.3,
    normalized=True,
    nms_eta=1.0,
    background_label=-1,
    return_index=True,
    return_rois_num=True,
    name=None,
):
    helper = LayerHelper('multiclass_nms3', **locals())

    if paddle.in_dynamic_mode():
        attrs = (
            score_threshold,
            nms_top_k,
            keep_top_k,
            nms_threshold,
            normalized,
            nms_eta,
            background_label,
        )
        output, index, nms_rois_num = _C_ops.multiclass_nms3(
            bboxes, scores, rois_num, *attrs
        )
        if not return_index:
            index = None
        return output, index, nms_rois_num
    else:
        output = helper.create_variable_for_type_inference(dtype=bboxes.dtype)
        index = helper.create_variable_for_type_inference(dtype='int32')

        inputs = {'BBoxes': bboxes, 'Scores': scores}
        outputs = {'Out': output, 'Index': index}

        if rois_num is not None:
            inputs['RoisNum'] = rois_num

        if return_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 not return_index:
            index = None
        if not return_rois_num:
            nms_rois_num = None

        return output, nms_rois_num, index


def softmax(x):
    # clip to shiftx, otherwise, when calc loss with
    # log(exp(shiftx)), may get log(0)=INF
    shiftx = (x - np.max(x)).clip(-64.0)
    exps = np.exp(shiftx)
    return exps / np.sum(exps)


def iou(box_a, box_b, norm):
    """Apply intersection-over-union overlap between box_a and box_b"""
    xmin_a = min(box_a[0], box_a[2])
    ymin_a = min(box_a[1], box_a[3])
    xmax_a = max(box_a[0], box_a[2])
    ymax_a = max(box_a[1], box_a[3])

    xmin_b = min(box_b[0], box_b[2])
    ymin_b = min(box_b[1], box_b[3])
    xmax_b = max(box_b[0], box_b[2])
    ymax_b = max(box_b[1], box_b[3])

    area_a = (ymax_a - ymin_a + (not norm)) * (xmax_a - xmin_a + (not norm))
    area_b = (ymax_b - ymin_b + (not norm)) * (xmax_b - xmin_b + (not norm))
    if area_a <= 0 and area_b <= 0:
        return 0.0

    xa = max(xmin_a, xmin_b)
    ya = max(ymin_a, ymin_b)
    xb = min(xmax_a, xmax_b)
    yb = min(ymax_a, ymax_b)

    inter_area = max(xb - xa + (not norm), 0.0) * max(yb - ya + (not norm), 0.0)

    iou_ratio = inter_area / (area_a + area_b - inter_area)

    return iou_ratio


def nms(
    boxes,
    scores,
    score_threshold,
    nms_threshold,
    top_k=200,
    normalized=True,
    eta=1.0,
):
    """Apply non-maximum suppression at test time to avoid detecting too many
    overlapping bounding boxes for a given object.
    Args:
        boxes: (tensor) The location preds for the img, Shape: [num_priors,4].
        scores: (tensor) The class predscores for the img, Shape:[num_priors].
        score_threshold: (float) The confidence thresh for filtering low
            confidence boxes.
        nms_threshold: (float) The overlap thresh for suppressing unnecessary
            boxes.
        top_k: (int) The maximum number of box preds to consider.
        eta: (float) The parameter for adaptive NMS.
    Return:
        The indices of the kept boxes with respect to num_priors.
    """
    all_scores = copy.deepcopy(scores)
    all_scores = all_scores.flatten()
    selected_indices = np.argwhere(all_scores > score_threshold)
    selected_indices = selected_indices.flatten()
    all_scores = all_scores[selected_indices]

    sorted_indices = np.argsort(-all_scores, axis=0, kind='mergesort')
    sorted_scores = all_scores[sorted_indices]
    sorted_indices = selected_indices[sorted_indices]
    if top_k > -1 and top_k < sorted_indices.shape[0]:
        sorted_indices = sorted_indices[:top_k]
        sorted_scores = sorted_scores[:top_k]

    selected_indices = []
    adaptive_threshold = nms_threshold
    for i in range(sorted_scores.shape[0]):
        idx = sorted_indices[i]
        keep = True
        for k in range(len(selected_indices)):
            if keep:
                kept_idx = selected_indices[k]
                overlap = iou(boxes[idx], boxes[kept_idx], normalized)
                keep = True if overlap <= adaptive_threshold else False
            else:
                break
        if keep:
            selected_indices.append(idx)
        if keep and eta < 1 and adaptive_threshold > 0.5:
            adaptive_threshold *= eta
    return selected_indices


def multiclass_nms(
    boxes,
    scores,
    background,
    score_threshold,
    nms_threshold,
    nms_top_k,
    keep_top_k,
    normalized,
    shared,
):
    if shared:
        class_num = scores.shape[0]
        priorbox_num = scores.shape[1]
    else:
        box_num = scores.shape[0]
        class_num = scores.shape[1]

    selected_indices = {}
    num_det = 0
    for c in range(class_num):
        if c == background:
            continue
        if shared:
            indices = nms(
                boxes,
                scores[c],
                score_threshold,
                nms_threshold,
                nms_top_k,
                normalized,
            )
        else:
            indices = nms(
                boxes[:, c, :],
                scores[:, c],
                score_threshold,
                nms_threshold,
                nms_top_k,
                normalized,
            )
        selected_indices[c] = indices
        num_det += len(indices)

    if keep_top_k > -1 and num_det > keep_top_k:
        score_index = []
        for c, indices in selected_indices.items():
            for idx in indices:
                if shared:
                    score_index.append((scores[c][idx], c, idx))
                else:
                    score_index.append((scores[idx][c], c, idx))

        sorted_score_index = sorted(
            score_index, key=lambda tup: tup[0], reverse=True
        )
        sorted_score_index = sorted_score_index[:keep_top_k]
        selected_indices = {}

        for _, c, _ in sorted_score_index:
            selected_indices[c] = []
        for s, c, idx in sorted_score_index:
            selected_indices[c].append(idx)
        if not shared:
            for labels in selected_indices:
                selected_indices[labels].sort()
        num_det = keep_top_k

    return selected_indices, num_det


def lod_multiclass_nms(
    boxes,
    scores,
    background,
    score_threshold,
    nms_threshold,
    nms_top_k,
    keep_top_k,
    box_lod,
    normalized,
):
    num_class = boxes.shape[1]
    det_outs = []
    lod = []
    head = 0
    for n in range(len(box_lod[0])):
        if box_lod[0][n] == 0:
            lod.append(0)
            continue
        box = boxes[head : head + box_lod[0][n]]
        score = scores[head : head + box_lod[0][n]]
        offset = head
        head = head + box_lod[0][n]
        nmsed_outs, nmsed_num = multiclass_nms(
            box,
            score,
            background,
            score_threshold,
            nms_threshold,
            nms_top_k,
            keep_top_k,
            normalized,
            shared=False,
        )
        lod.append(nmsed_num)

        if nmsed_num == 0:
            continue
        tmp_det_out = []
        for c, indices in nmsed_outs.items():
            for idx in indices:
                xmin, ymin, xmax, ymax = box[idx, c, :]
                tmp_det_out.append(
                    [
                        c,
                        score[idx][c],
                        xmin,
                        ymin,
                        xmax,
                        ymax,
                        offset * num_class + idx * num_class + c,
                    ]
                )
        sorted_det_out = sorted(
            tmp_det_out, key=lambda tup: tup[0], reverse=False
        )
        det_outs.extend(sorted_det_out)

    return det_outs, lod


def batched_multiclass_nms(
    boxes,
    scores,
    background,
    score_threshold,
    nms_threshold,
    nms_top_k,
    keep_top_k,
    normalized=True,
    gpu_logic=False,
):
    batch_size = scores.shape[0]
    num_boxes = scores.shape[2]
    det_outs = []
    index_outs = []
    lod = []
    for n in range(batch_size):
        nmsed_outs, nmsed_num = multiclass_nms(
            boxes[n],
            scores[n],
            background,
            score_threshold,
            nms_threshold,
            nms_top_k,
            keep_top_k,
            normalized,
            shared=True,
        )
        lod.append(nmsed_num)

        if nmsed_num == 0:
            continue
        tmp_det_out = []
        for c, indices in nmsed_outs.items():
            for idx in indices:
                xmin, ymin, xmax, ymax = boxes[n][idx][:]
                tmp_det_out.append(
                    [
                        c,
                        scores[n][c][idx],
                        xmin,
                        ymin,
                        xmax,
                        ymax,
                        idx + n * num_boxes,
                    ]
                )
        if gpu_logic:
            sorted_det_out = sorted(
                tmp_det_out, key=lambda tup: tup[1], reverse=True
            )
        else:
            sorted_det_out = sorted(
                tmp_det_out, key=lambda tup: tup[0], reverse=False
            )
        det_outs.extend(sorted_det_out)
    return det_outs, lod


class TestIOU(unittest.TestCase):
    def test_iou(self):
        box1 = np.array([4.0, 3.0, 7.0, 5.0]).astype('float32')
        box2 = np.array([3.0, 4.0, 6.0, 8.0]).astype('float32')

        expt_output = np.array([2.0 / 16.0]).astype('float32')
        calc_output = np.array([iou(box1, box2, True)]).astype('float32')
        np.testing.assert_allclose(calc_output, expt_output, rtol=1e-05)


class TestMulticlassNMS3Op(OpTest):
    def set_argument(self):
        self.score_threshold = 0.01

    def setUp(self):
        self.python_api = multiclass_nms3
        self.set_argument()
        N = 7
        M = 1200
        C = 21
        BOX_SIZE = 4
        background = 0
        nms_threshold = 0.3
        nms_top_k = 400
        keep_top_k = 200 if not hasattr(self, 'keep_top_k') else self.keep_top_k
        score_threshold = self.score_threshold

        scores = np.random.random((N * M, C)).astype('float32')

        scores = np.apply_along_axis(softmax, 1, scores)
        scores = np.reshape(scores, (N, M, C))
        scores = np.transpose(scores, (0, 2, 1))

        boxes = np.random.random((N, M, BOX_SIZE)).astype('float32')
        boxes[:, :, 0:2] = boxes[:, :, 0:2] * 0.5
        boxes[:, :, 2:4] = boxes[:, :, 2:4] * 0.5 + 0.5

        det_outs, lod = batched_multiclass_nms(
            boxes,
            scores,
            background,
            score_threshold,
            nms_threshold,
            nms_top_k,
            keep_top_k,
            gpu_logic=self.gpu_logic if hasattr(self, 'gpu_logic') else None,
        )
        det_outs = np.array(det_outs)

        nmsed_outs = (
            det_outs[:, :-1].astype('float32')
            if len(det_outs)
            else np.array([], dtype=np.float32).reshape([0, BOX_SIZE + 2])
        )
        index_outs = (
            det_outs[:, -1:].astype('int')
            if len(det_outs)
            else np.array([], dtype='int').reshape([0, 1])
        )
        self.op_type = 'multiclass_nms3'
        self.inputs = {'BBoxes': boxes, 'Scores': scores}
        self.outputs = {
            'Out': nmsed_outs,
            'Index': index_outs,
            'NmsRoisNum': np.array(lod).astype('int32'),
        }
        self.attrs = {
            'background_label': 0,
            'nms_threshold': nms_threshold,
            'nms_top_k': nms_top_k,
            'keep_top_k': keep_top_k,
            'score_threshold': score_threshold,
            'nms_eta': 1.0,
            'normalized': True,
        }

    def test_check_output(self):
        place = paddle.CPUPlace()
        self.check_output_with_place(place)


class TestMulticlassNMS3OpNoOutput(TestMulticlassNMS3Op):
    def set_argument(self):
        # Here set 2.0 to test the case there is no outputs.
        # In practical use, 0.0 < score_threshold < 1.0
        self.score_threshold = 2.0


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