person_monitor/dependence/PaddleDetection/static/ppdet/modeling/losses/fcos_loss.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 absolute_import
from __future__ import division
from __future__ import print_function

from paddle import fluid
from paddle.fluid.param_attr import ParamAttr
from ppdet.core.workspace import register, serializable

INF = 1e8
__all__ = ['FCOSLoss']


@register
@serializable
class FCOSLoss(object):
    """
    FCOSLoss
    Args:
        loss_alpha (float): alpha in focal loss 
        loss_gamma (float): gamma in focal loss
        iou_loss_type(str): location loss type, IoU/GIoU/LINEAR_IoU
        reg_weights(float): weight for location loss
    """

    def __init__(self,
                 loss_alpha=0.25,
                 loss_gamma=2.0,
                 iou_loss_type="IoU",
                 reg_weights=1.0):
        self.loss_alpha = loss_alpha
        self.loss_gamma = loss_gamma
        self.iou_loss_type = iou_loss_type
        self.reg_weights = reg_weights

    def __flatten_tensor(self, input, channel_first=False):
        """
        Flatten a Tensor
        Args:
            input   (Variables): Input Tensor
            channel_first(bool): if true the dimension order of
                Tensor is [N, C, H, W], otherwise is [N, H, W, C]
        Return:
            input_channel_last (Variables): The flattened Tensor in channel_last style
        """
        if channel_first:
            input_channel_last = fluid.layers.transpose(
                input, perm=[0, 2, 3, 1])
        else:
            input_channel_last = input
        input_channel_last = fluid.layers.flatten(input_channel_last, axis=3)
        return input_channel_last

    def __iou_loss(self, pred, targets, positive_mask, weights=None):
        """
        Calculate the loss for location prediction
        Args:
            pred          (Variables): bounding boxes prediction
            targets       (Variables): targets for positive samples
            positive_mask (Variables): mask of positive samples
            weights       (Variables): weights for each positive samples
        Return:
            loss (Varialbes): location loss
        """
        plw = fluid.layers.elementwise_mul(pred[:, 0], positive_mask, axis=0)
        pth = fluid.layers.elementwise_mul(pred[:, 1], positive_mask, axis=0)
        prw = fluid.layers.elementwise_mul(pred[:, 2], positive_mask, axis=0)
        pbh = fluid.layers.elementwise_mul(pred[:, 3], positive_mask, axis=0)
        tlw = fluid.layers.elementwise_mul(targets[:, 0], positive_mask, axis=0)
        tth = fluid.layers.elementwise_mul(targets[:, 1], positive_mask, axis=0)
        trw = fluid.layers.elementwise_mul(targets[:, 2], positive_mask, axis=0)
        tbh = fluid.layers.elementwise_mul(targets[:, 3], positive_mask, axis=0)
        tlw.stop_gradient = True
        trw.stop_gradient = True
        tth.stop_gradient = True
        tbh.stop_gradient = True
        area_target = (tlw + trw) * (tth + tbh)
        area_predict = (plw + prw) * (pth + pbh)
        ilw = fluid.layers.elementwise_min(plw, tlw)
        irw = fluid.layers.elementwise_min(prw, trw)
        ith = fluid.layers.elementwise_min(pth, tth)
        ibh = fluid.layers.elementwise_min(pbh, tbh)
        clw = fluid.layers.elementwise_max(plw, tlw)
        crw = fluid.layers.elementwise_max(prw, trw)
        cth = fluid.layers.elementwise_max(pth, tth)
        cbh = fluid.layers.elementwise_max(pbh, tbh)
        area_inter = (ilw + irw) * (ith + ibh)
        ious = (area_inter + 1.0) / (
            area_predict + area_target - area_inter + 1.0)
        ious = fluid.layers.elementwise_mul(ious, positive_mask, axis=0)
        if self.iou_loss_type.lower() == "linear_iou":
            loss = 1.0 - ious
        elif self.iou_loss_type.lower() == "giou":
            area_uniou = area_predict + area_target - area_inter
            area_circum = (clw + crw) * (cth + cbh) + 1e-7
            giou = ious - (area_circum - area_uniou) / area_circum
            loss = 1.0 - giou
        elif self.iou_loss_type.lower() == "iou":
            loss = 0.0 - fluid.layers.log(ious)
        else:
            raise KeyError
        if weights is not None:
            loss = loss * weights
        return loss

    def __call__(self, cls_logits, bboxes_reg, centerness, tag_labels,
                 tag_bboxes, tag_center):
        """
        Calculate the loss for classification, location and centerness
        Args:
            cls_logits (list): list of Variables, which is predicted
                score for all anchor points with shape [N, M, C]
            bboxes_reg (list): list of Variables, which is predicted
                offsets for all anchor points with shape [N, M, 4]
            centerness (list): list of Variables, which is predicted
                centerness for all anchor points with shape [N, M, 1]
            tag_labels (list): list of Variables, which is category
                targets for each anchor point
            tag_bboxes (list): list of Variables, which is bounding
                boxes targets for positive samples
            tag_center (list): list of Variables, which is centerness
                targets for positive samples
        Return:
            loss (dict): loss composed by classification loss, bounding box
        """
        cls_logits_flatten_list = []
        bboxes_reg_flatten_list = []
        centerness_flatten_list = []
        tag_labels_flatten_list = []
        tag_bboxes_flatten_list = []
        tag_center_flatten_list = []
        num_lvl = len(cls_logits)
        for lvl in range(num_lvl):
            cls_logits_flatten_list.append(
                self.__flatten_tensor(cls_logits[num_lvl - 1 - lvl], True))
            bboxes_reg_flatten_list.append(
                self.__flatten_tensor(bboxes_reg[num_lvl - 1 - lvl], True))
            centerness_flatten_list.append(
                self.__flatten_tensor(centerness[num_lvl - 1 - lvl], True))
            tag_labels_flatten_list.append(
                self.__flatten_tensor(tag_labels[lvl], False))
            tag_bboxes_flatten_list.append(
                self.__flatten_tensor(tag_bboxes[lvl], False))
            tag_center_flatten_list.append(
                self.__flatten_tensor(tag_center[lvl], False))

        cls_logits_flatten = fluid.layers.concat(
            cls_logits_flatten_list, axis=0)
        bboxes_reg_flatten = fluid.layers.concat(
            bboxes_reg_flatten_list, axis=0)
        centerness_flatten = fluid.layers.concat(
            centerness_flatten_list, axis=0)
        tag_labels_flatten = fluid.layers.concat(
            tag_labels_flatten_list, axis=0)
        tag_bboxes_flatten = fluid.layers.concat(
            tag_bboxes_flatten_list, axis=0)
        tag_center_flatten = fluid.layers.concat(
            tag_center_flatten_list, axis=0)
        tag_labels_flatten.stop_gradient = True
        tag_bboxes_flatten.stop_gradient = True
        tag_center_flatten.stop_gradient = True

        mask_positive = tag_labels_flatten > 0
        mask_positive.stop_gradient = True
        mask_positive_float = fluid.layers.cast(mask_positive, dtype="float32")
        mask_positive_float.stop_gradient = True
        num_positive_fp32 = fluid.layers.reduce_sum(mask_positive_float)
        num_positive_int32 = fluid.layers.cast(num_positive_fp32, dtype="int32")
        num_positive_int32 = num_positive_int32 * 0 + 1
        num_positive_fp32.stop_gradient = True
        num_positive_int32.stop_gradient = True
        normalize_sum = fluid.layers.sum(tag_center_flatten)
        normalize_sum.stop_gradient = True
        normalize_sum = fluid.layers.reduce_sum(mask_positive_float *
                                                normalize_sum)

        normalize_sum.stop_gradient = True
        cls_loss = fluid.layers.sigmoid_focal_loss(
            cls_logits_flatten, tag_labels_flatten,
            num_positive_int32) / num_positive_fp32
        reg_loss = self.__iou_loss(bboxes_reg_flatten, tag_bboxes_flatten,
                                   mask_positive_float, tag_center_flatten)
        reg_loss = fluid.layers.elementwise_mul(
            reg_loss, mask_positive_float, axis=0) / normalize_sum
        ctn_loss = fluid.layers.sigmoid_cross_entropy_with_logits(
            x=centerness_flatten, label=tag_center_flatten)
        ctn_loss = fluid.layers.elementwise_mul(
            ctn_loss, mask_positive_float, axis=0) / num_positive_fp32
        loss_all = {
            "loss_centerness": fluid.layers.reduce_sum(ctn_loss),
            "loss_cls": fluid.layers.reduce_sum(cls_loss),
            "loss_box": fluid.layers.reduce_sum(reg_loss)
        }
        return loss_all