# Copyright (c) 2021 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

import paddle
from paddlex.cv.nets.ppdet.core.workspace import register, create
from .meta_arch import BaseArch

__all__ = ['FCOS']


@register
class FCOS(BaseArch):
    __category__ = 'architecture'
    __inject__ = ['fcos_post_process']

    def __init__(self,
                 backbone,
                 neck,
                 fcos_head='FCOSHead',
                 fcos_post_process='FCOSPostProcess'):
        super(FCOS, self).__init__()
        self.backbone = backbone
        self.neck = neck
        self.fcos_head = fcos_head
        self.fcos_post_process = fcos_post_process

    @classmethod
    def from_config(cls, cfg, *args, **kwargs):
        backbone = create(cfg['backbone'])

        kwargs = {'input_shape': backbone.out_shape}
        neck = create(cfg['neck'], **kwargs)

        kwargs = {'input_shape': neck.out_shape}
        fcos_head = create(cfg['fcos_head'], **kwargs)

        return {
            'backbone': backbone,
            'neck': neck,
            "fcos_head": fcos_head,
        }

    def _forward(self):
        body_feats = self.backbone(self.inputs)
        fpn_feats = self.neck(body_feats)
        fcos_head_outs = self.fcos_head(fpn_feats, self.training)
        if not self.training:
            scale_factor = self.inputs['scale_factor']
            bboxes = self.fcos_post_process(fcos_head_outs, scale_factor)
            return bboxes
        else:
            return fcos_head_outs

    def get_loss(self, ):
        loss = {}
        tag_labels, tag_bboxes, tag_centerness = [], [], []
        for i in range(len(self.fcos_head.fpn_stride)):
            # reg_target, labels, scores, centerness
            k_lbl = 'labels{}'.format(i)
            if k_lbl in self.inputs:
                tag_labels.append(self.inputs[k_lbl])
            k_box = 'reg_target{}'.format(i)
            if k_box in self.inputs:
                tag_bboxes.append(self.inputs[k_box])
            k_ctn = 'centerness{}'.format(i)
            if k_ctn in self.inputs:
                tag_centerness.append(self.inputs[k_ctn])

        fcos_head_outs = self._forward()
        loss_fcos = self.fcos_head.get_loss(fcos_head_outs, tag_labels,
                                            tag_bboxes, tag_centerness)
        loss.update(loss_fcos)
        total_loss = paddle.add_n(list(loss.values()))
        loss.update({'loss': total_loss})
        return loss

    def get_pred(self):
        bboxes, bbox_num = self._forward()
        output = {'bbox': bboxes, 'bbox_num': bbox_num}
        return output
