# Copyright (c) 2022 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 ppdet.core.workspace import register, create
from .meta_arch import BaseArch

__all__ = ['YOLOF']


@register
class YOLOF(BaseArch):
    __category__ = 'architecture'

    def __init__(self,
                 backbone='ResNet',
                 neck='DilatedEncoder',
                 head='YOLOFHead',
                 for_mot=False):
        """
        YOLOF network, see https://arxiv.org/abs/2103.09460

        Args:
            backbone (nn.Layer): backbone instance
            neck (nn.Layer): DilatedEncoder instance
            head (nn.Layer): YOLOFHead instance
            for_mot (bool): whether return other features for multi-object tracking
                models, default False in pure object detection models.
        """
        super(YOLOF, self).__init__()
        self.backbone = backbone
        self.neck = neck
        self.head = head
        self.for_mot = for_mot

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

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

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

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

    def _forward(self):
        body_feats = self.backbone(self.inputs)
        neck_feats = self.neck(body_feats, self.for_mot)

        if self.training:
            yolo_losses = self.head(neck_feats, self.inputs)
            return yolo_losses
        else:
            yolo_head_outs = self.head(neck_feats)
            bbox, bbox_num = self.head.post_process(yolo_head_outs,
                                                    self.inputs['im_shape'],
                                                    self.inputs['scale_factor'])
            output = {'bbox': bbox, 'bbox_num': bbox_num}
            return output

    def get_loss(self):
        return self._forward()

    def get_pred(self):
        return self._forward()

0658aada0515ef35def944777109c7c1847629dc
<mask token>


@register
class YOLOF(BaseArch):
    <mask token>

    def __init__(self, backbone='ResNet', neck='DilatedEncoder', head=
        'YOLOFHead', for_mot=False):
        """
        YOLOF network, see https://arxiv.org/abs/2103.09460

        Args:
            backbone (nn.Layer): backbone instance
            neck (nn.Layer): DilatedEncoder instance
            head (nn.Layer): YOLOFHead instance
            for_mot (bool): whether return other features for multi-object tracking
                models, default False in pure object detection models.
        """
        super(YOLOF, self).__init__()
        self.backbone = backbone
        self.neck = neck
        self.head = head
        self.for_mot = for_mot
    <mask token>
    <mask token>

    def get_loss(self):
        return self._forward()

    def get_pred(self):
        return self._forward()

<mask token>


@register
class YOLOF(BaseArch):
    <mask token>

    def __init__(self, backbone='ResNet', neck='DilatedEncoder', head=
        'YOLOFHead', for_mot=False):
        """
        YOLOF network, see https://arxiv.org/abs/2103.09460

        Args:
            backbone (nn.Layer): backbone instance
            neck (nn.Layer): DilatedEncoder instance
            head (nn.Layer): YOLOFHead instance
            for_mot (bool): whether return other features for multi-object tracking
                models, default False in pure object detection models.
        """
        super(YOLOF, self).__init__()
        self.backbone = backbone
        self.neck = neck
        self.head = head
        self.for_mot = for_mot
    <mask token>

    def _forward(self):
        body_feats = self.backbone(self.inputs)
        neck_feats = self.neck(body_feats, self.for_mot)
        if self.training:
            yolo_losses = self.head(neck_feats, self.inputs)
            return yolo_losses
        else:
            yolo_head_outs = self.head(neck_feats)
            bbox, bbox_num = self.head.post_process(yolo_head_outs, self.
                inputs['im_shape'], self.inputs['scale_factor'])
            output = {'bbox': bbox, 'bbox_num': bbox_num}
            return output

    def get_loss(self):
        return self._forward()

    def get_pred(self):
        return self._forward()

<mask token>


@register
class YOLOF(BaseArch):
    __category__ = 'architecture'

    def __init__(self, backbone='ResNet', neck='DilatedEncoder', head=
        'YOLOFHead', for_mot=False):
        """
        YOLOF network, see https://arxiv.org/abs/2103.09460

        Args:
            backbone (nn.Layer): backbone instance
            neck (nn.Layer): DilatedEncoder instance
            head (nn.Layer): YOLOFHead instance
            for_mot (bool): whether return other features for multi-object tracking
                models, default False in pure object detection models.
        """
        super(YOLOF, self).__init__()
        self.backbone = backbone
        self.neck = neck
        self.head = head
        self.for_mot = for_mot

    @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}
        head = create(cfg['head'], **kwargs)
        return {'backbone': backbone, 'neck': neck, 'head': head}

    def _forward(self):
        body_feats = self.backbone(self.inputs)
        neck_feats = self.neck(body_feats, self.for_mot)
        if self.training:
            yolo_losses = self.head(neck_feats, self.inputs)
            return yolo_losses
        else:
            yolo_head_outs = self.head(neck_feats)
            bbox, bbox_num = self.head.post_process(yolo_head_outs, self.
                inputs['im_shape'], self.inputs['scale_factor'])
            output = {'bbox': bbox, 'bbox_num': bbox_num}
            return output

    def get_loss(self):
        return self._forward()

    def get_pred(self):
        return self._forward()

<mask token>
__all__ = ['YOLOF']


@register
class YOLOF(BaseArch):
    __category__ = 'architecture'

    def __init__(self, backbone='ResNet', neck='DilatedEncoder', head=
        'YOLOFHead', for_mot=False):
        """
        YOLOF network, see https://arxiv.org/abs/2103.09460

        Args:
            backbone (nn.Layer): backbone instance
            neck (nn.Layer): DilatedEncoder instance
            head (nn.Layer): YOLOFHead instance
            for_mot (bool): whether return other features for multi-object tracking
                models, default False in pure object detection models.
        """
        super(YOLOF, self).__init__()
        self.backbone = backbone
        self.neck = neck
        self.head = head
        self.for_mot = for_mot

    @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}
        head = create(cfg['head'], **kwargs)
        return {'backbone': backbone, 'neck': neck, 'head': head}

    def _forward(self):
        body_feats = self.backbone(self.inputs)
        neck_feats = self.neck(body_feats, self.for_mot)
        if self.training:
            yolo_losses = self.head(neck_feats, self.inputs)
            return yolo_losses
        else:
            yolo_head_outs = self.head(neck_feats)
            bbox, bbox_num = self.head.post_process(yolo_head_outs, self.
                inputs['im_shape'], self.inputs['scale_factor'])
            output = {'bbox': bbox, 'bbox_num': bbox_num}
            return output

    def get_loss(self):
        return self._forward()

    def get_pred(self):
        return self._forward()

# Copyright (c) 2022 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 ppdet.core.workspace import register, create
from .meta_arch import BaseArch

__all__ = ['YOLOF']


@register
class YOLOF(BaseArch):
    __category__ = 'architecture'

    def __init__(self,
                 backbone='ResNet',
                 neck='DilatedEncoder',
                 head='YOLOFHead',
                 for_mot=False):
        """
        YOLOF network, see https://arxiv.org/abs/2103.09460

        Args:
            backbone (nn.Layer): backbone instance
            neck (nn.Layer): DilatedEncoder instance
            head (nn.Layer): YOLOFHead instance
            for_mot (bool): whether return other features for multi-object tracking
                models, default False in pure object detection models.
        """
        super(YOLOF, self).__init__()
        self.backbone = backbone
        self.neck = neck
        self.head = head
        self.for_mot = for_mot

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

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

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

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

    def _forward(self):
        body_feats = self.backbone(self.inputs)
        neck_feats = self.neck(body_feats, self.for_mot)

        if self.training:
            yolo_losses = self.head(neck_feats, self.inputs)
            return yolo_losses
        else:
            yolo_head_outs = self.head(neck_feats)
            bbox, bbox_num = self.head.post_process(yolo_head_outs,
                                                    self.inputs['im_shape'],
                                                    self.inputs['scale_factor'])
            output = {'bbox': bbox, 'bbox_num': bbox_num}
            return output

    def get_loss(self):
        return self._forward()

    def get_pred(self):
        return self._forward()
