ttfnet.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

import paddle
from ppdet.core.workspace import register
from .meta_arch import BaseArch

__all__ = ['TTFNet']


@register
class TTFNet(BaseArch):
    """
    TTFNet network, see https://arxiv.org/abs/1909.00700

    Args:
        backbone (object): backbone instance
        neck (object): 'TTFFPN' instance
        ttf_head (object): 'TTFHead' instance
        post_process (object): 'BBoxPostProcess' instance
    """

    __category__ = 'architecture'
    __inject__ = [
        'backbone',
        'neck',
        'ttf_head',
        'post_process',
    ]

    def __init__(self,
                 backbone='DarkNet',
                 neck='TTFFPN',
                 ttf_head='TTFHead',
                 post_process='BBoxPostProcess'):
        super(TTFNet, self).__init__()
        self.backbone = backbone
        self.neck = neck
        self.ttf_head = ttf_head
        self.post_process = post_process

    def model_arch(self, ):
        # Backbone
        body_feats = self.backbone(self.inputs)
        # neck
        body_feats = self.neck(body_feats)
        # TTF Head
        self.hm, self.wh = self.ttf_head(body_feats)

    def get_loss(self, ):
        loss = {}
        heatmap = self.inputs['ttf_heatmap']
        box_target = self.inputs['ttf_box_target']
        reg_weight = self.inputs['ttf_reg_weight']
        head_loss = self.ttf_head.get_loss(self.hm, self.wh, heatmap,
                                           box_target, reg_weight)
        loss.update(head_loss)
        total_loss = paddle.add_n(list(loss.values()))
        loss.update({'loss': total_loss})
        return loss

    def get_pred(self):
        bbox, bbox_num = self.post_process(self.hm, self.wh,
                                           self.inputs['im_shape'],
                                           self.inputs['scale_factor'])
        outs = {
            "bbox": bbox,
            "bbox_num": bbox_num,
        }
        return outs