forked from PulseFocusPlatform/PulseFocusPlatform
144 lines
5.3 KiB
Python
144 lines
5.3 KiB
Python
# 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, create
|
|
from .meta_arch import BaseArch
|
|
|
|
__all__ = ['CascadeRCNN']
|
|
|
|
|
|
@register
|
|
class CascadeRCNN(BaseArch):
|
|
"""
|
|
Cascade R-CNN network, see https://arxiv.org/abs/1712.00726
|
|
|
|
Args:
|
|
backbone (object): backbone instance
|
|
rpn_head (object): `RPNHead` instance
|
|
bbox_head (object): `BBoxHead` instance
|
|
bbox_post_process (object): `BBoxPostProcess` instance
|
|
neck (object): 'FPN' instance
|
|
mask_head (object): `MaskHead` instance
|
|
mask_post_process (object): `MaskPostProcess` instance
|
|
"""
|
|
__category__ = 'architecture'
|
|
__inject__ = [
|
|
'bbox_post_process',
|
|
'mask_post_process',
|
|
]
|
|
|
|
def __init__(self,
|
|
backbone,
|
|
rpn_head,
|
|
bbox_head,
|
|
bbox_post_process,
|
|
neck=None,
|
|
mask_head=None,
|
|
mask_post_process=None):
|
|
super(CascadeRCNN, self).__init__()
|
|
self.backbone = backbone
|
|
self.rpn_head = rpn_head
|
|
self.bbox_head = bbox_head
|
|
self.bbox_post_process = bbox_post_process
|
|
self.neck = neck
|
|
self.mask_head = mask_head
|
|
self.mask_post_process = mask_post_process
|
|
self.with_mask = mask_head is not None
|
|
|
|
@classmethod
|
|
def from_config(cls, cfg, *args, **kwargs):
|
|
backbone = create(cfg['backbone'])
|
|
kwargs = {'input_shape': backbone.out_shape}
|
|
neck = cfg['neck'] and create(cfg['neck'], **kwargs)
|
|
|
|
out_shape = neck and neck.out_shape or backbone.out_shape
|
|
kwargs = {'input_shape': out_shape}
|
|
rpn_head = create(cfg['rpn_head'], **kwargs)
|
|
bbox_head = create(cfg['bbox_head'], **kwargs)
|
|
|
|
out_shape = neck and out_shape or bbox_head.get_head().out_shape
|
|
kwargs = {'input_shape': out_shape}
|
|
mask_head = cfg['mask_head'] and create(cfg['mask_head'], **kwargs)
|
|
return {
|
|
'backbone': backbone,
|
|
'neck': neck,
|
|
"rpn_head": rpn_head,
|
|
"bbox_head": bbox_head,
|
|
"mask_head": mask_head,
|
|
}
|
|
|
|
def _forward(self):
|
|
body_feats = self.backbone(self.inputs)
|
|
if self.neck is not None:
|
|
body_feats = self.neck(body_feats)
|
|
|
|
if self.training:
|
|
rois, rois_num, rpn_loss = self.rpn_head(body_feats, self.inputs)
|
|
bbox_loss, bbox_feat = self.bbox_head(body_feats, rois, rois_num,
|
|
self.inputs)
|
|
rois, rois_num = self.bbox_head.get_assigned_rois()
|
|
bbox_targets = self.bbox_head.get_assigned_targets()
|
|
if self.with_mask:
|
|
mask_loss = self.mask_head(body_feats, rois, rois_num,
|
|
self.inputs, bbox_targets, bbox_feat)
|
|
return rpn_loss, bbox_loss, mask_loss
|
|
else:
|
|
return rpn_loss, bbox_loss, {}
|
|
else:
|
|
rois, rois_num, _ = self.rpn_head(body_feats, self.inputs)
|
|
preds, _ = self.bbox_head(body_feats, rois, rois_num, self.inputs)
|
|
refined_rois = self.bbox_head.get_refined_rois()
|
|
|
|
im_shape = self.inputs['im_shape']
|
|
scale_factor = self.inputs['scale_factor']
|
|
|
|
bbox, bbox_num = self.bbox_post_process(
|
|
preds, (refined_rois, rois_num), im_shape, scale_factor)
|
|
# rescale the prediction back to origin image
|
|
bbox_pred = self.bbox_post_process.get_pred(bbox, bbox_num,
|
|
im_shape, scale_factor)
|
|
if not self.with_mask:
|
|
return bbox_pred, bbox_num, None
|
|
mask_out = self.mask_head(body_feats, bbox, bbox_num, self.inputs)
|
|
origin_shape = self.bbox_post_process.get_origin_shape()
|
|
mask_pred = self.mask_post_process(mask_out[:, 0, :, :], bbox_pred,
|
|
bbox_num, origin_shape)
|
|
return bbox_pred, bbox_num, mask_pred
|
|
|
|
def get_loss(self, ):
|
|
rpn_loss, bbox_loss, mask_loss = self._forward()
|
|
loss = {}
|
|
loss.update(rpn_loss)
|
|
loss.update(bbox_loss)
|
|
if self.with_mask:
|
|
loss.update(mask_loss)
|
|
total_loss = paddle.add_n(list(loss.values()))
|
|
loss.update({'loss': total_loss})
|
|
return loss
|
|
|
|
def get_pred(self):
|
|
bbox_pred, bbox_num, mask_pred = self._forward()
|
|
output = {
|
|
'bbox': bbox_pred,
|
|
'bbox_num': bbox_num,
|
|
}
|
|
if self.with_mask:
|
|
output.update({'mask': mask_pred})
|
|
return output
|