forked from PulseFocusPlatform/PulseFocusPlatform
332 lines
12 KiB
Python
332 lines
12 KiB
Python
# Copyright (c) 2019 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 numpy as np
|
|
|
|
from collections import OrderedDict
|
|
import copy
|
|
|
|
import paddle.fluid as fluid
|
|
from ppdet.core.workspace import register
|
|
from ppdet.utils.check import check_version
|
|
from .input_helper import multiscale_def
|
|
|
|
__all__ = ['CascadeRCNNClsAware']
|
|
|
|
|
|
@register
|
|
class CascadeRCNNClsAware(object):
|
|
"""
|
|
Cascade R-CNN architecture, see https://arxiv.org/abs/1712.00726
|
|
This is a kind of modification of Cascade R-CNN.
|
|
Specifically, it predicts bboxes for all classes with different weights,
|
|
while the standard vesion just predicts bboxes for foreground
|
|
Args:
|
|
backbone (object): backbone instance
|
|
rpn_head (object): `RPNhead` instance
|
|
bbox_assigner (object): `BBoxAssigner` instance
|
|
roi_extractor (object): ROI extractor instance
|
|
bbox_head (object): `BBoxHead` instance
|
|
fpn (object): feature pyramid network instance
|
|
"""
|
|
|
|
__category__ = 'architecture'
|
|
__inject__ = [
|
|
'backbone', 'fpn', 'rpn_head', 'bbox_assigner', 'roi_extractor',
|
|
'bbox_head'
|
|
]
|
|
|
|
def __init__(
|
|
self,
|
|
backbone,
|
|
rpn_head,
|
|
roi_extractor='FPNRoIAlign',
|
|
bbox_head='CascadeBBoxHead',
|
|
bbox_assigner='CascadeBBoxAssigner',
|
|
fpn='FPN', ):
|
|
super(CascadeRCNNClsAware, self).__init__()
|
|
check_version('2.0.0-rc0')
|
|
assert fpn is not None, "cascade RCNN requires FPN"
|
|
self.backbone = backbone
|
|
self.fpn = fpn
|
|
self.rpn_head = rpn_head
|
|
self.bbox_assigner = bbox_assigner
|
|
self.roi_extractor = roi_extractor
|
|
self.bbox_head = bbox_head
|
|
self.bbox_clip = np.log(1000. / 16.)
|
|
# Cascade local cfg
|
|
(brw0, brw1, brw2) = self.bbox_assigner.bbox_reg_weights
|
|
self.cascade_bbox_reg_weights = [
|
|
[1. / brw0, 1. / brw0, 2. / brw0, 2. / brw0],
|
|
[1. / brw1, 1. / brw1, 2. / brw1, 2. / brw1],
|
|
[1. / brw2, 1. / brw2, 2. / brw2, 2. / brw2]
|
|
]
|
|
self.cascade_rcnn_loss_weight = [1.0, 0.5, 0.25]
|
|
|
|
def build(self, feed_vars, mode='train'):
|
|
im = feed_vars['image']
|
|
im_info = feed_vars['im_info']
|
|
if mode == 'train':
|
|
gt_bbox = feed_vars['gt_bbox']
|
|
is_crowd = feed_vars['is_crowd']
|
|
gt_class = feed_vars['gt_class']
|
|
else:
|
|
im_shape = feed_vars['im_shape']
|
|
|
|
# backbone
|
|
body_feats = self.backbone(im)
|
|
|
|
# FPN
|
|
if self.fpn is not None:
|
|
body_feats, spatial_scale = self.fpn.get_output(body_feats)
|
|
|
|
# rpn proposals
|
|
rpn_rois = self.rpn_head.get_proposals(body_feats, im_info, mode=mode)
|
|
|
|
if mode == 'train':
|
|
rpn_loss = self.rpn_head.get_loss(im_info, gt_bbox, is_crowd)
|
|
|
|
proposal_list = []
|
|
roi_feat_list = []
|
|
rcnn_pred_list = []
|
|
rcnn_target_list = []
|
|
|
|
bbox_pred = None
|
|
|
|
self.cascade_var_v = []
|
|
for stage in range(3):
|
|
var_v = np.array(
|
|
self.cascade_bbox_reg_weights[stage], dtype="float32")
|
|
prior_box_var = fluid.layers.create_tensor(dtype="float32")
|
|
fluid.layers.assign(input=var_v, output=prior_box_var)
|
|
self.cascade_var_v.append(prior_box_var)
|
|
|
|
self.cascade_decoded_box = []
|
|
self.cascade_cls_prob = []
|
|
max_overlap = None
|
|
|
|
for stage in range(3):
|
|
if stage > 0:
|
|
pool_rois = decoded_assign_box
|
|
else:
|
|
pool_rois = rpn_rois
|
|
if mode == "train":
|
|
self.cascade_var_v[stage].stop_gradient = True
|
|
outs = self.bbox_assigner(
|
|
input_rois=pool_rois,
|
|
feed_vars=feed_vars,
|
|
curr_stage=stage,
|
|
max_overlap=max_overlap)
|
|
pool_rois = outs[0]
|
|
max_overlap = outs[-1]
|
|
rcnn_target_list.append(outs[:-1])
|
|
|
|
# extract roi features
|
|
roi_feat = self.roi_extractor(body_feats, pool_rois, spatial_scale)
|
|
roi_feat_list.append(roi_feat)
|
|
|
|
# bbox head
|
|
cls_score, bbox_pred = self.bbox_head.get_output(
|
|
roi_feat,
|
|
cls_agnostic_bbox_reg=self.bbox_head.num_classes,
|
|
wb_scalar=1.0 / self.cascade_rcnn_loss_weight[stage],
|
|
name='_' + str(stage + 1))
|
|
|
|
cls_prob = fluid.layers.softmax(cls_score, use_cudnn=False)
|
|
|
|
decoded_box, decoded_assign_box = fluid.layers.box_decoder_and_assign(
|
|
pool_rois, self.cascade_var_v[stage], bbox_pred, cls_prob,
|
|
self.bbox_clip)
|
|
|
|
if mode == "train":
|
|
decoded_box.stop_gradient = True
|
|
decoded_assign_box.stop_gradient = True
|
|
else:
|
|
self.cascade_cls_prob.append(cls_prob)
|
|
self.cascade_decoded_box.append(decoded_box)
|
|
|
|
rcnn_pred_list.append((cls_score, bbox_pred))
|
|
|
|
# out loop
|
|
if mode == 'train':
|
|
loss = self.bbox_head.get_loss(rcnn_pred_list, rcnn_target_list,
|
|
self.cascade_rcnn_loss_weight)
|
|
loss.update(rpn_loss)
|
|
total_loss = fluid.layers.sum(list(loss.values()))
|
|
loss.update({'loss': total_loss})
|
|
return loss
|
|
else:
|
|
pred = self.bbox_head.get_prediction_cls_aware(
|
|
im_info, im_shape, self.cascade_cls_prob,
|
|
self.cascade_decoded_box, self.cascade_bbox_reg_weights)
|
|
return pred
|
|
|
|
def build_multi_scale(self, feed_vars):
|
|
required_fields = ['image', 'im_shape', 'im_info']
|
|
self._input_check(required_fields, feed_vars)
|
|
|
|
result = {}
|
|
im_shape = feed_vars['im_shape']
|
|
result['im_shape'] = im_shape
|
|
|
|
for i in range(len(self.im_info_names) // 2):
|
|
im = feed_vars[self.im_info_names[2 * i]]
|
|
im_info = feed_vars[self.im_info_names[2 * i + 1]]
|
|
|
|
# backbone
|
|
body_feats = self.backbone(im)
|
|
result.update(body_feats)
|
|
# FPN
|
|
if self.fpn is not None:
|
|
body_feats, spatial_scale = self.fpn.get_output(body_feats)
|
|
|
|
# rpn proposals
|
|
rpn_rois = self.rpn_head.get_proposals(
|
|
body_feats, im_info, mode="test")
|
|
|
|
proposal_list = []
|
|
roi_feat_list = []
|
|
rcnn_pred_list = []
|
|
rcnn_target_list = []
|
|
|
|
bbox_pred = None
|
|
|
|
self.cascade_var_v = []
|
|
for stage in range(3):
|
|
var_v = np.array(
|
|
self.cascade_bbox_reg_weights[stage], dtype="float32")
|
|
prior_box_var = fluid.layers.create_tensor(dtype="float32")
|
|
fluid.layers.assign(input=var_v, output=prior_box_var)
|
|
self.cascade_var_v.append(prior_box_var)
|
|
|
|
self.cascade_decoded_box = []
|
|
self.cascade_cls_prob = []
|
|
|
|
for stage in range(3):
|
|
if stage > 0:
|
|
pool_rois = decoded_assign_box
|
|
else:
|
|
pool_rois = rpn_rois
|
|
|
|
# extract roi features
|
|
roi_feat = self.roi_extractor(body_feats, pool_rois,
|
|
spatial_scale)
|
|
roi_feat_list.append(roi_feat)
|
|
|
|
# bbox head
|
|
cls_score, bbox_pred = self.bbox_head.get_output(
|
|
roi_feat,
|
|
cls_agnostic_bbox_reg=self.bbox_head.num_classes,
|
|
wb_scalar=1.0 / self.cascade_rcnn_loss_weight[stage],
|
|
name='_' + str(stage + 1))
|
|
|
|
cls_prob = fluid.layers.softmax(cls_score, use_cudnn=False)
|
|
|
|
decoded_box, decoded_assign_box = fluid.layers.box_decoder_and_assign(
|
|
pool_rois, self.cascade_var_v[stage], bbox_pred, cls_prob,
|
|
self.bbox_clip)
|
|
|
|
self.cascade_cls_prob.append(cls_prob)
|
|
self.cascade_decoded_box.append(decoded_box)
|
|
|
|
rcnn_pred_list.append((cls_score, bbox_pred))
|
|
|
|
pred = self.bbox_head.get_prediction_cls_aware(
|
|
im_info,
|
|
im_shape,
|
|
self.cascade_cls_prob,
|
|
self.cascade_decoded_box,
|
|
self.cascade_bbox_reg_weights,
|
|
return_box_score=True)
|
|
|
|
bbox_name = 'bbox_' + str(i)
|
|
score_name = 'score_' + str(i)
|
|
if 'flip' in im.name:
|
|
bbox_name += '_flip'
|
|
score_name += '_flip'
|
|
result[bbox_name] = pred['bbox']
|
|
result[score_name] = pred['score']
|
|
|
|
return result
|
|
|
|
def _inputs_def(self, image_shape):
|
|
im_shape = [None] + image_shape
|
|
# yapf: disable
|
|
inputs_def = {
|
|
'image': {'shape': im_shape, 'dtype': 'float32', 'lod_level': 0},
|
|
'im_info': {'shape': [None, 3], 'dtype': 'float32', 'lod_level': 0},
|
|
'im_id': {'shape': [None, 1], 'dtype': 'int64', 'lod_level': 0},
|
|
'im_shape': {'shape': [None, 3], 'dtype': 'float32', 'lod_level': 0},
|
|
'gt_bbox': {'shape': [None, 4], 'dtype': 'float32', 'lod_level': 1},
|
|
'gt_class': {'shape': [None, 1], 'dtype': 'int32', 'lod_level': 1},
|
|
'is_crowd': {'shape': [None, 1], 'dtype': 'int32', 'lod_level': 1},
|
|
'is_difficult': {'shape': [None, 1], 'dtype': 'int32', 'lod_level': 1},
|
|
}
|
|
# yapf: enable
|
|
return inputs_def
|
|
|
|
def build_inputs(self,
|
|
image_shape=[3, None, None],
|
|
fields=[
|
|
'image', 'im_info', 'im_id', 'gt_bbox', 'gt_class',
|
|
'is_crowd', 'gt_mask'
|
|
],
|
|
multi_scale=False,
|
|
num_scales=-1,
|
|
use_flip=None,
|
|
use_dataloader=True,
|
|
iterable=False):
|
|
inputs_def = self._inputs_def(image_shape)
|
|
fields = copy.deepcopy(fields)
|
|
if multi_scale:
|
|
ms_def, ms_fields = multiscale_def(image_shape, num_scales,
|
|
use_flip)
|
|
inputs_def.update(ms_def)
|
|
fields += ms_fields
|
|
self.im_info_names = ['image', 'im_info'] + ms_fields
|
|
|
|
feed_vars = OrderedDict([(key, fluid.data(
|
|
name=key,
|
|
shape=inputs_def[key]['shape'],
|
|
dtype=inputs_def[key]['dtype'],
|
|
lod_level=inputs_def[key]['lod_level'])) for key in fields])
|
|
loader = fluid.io.DataLoader.from_generator(
|
|
feed_list=list(feed_vars.values()),
|
|
capacity=16,
|
|
use_double_buffer=True,
|
|
iterable=iterable) if use_dataloader else None
|
|
return feed_vars, loader
|
|
|
|
def _input_check(self, require_fields, feed_vars):
|
|
for var in require_fields:
|
|
assert var in feed_vars, \
|
|
"{} has no {} field".format(feed_vars, var)
|
|
|
|
def train(self, feed_vars):
|
|
return self.build(feed_vars, 'train')
|
|
|
|
def eval(self, feed_vars, multi_scale=None):
|
|
if multi_scale:
|
|
return self.build_multi_scale(feed_vars)
|
|
return self.build(feed_vars, 'test')
|
|
|
|
def test(self, feed_vars, exclude_nms=False):
|
|
assert not exclude_nms, "exclude_nms for {} is not support currently".format(
|
|
self.__class__.__name__)
|
|
return self.build(feed_vars, 'test')
|