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更换文档检测模型

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liuyebo
2024-08-27 14:42:45 +08:00
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paddle_detection/ppdet/modeling/heads/simota_head.py Normal file
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# 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.
# The code is based on:
# https://github.com/open-mmlab/mmdetection/blob/master/mmdet/models/dense_heads/yolox_head.py
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import math
from functools import partial
import numpy as np
import paddle
import paddle.nn as nn
import paddle.nn.functional as F
from paddle import ParamAttr
from paddle.nn.initializer import Normal, Constant
from ppdet.core.workspace import register
from ppdet.modeling.bbox_utils import distance2bbox, bbox2distance
from ppdet.data.transform.atss_assigner import bbox_overlaps
from .gfl_head import GFLHead
@register
class OTAHead(GFLHead):
"""
OTAHead
Args:
conv_feat (object): Instance of 'FCOSFeat'
num_classes (int): Number of classes
fpn_stride (list): The stride of each FPN Layer
prior_prob (float): Used to set the bias init for the class prediction layer
loss_qfl (object): Instance of QualityFocalLoss.
loss_dfl (object): Instance of DistributionFocalLoss.
loss_bbox (object): Instance of bbox loss.
assigner (object): Instance of label assigner.
reg_max: Max value of integral set :math: `{0, ..., reg_max}`
n QFL setting. Default: 16.
"""
__inject__ = [
'conv_feat', 'dgqp_module', 'loss_class', 'loss_dfl', 'loss_bbox',
'assigner', 'nms'
]
__shared__ = ['num_classes']
def __init__(self,
conv_feat='FCOSFeat',
dgqp_module=None,
num_classes=80,
fpn_stride=[8, 16, 32, 64, 128],
prior_prob=0.01,
loss_class='QualityFocalLoss',
loss_dfl='DistributionFocalLoss',
loss_bbox='GIoULoss',
assigner='SimOTAAssigner',
reg_max=16,
feat_in_chan=256,
nms=None,
nms_pre=1000,
cell_offset=0):
super(OTAHead, self).__init__(
conv_feat=conv_feat,
dgqp_module=dgqp_module,
num_classes=num_classes,
fpn_stride=fpn_stride,
prior_prob=prior_prob,
loss_class=loss_class,
loss_dfl=loss_dfl,
loss_bbox=loss_bbox,
reg_max=reg_max,
feat_in_chan=feat_in_chan,
nms=nms,
nms_pre=nms_pre,
cell_offset=cell_offset)
self.conv_feat = conv_feat
self.dgqp_module = dgqp_module
self.num_classes = num_classes
self.fpn_stride = fpn_stride
self.prior_prob = prior_prob
self.loss_qfl = loss_class
self.loss_dfl = loss_dfl
self.loss_bbox = loss_bbox
self.reg_max = reg_max
self.feat_in_chan = feat_in_chan
self.nms = nms
self.nms_pre = nms_pre
self.cell_offset = cell_offset
self.use_sigmoid = self.loss_qfl.use_sigmoid
self.assigner = assigner
def _get_target_single(self, flatten_cls_pred, flatten_center_and_stride,
flatten_bbox, gt_bboxes, gt_labels):
"""Compute targets for priors in a single image.
"""
pos_num, label, label_weight, bbox_target = self.assigner(
F.sigmoid(flatten_cls_pred), flatten_center_and_stride,
flatten_bbox, gt_bboxes, gt_labels)
return (pos_num, label, label_weight, bbox_target)
def get_loss(self, head_outs, gt_meta):
cls_scores, bbox_preds = head_outs
num_level_anchors = [
featmap.shape[-2] * featmap.shape[-1] for featmap in cls_scores
]
num_imgs = gt_meta['im_id'].shape[0]
featmap_sizes = [[featmap.shape[-2], featmap.shape[-1]]
for featmap in cls_scores]
decode_bbox_preds = []
center_and_strides = []
for featmap_size, stride, bbox_pred in zip(featmap_sizes,
self.fpn_stride, bbox_preds):
# center in origin image
yy, xx = self.get_single_level_center_point(featmap_size, stride,
self.cell_offset)
center_and_stride = paddle.stack([xx, yy, stride, stride], -1).tile(
[num_imgs, 1, 1])
center_and_strides.append(center_and_stride)
center_in_feature = center_and_stride.reshape(
[-1, 4])[:, :-2] / stride
bbox_pred = bbox_pred.transpose([0, 2, 3, 1]).reshape(
[num_imgs, -1, 4 * (self.reg_max + 1)])
pred_distances = self.distribution_project(bbox_pred)
decode_bbox_pred_wo_stride = distance2bbox(
center_in_feature, pred_distances).reshape([num_imgs, -1, 4])
decode_bbox_preds.append(decode_bbox_pred_wo_stride * stride)
flatten_cls_preds = [
cls_pred.transpose([0, 2, 3, 1]).reshape(
[num_imgs, -1, self.cls_out_channels])
for cls_pred in cls_scores
]
flatten_cls_preds = paddle.concat(flatten_cls_preds, axis=1)
flatten_bboxes = paddle.concat(decode_bbox_preds, axis=1)
flatten_center_and_strides = paddle.concat(center_and_strides, axis=1)
gt_boxes, gt_labels = gt_meta['gt_bbox'], gt_meta['gt_class']
pos_num_l, label_l, label_weight_l, bbox_target_l = [], [], [], []
for flatten_cls_pred,flatten_center_and_stride,flatten_bbox,gt_box, gt_label \
in zip(flatten_cls_preds.detach(),flatten_center_and_strides.detach(), \
flatten_bboxes.detach(),gt_boxes, gt_labels):
pos_num, label, label_weight, bbox_target = self._get_target_single(
flatten_cls_pred, flatten_center_and_stride, flatten_bbox,
gt_box, gt_label)
pos_num_l.append(pos_num)
label_l.append(label)
label_weight_l.append(label_weight)
bbox_target_l.append(bbox_target)
labels = paddle.to_tensor(np.stack(label_l, axis=0))
label_weights = paddle.to_tensor(np.stack(label_weight_l, axis=0))
bbox_targets = paddle.to_tensor(np.stack(bbox_target_l, axis=0))
center_and_strides_list = self._images_to_levels(
flatten_center_and_strides, num_level_anchors)
labels_list = self._images_to_levels(labels, num_level_anchors)
label_weights_list = self._images_to_levels(label_weights,
num_level_anchors)
bbox_targets_list = self._images_to_levels(bbox_targets,
num_level_anchors)
num_total_pos = sum(pos_num_l)
try:
paddle.distributed.all_reduce(paddle.to_tensor(num_total_pos))
num_total_pos = paddle.clip(
num_total_pos / paddle.distributed.get_world_size(), min=1.)
except:
num_total_pos = max(num_total_pos, 1)
loss_bbox_list, loss_dfl_list, loss_qfl_list, avg_factor = [], [], [], []
for cls_score, bbox_pred, center_and_strides, labels, label_weights, bbox_targets, stride in zip(
cls_scores, bbox_preds, center_and_strides_list, labels_list,
label_weights_list, bbox_targets_list, self.fpn_stride):
center_and_strides = center_and_strides.reshape([-1, 4])
cls_score = cls_score.transpose([0, 2, 3, 1]).reshape(
[-1, self.cls_out_channels])
bbox_pred = bbox_pred.transpose([0, 2, 3, 1]).reshape(
[-1, 4 * (self.reg_max + 1)])
bbox_targets = bbox_targets.reshape([-1, 4])
labels = labels.reshape([-1])
label_weights = label_weights.reshape([-1])
bg_class_ind = self.num_classes
pos_inds = paddle.nonzero(
paddle.logical_and((labels >= 0), (labels < bg_class_ind)),
as_tuple=False).squeeze(1)
score = np.zeros(labels.shape)
if len(pos_inds) > 0:
pos_bbox_targets = paddle.gather(bbox_targets, pos_inds, axis=0)
pos_bbox_pred = paddle.gather(bbox_pred, pos_inds, axis=0)
pos_centers = paddle.gather(
center_and_strides[:, :-2], pos_inds, axis=0) / stride
weight_targets = F.sigmoid(cls_score.detach())
weight_targets = paddle.gather(
weight_targets.max(axis=1, keepdim=True), pos_inds, axis=0)
pos_bbox_pred_corners = self.distribution_project(pos_bbox_pred)
pos_decode_bbox_pred = distance2bbox(pos_centers,
pos_bbox_pred_corners)
pos_decode_bbox_targets = pos_bbox_targets / stride
bbox_iou = bbox_overlaps(
pos_decode_bbox_pred.detach().numpy(),
pos_decode_bbox_targets.detach().numpy(),
is_aligned=True)
score[pos_inds.numpy()] = bbox_iou
pred_corners = pos_bbox_pred.reshape([-1, self.reg_max + 1])
target_corners = bbox2distance(pos_centers,
pos_decode_bbox_targets,
self.reg_max).reshape([-1])
# regression loss
loss_bbox = paddle.sum(
self.loss_bbox(pos_decode_bbox_pred,
pos_decode_bbox_targets) * weight_targets)
# dfl loss
loss_dfl = self.loss_dfl(
pred_corners,
target_corners,
weight=weight_targets.expand([-1, 4]).reshape([-1]),
avg_factor=4.0)
else:
loss_bbox = bbox_pred.sum() * 0
loss_dfl = bbox_pred.sum() * 0
weight_targets = paddle.to_tensor([0], dtype='float32')
# qfl loss
score = paddle.to_tensor(score)
loss_qfl = self.loss_qfl(
cls_score, (labels, score),
weight=label_weights,
avg_factor=num_total_pos)
loss_bbox_list.append(loss_bbox)
loss_dfl_list.append(loss_dfl)
loss_qfl_list.append(loss_qfl)
avg_factor.append(weight_targets.sum())
avg_factor = sum(avg_factor)
try:
paddle.distributed.all_reduce(paddle.to_tensor(avg_factor))
avg_factor = paddle.clip(
avg_factor / paddle.distributed.get_world_size(), min=1)
except:
avg_factor = max(avg_factor.item(), 1)
if avg_factor <= 0:
loss_qfl = paddle.to_tensor(0, dtype='float32', stop_gradient=False)
loss_bbox = paddle.to_tensor(
0, dtype='float32', stop_gradient=False)
loss_dfl = paddle.to_tensor(0, dtype='float32', stop_gradient=False)
else:
losses_bbox = list(map(lambda x: x / avg_factor, loss_bbox_list))
losses_dfl = list(map(lambda x: x / avg_factor, loss_dfl_list))
loss_qfl = sum(loss_qfl_list)
loss_bbox = sum(losses_bbox)
loss_dfl = sum(losses_dfl)
loss_states = dict(
loss_qfl=loss_qfl, loss_bbox=loss_bbox, loss_dfl=loss_dfl)
return loss_states
@register
class OTAVFLHead(OTAHead):
__inject__ = [
'conv_feat', 'dgqp_module', 'loss_class', 'loss_dfl', 'loss_bbox',
'assigner', 'nms'
]
__shared__ = ['num_classes']
def __init__(self,
conv_feat='FCOSFeat',
dgqp_module=None,
num_classes=80,
fpn_stride=[8, 16, 32, 64, 128],
prior_prob=0.01,
loss_class='VarifocalLoss',
loss_dfl='DistributionFocalLoss',
loss_bbox='GIoULoss',
assigner='SimOTAAssigner',
reg_max=16,
feat_in_chan=256,
nms=None,
nms_pre=1000,
cell_offset=0):
super(OTAVFLHead, self).__init__(
conv_feat=conv_feat,
dgqp_module=dgqp_module,
num_classes=num_classes,
fpn_stride=fpn_stride,
prior_prob=prior_prob,
loss_class=loss_class,
loss_dfl=loss_dfl,
loss_bbox=loss_bbox,
reg_max=reg_max,
feat_in_chan=feat_in_chan,
nms=nms,
nms_pre=nms_pre,
cell_offset=cell_offset)
self.conv_feat = conv_feat
self.dgqp_module = dgqp_module
self.num_classes = num_classes
self.fpn_stride = fpn_stride
self.prior_prob = prior_prob
self.loss_vfl = loss_class
self.loss_dfl = loss_dfl
self.loss_bbox = loss_bbox
self.reg_max = reg_max
self.feat_in_chan = feat_in_chan
self.nms = nms
self.nms_pre = nms_pre
self.cell_offset = cell_offset
self.use_sigmoid = self.loss_vfl.use_sigmoid
self.assigner = assigner
def get_loss(self, head_outs, gt_meta):
cls_scores, bbox_preds = head_outs
num_level_anchors = [
featmap.shape[-2] * featmap.shape[-1] for featmap in cls_scores
]
num_imgs = gt_meta['im_id'].shape[0]
featmap_sizes = [[featmap.shape[-2], featmap.shape[-1]]
for featmap in cls_scores]
decode_bbox_preds = []
center_and_strides = []
for featmap_size, stride, bbox_pred in zip(featmap_sizes,
self.fpn_stride, bbox_preds):
# center in origin image
yy, xx = self.get_single_level_center_point(featmap_size, stride,
self.cell_offset)
strides = paddle.full((len(xx), ), stride)
center_and_stride = paddle.stack([xx, yy, strides, strides],
-1).tile([num_imgs, 1, 1])
center_and_strides.append(center_and_stride)
center_in_feature = center_and_stride.reshape(
[-1, 4])[:, :-2] / stride
bbox_pred = bbox_pred.transpose([0, 2, 3, 1]).reshape(
[num_imgs, -1, 4 * (self.reg_max + 1)])
pred_distances = self.distribution_project(bbox_pred)
decode_bbox_pred_wo_stride = distance2bbox(
center_in_feature, pred_distances).reshape([num_imgs, -1, 4])
decode_bbox_preds.append(decode_bbox_pred_wo_stride * stride)
flatten_cls_preds = [
cls_pred.transpose([0, 2, 3, 1]).reshape(
[num_imgs, -1, self.cls_out_channels])
for cls_pred in cls_scores
]
flatten_cls_preds = paddle.concat(flatten_cls_preds, axis=1)
flatten_bboxes = paddle.concat(decode_bbox_preds, axis=1)
flatten_center_and_strides = paddle.concat(center_and_strides, axis=1)
gt_boxes, gt_labels = gt_meta['gt_bbox'], gt_meta['gt_class']
pos_num_l, label_l, label_weight_l, bbox_target_l = [], [], [], []
for flatten_cls_pred, flatten_center_and_stride, flatten_bbox,gt_box,gt_label \
in zip(flatten_cls_preds.detach(), flatten_center_and_strides.detach(), \
flatten_bboxes.detach(),gt_boxes,gt_labels):
pos_num, label, label_weight, bbox_target = self._get_target_single(
flatten_cls_pred, flatten_center_and_stride, flatten_bbox,
gt_box, gt_label)
pos_num_l.append(pos_num)
label_l.append(label)
label_weight_l.append(label_weight)
bbox_target_l.append(bbox_target)
labels = paddle.to_tensor(np.stack(label_l, axis=0))
label_weights = paddle.to_tensor(np.stack(label_weight_l, axis=0))
bbox_targets = paddle.to_tensor(np.stack(bbox_target_l, axis=0))
center_and_strides_list = self._images_to_levels(
flatten_center_and_strides, num_level_anchors)
labels_list = self._images_to_levels(labels, num_level_anchors)
label_weights_list = self._images_to_levels(label_weights,
num_level_anchors)
bbox_targets_list = self._images_to_levels(bbox_targets,
num_level_anchors)
num_total_pos = sum(pos_num_l)
try:
paddle.distributed.all_reduce(paddle.to_tensor(num_total_pos))
num_total_pos = paddle.clip(
num_total_pos / paddle.distributed.get_world_size(), min=1.)
except:
num_total_pos = max(num_total_pos, 1)
loss_bbox_list, loss_dfl_list, loss_vfl_list, avg_factor = [], [], [], []
for cls_score, bbox_pred, center_and_strides, labels, label_weights, bbox_targets, stride in zip(
cls_scores, bbox_preds, center_and_strides_list, labels_list,
label_weights_list, bbox_targets_list, self.fpn_stride):
center_and_strides = center_and_strides.reshape([-1, 4])
cls_score = cls_score.transpose([0, 2, 3, 1]).reshape(
[-1, self.cls_out_channels])
bbox_pred = bbox_pred.transpose([0, 2, 3, 1]).reshape(
[-1, 4 * (self.reg_max + 1)])
bbox_targets = bbox_targets.reshape([-1, 4])
labels = labels.reshape([-1])
bg_class_ind = self.num_classes
pos_inds = paddle.nonzero(
paddle.logical_and((labels >= 0), (labels < bg_class_ind)),
as_tuple=False).squeeze(1)
# vfl
vfl_score = np.zeros(cls_score.shape)
if len(pos_inds) > 0:
pos_bbox_targets = paddle.gather(bbox_targets, pos_inds, axis=0)
pos_bbox_pred = paddle.gather(bbox_pred, pos_inds, axis=0)
pos_centers = paddle.gather(
center_and_strides[:, :-2], pos_inds, axis=0) / stride
weight_targets = F.sigmoid(cls_score.detach())
weight_targets = paddle.gather(
weight_targets.max(axis=1, keepdim=True), pos_inds, axis=0)
pos_bbox_pred_corners = self.distribution_project(pos_bbox_pred)
pos_decode_bbox_pred = distance2bbox(pos_centers,
pos_bbox_pred_corners)
pos_decode_bbox_targets = pos_bbox_targets / stride
bbox_iou = bbox_overlaps(
pos_decode_bbox_pred.detach().numpy(),
pos_decode_bbox_targets.detach().numpy(),
is_aligned=True)
# vfl
pos_labels = paddle.gather(labels, pos_inds, axis=0)
vfl_score[pos_inds.numpy(), pos_labels] = bbox_iou
pred_corners = pos_bbox_pred.reshape([-1, self.reg_max + 1])
target_corners = bbox2distance(pos_centers,
pos_decode_bbox_targets,
self.reg_max).reshape([-1])
# regression loss
loss_bbox = paddle.sum(
self.loss_bbox(pos_decode_bbox_pred,
pos_decode_bbox_targets) * weight_targets)
# dfl loss
loss_dfl = self.loss_dfl(
pred_corners,
target_corners,
weight=weight_targets.expand([-1, 4]).reshape([-1]),
avg_factor=4.0)
else:
loss_bbox = bbox_pred.sum() * 0
loss_dfl = bbox_pred.sum() * 0
weight_targets = paddle.to_tensor([0], dtype='float32')
# vfl loss
num_pos_avg_per_gpu = num_total_pos
vfl_score = paddle.to_tensor(vfl_score)
loss_vfl = self.loss_vfl(
cls_score, vfl_score, avg_factor=num_pos_avg_per_gpu)
loss_bbox_list.append(loss_bbox)
loss_dfl_list.append(loss_dfl)
loss_vfl_list.append(loss_vfl)
avg_factor.append(weight_targets.sum())
avg_factor = sum(avg_factor)
try:
paddle.distributed.all_reduce(paddle.to_tensor(avg_factor))
avg_factor = paddle.clip(
avg_factor / paddle.distributed.get_world_size(), min=1)
except:
avg_factor = max(avg_factor.item(), 1)
if avg_factor <= 0:
loss_vfl = paddle.to_tensor(0, dtype='float32', stop_gradient=False)
loss_bbox = paddle.to_tensor(
0, dtype='float32', stop_gradient=False)
loss_dfl = paddle.to_tensor(0, dtype='float32', stop_gradient=False)
else:
losses_bbox = list(map(lambda x: x / avg_factor, loss_bbox_list))
losses_dfl = list(map(lambda x: x / avg_factor, loss_dfl_list))
loss_vfl = sum(loss_vfl_list)
loss_bbox = sum(losses_bbox)
loss_dfl = sum(losses_dfl)
loss_states = dict(
loss_vfl=loss_vfl, loss_bbox=loss_bbox, loss_dfl=loss_dfl)
return loss_states