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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
parent aea6f19951
commit 1514e09c40
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paddle_detection/ppdet/modeling/heads/centernet_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.
import math
import paddle
import paddle.nn as nn
import paddle.nn.functional as F
from paddle.nn.initializer import Constant, Uniform
from ppdet.core.workspace import register
from ppdet.modeling.losses import CTFocalLoss, GIoULoss
class ConvLayer(nn.Layer):
def __init__(self,
ch_in,
ch_out,
kernel_size,
stride=1,
padding=0,
dilation=1,
groups=1,
bias=False):
super(ConvLayer, self).__init__()
bias_attr = False
fan_in = ch_in * kernel_size**2
bound = 1 / math.sqrt(fan_in)
param_attr = paddle.ParamAttr(initializer=Uniform(-bound, bound))
if bias:
bias_attr = paddle.ParamAttr(initializer=Constant(0.))
self.conv = nn.Conv2D(
in_channels=ch_in,
out_channels=ch_out,
kernel_size=kernel_size,
stride=stride,
padding=padding,
dilation=dilation,
groups=groups,
weight_attr=param_attr,
bias_attr=bias_attr)
def forward(self, inputs):
out = self.conv(inputs)
return out
@register
class CenterNetHead(nn.Layer):
"""
Args:
in_channels (int): the channel number of input to CenterNetHead.
num_classes (int): the number of classes, 80 (COCO dataset) by default.
head_planes (int): the channel number in all head, 256 by default.
prior_bias (float): prior bias in heatmap head, -2.19 by default, -4.6 in CenterTrack
regress_ltrb (bool): whether to regress left/top/right/bottom or
width/height for a box, True by default.
size_loss (str): the type of size regression loss, 'L1' by default, can be 'giou'.
loss_weight (dict): the weight of each loss.
add_iou (bool): whether to add iou branch, False by default.
"""
__shared__ = ['num_classes']
def __init__(self,
in_channels,
num_classes=80,
head_planes=256,
prior_bias=-2.19,
regress_ltrb=True,
size_loss='L1',
loss_weight={
'heatmap': 1.0,
'size': 0.1,
'offset': 1.0,
'iou': 0.0,
},
add_iou=False):
super(CenterNetHead, self).__init__()
self.regress_ltrb = regress_ltrb
self.loss_weight = loss_weight
self.add_iou = add_iou
# heatmap head
self.heatmap = nn.Sequential(
ConvLayer(
in_channels, head_planes, kernel_size=3, padding=1, bias=True),
nn.ReLU(),
ConvLayer(
head_planes,
num_classes,
kernel_size=1,
stride=1,
padding=0,
bias=True))
with paddle.no_grad():
self.heatmap[2].conv.bias[:] = prior_bias
# size(ltrb or wh) head
self.size = nn.Sequential(
ConvLayer(
in_channels, head_planes, kernel_size=3, padding=1, bias=True),
nn.ReLU(),
ConvLayer(
head_planes,
4 if regress_ltrb else 2,
kernel_size=1,
stride=1,
padding=0,
bias=True))
self.size_loss = size_loss
# offset head
self.offset = nn.Sequential(
ConvLayer(
in_channels, head_planes, kernel_size=3, padding=1, bias=True),
nn.ReLU(),
ConvLayer(
head_planes, 2, kernel_size=1, stride=1, padding=0, bias=True))
# iou head (optinal)
if self.add_iou and 'iou' in self.loss_weight:
self.iou = nn.Sequential(
ConvLayer(
in_channels,
head_planes,
kernel_size=3,
padding=1,
bias=True),
nn.ReLU(),
ConvLayer(
head_planes,
4 if regress_ltrb else 2,
kernel_size=1,
stride=1,
padding=0,
bias=True))
@classmethod
def from_config(cls, cfg, input_shape):
if isinstance(input_shape, (list, tuple)):
input_shape = input_shape[0]
return {'in_channels': input_shape.channels}
def forward(self, feat, inputs):
heatmap = F.sigmoid(self.heatmap(feat))
size = self.size(feat)
offset = self.offset(feat)
head_outs = {'heatmap': heatmap, 'size': size, 'offset': offset}
if self.add_iou and 'iou' in self.loss_weight:
iou = self.iou(feat)
head_outs.update({'iou': iou})
if self.training:
losses = self.get_loss(inputs, self.loss_weight, head_outs)
return losses
else:
return head_outs
def get_loss(self, inputs, weights, head_outs):
# 1.heatmap(hm) head loss: CTFocalLoss
heatmap = head_outs['heatmap']
heatmap_target = inputs['heatmap']
heatmap = paddle.clip(heatmap, 1e-4, 1 - 1e-4)
ctfocal_loss = CTFocalLoss()
heatmap_loss = ctfocal_loss(heatmap, heatmap_target)
# 2.size(wh) head loss: L1 loss or GIoU loss
size = head_outs['size']
index = inputs['index']
mask = inputs['index_mask']
size = paddle.transpose(size, perm=[0, 2, 3, 1])
size_n, _, _, size_c = size.shape
size = paddle.reshape(size, shape=[size_n, -1, size_c])
index = paddle.unsqueeze(index, 2)
batch_inds = list()
for i in range(size_n):
batch_ind = paddle.full(
shape=[1, index.shape[1], 1], fill_value=i, dtype='int64')
batch_inds.append(batch_ind)
batch_inds = paddle.concat(batch_inds, axis=0)
index = paddle.concat(x=[batch_inds, index], axis=2)
pos_size = paddle.gather_nd(size, index=index)
mask = paddle.unsqueeze(mask, axis=2)
size_mask = paddle.expand_as(mask, pos_size)
size_mask = paddle.cast(size_mask, dtype=pos_size.dtype)
pos_num = size_mask.sum()
size_mask.stop_gradient = True
if self.size_loss == 'L1':
if self.regress_ltrb:
size_target = inputs['size']
# shape: [bs, max_per_img, 4]
else:
if inputs['size'].shape[-1] == 2:
# inputs['size'] is wh, and regress as wh
# shape: [bs, max_per_img, 2]
size_target = inputs['size']
else:
# inputs['size'] is ltrb, but regress as wh
# shape: [bs, max_per_img, 4]
size_target = inputs['size'][:, :, 0:2] + inputs[
'size'][:, :, 2:]
size_target.stop_gradient = True
size_loss = F.l1_loss(
pos_size * size_mask, size_target * size_mask, reduction='sum')
size_loss = size_loss / (pos_num + 1e-4)
elif self.size_loss == 'giou':
size_target = inputs['bbox_xys']
size_target.stop_gradient = True
centers_x = (size_target[:, :, 0:1] + size_target[:, :, 2:3]) / 2.0
centers_y = (size_target[:, :, 1:2] + size_target[:, :, 3:4]) / 2.0
x1 = centers_x - pos_size[:, :, 0:1]
y1 = centers_y - pos_size[:, :, 1:2]
x2 = centers_x + pos_size[:, :, 2:3]
y2 = centers_y + pos_size[:, :, 3:4]
pred_boxes = paddle.concat([x1, y1, x2, y2], axis=-1)
giou_loss = GIoULoss(reduction='sum')
size_loss = giou_loss(
pred_boxes * size_mask,
size_target * size_mask,
iou_weight=size_mask,
loc_reweight=None)
size_loss = size_loss / (pos_num + 1e-4)
# 3.offset(reg) head loss: L1 loss
offset = head_outs['offset']
offset_target = inputs['offset']
offset = paddle.transpose(offset, perm=[0, 2, 3, 1])
offset_n, _, _, offset_c = offset.shape
offset = paddle.reshape(offset, shape=[offset_n, -1, offset_c])
pos_offset = paddle.gather_nd(offset, index=index)
offset_mask = paddle.expand_as(mask, pos_offset)
offset_mask = paddle.cast(offset_mask, dtype=pos_offset.dtype)
pos_num = offset_mask.sum()
offset_mask.stop_gradient = True
offset_target.stop_gradient = True
offset_loss = F.l1_loss(
pos_offset * offset_mask,
offset_target * offset_mask,
reduction='sum')
offset_loss = offset_loss / (pos_num + 1e-4)
# 4.iou head loss: GIoU loss (optinal)
if self.add_iou and 'iou' in self.loss_weight:
iou = head_outs['iou']
iou = paddle.transpose(iou, perm=[0, 2, 3, 1])
iou_n, _, _, iou_c = iou.shape
iou = paddle.reshape(iou, shape=[iou_n, -1, iou_c])
pos_iou = paddle.gather_nd(iou, index=index)
iou_mask = paddle.expand_as(mask, pos_iou)
iou_mask = paddle.cast(iou_mask, dtype=pos_iou.dtype)
pos_num = iou_mask.sum()
iou_mask.stop_gradient = True
gt_bbox_xys = inputs['bbox_xys']
gt_bbox_xys.stop_gradient = True
centers_x = (gt_bbox_xys[:, :, 0:1] + gt_bbox_xys[:, :, 2:3]) / 2.0
centers_y = (gt_bbox_xys[:, :, 1:2] + gt_bbox_xys[:, :, 3:4]) / 2.0
x1 = centers_x - pos_size[:, :, 0:1]
y1 = centers_y - pos_size[:, :, 1:2]
x2 = centers_x + pos_size[:, :, 2:3]
y2 = centers_y + pos_size[:, :, 3:4]
pred_boxes = paddle.concat([x1, y1, x2, y2], axis=-1)
giou_loss = GIoULoss(reduction='sum')
iou_loss = giou_loss(
pred_boxes * iou_mask,
gt_bbox_xys * iou_mask,
iou_weight=iou_mask,
loc_reweight=None)
iou_loss = iou_loss / (pos_num + 1e-4)
losses = {
'heatmap_loss': heatmap_loss,
'size_loss': size_loss,
'offset_loss': offset_loss,
}
det_loss = weights['heatmap'] * heatmap_loss + weights[
'size'] * size_loss + weights['offset'] * offset_loss
if self.add_iou and 'iou' in self.loss_weight:
losses.update({'iou_loss': iou_loss})
det_loss += weights['iou'] * iou_loss
losses.update({'det_loss': det_loss})
return losses