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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/yolo_head.py Normal file
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# 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.
import paddle
import paddle.nn as nn
import paddle.nn.functional as F
from paddle import ParamAttr
from paddle.regularizer import L2Decay
from ppdet.core.workspace import register
import math
import numpy as np
from ..initializer import bias_init_with_prob, constant_
from ..backbones.csp_darknet import BaseConv, DWConv
from ..losses import IouLoss
from ppdet.modeling.assigners.simota_assigner import SimOTAAssigner
from ppdet.modeling.bbox_utils import bbox_overlaps
from ppdet.modeling.layers import MultiClassNMS
__all__ = ['YOLOv3Head', 'YOLOXHead']
def _de_sigmoid(x, eps=1e-7):
x = paddle.clip(x, eps, 1. / eps)
x = paddle.clip(1. / x - 1., eps, 1. / eps)
x = -paddle.log(x)
return x
@register
class YOLOv3Head(nn.Layer):
__shared__ = ['num_classes', 'data_format']
__inject__ = ['loss']
def __init__(self,
in_channels=[1024, 512, 256],
anchors=[[10, 13], [16, 30], [33, 23], [30, 61], [62, 45],
[59, 119], [116, 90], [156, 198], [373, 326]],
anchor_masks=[[6, 7, 8], [3, 4, 5], [0, 1, 2]],
num_classes=80,
loss='YOLOv3Loss',
iou_aware=False,
iou_aware_factor=0.4,
data_format='NCHW'):
"""
Head for YOLOv3 network
Args:
num_classes (int): number of foreground classes
anchors (list): anchors
anchor_masks (list): anchor masks
loss (object): YOLOv3Loss instance
iou_aware (bool): whether to use iou_aware
iou_aware_factor (float): iou aware factor
data_format (str): data format, NCHW or NHWC
"""
super(YOLOv3Head, self).__init__()
assert len(in_channels) > 0, "in_channels length should > 0"
self.in_channels = in_channels
self.num_classes = num_classes
self.loss = loss
self.iou_aware = iou_aware
self.iou_aware_factor = iou_aware_factor
self.parse_anchor(anchors, anchor_masks)
self.num_outputs = len(self.anchors)
self.data_format = data_format
self.yolo_outputs = []
for i in range(len(self.anchors)):
if self.iou_aware:
num_filters = len(self.anchors[i]) * (self.num_classes + 6)
else:
num_filters = len(self.anchors[i]) * (self.num_classes + 5)
name = 'yolo_output.{}'.format(i)
conv = nn.Conv2D(
in_channels=self.in_channels[i],
out_channels=num_filters,
kernel_size=1,
stride=1,
padding=0,
data_format=data_format,
bias_attr=ParamAttr(regularizer=L2Decay(0.)))
conv.skip_quant = True
yolo_output = self.add_sublayer(name, conv)
self.yolo_outputs.append(yolo_output)
def parse_anchor(self, anchors, anchor_masks):
self.anchors = [[anchors[i] for i in mask] for mask in anchor_masks]
self.mask_anchors = []
anchor_num = len(anchors)
for masks in anchor_masks:
self.mask_anchors.append([])
for mask in masks:
assert mask < anchor_num, "anchor mask index overflow"
self.mask_anchors[-1].extend(anchors[mask])
def forward(self, feats, targets=None):
assert len(feats) == len(self.anchors)
yolo_outputs = []
for i, feat in enumerate(feats):
yolo_output = self.yolo_outputs[i](feat)
if self.data_format == 'NHWC':
yolo_output = paddle.transpose(yolo_output, [0, 3, 1, 2])
yolo_outputs.append(yolo_output)
if self.training:
return self.loss(yolo_outputs, targets, self.anchors)
else:
if self.iou_aware:
y = []
for i, out in enumerate(yolo_outputs):
na = len(self.anchors[i])
ioup, x = out[:, 0:na, :, :], out[:, na:, :, :]
b, c, h, w = x.shape
no = c // na
x = x.reshape((b, na, no, h * w))
ioup = ioup.reshape((b, na, 1, h * w))
obj = x[:, :, 4:5, :]
ioup = F.sigmoid(ioup)
obj = F.sigmoid(obj)
obj_t = (obj**(1 - self.iou_aware_factor)) * (
ioup**self.iou_aware_factor)
obj_t = _de_sigmoid(obj_t)
loc_t = x[:, :, :4, :]
cls_t = x[:, :, 5:, :]
y_t = paddle.concat([loc_t, obj_t, cls_t], axis=2)
y_t = y_t.reshape((b, c, h, w))
y.append(y_t)
return y
else:
return yolo_outputs
@classmethod
def from_config(cls, cfg, input_shape):
return {'in_channels': [i.channels for i in input_shape], }
@register
class YOLOXHead(nn.Layer):
__shared__ = ['num_classes', 'width_mult', 'act', 'trt', 'exclude_nms']
__inject__ = ['assigner', 'nms']
def __init__(self,
num_classes=80,
width_mult=1.0,
depthwise=False,
in_channels=[256, 512, 1024],
feat_channels=256,
fpn_strides=(8, 16, 32),
l1_epoch=285,
act='silu',
assigner=SimOTAAssigner(use_vfl=False),
nms='MultiClassNMS',
loss_weight={
'cls': 1.0,
'obj': 1.0,
'iou': 5.0,
'l1': 1.0,
},
trt=False,
exclude_nms=False):
super(YOLOXHead, self).__init__()
self._dtype = paddle.framework.get_default_dtype()
self.num_classes = num_classes
assert len(in_channels) > 0, "in_channels length should > 0"
self.in_channels = in_channels
feat_channels = int(feat_channels * width_mult)
self.fpn_strides = fpn_strides
self.l1_epoch = l1_epoch
self.assigner = assigner
self.nms = nms
if isinstance(self.nms, MultiClassNMS) and trt:
self.nms.trt = trt
self.exclude_nms = exclude_nms
self.loss_weight = loss_weight
self.iou_loss = IouLoss(loss_weight=1.0) # default loss_weight 2.5
ConvBlock = DWConv if depthwise else BaseConv
self.stem_conv = nn.LayerList()
self.conv_cls = nn.LayerList()
self.conv_reg = nn.LayerList() # reg [x,y,w,h] + obj
for in_c in self.in_channels:
self.stem_conv.append(BaseConv(in_c, feat_channels, 1, 1, act=act))
self.conv_cls.append(
nn.Sequential(* [
ConvBlock(
feat_channels, feat_channels, 3, 1, act=act), ConvBlock(
feat_channels, feat_channels, 3, 1, act=act),
nn.Conv2D(
feat_channels,
self.num_classes,
1,
bias_attr=ParamAttr(regularizer=L2Decay(0.0)))
]))
self.conv_reg.append(
nn.Sequential(* [
ConvBlock(
feat_channels, feat_channels, 3, 1, act=act),
ConvBlock(
feat_channels, feat_channels, 3, 1, act=act),
nn.Conv2D(
feat_channels,
4 + 1, # reg [x,y,w,h] + obj
1,
bias_attr=ParamAttr(regularizer=L2Decay(0.0)))
]))
self._init_weights()
@classmethod
def from_config(cls, cfg, input_shape):
return {'in_channels': [i.channels for i in input_shape], }
def _init_weights(self):
bias_cls = bias_init_with_prob(0.01)
bias_reg = paddle.full([5], math.log(5.), dtype=self._dtype)
bias_reg[:2] = 0.
bias_reg[-1] = bias_cls
for cls_, reg_ in zip(self.conv_cls, self.conv_reg):
constant_(cls_[-1].weight)
constant_(cls_[-1].bias, bias_cls)
constant_(reg_[-1].weight)
reg_[-1].bias.set_value(bias_reg)
def _generate_anchor_point(self, feat_sizes, strides, offset=0.):
anchor_points, stride_tensor = [], []
num_anchors_list = []
for feat_size, stride in zip(feat_sizes, strides):
h, w = feat_size
x = (paddle.arange(w) + offset) * stride
y = (paddle.arange(h) + offset) * stride
y, x = paddle.meshgrid(y, x)
anchor_points.append(paddle.stack([x, y], axis=-1).reshape([-1, 2]))
stride_tensor.append(
paddle.full(
[len(anchor_points[-1]), 1], stride, dtype=self._dtype))
num_anchors_list.append(len(anchor_points[-1]))
anchor_points = paddle.concat(anchor_points).astype(self._dtype)
anchor_points.stop_gradient = True
stride_tensor = paddle.concat(stride_tensor)
stride_tensor.stop_gradient = True
return anchor_points, stride_tensor, num_anchors_list
def forward(self, feats, targets=None):
assert len(feats) == len(self.fpn_strides), \
"The size of feats is not equal to size of fpn_strides"
feat_sizes = [[f.shape[-2], f.shape[-1]] for f in feats]
cls_score_list, reg_pred_list = [], []
obj_score_list = []
for i, feat in enumerate(feats):
feat = self.stem_conv[i](feat)
cls_logit = self.conv_cls[i](feat)
reg_pred = self.conv_reg[i](feat)
# cls prediction
cls_score = F.sigmoid(cls_logit)
cls_score_list.append(cls_score.flatten(2).transpose([0, 2, 1]))
# reg prediction
reg_xywh, obj_logit = paddle.split(reg_pred, [4, 1], axis=1)
reg_xywh = reg_xywh.flatten(2).transpose([0, 2, 1])
reg_pred_list.append(reg_xywh)
# obj prediction
obj_score = F.sigmoid(obj_logit)
obj_score_list.append(obj_score.flatten(2).transpose([0, 2, 1]))
cls_score_list = paddle.concat(cls_score_list, axis=1)
reg_pred_list = paddle.concat(reg_pred_list, axis=1)
obj_score_list = paddle.concat(obj_score_list, axis=1)
# bbox decode
anchor_points, stride_tensor, _ =\
self._generate_anchor_point(feat_sizes, self.fpn_strides)
reg_xy, reg_wh = paddle.split(reg_pred_list, 2, axis=-1)
reg_xy += (anchor_points / stride_tensor)
reg_wh = paddle.exp(reg_wh) * 0.5
bbox_pred_list = paddle.concat(
[reg_xy - reg_wh, reg_xy + reg_wh], axis=-1)
if self.training:
anchor_points, stride_tensor, num_anchors_list =\
self._generate_anchor_point(feat_sizes, self.fpn_strides, 0.5)
yolox_losses = self.get_loss([
cls_score_list, bbox_pred_list, obj_score_list, anchor_points,
stride_tensor, num_anchors_list
], targets)
return yolox_losses
else:
pred_scores = (cls_score_list * obj_score_list).sqrt()
return pred_scores, bbox_pred_list, stride_tensor
def get_loss(self, head_outs, targets):
pred_cls, pred_bboxes, pred_obj,\
anchor_points, stride_tensor, num_anchors_list = head_outs
gt_labels = targets['gt_class']
gt_bboxes = targets['gt_bbox']
pred_scores = (pred_cls * pred_obj).sqrt()
# label assignment
center_and_strides = paddle.concat(
[anchor_points, stride_tensor, stride_tensor], axis=-1)
pos_num_list, label_list, bbox_target_list = [], [], []
for pred_score, pred_bbox, gt_box, gt_label in zip(
pred_scores.detach(),
pred_bboxes.detach() * stride_tensor, gt_bboxes, gt_labels):
pos_num, label, _, bbox_target = self.assigner(
pred_score, center_and_strides, pred_bbox, gt_box, gt_label)
pos_num_list.append(pos_num)
label_list.append(label)
bbox_target_list.append(bbox_target)
labels = paddle.to_tensor(np.stack(label_list, axis=0))
bbox_targets = paddle.to_tensor(np.stack(bbox_target_list, axis=0))
bbox_targets /= stride_tensor # rescale bbox
# 1. obj score loss
mask_positive = (labels != self.num_classes)
loss_obj = F.binary_cross_entropy(
pred_obj,
mask_positive.astype(pred_obj.dtype).unsqueeze(-1),
reduction='sum')
num_pos = sum(pos_num_list)
if num_pos > 0:
num_pos = paddle.to_tensor(num_pos, dtype=self._dtype).clip(min=1)
loss_obj /= num_pos
# 2. iou loss
bbox_mask = mask_positive.unsqueeze(-1).tile([1, 1, 4])
pred_bboxes_pos = paddle.masked_select(pred_bboxes,
bbox_mask).reshape([-1, 4])
assigned_bboxes_pos = paddle.masked_select(
bbox_targets, bbox_mask).reshape([-1, 4])
bbox_iou = bbox_overlaps(pred_bboxes_pos, assigned_bboxes_pos)
bbox_iou = paddle.diag(bbox_iou)
loss_iou = self.iou_loss(
pred_bboxes_pos.split(
4, axis=-1),
assigned_bboxes_pos.split(
4, axis=-1))
loss_iou = loss_iou.sum() / num_pos
# 3. cls loss
cls_mask = mask_positive.unsqueeze(-1).tile(
[1, 1, self.num_classes])
pred_cls_pos = paddle.masked_select(
pred_cls, cls_mask).reshape([-1, self.num_classes])
assigned_cls_pos = paddle.masked_select(labels, mask_positive)
assigned_cls_pos = F.one_hot(assigned_cls_pos,
self.num_classes + 1)[..., :-1]
assigned_cls_pos *= bbox_iou.unsqueeze(-1)
loss_cls = F.binary_cross_entropy(
pred_cls_pos, assigned_cls_pos, reduction='sum')
loss_cls /= num_pos
# 4. l1 loss
if targets['epoch_id'] >= self.l1_epoch:
loss_l1 = F.l1_loss(
pred_bboxes_pos, assigned_bboxes_pos, reduction='sum')
loss_l1 /= num_pos
else:
loss_l1 = paddle.zeros([1])
loss_l1.stop_gradient = False
else:
loss_cls = paddle.zeros([1])
loss_iou = paddle.zeros([1])
loss_l1 = paddle.zeros([1])
loss_cls.stop_gradient = False
loss_iou.stop_gradient = False
loss_l1.stop_gradient = False
loss = self.loss_weight['obj'] * loss_obj + \
self.loss_weight['cls'] * loss_cls + \
self.loss_weight['iou'] * loss_iou
if targets['epoch_id'] >= self.l1_epoch:
loss += (self.loss_weight['l1'] * loss_l1)
yolox_losses = {
'loss': loss,
'loss_cls': loss_cls,
'loss_obj': loss_obj,
'loss_iou': loss_iou,
'loss_l1': loss_l1,
}
return yolox_losses
def post_process(self, head_outs, img_shape, scale_factor):
pred_scores, pred_bboxes, stride_tensor = head_outs
pred_scores = pred_scores.transpose([0, 2, 1])
pred_bboxes *= stride_tensor
# scale bbox to origin image
scale_factor = scale_factor.flip(-1).tile([1, 2]).unsqueeze(1)
pred_bboxes /= scale_factor
if self.exclude_nms:
# `exclude_nms=True` just use in benchmark
return pred_bboxes.sum(), pred_scores.sum()
else:
bbox_pred, bbox_num, _ = self.nms(pred_bboxes, pred_scores)
return bbox_pred, bbox_num