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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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# 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/gfl_head.py
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import math
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, batch_distance2bbox
from ppdet.data.transform.atss_assigner import bbox_overlaps
__all__ = ['GFLHead', 'LDGFLHead']
class ScaleReg(nn.Layer):
"""
Parameter for scaling the regression outputs.
"""
def __init__(self):
super(ScaleReg, self).__init__()
self.scale_reg = self.create_parameter(
shape=[1],
attr=ParamAttr(initializer=Constant(value=1.)),
dtype="float32")
def forward(self, inputs):
out = inputs * self.scale_reg
return out
class Integral(nn.Layer):
"""A fixed layer for calculating integral result from distribution.
This layer calculates the target location by :math: `sum{P(y_i) * y_i}`,
P(y_i) denotes the softmax vector that represents the discrete distribution
y_i denotes the discrete set, usually {0, 1, 2, ..., reg_max}
Args:
reg_max (int): The maximal value of the discrete set. Default: 16. You
may want to reset it according to your new dataset or related
settings.
"""
def __init__(self, reg_max=16):
super(Integral, self).__init__()
self.reg_max = reg_max
self.register_buffer('project',
paddle.linspace(0, self.reg_max, self.reg_max + 1))
def forward(self, x):
"""Forward feature from the regression head to get integral result of
bounding box location.
Args:
x (Tensor): Features of the regression head, shape (N, 4*(n+1)),
n is self.reg_max.
Returns:
x (Tensor): Integral result of box locations, i.e., distance
offsets from the box center in four directions, shape (N, 4).
"""
x = F.softmax(x.reshape([-1, self.reg_max + 1]), axis=1)
x = F.linear(x, self.project)
if self.training:
x = x.reshape([-1, 4])
return x
@register
class DGQP(nn.Layer):
"""Distribution-Guided Quality Predictor of GFocal head
Args:
reg_topk (int): top-k statistics of distribution to guide LQE
reg_channels (int): hidden layer unit to generate LQE
add_mean (bool): Whether to calculate the mean of top-k statistics
"""
def __init__(self, reg_topk=4, reg_channels=64, add_mean=True):
super(DGQP, self).__init__()
self.reg_topk = reg_topk
self.reg_channels = reg_channels
self.add_mean = add_mean
self.total_dim = reg_topk
if add_mean:
self.total_dim += 1
self.reg_conv1 = self.add_sublayer(
'dgqp_reg_conv1',
nn.Conv2D(
in_channels=4 * self.total_dim,
out_channels=self.reg_channels,
kernel_size=1,
weight_attr=ParamAttr(initializer=Normal(
mean=0., std=0.01)),
bias_attr=ParamAttr(initializer=Constant(value=0))))
self.reg_conv2 = self.add_sublayer(
'dgqp_reg_conv2',
nn.Conv2D(
in_channels=self.reg_channels,
out_channels=1,
kernel_size=1,
weight_attr=ParamAttr(initializer=Normal(
mean=0., std=0.01)),
bias_attr=ParamAttr(initializer=Constant(value=0))))
def forward(self, x):
"""Forward feature from the regression head to get integral result of
bounding box location.
Args:
x (Tensor): Features of the regression head, shape (N, 4*(n+1)),
n is self.reg_max.
Returns:
x (Tensor): Integral result of box locations, i.e., distance
offsets from the box center in four directions, shape (N, 4).
"""
N, _, H, W = x.shape[:]
prob = F.softmax(x.reshape([N, 4, -1, H, W]), axis=2)
prob_topk, _ = prob.topk(self.reg_topk, axis=2)
if self.add_mean:
stat = paddle.concat(
[prob_topk, prob_topk.mean(
axis=2, keepdim=True)], axis=2)
else:
stat = prob_topk
y = F.relu(self.reg_conv1(stat.reshape([N, 4 * self.total_dim, H, W])))
y = F.sigmoid(self.reg_conv2(y))
return y
@register
class GFLHead(nn.Layer):
"""
GFLHead
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_class (object): Instance of QualityFocalLoss.
loss_dfl (object): Instance of DistributionFocalLoss.
loss_bbox (object): Instance of bbox loss.
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', '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',
reg_max=16,
feat_in_chan=256,
nms=None,
nms_pre=1000,
cell_offset=0):
super(GFLHead, self).__init__()
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
if self.use_sigmoid:
self.cls_out_channels = self.num_classes
else:
self.cls_out_channels = self.num_classes + 1
conv_cls_name = "gfl_head_cls"
bias_init_value = -math.log((1 - self.prior_prob) / self.prior_prob)
self.gfl_head_cls = self.add_sublayer(
conv_cls_name,
nn.Conv2D(
in_channels=self.feat_in_chan,
out_channels=self.cls_out_channels,
kernel_size=3,
stride=1,
padding=1,
weight_attr=ParamAttr(initializer=Normal(
mean=0., std=0.01)),
bias_attr=ParamAttr(
initializer=Constant(value=bias_init_value))))
conv_reg_name = "gfl_head_reg"
self.gfl_head_reg = self.add_sublayer(
conv_reg_name,
nn.Conv2D(
in_channels=self.feat_in_chan,
out_channels=4 * (self.reg_max + 1),
kernel_size=3,
stride=1,
padding=1,
weight_attr=ParamAttr(initializer=Normal(
mean=0., std=0.01)),
bias_attr=ParamAttr(initializer=Constant(value=0))))
self.scales_regs = []
for i in range(len(self.fpn_stride)):
lvl = int(math.log(int(self.fpn_stride[i]), 2))
feat_name = 'p{}_feat'.format(lvl)
scale_reg = self.add_sublayer(feat_name, ScaleReg())
self.scales_regs.append(scale_reg)
self.distribution_project = Integral(self.reg_max)
def forward(self, fpn_feats):
assert len(fpn_feats) == len(
self.fpn_stride
), "The size of fpn_feats is not equal to size of fpn_stride"
cls_logits_list = []
bboxes_reg_list = []
for stride, scale_reg, fpn_feat in zip(self.fpn_stride,
self.scales_regs, fpn_feats):
conv_cls_feat, conv_reg_feat = self.conv_feat(fpn_feat)
cls_score = self.gfl_head_cls(conv_cls_feat)
bbox_pred = scale_reg(self.gfl_head_reg(conv_reg_feat))
if self.dgqp_module:
quality_score = self.dgqp_module(bbox_pred)
cls_score = F.sigmoid(cls_score) * quality_score
if not self.training:
cls_score = F.sigmoid(cls_score.transpose([0, 2, 3, 1]))
bbox_pred = bbox_pred.transpose([0, 2, 3, 1])
b, cell_h, cell_w, _ = paddle.shape(cls_score)
y, x = self.get_single_level_center_point(
[cell_h, cell_w], stride, cell_offset=self.cell_offset)
center_points = paddle.stack([x, y], axis=-1)
cls_score = cls_score.reshape([b, -1, self.cls_out_channels])
bbox_pred = self.distribution_project(bbox_pred) * stride
bbox_pred = bbox_pred.reshape([-1, cell_h * cell_w, 4])
# NOTE: If keep_ratio=False and image shape value that
# multiples of 32, distance2bbox not set max_shapes parameter
# to speed up model prediction. If need to set max_shapes,
# please use inputs['im_shape'].
bbox_pred = batch_distance2bbox(
center_points, bbox_pred, max_shapes=None)
cls_logits_list.append(cls_score)
bboxes_reg_list.append(bbox_pred)
return (cls_logits_list, bboxes_reg_list)
def _images_to_levels(self, target, num_level_anchors):
"""
Convert targets by image to targets by feature level.
"""
level_targets = []
start = 0
for n in num_level_anchors:
end = start + n
level_targets.append(target[:, start:end].squeeze(0))
start = end
return level_targets
def _grid_cells_to_center(self, grid_cells):
"""
Get center location of each gird cell
Args:
grid_cells: grid cells of a feature map
Returns:
center points
"""
cells_cx = (grid_cells[:, 2] + grid_cells[:, 0]) / 2
cells_cy = (grid_cells[:, 3] + grid_cells[:, 1]) / 2
return paddle.stack([cells_cx, cells_cy], axis=-1)
def get_loss(self, gfl_head_outs, gt_meta):
cls_logits, bboxes_reg = gfl_head_outs
num_level_anchors = [
featmap.shape[-2] * featmap.shape[-1] for featmap in cls_logits
]
grid_cells_list = self._images_to_levels(gt_meta['grid_cells'],
num_level_anchors)
labels_list = self._images_to_levels(gt_meta['labels'],
num_level_anchors)
label_weights_list = self._images_to_levels(gt_meta['label_weights'],
num_level_anchors)
bbox_targets_list = self._images_to_levels(gt_meta['bbox_targets'],
num_level_anchors)
num_total_pos = sum(gt_meta['pos_num'])
try:
paddle.distributed.all_reduce(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, grid_cells, labels, label_weights, bbox_targets, stride in zip(
cls_logits, bboxes_reg, grid_cells_list, labels_list,
label_weights_list, bbox_targets_list, self.fpn_stride):
grid_cells = grid_cells.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_grid_cells = paddle.gather(grid_cells, pos_inds, axis=0)
pos_grid_cell_centers = self._grid_cells_to_center(
pos_grid_cells) / 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_grid_cell_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_grid_cell_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(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
def get_single_level_center_point(self, featmap_size, stride,
cell_offset=0):
"""
Generate pixel centers of a single stage feature map.
Args:
featmap_size: height and width of the feature map
stride: down sample stride of the feature map
Returns:
y and x of the center points
"""
h, w = featmap_size
x_range = (paddle.arange(w, dtype='float32') + cell_offset) * stride
y_range = (paddle.arange(h, dtype='float32') + cell_offset) * stride
y, x = paddle.meshgrid(y_range, x_range)
y = y.flatten()
x = x.flatten()
return y, x
def post_process(self, gfl_head_outs, im_shape, scale_factor):
cls_scores, bboxes_reg = gfl_head_outs
bboxes = paddle.concat(bboxes_reg, axis=1)
# rescale: [h_scale, w_scale] -> [w_scale, h_scale, w_scale, h_scale]
im_scale = scale_factor.flip([1]).tile([1, 2]).unsqueeze(1)
bboxes /= im_scale
mlvl_scores = paddle.concat(cls_scores, axis=1)
mlvl_scores = mlvl_scores.transpose([0, 2, 1])
bbox_pred, bbox_num, _ = self.nms(bboxes, mlvl_scores)
return bbox_pred, bbox_num
@register
class LDGFLHead(GFLHead):
"""
GFLHead for LD distill
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_class (object): Instance of QualityFocalLoss.
loss_dfl (object): Instance of DistributionFocalLoss.
loss_bbox (object): Instance of bbox loss.
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',
'loss_ld', 'loss_ld_vlr', 'loss_kd', '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',
loss_ld='KnowledgeDistillationKLDivLoss',
loss_ld_vlr='KnowledgeDistillationKLDivLoss',
loss_kd='KnowledgeDistillationKLDivLoss',
reg_max=16,
feat_in_chan=256,
nms=None,
nms_pre=1000,
cell_offset=0):
super(LDGFLHead, 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.loss_ld = loss_ld
self.loss_kd = loss_kd
self.loss_ld_vlr = loss_ld_vlr
def forward(self, fpn_feats):
assert len(fpn_feats) == len(
self.fpn_stride
), "The size of fpn_feats is not equal to size of fpn_stride"
cls_logits_list = []
bboxes_reg_list = []
for stride, scale_reg, fpn_feat in zip(self.fpn_stride,
self.scales_regs, fpn_feats):
conv_cls_feat, conv_reg_feat = self.conv_feat(fpn_feat)
cls_score = self.gfl_head_cls(conv_cls_feat)
bbox_pred = scale_reg(self.gfl_head_reg(conv_reg_feat))
if self.dgqp_module:
quality_score = self.dgqp_module(bbox_pred)
cls_score = F.sigmoid(cls_score) * quality_score
if not self.training:
cls_score = F.sigmoid(cls_score.transpose([0, 2, 3, 1]))
bbox_pred = bbox_pred.transpose([0, 2, 3, 1])
b, cell_h, cell_w, _ = paddle.shape(cls_score)
y, x = self.get_single_level_center_point(
[cell_h, cell_w], stride, cell_offset=self.cell_offset)
center_points = paddle.stack([x, y], axis=-1)
cls_score = cls_score.reshape([b, -1, self.cls_out_channels])
bbox_pred = self.distribution_project(bbox_pred) * stride
bbox_pred = bbox_pred.reshape([b, cell_h * cell_w, 4])
# NOTE: If keep_ratio=False and image shape value that
# multiples of 32, distance2bbox not set max_shapes parameter
# to speed up model prediction. If need to set max_shapes,
# please use inputs['im_shape'].
bbox_pred = batch_distance2bbox(
center_points, bbox_pred, max_shapes=None)
cls_logits_list.append(cls_score)
bboxes_reg_list.append(bbox_pred)
return (cls_logits_list, bboxes_reg_list)
def get_loss(self, gfl_head_outs, gt_meta, soft_label_list,
soft_targets_list):
cls_logits, bboxes_reg = gfl_head_outs
num_level_anchors = [
featmap.shape[-2] * featmap.shape[-1] for featmap in cls_logits
]
grid_cells_list = self._images_to_levels(gt_meta['grid_cells'],
num_level_anchors)
labels_list = self._images_to_levels(gt_meta['labels'],
num_level_anchors)
label_weights_list = self._images_to_levels(gt_meta['label_weights'],
num_level_anchors)
bbox_targets_list = self._images_to_levels(gt_meta['bbox_targets'],
num_level_anchors)
# vlr regions
vlr_regions_list = self._images_to_levels(gt_meta['vlr_regions'],
num_level_anchors)
num_total_pos = sum(gt_meta['pos_num'])
try:
paddle.distributed.all_reduce(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, loss_ld_list, avg_factor = [], [], [], [], []
loss_ld_vlr_list, loss_kd_list = [], []
for cls_score, bbox_pred, grid_cells, labels, label_weights, bbox_targets, stride, soft_targets,\
soft_label, vlr_region in zip(
cls_logits, bboxes_reg, grid_cells_list, labels_list,
label_weights_list, bbox_targets_list, self.fpn_stride, soft_targets_list,
soft_label_list, vlr_regions_list):
grid_cells = grid_cells.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)])
soft_targets = soft_targets.transpose([0, 2, 3, 1]).reshape(
[-1, 4 * (self.reg_max + 1)])
soft_label = soft_label.transpose([0, 2, 3, 1]).reshape(
[-1, self.cls_out_channels])
# feture im
# teacher_x = teacher_x.transpose([0, 2, 3, 1]).reshape([-1, 256])
# x = x.transpose([0, 2, 3, 1]).reshape([-1, 256])
bbox_targets = bbox_targets.reshape([-1, 4])
labels = labels.reshape([-1])
label_weights = label_weights.reshape([-1])
vlr_region = vlr_region.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)
remain_inds = (vlr_region > 0).nonzero()
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_grid_cells = paddle.gather(grid_cells, pos_inds, axis=0)
pos_grid_cell_centers = self._grid_cells_to_center(
pos_grid_cells) / 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_grid_cell_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])
pos_soft_targets = paddle.gather(soft_targets, pos_inds, axis=0)
soft_corners = pos_soft_targets.reshape([-1, self.reg_max + 1])
target_corners = bbox2distance(pos_grid_cell_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)
# ld loss
loss_ld = self.loss_ld(
pred_corners,
soft_corners,
weight=weight_targets.expand([-1, 4]).reshape([-1]),
avg_factor=4.0)
loss_kd = self.loss_kd(
paddle.gather(
cls_score, pos_inds, axis=0),
paddle.gather(
soft_label, pos_inds, axis=0),
weight=paddle.gather(
label_weights, pos_inds, axis=0),
avg_factor=pos_inds.shape[0])
else:
loss_bbox = bbox_pred.sum() * 0
loss_dfl = bbox_pred.sum() * 0
loss_ld = bbox_pred.sum() * 0
loss_kd = bbox_pred.sum() * 0
weight_targets = paddle.to_tensor([0], dtype='float32')
if len(remain_inds) > 0:
neg_pred_corners = bbox_pred[remain_inds].reshape(
[-1, self.reg_max + 1])
neg_soft_corners = soft_targets[remain_inds].reshape(
[-1, self.reg_max + 1])
remain_targets = vlr_region[remain_inds]
loss_ld_vlr = self.loss_ld_vlr(
neg_pred_corners,
neg_soft_corners,
weight=remain_targets.expand([-1, 4]).reshape([-1]),
avg_factor=16.0)
else:
loss_ld_vlr = bbox_pred.sum() * 0
# 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)
loss_ld_list.append(loss_ld)
loss_ld_vlr_list.append(loss_ld_vlr)
loss_kd_list.append(loss_kd)
avg_factor.append(weight_targets.sum())
avg_factor = sum(avg_factor) # + 1e-6
try:
paddle.distributed.all_reduce(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)
loss_ld = paddle.to_tensor(0, dtype='float32', stop_gradient=False)
loss_ld_vlr = paddle.to_tensor(
0, dtype='float32', stop_gradient=False)
loss_kd = 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_ld = sum(loss_ld_list)
loss_ld_vlr = sum(loss_ld_vlr_list)
loss_kd = sum(loss_kd_list)
loss_states = dict(
loss_qfl=loss_qfl,
loss_bbox=loss_bbox,
loss_dfl=loss_dfl,
loss_ld=loss_ld,
loss_ld_vlr=loss_ld_vlr,
loss_kd=loss_kd)
return loss_states