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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/csuhan/s2anet/blob/master/mmdet/models/anchor_heads_rotated/s2anet_head.py
import paddle
from paddle import ParamAttr
import paddle.nn as nn
import paddle.nn.functional as F
from paddle.nn.initializer import Normal, Constant
from ppdet.core.workspace import register
from ppdet.modeling.proposal_generator.target_layer import RBoxAssigner
from ppdet.modeling.proposal_generator.anchor_generator import S2ANetAnchorGenerator
from ppdet.modeling.layers import AlignConv
from ..cls_utils import _get_class_default_kwargs
import numpy as np
@register
class S2ANetHead(nn.Layer):
"""
S2Anet head
Args:
stacked_convs (int): number of stacked_convs
feat_in (int): input channels of feat
feat_out (int): output channels of feat
num_classes (int): num_classes
anchor_strides (list): stride of anchors
anchor_scales (list): scale of anchors
anchor_ratios (list): ratios of anchors
target_means (list): target_means
target_stds (list): target_stds
align_conv_type (str): align_conv_type ['Conv', 'AlignConv']
align_conv_size (int): kernel size of align_conv
use_sigmoid_cls (bool): use sigmoid_cls or not
reg_loss_weight (list): loss weight for regression
"""
__shared__ = ['num_classes']
__inject__ = ['anchor_assign', 'nms']
def __init__(self,
stacked_convs=2,
feat_in=256,
feat_out=256,
num_classes=15,
anchor_strides=[8, 16, 32, 64, 128],
anchor_scales=[4],
anchor_ratios=[1.0],
target_means=0.0,
target_stds=1.0,
align_conv_type='AlignConv',
align_conv_size=3,
use_sigmoid_cls=True,
anchor_assign=_get_class_default_kwargs(RBoxAssigner),
reg_loss_weight=[1.0, 1.0, 1.0, 1.0, 1.1],
cls_loss_weight=[1.1, 1.05],
reg_loss_type='l1',
nms_pre=2000,
nms='MultiClassNMS'):
super(S2ANetHead, self).__init__()
self.stacked_convs = stacked_convs
self.feat_in = feat_in
self.feat_out = feat_out
self.anchor_list = None
self.anchor_scales = anchor_scales
self.anchor_ratios = anchor_ratios
self.anchor_strides = anchor_strides
self.anchor_strides = paddle.to_tensor(anchor_strides)
self.anchor_base_sizes = list(anchor_strides)
self.means = paddle.ones(shape=[5]) * target_means
self.stds = paddle.ones(shape=[5]) * target_stds
assert align_conv_type in ['AlignConv', 'Conv', 'DCN']
self.align_conv_type = align_conv_type
self.align_conv_size = align_conv_size
self.use_sigmoid_cls = use_sigmoid_cls
self.cls_out_channels = num_classes if self.use_sigmoid_cls else num_classes + 1
self.sampling = False
self.anchor_assign = anchor_assign
self.reg_loss_weight = reg_loss_weight
self.cls_loss_weight = cls_loss_weight
self.alpha = 1.0
self.beta = 1.0
self.reg_loss_type = reg_loss_type
self.nms_pre = nms_pre
self.nms = nms
self.fake_bbox = paddle.to_tensor(
np.array(
[[-1, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]],
dtype='float32'))
self.fake_bbox_num = paddle.to_tensor(np.array([1], dtype='int32'))
# anchor
self.anchor_generators = []
for anchor_base in self.anchor_base_sizes:
self.anchor_generators.append(
S2ANetAnchorGenerator(anchor_base, anchor_scales,
anchor_ratios))
self.anchor_generators = nn.LayerList(self.anchor_generators)
self.fam_cls_convs = nn.Sequential()
self.fam_reg_convs = nn.Sequential()
for i in range(self.stacked_convs):
chan_in = self.feat_in if i == 0 else self.feat_out
self.fam_cls_convs.add_sublayer(
'fam_cls_conv_{}'.format(i),
nn.Conv2D(
in_channels=chan_in,
out_channels=self.feat_out,
kernel_size=3,
padding=1,
weight_attr=ParamAttr(initializer=Normal(0.0, 0.01)),
bias_attr=ParamAttr(initializer=Constant(0))))
self.fam_cls_convs.add_sublayer('fam_cls_conv_{}_act'.format(i),
nn.ReLU())
self.fam_reg_convs.add_sublayer(
'fam_reg_conv_{}'.format(i),
nn.Conv2D(
in_channels=chan_in,
out_channels=self.feat_out,
kernel_size=3,
padding=1,
weight_attr=ParamAttr(initializer=Normal(0.0, 0.01)),
bias_attr=ParamAttr(initializer=Constant(0))))
self.fam_reg_convs.add_sublayer('fam_reg_conv_{}_act'.format(i),
nn.ReLU())
self.fam_reg = nn.Conv2D(
self.feat_out,
5,
1,
weight_attr=ParamAttr(initializer=Normal(0.0, 0.01)),
bias_attr=ParamAttr(initializer=Constant(0)))
prior_prob = 0.01
bias_init = float(-np.log((1 - prior_prob) / prior_prob))
self.fam_cls = nn.Conv2D(
self.feat_out,
self.cls_out_channels,
1,
weight_attr=ParamAttr(initializer=Normal(0.0, 0.01)),
bias_attr=ParamAttr(initializer=Constant(bias_init)))
if self.align_conv_type == "AlignConv":
self.align_conv = AlignConv(self.feat_out, self.feat_out,
self.align_conv_size)
elif self.align_conv_type == "Conv":
self.align_conv = nn.Conv2D(
self.feat_out,
self.feat_out,
self.align_conv_size,
padding=(self.align_conv_size - 1) // 2,
bias_attr=ParamAttr(initializer=Constant(0)))
elif self.align_conv_type == "DCN":
self.align_conv_offset = nn.Conv2D(
self.feat_out,
2 * self.align_conv_size**2,
1,
weight_attr=ParamAttr(initializer=Normal(0.0, 0.01)),
bias_attr=ParamAttr(initializer=Constant(0)))
self.align_conv = paddle.vision.ops.DeformConv2D(
self.feat_out,
self.feat_out,
self.align_conv_size,
padding=(self.align_conv_size - 1) // 2,
weight_attr=ParamAttr(initializer=Normal(0.0, 0.01)),
bias_attr=False)
self.or_conv = nn.Conv2D(
self.feat_out,
self.feat_out,
kernel_size=3,
padding=1,
weight_attr=ParamAttr(initializer=Normal(0.0, 0.01)),
bias_attr=ParamAttr(initializer=Constant(0)))
# ODM
self.odm_cls_convs = nn.Sequential()
self.odm_reg_convs = nn.Sequential()
for i in range(self.stacked_convs):
ch_in = self.feat_out
# ch_in = int(self.feat_out / 8) if i == 0 else self.feat_out
self.odm_cls_convs.add_sublayer(
'odm_cls_conv_{}'.format(i),
nn.Conv2D(
in_channels=ch_in,
out_channels=self.feat_out,
kernel_size=3,
stride=1,
padding=1,
weight_attr=ParamAttr(initializer=Normal(0.0, 0.01)),
bias_attr=ParamAttr(initializer=Constant(0))))
self.odm_cls_convs.add_sublayer('odm_cls_conv_{}_act'.format(i),
nn.ReLU())
self.odm_reg_convs.add_sublayer(
'odm_reg_conv_{}'.format(i),
nn.Conv2D(
in_channels=self.feat_out,
out_channels=self.feat_out,
kernel_size=3,
stride=1,
padding=1,
weight_attr=ParamAttr(initializer=Normal(0.0, 0.01)),
bias_attr=ParamAttr(initializer=Constant(0))))
self.odm_reg_convs.add_sublayer('odm_reg_conv_{}_act'.format(i),
nn.ReLU())
self.odm_cls = nn.Conv2D(
self.feat_out,
self.cls_out_channels,
3,
padding=1,
weight_attr=ParamAttr(initializer=Normal(0.0, 0.01)),
bias_attr=ParamAttr(initializer=Constant(bias_init)))
self.odm_reg = nn.Conv2D(
self.feat_out,
5,
3,
padding=1,
weight_attr=ParamAttr(initializer=Normal(0.0, 0.01)),
bias_attr=ParamAttr(initializer=Constant(0)))
def forward(self, feats, targets=None):
fam_reg_list, fam_cls_list = [], []
odm_reg_list, odm_cls_list = [], []
num_anchors_list, base_anchors_list, refine_anchors_list = [], [], []
for i, feat in enumerate(feats):
# get shape
B = feat.shape[0]
H, W = paddle.shape(feat)[2], paddle.shape(feat)[3]
NA = H * W
num_anchors_list.append(NA)
fam_cls_feat = self.fam_cls_convs(feat)
fam_cls = self.fam_cls(fam_cls_feat)
# [N, CLS, H, W] --> [N, H, W, CLS]
fam_cls = fam_cls.transpose([0, 2, 3, 1]).reshape(
[B, NA, self.cls_out_channels])
fam_cls_list.append(fam_cls)
fam_reg_feat = self.fam_reg_convs(feat)
fam_reg = self.fam_reg(fam_reg_feat)
# [N, 5, H, W] --> [N, H, W, 5]
fam_reg = fam_reg.transpose([0, 2, 3, 1]).reshape([B, NA, 5])
fam_reg_list.append(fam_reg)
# prepare anchor
init_anchors = self.anchor_generators[i]((H, W),
self.anchor_strides[i])
init_anchors = init_anchors.reshape([1, NA, 5])
base_anchors_list.append(init_anchors.squeeze(0))
if self.training:
refine_anchor = self.bbox_decode(fam_reg.detach(), init_anchors)
else:
refine_anchor = self.bbox_decode(fam_reg, init_anchors)
refine_anchors_list.append(refine_anchor)
if self.align_conv_type == 'AlignConv':
align_feat = self.align_conv(feat,
refine_anchor.clone(), (H, W),
self.anchor_strides[i])
elif self.align_conv_type == 'DCN':
align_offset = self.align_conv_offset(feat)
align_feat = self.align_conv(feat, align_offset)
elif self.align_conv_type == 'Conv':
align_feat = self.align_conv(feat)
or_feat = self.or_conv(align_feat)
odm_reg_feat = or_feat
odm_cls_feat = or_feat
odm_reg_feat = self.odm_reg_convs(odm_reg_feat)
odm_cls_feat = self.odm_cls_convs(odm_cls_feat)
odm_cls = self.odm_cls(odm_cls_feat)
# [N, CLS, H, W] --> [N, H, W, CLS]
odm_cls = odm_cls.transpose([0, 2, 3, 1]).reshape(
[B, NA, self.cls_out_channels])
odm_cls_list.append(odm_cls)
odm_reg = self.odm_reg(odm_reg_feat)
# [N, 5, H, W] --> [N, H, W, 5]
odm_reg = odm_reg.transpose([0, 2, 3, 1]).reshape([B, NA, 5])
odm_reg_list.append(odm_reg)
if self.training:
return self.get_loss([
fam_cls_list, fam_reg_list, odm_cls_list, odm_reg_list,
num_anchors_list, base_anchors_list, refine_anchors_list
], targets)
else:
odm_bboxes_list = []
for odm_reg, refine_anchor in zip(odm_reg_list,
refine_anchors_list):
odm_bboxes = self.bbox_decode(odm_reg, refine_anchor)
odm_bboxes_list.append(odm_bboxes)
return [odm_bboxes_list, odm_cls_list]
def get_bboxes(self, head_outs):
perd_bboxes_list, pred_scores_list = head_outs
batch = paddle.shape(pred_scores_list[0])[0]
bboxes, bbox_num = [], []
for i in range(batch):
pred_scores_per_image = [t[i] for t in pred_scores_list]
pred_bboxes_per_image = [t[i] for t in perd_bboxes_list]
bbox_per_image, bbox_num_per_image = self.get_bboxes_single(
pred_scores_per_image, pred_bboxes_per_image)
bboxes.append(bbox_per_image)
bbox_num.append(bbox_num_per_image)
bboxes = paddle.concat(bboxes)
bbox_num = paddle.concat(bbox_num)
return bboxes, bbox_num
def get_pred(self, bboxes, bbox_num, im_shape, scale_factor):
"""
Rescale, clip and filter the bbox from the output of NMS to
get final prediction.
Args:
bboxes(Tensor): bboxes [N, 10]
bbox_num(Tensor): bbox_num
im_shape(Tensor): [1 2]
scale_factor(Tensor): [1 2]
Returns:
bbox_pred(Tensor): The output is the prediction with shape [N, 8]
including labels, scores and bboxes. The size of
bboxes are corresponding to the original image.
"""
origin_shape = paddle.floor(im_shape / scale_factor + 0.5)
origin_shape_list = []
scale_factor_list = []
# scale_factor: scale_y, scale_x
for i in range(bbox_num.shape[0]):
expand_shape = paddle.expand(origin_shape[i:i + 1, :],
[bbox_num[i], 2])
scale_y, scale_x = scale_factor[i, 0:1], scale_factor[i, 1:2]
scale = paddle.concat([
scale_x, scale_y, scale_x, scale_y, scale_x, scale_y, scale_x,
scale_y
])
expand_scale = paddle.expand(scale, [bbox_num[i], 8])
origin_shape_list.append(expand_shape)
scale_factor_list.append(expand_scale)
origin_shape_list = paddle.concat(origin_shape_list)
scale_factor_list = paddle.concat(scale_factor_list)
# bboxes: [N, 10], label, score, bbox
pred_label_score = bboxes[:, 0:2]
pred_bbox = bboxes[:, 2:]
# rescale bbox to original image
pred_bbox = pred_bbox.reshape([-1, 8])
scaled_bbox = pred_bbox / scale_factor_list
origin_h = origin_shape_list[:, 0]
origin_w = origin_shape_list[:, 1]
bboxes = scaled_bbox
zeros = paddle.zeros_like(origin_h)
x1 = paddle.maximum(paddle.minimum(bboxes[:, 0], origin_w - 1), zeros)
y1 = paddle.maximum(paddle.minimum(bboxes[:, 1], origin_h - 1), zeros)
x2 = paddle.maximum(paddle.minimum(bboxes[:, 2], origin_w - 1), zeros)
y2 = paddle.maximum(paddle.minimum(bboxes[:, 3], origin_h - 1), zeros)
x3 = paddle.maximum(paddle.minimum(bboxes[:, 4], origin_w - 1), zeros)
y3 = paddle.maximum(paddle.minimum(bboxes[:, 5], origin_h - 1), zeros)
x4 = paddle.maximum(paddle.minimum(bboxes[:, 6], origin_w - 1), zeros)
y4 = paddle.maximum(paddle.minimum(bboxes[:, 7], origin_h - 1), zeros)
pred_bbox = paddle.stack([x1, y1, x2, y2, x3, y3, x4, y4], axis=-1)
pred_result = paddle.concat([pred_label_score, pred_bbox], axis=1)
return pred_result
def get_bboxes_single(self, cls_score_list, bbox_pred_list):
mlvl_bboxes = []
mlvl_scores = []
for cls_score, bbox_pred in zip(cls_score_list, bbox_pred_list):
if self.use_sigmoid_cls:
scores = F.sigmoid(cls_score)
else:
scores = F.softmax(cls_score, axis=-1)
if scores.shape[0] > self.nms_pre:
# Get maximum scores for foreground classes.
if self.use_sigmoid_cls:
max_scores = paddle.max(scores, axis=1)
else:
max_scores = paddle.max(scores[:, :-1], axis=1)
topk_val, topk_inds = paddle.topk(max_scores, self.nms_pre)
bbox_pred = paddle.gather(bbox_pred, topk_inds)
scores = paddle.gather(scores, topk_inds)
mlvl_bboxes.append(bbox_pred)
mlvl_scores.append(scores)
mlvl_bboxes = paddle.concat(mlvl_bboxes)
mlvl_scores = paddle.concat(mlvl_scores)
mlvl_polys = self.rbox2poly(mlvl_bboxes).unsqueeze(0)
mlvl_scores = paddle.transpose(mlvl_scores, [1, 0]).unsqueeze(0)
bbox, bbox_num, _ = self.nms(mlvl_polys, mlvl_scores)
if bbox.shape[0] <= 0:
bbox = self.fake_bbox
bbox_num = self.fake_bbox_num
return bbox, bbox_num
def smooth_l1_loss(self, pred, label, delta=1.0 / 9.0):
"""
Args:
pred: pred score
label: label
delta: delta
Returns: loss
"""
assert pred.shape == label.shape and label.numel() > 0
assert delta > 0
diff = paddle.abs(pred - label)
loss = paddle.where(diff < delta, 0.5 * diff * diff / delta,
diff - 0.5 * delta)
return loss
def get_fam_loss(self, fam_target, s2anet_head_out, reg_loss_type='l1'):
(labels, label_weights, bbox_targets, bbox_weights, bbox_gt_bboxes,
pos_inds, neg_inds) = fam_target
fam_cls_branch_list, fam_reg_branch_list, odm_cls_branch_list, odm_reg_branch_list, num_anchors_list = s2anet_head_out
fam_cls_losses = []
fam_bbox_losses = []
st_idx = 0
num_total_samples = len(pos_inds) + len(
neg_inds) if self.sampling else len(pos_inds)
num_total_samples = max(1, num_total_samples)
for idx, feat_anchor_num in enumerate(num_anchors_list):
# step1: get data
feat_labels = labels[st_idx:st_idx + feat_anchor_num]
feat_label_weights = label_weights[st_idx:st_idx + feat_anchor_num]
feat_bbox_targets = bbox_targets[st_idx:st_idx + feat_anchor_num, :]
feat_bbox_weights = bbox_weights[st_idx:st_idx + feat_anchor_num, :]
# step2: calc cls loss
feat_labels = feat_labels.reshape(-1)
feat_label_weights = feat_label_weights.reshape(-1)
fam_cls_score = fam_cls_branch_list[idx]
fam_cls_score = paddle.squeeze(fam_cls_score, axis=0)
fam_cls_score1 = fam_cls_score
feat_labels = paddle.to_tensor(feat_labels)
feat_labels_one_hot = paddle.nn.functional.one_hot(
feat_labels, self.cls_out_channels + 1)
feat_labels_one_hot = feat_labels_one_hot[:, 1:]
feat_labels_one_hot.stop_gradient = True
num_total_samples = paddle.to_tensor(
num_total_samples, dtype='float32', stop_gradient=True)
fam_cls = F.sigmoid_focal_loss(
fam_cls_score1,
feat_labels_one_hot,
normalizer=num_total_samples,
reduction='none')
feat_label_weights = feat_label_weights.reshape(
feat_label_weights.shape[0], 1)
feat_label_weights = np.repeat(
feat_label_weights, self.cls_out_channels, axis=1)
feat_label_weights = paddle.to_tensor(
feat_label_weights, stop_gradient=True)
fam_cls = fam_cls * feat_label_weights
fam_cls_total = paddle.sum(fam_cls)
fam_cls_losses.append(fam_cls_total)
# step3: regression loss
feat_bbox_targets = paddle.to_tensor(
feat_bbox_targets, dtype='float32', stop_gradient=True)
feat_bbox_targets = paddle.reshape(feat_bbox_targets, [-1, 5])
fam_bbox_pred = fam_reg_branch_list[idx]
fam_bbox_pred = paddle.squeeze(fam_bbox_pred, axis=0)
fam_bbox_pred = paddle.reshape(fam_bbox_pred, [-1, 5])
fam_bbox = self.smooth_l1_loss(fam_bbox_pred, feat_bbox_targets)
loss_weight = paddle.to_tensor(
self.reg_loss_weight, dtype='float32', stop_gradient=True)
fam_bbox = paddle.multiply(fam_bbox, loss_weight)
feat_bbox_weights = paddle.to_tensor(
feat_bbox_weights, stop_gradient=True)
fam_bbox = fam_bbox * feat_bbox_weights
fam_bbox_total = paddle.sum(fam_bbox) / num_total_samples
fam_bbox_losses.append(fam_bbox_total)
st_idx += feat_anchor_num
fam_cls_loss = paddle.add_n(fam_cls_losses)
fam_cls_loss_weight = paddle.to_tensor(
self.cls_loss_weight[0], dtype='float32', stop_gradient=True)
fam_cls_loss = fam_cls_loss * fam_cls_loss_weight
fam_reg_loss = paddle.add_n(fam_bbox_losses)
return fam_cls_loss, fam_reg_loss
def get_odm_loss(self, odm_target, s2anet_head_out, reg_loss_type='l1'):
(labels, label_weights, bbox_targets, bbox_weights, bbox_gt_bboxes,
pos_inds, neg_inds) = odm_target
fam_cls_branch_list, fam_reg_branch_list, odm_cls_branch_list, odm_reg_branch_list, num_anchors_list = s2anet_head_out
odm_cls_losses = []
odm_bbox_losses = []
st_idx = 0
num_total_samples = len(pos_inds) + len(
neg_inds) if self.sampling else len(pos_inds)
num_total_samples = max(1, num_total_samples)
for idx, feat_anchor_num in enumerate(num_anchors_list):
# step1: get data
feat_labels = labels[st_idx:st_idx + feat_anchor_num]
feat_label_weights = label_weights[st_idx:st_idx + feat_anchor_num]
feat_bbox_targets = bbox_targets[st_idx:st_idx + feat_anchor_num, :]
feat_bbox_weights = bbox_weights[st_idx:st_idx + feat_anchor_num, :]
# step2: calc cls loss
feat_labels = feat_labels.reshape(-1)
feat_label_weights = feat_label_weights.reshape(-1)
odm_cls_score = odm_cls_branch_list[idx]
odm_cls_score = paddle.squeeze(odm_cls_score, axis=0)
odm_cls_score1 = odm_cls_score
feat_labels = paddle.to_tensor(feat_labels)
feat_labels_one_hot = paddle.nn.functional.one_hot(
feat_labels, self.cls_out_channels + 1)
feat_labels_one_hot = feat_labels_one_hot[:, 1:]
feat_labels_one_hot.stop_gradient = True
num_total_samples = paddle.to_tensor(
num_total_samples, dtype='float32', stop_gradient=True)
odm_cls = F.sigmoid_focal_loss(
odm_cls_score1,
feat_labels_one_hot,
normalizer=num_total_samples,
reduction='none')
feat_label_weights = feat_label_weights.reshape(
feat_label_weights.shape[0], 1)
feat_label_weights = np.repeat(
feat_label_weights, self.cls_out_channels, axis=1)
feat_label_weights = paddle.to_tensor(feat_label_weights)
feat_label_weights.stop_gradient = True
odm_cls = odm_cls * feat_label_weights
odm_cls_total = paddle.sum(odm_cls)
odm_cls_losses.append(odm_cls_total)
# # step3: regression loss
feat_bbox_targets = paddle.to_tensor(
feat_bbox_targets, dtype='float32')
feat_bbox_targets = paddle.reshape(feat_bbox_targets, [-1, 5])
feat_bbox_targets.stop_gradient = True
odm_bbox_pred = odm_reg_branch_list[idx]
odm_bbox_pred = paddle.squeeze(odm_bbox_pred, axis=0)
odm_bbox_pred = paddle.reshape(odm_bbox_pred, [-1, 5])
odm_bbox = self.smooth_l1_loss(odm_bbox_pred, feat_bbox_targets)
loss_weight = paddle.to_tensor(
self.reg_loss_weight, dtype='float32', stop_gradient=True)
odm_bbox = paddle.multiply(odm_bbox, loss_weight)
feat_bbox_weights = paddle.to_tensor(
feat_bbox_weights, stop_gradient=True)
odm_bbox = odm_bbox * feat_bbox_weights
odm_bbox_total = paddle.sum(odm_bbox) / num_total_samples
odm_bbox_losses.append(odm_bbox_total)
st_idx += feat_anchor_num
odm_cls_loss = paddle.add_n(odm_cls_losses)
odm_cls_loss_weight = paddle.to_tensor(
self.cls_loss_weight[1], dtype='float32', stop_gradient=True)
odm_cls_loss = odm_cls_loss * odm_cls_loss_weight
odm_reg_loss = paddle.add_n(odm_bbox_losses)
return odm_cls_loss, odm_reg_loss
def get_loss(self, head_outs, inputs):
fam_cls_list, fam_reg_list, odm_cls_list, odm_reg_list, \
num_anchors_list, base_anchors_list, refine_anchors_list = head_outs
# compute loss
fam_cls_loss_lst = []
fam_reg_loss_lst = []
odm_cls_loss_lst = []
odm_reg_loss_lst = []
batch = len(inputs['gt_rbox'])
for i in range(batch):
# data_format: (xc, yc, w, h, theta)
gt_mask = inputs['pad_gt_mask'][i, :, 0]
gt_idx = paddle.nonzero(gt_mask).squeeze(-1)
gt_bboxes = paddle.gather(inputs['gt_rbox'][i], gt_idx).numpy()
gt_labels = paddle.gather(inputs['gt_class'][i], gt_idx).numpy()
is_crowd = paddle.gather(inputs['is_crowd'][i], gt_idx).numpy()
gt_labels = gt_labels + 1
anchors_per_image = np.concatenate(base_anchors_list)
fam_cls_per_image = [t[i] for t in fam_cls_list]
fam_reg_per_image = [t[i] for t in fam_reg_list]
odm_cls_per_image = [t[i] for t in odm_cls_list]
odm_reg_per_image = [t[i] for t in odm_reg_list]
im_s2anet_head_out = (fam_cls_per_image, fam_reg_per_image,
odm_cls_per_image, odm_reg_per_image,
num_anchors_list)
# FAM
im_fam_target = self.anchor_assign(anchors_per_image, gt_bboxes,
gt_labels, is_crowd)
if im_fam_target is not None:
im_fam_cls_loss, im_fam_reg_loss = self.get_fam_loss(
im_fam_target, im_s2anet_head_out, self.reg_loss_type)
fam_cls_loss_lst.append(im_fam_cls_loss)
fam_reg_loss_lst.append(im_fam_reg_loss)
# ODM
refine_anchors_per_image = [t[i] for t in refine_anchors_list]
refine_anchors_per_image = paddle.concat(
refine_anchors_per_image).numpy()
im_odm_target = self.anchor_assign(refine_anchors_per_image,
gt_bboxes, gt_labels, is_crowd)
if im_odm_target is not None:
im_odm_cls_loss, im_odm_reg_loss = self.get_odm_loss(
im_odm_target, im_s2anet_head_out, self.reg_loss_type)
odm_cls_loss_lst.append(im_odm_cls_loss)
odm_reg_loss_lst.append(im_odm_reg_loss)
fam_cls_loss = paddle.add_n(fam_cls_loss_lst) / batch
fam_reg_loss = paddle.add_n(fam_reg_loss_lst) / batch
odm_cls_loss = paddle.add_n(odm_cls_loss_lst) / batch
odm_reg_loss = paddle.add_n(odm_reg_loss_lst) / batch
loss = fam_cls_loss + fam_reg_loss + odm_cls_loss + odm_reg_loss
return {
'loss': loss,
'fam_cls_loss': fam_cls_loss,
'fam_reg_loss': fam_reg_loss,
'odm_cls_loss': odm_cls_loss,
'odm_reg_loss': odm_reg_loss
}
def bbox_decode(self, preds, anchors, wh_ratio_clip=1e-6):
"""decode bbox from deltas
Args:
preds: [B, L, 5]
anchors: [1, L, 5]
return:
bboxes: [B, L, 5]
"""
preds = paddle.add(paddle.multiply(preds, self.stds), self.means)
dx, dy, dw, dh, dangle = paddle.split(preds, 5, axis=-1)
max_ratio = np.abs(np.log(wh_ratio_clip))
dw = paddle.clip(dw, min=-max_ratio, max=max_ratio)
dh = paddle.clip(dh, min=-max_ratio, max=max_ratio)
rroi_x, rroi_y, rroi_w, rroi_h, rroi_angle = paddle.split(
anchors, 5, axis=-1)
gx = dx * rroi_w * paddle.cos(rroi_angle) - dy * rroi_h * paddle.sin(
rroi_angle) + rroi_x
gy = dx * rroi_w * paddle.sin(rroi_angle) + dy * rroi_h * paddle.cos(
rroi_angle) + rroi_y
gw = rroi_w * dw.exp()
gh = rroi_h * dh.exp()
ga = np.pi * dangle + rroi_angle
ga = (ga + np.pi / 4) % np.pi - np.pi / 4
bboxes = paddle.concat([gx, gy, gw, gh, ga], axis=-1)
return bboxes
def rbox2poly(self, rboxes):
"""
rboxes: [x_ctr,y_ctr,w,h,angle]
to
polys: [x0,y0,x1,y1,x2,y2,x3,y3]
"""
N = paddle.shape(rboxes)[0]
x_ctr = rboxes[:, 0]
y_ctr = rboxes[:, 1]
width = rboxes[:, 2]
height = rboxes[:, 3]
angle = rboxes[:, 4]
tl_x, tl_y, br_x, br_y = -width * 0.5, -height * 0.5, width * 0.5, height * 0.5
normal_rects = paddle.stack(
[tl_x, br_x, br_x, tl_x, tl_y, tl_y, br_y, br_y], axis=0)
normal_rects = paddle.reshape(normal_rects, [2, 4, N])
normal_rects = paddle.transpose(normal_rects, [2, 0, 1])
sin, cos = paddle.sin(angle), paddle.cos(angle)
# M: [N,2,2]
M = paddle.stack([cos, -sin, sin, cos], axis=0)
M = paddle.reshape(M, [2, 2, N])
M = paddle.transpose(M, [2, 0, 1])
# polys: [N,8]
polys = paddle.matmul(M, normal_rects)
polys = paddle.transpose(polys, [2, 1, 0])
polys = paddle.reshape(polys, [-1, N])
polys = paddle.transpose(polys, [1, 0])
tmp = paddle.stack(
[x_ctr, y_ctr, x_ctr, y_ctr, x_ctr, y_ctr, x_ctr, y_ctr], axis=1)
polys = polys + tmp
return polys