798 lines
31 KiB
Python
798 lines
31 KiB
Python
# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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from __future__ import absolute_import
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from __future__ import division
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from __future__ import print_function
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import math
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import numpy as np
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import paddle
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import paddle.nn as nn
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import paddle.nn.functional as F
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from paddle import ParamAttr
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from paddle.nn.initializer import Normal, Constant
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from ppdet.modeling.ops import get_static_shape
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from ..initializer import normal_
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from ..assigners.utils import generate_anchors_for_grid_cell
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from ..bbox_utils import bbox_center, batch_distance2bbox, bbox2distance
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from ppdet.core.workspace import register
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from ppdet.modeling.layers import ConvNormLayer
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from .simota_head import OTAVFLHead
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from .gfl_head import Integral, GFLHead
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from ppdet.modeling.necks.csp_pan import DPModule
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eps = 1e-9
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__all__ = ['PicoHead', 'PicoHeadV2', 'PicoFeat']
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class PicoSE(nn.Layer):
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def __init__(self, feat_channels):
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super(PicoSE, self).__init__()
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self.fc = nn.Conv2D(feat_channels, feat_channels, 1)
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self.conv = ConvNormLayer(feat_channels, feat_channels, 1, 1)
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self._init_weights()
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def _init_weights(self):
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normal_(self.fc.weight, std=0.001)
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def forward(self, feat, avg_feat):
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weight = F.sigmoid(self.fc(avg_feat))
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out = self.conv(feat * weight)
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return out
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@register
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class PicoFeat(nn.Layer):
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"""
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PicoFeat of PicoDet
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Args:
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feat_in (int): The channel number of input Tensor.
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feat_out (int): The channel number of output Tensor.
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num_convs (int): The convolution number of the LiteGFLFeat.
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norm_type (str): Normalization type, 'bn'/'sync_bn'/'gn'.
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share_cls_reg (bool): Whether to share the cls and reg output.
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act (str): The act of per layers.
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use_se (bool): Whether to use se module.
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"""
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def __init__(self,
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feat_in=256,
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feat_out=96,
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num_fpn_stride=3,
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num_convs=2,
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norm_type='bn',
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share_cls_reg=False,
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act='hard_swish',
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use_se=False):
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super(PicoFeat, self).__init__()
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self.num_convs = num_convs
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self.norm_type = norm_type
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self.share_cls_reg = share_cls_reg
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self.act = act
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self.use_se = use_se
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self.cls_convs = []
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self.reg_convs = []
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if use_se:
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assert share_cls_reg == True, \
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'In the case of using se, share_cls_reg must be set to True'
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self.se = nn.LayerList()
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for stage_idx in range(num_fpn_stride):
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cls_subnet_convs = []
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reg_subnet_convs = []
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for i in range(self.num_convs):
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in_c = feat_in if i == 0 else feat_out
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cls_conv_dw = self.add_sublayer(
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'cls_conv_dw{}.{}'.format(stage_idx, i),
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ConvNormLayer(
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ch_in=in_c,
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ch_out=feat_out,
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filter_size=5,
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stride=1,
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groups=feat_out,
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norm_type=norm_type,
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bias_on=False,
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lr_scale=2.))
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cls_subnet_convs.append(cls_conv_dw)
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cls_conv_pw = self.add_sublayer(
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'cls_conv_pw{}.{}'.format(stage_idx, i),
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ConvNormLayer(
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ch_in=in_c,
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ch_out=feat_out,
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filter_size=1,
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stride=1,
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norm_type=norm_type,
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bias_on=False,
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lr_scale=2.))
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cls_subnet_convs.append(cls_conv_pw)
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if not self.share_cls_reg:
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reg_conv_dw = self.add_sublayer(
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'reg_conv_dw{}.{}'.format(stage_idx, i),
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ConvNormLayer(
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ch_in=in_c,
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ch_out=feat_out,
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filter_size=5,
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stride=1,
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groups=feat_out,
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norm_type=norm_type,
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bias_on=False,
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lr_scale=2.))
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reg_subnet_convs.append(reg_conv_dw)
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reg_conv_pw = self.add_sublayer(
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'reg_conv_pw{}.{}'.format(stage_idx, i),
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ConvNormLayer(
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ch_in=in_c,
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ch_out=feat_out,
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filter_size=1,
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stride=1,
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norm_type=norm_type,
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bias_on=False,
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lr_scale=2.))
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reg_subnet_convs.append(reg_conv_pw)
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self.cls_convs.append(cls_subnet_convs)
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self.reg_convs.append(reg_subnet_convs)
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if use_se:
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self.se.append(PicoSE(feat_out))
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def act_func(self, x):
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if self.act == "leaky_relu":
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x = F.leaky_relu(x)
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elif self.act == "hard_swish":
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x = F.hardswish(x)
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elif self.act == "relu6":
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x = F.relu6(x)
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return x
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def forward(self, fpn_feat, stage_idx):
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assert stage_idx < len(self.cls_convs)
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cls_feat = fpn_feat
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reg_feat = fpn_feat
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for i in range(len(self.cls_convs[stage_idx])):
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cls_feat = self.act_func(self.cls_convs[stage_idx][i](cls_feat))
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reg_feat = cls_feat
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if not self.share_cls_reg:
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reg_feat = self.act_func(self.reg_convs[stage_idx][i](reg_feat))
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if self.use_se:
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avg_feat = F.adaptive_avg_pool2d(cls_feat, (1, 1))
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se_feat = self.act_func(self.se[stage_idx](cls_feat, avg_feat))
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return cls_feat, se_feat
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return cls_feat, reg_feat
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@register
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class PicoHead(OTAVFLHead):
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"""
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PicoHead
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Args:
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conv_feat (object): Instance of 'PicoFeat'
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num_classes (int): Number of classes
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fpn_stride (list): The stride of each FPN Layer
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prior_prob (float): Used to set the bias init for the class prediction layer
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loss_class (object): Instance of VariFocalLoss.
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loss_dfl (object): Instance of DistributionFocalLoss.
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loss_bbox (object): Instance of bbox loss.
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assigner (object): Instance of label assigner.
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reg_max: Max value of integral set :math: `{0, ..., reg_max}`
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n QFL setting. Default: 7.
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"""
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__inject__ = [
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'conv_feat', 'dgqp_module', 'loss_class', 'loss_dfl', 'loss_bbox',
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'assigner', 'nms'
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]
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__shared__ = ['num_classes', 'eval_size']
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def __init__(self,
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conv_feat='PicoFeat',
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dgqp_module=None,
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num_classes=80,
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fpn_stride=[8, 16, 32],
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prior_prob=0.01,
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loss_class='VariFocalLoss',
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loss_dfl='DistributionFocalLoss',
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loss_bbox='GIoULoss',
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assigner='SimOTAAssigner',
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reg_max=16,
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feat_in_chan=96,
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nms=None,
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nms_pre=1000,
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cell_offset=0,
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eval_size=None):
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super(PicoHead, self).__init__(
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conv_feat=conv_feat,
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dgqp_module=dgqp_module,
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num_classes=num_classes,
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fpn_stride=fpn_stride,
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prior_prob=prior_prob,
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loss_class=loss_class,
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loss_dfl=loss_dfl,
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loss_bbox=loss_bbox,
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assigner=assigner,
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reg_max=reg_max,
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feat_in_chan=feat_in_chan,
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nms=nms,
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nms_pre=nms_pre,
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cell_offset=cell_offset)
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self.conv_feat = conv_feat
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self.num_classes = num_classes
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self.fpn_stride = fpn_stride
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self.prior_prob = prior_prob
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self.loss_vfl = loss_class
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self.loss_dfl = loss_dfl
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self.loss_bbox = loss_bbox
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self.assigner = assigner
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self.reg_max = reg_max
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self.feat_in_chan = feat_in_chan
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self.nms = nms
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self.nms_pre = nms_pre
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self.cell_offset = cell_offset
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self.eval_size = eval_size
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self.device = paddle.device.get_device()
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self.use_sigmoid = self.loss_vfl.use_sigmoid
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if self.use_sigmoid:
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self.cls_out_channels = self.num_classes
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else:
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self.cls_out_channels = self.num_classes + 1
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bias_init_value = -math.log((1 - self.prior_prob) / self.prior_prob)
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# Clear the super class initialization
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self.gfl_head_cls = None
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self.gfl_head_reg = None
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self.scales_regs = None
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self.head_cls_list = []
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self.head_reg_list = []
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for i in range(len(fpn_stride)):
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head_cls = self.add_sublayer(
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"head_cls" + str(i),
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nn.Conv2D(
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in_channels=self.feat_in_chan,
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out_channels=self.cls_out_channels + 4 * (self.reg_max + 1)
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if self.conv_feat.share_cls_reg else self.cls_out_channels,
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kernel_size=1,
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stride=1,
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padding=0,
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weight_attr=ParamAttr(initializer=Normal(
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mean=0., std=0.01)),
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bias_attr=ParamAttr(
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initializer=Constant(value=bias_init_value))))
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self.head_cls_list.append(head_cls)
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if not self.conv_feat.share_cls_reg:
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head_reg = self.add_sublayer(
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"head_reg" + str(i),
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nn.Conv2D(
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in_channels=self.feat_in_chan,
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out_channels=4 * (self.reg_max + 1),
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kernel_size=1,
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stride=1,
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padding=0,
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weight_attr=ParamAttr(initializer=Normal(
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mean=0., std=0.01)),
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bias_attr=ParamAttr(initializer=Constant(value=0))))
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self.head_reg_list.append(head_reg)
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# initialize the anchor points
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if self.eval_size:
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self.anchor_points, self.stride_tensor = self._generate_anchors()
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def forward(self, fpn_feats, export_post_process=True):
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assert len(fpn_feats) == len(
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self.fpn_stride
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), "The size of fpn_feats is not equal to size of fpn_stride"
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if self.training:
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return self.forward_train(fpn_feats)
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else:
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return self.forward_eval(
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fpn_feats, export_post_process=export_post_process)
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def forward_train(self, fpn_feats):
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cls_logits_list, bboxes_reg_list = [], []
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for i, fpn_feat in enumerate(fpn_feats):
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conv_cls_feat, conv_reg_feat = self.conv_feat(fpn_feat, i)
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if self.conv_feat.share_cls_reg:
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cls_logits = self.head_cls_list[i](conv_cls_feat)
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cls_score, bbox_pred = paddle.split(
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cls_logits,
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[self.cls_out_channels, 4 * (self.reg_max + 1)],
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axis=1)
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else:
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cls_score = self.head_cls_list[i](conv_cls_feat)
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bbox_pred = self.head_reg_list[i](conv_reg_feat)
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if self.dgqp_module:
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quality_score = self.dgqp_module(bbox_pred)
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cls_score = F.sigmoid(cls_score) * quality_score
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cls_logits_list.append(cls_score)
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bboxes_reg_list.append(bbox_pred)
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return (cls_logits_list, bboxes_reg_list)
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def forward_eval(self, fpn_feats, export_post_process=True):
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if self.eval_size:
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anchor_points, stride_tensor = self.anchor_points, self.stride_tensor
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else:
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anchor_points, stride_tensor = self._generate_anchors(fpn_feats)
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cls_logits_list, bboxes_reg_list = [], []
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for i, fpn_feat in enumerate(fpn_feats):
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conv_cls_feat, conv_reg_feat = self.conv_feat(fpn_feat, i)
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if self.conv_feat.share_cls_reg:
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cls_logits = self.head_cls_list[i](conv_cls_feat)
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cls_score, bbox_pred = paddle.split(
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cls_logits,
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[self.cls_out_channels, 4 * (self.reg_max + 1)],
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axis=1)
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else:
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cls_score = self.head_cls_list[i](conv_cls_feat)
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bbox_pred = self.head_reg_list[i](conv_reg_feat)
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if self.dgqp_module:
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quality_score = self.dgqp_module(bbox_pred)
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cls_score = F.sigmoid(cls_score) * quality_score
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if not export_post_process:
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# Now only supports batch size = 1 in deploy
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# TODO(ygh): support batch size > 1
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cls_score_out = F.sigmoid(cls_score).reshape(
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[1, self.cls_out_channels, -1]).transpose([0, 2, 1])
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bbox_pred = bbox_pred.reshape([1, (self.reg_max + 1) * 4,
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-1]).transpose([0, 2, 1])
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else:
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_, _, h, w = fpn_feat.shape
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l = h * w
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cls_score_out = F.sigmoid(
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cls_score.reshape([-1, self.cls_out_channels, l]))
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bbox_pred = bbox_pred.transpose([0, 2, 3, 1])
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bbox_pred = self.distribution_project(bbox_pred)
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bbox_pred = bbox_pred.reshape([-1, l, 4])
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cls_logits_list.append(cls_score_out)
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bboxes_reg_list.append(bbox_pred)
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if export_post_process:
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cls_logits_list = paddle.concat(cls_logits_list, axis=-1)
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bboxes_reg_list = paddle.concat(bboxes_reg_list, axis=1)
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bboxes_reg_list = batch_distance2bbox(anchor_points,
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bboxes_reg_list)
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bboxes_reg_list *= stride_tensor
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return (cls_logits_list, bboxes_reg_list)
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def _generate_anchors(self, feats=None):
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# just use in eval time
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anchor_points = []
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stride_tensor = []
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for i, stride in enumerate(self.fpn_stride):
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if feats is not None:
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_, _, h, w = feats[i].shape
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else:
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h = math.ceil(self.eval_size[0] / stride)
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w = math.ceil(self.eval_size[1] / stride)
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shift_x = paddle.arange(end=w) + self.cell_offset
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shift_y = paddle.arange(end=h) + self.cell_offset
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shift_y, shift_x = paddle.meshgrid(shift_y, shift_x)
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anchor_point = paddle.cast(
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paddle.stack(
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[shift_x, shift_y], axis=-1), dtype='float32')
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anchor_points.append(anchor_point.reshape([-1, 2]))
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stride_tensor.append(
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paddle.full(
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[h * w, 1], stride, dtype='float32'))
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anchor_points = paddle.concat(anchor_points)
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stride_tensor = paddle.concat(stride_tensor)
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return anchor_points, stride_tensor
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def post_process(self,
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head_outs,
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scale_factor,
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export_nms=True,
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nms_cpu=False):
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pred_scores, pred_bboxes = head_outs
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if not export_nms:
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return pred_bboxes, pred_scores
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else:
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# rescale: [h_scale, w_scale] -> [w_scale, h_scale, w_scale, h_scale]
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scale_y, scale_x = paddle.split(scale_factor, 2, axis=-1)
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scale_factor = paddle.concat(
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[scale_x, scale_y, scale_x, scale_y],
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axis=-1).reshape([-1, 1, 4])
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# scale bbox to origin image size.
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pred_bboxes /= scale_factor
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if nms_cpu:
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paddle.set_device("cpu")
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bbox_pred, bbox_num, _ = self.nms(pred_bboxes, pred_scores)
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paddle.set_device(self.device)
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else:
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bbox_pred, bbox_num, _ = self.nms(pred_bboxes, pred_scores)
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return bbox_pred, bbox_num
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@register
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class PicoHeadV2(GFLHead):
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"""
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PicoHeadV2
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Args:
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conv_feat (object): Instance of 'PicoFeat'
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num_classes (int): Number of classes
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fpn_stride (list): The stride of each FPN Layer
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prior_prob (float): Used to set the bias init for the class prediction layer
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loss_class (object): Instance of VariFocalLoss.
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loss_dfl (object): Instance of DistributionFocalLoss.
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loss_bbox (object): Instance of bbox loss.
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assigner (object): Instance of label assigner.
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reg_max: Max value of integral set :math: `{0, ..., reg_max}`
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n QFL setting. Default: 7.
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"""
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__inject__ = [
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'conv_feat', 'dgqp_module', 'loss_class', 'loss_dfl', 'loss_bbox',
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'static_assigner', 'assigner', 'nms'
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]
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__shared__ = ['num_classes', 'eval_size']
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def __init__(self,
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conv_feat='PicoFeatV2',
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dgqp_module=None,
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num_classes=80,
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fpn_stride=[8, 16, 32],
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prior_prob=0.01,
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use_align_head=True,
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loss_class='VariFocalLoss',
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loss_dfl='DistributionFocalLoss',
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loss_bbox='GIoULoss',
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static_assigner_epoch=60,
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static_assigner='ATSSAssigner',
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assigner='TaskAlignedAssigner',
|
|
reg_max=16,
|
|
feat_in_chan=96,
|
|
nms=None,
|
|
nms_pre=1000,
|
|
cell_offset=0,
|
|
act='hard_swish',
|
|
grid_cell_scale=5.0,
|
|
eval_size=None):
|
|
super(PicoHeadV2, 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.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.static_assigner_epoch = static_assigner_epoch
|
|
self.static_assigner = static_assigner
|
|
self.assigner = assigner
|
|
|
|
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.act = act
|
|
self.grid_cell_scale = grid_cell_scale
|
|
self.use_align_head = use_align_head
|
|
self.cls_out_channels = self.num_classes
|
|
self.eval_size = eval_size
|
|
|
|
bias_init_value = -math.log((1 - self.prior_prob) / self.prior_prob)
|
|
# Clear the super class initialization
|
|
self.gfl_head_cls = None
|
|
self.gfl_head_reg = None
|
|
self.scales_regs = None
|
|
|
|
self.head_cls_list = nn.LayerList()
|
|
self.head_reg_list = nn.LayerList()
|
|
self.cls_align = nn.LayerList()
|
|
|
|
for i in range(len(fpn_stride)):
|
|
head_cls = self.add_sublayer(
|
|
"head_cls" + str(i),
|
|
nn.Conv2D(
|
|
in_channels=self.feat_in_chan,
|
|
out_channels=self.cls_out_channels,
|
|
kernel_size=1,
|
|
stride=1,
|
|
padding=0,
|
|
weight_attr=ParamAttr(initializer=Normal(
|
|
mean=0., std=0.01)),
|
|
bias_attr=ParamAttr(
|
|
initializer=Constant(value=bias_init_value))))
|
|
self.head_cls_list.append(head_cls)
|
|
head_reg = self.add_sublayer(
|
|
"head_reg" + str(i),
|
|
nn.Conv2D(
|
|
in_channels=self.feat_in_chan,
|
|
out_channels=4 * (self.reg_max + 1),
|
|
kernel_size=1,
|
|
stride=1,
|
|
padding=0,
|
|
weight_attr=ParamAttr(initializer=Normal(
|
|
mean=0., std=0.01)),
|
|
bias_attr=ParamAttr(initializer=Constant(value=0))))
|
|
self.head_reg_list.append(head_reg)
|
|
if self.use_align_head:
|
|
self.cls_align.append(
|
|
DPModule(
|
|
self.feat_in_chan,
|
|
1,
|
|
5,
|
|
act=self.act,
|
|
use_act_in_out=False))
|
|
|
|
# initialize the anchor points
|
|
if self.eval_size:
|
|
self.anchor_points, self.stride_tensor = self._generate_anchors()
|
|
|
|
def forward(self, fpn_feats, export_post_process=True):
|
|
assert len(fpn_feats) == len(
|
|
self.fpn_stride
|
|
), "The size of fpn_feats is not equal to size of fpn_stride"
|
|
|
|
if self.training:
|
|
return self.forward_train(fpn_feats)
|
|
else:
|
|
return self.forward_eval(
|
|
fpn_feats, export_post_process=export_post_process)
|
|
|
|
def forward_train(self, fpn_feats):
|
|
cls_score_list, reg_list, box_list = [], [], []
|
|
for i, (fpn_feat, stride) in enumerate(zip(fpn_feats, self.fpn_stride)):
|
|
b, _, h, w = get_static_shape(fpn_feat)
|
|
# task decomposition
|
|
conv_cls_feat, se_feat = self.conv_feat(fpn_feat, i)
|
|
cls_logit = self.head_cls_list[i](se_feat)
|
|
reg_pred = self.head_reg_list[i](se_feat)
|
|
|
|
# cls prediction and alignment
|
|
if self.use_align_head:
|
|
cls_prob = F.sigmoid(self.cls_align[i](conv_cls_feat))
|
|
cls_score = (F.sigmoid(cls_logit) * cls_prob + eps).sqrt()
|
|
else:
|
|
cls_score = F.sigmoid(cls_logit)
|
|
|
|
cls_score_out = cls_score.transpose([0, 2, 3, 1])
|
|
bbox_pred = reg_pred.transpose([0, 2, 3, 1])
|
|
b, cell_h, cell_w, _ = paddle.shape(cls_score_out)
|
|
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_out = cls_score_out.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])
|
|
bbox_pred = batch_distance2bbox(
|
|
center_points, bbox_pred, max_shapes=None)
|
|
cls_score_list.append(cls_score.flatten(2).transpose([0, 2, 1]))
|
|
reg_list.append(reg_pred.flatten(2).transpose([0, 2, 1]))
|
|
box_list.append(bbox_pred / stride)
|
|
|
|
cls_score_list = paddle.concat(cls_score_list, axis=1)
|
|
box_list = paddle.concat(box_list, axis=1)
|
|
reg_list = paddle.concat(reg_list, axis=1)
|
|
return cls_score_list, reg_list, box_list, fpn_feats
|
|
|
|
def forward_eval(self, fpn_feats, export_post_process=True):
|
|
if self.eval_size:
|
|
anchor_points, stride_tensor = self.anchor_points, self.stride_tensor
|
|
else:
|
|
anchor_points, stride_tensor = self._generate_anchors(fpn_feats)
|
|
cls_score_list, box_list = [], []
|
|
for i, (fpn_feat, stride) in enumerate(zip(fpn_feats, self.fpn_stride)):
|
|
_, _, h, w = fpn_feat.shape
|
|
# task decomposition
|
|
conv_cls_feat, se_feat = self.conv_feat(fpn_feat, i)
|
|
cls_logit = self.head_cls_list[i](se_feat)
|
|
reg_pred = self.head_reg_list[i](se_feat)
|
|
|
|
# cls prediction and alignment
|
|
if self.use_align_head:
|
|
cls_prob = F.sigmoid(self.cls_align[i](conv_cls_feat))
|
|
cls_score = (F.sigmoid(cls_logit) * cls_prob + eps).sqrt()
|
|
else:
|
|
cls_score = F.sigmoid(cls_logit)
|
|
|
|
if not export_post_process:
|
|
# Now only supports batch size = 1 in deploy
|
|
cls_score_list.append(
|
|
cls_score.reshape([1, self.cls_out_channels, -1]).transpose(
|
|
[0, 2, 1]))
|
|
box_list.append(
|
|
reg_pred.reshape([1, (self.reg_max + 1) * 4, -1]).transpose(
|
|
[0, 2, 1]))
|
|
else:
|
|
l = h * w
|
|
cls_score_out = cls_score.reshape(
|
|
[-1, self.cls_out_channels, l])
|
|
bbox_pred = reg_pred.transpose([0, 2, 3, 1])
|
|
bbox_pred = self.distribution_project(bbox_pred)
|
|
bbox_pred = bbox_pred.reshape([-1, l, 4])
|
|
cls_score_list.append(cls_score_out)
|
|
box_list.append(bbox_pred)
|
|
|
|
if export_post_process:
|
|
cls_score_list = paddle.concat(cls_score_list, axis=-1)
|
|
box_list = paddle.concat(box_list, axis=1)
|
|
box_list = batch_distance2bbox(anchor_points, box_list)
|
|
box_list *= stride_tensor
|
|
|
|
return cls_score_list, box_list
|
|
|
|
def get_loss(self, head_outs, gt_meta):
|
|
pred_scores, pred_regs, pred_bboxes, fpn_feats = head_outs
|
|
gt_labels = gt_meta['gt_class']
|
|
gt_bboxes = gt_meta['gt_bbox']
|
|
gt_scores = gt_meta['gt_score'] if 'gt_score' in gt_meta else None
|
|
num_imgs = gt_meta['im_id'].shape[0]
|
|
pad_gt_mask = gt_meta['pad_gt_mask']
|
|
|
|
anchors, _, num_anchors_list, stride_tensor_list = generate_anchors_for_grid_cell(
|
|
fpn_feats, self.fpn_stride, self.grid_cell_scale, self.cell_offset)
|
|
|
|
centers = bbox_center(anchors)
|
|
|
|
# label assignment
|
|
if gt_meta['epoch_id'] < self.static_assigner_epoch:
|
|
assigned_labels, assigned_bboxes, assigned_scores = self.static_assigner(
|
|
anchors,
|
|
num_anchors_list,
|
|
gt_labels,
|
|
gt_bboxes,
|
|
pad_gt_mask,
|
|
bg_index=self.num_classes,
|
|
gt_scores=gt_scores,
|
|
pred_bboxes=pred_bboxes.detach() * stride_tensor_list)
|
|
|
|
else:
|
|
assigned_labels, assigned_bboxes, assigned_scores = self.assigner(
|
|
pred_scores.detach(),
|
|
pred_bboxes.detach() * stride_tensor_list,
|
|
centers,
|
|
num_anchors_list,
|
|
gt_labels,
|
|
gt_bboxes,
|
|
pad_gt_mask,
|
|
bg_index=self.num_classes,
|
|
gt_scores=gt_scores)
|
|
|
|
assigned_bboxes /= stride_tensor_list
|
|
|
|
centers_shape = centers.shape
|
|
flatten_centers = centers.expand(
|
|
[num_imgs, centers_shape[0], centers_shape[1]]).reshape([-1, 2])
|
|
flatten_strides = stride_tensor_list.expand(
|
|
[num_imgs, centers_shape[0], 1]).reshape([-1, 1])
|
|
flatten_cls_preds = pred_scores.reshape([-1, self.num_classes])
|
|
flatten_regs = pred_regs.reshape([-1, 4 * (self.reg_max + 1)])
|
|
flatten_bboxes = pred_bboxes.reshape([-1, 4])
|
|
flatten_bbox_targets = assigned_bboxes.reshape([-1, 4])
|
|
flatten_labels = assigned_labels.reshape([-1])
|
|
flatten_assigned_scores = assigned_scores.reshape(
|
|
[-1, self.num_classes])
|
|
|
|
pos_inds = paddle.nonzero(
|
|
paddle.logical_and((flatten_labels >= 0),
|
|
(flatten_labels < self.num_classes)),
|
|
as_tuple=False).squeeze(1)
|
|
|
|
num_total_pos = len(pos_inds)
|
|
|
|
if num_total_pos > 0:
|
|
pos_bbox_targets = paddle.gather(
|
|
flatten_bbox_targets, pos_inds, axis=0)
|
|
pos_decode_bbox_pred = paddle.gather(
|
|
flatten_bboxes, pos_inds, axis=0)
|
|
pos_reg = paddle.gather(flatten_regs, pos_inds, axis=0)
|
|
pos_strides = paddle.gather(flatten_strides, pos_inds, axis=0)
|
|
pos_centers = paddle.gather(
|
|
flatten_centers, pos_inds, axis=0) / pos_strides
|
|
|
|
weight_targets = flatten_assigned_scores.detach()
|
|
weight_targets = paddle.gather(
|
|
weight_targets.max(axis=1, keepdim=True), pos_inds, axis=0)
|
|
|
|
pred_corners = pos_reg.reshape([-1, self.reg_max + 1])
|
|
target_corners = bbox2distance(pos_centers, pos_bbox_targets,
|
|
self.reg_max).reshape([-1])
|
|
# regression loss
|
|
loss_bbox = paddle.sum(
|
|
self.loss_bbox(pos_decode_bbox_pred,
|
|
pos_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 = paddle.zeros([1])
|
|
loss_dfl = paddle.zeros([1])
|
|
|
|
avg_factor = flatten_assigned_scores.sum()
|
|
if paddle.distributed.get_world_size() > 1:
|
|
paddle.distributed.all_reduce(avg_factor)
|
|
avg_factor = paddle.clip(
|
|
avg_factor / paddle.distributed.get_world_size(), min=1)
|
|
loss_vfl = self.loss_vfl(
|
|
flatten_cls_preds, flatten_assigned_scores, avg_factor=avg_factor)
|
|
|
|
loss_bbox = loss_bbox / avg_factor
|
|
loss_dfl = loss_dfl / avg_factor
|
|
|
|
loss_states = dict(
|
|
loss_vfl=loss_vfl, loss_bbox=loss_bbox, loss_dfl=loss_dfl)
|
|
|
|
return loss_states
|
|
|
|
def _generate_anchors(self, feats=None):
|
|
# just use in eval time
|
|
anchor_points = []
|
|
stride_tensor = []
|
|
for i, stride in enumerate(self.fpn_stride):
|
|
if feats is not None:
|
|
_, _, h, w = feats[i].shape
|
|
else:
|
|
h = math.ceil(self.eval_size[0] / stride)
|
|
w = math.ceil(self.eval_size[1] / stride)
|
|
shift_x = paddle.arange(end=w) + self.cell_offset
|
|
shift_y = paddle.arange(end=h) + self.cell_offset
|
|
shift_y, shift_x = paddle.meshgrid(shift_y, shift_x)
|
|
anchor_point = paddle.cast(
|
|
paddle.stack(
|
|
[shift_x, shift_y], axis=-1), dtype='float32')
|
|
anchor_points.append(anchor_point.reshape([-1, 2]))
|
|
stride_tensor.append(
|
|
paddle.full(
|
|
[h * w, 1], stride, dtype='float32'))
|
|
anchor_points = paddle.concat(anchor_points)
|
|
stride_tensor = paddle.concat(stride_tensor)
|
|
return anchor_points, stride_tensor
|
|
|
|
def post_process(self,
|
|
head_outs,
|
|
scale_factor,
|
|
export_nms=True,
|
|
nms_cpu=False):
|
|
pred_scores, pred_bboxes = head_outs
|
|
if not export_nms:
|
|
return pred_bboxes, pred_scores
|
|
else:
|
|
# rescale: [h_scale, w_scale] -> [w_scale, h_scale, w_scale, h_scale]
|
|
scale_y, scale_x = paddle.split(scale_factor, 2, axis=-1)
|
|
scale_factor = paddle.concat(
|
|
[scale_x, scale_y, scale_x, scale_y],
|
|
axis=-1).reshape([-1, 1, 4])
|
|
# scale bbox to origin image size.
|
|
pred_bboxes /= scale_factor
|
|
bbox_pred, bbox_num, _ = self.nms(pred_bboxes, pred_scores)
|
|
return bbox_pred, bbox_num
|