500 lines
19 KiB
Python
500 lines
19 KiB
Python
# Copyright (c) 2023 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 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.core.workspace import register
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from ppdet.modeling.layers import ConvNormLayer, MultiClassNMS
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__all__ = ['FCOSFeat', 'FCOSHead', 'FCOSHead_ARSL']
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class ScaleReg(nn.Layer):
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"""
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Parameter for scaling the regression outputs.
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"""
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def __init__(self):
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super(ScaleReg, self).__init__()
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self.scale_reg = self.create_parameter(
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shape=[1],
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attr=ParamAttr(initializer=Constant(value=1.)),
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dtype="float32")
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def forward(self, inputs):
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out = inputs * self.scale_reg
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return out
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@register
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class FCOSFeat(nn.Layer):
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"""
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FCOSFeat of FCOS
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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 FCOSFeat.
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norm_type (str): Normalization type, 'bn'/'sync_bn'/'gn'.
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use_dcn (bool): Whether to use dcn in tower or not.
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"""
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def __init__(self,
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feat_in=256,
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feat_out=256,
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num_convs=4,
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norm_type='bn',
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use_dcn=False):
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super(FCOSFeat, self).__init__()
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self.feat_in = feat_in
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self.feat_out = feat_out
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self.num_convs = num_convs
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self.norm_type = norm_type
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self.cls_subnet_convs = []
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self.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_name = 'fcos_head_cls_tower_conv_{}'.format(i)
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cls_conv = self.add_sublayer(
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cls_conv_name,
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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=3,
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stride=1,
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norm_type=norm_type,
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use_dcn=use_dcn,
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bias_on=True,
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lr_scale=2.))
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self.cls_subnet_convs.append(cls_conv)
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reg_conv_name = 'fcos_head_reg_tower_conv_{}'.format(i)
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reg_conv = self.add_sublayer(
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reg_conv_name,
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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=3,
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stride=1,
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norm_type=norm_type,
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use_dcn=use_dcn,
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bias_on=True,
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lr_scale=2.))
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self.reg_subnet_convs.append(reg_conv)
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def forward(self, fpn_feat):
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cls_feat = fpn_feat
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reg_feat = fpn_feat
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for i in range(self.num_convs):
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cls_feat = F.relu(self.cls_subnet_convs[i](cls_feat))
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reg_feat = F.relu(self.reg_subnet_convs[i](reg_feat))
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return cls_feat, reg_feat
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@register
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class FCOSHead(nn.Layer):
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"""
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FCOSHead
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Args:
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num_classes (int): Number of classes
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fcos_feat (object): Instance of 'FCOSFeat'
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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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norm_reg_targets (bool): Normalization the regression target if true
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centerness_on_reg (bool): The prediction of centerness on regression or clssification branch
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num_shift (float): Relative offset between the center of the first shift and the top-left corner of img
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fcos_loss (object): Instance of 'FCOSLoss'
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nms (object): Instance of 'MultiClassNMS'
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trt (bool): Whether to use trt in nms of deploy
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"""
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__inject__ = ['fcos_feat', 'fcos_loss', 'nms']
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__shared__ = ['num_classes', 'trt']
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def __init__(self,
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num_classes=80,
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fcos_feat='FCOSFeat',
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fpn_stride=[8, 16, 32, 64, 128],
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prior_prob=0.01,
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multiply_strides_reg_targets=False,
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norm_reg_targets=True,
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centerness_on_reg=True,
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num_shift=0.5,
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sqrt_score=False,
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fcos_loss='FCOSLoss',
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nms='MultiClassNMS',
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trt=False):
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super(FCOSHead, self).__init__()
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self.fcos_feat = fcos_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.fcos_loss = fcos_loss
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self.norm_reg_targets = norm_reg_targets
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self.centerness_on_reg = centerness_on_reg
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self.multiply_strides_reg_targets = multiply_strides_reg_targets
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self.num_shift = num_shift
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self.nms = nms
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if isinstance(self.nms, MultiClassNMS) and trt:
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self.nms.trt = trt
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self.sqrt_score = sqrt_score
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self.is_teacher = False
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conv_cls_name = "fcos_head_cls"
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bias_init_value = -math.log((1 - self.prior_prob) / self.prior_prob)
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self.fcos_head_cls = self.add_sublayer(
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conv_cls_name,
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nn.Conv2D(
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in_channels=256,
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out_channels=self.num_classes,
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kernel_size=3,
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stride=1,
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padding=1,
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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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conv_reg_name = "fcos_head_reg"
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self.fcos_head_reg = self.add_sublayer(
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conv_reg_name,
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nn.Conv2D(
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in_channels=256,
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out_channels=4,
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kernel_size=3,
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stride=1,
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padding=1,
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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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conv_centerness_name = "fcos_head_centerness"
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self.fcos_head_centerness = self.add_sublayer(
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conv_centerness_name,
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nn.Conv2D(
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in_channels=256,
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out_channels=1,
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kernel_size=3,
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stride=1,
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padding=1,
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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.scales_regs = []
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for i in range(len(self.fpn_stride)):
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lvl = int(math.log(int(self.fpn_stride[i]), 2))
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feat_name = 'p{}_feat'.format(lvl)
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scale_reg = self.add_sublayer(feat_name, ScaleReg())
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self.scales_regs.append(scale_reg)
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def _compute_locations_by_level(self, fpn_stride, feature, num_shift=0.5):
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"""
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Compute locations of anchor points of each FPN layer
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Args:
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fpn_stride (int): The stride of current FPN feature map
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feature (Tensor): Tensor of current FPN feature map
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Return:
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Anchor points locations of current FPN feature map
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"""
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h, w = feature.shape[2], feature.shape[3]
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shift_x = paddle.arange(0, w * fpn_stride, fpn_stride)
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shift_y = paddle.arange(0, h * fpn_stride, fpn_stride)
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shift_x = paddle.unsqueeze(shift_x, axis=0)
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shift_y = paddle.unsqueeze(shift_y, axis=1)
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shift_x = paddle.expand(shift_x, shape=[h, w])
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shift_y = paddle.expand(shift_y, shape=[h, w])
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shift_x = paddle.reshape(shift_x, shape=[-1])
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shift_y = paddle.reshape(shift_y, shape=[-1])
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location = paddle.stack(
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[shift_x, shift_y], axis=-1) + float(fpn_stride * num_shift)
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return location
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def forward(self, fpn_feats, targets=None):
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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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cls_logits_list = []
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bboxes_reg_list = []
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centerness_list = []
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for scale_reg, fpn_stride, fpn_feat in zip(self.scales_regs,
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self.fpn_stride, fpn_feats):
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fcos_cls_feat, fcos_reg_feat = self.fcos_feat(fpn_feat)
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cls_logits = self.fcos_head_cls(fcos_cls_feat)
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bbox_reg = scale_reg(self.fcos_head_reg(fcos_reg_feat))
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if self.centerness_on_reg:
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centerness = self.fcos_head_centerness(fcos_reg_feat)
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else:
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centerness = self.fcos_head_centerness(fcos_cls_feat)
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if self.norm_reg_targets:
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bbox_reg = F.relu(bbox_reg)
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if self.multiply_strides_reg_targets:
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bbox_reg = bbox_reg * fpn_stride
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else:
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if not self.training or targets.get(
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'get_data',
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False) or targets.get('is_teacher', False):
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bbox_reg = bbox_reg * fpn_stride
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else:
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bbox_reg = paddle.exp(bbox_reg)
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cls_logits_list.append(cls_logits)
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bboxes_reg_list.append(bbox_reg)
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centerness_list.append(centerness)
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if targets is not None:
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self.is_teacher = targets.get('is_teacher', False)
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if self.is_teacher:
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return [cls_logits_list, bboxes_reg_list, centerness_list]
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if self.training and targets is not None:
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get_data = targets.get('get_data', False)
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if get_data:
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return [cls_logits_list, bboxes_reg_list, centerness_list]
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losses = {}
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fcos_head_outs = [cls_logits_list, bboxes_reg_list, centerness_list]
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losses_fcos = self.get_loss(fcos_head_outs, targets)
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losses.update(losses_fcos)
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total_loss = paddle.add_n(list(losses.values()))
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losses.update({'loss': total_loss})
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return losses
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else:
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# eval or infer
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locations_list = []
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for fpn_stride, feature in zip(self.fpn_stride, fpn_feats):
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location = self._compute_locations_by_level(fpn_stride, feature,
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self.num_shift)
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locations_list.append(location)
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fcos_head_outs = [
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locations_list, cls_logits_list, bboxes_reg_list,
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centerness_list
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]
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return fcos_head_outs
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def get_loss(self, fcos_head_outs, targets):
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cls_logits, bboxes_reg, centerness = fcos_head_outs
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# get labels,reg_target,centerness
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tag_labels, tag_bboxes, tag_centerness = [], [], []
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for i in range(len(self.fpn_stride)):
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k_lbl = 'labels{}'.format(i)
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if k_lbl in targets:
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tag_labels.append(targets[k_lbl])
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k_box = 'reg_target{}'.format(i)
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if k_box in targets:
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tag_bboxes.append(targets[k_box])
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k_ctn = 'centerness{}'.format(i)
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if k_ctn in targets:
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tag_centerness.append(targets[k_ctn])
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losses_fcos = self.fcos_loss(cls_logits, bboxes_reg, centerness,
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tag_labels, tag_bboxes, tag_centerness)
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return losses_fcos
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def _post_process_by_level(self,
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locations,
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box_cls,
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box_reg,
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box_ctn,
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sqrt_score=False):
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box_scores = F.sigmoid(box_cls).flatten(2).transpose([0, 2, 1])
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box_centerness = F.sigmoid(box_ctn).flatten(2).transpose([0, 2, 1])
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pred_scores = box_scores * box_centerness
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if sqrt_score:
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pred_scores = paddle.sqrt(pred_scores)
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box_reg_ch_last = box_reg.flatten(2).transpose([0, 2, 1])
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box_reg_decoding = paddle.stack(
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[
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locations[:, 0] - box_reg_ch_last[:, :, 0],
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locations[:, 1] - box_reg_ch_last[:, :, 1],
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locations[:, 0] + box_reg_ch_last[:, :, 2],
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locations[:, 1] + box_reg_ch_last[:, :, 3]
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],
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axis=1)
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pred_boxes = box_reg_decoding.transpose([0, 2, 1])
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return pred_scores, pred_boxes
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def post_process(self, fcos_head_outs, scale_factor):
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locations, cls_logits, bboxes_reg, centerness = fcos_head_outs
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pred_bboxes, pred_scores = [], []
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for pts, cls, reg, ctn in zip(locations, cls_logits, bboxes_reg,
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centerness):
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scores, boxes = self._post_process_by_level(pts, cls, reg, ctn,
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self.sqrt_score)
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pred_scores.append(scores)
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pred_bboxes.append(boxes)
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pred_bboxes = paddle.concat(pred_bboxes, axis=1)
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pred_scores = paddle.concat(pred_scores, axis=1)
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# scale bbox to origin
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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], axis=-1).reshape([-1, 1, 4])
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pred_bboxes /= scale_factor
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pred_scores = pred_scores.transpose([0, 2, 1])
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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 FCOSHead_ARSL(FCOSHead):
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"""
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FCOSHead of ARSL for semi-det(ssod)
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Args:
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fcos_feat (object): Instance of 'FCOSFeat'
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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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fcos_loss (object): Instance of 'FCOSLoss'
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norm_reg_targets (bool): Normalization the regression target if true
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centerness_on_reg (bool): The prediction of centerness on regression or clssification branch
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nms (object): Instance of 'MultiClassNMS'
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trt (bool): Whether to use trt in nms of deploy
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"""
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__inject__ = ['fcos_feat', 'fcos_loss', 'nms']
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__shared__ = ['num_classes', 'trt']
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def __init__(self,
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num_classes=80,
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fcos_feat='FCOSFeat',
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fpn_stride=[8, 16, 32, 64, 128],
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prior_prob=0.01,
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multiply_strides_reg_targets=False,
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norm_reg_targets=True,
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centerness_on_reg=True,
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num_shift=0.5,
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sqrt_score=False,
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fcos_loss='FCOSLossMILC',
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nms='MultiClassNMS',
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trt=False):
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super(FCOSHead_ARSL, self).__init__()
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self.fcos_feat = fcos_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.fcos_loss = fcos_loss
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self.norm_reg_targets = norm_reg_targets
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self.centerness_on_reg = centerness_on_reg
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self.multiply_strides_reg_targets = multiply_strides_reg_targets
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self.num_shift = num_shift
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self.nms = nms
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if isinstance(self.nms, MultiClassNMS) and trt:
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self.nms.trt = trt
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self.sqrt_score = sqrt_score
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conv_cls_name = "fcos_head_cls"
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bias_init_value = -math.log((1 - self.prior_prob) / self.prior_prob)
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self.fcos_head_cls = self.add_sublayer(
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conv_cls_name,
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nn.Conv2D(
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in_channels=256,
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out_channels=self.num_classes,
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kernel_size=3,
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stride=1,
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padding=1,
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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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conv_reg_name = "fcos_head_reg"
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self.fcos_head_reg = self.add_sublayer(
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conv_reg_name,
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nn.Conv2D(
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in_channels=256,
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out_channels=4,
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kernel_size=3,
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stride=1,
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padding=1,
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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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conv_centerness_name = "fcos_head_centerness"
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self.fcos_head_centerness = self.add_sublayer(
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conv_centerness_name,
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nn.Conv2D(
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in_channels=256,
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out_channels=1,
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kernel_size=3,
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stride=1,
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padding=1,
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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.scales_regs = []
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for i in range(len(self.fpn_stride)):
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lvl = int(math.log(int(self.fpn_stride[i]), 2))
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feat_name = 'p{}_feat'.format(lvl)
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scale_reg = self.add_sublayer(feat_name, ScaleReg())
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self.scales_regs.append(scale_reg)
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def forward(self, fpn_feats, targets=None):
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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"
|
|
cls_logits_list = []
|
|
bboxes_reg_list = []
|
|
centerness_list = []
|
|
for scale_reg, fpn_stride, fpn_feat in zip(self.scales_regs,
|
|
self.fpn_stride, fpn_feats):
|
|
fcos_cls_feat, fcos_reg_feat = self.fcos_feat(fpn_feat)
|
|
cls_logits = self.fcos_head_cls(fcos_cls_feat)
|
|
bbox_reg = scale_reg(self.fcos_head_reg(fcos_reg_feat))
|
|
if self.centerness_on_reg:
|
|
centerness = self.fcos_head_centerness(fcos_reg_feat)
|
|
else:
|
|
centerness = self.fcos_head_centerness(fcos_cls_feat)
|
|
if self.norm_reg_targets:
|
|
bbox_reg = F.relu(bbox_reg)
|
|
if not self.training:
|
|
bbox_reg = bbox_reg * fpn_stride
|
|
else:
|
|
bbox_reg = paddle.exp(bbox_reg)
|
|
cls_logits_list.append(cls_logits)
|
|
bboxes_reg_list.append(bbox_reg)
|
|
centerness_list.append(centerness)
|
|
|
|
if not self.training:
|
|
locations_list = []
|
|
for fpn_stride, feature in zip(self.fpn_stride, fpn_feats):
|
|
location = self._compute_locations_by_level(fpn_stride, feature)
|
|
locations_list.append(location)
|
|
|
|
return locations_list, cls_logits_list, bboxes_reg_list, centerness_list
|
|
else:
|
|
return cls_logits_list, bboxes_reg_list, centerness_list
|
|
|
|
def get_loss(self, fcos_head_outs, tag_labels, tag_bboxes, tag_centerness):
|
|
cls_logits, bboxes_reg, centerness = fcos_head_outs
|
|
return self.fcos_loss(cls_logits, bboxes_reg, centerness, tag_labels,
|
|
tag_bboxes, tag_centerness)
|