# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. from __future__ import absolute_import from __future__ import division from __future__ import print_function import math import paddle import paddle.nn as nn import paddle.nn.functional as F from paddle import ParamAttr from paddle.nn.initializer import Normal, Constant from ppdet.core.workspace import register from ppdet.modeling.layers import ConvNormLayer class ScaleReg(nn.Layer): """ Parameter for scaling the regression outputs. """ def __init__(self): super(ScaleReg, self).__init__() self.scale_reg = self.create_parameter( shape=[1], attr=ParamAttr(initializer=Constant(value=1.)), dtype="float32") def forward(self, inputs): out = inputs * self.scale_reg return out @register class FCOSFeat(nn.Layer): """ FCOSFeat of FCOS Args: feat_in (int): The channel number of input Tensor. feat_out (int): The channel number of output Tensor. num_convs (int): The convolution number of the FCOSFeat. norm_type (str): Normalization type, 'bn'/'sync_bn'/'gn'. use_dcn (bool): Whether to use dcn in tower or not. """ def __init__(self, feat_in=256, feat_out=256, num_convs=4, norm_type='bn', use_dcn=False): super(FCOSFeat, self).__init__() self.feat_in = feat_in self.feat_out = feat_out self.num_convs = num_convs self.norm_type = norm_type self.cls_subnet_convs = [] self.reg_subnet_convs = [] for i in range(self.num_convs): in_c = feat_in if i == 0 else feat_out cls_conv_name = 'fcos_head_cls_tower_conv_{}'.format(i) cls_conv = self.add_sublayer( cls_conv_name, ConvNormLayer( ch_in=in_c, ch_out=feat_out, filter_size=3, stride=1, norm_type=norm_type, use_dcn=use_dcn, bias_on=True, lr_scale=2.)) self.cls_subnet_convs.append(cls_conv) reg_conv_name = 'fcos_head_reg_tower_conv_{}'.format(i) reg_conv = self.add_sublayer( reg_conv_name, ConvNormLayer( ch_in=in_c, ch_out=feat_out, filter_size=3, stride=1, norm_type=norm_type, use_dcn=use_dcn, bias_on=True, lr_scale=2.)) self.reg_subnet_convs.append(reg_conv) def forward(self, fpn_feat): cls_feat = fpn_feat reg_feat = fpn_feat for i in range(self.num_convs): cls_feat = F.relu(self.cls_subnet_convs[i](cls_feat)) reg_feat = F.relu(self.reg_subnet_convs[i](reg_feat)) return cls_feat, reg_feat @register class FCOSHead(nn.Layer): """ FCOSHead Args: fcos_feat (object): Instance of 'FCOSFeat' num_classes (int): Number of classes fpn_stride (list): The stride of each FPN Layer prior_prob (float): Used to set the bias init for the class prediction layer fcos_loss (object): Instance of 'FCOSLoss' norm_reg_targets (bool): Normalization the regression target if true centerness_on_reg (bool): The prediction of centerness on regression or clssification branch """ __inject__ = ['fcos_feat', 'fcos_loss'] __shared__ = ['num_classes'] def __init__(self, fcos_feat, num_classes=80, fpn_stride=[8, 16, 32, 64, 128], prior_prob=0.01, fcos_loss='FCOSLoss', norm_reg_targets=True, centerness_on_reg=True): super(FCOSHead, self).__init__() self.fcos_feat = fcos_feat self.num_classes = num_classes self.fpn_stride = fpn_stride self.prior_prob = prior_prob self.fcos_loss = fcos_loss self.norm_reg_targets = norm_reg_targets self.centerness_on_reg = centerness_on_reg conv_cls_name = "fcos_head_cls" bias_init_value = -math.log((1 - self.prior_prob) / self.prior_prob) self.fcos_head_cls = self.add_sublayer( conv_cls_name, nn.Conv2D( in_channels=256, out_channels=self.num_classes, kernel_size=3, stride=1, padding=1, weight_attr=ParamAttr(initializer=Normal( mean=0., std=0.01)), bias_attr=ParamAttr( initializer=Constant(value=bias_init_value)))) conv_reg_name = "fcos_head_reg" self.fcos_head_reg = self.add_sublayer( conv_reg_name, nn.Conv2D( in_channels=256, out_channels=4, kernel_size=3, stride=1, padding=1, weight_attr=ParamAttr(initializer=Normal( mean=0., std=0.01)), bias_attr=ParamAttr(initializer=Constant(value=0)))) conv_centerness_name = "fcos_head_centerness" self.fcos_head_centerness = self.add_sublayer( conv_centerness_name, nn.Conv2D( in_channels=256, out_channels=1, kernel_size=3, stride=1, padding=1, weight_attr=ParamAttr(initializer=Normal( mean=0., std=0.01)), bias_attr=ParamAttr(initializer=Constant(value=0)))) self.scales_regs = [] for i in range(len(self.fpn_stride)): lvl = int(math.log(int(self.fpn_stride[i]), 2)) feat_name = 'p{}_feat'.format(lvl) scale_reg = self.add_sublayer(feat_name, ScaleReg()) self.scales_regs.append(scale_reg) def _compute_locations_by_level(self, fpn_stride, feature): """ Compute locations of anchor points of each FPN layer Args: fpn_stride (int): The stride of current FPN feature map feature (Tensor): Tensor of current FPN feature map Return: Anchor points locations of current FPN feature map """ shape_fm = paddle.shape(feature) shape_fm.stop_gradient = True h, w = shape_fm[2], shape_fm[3] shift_x = paddle.arange(0, w * fpn_stride, fpn_stride) shift_y = paddle.arange(0, h * fpn_stride, fpn_stride) shift_x = paddle.unsqueeze(shift_x, axis=0) shift_y = paddle.unsqueeze(shift_y, axis=1) shift_x = paddle.expand(shift_x, shape=[h, w]) shift_y = paddle.expand(shift_y, shape=[h, w]) shift_x.stop_gradient = True shift_y.stop_gradient = True shift_x = paddle.reshape(shift_x, shape=[-1]) shift_y = paddle.reshape(shift_y, shape=[-1]) location = paddle.stack( [shift_x, shift_y], axis=-1) + float(fpn_stride) / 2 location.stop_gradient = True return location def forward(self, fpn_feats, is_training): assert len(fpn_feats) == len( self.fpn_stride ), "The size of fpn_feats is not equal to size of fpn_stride" cls_logits_list = [] bboxes_reg_list = [] 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 is_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 is_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)