# 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. from __future__ import absolute_import from __future__ import division from __future__ import print_function import math import numpy as np import paddle import paddle.nn as nn import paddle.nn.functional as F from paddle import ParamAttr from paddle.nn.initializer import Normal, Constant from ppdet.modeling.ops import get_static_shape from ..initializer import normal_ from ..assigners.utils import generate_anchors_for_grid_cell from ..bbox_utils import bbox_center, batch_distance2bbox, bbox2distance from ppdet.core.workspace import register from ppdet.modeling.layers import ConvNormLayer from .simota_head import OTAVFLHead from .gfl_head import Integral, GFLHead from ppdet.modeling.necks.csp_pan import DPModule eps = 1e-9 __all__ = ['PicoHead', 'PicoHeadV2', 'PicoFeat'] class PicoSE(nn.Layer): def __init__(self, feat_channels): super(PicoSE, self).__init__() self.fc = nn.Conv2D(feat_channels, feat_channels, 1) self.conv = ConvNormLayer(feat_channels, feat_channels, 1, 1) self._init_weights() def _init_weights(self): normal_(self.fc.weight, std=0.001) def forward(self, feat, avg_feat): weight = F.sigmoid(self.fc(avg_feat)) out = self.conv(feat * weight) return out @register class PicoFeat(nn.Layer): """ PicoFeat of PicoDet 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 LiteGFLFeat. norm_type (str): Normalization type, 'bn'/'sync_bn'/'gn'. share_cls_reg (bool): Whether to share the cls and reg output. act (str): The act of per layers. use_se (bool): Whether to use se module. """ def __init__(self, feat_in=256, feat_out=96, num_fpn_stride=3, num_convs=2, norm_type='bn', share_cls_reg=False, act='hard_swish', use_se=False): super(PicoFeat, self).__init__() self.num_convs = num_convs self.norm_type = norm_type self.share_cls_reg = share_cls_reg self.act = act self.use_se = use_se self.cls_convs = [] self.reg_convs = [] if use_se: assert share_cls_reg == True, \ 'In the case of using se, share_cls_reg must be set to True' self.se = nn.LayerList() for stage_idx in range(num_fpn_stride): cls_subnet_convs = [] reg_subnet_convs = [] for i in range(self.num_convs): in_c = feat_in if i == 0 else feat_out cls_conv_dw = self.add_sublayer( 'cls_conv_dw{}.{}'.format(stage_idx, i), ConvNormLayer( ch_in=in_c, ch_out=feat_out, filter_size=5, stride=1, groups=feat_out, norm_type=norm_type, bias_on=False, lr_scale=2.)) cls_subnet_convs.append(cls_conv_dw) cls_conv_pw = self.add_sublayer( 'cls_conv_pw{}.{}'.format(stage_idx, i), ConvNormLayer( ch_in=in_c, ch_out=feat_out, filter_size=1, stride=1, norm_type=norm_type, bias_on=False, lr_scale=2.)) cls_subnet_convs.append(cls_conv_pw) if not self.share_cls_reg: reg_conv_dw = self.add_sublayer( 'reg_conv_dw{}.{}'.format(stage_idx, i), ConvNormLayer( ch_in=in_c, ch_out=feat_out, filter_size=5, stride=1, groups=feat_out, norm_type=norm_type, bias_on=False, lr_scale=2.)) reg_subnet_convs.append(reg_conv_dw) reg_conv_pw = self.add_sublayer( 'reg_conv_pw{}.{}'.format(stage_idx, i), ConvNormLayer( ch_in=in_c, ch_out=feat_out, filter_size=1, stride=1, norm_type=norm_type, bias_on=False, lr_scale=2.)) reg_subnet_convs.append(reg_conv_pw) self.cls_convs.append(cls_subnet_convs) self.reg_convs.append(reg_subnet_convs) if use_se: self.se.append(PicoSE(feat_out)) def act_func(self, x): if self.act == "leaky_relu": x = F.leaky_relu(x) elif self.act == "hard_swish": x = F.hardswish(x) elif self.act == "relu6": x = F.relu6(x) return x def forward(self, fpn_feat, stage_idx): assert stage_idx < len(self.cls_convs) cls_feat = fpn_feat reg_feat = fpn_feat for i in range(len(self.cls_convs[stage_idx])): cls_feat = self.act_func(self.cls_convs[stage_idx][i](cls_feat)) reg_feat = cls_feat if not self.share_cls_reg: reg_feat = self.act_func(self.reg_convs[stage_idx][i](reg_feat)) if self.use_se: avg_feat = F.adaptive_avg_pool2d(cls_feat, (1, 1)) se_feat = self.act_func(self.se[stage_idx](cls_feat, avg_feat)) return cls_feat, se_feat return cls_feat, reg_feat @register class PicoHead(OTAVFLHead): """ PicoHead Args: conv_feat (object): Instance of 'PicoFeat' num_classes (int): Number of classes fpn_stride (list): The stride of each FPN Layer prior_prob (float): Used to set the bias init for the class prediction layer loss_class (object): Instance of VariFocalLoss. loss_dfl (object): Instance of DistributionFocalLoss. loss_bbox (object): Instance of bbox loss. assigner (object): Instance of label assigner. reg_max: Max value of integral set :math: `{0, ..., reg_max}` n QFL setting. Default: 7. """ __inject__ = [ 'conv_feat', 'dgqp_module', 'loss_class', 'loss_dfl', 'loss_bbox', 'assigner', 'nms' ] __shared__ = ['num_classes', 'eval_size'] def __init__(self, conv_feat='PicoFeat', dgqp_module=None, num_classes=80, fpn_stride=[8, 16, 32], prior_prob=0.01, loss_class='VariFocalLoss', loss_dfl='DistributionFocalLoss', loss_bbox='GIoULoss', assigner='SimOTAAssigner', reg_max=16, feat_in_chan=96, nms=None, nms_pre=1000, cell_offset=0, eval_size=None): super(PicoHead, 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, assigner=assigner, 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.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.eval_size = eval_size self.use_sigmoid = self.loss_vfl.use_sigmoid if self.use_sigmoid: self.cls_out_channels = self.num_classes else: self.cls_out_channels = self.num_classes + 1 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 = [] self.head_reg_list = [] 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 + 4 * (self.reg_max + 1) if self.conv_feat.share_cls_reg else 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) if not self.conv_feat.share_cls_reg: 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) # 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_logits_list, bboxes_reg_list = [], [] for i, fpn_feat in enumerate(fpn_feats): conv_cls_feat, conv_reg_feat = self.conv_feat(fpn_feat, i) if self.conv_feat.share_cls_reg: cls_logits = self.head_cls_list[i](conv_cls_feat) cls_score, bbox_pred = paddle.split( cls_logits, [self.cls_out_channels, 4 * (self.reg_max + 1)], axis=1) else: cls_score = self.head_cls_list[i](conv_cls_feat) bbox_pred = self.head_reg_list[i](conv_reg_feat) if self.dgqp_module: quality_score = self.dgqp_module(bbox_pred) cls_score = F.sigmoid(cls_score) * quality_score cls_logits_list.append(cls_score) bboxes_reg_list.append(bbox_pred) return (cls_logits_list, bboxes_reg_list) 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_logits_list, bboxes_reg_list = [], [] for i, fpn_feat in enumerate(fpn_feats): conv_cls_feat, conv_reg_feat = self.conv_feat(fpn_feat, i) if self.conv_feat.share_cls_reg: cls_logits = self.head_cls_list[i](conv_cls_feat) cls_score, bbox_pred = paddle.split( cls_logits, [self.cls_out_channels, 4 * (self.reg_max + 1)], axis=1) else: cls_score = self.head_cls_list[i](conv_cls_feat) bbox_pred = self.head_reg_list[i](conv_reg_feat) if self.dgqp_module: quality_score = self.dgqp_module(bbox_pred) cls_score = F.sigmoid(cls_score) * quality_score if not export_post_process: # Now only supports batch size = 1 in deploy # TODO(ygh): support batch size > 1 cls_score_out = F.sigmoid(cls_score).reshape( [1, self.cls_out_channels, -1]).transpose([0, 2, 1]) bbox_pred = bbox_pred.reshape([1, (self.reg_max + 1) * 4, -1]).transpose([0, 2, 1]) else: _, _, h, w = fpn_feat.shape l = h * w cls_score_out = F.sigmoid( cls_score.reshape([-1, self.cls_out_channels, l])) bbox_pred = bbox_pred.transpose([0, 2, 3, 1]) bbox_pred = self.distribution_project(bbox_pred) bbox_pred = bbox_pred.reshape([-1, l, 4]) cls_logits_list.append(cls_score_out) bboxes_reg_list.append(bbox_pred) if export_post_process: cls_logits_list = paddle.concat(cls_logits_list, axis=-1) bboxes_reg_list = paddle.concat(bboxes_reg_list, axis=1) bboxes_reg_list = batch_distance2bbox(anchor_points, bboxes_reg_list) bboxes_reg_list *= stride_tensor return (cls_logits_list, bboxes_reg_list) 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): 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 @register class PicoHeadV2(GFLHead): """ PicoHeadV2 Args: conv_feat (object): Instance of 'PicoFeat' num_classes (int): Number of classes fpn_stride (list): The stride of each FPN Layer prior_prob (float): Used to set the bias init for the class prediction layer loss_class (object): Instance of VariFocalLoss. loss_dfl (object): Instance of DistributionFocalLoss. loss_bbox (object): Instance of bbox loss. assigner (object): Instance of label assigner. reg_max: Max value of integral set :math: `{0, ..., reg_max}` n QFL setting. Default: 7. """ __inject__ = [ 'conv_feat', 'dgqp_module', 'loss_class', 'loss_dfl', 'loss_bbox', 'static_assigner', 'assigner', 'nms' ] __shared__ = ['num_classes', 'eval_size'] def __init__(self, conv_feat='PicoFeatV2', dgqp_module=None, num_classes=80, fpn_stride=[8, 16, 32], prior_prob=0.01, use_align_head=True, loss_class='VariFocalLoss', loss_dfl='DistributionFocalLoss', loss_bbox='GIoULoss', static_assigner_epoch=60, static_assigner='ATSSAssigner', 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): 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