369 lines
14 KiB
Python
369 lines
14 KiB
Python
# Copyright (c) 2022 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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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.regularizer import L2Decay
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from paddle.nn.initializer import Normal, Constant
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from ppdet.modeling.layers import MultiClassNMS
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from ppdet.core.workspace import register
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from ppdet.modeling.bbox_utils import delta2bbox_v2
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__all__ = ['YOLOFHead']
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INF = 1e8
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def reduce_mean(tensor):
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world_size = paddle.distributed.get_world_size()
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if world_size == 1:
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return tensor
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paddle.distributed.all_reduce(tensor)
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return tensor / world_size
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def find_inside_anchor(feat_size, stride, num_anchors, im_shape):
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feat_h, feat_w = feat_size[:2]
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im_h, im_w = im_shape[:2]
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inside_h = min(int(np.ceil(im_h / stride)), feat_h)
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inside_w = min(int(np.ceil(im_w / stride)), feat_w)
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inside_mask = paddle.zeros([feat_h, feat_w], dtype=paddle.bool)
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inside_mask[:inside_h, :inside_w] = True
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inside_mask = inside_mask.unsqueeze(-1).expand(
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[feat_h, feat_w, num_anchors])
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return inside_mask.reshape([-1])
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@register
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class YOLOFFeat(nn.Layer):
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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_cls_convs=2,
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num_reg_convs=4,
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norm_type='bn'):
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super(YOLOFFeat, self).__init__()
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assert norm_type == 'bn', "YOLOFFeat only support BN now."
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self.feat_in = feat_in
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self.feat_out = feat_out
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self.num_cls_convs = num_cls_convs
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self.num_reg_convs = num_reg_convs
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self.norm_type = norm_type
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cls_subnet, reg_subnet = [], []
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for i in range(self.num_cls_convs):
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feat_in = self.feat_in if i == 0 else self.feat_out
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cls_subnet.append(
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nn.Conv2D(
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feat_in,
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self.feat_out,
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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=0.0, std=0.01)),
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bias_attr=ParamAttr(initializer=Constant(value=0.0))))
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cls_subnet.append(
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nn.BatchNorm2D(
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self.feat_out,
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weight_attr=ParamAttr(regularizer=L2Decay(0.0)),
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bias_attr=ParamAttr(regularizer=L2Decay(0.0))))
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cls_subnet.append(nn.ReLU())
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for i in range(self.num_reg_convs):
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feat_in = self.feat_in if i == 0 else self.feat_out
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reg_subnet.append(
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nn.Conv2D(
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feat_in,
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self.feat_out,
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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=0.0, std=0.01)),
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bias_attr=ParamAttr(initializer=Constant(value=0.0))))
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reg_subnet.append(
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nn.BatchNorm2D(
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self.feat_out,
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weight_attr=ParamAttr(regularizer=L2Decay(0.0)),
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bias_attr=ParamAttr(regularizer=L2Decay(0.0))))
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reg_subnet.append(nn.ReLU())
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self.cls_subnet = nn.Sequential(*cls_subnet)
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self.reg_subnet = nn.Sequential(*reg_subnet)
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def forward(self, fpn_feat):
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cls_feat = self.cls_subnet(fpn_feat)
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reg_feat = self.reg_subnet(fpn_feat)
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return cls_feat, reg_feat
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@register
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class YOLOFHead(nn.Layer):
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__shared__ = ['num_classes', 'trt', 'exclude_nms']
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__inject__ = [
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'conv_feat', 'anchor_generator', 'bbox_assigner', 'loss_class',
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'loss_bbox', 'nms'
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]
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def __init__(self,
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num_classes=80,
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conv_feat='YOLOFFeat',
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anchor_generator='AnchorGenerator',
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bbox_assigner='UniformAssigner',
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loss_class='FocalLoss',
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loss_bbox='GIoULoss',
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ctr_clip=32.0,
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delta_mean=[0.0, 0.0, 0.0, 0.0],
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delta_std=[1.0, 1.0, 1.0, 1.0],
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nms='MultiClassNMS',
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prior_prob=0.01,
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nms_pre=1000,
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use_inside_anchor=False,
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trt=False,
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exclude_nms=False):
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super(YOLOFHead, self).__init__()
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self.num_classes = num_classes
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self.conv_feat = conv_feat
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self.anchor_generator = anchor_generator
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self.na = self.anchor_generator.num_anchors
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self.bbox_assigner = bbox_assigner
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self.loss_class = loss_class
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self.loss_bbox = loss_bbox
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self.ctr_clip = ctr_clip
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self.delta_mean = delta_mean
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self.delta_std = delta_std
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self.nms = nms
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self.nms_pre = nms_pre
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self.use_inside_anchor = use_inside_anchor
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if isinstance(self.nms, MultiClassNMS) and trt:
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self.nms.trt = trt
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self.exclude_nms = exclude_nms
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bias_init_value = -math.log((1 - prior_prob) / prior_prob)
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self.cls_score = self.add_sublayer(
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'cls_score',
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nn.Conv2D(
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in_channels=conv_feat.feat_out,
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out_channels=self.num_classes * self.na,
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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=0.0, std=0.01)),
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bias_attr=ParamAttr(initializer=Constant(
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value=bias_init_value))))
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self.bbox_pred = self.add_sublayer(
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'bbox_pred',
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nn.Conv2D(
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in_channels=conv_feat.feat_out,
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out_channels=4 * self.na,
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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=0.0, std=0.01)),
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bias_attr=ParamAttr(initializer=Constant(value=0))))
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self.object_pred = self.add_sublayer(
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'object_pred',
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nn.Conv2D(
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in_channels=conv_feat.feat_out,
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out_channels=self.na,
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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=0.0, std=0.01)),
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bias_attr=ParamAttr(initializer=Constant(value=0))))
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def forward(self, feats, targets=None):
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assert len(feats) == 1, "YOLOF only has one level feature."
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conv_cls_feat, conv_reg_feat = self.conv_feat(feats[0])
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cls_logits = self.cls_score(conv_cls_feat)
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objectness = self.object_pred(conv_reg_feat)
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bboxes_reg = self.bbox_pred(conv_reg_feat)
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N, C, H, W = paddle.shape(cls_logits)[:]
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cls_logits = cls_logits.reshape((N, self.na, self.num_classes, H, W))
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objectness = objectness.reshape((N, self.na, 1, H, W))
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norm_cls_logits = cls_logits + objectness - paddle.log(
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1.0 + paddle.clip(
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cls_logits.exp(), max=INF) + paddle.clip(
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objectness.exp(), max=INF))
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norm_cls_logits = norm_cls_logits.reshape((N, C, H, W))
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anchors = self.anchor_generator([norm_cls_logits])
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if self.training:
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yolof_losses = self.get_loss(
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[anchors[0], norm_cls_logits, bboxes_reg], targets)
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return yolof_losses
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else:
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return anchors[0], norm_cls_logits, bboxes_reg
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def get_loss(self, head_outs, targets):
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anchors, cls_logits, bbox_preds = head_outs
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feat_size = cls_logits.shape[-2:]
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cls_logits = cls_logits.transpose([0, 2, 3, 1])
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cls_logits = cls_logits.reshape([0, -1, self.num_classes])
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bbox_preds = bbox_preds.transpose([0, 2, 3, 1])
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bbox_preds = bbox_preds.reshape([0, -1, 4])
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num_pos_list = []
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cls_pred_list, cls_tar_list = [], []
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reg_pred_list, reg_tar_list = [], []
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# find and gather preds and targets in each image
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for cls_logit, bbox_pred, gt_bbox, gt_class, im_shape in zip(
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cls_logits, bbox_preds, targets['gt_bbox'], targets['gt_class'],
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targets['im_shape']):
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if self.use_inside_anchor:
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inside_mask = find_inside_anchor(
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feat_size, self.anchor_generator.strides[0], self.na,
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im_shape.tolist())
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cls_logit = cls_logit[inside_mask]
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bbox_pred = bbox_pred[inside_mask]
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anchors = anchors[inside_mask]
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bbox_pred = delta2bbox_v2(
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bbox_pred,
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anchors,
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self.delta_mean,
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self.delta_std,
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ctr_clip=self.ctr_clip)
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bbox_pred = bbox_pred.reshape([-1, bbox_pred.shape[-1]])
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# -2:ignore, -1:neg, >=0:pos
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match_labels, pos_bbox_pred, pos_bbox_tar = self.bbox_assigner(
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bbox_pred, anchors, gt_bbox)
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pos_mask = (match_labels >= 0)
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neg_mask = (match_labels == -1)
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chosen_mask = paddle.logical_or(pos_mask, neg_mask)
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gt_class = gt_class.reshape([-1])
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bg_class = paddle.to_tensor(
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[self.num_classes], dtype=gt_class.dtype)
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# a trick to assign num_classes to negative targets
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gt_class = paddle.concat([gt_class, bg_class], axis=-1)
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match_labels = paddle.where(
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neg_mask,
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paddle.full_like(match_labels, gt_class.size - 1), match_labels)
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num_pos_list.append(max(1.0, pos_mask.sum().item()))
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cls_pred_list.append(cls_logit[chosen_mask])
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cls_tar_list.append(gt_class[match_labels[chosen_mask]])
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reg_pred_list.append(pos_bbox_pred)
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reg_tar_list.append(pos_bbox_tar)
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num_tot_pos = paddle.to_tensor(sum(num_pos_list))
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num_tot_pos = reduce_mean(num_tot_pos).item()
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num_tot_pos = max(1.0, num_tot_pos)
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cls_pred = paddle.concat(cls_pred_list)
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cls_tar = paddle.concat(cls_tar_list)
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cls_loss = self.loss_class(
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cls_pred, cls_tar, reduction='sum') / num_tot_pos
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reg_pred_list = [_ for _ in reg_pred_list if _ is not None]
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reg_tar_list = [_ for _ in reg_tar_list if _ is not None]
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if len(reg_pred_list) == 0:
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reg_loss = bbox_preds.sum() * 0.0
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else:
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reg_pred = paddle.concat(reg_pred_list)
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reg_tar = paddle.concat(reg_tar_list)
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reg_loss = self.loss_bbox(reg_pred, reg_tar).sum() / num_tot_pos
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yolof_losses = {
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'loss': cls_loss + reg_loss,
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'loss_cls': cls_loss,
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'loss_reg': reg_loss,
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}
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return yolof_losses
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def get_bboxes_single(self,
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anchors,
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cls_scores,
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bbox_preds,
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im_shape,
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scale_factor,
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rescale=True):
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assert len(cls_scores) == len(bbox_preds)
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mlvl_bboxes = []
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mlvl_scores = []
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for anchor, cls_score, bbox_pred in zip(anchors, cls_scores,
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bbox_preds):
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cls_score = cls_score.reshape([-1, self.num_classes])
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bbox_pred = bbox_pred.reshape([-1, 4])
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if self.nms_pre is not None and cls_score.shape[0] > self.nms_pre:
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max_score = cls_score.max(axis=1)
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_, topk_inds = max_score.topk(self.nms_pre)
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bbox_pred = bbox_pred.gather(topk_inds)
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anchor = anchor.gather(topk_inds)
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cls_score = cls_score.gather(topk_inds)
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bbox_pred = delta2bbox_v2(
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bbox_pred,
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anchor,
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self.delta_mean,
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self.delta_std,
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max_shape=im_shape,
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ctr_clip=self.ctr_clip).squeeze()
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mlvl_bboxes.append(bbox_pred)
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mlvl_scores.append(F.sigmoid(cls_score))
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mlvl_bboxes = paddle.concat(mlvl_bboxes)
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mlvl_bboxes = paddle.squeeze(mlvl_bboxes)
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if rescale:
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mlvl_bboxes = mlvl_bboxes / paddle.concat(
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[scale_factor[::-1], scale_factor[::-1]])
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mlvl_scores = paddle.concat(mlvl_scores)
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mlvl_scores = mlvl_scores.transpose([1, 0])
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return mlvl_bboxes, mlvl_scores
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def decode(self, anchors, cls_scores, bbox_preds, im_shape, scale_factor):
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batch_bboxes = []
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batch_scores = []
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for img_id in range(cls_scores[0].shape[0]):
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num_lvls = len(cls_scores)
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cls_score_list = [cls_scores[i][img_id] for i in range(num_lvls)]
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bbox_pred_list = [bbox_preds[i][img_id] for i in range(num_lvls)]
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bboxes, scores = self.get_bboxes_single(
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anchors, cls_score_list, bbox_pred_list, im_shape[img_id],
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scale_factor[img_id])
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batch_bboxes.append(bboxes)
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batch_scores.append(scores)
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batch_bboxes = paddle.stack(batch_bboxes, 0)
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batch_scores = paddle.stack(batch_scores, 0)
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return batch_bboxes, batch_scores
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def post_process(self, head_outs, im_shape, scale_factor):
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anchors, cls_scores, bbox_preds = head_outs
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cls_scores = cls_scores.transpose([0, 2, 3, 1])
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bbox_preds = bbox_preds.transpose([0, 2, 3, 1])
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pred_bboxes, pred_scores = self.decode(
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[anchors], [cls_scores], [bbox_preds], im_shape, scale_factor)
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if self.exclude_nms:
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# `exclude_nms=True` just use in benchmark
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return pred_bboxes.sum(), pred_scores.sum()
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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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