294 lines
11 KiB
Python
294 lines
11 KiB
Python
# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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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.nn.initializer import Constant, Uniform
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from ppdet.core.workspace import register
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from ppdet.modeling.losses import CTFocalLoss, GIoULoss
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class ConvLayer(nn.Layer):
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def __init__(self,
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ch_in,
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ch_out,
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kernel_size,
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stride=1,
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padding=0,
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dilation=1,
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groups=1,
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bias=False):
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super(ConvLayer, self).__init__()
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bias_attr = False
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fan_in = ch_in * kernel_size**2
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bound = 1 / math.sqrt(fan_in)
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param_attr = paddle.ParamAttr(initializer=Uniform(-bound, bound))
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if bias:
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bias_attr = paddle.ParamAttr(initializer=Constant(0.))
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self.conv = nn.Conv2D(
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in_channels=ch_in,
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out_channels=ch_out,
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kernel_size=kernel_size,
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stride=stride,
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padding=padding,
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dilation=dilation,
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groups=groups,
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weight_attr=param_attr,
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bias_attr=bias_attr)
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def forward(self, inputs):
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out = self.conv(inputs)
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return out
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@register
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class CenterNetHead(nn.Layer):
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"""
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Args:
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in_channels (int): the channel number of input to CenterNetHead.
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num_classes (int): the number of classes, 80 (COCO dataset) by default.
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head_planes (int): the channel number in all head, 256 by default.
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prior_bias (float): prior bias in heatmap head, -2.19 by default, -4.6 in CenterTrack
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regress_ltrb (bool): whether to regress left/top/right/bottom or
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width/height for a box, True by default.
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size_loss (str): the type of size regression loss, 'L1' by default, can be 'giou'.
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loss_weight (dict): the weight of each loss.
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add_iou (bool): whether to add iou branch, False by default.
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"""
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__shared__ = ['num_classes']
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def __init__(self,
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in_channels,
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num_classes=80,
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head_planes=256,
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prior_bias=-2.19,
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regress_ltrb=True,
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size_loss='L1',
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loss_weight={
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'heatmap': 1.0,
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'size': 0.1,
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'offset': 1.0,
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'iou': 0.0,
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},
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add_iou=False):
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super(CenterNetHead, self).__init__()
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self.regress_ltrb = regress_ltrb
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self.loss_weight = loss_weight
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self.add_iou = add_iou
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# heatmap head
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self.heatmap = nn.Sequential(
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ConvLayer(
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in_channels, head_planes, kernel_size=3, padding=1, bias=True),
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nn.ReLU(),
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ConvLayer(
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head_planes,
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num_classes,
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kernel_size=1,
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stride=1,
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padding=0,
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bias=True))
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with paddle.no_grad():
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self.heatmap[2].conv.bias[:] = prior_bias
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# size(ltrb or wh) head
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self.size = nn.Sequential(
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ConvLayer(
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in_channels, head_planes, kernel_size=3, padding=1, bias=True),
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nn.ReLU(),
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ConvLayer(
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head_planes,
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4 if regress_ltrb else 2,
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kernel_size=1,
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stride=1,
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padding=0,
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bias=True))
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self.size_loss = size_loss
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# offset head
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self.offset = nn.Sequential(
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ConvLayer(
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in_channels, head_planes, kernel_size=3, padding=1, bias=True),
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nn.ReLU(),
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ConvLayer(
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head_planes, 2, kernel_size=1, stride=1, padding=0, bias=True))
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# iou head (optinal)
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if self.add_iou and 'iou' in self.loss_weight:
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self.iou = nn.Sequential(
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ConvLayer(
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in_channels,
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head_planes,
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kernel_size=3,
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padding=1,
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bias=True),
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nn.ReLU(),
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ConvLayer(
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head_planes,
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4 if regress_ltrb else 2,
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kernel_size=1,
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stride=1,
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padding=0,
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bias=True))
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@classmethod
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def from_config(cls, cfg, input_shape):
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if isinstance(input_shape, (list, tuple)):
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input_shape = input_shape[0]
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return {'in_channels': input_shape.channels}
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def forward(self, feat, inputs):
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heatmap = F.sigmoid(self.heatmap(feat))
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size = self.size(feat)
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offset = self.offset(feat)
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head_outs = {'heatmap': heatmap, 'size': size, 'offset': offset}
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if self.add_iou and 'iou' in self.loss_weight:
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iou = self.iou(feat)
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head_outs.update({'iou': iou})
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if self.training:
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losses = self.get_loss(inputs, self.loss_weight, head_outs)
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return losses
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else:
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return head_outs
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def get_loss(self, inputs, weights, head_outs):
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# 1.heatmap(hm) head loss: CTFocalLoss
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heatmap = head_outs['heatmap']
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heatmap_target = inputs['heatmap']
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heatmap = paddle.clip(heatmap, 1e-4, 1 - 1e-4)
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ctfocal_loss = CTFocalLoss()
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heatmap_loss = ctfocal_loss(heatmap, heatmap_target)
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# 2.size(wh) head loss: L1 loss or GIoU loss
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size = head_outs['size']
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index = inputs['index']
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mask = inputs['index_mask']
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size = paddle.transpose(size, perm=[0, 2, 3, 1])
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size_n, _, _, size_c = size.shape
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size = paddle.reshape(size, shape=[size_n, -1, size_c])
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index = paddle.unsqueeze(index, 2)
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batch_inds = list()
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for i in range(size_n):
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batch_ind = paddle.full(
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shape=[1, index.shape[1], 1], fill_value=i, dtype='int64')
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batch_inds.append(batch_ind)
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batch_inds = paddle.concat(batch_inds, axis=0)
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index = paddle.concat(x=[batch_inds, index], axis=2)
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pos_size = paddle.gather_nd(size, index=index)
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mask = paddle.unsqueeze(mask, axis=2)
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size_mask = paddle.expand_as(mask, pos_size)
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size_mask = paddle.cast(size_mask, dtype=pos_size.dtype)
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pos_num = size_mask.sum()
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size_mask.stop_gradient = True
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if self.size_loss == 'L1':
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if self.regress_ltrb:
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size_target = inputs['size']
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# shape: [bs, max_per_img, 4]
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else:
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if inputs['size'].shape[-1] == 2:
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# inputs['size'] is wh, and regress as wh
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# shape: [bs, max_per_img, 2]
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size_target = inputs['size']
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else:
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# inputs['size'] is ltrb, but regress as wh
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# shape: [bs, max_per_img, 4]
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size_target = inputs['size'][:, :, 0:2] + inputs[
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'size'][:, :, 2:]
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size_target.stop_gradient = True
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size_loss = F.l1_loss(
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pos_size * size_mask, size_target * size_mask, reduction='sum')
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size_loss = size_loss / (pos_num + 1e-4)
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elif self.size_loss == 'giou':
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size_target = inputs['bbox_xys']
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size_target.stop_gradient = True
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centers_x = (size_target[:, :, 0:1] + size_target[:, :, 2:3]) / 2.0
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centers_y = (size_target[:, :, 1:2] + size_target[:, :, 3:4]) / 2.0
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x1 = centers_x - pos_size[:, :, 0:1]
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y1 = centers_y - pos_size[:, :, 1:2]
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x2 = centers_x + pos_size[:, :, 2:3]
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y2 = centers_y + pos_size[:, :, 3:4]
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pred_boxes = paddle.concat([x1, y1, x2, y2], axis=-1)
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giou_loss = GIoULoss(reduction='sum')
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size_loss = giou_loss(
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pred_boxes * size_mask,
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size_target * size_mask,
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iou_weight=size_mask,
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loc_reweight=None)
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size_loss = size_loss / (pos_num + 1e-4)
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# 3.offset(reg) head loss: L1 loss
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offset = head_outs['offset']
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offset_target = inputs['offset']
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offset = paddle.transpose(offset, perm=[0, 2, 3, 1])
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offset_n, _, _, offset_c = offset.shape
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offset = paddle.reshape(offset, shape=[offset_n, -1, offset_c])
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pos_offset = paddle.gather_nd(offset, index=index)
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offset_mask = paddle.expand_as(mask, pos_offset)
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offset_mask = paddle.cast(offset_mask, dtype=pos_offset.dtype)
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pos_num = offset_mask.sum()
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offset_mask.stop_gradient = True
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offset_target.stop_gradient = True
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offset_loss = F.l1_loss(
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pos_offset * offset_mask,
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offset_target * offset_mask,
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reduction='sum')
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offset_loss = offset_loss / (pos_num + 1e-4)
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# 4.iou head loss: GIoU loss (optinal)
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if self.add_iou and 'iou' in self.loss_weight:
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iou = head_outs['iou']
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iou = paddle.transpose(iou, perm=[0, 2, 3, 1])
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iou_n, _, _, iou_c = iou.shape
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iou = paddle.reshape(iou, shape=[iou_n, -1, iou_c])
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pos_iou = paddle.gather_nd(iou, index=index)
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iou_mask = paddle.expand_as(mask, pos_iou)
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iou_mask = paddle.cast(iou_mask, dtype=pos_iou.dtype)
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pos_num = iou_mask.sum()
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iou_mask.stop_gradient = True
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gt_bbox_xys = inputs['bbox_xys']
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gt_bbox_xys.stop_gradient = True
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centers_x = (gt_bbox_xys[:, :, 0:1] + gt_bbox_xys[:, :, 2:3]) / 2.0
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centers_y = (gt_bbox_xys[:, :, 1:2] + gt_bbox_xys[:, :, 3:4]) / 2.0
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x1 = centers_x - pos_size[:, :, 0:1]
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y1 = centers_y - pos_size[:, :, 1:2]
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x2 = centers_x + pos_size[:, :, 2:3]
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y2 = centers_y + pos_size[:, :, 3:4]
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pred_boxes = paddle.concat([x1, y1, x2, y2], axis=-1)
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giou_loss = GIoULoss(reduction='sum')
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iou_loss = giou_loss(
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pred_boxes * iou_mask,
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gt_bbox_xys * iou_mask,
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iou_weight=iou_mask,
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loc_reweight=None)
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iou_loss = iou_loss / (pos_num + 1e-4)
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losses = {
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'heatmap_loss': heatmap_loss,
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'size_loss': size_loss,
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'offset_loss': offset_loss,
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}
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det_loss = weights['heatmap'] * heatmap_loss + weights[
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'size'] * size_loss + weights['offset'] * offset_loss
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if self.add_iou and 'iou' in self.loss_weight:
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losses.update({'iou_loss': iou_loss})
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det_loss += weights['iou'] * iou_loss
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losses.update({'det_loss': det_loss})
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return losses
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