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- # 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.
- import math
- import paddle
- import paddle.nn as nn
- import paddle.nn.functional as F
- from paddle.nn.initializer import KaimingUniform
- from paddlex.ppdet.core.workspace import register
- from paddlex.ppdet.modeling.losses import CTFocalLoss
- class ConvLayer(nn.Layer):
- def __init__(self,
- ch_in,
- ch_out,
- kernel_size,
- stride=1,
- padding=0,
- dilation=1,
- groups=1,
- bias=False):
- super(ConvLayer, self).__init__()
- bias_attr = False
- fan_in = ch_in * kernel_size**2
- bound = 1 / math.sqrt(fan_in)
- param_attr = paddle.ParamAttr(initializer=KaimingUniform())
- if bias:
- bias_attr = paddle.ParamAttr(
- initializer=nn.initializer.Uniform(-bound, bound))
- self.conv = nn.Conv2D(
- in_channels=ch_in,
- out_channels=ch_out,
- kernel_size=kernel_size,
- stride=stride,
- padding=padding,
- dilation=dilation,
- groups=groups,
- weight_attr=param_attr,
- bias_attr=bias_attr)
- def forward(self, inputs):
- out = self.conv(inputs)
- return out
- @register
- class CenterNetHead(nn.Layer):
- """
- Args:
- in_channels (int): the channel number of input to CenterNetHead.
- num_classes (int): the number of classes, 80 by default.
- head_planes (int): the channel number in all head, 256 by default.
- heatmap_weight (float): the weight of heatmap loss, 1 by default.
- regress_ltrb (bool): whether to regress left/top/right/bottom or
- width/height for a box, true by default
- size_weight (float): the weight of box size loss, 0.1 by default.
- offset_weight (float): the weight of center offset loss, 1 by default.
- """
- __shared__ = ['num_classes']
- def __init__(self,
- in_channels,
- num_classes=80,
- head_planes=256,
- heatmap_weight=1,
- regress_ltrb=True,
- size_weight=0.1,
- offset_weight=1):
- super(CenterNetHead, self).__init__()
- self.weights = {
- 'heatmap': heatmap_weight,
- 'size': size_weight,
- 'offset': offset_weight
- }
- self.heatmap = nn.Sequential(
- ConvLayer(
- in_channels, head_planes, kernel_size=3, padding=1, bias=True),
- nn.ReLU(),
- ConvLayer(
- head_planes,
- num_classes,
- kernel_size=1,
- stride=1,
- padding=0,
- bias=True))
- with paddle.no_grad():
- self.heatmap[2].conv.bias[:] = -2.19
- self.size = nn.Sequential(
- ConvLayer(
- in_channels, head_planes, kernel_size=3, padding=1, bias=True),
- nn.ReLU(),
- ConvLayer(
- head_planes,
- 4 if regress_ltrb else 2,
- kernel_size=1,
- stride=1,
- padding=0,
- bias=True))
- self.offset = nn.Sequential(
- ConvLayer(
- in_channels, head_planes, kernel_size=3, padding=1, bias=True),
- nn.ReLU(),
- ConvLayer(
- head_planes, 2, kernel_size=1, stride=1, padding=0, bias=True))
- self.focal_loss = CTFocalLoss()
- @classmethod
- def from_config(cls, cfg, input_shape):
- if isinstance(input_shape, (list, tuple)):
- input_shape = input_shape[0]
- return {'in_channels': input_shape.channels}
- def forward(self, feat, inputs):
- heatmap = self.heatmap(feat)
- size = self.size(feat)
- offset = self.offset(feat)
- if self.training:
- loss = self.get_loss(heatmap, size, offset, self.weights, inputs)
- return loss
- else:
- heatmap = F.sigmoid(heatmap)
- return {'heatmap': heatmap, 'size': size, 'offset': offset}
- def get_loss(self, heatmap, size, offset, weights, inputs):
- heatmap_target = inputs['heatmap']
- size_target = inputs['size']
- offset_target = inputs['offset']
- index = inputs['index']
- mask = inputs['index_mask']
- heatmap = paddle.clip(F.sigmoid(heatmap), 1e-4, 1 - 1e-4)
- heatmap_loss = self.focal_loss(heatmap, heatmap_target)
- size = paddle.transpose(size, perm=[0, 2, 3, 1])
- size_n, size_h, size_w, size_c = size.shape
- size = paddle.reshape(size, shape=[size_n, -1, size_c])
- index = paddle.unsqueeze(index, 2)
- batch_inds = list()
- for i in range(size_n):
- batch_ind = paddle.full(
- shape=[1, index.shape[1], 1], fill_value=i, dtype='int64')
- batch_inds.append(batch_ind)
- batch_inds = paddle.concat(batch_inds, axis=0)
- index = paddle.concat(x=[batch_inds, index], axis=2)
- pos_size = paddle.gather_nd(size, index=index)
- mask = paddle.unsqueeze(mask, axis=2)
- size_mask = paddle.expand_as(mask, pos_size)
- size_mask = paddle.cast(size_mask, dtype=pos_size.dtype)
- pos_num = size_mask.sum()
- size_mask.stop_gradient = True
- size_target.stop_gradient = True
- size_loss = F.l1_loss(
- pos_size * size_mask, size_target * size_mask, reduction='sum')
- size_loss = size_loss / (pos_num + 1e-4)
- offset = paddle.transpose(offset, perm=[0, 2, 3, 1])
- offset_n, offset_h, offset_w, offset_c = offset.shape
- offset = paddle.reshape(offset, shape=[offset_n, -1, offset_c])
- pos_offset = paddle.gather_nd(offset, index=index)
- offset_mask = paddle.expand_as(mask, pos_offset)
- offset_mask = paddle.cast(offset_mask, dtype=pos_offset.dtype)
- pos_num = offset_mask.sum()
- offset_mask.stop_gradient = True
- offset_target.stop_gradient = True
- offset_loss = F.l1_loss(
- pos_offset * offset_mask,
- offset_target * offset_mask,
- reduction='sum')
- offset_loss = offset_loss / (pos_num + 1e-4)
- det_loss = weights['heatmap'] * heatmap_loss + weights[
- 'size'] * size_loss + weights['offset'] * offset_loss
- return {
- 'det_loss': det_loss,
- 'heatmap_loss': heatmap_loss,
- 'size_loss': size_loss,
- 'offset_loss': offset_loss
- }
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