forked from PulseFocusPlatform/PulseFocusPlatform
109 lines
3.8 KiB
Python
109 lines
3.8 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 paddle
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import paddle.nn as nn
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from ppdet.core.workspace import register
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from .. import layers as L
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from ..backbones.hrnet import BasicBlock
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@register
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class HrHRNetHead(nn.Layer):
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__inject__ = ['loss']
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def __init__(self, num_joints, loss='HrHRNetLoss', swahr=False, width=32):
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"""
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Head for HigherHRNet network
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Args:
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num_joints (int): number of keypoints
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hrloss (object): HrHRNetLoss instance
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swahr (bool): whether to use swahr
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width (int): hrnet channel width
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"""
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super(HrHRNetHead, self).__init__()
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self.loss = loss
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self.num_joints = num_joints
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num_featout1 = num_joints * 2
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num_featout2 = num_joints
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self.swahr = swahr
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self.conv1 = L.Conv2d(width, num_featout1, 1, 1, 0, bias=True)
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self.conv2 = L.Conv2d(width, num_featout2, 1, 1, 0, bias=True)
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self.deconv = nn.Sequential(
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L.ConvTranspose2d(
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num_featout1 + width, width, 4, 2, 1, 0, bias=False),
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L.BatchNorm2d(width),
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L.ReLU())
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self.blocks = nn.Sequential(*(BasicBlock(
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num_channels=width,
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num_filters=width,
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has_se=False,
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freeze_norm=False,
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name='HrHRNetHead_{}'.format(i)) for i in range(4)))
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self.interpolate = L.Upsample(2, mode='bilinear')
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self.concat = L.Concat(dim=1)
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if swahr:
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self.scalelayer0 = nn.Sequential(
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L.Conv2d(
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width, num_joints, 1, 1, 0, bias=True),
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L.BatchNorm2d(num_joints),
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L.ReLU(),
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L.Conv2d(
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num_joints,
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num_joints,
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9,
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1,
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4,
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groups=num_joints,
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bias=True))
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self.scalelayer1 = nn.Sequential(
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L.Conv2d(
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width, num_joints, 1, 1, 0, bias=True),
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L.BatchNorm2d(num_joints),
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L.ReLU(),
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L.Conv2d(
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num_joints,
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num_joints,
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9,
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1,
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4,
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groups=num_joints,
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bias=True))
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def forward(self, feats, targets=None):
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x1 = feats[0]
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xo1 = self.conv1(x1)
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x2 = self.blocks(self.deconv(self.concat((x1, xo1))))
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xo2 = self.conv2(x2)
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num_joints = self.num_joints
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if self.training:
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heatmap1, tagmap = paddle.split(xo1, 2, axis=1)
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if self.swahr:
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so1 = self.scalelayer0(x1)
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so2 = self.scalelayer1(x2)
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hrhrnet_outputs = ([heatmap1, so1], [xo2, so2], tagmap)
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return self.loss(hrhrnet_outputs, targets)
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else:
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hrhrnet_outputs = (heatmap1, xo2, tagmap)
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return self.loss(hrhrnet_outputs, targets)
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# averaged heatmap, upsampled tagmap
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upsampled = self.interpolate(xo1)
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avg = (upsampled[:, :num_joints] + xo2[:, :num_joints]) / 2
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return avg, upsampled[:, num_joints:]
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