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129 lines
4.6 KiB
Python
129 lines
4.6 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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import paddle.nn.functional as F
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from ..registry import BACKBONES
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class GCN(nn.Layer):
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def __init__(self, in_channels, out_channels, vertex_nums=25, stride=1):
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super(GCN, self).__init__()
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self.conv1 = nn.Conv2D(in_channels=in_channels,
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out_channels=3 * out_channels,
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kernel_size=1,
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stride=1)
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self.conv2 = nn.Conv2D(in_channels=vertex_nums * 3,
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out_channels=vertex_nums,
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kernel_size=1)
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def forward(self, x):
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# x --- N,C,T,V
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x = self.conv1(x) # N,3C,T,V
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N, C, T, V = x.shape
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x = paddle.reshape(x, [N, C // 3, 3, T, V]) # N,C,3,T,V
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x = paddle.transpose(x, perm=[0, 1, 2, 4, 3]) # N,C,3,V,T
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x = paddle.reshape(x, [N, C // 3, 3 * V, T]) # N,C,3V,T
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x = paddle.transpose(x, perm=[0, 2, 1, 3]) # N,3V,C,T
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x = self.conv2(x) # N,V,C,T
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x = paddle.transpose(x, perm=[0, 2, 3, 1]) # N,C,T,V
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return x
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class Block(paddle.nn.Layer):
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def __init__(self,
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in_channels,
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out_channels,
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vertex_nums=25,
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temporal_size=9,
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stride=1,
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residual=True):
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super(Block, self).__init__()
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self.residual = residual
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self.out_channels = out_channels
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self.bn_res = nn.BatchNorm2D(out_channels)
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self.conv_res = nn.Conv2D(in_channels=in_channels,
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out_channels=out_channels,
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kernel_size=1,
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stride=(stride, 1))
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self.gcn = GCN(in_channels=in_channels,
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out_channels=out_channels,
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vertex_nums=vertex_nums)
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self.tcn = nn.Sequential(
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nn.BatchNorm2D(out_channels),
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nn.ReLU(),
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nn.Conv2D(in_channels=out_channels,
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out_channels=out_channels,
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kernel_size=(temporal_size, 1),
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padding=((temporal_size - 1) // 2, 0),
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stride=(stride, 1)),
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nn.BatchNorm2D(out_channels),
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)
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def forward(self, x):
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if self.residual:
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y = self.conv_res(x)
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y = self.bn_res(y)
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x = self.gcn(x)
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x = self.tcn(x)
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out = x + y if self.residual else x
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out = F.relu(out)
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return out
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@BACKBONES.register()
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class AGCN(nn.Layer):
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"""
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AGCN model improves the performance of ST-GCN using
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Adaptive Graph Convolutional Networks.
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Args:
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in_channels: int, channels of vertex coordinate. 2 for (x,y), 3 for (x,y,z). Default 2.
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"""
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def __init__(self, in_channels=2, **kwargs):
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super(AGCN, self).__init__()
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self.data_bn = nn.BatchNorm1D(25 * 2)
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self.agcn = nn.Sequential(
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Block(in_channels=in_channels,
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out_channels=64,
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residual=False,
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**kwargs), Block(in_channels=64, out_channels=64, **kwargs),
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Block(in_channels=64, out_channels=64, **kwargs),
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Block(in_channels=64, out_channels=64, **kwargs),
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Block(in_channels=64, out_channels=128, stride=2, **kwargs),
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Block(in_channels=128, out_channels=128, **kwargs),
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Block(in_channels=128, out_channels=128, **kwargs),
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Block(in_channels=128, out_channels=256, stride=2, **kwargs),
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Block(in_channels=256, out_channels=256, **kwargs),
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Block(in_channels=256, out_channels=256, **kwargs))
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self.pool = nn.AdaptiveAvgPool2D(output_size=(1, 1))
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def forward(self, x):
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# data normalization
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N, C, T, V, M = x.shape
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x = x.transpose((0, 4, 1, 2, 3)) # N, M, C, T, V
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x = x.reshape((N * M, C, T, V))
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x = self.agcn(x)
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x = self.pool(x) # NM,C,T,V --> NM,C,1,1
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C = x.shape[1]
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x = paddle.reshape(x, (N, M, C, 1, 1)).mean(axis=1) # N,C,1,1
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return x
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