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230 lines
7.6 KiB
Python
230 lines
7.6 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 numpy as np
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from ..registry import BACKBONES
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def import_class(name):
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components = name.split('.')
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mod = __import__(components[0])
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for comp in components[1:]:
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mod = getattr(mod, comp)
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return mod
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class UnitTCN(nn.Layer):
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def __init__(self, in_channels, out_channels, kernel_size=9, stride=1):
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super(UnitTCN, self).__init__()
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pad = int((kernel_size - 1) / 2)
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self.conv = nn.Conv2D(in_channels,
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out_channels,
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kernel_size=(kernel_size, 1),
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padding=(pad, 0),
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stride=(stride, 1))
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self.bn = nn.BatchNorm2D(out_channels)
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self.relu = nn.ReLU()
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def forward(self, x):
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" input size : (N*M, C, T, V)"
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x = self.bn(self.conv(x))
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return x
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class UnitGCN(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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A,
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coff_embedding=4,
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num_subset=3):
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super(UnitGCN, self).__init__()
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inter_channels = out_channels // coff_embedding
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self.inter_c = inter_channels
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PA = self.create_parameter(shape=A.shape, dtype='float32')
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self.PA = PA
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self.A = paddle.to_tensor(A.astype(np.float32))
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self.num_subset = num_subset
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self.conv_a = nn.LayerList()
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self.conv_b = nn.LayerList()
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self.conv_d = nn.LayerList()
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for i in range(self.num_subset):
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self.conv_a.append(nn.Conv2D(in_channels, inter_channels, 1))
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self.conv_b.append(nn.Conv2D(in_channels, inter_channels, 1))
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self.conv_d.append(nn.Conv2D(in_channels, out_channels, 1))
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if in_channels != out_channels:
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self.down = nn.Sequential(nn.Conv2D(in_channels, out_channels, 1),
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nn.BatchNorm2D(out_channels))
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else:
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self.down = lambda x: x
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self.bn = nn.BatchNorm2D(out_channels)
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self.soft = nn.Softmax(-2)
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self.relu = nn.ReLU()
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def forward(self, x):
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N, C, T, V = x.shape
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A = self.A + self.PA
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y = None
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for i in range(self.num_subset):
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A1 = paddle.transpose(self.conv_a[i](x),
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perm=[0, 3, 1,
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2]).reshape([N, V, self.inter_c * T])
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A2 = self.conv_b[i](x).reshape([N, self.inter_c * T, V])
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A1 = self.soft(paddle.matmul(A1, A2) / A1.shape[-1])
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A1 = A1 + A[i]
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A2 = x.reshape([N, C * T, V])
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z = self.conv_d[i](paddle.matmul(A2, A1).reshape([N, C, T, V]))
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y = z + y if y is not None else z
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y = self.bn(y)
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y += self.down(x)
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return self.relu(y)
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class Block(nn.Layer):
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def __init__(self, in_channels, out_channels, A, stride=1, residual=True):
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super(Block, self).__init__()
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self.gcn1 = UnitGCN(in_channels, out_channels, A)
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self.tcn1 = UnitTCN(out_channels, out_channels, stride=stride)
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self.relu = nn.ReLU()
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if not residual:
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self.residual = lambda x: 0
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elif (in_channels == out_channels) and (stride == 1):
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self.residual = lambda x: x
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else:
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self.residual = UnitTCN(in_channels,
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out_channels,
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kernel_size=1,
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stride=stride)
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def forward(self, x):
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x = self.tcn1(self.gcn1(x)) + self.residual(x)
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return self.relu(x)
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# This Graph structure is for the NTURGB+D dataset. If you use a custom dataset, modify num_node and the corresponding graph adjacency structure.
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class Graph:
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def __init__(self, labeling_mode='spatial'):
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num_node = 25
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self_link = [(i, i) for i in range(num_node)]
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inward_ori_index = [(1, 2), (2, 21), (3, 21), (4, 3), (5, 21), (6, 5),
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(7, 6), (8, 7), (9, 21), (10, 9), (11, 10),
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(12, 11), (13, 1), (14, 13), (15, 14), (16, 15),
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(17, 1), (18, 17), (19, 18), (20, 19), (22, 23),
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(23, 8), (24, 25), (25, 12)]
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inward = [(i - 1, j - 1) for (i, j) in inward_ori_index]
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outward = [(j, i) for (i, j) in inward]
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neighbor = inward + outward
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self.num_node = num_node
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self.self_link = self_link
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self.inward = inward
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self.outward = outward
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self.neighbor = neighbor
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self.A = self.get_adjacency_matrix(labeling_mode)
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def edge2mat(self, link, num_node):
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A = np.zeros((num_node, num_node))
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for i, j in link:
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A[j, i] = 1
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return A
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def normalize_digraph(self, A):
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Dl = np.sum(A, 0)
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h, w = A.shape
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Dn = np.zeros((w, w))
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for i in range(w):
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if Dl[i] > 0:
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Dn[i, i] = Dl[i]**(-1)
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AD = np.dot(A, Dn)
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return AD
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def get_spatial_graph(self, num_node, self_link, inward, outward):
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I = self.edge2mat(self_link, num_node)
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In = self.normalize_digraph(self.edge2mat(inward, num_node))
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Out = self.normalize_digraph(self.edge2mat(outward, num_node))
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A = np.stack((I, In, Out))
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return A
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def get_adjacency_matrix(self, labeling_mode=None):
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if labeling_mode is None:
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return self.A
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if labeling_mode == 'spatial':
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A = self.get_spatial_graph(self.num_node, self.self_link,
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self.inward, self.outward)
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else:
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raise ValueError()
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return A
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@BACKBONES.register()
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class AGCN2s(nn.Layer):
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def __init__(self,
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num_point=25,
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num_person=2,
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graph='ntu_rgb_d',
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graph_args=dict(),
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in_channels=3):
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super(AGCN2s, self).__init__()
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if graph == 'ntu_rgb_d':
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self.graph = Graph(**graph_args)
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else:
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raise ValueError()
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A = self.graph.A
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self.data_bn = nn.BatchNorm1D(num_person * in_channels * num_point)
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self.l1 = Block(in_channels, 64, A, residual=False)
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self.l2 = Block(64, 64, A)
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self.l3 = Block(64, 64, A)
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self.l4 = Block(64, 64, A)
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self.l5 = Block(64, 128, A, stride=2)
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self.l6 = Block(128, 128, A)
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self.l7 = Block(128, 128, A)
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self.l8 = Block(128, 256, A, stride=2)
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self.l9 = Block(256, 256, A)
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self.l10 = Block(256, 256, A)
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def forward(self, x):
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N, C, T, V, M = x.shape
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x = x.transpose([0, 4, 3, 1, 2]).reshape_([N, M * V * C, T])
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x = self.data_bn(x)
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x = x.reshape_([N, M, V, C,
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T]).transpose([0, 1, 3, 4,
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2]).reshape_([N * M, C, T, V])
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x = self.l1(x)
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x = self.l2(x)
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x = self.l3(x)
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x = self.l4(x)
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x = self.l5(x)
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x = self.l6(x)
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x = self.l7(x)
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x = self.l8(x)
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x = self.l9(x)
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x = self.l10(x)
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return x
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