Inheritance diagram for stgcn.STGCNBlock:
Public Member Functions | |
| __init__ (self, in_channels, out_channels, kernel_size, stride=1, dropout=0, residual=True) | |
| forward (self, x, A) | |
Public Attributes | |
| gcn | |
| tcn | |
| residual | |
| relu | |
Detailed Description
Applies a spatial temporal graph convolution over an input graph sequence.
Args:
in_channels (int): Number of channels in the input sequence data
out_channels (int): Number of channels produced by the convolution
kernel_size (tuple): Size of the temporal convolving kernel and graph convolving kernel
stride (int, optional): Stride of the temporal convolution. Default: 1
dropout (int, optional): Dropout rate of the final output. Default: 0
residual (bool, optional): If ``True``, applies a residual mechanism. Default: ``True``
Shape:
- Input[0]: Input graph sequence in :math:`(N, in_channels, T_{in}, V)` format
- Input[1]: Input graph adjacency matrix in :math:`(K, V, V)` format
- Output[0]: Outpu graph sequence in :math:`(N, out_channels, T_{out}, V)` format
- Output[1]: Graph adjacency matrix for output data in :math:`(K, V, V)` format
where
:math:`N` is a batch size,
:math:`K` is the spatial kernel size, as :math:`K == kernel_size[1]`,
:math:`T_{in}/T_{out}` is a length of input/output sequence,
:math:`V` is the number of graph nodes.
Constructor & Destructor Documentation
◆ __init__()
| stgcn.STGCNBlock.__init__ | ( | self, | |
| in_channels, | |||
| out_channels, | |||
| kernel_size, | |||
| stride = 1, | |||
| dropout = 0, | |||
| residual = True ) |
Definition at line 32 of file stgcn.py.
38 residual=True):
39 super().__init__()
40
41 assert len(kernel_size) == 2
42 assert kernel_size[0] % 2 == 1
43
44 self.gcn = GCNBlock(in_channels, out_channels, kernel_size[1])
45 self.tcn = TCNBlock(out_channels, kernel_size[0], stride, dropout)
46
47 if not residual:
48 self.residual = lambda x: 0
49
50 elif (in_channels == out_channels) and (stride == 1):
51 self.residual = lambda x: x
52
53 else:
54 self.residual = nn.Sequential(
55 nn.Conv2d(
56 in_channels,
57 out_channels,
58 kernel_size=1,
59 stride=(stride, 1)),
60 nn.BatchNorm2d(out_channels),
61 )
62
63 self.relu = nn.ReLU(inplace=True)
64
Member Function Documentation
◆ forward()
| stgcn.STGCNBlock.forward | ( | self, | |
| x, | |||
| A ) |
Member Data Documentation
◆ gcn
◆ relu
◆ residual
◆ tcn
The documentation for this class was generated from the following file:
- smaction/models/backbones/stgcn.py
