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st_gcn.py
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st_gcn.py
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import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.autograd import Variable
from net.utils.tgcn import ConvTemporalGraphical
from net.utils.graph import Graph
class Model(nn.Module):
r"""Spatial temporal graph convolutional networks.
Args:
in_channels (int): Number of channels in the input data
num_class (int): Number of classes for the classification task
graph_args (dict): The arguments for building the graph
edge_importance_weighting (bool): If ``True``, adds a learnable
importance weighting to the edges of the graph
**kwargs (optional): Other parameters for graph convolution units
Shape:
- Input: :math:`(N, in_channels, T_{in}, V_{in}, M_{in})`
- Output: :math:`(N, num_class)` where
:math:`N` is a batch size,
:math:`T_{in}` is a length of input sequence,
:math:`V_{in}` is the number of graph nodes,
:math:`M_{in}` is the number of instance in a frame.
"""
def __init__(self, in_channels, num_class, graph_args,
edge_importance_weighting, **kwargs):
super().__init__()
# load graph
self.graph = Graph(**graph_args)
A = torch.tensor(self.graph.A, dtype=torch.float32, requires_grad=False)
self.register_buffer('A', A)
# build networks
spatial_kernel_size = A.size(0)
temporal_kernel_size = 9
kernel_size = (temporal_kernel_size, spatial_kernel_size)
self.data_bn = nn.BatchNorm1d(in_channels * A.size(1))
kwargs0 = {k: v for k, v in kwargs.items() if k != 'dropout'}
self.st_gcn_networks = nn.ModuleList((
st_gcn(in_channels, 64, kernel_size, 1, residual=False, **kwargs0),
st_gcn(64, 64, kernel_size, 1, **kwargs),
st_gcn(64, 64, kernel_size, 1, **kwargs),
st_gcn(64, 64, kernel_size, 1, **kwargs),
st_gcn(64, 128, kernel_size, 2, **kwargs),
st_gcn(128, 128, kernel_size, 1, **kwargs),
st_gcn(128, 128, kernel_size, 1, **kwargs),
st_gcn(128, 256, kernel_size, 2, **kwargs),
st_gcn(256, 256, kernel_size, 1, **kwargs),
st_gcn(256, 256, kernel_size, 1, **kwargs),
))
# initialize parameters for edge importance weighting
if edge_importance_weighting:
self.edge_importance = nn.ParameterList([
nn.Parameter(torch.ones(self.A.size()))
for i in self.st_gcn_networks
])
else:
self.edge_importance = [1] * len(self.st_gcn_networks)
# fcn for prediction
self.fcn = nn.Conv2d(256, num_class, kernel_size=1)
def forward(self, x):
# data normalization
N, C, T, V, M = x.size()
x = x.permute(0, 4, 3, 1, 2).contiguous()
x = x.view(N * M, V * C, T)
x = self.data_bn(x)
x = x.view(N, M, V, C, T)
x = x.permute(0, 1, 3, 4, 2).contiguous()
x = x.view(N * M, C, T, V)
# forwad
for gcn, importance in zip(self.st_gcn_networks, self.edge_importance):
x, _ = gcn(x, self.A * importance)
# global pooling
x = F.avg_pool2d(x, x.size()[2:])
x = x.view(N, M, -1, 1, 1).mean(dim=1)
# prediction
x = self.fcn(x)
x = x.view(x.size(0), -1)
return x
def extract_feature(self, x):
# data normalization
N, C, T, V, M = x.size()
x = x.permute(0, 4, 3, 1, 2).contiguous()
x = x.view(N * M, V * C, T)
x = self.data_bn(x)
x = x.view(N, M, V, C, T)
x = x.permute(0, 1, 3, 4, 2).contiguous()
x = x.view(N * M, C, T, V)
# forwad
for gcn, importance in zip(self.st_gcn_networks, self.edge_importance):
x, _ = gcn(x, self.A * importance)
_, c, t, v = x.size()
feature = x.view(N, M, c, t, v).permute(0, 2, 3, 4, 1)
# prediction
x = self.fcn(x)
output = x.view(N, M, -1, t, v).permute(0, 2, 3, 4, 1)
return output, feature
class st_gcn(nn.Module):
r"""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.
"""
def __init__(self,
in_channels,
out_channels,
kernel_size,
stride=1,
dropout=0,
residual=True):
super().__init__()
assert len(kernel_size) == 2
assert kernel_size[0] % 2 == 1
padding = ((kernel_size[0] - 1) // 2, 0)
self.gcn = ConvTemporalGraphical(in_channels, out_channels,
kernel_size[1])
self.tcn = nn.Sequential(
nn.BatchNorm2d(out_channels),
nn.ReLU(inplace=True),
nn.Conv2d(
out_channels,
out_channels,
(kernel_size[0], 1),
(stride, 1),
padding,
),
nn.BatchNorm2d(out_channels),
nn.Dropout(dropout, inplace=True),
)
if not residual:
self.residual = lambda x: 0
elif (in_channels == out_channels) and (stride == 1):
self.residual = lambda x: x
else:
self.residual = nn.Sequential(
nn.Conv2d(
in_channels,
out_channels,
kernel_size=1,
stride=(stride, 1)),
nn.BatchNorm2d(out_channels),
)
self.relu = nn.ReLU(inplace=True)
def forward(self, x, A):
res = self.residual(x)
x, A = self.gcn(x, A)
x = self.tcn(x) + res
return self.relu(x), A