(self, num_inputs, num_channels, kernel_size=2, dropout=0.2)
| 85 | |
| 86 | class _TemporalConvNet(nn.Module): |
| 87 | def __init__(self, num_inputs, num_channels, kernel_size=2, dropout=0.2): |
| 88 | super(_TemporalConvNet, self).__init__() |
| 89 | layers = [] |
| 90 | num_levels = len(num_channels) |
| 91 | for i in range(num_levels): |
| 92 | dilation_size = 2 ** i |
| 93 | in_channels = num_inputs if i == 0 else num_channels[i-1] |
| 94 | out_channels = num_channels[i] |
| 95 | layers += [_TemporalBlock2(in_channels, out_channels, kernel_size, stride=1, dilation=dilation_size, |
| 96 | padding=(kernel_size-1) * dilation_size, dropout=dropout)] |
| 97 | |
| 98 | self.network = nn.Sequential(*layers) |
| 99 | |
| 100 | def forward(self, x): |
| 101 | return self.network(x) |
nothing calls this directly
no test coverage detected