| 77 | |
| 78 | |
| 79 | class MovementConvEncoder(nn.Module): |
| 80 | def __init__(self, input_size, hidden_size, output_size): |
| 81 | super(MovementConvEncoder, self).__init__() |
| 82 | self.main = nn.Sequential( |
| 83 | nn.Conv1d(input_size, hidden_size, 4, 2, 1), |
| 84 | nn.Dropout(0.2, inplace=True), |
| 85 | nn.LeakyReLU(0.2, inplace=True), |
| 86 | nn.Conv1d(hidden_size, output_size, 4, 2, 1), |
| 87 | nn.Dropout(0.2, inplace=True), |
| 88 | nn.LeakyReLU(0.2, inplace=True), |
| 89 | ) |
| 90 | self.out_net = nn.Linear(output_size, output_size) |
| 91 | self.main.apply(init_weight) |
| 92 | self.out_net.apply(init_weight) |
| 93 | |
| 94 | def forward(self, inputs): |
| 95 | inputs = inputs.permute(0, 2, 1) |
| 96 | outputs = self.main(inputs).permute(0, 2, 1) |
| 97 | # print(outputs.shape) |
| 98 | return self.out_net(outputs) |
| 99 | |
| 100 | |
| 101 | class MovementConvDecoder(nn.Module): |