(self, model_input)
| 117 | self.output_linear.apply(mfn_weights_init) |
| 118 | |
| 119 | def forward(self, model_input): |
| 120 | |
| 121 | input_dict = {key: input.clone().detach().requires_grad_(True) |
| 122 | for key, input in model_input.items()} |
| 123 | coords = input_dict['coords'] |
| 124 | |
| 125 | out = self.filters[0](coords) |
| 126 | for i in range(1, len(self.filters)): |
| 127 | out = self.filters[i](coords) * self.linear[i - 1](out) |
| 128 | out = self.output_linear(out) |
| 129 | |
| 130 | if self.output_act: |
| 131 | out = torch.sin(out) |
| 132 | |
| 133 | return {'model_in': input_dict, 'model_out': {'output': out}} |
| 134 | |
| 135 | |
| 136 | class FourierLayer(nn.Module): |
nothing calls this directly
no outgoing calls
no test coverage detected