(self, do_vae, out_channels=128, num_pts=256)
| 111 | |
| 112 | class StrandEncoder1dCNNWN(nn.Module): |
| 113 | def __init__(self, do_vae, out_channels=128, num_pts=256): |
| 114 | super(StrandEncoder1dCNNWN, self).__init__() |
| 115 | |
| 116 | self.do_vae = do_vae |
| 117 | self.num_pts = num_pts |
| 118 | |
| 119 | # self.training = False |
| 120 | |
| 121 | |
| 122 | in_channels = 0 |
| 123 | in_channels += 3 # 3 for xyz |
| 124 | in_channels += 3 # 3 for dirs |
| 125 | |
| 126 | |
| 127 | self.cnn_encoder = torch.nn.Sequential( |
| 128 | Conv1dWN_v2(in_channels, 64, kernel_size=4, stride=2, padding=1, padding_mode='replicate'),torch.nn.SiLU(), |
| 129 | Conv1dWN_v2(64, 64, kernel_size=4, stride=2, padding=1, padding_mode='replicate'), torch.nn.SiLU(), |
| 130 | Conv1dWN_v2(64, 128, kernel_size=4, stride=2, padding=1, padding_mode='replicate'), torch.nn.SiLU(), |
| 131 | Conv1dWN_v2(128, 128, kernel_size=4, stride=2, padding=1, padding_mode='replicate'), torch.nn.SiLU(), |
| 132 | Conv1dWN_v2(128, 256, kernel_size=4, stride=2, padding=1, padding_mode='replicate'), torch.nn.SiLU(), |
| 133 | Conv1dWN_v2(256, 256, kernel_size=4, stride=2, padding=1, padding_mode='replicate'), torch.nn.SiLU(), |
| 134 | ) |
| 135 | |
| 136 | |
| 137 | self.aggregate_towards_mean = torch.nn.Sequential( |
| 138 | LinearWN_v2(256 * 4, 512), torch.nn.SiLU(), |
| 139 | ) |
| 140 | self.pred_mean = LinearWN_v2(512, out_channels) |
| 141 | |
| 142 | self.aggregate_towards_logstd = torch.nn.Sequential( |
| 143 | LinearWN_v2(256 * 4, 512), torch.nn.SiLU(), |
| 144 | ) |
| 145 | self.pred_logstd = LinearWN_v2(512, out_channels) |
| 146 | |
| 147 | |
| 148 | self.apply(lambda x: kaiming_init(x, False, nonlinearity="silu")) |
| 149 | kaiming_init(self.pred_mean, True) |
| 150 | kaiming_init(self.pred_logstd, True) |
| 151 | |
| 152 | |
| 153 | self.tanh = torch.nn.Tanh() |
| 154 | |
| 155 | |
| 156 | def forward(self, gt_dict): |
nothing calls this directly
no test coverage detected