| 127 | return results |
| 128 | |
| 129 | class Predictor(torch.nn.Module): |
| 130 | def __init__(self, opt: Options, **model_kwargs): |
| 131 | super().__init__() |
| 132 | self.opt = opt |
| 133 | self.depth_prior = DepthAnythingWrapper(opt.depth_model_name) |
| 134 | |
| 135 | def forward(self, frames, depths, cond_times=None): |
| 136 | with torch.no_grad(): |
| 137 | predicted_depth = self.depth_prior(depths).detach() # [B, H, W] |
| 138 | predicted_depth = F.interpolate(predicted_depth[:, None], size=frames.shape[-2:], mode="bilinear", align_corners=True) |
| 139 | return predicted_depth # [B, C, H, W] |
| 140 | |
| 141 | def run_inference(opt: Options): |
| 142 | train_dataset = DAVISDataset(opt, training=True) |