(self, batch, batch_idx)
| 78 | # self.global_step = int(args.pretrained.split("/")[-1].split(".")[0]) # dirty implementation |
| 79 | |
| 80 | def training_step(self, batch, batch_idx): |
| 81 | x = batch["video"].to(self.device) |
| 82 | video_name = batch["video_name"] |
| 83 | |
| 84 | with torch.no_grad(): |
| 85 | b, _, _, _, _ = x.shape |
| 86 | x = rearrange(x, "b f c h w -> (b f) c h w").contiguous() |
| 87 | x = self.vae.encode(x).latent_dist.sample().mul_(0.18215) |
| 88 | x = rearrange(x, "(b f) c h w -> b f c h w", b=b).contiguous() |
| 89 | |
| 90 | if self.args.extras == 78: # text-to-video |
| 91 | raise ValueError("T2V training is not supported at this moment!") |
| 92 | elif self.args.extras == 2: |
| 93 | model_kwargs = dict(y=video_name) |
| 94 | else: |
| 95 | model_kwargs = dict(y=None) |
| 96 | |
| 97 | t = torch.randint(0, self.diffusion.num_timesteps, (x.shape[0],), device=self.device) |
| 98 | loss_dict = self.diffusion.training_losses(self.model, x, t, model_kwargs) |
| 99 | loss = loss_dict["loss"].mean() |
| 100 | |
| 101 | if self.global_step < self.args.start_clip_iter: |
| 102 | gradient_norm = clip_grad_norm_(self.model.parameters(), self.args.clip_max_norm, clip_grad=False) |
| 103 | else: |
| 104 | gradient_norm = clip_grad_norm_(self.model.parameters(), self.args.clip_max_norm, clip_grad=True) |
| 105 | |
| 106 | self.log("train_loss", loss) |
| 107 | self.log("gradient_norm", gradient_norm) |
| 108 | |
| 109 | if (self.global_step+1) % self.args.log_every == 0: |
| 110 | self.logging.info( |
| 111 | f"(step={self.global_step+1:07d}/epoch={self.current_epoch:04d}) Train Loss: {loss:.4f}, Gradient Norm: {gradient_norm:.4f}" |
| 112 | ) |
| 113 | return loss |
| 114 | |
| 115 | def on_train_batch_end(self, *args, **kwargs): |
| 116 | update_ema(self.ema, self.model) |
nothing calls this directly
no test coverage detected