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0000000000000000000000000000000000000000..ec01728ffadb21967c89bcc3267357d56a9f29ff --- /dev/null +++ b/samples/unet_384x384_0.jpg @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:a0448fb1f0d7f7317849a198660b40c1ac2b6a22c883243ef5ee3f17dd3f7eef +size 66747 diff --git a/src/dataset_from_folder.py b/src/dataset_from_folder.py index 27deb3a213c99adec3338f7055cb3d73f7847e64..8c7b6e5a19de2a638f5f0561548cdd626b7ffc1b 100644 --- a/src/dataset_from_folder.py +++ b/src/dataset_from_folder.py @@ -27,8 +27,8 @@ step = 64 empty_share = 0.05 limit = 0 # Основная процедура обработки -folder_path = "/workspace/butterfly" #alchemist" -save_path = "/workspace/sdxs3d/datasets/butterfly" #"alchemist" +folder_path = "/workspace/mjnj" #alchemist" +save_path = "/workspace/sdxs3d/datasets/mjnj" #"alchemist" os.makedirs(save_path, exist_ok=True) # Функция для очистки CUDA памяти diff --git a/train.py b/train.py index f9af0e0887fa4252faf7a40ac7ea2cc74af21ac2..ff8e86434d1bbbbfb28106d59a342eb3024a4886 100644 --- a/train.py +++ b/train.py @@ -26,12 +26,12 @@ import torch.nn.functional as F from collections import deque # --------------------------- Параметры --------------------------- -ds_path = "/workspace/sdxs3d/datasets/butterfly" +ds_path = "/workspace/sdxs3d/datasets/alchemist" project = "unet" -batch_size = 16 +batch_size = 32 base_learning_rate = 9e-5 min_learning_rate = 1e-5 -num_epochs = 30 +num_epochs = 300 # samples/save per epoch sample_interval_share = 1 use_wandb = True @@ -71,7 +71,7 @@ device = accelerator.device # Параметры для диффузии n_diffusion_steps = 50 samples_to_generate = 12 -guidance_scale = 5 +guidance_scale = 1 # Папки для сохранения результатов generated_folder = "samples" @@ -133,9 +133,6 @@ def logit_normal_samples(shape, mu=0.0, sigma=1.0, device=None, dtype=None): return logit_normal_samples - - - # --------------------------- Инициализация WandB --------------------------- if accelerator.is_main_process: if use_wandb: @@ -574,7 +571,7 @@ progress_bar = tqdm(total=total_training_steps, disable=not accelerator.is_local steps_per_epoch = len(dataloader) sample_interval = max(1, steps_per_epoch // sample_interval_share) -min_loss = 1. +min_loss = 2. for epoch in range(start_epoch, start_epoch + num_epochs): batch_losses = [] @@ -588,11 +585,9 @@ for epoch in range(start_epoch, start_epoch + num_epochs): if save_model == False and step == 5 : used_gb = torch.cuda.max_memory_allocated() / 1024**3 print(f"Шаг {step}: {used_gb:.2f} GB") - + noise = torch.randn_like(latents, dtype=latents.dtype) - - t = logit_normal_samples((batch_size, 1, 1, 1), mu=0.0, sigma=1.0, device=latents.device, dtype=latents.dtype) - + t = logit_normal_samples((latents.shape[0], 1, 1, 1), mu=0.0, sigma=1.0, device=latents.device, dtype=latents.dtype) noisy_latents = (1 - t) * noise + t * latents t_for_unet = (t * 1000).long().view(-1) @@ -648,31 +643,24 @@ for epoch in range(start_epoch, start_epoch + num_epochs): if global_step % sample_interval == 0: generate_and_save_samples(fixed_samples,global_step) last_n = sample_interval - avg_loss = float(np.mean(batch_losses[-last_n:])) if len(batch_losses) > 0 else 0.0 - avg_grad = float(np.mean(batch_grads[-last_n:])) if len(batch_grads) > 0 else 0.0 - - avg_data = {} - avg_data["avg_loss"] = avg_loss - avg_data["avg_grad"] = avg_grad - - print(f"Эпоха {epoch}, шаг {global_step}, средний лосс: {avg_loss:.6f}, grad: {avg_grad:.6f}") if save_model: - print("saving:",avg_loss < min_loss*save_barrier) - if avg_loss < min_loss*save_barrier: - min_loss = avg_loss + print("saving:",log_data["loss"] < min_loss*save_barrier) + if log_data["loss"] < min_loss*save_barrier: + min_loss = log_data["loss"] save_checkpoint(unet) if use_wandb: - wandb.log(avg_data, step=global_step) + wandb.log(log_data, step=global_step) if use_comet_ml: - comet_experiment.log_metrics(avg_data, step=global_step) + comet_experiment.log_metrics(log_data, step=global_step) if accelerator.is_main_process: - avg_epoch_loss = np.mean(batch_losses) if len(batch_losses)>0 else 0.0 + avg_epoch_loss = np.mean(batch_losses[-steps_per_epoch:]) if len(batch_losses)>0 else 0.0 + avg_epoch_grad = np.mean(batch_grads[-steps_per_epoch:]) if len(batch_grads)>0 else 0.0 print(f"\nЭпоха {epoch} завершена. 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