mirror of
https://github.com/finegrain-ai/refiners.git
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Make Dataset part of the trainer
This commit is contained in:
limiteinductive
Benjamin Trom
parent
9883f24f9a
commit
2e526d35d1
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@ -1,11 +0,0 @@
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from refiners.training_utils.latent_diffusion import FinetuneLatentDiffusionConfig, LatentDiffusionTrainer
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if __name__ == "__main__":
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import sys
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config_path = sys.argv[1]
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config = FinetuneLatentDiffusionConfig.load_from_toml(
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toml_path=config_path,
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)
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trainer = LatentDiffusionTrainer(config=config)
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trainer.train()
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@ -4,6 +4,9 @@ from importlib.metadata import requires
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from packaging.requirements import Requirement
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from refiners.training_utils.config import BaseConfig
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from refiners.training_utils.trainer import Trainer
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refiners_requires = requires("refiners")
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assert refiners_requires is not None
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@ -21,3 +24,9 @@ for dep in refiners_requires:
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file=sys.stderr,
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)
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sys.exit(1)
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__all__ = [
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"Trainer",
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"BaseConfig",
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]
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@ -1,260 +0,0 @@
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import random
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from dataclasses import dataclass
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from functools import cached_property
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from typing import Any, Callable, TypedDict, TypeVar
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from loguru import logger
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from PIL import Image
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from pydantic import BaseModel
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from torch import Generator, Tensor, cat, device as Device, dtype as DType, randn
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from torch.nn import Module
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from torch.nn.functional import mse_loss
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from torch.utils.data import Dataset
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from torchvision.transforms import Compose, RandomCrop, RandomHorizontalFlip # type: ignore
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import refiners.fluxion.layers as fl
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from refiners.foundationals.clip.text_encoder import CLIPTextEncoderL
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from refiners.foundationals.latent_diffusion import (
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DPMSolver,
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SD1UNet,
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StableDiffusion_1,
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)
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from refiners.foundationals.latent_diffusion.solvers import DDPM, Solver
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from refiners.foundationals.latent_diffusion.stable_diffusion_1.model import SD1Autoencoder
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from refiners.training_utils.callback import Callback
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from refiners.training_utils.config import BaseConfig
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from refiners.training_utils.huggingface_datasets import HuggingfaceDataset, HuggingfaceDatasetConfig, load_hf_dataset
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from refiners.training_utils.trainer import Trainer
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from refiners.training_utils.wandb import WandbLoggable
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class LatentDiffusionConfig(BaseModel):
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unconditional_sampling_probability: float = 0.2
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offset_noise: float = 0.1
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min_step: int = 0
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max_step: int = 999
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class TestDiffusionConfig(BaseModel):
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seed: int = 0
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num_inference_steps: int = 30
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use_short_prompts: bool = False
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prompts: list[str] = []
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num_images_per_prompt: int = 1
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class FinetuneLatentDiffusionConfig(BaseConfig):
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dataset: HuggingfaceDatasetConfig
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latent_diffusion: LatentDiffusionConfig
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test_diffusion: TestDiffusionConfig
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@dataclass
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class TextEmbeddingLatentsBatch:
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text_embeddings: Tensor
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latents: Tensor
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class CaptionImage(TypedDict):
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caption: str
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image: Image.Image
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ConfigType = TypeVar("ConfigType", bound=FinetuneLatentDiffusionConfig)
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class TextEmbeddingLatentsDataset(Dataset[TextEmbeddingLatentsBatch]):
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def __init__(self, trainer: "LatentDiffusionTrainer[Any]") -> None:
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self.trainer = trainer
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self.config = trainer.config
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self.device = self.trainer.device
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self.lda = self.trainer.lda
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self.text_encoder = self.trainer.text_encoder
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self.dataset = self.load_huggingface_dataset()
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self.process_image = self.build_image_processor()
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logger.info(f"Loaded {len(self.dataset)} samples from dataset")
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def build_image_processor(self) -> Callable[[Image.Image], Image.Image]:
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# TODO: make this configurable and add other transforms
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transforms: list[Module] = []
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if self.config.dataset.random_crop:
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transforms.append(RandomCrop(size=512))
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if self.config.dataset.horizontal_flip:
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transforms.append(RandomHorizontalFlip(p=0.5))
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if not transforms:
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return lambda image: image
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return Compose(transforms)
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def load_huggingface_dataset(self) -> HuggingfaceDataset[CaptionImage]:
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dataset_config = self.config.dataset
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logger.info(f"Loading dataset from {dataset_config.hf_repo} revision {dataset_config.revision}")
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return load_hf_dataset(
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path=dataset_config.hf_repo, revision=dataset_config.revision, split=dataset_config.split
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)
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def resize_image(self, image: Image.Image, min_size: int = 512, max_size: int = 576) -> Image.Image:
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return resize_image(image=image, min_size=min_size, max_size=max_size)
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def process_caption(self, caption: str) -> str:
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return caption if random.random() > self.config.latent_diffusion.unconditional_sampling_probability else ""
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def get_caption(self, index: int) -> str:
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return self.dataset[index]["caption"]
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def get_image(self, index: int) -> Image.Image:
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return self.dataset[index]["image"]
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def __getitem__(self, index: int) -> TextEmbeddingLatentsBatch:
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caption = self.get_caption(index=index)
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image = self.get_image(index=index)
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resized_image = self.resize_image(
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image=image,
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min_size=self.config.dataset.resize_image_min_size,
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max_size=self.config.dataset.resize_image_max_size,
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)
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processed_image = self.process_image(resized_image)
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latents = self.lda.image_to_latents(image=processed_image).to(device=self.device)
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processed_caption = self.process_caption(caption=caption)
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clip_text_embedding = self.text_encoder(processed_caption).to(device=self.device)
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return TextEmbeddingLatentsBatch(text_embeddings=clip_text_embedding, latents=latents)
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def collate_fn(self, batch: list[TextEmbeddingLatentsBatch]) -> TextEmbeddingLatentsBatch:
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text_embeddings = cat(tensors=[item.text_embeddings for item in batch])
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latents = cat(tensors=[item.latents for item in batch])
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return TextEmbeddingLatentsBatch(text_embeddings=text_embeddings, latents=latents)
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def __len__(self) -> int:
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return len(self.dataset)
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class LatentDiffusionTrainer(Trainer[ConfigType, TextEmbeddingLatentsBatch]):
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@cached_property
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def unet(self) -> SD1UNet:
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assert self.config.models["unet"] is not None, "The config must contain a unet entry."
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return SD1UNet(in_channels=4, device=self.device).to(device=self.device)
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@cached_property
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def text_encoder(self) -> CLIPTextEncoderL:
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assert self.config.models["text_encoder"] is not None, "The config must contain a text_encoder entry."
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return CLIPTextEncoderL(device=self.device).to(device=self.device)
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@cached_property
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def lda(self) -> SD1Autoencoder:
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assert self.config.models["lda"] is not None, "The config must contain a lda entry."
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return SD1Autoencoder(device=self.device).to(device=self.device)
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def load_models(self) -> dict[str, fl.Module]:
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return {"unet": self.unet, "text_encoder": self.text_encoder, "lda": self.lda}
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def load_dataset(self) -> Dataset[TextEmbeddingLatentsBatch]:
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return TextEmbeddingLatentsDataset(trainer=self)
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@cached_property
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def ddpm_solver(self) -> Solver:
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return DDPM(
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num_inference_steps=1000,
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device=self.device,
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).to(device=self.device)
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def sample_timestep(self) -> Tensor:
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random_step = random.randint(a=self.config.latent_diffusion.min_step, b=self.config.latent_diffusion.max_step)
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self.current_step = random_step
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return self.ddpm_solver.timesteps[random_step].unsqueeze(dim=0)
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def sample_noise(self, size: tuple[int, ...], dtype: DType | None = None) -> Tensor:
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return sample_noise(
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size=size, offset_noise=self.config.latent_diffusion.offset_noise, device=self.device, dtype=dtype
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)
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def compute_loss(self, batch: TextEmbeddingLatentsBatch) -> Tensor:
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clip_text_embedding, latents = batch.text_embeddings, batch.latents
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timestep = self.sample_timestep()
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noise = self.sample_noise(size=latents.shape, dtype=latents.dtype)
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noisy_latents = self.ddpm_solver.add_noise(x=latents, noise=noise, step=self.current_step)
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self.unet.set_timestep(timestep=timestep)
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self.unet.set_clip_text_embedding(clip_text_embedding=clip_text_embedding)
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prediction = self.unet(noisy_latents)
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loss = mse_loss(input=prediction, target=noise)
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return loss
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def compute_evaluation(self) -> None:
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sd = StableDiffusion_1(
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unet=self.unet,
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lda=self.lda,
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clip_text_encoder=self.text_encoder,
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solver=DPMSolver(num_inference_steps=self.config.test_diffusion.num_inference_steps),
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device=self.device,
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)
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prompts = self.config.test_diffusion.prompts
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num_images_per_prompt = self.config.test_diffusion.num_images_per_prompt
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if self.config.test_diffusion.use_short_prompts:
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prompts = [prompt.split(sep=",")[0] for prompt in prompts]
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images: dict[str, WandbLoggable] = {}
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for prompt in prompts:
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canvas_image: Image.Image = Image.new(mode="RGB", size=(512, 512 * num_images_per_prompt))
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for i in range(num_images_per_prompt):
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logger.info(f"Generating image {i+1}/{num_images_per_prompt} for prompt: {prompt}")
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x = randn(1, 4, 64, 64, device=self.device)
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clip_text_embedding = sd.compute_clip_text_embedding(text=prompt).to(device=self.device)
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for step in sd.steps:
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x = sd(
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x,
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step=step,
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clip_text_embedding=clip_text_embedding,
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)
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canvas_image.paste(sd.lda.latents_to_image(x=x), box=(0, 512 * i))
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images[prompt] = canvas_image
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# TODO: wandb_log images
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def sample_noise(
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size: tuple[int, ...],
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offset_noise: float = 0.1,
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device: Device | str = "cpu",
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dtype: DType | None = None,
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generator: Generator | None = None,
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) -> Tensor:
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"""Sample noise from a normal distribution.
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If `offset_noise` is more than 0, the noise will be offset by a small amount. It allows the model to generate
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images with a wider range of contrast https://www.crosslabs.org/blog/diffusion-with-offset-noise.
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"""
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device = Device(device)
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noise = randn(*size, generator=generator, device=device, dtype=dtype)
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return noise + offset_noise * randn(*size[:2], 1, 1, generator=generator, device=device, dtype=dtype)
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def resize_image(image: Image.Image, min_size: int = 512, max_size: int = 576) -> Image.Image:
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image_min_size = min(image.size)
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if image_min_size > max_size:
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if image_min_size == image.size[0]:
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image = image.resize(size=(max_size, int(max_size * image.size[1] / image.size[0])))
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else:
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image = image.resize(size=(int(max_size * image.size[0] / image.size[1]), max_size))
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if image_min_size < min_size:
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if image_min_size == image.size[0]:
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image = image.resize(size=(min_size, int(min_size * image.size[1] / image.size[0])))
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else:
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image = image.resize(size=(int(min_size * image.size[0] / image.size[1]), min_size))
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return image
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class MonitorTimestepLoss(Callback[LatentDiffusionTrainer[Any]]):
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def on_train_begin(self, trainer: LatentDiffusionTrainer[Any]) -> None:
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self.timestep_bins: dict[int, list[float]] = {i: [] for i in range(10)}
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def on_compute_loss_end(self, trainer: LatentDiffusionTrainer[Any]) -> None:
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loss_value = trainer.loss.detach().cpu().item()
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current_step = trainer.current_step
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bin_index = min(current_step // 100, 9)
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self.timestep_bins[bin_index].append(loss_value)
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def on_epoch_end(self, trainer: LatentDiffusionTrainer[Any]) -> None:
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log_data: dict[str, WandbLoggable] = {}
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for bin_index, losses in self.timestep_bins.items():
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if losses:
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avg_loss = sum(losses) / len(losses)
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log_data[f"average_loss_timestep_bin_{bin_index * 100}"] = avg_loss
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self.timestep_bins[bin_index] = []
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# TODO: wandb_log log_data
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@ -1,6 +1,6 @@
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import random
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import time
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from abc import ABC, abstractmethod
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from abc import ABC, abstractmethod, abstractproperty
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from functools import cached_property, wraps
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from typing import Any, Callable, Generic, Iterable, TypeVar, cast
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@ -250,6 +250,23 @@ Batch = TypeVar("Batch")
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ConfigType = TypeVar("ConfigType", bound=BaseConfig)
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class _Dataset(Dataset[Batch]):
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"""
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A wrapper around the `get_item` method to create a [`torch.utils.data.Dataset`]
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"""
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def __init__(self, get_item: Callable[[int], Batch], length: int) -> None:
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assert length > 0, "Dataset length must be greater than 0."
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self.length = length
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self.get_item = get_item
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def __getitem__(self, index: int) -> Batch:
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return self.get_item(index)
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def __len__(self) -> int:
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return self.length
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class Trainer(Generic[ConfigType, Batch], ABC):
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def __init__(self, config: ConfigType, callbacks: list[Callback[Any]] | None = None) -> None:
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self.config = config
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@ -429,23 +446,44 @@ class Trainer(Generic[ConfigType, Batch], ABC):
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...
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@abstractmethod
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def load_dataset(self) -> Dataset[Batch]:
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def get_item(self, _: int) -> Batch:
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"""
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Returns a batch of data.
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This function is used by the dataloader to fetch a batch of data.
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"""
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...
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@abstractproperty
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def dataset_length(self) -> int:
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"""
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Returns the length of the dataset.
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This is used to compute the number of batches per epoch.
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"""
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...
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@abstractmethod
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def collate_fn(self, batch: list[Batch]) -> Batch:
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"""
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Collate function for the dataloader.
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This function is used to tell the dataloader how to combine a list of
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batches into a single batch.
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"""
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...
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@cached_property
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def dataset(self) -> Dataset[Batch]:
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return self.load_dataset()
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"""
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Returns the dataset constructed with the `get_item` method.
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"""
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return _Dataset(get_item=self.get_item, length=self.dataset_length)
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@cached_property
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def dataset_length(self) -> int:
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assert hasattr(self.dataset, "__len__"), "The dataset must implement the `__len__` method."
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return len(self.dataset) # type: ignore
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@cached_property
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def dataloader(self) -> DataLoader[Batch]:
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collate_fn = getattr(self.dataset, "collate_fn", None)
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def dataloader(self) -> DataLoader[Any]:
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return DataLoader(
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dataset=self.dataset, batch_size=self.config.training.batch_size, shuffle=True, collate_fn=collate_fn
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dataset=self.dataset, batch_size=self.config.training.batch_size, shuffle=True, collate_fn=self.collate_fn
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)
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@abstractmethod
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|
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@ -7,7 +7,6 @@ import pytest
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import torch
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from torch import Tensor, nn
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from torch.optim import SGD
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from torch.utils.data import Dataset
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from refiners.fluxion import layers as fl
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from refiners.fluxion.utils import norm
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@ -27,20 +26,6 @@ class MockBatch:
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targets: torch.Tensor
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class MockDataset(Dataset[MockBatch]):
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def __len__(self):
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return 20
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def __getitem__(self, _: int) -> MockBatch:
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return MockBatch(inputs=torch.randn(1, 10), targets=torch.randn(1, 10))
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def collate_fn(self, batch: list[MockBatch]) -> MockBatch:
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return MockBatch(
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inputs=torch.cat([b.inputs for b in batch]),
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targets=torch.cat([b.targets for b in batch]),
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)
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class MockConfig(BaseConfig):
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pass
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@ -57,13 +42,23 @@ class MockModel(fl.Chain):
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class MockTrainer(Trainer[MockConfig, MockBatch]):
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step_counter: int = 0
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@property
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def dataset_length(self) -> int:
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return 20
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def get_item(self, _: int) -> MockBatch:
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return MockBatch(inputs=torch.randn(1, 10), targets=torch.randn(1, 10))
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def collate_fn(self, batch: list[MockBatch]) -> MockBatch:
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return MockBatch(
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inputs=torch.cat([b.inputs for b in batch]),
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targets=torch.cat([b.targets for b in batch]),
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)
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@cached_property
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def mock_model(self) -> MockModel:
|
||||
return MockModel()
|
||||
|
||||
def load_dataset(self) -> Dataset[MockBatch]:
|
||||
return MockDataset()
|
||||
|
||||
def load_models(self) -> dict[str, fl.Module]:
|
||||
return {"mock_model": self.mock_model}
|
||||
|
||||
|
|
|
|||
Loading…
Reference in a new issue