mirror of
https://github.com/finegrain-ai/refiners.git
synced 2024-11-21 13:48:46 +00:00
Make Dataset part of the trainer
This commit is contained in:
limiteinductive
Benjamin Trom
parent
9883f24f9a
commit
2e526d35d1
|
|
@ -1,11 +0,0 @@
|
|||
from refiners.training_utils.latent_diffusion import FinetuneLatentDiffusionConfig, LatentDiffusionTrainer
|
||||
|
||||
if __name__ == "__main__":
|
||||
import sys
|
||||
|
||||
config_path = sys.argv[1]
|
||||
config = FinetuneLatentDiffusionConfig.load_from_toml(
|
||||
toml_path=config_path,
|
||||
)
|
||||
trainer = LatentDiffusionTrainer(config=config)
|
||||
trainer.train()
|
||||
|
|
@ -4,6 +4,9 @@ from importlib.metadata import requires
|
|||
|
||||
from packaging.requirements import Requirement
|
||||
|
||||
from refiners.training_utils.config import BaseConfig
|
||||
from refiners.training_utils.trainer import Trainer
|
||||
|
||||
refiners_requires = requires("refiners")
|
||||
assert refiners_requires is not None
|
||||
|
||||
|
|
@ -21,3 +24,9 @@ for dep in refiners_requires:
|
|||
file=sys.stderr,
|
||||
)
|
||||
sys.exit(1)
|
||||
|
||||
|
||||
__all__ = [
|
||||
"Trainer",
|
||||
"BaseConfig",
|
||||
]
|
||||
|
|
|
|||
|
|
@ -1,260 +0,0 @@
|
|||
import random
|
||||
from dataclasses import dataclass
|
||||
from functools import cached_property
|
||||
from typing import Any, Callable, TypedDict, TypeVar
|
||||
|
||||
from loguru import logger
|
||||
from PIL import Image
|
||||
from pydantic import BaseModel
|
||||
from torch import Generator, Tensor, cat, device as Device, dtype as DType, randn
|
||||
from torch.nn import Module
|
||||
from torch.nn.functional import mse_loss
|
||||
from torch.utils.data import Dataset
|
||||
from torchvision.transforms import Compose, RandomCrop, RandomHorizontalFlip # type: ignore
|
||||
|
||||
import refiners.fluxion.layers as fl
|
||||
from refiners.foundationals.clip.text_encoder import CLIPTextEncoderL
|
||||
from refiners.foundationals.latent_diffusion import (
|
||||
DPMSolver,
|
||||
SD1UNet,
|
||||
StableDiffusion_1,
|
||||
)
|
||||
from refiners.foundationals.latent_diffusion.solvers import DDPM, Solver
|
||||
from refiners.foundationals.latent_diffusion.stable_diffusion_1.model import SD1Autoencoder
|
||||
from refiners.training_utils.callback import Callback
|
||||
from refiners.training_utils.config import BaseConfig
|
||||
from refiners.training_utils.huggingface_datasets import HuggingfaceDataset, HuggingfaceDatasetConfig, load_hf_dataset
|
||||
from refiners.training_utils.trainer import Trainer
|
||||
from refiners.training_utils.wandb import WandbLoggable
|
||||
|
||||
|
||||
class LatentDiffusionConfig(BaseModel):
|
||||
unconditional_sampling_probability: float = 0.2
|
||||
offset_noise: float = 0.1
|
||||
min_step: int = 0
|
||||
max_step: int = 999
|
||||
|
||||
|
||||
class TestDiffusionConfig(BaseModel):
|
||||
seed: int = 0
|
||||
num_inference_steps: int = 30
|
||||
use_short_prompts: bool = False
|
||||
prompts: list[str] = []
|
||||
num_images_per_prompt: int = 1
|
||||
|
||||
|
||||
class FinetuneLatentDiffusionConfig(BaseConfig):
|
||||
dataset: HuggingfaceDatasetConfig
|
||||
latent_diffusion: LatentDiffusionConfig
|
||||
test_diffusion: TestDiffusionConfig
|
||||
|
||||
|
||||
@dataclass
|
||||
class TextEmbeddingLatentsBatch:
|
||||
text_embeddings: Tensor
|
||||
latents: Tensor
|
||||
|
||||
|
||||
class CaptionImage(TypedDict):
|
||||
caption: str
|
||||
image: Image.Image
|
||||
|
||||
|
||||
ConfigType = TypeVar("ConfigType", bound=FinetuneLatentDiffusionConfig)
|
||||
|
||||
|
||||
class TextEmbeddingLatentsDataset(Dataset[TextEmbeddingLatentsBatch]):
|
||||
def __init__(self, trainer: "LatentDiffusionTrainer[Any]") -> None:
|
||||
self.trainer = trainer
|
||||
self.config = trainer.config
|
||||
self.device = self.trainer.device
|
||||
self.lda = self.trainer.lda
|
||||
self.text_encoder = self.trainer.text_encoder
|
||||
self.dataset = self.load_huggingface_dataset()
|
||||
self.process_image = self.build_image_processor()
|
||||
logger.info(f"Loaded {len(self.dataset)} samples from dataset")
|
||||
|
||||
def build_image_processor(self) -> Callable[[Image.Image], Image.Image]:
|
||||
# TODO: make this configurable and add other transforms
|
||||
transforms: list[Module] = []
|
||||
if self.config.dataset.random_crop:
|
||||
transforms.append(RandomCrop(size=512))
|
||||
if self.config.dataset.horizontal_flip:
|
||||
transforms.append(RandomHorizontalFlip(p=0.5))
|
||||
if not transforms:
|
||||
return lambda image: image
|
||||
return Compose(transforms)
|
||||
|
||||
def load_huggingface_dataset(self) -> HuggingfaceDataset[CaptionImage]:
|
||||
dataset_config = self.config.dataset
|
||||
logger.info(f"Loading dataset from {dataset_config.hf_repo} revision {dataset_config.revision}")
|
||||
return load_hf_dataset(
|
||||
path=dataset_config.hf_repo, revision=dataset_config.revision, split=dataset_config.split
|
||||
)
|
||||
|
||||
def resize_image(self, image: Image.Image, min_size: int = 512, max_size: int = 576) -> Image.Image:
|
||||
return resize_image(image=image, min_size=min_size, max_size=max_size)
|
||||
|
||||
def process_caption(self, caption: str) -> str:
|
||||
return caption if random.random() > self.config.latent_diffusion.unconditional_sampling_probability else ""
|
||||
|
||||
def get_caption(self, index: int) -> str:
|
||||
return self.dataset[index]["caption"]
|
||||
|
||||
def get_image(self, index: int) -> Image.Image:
|
||||
return self.dataset[index]["image"]
|
||||
|
||||
def __getitem__(self, index: int) -> TextEmbeddingLatentsBatch:
|
||||
caption = self.get_caption(index=index)
|
||||
image = self.get_image(index=index)
|
||||
resized_image = self.resize_image(
|
||||
image=image,
|
||||
min_size=self.config.dataset.resize_image_min_size,
|
||||
max_size=self.config.dataset.resize_image_max_size,
|
||||
)
|
||||
processed_image = self.process_image(resized_image)
|
||||
latents = self.lda.image_to_latents(image=processed_image).to(device=self.device)
|
||||
processed_caption = self.process_caption(caption=caption)
|
||||
clip_text_embedding = self.text_encoder(processed_caption).to(device=self.device)
|
||||
return TextEmbeddingLatentsBatch(text_embeddings=clip_text_embedding, latents=latents)
|
||||
|
||||
def collate_fn(self, batch: list[TextEmbeddingLatentsBatch]) -> TextEmbeddingLatentsBatch:
|
||||
text_embeddings = cat(tensors=[item.text_embeddings for item in batch])
|
||||
latents = cat(tensors=[item.latents for item in batch])
|
||||
return TextEmbeddingLatentsBatch(text_embeddings=text_embeddings, latents=latents)
|
||||
|
||||
def __len__(self) -> int:
|
||||
return len(self.dataset)
|
||||
|
||||
|
||||
class LatentDiffusionTrainer(Trainer[ConfigType, TextEmbeddingLatentsBatch]):
|
||||
@cached_property
|
||||
def unet(self) -> SD1UNet:
|
||||
assert self.config.models["unet"] is not None, "The config must contain a unet entry."
|
||||
return SD1UNet(in_channels=4, device=self.device).to(device=self.device)
|
||||
|
||||
@cached_property
|
||||
def text_encoder(self) -> CLIPTextEncoderL:
|
||||
assert self.config.models["text_encoder"] is not None, "The config must contain a text_encoder entry."
|
||||
return CLIPTextEncoderL(device=self.device).to(device=self.device)
|
||||
|
||||
@cached_property
|
||||
def lda(self) -> SD1Autoencoder:
|
||||
assert self.config.models["lda"] is not None, "The config must contain a lda entry."
|
||||
return SD1Autoencoder(device=self.device).to(device=self.device)
|
||||
|
||||
def load_models(self) -> dict[str, fl.Module]:
|
||||
return {"unet": self.unet, "text_encoder": self.text_encoder, "lda": self.lda}
|
||||
|
||||
def load_dataset(self) -> Dataset[TextEmbeddingLatentsBatch]:
|
||||
return TextEmbeddingLatentsDataset(trainer=self)
|
||||
|
||||
@cached_property
|
||||
def ddpm_solver(self) -> Solver:
|
||||
return DDPM(
|
||||
num_inference_steps=1000,
|
||||
device=self.device,
|
||||
).to(device=self.device)
|
||||
|
||||
def sample_timestep(self) -> Tensor:
|
||||
random_step = random.randint(a=self.config.latent_diffusion.min_step, b=self.config.latent_diffusion.max_step)
|
||||
self.current_step = random_step
|
||||
return self.ddpm_solver.timesteps[random_step].unsqueeze(dim=0)
|
||||
|
||||
def sample_noise(self, size: tuple[int, ...], dtype: DType | None = None) -> Tensor:
|
||||
return sample_noise(
|
||||
size=size, offset_noise=self.config.latent_diffusion.offset_noise, device=self.device, dtype=dtype
|
||||
)
|
||||
|
||||
def compute_loss(self, batch: TextEmbeddingLatentsBatch) -> Tensor:
|
||||
clip_text_embedding, latents = batch.text_embeddings, batch.latents
|
||||
timestep = self.sample_timestep()
|
||||
noise = self.sample_noise(size=latents.shape, dtype=latents.dtype)
|
||||
noisy_latents = self.ddpm_solver.add_noise(x=latents, noise=noise, step=self.current_step)
|
||||
self.unet.set_timestep(timestep=timestep)
|
||||
self.unet.set_clip_text_embedding(clip_text_embedding=clip_text_embedding)
|
||||
prediction = self.unet(noisy_latents)
|
||||
loss = mse_loss(input=prediction, target=noise)
|
||||
return loss
|
||||
|
||||
def compute_evaluation(self) -> None:
|
||||
sd = StableDiffusion_1(
|
||||
unet=self.unet,
|
||||
lda=self.lda,
|
||||
clip_text_encoder=self.text_encoder,
|
||||
solver=DPMSolver(num_inference_steps=self.config.test_diffusion.num_inference_steps),
|
||||
device=self.device,
|
||||
)
|
||||
prompts = self.config.test_diffusion.prompts
|
||||
num_images_per_prompt = self.config.test_diffusion.num_images_per_prompt
|
||||
if self.config.test_diffusion.use_short_prompts:
|
||||
prompts = [prompt.split(sep=",")[0] for prompt in prompts]
|
||||
images: dict[str, WandbLoggable] = {}
|
||||
for prompt in prompts:
|
||||
canvas_image: Image.Image = Image.new(mode="RGB", size=(512, 512 * num_images_per_prompt))
|
||||
for i in range(num_images_per_prompt):
|
||||
logger.info(f"Generating image {i+1}/{num_images_per_prompt} for prompt: {prompt}")
|
||||
x = randn(1, 4, 64, 64, device=self.device)
|
||||
clip_text_embedding = sd.compute_clip_text_embedding(text=prompt).to(device=self.device)
|
||||
for step in sd.steps:
|
||||
x = sd(
|
||||
x,
|
||||
step=step,
|
||||
clip_text_embedding=clip_text_embedding,
|
||||
)
|
||||
canvas_image.paste(sd.lda.latents_to_image(x=x), box=(0, 512 * i))
|
||||
images[prompt] = canvas_image
|
||||
# TODO: wandb_log images
|
||||
|
||||
|
||||
def sample_noise(
|
||||
size: tuple[int, ...],
|
||||
offset_noise: float = 0.1,
|
||||
device: Device | str = "cpu",
|
||||
dtype: DType | None = None,
|
||||
generator: Generator | None = None,
|
||||
) -> Tensor:
|
||||
"""Sample noise from a normal distribution.
|
||||
|
||||
If `offset_noise` is more than 0, the noise will be offset by a small amount. It allows the model to generate
|
||||
images with a wider range of contrast https://www.crosslabs.org/blog/diffusion-with-offset-noise.
|
||||
"""
|
||||
device = Device(device)
|
||||
noise = randn(*size, generator=generator, device=device, dtype=dtype)
|
||||
return noise + offset_noise * randn(*size[:2], 1, 1, generator=generator, device=device, dtype=dtype)
|
||||
|
||||
|
||||
def resize_image(image: Image.Image, min_size: int = 512, max_size: int = 576) -> Image.Image:
|
||||
image_min_size = min(image.size)
|
||||
if image_min_size > max_size:
|
||||
if image_min_size == image.size[0]:
|
||||
image = image.resize(size=(max_size, int(max_size * image.size[1] / image.size[0])))
|
||||
else:
|
||||
image = image.resize(size=(int(max_size * image.size[0] / image.size[1]), max_size))
|
||||
if image_min_size < min_size:
|
||||
if image_min_size == image.size[0]:
|
||||
image = image.resize(size=(min_size, int(min_size * image.size[1] / image.size[0])))
|
||||
else:
|
||||
image = image.resize(size=(int(min_size * image.size[0] / image.size[1]), min_size))
|
||||
return image
|
||||
|
||||
|
||||
class MonitorTimestepLoss(Callback[LatentDiffusionTrainer[Any]]):
|
||||
def on_train_begin(self, trainer: LatentDiffusionTrainer[Any]) -> None:
|
||||
self.timestep_bins: dict[int, list[float]] = {i: [] for i in range(10)}
|
||||
|
||||
def on_compute_loss_end(self, trainer: LatentDiffusionTrainer[Any]) -> None:
|
||||
loss_value = trainer.loss.detach().cpu().item()
|
||||
current_step = trainer.current_step
|
||||
bin_index = min(current_step // 100, 9)
|
||||
self.timestep_bins[bin_index].append(loss_value)
|
||||
|
||||
def on_epoch_end(self, trainer: LatentDiffusionTrainer[Any]) -> None:
|
||||
log_data: dict[str, WandbLoggable] = {}
|
||||
for bin_index, losses in self.timestep_bins.items():
|
||||
if losses:
|
||||
avg_loss = sum(losses) / len(losses)
|
||||
log_data[f"average_loss_timestep_bin_{bin_index * 100}"] = avg_loss
|
||||
self.timestep_bins[bin_index] = []
|
||||
|
||||
# TODO: wandb_log log_data
|
||||
|
|
@ -1,6 +1,6 @@
|
|||
import random
|
||||
import time
|
||||
from abc import ABC, abstractmethod
|
||||
from abc import ABC, abstractmethod, abstractproperty
|
||||
from functools import cached_property, wraps
|
||||
from typing import Any, Callable, Generic, Iterable, TypeVar, cast
|
||||
|
||||
|
|
@ -250,6 +250,23 @@ Batch = TypeVar("Batch")
|
|||
ConfigType = TypeVar("ConfigType", bound=BaseConfig)
|
||||
|
||||
|
||||
class _Dataset(Dataset[Batch]):
|
||||
"""
|
||||
A wrapper around the `get_item` method to create a [`torch.utils.data.Dataset`]
|
||||
"""
|
||||
|
||||
def __init__(self, get_item: Callable[[int], Batch], length: int) -> None:
|
||||
assert length > 0, "Dataset length must be greater than 0."
|
||||
self.length = length
|
||||
self.get_item = get_item
|
||||
|
||||
def __getitem__(self, index: int) -> Batch:
|
||||
return self.get_item(index)
|
||||
|
||||
def __len__(self) -> int:
|
||||
return self.length
|
||||
|
||||
|
||||
class Trainer(Generic[ConfigType, Batch], ABC):
|
||||
def __init__(self, config: ConfigType, callbacks: list[Callback[Any]] | None = None) -> None:
|
||||
self.config = config
|
||||
|
|
@ -429,23 +446,44 @@ class Trainer(Generic[ConfigType, Batch], ABC):
|
|||
...
|
||||
|
||||
@abstractmethod
|
||||
def load_dataset(self) -> Dataset[Batch]:
|
||||
def get_item(self, _: int) -> Batch:
|
||||
"""
|
||||
Returns a batch of data.
|
||||
|
||||
This function is used by the dataloader to fetch a batch of data.
|
||||
"""
|
||||
...
|
||||
|
||||
@abstractproperty
|
||||
def dataset_length(self) -> int:
|
||||
"""
|
||||
Returns the length of the dataset.
|
||||
|
||||
This is used to compute the number of batches per epoch.
|
||||
"""
|
||||
...
|
||||
|
||||
@abstractmethod
|
||||
def collate_fn(self, batch: list[Batch]) -> Batch:
|
||||
"""
|
||||
Collate function for the dataloader.
|
||||
|
||||
This function is used to tell the dataloader how to combine a list of
|
||||
batches into a single batch.
|
||||
"""
|
||||
...
|
||||
|
||||
@cached_property
|
||||
def dataset(self) -> Dataset[Batch]:
|
||||
return self.load_dataset()
|
||||
"""
|
||||
Returns the dataset constructed with the `get_item` method.
|
||||
"""
|
||||
return _Dataset(get_item=self.get_item, length=self.dataset_length)
|
||||
|
||||
@cached_property
|
||||
def dataset_length(self) -> int:
|
||||
assert hasattr(self.dataset, "__len__"), "The dataset must implement the `__len__` method."
|
||||
return len(self.dataset) # type: ignore
|
||||
|
||||
@cached_property
|
||||
def dataloader(self) -> DataLoader[Batch]:
|
||||
collate_fn = getattr(self.dataset, "collate_fn", None)
|
||||
def dataloader(self) -> DataLoader[Any]:
|
||||
return DataLoader(
|
||||
dataset=self.dataset, batch_size=self.config.training.batch_size, shuffle=True, collate_fn=collate_fn
|
||||
dataset=self.dataset, batch_size=self.config.training.batch_size, shuffle=True, collate_fn=self.collate_fn
|
||||
)
|
||||
|
||||
@abstractmethod
|
||||
|
|
|
|||
|
|
@ -7,7 +7,6 @@ import pytest
|
|||
import torch
|
||||
from torch import Tensor, nn
|
||||
from torch.optim import SGD
|
||||
from torch.utils.data import Dataset
|
||||
|
||||
from refiners.fluxion import layers as fl
|
||||
from refiners.fluxion.utils import norm
|
||||
|
|
@ -27,20 +26,6 @@ class MockBatch:
|
|||
targets: torch.Tensor
|
||||
|
||||
|
||||
class MockDataset(Dataset[MockBatch]):
|
||||
def __len__(self):
|
||||
return 20
|
||||
|
||||
def __getitem__(self, _: int) -> MockBatch:
|
||||
return MockBatch(inputs=torch.randn(1, 10), targets=torch.randn(1, 10))
|
||||
|
||||
def collate_fn(self, batch: list[MockBatch]) -> MockBatch:
|
||||
return MockBatch(
|
||||
inputs=torch.cat([b.inputs for b in batch]),
|
||||
targets=torch.cat([b.targets for b in batch]),
|
||||
)
|
||||
|
||||
|
||||
class MockConfig(BaseConfig):
|
||||
pass
|
||||
|
||||
|
|
@ -57,13 +42,23 @@ class MockModel(fl.Chain):
|
|||
class MockTrainer(Trainer[MockConfig, MockBatch]):
|
||||
step_counter: int = 0
|
||||
|
||||
@property
|
||||
def dataset_length(self) -> int:
|
||||
return 20
|
||||
|
||||
def get_item(self, _: int) -> MockBatch:
|
||||
return MockBatch(inputs=torch.randn(1, 10), targets=torch.randn(1, 10))
|
||||
|
||||
def collate_fn(self, batch: list[MockBatch]) -> MockBatch:
|
||||
return MockBatch(
|
||||
inputs=torch.cat([b.inputs for b in batch]),
|
||||
targets=torch.cat([b.targets for b in batch]),
|
||||
)
|
||||
|
||||
@cached_property
|
||||
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