使用DDPM(扩散模型)训练自己的数据集实现数据集扩容pytorch_ddpm-model-128

一、简介

DDPM扩散模型包括两个过程：前向过程（forward process）和反向过程（reverse process），其中前向过程又称为扩散过程（diffusion process）。无论是前向过程还是反向过程都是一个参数化的马尔可夫链（Markov chain），其中反向过程可以用来生成数据，可通过变分推断来进行建模和求解。在DDPM中，通过连续添加高斯噪声来破坏训练数据,然后通过反转这个噪声过程,来学习恢复数据。测试时,可以使用DDPM将随机采样的噪声传入模型中,通过学习去噪过程来生成数据。

二、实战

首先，下载安装指定的python包

pip install diffusers

我使用的数据集为柑橘病害叶片数据集（未开源），三种类型，分别为黄龙病、缺镁、正常三种性状。

我使用数据集格式：（数据集不需要划分为train、val、test）

dataset_orgin
--Huanglong_disease
----0.jpg
----1.jpg
----~~~~~
--Magnesium_deficiency
----0.jpg
----1.jpg
----~~~~~
--Normal
----0.jpg
----1.jpg
----~~~~~

新建python文件，用于训练

train_unconditional.py

import argparse
import inspect
import math
import os
from pathlib import Path
from typing import Optional
 
import torch
import torch.nn.functional as F
 
from accelerate import Accelerator
from accelerate.logging import get_logger
from datasets import load_dataset
from diffusers import DDPMPipeline, DDPMScheduler, UNet2DModel
from diffusers.optimization import get_scheduler
from diffusers.training_utils import EMAModel
from diffusers.utils import check_min_version
from huggingface_hub import HfFolder, Repository, whoami
from torchvision.transforms import (
    CenterCrop,
    Compose,
    InterpolationMode,
    Normalize,
    RandomHorizontalFlip,
    Resize,
    ToTensor,
)
from tqdm.auto import tqdm
 
 
# Will error if the minimal version of diffusers is not installed. Remove at your own risks.
check_min_version("0.10.0.dev0")
 
 
device = torch.device('cuda:0' if torch.cuda.is_available() else 'cpu')   # 设备
 
logger = get_logger(__name__)
 
 
def _extract_into_tensor(arr, timesteps, broadcast_shape):
    """
    Extract values from a 1-D numpy array for a batch of indices.
    :param arr: the 1-D numpy array.
    :param timesteps: a tensor of indices into the array to extract.
    :param broadcast_shape: a larger shape of K dimensions with the batch
                            dimension equal to the length of timesteps.
    :return: a tensor of shape [batch_size, 1, ...] where the shape has K dims.
    """
    if not isinstance(arr, torch.Tensor):
        arr = torch.from_numpy(arr)
    res = arr[timesteps].float().to(timesteps.device)
    while len(res.shape) < len(broadcast_shape):
        res = res[..., None]
    return res.expand(broadcast_shape)
 
 
def parse_args():
    parser = argparse.ArgumentParser(description="Simple example of a training script.")
    parser.add_argument(
        "--dataset_name",
        type=str,
        default=r'D:\pyCharmdata\datasets_orgin\Huanglong_disease',#None
        help=(
            "The name of the Dataset (from the HuggingFace hub) to train on (could be your own, possibly private,"
            " dataset). It can also be a path pointing to a local copy of a dataset in your filesystem,"
            " or to a folder containing files that HF Datasets can understand."
        ),
    )
    parser.add_argument(
        "--dataset_config_name",
        type=str,
        default=None,#None
        help="The config of the Dataset, leave as None if there's only one config.",
    )
    parser.add_argument(
        "--train_data_dir",
        type=str,
        default=r'D:\pyCharmdata\datasets_orgin\Huanglong_disease',# None
        help=(
            "A folder containing the training data. Folder contents must follow the structure described in"
            " https://huggingface.co/docs/datasets/image_dataset#imagefolder. In particular, a `metadata.jsonl` file"
            " must exist to provide the captions for the images. Ignored if `dataset_name` is specified."
        ),
    )
    parser.add_argument(
        "--output_dir",
        type=str,
        default="ddpm-model-128",
        help="The output directory where the model predictions and checkpoints will be written.",
    )
    parser.add_argument("--overwrite_output_dir", action="store_true")
    parser.add_argument(
        "--cache_dir",
        type=str,
        default=None,
        help="The directory where the downloaded models and datasets will be stored.",
    )
    parser.add_argument(
        "--resolution",
        type=int,
        default=128,
        help=(
            "The resolution for input images, all the images in the train/validation dataset will be resized to this"
            " resolution"
        ),
    )
    parser.add_argument(
        "--train_batch_size", type=int, default=1, help="Batch size (per device) for the training dataloader."
    )
    parser.add_argument(
        "--eval_batch_size", type=int, default=1, help="The number of images to generate for evaluation."
    )
    parser.add_argument(
        "--dataloader_num_workers",
        type=int,
        default=0,
        help=(
            "The number of subprocesses to use for data loading. 0 means that the data will be loaded in the main"
            " process."
        ),
    )
    parser.add_argument("--num_epochs", type=int, default=300)
    parser.add_argument("--save_images_epochs", type=int, default=10, help="How often to save images during training.")
    parser.add_argument(
        "--save_model_epochs", type=int, default=10, help="How often to save the model during training."
    )
    parser.add_argument(
        "--gradient_accumulation_steps",
        type=int,
        default=1,
        help="Number of updates steps to accumulate before performing a backward/update pass.",
    )
    parser.add_argument(
        "--learning_rate",
        type=float,
        default=1e-4,
        help="Initial learning rate (after the potential warmup period) to use.",
    )
    parser.add_argument(
        "--lr_scheduler",
        type=str,
        default="cosine",
        help=(
            'The scheduler type to use. Choose between ["linear", "cosine", "cosine_with_restarts", "polynomial",'
            ' "constant", "constant_with_warmup"]'
        ),
    )
    parser.add_argument(
        "--lr_warmup_steps", type=int, default=500, help="Number of steps for the warmup in the lr scheduler."
    )
    parser.add_argument("--adam_beta1", type=float, default=0.95, help="The beta1 parameter for the Adam optimizer.")
    parser.add_argument("--adam_beta2", type=float, default=0.999, help="The beta2 parameter for the Adam optimizer.")
    parser.add_argument(
        "--adam_weight_decay", type=float, default=1e-6, help="Weight decay magnitude for the Adam optimizer."
    )
    parser.add_argument("--adam_epsilon", type=float, default=1e-08, help="Epsilon value for the Adam optimizer.")
    parser.add_argument(
        "--use_ema",
        action="store_true",
        default=True,
        help="Whether to use Exponential Moving Average for the final model weights.",
    )
    parser.add_argument("--ema_inv_gamma", type=float, default=1.0, help="The inverse gamma value for the EMA decay.")
    parser.add_argument("--ema_power", type=float, default=3 / 4, help="The power value for the EMA decay.")
    parser.add_argument("--ema_max_decay", type=float, default=0.9999, help="The maximum decay magnitude for EMA.")
    parser.add_argument("--push_to_hub", action="store_true", help="Whether or not to push the model to the Hub.")
    parser.add_argument("--hub_token", type=str, default=None, help="The token to use to push to the Model Hub.")
    parser.add_argument(
        "--hub_model_id",
        type=str,
        default=None,
        help="The name of the repository to keep in sync with the local `output_dir`.",
    )
    parser.add_argument(
        "--hub_private_repo", action="store_true", help="Whether or not to create a private repository."
    )
    parser.add_argument(
        "--logging_dir",
        type=str,
        default="logs",
        help=(
            "[TensorBoard](https://www.tensorflow.org/tensorboard) log directory. Will default to"
            " *output_dir/runs/**CURRENT_DATETIME_HOSTNAME***."
        ),
    )
    parser.add_argument("--local_rank", type=int, default=-1, help="For distributed training: local_rank")
    parser.add_argument(
        "--mixed_precision",
        type=str,
        default="fp16",#"no"
        choices=["no", "fp16", "bf16"],
        help=(
            "Whether to use mixed precision. Choose"
            "between fp16 and bf16 (bfloat16). Bf16 requires PyTorch >= 1.10."
            "and an Nvidia Ampere GPU."
        ),
    )
 
    parser.add_argument(
        "--prediction_type",
        type=str,
        default="epsilon",
        choices=["epsilon", "sample"],
        help="Whether the model should predict the 'epsilon'/noise error or directly the reconstructed image 'x0'.",
    )
 
    parser.add_argument("--ddpm_num_steps", type=int, default=1000)
    parser.add_argument("--ddpm_beta_schedule", type=str, default="linear")
 
    args = parser.parse_args()
    env_local_rank = int(os.environ.get("LOCAL_RANK", -1))
    if env_local_rank != -1 and env_local_rank != args.local_rank:
        args.local_rank = env_local_rank
 
    if args.dataset_name is None and args.train_data_dir is None:
        raise ValueError("You must specify either a dataset name from the hub or a train data directory.")
 
    return args
 
 
def get_full_repo_name(model_id: str, organization: Optional[str] = None, token: Optional[str] = None):
    if token is None:
        token = HfFolder.get_token()
    if organization is None:
        username = whoami(token)["name"]
        return f"{username}/{model_id}"
    else:
        return f"{organization}/{model_id}"
 
 
def main(args):
    logging_dir = os.path.join(args.output_dir, args.logging_dir)
    accelerator = Accelerator(
        gradient_accumulation_steps=args.gradient_accumulation_steps,
        mixed_precision=args.mixed_precision,
        log_with="tensorboard",
        logging_dir=logging_dir,
    )
 
    model = UNet2DModel(
        sample_size=args.resolution,
        in_channels=3,
        out_channels=3,
        layers_per_block=2,
        block_out_channels=(128, 128, 256, 256, 512, 512),
        down_block_types=(
            "DownBlock2D",
            "DownBlock2D",
            "DownBlock2D",
            "DownBlock2D",
            "AttnDownBlock2D",
            "DownBlock2D",
        ),
        up_block_types=(
            "UpBlock2D",
            "AttnUpBlock2D",
            "UpBlock2D",
            "UpBlock2D",
            "UpBlock2D",
            "UpBlock2D",
        ),
    )
    accepts_prediction_type = "prediction_type" in set(inspect.signature(DDPMScheduler.__init__).parameters.keys())
 
    if accepts_prediction_type:
        noise_scheduler = DDPMScheduler(
            num_train_timesteps=args.ddpm_num_steps,
            beta_schedule=args.ddpm_beta_schedule,
            prediction_type=args.prediction_type,
        )
    else:
        noise_scheduler = DDPMScheduler(num_train_timesteps=args.ddpm_num_steps, beta_schedule=args.ddpm_beta_schedule)
 
    optimizer = torch.optim.AdamW(
        model.parameters(),
        lr=args.learning_rate,
        betas=(args.adam_beta1, args.adam_beta2),
        weight_decay=args.adam_weight_decay,
        eps=args.adam_epsilon,
    )
 
    augmentations = Compose(
        [
            Resize(args.resolution, interpolation=InterpolationMode.BILINEAR),
            CenterCrop(args.resolution),
            RandomHorizontalFlip(),
            ToTensor(),
            Normalize([0.5], [0.5]),
        ]
    )
 
    if args.dataset_name is not None:
        dataset = load_dataset(
            args.dataset_name,
            args.dataset_config_name,
            cache_dir=args.cache_dir,
            split="train",
        )
    else:
        dataset = load_dataset("imagefolder", data_dir=args.train_data_dir, cache_dir=args.cache_dir, split="train")
 
    def transforms(examples):
        images = [augmentations(image.convert("RGB")) for image in examples["image"]]
        return {"input": images}
 
    logger.info(f"Dataset size: {len(dataset)}")
 
    dataset.set_transform(transforms)
    train_dataloader = torch.utils.data.DataLoader(
        dataset, batch_size=args.train_batch_size, shuffle=True, num_workers=args.dataloader_num_workers
    )
 
    lr_scheduler = get_scheduler(
        args.lr_scheduler,
        optimizer=optimizer,
        num_warmup_steps=args.lr_warmup_steps,
        num_training_steps=(len(train_dataloader) * args.num_epochs) // args.gradient_accumulation_steps,
    )
 
    model, optimizer, train_dataloader, lr_scheduler = accelerator.prepare(
        model, optimizer, train_dataloader, lr_scheduler
    )
 
    num_update_steps_per_epoch = math.ceil(len(train_dataloader) / args.gradient_accumulation_steps)
 
    ema_model = EMAModel(
        accelerator.unwrap_model(model),
        inv_gamma=args.ema_inv_gamma,
        power=args.ema_power,
        max_value=args.ema_max_decay,
    )
 
    # Handle the repository creation
    if accelerator.is_main_process:
        if args.push_to_hub:
            if args.hub_model_id is None:
                repo_name = get_full_repo_name(Path(args.output_dir).name, token=args.hub_token)
            else:
                repo_name = args.hub_model_id
            repo = Repository(args.output_dir, clone_from=repo_name)
 
            with open(os.path.join(args.output_dir, ".gitignore"), "w+") as gitignore:
                if "step_*" not in gitignore:
                    gitignore.write("step_*\n")
                if "epoch_*" not in gitignore:
                    gitignore.write("epoch_*\n")
        elif args.output_dir is not None:
            os.makedirs(args.output_dir, exist_ok=True)
 
    if accelerator.is_main_process:
        run = os.path.split(__file__)[-1].split(".")[0]
        accelerator.init_trackers(run)
 
    global_step = 0
    for epoch in range(args.num_epochs):
        model.train()
        progress_bar = tqdm(total=num_update_steps_per_epoch, disable=not accelerator.is_local_main_process)
        progress_bar.set_description(f"Epoch {epoch}")
        for step, batch in enumerate(train_dataloader):
            clean_images = batch["input"]
            # Sample noise that we'll add to the images
            noise = torch.randn(clean_images.shape).to(clean_images.device)
            bsz = clean_images.shape[0]
            # Sample a random timestep for each image
            timesteps = torch.randint(
                0, noise_scheduler.config.num_train_timesteps, (bsz,), device=clean_images.device
            ).long()
            # Add noise to the clean images according to the noise magnitude at each timestep
            # (this is the forward diffusion process)
            noisy_images = noise_scheduler.add_noise(clean_images, noise, timesteps)
            with accelerator.accumulate(model):
                # Predict the noise residual
                model_output = model(noisy_images, timesteps).sample
                if args.prediction_type == "epsilon":
                    loss = F.mse_loss(model_output, noise)  # this could have different weights!
                elif args.prediction_type == "sample":
                    alpha_t = _extract_into_tensor(
                        noise_scheduler.alphas_cumprod, timesteps, (clean_images.shape[0], 1, 1, 1)
                    )
                    snr_weights = alpha_t / (1 - alpha_t)
                    loss = snr_weights * F.mse_loss(
                        model_output, clean_images, reduction="none"
                    )  # use SNR weighting from distillation paper
                    loss = loss.mean()
                else:
                    raise ValueError(f"Unsupported prediction type: {args.prediction_type}")
                accelerator.backward(loss)
                if accelerator.sync_gradients:
                    accelerator.clip_grad_norm_(model.parameters(), 1.0)
                optimizer.step()
                lr_scheduler.step()
                if args.use_ema:
                    ema_model.step(model)
                optimizer.zero_grad()
            # Checks if the accelerator has performed an optimization step behind the scenes
            if accelerator.sync_gradients:
                progress_bar.update(1)
                global_step += 1
            logs = {"loss": loss.detach().item(), "lr": lr_scheduler.get_last_lr()[0], "step": global_step}
            if args.use_ema:
                logs["ema_decay"] = ema_model.decay
            progress_bar.set_postfix(**logs)
            accelerator.log(logs, step=global_step)
        progress_bar.close()
        accelerator.wait_for_everyone()
        # Generate sample images for visual inspection
        if accelerator.is_main_process:
            if epoch % args.save_images_epochs == 0 or epoch == args.num_epochs - 1:
                pipeline = DDPMPipeline(
                    unet=accelerator.unwrap_model(ema_model.averaged_model if args.use_ema else model),
                    scheduler=noise_scheduler,
                )
                generator = torch.Generator(device=pipeline.device).manual_seed(0)
                # run pipeline in inference (sample random noise and denoise)
                images = pipeline(
                    generator=generator,
                    batch_size=args.eval_batch_size,
                    output_type="numpy",
                ).images
                # denormalize the images and save to tensorboard
                images_processed = (images * 255).round().astype("uint8")
                accelerator.trackers[0].writer.add_images(
                    "test_samples", images_processed.transpose(0, 3, 1, 2), epoch
                )
            if epoch % args.save_model_epochs == 0 or epoch == args.num_epochs - 1:
                # save the model
                pipeline.save_pretrained(args.output_dir)
                if args.push_to_hub:
                    repo.push_to_hub(commit_message=f"Epoch {epoch}", blocking=False)
        accelerator.wait_for_everyone()
    accelerator.end_training()
if __name__ == "__main__":
    args = parse_args()
    main(args)

开始训练前对argparse部分代码修改：
将"--dataset_name"部分修改为你使用的数据集的文件路径位置；

将"--train_data_dir"部分修改为你使用的数据集的文件路径位置；

将"--output_dir"部分修改为你训练得到模型文件夹的输出名称；

将"--resolution"部分修改为你想要后续生成的假图像的大小分辨率，比较考验电脑算力，可以自行尝试不同分辨率的，可以从64*64不断往上尝试，比如：64*64、128*128、256*256、512*512等

将"--train_batch_size"、"--eval_batch_size"、"--dataloader_num_workers"这三个参数与训练是否能继续息息相关，DDPM模型较大，对电脑算力要求高，显存不足时需要调小这三个参数才能正常运行程序；

将"--num_epochs"修改为你想要训练的轮数，不同数据集要求不同，数据集简单的100轮可能可以搞定，数据集复杂得自行尝试确定轮数。

总而言之，看菜吃饭，算力充足的可以任意调整，算力有限则需要慢慢调试参数进行运行训练。

新建python文件，用于生产“假”图像

generate.py

# !pip install diffusers
from diffusers import DDPMPipeline, DDIMPipeline, PNDMPipeline
import os
 
model_id = "ddpm-model-512-Huanglong_disease"
# 生成的图像放的位置
img_path = 'results' + '/' + model_id + '-img'
if not os.path.exists(img_path): os.mkdir(img_path)
device = "cuda"
 
# load model and scheduler
ddpm = DDPMPipeline.from_pretrained(
    model_id)  # you can replace DDPMPipeline with DDIMPipeline or PNDMPipeline for faster inference
ddpm.to(device)
for i in range(1000):
    # run pipeline in inference (sample random noise and denoise)
    image = ddpm().images[0]
    # save image
    # 不修改格式
    image.save(os.path.join(img_path,f'{i}.png'))
    # 改成单通道
    #image.convert('L').save(os.path.join(img_path, f'{i}.png'))
    # 看看跑到哪里了
    if i % 10 == 0: print(f"i={i}")
 

model_id为项目根目录下，训练好的模型文件夹名称。

修改for i in range()的循环次数，可以指定为你想要生成的合成图像的数量

生成彩色图像则使用

image.save(os.path.join(img_path,f'{i}.png'))

生成黑白图像则使用

#image.convert('L').save(os.path.join(img_path, f'{i}.png'))

两种各自使用，需要屏蔽另外一种的代码。

部分生成的图像：

                                                                          黄龙病

缺镁：

正常：

此外，由于是train_unconditional，当使用多个种类的数据集一起训练时，生成的图像类型不可控（即不能做到生成指定类型的图像数据），且由于扩散模型训练的是扩散（去噪）的模型，本身不带有鉴别的功能，训练生成的彩色合成图像可能带有一定的色差，我的建议是，先使用ResNet50等分类网络对原始数据集进行训练，得到一个类似于GAN算法中鉴别器的模型，再对生成好的合成图像进行分类并且筛选掉质量较差的图像。

三、小结

总的来说，对比效果实现数据扩容的合成图像方向的算法GAN，GAN是同时训练生成器和鉴别器，让两者相互作用进而实现拟合，但是大多数情况下GAN算法很难训练到很高程度的拟合，且常常会出现梯度消失或者梯度爆炸等不稳定的情况。

DDPM（扩散模型）训练的是模型对图像的去噪能力，比较容易拟合，训练得到的模型，输入无序的高斯噪声再执行模型的去噪能力，来实现“生产”合成图像的功能。优点是训练得到的图像精度较高，与原始图像更相似；缺点是模型训练需要的算力较大，且生成合成图像所需时间较长。

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