innerRace.py

# -*- coding: utf-8 -*-
"""

Original file is located at
    https://colab.research.google.com/drive/1SbxWXhffEnCJ6tVT6ZfTDbY2-cxb063U

Adapted by Nathaniel Coe

"""

#Login to Huggingface
from huggingface_hub import notebook_login

notebook_login()




"""
## Training configuration
"""

from dataclasses import dataclass


@dataclass
class TrainingConfig:
    image_size = 256  # the generated image resolution
    train_batch_size = 10 #Images to sample when training
    eval_batch_size = 16  # how many images to sample during evaluation
    num_epochs = 250 #Number of epochs to run
    gradient_accumulation_steps = 1 # 1 == not utilisesd
    learning_rate = 1e-4
    lr_warmup_steps = 500
    save_image_epochs = 100 #When to sample current model output
    save_model_epochs = 100 #When to generate ALL data
    mixed_precision = "fp16"  # `no` for float32, `fp16` for automatic mixed precision
    output_dir = "7inchInnerRace1730"  # the model name locally and on the HF Hub

    push_to_hub = True  # whether to upload the saved model to the HF Hub
    hub_private_repo = False
    overwrite_output_dir = False  # KEEP THIS AS FALSE
    seed = 0


config = TrainingConfig()

"""## Load the dataset
"""

from datasets import load_dataset

config.dataset_name = "GaumlessGraham/7inchInnerRace1730AugData"
dataset = load_dataset(config.dataset_name, split="train")

"""
Preprocess Image:

* `Resize` changes the image size to the one defined in `config.image_size`.
* `Normalize` is important to rescale the pixel values into a [-1, 1] range, which is what the model expects.
"""

from torchvision import transforms

preprocess = transforms.Compose(
    [
        transforms.Resize((config.image_size, config.image_size)),
      
        transforms.ToTensor(),
        transforms.Normalize([0.5], [0.5]),
    ]
)

"""Use 🤗 Datasets' [set_transform] method to apply the `preprocess` function on the fly during training:"""

def transform(examples):
    images = [preprocess(image) for image in examples["image"]]
    return {"images": images}


dataset.set_transform(transform)

"""Visualize the images 
"""
import torch

train_dataloader = torch.utils.data.DataLoader(dataset, batch_size=config.train_batch_size, shuffle=True)

fig.show()

"""## Create a UNet2DModel
"""

from diffusers import UNet2DModel

model = UNet2DModel(
    sample_size=config.image_size,  # the target image resolution
    in_channels=1,  # the number of input channels, 3 for RGB images, 1 for grey
    out_channels=1,  # the number of output channels
    layers_per_block=2,  # how many ResNet layers to use per UNet block
    block_out_channels=(128, 128, 256, 256, 512, 512),  # the number of output channels for each UNet block
    down_block_types=(
        "DownBlock2D",  # a regular ResNet downsampling block
        "DownBlock2D",
        "DownBlock2D",
        "DownBlock2D",
        "AttnDownBlock2D",  # a ResNet downsampling block with spatial self-attention
        "DownBlock2D",
    ),
    up_block_types=(
        "UpBlock2D",  # a regular ResNet upsampling block
        "AttnUpBlock2D",  # a ResNet upsampling block with spatial self-attention
        "UpBlock2D",
        "UpBlock2D",
        "UpBlock2D",
        "UpBlock2D",
    ),
)

"""Check the sample image shape matches the model output shape:"""

sample_image = dataset[0]["images"].unsqueeze(0)
print("Input shape:", sample_image.shape)

print("Output shape:", model(sample_image, timestep=0).sample.shape)

""".

## Create a scheduler (Add noise)

"""

import torch
from PIL import Image
from diffusers import DDPMScheduler

noise_scheduler = DDPMScheduler(num_train_timesteps=1000)
noise = torch.randn(sample_image.shape)
timesteps = torch.LongTensor([50])
noisy_image = noise_scheduler.add_noise(sample_image, noise, timesteps)



"""
The training objective of the model is to predict the noise added to the image. The loss at this step can be calculated by:
"""

import torch.nn.functional as F

noise_pred = model(noisy_image, timesteps).sample
loss = F.mse_loss(noise_pred, noise)

"""## Train the model

optimizer and a learning rate scheduler:
"""

from diffusers.optimization import get_cosine_schedule_with_warmup

optimizer = torch.optim.AdamW(model.parameters(), lr=config.learning_rate)
lr_scheduler = get_cosine_schedule_with_warmup(
    optimizer=optimizer,
    num_warmup_steps=config.lr_warmup_steps,
    num_training_steps=(len(train_dataloader) * config.num_epochs),
)

"""Model Evaluation"""

from diffusers import DDPMPipeline
import math
import os

#Make grid when sampling images
def make_grid(images, rows, cols):
    w, h = images[0].size
    grid = Image.new("RGB", size=(cols * w, rows * h))
    for i, image in enumerate(images):
        grid.paste(image, box=(i % cols * w, i // cols * h))
    return grid

def evalfirst(config, epoch, pipeline):
    #Function evaluates 16 images ONCE to ensure the actual generated images are correct
    #Takes approx 5 mins to evaluate on an RTX 4090

    #Generate images from noise
    images = pipeline(
        batch_size=config.eval_batch_size,
        generator=torch.manual_seed(config.seed),
    ).images

    # Make a grid out of the images
    image_grid = make_grid(images, rows=4, cols=4)

    # Save the images
    test_dir = os.path.join(config.output_dir, "samples")
    os.makedirs(test_dir, exist_ok=True)
    image_grid.save(f"{test_dir}/{epoch:04d}.png")

    





def evaluate(config, epoch, pipeline):
    import random
    #Function generates (16*k) images, please use function "evalfirst" to ensure model has trained correctly before running
    #for k = 20, function takes approx. 1 hour, 45 mins on an RTX 4090
    for k in range(1, 14):

        #Generate Images
        images = pipeline(
            batch_size=config.eval_batch_size,
            generator=torch.manual_seed(config.seed),
        ).images
    
    
        # Save the images
        test_dir = os.path.join(config.output_dir, "samples_generated")
        if not os.path.exists(test_dir):
            os.makedirs(test_dir)
    
        for i, image in enumerate(images):
            image.save(f"{test_dir}/{(i+((k-1)*16)):04d}.png")

        #Randomise noise seed
        config.seed = random.randint(1, 1000)

"""
Training Loop
"""

from accelerate import Accelerator
from huggingface_hub import HfFolder, Repository, whoami
from tqdm.auto import tqdm
from pathlib import Path
import os


def get_full_repo_name(model_id: str, organization: str = None, token: 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 train_loop(config, model, noise_scheduler, optimizer, train_dataloader, lr_scheduler):
    # Initialize accelerator and tensorboard logging
    accelerator = Accelerator(
        mixed_precision=config.mixed_precision,
        gradient_accumulation_steps=config.gradient_accumulation_steps,
        log_with="tensorboard",
        project_dir=os.path.join(config.output_dir, "logs"),
    )
    if accelerator.is_main_process:
        if config.push_to_hub:
            repo_name = get_full_repo_name(Path(config.output_dir).name)
            repo = Repository(config.output_dir, clone_from=repo_name)
        elif config.output_dir is not None:
            os.makedirs(config.output_dir, exist_ok=True)
        accelerator.init_trackers("train_example")

    # Prepare everything
    model, optimizer, train_dataloader, lr_scheduler = accelerator.prepare(
        model, optimizer, train_dataloader, lr_scheduler
    )

    global_step = 0

    #Train Model
    for epoch in range(config.num_epochs):
        progress_bar = tqdm(total=len(train_dataloader), disable=not accelerator.is_local_main_process)
        progress_bar.set_description(f"Epoch {epoch}")

        for step, batch in enumerate(train_dataloader):
            clean_images = batch["images"]
            # Sample noise to add to the images
            noise = torch.randn(clean_images.shape).to(clean_images.device)
            bs = clean_images.shape[0]

            # Sample a random timestep for each image
            timesteps = torch.randint(
                0, noise_scheduler.config.num_train_timesteps, (bs,), 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
                noise_pred = model(noisy_images, timesteps, return_dict=False)[0]
                loss = F.mse_loss(noise_pred, noise)
                accelerator.backward(loss)

                accelerator.clip_grad_norm_(model.parameters(), 1.0)
                optimizer.step()
                lr_scheduler.step()
                optimizer.zero_grad()

            progress_bar.update(1)
            logs = {"loss": loss.detach().item(), "lr": lr_scheduler.get_last_lr()[0], "step": global_step}
            progress_bar.set_postfix(**logs)
            accelerator.log(logs, step=global_step)
            global_step += 1

        # Image sampling/model saving
        if accelerator.is_main_process:
            pipeline = DDPMPipeline(unet=accelerator.unwrap_model(model), scheduler=noise_scheduler)

            #Determines whether to sample 16 images (Ensures model has been trained correctly)
            if (epoch + 1) % config.save_image_epochs == 0 or epoch == config.num_epochs - 1:
                evalfirst(config, epoch, pipeline)

                model_dir = os.path.join(config.output_dir, str(epoch))
                os.makedirs(model_dir, exist_ok=True)
                    
                pipeline.save_pretrained(model_dir)
                repo.push_to_hub(commit_message=f"Sample Images Epoch {epoch}", blocking=True)
                
                evaluate(config, epoch, pipeline)

            #Determines when to save the model
            if (epoch + 1) % config.save_model_epochs == 0 or epoch == config.num_epochs - 1:
                if config.push_to_hub:

                    model_dir = os.path.join(config.output_dir, str(epoch))
                    os.makedirs(model_dir, exist_ok=True)
                    
                    pipeline.save_pretrained(model_dir)
                    repo.push_to_hub(commit_message=f"Epoch {epoch}", blocking=True)
                else:
                    pipeline.save_pretrained(config.output_dir)



from accelerate import notebook_launcher


args = (config, model, noise_scheduler, optimizer, train_dataloader, lr_scheduler)


notebook_launcher(train_loop, args, num_processes=1)

"""Once training is complete, take a look at the final images"""

import glob

sample_images = sorted(glob.glob(f"{config.output_dir}/samples/*.png"))
Image.open(sample_images[-1])