Note
Go to the end to download the full example code.
Self-supervised denoising with the Neighbor2Neighbor loss.
This example shows you how to train a denoiser network in a fully self-supervised way, i.e., using noisy images only via the Neighbor2Neighbor loss, which exploits the local correlation of natural images.
The Neighbor2Neighbor loss is presented in “Neighbor2Neighbor: Self-Supervised Denoising from Single Noisy Images” and is defined as:
where \(A_1\) and \(A_2\) are two masks, each choosing a different neighboring map, \(R\) is the trainable denoiser network, \(\gamma>0\) is a regularization parameter and no gradient is propagated when computing \(R(y)\).
import deepinv as dinv
from torch.utils.data import DataLoader
import torch
from pathlib import Path
from torchvision import transforms, datasets
from deepinv.models.utils import get_weights_url
Setup paths for data loading and results.
BASE_DIR = Path(".")
ORIGINAL_DATA_DIR = BASE_DIR / "datasets"
DATA_DIR = BASE_DIR / "measurements"
CKPT_DIR = BASE_DIR / "ckpts"
# Set the global random seed from pytorch to ensure reproducibility of the example.
torch.manual_seed(0)
device = dinv.utils.get_freer_gpu() if torch.cuda.is_available() else "cpu"
Load base image datasets
In this example, we use the MNIST dataset as the base image dataset.
operation = "denoising"
train_dataset_name = "MNIST"
transform = transforms.Compose([transforms.ToTensor()])
train_dataset = datasets.MNIST(
root="../datasets/", train=True, transform=transform, download=True
)
test_dataset = datasets.MNIST(
root="../datasets/", train=False, transform=transform, download=True
)
Generate a dataset of noisy images
We generate a dataset of noisy images corrupted by Poisson noise.
Note
We use a subset of the whole training set to reduce the computational load of the example.
We recommend to use the whole set by setting n_images_max=None
to get the best results.
# defined physics
physics = dinv.physics.Denoising(dinv.physics.PoissonNoise(0.1))
# Use parallel dataloader if using a GPU to fasten training,
# otherwise, as all computes are on CPU, use synchronous data loading.
num_workers = 4 if torch.cuda.is_available() else 0
n_images_max = (
100 if torch.cuda.is_available() else 5
) # number of images used for training
my_dataset_name = "demo_n2n"
measurement_dir = DATA_DIR / train_dataset_name / operation
deepinv_datasets_path = dinv.datasets.generate_dataset(
train_dataset=train_dataset,
test_dataset=test_dataset,
physics=physics,
device=device,
save_dir=measurement_dir,
train_datapoints=n_images_max,
test_datapoints=n_images_max,
num_workers=num_workers,
dataset_filename=str(my_dataset_name),
)
train_dataset = dinv.datasets.HDF5Dataset(path=deepinv_datasets_path, train=True)
test_dataset = dinv.datasets.HDF5Dataset(path=deepinv_datasets_path, train=False)
Dataset has been saved in measurements/MNIST/denoising
Set up the denoiser network
We use a simple U-Net architecture with 2 scales as the denoiser network.
model = dinv.models.ArtifactRemoval(
dinv.models.UNet(in_channels=1, out_channels=1, scales=2).to(device)
)
Set up the training parameters
We set deepinv.loss.Neighbor2Neighbor
as the training loss.
Note
We use a pretrained model to reduce training time. You can get the same results by training from scratch for 50 epochs.
epochs = 1 # choose training epochs
learning_rate = 5e-4
batch_size = 32 if torch.cuda.is_available() else 1
# choose self-supervised training loss
loss = dinv.loss.Neighbor2Neighbor()
# choose optimizer and scheduler
optimizer = torch.optim.Adam(model.parameters(), lr=learning_rate, weight_decay=1e-8)
scheduler = torch.optim.lr_scheduler.StepLR(optimizer, step_size=int(epochs * 0.8) + 1)
# start with a pretrained model to reduce training time
file_name = "ckp_50_demo_n2n.pth"
url = get_weights_url(model_name="demo", file_name=file_name)
ckpt = torch.hub.load_state_dict_from_url(
url, map_location=lambda storage, loc: storage, file_name=file_name
)
# load a checkpoint to reduce training time
model.load_state_dict(ckpt["state_dict"])
optimizer.load_state_dict(ckpt["optimizer"])
Downloading: "https://huggingface.co/deepinv/demo/resolve/main/ckp_50_demo_n2n.pth?download=true" to /home/runner/.cache/torch/hub/checkpoints/ckp_50_demo_n2n.pth
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Train the network
verbose = True # print training information
wandb_vis = False # plot curves and images in Weight&Bias
train_dataloader = DataLoader(
train_dataset, batch_size=batch_size, num_workers=num_workers, shuffle=True
)
test_dataloader = DataLoader(
test_dataset, batch_size=batch_size, num_workers=num_workers, shuffle=False
)
# Initialize the trainer
trainer = dinv.Trainer(
model=model,
physics=physics,
epochs=epochs,
scheduler=scheduler,
losses=loss,
optimizer=optimizer,
device=device,
train_dataloader=train_dataloader,
eval_dataloader=test_dataloader,
plot_images=True,
save_path=str(CKPT_DIR / operation),
verbose=verbose,
show_progress_bar=False, # disable progress bar for better vis in sphinx gallery.
wandb_vis=wandb_vis,
)
model = trainer.train()
The model has 444737 trainable parameters
Train epoch 0: TotalLoss=0.07, PSNR=24.558
Eval epoch 0: PSNR=24.633
Test the network
trainer.test(test_dataloader)
Eval epoch 0: PSNR=24.633, PSNR no learning=19.346
Test results:
PSNR no learning: 19.346 +- 1.786
PSNR: 24.633 +- 1.501
{'PSNR no learning': 19.346022415161134, 'PSNR no learning_std': 1.7863402359700375, 'PSNR': 24.633205032348634, 'PSNR_std': 1.5013128598076784}
Total running time of the script: (0 minutes 1.393 seconds)