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Reproduce the table 2 of paper in description #4

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65afbd6
Add motion blur
Melvin-klein Apr 14, 2025
043a7af
add tasks and datasets from FiRe paper
Melvin-klein Apr 18, 2025
12d2430
Add pyiqa to requirements
Melvin-klein Apr 18, 2025
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8 changes: 7 additions & 1 deletion benchmark_utils/image_dataset.py
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Original file line number Diff line number Diff line change
@@ -1,16 +1,22 @@
import os
import random

from torch.utils.data import Dataset
from typing import Callable
from PIL import Image


class ImageDataset(Dataset):
def __init__(self, folder: str, transform: Callable = None) -> None:
def __init__(self, folder: str, transform: Callable = None, num_images=None):
self.folder = folder
self.transform = transform
self.files = [f for f in os.listdir(folder) if f.endswith((
'.png', '.jpg', '.jpeg'))]

if num_images is not None:
self.files.sort()
self.files = random.sample(self.files, num_images)
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Is this whithout replacement?


def __len__(self):
return len(self.files)

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110 changes: 110 additions & 0 deletions datasets/bsd500_bsd20.py
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@@ -0,0 +1,110 @@
from benchopt import BaseDataset, safe_import_context, config

with safe_import_context() as import_ctx:
import deepinv as dinv
import torch
from torchvision import transforms
from benchmark_utils.image_dataset import ImageDataset
from deepinv.physics import Downsampling, Denoising, GaussianNoise
from deepinv.physics.generator import MotionBlurGenerator


class Dataset(BaseDataset):

name = "BSD500_BSD20"

parameters = {
'task': ['denoising', 'gaussian-debluring', 'motion-debluring', 'SRx4'],
'img_size': [256],
}

requirements = ["datasets"]

def get_data(self):
# TODO: Remove
device = (
dinv.utils.get_freer_gpu()) if torch.cuda.is_available() else "cpu"

n_channels = 3

if self.task == "denoising":
noise_level_img = 0.03
physics = Denoising(GaussianNoise(sigma=noise_level_img))
elif self.task == "gaussian-debluring":
filter_torch = dinv.physics.blur.gaussian_blur(sigma=(3, 3))
noise_level_img = 0.03
n_channels = 3

physics = dinv.physics.BlurFFT(
img_size=(n_channels, self.img_size, self.img_size),
filter=filter_torch,
noise_model=dinv.physics.GaussianNoise(sigma=noise_level_img),
device=device
)
elif self.task == "motion-debluring":
psf_size = 31
n_channels = 3
motion_generator = MotionBlurGenerator((psf_size, psf_size), device=device)

filters = motion_generator.step(batch_size=1)

physics = dinv.physics.BlurFFT(
img_size=(n_channels, self.img_size, self.img_size),
filter=filters["filter"],
device=device
)
elif self.task == "SRx4":
physics = Downsampling(img_size=(n_channels, self.img_size, self.img_size),
filter="bicubic",
factor=4,
device=device)
else:
raise Exception("Unknown task")

transform = transforms.Compose([
transforms.Resize((self.img_size, self.img_size)),
transforms.ToTensor()
])

train_dataset = ImageDataset(
config.get_data_path("BSD500") / "train",
transform=transform
)

test_dataset = ImageDataset(
config.get_data_path("BSD500") / "val",
transform=transform,
num_images=20
)

dinv_dataset_path = dinv.datasets.generate_dataset(
train_dataset=train_dataset,
test_dataset=test_dataset,
physics=physics,
save_dir=config.get_data_path(
key="generated_datasets"
) / "bsd500_bsd20",
dataset_filename=self.task,
device=device
)

train_dataset = dinv.datasets.HDF5Dataset(
path=dinv_dataset_path, train=True
)
test_dataset = dinv.datasets.HDF5Dataset(
path=dinv_dataset_path, train=False
)

x, y = train_dataset[0]
dinv.utils.plot([x.unsqueeze(0), y.unsqueeze(0)])

x, y = test_dataset[0]
dinv.utils.plot([x.unsqueeze(0), y.unsqueeze(0)])

return dict(
train_dataset=train_dataset,
test_dataset=test_dataset,
physics=physics,
dataset_name="BSD68",
task_name=self.task
)
27 changes: 23 additions & 4 deletions datasets/bsd500_cbsd68.py
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Original file line number Diff line number Diff line change
Expand Up @@ -9,15 +9,16 @@
from benchmark_utils.hugging_face_torch_dataset import (
HuggingFaceTorchDataset
)
from deepinv.physics import Denoising, GaussianNoise
from deepinv.physics import Denoising, GaussianNoise, Downsampling
from deepinv.physics.generator import MotionBlurGenerator


class Dataset(BaseDataset):

name = "BSD500_CBSD68"

parameters = {
'task': ['denoising', 'debluring'],
'task': ['denoising', 'gaussian-debluring', 'motion-debluring', 'SRx4'],
'img_size': [256],
}

Expand All @@ -31,17 +32,35 @@ def get_data(self):
if self.task == "denoising":
noise_level_img = 0.03
physics = Denoising(GaussianNoise(sigma=noise_level_img))
elif self.task == "debluring":
elif self.task == "gaussian-debluring":
filter_torch = dinv.physics.blur.gaussian_blur(sigma=(3, 3))
noise_level_img = 0.03
n_channels = 3 # 3 for color images, 1 for gray-scale images
n_channels = 3

physics = dinv.physics.BlurFFT(
img_size=(n_channels, self.img_size, self.img_size),
filter=filter_torch,
noise_model=dinv.physics.GaussianNoise(sigma=noise_level_img),
device=device
)
elif self.task == "motion-debluring":
psf_size = 31
n_channels = 3
motion_generator = MotionBlurGenerator((psf_size, psf_size), device=device)

filters = motion_generator.step(batch_size=1)

physics = dinv.physics.BlurFFT(
img_size=(n_channels, self.img_size, self.img_size),
filter=filters["filter"],
device=device
)
elif self.task == "SRx4":
n_channels = 3
physics = Downsampling(img_size=(n_channels, self.img_size, self.img_size),
filter="bicubic",
factor=4,
device=device)
else:
raise Exception("Unknown task")

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110 changes: 110 additions & 0 deletions datasets/bsd500_imnet100.py
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Original file line number Diff line number Diff line change
@@ -0,0 +1,110 @@
from benchopt import BaseDataset, safe_import_context, config

with safe_import_context() as import_ctx:
import deepinv as dinv
import torch
from torchvision import transforms
from benchmark_utils.image_dataset import ImageDataset
from benchmark_utils.hugging_face_torch_dataset import HuggingFaceTorchDataset
from deepinv.physics import Downsampling, Denoising, GaussianNoise
from deepinv.physics.generator import MotionBlurGenerator
from datasets import load_dataset


class Dataset(BaseDataset):

name = "BSD500_imnet100"

parameters = {
'task': ['denoising', 'gaussian-debluring', 'motion-debluring', 'SRx4'],
'img_size': [256],
}

requirements = ["datasets"]

def get_data(self):
# TODO: Remove
device = (
dinv.utils.get_freer_gpu()) if torch.cuda.is_available() else "cpu"

if self.task == "denoising":
noise_level_img = 0.03
physics = Denoising(GaussianNoise(sigma=noise_level_img))
elif self.task == "gaussian-debluring":
filter_torch = dinv.physics.blur.gaussian_blur(sigma=(3, 3))
noise_level_img = 0.03
n_channels = 3

physics = dinv.physics.BlurFFT(
img_size=(n_channels, self.img_size, self.img_size),
filter=filter_torch,
noise_model=dinv.physics.GaussianNoise(sigma=noise_level_img),
device=device
)
elif self.task == "motion-debluring":
psf_size = 31
n_channels = 3
motion_generator = MotionBlurGenerator((psf_size, psf_size), device=device)

filters = motion_generator.step(batch_size=1)

physics = dinv.physics.BlurFFT(
img_size=(n_channels, self.img_size, self.img_size),
filter=filters["filter"],
device=device
)
elif self.task == "SRx4":
n_channels = 3
physics = Downsampling(img_size=(n_channels, self.img_size, self.img_size),
filter="bicubic",
factor=4,
device=device)
else:
raise Exception("Unknown task")

transform = transforms.Compose([
transforms.Resize((self.img_size, self.img_size)),
transforms.ToTensor()
])

train_dataset = ImageDataset(
config.get_data_path("BSD500") / "train",
transform=transform
)

dataset_miniImnet100 = load_dataset("mterris/miniImnet100")
test_dataset = HuggingFaceTorchDataset(
dataset_miniImnet100["validation"], key="image", transform=transform
)

dinv_dataset_path = dinv.datasets.generate_dataset(
train_dataset=train_dataset,
test_dataset=test_dataset,
physics=physics,
save_dir=config.get_data_path(
key="generated_datasets"
) / "bsd500_imnet100",
dataset_filename=self.task,
device=device
)

train_dataset = dinv.datasets.HDF5Dataset(
path=dinv_dataset_path, train=True
)
test_dataset = dinv.datasets.HDF5Dataset(
path=dinv_dataset_path, train=False
)

x, y = train_dataset[0]
dinv.utils.plot([x.unsqueeze(0), y.unsqueeze(0)])

x, y = test_dataset[0]
dinv.utils.plot([x.unsqueeze(0), y.unsqueeze(0)])

return dict(
train_dataset=train_dataset,
test_dataset=test_dataset,
physics=physics,
dataset_name="BSD68",
task_name=self.task
)
27 changes: 23 additions & 4 deletions datasets/cbsd68_set3c.py
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Original file line number Diff line number Diff line change
Expand Up @@ -8,15 +8,16 @@
from benchmark_utils.hugging_face_torch_dataset import (
HuggingFaceTorchDataset
)
from deepinv.physics import Denoising, GaussianNoise
from deepinv.physics import Denoising, GaussianNoise, Downsampling
from deepinv.physics.generator import MotionBlurGenerator


class Dataset(BaseDataset):

name = "CBSD68_Set3c"

parameters = {
'task': ['denoising', 'debluring'],
'task': ['denoising', 'gaussian-debluring', 'motion-debluring', 'SRx4'],
'img_size': [256],
}

Expand All @@ -31,17 +32,35 @@ def get_data(self):
if self.task == "denoising":
noise_level_img = 0.03
physics = Denoising(GaussianNoise(sigma=noise_level_img))
elif self.task == "debluring":
elif self.task == "gaussian-debluring":
filter_torch = dinv.physics.blur.gaussian_blur(sigma=(3, 3))
noise_level_img = 0.03
n_channels = 3 # 3 for color images, 1 for gray-scale images
n_channels = 3

physics = dinv.physics.BlurFFT(
img_size=(n_channels, self.img_size, self.img_size),
filter=filter_torch,
noise_model=dinv.physics.GaussianNoise(sigma=noise_level_img),
device=device
)
elif self.task == "motion-debluring":
psf_size = 31
n_channels = 3
motion_generator = MotionBlurGenerator((psf_size, psf_size), device=device)

filters = motion_generator.step(batch_size=1)

physics = dinv.physics.BlurFFT(
img_size=(n_channels, self.img_size, self.img_size),
filter=filters["filter"],
device=device
)
elif self.task == "SRx4":
n_channels = 3
physics = Downsampling(img_size=(n_channels, self.img_size, self.img_size),
filter="bicubic",
factor=4,
device=device)
else:
raise Exception("Unknown task")

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