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affirm3d_noGT_normaliser.py
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affirm3d_noGT_normaliser.py
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import os
from pathlib import Path
import torch
from torch.utils.data import DataLoader
import albumentations
from albumentations.pytorch import ToTensorV2
from affirm3d_noGT_classes import Custom_Dataset
def mean_and_std(dataloader):
signal_sum = 0.
signal_squared_sum = 0.
n_batches = 0.
for data in dataloader:
signal_sum += torch.mean(data, dim=[0, -3, -2, -1])
signal_squared_sum += torch.mean(data ** 2, dim=[0, -3, -2, -1])
n_batches += 1
mean = signal_sum / n_batches
std = ((signal_squared_sum / n_batches) - (mean ** 2)) ** 0.5
return mean, std
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
n_workers = 2
img_size = [32, 96, 96]
batch_size = 12
# path to data
path_data = os.path.join(
os.getcwd(), Path("images/sims/microtubules/noGT_LD_zres5xWorse")
)
# glob of filenames
filename = "mtubs_sim_*.tif"
transformoid = albumentations.Compose(
[
# albumentations.Resize(img_size[-1], width=img_size[-2]),
albumentations.HorizontalFlip(),
albumentations.Normalize(mean=0.0309, std=0.0740),
ToTensorV2(),
]
)
# image datasets
dataset = Custom_Dataset(
dir_data=path_data,
filename=filename,
transform=None,
)
# image dataloaders when loading in hires and lores together
dataloader = DataLoader(dataset, batch_size=batch_size, shuffle=True, num_workers=n_workers)
print(mean_and_std(dataloader))