dnlcrl
/ s3fs_nifti.ipynb
Created
December 21, 2022 12:28
— forked from arokem/s3fs_nifti.ipynb
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| version: 2 | |
| updates: | |
| # Maintain dependencies for GitHub Actions | |
| - package-ecosystem: "github-actions" | |
| directory: "/" | |
| schedule: | |
| interval: "daily" | |
| commit-message: | |
| prefix: "chore:" | |
| include: "scope" |
dnlcrl
/ average_model.py
Last active
October 2, 2020 07:09
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| import torch | |
| import sys | |
| import os | |
| print("Warning: Do not use this for BatchNorm-using models!") | |
| model_names = sys.argv[1:] | |
| if len(model_names) < 2: | |
| print("need at least two models to average") |
dnlcrl
/ convert_dicom.py
Created
October 11, 2019 09:18
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| #!/usr/bin/env python | |
| import SimpleITK as sitk | |
| import sys, os | |
| if len ( sys.argv ) < 3: | |
| print( "Usage: DicomSeriesReader <input_directory> <output_file>" ) | |
| sys.exit ( 1 ) | |
| print( "Reading Dicom directory:", sys.argv[1] ) | |
| reader = sitk.ImageSeriesReader() | |
| dicom_names = reader.GetGDCMSeriesFileNames( sys.argv[1] ) | |
| reader.SetFileNames(dicom_names) |
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| import torch | |
| import torch.nn as nn | |
| class Model(nn.Module): | |
| def __init__(self): | |
| super(Model, self).__init__() | |
| self.linear = nn.Linear(1,1) | |
| def forward(self, x): | |
| y = self.linear(x) |
dnlcrl
/ einsum_vs_torch-bigger_data.ipynb
Created
May 2, 2018 16:36
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dnlcrl
/ print_img.py
Created
February 9, 2018 14:30
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| from __future__ import print_function | |
| from PIL import Image | |
| from xtermcolor import colorize | |
| from skimage.exposure import rescale_intensity | |
| import argparse | |
| import numpy as np | |
| import matplotlib.pyplot as plt | |
| import scipy.misc | |
| PIXEL = ' ' |
dnlcrl
/ gan_vid.sh
Created
February 6, 2018 12:06
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| for f in `find . -name "fake*.png"`; do convert real_samples.png $f +append $f; done | |
dnlcrl
/ numpy-bbox.py
Created
January 25, 2018 08:56
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| def bbox(img): | |
| rows = np.any(img, axis=1) | |
| cols = np.any(img, axis=0) | |
| rmin, rmax = np.where(rows)[0][[0, -1]] | |
| cmin, cmax = np.where(cols)[0][[0, -1]] | |
| return slice(rmin, rmax), slice(cmin, cmax) |
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