JDWarner

"""
_dice.py : Dice coefficient for comparing set similarity.

"""

import numpy as np


def dice(im1, im2):
    """

brunodoamaral

def dice(im1, im2, empty_score=1.0):
    """
    Computes the Dice coefficient, a measure of set similarity.
    Parameters
    ----------
    im1 : array-like, bool
        Any array of arbitrary size. If not boolean, will be converted.
    im2 : array-like, bool
        Any other array of identical size. If not boolean, will be converted.
    Returns

brookisme

mingrui

import nibabel as nib

def test_nifti_all_labels_dice_score(seg_path, seg_file, truth_path, truth_file):
    truth_uid = os.listdir(truth_path)
    print(truth_uid)
    dice_score = 0
    for uid in  truth_uid:
        seg_file_path = os.path.join(seg_path, uid, seg_file)
        truth_file_path = os.path.join(truth_path, uid, truth_file)
        seg_nib = nib.load(seg_file_path)

Shreeyak

#!/usr/bin/env python3

import numpy as np
from PIL import Image
import imageio
import OpenEXR
import struct
import os

def get_pointcloud(color_image,depth_image,camera_intrinsics):

Birch-san

Installing CUDA 12.1.1 + PyTorch nightly + Python 3.10 on Ubuntu 22.10

Should you keep your NVIDIA driver?

CUDA 12.1.1 toolkit is gonna offer to install Nvidia driver 530 for us. It's from New Feature branch. It's likely to be newer than the default Nvidia driver you would've installed via apt-get (apt would prefer to give you 525, i.e. Production Branch).

If you're confident that you already have a new enough Nvidia driver for CUDA 12.1.1, and you'd like to keep your driver: feel free to skip this "uninstall driver" step.

But if you're not sure, or you know your driver is too old: let's uninstall it. CUDA will install a new driver for us later.