authman

import math
from torch.optim.optimizer import Optimizer

# This version of Adam keeps an fp32 copy of the parameters and 
# does all of the parameter updates in fp32, while still doing the
# forwards and backwards passes using fp16 (i.e. fp16 copies of the 
# parameters and fp16 activations).
#
# Note that this calls .float().cuda() on the params such that it 
# moves them to gpu 0--if you're using a different GPU or want to 

authman

import math
from torch.optim.optimizer import Optimizer

# This version of Adam keeps an fp32 copy of the parameters and 
# does all of the parameter updates in fp32, while still doing the
# forwards and backwards passes using fp16 (i.e. fp16 copies of the 
# parameters and fp16 activations).
#
# Note that this calls .float().cuda() on the params such that it 
# moves them to gpu 0--if you're using a different GPU or want to 

authman

import numpy as np
import matplotlib.pyplot as plt
import torch
import torch.nn as nn
import torch.optim as optim
import torch.nn.functional as F
from torch.autograd import Variable
import torchvision
import torchvision.transforms as transforms
import numpy as np

authman

import numpy as np

# start_point = (1,2,3)  # Z, X, Y coordinate values

def region_grow(vol, mask, start_point, epsilon=5, HU_mid=0, HU_range=0, fill_with=1):

    sizez = vol.shape[0] - 1
    sizex = vol.shape[1] - 1
    sizey = vol.shape[2] - 1

authman

var fs = require('fs');

//this is the path that QTNetwork classes uses for caching files for it's http client
//the path should be the one that has 16 folders labeled 0,1,2,3,...,F
exports.cachePath = '/path/to/phantomjs/cache/data/folder';

//this is the extension used for files in the cache path
exports.cacheExtension = "d";

//the resources that are to be saved