mingrui

using UnityEngine;
using System.Collections;

public class SmoothFollow : MonoBehaviour {

    // The target we are following
    public Transform target;
    // The target angle offset, 180 is in front of the target, 0 is behind the target
    public float angleOffset;
    // The distance in the x-z plane to the target

mingrui

using System.Collections;
using System.Collections.Generic;
using UnityEngine;
using UnityEditor;

public class CleanupMissingScriptsHelper{
    [MenuItem("Edit/Cleanup Missing Scripts")]
    static void CleanupMissingScripts() {
        for (int i = 0; i < Selection.gameObjects.Length; i++) {
            var gameObject = Selection.gameObjects[i];

mingrui

import abc

#class Base1(metaclass=abc.ABCMeta):
class Base1():
    class_variable_one = 0

    @abc.abstractmethod
    def test_func(self):
        pass

mingrui

Verifying my Blockstack ID is secured with the address 16kTL3Le9AAX8A2nc4oUZ7VKVb5G4DKPHG https://explorer.blockstack.org/address/16kTL3Le9AAX8A2nc4oUZ7VKVb5G4DKPHG

mingrui

When installing latest docker-ec 18, I encountered libseccomp2 version problem

https://www.ubuntuupdates.org/ppa/ubuntu_sdk_release?dist=xenial

add this to update libseccomp2

then install docker-ce

mingrui

class DICELossMultiClass(nn.Module):

    def __init__(self):
        super(DICELossMultiClass, self).__init__()

    def forward(self, output, mask):

        probs = output[:, 1, :, :]
        mask = torch.squeeze(mask, 1)

mingrui

# source: https://github.com/shreyaspadhy/UNet-Zoo

def test(train_accuracy=False, save_output=False):
    test_loss = 0
    correct = 0

    if train_accuracy:
        loader = train_loader
    else:
        loader = test_loader

mingrui

total: 64000 cny

config:
gtx 1080 ti * 4
server motherboard with dual cpu slot * 1
cpu * 2
ram 128gb * 1
hhd 10tb * 1
server case * 1
power supply * 1

mingrui

#!/bin/sh
echo "# git secret hide!"
exec git secret hide

mingrui

def load_async(arg):
    print('load_async')
    tick = time.time()

    filename, reader_location, level, reader_size, location, size = arg
    slide = openslide.open_slide(filename)
    region = slide.read_region(location=reader_location, level=level, size=reader_size)
    # Convert to numpy array
    cachedImage = np.array(region, dtype=np.uint8)