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

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

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)

mingrui

(Result, number of patches predicted)

2018-03768 is Liver 穿刺 from Breast

data/unknown/2018-03768
[('TCGA_Kidney', 17), ('TCGA_Lung', 9), ('TCGA_Breast', 8), ('TCGA_Glioma', 3), ('TCGA_Uterus', 0), ('TCGA_Thyroid', 0), ('TCGA_Colorectal', 0), ('TCGA_Bladder', 0)]

2017-17894 is Spleen from Uterus

data/unknown/2017-17894
[('TCGA_Lung', 47), ('TCGA_Colorectal', 1), ('TCGA_Uterus', 0), ('TCGA_Thyroid', 0), ('TCGA_Kidney', 0), ('TCGA_Glioma', 0), ('TCGA_Breast', 0), ('TCGA_Bladder', 0)]

mingrui

# https://bic-berkeley.github.io/psych-214-fall-2016/saving_images.html

def array_to_nii(array_3d, scan, output_path):
    '''
    Same affine and same header
    convert mask only contains 0 and 1, so convert data type to uint8
    :param array_3d:
    :param scan:
    :param output_path:
    :return: