quanhua92

#!/bin/sh
curl -s https://bunnycdn.com/api/system/edgeserverlist -H "Accept: application/json" | jq -r .[] > /tmp/bunny_ips
echo "" >> /tmp/bunny_ips
curl -s https://bunnycdn.com/api/system/edgeserverlist/ipv6 -H "Accept: application/json" | jq -r .[] >> /tmp/bunny_ips

for ip in `cat /tmp/bunny_ips`; do ufw allow proto tcp from $ip comment 'Bunny IP'; done;

quanhua92

curl -sL yabs.sh | bash -s -- -r9

3x vCPU Cores
75 GB PURE SSD RAID-10 Storage
4 GB RAM
10,000GB Monthly Premium Bandwidth
1Gbps Public Network Port
Full Root Admin Access
1 Dedicated IPv4 Address
KVM / SolusVM Control Panel - Reboot, Reinstall, Manage rDNS, & much more

quanhua92

Command: 
curl -s wget.racing/nench.sh | bash; curl -s wget.racing/nench.sh | bash

-------------------------------------------------
 nench.sh v2019.07.20 -- https://git.io/nench.sh
 benchmark timestamp:    2021-12-22 13:27:07 UTC
-------------------------------------------------

Processor:    Intel(R) Xeon(R) CPU E5-2697 v2 @ 2.70GHz
CPU cores:    3

quanhua92

import torch
import numpy as np
import copy

from deodr import read_obj
from deodr.pytorch import Scene3DPytorch, CameraPytorch
from deodr.pytorch.triangulated_mesh_pytorch import ColoredTriMeshPytorch as ColoredTriMesh

def get_camera(camera_center, width, height, focal=None):
    if focal is None:

quanhua92

function addnoise_asl(cleanfile, noisefile, outfile, snr) 
% ----------------------------------------------------------------------
%   This function adds noise to a file at a specified SNR level. It uses
%   the active speech level to compute the speech energy. The
%   active speech level1 is computed as per ITU-T P.56 standard [1].
%
%   Usage:  addnoise_asl(cleanFile.wav, noiseFile.wav, noisyFile.wav, SNR)
%           
%         cleanFile.wav  - clean input file in .wav format
%         noiseFile.wav  - file containing the noise signal in .wav format

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function scroll_to(pos){
    $('html,body').animate({ scrollTop: pos }, 'fast');
}

function scroll_and_grid(current){
    scroll_to(9999999); grid(false);

    if(current > 0){
        scroll_and_grid(current - 1);

quanhua92

"""
This is the implementation of AlexNet which is modified from [Jeicaoyu's AlexNet].
Note:
- The number of Conv2d filters now matches with the original paper.
- Use PyTorch's Local Response Normalization layer which is implemented in Jan 2018. [PR #4667]
- This is for educational purpose only. We don't have pretrained weights for this model.
References:
- Jeicaoyu's AlexNet Model: [jiecaoyu](https://github.com/jiecaoyu/pytorch_imagenet/blob/984a2a988ba17b37e1173dd2518fa0f4dc4a1879/networks/model_list/alexnet.py)
- PR #4667: https://github.com/pytorch/pytorch/pull/4667
"""

quanhua92

"""
This is AlexNet implementation from pytorch/torchvision.
Note:
- The number of nn.Conv2d doesn't match with the original paper.
- This model uses `nn.AdaptiveAvgPool2d` to allow the model to process images with arbitrary image size. [PR #746]
- This model doesn't use Local Response Normalization as described in the original paper.
    - This model is implemented in Jan 2017 with pretrained model.
    - PyTorch's Local Response Normalization layer is implemented in Jan 2018. [PR #4667]
References:
- Model: https://github.com/pytorch/vision/blob/ac2e995a4352267f65e7cc6d354bde683a4fb402/torchvision/models/alexnet.py

quanhua92

"""
This is the implementation of AlexNet which is modified from [Jeicaoyu's AlexNet].

Note:
- The number of Conv2d filters now matches with the original paper.
- Use PyTorch's Local Response Normalization layer which is implemented in Jan 2018. [PR #4667]
- This is for educational purpose only. We don't have pretrained weights for this model.

References:

quanhua92

Install JupyterLab and JupyText

conda install -c conda-forge jupyterlab jupytext

Install JupyterLab Table of Contents

jupyter labextension install @jupyterlab/toc