conda install -c conda-forge jupyterlab jupytext
jupyter labextension install @jupyterlab/toc
#!/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; |
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 |
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 |
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: |
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 |
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); |
""" | |
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 | |
""" |
""" | |
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 |
""" | |
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: |