start new:
tmux
start new with session name:
tmux new -s myname
-- Available torch7 pre-trained models for download | |
Alexnet (trained by me) | |
cudnn: https://ln.sync.com/dl/3d9c28e80#cvjp4mjs-mc2y8jrt-9wq5yebx-yrdchcei | |
mean = {0.48037518790839, 0.45039056120456, 0.39922636057037} | |
std = {0.27660147027775, 0.26883440068399, 0.28014687231841} | |
img size: 3x224x224 | |
overfeat: https://github.com/jhjin/overfeat-torch | |
mean = {118.380948, 118.380948, 118.380948} |
Trained by facebook using https://github.com/facebook/fb.resnet.torch
The model was converted to nn
backend and BatchNorm folded into convolutional layers with this script
https://github.com/szagoruyko/imagine-nn/blob/utils/utils.lua
gradWeight
and gradBias
were removed from convolutional layers.
--[[ | |
Neural-mirage 5 | |
by @htoyryla 3/2016, 3/2017 | |
based on neural-style by @jcjohnson | |
for more info see | |
http://liipetti.net/erratic/2016/03/31/i-have-seen-a-neural-mirage/ | |
http://liipetti.net/erratic/2016/04/20/getting-the-space-back/ |
import math | |
from moviepy.editor import concatenate, ImageClip | |
import os | |
import platform | |
import subprocess | |
import random | |
import torch | |
# pip install pytorch-pretrained-biggan | |
from pytorch_pretrained_biggan import (BigGAN, truncated_noise_sample, convert_to_images) | |
import numpy as np |