ProGamerGov

Multiscale Resolution Scripts

Setup

First download the linear-color-transfer.py, and lum-transfer.py scripts from Neural-Tools:

wget -c https://raw.githubusercontent.com/ProGamerGov/Neural-Tools/master/linear-color-transfer.py

wget -c https://raw.githubusercontent.com/ProGamerGov/Neural-Tools/master/lum-transfer.py

	-- This modified version of Neural-Style was created by github.com/ProGamerGov on 2017-09-29
	-- Use: "luarocks install paths" to install the required package.
	-- The -style_image parameter will now automatically determine if the input is a directory, or a string of one or more images.

	-- The FC layers as content layers feature comes from:
	-- github.com/htoyryla's gist.github.com/htoyryla/806ca4d978f0528114282cd00022ad71

	-- Code from: https://github.com/jcjohnson/neural-style/pull/408
	-- The NIN ImageNet model uses dropout layers with random values,
	-- so this version of neural_style.lua removes the unneeded dropout layers entirely.

	from PIL import Image

	import argparse
	parser = argparse.ArgumentParser()
	# Basic options
	parser.add_argument("-content", help="Original content target image", default='examples/inputs/tubingen.jpg')
	parser.add_argument("-generated", help="Stylized image", default='examples/outputs/tubingen_starry.png')
	parser.add_argument("-output_image", default='out.png')
	params = parser.parse_args()

	# Inspired by: https://github.com/torch/nn/blob/master/GPU.lua
	# And: https://github.com/jcjohnson/neural-style/blob/master/neural_style.lua#L360
	# As seen in: https://github.com/ProGamerGov/neural-style-pt

	import torch
	import torch.nn as nn


	class ModelParallel(nn.Module):
	r"""Splits a sequential network across multiple devices.

	import torch
	import torch.nn as nn


	class VGG(nn.Module):
	def __init__(self, features, num_classes=1000):
	super(VGG, self).__init__()
	self.features = features
	self.classifier = nn.Sequential(
	nn.Linear(512 * 7 * 7, 4096),

	import os
	import copy
	import torch
	import torch.nn as nn
	import torch.optim as optim
	import torchvision.transforms as transforms

	from PIL import Image
	from CaffeLoader import loadCaffemodel, ModelParallel

	import os
	import copy
	import time
	import torch
	import torch.nn as nn
	import torch.optim as optim
	import torchvision.transforms as transforms

	from PIL import Image
	from CaffeLoader import loadCaffemodel, ModelParallel

	import torch
	from collections import OrderedDict

	import argparse
	parser = argparse.ArgumentParser()
	parser.add_argument("-model_file", type=str, default='')
	parser.add_argument("-output_name", type=str, default='')
	params = parser.parse_args()