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

	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

	#!/bin/bash
	fileid="1qWjJd-mVNxvHhzc9XsU6LjC0pQXIwW4t"
	filename="models/VGG16-Stylized-ImageNet.pth"
	curl -c ./cookie -s -L "https://drive.google.com/uc?export=download&id=${fileid}" > /dev/null
	curl -Lb ./cookie "https://drive.google.com/uc?export=download&confirm=`awk '/download/ {print $NF}' ./cookie`&id=${fileid}" -o ${filename}

	fileid="1EU-F9ugeIeTO9ay4PinzsBXgEuCYBu0Z"
	filename="models/VGG16_SOD_finetune.pth"
	curl -c ./cookie -s -L "https://drive.google.com/uc?export=download&id=${fileid}" > /dev/null
	curl -Lb ./cookie "https://drive.google.com/uc?export=download&confirm=`awk '/download/ {print $NF}' ./cookie`&id=${fileid}" -o ${filename}

	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()

	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
	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),

	# 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.