generate.py

import numpy as np
import argparse, json, math
import os, glob

import flow, utils, source

import torch, torchvision
from torch import nn

from encoder import rans, coder
from utils import cdfDiscreteLogitstic, cdfMixDiscreteLogistic
from matplotlib import pyplot as plt

#torch.manual_seed(42)

parser = argparse.ArgumentParser(description="")

parser.add_argument("-folder", default=None, help="Path to load the trained model")
parser.add_argument("-cuda", type=int, default=-1, help="Which device to use with -1 standing for CPU, number bigger than -1 is N.O. of GPU.")
parser.add_argument("-baseScale", type=float, default=-2.0, help="exp scaling of distribution's logscale to achieve better quality")
parser.add_argument("-best", action='store_false', help="if load the best model")
parser.add_argument("-num", type=int, default=10, help="num of image to demo")

args = parser.parse_args()

device = torch.device("cpu" if args.cuda < 0 else "cuda:" + str(args.cuda))

if args.folder is None:
    raise Exception("No loading")
else:
    rootFolder = args.folder
    if rootFolder[-1] != '/':
        rootFolder += '/'
    with open(rootFolder + "parameter.json", 'r') as f:
        config = json.load(f)
        locals().update(config)

        target = config['target']
        repeat = config['repeat']
        nMixing = config['nMixing']
        batch = config['batch']

# update batch using passing parameter
batch = args.num ** 2

if target == "CIFAR":
    # Define dimensions
    targetSize = [3, 32, 32]
    dimensional = 2
    channel = targetSize[0]
    blockLength = targetSize[-1]

    # Define nomaliziation and decimal
    decimal = flow.ScalingNshifting(256, -128)
    rounding = utils.roundingWidentityGradient
elif target == "ImageNet32":
    # Define dimensions
    targetSize = [3, 32, 32]
    dimensional = 2
    channel = targetSize[0]
    blockLength = targetSize[-1]

    # Define nomaliziation and decimal
    decimal = flow.ScalingNshifting(256, -128)
    rounding = utils.roundingWidentityGradient
elif target == "ImageNet64":
    # Define dimensions
    targetSize = [3, 64, 64]
    dimensional = 2
    channel = targetSize[0]
    blockLength = targetSize[-1]

    # Define nomaliziation and decimal
    decimal = flow.ScalingNshifting(256, -128)
    rounding = utils.roundingWidentityGradient


# decide which model to load
if args.best:
    name = max(glob.iglob(os.path.join(rootFolder, '*.saving')), key=os.path.getctime)
else:
    name = max(glob.iglob(os.path.join(rootFolder, 'savings', '*.saving')), key=os.path.getctime)

# load the model
print("load saving at " + name)
loadedF = torch.load(name, map_location=device)

if 'easyMera' in name:
    layerList = loadedF.layerList[:(4 * repeat)]
    layerList = [layerList[no] for no in range(4 * repeat)]
elif '1to2Mera' in name:
    layerList = loadedF.layerList[:(2 * repeat)]
    layerList = [layerList[no] for no in range(2 * repeat)]
else:
    raise Exception("model not define")

# Define nomaliziation and decimal
if 'easyMera' in name:
    decimal = flow.ScalingNshifting(256, -128)
elif '1to2Mera' in name:
    decimal = flow.ScalingNshifting(256, 0)
else:
    raise Exception("model not define")

if 'simplePrior_False' in name:
    meanNNlist = [loadedF.meanNNlist[0]]
    scaleNNlist = [loadedF.scaleNNlist[0]]
else:
    meanNNlist = None
    scaleNNlist = None

rounding = utils.roundingWidentityGradient

prior = loadedF.prior

# Building MERA mode
if 'easyMera' in name:
    f = flow.SimpleMERA(blockLength, layerList, meanNNlist, scaleNNlist, repeat, None, nMixing, decimal=decimal, rounding=utils.roundingWidentityGradient).to(device)
elif '1to2Mera' in name:
    f = flow.OneToTwoMERA(blockLength, layerList, meanNNlist, scaleNNlist, repeat, None, nMixing, decimal=decimal, rounding=utils.roundingWidentityGradient).to(device)

f.prior = prior

samples = f.sample(batch)


# define renorm fn
def back01(tensor):
    ten = tensor.clone().float()
    ten = ten.view(ten.shape[0] * ten.shape[1], -1)
    ten -= ten.min(1, keepdim=True)[0]
    ten /= ten.max(1, keepdim=True)[0]
    ten = ten.view(tensor.shape)
    return ten


# another renorm fn
def clip(tensor, l=0, h=255):
    return torch.clamp(tensor, l, h).int()


# yet another renorm fn
def batchNorm(tensor, base=1.0):
    m = nn.BatchNorm2d(tensor.shape[1], affine=False)
    return m(tensor).float() + base


renormFn = lambda x: back01(batchNorm(x))

samples = renormFn(samples).detach()

grid_img = torchvision.utils.make_grid(samples, nrow=args.num)

plt.figure(figsize=(12, 12))

plt.imshow(grid_img.permute(1, 2, 0))

plt.show()