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score_sde_fza_demo.py
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#@title Autoload all modules
#%load_ext autoreload
#%autoreload 2
from dataclasses import dataclass, field
import matplotlib.pyplot as plt
import io
import csv
import numpy as np
import pandas as pd
import seaborn as sns
import matplotlib
import importlib
import os
import functools
import itertools
import torch
from losses import get_optimizer
from models.ema import ExponentialMovingAverage
import torch.nn as nn
import numpy as np
import tensorflow as tf
import tensorflow_datasets as tfds
import tensorflow_gan as tfgan
import tqdm
import io
import likelihood
import controllable_generation_fza
from utils import restore_checkpoint
sns.set(font_scale=2)
sns.set(style="whitegrid")
import cv2
import models
from models import utils as mutils
from models import ncsnv2
from models import ncsnpp
from models import ddpm as ddpm_model
from models import layerspp
from models import layers
from models import normalization
import sampling_fza
import sampling_pc
import sampling_3noise
from likelihood import get_likelihood_fn
from sde_lib import VESDE, VPSDE, subVPSDE
from sampling_3noise import (ReverseDiffusionPredictor,
LangevinCorrector,
EulerMaruyamaPredictor,
AncestralSamplingPredictor,
NoneCorrector,
NonePredictor,
AnnealedLangevinDynamics)
import datasets
import scipy.io as io
from operator_fza import forward,backward,forward_torch,backward_torch
#from skimage.measure import compare_psnr,compare_ssim
from skimage.metrics import structural_similarity
# @title Load the score-based model
sde = 'VESDE' #@param ['VESDE', 'VPSDE', 'subVPSDE'] {"type": "string"}
if sde.lower() == 'vesde':
#from configs.ve import cifar10_ncsnpp as configs
#ckpt_filename = "exp/ve/cifar10_ncsnpp/checkpoint_9.pth"
#from configs.ve import bedroom_ncsnpp_continuous as configs
#ckpt_filename = "exp_train_bedroom_max380_N1000/checkpoints/checkpoint_50.pth"
#from configs.ve import bedroom_ncsnpp_continuous as configs
#ckpt_filename = "exp/ve/bedroom_ncsnpp_continuous/checkpoint_127.pth"
from configs.ve import church_ncsnpp_continuous as configs
ckpt_filename = "exp_train_church_max380_N1000/checkpoints/checkpoint_9.pth" #(9:(20.2,0.5)
#from configs.ve import church_ncsnpp_continuous as configs
#ckpt_filename = "exp/ve/church_ncsnpp_continuous/checkpoint_126.pth"
config = configs.get_config()
sde = VESDE(sigma_min=config.model.sigma_min, sigma_max=config.model.sigma_max, N=config.model.num_scales)
sampling_eps = 1e-5
elif sde.lower() == 'vpsde':
from configs.vp import cifar10_ddpmpp_continuous as configs
ckpt_filename = "exp/vp/cifar10_ddpmpp_continuous/checkpoint_8.pth"
config = configs.get_config()
sde = VPSDE(beta_min=config.model.beta_min, beta_max=config.model.beta_max, N=config.model.num_scales)
sampling_eps = 1e-3
elif sde.lower() == 'subvpsde':
from configs.subvp import cifar10_ddpmpp_continuous as configs
ckpt_filename = "exp/subvp/cifar10_ddpmpp_continuous/checkpoint_26.pth"
config = configs.get_config()
sde = subVPSDE(beta_min=config.model.beta_min, beta_max=config.model.beta_max, N=config.model.num_scales)
sampling_eps = 1e-3
batch_size = 1 #64#@param {"type":"integer"}
config.training.batch_size = batch_size
config.eval.batch_size = batch_size
random_seed = 0 #@param {"type": "integer"}
sigmas = mutils.get_sigmas(config)
scaler = datasets.get_data_scaler(config)
inverse_scaler = datasets.get_data_inverse_scaler(config)
score_model = mutils.create_model(config)
optimizer = get_optimizer(config, score_model.parameters())
ema = ExponentialMovingAverage(score_model.parameters(),
decay=config.model.ema_rate)
state = dict(step=0, optimizer=optimizer,
model=score_model, ema=ema)
state = restore_checkpoint(ckpt_filename, state, config.device)
ema.copy_to(score_model.parameters())
#@title Visualization code
def image_grid(x):
size = config.data.image_size
channels = config.data.num_channels
img = x.reshape(-1, size, size, channels)
w = int(np.sqrt(img.shape[0]))
img = img.reshape((w, w, size, size, channels)).transpose((0, 2, 1, 3, 4)).reshape((w * size, w * size, channels))
#img = img.reshape(( size, size, channels*2))
return img
def show_samples(x):
x = x.permute(0, 2, 3, 1).detach().cpu().numpy()
img = image_grid(x)
plt.figure(figsize=(8,8))
plt.axis('off')
plt.imshow(img)
plt.show()
#@title PC inpainting
predictor = ReverseDiffusionPredictor #@param ["EulerMaruyamaPredictor", "AncestralSamplingPredictor", "ReverseDiffusionPredictor", "None"] {"type": "raw"}
corrector = LangevinCorrector #@param ["LangevinCorrector", "AnnealedLangevinDynamics", "None"] {"type": "raw"}
snr = 0.16 #@param {"type": "number"}
n_steps = 1 #@param {"type": "integer"}
probability_flow = False #@param {"type": "boolean"}
pc_inpainter = controllable_generation_fza.get_pc_inpainter(sde,
predictor, corrector,
inverse_scaler,
snr=snr,
n_steps=n_steps,
probability_flow=probability_flow,
continuous=config.training.continuous,
denoise=True)
'''
batch = next(eval_iter)
img = batch['image']._numpy()
print(img)
plt.figure(figsize=(8,8))
plt.axis('off')
plt.imshow(img[0,:,:,:])
plt.show()
'''
psnr_result=[ ]
ssim_result=[ ]
for j in range(0,1,1):
print('****************'+'第{}张图'.format(j+1)+'******************')
img=io.loadmat('./church_mat/Img/Img_bed{}_256mat.mat'.format(j+1))['Img']
print(type(img))
print(img)
assert 0
img_ob=io.loadmat('./church_mat/ob/ob_bed{}_256mat.mat'.format(j+1))['ob']
img_ob=torch.tensor(img_ob).cuda()
img=np.expand_dims(img,axis=0)
'''
img=cv2.imread('1.jpg',-1)/255
img=np.stack((img[:,:,2],img[:,:,1],img[:,:,0]),axis=2)
print(img.shape)
img=cv2.resize(img,(256,256))
img=np.expand_dims(img,axis=0)
'''
img = torch.from_numpy(img).permute(0, 3, 1, 2).to(config.device) #1,3,128,128
#show_samples(img)
dp=0.014
di=3
z1=20
r1=0.23
M=di/z1
ri=(1+M)*r1
NX,NY=256,256
fu_max,fv_max=0.5/dp,0.5/dp
du,dv=2*fu_max/NX,2*fv_max/NY
u,v=np.mgrid[-fu_max:fu_max:du,-fv_max:fv_max:dv]
u=u.T
v=v.T
H=1j*(np.exp(-1j*(np.dot(np.pi,ri**2))*(u**2+v**2)))
H=np.array(H,dtype=np.complex128)
#H=torch.tensor(H,dtype=torch.complex128).cuda()
img_forward=backward(img_ob[:,:,0],H).cpu().numpy()#(-0.6,0.6)
'''
plt.figure(figsize=(8,8))
plt.axis('off')
plt.imshow(img_forward,cmap='gray')
plt.show()
'''
'''
mask = torch.ones_like(img)
mask[:, :, :, 128:] = 0.
show_samples(img * mask)
'''
psnr_max_1=0
for i in range(1):
print('##################'+str(i)+'#######################')
img_size = config.data.image_size
channels = config.data.num_channels
shape = (batch_size, channels, img_size, img_size)
sampling_fn = sampling_3noise.get_pc_sampler(sde, shape, predictor, corrector,
inverse_scaler, snr, n_steps=n_steps,
probability_flow=probability_flow,
continuous=config.training.continuous,
eps=sampling_eps, device=config.device)
x,psnr_max,ssim_max = sampling_fn(score_model,img,H,img_ob)
'''
if psnr_max>psnr_max_1:
psnr_max_1=psnr_max
ssim_max_1=ssim_max
'''
cv2.imwrite('./try/fza_rec_{}.png'.format(j),x*255)
#print('psnr_max_1',psnr_max)
psnr_result.append(psnr_max)
ssim_result.append(ssim_max)
print('psnr_result',psnr_result)
print('ssim_result',ssim_result)
psnr_result=sum(psnr_result)/(len(psnr_result))
ssim_result=sum(ssim_result)/(len(ssim_result))
print(psnr_result,ssim_result)
'''
x,psnr_max,ssim_max = pc_inpainter(score_model, scaler(img),H,img_ob)
if psnr_max>psnr_max_1:
psnr_max_1=psnr_max
ssim_max_1=ssim_max
cv2.imwrite('./try/fza_rec_{}.png'.format(j),x*255)
print('psnr_max_1',psnr_max_1)
psnr_result.append(psnr_max_1)
ssim_result.append(ssim_max_1)
print('psnr_result',psnr_result)
print('ssim_result',ssim_result)
psnr_result=sum(psnr_result)/(len(psnr_result))
ssim_result=sum(ssim_result)/(len(ssim_result))
print(psnr_result,ssim_result)
'''