test.py

import time
import os
from options.test_options import TestOptions
from data.data_loader import CreateDataLoader
from models.models import create_model
from util.visualizer import Visualizer
from pdb import set_trace as st
from util import html
from util.metrics import PSNR
from util.metrics import SSIM
from PIL import Image
import torch

import warnings
warnings.filterwarnings("ignore")

opt = TestOptions().parse()
opt.nThreads = 1  # test code only supports nThreads = 1
opt.batch_size = 1  # test code only supports batchSize = 1
opt.serial_batches = True  # no shuffle
opt.no_flip = True  # no flip

data_loader = CreateDataLoader(opt)
dataset = data_loader.load_data()
model = create_model(opt)
visualizer = Visualizer(opt)
# create website
web_dir = os.path.join(opt.results_dir, opt.name,
                       '%s_%s' % (opt.phase, opt.which_epoch))
webpage = html.HTML(
    web_dir, 'Experiment = %s, Phase = %s, Epoch = %s' % (opt.name, opt.phase,
                                                          opt.which_epoch))
# test
avgPSNR = 0.0
avgPSNR_b = 0.0
#avgSSIM = 0.0
#avgSSIM_b = 0.0
counter = 0

with torch.no_grad():
    for i, data in enumerate(dataset):
        if i >= opt.how_many:
            break
        counter = i
        model.set_input(data)
        model.test()
        visuals = model.get_current_visuals()
        PSNR_b = PSNR(visuals['real_A'], visuals['real_B'])
        PSNR_d = PSNR(visuals['fake_B'], visuals['real_B'])
        avgPSNR += PSNR_d
        avgPSNR_b += PSNR_b
        #pilReala = Image.fromarray(visuals['real_A'])
        #pilFake = Image.fromarray(visuals['fake_B'])
        #pilReal = Image.fromarray(visuals['real_B'])
        #SSIM_b = SSIM(pilReala,pilReal)
        #SSIM_b = SSIM(pilReala).cw_ssim_value(pilReal)
        #SSIM_d = SSIM(pilFake).cw_ssim_value(pilReal)
        #avgSSIM += SSIM_d
        #avgSSIM_b += SSIM_b
        img_path = model.get_image_paths()
        #print('process image... %s ... Deblurred PSNR ... %f' % (img_path, PSNR_d))
        print(
            'process image... %s ... Blurred PSNR ... %f ... Deblurred PSNR ... %f'
            % (img_path, PSNR_b, PSNR_d))
        #print('process image... %s ... Blurred SSIM ... %f ... Deblurred SSIM ... %f' % (img_path, SSIM_b, SSIM_d))
        visualizer.save_images(webpage, visuals, img_path)
    
    avgPSNR /= counter
    avgPSNR_b /= counter
    #avgSSIM /= counter
    #avgSSIM_b /= counter
    print('Blurred PSNR = %f, Deblurred PSNR = %f' % (avgPSNR_b, avgPSNR))
    #print('PSNR = %f, SSIM = %f' %
    #				  (avgPSNR, avgSSIM))
    
    webpage.save()