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tester.py
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tester.py
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#-*- coding:utf-8 -*-
import os
import time
import torch
import datetime
import torch.nn as nn
from torchvision.utils import save_image
from losses import PerceptualLoss, TVLoss
from utils import Logger, denorm, ImagePool, GaussianNoise
from models import Generator, Discriminator
from metrics.NIMA.CalcNIMA import calc_nima
from metrics.CalcPSNR import calc_psnr
from metrics.CalcSSIM import calc_ssim
from tqdm import *
from data_loader import InputFetcher
class Tester(object):
def __init__(self, loaders, args):
# data loader
self.loaders = loaders
# Model configuration.
self.args = args
self.device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
# Directories.
self.model_save_path = os.path.join(args.save_root_dir, args.version, args.model_save_path)
self.sample_path = os.path.join(args.save_root_dir, args.version, args.sample_path)
self.log_path = os.path.join(args.save_root_dir, args.version, args.log_path)
self.test_result_path = os.path.join(args.save_root_dir, args.version, args.test_result_path)
# Build the model and tensorboard.
self.build_model()
if self.args.use_tensorboard:
self.build_tensorboard()
def test(self):
""" Test UEGAN ."""
self.load_pretrained_model(self.args.pretrained_model)
start_time = time.time()
test_start = 0
test_total_steps = len(self.loaders.tes)
self.fetcher_test = InputFetcher(self.loaders.tes)
test = {}
test_save_path = self.test_result_path + '/' + 'test_results'
test_compare_save_path = self.test_result_path + '/' + 'test_compare'
if not os.path.exists(test_save_path):
os.makedirs(test_save_path)
if not os.path.exists(test_compare_save_path):
os.makedirs(test_compare_save_path)
self.G.eval()
pbar = tqdm(total=(test_total_steps - test_start), desc='Test epoches', position=test_start)
pbar.write("============================== Start tesing ==============================")
with torch.no_grad():
for test_step in range(test_start, test_total_steps):
input = next(self.fetcher_test)
test_real_raw, test_name = input.img_raw, input.img_name
test_fake_exp = self.G(test_real_raw)
for i in range(0, denorm(test_real_raw.data).size(0)):
save_imgs = denorm(test_fake_exp.data)[i:i + 1,:,:,:]
save_image(save_imgs, os.path.join(test_save_path, '{:s}_{:0>3.2f}_testFakeExp.png'.format(test_name[i], self.args.pretrained_model)))
save_imgs_compare = torch.cat([denorm(test_real_raw.data)[i:i + 1,:,:,:], denorm(test_fake_exp.data)[i:i + 1,:,:,:]], 3)
save_image(save_imgs_compare, os.path.join(test_compare_save_path, '{:s}_{:0>3.2f}_testRealRaw_testFakeExp.png'.format(test_name[i], self.args.pretrained_model)))
elapsed = time.time() - start_time
elapsed = str(datetime.timedelta(seconds=elapsed))
if test_step % self.args.info_step == 0:
pbar.write("=== Elapse:{}, Save {:>3d}-th test_fake_exp images into {} ===".format(elapsed, test_step, test_save_path))
test['test/testFakeExp'] = denorm(test_fake_exp.detach().cpu())
test['test_compare/testRealRaw_testFakeExp'] = torch.cat([denorm(test_real_raw.cpu()), denorm(test_fake_exp.detach().cpu())], 3)
pbar.update(1)
if self.args.use_tensorboard:
for tag, images in test.items():
self.logger.images_summary(tag, images, test_step + 1)
if self.args.is_test_nima:
self.nima_result_save_path = './results/nima_test_results/'
curr_nima = calc_nima(test_save_path, self.nima_result_save_path, self.args.pretrained_model)
print("====== Avg. NIMA: {:>.4f} ======".format(curr_nima))
if self.args.is_test_psnr_ssim:
self.psnr_save_path = './results/psnr_test_results/'
curr_psnr = calc_psnr(test_save_path, self.args.test_label_dir, self.psnr_save_path, self.args.pretrained_model)
print("====== Avg. PSNR: {:>.4f} dB ======".format(curr_psnr))
self.ssim_save_path = './results/ssim_test_results/'
curr_ssim = calc_ssim(test_save_path, self.args.test_label_dir, self.ssim_save_path, self.args.pretrained_model)
print("====== Avg. SSIM: {:>.4f} ======".format(curr_ssim))
"""define some functions"""
def build_model(self):
"""Create a generator and a discriminator."""
self.G = Generator(self.args.g_conv_dim, self.args.g_norm_fun, self.args.g_act_fun, self.args.g_use_sn).to(self.device)
self.D = Discriminator(self.args.d_conv_dim, self.args.d_norm_fun, self.args.d_act_fun, self.args.d_use_sn, self.args.adv_loss_type).to(self.device)
if self.args.parallel:
self.G.to(self.args.gpu_ids[0])
self.D.to(self.args.gpu_ids[0])
self.G = nn.DataParallel(self.G, self.args.gpu_ids)
self.D = nn.DataParallel(self.D, self.args.gpu_ids)
print("=== Models have been created ===")
# print network
if self.args.is_print_network:
self.print_network(self.G, 'Generator')
self.print_network(self.D, 'Discriminator')
def print_network(self, model, name):
"""Print out the network information."""
num_params = 0
for p in model.parameters():
num_params += p.numel()
# print(model)
print("=== The number of parameters of the above model [{}] is [{}] or [{:>.4f}M] ===".format(name, num_params, num_params / 1e6))
def load_pretrained_model(self, resume_epochs):
checkpoint_path = os.path.join(self.model_save_path, '{}_{}_{}.pth'.format(self.args.version, self.args.adv_loss_type, resume_epochs))
if torch.cuda.is_available():
# save on GPU, load on GPU
checkpoint = torch.load(checkpoint_path)
self.G.load_state_dict(checkpoint['G_net'])
self.D.load_state_dict(checkpoint['D_net'])
else:
# save on GPU, load on CPU
checkpoint = torch.load(checkpoint_path, map_location=lambda storage, loc: storage)
self.G.load_state_dict(checkpoint['G_net'])
self.D.load_state_dict(checkpoint['D_net'])
print("=========== loaded trained models (epochs: {})! ===========".format(resume_epochs))
def build_tensorboard(self):
"""Build a tensorboard logger."""
self.logger = Logger(self.log_path)