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image_adaptive_lut_evaluation.py
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image_adaptive_lut_evaluation.py
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import argparse
import time
import torch
from torchvision.utils import save_image
from torch.utils.data import DataLoader
from torch.autograd import Variable
from models import *
from datasets import *
parser = argparse.ArgumentParser()
parser.add_argument("--epoch", type=int, default=145, help="epoch to load the saved checkpoint")
parser.add_argument("--dataset_name", type=str, default="fiveK", help="name of the dataset")
parser.add_argument("--input_color_space", type=str, default="sRGB", help="input color space: sRGB or XYZ")
parser.add_argument("--model_dir", type=str, default="LUTs/paired/fiveK_480p_3LUT_sm_1e-4_mn_10", help="directory of saved models")
opt = parser.parse_args()
opt.model_dir = opt.model_dir + '_' + opt.input_color_space
# use gpu when detect cuda
cuda = True if torch.cuda.is_available() else False
# Tensor type
Tensor = torch.cuda.FloatTensor if cuda else torch.FloatTensor
criterion_pixelwise = torch.nn.MSELoss()
LUT0 = Generator3DLUT_identity()
LUT1 = Generator3DLUT_zero()
LUT2 = Generator3DLUT_zero()
#LUT3 = Generator3DLUT_zero()
#LUT4 = Generator3DLUT_zero()
classifier = Classifier()
trilinear_ = TrilinearInterpolation()
if cuda:
LUT0 = LUT0.cuda()
LUT1 = LUT1.cuda()
LUT2 = LUT2.cuda()
#LUT3 = LUT3.cuda()
#LUT4 = LUT4.cuda()
classifier = classifier.cuda()
criterion_pixelwise.cuda()
# Load pretrained models
LUTs = torch.load("saved_models/%s/LUTs_%d.pth" % (opt.model_dir, opt.epoch))
LUT0.load_state_dict(LUTs["0"])
LUT1.load_state_dict(LUTs["1"])
LUT2.load_state_dict(LUTs["2"])
#LUT3.load_state_dict(LUTs["3"])
#LUT4.load_state_dict(LUTs["4"])
LUT0.eval()
LUT1.eval()
LUT2.eval()
#LUT3.eval()
#LUT4.eval()
classifier.load_state_dict(torch.load("saved_models/%s/classifier_%d.pth" % (opt.model_dir, opt.epoch)))
classifier.eval()
if opt.input_color_space == 'sRGB':
dataloader = DataLoader(
ImageDataset_sRGB("../data/%s" % opt.dataset_name, mode="test"),
batch_size=1,
shuffle=False,
num_workers=1,
)
elif opt.input_color_space == 'XYZ':
dataloader = DataLoader(
ImageDataset_XYZ("../data/%s" % opt.dataset_name, mode="test"),
batch_size=1,
shuffle=False,
num_workers=1,
)
def generator(img):
pred = classifier(img).squeeze()
LUT = pred[0] * LUT0.LUT + pred[1] * LUT1.LUT + pred[2] * LUT2.LUT #+ pred[3] * LUT3.LUT + pred[4] * LUT4.LUT
combine_A = img.new(img.size())
combine_A = trilinear_(LUT,img)
return combine_A
def visualize_result():
"""Saves a generated sample from the validation set"""
out_dir = "images/%s_%d" % (opt.model_dir, opt.epoch)
os.makedirs(out_dir, exist_ok=True)
for i, batch in enumerate(dataloader):
real_A = Variable(batch["A_input"].type(Tensor))
img_name = batch["input_name"]
fake_B = generator(real_A)
#real_B = Variable(batch["A_exptC"].type(Tensor))
#img_sample = torch.cat((real_A.data, fake_B.data, real_B.data), -1)
#save_image(img_sample, "images/LUTs/paired/JPGsRGB8_to_JPGsRGB8_WB_original_5LUT/%s.png" % (img_name[0][:-4]), nrow=3, normalize=False)
save_image(fake_B, os.path.join(out_dir,"%s.png" % (img_name[0][:-4])), nrow=1, normalize=False)
def test_speed():
t_list = []
for i in range(1,10):
img_input = Image.open(os.path.join("../data/fiveK/input/JPG","original","a000%d.jpg"%i))
img_input = torch.unsqueeze(TF.to_tensor(TF.resize(img_input,(4000,6000))),0)
real_A = Variable(img_input.type(Tensor))
torch.cuda.synchronize()
t0 = time.time()
for j in range(0,100):
fake_B = generator(real_A)
torch.cuda.synchronize()
t1 = time.time()
t_list.append(t1 - t0)
print((t1 - t0))
print(t_list)
# ----------
# evaluation
# ----------
visualize_result()
#test_speed()