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test.py
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import torch
import argparse
from torch.utils.data import Dataset, DataLoader
from torchvision import transforms
from torchvision.io import read_image
from torchvision.utils import save_image
import os
import cv2
import numpy as np
from tqdm import tqdm
from utils import functions as udf
from utils.guided_filter import GuidedFilter
from models import layers, loss
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
print(f"Using {device} for testing.")
def arg_parser():
parser = argparse.ArgumentParser()
parser.add_argument("--model_version", default = 0, type = int)
parser.add_argument("--batch", default = 0, type = int)
args = parser.parse_args()
return args
# from options.test_options import TestOptions
# opt = TestOptions().parse() # get testing options
class FaceDataset(Dataset):
def __init__(self, img_dir, transform=None, target_transform=None):
self.img_dir = img_dir
self.transform = transform
self.target_transform = target_transform
self.img_list = list()
for name in os.listdir(self.img_dir):
self.img_list.append(os.path.join(self.img_dir, name))
self.img_list.sort()
def __len__(self):
return len(self.img_list)
def __getitem__(self, idx):
image = read_image(self.img_list[idx])
label = None
if self.transform:
image = self.transform(image)
if self.target_transform:
label = self.target_transform(label)
return image
def cartoonize(input_photo, input_cartoon, model_path, save_folder):
save_image((input_photo+1)/2, save_folder + f'/in-{args.model_version}-{args.batch}.png', nrow=4)
## load model
G = layers.UnetGenerator(channel=32, num_blocks=4).to(device)
if torch.cuda.is_available():
state_dict = torch.load(model_path)
else:
state_dict = torch.load(model_path, map_location=torch.device('cpu'))
G.load_state_dict(state_dict)
G.eval()
network_out = G(input_photo)
final_out = GuidedFilter(r=1)(input_photo, network_out)
save_image((final_out+1)/2, save_folder + f'/out-{args.model_version}-{args.batch}.png', nrow=4)
# save_image(input_photo - final_out, save_folder + '/final_out--.png')
print(f'Test loss: {torch.nn.L1Loss()(input_photo, final_out):>5f}')
inter_out = final_out.detach().numpy()
# superpixel = udf.selective_adacolor(inter_out, power=1.2, seg_num=1000)
superpixel = udf.simple_superpixel(inter_out, seg_num=200)
save_image((torch.tensor(superpixel)+1)/2, save_folder + '/superpixel.png')
save_image((torch.tensor(superpixel) - final_out+1)/2, save_folder + '/superpixel--.png')
if __name__ == '__main__':
args = arg_parser()
# model_path = f'checkpoints/saved_models/pre_gen_batch_{args.batch}.pth'
model_path = f'checkpoints/saved_models/gen{args.model_version}_batch_{args.batch}.pth'
save_folder = 'playground'
if not os.path.exists(save_folder):
os.mkdir(save_folder)
## load datasets
transforms = transforms.Compose([
transforms.ToPILImage(),
transforms.Resize(128),
transforms.CenterCrop(128),
transforms.ToTensor(),
transforms.Normalize((0.5,0.5,0.5), (0.5,0.5,0.5)),
])
face_photo_dir = 'datasets/ffhq/128px/00000'
face_photo_dataset = FaceDataset(face_photo_dir, transform=transforms)
face_photo_loader = DataLoader(face_photo_dataset, batch_size=1, num_workers=4, shuffle=True)
face_cartoon_dir = 'datasets/animeGAN/Hayao/style'
face_cartoon_dataset = FaceDataset(face_cartoon_dir, transform=transforms)
face_cartoon_loader = DataLoader(face_cartoon_dataset, batch_size=32, num_workers=4, shuffle=True)
cartoonize(next(iter(face_photo_loader)).to(device), next(iter(face_cartoon_loader)).to(device), model_path, save_folder)