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import torch
import os
import numpy as np
import torchvision.utils as vutils
from PIL import Image
import torchvision.transforms as transforms
from torch.autograd import Variable
from network.Transformer import Transformer
import argparse
parser = argparse.ArgumentParser()
parser.add_argument("--input_dir", default="test_img")
parser.add_argument("--load_size", default=1280)
parser.add_argument("--model_path", default="./pretrained_model")
parser.add_argument("--style", default="Shinkai")
parser.add_argument("--output_dir", default="test_output")
parser.add_argument("--gpu", type=int, default=0)
opt = parser.parse_args()
valid_ext = [".jpg", ".png"]
# setup
if not os.path.exists(opt.input_dir):
os.makedirs(opt.input_dir)
if not os.path.exists(opt.output_dir):
os.makedirs(opt.output_dir)
# load pretrained model
model = Transformer()
model.load_state_dict(
torch.load(os.path.join(opt.model_path, opt.style + "_net_G_float.pth"))
)
model.eval()
disable_gpu = opt.gpu == -1 or not torch.cuda.is_available()
if disable_gpu:
print("CPU mode")
model.float()
else:
print("GPU mode")
model.cuda()
for files in os.listdir(opt.input_dir):
ext = os.path.splitext(files)[1]
if ext not in valid_ext:
continue
# load image
input_image = Image.open(os.path.join(opt.input_dir, files)).convert("RGB")
input_image = np.asarray(input_image)
# RGB -> BGR
input_image = input_image[:, :, [2, 1, 0]]
input_image = transforms.ToTensor()(input_image).unsqueeze(0)
# preprocess, (-1, 1)
input_image = -1 + 2 * input_image
if disable_gpu:
input_image = Variable(input_image).float()
else:
input_image = Variable(input_image).cuda()
# forward
output_image = model(input_image)
output_image = output_image[0]
# BGR -> RGB
output_image = output_image[[2, 1, 0], :, :]
output_image = output_image.data.cpu().float() * 0.5 + 0.5
# save
vutils.save_image(
output_image,
os.path.join(opt.output_dir, files[:-4] + "_" + opt.style + ".jpg"),
)
print("Done!")
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