inference_manga_v2.py

import sys
import cv2
import numpy as np
import torch
import ESRGAN.architecture as arch
from ESRGANer import ESRGANer

def is_cuda():
    if torch.cuda.is_available():
        return True
    else:
        return False

model_path = 'models/4x_eula_digimanga_bw_v2_nc1_307k.pth'
OUTPUT_PATH = sys.argv[1]
device = torch.device('cuda' if is_cuda() else 'cpu')

model = arch.RRDB_Net(1, 1, 64, 23, gc=32, upscale=4, norm_type=None, act_type='leakyrelu', mode='CNA', res_scale=1, upsample_mode='upconv')

if is_cuda():
    print("Using GPU 🥶")
    model.load_state_dict(torch.load(model_path), strict=True)
else:
    print("Using CPU 😒")
    model.load_state_dict(torch.load(model_path, map_location=torch.device('cpu')), strict=True)

model.eval()

for k, v in model.named_parameters():
    v.requires_grad = False
model = model.to(device)

# Read image
img = cv2.imread(OUTPUT_PATH, cv2.IMREAD_GRAYSCALE)
img = img * 1.0 / 255
img = torch.from_numpy(img[np.newaxis, :, :]).float()
img_LR = img.unsqueeze(0)
img_LR = img_LR.to(device)

upsampler = ESRGANer(model=model)
output = upsampler.enhance(img_LR)

output = output.squeeze(dim=0).float().cpu().clamp_(0, 1).numpy()
output = np.transpose(output, (1, 2, 0))
output = (output * 255.0).round()
cv2.imwrite(OUTPUT_PATH, output, [int(cv2.IMWRITE_PNG_COMPRESSION), 5])