inference.py

import sys
import os.path
import cv2
import numpy as np
import torch
import architecture as arch

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

model_type = sys.argv[3]

if model_type == "Anime":
    model_path = "models/4x-AnimeSharp.pth"
else:
    model_path = "models/4x-UniScaleV2_Sharp.pth"

img_path = sys.argv[1]
output_dir = sys.argv[2]
device = torch.device('cuda' if is_cuda() else 'cpu')

model = arch.RRDB_Net(3, 3, 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)

base = os.path.splitext(os.path.basename(img_path))[0]

# Read image
img = cv2.imread(img_path, cv2.IMREAD_COLOR)
img = img * 1.0 / 255
img = torch.from_numpy(np.transpose(img[:, :, [2, 1, 0]], (2, 0, 1))).float()
img_LR = img.unsqueeze(0)
img_LR = img_LR.to(device)

print('Start upscaling...')
with torch.no_grad():
    output = model(img_LR).data.squeeze().float().cpu().clamp_(0, 1).numpy()
    output = np.transpose(output[[2, 1, 0], :, :], (1, 2, 0))
    output = (output * 255.0).round()
print('Finished upscaling, saving image.')
cv2.imwrite(output_dir, output, [int(cv2.IMWRITE_PNG_COMPRESSION), 9])