用代码调用RealESRGAN_x2plus_from basicsr.archs.rrdbnet_arch import rrdbnet

import time
 
import cv2
from basicsr.archs.rrdbnet_arch import RRDBNet
from realesrgan import RealESRGANer
 
if __name__ == '__main__':
    outscale = 1.5
    # RealESRGAN_x2plus
    model = RRDBNet(num_in_ch=3, num_out_ch=3, num_feat=64, num_block=23, num_grow_ch=32, scale=2)
    netscale = 2
    # RealESRNet_x4plus
    # model = RRDBNet(num_in_ch=3, num_out_ch=3, num_feat=64, num_block=23, num_grow_ch=32, scale=4)
    # netscale = 4
    model_path = 'weights/RealESRGAN_x2plus.pth'
 
    # restorer
    upsampler = RealESRGANer(
        scale=netscale,
        model_path=model_path,
        dni_weight=1,  # RealESRGAN_x2plus不需要这个参数
        model=model,
        tile=0,
        tile_pad=10,
        pre_pad=0,
        half=True,  # 半精度计算
        gpu_id=0)
 
    img = cv2.imread("./demo.jpg", cv2.IMREAD_COLOR)
    print(img.shape)
 
    start_time = time.time()
    try:
        output, _ = upsampler.enhance(img, outscale=outscale)
    except RuntimeError as error:
        print('Error', error)
        print('If you encounter CUDA out of memory, try to set --tile with a smaller number.')
    print("time:", time.time() - start_time)
    print(output.shape)
    cv2.imwrite("./demo2.jpg", output)
 

经测试RTX3090 512->2048 0.5s