from network import REAL
import torch 


def getEDVRSmallModel():
    model_path = './200000.pth'
    net = REAL(
            nbr=2,
            )
    net.eval()
    net.load_state_dict(torch.load(model_path), strict=True)
    print("opt. edvr have {:.3f}M paramerters in total".format(sum(x.numel() for x in net.parameters())/1000000.0))
    return net 



9d6789e72c3a0d1f1cf349b26e74dc6d10ae31ec
<mask token>

<mask token>


def getEDVRSmallModel():
    model_path = './200000.pth'
    net = REAL(nbr=2)
    net.eval()
    net.load_state_dict(torch.load(model_path), strict=True)
    print('opt. edvr have {:.3f}M paramerters in total'.format(sum(x.numel(
        ) for x in net.parameters()) / 1000000.0))
    return net

from network import REAL
import torch


def getEDVRSmallModel():
    model_path = './200000.pth'
    net = REAL(nbr=2)
    net.eval()
    net.load_state_dict(torch.load(model_path), strict=True)
    print('opt. edvr have {:.3f}M paramerters in total'.format(sum(x.numel(
        ) for x in net.parameters()) / 1000000.0))
    return net

from network import REAL
import torch 


def getEDVRSmallModel():
    model_path = './200000.pth'
    net = REAL(
            nbr=2,
            )
    net.eval()
    net.load_state_dict(torch.load(model_path), strict=True)
    print("opt. edvr have {:.3f}M paramerters in total".format(sum(x.numel() for x in net.parameters())/1000000.0))
    return net 


