TypeError: Variable data has to be a tensor, but got builtin_function_or_method

[CybotDNA]

Namespace(chop_forward=False, data_dir='./Vid4', file_list='foliage.txt', future_frame=True, gpu_mode=True, gpus=1, model='weights/RBPN_4x.pth', model_type='RBPN', nFrames=7, other_dataset=True, output='Results/', residual=False, seed=123, testBatchSize=1, threads=1, upscale_factor=4)
===> Loading datasets
===> Building model  RBPN
Pre-trained SR model is loaded.
Traceback (most recent call last):
  File "eval.py", line 82, in <module>
    input = Variable(input).cuda(gpus_list[0])
TypeError: Variable data has to be a tensor, but got builtin_function_or_method

my eval.py:

from __future__ import print_function
import argparse

import os
import torch
import torch.nn as nn
import torch.optim as optim
from torch.autograd import Variable
from torch.utils.data import DataLoader
from rbpn import Net as RBPN
from data import get_test_set
from functools import reduce
import numpy as np

from scipy.misc import imsave
#from keras.preprocessing.image import save_img
import scipy.io as sio
import time
import cv2
import math
import pdb

# Training settings
parser = argparse.ArgumentParser(description='PyTorch Super Res Example')
parser.add_argument('--upscale_factor', type=int, default=4, help="super resolution upscale factor")
parser.add_argument('--testBatchSize', type=int, default=1, help='testing batch size')
parser.add_argument('--gpu_mode', type=bool, default=True)
parser.add_argument('--chop_forward', type=bool, default=False)
parser.add_argument('--threads', type=int, default=1, help='number of threads for data loader to use')
parser.add_argument('--seed', type=int, default=123, help='random seed to use. Default=123')
parser.add_argument('--gpus', default=1, type=int, help='number of gpu')
parser.add_argument('--data_dir', type=str, default='./Vid4')
parser.add_argument('--file_list', type=str, default='foliage.txt')
parser.add_argument('--other_dataset', type=bool, default=True, help="use other dataset than vimeo-90k")
parser.add_argument('--future_frame', type=bool, default=True, help="use future frame")
parser.add_argument('--nFrames', type=int, default=7)
parser.add_argument('--model_type', type=str, default='RBPN')
parser.add_argument('--residual', type=bool, default=False)
parser.add_argument('--output', default='Results/', help='Location to save checkpoint models')
parser.add_argument('--model', default='weights/RBPN_4x.pth', help='sr pretrained base model')

opt = parser.parse_args()

gpus_list=range(opt.gpus)
print(opt)

cuda = opt.gpu_mode
if cuda and not torch.cuda.is_available():
    raise Exception("No GPU found, please run without --cuda")

torch.manual_seed(opt.seed)
if cuda:
    torch.cuda.manual_seed(opt.seed)

print('===> Loading datasets')
test_set = get_test_set(opt.data_dir, opt.nFrames, opt.upscale_factor, opt.file_list, opt.other_dataset, opt.future_frame)
testing_data_loader = DataLoader(dataset=test_set, num_workers=opt.threads, batch_size=opt.testBatchSize, shuffle=False)

print('===> Building model ', opt.model_type)
if opt.model_type == 'RBPN':
    model = RBPN(num_channels=3, base_filter=256,  feat = 64, num_stages=3, n_resblock=5, nFrames=opt.nFrames, scale_factor=opt.upscale_factor)

if cuda:
    model = torch.nn.DataParallel(model, device_ids=gpus_list)

model.load_state_dict(torch.load(opt.model, map_location=lambda storage, loc: storage))
print('Pre-trained SR model is loaded.')

if cuda:
    model = model.cuda(gpus_list[0])

def eval():
    model.eval()
    count=1
    avg_psnr_predicted = 0.0
    for batch in testing_data_loader:
        input, target, neigbor, flow, bicubic = batch[0], batch[1], batch[2], batch[3], batch[4]

if __name__ == '__main__':	<<<---- this is because i use Windows
        
        with torch.no_grad():
            input = Variable(input).cuda(gpus_list[0])
            bicubic = Variable(bicubic).cuda(gpus_list[0])
            neigbor = [Variable(j).cuda(gpus_list[0]) for j in neigbor]
            flow = [Variable(j).cuda(gpus_list[0]).float() for j in flow]

        t0 = time.time()
        if opt.chop_forward:
            with torch.no_grad():
                prediction = chop_forward(input, neigbor, flow, model, opt.upscale_factor)
        else:
            with torch.no_grad():
                prediction = model(input, neigbor, flow) 
        
        if opt.residual:
            prediction = prediction + bicubic
            
        t1 = time.time()
        print("===> Processing: %s || Timer: %.4f sec." % (str(count), (t1 - t0)))
        save_img(prediction.cpu().data, str(count), True)
        #save_img(target, str(count), False)
        
        #prediction=prediction.cpu()
        #prediction = prediction.data[0].numpy().astype(np.float32)
        #prediction = prediction*255.
        
        #target = target.squeeze().numpy().astype(np.float32)
        #target = target*255.
                
        #psnr_predicted = PSNR(prediction,target, shave_border=opt.upscale_factor)
        #avg_psnr_predicted += psnr_predicted
        count+=1
    
    #print("PSNR_predicted=", avg_psnr_predicted/count)

def save_img(img, img_name, pred_flag):
    save_img = img.squeeze().clamp(0, 1).numpy().transpose(1,2,0)

    # save img
    save_dir=os.path.join(opt.output, opt.data_dir, os.path.splitext(opt.file_list)[0]+'_'+str(opt.upscale_factor)+'x')
    if not os.path.exists(save_dir):
        os.makedirs(save_dir)
        
    if pred_flag:
        save_fn = save_dir +'/'+ img_name+'_'+opt.model_type+'F'+str(opt.nFrames)+'.png'
    else:
        save_fn = save_dir +'/'+ img_name+'.png'
    cv2.imwrite(save_fn, cv2.cvtColor(save_img*255, cv2.COLOR_BGR2RGB),  [cv2.IMWRITE_PNG_COMPRESSION, 0])

def PSNR(pred, gt, shave_border=0):
    height, width = pred.shape[:2]
    pred = pred[1+shave_border:height - shave_border, 1+shave_border:width - shave_border, :]
    gt = gt[1+shave_border:height - shave_border, 1+shave_border:width - shave_border, :]
    imdff = pred - gt
    rmse = math.sqrt(np.mean(imdff ** 2))
    if rmse == 0:
        return 100
    return 20 * math.log10(255.0 / rmse)
    
def chop_forward(x, neigbor, flow, model, scale, shave=8, min_size=2000, nGPUs=opt.gpus):
    b, c, h, w = x.size()
    h_half, w_half = h // 2, w // 2
    h_size, w_size = h_half + shave, w_half + shave
    inputlist = [
        [x[:, :, 0:h_size, 0:w_size], [j[:, :, 0:h_size, 0:w_size] for j in neigbor], [j[:, :, 0:h_size, 0:w_size] for j in flow]],
        [x[:, :, 0:h_size, (w - w_size):w], [j[:, :, 0:h_size, (w - w_size):w] for j in neigbor], [j[:, :, 0:h_size, (w - w_size):w] for j in flow]],
        [x[:, :, (h - h_size):h, 0:w_size], [j[:, :, (h - h_size):h, 0:w_size] for j in neigbor], [j[:, :, (h - h_size):h, 0:w_size] for j in flow]],
        [x[:, :, (h - h_size):h, (w - w_size):w], [j[:, :, (h - h_size):h, (w - w_size):w] for j in neigbor], [j[:, :, (h - h_size):h, (w - w_size):w] for j in flow]]]

    if w_size * h_size < min_size:
        outputlist = []
        for i in range(0, 4, nGPUs):
            with torch.no_grad():
                input_batch = inputlist[i]#torch.cat(inputlist[i:(i + nGPUs)], dim=0)
                output_batch = model(input_batch[0], input_batch[1], input_batch[2])
            outputlist.extend(output_batch.chunk(nGPUs, dim=0))
    else:
        outputlist = [
            chop_forward(patch[0], patch[1], patch[2], model, scale, shave, min_size, nGPUs) \
            for patch in inputlist]

    h, w = scale * h, scale * w
    h_half, w_half = scale * h_half, scale * w_half
    h_size, w_size = scale * h_size, scale * w_size
    shave *= scale

    with torch.no_grad():
        output = Variable(x.data.new(b, c, h, w))
    output[:, :, 0:h_half, 0:w_half] \
        = outputlist[0][:, :, 0:h_half, 0:w_half]
    output[:, :, 0:h_half, w_half:w] \
        = outputlist[1][:, :, 0:h_half, (w_size - w + w_half):w_size]
    output[:, :, h_half:h, 0:w_half] \
        = outputlist[2][:, :, (h_size - h + h_half):h_size, 0:w_half]
    output[:, :, h_half:h, w_half:w] \
        = outputlist[3][:, :, (h_size - h + h_half):h_size, (w_size - w + w_half):w_size]

    return output

##Eval Start!!!!
eval()