I have to give multiple image inputs to the following code. I have my input images in a folder. How can I give it as an input one by one and save the output in every single iteration?
This is my code :
from __future__ import print_function
import matplotlib.pyplot as plt
%matplotlib inline

import argparse
import os
os.environ['CUDA_VISIBLE_DEVICES'] = '0'

import numpy as np
from models import *

import torch
import torch.optim

from skimage.measure import compare_psnr
from models.downsampler import Downsampler

from utils.sr_utils import *

torch.backends.cudnn.enabled = True
torch.backends.cudnn.benchmark =True
dtype = torch.cuda.FloatTensor

imsize = -1 
factor = 4 # 8
enforse_div32 = 'CROP' # we usually need the dimensions to be divisible by a power of two (32 in this case)
PLOT = True

# To produce images from the paper we took *_GT.png images from LapSRN viewer for corresponding factor,
# e.g. x4/zebra_GT.png for factor=4, and x8/zebra_GT.png for factor=8 
path_to_image = '/home/smitha/Documents/Falcon.png'
imgs = load_LR_HR_imgs_sr(path_to_image , imsize, factor, enforse_div32)

imgs['bicubic_np'], imgs['sharp_np'], imgs['nearest_np'] = get_baselines(imgs['LR_pil'], imgs['HR_pil'])

if PLOT:
    plot_image_grid([imgs['HR_np'], imgs['bicubic_np'], imgs['sharp_np'], imgs['nearest_np']], 4,12);
    print ('PSNR bicubic: %.4f   PSNR nearest: %.4f' %  (
                                        compare_psnr(imgs['HR_np'], imgs['bicubic_np']), 
                                        compare_psnr(imgs['HR_np'], imgs['nearest_np'])))
input_depth = 32

INPUT =     'noise'
pad   =     'reflection'
OPT_OVER =  'net'
KERNEL_TYPE='lanczos2'

LR = 0.01
tv_weight = 0.0

OPTIMIZER = 'adam'

if factor == 4: 
    num_iter = 2000
    reg_noise_std = 0.03
elif factor == 8:
    num_iter = 4000
    reg_noise_std = 0.05
else:
    assert False, 'We did not experiment with other factors'
net_input = get_noise(input_depth, INPUT, (imgs['HR_pil'].size[1], imgs['HR_pil'].size[0])).type(dtype).detach()

NET_TYPE = 'skip' # UNet, ResNet
net = get_net(input_depth, 'skip', pad,
              skip_n33d=128, 
              skip_n33u=128, 
              skip_n11=4, 
              num_scales=5,
              upsample_mode='bilinear').type(dtype)

# Losses
mse = torch.nn.MSELoss().type(dtype)

img_LR_var = np_to_torch(imgs['LR_np']).type(dtype)

downsampler = Downsampler(n_planes=3, factor=factor, kernel_type=KERNEL_TYPE, phase=0.5, preserve_size=True).type(dtype)
def closure():
    global i, net_input

    if reg_noise_std > 0:
        net_input = net_input_saved + (noise.normal_() * reg_noise_std)

    out_HR = net(net_input)
    out_LR = downsampler(out_HR)

    total_loss = mse(out_LR, img_LR_var) 

    if tv_weight > 0:
        total_loss += tv_weight * tv_loss(out_HR)

    total_loss.backward()

    # Log
    psnr_LR = compare_psnr(imgs['LR_np'], torch_to_np(out_LR))
    psnr_HR = compare_psnr(imgs['HR_np'], torch_to_np(out_HR))
    print ('Iteration %05d    PSNR_LR %.3f   PSNR_HR %.3f' % (i, psnr_LR, psnr_HR), '\r', end='')

    # History
    psnr_history.append([psnr_LR, psnr_HR])

    if PLOT and i % 100 == 0:
        out_HR_np = torch_to_np(out_HR)
        plot_image_grid([imgs['HR_np'], imgs['bicubic_np'], np.clip(out_HR_np, 0, 1)], factor=13, nrow=3)

    i += 1

    return total_loss
psnr_history = [] 
net_input_saved = net_input.detach().clone()
noise = net_input.detach().clone()

i = 0
p = get_params(OPT_OVER, net, net_input)
optimize(OPTIMIZER, p, closure, LR, num_iter)
out_HR_np = np.clip(torch_to_np(net(net_input)), 0, 1)
result_deep_prior = put_in_center(out_HR_np, imgs['orig_np'].shape[1:])

# For the paper we acually took `_bicubic.png` files from LapSRN viewer and used `result_deep_prior` as our result
plot_image_grid([imgs['HR_np'],
                 imgs['bicubic_np'],
                 out_HR_np], factor=4, nrow=1);

And for saving the images I am using the following code.

tensors_to_plot1 = torch.from_numpy(imgs[‘bicubic_np’]) tensors_to_plot2 = torch.from_numpy(imgs[‘HR_np’]) tensors_to_plot3 = torch.from_numpy(out_HR_np) torchvision.utils.save_image(tensors_to_plot1, ‘bicubic9.tif’) torchvision.utils.save_image(tensors_to_plot2, ‘HR9.tif’) torchvision.utils.save_image(tensors_to_plot3, ‘out9.tif’)