I am trying to save the bicubic and HR_np images. I can plot it right but I am having a hard time in saving it. Below is my code. I tried using torchvision.utils.save_image(imgs[‘bicubic_np’], ‘ccc.png’.format(), nrow=8, padding=2, normalize=False, range=None, scale_each=False, pad_value=0) but it did not work. Tell me where I am going wrong.

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);