No increase in resolution

[pra-dan]

Hi, I ran the inference on a test image

And used the pre-trained weights provided by you.
I got 2 results:

and

But I got no improvement. That is, the resolution of all the images remained almost same: 579x1032 pixels for all 3 images.

What went wrong ?

[WilsonFok2020]

The test.py is intended for testing only. Consequently, the script takes a high-resolution image, down-samples it to low resolution, and uses espcn to restore the resolution. For comparison sake, it calculates signal to noise ratio (psnr) on the input and plots the effects of simple bicubic interpolation. Therefore, both the bicubic and espcn outputs have as high resolution as the input.

To achieve what you are asking, I suggest you try the following code that I have written based on the test.py script.

[WilsonFok2020]

import argparse

import torch
import torch.backends.cudnn as cudnn
import numpy as np
import PIL.Image as pil_image

from models import ESPCN
from utils import convert_ycbcr_to_rgb, preprocess, calc_psnr

if name == 'main':
parser = argparse.ArgumentParser()
parser.add_argument('--weights-file', type=str, required=True)
parser.add_argument('--image-file', type=str, required=True)
parser.add_argument('--scale', type=int, default=3)
args = parser.parse_args()

cudnn.benchmark = True

device = torch.device('cuda:0' if torch.cuda.is_available() else 'cpu')
print ('device = ', device)

model = ESPCN(scale_factor=args.scale).to(device)

state_dict = model.state_dict()
for n, p in torch.load(args.weights_file, map_location=lambda storage, loc: storage).items():
    if n in state_dict.keys():
        state_dict[n].copy_(p)
    else:
        raise KeyError(n)

model.eval()

# image = pil_image.open(args.image_file).convert('RGB')
lr = pil_image.open(args.image_file).convert('RGB')

# image_width = (image.width // args.scale) * args.scale
# image_height = (image.height // args.scale) * args.scale

# hr = image.resize((image_width, image_height), resample=pil_image.BICUBIC)
# lr = hr.resize((hr.width // args.scale, hr.height // args.scale), resample=pil_image.BICUBIC)
bicubic = lr.resize((lr.width * args.scale, lr.height * args.scale), resample=pil_image.BICUBIC)
# bicubic.save(args.image_file.replace('.', '_bicubic_x{}.'.format(args.scale)))

lr, _ = preprocess(lr, device)
# hr, _ = preprocess(hr, device)
_, ycbcr = preprocess(bicubic, device)

with torch.no_grad():
    preds = model(lr).clamp(0.0, 1.0)

# psnr = calc_psnr(hr, preds)
# print('PSNR: {:.2f}'.format(psnr))

preds = preds.mul(255.0).cpu().numpy().squeeze(0).squeeze(0)

output = np.array([preds, ycbcr[..., 1], ycbcr[..., 2]]).transpose([1, 2, 0])
output = np.clip(convert_ycbcr_to_rgb(output), 0.0, 255.0).astype(np.uint8)
output = pil_image.fromarray(output)
output.save(args.image_file.replace('.', '_espcn_x{}.'.format(args.scale)))