forked from mrq/DL-Art-School
75 lines
2.8 KiB
Python
75 lines
2.8 KiB
Python
import torch
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import os
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from PIL import Image
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import numpy as np
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import options.options as option
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from data import create_dataloader, create_dataset
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import math
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from tqdm import tqdm
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from torchvision import transforms
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from utils.fdpl_util import dct_2d, extract_patches_2d
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import random
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import matplotlib.pyplot as plt
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from mpl_toolkits.axes_grid1 import make_axes_locatable
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from utils.colors import rgb2ycbcr
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import torch.nn.functional as F
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input_config = "../../options/train_imgset_pixgan_srg4_fdpl.yml"
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output_file = "fdpr_diff_means.pt"
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device = 'cuda'
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patch_size=128
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if __name__ == '__main__':
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opt = option.parse(input_config, is_train=True)
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opt['dist'] = False
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# Create a dataset to load from (this dataset loads HR/LR images and performs any distortions specified by the YML.
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dataset_opt = opt['datasets']['train']
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train_set = create_dataset(dataset_opt)
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train_size = int(math.ceil(len(train_set) / dataset_opt['batch_size']))
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total_iters = int(opt['train']['niter'])
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total_epochs = int(math.ceil(total_iters / train_size))
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train_loader = create_dataloader(train_set, dataset_opt, opt, None)
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print('Number of train images: {:,d}, iters: {:,d}'.format(
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len(train_set), train_size))
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# calculate the perceptual weights
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master_diff = np.zeros((patch_size, patch_size))
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num_patches = 0
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all_diff_patches = []
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tq = tqdm(train_loader)
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sampled = 0
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for train_data in tq:
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if sampled > 200:
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break
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sampled += 1
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im = rgb2ycbcr(train_data['GT'].double())
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im_LR = rgb2ycbcr(F.interpolate(train_data['LQ'].double(),
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size=im.shape[2:],
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mode="bicubic", align_corners=False))
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patches_hr = extract_patches_2d(img=im, patch_shape=(patch_size,patch_size), batch_first=True)
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patches_hr = dct_2d(patches_hr, norm='ortho')
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patches_lr = extract_patches_2d(img=im_LR, patch_shape=(patch_size,patch_size), batch_first=True)
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patches_lr = dct_2d(patches_lr, norm='ortho')
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b, p, c, w, h = patches_hr.shape
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diffs = torch.abs(patches_lr - patches_hr) / ((torch.abs(patches_lr) + torch.abs(patches_hr)) / 2 + .00000001)
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num_patches += b * p
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all_diff_patches.append(torch.sum(diffs, dim=(0, 1)))
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diff_patches = torch.stack(all_diff_patches, dim=0)
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diff_means = torch.sum(diff_patches, dim=0) / num_patches
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torch.save(diff_means, output_file)
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print(diff_means)
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for i in range(3):
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fig, ax = plt.subplots()
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divider = make_axes_locatable(ax)
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cax = divider.append_axes('right', size='5%', pad=0.05)
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im = ax.imshow(diff_means[i].numpy())
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ax.set_title("mean_diff for channel %i" % (i,))
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fig.colorbar(im, cax=cax, orientation='vertical')
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plt.show()
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