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utils.py
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import time
import torch
import torch.nn.functional as F
import torchvision.utils as utils
from math import log10
from skimage import measure
import cv2
import skimage
import cv2
from skimage.measure import compare_psnr, compare_ssim
import pdb
def calc_psnr(im1, im2):
im1 = im1[0].view(im1.shape[2],im1.shape[3],3).detach().cpu().numpy()
im2 = im2[0].view(im2.shape[2],im2.shape[3],3).detach().cpu().numpy()
im1_y = cv2.cvtColor(im1, cv2.COLOR_BGR2YCR_CB)[:, :, 0]
im2_y = cv2.cvtColor(im2, cv2.COLOR_BGR2YCR_CB)[:, :, 0]
ans = [compare_psnr(im1_y, im2_y)]
return ans
def calc_ssim(im1, im2):
im1 = im1[0].view(im1.shape[2],im1.shape[3],3).detach().cpu().numpy()
im2 = im2[0].view(im2.shape[2],im2.shape[3],3).detach().cpu().numpy()
im1_y = cv2.cvtColor(im1, cv2.COLOR_BGR2YCR_CB)[:, :, 0]
im2_y = cv2.cvtColor(im2, cv2.COLOR_BGR2YCR_CB)[:, :, 0]
ans = [compare_ssim(im1_y, im2_y)]
return ans
def to_psnr(pred_image, gt):
mse = F.mse_loss(pred_image, gt, reduction='none')
mse_split = torch.split(mse, 1, dim=0)
mse_list = [torch.mean(torch.squeeze(mse_split[ind])).item() for ind in range(len(mse_split))]
intensity_max = 1.0
psnr_list = [10.0 * log10(intensity_max / mse) for mse in mse_list]
return psnr_list
def to_ssim_skimage(pred_image, gt):
pred_image_list = torch.split(pred_image, 1, dim=0)
gt_list = torch.split(gt, 1, dim=0)
pred_image_list_np = [pred_image_list[ind].permute(0, 2, 3, 1).data.cpu().numpy().squeeze() for ind in range(len(pred_image_list))]
gt_list_np = [gt_list[ind].permute(0, 2, 3, 1).data.cpu().numpy().squeeze() for ind in range(len(pred_image_list))]
ssim_list = [measure.compare_ssim(pred_image_list_np[ind], gt_list_np[ind], data_range=1, multichannel=True) for ind in range(len(pred_image_list))]
return ssim_list
def validation(net, val_data_loader, device, exp_name, save_tag=False):
psnr_list = []
ssim_list = []
for batch_id, val_data in enumerate(val_data_loader):
with torch.no_grad():
input_im, gt, imgid = val_data
input_im = input_im.to(device)
gt = gt.to(device)
pred_image = net(input_im)
# --- Calculate the average PSNR --- #
psnr_list.extend(calc_psnr(pred_image, gt))
# --- Calculate the average SSIM --- #
ssim_list.extend(calc_ssim(pred_image, gt))
# --- Save image --- #
if save_tag:
# print()
save_image(pred_image, imgid, exp_name)
avr_psnr = sum(psnr_list) / len(psnr_list)
avr_ssim = sum(ssim_list) / len(ssim_list)
return avr_psnr, avr_ssim
def validation_val(net, val_data_loader, device, exp_name, category, save_tag=False):
psnr_list = []
ssim_list = []
for batch_id, val_data in enumerate(val_data_loader):
with torch.no_grad():
input_im, gt, imgid = val_data
input_im = input_im.to(device)
gt = gt.to(device)
pred_image = net(input_im)
# --- Calculate the average PSNR --- #
psnr_list.extend(calc_psnr(pred_image, gt))
# --- Calculate the average SSIM --- #
ssim_list.extend(calc_ssim(pred_image, gt))
# --- Save image --- #
if save_tag:
# print()
save_image(pred_image, imgid, exp_name,category)
avr_psnr = sum(psnr_list) / len(psnr_list)
avr_ssim = sum(ssim_list) / len(ssim_list)
return avr_psnr, avr_ssim
def save_image(pred_image, image_name, exp_name, category):
pred_image_images = torch.split(pred_image, 1, dim=0)
batch_num = len(pred_image_images)
for ind in range(batch_num):
image_name_1 = image_name[ind].split('/')[-1]
print(image_name_1)
utils.save_image(pred_image_images[ind], './results/{}/{}/{}'.format(category,exp_name,image_name_1))
def print_log(epoch, num_epochs, one_epoch_time, train_psnr, val_psnr, val_ssim, exp_name):
print('({0:.0f}s) Epoch [{1}/{2}], Train_PSNR:{3:.2f}, Val_PSNR:{4:.2f}, Val_SSIM:{5:.4f}'
.format(one_epoch_time, epoch, num_epochs, train_psnr, val_psnr, val_ssim))
# --- Write the training log --- #
with open('./training_log/{}_log.txt'.format( exp_name), 'a') as f:
print('Date: {0}s, Time_Cost: {1:.0f}s, Epoch: [{2}/{3}], Train_PSNR: {4:.2f}, Val_PSNR: {5:.2f}, Val_SSIM: {6:.4f}'
.format(time.strftime("%Y-%m-%d %H:%M:%S", time.localtime()),
one_epoch_time, epoch, num_epochs, train_psnr, val_psnr, val_ssim), file=f)
def adjust_learning_rate(optimizer, epoch, lr_decay=0.5):
# --- Decay learning rate --- #
step = 100
torch.Exponential(optimizer, gamma=0.95)
if not epoch % step and epoch > 0:
for param_group in optimizer.param_groups:
param_group['lr'] *= lr_decay
print('Learning rate sets to {}.'.format(param_group['lr']))
else:
for param_group in optimizer.param_groups:
print('Learning rate sets to {}.'.format(param_group['lr']))