def valid_loss_function(model, val_loader,epoch,save_freq):
psnr=0
final_loss=0
g_loss = np.zeros((5000, 1))
model.eval()
for batch_idx, (inputs, targets) in enumerate(val_loader):
inputs, targets = inputs, targets
in_img = inputs
target = targets
out_img = model(in_img)
out_img = out_img
loss = reduce_mean(out_img['output1'], target)
g_loss[batch_idx] = loss.data.cpu()
final_loss = np.mean(g_loss[np.where(g_loss)])
print("%d %d Loss=%.10f" % (epoch, batch_idx, final_loss))
if epoch % save_freq == 0:
if not os.path.isdir(result_dir_val + '%04d' % epoch):
os.makedirs(result_dir_val + '%04d' % epoch)
out_img=transforms.ToPILImage()(out_img['output1'][0].cpu())
target_img = transforms.ToPILImage()(target[0].cpu())
out_img.save(result_dir_val +'/%04d/' % epoch+ '%04dDBN-FlorinDS_MSE_FS_30_00_train_%d.jpg' % (epoch, batch_idx))
target_img.save(result_dir_val +'/%04d/' % epoch+ '%04dDBN-FlorinDS_MSE_FS_30_00_target_%d.jpg' % (epoch, batch_idx))
psnr = metrics.PSNR(target_img, out_img)
return final_loss, psnr
def test_function(model, test_loader,epoch,save_freq, result_dir_val,experiment_name,plotter1,plotter2):
out_imgs=[]
target_imgs=[]
input_imgs=[]
batch_i=0
psnrs= []
ssims=[]
average_psnr=0
average_ssim=0
final_loss=0
model.eval()
for batch_idx, (inputs, targets,input_refs) in enumerate(test_loader):
inputs, targets,input_refs = inputs, targets,input_refs
in_img = inputs
target = targets
input_ref= input_refs
out_img = model(in_img)
out_img = out_img
out_imgs=transforms.ToPILImage()(out_img['output1'][0].cpu())
target_imgs = transforms.ToPILImage()(target[0].cpu())
input_imgs = transforms.ToPILImage()(input_ref[0].cpu())
if epoch % save_freq == 0:
if not os.path.isdir(result_dir_val + '%04d' % epoch):
os.makedirs(result_dir_val + '%04d' % epoch)
psnrs.append(metrics.PSNR(target_imgs, out_imgs))
ssims.append(metrics.SSIM(target_imgs, out_imgs))
# paralel_imgs = []
if batch_idx% 40==0:
input_imgs.save(
result_dir_val + '/%04d/' % epoch + '%04dDBN_FD_20s_512_00_input_%d.jpg' % (epoch, batch_idx))
out_imgs.save(
result_dir_val + '/%04d/' % epoch + '%04dDBN_FD_20s_512_00_train_%d-PSNR-%f.jpg' % (
epoch, batch_idx,psnrs[batch_idx]))
target_imgs.save(
result_dir_val + '/%04d/' % epoch + '%04dDBN_FD_20s_512_00_target_%d.jpg' % (epoch, batch_idx))
# paralel_imgs.append(input_imgs)
# paralel_imgs.append(out_imgs)
# paralel_imgs.append(target_imgs)
# UtilsImage.uniImage(paralel_imgs).save(
# result_dir_val + '/%04d/' % epoch + '%04dDBN_D_ER_FDS_MSE_FS_Result_%d-PSNR-%d.jpg' % (
# epoch, batch_i, psnrs[batch_idx]))
# if epoch % save_freq == 0:
# if not os.path.isdir(result_dir_val + '%04d' % epoch):
# os.makedirs(result_dir_val + '%04d' % epoch)
# for batch_i in range(0,test_loader):
# psnrs[batch_i] = metrics.PSNR(target_imgs[batch_i], out_imgs[batch_i])
# ssims[batch_i] = metrics.SSIM(target_imgs[batch_i], out_imgs[batch_i])
# if batch_i % 20 ==0:
# paralel_imgs =[]
# input_imgs[batch_i].save(result_dir_val +'/%04d/' % epoch+ '%04dDBNP_D_ER_FDS_MSE_FS_00_input_%d.jpg' % (epoch, batch_i))
# out_imgs[batch_i].save(result_dir_val +'/%04d/' % epoch+ '%04dDBNP_D_ER_FDS_MSE_FS_00_train_%d-PSNR-%d.jpg' % (epoch, batch_i,psnrs[batch_i]))
# target_imgs[batch_i].save(result_dir_val +'/%04d/' % epoch+ '%04dDBN_D_ER_FDS_MSE_FS_00_target_%d.jpg' % (epoch, batch_i))
# paralel_imgs.append( input_imgs[batch_i] )
# paralel_imgs.append (out_imgs[batch_i] )
# paralel_imgs.append(target_imgs[batch_i] )
# UtilsImage.uniImage(paralel_imgs).save(result_dir_val +'/%04d/' % epoch+ '%04dDBN_D_ER_FDS_MSE_FS_Result_%d-PSNR-%d.jpg' % (epoch, batch_i,psnrs[batch_i]))
# # wandb.log({ '%04dDBN_D_ER_FDS_MSE_FS_Result_%d.jpg' % (epoch, batch_i) : wandb.Image( Utils.uniImage(paralel_imgs))
# # , "PSNR: " :psnrs[batch_i]
# # })
for i in range(0,psnrs.__len__()):
average_psnr += psnrs[i]
average_ssim += ssims[i]
average_psnr=average_psnr/psnrs.__len__()
average_ssim=average_ssim/ssims.__len__()
print(epoch,average_psnr,average_ssim)
writer.writerow([epoch, average_psnr, average_ssim])
plotter1.plot("average_PSNR", 'Val-PSNR', "Epoch", epoch, average_psnr)
plotter2.plot("average_SSIM", 'Val-SSIM', "Epoch", epoch, average_ssim)
out_img=transforms.ToPILImage()(outputs['output1'][0].cpu())
target_img = transforms.ToPILImage()(target[0].cpu())
out_img.save(result_dir_train +'/%04d/' % epoch+ '%04dFlorinDS_MSE_FS_30_00_train_%d.jpg' % (epoch, batch_idx))
target_img.save(result_dir_train +'/%04d/' % epoch+ '%04dFlorinDS_MSE_FS_30_00_target_%d.jpg' % (epoch, batch_idx))
if epoch % save_freq_model == 0: torch.save(model.state_dict(), model_dir + 'FlorinDS_MSE_FS_30_Checkpoint_e%04d' % epoch)
model_name='FlorinDS_MSE_FS_30_Checkpoint_e%04d' % epoch
if epoch % save_freq == 0 and batch_idx == (len(train_loader) - 1):
model_V= M.DeBlurNet().cpu()
model_V.load_state_dict(torch.load(model_dir + model_name))
my_val_loss, val_psnr = valid_loss_function(model_V.cpu(), val_loader, epoch, save_freq)
print("Validation Loss=%.10f" % my_val_loss)
plotter1V.plot('loss', 'Validation Loss', 'Epoch', epoch, my_val_loss)
print("PSNR: " + str(val_psnr))
plotter2V.plot("PSNR", 'Val_PSNR', 'Epoch', epoch, val_psnr)
psnr=metrics.PSNR(target_img,out_img)
print("PSNR: " + str(psnr))
# ssim=metrics.SSIM(target_img,out_img)
plotter1.plot('Loss', 'Train Loss', 'Epoch', epoch, final_loss)
plotter2.plot("PSNR", 'Train_PSNR', 'Epoch', epoch, psnr)
t1=time.time()
total=t1-to
print(total)
# torch.save(model.state_dict(), model_dir + 'checkpoint_curr_e%04d' % epoch)