A question about how to test

.gitignore

@@ -1,101 +1,7 @@
-# Byte-compiled / optimized / DLL files
+#python cache
 __pycache__/
-*.py[cod]
-*$py.class
 
-# C extensions
-*.so
-
-# Distribution / packaging
-.Python
-env/
-build/
-develop-eggs/
-dist/
-downloads/
-eggs/
-.eggs/
-lib/
-lib64/
-parts/
-sdist/
-var/
-wheels/
-*.egg-info/
-.installed.cfg
-*.egg
-
-# PyInstaller
-#  Usually these files are written by a python script from a template
-#  before PyInstaller builds the exe, so as to inject date/other infos into it.
-*.manifest
-*.spec
-
-# Installer logs
-pip-log.txt
-pip-delete-this-directory.txt
-
-# Unit test / coverage reports
-htmlcov/
-.tox/
-.coverage
-.coverage.*
-.cache
-nosetests.xml
-coverage.xml
-*.cover
-.hypothesis/
-
-# Translations
-*.mo
-*.pot
-
-# Django stuff:
-*.log
-local_settings.py
-
-# Flask stuff:
-instance/
-.webassets-cache
-
-# Scrapy stuff:
-.scrapy
-
-# Sphinx documentation
-docs/_build/
-
-# PyBuilder
-target/
-
-# Jupyter Notebook
-.ipynb_checkpoints
-
-# pyenv
-.python-version
-
-# celery beat schedule file
-celerybeat-schedule
-
-# SageMath parsed files
-*.sage.py
-
-# dotenv
-.env
-
-# virtualenv
-.venv
-venv/
-ENV/
-
-# Spyder project settings
-.spyderproject
-.spyproject
-
-# Rope project settings
-.ropeproject
-
-# mkdocs documentation
-/site
-
-# mypy
-.mypy_cache/
+img_align_celeba/
+model/*/*.pth
+!model/*/*epoch_500*.pth
+results/

dataset.py

@@ -29,7 +29,7 @@ def loadFromFile(path, datasize):
     f=open(path)
     data=[]
     label=[]
-    for idx in xrange(0, datasize):
+    for idx in range(0, datasize):
       line = f.readline().split()
       data.append(line[0])         
       label.append(line[1])

main.py

@@ -19,18 +19,19 @@
 from math import log10
 import torchvision
 import cv2
+from PIL import Image, ImageOps
 
 parser = argparse.ArgumentParser()
 parser.add_argument('--test', action='store_true', help='enables test during training')
 parser.add_argument('--mse_avg', action='store_true', help='enables mse avg')
 parser.add_argument('--num_layers_res', type=int, help='number of the layers in residual block', default=2)
 parser.add_argument('--nrow', type=int, help='number of the rows to save images', default=10)
-parser.add_argument('--trainfiles', default="path/celeba/train.list", type=str, help='the list of training files')
-parser.add_argument('--dataroot', default="path/celeba", type=str, help='path to dataset')
-parser.add_argument('--testfiles', default="path/test.list", type=str, help='the list of training files')
-parser.add_argument('--testroot', default="path/celeba", type=str, help='path to dataset')
-parser.add_argument('--trainsize', type=int, help='number of training data', default=162770)
-parser.add_argument('--testsize', type=int, help='number of testing data', default=19962)
+parser.add_argument('--trainfiles', default="train.list", type=str, help='the list of training files')
+parser.add_argument('--dataroot', default="img_align_celeba", type=str, help='path to dataset')
+parser.add_argument('--testfiles', default="test.list", type=str, help='the list of training files')
+parser.add_argument('--testroot', default="img_align_celeba", type=str, help='path to dataset')
+parser.add_argument('--trainsize', type=int, help='number of training data', default=4000)
+parser.add_argument('--testsize', type=int, help='number of testing data', default=500)
 parser.add_argument('--workers', type=int, help='number of data loading workers', default=2)
 parser.add_argument('--batchSize', type=int, default=64, help='input batch size')
 parser.add_argument('--test_batchSize', type=int, default=64, help='test batch size')
@@ -56,7 +57,15 @@
 parser.add_argument('--manualSeed', type=int, help='manual seed')
 parser.add_argument("--pretrained", default="", type=str, help="path to pretrained model (default: none)")
 
+# NetStruct_WavetletDepth_batch-testBatch_Change#1_Change#2...
+TAG = "removeBNinResBlocks_3_16-16_testAtLastEpoch" 
+
 def main():
+
+    if not os.path.exists("model/{0}".format(TAG)):
+        os.makedirs("model/{0}".format(TAG))
+
+    f_psrn = open("model/{0}/psnr_{0}.txt".format(TAG,TAG), "a")
 
     global opt, model
     opt = parser.parse_args()
@@ -140,6 +149,7 @@ def main():
                   crop_height=None, crop_width=None,
                   is_random_crop=False, is_mirror=False, is_gray=False, 
                   upscale=mag, is_scale_back=is_scale_back)    
+
     test_data_loader = torch.utils.data.DataLoader(test_set, batch_size=opt.test_batchSize,
                                          shuffle=False, num_workers=int(opt.workers))
 
@@ -153,37 +163,16 @@ def main():
             save_checkpoint(srnet, epoch, 0, 'sr_')
 
         for iteration, batch in enumerate(train_data_loader, 0):
-            #--------------test-------------
-            if iteration % opt.test_iter is 0 and opt.test:
-                srnet.eval()
-                avg_psnr = 0
-                for titer, batch in enumerate(test_data_loader,0):
-                    input, target = Variable(batch[0]), Variable(batch[1])
-                    if opt.cuda:
-                        input = input.cuda()
-                        target = target.cuda()    
-
-                    wavelets = forward_parallel(srnet, input, opt.ngpu)                    
-                    prediction = wavelet_rec(wavelets)
-                    mse = criterion_m(prediction, target)
-                    psnr = 10 * log10(1 / (mse.data[0]) )
-                    avg_psnr += psnr
-
-                    save_images(prediction, "Epoch_{:03d}_Iter_{:06d}_{:02d}_o.jpg".format(epoch, iteration, titer), 
-                                path=opt.outf, nrow=opt.nrow)
-
-
-                print("===> Avg. PSNR: {:.4f} dB".format(avg_psnr / len(test_data_loader)))
-                srnet.train()
+
 
             #--------------train------------
             input, target = Variable(batch[0]), Variable(batch[1], requires_grad=False)          
             if opt.cuda:
               input = input.cuda()
               target = target.cuda()
-              
+
             target_wavelets = wavelet_dec(target)
-          
+
             batch_size = target.size(0)
             wavelets_lr = target_wavelets[:,0:3,:,:]
             wavelets_sr = target_wavelets[:,3:,:,:]
@@ -204,11 +193,43 @@ def main():
             optimizer_sr.step()
 
             info = "===> Epoch[{}]({}/{}): time: {:4.4f}:".format(epoch, iteration, len(train_data_loader), time.time()-start_time)
-            info += "Rec: {:.4f}, {:.4f}, {:.4f}, Texture: {:.4f}".format(loss_lr.data[0], loss_sr.data[0], 
-                                loss_img.data[0], loss_textures.data[0])            
-                          
+            info += "Rec: {:.4f}, {:.4f}, {:.4f}, Texture: {:.4f}".format(loss_lr.item(), loss_sr.item(), 
+                                loss_img.item(), loss_textures.item())            
+
             print(info)
-
+
+
+
+            #--------------test-------------
+            #if iteration % opt.test_iter is 0 and opt.test:
+            if iteration == len(train_data_loader) - 1 and opt.test:
+                srnet.eval()
+                avg_psnr = 0
+                for titer, batch in enumerate(test_data_loader,0):
+                    input, target = Variable(batch[0]), Variable(batch[1])
+                    if opt.cuda:
+                        input = input.cuda()
+                        target = target.cuda()    
+
+                    wavelets = forward_parallel(srnet, input, opt.ngpu)                    
+                    prediction = wavelet_rec(wavelets)
+                    mse = criterion_m(prediction, target)
+                    psnr = 10 * log10(1 / (mse.item()) )
+                    avg_psnr += psnr
+
+                    save_images(prediction, "Epoch_{:03d}_Iter_{:06d}_{:02d}_o.jpg".format(epoch, iteration, titer), 
+                                path=opt.outf, nrow=opt.nrow)
+
+                    if epoch%50 == 0:
+                        save_images(prediction, "Epoch_{:03d}_Iter_{:06d}_{:02d}_o.jpg".format(epoch, iteration, titer), 
+                                    path="result_step50/{0}/".format(TAG), nrow=opt.nrow)
+
+
+                print("===> Avg. PSNR: {:.4f} dB".format(avg_psnr / len(test_data_loader)))
+                f_psrn.write("{:.4f}\n".format(avg_psnr / len(test_data_loader)))
+                srnet.train()
+
+    f_psrn.close()
 
 def forward_parallel(net, input, ngpu):
     if ngpu > 1:
@@ -217,16 +238,20 @@ def forward_parallel(net, input, ngpu):
         return net(input)
 
 def save_checkpoint(model, epoch, iteration, prefix=""):
-    model_out_path = "model/" + prefix +"model_epoch_{}_iter_{}.pth".format(epoch, iteration)
+    model_out_path = "model/{0}/".format(TAG) + prefix +"model_epoch_{}_iter_{}.pth".format(epoch, iteration)
     state = {"epoch": epoch ,"model": model}
-    if not os.path.exists("model/"):
-        os.makedirs("model/")
+    if not os.path.exists("model/{0}/".format(TAG)):
+        os.makedirs("model/{0}/".format(TAG))
 
     torch.save(state, model_out_path)
 
     print("Checkpoint saved to {}".format(model_out_path))
 
 def save_images(images, name, path, nrow=10):   
+
+  # Create target Directory if don't exist
+  if not os.path.exists(path):
+    os.makedirs(path)
   #print(images.size())
   img = images.cpu()
   im = img.data.numpy().astype(np.float32)