train

.gitignore

@@ -3,3 +3,4 @@ test.py
 *.log
 Dataset/*
 *.ipynb
+*.swp

.vscode/launch.json

@@ -0,0 +1,34 @@
+{
+    // Use IntelliSense to learn about possible attributes.
+    // Hover to view descriptions of existing attributes.
+    // For more information, visit: https://go.microsoft.com/fwlink/?linkid=830387
+    "version": "0.2.0",
+    "configurations": [
+        {
+            "name": "Python: 当前文件",
+            "type": "python",
+            "request": "launch",
+            "program": "${file}",
+            "console": "integratedTerminal",
+            "justMyCode": true,
+            "args": [
+                "--lr",
+                "10e-3",
+                "--epochs",
+                "50",
+                "--batch_size",
+                "512",
+                "--dataset",
+                "cifar10",
+                "--num_workers",
+                "4",
+                "--device",
+                "cuda:1",
+                "--ratio_list",
+                "1/3",
+                "--snr_list",
+                "100"
+            ]
+        }
+    ]
+}

eval.py

@@ -15,14 +15,14 @@ def config_parser():
     parser.add_argument('--saved', type=str, help='saved_path')
     parser.add_argument('--snr', default=20, type=int, help='snr')
     parser.add_argument('--test_image', default='./demo/kodim08.png', type=str, help='demo_image')
-    parser.add_argument('--times', default=100, type=int, help='num_workers')
+    parser.add_argument('--times', default=10, type=int, help='num_workers')
     return parser.parse_args()
 
 
 def main():
     args = config_parser()
     transform = transforms.Compose([transforms.ToTensor()])
-    # args.saved = './saved/model_cifar10_0.33_19.00_40.pth' # to be deleted
+    args.saved = './saved/cifar10_50_0.33_100.00_41.pth' # to be deleted
     test_image = Image.open(args.test_image)
     test_image.load()
     test_image = transform(test_image)
@@ -35,14 +35,15 @@ def main():
     model.change_channel(args.channel, args.snr)
 
     psnr_all = 0.0
+    demo_image = model(test_image)
+    demo_image = image_normalization('denormalization')(demo_image)
+    gt = image_normalization('denormalization')(test_image)
     for i in range(args.times):
-        demo_image = model(test_image)
-        demo_image = image_normalization('denormalization')(demo_image)
-        gt = image_normalization('denormalization')(test_image)
         psnr_all += get_psnr(demo_image, gt)
     demo_image = torch.cat([test_image, demo_image], dim=1)
     demo_image = transforms.ToPILImage()(demo_image)
-    demo_image.save('./demo/demo.png')
+    temp = args.saved.split('/')[-1]
+    demo_image.save('./run/{}.png'.format(args.saved.split('/')[-1]))
     print("psnr on {} is {}".format(args.test_image, psnr_all / args.times))
 
 

model.py

@@ -32,7 +32,7 @@ def ratio2filtersize(x: torch.Tensor, ratio):
     encoder_temp = _Encoder(is_temp=True)
     z_temp = encoder_temp(x)
     c = before_size * ratio / np.prod(z_temp.size()[-2:])
-    return int(c)
+    return int(c) + 1
 
 
 class _ConvWithPReLU(nn.Module):

train.py

@@ -38,7 +38,7 @@ def config_parser():
     parser.add_argument('--dataset', default='cifar10', type=str,
                         choices=['cifar10', 'imagenet'], help='dataset')
     parser.add_argument('--parallel', default=False, type=bool, help='parallel')
-    parser.add_argument('--if_scheduler', default=True, type=bool, help='if_scheduler')
+    parser.add_argument('--if_scheduler', default=False, type=bool, help='if_scheduler')
     parser.add_argument('--step_size', default=640, type=int, help='scheduler')
     parser.add_argument('--device', default='cuda:0', type=str, help='device')
     return parser.parse_args()
@@ -53,7 +53,6 @@ def main():
         for snr in args.snr_list:
             train(args, ratio, snr)
 
-
 def train(args: config_parser(), ratio: float, snr: float):
 
     device = torch.device(args.device if torch.cuda.is_available() else 'cpu')
@@ -72,6 +71,7 @@ def train(args: config_parser(), ratio: float, snr: float):
     elif args.dataset == 'imagenet':
         transform = transforms.Compose(
             [transforms.ToTensor(), transforms.Resize((128, 128))])  # the size of paper is 128
+        print("loading data of imagenet")
         train_dataset = datasets.ImageFolder(root='./Dataset/ImageNet/train', transform=transform)
 
         train_loader = DataLoader(train_dataset, shuffle=True,
@@ -113,7 +113,7 @@ def train(args: config_parser(), ratio: float, snr: float):
             loss.backward()
             optimizer.step()
             run_loss += loss.item()
-            if args.if_scheduler:
+            if args.if_scheduler: # the scheduler is wrong before
                 scheduler.step()
         with torch.no_grad():
             model.eval()