utils.py added

.gitignore

@@ -1,3 +1,4 @@
 test.py
 *.pyc
-*.log
+*.log
+Dataset/*

README.md

@@ -13,12 +13,18 @@ pip install requirements.txt
 
 ## Usage
 ### Training Model
-Run(example)
+Run(example presented in paper)
 ```
 cd ./Deep-JSCC-PyTorch
-python train.py --seed 2048 --epochs 200 --batch_size 256 --channel 'AWGN' --saved ./saved --snr_list [1,4,7,13,19] --ratio_list [1/6,1/12] --dataset imagenet
 ```
 
+```
+python train.py --lr 10e-4 --epochs 100 --batch_size 32 --channel 'AWGN' --saved ./saved --snr_list [1,4,7,13,19] --ratio_list [1/6,1/12] --dataset imagenet
+```
+or
+```
+python train.py --lr 10e-3 --epochs 100 --batch_size 64 --channel 'AWGN' --saved ./saved --snr_list [1,4,7,13,19] --ratio_list [1/6,1/12] --dataset cifar10
+```
 ### Evaluation
 
 

eval.py

@@ -2,13 +2,36 @@
 import torch
 import torch.nn as nn
 from PIL import Image
-
+from torchvision import transforms
+from utils import get_psnr
 
 def config_parser():
     import argparse
     parser = argparse.ArgumentParser()
     parser.add_argument('--channel', default='AWGN', type=str, help='channel type')
-    parser.add_argument('--saved', default='./saved', type=str, help='saved_path')
+    parser.add_argument('--saved', type=str, help='saved_path')
     parser.add_argument('--snr_list', default=range(1, 19, 3), type=list, help='snr_list')
-    parser.add_argument('--demo_image', default='./demo/kodim08.png', type=str, help='demo_image')
+    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')
     return parser.parse_args()
+
+
+def main():
+    args = config_parser()
+    transform = transforms.Compose([transforms.ToTensor(), ])
+
+    test_image = Image.open(args.test_image)
+    test_image.load()
+    test_image = transform(test_image)
+    model = torch.load(args.saved)
+    psnr_all = 0.0
+    for i in range(args.times):
+        demo_image = model(test_image)
+        psnr_all += get_psnr(demo_image, test_image)
+    demo_image = torch.cat([test_image, demo_image], dim=1)
+    demo_image = transforms.ToPILImage()(demo_image)
+    demo_image.save('./demo/demo.png')
+    print("psnr on {} is {}".format(args.test_image, psnr_all / args.times))
+
+if __name__ == '__main__':
+    main()

scripts.py

@@ -1,18 +0,0 @@
-import torch
-import torch.nn as nn
-
-
-def image_normalization(norm_type):
-    def _inner(tensor: torch.Tensor):
-        if norm_type == 'nomalization':
-            return tensor / 255.0
-        elif norm_type == 'denormalization':
-            return (tensor * 255.0).type(torch.FloatTensor)
-        else:
-            raise Exception('Unknown type of normalization')
-    return _inner
-
-def get_psnr(image,gt,max=255):
-    psnr = 10 * torch.log10(max**2 / torch.mean((image - gt)**2))
-    return psnr
-    

train.py

@@ -14,6 +14,7 @@ import torch.optim as optim
 from tqdm import tqdm
 from model import DeepJSCC, ratio2filtersize
 from torch.nn.parallel import DataParallel
+from utils import image_normalization
 
 
 def config_parser():
@@ -24,12 +25,14 @@ def config_parser():
     parser.add_argument('--epochs', default=100, type=int, help='number of epochs')
     parser.add_argument('--batch_size', default=256, type=int, help='batch size')
     parser.add_argument('--weight_decay', default=5e-4, type=float, help='weight decay')
-    parser.add_argument('--channel', default='AWGN', type=str, help='channel type')
+    parser.add_argument('--channel', default='AWGN', type=str,
+                        choices=['AWGN', 'Rayleigh'], help='channel type')
     parser.add_argument('--saved', default='./saved', type=str, help='saved_path')
     parser.add_argument('--snr_list', default=range(1, 19, 3), type=list, help='snr_list')
     parser.add_argument('--ratio_list', default=[1/3, 1/6, 1/12], type=list, help='ratio_list')
     parser.add_argument('--num_workers', default=0, type=int, help='num_workers')
-    parser.add_argument('--dataset', default='imagenet', type=str, help='dataset')
+    parser.add_argument('--dataset', default='cifar10', type=str,
+                        choices=['cifar10', 'imagenet'], help='dataset')
     return parser.parse_args()
 
 
@@ -55,7 +58,8 @@ def train(args: config_parser(), ratio: float, snr: float):
                                   batch_size=args.batch_size, num_workers=args.num_workers)
         test_dataset = datasets.CIFAR10(root='./Dataset/', train=False,
                                         download=True, transform=transform)
-        test_loader = RandomSampler(test_dataset, replacement=True, num_samples=args.batch_size)
+        test_loader = DataLoader(test_dataset, shuffle=True,
+                                 batch_size=args.batch_size, num_workers=args.num_workers)
     elif args.dataset == 'imagenet':
         transform = transforms.Compose([transforms.ToTensor(), ])
         train_dataset = datasets.ImageNet(root='./Dataset/', train=True,
@@ -65,7 +69,8 @@ def train(args: config_parser(), ratio: float, snr: float):
                                   batch_size=args.batch_size, num_workers=args.num_workers)
         test_dataset = datasets.ImageNet(root='./Dataset/', train=False,
                                          download=True, transform=transform)
-        test_loader = RandomSampler(test_dataset, replacement=True, num_samples=args.batch_size)
+        test_loader = DataLoader(test_dataset, shuffle=True,
+                                 batch_size=args.batch_size, num_workers=args.num_workers)
     else:
         raise Exception('Unknown dataset')
 
@@ -75,7 +80,7 @@ def train(args: config_parser(), ratio: float, snr: float):
     c = ratio2filtersize(image_fisrt, ratio)
     model = DeepJSCC(c=c, channel_type=args.channel, snr=snr).cuda(device=device)
 
-    criterion = nn.MSELoss(reduction='sum').cuda(device=device)
+    criterion = nn.MSELoss(reduction='mean').cuda(device=device)
     optimizer = optim.Adam(model.parameters(), lr=args.lr, weight_decay=args.weight_decay)
     epoch_loop = tqdm(range(args.epochs), total=args.epochs, leave=False)
 
@@ -85,13 +90,23 @@ def train(args: config_parser(), ratio: float, snr: float):
             optimizer.zero_grad()
             images = images.cuda(device=device)
             outputs = model(images)
-            loss = criterion(outputs, images) / args.batch_size
+            loss = criterion(image_normalization('denormalization')(outputs),
+                             image_normalization('denormalization')(images))
             loss.backward()
             optimizer.step()
             run_loss += loss.item()
-
+        with torch.no_grad():
-        epoch_loop.set_description(f'Epoch [{epoch}/{args.epochs}]')
+            model.eval()
-        epoch_loop.set_postfix(loss=run_loss/len(train_loader))
+            test_mse = 0.0
+            for images, _ in tqdm((test_loader), leave=False):
+                images = images.cuda(device=device)
+                outputs = model(images)
+                images = image_normalization('normalization')(images)
+                outputs = image_normalization('normalization')(outputs)
+                loss = criterion(outputs, images)
+                test_mse += loss.item()
+            model.train()
+        epoch_loop.set_postfix(loss=run_loss/len(train_loader), test_mse=test_mse/len(test_loader))
     save_model(model, args.saved + '/model{}_{:2f}_{:2f}.pth'.format(args.dataset, ratio, snr))
 
 

utils.py

@@ -0,0 +1,27 @@
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+
+
+def image_normalization(norm_type):
+    def _inner(tensor: torch.Tensor):
+        if norm_type == 'nomalization':
+            return tensor / 255.0
+        elif norm_type == 'denormalization':
+            return tensor * 255.0
+        else:
+            raise Exception('Unknown type of normalization')
+    return _inner
+
+
+def get_psnr(image, gt, max=255):
+    image = image_normalization('denormalization')(image)
+    gt = image_normalization('denormalization')(gt)
+    mse = F.mse_loss(image, gt)
+
+    psnr = 10 * torch.log10(max**2 / mse)
+    return psnr
+
+
+a = torch.randn(2, 3, 32, 32)
+b = image_normalization('nomalization')(a)