requirements modified

README.md

@@ -16,7 +16,7 @@ pip install requirements.txt
 Run(example)
 ```
 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]
+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
 ```
 
 ### Evaluation

eval.py

@@ -1 +1,14 @@
 # to be implemented
+import torch
+import torch.nn as nn
+from PIL import Image
+
+
+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('--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')
+    return parser.parse_args()

requirements.txt

@@ -3,4 +3,4 @@ torchvison
 matplotlib
 tqdm
 numpy
-fraction
+pillow

scripts.py

@@ -11,3 +11,8 @@ def image_normalization(norm_type):
         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

@@ -22,13 +22,14 @@ def config_parser():
     parser.add_argument('--seed', default=2048, type=int, help='Random seed')
     parser.add_argument('--lr', default=1e-3, type=float, help='learning rate')
     parser.add_argument('--epochs', default=100, type=int, help='number of epochs')
-    parser.add_argument('--batch_size', default=64, type=int, help='batch size')
+    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('--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=[1/6, 1/12], type=list, help='ratio_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')
     return parser.parse_args()
 
 
@@ -36,25 +37,37 @@ def main():
     args = config_parser()
 
     print("Training Start")
-    # for ratio in args.ratio_list:
+    for ratio in args.ratio_list:
-    #     for snr in args.snr_list:
+        for snr in args.snr_list:
-    #         train(args, ratio, snr)
+            train(args, ratio, snr)
-    train(args, 1/6, 20)
 
 
 def train(args: config_parser(), ratio: float, snr: float):
 
     device = torch.device('cuda')
     # load data
-    transform = transforms.Compose([transforms.ToTensor(), ])
+    if args.dataset == 'cifar10':
-    train_dataset = datasets.CIFAR10(root='./Dataset/', train=True,
+        transform = transforms.Compose([transforms.ToTensor(), ])
-                                     download=True, transform=transform)
+        train_dataset = datasets.CIFAR10(root='./Dataset/', train=True,
+                                         download=True, transform=transform)
 
-    train_loader = DataLoader(train_dataset, shuffle=True,
+        train_loader = DataLoader(train_dataset, shuffle=True,
-                              batch_size=args.batch_size, num_workers=args.num_workers)
+                                  batch_size=args.batch_size, num_workers=args.num_workers)
-    test_dataset = datasets.CIFAR10(root='./Dataset/', train=False,
+        test_dataset = datasets.CIFAR10(root='./Dataset/', train=False,
-                                    download=True, transform=transform)
+                                        download=True, transform=transform)
-    test_loader = RandomSampler(test_dataset, replacement=True, num_samples=args.batch_size)
+        test_loader = RandomSampler(test_dataset, replacement=True, num_samples=args.batch_size)
+    elif args.dataset == 'imagenet':
+        transform = transforms.Compose([transforms.ToTensor(), ])
+        train_dataset = datasets.ImageNet(root='./Dataset/', train=True,
+                                          download=True, transform=transform)
+
+        train_loader = DataLoader(train_dataset, shuffle=True,
+                                  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)
+    else:
+        raise Exception('Unknown dataset')
 
     print("training with ratio: {:2f}, snr_db: {}, channel: {}".format(ratio, snr, args.channel))
 
@@ -79,12 +92,12 @@ def train(args: config_parser(), ratio: float, snr: float):
 
         epoch_loop.set_description(f'Epoch [{epoch}/{args.epochs}]')
         epoch_loop.set_postfix(loss=run_loss/len(train_loader))
-    save_model(model, args.saved + '/model_{:2f}_{:2f}.pth'.format(ratio, snr))
+    save_model(model, args.saved + '/model{}_{:2f}_{:2f}.pth'.format(args.dataset, ratio, snr))
 
 
 def save_model(model, path):
     os.makedirs(path, exist_ok=True)
-    torch.save(model.state_dict(), path)
+    torch.save(model, path)
     print("Model saved in {}".format(path))