get_num_workers.py added

best_number_workers.py

@@ -0,0 +1,31 @@
+from time import time
+import multiprocessing as mp
+import torch
+import torchvision
+from torchvision import transforms
+from torch.utils.data import DataLoader, RandomSampler
+
+
+if __name__ == '__main__':
+    transform = transforms.Compose([
+        torchvision.transforms.ToTensor(),
+        torchvision.transforms.Normalize((0.1307,), (0.3081,))
+    ])
+
+    trainset = torchvision.datasets.MNIST(
+        root='./Dataset/MNIST/',
+        train=True,  # 如果为True，从 training.pt 创建数据，否则从 test.pt 创建数据。
+        download=True,  # 如果为true，则从 Internet 下载数据集并将其放在根目录中。 如果已下载数据集，则不会再次下载。
+        transform=transform
+    )
+
+    print(f"num of CPU: {mp.cpu_count()}")
+    for num_workers in range(2, mp.cpu_count(), 2):
+        train_loader = torch.utils.data.DataLoader(
+            trainset, shuffle=True, num_workers=num_workers, batch_size=64, pin_memory=True)
+        start = time()
+        for epoch in range(1, 3):
+            for i, data in enumerate(train_loader, 0):
+                pass
+        end = time()
+        print("Finish with:{} second, num_workers={}".format(end - start, num_workers))

eval.py

@@ -1 +1 @@
-# to be implemented
+# to be implemented

train.py

@@ -23,12 +23,12 @@ def config_parser():
     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('--momentum', default=0.9, type=float, help='momentum')
     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('--num_workers', default=0, type=int, help='num_workers')
     return parser.parse_args()
 
 
@@ -44,20 +44,20 @@ def main():
 
 def train(args: config_parser(), ratio: float, snr: float):
 
-
     device = torch.device('cuda:1')
     # load data
     transform = transforms.Compose([transforms.ToTensor(), ])
     train_dataset = datasets.CIFAR10(root='./Dataset/', train=True,
                                      download=True, transform=transform)
 
-    train_loader = DataLoader(train_dataset, shuffle=True, batch_size=args.batch_size)
+    train_loader = DataLoader(train_dataset, shuffle=True,
+                              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)
-    
+
     print("training with ratio: {}, snr_db: {}, channel: {}".format(ratio, snr, args.channel))
-    
+
     image_fisrt = train_dataset.__getitem__(0)[0]
     c = ratio2filtersize(image_fisrt, ratio)
     model = DeepJSCC(c=c, channel_type=args.channel, snr=snr).cuda(device=device)