update pretrain progress bar tutorial

Author: aladdinpersson

ML/Pytorch/Basics/pytorch_pretrain_finetune.py

@@ -3,11 +3,9 @@
 and modifies this to train on the CIFAR10 dataset. The same method generalizes
 well to other datasets, but the modifications to the network may need to be changed.
 
-Video explanation: https://youtu.be/U4bHxEhMGNk
-Got any questions leave a comment on youtube :)
-
 Programmed by Aladdin Persson <aladdin.persson at hotmail dot com>
 *    2020-04-08 Initial coding
+*    2022-12-19 Updated comments, minor code changes, made sure it works with latest PyTorch
 
 """
 
@@ -22,8 +20,8 @@
 )  # Gives easier dataset managment and creates mini batches
 import torchvision.datasets as datasets  # Has standard datasets we can import in a nice way
 import torchvision.transforms as transforms  # Transformations we can perform on our dataset
+from tqdm import tqdm
 
-# Set device
 device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
 
 # Hyperparameters
@@ -32,25 +30,16 @@
 batch_size = 1024
 num_epochs = 5
 
-# Simple Identity class that let's input pass without changes
-class Identity(nn.Module):
-    def __init__(self):
-        super(Identity, self).__init__()
-
-    def forward(self, x):
-        return x
-
-
 # Load pretrain model & modify it
-model = torchvision.models.vgg16(pretrained=True)
+model = torchvision.models.vgg16(weights="DEFAULT")
 
 # If you want to do finetuning then set requires_grad = False
 # Remove these two lines if you want to train entire model,
 # and only want to load the pretrain weights.
 for param in model.parameters():
     param.requires_grad = False
 
-model.avgpool = Identity()
+model.avgpool = nn.Identity()
 model.classifier = nn.Sequential(
     nn.Linear(512, 100), nn.ReLU(), nn.Linear(100, num_classes)
 )
@@ -71,7 +60,7 @@ def forward(self, x):
 for epoch in range(num_epochs):
     losses = []
 
-    for batch_idx, (data, targets) in enumerate(train_loader):
+    for batch_idx, (data, targets) in enumerate(tqdm(train_loader)):
         # Get data to cuda if possible
         data = data.to(device=device)
         targets = targets.to(device=device)

ML/Pytorch/Basics/pytorch_progress_bar.py

@@ -3,22 +3,20 @@
 from tqdm import tqdm
 from torch.utils.data import TensorDataset, DataLoader
 
-# Create a simple toy dataset example, normally this
-# would be doing custom class with __getitem__ etc,
-# which we have done in custom dataset tutorials
+# Create a simple toy dataset
 x = torch.randn((1000, 3, 224, 224))
 y = torch.randint(low=0, high=10, size=(1000, 1))
 ds = TensorDataset(x, y)
 loader = DataLoader(ds, batch_size=8)
 
 
 model = nn.Sequential(
-    nn.Conv2d(3, 10, kernel_size=3, padding=1, stride=1),
+    nn.Conv2d(in_channels=3, out_channels=10, kernel_size=3, padding=1, stride=1),
     nn.Flatten(),
-    nn.Linear(10*224*224, 10),
+    nn.Linear(10 * 224 * 224, 10),
 )
 
-NUM_EPOCHS = 100
+NUM_EPOCHS = 10
 for epoch in range(NUM_EPOCHS):
     loop = tqdm(loader)
     for idx, (x, y) in enumerate(loop):
@@ -35,7 +33,3 @@
         loop.set_postfix(loss=torch.rand(1).item(), acc=torch.rand(1).item())
 
 # There you go. Hope it was useful :)
-
-
-
-