Initial commit

ML/Pytorch/Basics/pytorch_progress_bar.py

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+import torch
+import torch.nn as nn
+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
+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.Flatten(),
+    nn.Linear(10*224*224, 10),
+)
+
+NUM_EPOCHS = 100
+for epoch in range(NUM_EPOCHS):
+    loop = tqdm(loader)
+    for idx, (x, y) in enumerate(loop):
+        scores = model(x)
+
+        # here we would compute loss, backward, optimizer step etc.
+        # you know how it goes, but now you have a nice progress bar
+        # with tqdm
+
+        # then at the bottom if you want additional info shown, you can
+        # add it here, for loss and accuracy you would obviously compute
+        # but now we just set them to random values
+        loop.set_description(f"Epoch [{epoch}/{NUM_EPOCHS}]")
+        loop.set_postfix(loss=torch.rand(1).item(), acc=torch.rand(1).item())
+
+# There you go. Hope it was useful :)
+
+
+
+