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ML/Pytorch/Basics/pytorch_loadsave.py

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+"""
+Small code example of how to save and load checkpoint of a model.
+This example doesn't perform any training, so it would be quite useless.
+
+In practice you would save the model as you train, and then load before 
+continuining training at another point.
+
+Video explanation of code & how to save and load model: https://youtu.be/g6kQl_EFn84
+Got any questions leave a comment on youtube :)
+
+Coded by Aladdin Persson <aladdin dot person at hotmail dot com>
+-   2020-04-07 Initial programming
+
+"""
+
+# Imports
+import torch
+import torchvision
+import torch.nn as nn  # All neural network modules, nn.Linear, nn.Conv2d, BatchNorm, Loss functions
+import torch.optim as optim  # For all Optimization algorithms, SGD, Adam, etc.
+import torch.nn.functional as F  # All functions that don't have any parameters
+from torch.utils.data import (
+    DataLoader,
+)  # 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
+
+
+def save_checkpoint(state, filename="my_checkpoint.pth.tar"):
+    print("=> Saving checkpoint")
+    torch.save(state, filename)
+
+
+def load_checkpoint(checkpoint, model, optimizer):
+    print("=> Loading checkpoint")
+    model.load_state_dict(checkpoint["state_dict"])
+    optimizer.load_state_dict(checkpoint["optimizer"])
+
+
+def main():
+    # Initialize network
+    model = torchvision.models.vgg16(pretrained=False)
+    optimizer = optim.Adam(model.parameters())
+
+    checkpoint = {"state_dict": model.state_dict(), "optimizer": optimizer.state_dict()}
+    # Try save checkpoint
+    save_checkpoint(checkpoint)
+
+    # Try load checkpoint
+    load_checkpoint(torch.load("my_checkpoint.pth.tar"), model, optimizer)
+
+
+if __name__ == "__main__":
+    main()