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What is a state_dict in PyTorch#
Created On: Apr 17, 2020 | Last Updated: Feb 06, 2024 | Last Verified: Nov 05, 2024
In PyTorch, the learnable parameters (i.e. weights and biases) of a
torch.nn.Module model are contained in the model’s parameters
(accessed with model.parameters()). A state_dict is simply a
Python dictionary object that maps each layer to its parameter tensor.
Introduction#
A state_dict is an integral entity if you are interested in saving
or loading models from PyTorch.
Because state_dict objects are Python dictionaries, they can be
easily saved, updated, altered, and restored, adding a great deal of
modularity to PyTorch models and optimizers.
Note that only layers with learnable parameters (convolutional layers,
linear layers, etc.) and registered buffers (batchnorm’s running_mean)
have entries in the model’s state_dict. Optimizer objects
(torch.optim) also have a state_dict, which contains information
about the optimizer’s state, as well as the hyperparameters used.
In this recipe, we will see how state_dict is used with a simple
model.
Setup#
Before we begin, we need to install torch if it isn’t already
available.
pip install torch
Steps#
Import all necessary libraries for loading our data
Define and initialize the neural network
Initialize the optimizer
Access the model and optimizer
state_dict
1. Import necessary libraries for loading our data#
For this recipe, we will use torch and its subsidiaries torch.nn
and torch.optim.
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
2. Define and initialize the neural network#
For sake of example, we will create a neural network for training images. To learn more see the Defining a Neural Network recipe.
class Net(nn.Module):
def __init__(self):
super(Net, self).__init__()
self.conv1 = nn.Conv2d(3, 6, 5)
self.pool = nn.MaxPool2d(2, 2)
self.conv2 = nn.Conv2d(6, 16, 5)
self.fc1 = nn.Linear(16 * 5 * 5, 120)
self.fc2 = nn.Linear(120, 84)
self.fc3 = nn.Linear(84, 10)
def forward(self, x):
x = self.pool(F.relu(self.conv1(x)))
x = self.pool(F.relu(self.conv2(x)))
x = x.view(-1, 16 * 5 * 5)
x = F.relu(self.fc1(x))
x = F.relu(self.fc2(x))
x = self.fc3(x)
return x
net = Net()
print(net)
3. Initialize the optimizer#
We will use SGD with momentum.
optimizer = optim.SGD(net.parameters(), lr=0.001, momentum=0.9)
4. Access the model and optimizer state_dict#
Now that we have constructed our model and optimizer, we can understand
what is preserved in their respective state_dict properties.
# Print model's state_dict
print("Model's state_dict:")
for param_tensor in net.state_dict():
print(param_tensor, "\t", net.state_dict()[param_tensor].size())
print()
# Print optimizer's state_dict
print("Optimizer's state_dict:")
for var_name in optimizer.state_dict():
print(var_name, "\t", optimizer.state_dict()[var_name])
This information is relevant for saving and loading the model and optimizers for future use.
Congratulations! You have successfully used state_dict in PyTorch.
Learn More#
Take a look at these other recipes to continue your learning:
