Saving and Loading Entire Models vs. Only Parameters

When it comes to saving your PyTorch models, you generally have two main choices: saving the entire model object, or saving only its learned parameters (the state_dict). While the previous section introduced the state_dict as PyTorch's primary mechanism for model persistence, contrasting it with TensorFlow's formats, this section gets into the practical differences and implications of these two saving approaches in PyTorch. Understanding these distinctions is significant for managing your models effectively, especially when collaborating or moving models to different environments.

Saving and Loading Only the Model Parameters (state_dict)

The recommended and most common PyTorch practice is to save and load only the model's state_dict. As a quick reminder, the state_dict is a Python dictionary object that maps each layer to its learnable parameters (weights and biases).

Saving the state_dict

To save the state_dict, you access it via model.state_dict() and then use torch.save():

import torch
import torch.nn as nn
import os

# Define a simple model for demonstration
class SimpleNet(nn.Module):
    def __init__(self, input_size=10, hidden_size=5, output_size=2):
        super(SimpleNet, self).__init__()
        self.fc1 = nn.Linear(input_size, hidden_size)
        self.relu = nn.ReLU()
        self.fc2 = nn.Linear(hidden_size, output_size)

    def forward(self, x):
        x = self.fc1(x)
        x = self.relu(x)
        x = self.fc2(x)
        return x

# Instantiate the model
model = SimpleNet()

# --- Saving the state_dict ---
STATE_DICT_PATH = "simple_net_state_dict.pth"
torch.save(model.state_dict(), STATE_DICT_PATH)
print(f"Model state_dict saved to {STATE_DICT_PATH}")

This creates a file (e.g., simple_net_state_dict.pth) containing only the weights and biases of your SimpleNet model.

Loading the state_dict

To load the parameters back into a model, you first need to instantiate an object of your model class. Then, you use torch.load() to load the state_dict from the file and model.load_state_dict() to populate your model instance with these parameters:

# --- Loading the state_dict ---
# First, instantiate the model structure
loaded_model_from_state_dict = SimpleNet() # Ensure class definition is available

# Load the state_dict
state_dict = torch.load(STATE_DICT_PATH)
loaded_model_from_state_dict.load_state_dict(state_dict)

# Remember to call model.eval() if you are using the model for inference
# to set dropout and batch normalization layers to evaluation mode.
loaded_model_from_state_dict.eval()

print("Model loaded successfully from state_dict.")
# You can now use loaded_model_from_state_dict for inference
# For example:
# dummy_input = torch.randn(1, 10)
# output = loaded_model_from_state_dict(dummy_input)
# print("Output from loaded model:", output)

Advantages of using state_dict:

• Flexibility: This is the most flexible method. The saved state_dict is independent of the exact Python code defining the model, as long as the new model's architecture has layers with matching names and parameter shapes. You can refactor your model class, move it to different files, or even load parameters into a slightly different architecture (if you carefully manage the state_dict keys).
• Robustness: It's less prone to breaking when PyTorch versions change or minor code modifications occur, as it only serializes the raw tensor data and their layer associations.
• Transparency: It makes explicit that you are only restoring the learned parameters, not arbitrary Python code. This is generally safer.
• Standard Practice: This is the method recommended by the PyTorch team for most use cases.

Disadvantage of using state_dict:

• You must have the model's class definition available to instantiate the model before you can load the state_dict. This means you need access to the Python code that defines the model architecture.

Saving and Loading the Entire Model Object

PyTorch also allows you to save the entire model object directly using torch.save(). This method uses Python's pickle module behind the scenes to serialize the model object itself.

Saving the entire model

# --- Saving the entire model object ---
ENTIRE_MODEL_PATH = "simple_net_entire_model.pth"
torch.save(model, ENTIRE_MODEL_PATH)
print(f"Entire model saved to {ENTIRE_MODEL_PATH}")

Loading the entire model

Loading is straightforward; torch.load() directly returns the model object:

# --- Loading the entire model object ---
loaded_entire_model = torch.load(ENTIRE_MODEL_PATH)

# Remember to call model.eval() for inference
loaded_entire_model.eval()

print("Entire model loaded successfully.")
# You can now use loaded_entire_model for inference
# For example:
# output_entire = loaded_entire_model(dummy_input)
# print("Output from entirely loaded model:", output_entire)

Advantages of saving the entire model:

• Syntactic Simplicity: The code for saving and loading appears more concise as you don't need to instantiate the model class separately before loading.

Disadvantages of saving the entire model:

• Brittleness: This method is less robust. The serialized data is tightly coupled to the specific Python classes and directory structure used when the model was saved. If you refactor your code (e.g., rename the class, move the file defining the class), change PyTorch versions, or try to load the model in a different project without the exact same code structure, loading can easily fail.
• Security Risks: Since it uses pickle, loading a model saved this way from an untrusted source can be a security risk, as pickle can execute arbitrary code.
• Portability Issues: These files are often less portable across different Python environments or PyTorch versions.

Which Approach Should You Choose?

For most situations, especially when sharing models, deploying them to production, or planning for long-term use, saving and loading the state_dict is the strongly recommended approach. It offers greater flexibility, robustness, and security.

Saving the entire model might be convenient for:

• Quick, temporary saves during personal experimentation where you are certain the environment and code will not change.
• Simple scripts where the model definition is always available and unchanged.

However, be mindful of its limitations and potential for breaking.

Relating to TensorFlow Practices

If you're coming from TensorFlow, these PyTorch methods have somewhat analogous counterparts:

1. Saving/Loading state_dict (PyTorch) is similar to saving/loading weights in TensorFlow (e.g., model.save_weights('my_weights.h5') and model.load_weights('my_weights.h5')). In both cases, you save only the learned parameters, and you need the model's architecture defined in code to restore the model. To load the weights in TensorFlow, you first construct the model (e.g., model = create_my_model()) and then call model.load_weights(). This parallels instantiating your nn.Module class and then calling model.load_state_dict().

2. Saving/Loading the entire model (PyTorch's torch.save(model, PATH)) might initially seem like TensorFlow's model.save('my_model.h5') or tf.saved_model.save(model, 'my_saved_model_dir'). Both aim to save more than just weights. However, there's a significant difference in how the model's architecture is persisted.

   • PyTorch's torch.save(model, PATH) uses Python's pickle to serialize the model object, including its code. This makes it dependent on the exact Python class definitions and file structure being available and identical during loading.
   • TensorFlow's SavedModel format, on the other hand, saves the model's architecture as a computation graph in a more language-agnostic and robust way. This makes SavedModel generally more reliable for deployment and sharing, as it's less tied to the original Python code structure. The older Keras HDF5 format (model.save('my_model.h5')) also saves the architecture, but SavedModel is the preferred format in modern TensorFlow.

PyTorch's answer to a more robust, graph-based serialization format, akin to TensorFlow's SavedModel, is TorchScript, which you'll learn about later in this chapter. TorchScript allows you to convert your PyTorch model into an intermediate representation that can be run independently of Python, offering better portability and performance for deployment.

By understanding these two primary ways of saving and loading models in PyTorch, and their respective trade-offs, you are better equipped to manage your trained models effectively. Opting for the state_dict method will generally lead to more maintainable and shareable code, aligning with best practices in the PyTorch community.