Loading model state_dict in PyTorch - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to load the saved model weights into the model.

PyTorch

model = MyModel()
model.[1](torch.load('model_weights.pth'))

Drag options to blanks, or click blank then click option'

Aload_state_dict

Bsave_state_dict

Csave_weights

Dload_weights

Attempts:

3 left

💡 Hint

Common Mistakes

Using save_state_dict instead of load_state_dict

Trying to load weights with a method that doesn't exist

The method load_state_dict loads the saved weights into the model.

2fill in blank

medium

Complete the code to load the model weights onto the CPU device.

PyTorch

state_dict = torch.load('model_weights.pth', map_location=[1])
model.load_state_dict(state_dict)

Drag options to blanks, or click blank then click option'

A'cpu'

B'cuda'

C'gpu'

D'device'

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'cuda' when no GPU is available

Using an invalid device string like 'gpu'

Using map_location='cpu' loads the model weights onto the CPU device.

3fill in blank

hard

Fix the error in loading the model weights by completing the code.

PyTorch

model = MyModel()
state_dict = torch.load('weights.pth')
model.[1](state_dict, strict=False)

Drag options to blanks, or click blank then click option'

Aload_weights

Bload_model

Cload_state_dict

Dload_params

Attempts:

3 left

💡 Hint

Common Mistakes

Using a non-existent method like load_weights

Not passing the state dictionary to the correct method

The correct method to load weights is load_state_dict, which accepts the state dictionary.

4fill in blank

hard

Fill both blanks to load the model weights and set the model to evaluation mode.

PyTorch

model = MyModel()
model.[1](torch.load('model.pth'))
model.[2]()

Drag options to blanks, or click blank then click option'

Aload_state_dict

Btrain

Ceval

Dsave_state_dict

Attempts:

3 left

💡 Hint

Common Mistakes

Calling train() instead of eval() after loading weights

Using save_state_dict instead of load_state_dict

First, load_state_dict loads the weights, then eval() sets the model to evaluation mode.

5fill in blank

hard

Fill all three blanks to load the model weights, move the model to GPU, and set it to evaluation mode.

PyTorch

model = MyModel()
model.[1](torch.load('weights.pth', map_location=[2]))
model.to([3])
model.eval()

Drag options to blanks, or click blank then click option'

Aload_state_dict

B'cpu'

C'cuda'

Dsave_state_dict

Attempts:

3 left

💡 Hint

Common Mistakes

Loading weights directly on GPU without map_location

Using save_state_dict instead of load_state_dict

Moving model to 'cpu' instead of 'cuda'

Load weights with load_state_dict, load them on CPU with map_location='cpu', then move model to GPU with to('cuda').

Practice

1. What does model.load_state_dict() do in PyTorch?

easy

A. It loads saved model weights into the model.

B. It saves the current model weights to a file.

C. It initializes a new model architecture.

D. It compiles the model for training.

Solution

1. Step 1: Understand the purpose of load_state_dict

   This function is used to load previously saved weights into a model.

2. Step 2: Differentiate from other functions

   Saving weights uses state_dict() with torch.save(), not load_state_dict().

3. Final Answer:

   It loads saved model weights into the model. -> Option A

4. Quick Check:

   Load weights = load_state_dict() [OK]

Hint: Remember: load_state_dict loads weights, not saves them [OK]

Common Mistakes:

• Confusing loading weights with saving weights
• Thinking it initializes model architecture
• Assuming it compiles the model

2. Which of the following is the correct syntax to load a saved state dictionary from a file model.pth into a model named model?

easy

A. model.load_state_dict(torch.load('model.pth'))

B. model.load(torch.load_state_dict('model.pth'))

C. torch.load_state_dict(model, 'model.pth')

D. model.load_state_dict('model.pth')

Solution

1. Step 1: Identify correct function usage

   The correct way is to first load the saved weights with torch.load() and then pass them to model.load_state_dict().

2. Step 2: Check syntax correctness

   model.load_state_dict(torch.load('model.pth')) correctly calls torch.load('model.pth') inside model.load_state_dict(). Other options misuse function names or argument order.

3. Final Answer:

   model.load_state_dict(torch.load('model.pth')) -> Option A

4. Quick Check:

   Load weights with torch.load, then load_state_dict [OK]

Hint: Load file with torch.load, then pass to load_state_dict [OK]

Common Mistakes:

• Passing filename directly to load_state_dict
• Using wrong function names or order
• Confusing torch.load and load_state_dict

3. Given the code below, what will be printed?

import torch
import torch.nn as nn

class SimpleModel(nn.Module):
    def __init__(self):
        super().__init__()
        self.linear = nn.Linear(2, 1)

model = SimpleModel()
torch.save(model.state_dict(), 'temp.pth')

new_model = SimpleModel()
new_model.load_state_dict(torch.load('temp.pth'))

print(all(torch.equal(p1, p2) for p1, p2 in zip(model.parameters(), new_model.parameters())))

medium

A. Raises an error

B. False

C. True

D. None

Solution

1. Step 1: Understand saving and loading state_dict

   The code saves the original model's weights and loads them into a new model instance.

2. Step 2: Compare parameters of both models

   Since the new model loads the exact saved weights, parameters should be identical, so the comparison returns True.

3. Final Answer:

   True -> Option C

4. Quick Check:

   Loaded weights match saved weights = True [OK]

Hint: Loaded model matches saved weights exactly [OK]

Common Mistakes:

• Assuming new model has random weights after loading
• Thinking load_state_dict changes model architecture
• Expecting an error due to missing device argument

4. You try to load a saved state_dict into your model but get this error: RuntimeError: Error(s) in loading state_dict for Model: Missing key(s) in state_dict: "fc.weight". What is the most likely cause?

medium

A. The file path to the saved state_dict is incorrect.

B. The saved state_dict is from a different model architecture.

C. You forgot to call torch.load() before loading.

D. The model was not moved to the correct device before loading.

Solution

1. Step 1: Analyze the error message

   The error says some keys are missing in the loaded state_dict, meaning the model expects parameters not found in the saved weights.

2. Step 2: Identify cause of missing keys

   This usually happens when the saved weights come from a different model architecture than the current model.

3. Final Answer:

   The saved state_dict is from a different model architecture. -> Option B

4. Quick Check:

   Missing keys = architecture mismatch [OK]

Hint: Missing keys usually mean model architectures differ [OK]

Common Mistakes:

• Assuming file path error causes missing keys
• Forgetting to load file before loading state_dict
• Thinking device mismatch causes missing keys

5. You have a model trained on GPU and saved its state_dict. Now you want to load it on a CPU-only machine. Which code snippet correctly loads the weights without error?

hard

A. model.load_state_dict(torch.load('model_gpu.pth', device='cpu'))

B. model.load_state_dict(torch.load('model_gpu.pth'))

C. model.load_state_dict(torch.load('model_gpu.pth', map_location='cuda'))

D. model.load_state_dict(torch.load('model_gpu.pth', map_location=torch.device('cpu')))

Solution

1. Step 1: Understand device mismatch issue

   Loading GPU-trained weights on CPU requires mapping the storage to CPU to avoid errors.

2. Step 2: Use correct map_location argument

   Passing map_location=torch.device('cpu') to torch.load() correctly maps tensors to CPU.

3. Final Answer:

   model.load_state_dict(torch.load('model_gpu.pth', map_location=torch.device('cpu'))) -> Option D

4. Quick Check:

   Use map_location to load GPU weights on CPU [OK]

Hint: Use map_location=torch.device('cpu') when loading GPU weights on CPU [OK]

Common Mistakes:

• Not using map_location causes runtime errors
• Passing wrong device string like 'cuda' on CPU
• Using non-existent 'device' argument in torch.load