Mobile deployment (PyTorch Mobile) - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to load a PyTorch model for mobile deployment.

PyTorch

import torch

model = torch.jit.load([1])
model.eval()

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

A"model.pt"

Bmodel.pt

Ctorch_model.pt

D'model.pth'

Attempts:

3 left

💡 Hint

Common Mistakes

Forgetting quotes around the file path string.

Using the wrong file extension like '.pth' instead of '.pt'.

The model file path must be a string with quotes, like "model.pt".

2fill in blank

medium

Complete the code to convert a PyTorch model to TorchScript for mobile deployment.

PyTorch

import torch

scripted_model = torch.jit.[1](model)
scripted_model.save("model.pt")

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

Atrace

Bload

Cscript

Dsave

Attempts:

3 left

💡 Hint

Common Mistakes

Using torch.jit.trace instead of torch.jit.script for models with control flow.

Confusing save and load methods.

torch.jit.script converts the model to TorchScript by scripting it.

3fill in blank

hard

Fix the error in the code to run inference on a mobile model.

PyTorch

input_tensor = torch.randn(1, 3, 224, 224)
output = model.[1](input_tensor)
print(output)

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

Acall

Bforward

Crun

Dpredict

Attempts:

3 left

💡 Hint

Common Mistakes

Using non-existent methods like predict or run.

Trying to call the model object directly without parentheses.

In PyTorch, the forward method runs the model on input data.

4fill in blank

hard

Fill both blanks to prepare input and run inference on a mobile model.

PyTorch

input_data = torch.randn([1])
output = model.[2](input_data)
print(output)

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

A1, 3, 224, 224

Bforward

Cpredict

D3, 224, 224

Attempts:

3 left

💡 Hint

Common Mistakes

Using wrong input shape missing batch dimension.

Using incorrect method names for inference.

Input tensor shape must match model input; use forward to run inference.

5fill in blank

hard

Fill all three blanks to save a scripted model and load it for mobile deployment.

PyTorch

scripted_model = torch.jit.[1](model)
scripted_model.[2]("model.pt")
loaded_model = torch.jit.[3]("model.pt")

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

Ascript

Bsave

Cload

Dtrace

Attempts:

3 left

💡 Hint

Common Mistakes

Mixing up save and load methods.

Using trace instead of script for models with control flow.

Use script to convert, save to write file, and load to read file.

Practice

1. What is the main advantage of using PyTorch Mobile for AI models on phones?

easy

A. It converts models to TensorFlow format

B. It requires an internet connection to work

C. It runs models directly on the phone for faster response and privacy

D. It only supports training models on the phone

Solution

1. Step 1: Understand PyTorch Mobile purpose

   PyTorch Mobile allows running AI models directly on mobile devices without needing a server.

2. Step 2: Identify benefits of local model execution

   Running models locally improves speed and keeps data private since no internet is needed.

3. Final Answer:

   It runs models directly on the phone for faster response and privacy -> Option C

4. Quick Check:

   Local model execution = faster and private [OK]

Hint: Remember: Mobile deployment means running models on the device [OK]

Common Mistakes:

• Thinking PyTorch Mobile needs internet
• Confusing training with inference on mobile
• Mixing PyTorch Mobile with TensorFlow

2. Which PyTorch function is used to convert a model to TorchScript for mobile deployment?

easy

A. torch.jit.trace()

B. torch.save()

C. torch.load()

D. torch.nn.Module()

Solution

1. Step 1: Recall TorchScript conversion methods

   PyTorch uses torch.jit.trace() or torch.jit.script() to convert models to TorchScript.

2. Step 2: Identify correct function for conversion

   torch.jit.trace() records operations to create a TorchScript model for mobile use.

3. Final Answer:

   torch.jit.trace() -> Option A

4. Quick Check:

   Model conversion = torch.jit.trace() [OK]

Hint: Trace or script to convert model for mobile [OK]

Common Mistakes:

• Using torch.save() which only saves models
• Confusing torch.load() which loads models
• Using torch.nn.Module() which defines models

3. What will be the output type of this code snippet?

import torch
class MyModel(torch.nn.Module):
    def forward(self, x):
        return x * 2
model = MyModel()
traced_model = torch.jit.trace(model, torch.tensor([1]))
print(type(traced_model))

medium

A. <class 'torch.nn.Module'>

B. <class 'torch.jit.ScriptModule'>

C. <class 'torch.jit.TopLevelTracedModule'>

D. <class 'torch.jit.TracedModule'>

Solution

1. Step 1: Understand torch.jit.trace output type

   Tracing a model returns a TorchScript object of type ScriptModule in PyTorch.

2. Step 2: Check printed type

   Printing type(traced_model) shows <class 'torch.jit.ScriptModule'> indicating the traced script module.

3. Final Answer:

   <class 'torch.jit.ScriptModule'> -> Option B

4. Quick Check:

   Tracing returns ScriptModule [OK]

Hint: Trace returns ScriptModule type [OK]

Common Mistakes:

• Assuming output is TopLevelTracedModule
• Confusing traced model with normal nn.Module
• Choosing non-existent TracedModule class

4. Identify the error in this PyTorch Mobile model saving code:

import torch
model = torch.nn.Linear(2, 2)
traced = torch.jit.trace(model, torch.randn(2))
torch.save(traced, 'model.pt')
loaded = torch.jit.load('model.pt')
print(loaded(torch.randn(2)))

medium

A. torch.save() cannot save traced models

B. torch.jit.load() cannot load saved models

C. Input tensor shape for tracing is incorrect

D. Model must be scripted, not traced

Solution

1. Step 1: Identify the saving issue

   TorchScript models from torch.jit.trace() must use torch.jit.save(traced, 'model.pt'); torch.save() fails as ScriptModules are not picklable.

2. Step 2: Verify other components

   torch.randn(2) matches Linear(2,2) input shape; torch.jit.load() works after proper save.

3. Final Answer:

   torch.save() cannot save traced models -> Option A

4. Quick Check:

   TorchScript requires torch.jit.save() [OK]

Hint: torch.jit.save() for traced/ScriptModules [OK]

Common Mistakes:

• torch.save() doesn't pickle ScriptModules
• torch.jit.load() works correctly
• Traced models save with torch.jit.save()

5. You want to deploy a PyTorch model on Android. Which sequence correctly prepares the model for mobile use?

hard

A. Train model -> Convert to TorchScript with torch.save() -> Save as .pt -> Load in Android app

B. Train model -> Save as .pt -> Load directly in Android app -> Convert to TorchScript

C. Train model -> Convert to ONNX -> Save as .pt -> Load in Android app

D. Train model -> Convert to TorchScript with torch.jit.trace() -> Save as .pt -> Load in Android app

Solution

1. Step 1: Understand model conversion for mobile

   PyTorch Mobile requires models in TorchScript format, created by tracing or scripting.

2. Step 2: Identify correct conversion and saving steps

   Tracing with torch.jit.trace(), saving as .pt, then loading in Android app is the correct flow.

3. Final Answer:

   Train model -> Convert to TorchScript with torch.jit.trace() -> Save as .pt -> Load in Android app -> Option D

4. Quick Check:

   Trace -> save -> load for mobile deployment [OK]

Hint: Trace or script, save .pt, then load on mobile [OK]

Common Mistakes:

• Trying to load raw PyTorch model on mobile
• Using ONNX instead of TorchScript for PyTorch Mobile
• Saving before converting to TorchScript