Compose transforms in PyTorch - Model Pipeline Trace

Model Pipeline - Compose transforms

This pipeline shows how multiple image transformations are combined using Compose in PyTorch to prepare data for training a model. Compose applies each transform step-by-step to the input images, making preprocessing easy and organized.

Data Flow - 3 Stages

1Raw Image Input

1000 images x 3 channels x 256 height x 256 width→Original dataset of color images→1000 images x 3 channels x 256 height x 256 width

An image of a cat with size 256x256 pixels and 3 color channels (RGB)

↓

2Compose Transforms Applied

1000 images x 3 channels x 256 height x 256 width→Resize to 128x128, convert to tensor, normalize pixel values→1000 images x 3 channels x 128 height x 128 width

The cat image resized smaller, pixel values scaled to range [-1,1]

↓

3Batch Formation

1000 images x 3 channels x 128 height x 128 width→Group images into batches of 32 for training→32 images x 3 channels x 128 height x 128 width

A batch containing 32 resized and normalized cat images

Training Trace - Epoch by Epoch

Epochs
1 |***************
2 |************
3 |*********
4 |******
5 |****
Loss

Epoch	Loss ↓	Accuracy ↑	Observation
1	1.2	0.45	Initial training with high loss and low accuracy
2	0.9	0.60	Loss decreased, accuracy improved as model learns
3	0.7	0.72	Continued improvement in training metrics
4	0.55	0.80	Model is converging with better accuracy
5	0.45	0.85	Training loss low and accuracy high, good convergence

Prediction Trace - 4 Layers

Layer 1: Resize

Layer 2: ToTensor

Layer 3: Normalize

Layer 4: Model Prediction

Model Quiz - 3 Questions

What does the Compose transform do in this pipeline?

A Trains the model on batches

B Splits data into training and testing sets

C Applies multiple image transformations in sequence

D Calculates accuracy after each epoch

Key Insight

Using Compose to chain transforms simplifies preprocessing and ensures consistent input format. This helps the model learn better, shown by decreasing loss and increasing accuracy during training.

Practice

1. What is the main purpose of Compose in PyTorch transforms?

easy

A. To chain multiple image transforms into one step

B. To train a neural network model

C. To load datasets from disk

D. To visualize images in a grid

Solution

1. Step 1: Understand Compose functionality

   Compose is used to combine several image transformations so they run one after another.

2. Step 2: Compare with other options

   Training models, loading datasets, and visualization are different tasks not handled by Compose.

3. Final Answer:

   To chain multiple image transforms into one step -> Option A

4. Quick Check:

   Compose chains transforms = C [OK]

Hint: Compose chains transforms in order for easy preprocessing [OK]

Common Mistakes:

• Thinking Compose trains models
• Confusing Compose with dataset loading
• Assuming Compose visualizes images

2. Which of the following is the correct way to create a Compose transform that converts images to tensor and normalizes them?

easy

A. transforms.Compose(transforms.ToTensor(), transforms.Normalize((0.5,), (0.5,)))

B. transforms.Compose([transforms.ToTensor(), transforms.Normalize((0.5,), (0.5,))])

C. transforms.Compose{transforms.ToTensor(), transforms.Normalize((0.5,), (0.5,))}

D. transforms.Compose([transforms.Normalize((0.5,), (0.5,)), transforms.ToTensor()])

Solution

1. Step 1: Check Compose syntax

   Compose takes a list of transforms inside square brackets [].

2. Step 2: Verify order and syntax

   transforms.Compose([transforms.ToTensor(), transforms.Normalize((0.5,), (0.5,))]) uses correct list syntax and order: ToTensor first, then Normalize. transforms.Compose(transforms.ToTensor(), transforms.Normalize((0.5,), (0.5,))) misses list brackets, C uses curly braces, D reverses order which is incorrect.

3. Final Answer:

   transforms.Compose([transforms.ToTensor(), transforms.Normalize((0.5,), (0.5,))]) -> Option B

4. Quick Check:

   Compose needs list and correct order = A [OK]

Hint: Use square brackets [] inside Compose and correct transform order [OK]

Common Mistakes:

• Omitting list brackets in Compose
• Using curly braces instead of brackets
• Reversing transform order

3. What will be the output shape of the tensor after applying this Compose transform on a 28x28 grayscale image?

transform = transforms.Compose([
    transforms.ToTensor(),
    transforms.Resize((14, 14))
])
img_tensor = transform(image)

medium

A. [3, 14, 14]

B. [1, 28, 28]

C. [1, 14, 14]

D. [3, 28, 28]

Solution

1. Step 1: Understand ToTensor output

   For a grayscale image, ToTensor() converts it to a tensor with shape [channels, height, width] where channels=1.

2. Step 2: Apply Resize transform

   Resize((14,14)) changes height and width to 14 each, channels remain 1.

3. Final Answer:

   [1, 14, 14] -> Option C

4. Quick Check:

   Grayscale tensor shape after resize = [1,14,14] [OK]

Hint: Grayscale images have 1 channel; Resize changes height and width [OK]

Common Mistakes:

• Assuming 3 channels for grayscale
• Ignoring Resize effect on shape
• Confusing height and width order

4. Identify the error in this Compose transform code:

transform = transforms.Compose([
    transforms.Normalize((0.5,), (0.5,)),
    transforms.ToTensor()
])

medium

A. ToTensor should be replaced with ToPILImage

B. Normalize parameters are incorrect

C. Compose requires a tuple, not a list

D. Normalize should come after ToTensor

Solution

1. Step 1: Check transform order

   Normalize expects a tensor input, so ToTensor() must come first to convert image to tensor.

2. Step 2: Validate other options

   Parameters for Normalize are correct, Compose accepts list, and ToPILImage is not needed here.

3. Final Answer:

   Normalize should come after ToTensor -> Option D

4. Quick Check:

   Normalize after ToTensor = A [OK]

Hint: Always put ToTensor before Normalize in Compose [OK]

Common Mistakes:

• Placing Normalize before ToTensor
• Confusing list and tuple for Compose
• Replacing ToTensor incorrectly

5. You want to apply a Compose transform that first randomly flips an image horizontally, then converts it to a tensor, and finally normalizes it. Which code snippet correctly implements this?

hard

A. transforms.Compose([transforms.RandomHorizontalFlip(), transforms.ToTensor(), transforms.Normalize((0.5,), (0.5,))])

B. transforms.Compose([transforms.ToTensor(), transforms.RandomHorizontalFlip(), transforms.Normalize((0.5,), (0.5,))])

C. transforms.Compose([transforms.Normalize((0.5,), (0.5,)), transforms.RandomHorizontalFlip(), transforms.ToTensor()])

D. transforms.Compose([transforms.RandomHorizontalFlip(), transforms.Normalize((0.5,), (0.5,)), transforms.ToTensor()])

Solution

1. Step 1: Order of transforms

   RandomHorizontalFlip works on PIL images, so it must come before ToTensor which converts to tensor.

2. Step 2: Normalize after ToTensor

   Normalize requires tensor input, so it must come after ToTensor.

3. Step 3: Verify options

   transforms.Compose([transforms.RandomHorizontalFlip(), transforms.ToTensor(), transforms.Normalize((0.5,), (0.5,))]) has correct order: flip, then tensor, then normalize. Others have wrong order causing errors or wrong behavior.

4. Final Answer:

   transforms.Compose([transforms.RandomHorizontalFlip(), transforms.ToTensor(), transforms.Normalize((0.5,), (0.5,))]) -> Option A

5. Quick Check:

   Flip before ToTensor, Normalize last = D [OK]

Hint: Flip before ToTensor; Normalize last in Compose chain [OK]

Common Mistakes:

• Placing Normalize before ToTensor
• Applying flip after ToTensor (wrong input type)
• Mixing order causing runtime errors