Setting Probabilities for Transforms in Augmentation Pipelines 🔗

Each augmentation in Albumentations has a parameter named p that controls the probability of applying that augmentation to input data. Understanding how probabilities work is essential for creating effective and predictable augmentation pipelines.

Quick Reference 🔗

Key Concepts:

• p=1.0: Transform is always considered for application
• p=0.0: Transform is never considered for application
• p=0.5: Transform has a 50% chance of being considered
• Pipeline probability: Overall chance the entire pipeline runs
• Nested probabilities: How probabilities combine in composition blocks like OneOf

Basic Probability Mechanics 🔗

Individual Transform Probability 🔗

Setting p=1 means the transform will always be considered for application, while p=0 means it will never be considered. A value between 0 and 1 represents the chance it will be considered.

Some transforms default to p=1, while others default to p=0.5. Since default values can vary, it is recommended to explicitly set the p value for each transform in your pipeline to ensure clarity and avoid unexpected behavior.

Probability in Practice 🔗

import albumentations as A
import numpy as np

# Create a simple example
transform = A.Compose([
    A.HorizontalFlip(p=0.5),        # 50% chance to flip
    A.RandomBrightnessContrast(p=0.8),  # 80% chance to adjust brightness/contrast
    A.GaussianBlur(p=0.3),         # 30% chance to blur
])

# Each transform runs independently based on its p value
image = np.random.randint(0, 256, (100, 100, 3), dtype=np.uint8)
transformed = transform(image=image)

Complex Probability Example 🔗

Let's examine a more complex pipeline to understand how probabilities interact:

import albumentations as A
import numpy as np

# Define probabilities for clarity
prob_pipeline = 0.95
prob_rotate = 0.85
prob_oneof_noise = 0.75

# Define the augmentation pipeline
transform = A.Compose([
    A.RandomRotate90(p=prob_rotate),     # 85% chance to be considered
    A.OneOf([
        A.GaussNoise(p=0.9),             # 90% weight within OneOf block
        A.ISONoise(p=0.7),               # 70% weight within OneOf block
    ], p=prob_oneof_noise)               # 75% chance for OneOf block to run
], p=prob_pipeline,                      # 95% chance for entire pipeline to run
  seed=137)                             # Seed for reproducibility

# Apply the transform
image = np.random.randint(0, 256, (100, 100, 3), dtype=np.uint8)
transformed = transform(image=image)
transformed_image = transformed['image']

print("Transformation applied:", not np.array_equal(image, transformed_image))

How Different Probability Levels Work 🔗

Pipeline Probability (prob_pipeline) 🔗

The p parameter in A.Compose determines if any augmentations within it are applied:

• p=0.0: Pipeline never runs, input is always returned unchanged
• p=1.0: Pipeline always runs, inner transforms get a chance based on their own probabilities
• 0 < p < 1: Pipeline runs with that specific probability

In our example (prob_pipeline = 0.95), the pipeline runs 95% of the time.

Individual Transform Probability (prob_rotate) 🔗

Once the pipeline runs, each transform has its own probability:

• RandomRotate90 with p=0.85 has an 85% chance of being applied
• This is independent of other transforms in the pipeline

Composition Block Probability (prob_oneof_noise) 🔗

Composition utilities like OneOf have their own probability layer:

• OneOf block with p=0.75 has a 75% chance to run
• If it runs, it executes exactly one of its contained transforms

Probability Calculations in OneOf 🔗

How Selection Works 🔗

When a OneOf block runs, it normalizes the probabilities of inner transforms to determine selection weights:

Example transforms in OneOf:

• GaussNoise(p=0.9)
• ISONoise(p=0.7)

Normalization process:

1. Sum of probabilities: 0.9 + 0.7 = 1.6
2. Normalized probability for GaussNoise: 0.9 ÷ 1.6 = 0.5625 (56.25%)
3. Normalized probability for ISONoise: 0.7 ÷ 1.6 = 0.4375 (43.75%)

Selection Results 🔗

If the OneOf block runs:

• GaussNoise is selected 56.25% of the time
• ISONoise is selected 43.75% of the time

Overall Probability Calculations 🔗

The actual probability of each transform being applied to the original image is the product of all probability layers:

Mathematical Breakdown 🔗

Formula Pattern 🔗

Final Probability = Pipeline_p × Block_p × Normalized_Transform_p

Edge Cases: When p=1 Doesn't Change the Image 🔗

Even when a transform is applied (p=1 or probability check succeeds), the image might not visually change in certain cases:

Identity Operations 🔗

Transforms with identity operations in their selection:

• RandomRotate90: Can select 0° rotation (no change)
• D4: Can select identity transformation
• RandomGridShuffle: Might shuffle back to original positions

Identity Parameter Sampling 🔗

Geometric transforms can sample identity parameters:

• Affine(rotate=(-10, 10)): Might sample rotation = 0°
• ShiftScaleRotate: Could sample shift=0, scale=1, rotate=0

Example:

# This always applies identity transformation
A.Affine(rotate=0, scale=1, translate_px=0, p=1)

# This might randomly sample identity parameters
A.Affine(rotate=(-10, 10), scale=(0.9, 1.1), p=1)

Key Point: The p parameter controls whether a transform runs, but the transform's internal logic determines whether that execution visually changes the image.

Best Practices 🔗

Probability Setting Guidelines 🔗

1. Be Explicit: Always set p values explicitly rather than relying on defaults
2. Consider Independence: Remember that transform probabilities are independent within Compose
3. Calculate Overall Effects: Use the multiplication rule to understand final probabilities
4. Test Your Pipeline: Verify that your probability settings achieve the desired augmentation frequency

Common Patterns 🔗

# Light augmentation (conservative)
light_transform = A.Compose([
    A.HorizontalFlip(p=0.3),
    A.RandomBrightnessContrast(p=0.2),
    A.GaussianBlur(p=0.1),
])

# Moderate augmentation (balanced)
moderate_transform = A.Compose([
    A.HorizontalFlip(p=0.5),
    A.OneOf([
        A.RandomBrightnessContrast(p=1.0),
        A.HueSaturationValue(p=1.0),
    ], p=0.3),
    A.GaussianBlur(p=0.2),
])

# Heavy augmentation (aggressive)
heavy_transform = A.Compose([
    A.HorizontalFlip(p=0.7),
    A.RandomBrightnessContrast(p=0.6),
    A.OneOf([
        A.GaussianBlur(p=1.0),
        A.MedianBlur(p=1.0),
        A.MotionBlur(p=1.0),
    ], p=0.4),
])

Where to Go Next? 🔗

Understanding probabilities is crucial for controlling your augmentation pipelines. Now you can:

• Review Pipelines: See how probabilities function within different composition utilities like Compose, OneOf, SomeOf, and Sequential.
• Visually Explore Transforms: Experiment with different augmentations, their parameters, and consider the impact of their p values.
• See Basic Usage Examples: Look at practical code applying pipelines with specific probabilities for different tasks.
• Learn How to Pick Augmentations: Get insights into choosing appropriate transforms and their probabilities.
• Understand Reproducibility: Learn how seeds interact with probabilities to ensure consistent results when needed.